idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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900 | def rename_motifs ( motifs , stats = None ) : final_motifs = [ ] for i , motif in enumerate ( motifs ) : old = str ( motif ) motif . id = "GimmeMotifs_{}" . format ( i + 1 ) final_motifs . append ( motif ) if stats : stats [ str ( motif ) ] = stats [ old ] . copy ( ) if stats : return final_motifs , stats else : return... | Rename motifs to GimmeMotifs_1 .. GimmeMotifs_N . If stats object is passed stats will be copied . |
901 | def register_db ( cls , dbname ) : def decorator ( subclass ) : cls . _dbs [ dbname ] = subclass subclass . name = dbname return subclass return decorator | Register method to keep list of dbs . |
902 | def create ( cls , name , ncpus = None ) : try : return cls . _predictors [ name . lower ( ) ] ( ncpus = ncpus ) except KeyError : raise Exception ( "Unknown class" ) | Create a Moap instance based on the predictor name . |
903 | def register_predictor ( cls , name ) : def decorator ( subclass ) : cls . _predictors [ name . lower ( ) ] = subclass subclass . name = name . lower ( ) return subclass return decorator | Register method to keep list of predictors . |
904 | def list_classification_predictors ( self ) : preds = [ self . create ( x ) for x in self . _predictors . keys ( ) ] return [ x . name for x in preds if x . ptype == "classification" ] | List available classification predictors . |
905 | def _activate ( self ) : if six . callable ( self . streamer ) : self . stream_ = self . streamer ( * ( self . args ) , ** ( self . kwargs ) ) else : self . stream_ = iter ( self . streamer ) | Activates the stream . |
906 | def iterate ( self , max_iter = None ) : with self as active_streamer : for n , obj in enumerate ( active_streamer . stream_ ) : if max_iter is not None and n >= max_iter : break yield obj | Instantiate an iterator . |
907 | def cycle ( self , max_iter = None ) : count = 0 while True : for obj in self . iterate ( ) : count += 1 if max_iter is not None and count > max_iter : return yield obj | Iterate from the streamer infinitely . |
908 | def rank_motifs ( stats , metrics = ( "roc_auc" , "recall_at_fdr" ) ) : rank = { } combined_metrics = [ ] motif_ids = stats . keys ( ) background = list ( stats . values ( ) ) [ 0 ] . keys ( ) for metric in metrics : mean_metric_stats = [ np . mean ( [ stats [ m ] [ bg ] [ metric ] for bg in background ] ) for m in mot... | Determine mean rank of motifs based on metrics . |
909 | def write_stats ( stats , fname , header = None ) : for bg in list ( stats . values ( ) ) [ 0 ] . keys ( ) : f = open ( fname . format ( bg ) , "w" ) if header : f . write ( header ) stat_keys = sorted ( list ( list ( stats . values ( ) ) [ 0 ] . values ( ) ) [ 0 ] . keys ( ) ) f . write ( "{}\t{}\n" . format ( "Motif"... | write motif statistics to text file . |
910 | def get_roc_values ( motif , fg_file , bg_file ) : try : stats = calc_stats ( motif , fg_file , bg_file , stats = [ "roc_values" ] , ncpus = 1 ) ( x , y ) = list ( stats . values ( ) ) [ 0 ] [ "roc_values" ] return None , x , y except Exception as e : print ( motif ) print ( motif . id ) raise error = e return error , ... | Calculate ROC AUC values for ROC plots . |
911 | def create_roc_plots ( pwmfile , fgfa , background , outdir ) : motifs = read_motifs ( pwmfile , fmt = "pwm" , as_dict = True ) ncpus = int ( MotifConfig ( ) . get_default_params ( ) [ 'ncpus' ] ) pool = Pool ( processes = ncpus ) jobs = { } for bg , fname in background . items ( ) : for m_id , m in motifs . items ( ) ... | Make ROC plots for all motifs . |
912 | def _create_text_report ( inputfile , motifs , closest_match , stats , outdir ) : my_stats = { } for motif in motifs : match = closest_match [ motif . id ] my_stats [ str ( motif ) ] = { } for bg in list ( stats . values ( ) ) [ 0 ] . keys ( ) : if str ( motif ) not in stats : logger . error ( "####" ) logger . error (... | Create text report of motifs with statistics and database match . |
913 | def axes_off ( ax ) : ax . set_frame_on ( False ) ax . axes . get_yaxis ( ) . set_visible ( False ) ax . axes . get_xaxis ( ) . set_visible ( False ) | Get rid of all axis ticks lines etc . |
