def raw(files=items):
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "ppattach", file)
for line in open(path).readlines():
yield tuple(line.split())
def raw(files = ['female', 'male']):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "names", file+".txt")
for word in open(path).readlines():
yield word.strip()
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ppattach", file)
for line in open(path).readlines():
yield tuple(line.split())
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "words", file)
for word in open(path).readlines():
yield word.strip()
def chunked(files = items, chunk_types=('NP',)):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "conll2000", file + ".txt")
s = open(path).read()
for sent in tokenize.blankline(s):
yield tree.conll_chunk(sent, chunk_types)
def raw(files='english'):
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "stopwords", file)
for word in open(path).readlines():
yield word.strip()
def raw(files = ['female', 'male']):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "names", file+".txt")
for word in open(path).readlines():
yield word.strip()
def tagged(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "conll2000", file + ".txt")
s = open(path).read()
for sent in tokenize.blankline(s):
yield [(word, tag) for (word, tag, chunk) in _list_sent(sent)]
def bracket_parse(files=items):
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "ycoe/psd", file + ".psd")
s = open(path).read()
data = _parse(s)
for sent in data:
yield tree.bracket_parse(sent)
def _read(files, conversion_function):
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "brown", file)
f = open(path).read()
for sent in tokenize.blankline(f):
yield conversion_function(sent)
def bracket_parse(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ycoe/psd", file + ".psd")
s = open(path).read()
data = _parse(s)
for sent in data:
yield tree.bracket_parse(sent)
def _read(files, conversion_function):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "brown", file)
f = open(path).read()
for sent in tokenize.blankline(f):
yield conversion_function(sent)
def chunked(files=items, chunk_types=("NP",)):
if type(files) is str:
files = (files,)
for file in files:
path = os.path.join(get_basedir(), "conll2000", file + ".txt")
s = open(path).read()
for sent in tokenize.blankline(s):
yield tree.conll_chunk(sent, chunk_types)
def tagged(files=items):
if type(files) is str:
files = (files,)
for file in files:
path = os.path.join(get_basedir(), "conll2000", file + ".txt")
s = open(path).read()
for sent in tokenize.blankline(s):
yield [(word, tag) for (word, tag, chunk) in _list_sent(sent)]
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ieer", file)
for doc in open(path).read().split('</DOC>'):
doc = doc.split('<DOC>')
if len(doc) == 2:
yield "<DOC>" + doc[1] + "</DOC>\n"
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ieer", file)
for doc in open(path).read().split('</DOC>'):
doc = doc.split('<DOC>')
if len(doc) == 2:
yield "<DOC>" + doc[1] + "</DOC>\n"
def demo() :
path = os.path.join(get_basedir(), "shoebox", "rotokas.dic")
l = Lexicon(path)
l.parse(key_fields=['lx','ps','sn'], unique_entry=False)
h = l.get_header()
for e in l.get_entries() :
print "<%s><%s><%s>" % (e.get_field_as_string("lx", ""),
e.get_field_as_string("ps", ""),
e.get_field_as_string("sn", ""))
def demo():
path = os.path.join(get_basedir(), "shoebox", "rotokas.dic")
l = Lexicon(path)
l.parse(key_fields=['lx', 'ps', 'sn'], unique_entry=False)
h = l.get_header()
for e in l.get_entries():
print "<%s><%s><%s>" % (e.get_field_as_string(
"lx", ""), e.get_field_as_string(
"ps", ""), e.get_field_as_string("sn", ""))
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "inaugural", file + ".txt")
f = open(path)
preamble = True
text = f.read()
for t in tokenize.wordpunct(text):
yield t
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "state_union", file + ".txt")
f = open(path)
preamble = True
text = f.read()
for t in tokenize.wordpunct(text):
yield t
def raw(files=items):
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "gutenberg", file + ".txt")
f = open(path)
preamble = True
for line in f.readlines():
if not preamble:
for t in tokenize.wordpunct(line):
yield t
if line[:5] == '*END*':
preamble = False
def raw(files = items):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "gutenberg", file + ".txt")
f = open(path)
preamble = True
for line in f.readlines():
if not preamble:
for t in tokenize.wordpunct(line):
yield t
if line[:5] == '*END*':
preamble = False
def raw(files = 'raw'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{list(string)}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "sinica_treebank", file)
