Datasets:

mengmengmmm

/

csn_python_trainuse

like 1

def split_phylogeny ( p , level = "s" ) : level = level + "__" result = p . split ( level ) return result [ 0 ] + level + result [ 1 ] . split ( ";" ) [ 0 ]

Return either the full or truncated version of a QIIME - formatted taxonomy string .

def ensure_dir ( d ) : if not os . path . exists ( d ) : try : os . makedirs ( d ) except OSError as oe : if os . errno == errno . ENOENT : msg = twdd ( ) return msg . format ( d ) else : msg = twdd ( ) return msg . format ( d , oe . strerror )

Check to make sure the supplied directory path does not exist if so create it . The method catches OSError exceptions and returns a descriptive message instead of re - raising the error .

def file_handle ( fnh , mode = "rU" ) : handle = None if isinstance ( fnh , file ) : if fnh . closed : raise ValueError ( "Input file is closed." ) handle = fnh elif isinstance ( fnh , str ) : handle = open ( fnh , mode ) return handle

Takes either a file path or an open file handle checks validity and returns an open file handle or raises an appropriate Exception .

def gather_categories ( imap , header , categories = None ) : if categories is None : return { "default" : DataCategory ( set ( imap . keys ( ) ) , { } ) } cat_ids = [ header . index ( cat ) for cat in categories if cat in header and "=" not in cat ] table = OrderedDict ( ) conditions = defaultdict ( set ) for i , cat in enumerate ( categories ) : if "=" in cat and cat . split ( "=" ) [ 0 ] in header : cat_name = header [ header . index ( cat . split ( "=" ) [ 0 ] ) ] conditions [ cat_name ] . add ( cat . split ( "=" ) [ 1 ] ) if not cat_ids and not conditions : return { "default" : DataCategory ( set ( imap . keys ( ) ) , { } ) } if cat_ids and not conditions : for sid , row in imap . items ( ) : cat_name = "_" . join ( [ row [ cid ] for cid in cat_ids ] ) if cat_name not in table : table [ cat_name ] = DataCategory ( set ( ) , { } ) table [ cat_name ] . sids . add ( sid ) return table cond_ids = set ( ) for k in conditions : try : cond_ids . add ( header . index ( k ) ) except ValueError : continue idx_to_test = set ( cat_ids ) . union ( cond_ids ) for sid , row in imap . items ( ) : if all ( [ row [ header . index ( c ) ] in conditions [ c ] for c in conditions ] ) : key = "_" . join ( [ row [ idx ] for idx in idx_to_test ] ) try : assert key in table . keys ( ) except AssertionError : table [ key ] = DataCategory ( set ( ) , { } ) table [ key ] . sids . add ( sid ) try : assert len ( table ) > 0 except AssertionError : return { "default" : DataCategory ( set ( imap . keys ( ) ) , { } ) } else : return table

Find the user specified categories in the map and create a dictionary to contain the relevant data for each type within the categories . Multiple categories will have their types combined such that each possible combination will have its own entry in the dictionary .

def parse_unifrac ( unifracFN ) : with open ( unifracFN , "rU" ) as uF : first = uF . next ( ) . split ( "\t" ) lines = [ line . strip ( ) for line in uF ] unifrac = { "pcd" : OrderedDict ( ) , "eigvals" : [ ] , "varexp" : [ ] } if first [ 0 ] == "pc vector number" : return parse_unifrac_v1_8 ( unifrac , lines ) elif first [ 0 ] == "Eigvals" : return parse_unifrac_v1_9 ( unifrac , lines ) else : raise ValueError ( "File format not supported/recognized. Please check input " "unifrac file." )

Parses the unifrac results file into a dictionary

def parse_unifrac_v1_8 ( unifrac , file_data ) : for line in file_data : if line == "" : break line = line . split ( "\t" ) unifrac [ "pcd" ] [ line [ 0 ] ] = [ float ( e ) for e in line [ 1 : ] ] unifrac [ "eigvals" ] = [ float ( entry ) for entry in file_data [ - 2 ] . split ( "\t" ) [ 1 : ] ] unifrac [ "varexp" ] = [ float ( entry ) for entry in file_data [ - 1 ] . split ( "\t" ) [ 1 : ] ] return unifrac

Function to parse data from older version of unifrac file obtained from Qiime version 1 . 8 and earlier .

def parse_unifrac_v1_9 ( unifrac , file_data ) : unifrac [ "eigvals" ] = [ float ( entry ) for entry in file_data [ 0 ] . split ( "\t" ) ] unifrac [ "varexp" ] = [ float ( entry ) * 100 for entry in file_data [ 3 ] . split ( "\t" ) ] for line in file_data [ 8 : ] : if line == "" : break line = line . split ( "\t" ) unifrac [ "pcd" ] [ line [ 0 ] ] = [ float ( e ) for e in line [ 1 : ] ] return unifrac

Function to parse data from newer version of unifrac file obtained from Qiime version 1 . 9 and later .

def color_mapping ( sample_map , header , group_column , color_column = None ) : group_colors = OrderedDict ( ) group_gather = gather_categories ( sample_map , header , [ group_column ] ) if color_column is not None : color_gather = gather_categories ( sample_map , header , [ color_column ] ) for group in group_gather : for color in color_gather : if group_gather [ group ] . sids . intersection ( color_gather [ color ] . sids ) : group_colors [ group ] = color else : bcolors = itertools . cycle ( Set3_12 . hex_colors ) for group in group_gather : group_colors [ group ] = bcolors . next ( ) return group_colors

Determine color - category mapping . If color_column was specified then map the category names to color values . Otherwise use the palettable colors to automatically generate a set of colors for the group values .

def rev_c ( read ) : rc = [ ] rc_nucs = { 'A' : 'T' , 'T' : 'A' , 'G' : 'C' , 'C' : 'G' , 'N' : 'N' } for base in read : rc . extend ( rc_nucs [ base . upper ( ) ] ) return rc [ : : - 1 ]

return reverse completment of read

def shuffle_genome ( genome , cat , fraction = float ( 100 ) , plot = True , alpha = 0.1 , beta = 100000 , min_length = 1000 , max_length = 200000 ) : header = '>randomized_%s' % ( genome . name ) sequence = list ( '' . join ( [ i [ 1 ] for i in parse_fasta ( genome ) ] ) ) length = len ( sequence ) shuffled = [ ] while sequence is not False : s = int ( random . gammavariate ( alpha , beta ) ) if s <= min_length or s >= max_length : continue if len ( sequence ) < s : seq = sequence [ 0 : ] else : seq = sequence [ 0 : s ] sequence = sequence [ s : ] shuffled . append ( '' . join ( seq ) ) if sequence == [ ] : break random . shuffle ( shuffled ) if fraction == float ( 100 ) : subset = shuffled else : max_pieces = int ( length * fraction / 100 ) subset , total = [ ] , 0 for fragment in shuffled : length = len ( fragment ) if total + length <= max_pieces : subset . append ( fragment ) total += length else : diff = max_pieces - total subset . append ( fragment [ 0 : diff ] ) break if cat is True : yield [ header , '' . join ( subset ) ] else : for i , seq in enumerate ( subset ) : yield [ '%s fragment:%s' % ( header , i ) , seq ]

randomly shuffle genome

def _prune ( self , fit , p_max ) : def remove_from_model_desc ( x , model_desc ) : rhs_termlist = [ ] for t in model_desc . rhs_termlist : if not t . factors : rhs_termlist . append ( t ) elif not x == t . factors [ 0 ] . _varname : rhs_termlist . append ( t ) md = ModelDesc ( model_desc . lhs_termlist , rhs_termlist ) return md corrected_model_desc = ModelDesc ( fit . model . formula . lhs_termlist [ : ] , fit . model . formula . rhs_termlist [ : ] ) pars_to_prune = fit . pvalues . where ( fit . pvalues > p_max ) . dropna ( ) . index . tolist ( ) try : pars_to_prune . remove ( 'Intercept' ) except : pass while pars_to_prune : corrected_model_desc = remove_from_model_desc ( pars_to_prune [ 0 ] , corrected_model_desc ) fit = fm . ols ( corrected_model_desc , data = self . df ) . fit ( ) pars_to_prune = fit . pvalues . where ( fit . pvalues > p_max ) . dropna ( ) . index . tolist ( ) try : pars_to_prune . remove ( 'Intercept' ) except : pass return fit

