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Source code for galaxy.datatypes.mothur

"""
Mothur Metagenomics Datatypes
"""
import logging
import re
import sys

from galaxy.datatypes.data import Text
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.sniff import (
    get_headers,
    iter_headers
)
from galaxy.datatypes.tabular import Tabular

log = logging.getLogger(__name__)


[docs]class Otu(Text): file_ext = 'mothur.otu' MetadataElement(name="columns", default=0, desc="Number of columns", readonly=True, visible=True, no_value=0) MetadataElement(name="labels", default=[], desc="Label Names", readonly=True, visible=True, no_value=[]) MetadataElement(name="otulabels", default=[], desc="OTU Names", readonly=True, visible=True, no_value=[])
[docs] def __init__(self, **kwd): super(Otu, self).__init__(**kwd)
[docs] def set_meta(self, dataset, overwrite=True, **kwd): """ Set metadata for Otu files. >>> from galaxy.datatypes.sniff import get_test_fname >>> from galaxy.util.bunch import Bunch >>> dataset = Bunch() >>> dataset.metadata = Bunch >>> otu = Otu() >>> dataset.file_name = get_test_fname( 'mothur_datatypetest_true.mothur.otu' ) >>> dataset.has_data = lambda: True >>> otu.set_meta(dataset) >>> dataset.metadata.columns 100 >>> len(dataset.metadata.labels) == 37 True >>> len(dataset.metadata.otulabels) == 98 True """ super(Otu, self).set_meta(dataset, overwrite=overwrite, **kwd) if dataset.has_data(): label_names = set() otulabel_names = set() ncols = 0 data_lines = 0 comment_lines = 0 headers = iter_headers(dataset.file_name, sep='\t', count=-1) first_line = get_headers(dataset.file_name, sep='\t', count=1) if first_line: first_line = first_line[0] # set otulabels if len(first_line) > 2: otulabel_names = first_line[2:] # set label names and number of lines for line in headers: if len(line) >= 2 and not line[0].startswith('@'): data_lines += 1 ncols = max(ncols, len(line)) label_names.add(line[0]) else: comment_lines += 1 # Set the discovered metadata values for the dataset dataset.metadata.data_lines = data_lines dataset.metadata.columns = ncols dataset.metadata.labels = list(label_names) dataset.metadata.labels.sort() dataset.metadata.otulabels = list(otulabel_names) dataset.metadata.otulabels.sort()
[docs] def sniff(self, filename): """ Determines whether the file is otu (operational taxonomic unit) format >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.otu' ) >>> Otu().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.otu' ) >>> Otu().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@'): if len(line) < 2: return False if count >= 1: try: check = int(line[1]) if check + 2 != len(line): return False except ValueError: return False count += 1 if count > 2: return True return False
[docs]class Sabund(Otu): file_ext = 'mothur.sabund'
[docs] def __init__(self, **kwd): """ http://www.mothur.org/wiki/Sabund_file """ super(Sabund, self).__init__(**kwd)
[docs] def init_meta(self, dataset, copy_from=None): super(Sabund, self).init_meta(dataset, copy_from=copy_from)
[docs] def sniff(self, filename): """ Determines whether the file is otu (operational taxonomic unit) format label<TAB>count[<TAB>value(1..n)] >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.sabund' ) >>> Sabund().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.sabund' ) >>> Sabund().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@'): if len(line) < 2: return False try: check = int(line[1]) if check + 2 != len(line): return False for i in range(2, len(line)): int(line[i]) except ValueError: return False count += 1 if count > 0: return True return False
[docs]class GroupAbund(Otu): file_ext = 'mothur.shared' MetadataElement(name="groups", default=[], desc="Group Names", readonly=True, visible=True, no_value=[])
[docs] def __init__(self, **kwd): super(GroupAbund, self).__init__(**kwd)
[docs] def init_meta(self, dataset, copy_from=None): super(GroupAbund, self).init_meta(dataset, copy_from=copy_from)