914 | def motif_tree_plot ( outfile , tree , data , circle = True , vmin = None , vmax = None , dpi = 300 ) : try : from ete3 import Tree , faces , AttrFace , TreeStyle , NodeStyle except ImportError : print ( "Please install ete3 to use this functionality" ) sys . exit ( 1 ) t , ts = _get_motif_tree ( tree , data , circle ,... | Plot a phylogenetic tree |
915 | def check_bed_file ( fname ) : if not os . path . exists ( fname ) : logger . error ( "Inputfile %s does not exist!" , fname ) sys . exit ( 1 ) for i , line in enumerate ( open ( fname ) ) : if line . startswith ( "#" ) or line . startswith ( "track" ) or line . startswith ( "browser" ) : pass else : vals = line . stri... | Check if the inputfile is a valid bed - file |
916 | def check_denovo_input ( inputfile , params ) : background = params [ "background" ] input_type = determine_file_type ( inputfile ) if input_type == "fasta" : valid_bg = FA_VALID_BGS elif input_type in [ "bed" , "narrowpeak" ] : genome = params [ "genome" ] valid_bg = BED_VALID_BGS if "genomic" in background or "gc" in... | Check if an input file is valid which means BED narrowPeak or FASTA |
917 | def scan_to_best_match ( fname , motifs , ncpus = None , genome = None , score = False ) : s = Scanner ( ncpus = ncpus ) s . set_motifs ( motifs ) s . set_threshold ( threshold = 0.0 ) if genome : s . set_genome ( genome ) if isinstance ( motifs , six . string_types ) : motifs = read_motifs ( motifs ) logger . debug ( ... | Scan a FASTA file with motifs . |
918 | def set_background ( self , fname = None , genome = None , length = 200 , nseq = 10000 ) : length = int ( length ) if genome and fname : raise ValueError ( "Need either genome or filename for background." ) if fname : if not os . path . exists ( fname ) : raise IOError ( "Background file {} does not exist!" . format ( ... | Set the background to use for FPR and z - score calculations . |
919 | def set_threshold ( self , fpr = None , threshold = None ) : if threshold and fpr : raise ValueError ( "Need either fpr or threshold." ) if fpr : fpr = float ( fpr ) if not ( 0.0 < fpr < 1.0 ) : raise ValueError ( "Parameter fpr should be between 0 and 1" ) if not self . motifs : raise ValueError ( "please run set_moti... | Set motif scanning threshold based on background sequences . |
920 | def best_score ( self , seqs , scan_rc = True , normalize = False ) : self . set_threshold ( threshold = 0.0 ) if normalize and len ( self . meanstd ) == 0 : self . set_meanstd ( ) means = np . array ( [ self . meanstd [ m ] [ 0 ] for m in self . motif_ids ] ) stds = np . array ( [ self . meanstd [ m ] [ 1 ] for m in s... | give the score of the best match of each motif in each sequence returns an iterator of lists containing floats |
921 | def roc ( args ) : outputfile = args . outfile if outputfile and not outputfile . endswith ( ".png" ) : outputfile += ".png" motifs = read_motifs ( args . pwmfile , fmt = "pwm" ) ids = [ ] if args . ids : ids = args . ids . split ( "," ) else : ids = [ m . id for m in motifs ] motifs = [ m for m in motifs if ( m . id i... | Calculate ROC_AUC and other metrics and optionally plot ROC curve . |
922 | def seqcor ( m1 , m2 , seq = None ) : l1 = len ( m1 ) l2 = len ( m2 ) l = max ( l1 , l2 ) if seq is None : seq = RCDB L = len ( seq ) result1 = pfmscan ( seq , m1 . pwm , m1 . pwm_min_score ( ) , len ( seq ) , False , True ) result2 = pfmscan ( seq , m2 . pwm , m2 . pwm_min_score ( ) , len ( seq ) , False , True ) resu... | Calculates motif similarity based on Pearson correlation of scores . |
923 | def compare_motifs ( self , m1 , m2 , match = "total" , metric = "wic" , combine = "mean" , pval = False ) : if metric == "seqcor" : return seqcor ( m1 , m2 ) elif match == "partial" : if pval : return self . pvalue ( m1 , m2 , "total" , metric , combine , self . max_partial ( m1 . pwm , m2 . pwm , metric , combine ) )... | Compare two motifs . The similarity metric can be any of seqcor pcc ed distance wic chisq akl or ssd . If match is total the similarity score is calculated for the whole match including positions that are not present in both motifs . If match is partial or subtotal only the matching psotiions are used to calculate the ... |