If the fit contains statistically insignificant parameters remove them . Returns a pruned fit where all parameters have p - values of the t - statistic below p_max

def find_best_rsquared ( list_of_fits ) : res = sorted ( list_of_fits , key = lambda x : x . rsquared ) return res [ - 1 ]

Return the best fit based on rsquared

def _predict ( self , fit , df ) : df_res = df . copy ( ) if 'Intercept' in fit . model . exog_names : df_res [ 'Intercept' ] = 1.0 df_res [ 'predicted' ] = fit . predict ( df_res ) if not self . allow_negative_predictions : df_res . loc [ df_res [ 'predicted' ] < 0 , 'predicted' ] = 0 prstd , interval_l , interval_u = wls_prediction_std ( fit , df_res [ fit . model . exog_names ] , alpha = 1 - self . confint ) df_res [ 'interval_l' ] = interval_l df_res [ 'interval_u' ] = interval_u if 'Intercept' in df_res : df_res . drop ( labels = [ 'Intercept' ] , axis = 1 , inplace = True ) return df_res

Return a df with predictions and confidence interval

def relative_abundance ( biomf , sampleIDs = None ) : if sampleIDs is None : sampleIDs = biomf . ids ( ) else : try : for sid in sampleIDs : assert sid in biomf . ids ( ) except AssertionError : raise ValueError ( "\nError while calculating relative abundances: The sampleIDs provided do" " not match the sampleIDs in biom file. Please double check the sampleIDs" " provided.\n" ) otuIDs = biomf . ids ( axis = "observation" ) norm_biomf = biomf . norm ( inplace = False ) return { sample : { otuID : norm_biomf . get_value_by_ids ( otuID , sample ) for otuID in otuIDs } for sample in sampleIDs }

Calculate the relative abundance of each OTUID in a Sample .

def mean_otu_pct_abundance ( ra , otuIDs ) : sids = ra . keys ( ) otumeans = defaultdict ( int ) for oid in otuIDs : otumeans [ oid ] = sum ( [ ra [ sid ] [ oid ] for sid in sids if oid in ra [ sid ] ] ) / len ( sids ) * 100 return otumeans

Calculate the mean OTU abundance percentage .

def MRA ( biomf , sampleIDs = None , transform = None ) : ra = relative_abundance ( biomf , sampleIDs ) if transform is not None : ra = { sample : { otuID : transform ( abd ) for otuID , abd in ra [ sample ] . items ( ) } for sample in ra . keys ( ) } otuIDs = biomf . ids ( axis = "observation" ) return mean_otu_pct_abundance ( ra , otuIDs )

Calculate the mean relative abundance percentage .

def raw_abundance ( biomf , sampleIDs = None , sample_abd = True ) : results = defaultdict ( int ) if sampleIDs is None : sampleIDs = biomf . ids ( ) else : try : for sid in sampleIDs : assert sid in biomf . ids ( ) except AssertionError : raise ValueError ( "\nError while calculating raw total abundances: The sampleIDs provided " "do not match the sampleIDs in biom file. Please double check the " "sampleIDs provided.\n" ) otuIDs = biomf . ids ( axis = "observation" ) for sampleID in sampleIDs : for otuID in otuIDs : abd = biomf . get_value_by_ids ( otuID , sampleID ) if sample_abd : results [ sampleID ] += abd else : results [ otuID ] += abd return results

Calculate the total number of sequences in each OTU or SampleID .

def transform_raw_abundance ( biomf , fn = math . log10 , sampleIDs = None , sample_abd = True ) : totals = raw_abundance ( biomf , sampleIDs , sample_abd ) return { sid : fn ( abd ) for sid , abd in totals . items ( ) }

Function to transform the total abundance calculation for each sample ID to another format based on user given transformation function .

def print_MannWhitneyU ( div_calc ) : try : x = div_calc . values ( ) [ 0 ] . values ( ) y = div_calc . values ( ) [ 1 ] . values ( ) except : return "Error setting up input arrays for Mann-Whitney U Test. Skipping " "significance testing." T , p = stats . mannwhitneyu ( x , y ) print "\nMann-Whitney U test statistic:" , T print "Two-tailed p-value: {}" . format ( 2 * p )

Compute the Mann - Whitney U test for unequal group sample sizes .

def print_KruskalWallisH ( div_calc ) : calc = defaultdict ( list ) try : for k1 , v1 in div_calc . iteritems ( ) : for k2 , v2 in v1 . iteritems ( ) : calc [ k1 ] . append ( v2 ) except : return "Error setting up input arrays for Kruskal-Wallis H-Test. Skipping " "significance testing." h , p = stats . kruskal ( * calc . values ( ) ) print "\nKruskal-Wallis H-test statistic for {} groups: {}" . format ( str ( len ( div_calc ) ) , h ) print "p-value: {}" . format ( p )

Compute the Kruskal - Wallis H - test for independent samples . A typical rule is that each group must have at least 5 measurements .

def handle_program_options ( ) : parser = argparse . ArgumentParser ( description = "Calculate the alpha diversity\ of a set of samples using one or more \ metrics and output a kernal density \ estimator-smoothed histogram of the \ results." ) parser . add_argument ( "-m" , "--map_file" , help = "QIIME mapping file." ) parser . add_argument ( "-i" , "--biom_fp" , help = "Path to the BIOM table" ) parser . add_argument ( "-c" , "--category" , help = "Specific category from the mapping file." ) parser . add_argument ( "-d" , "--diversity" , default = [ "shannon" ] , nargs = "+" , help = "The alpha diversity metric. Default \ value is 'shannon', which will calculate the Shannon\ entropy. Multiple metrics can be specified (space separated).\ The full list of metrics is available at:\ http://scikit-bio.org/docs/latest/generated/skbio.diversity.alpha.html.\ Beta diversity metrics will be supported in the future." ) parser . add_argument ( "--x_label" , default = [ None ] , nargs = "+" , help = "The name of the diversity metric to be displayed on the\ plot as the X-axis label. If multiple metrics are specified,\ then multiple entries for the X-axis label should be given." ) parser . add_argument ( "--color_by" , help = "A column name in the mapping file containing\ hexadecimal (#FF0000) color values that will\ be used to color the groups. Each sample ID must\ have a color entry." ) parser . add_argument ( "--plot_title" , default = "" , help = "A descriptive title that will appear at the top \ of the output plot. Surround with quotes if there are\ spaces in the title." ) parser . add_argument ( "-o" , "--output_dir" , default = "." , help = "The directory plots will be saved to." ) parser . add_argument ( "--image_type" , default = "png" , help = "The type of image to save: png, svg, pdf, eps, etc..." ) parser . add_argument ( "--save_calculations" , help = "Path and name of text file to store the calculated " "diversity metrics." ) parser . add_argument ( "--suppress_stats" , action = "store_true" , help = "Do not display " "significance testing results which are shown by default." ) parser . add_argument ( "--show_available_metrics" , action = "store_true" , help = "Supply this parameter to see which alpha diversity metrics " " are available for usage. No calculations will be performed" " if this parameter is provided." ) return parser . parse_args ( )

Parses the given options passed in at the command line .

def blastdb ( fasta , maxfile = 10000000 ) : db = fasta . rsplit ( '.' , 1 ) [ 0 ] type = check_type ( fasta ) if type == 'nucl' : type = [ 'nhr' , type ] else : type = [ 'phr' , type ] if os . path . exists ( '%s.%s' % ( db , type [ 0 ] ) ) is False and os . path . exists ( '%s.00.%s' % ( db , type [ 0 ] ) ) is False : print ( '# ... making blastdb for: %s' % ( fasta ) , file = sys . stderr ) os . system ( 'makeblastdb \ -in %s -out %s -dbtype %s -max_file_sz %s >> log.txt' % ( fasta , db , type [ 1 ] , maxfile ) ) else : print ( '# ... database found for: %s' % ( fasta ) , file = sys . stderr ) return db

make blast db

def usearchdb ( fasta , alignment = 'local' , usearch_loc = 'usearch' ) : if '.udb' in fasta : print ( '# ... database found: %s' % ( fasta ) , file = sys . stderr ) return fasta type = check_type ( fasta ) db = '%s.%s.udb' % ( fasta . rsplit ( '.' , 1 ) [ 0 ] , type ) if os . path . exists ( db ) is False : print ( '# ... making usearch db for: %s' % ( fasta ) , file = sys . stderr ) if alignment == 'local' : os . system ( '%s -makeudb_ublast %s -output %s >> log.txt' % ( usearch_loc , fasta , db ) ) elif alignment == 'global' : os . system ( '%s -makeudb_usearch %s -output %s >> log.txt' % ( usearch_loc , fasta , db ) ) else : print ( '# ... database found for: %s' % ( fasta ) , file = sys . stderr ) return db

make usearch db

def _pp ( dict_data ) : for key , val in dict_data . items ( ) : print ( '{0:<11}: {1}' . format ( key , val ) )