[docs] def set_meta(self, dataset, overwrite=True, skip=1, **kwd): super(GroupAbund, self).set_meta(dataset, overwrite=overwrite, **kwd) # See if file starts with header line if dataset.has_data(): label_names = set() group_names = set() data_lines = 0 comment_lines = 0 ncols = 0 headers = iter_headers(dataset.file_name, sep='\t', count=-1) for line in headers: if line[0] == 'label' and line[1] == 'Group': skip = 1 comment_lines += 1 else: skip = 0 data_lines += 1 ncols = max(ncols, len(line)) label_names.add(line[0]) group_names.add(line[1]) # Set the discovered metadata values for the dataset dataset.metadata.data_lines = data_lines dataset.metadata.columns = ncols dataset.metadata.labels = list(label_names) dataset.metadata.labels.sort() dataset.metadata.groups = list(group_names) dataset.metadata.groups.sort() dataset.metadata.skip = skip
[docs] def sniff(self, filename, vals_are_int=False): """ Determines whether the file is a otu (operational taxonomic unit) Shared format label<TAB>group<TAB>count[<TAB>value(1..n)] The first line is column headings as of Mothur v 1.2 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.shared' ) >>> GroupAbund().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.shared' ) >>> GroupAbund().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@'): if len(line) < 3: return False if count > 0 or line[0] != 'label': try: check = int(line[2]) if check + 3 != len(line): return False for i in range(3, len(line)): if vals_are_int: int(line[i]) else: float(line[i]) except ValueError: return False count += 1 if count > 1: return True return False
[docs]class SecondaryStructureMap(Tabular): file_ext = 'mothur.map'
[docs] def __init__(self, **kwd): """Initialize secondary structure map datatype""" super(SecondaryStructureMap, self).__init__(**kwd) self.column_names = ['Map']
[docs] def sniff(self, filename): """ Determines whether the file is a secondary structure map format A single column with an integer value which indicates the row that this row maps to. Check to make sure if structMap[10] = 380 then structMap[380] = 10 and vice versa. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.map' ) >>> SecondaryStructureMap().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.map' ) >>> SecondaryStructureMap().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') line_num = 0 rowidxmap = {} for line in headers: line_num += 1 if len(line) > 1: return False try: pointer = int(line[0]) if pointer > line_num: rowidxmap[pointer] = line_num elif pointer > 0 or line_num in rowidxmap: if rowidxmap[line_num] != pointer: return False except (ValueError, KeyError): return False if line_num < 3: return False return True
[docs]class AlignCheck(Tabular): file_ext = 'mothur.align.check'
[docs] def __init__(self, **kwd): """Initialize AlignCheck datatype""" super(AlignCheck, self).__init__(**kwd) self.column_names = ['name', 'pound', 'dash', 'plus', 'equal', 'loop', 'tilde', 'total'] self.column_types = ['str', 'int', 'int', 'int', 'int', 'int', 'int', 'int'] self.comment_lines = 1
[docs] def set_meta(self, dataset, overwrite=True, **kwd): super(AlignCheck, self).set_meta(dataset, overwrite=overwrite, **kwd) dataset.metadata.column_names = self.column_names dataset.metadata.column_types = self.column_types dataset.metadata.comment_lines = self.comment_lines if isinstance(dataset.metadata.data_lines, int): dataset.metadata.data_lines -= self.comment_lines
[docs]class AlignReport(Tabular): """ QueryName QueryLength TemplateName TemplateLength SearchMethod SearchScore AlignmentMethod QueryStart QueryEnd TemplateStart TemplateEnd PairwiseAlignmentLength GapsInQuery GapsInTemplate LongestInsert SimBtwnQuery&Template AY457915 501 82283 1525 kmer 89.07 needleman 5 501 1 499 499 2 0 0 97.6 """ file_ext = 'mothur.align.report'
[docs] def __init__(self, **kwd): """Initialize AlignCheck datatype""" super(AlignReport, self).__init__(**kwd) self.column_names = ['QueryName', 'QueryLength', 'TemplateName', 'TemplateLength', 'SearchMethod', 'SearchScore', 'AlignmentMethod', 'QueryStart', 'QueryEnd', 'TemplateStart', 'TemplateEnd', 'PairwiseAlignmentLength', 'GapsInQuery', 'GapsInTemplate', 'LongestInsert', 'SimBtwnQuery&Template' ]
[docs]class DistanceMatrix(Text): file_ext = 'mothur.dist' """Add metadata elements""" MetadataElement(name="sequence_count", default=0, desc="Number of sequences", readonly=True, visible=True, optional=True, no_value='?')