924 | def get_all_scores ( self , motifs , dbmotifs , match , metric , combine , pval = False , parallel = True , trim = None , ncpus = None ) : if trim : for m in motifs : m . trim ( trim ) for m in dbmotifs : m . trim ( trim ) scores = { } if parallel : if ncpus is None : ncpus = int ( MotifConfig ( ) . get_default_params ... | Pairwise comparison of a set of motifs compared to reference motifs . |
925 | def get_closest_match ( self , motifs , dbmotifs = None , match = "partial" , metric = "wic" , combine = "mean" , parallel = True , ncpus = None ) : if dbmotifs is None : pwm = self . config . get_default_params ( ) [ "motif_db" ] pwmdir = self . config . get_motif_dir ( ) dbmotifs = os . path . join ( pwmdir , pwm ) m... | Return best match in database for motifs . |
926 | def list_regions ( service ) : for region in service . regions ( ) : print '%(name)s: %(endpoint)s' % { 'name' : region . name , 'endpoint' : region . endpoint , } | List regions for the service |
927 | def elb_table ( balancers ) : t = prettytable . PrettyTable ( [ 'Name' , 'DNS' , 'Ports' , 'Zones' , 'Created' ] ) t . align = 'l' for b in balancers : ports = [ '%s: %s -> %s' % ( l [ 2 ] , l [ 0 ] , l [ 1 ] ) for l in b . listeners ] ports = '\n' . join ( ports ) zones = '\n' . join ( b . availability_zones ) t . add... | Print nice looking table of information from list of load balancers |
928 | def ec2_table ( instances ) : t = prettytable . PrettyTable ( [ 'ID' , 'State' , 'Monitored' , 'Image' , 'Name' , 'Type' , 'SSH key' , 'DNS' ] ) t . align = 'l' for i in instances : name = i . tags . get ( 'Name' , '' ) t . add_row ( [ i . id , i . state , i . monitored , i . image_id , name , i . instance_type , i . k... | Print nice looking table of information from list of instances |
929 | def ec2_image_table ( images ) : t = prettytable . PrettyTable ( [ 'ID' , 'State' , 'Name' , 'Owner' , 'Root device' , 'Is public' , 'Description' ] ) t . align = 'l' for i in images : t . add_row ( [ i . id , i . state , i . name , i . ownerId , i . root_device_type , i . is_public , i . description ] ) return t | Print nice looking table of information from images |
930 | def ec2_fab ( service , args ) : instance_ids = args . instances instances = service . list ( elb = args . elb , instance_ids = instance_ids ) hosts = service . resolve_hosts ( instances ) fab . env . hosts = hosts fab . env . key_filename = settings . get ( 'SSH' , 'KEY_FILE' ) fab . env . user = settings . get ( 'SSH... | Run Fabric commands against EC2 instances |
931 | def buffer_stream ( stream , buffer_size , partial = False , axis = None ) : data = [ ] count = 0 for item in stream : data . append ( item ) count += 1 if count < buffer_size : continue try : yield __stack_data ( data , axis = axis ) except ( TypeError , AttributeError ) : raise DataError ( "Malformed data stream: {}"... | Buffer data from an stream into one data object . |
932 | def tuples ( stream , * keys ) : if not keys : raise PescadorError ( 'Unable to generate tuples from ' 'an empty item set' ) for data in stream : try : yield tuple ( data [ key ] for key in keys ) except TypeError : raise DataError ( "Malformed data stream: {}" . format ( data ) ) | Reformat data as tuples . |
933 | def keras_tuples ( stream , inputs = None , outputs = None ) : flatten_inputs , flatten_outputs = False , False if inputs and isinstance ( inputs , six . string_types ) : inputs = [ inputs ] flatten_inputs = True if outputs and isinstance ( outputs , six . string_types ) : outputs = [ outputs ] flatten_outputs = True i... | Reformat data objects as keras - compatible tuples . |
934 | def location ( args ) : fastafile = args . fastafile pwmfile = args . pwmfile lwidth = args . width if not lwidth : f = Fasta ( fastafile ) lwidth = len ( f . items ( ) [ 0 ] [ 1 ] ) f = None jobs = [ ] motifs = pwmfile_to_motifs ( pwmfile ) ids = [ motif . id for motif in motifs ] if args . ids : ids = args . ids . sp... | Creates histrogram of motif location . |
935 | def which ( fname ) : if "PATH" not in os . environ or not os . environ [ "PATH" ] : path = os . defpath else : path = os . environ [ "PATH" ] for p in [ fname ] + [ os . path . join ( x , fname ) for x in path . split ( os . pathsep ) ] : p = os . path . abspath ( p ) if os . access ( p , os . X_OK ) and not os . path... | Find location of executable . |