for line in open(path).readlines():
yield line.split()[1:]
def parsed(files = 'parsed'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "sinica_treebank", file)
for sent in open(path).readlines():
yield tree.bracket_parse(sent)
def parsed(files = 'parsed'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "sinica_treebank", file)
for sent in open(path).readlines():
yield tree.bracket_parse(sent)
def raw(files = 'raw'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{list(string)}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "sinica_treebank", file)
for line in open(path).readlines():
yield line.split()[1:]
def raw(files=items):
"""
@param files: One or more Senseval files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tuple}
"""
if type(files) is str: files = (files, )
parser = SensevalParser()
for file in files:
path = os.path.join(get_basedir(), "senseval", file + ".pos")
f = open(path).read()
for entry in parser.parse(f):
yield entry
def raw(files = items):
"""
@param files: One or more Senseval files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tuple}
"""
if type(files) is str: files = (files,)
parser = SensevalParser()
for file in files:
path = os.path.join(get_basedir(), "senseval", file+".pos")
f = open(path).read()
for entry in parser.parse(f):
yield entry
def raw(files='english-kjv'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "genesis", file + ".txt")
s = open(path).read()
for t in tokenize.whitespace(s):
yield t
def raw(files = 'english-kjv'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "genesis", file+".txt")
s = open(path).read()
for t in tokenize.whitespace(s):
yield t
def raw(files='cmudict'):
"""
@param files: One or more cmudict files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "cmudict", file)
for line in open(path).readlines():
fields = line.strip().split(' ')
yield (fields[0], int(fields[1]), tuple(fields[2:]))
def raw(files = 'cmudict'):
"""
@param files: One or more cmudict files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "cmudict", file)
for line in open(path).readlines():
fields = line.strip().split(' ')
yield (fields[0], int(fields[1]), tuple(fields[2:]))
def _read(files, conversion_function):
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ycoe/pos", file)
f = open(path).read()
rx_pattern = re.compile(r"""
<.*>_CODE
|\s.*_ID
""", re.VERBOSE|re.UNICODE)
mySents = tokenize.blankline(f)
for sent in mySents:
sent= re.sub(rx_pattern, '', sent)
if sent != "":
yield conversion_function(sent, sep="_")
def chunked(files='chunked'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{tree}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "treebank", file)
s = open(path).read()
for t in tokenize.blankline(s):
yield tree.chunk(t)
def _read(files, conversion_function):
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "ycoe/pos", file)
f = open(path).read()
rx_pattern = re.compile(
r"""
<.*>_CODE
|\s.*_ID
""", re.VERBOSE | re.UNICODE)
mySents = tokenize.blankline(f)
for sent in mySents:
sent = re.sub(rx_pattern, '', sent)
if sent != "":
yield conversion_function(sent, sep="_")
def raw(files='raw'):
"""
@param files: One or more treebank files to be processed
@type files: L{string} or L{tuple(string)}
@rtype: iterator over L{list(string)}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "treebank", file)
f = open(path).read()
for sent in tokenize.blankline(f):
l = []
for t in tokenize.whitespace(sent):
l.append(t)
yield l
def parse_corpus(file_name, key=None, **kwargs):
"""
Return an element tree resulting from parsing the toolbox datafile.
A convenience function that creates a C{ToolboxData} object, opens and
parses the toolbox data file. The data file is assumed to be in the toolbox
subdirectory of the directory where NLTK looks for corpora,
see L{corpora.get_basedir()}.
@param file_name: Name of file in toolbox corpus directory
@type file_name: string
@param key: marker at the start of each record
@type key: string
@param kwargs: Keyword arguments passed to L{ToolboxData.parse()}
@type kwargs: keyword arguments dictionary
@rtype: ElementTree._ElementInterface
@return: contents of toolbox data divided into header and records
"""
db = ToolboxData()
db.open(os.path.join(get_basedir(), 'toolbox', file_name))
return db.parse(key, **kwargs)
def parse_corpus(file_name, key=None, **kwargs):
"""
Return an element tree resulting from parsing the toolbox datafile.
A convenience function that creates a C{ToolboxData} object, opens and
parses the toolbox data file. The data file is assumed to be in the toolbox
subdirectory of the directory where NLTK looks for corpora,
see L{corpora.get_basedir()}.