Pretty print .

def print_licences ( params , metadata ) : if hasattr ( params , 'licenses' ) : if params . licenses : _pp ( metadata . licenses_desc ( ) ) sys . exit ( 0 )

Print licenses .

def check_repository_existence ( params ) : repodir = os . path . join ( params . outdir , params . name ) if os . path . isdir ( repodir ) : raise Conflict ( 'Package repository "{0}" has already exists.' . format ( repodir ) )

Check repository existence .

def generate_package ( params ) : pkg_data = package . PackageData ( params ) pkg_tree = package . PackageTree ( pkg_data ) pkg_tree . generate ( ) pkg_tree . move ( ) VCS ( os . path . join ( pkg_tree . outdir , pkg_tree . name ) , pkg_tree . pkg_data )

Generate package repository .

def print_single ( line , rev ) : if rev is True : seq = rc ( [ '' , line [ 9 ] ] ) [ 1 ] qual = line [ 10 ] [ : : - 1 ] else : seq = line [ 9 ] qual = line [ 10 ] fq = [ '@%s' % line [ 0 ] , seq , '+%s' % line [ 0 ] , qual ] print ( '\n' . join ( fq ) , file = sys . stderr )

print single reads to stderr

def sam2fastq ( sam , singles = False , force = False ) : L , R = None , None for line in sam : if line . startswith ( '@' ) is True : continue line = line . strip ( ) . split ( ) bit = [ True if i == '1' else False for i in bin ( int ( line [ 1 ] ) ) . split ( 'b' ) [ 1 ] [ : : - 1 ] ] while len ( bit ) < 8 : bit . append ( False ) pair , proper , na , nap , rev , mrev , left , right = bit if pair is False : if singles is True : print_single ( line , rev ) continue if rev is True : seq = rc ( [ '' , line [ 9 ] ] ) [ 1 ] qual = line [ 10 ] [ : : - 1 ] else : seq = line [ 9 ] qual = line [ 10 ] if left is True : if L is not None and force is False : print ( 'sam file is not sorted' , file = sys . stderr ) print ( '\te.g.: %s' % ( line [ 0 ] ) , file = sys . stderr ) exit ( ) if L is not None : L = None continue L = [ '@%s' % line [ 0 ] , seq , '+%s' % line [ 0 ] , qual ] if R is not None : yield L yield R L , R = None , None if right is True : if R is not None and force is False : print ( 'sam file is not sorted' , file = sys . stderr ) print ( '\te.g.: %s' % ( line [ 0 ] ) , file = sys . stderr ) exit ( ) if R is not None : R = None continue R = [ '@%s' % line [ 0 ] , seq , '+%s' % line [ 0 ] , qual ] if L is not None : yield L yield R L , R = None , None

convert sam to fastq

def sort_sam ( sam , sort ) : tempdir = '%s/' % ( os . path . abspath ( sam ) . rsplit ( '/' , 1 ) [ 0 ] ) if sort is True : mapping = '%s.sorted.sam' % ( sam . rsplit ( '.' , 1 ) [ 0 ] ) if sam != '-' : if os . path . exists ( mapping ) is False : os . system ( "\ sort -k1 --buffer-size=%sG -T %s -o %s %s\ " % ( sbuffer , tempdir , mapping , sam ) ) else : mapping = 'stdin-sam.sorted.sam' p = Popen ( "sort -k1 --buffer-size=%sG -T %s -o %s" % ( sbuffer , tempdir , mapping ) , stdin = sys . stdin , shell = True ) p . communicate ( ) mapping = open ( mapping ) else : if sam == '-' : mapping = sys . stdin else : mapping = open ( sam ) return mapping

sort sam file

def sub_sam ( sam , percent , sort = True , sbuffer = False ) : mapping = sort_sam ( sam , sort ) pool = [ 1 for i in range ( 0 , percent ) ] + [ 0 for i in range ( 0 , 100 - percent ) ] c = cycle ( [ 1 , 2 ] ) for line in mapping : line = line . strip ( ) . split ( ) if line [ 0 ] . startswith ( '@' ) : yield line continue if int ( line [ 1 ] ) <= 20 : if random . choice ( pool ) == 1 : yield line else : n = next ( c ) if n == 1 : prev = line if n == 2 and random . choice ( pool ) == 1 : yield prev yield line

randomly subset sam file

def fq2fa ( fq ) : c = cycle ( [ 1 , 2 , 3 , 4 ] ) for line in fq : n = next ( c ) if n == 1 : seq = [ '>%s' % ( line . strip ( ) . split ( '@' , 1 ) [ 1 ] ) ] if n == 2 : seq . append ( line . strip ( ) ) yield seq

convert fq to fa

def change_return_type ( f ) : @ wraps ( f ) def wrapper ( * args , ** kwargs ) : if kwargs . has_key ( 'return_type' ) : return_type = kwargs [ 'return_type' ] kwargs . pop ( 'return_type' ) return return_type ( f ( * args , ** kwargs ) ) elif len ( args ) > 0 : return_type = type ( args [ 0 ] ) return return_type ( f ( * args , ** kwargs ) ) else : return f ( * args , ** kwargs ) return wrapper

Converts the returned value of wrapped function to the type of the first arg or to the type specified by a kwarg key return_type s value .

def convert_args_to_sets ( f ) : @ wraps ( f ) def wrapper ( * args , ** kwargs ) : args = ( setify ( x ) for x in args ) return f ( * args , ** kwargs ) return wrapper

Converts all args to set type via self . setify function .

def _init_entri ( self , laman ) : sup = BeautifulSoup ( laman . text , 'html.parser' ) estr = '' for label in sup . find ( 'hr' ) . next_siblings : if label . name == 'hr' : self . entri . append ( Entri ( estr ) ) break if label . name == 'h2' : if estr : self . entri . append ( Entri ( estr ) ) estr = '' estr += str ( label ) . strip ( )

Membuat objek - objek entri dari laman yang diambil .

def _init_kata_dasar ( self , dasar ) : for tiap in dasar : kata = tiap . find ( 'a' ) dasar_no = kata . find ( 'sup' ) kata = ambil_teks_dalam_label ( kata ) self . kata_dasar . append ( kata + ' [{}]' . format ( dasar_no . text . strip ( ) ) if dasar_no else kata )

Memproses kata dasar yang ada dalam nama entri .

def serialisasi ( self ) : return { "nama" : self . nama , "nomor" : self . nomor , "kata_dasar" : self . kata_dasar , "pelafalan" : self . pelafalan , "bentuk_tidak_baku" : self . bentuk_tidak_baku , "varian" : self . varian , "makna" : [ makna . serialisasi ( ) for makna in self . makna ] }

Mengembalikan hasil serialisasi objek Entri ini .

def _makna ( self ) : if len ( self . makna ) > 1 : return '\n' . join ( str ( i ) + ". " + str ( makna ) for i , makna in enumerate ( self . makna , 1 ) ) return str ( self . makna [ 0 ] )

Mengembalikan representasi string untuk semua makna entri ini .

def _nama ( self ) : hasil = self . nama if self . nomor : hasil += " [{}]" . format ( self . nomor ) if self . kata_dasar : hasil = " » ". j oin( s elf. k ata_dasar) » " + h sil return hasil

Mengembalikan representasi string untuk nama entri ini .

def _varian ( self , varian ) : if varian == self . bentuk_tidak_baku : nama = "Bentuk tidak baku" elif varian == self . varian : nama = "Varian" else : return '' return nama + ': ' + ', ' . join ( varian )