936 | def find_by_ext ( dirname , ext ) : try : files = os . listdir ( dirname ) except OSError : if os . path . exists ( dirname ) : cmd = "find {0} -maxdepth 1 -name \"*\"" . format ( dirname ) p = sp . Popen ( cmd , shell = True , stdout = sp . PIPE , stderr = sp . PIPE ) stdout , _stderr = p . communicate ( ) files = [ o... | Find all files in a directory by extension . |
937 | def default_motifs ( ) : config = MotifConfig ( ) d = config . get_motif_dir ( ) m = config . get_default_params ( ) [ 'motif_db' ] if not d or not m : raise ValueError ( "default motif database not configured" ) fname = os . path . join ( d , m ) with open ( fname ) as f : motifs = read_motifs ( f ) return motifs | Return list of Motif instances from default motif database . |
938 | def motif_from_align ( align ) : width = len ( align [ 0 ] ) nucs = { "A" : 0 , "C" : 1 , "G" : 2 , "T" : 3 } pfm = [ [ 0 for _ in range ( 4 ) ] for _ in range ( width ) ] for row in align : for i in range ( len ( row ) ) : pfm [ i ] [ nucs [ row [ i ] ] ] += 1 m = Motif ( pfm ) m . align = align [ : ] return m | Convert alignment to motif . |
939 | def motif_from_consensus ( cons , n = 12 ) : width = len ( cons ) nucs = { "A" : 0 , "C" : 1 , "G" : 2 , "T" : 3 } pfm = [ [ 0 for _ in range ( 4 ) ] for _ in range ( width ) ] m = Motif ( ) for i , char in enumerate ( cons ) : for nuc in m . iupac [ char . upper ( ) ] : pfm [ i ] [ nucs [ nuc ] ] = n / len ( m . iupac... | Convert consensus sequence to motif . |
940 | def parse_motifs ( motifs ) : if isinstance ( motifs , six . string_types ) : with open ( motifs ) as f : if motifs . endswith ( "pwm" ) or motifs . endswith ( "pfm" ) : motifs = read_motifs ( f , fmt = "pwm" ) elif motifs . endswith ( "transfac" ) : motifs = read_motifs ( f , fmt = "transfac" ) else : motifs = read_mo... | Parse motifs in a variety of formats to return a list of motifs . |
941 | def read_motifs ( infile = None , fmt = "pwm" , as_dict = False ) : if infile is None or isinstance ( infile , six . string_types ) : infile = pwmfile_location ( infile ) with open ( infile ) as f : motifs = _read_motifs_from_filehandle ( f , fmt ) else : motifs = _read_motifs_from_filehandle ( infile , fmt ) if as_dic... | Read motifs from a file or stream or file - like object . |
942 | def information_content ( self ) : ic = 0 for row in self . pwm : ic += 2.0 + np . sum ( [ row [ x ] * log ( row [ x ] ) / log ( 2 ) for x in range ( 4 ) if row [ x ] > 0 ] ) return ic | Return the total information content of the motif . |
943 | def pwm_min_score ( self ) : if self . min_score is None : score = 0 for row in self . pwm : score += log ( min ( row ) / 0.25 + 0.01 ) self . min_score = score return self . min_score | Return the minimum PWM score . |
944 | def pwm_max_score ( self ) : if self . max_score is None : score = 0 for row in self . pwm : score += log ( max ( row ) / 0.25 + 0.01 ) self . max_score = score return self . max_score | Return the maximum PWM score . |
945 | def score_kmer ( self , kmer ) : if len ( kmer ) != len ( self . pwm ) : raise Exception ( "incorrect k-mer length" ) score = 0.0 d = { "A" : 0 , "C" : 1 , "G" : 2 , "T" : 3 } for nuc , row in zip ( kmer . upper ( ) , self . pwm ) : score += log ( row [ d [ nuc ] ] / 0.25 + 0.01 ) return score | Calculate the log - odds score for a specific k - mer . |
946 | def pfm_to_pwm ( self , pfm , pseudo = 0.001 ) : return [ [ ( x + pseudo ) / ( float ( np . sum ( row ) ) + pseudo * 4 ) for x in row ] for row in pfm ] | Convert PFM with counts to a PFM with fractions . |
947 | def ic_pos ( self , row1 , row2 = None ) : if row2 is None : row2 = [ 0.25 , 0.25 , 0.25 , 0.25 ] score = 0 for a , b in zip ( row1 , row2 ) : if a > 0 : score += a * log ( a / b ) / log ( 2 ) return score | Calculate the information content of one position . |
948 | def pcc_pos ( self , row1 , row2 ) : mean1 = np . mean ( row1 ) mean2 = np . mean ( row2 ) a = 0 x = 0 y = 0 for n1 , n2 in zip ( row1 , row2 ) : a += ( n1 - mean1 ) * ( n2 - mean2 ) x += ( n1 - mean1 ) ** 2 y += ( n2 - mean2 ) ** 2 if a == 0 : return 0 else : return a / sqrt ( x * y ) | Calculate the Pearson correlation coefficient of one position compared to another position . |