[docs] def init_meta(self, dataset, copy_from=None): super(DistanceMatrix, self).init_meta(dataset, copy_from=copy_from)
[docs] def set_meta(self, dataset, overwrite=True, skip=0, **kwd): super(DistanceMatrix, self).set_meta(dataset, overwrite=overwrite, skip=skip, **kwd) headers = iter_headers(dataset.file_name, sep='\t') for line in headers: if not line[0].startswith('@'): try: dataset.metadata.sequence_count = int(''.join(line)) # seq count sometimes preceded by tab break except Exception as e: if not isinstance(self, PairwiseDistanceMatrix): log.warning("DistanceMatrix set_meta %s" % e)
[docs]class LowerTriangleDistanceMatrix(DistanceMatrix): file_ext = 'mothur.lower.dist'
[docs] def __init__(self, **kwd): """Initialize secondary structure map datatype""" super(LowerTriangleDistanceMatrix, self).__init__(**kwd)
[docs] def init_meta(self, dataset, copy_from=None): super(LowerTriangleDistanceMatrix, self).init_meta(dataset, copy_from=copy_from)
[docs] def sniff(self, filename): """ Determines whether the file is a lower-triangle distance matrix (phylip) format The first line has the number of sequences in the matrix. The remaining lines have the sequence name followed by a list of distances from all preceeding sequences 5 # possibly but not always preceded by a tab :/ U68589 U68590 0.3371 U68591 0.3609 0.3782 U68592 0.4155 0.3197 0.4148 U68593 0.2872 0.1690 0.3361 0.2842 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.lower.dist' ) >>> LowerTriangleDistanceMatrix().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.lower.dist' ) >>> LowerTriangleDistanceMatrix().sniff( fname ) False """ numlines = 300 headers = iter_headers(filename, sep='\t', count=numlines) line_num = 0 for line in headers: if not line[0].startswith('@'): # first line should contain the number of sequences in the file if line_num == 0: if len(line) > 2: return False else: try: sequence_count = int(''.join(line)) except ValueError: return False else: # number of fields should equal the line number if len(line) != (line_num): return False try: # Distances should be floats for column in line[2:]: float(column) except ValueError: return False line_num += 1 # check if the number of lines in the file was as expected if line_num == sequence_count + 1 or line_num == numlines + 1: return True return False
[docs]class SquareDistanceMatrix(DistanceMatrix): file_ext = 'mothur.square.dist'
[docs] def __init__(self, **kwd): super(SquareDistanceMatrix, self).__init__(**kwd)
[docs] def init_meta(self, dataset, copy_from=None): super(SquareDistanceMatrix, self).init_meta(dataset, copy_from=copy_from)
[docs] def sniff(self, filename): """ Determines whether the file is a square distance matrix (Column-formatted distance matrix) format The first line has the number of sequences in the matrix. The following lines have the sequence name in the first column plus a column for the distance to each sequence in the row order in which they appear in the matrix. 3 U68589 0.0000 0.3371 0.3610 U68590 0.3371 0.0000 0.3783 U68590 0.3371 0.0000 0.3783 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.square.dist' ) >>> SquareDistanceMatrix().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.square.dist' ) >>> SquareDistanceMatrix().sniff( fname ) False """ numlines = 300 headers = iter_headers(filename, sep='\t', count=numlines) line_num = 0 for line in headers: if not line[0].startswith('@'): if line_num == 0: if len(line) > 2: return False else: try: sequence_count = int(''.join(line)) except ValueError: return False else: # number of fields should equal the number of sequences if len(line) != sequence_count + 1: return False try: # Distances should be floats for column in line[2:]: float(column) except ValueError: return False line_num += 1 # check if the number of lines in the file was as expected if line_num == sequence_count + 1 or line_num == numlines + 1: return True return False
[docs]class PairwiseDistanceMatrix(DistanceMatrix, Tabular): file_ext = 'mothur.pair.dist'
[docs] def __init__(self, **kwd): """Initialize secondary structure map datatype""" super(PairwiseDistanceMatrix, self).__init__(**kwd) self.column_names = ['Sequence', 'Sequence', 'Distance'] self.column_types = ['str', 'str', 'float']