Mengembalikan representasi string untuk varian entri ini . Dapat digunakan untuk Varian maupun Bentuk tidak baku .

def _init_kelas ( self , makna_label ) : kelas = makna_label . find ( color = 'red' ) lain = makna_label . find ( color = 'darkgreen' ) info = makna_label . find ( color = 'green' ) if kelas : kelas = kelas . find_all ( 'span' ) if lain : self . kelas = { lain . text . strip ( ) : lain [ 'title' ] . strip ( ) } self . submakna = lain . next_sibling . strip ( ) self . submakna += ' ' + makna_label . find ( color = 'grey' ) . text . strip ( ) else : self . kelas = { k . text . strip ( ) : k [ 'title' ] . strip ( ) for k in kelas } if kelas else { } self . info = info . text . strip ( ) if info else ''

Memproses kelas kata yang ada dalam makna .

def _init_contoh ( self , makna_label ) : indeks = makna_label . text . find ( ': ' ) if indeks != - 1 : contoh = makna_label . text [ indeks + 2 : ] . strip ( ) self . contoh = contoh . split ( '; ' ) else : self . contoh = [ ]

Memproses contoh yang ada dalam makna .

def serialisasi ( self ) : return { "kelas" : self . kelas , "submakna" : self . submakna , "info" : self . info , "contoh" : self . contoh }

Mengembalikan hasil serialisasi objek Makna ini .

def build_sphinx ( pkg_data , projectdir ) : try : version , _minor_version = pkg_data . version . rsplit ( '.' , 1 ) except ValueError : version = pkg_data . version args = ' ' . join ( ( 'sphinx-quickstart' , '--sep' , '-q' , '-p "{name}"' , '-a "{author}"' , '-v "{version}"' , '-r "{release}"' , '-l en' , '--suffix=.rst' , '--master=index' , '--ext-autodoc' , '--ext-viewcode' , '--makefile' , '{projectdir}' ) ) . format ( name = pkg_data . name , author = pkg_data . author , version = version , release = pkg_data . version , projectdir = projectdir ) if subprocess . call ( shlex . split ( args ) ) == 0 : _touch_gitkeep ( projectdir )

Build sphinx documentation .

def bowtiedb ( fa , keepDB ) : btdir = '%s/bt2' % ( os . getcwd ( ) ) if not os . path . exists ( btdir ) : os . mkdir ( btdir ) btdb = '%s/%s' % ( btdir , fa . rsplit ( '/' , 1 ) [ - 1 ] ) if keepDB is True : if os . path . exists ( '%s.1.bt2' % ( btdb ) ) : return btdb p = subprocess . Popen ( 'bowtie2-build -q %s %s' % ( fa , btdb ) , shell = True ) p . communicate ( ) return btdb

make bowtie db

def bowtie ( sam , btd , f , r , u , opt , no_shrink , threads ) : bt2 = 'bowtie2 -x %s -p %s ' % ( btd , threads ) if f is not False : bt2 += '-1 %s -2 %s ' % ( f , r ) if u is not False : bt2 += '-U %s ' % ( u ) bt2 += opt if no_shrink is False : if f is False : bt2 += ' | shrinksam -u -k %s-shrunk.sam ' % ( sam ) else : bt2 += ' | shrinksam -k %s-shrunk.sam ' % ( sam ) else : bt2 += ' > %s.sam' % ( sam ) return bt2

generate bowtie2 command

def crossmap ( fas , reads , options , no_shrink , keepDB , threads , cluster , nodes ) : if cluster is True : threads = '48' btc = [ ] for fa in fas : btd = bowtiedb ( fa , keepDB ) F , R , U = reads if F is not False : if U is False : u = False for i , f in enumerate ( F ) : r = R [ i ] if U is not False : u = U [ i ] sam = '%s/%s-vs-%s' % ( os . getcwd ( ) , fa . rsplit ( '/' , 1 ) [ - 1 ] , f . rsplit ( '/' , 1 ) [ - 1 ] . rsplit ( '.' , 3 ) [ 0 ] ) btc . append ( bowtie ( sam , btd , f , r , u , options , no_shrink , threads ) ) else : f = False r = False for u in U : sam = '%s/%s-vs-%s' % ( os . getcwd ( ) , fa . rsplit ( '/' , 1 ) [ - 1 ] , u . rsplit ( '/' , 1 ) [ - 1 ] . rsplit ( '.' , 3 ) [ 0 ] ) btc . append ( bowtie ( sam , btd , f , r , u , options , no_shrink , threads ) ) if cluster is False : for i in btc : p = subprocess . Popen ( i , shell = True ) p . communicate ( ) else : ID = '' . join ( random . choice ( [ str ( i ) for i in range ( 0 , 9 ) ] ) for _ in range ( 5 ) ) for node , commands in enumerate ( chunks ( btc , nodes ) , 1 ) : bs = open ( '%s/crossmap-qsub.%s.%s.sh' % ( os . getcwd ( ) , ID , node ) , 'w' ) print ( '\n' . join ( commands ) , file = bs ) bs . close ( ) p = subprocess . Popen ( 'qsub -V -N crossmap %s' % ( bs . name ) , shell = True ) p . communicate ( )

map all read sets against all fasta files

def get_conn ( self , * args , ** kwargs ) : connections = self . __connections_for ( 'get_conn' , args = args , kwargs = kwargs ) if len ( connections ) is 1 : return connections [ 0 ] else : return connections

Returns a connection object from the router given args .

def __get_nondirect_init ( self , init ) : crc = init for i in range ( self . Width ) : bit = crc & 0x01 if bit : crc ^= self . Poly crc >>= 1 if bit : crc |= self . MSB_Mask return crc & self . Mask

return the non - direct init if the direct algorithm has been selected .

def reflect ( self , data , width ) : x = data & 0x01 for i in range ( width - 1 ) : data >>= 1 x = ( x << 1 ) | ( data & 0x01 ) return x

reflect a data word i . e . reverts the bit order .

def bit_by_bit ( self , in_data ) : if isinstance ( in_data , str ) : in_data = [ ord ( c ) for c in in_data ] register = self . NonDirectInit for octet in in_data : if self . ReflectIn : octet = self . reflect ( octet , 8 ) for i in range ( 8 ) : topbit = register & self . MSB_Mask register = ( ( register << 1 ) & self . Mask ) | ( ( octet >> ( 7 - i ) ) & 0x01 ) if topbit : register ^= self . Poly for i in range ( self . Width ) : topbit = register & self . MSB_Mask register = ( ( register << 1 ) & self . Mask ) if topbit : register ^= self . Poly if self . ReflectOut : register = self . reflect ( register , self . Width ) return register ^ self . XorOut

Classic simple and slow CRC implementation . This function iterates bit by bit over the augmented input message and returns the calculated CRC value at the end .

def gen_table ( self ) : table_length = 1 << self . TableIdxWidth tbl = [ 0 ] * table_length for i in range ( table_length ) : register = i if self . ReflectIn : register = self . reflect ( register , self . TableIdxWidth ) register = register << ( self . Width - self . TableIdxWidth + self . CrcShift ) for j in range ( self . TableIdxWidth ) : if register & ( self . MSB_Mask << self . CrcShift ) != 0 : register = ( register << 1 ) ^ ( self . Poly << self . CrcShift ) else : register = ( register << 1 ) if self . ReflectIn : register = self . reflect ( register >> self . CrcShift , self . Width ) << self . CrcShift tbl [ i ] = register & ( self . Mask << self . CrcShift ) return tbl

This function generates the CRC table used for the table_driven CRC algorithm . The Python version cannot handle tables of an index width other than 8 . See the generated C code for tables with different sizes instead .

def table_driven ( self , in_data ) : if isinstance ( in_data , str ) : in_data = [ ord ( c ) for c in in_data ] tbl = self . gen_table ( ) register = self . DirectInit << self . CrcShift if not self . ReflectIn : for octet in in_data : tblidx = ( ( register >> ( self . Width - self . TableIdxWidth + self . CrcShift ) ) ^ octet ) & 0xff register = ( ( register << ( self . TableIdxWidth - self . CrcShift ) ) ^ tbl [ tblidx ] ) & ( self . Mask << self . CrcShift ) register = register >> self . CrcShift else : register = self . reflect ( register , self . Width + self . CrcShift ) << self . CrcShift for octet in in_data : tblidx = ( ( register >> self . CrcShift ) ^ octet ) & 0xff register = ( ( register >> self . TableIdxWidth ) ^ tbl [ tblidx ] ) & ( self . Mask << self . CrcShift ) register = self . reflect ( register , self . Width + self . CrcShift ) & self . Mask if self . ReflectOut : register = self . reflect ( register , self . Width ) return register ^ self . XorOut

