annotate dml-cla/python/rdf_wrapper.py @ 0:718306e29690 tip

commiting public release

# Part of DML (Digital Music Laboratory)
# Copyright 2014-2015 Steven Hargreaves
 
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
# 
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
# 
# You should have received a copy of the GNU General Public
# License along with this library; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA

#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__="hargreavess"

import ConfigParser
import logging
import sys
import os
import time
import shutil
import argparse
from os import walk
import rdflib
from rdflib import Graph, Namespace, BNode, Literal, RDF, RDFS
from RDFClosure import DeductiveClosure, OWLRL_Semantics
import transforms.keyTonicHistogram
import transforms.tuningFrequencyStatistics
import transforms.semitoneHistogram
import math

cla = Namespace("http://dml.org/cla#")

def print_status(msg):
    sys.stderr.write(msg+'\n')

def main():

    # get config
    # print_status("Reading configuration...")
    # config = ConfigParser.ConfigParser()
    # config.read('dml-analyser.cfg')

    # parse dmlcla ontolgy and input graph
    input_graph = Graph()
    # print_status("Reading DML CLA ontology...")
    # input_graph.parse(config.get('Ontology', 'dmlclaOntology_URI'), format="n3")
    # print_status("Reading input triples...")
    input_graph.parse(sys.stdin, format="n3")
    # print_status("------")
    # input_graph.serialize(destination=sys.stderr,format='n3')
    # print_status("------")
    # print_status("Forward chaining OWL entailments...")
    # DeductiveClosure(OWLRL_Semantics).expand(input_graph)

    # initialise output rdf graph
    # bnode = BNode()
    # print_status("Building output graph...")
    output_graph = Graph()
    output_graph.bind("dmlcla",cla)

    for comp in input_graph.subjects(cla.function,None):
        func= input_graph.value(comp, cla.function)
        inp = input_graph.value(comp, cla.input)
        print_status("Got computation %s: %s(%s)" % (comp,func,inp))
        fn = eval(func)
        output = fn(inp.value)
        print_status("Result is %s" % output)
        output_graph.set((comp,cla.output,Literal(output)))
    # comps = input_graph.query(
    #     """prefix cla:     <http://dml.org/cla#>
    #         SELECT ?comp ?function ?input
    #         WHERE {
    #             ?comp cla:function ?function .
    #             ?comp cla:input ?input
    #         }""")

    # for row in comps:
    #     print_status("Got computation %s: %s(%s)" % (row.comp,row.function,row.input))
    #     fn = eval(row.function)
    #     output = fn(row.input)
    #     print_state("Result is %s" % output)


    # # Determine which transforms are to be applied, and
    # # the associated input files
    # transforms = find_transforms_in_n3(input_graph)

    # # Apply the transform(s) to each file and create 
    # # rdf results graph
    # execute_transforms(transforms, output_graph)

    # # Write output rdf to stdout
    # print_status("Writing output triples...")
    output_graph.serialize(destination=sys.stdout,format='n3')

# # Loop through all transforms, process the corresponding
# # input files appropriately and add the (RDF) result to output_graph
# def execute_transforms(transforms, output_graph):

#     transform_iter = transforms.iterkeys()
#     key_histogram = []

#     for (transform, transform_type) in transforms:

#         input_f_files = transforms.get((transform, transform_type))

#         # Add additional clauses to this if statement
#         # for each transform type
#         if transform_type == rdflib.term.URIRef(u'http://dml.org/dml/cla#CollectionLevelKeyTonic'):

#             transforms.keyTonicHistogram.run(transform,input_f_files, output_graph)
            
#         elif transform_type == rdflib.term.URIRef(u'http://dml.org/dml/cla#CollectionLevelTuningFrequencyStatistics'):

#             transforms.tuningFrequencyStatistics.run(transform,input_f_files, output_graph)

#         elif transform_type == rdflib.term.URIRef(u'http://dml.org/dml/cla#CollectionLevelSemitone'):
#             transforms.semitoneHistogram.run(transform, input_f_files, output_graph)


# # Find all transforms, and their associated input files,
# # from rdf_graph
# def find_transforms_in_n3(rdf_graph):

#     q1 = rdf_graph.query(
#         """prefix dml:     <http://dml.org/dml/cla#>
#             SELECT ?comp ?function
#             WHERE {
#                 ?comp a dml:Computation .
#                 ?comp dml:function ?function .
#             }""")

#     for row in q1:

#         inputs = rdf_graph.query(
#             """prefix dml:     <http://dml.org/dml/cla#>
#                 SELECT ?input 
#                 WHERE {
#                     ?comp dml:input ?input .
#                 }""")

#     computations = dict()
    
#     for row in qres:

#         comp = row.comp
#         input = row.input
#         transform_type = row.transform_type

#         if transforms.has_key((transform_bnode, transform_type)):

#             transform_key = transforms.get((transform_bnode, transform_type))
#             transform_key.append(dml_input)

#         else:

#             transforms[(transform_bnode, transform_type)] = [dml_input]

#     return transforms

# # Determine the mapping between feature file URIs and
# # their source audio file URIs
# def map_audio_to_feature_files():    

#     # Loop through audio files
#     lines = [line.strip() for line in args.audio_files]

#     for audio_file in lines:

#         print "sonic-annotator -T " + args.transforms + " --rdf-basedir " + args.basedir + " <" + audio_file + ">"

#     audio_to_feature_file_dict = dict()

#     for (dirpath, dirnames, filenames) in walk(args.basedir):
#         for file in filenames:

#             print "found file: " + file

#             if file.endswith(".n3"):

#                 print "found n3 file: " + file

#                 # open and parse n3 file
#                 rdf_graph = Graph()
#                 rdf_graph.parse(os.path.join(dirpath, file), format="n3")

#                 # find subject in ?subject a mo:AudioFile
#                 qres = rdf_graph.query(
#                     """SELECT ?audio_file
#                        WHERE {
#                           ?audio_file a mo:AudioFile .
#                        }""")

#                 print len(qres)

#                 for row in qres:

#                     print("audio file URI is %s" % row.audio_file.n3())
#                     print("feature file URI is %s" % os.path.join(os.getcwd(), dirpath, file))
#                     audio_to_feature_file_dict[row.audio_file.n3()] = os.path.join(os.getcwd(), dirpath, file)

#                 # add full file URI, subject to dict

#     print audio_to_feature_file_dict

if __name__ == "__main__":

    # parser = argparse.ArgumentParser()

    # # parser.add_argument("-T", "--transforms", help="the URI of an n3 (RDF) file describing one or more transforms, and the files to which they should be applied")
    # parser.add_argument("-b", "--basedir", help="the URI of the base output directory")

    # args = parser.parse_args()

    main()