[docs] def set_meta(self, dataset, overwrite=True, skip=None, **kwd): super(PairwiseDistanceMatrix, self).set_meta(dataset, overwrite=overwrite, skip=skip, **kwd)
[docs] def sniff(self, filename): """ Determines whether the file is a pairwise distance matrix (Column-formatted distance matrix) format The first and second columns have the sequence names and the third column is the distance between those sequences. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.pair.dist' ) >>> PairwiseDistanceMatrix().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.pair.dist' ) >>> PairwiseDistanceMatrix().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@'): if len(line) != 3: return False try: float(line[2]) try: # See if it's also an integer int(line[2]) except ValueError: # At least one value is not an integer all_ints = False except ValueError: return False count += 1 if count > 2: return not all_ints return False
[docs]class Names(Tabular): file_ext = 'mothur.names'
[docs] def __init__(self, **kwd): """ http://www.mothur.org/wiki/Name_file Name file shows the relationship between a representative sequence(col 1) and the sequences(comma-separated) it represents(col 2) """ super(Names, self).__init__(**kwd) self.column_names = ['name', 'representatives'] self.columns = 2
[docs]class Summary(Tabular): file_ext = 'mothur.summary'
[docs] def __init__(self, **kwd): """summarizes the quality of sequences in an unaligned or aligned fasta-formatted sequence file""" super(Summary, self).__init__(**kwd) self.column_names = ['seqname', 'start', 'end', 'nbases', 'ambigs', 'polymer'] self.columns = 6
[docs]class Group(Tabular): file_ext = 'mothur.groups' MetadataElement(name="groups", default=[], desc="Group Names", readonly=True, visible=True, no_value=[])
[docs] def __init__(self, **kwd): """ http://www.mothur.org/wiki/Groups_file Group file assigns sequence (col 1) to a group (col 2) """ super(Group, self).__init__(**kwd) self.column_names = ['name', 'group'] self.columns = 2
[docs] def set_meta(self, dataset, overwrite=True, skip=None, max_data_lines=None, **kwd): super(Group, self).set_meta(dataset, overwrite, skip, max_data_lines) group_names = set() headers = iter_headers(dataset.file_name, sep='\t', count=-1) for line in headers: if len(line) > 1: group_names.add(line[1]) dataset.metadata.groups = list(group_names)
[docs]class AccNos(Tabular): file_ext = 'mothur.accnos'
[docs] def __init__(self, **kwd): """A list of names""" super(AccNos, self).__init__(**kwd) self.column_names = ['name'] self.columns = 1
[docs]class Oligos(Text): file_ext = 'mothur.oligos'
[docs] def sniff(self, filename): """ http://www.mothur.org/wiki/Oligos_File Determines whether the file is a otu (operational taxonomic unit) format >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.oligos' ) >>> Oligos().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.oligos' ) >>> Oligos().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@') and not line[0].startswith('#'): if len(line) == 2 and line[0] in ['forward', 'reverse']: count += 1 continue elif len(line) == 3 and line[0] == 'barcode': count += 1 continue else: return False if count > 0: return True return False
[docs]class Frequency(Tabular): file_ext = 'mothur.freq'
[docs] def __init__(self, **kwd): """A list of names""" super(Frequency, self).__init__(**kwd) self.column_names = ['position', 'frequency'] self.column_types = ['int', 'float']
[docs] def sniff(self, filename): """ Determines whether the file is a frequency tabular format for chimera analysis #1.14.0 0 0.000 1 0.000 ... 155 0.975 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.freq' ) >>> Frequency().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.freq' ) >>> Frequency().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@'): if count == 0: # first line should be #<version string> if not line[0].startswith('#') and len(line) == 1: return False else: # all other lines should be <int> <float> if len(line) != 2: return False try: int(line[0]) float(line[1]) except Exception: return False count += 1 if count > 1: return True return False
[docs]class Quantile(Tabular): file_ext = 'mothur.quan' MetadataElement(name="filtered", default=False, no_value=False, optional=True, desc="Quantiles calculated using a mask", readonly=True) MetadataElement(name="masked", default=False, no_value=False, optional=True, desc="Quantiles calculated using a frequency filter", readonly=True)