The Standard table_driven CRC algorithm .

def parse_masked ( seq , min_len ) : nm , masked = [ ] , [ [ ] ] prev = None for base in seq [ 1 ] : if base . isupper ( ) : nm . append ( base ) if masked != [ [ ] ] and len ( masked [ - 1 ] ) < min_len : nm . extend ( masked [ - 1 ] ) del masked [ - 1 ] prev = False elif base . islower ( ) : if prev is False : masked . append ( [ ] ) masked [ - 1 ] . append ( base ) prev = True return nm , masked

parse masked sequence into non - masked and masked regions

def strip_masked ( fasta , min_len , print_masked ) : for seq in parse_fasta ( fasta ) : nm , masked = parse_masked ( seq , min_len ) nm = [ '%s removed_masked >=%s' % ( seq [ 0 ] , min_len ) , '' . join ( nm ) ] yield [ 0 , nm ] if print_masked is True : for i , m in enumerate ( [ i for i in masked if i != [ ] ] , 1 ) : m = [ '%s insertion:%s' % ( seq [ 0 ] , i ) , '' . join ( m ) ] yield [ 1 , m ]

remove masked regions from fasta file as long as they are longer than min_len

def get_relative_abundance ( biomfile ) : biomf = biom . load_table ( biomfile ) norm_biomf = biomf . norm ( inplace = False ) rel_abd = { } for sid in norm_biomf . ids ( ) : rel_abd [ sid ] = { } for otuid in norm_biomf . ids ( "observation" ) : otuname = oc . otu_name ( norm_biomf . metadata ( otuid , axis = "observation" ) [ "taxonomy" ] ) otuname = " " . join ( otuname . split ( "_" ) ) abd = norm_biomf . get_value_by_ids ( otuid , sid ) rel_abd [ sid ] [ otuname ] = abd ast_rel_abd = bc . arcsine_sqrt_transform ( rel_abd ) return ast_rel_abd

Return arcsine transformed relative abundance from a BIOM format file .

def find_otu ( otuid , tree ) : for m in re . finditer ( otuid , tree ) : before , after = tree [ m . start ( ) - 1 ] , tree [ m . start ( ) + len ( otuid ) ] if before in [ "(" , "," , ")" ] and after in [ ":" , ";" ] : return m . start ( ) return None

Find an OTU ID in a Newick - format tree . Return the starting position of the ID or None if not found .

def newick_replace_otuids ( tree , biomf ) : for val , id_ , md in biomf . iter ( axis = "observation" ) : otu_loc = find_otu ( id_ , tree ) if otu_loc is not None : tree = tree [ : otu_loc ] + oc . otu_name ( md [ "taxonomy" ] ) + tree [ otu_loc + len ( id_ ) : ] return tree

Replace the OTU ids in the Newick phylogenetic tree format with truncated OTU names

def genome_info ( genome , info ) : try : scg = info [ '#SCGs' ] dups = info [ '#SCG duplicates' ] length = info [ 'genome size (bp)' ] return [ scg - dups , length , genome ] except : return [ False , False , info [ 'genome size (bp)' ] , genome ]

return genome info for choosing representative

def print_clusters ( fastas , info , ANI ) : header = [ '#cluster' , 'num. genomes' , 'rep.' , 'genome' , '#SCGs' , '#SCG duplicates' , 'genome size (bp)' , 'fragments' , 'list' ] yield header in_cluster = [ ] for cluster_num , cluster in enumerate ( connected_components ( ANI ) ) : cluster = sorted ( [ genome_info ( genome , info [ genome ] ) for genome in cluster ] , key = lambda x : x [ 0 : ] , reverse = True ) rep = cluster [ 0 ] [ - 1 ] cluster = [ i [ - 1 ] for i in cluster ] size = len ( cluster ) for genome in cluster : in_cluster . append ( genome ) try : stats = [ size , rep , genome , info [ genome ] [ '#SCGs' ] , info [ genome ] [ '#SCG duplicates' ] , info [ genome ] [ 'genome size (bp)' ] , info [ genome ] [ '# contigs' ] , cluster ] except : stats = [ size , rep , genome , 'n/a' , 'n/a' , info [ genome ] [ 'genome size (bp)' ] , info [ genome ] [ '# contigs' ] , cluster ] if rep == genome : stats = [ '*%s' % ( cluster_num ) ] + stats else : stats = [ cluster_num ] + stats yield stats try : start = cluster_num + 1 except : start = 0 fastas = set ( [ i . rsplit ( '.' , 1 ) [ 0 ] . rsplit ( '/' , 1 ) [ - 1 ] . rsplit ( '.contigs' ) [ 0 ] for i in fastas ] ) for cluster_num , genome in enumerate ( fastas . difference ( set ( in_cluster ) ) , start ) : try : stats = [ '*%s' % ( cluster_num ) , 1 , genome , genome , info [ genome ] [ '#SCGs' ] , info [ genome ] [ '#SCG duplicates' ] , info [ genome ] [ 'genome size (bp)' ] , info [ genome ] [ '# contigs' ] , [ genome ] ] except : stats = [ '*%s' % ( cluster_num ) , 1 , genome , genome , 'n/a' , 'n/a' , info [ genome ] [ 'genome size (bp)' ] , info [ genome ] [ '# contigs' ] , [ genome ] ] yield stats

choose represenative genome and print cluster information

def parse_ggKbase_tables ( tables , id_type ) : g2info = { } for table in tables : for line in open ( table ) : line = line . strip ( ) . split ( '\t' ) if line [ 0 ] . startswith ( 'name' ) : header = line header [ 4 ] = 'genome size (bp)' header [ 12 ] = '#SCGs' header [ 13 ] = '#SCG duplicates' continue name , code , info = line [ 0 ] , line [ 1 ] , line info = [ to_int ( i ) for i in info ] if id_type is False : if 'UNK' in code or 'unknown' in code : code = name if ( name != code ) and ( name and code in g2info ) : print ( '# duplicate name or code in table(s)' , file = sys . stderr ) print ( '# %s and/or %s' % ( name , code ) , file = sys . stderr ) exit ( ) if name not in g2info : g2info [ name ] = { item : stat for item , stat in zip ( header , info ) } if code not in g2info : g2info [ code ] = { item : stat for item , stat in zip ( header , info ) } else : if id_type == 'name' : ID = name elif id_type == 'code' : ID = code else : print ( '# specify name or code column using -id' , file = sys . stderr ) exit ( ) ID = ID . replace ( ' ' , '' ) g2info [ ID ] = { item : stat for item , stat in zip ( header , info ) } if g2info [ ID ] [ 'genome size (bp)' ] == '' : g2info [ ID ] [ 'genome size (bp)' ] = 0 return g2info

convert ggKbase genome info tables to dictionary

def parse_checkM_tables ( tables ) : g2info = { } for table in tables : for line in open ( table ) : line = line . strip ( ) . split ( '\t' ) if line [ 0 ] . startswith ( 'Bin Id' ) : header = line header [ 8 ] = 'genome size (bp)' header [ 5 ] = '#SCGs' header [ 6 ] = '#SCG duplicates' continue ID , info = line [ 0 ] , line info = [ to_int ( i ) for i in info ] ID = ID . replace ( ' ' , '' ) g2info [ ID ] = { item : stat for item , stat in zip ( header , info ) } if g2info [ ID ] [ 'genome size (bp)' ] == '' : g2info [ ID ] [ 'genome size (bp)' ] = 0 return g2info

convert checkM genome info tables to dictionary

def genome_lengths ( fastas , info ) : if info is False : info = { } for genome in fastas : name = genome . rsplit ( '.' , 1 ) [ 0 ] . rsplit ( '/' , 1 ) [ - 1 ] . rsplit ( '.contigs' ) [ 0 ] if name in info : continue length = 0 fragments = 0 for seq in parse_fasta ( genome ) : length += len ( seq [ 1 ] ) fragments += 1 info [ name ] = { 'genome size (bp)' : length , '# contigs' : fragments } return info

get genome lengths

def get_dbs ( self , attr , args , kwargs , ** fkwargs ) : if not self . _ready : if not self . setup_router ( args = args , kwargs = kwargs , ** fkwargs ) : raise self . UnableToSetupRouter ( ) retval = self . _pre_routing ( attr = attr , args = args , kwargs = kwargs , ** fkwargs ) if retval is not None : args , kwargs = retval if not ( args or kwargs ) : return self . cluster . hosts . keys ( ) try : db_nums = self . _route ( attr = attr , args = args , kwargs = kwargs , ** fkwargs ) except Exception as e : self . _handle_exception ( e ) db_nums = [ ] return self . _post_routing ( attr = attr , db_nums = db_nums , args = args , kwargs = kwargs , ** fkwargs )