@param file_name: Name of file in toolbox corpus directory
@type file_name: string
@param key: marker at the start of each record
@type key: string
@param kwargs: Keyword arguments passed to L{ToolboxData.parse()}
@type kwargs: keyword arguments dictionary
@rtype: ElementTree._ElementInterface
@return: contents of toolbox data divided into header and records
"""
db = ToolboxData()
db.open(os.path.join(get_basedir(), "toolbox", file_name))
return db.parse(key, **kwargs)
def raw(files='rotokas.dic', include_header=False, head_field_marker=None):
"""
Deprecated: use C{StandardFormat.fields()}
@param files: One or more toolbox files to be processed
@type files: L{string} or L{tuple(string)}
@param include_header: flag that determines whether to treat header as record (default is no)
@type include_header: boolean
@param head_field_marker: option for explicitly setting which marker to use as the head field
when parsing the file (default is automatically determining it from
the first field of the first record)
@type head_field_marker: string
@rtype: iterator over L{list(string)}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "toolbox", file)
fc = open(path, "U").read()
if fc.strip().startswith(r"\_"):
(header, body) = split(fc, sep="\n\n", maxsplit=1)
if include_header:
yield list(_parse_record(header))
else:
body = fc
# Deal with head field marker
if head_field_marker:
hfm_with_backslash = "\\" + hfm
else:
ff = split(body, sep="\n", maxsplit=1)[0] # first field
hfm_with_backslash = split(
ff, sep=" ", maxsplit=1)[0] # raw marker of first field
recordsep = "\n\n" + hfm_with_backslash # separates records from one another
# Parse records
for r in split("\n\n" + body, sep=recordsep)[1:]:
yield list(_parse_record(hfm_with_backslash + r))
def raw(files="rotokas.dic", include_header=False, head_field_marker=None):
"""
Deprecated: use C{StandardFormat.fields()}
@param files: One or more toolbox files to be processed
@type files: L{string} or L{tuple(string)}
@param include_header: flag that determines whether to treat header as record (default is no)
@type include_header: boolean
@param head_field_marker: option for explicitly setting which marker to use as the head field
when parsing the file (default is automatically determining it from
the first field of the first record)
@type head_field_marker: string
@rtype: iterator over L{list(string)}
"""
# Just one file to process? If so convert to a tuple so we can iterate
if type(files) is str:
files = (files,)
for file in files:
path = os.path.join(get_basedir(), "toolbox", file)
fc = open(path, "U").read()
if fc.strip().startswith(r"\_"):
(header, body) = split(fc, sep="\n\n", maxsplit=1)
if include_header:
yield list(_parse_record(header))
else:
body = fc
# Deal with head field marker
if head_field_marker:
hfm_with_backslash = "\\" + hfm
else:
ff = split(body, sep="\n", maxsplit=1)[0] # first field
hfm_with_backslash = split(ff, sep=" ", maxsplit=1)[0] # raw marker of first field
recordsep = "\n\n" + hfm_with_backslash # separates records from one another
# Parse records
for r in split("\n\n" + body, sep=recordsep)[1:]:
yield list(_parse_record(hfm_with_backslash + r))
def _chunk_parse(files, chunk_types, top_node, partial_match, collapse_partials, cascade):
# allow any kind of bracketing for flexibility
L_BRACKET = re.compile(r'[\(\[\{<]')
R_BRACKET = re.compile(r'[\)\]\}>]')
if type(files) is str: files = (files,)
for file in files:
path = os.path.join(get_basedir(), "ycoe/psd", file + ".psd")
s = open(path).read()
data = _parse(s)
for s in data:
bracket = 0
itmType = None
stack = [tree.Tree(top_node, [])]
inTag = []
for itm in list(tokenize.whitespace(s)):
if L_BRACKET.match(itm[0]):
bracket += 1
itm = itm[1:]
matched = False
if partial_match == True:
