def load_from_dir(cls, d):
'''
This class method returns a FeatureMatrix object that has been
constructed using data loaded from a feature matrix directory.
Args:
| **d** *(str)*: The path to the feature matrix directory.
Raises:
'''
# initilaze empty feature matrix object
fm = cls()
# first load object ids, if available
f = os.path.join(d, cls.OBJECT_IDS_F)
if (os.path.exists(f)):
fm.load_object_ids(f)
# read and add labelings
lab_d = os.path.join(d, cls.LABELING_D)
if (os.path.exists(lab_d)):
for f in glob.glob(os.path.join(lab_d, '*.txt')):
lname = os.path.splitext(os.path.basename(f))[0]
if not (lname == cls.ONE_CLASS_LABELING):
(label_dict, class_names) = file_io.read_labeling(f)
fm.add_labeling(lname, label_dict, class_names)
fids = None
fnames = None
featmat = None
# read feature ids
f = os.path.join(d, cls.FEATURE_IDS_F)
if (os.path.exists(f)):
with open(f, 'r') as fin:
fids = [i for i in file_io.read_ids(fin)]
# read feature names
f = os.path.join(d, cls.FEATURE_NAMES_F)
if (os.path.exists(f)):
with open(f, 'r') as fin:
fnames = [n for n in file_io.read_names(fin)]
# read feature matrix
f = os.path.join(d, cls.FEATURE_MATRIX_F)
if (os.path.exists(f)):
featmat = numpy.loadtxt(f)
# in case of 1D matrix, reshape to single column 2D matrix
fm_shape = featmat.shape
if (len(fm_shape) == 1):
n = fm_shape[0]
featmat = featmat.reshape((n, 1))
if not (featmat is None):
fm.add_features(fids, featmat, fnames)
return fm
def load_from_dir(cls, d):
'''
This class method returns a FeatureMatrix object that has been
constructed using data loaded from a feature matrix directory.
Args:
| **d** *(str)*: The path to the feature matrix directory.
Raises:
'''
# initilaze empty feature matrix object
fm = cls()
# first load object ids, if available
f = os.path.join(d, cls.OBJECT_IDS_F)
if(os.path.exists(f)):
fm.load_object_ids(f)
# read and add labelings
lab_d = os.path.join(d, cls.LABELING_D)
if(os.path.exists(lab_d)):
for f in glob.glob(os.path.join(lab_d, '*.txt')):
lname = os.path.splitext(os.path.basename(f))[0]
if not(lname == cls.ONE_CLASS_LABELING):
(label_dict, class_names) = file_io.read_labeling(f)
fm.add_labeling(lname, label_dict, class_names)
fids = None
fnames = None
featmat = None
# read feature ids
f = os.path.join(d, cls.FEATURE_IDS_F)
if(os.path.exists(f)):
with open(f, 'r') as fin:
fids = [i for i in file_io.read_ids(fin)]
# read feature names
f = os.path.join(d, cls.FEATURE_NAMES_F)
if(os.path.exists(f)):
with open(f, 'r') as fin:
fnames = [n for n in file_io.read_names(fin)]
# read feature matrix
f = os.path.join(d, cls.FEATURE_MATRIX_F)
if(os.path.exists(f)):
featmat = numpy.loadtxt(f)
# in case of 1D matrix, reshape to single column 2D matrix
fm_shape = featmat.shape
if(len(fm_shape) == 1):
n = fm_shape[0]
featmat = featmat.reshape((n, 1))
if not(featmat is None):
fm.add_features(fids, featmat, fnames)
return fm
def load_object_ids(self, object_ids_f):
'''
This function reads ids from files and sets them as the object ids.
Args:
object_ids_f (str or file): The ids file.
Raises:
FileIOError: If the file does not exist.
'''
with open(object_ids_f, 'r') as fin:
ids = [i for i in file_io.read_ids(fin)]
self.object_ids = ids
def load_object_ids(self, object_ids_f):
'''
This function reads ids from files and sets them as the object ids.
Args:
object_ids_f (str or file): The ids file.
Raises:
FileIOError: If the file does not exist.