Returns a list of db keys to route the given call to .

def setup_router ( self , args , kwargs , ** fkwargs ) : self . _ready = self . _setup_router ( args = args , kwargs = kwargs , ** fkwargs ) return self . _ready

Call method to perform any setup

def _route ( self , attr , args , kwargs , ** fkwargs ) : return self . cluster . hosts . keys ( )

Perform routing and return db_nums

def check_down_connections ( self ) : now = time . time ( ) for db_num , marked_down_at in self . _down_connections . items ( ) : if marked_down_at + self . retry_timeout <= now : self . mark_connection_up ( db_num )

Iterates through all connections which were previously listed as unavailable and marks any that have expired their retry_timeout as being up .

def flush_down_connections ( self ) : self . _get_db_attempts = 0 for db_num in self . _down_connections . keys ( ) : self . mark_connection_up ( db_num )

Marks all connections which were previously listed as unavailable as being up .

def standby ( df , resolution = '24h' , time_window = None ) : if df . empty : raise EmptyDataFrame ( ) df = pd . DataFrame ( df ) def parse_time ( t ) : if isinstance ( t , numbers . Number ) : return pd . Timestamp . utcfromtimestamp ( t ) . time ( ) else : return pd . Timestamp ( t ) . time ( ) if time_window is not None : t_start = parse_time ( time_window [ 0 ] ) t_end = parse_time ( time_window [ 1 ] ) if t_start > t_end : df = df [ ( df . index . time >= t_start ) | ( df . index . time < t_end ) ] else : df = df [ ( df . index . time >= t_start ) & ( df . index . time < t_end ) ] return df . resample ( resolution ) . min ( )

Compute standby power

def share_of_standby ( df , resolution = '24h' , time_window = None ) : p_sb = standby ( df , resolution , time_window ) df = df . resample ( resolution ) . mean ( ) p_tot = df . sum ( ) p_standby = p_sb . sum ( ) share_standby = p_standby / p_tot res = share_standby . iloc [ 0 ] return res

Compute the share of the standby power in the total consumption .

def count_peaks ( ts ) : on_toggles = ts . diff ( ) > 3000 shifted = np . logical_not ( on_toggles . shift ( 1 ) ) result = on_toggles & shifted count = result . sum ( ) return count

Toggle counter for gas boilers

def load_factor ( ts , resolution = None , norm = None ) : if norm is None : norm = ts . max ( ) if resolution is not None : ts = ts . resample ( rule = resolution ) . mean ( ) lf = ts / norm return lf

Calculate the ratio of input vs . norm over a given interval .

def top_hits ( hits , num , column , reverse ) : hits . sort ( key = itemgetter ( column ) , reverse = reverse ) for hit in hits [ 0 : num ] : yield hit

get top hits after sorting by column number

def numBlast_sort ( blast , numHits , evalueT , bitT ) : header = [ '#query' , 'target' , 'pident' , 'alen' , 'mismatch' , 'gapopen' , 'qstart' , 'qend' , 'tstart' , 'tend' , 'evalue' , 'bitscore' ] yield header hmm = { h : [ ] for h in header } for line in blast : if line . startswith ( '#' ) : continue line = line . strip ( ) . split ( '\t' ) line [ 10 ] , line [ 11 ] = float ( line [ 10 ] ) , float ( line [ 11 ] ) evalue , bit = line [ 10 ] , line [ 11 ] if evalueT is not False and evalue > evalueT : continue if bitT is not False and bit < bitT : continue for i , h in zip ( line , header ) : hmm [ h ] . append ( i ) hmm = pd . DataFrame ( hmm ) for query , df in hmm . groupby ( by = [ '#query' ] ) : df = df . sort_values ( by = [ 'bitscore' ] , ascending = False ) for hit in df [ header ] . values [ 0 : numHits ] : yield hit

parse b6 output with sorting

def numBlast ( blast , numHits , evalueT = False , bitT = False , sort = False ) : if sort is True : for hit in numBlast_sort ( blast , numHits , evalueT , bitT ) : yield hit return header = [ '#query' , 'target' , 'pident' , 'alen' , 'mismatch' , 'gapopen' , 'qstart' , 'qend' , 'tstart' , 'tend' , 'evalue' , 'bitscore' ] yield header prev , hits = None , [ ] for line in blast : line = line . strip ( ) . split ( '\t' ) ID = line [ 0 ] line [ 10 ] , line [ 11 ] = float ( line [ 10 ] ) , float ( line [ 11 ] ) evalue , bit = line [ 10 ] , line [ 11 ] if ID != prev : if len ( hits ) > 0 : for hit in top_hits ( hits , numHits , 11 , True ) : yield hit hits = [ ] if evalueT == False and bitT == False : hits . append ( line ) elif evalue <= evalueT and bitT == False : hits . append ( line ) elif evalue <= evalueT and bit >= bitT : hits . append ( line ) elif evalueT == False and bit >= bitT : hits . append ( line ) prev = ID for hit in top_hits ( hits , numHits , 11 , True ) : yield hit

parse b6 output

def numDomtblout ( domtblout , numHits , evalueT , bitT , sort ) : if sort is True : for hit in numDomtblout_sort ( domtblout , numHits , evalueT , bitT ) : yield hit return header = [ '#target name' , 'target accession' , 'tlen' , 'query name' , 'query accession' , 'qlen' , 'full E-value' , 'full score' , 'full bias' , 'domain #' , '# domains' , 'domain c-Evalue' , 'domain i-Evalue' , 'domain score' , 'domain bias' , 'hmm from' , 'hmm to' , 'seq from' , 'seq to' , 'env from' , 'env to' , 'acc' , 'target description' ] yield header prev , hits = None , [ ] for line in domtblout : if line . startswith ( '#' ) : continue line = line . strip ( ) . split ( ) desc = ' ' . join ( line [ 18 : ] ) line = line [ 0 : 18 ] line . append ( desc ) ID = line [ 0 ] + line [ 9 ] line [ 11 ] , line [ 13 ] = float ( line [ 11 ] ) , float ( line [ 13 ] ) evalue , bitscore = line [ 11 ] , line [ 13 ] line [ 11 ] , line [ 13 ] = evalue , bitscore if ID != prev : if len ( hits ) > 0 : for hit in top_hits ( hits , numHits , 13 , True ) : yield hit hits = [ ] if evalueT == False and bitT == False : hits . append ( line ) elif evalue <= evalueT and bitT == False : hits . append ( line ) elif evalue <= evalueT and bit >= bitT : hits . append ( line ) elif evalueT == False and bit >= bitT : hits . append ( line ) prev = ID for hit in top_hits ( hits , numHits , 13 , True ) : yield hit

parse hmm domain table output this version is faster but does not work unless the table is sorted

def stock2fa ( stock ) : seqs = { } for line in stock : if line . startswith ( '#' ) is False and line . startswith ( ' ' ) is False and len ( line ) > 3 : id , seq = line . strip ( ) . split ( ) id = id . rsplit ( '/' , 1 ) [ 0 ] id = re . split ( '[0-9]\|' , id , 1 ) [ - 1 ] if id not in seqs : seqs [ id ] = [ ] seqs [ id ] . append ( seq ) if line . startswith ( '//' ) : break return seqs

convert stockholm to fasta

def week_schedule ( index , on_time = None , off_time = None , off_days = None ) : if on_time is None : on_time = '9:00' if off_time is None : off_time = '17:00' if off_days is None : off_days = [ 'Sunday' , 'Monday' ] if not isinstance ( on_time , datetime . time ) : on_time = pd . to_datetime ( on_time , format = '%H:%M' ) . time ( ) if not isinstance ( off_time , datetime . time ) : off_time = pd . to_datetime ( off_time , format = '%H:%M' ) . time ( ) times = ( index . time >= on_time ) & ( index . time < off_time ) & ( ~ index . weekday_name . isin ( off_days ) ) return pd . Series ( times , index = index )