for eachItm in chunk_types:
if (len(eachItm) <= len(itm) and
eachItm == itm[:len(eachItm)]):
matched = True
if collapse_partials == True:
itm = eachItm
else:
if (chunk_types is not None and
itm in chunk_types):
matched = True
if matched == True: # and inTag == 0:
chunk = tree.Tree(itm, [])
if cascade == True:
stack.append(chunk)
inTag += [bracket]
else:
if len(inTag) == 0:
stack[-1].append(chunk)
inTag += [bracket]
itmType=itm
if R_BRACKET.match(itm[-1]):
tmpItm = split(itm, itm[-1])
if tmpItm != "":
if len(inTag) > 0 and inTag[-1] <= bracket: #inTag <= bracket:
if cascade == True:
stack[-1].append( (itmType, tmpItm[0]) )
else:
stack[-1][-1].append( (itmType, tmpItm[0]) )
else:
if cascade == True:
if len(stack) > 1:
stack[-2].append(stack[-1])
stack = stack[:-1]
stack[-1].append( (itmType, tmpItm[0]) )
inTag = [] + inTag[:-2]
bracket -= (len(tmpItm)-1)
while( len(inTag) > 0 and bracket < inTag[-1] ):
if cascade == True:
if len(stack) > 1:
stack[-2].append(stack[-1])
stack = stack[:-1]
inTag = [] + inTag[:-2]
yield stack
import ossaudiodev
import time
from en.parser.nltk_lite.corpora import get_basedir
if sys.platform.startswith('linux') or sys.platform.startswith('freebsd'):
PLAY_ENABLED = True
else:
PLAY_ENABLED = False
__all__ = [
"items", "raw", "phonetic", "speakers", "dictionary", "spkrinfo",
"audiodata", "play"
]
PREFIX = os.path.join(get_basedir(), "timit")
speakers = []
items = []
dictionary = {}
spkrinfo = {}
for f in os.listdir(PREFIX):
if re.match("^dr[0-9]-[a-z]{4}[0-9]$", f):
speakers.append(f)
for g in os.listdir(os.path.join(PREFIX, f)):
if g.endswith(".txt"):
items.append(f + ':' + g[:-4])
speakers.sort()
items.sort()
def loadParadigm(self, p_filename ):
"""
Load the given paradigm (XML file)
Attributes are stored in self.attributes
Data are stored in self.data
They can be accessed as follows:
self.attributes['gender'] # list of genders
self.data[6]['gender'] # gender for the sixth data object
self.data[6]['content'] # content for the sixth data object
"""
from en.parser.nltk_lite.corpora import get_basedir
basedir = get_basedir()
# Look for the file
try_filename = os.path.join(get_basedir(), "paradigms", p_filename)
try:
f = open(try_filename)
p_filename = try_filename
except IOError:
print "Cannot find file"
return None
f.close()
# These variables will be set by this method
self.attributes = {} # A new dictionary
self.data = [] # A new list
# XML admin: create Reader object, parse document
reader = Sax2.Reader()
doc = reader.fromStream(p_filename)
# Cycle through the given attributes and add them to self.attributes
# for <name> in <attributes>
attributes = doc.getElementsByTagName('attributes')[0]
for name in attributes.getElementsByTagName('name'):
# Setup a list of attribute values
tmp_list = []
# for each value under name, store in list
for value in name.getElementsByTagName('value'):
tmp_list.append(value.getAttribute('value'))
# Store list of values in dictionary
self.attributes[name.getAttribute('name')] = tmp_list
# Cycle through data objects and add them to self.data
# for <form> in <paradigm>
forms = doc.getElementsByTagName('paradigm')[0]
for form in forms.getElementsByTagName('form'):
# Initialise a temporary dictionary
tmp_dict = {}
for value in form.getElementsByTagName('attribute'):
tmp_dict[value.getAttribute('name')] = value.getAttribute('value')
# Add the new dictionary to the data list
self.data.append(tmp_dict)
# Talk to the user
print "Paradigm information successfully loaded from file:", p_filename
# State the number and print out a list of attributes
print " "*4 + str(len(self.attributes)) + " attributes imported:",
for att in self.attributes:
print att,
print
# State the number of paradigm objects imported
print " "*4 + str(len(self.data)) + " paradigm objects imported."