949 | def rc ( self ) : m = Motif ( ) m . pfm = [ row [ : : - 1 ] for row in self . pfm [ : : - 1 ] ] m . pwm = [ row [ : : - 1 ] for row in self . pwm [ : : - 1 ] ] m . id = self . id + "_revcomp" return m | Return the reverse complemented motif . |
950 | def trim ( self , edge_ic_cutoff = 0.4 ) : pwm = self . pwm [ : ] while len ( pwm ) > 0 and self . ic_pos ( pwm [ 0 ] ) < edge_ic_cutoff : pwm = pwm [ 1 : ] self . pwm = self . pwm [ 1 : ] self . pfm = self . pfm [ 1 : ] while len ( pwm ) > 0 and self . ic_pos ( pwm [ - 1 ] ) < edge_ic_cutoff : pwm = pwm [ : - 1 ] self... | Trim positions with an information content lower than the threshold . |
951 | def consensus_scan ( self , fa ) : regexp = "" . join ( [ "[" + "" . join ( self . iupac [ x . upper ( ) ] ) + "]" for x in self . to_consensusv2 ( ) ] ) p = re . compile ( regexp ) matches = { } for name , seq in fa . items ( ) : matches [ name ] = [ ] for match in p . finditer ( seq ) : middle = ( match . span ( ) [ ... | Scan FASTA with the motif as a consensus sequence . |
952 | def pwm_scan_to_gff ( self , fa , gfffile , cutoff = 0.9 , nreport = 50 , scan_rc = True , append = False ) : if append : out = open ( gfffile , "a" ) else : out = open ( gfffile , "w" ) c = self . pwm_min_score ( ) + ( self . pwm_max_score ( ) - self . pwm_min_score ( ) ) * cutoff pwm = self . pwm strandmap = { - 1 : ... | Scan sequences with this motif and save to a GFF file . |
953 | def average_motifs ( self , other , pos , orientation , include_bg = False ) : pfm1 = self . pfm [ : ] pfm2 = other . pfm [ : ] if orientation < 0 : pfm2 = [ row [ : : - 1 ] for row in pfm2 [ : : - 1 ] ] pfm1_count = float ( np . sum ( pfm1 [ 0 ] ) ) pfm2_count = float ( np . sum ( pfm2 [ 0 ] ) ) if include_bg : if len... | Return the average of two motifs . |
954 | def _pwm_to_str ( self , precision = 4 ) : if not self . pwm : return "" fmt = "{{:.{:d}f}}" . format ( precision ) return "\n" . join ( [ "\t" . join ( [ fmt . format ( p ) for p in row ] ) for row in self . pwm ] ) | Return string representation of pwm . |
955 | def to_pwm ( self , precision = 4 , extra_str = "" ) : motif_id = self . id if extra_str : motif_id += "_%s" % extra_str if not self . pwm : self . pwm = [ self . iupac_pwm [ char ] for char in self . consensus . upper ( ) ] return ">%s\n%s" % ( motif_id , self . _pwm_to_str ( precision ) ) | Return pwm as string . |
956 | def to_img ( self , fname , fmt = "PNG" , add_left = 0 , seqlogo = None , height = 6 ) : if not seqlogo : seqlogo = self . seqlogo if not seqlogo : raise ValueError ( "seqlogo not specified or configured" ) VALID_FORMATS = [ "EPS" , "GIF" , "PDF" , "PNG" ] N = 1000 fmt = fmt . upper ( ) if not fmt in VALID_FORMATS : sy... | Create a sequence logo using seqlogo . |
957 | def randomize ( self ) : random_pfm = [ [ c for c in row ] for row in self . pfm ] random . shuffle ( random_pfm ) m = Motif ( pfm = random_pfm ) m . id = "random" return m | Create a new motif with shuffled positions . |
958 | def maelstrom ( args ) : infile = args . inputfile genome = args . genome outdir = args . outdir pwmfile = args . pwmfile methods = args . methods ncpus = args . ncpus if not os . path . exists ( infile ) : raise ValueError ( "file {} does not exist" . format ( infile ) ) if methods : methods = [ x . strip ( ) for x in... | Run the maelstrom method . |
959 | def zmq_recv_data ( socket , flags = 0 , copy = True , track = False ) : data = dict ( ) msg = socket . recv_multipart ( flags = flags , copy = copy , track = track ) headers = json . loads ( msg [ 0 ] . decode ( 'ascii' ) ) if len ( headers ) == 0 : raise StopIteration for header , payload in zip ( headers , msg [ 1 :... | Receive data over a socket . |
960 | def hardmask ( self ) : p = re . compile ( "a|c|g|t|n" ) for seq_id in self . fasta_dict . keys ( ) : self . fasta_dict [ seq_id ] = p . sub ( "N" , self . fasta_dict [ seq_id ] ) return self | Mask all lowercase nucleotides with N s |
961 | def get_random ( self , n , l = None ) : random_f = Fasta ( ) if l : ids = self . ids [ : ] random . shuffle ( ids ) i = 0 while ( i < n ) and ( len ( ids ) > 0 ) : seq_id = ids . pop ( ) if ( len ( self [ seq_id ] ) >= l ) : start = random . randint ( 0 , len ( self [ seq_id ] ) - l ) random_f [ "random%s" % ( i + 1 )... | Return n random sequences from this Fasta object |