Return boolean time series following given week schedule .

def carpet ( timeseries , ** kwargs ) : cmap = kwargs . pop ( 'cmap' , cm . coolwarm ) norm = kwargs . pop ( 'norm' , LogNorm ( ) ) interpolation = kwargs . pop ( 'interpolation' , 'nearest' ) cblabel = kwargs . pop ( 'zlabel' , timeseries . name if timeseries . name else '' ) title = kwargs . pop ( 'title' , 'carpet plot: ' + timeseries . name if timeseries . name else '' ) if timeseries . dropna ( ) . empty : print ( 'skipped {} - no data' . format ( title ) ) return ts = timeseries . resample ( '15min' ) . interpolate ( ) vmin = max ( 0.1 , kwargs . pop ( 'vmin' , ts [ ts > 0 ] . min ( ) ) ) vmax = max ( vmin , kwargs . pop ( 'vmax' , ts . quantile ( .999 ) ) ) mpldatetimes = date2num ( ts . index . to_pydatetime ( ) ) ts . index = pd . MultiIndex . from_arrays ( [ np . floor ( mpldatetimes ) , 2 + mpldatetimes % 1 ] ) df = ts . unstack ( ) fig , ax = plt . subplots ( ) extent = [ df . columns [ 0 ] , df . columns [ - 1 ] , df . index [ - 1 ] + 0.5 , df . index [ 0 ] - 0.5 ] im = plt . imshow ( df , vmin = vmin , vmax = vmax , extent = extent , cmap = cmap , aspect = 'auto' , norm = norm , interpolation = interpolation , ** kwargs ) ax . xaxis_date ( ) ax . xaxis . set_major_locator ( HourLocator ( interval = 2 ) ) ax . xaxis . set_major_formatter ( DateFormatter ( '%H:%M' ) ) ax . xaxis . grid ( True ) plt . xlabel ( 'UTC Time' ) ax . yaxis_date ( ) dmin , dmax = ax . yaxis . get_data_interval ( ) number_of_days = ( num2date ( dmax ) - num2date ( dmin ) ) . days if abs ( number_of_days ) <= 35 : ax . yaxis . set_major_locator ( DayLocator ( ) ) else : ax . yaxis . set_major_locator ( AutoDateLocator ( ) ) ax . yaxis . set_major_formatter ( DateFormatter ( "%a, %d %b %Y" ) ) cbticks = np . logspace ( np . log10 ( vmin ) , np . log10 ( vmax ) , 11 , endpoint = True ) cb = plt . colorbar ( format = '%.0f' , ticks = cbticks ) cb . set_label ( cblabel ) plt . title ( title ) return im

Draw a carpet plot of a pandas timeseries .

def calc_pident_ignore_gaps ( a , b ) : m = 0 mm = 0 for A , B in zip ( list ( a ) , list ( b ) ) : if A == '-' or A == '.' or B == '-' or B == '.' : continue if A == B : m += 1 else : mm += 1 try : return float ( float ( m ) / float ( ( m + mm ) ) ) * 100 except : return 0

calculate percent identity

def remove_gaps ( A , B ) : a_seq , b_seq = [ ] , [ ] for a , b in zip ( list ( A ) , list ( B ) ) : if a == '-' or a == '.' or b == '-' or b == '.' : continue a_seq . append ( a ) b_seq . append ( b ) return '' . join ( a_seq ) , '' . join ( b_seq )

skip column if either is a gap

def compare_seqs ( seqs ) : A , B , ignore_gaps = seqs a , b = A [ 1 ] , B [ 1 ] if len ( a ) != len ( b ) : print ( '# reads are not the same length' , file = sys . stderr ) exit ( ) if ignore_gaps is True : pident = calc_pident_ignore_gaps ( a , b ) else : pident = calc_pident ( a , b ) return A [ 0 ] , B [ 0 ] , pident

compare pairs of sequences

def compare_seqs_leven ( seqs ) : A , B , ignore_gaps = seqs a , b = remove_gaps ( A [ 1 ] , B [ 1 ] ) if len ( a ) != len ( b ) : print ( '# reads are not the same length' , file = sys . stderr ) exit ( ) pident = lr ( a , b ) * 100 return A [ 0 ] , B [ 0 ] , pident

calculate Levenshtein ratio of sequences

def pairwise_compare ( afa , leven , threads , print_list , ignore_gaps ) : seqs = { seq [ 0 ] : seq for seq in nr_fasta ( [ afa ] , append_index = True ) } num_seqs = len ( seqs ) pairs = ( ( i [ 0 ] , i [ 1 ] , ignore_gaps ) for i in itertools . combinations ( list ( seqs . values ( ) ) , 2 ) ) pool = multithread ( threads ) if leven is True : pident = pool . map ( compare_seqs_leven , pairs ) else : compare = pool . imap_unordered ( compare_seqs , pairs ) pident = [ i for i in tqdm ( compare , total = ( num_seqs * num_seqs ) / 2 ) ] pool . close ( ) pool . terminate ( ) pool . join ( ) return to_dictionary ( pident , print_list )

make pairwise sequence comparisons between aligned sequences

def print_pairwise ( pw , median = False ) : names = sorted ( set ( [ i for i in pw ] ) ) if len ( names ) != 0 : if '>' in names [ 0 ] : yield [ '#' ] + [ i . split ( '>' ) [ 1 ] for i in names if '>' in i ] else : yield [ '#' ] + names for a in names : if '>' in a : yield [ a . split ( '>' ) [ 1 ] ] + [ pw [ a ] [ b ] for b in names ] else : out = [ ] for b in names : if b in pw [ a ] : if median is False : out . append ( max ( pw [ a ] [ b ] ) ) else : out . append ( np . median ( pw [ a ] [ b ] ) ) else : out . append ( '-' ) yield [ a ] + out

print matrix of pidents to stdout

def print_comps ( comps ) : if comps == [ ] : print ( 'n/a' ) else : print ( '# min: %s, max: %s, mean: %s' % ( min ( comps ) , max ( comps ) , np . mean ( comps ) ) )

print stats for comparisons

def compare_clades ( pw ) : names = sorted ( set ( [ i for i in pw ] ) ) for i in range ( 0 , 4 ) : wi , bt = { } , { } for a in names : for b in pw [ a ] : if ';' not in a or ';' not in b : continue pident = pw [ a ] [ b ] cA , cB = a . split ( ';' ) [ i ] , b . split ( ';' ) [ i ] if i == 0 and '_' in cA and '_' in cB : cA = cA . rsplit ( '_' , 1 ) [ 1 ] cB = cB . rsplit ( '_' , 1 ) [ 1 ] elif '>' in cA or '>' in cB : cA = cA . split ( '>' ) [ 1 ] cB = cB . split ( '>' ) [ 1 ] if cA == cB : if cA not in wi : wi [ cA ] = [ ] wi [ cA ] . append ( pident ) else : if cA not in bt : bt [ cA ] = { } if cB not in bt [ cA ] : bt [ cA ] [ cB ] = [ ] bt [ cA ] [ cB ] . append ( pident ) print ( '\n# min. within' ) for clade , pidents in list ( wi . items ( ) ) : print ( '\t' . join ( [ 'wi:%s' % str ( i ) , clade , str ( min ( pidents ) ) ] ) ) comps = [ ] print ( '\n# max. between' ) for comp in print_pairwise ( bt ) : if comp is not None : print ( '\t' . join ( [ 'bt:%s' % str ( i ) ] + [ str ( j ) for j in comp ] ) ) if comp [ 0 ] != '#' : comps . extend ( [ j for j in comp [ 1 : ] if j != '-' ] ) print_comps ( comps ) comps = [ ] print ( '\n# median between' ) for comp in print_pairwise ( bt , median = True ) : if comp is not None : print ( '\t' . join ( [ 'bt:%s' % str ( i ) ] + [ str ( j ) for j in comp ] ) ) if comp [ 0 ] != '#' : comps . extend ( [ j for j in comp [ 1 : ] if j != '-' ] ) print_comps ( comps )

print min . pident within each clade and then matrix of between - clade max .

