Added read_ftsv in dataset.py. This method reads fluents tab separated values
files and returns a dataset.
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060732d980
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@ -1,8 +1,9 @@
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from scipy import ndarray,atleast_2d,asarray,intersect1d
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from scipy import ndarray,atleast_2d,asarray,intersect1d,zeros
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from scipy import sort as array_sort
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from itertools import izip
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import shelve
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import copy
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import re
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class Dataset:
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"""The Dataset base class.
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@ -381,3 +382,73 @@ class Selection(dict):
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def select(self, axis, labels):
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self[axis] = labels
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def read_ftsv(fd):
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split_re = re.compile('^#\s*(\w+)\s*:\s*(.+)')
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dimensions = []
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identifiers = {}
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type = 'dataset'
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name = 'Unnamed dataset'
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graphtype = 'graph'
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# Read header lines from file.
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line = fd.readline()
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while line:
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m = split_re.match(line)
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if m:
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key, val = m.groups()
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# The line is on the form;
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# dimension: dimname id1 id2 id3 ...
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if key == 'dimension':
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values = [v.strip() for v in val.split(' ')]
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dimensions.append(values[0])
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identifiers[values[0]] = values[1:]
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# Read type of dataset.
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# Should be dataset, category, or network
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elif key == 'type':
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type = val
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elif key == 'name':
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name = val
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elif key == 'graphtype':
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graphtype = val
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else:
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break
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line = fd.readline()
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# Dimensions in the form [(dim1, [id1, id2, id3 ..) ...]
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dims = [(x, identifiers[x]) for x in dimensions]
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dim_lengths = [len(identifiers[x]) for x in dimensions]
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# Create matrix
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if type == 'category':
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matrix = zeros(dim_lengths, dtype=bool)
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elif type == 'network':
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matrix = zeros(dim_lengths)
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else:
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matrix = zeros(dim_lengths)
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line = fd.readline()
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y = 0
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while line:
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values = line.split()
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for x, v in enumerate(values):
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matrix[y,x] = float(v)
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y += 1
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line = fd.readline()
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# Create dataset of specified type
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if type == 'category':
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ds = CategoryDataset(matrix, dims)
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elif type == 'network':
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ds = GraphDataset(matrix, dims)
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else:
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ds = Dataset(matrix, dims)
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return ds
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@ -4,6 +4,7 @@ import geneontology
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#import gostat
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from scipy import array, randn, log, ones, zeros
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import networkx
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import re
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EVIDENCE_CODES=[('IMP', 'Inferred from mutant phenotype'),
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('IGI', 'Inferred from genetic interaction'),
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@ -137,80 +138,9 @@ class LoadTextDatasetFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'load-text-ds', 'Load text dataset')
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def read_text_dataset(self, fd):
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split_re = re.compile('^#\s*(\w+)\s*:\s*(.)')
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dimensions = []
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identifiers = {}
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type = 'dataset'
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name = 'Unnamed dataset'
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graphtype = 'graph'
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# Read header lines from file.
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line = fd.readline()
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while line:
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m = split_re.match(line)
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if m:
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key, val = m
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# The line is on the form;
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# dimension: dimname id1 id2 id3 ...
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if key == 'dimension':
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values = [v.strip() for v in val.split(' ')]
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dimensions.append(values[0])
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identifiers[values[0]] = values[1:]
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headers[key] = val.strip()
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# Read type of dataset.
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# Should be dataset, category, or network
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elif key == 'type':
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type = val
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elif key == 'name':
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name = val
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elif key == 'graphtype':
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graphtype = val
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else:
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break
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line = f.readline()
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# Dimensions in the form [(dim1, [id1, id2, id3 ..) ...]
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dims = [(x, identifiers[x]) for x in dimensions]
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dim_lengths = [len(identifiers[x]) for x in dimensions]
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# Create dataset of specified type
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if type == 'category':
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matrix = zeros(dim_lengths, dtype=bool)
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ds = dataset.CategoryDataset(matrix, dims)
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elif type == 'network':
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matrix = zeros(dim_lengths)
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ds = dataset.GraphDataset(matrix, dims)
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else:
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matrix = zeros(dim_lengths)
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ds = dataset.Dataset(matrix, dims)
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line = f.readline()
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y = 0
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while line:
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values = line.split()
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for x, v in enumerate(values):
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matrix[x,y] = float(v)
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y += 1
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line = f.readline()
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# Build NetowrkX graph from matrix.
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if type == 'network':
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matrix = zeros(dim_lengths)
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ds = dataset.NetworkDataset(matrix, dims)
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def run(self):
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f = open('/home/einarr/foodata.fcsv')
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return read_text_dataset(f)
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f = open('/home/einarr/data/goa-condensed.ftsv')
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return [dataset.read_ftsv(f)]
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class LoadAnnotationsFunction(workflow.Function):
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