962 | def writefasta ( self , fname ) : f = open ( fname , "w" ) fa_str = "\n" . join ( [ ">%s\n%s" % ( id , self . _format_seq ( seq ) ) for id , seq in self . items ( ) ] ) f . write ( fa_str ) f . close ( ) | Write sequences to FASTA formatted file |
963 | def batch_length ( batch ) : n = None for value in six . itervalues ( batch ) : if n is None : n = len ( value ) elif len ( value ) != n : raise PescadorError ( 'Unequal field lengths' ) return n | Determine the number of samples in a batch . |
964 | def _activate ( self ) : self . distribution_ = 1. / self . n_streams * np . ones ( self . n_streams ) self . valid_streams_ = np . ones ( self . n_streams , dtype = bool ) self . streams_ = [ None ] * self . k self . stream_weights_ = np . zeros ( self . k ) self . stream_counts_ = np . zeros ( self . k , dtype = int ... | Activates a number of streams |
965 | def iterate ( self , max_iter = None ) : if max_iter is None : max_iter = np . inf with self as active_mux : n = 0 while n < max_iter and active_mux . _streamers_available ( ) : idx = active_mux . _next_sample_index ( ) try : yield six . advance_iterator ( active_mux . streams_ [ idx ] ) n += 1 active_mux . stream_coun... | Yields items from the mux and handles stream exhaustion and replacement . |
966 | def _next_sample_index ( self ) : return self . rng . choice ( self . n_active , p = ( self . stream_weights_ / self . weight_norm_ ) ) | StochasticMux chooses its next sample stream randomly |
967 | def _activate ( self ) : self . streams_ = [ None ] * self . n_streams self . stream_weights_ = np . array ( self . weights , dtype = float ) self . stream_counts_ = np . zeros ( self . n_streams , dtype = int ) for idx in range ( self . n_streams ) : self . _new_stream ( idx ) self . weight_norm_ = np . sum ( self . s... | ShuffledMux s activate is similar to StochasticMux but there is no n_active since all the streams are always available . |
968 | def _next_sample_index ( self ) : return self . rng . choice ( self . n_streams , p = ( self . stream_weights_ / self . weight_norm_ ) ) | ShuffledMux chooses its next sample stream randomly conditioned on the stream weights . |
969 | def _next_sample_index ( self ) : idx = self . active_index_ self . active_index_ += 1 if self . active_index_ >= len ( self . streams_ ) : self . active_index_ = 0 while self . streams_ [ idx ] is None : idx = self . active_index_ self . active_index_ += 1 if self . active_index_ >= len ( self . streams_ ) : self . ac... | Rotates through each active sampler by incrementing the index |
970 | def _new_stream ( self , idx ) : stream_index = self . stream_idxs_ [ idx ] self . streams_ [ idx ] = self . streamers [ stream_index ] . iterate ( ) self . stream_counts_ [ idx ] = 0 | Activate a new stream given the index into the stream pool . |
971 | def _new_stream ( self ) : try : next_stream = six . advance_iterator ( self . stream_generator_ ) except StopIteration : if self . mode == "cycle" : self . stream_generator_ = self . chain_streamer_ . iterate ( ) next_stream = six . advance_iterator ( self . stream_generator_ ) else : next_stream = None if next_stream... | Grab the next stream from the input streamers and start it . |
972 | def npz_generator ( npz_path ) : npz_data = np . load ( npz_path ) X = npz_data [ 'X' ] y = npz_data [ 'Y' ] n = X . shape [ 0 ] while True : i = np . random . randint ( 0 , n ) yield { 'X' : X [ i ] , 'Y' : y [ i ] } | Generate data from an npz file . |
973 | def phyper ( k , good , bad , N ) : pvalues = [ phyper_single ( x , good , bad , N ) for x in range ( k + 1 , N + 1 ) ] return np . sum ( pvalues ) | Current hypergeometric implementation in scipy is broken so here s the correct version |
974 | def calc_motif_enrichment ( sample , background , mtc = None , len_sample = None , len_back = None ) : INF = "Inf" if mtc not in [ None , "Bonferroni" , "Benjamini-Hochberg" , "None" ] : raise RuntimeError ( "Unknown correction: %s" % mtc ) sig = { } p_value = { } n_sample = { } n_back = { } if not ( len_sample ) : len... | Calculate enrichment based on hypergeometric distribution |