[docs] def __init__(self, **kwd): """Quantiles for chimera analysis""" super(Quantile, self).__init__(**kwd) self.column_names = ['num', 'ten', 'twentyfive', 'fifty', 'seventyfive', 'ninetyfive', 'ninetynine'] self.column_types = ['int', 'float', 'float', 'float', 'float', 'float', 'float']
[docs] def sniff(self, filename): """ Determines whether the file is a quantiles tabular format for chimera analysis 1 0 0 0 0 0 0 2 0.309198 0.309198 0.37161 0.37161 0.37161 0.37161 3 0.510982 0.563213 0.693529 0.858939 1.07442 1.20608 ... >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.quan' ) >>> Quantile().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.quan' ) >>> Quantile().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 for line in headers: if not line[0].startswith('@') and not line[0].startswith('#'): if len(line) != 7: return False try: int(line[0]) float(line[1]) float(line[2]) float(line[3]) float(line[4]) float(line[5]) float(line[6]) except Exception: return False count += 1 if count > 0: return True return False
[docs]class LaneMask(Text): file_ext = 'mothur.filter'
[docs] def sniff(self, filename): """ Determines whether the file is a lane mask filter: 1 line consisting of zeros and ones. >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.filter' ) >>> LaneMask().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.filter' ) >>> LaneMask().sniff( fname ) False """ headers = get_headers(filename, sep='\t', count=2) if len(headers) != 1 or len(headers[0]) != 1: return False if not re.match('^[01]+$', headers[0][0]): return False return True
[docs]class CountTable(Tabular): MetadataElement(name="groups", default=[], desc="Group Names", readonly=True, visible=True, no_value=[]) file_ext = 'mothur.count_table'
[docs] def __init__(self, **kwd): """ http://www.mothur.org/wiki/Count_File A table with first column names and following columns integer counts # Example 1: Representative_Sequence total U68630 1 U68595 1 U68600 1 # Example 2 (with group columns): Representative_Sequence total forest pasture U68630 1 1 0 U68595 1 1 0 U68600 1 1 0 U68591 1 1 0 U68647 1 0 1 """ super(CountTable, self).__init__(**kwd) self.column_names = ['name', 'total']
[docs] def set_meta(self, dataset, overwrite=True, skip=1, max_data_lines=None, **kwd): super(CountTable, self).set_meta(dataset, overwrite=overwrite, **kwd) headers = get_headers(dataset.file_name, sep='\t', count=1) colnames = headers[0] dataset.metadata.column_types = ['str'] + (['int'] * (len(headers[0]) - 1)) if len(colnames) > 1: dataset.metadata.columns = len(colnames) if len(colnames) > 2: dataset.metadata.groups = colnames[2:] dataset.metadata.comment_lines = 1 if isinstance(dataset.metadata.data_lines, int): dataset.metadata.data_lines -= 1
[docs]class RefTaxonomy(Tabular): file_ext = 'mothur.ref.taxonomy'
[docs] def __init__(self, **kwd): super(RefTaxonomy, self).__init__(**kwd) self.column_names = ['name', 'taxonomy']
[docs] def sniff(self, filename): """ Determines whether the file is a Reference Taxonomy http://www.mothur.org/wiki/Taxonomy_outline A table with 2 or 3 columns: - SequenceName - Taxonomy (semicolon-separated taxonomy in descending order) - integer ? Example: 2-column (http://www.mothur.org/wiki/Taxonomy_outline) X56533.1 Eukaryota;Alveolata;Ciliophora;Intramacronucleata;Oligohymenophorea;Hymenostomatida;Tetrahymenina;Glaucomidae;Glaucoma; X97975.1 Eukaryota;Parabasalidea;Trichomonada;Trichomonadida;unclassified_Trichomonadida; AF052717.1 Eukaryota;Parabasalidea; Example: 3-column (http://vamps.mbl.edu/resources/databases.php) v3_AA008 Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus 5 v3_AA016 Bacteria 120 v3_AA019 Archaea;Crenarchaeota;Marine_Group_I 1 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.ref.taxonomy' ) >>> RefTaxonomy().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.ref.taxonomy' ) >>> RefTaxonomy().sniff( fname ) False """ headers = iter_headers(filename, sep='\t', count=300) count = 0 pat_prog = re.compile('^([^ \t\n\r\x0c\x0b;]+([(]\\d+[)])?(;[^ \t\n\r\x0c\x0b;]+([(]\\d+[)])?)*(;)?)$') found_semicolons = False for line in headers: if not line[0].startswith('@') and not line[0].startswith('#'): if not (2 <= len(line) <= 3): return False if not pat_prog.match(line[1]): return False if not found_semicolons and line[1].find(';') > -1: found_semicolons = True if len(line) == 3: try: int(line[2]) except Exception: return False count += 1 if count > 0: # Require that at least one entry has semicolons in the 2nd column return found_semicolons return False