return
def _chunk_parse(files, chunk_types, top_node, partial_match,
collapse_partials, cascade):
# allow any kind of bracketing for flexibility
L_BRACKET = re.compile(r'[\(\[\{<]')
R_BRACKET = re.compile(r'[\)\]\}>]')
if type(files) is str: files = (files, )
for file in files:
path = os.path.join(get_basedir(), "ycoe/psd", file + ".psd")
s = open(path).read()
data = _parse(s)
for s in data:
bracket = 0
itmType = None
stack = [tree.Tree(top_node, [])]
inTag = []
for itm in list(tokenize.whitespace(s)):
if L_BRACKET.match(itm[0]):
bracket += 1
itm = itm[1:]
matched = False
if partial_match == True:
for eachItm in chunk_types:
if (len(eachItm) <= len(itm)
and eachItm == itm[:len(eachItm)]):
matched = True
if collapse_partials == True:
itm = eachItm
else:
if (chunk_types is not None and itm in chunk_types):
matched = True
if matched == True: # and inTag == 0:
chunk = tree.Tree(itm, [])
if cascade == True:
stack.append(chunk)
inTag += [bracket]
else:
if len(inTag) == 0:
stack[-1].append(chunk)
inTag += [bracket]
itmType = itm
if R_BRACKET.match(itm[-1]):
tmpItm = split(itm, itm[-1])
if tmpItm != "":
if len(inTag) > 0 and inTag[
-1] <= bracket: #inTag <= bracket:
if cascade == True:
stack[-1].append((itmType, tmpItm[0]))
else:
stack[-1][-1].append((itmType, tmpItm[0]))
else:
if cascade == True:
if len(stack) > 1:
stack[-2].append(stack[-1])
stack = stack[:-1]
stack[-1].append((itmType, tmpItm[0]))
inTag = [] + inTag[:-2]
bracket -= (len(tmpItm) - 1)
while (len(inTag) > 0 and bracket < inTag[-1]):
if cascade == True:
if len(stack) > 1:
stack[-2].append(stack[-1])
stack = stack[:-1]
inTag = [] + inTag[:-2]
yield stack
from en.parser.nltk_lite.corpora import get_basedir
from en.parser.nltk_lite import tokenize
from itertools import islice
import ossaudiodev, time
import sys, os, re
if sys.platform.startswith('linux') or sys.platform.startswith('freebsd'):
PLAY_ENABLED = True
else:
PLAY_ENABLED = False
__all__ = ["items", "raw", "phonetic", "speakers", "dictionary", "spkrinfo",
"audiodata", "play"]
PREFIX = os.path.join(get_basedir(),"timit")
speakers = []
items = []
dictionary = {}
spkrinfo = {}
for f in os.listdir(PREFIX):
if re.match("^dr[0-9]-[a-z]{4}[0-9]$", f):
speakers.append(f)
for g in os.listdir(os.path.join(PREFIX,f)):
if g.endswith(".txt"):
items.append(f+':'+g[:-4])
speakers.sort()
items.sort()
def loadParadigm(self, p_filename):
"""
Load the given paradigm (XML file)
Attributes are stored in self.attributes
Data are stored in self.data
They can be accessed as follows:
self.attributes['gender'] # list of genders
self.data[6]['gender'] # gender for the sixth data object
self.data[6]['content'] # content for the sixth data object
"""
from en.parser.nltk_lite.corpora import get_basedir
basedir = get_basedir()
# Look for the file
try_filename = os.path.join(get_basedir(), "paradigms", p_filename)
try:
f = open(try_filename)
p_filename = try_filename
except IOError:
print "Cannot find file"
return None
f.close()
# These variables will be set by this method
self.attributes = {} # A new dictionary
self.data = [] # A new list
# XML admin: create Reader object, parse document
reader = Sax2.Reader()
doc = reader.fromStream(p_filename)
# Cycle through the given attributes and add them to self.attributes
# for <name> in <attributes>
attributes = doc.getElementsByTagName('attributes')[0]
for name in attributes.getElementsByTagName('name'):
# Setup a list of attribute values
tmp_list = []
# for each value under name, store in list
for value in name.getElementsByTagName('value'):
tmp_list.append(value.getAttribute('value'))
# Store list of values in dictionary
self.attributes[name.getAttribute('name')] = tmp_list
# Cycle through data objects and add them to self.data
# for <form> in <paradigm>
forms = doc.getElementsByTagName('paradigm')[0]
for form in forms.getElementsByTagName('form'):
# Initialise a temporary dictionary
tmp_dict = {}
for value in form.getElementsByTagName('attribute'):
tmp_dict[value.getAttribute('name')] = value.getAttribute(
'value')
# Add the new dictionary to the data list
self.data.append(tmp_dict)
# Talk to the user
print "Paradigm information successfully loaded from file:", p_filename
# State the number and print out a list of attributes
print " " * 4 + str(len(self.attributes)) + " attributes imported:",
for att in self.attributes:
print att,
print
# State the number of paradigm objects imported
print " " * 4 + str(len(self.data)) + " paradigm objects imported."
return