975 | def parse_cutoff ( motifs , cutoff , default = 0.9 ) : cutoffs = { } if os . path . isfile ( str ( cutoff ) ) : for i , line in enumerate ( open ( cutoff ) ) : if line != "Motif\tScore\tCutoff\n" : try : motif , _ , c = line . strip ( ) . split ( "\t" ) c = float ( c ) cutoffs [ motif ] = c except Exception as e : sys ... | Provide either a file with one cutoff per motif or a single cutoff returns a hash with motif id as key and cutoff as value |
976 | def determine_file_type ( fname ) : if not ( isinstance ( fname , str ) or isinstance ( fname , unicode ) ) : raise ValueError ( "{} is not a file name!" , fname ) if not os . path . isfile ( fname ) : raise ValueError ( "{} is not a file!" , fname ) ext = os . path . splitext ( fname ) [ 1 ] . lower ( ) if ext in [ "b... | Detect file type . |
977 | def file_checksum ( fname ) : size = os . path . getsize ( fname ) with open ( fname , "r+" ) as f : checksum = hashlib . md5 ( mmap . mmap ( f . fileno ( ) , size ) ) . hexdigest ( ) return checksum | Return md5 checksum of file . |
978 | def download_annotation ( genomebuild , gene_file ) : pred_bin = "genePredToBed" pred = find_executable ( pred_bin ) if not pred : sys . stderr . write ( "{} not found in path!\n" . format ( pred_bin ) ) sys . exit ( 1 ) tmp = NamedTemporaryFile ( delete = False , suffix = ".gz" ) anno = [ ] f = urlopen ( UCSC_GENE_URL... | Download gene annotation from UCSC based on genomebuild . |
979 | def _make_index ( self , fasta , index ) : out = open ( index , "wb" ) f = open ( fasta ) line = f . readline ( ) offset = f . tell ( ) line = f . readline ( ) while line : out . write ( pack ( self . pack_char , offset ) ) offset = f . tell ( ) line = f . readline ( ) f . close ( ) out . close ( ) | Index a single one - sequence fasta - file |
980 | def _read_index_file ( self ) : param_file = os . path . join ( self . index_dir , self . param_file ) with open ( param_file ) as f : for line in f . readlines ( ) : ( name , fasta_file , index_file , line_size , total_size ) = line . strip ( ) . split ( "\t" ) self . size [ name ] = int ( total_size ) self . fasta_fi... | read the param_file index_dir should already be set |
981 | def _read_seq_from_fasta ( self , fasta , offset , nr_lines ) : fasta . seek ( offset ) lines = [ fasta . readline ( ) . strip ( ) for _ in range ( nr_lines ) ] return "" . join ( lines ) | retrieve a number of lines from a fasta file - object starting at offset |
982 | def get_sequence ( self , chrom , start , end , strand = None ) : if not self . index_dir : print ( "Index dir is not defined!" ) sys . exit ( ) fasta_file = self . fasta_file [ chrom ] index_file = self . index_file [ chrom ] line_size = self . line_size [ chrom ] total_size = self . size [ chrom ] if start > total_si... | Retrieve a sequence |
983 | def get_size ( self , chrom = None ) : if len ( self . size ) == 0 : raise LookupError ( "no chromosomes in index, is the index correct?" ) if chrom : if chrom in self . size : return self . size [ chrom ] else : raise KeyError ( "chromosome {} not in index" . format ( chrom ) ) total = 0 for size in self . size . valu... | Return the sizes of all sequences in the index or the size of chrom if specified as an optional argument |
984 | def get_tool ( name ) : tool = name . lower ( ) if tool not in __tools__ : raise ValueError ( "Tool {0} not found!\n" . format ( name ) ) t = __tools__ [ tool ] ( ) if not t . is_installed ( ) : sys . stderr . write ( "Tool {0} not installed!\n" . format ( tool ) ) if not t . is_configured ( ) : sys . stderr . write ( ... | Returns an instance of a specific tool . |
985 | def locate_tool ( name , verbose = True ) : m = get_tool ( name ) tool_bin = which ( m . cmd ) if tool_bin : if verbose : print ( "Found {} in {}" . format ( m . name , tool_bin ) ) return tool_bin else : print ( "Couldn't find {}" . format ( m . name ) ) | Returns the binary of a tool . |
986 | def bin ( self ) : if self . local_bin : return self . local_bin else : return self . config . bin ( self . name ) | Get the command used to run the tool . |
987 | def run ( self , fastafile , params = None , tmp = None ) : if not self . is_configured ( ) : raise ValueError ( "%s is not configured" % self . name ) if not self . is_installed ( ) : raise ValueError ( "%s is not installed or not correctly configured" % self . name ) self . tmpdir = mkdtemp ( prefix = "{0}." . format... | Run the tool and predict motifs from a FASTA file . |