[docs]class ConsensusTaxonomy(Tabular): file_ext = 'mothur.cons.taxonomy'
[docs] def __init__(self, **kwd): """A list of names""" super(ConsensusTaxonomy, self).__init__(**kwd) self.column_names = ['OTU', 'count', 'taxonomy']
[docs]class TaxonomySummary(Tabular): file_ext = 'mothur.tax.summary'
[docs] def __init__(self, **kwd): """A Summary of taxon classification""" super(TaxonomySummary, self).__init__(**kwd) self.column_names = ['taxlevel', 'rankID', 'taxon', 'daughterlevels', 'total']
[docs]class Axes(Tabular): file_ext = 'mothur.axes'
[docs] def __init__(self, **kwd): """Initialize axes datatype""" super(Axes, self).__init__(**kwd)
[docs] def sniff(self, filename): """ Determines whether the file is an axes format The first line may have column headings. The following lines have the name in the first column plus float columns for each axis. ==> 98_sq_phylip_amazon.fn.unique.pca.axes <== group axis1 axis2 forest 0.000000 0.145743 pasture 0.145743 0.000000 ==> 98_sq_phylip_amazon.nmds.axes <== axis1 axis2 U68589 0.262608 -0.077498 U68590 0.027118 0.195197 U68591 0.329854 0.014395 >>> from galaxy.datatypes.sniff import get_test_fname >>> fname = get_test_fname( 'mothur_datatypetest_true.mothur.axes' ) >>> Axes().sniff( fname ) True >>> fname = get_test_fname( 'mothur_datatypetest_false.mothur.axes' ) >>> Axes().sniff( fname ) False """ headers = iter_headers(filename, sep='\t') count = 0 col_cnt = None all_integers = True for line in headers: if count != 0: if col_cnt is None: col_cnt = len(line) if col_cnt < 2: return False else: if len(line) != col_cnt: return False try: for i in range(1, col_cnt): check = float(line[i]) # Check abs value is <= 1.0 if abs(check) > 1.0: return False # Also test for whether value is an integer try: check = int(line[i]) except ValueError: all_integers = False except ValueError: return False count += 1 if count > 0: return not all_integers return False
[docs]class SffFlow(Tabular): MetadataElement(name="flow_values", default="", no_value="", optional=True, desc="Total number of flow values", readonly=True) MetadataElement(name="flow_order", default="TACG", no_value="TACG", desc="Total number of flow values", readonly=False) file_ext = 'mothur.sff.flow' """ http://www.mothur.org/wiki/Flow_file The first line is the total number of flow values - 800 for Titanium data. For GS FLX it would be 400. Following lines contain: - SequenceName - the number of useable flows as defined by 454's software - the flow intensity for each base going in the order of TACG. Example: 800 GQY1XT001CQL4K 85 1.04 0.00 1.00 0.02 0.03 1.02 0.05 ... GQY1XT001CQIRF 84 1.02 0.06 0.98 0.06 0.09 1.05 0.07 ... GQY1XT001CF5YW 88 1.02 0.02 1.01 0.04 0.06 1.02 0.03 ... """
[docs] def __init__(self, **kwd): super(SffFlow, self).__init__(**kwd)
[docs] def set_meta(self, dataset, overwrite=True, skip=1, max_data_lines=None, **kwd): super(SffFlow, self).set_meta(dataset, overwrite, 1, max_data_lines) headers = get_headers(dataset.file_name, sep='\t', count=1) try: flow_values = int(headers[0][0]) dataset.metadata.flow_values = flow_values except Exception as e: log.warning("SffFlow set_meta %s" % e)
[docs] def make_html_table(self, dataset, skipchars=[]): """Create HTML table, used for displaying peek""" try: out = '<table cellspacing="0" cellpadding="3">' # Generate column header out += '<tr>' out += '<th>%d. Name</th>' % 1 out += '<th>%d. Flows</th>' % 2 for i in range(3, dataset.metadata.columns + 1): base = dataset.metadata.flow_order[(i + 1) % 4] out += '<th>%d. %d %s</th>' % (i - 2, base) out += '</tr>' out += self.make_html_peek_rows(dataset, skipchars=skipchars) out += '</table>' except Exception as exc: out = "Can't create peek %s" % str(exc) return out
if __name__ == '__main__': import doctest doctest.testmod(sys.modules[__name__])