def matrix2dictionary ( matrix ) : pw = { } for line in matrix : line = line . strip ( ) . split ( '\t' ) if line [ 0 ] . startswith ( '#' ) : names = line [ 1 : ] continue a = line [ 0 ] for i , pident in enumerate ( line [ 1 : ] ) : b = names [ i ] if a not in pw : pw [ a ] = { } if b not in pw : pw [ b ] = { } if pident != '-' : pident = float ( pident ) pw [ a ] [ b ] = pident pw [ b ] [ a ] = pident return pw

convert matrix to dictionary of comparisons

def setoption ( parser , metadata = None ) : parser . add_argument ( '-v' , action = 'version' , version = __version__ ) subparsers = parser . add_subparsers ( help = 'sub commands help' ) create_cmd = subparsers . add_parser ( 'create' ) create_cmd . add_argument ( 'name' , help = 'Specify Python package name.' ) create_cmd . add_argument ( '-d' , dest = 'description' , action = 'store' , help = 'Short description about your package.' ) create_cmd . add_argument ( '-a' , dest = 'author' , action = 'store' , required = True , help = 'Python package author name.' ) create_cmd . add_argument ( '-e' , dest = 'email' , action = 'store' , required = True , help = 'Python package author email address.' ) create_cmd . add_argument ( '-l' , dest = 'license' , choices = metadata . licenses ( ) . keys ( ) , default = 'GPLv3+' , help = 'Specify license. (default: %(default)s)' ) create_cmd . add_argument ( '-s' , dest = 'status' , choices = metadata . status ( ) . keys ( ) , default = 'Alpha' , help = ( 'Specify development status. ' '(default: %(default)s)' ) ) create_cmd . add_argument ( '--no-check' , action = 'store_true' , help = 'No checking package name in PyPI.' ) create_cmd . add_argument ( '--with-samples' , action = 'store_true' , help = 'Generate package with sample code.' ) group = create_cmd . add_mutually_exclusive_group ( required = True ) group . add_argument ( '-U' , dest = 'username' , action = 'store' , help = 'Specify GitHub username.' ) group . add_argument ( '-u' , dest = 'url' , action = 'store' , type = valid_url , help = 'Python package homepage url.' ) create_cmd . add_argument ( '-o' , dest = 'outdir' , action = 'store' , default = os . path . abspath ( os . path . curdir ) , help = 'Specify output directory. (default: $PWD)' ) list_cmd = subparsers . add_parser ( 'list' ) list_cmd . add_argument ( '-l' , dest = 'licenses' , action = 'store_true' , help = 'show license choices.' )

Set argument parser option .

def parse_options ( metadata ) : parser = argparse . ArgumentParser ( description = '%(prog)s usage:' , prog = __prog__ ) setoption ( parser , metadata = metadata ) return parser

Parse argument options .

def main ( ) : try : pkg_version = Update ( ) if pkg_version . updatable ( ) : pkg_version . show_message ( ) metadata = control . retreive_metadata ( ) parser = parse_options ( metadata ) argvs = sys . argv if len ( argvs ) <= 1 : parser . print_help ( ) sys . exit ( 1 ) args = parser . parse_args ( ) control . print_licences ( args , metadata ) control . check_repository_existence ( args ) control . check_package_existence ( args ) control . generate_package ( args ) except ( RuntimeError , BackendFailure , Conflict ) as exc : sys . stderr . write ( '{0}\n' . format ( exc ) ) sys . exit ( 1 )

Execute main processes .

def _check_or_set_default_params ( self ) : if not hasattr ( self , 'date' ) : self . _set_param ( 'date' , datetime . utcnow ( ) . strftime ( '%Y-%m-%d' ) ) if not hasattr ( self , 'version' ) : self . _set_param ( 'version' , self . default_version ) if not hasattr ( self , 'description' ) or self . description is None : getattr ( self , '_set_param' ) ( 'description' , self . warning_message )

Check key and set default vaule when it does not exists .

def move ( self ) : if not os . path . isdir ( self . outdir ) : os . makedirs ( self . outdir ) shutil . move ( self . tmpdir , os . path . join ( self . outdir , self . name ) )

Move directory from working directory to output directory .

def vcs_init ( self ) : VCS ( os . path . join ( self . outdir , self . name ) , self . pkg_data )

Initialize VCS repository .

def find_steam_location ( ) : if registry is None : return None key = registry . CreateKey ( registry . HKEY_CURRENT_USER , "Software\Valve\Steam" ) return registry . QueryValueEx ( key , "SteamPath" ) [ 0 ]

Finds the location of the current Steam installation on Windows machines . Returns None for any non - Windows machines or for Windows machines where Steam is not installed .

def plot_PCoA ( cat_data , otu_name , unifrac , names , colors , xr , yr , outDir , save_as , plot_style ) : fig = plt . figure ( figsize = ( 14 , 8 ) ) ax = fig . add_subplot ( 111 ) for i , cat in enumerate ( cat_data ) : plt . scatter ( cat_data [ cat ] [ "pc1" ] , cat_data [ cat ] [ "pc2" ] , cat_data [ cat ] [ "size" ] , color = colors [ cat ] , alpha = 0.85 , marker = "o" , edgecolor = "black" , label = cat ) lgnd = plt . legend ( loc = "best" , scatterpoints = 3 , fontsize = 13 ) for i in range ( len ( colors . keys ( ) ) ) : lgnd . legendHandles [ i ] . _sizes = [ 80 ] plt . title ( " " . join ( otu_name . split ( "_" ) ) , style = "italic" ) plt . ylabel ( "PC2 (Percent Explained Variance {:.3f}%)" . format ( float ( unifrac [ "varexp" ] [ 1 ] ) ) ) plt . xlabel ( "PC1 (Percent Explained Variance {:.3f}%)" . format ( float ( unifrac [ "varexp" ] [ 0 ] ) ) ) plt . xlim ( round ( xr [ 0 ] * 1.5 , 1 ) , round ( xr [ 1 ] * 1.5 , 1 ) ) plt . ylim ( round ( yr [ 0 ] * 1.5 , 1 ) , round ( yr [ 1 ] * 1.5 , 1 ) ) if plot_style : gu . ggplot2_style ( ax ) fc = "0.8" else : fc = "none" fig . savefig ( os . path . join ( outDir , "_" . join ( otu_name . split ( ) ) ) + "." + save_as , facecolor = fc , edgecolor = "none" , format = save_as , bbox_inches = "tight" , pad_inches = 0.2 ) plt . close ( fig )

Plot PCoA principal coordinates scaled by the relative abundances of otu_name .

def split_by_category ( biom_cols , mapping , category_id ) : columns = defaultdict ( list ) for i , col in enumerate ( biom_cols ) : columns [ mapping [ col [ 'id' ] ] [ category_id ] ] . append ( ( i , col ) ) return columns

Split up the column data in a biom table by mapping category value .

def print_line ( l ) : print_lines = [ '# STOCKHOLM' , '#=GF' , '#=GS' , ' ' ] if len ( l . split ( ) ) == 0 : return True for start in print_lines : if l . startswith ( start ) : return True return False

print line if starts with ...

def stock2one ( stock ) : lines = { } for line in stock : line = line . strip ( ) if print_line ( line ) is True : yield line continue if line . startswith ( '//' ) : continue ID , seq = line . rsplit ( ' ' , 1 ) if ID not in lines : lines [ ID ] = '' else : seq = seq . strip ( ) lines [ ID ] += seq for ID , line in lines . items ( ) : yield '\t' . join ( [ ID , line ] ) yield '\n//'

convert stockholm to single line format

def math_func ( f ) : @ wraps ( f ) def wrapper ( * args , ** kwargs ) : if len ( args ) > 0 : return_type = type ( args [ 0 ] ) if kwargs . has_key ( 'return_type' ) : return_type = kwargs [ 'return_type' ] kwargs . pop ( 'return_type' ) return return_type ( f ( * args , ** kwargs ) ) args = list ( ( setify ( x ) for x in args ) ) return return_type ( f ( * args , ** kwargs ) ) return wrapper

Statics the methods . wut .