988 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) outfile = os . path . join ( self . tmpdir , os . path . basename ( fastafile . replace ( ".fa" , ".pwm" ) ) ) stdout = "" stderr = "" cmd = "%s %s %s --localization --batch %s %s" % ( bin , self . tmpdir , fastafile ... | Run XXmotif and predict motifs from a FASTA file . |
989 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) outfile = NamedTemporaryFile ( mode = "w" , dir = self . tmpdir , prefix = "homer_w{}." . format ( params [ "width" ] ) ) . name cmd = "%s denovo -i %s -b %s -len %s -S %s %s -o %s -p 8" % ( bin , fastafile , params [... | Run Homer and predict motifs from a FASTA file . |
990 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) default_params = { "width" : 10 } if params is not None : default_params . update ( params ) fgfile , summitfile , outfile = self . _prepare_files ( fastafile ) current_path = os . getcwd ( ) os . chdir ( self . tmpdi... | Run HMS and predict motifs from a FASTA file . |
991 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) fgfile = os . path . join ( self . tmpdir , "AMD.in.fa" ) outfile = fgfile + ".Matrix" shutil . copy ( fastafile , fgfile ) current_path = os . getcwd ( ) os . chdir ( self . tmpdir ) stdout = "" stderr = "" cmd = "%s... | Run AMD and predict motifs from a FASTA file . |
992 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) tmp = NamedTemporaryFile ( mode = "w" , dir = self . tmpdir , delete = False ) shutil . copy ( fastafile , tmp . name ) fastafile = tmp . name current_path = os . getcwd ( ) os . chdir ( self . dir ( ) ) motifs = [ ] ... | Run Trawler and predict motifs from a FASTA file . |
993 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) organism = params [ "organism" ] weeder_organisms = { "hg18" : "HS" , "hg19" : "HS" , "hg38" : "HS" , "mm9" : "MM" , "mm10" : "MM" , "dm3" : "DM" , "dm5" : "DM" , "dm6" : "DM" , "yeast" : "SC" , "sacCer2" : "SC" , "sa... | Run Weeder and predict motifs from a FASTA file . |
994 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) cmd = "%s -f %s -b %s -m %s -w %s -n %s -o %s -s %s" % ( bin , fastafile , params [ "background_model" ] , params [ "pwmfile" ] , params [ "width" ] , params [ "number" ] , params [ "outfile" ] , params [ "strand" ] ,... | Run MotifSampler and predict motifs from a FASTA file . |
995 | def _run_program ( self , bin , fastafile , params = None ) : default_params = { "width" : 10 , "number" : 10 } if params is not None : default_params . update ( params ) new_file = os . path . join ( self . tmpdir , "mdmodule_in.fa" ) shutil . copy ( fastafile , new_file ) fastafile = new_file pwmfile = fastafile + ".... | Run MDmodule and predict motifs from a FASTA file . |
996 | def _run_program ( self , bin , fastafile , params = None ) : params = self . _parse_params ( params ) basename = "munk_in.fa" new_file = os . path . join ( self . tmpdir , basename ) out = open ( new_file , "w" ) f = Fasta ( fastafile ) for seq in f . seqs : header = len ( seq ) // 2 out . write ( ">%s\n" % header ) o... | Run ChIPMunk and predict motifs from a FASTA file . |
997 | def _run_program ( self , bin , fastafile , params = None ) : default_params = { } if params is not None : default_params . update ( params ) width = params . get ( "width" , 8 ) basename = "posmo_in.fa" new_file = os . path . join ( self . tmpdir , basename ) shutil . copy ( fastafile , new_file ) fastafile = new_file... | Run Posmo and predict motifs from a FASTA file . |
998 | def _run_program ( self , bin , fastafile , params = None ) : fname = os . path . join ( self . config . get_motif_dir ( ) , "JASPAR2010_vertebrate.pwm" ) motifs = read_motifs ( fname , fmt = "pwm" ) for motif in motifs : motif . id = "JASPAR_%s" % motif . id return motifs , "" , "" | Get enriched JASPAR motifs in a FASTA file . |
999 | def _run_program ( self , bin , fastafile , params = None ) : default_params = { "width" : 10 , "single" : False , "number" : 10 } if params is not None : default_params . update ( params ) tmp = NamedTemporaryFile ( dir = self . tmpdir ) tmpname = tmp . name strand = "-revcomp" width = default_params [ "width" ] numbe... | Run MEME and predict motifs from a FASTA file . |
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