'''
with open(object_ids_f, 'r') as fin:
ids = [i for i in file_io.read_ids(fin)]
self.object_ids = ids
def add_custom_features(self, project_id, object_ids_f, feature_matrix_f):
'''
'''
self.set_project(project_id)
try:
object_ids = [i for i in file_io.read_ids(object_ids_f.file)]
except Exception as e:
print '\n%s\n%s\n%s\n' % (e, type(e), e.args)
return 'Error in object ids file'
object_ids_f.file.close()
try:
featmat = numpy.loadtxt(feature_matrix_f.file)
except Exception as e:
print '\n%s\n%s\n%s\n' % (e, type(e), e.args)
return 'Error in feature matrix file'
feature_matrix_f.file.close()
fm = self.get_feature_matrix()
if not(sorted(object_ids) == sorted(fm.object_ids)):
return 'The protein ids do not correspond to the proteins ' +\
'in this project'
if not(featmat.shape[0] == len(fm.object_ids)):
return 'The number of rows in the feature matrix does not ' +\
'correspond to the number of proteins in this project.'
# reorder feature matrix rows
featmat = featmat[fm.object_indices(object_ids)]
try:
fm.add_custom_features(featmat)
except ValueError as e:
return str(e)
except Exception as e:
print e
return 'Something went wrong while adding custom features'
fm.save_to_dir(self.fm_dir)
return ''
def add_custom_features(self, project_id, object_ids_f, feature_matrix_f):
'''
'''
self.set_project(project_id)
try:
object_ids = [i for i in file_io.read_ids(object_ids_f.file)]
except Exception as e:
print '\n%s\n%s\n%s\n' % (e, type(e), e.args)
return 'Error in object ids file'
object_ids_f.file.close()
try:
featmat = numpy.loadtxt(feature_matrix_f.file)
except Exception as e:
print '\n%s\n%s\n%s\n' % (e, type(e), e.args)
return 'Error in feature matrix file'
feature_matrix_f.file.close()
fm = self.get_feature_matrix()
if not (sorted(object_ids) == sorted(fm.object_ids)):
return 'The protein ids do not correspond to the proteins ' +\
'in this project'
if not (featmat.shape[0] == len(fm.object_ids)):
return 'The number of rows in the feature matrix does not ' +\
'correspond to the number of proteins in this project.'
# reorder feature matrix rows
featmat = featmat[fm.object_indices(object_ids)]
try:
fm.add_custom_features(featmat)
except ValueError as e:
return str(e)
except Exception as e:
print e
return 'Something went wrong while adding custom features'
fm.save_to_dir(self.fm_dir)
return ''
def taxon_list(self, taxon_domain=None):
self.fetch_session_data()
pm = self.project_manager
taxon_id = int(taxon_domain)
#top_lists = self.FAVO_TAXONS
#top_list = top_lists[taxon_id]
# obtain all taxons of this domain from uniprot
#url = 'http://www.uniprot.org/taxonomy/' +\
# '?query=complete:yes+ancestor:%i&format=tab' % (taxon_id)
#response = urllib2.urlopen(url)
#full_taxon_list = response.read()
f = os.path.join(pm.ref_data_dir, '%i.txt' % (taxon_id))
f_favo = os.path.join(pm.ref_data_dir, '%i_favo.txt' % (taxon_id))
taxon_tuples = []
if(os.path.exists(f)):
with open(f, 'r') as fin:
for line in fin:
tokens = line.split()
taxon_tuples.append((int(tokens[0]), ' '.join(tokens[1:])))
ids_favo = []
if(os.path.exists(f_favo)):
ids_favo = [int(i) for i in file_io.read_ids(f_favo)]
# parse result, fetch ids and names
'''
ids = []
names = []
first_line = True
for line in full_taxon_list.split('\n'):
if(len(line.strip()) > 0):
if(first_line):
first_line = False
else:
tokens = line.split('\t')
ids.append(int(tokens[0]))
names.append(tokens[2])
'''
# turn it into select list, would be nicer to let javascript do this
select_str = ''
if(len(ids_favo) > 0):
taxon_dict = dict(taxon_tuples)
select_str += '<optgroup label="Short list">\n'
for i in ids_favo:
select_str += '<option value="%i">%s (taxon id: %i)</option>\n' % (i, taxon_dict[i], i)
select_str += '</optgroup>\n'
select_str += '<optgroup label="All uniprot complete proteome taxonomies">\n'
for i, name in taxon_tuples:
select_str += '<option value="%i">%s (taxon id: %i)</option>\n' % (i, name, i)
select_str += '</optgroup>\n'
cherrypy.response.headers['Content-Type'] = 'application/json'
return simplejson.dumps(dict(taxon_list=select_str))
def load_proteins(self, protein_ids_f):
with open(protein_ids_f, 'r') as fin:
protein_ids = [i for i in file_io.read_ids(fin)]
self.set_proteins(protein_ids)
def load_proteins(self, protein_ids_f):
with open(protein_ids_f, 'r') as fin:
protein_ids = [i for i in file_io.read_ids(fin)]
self.set_proteins(protein_ids)
