diff pyspark/dml-analyser.py @ 0:e34cf1b6fe09 tip

commit

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pyspark/dml-analyser.py	Sat Feb 20 18:14:24 2016 +0100
@@ -0,0 +1,188 @@
+# Part of DML (Digital Music Laboratory)
+#
+# 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 os
+import time
+import shutil
+import argparse
+from os import walk
+import rdflib
+from rdflib import Graph
+from RDFClosure import DeductiveClosure, OWLRL_Semantics
+from transforms.tonicHistogram import find_cla_tonic_histogram, add_tonic_histogram_to_graph
+from transforms.tuningFrequencyStatistics import find_cla_tf_statistics, add_tf_statistics_to_graph
+from transforms.semitoneHistogram import find_cla_semitone_histogram, add_semitone_histogram_to_graph
+from transforms.tonicNormSemitoneHistogram import find_cla_tonic_norm_semitone_histogram, add_tonic_norm_semitone_histogram_to_graph
+
+input_rdf_graph = Graph()
+
+def main():
+
+    # get config
+    config = ConfigParser.ConfigParser()
+    config.read('dml-analyser.cfg')
+
+    # parse dmlcla ontolgy
+    input_rdf_graph.parse(config.get('Ontology', 'dmlclaOntology_URI'), format="n3")
+    DeductiveClosure(OWLRL_Semantics).expand(input_rdf_graph)
+
+    # parse input rdf
+    input_rdf_graph.parse(args.transforms, format="n3")
+    DeductiveClosure(OWLRL_Semantics).expand(input_rdf_graph)
+
+    # initialise output rdf graph
+    output_rdf_graph = Graph()
+
+    # Determine which transforms are to be applied, and
+    # the associated input files
+    transforms = find_transforms_in_n3(input_rdf_graph)
+
+    # Apply the transform(s) to each file and create 
+    # rdf results graph
+    output_rdf_graph = execute_transforms(transforms, output_rdf_graph)
+
+    # Write output rdf to stdout
+    print(output_rdf_graph.serialize(format='n3'))
+
+# Loop through all transforms, process the corresponding
+# input files appropriately and add the (RDF) result to output_rdf_graph
+def execute_transforms(transforms, output_rdf_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#CollectionLevelTonic'):
+
+            (tonic_histogram, sample_count) = find_cla_tonic_histogram(input_f_files)
+            output_rdf_graph = add_tonic_histogram_to_graph(tonic_histogram, output_rdf_graph, transform, sample_count, input_f_files)
+            
+        elif transform_type == rdflib.term.URIRef(u'http://dml.org/dml/cla#CollectionLevelTuningFrequencyStatistics'):
+
+            statistics, sample_count = find_cla_tf_statistics(input_f_files)
+            output_rdf_graph = add_tf_statistics_to_graph(statistics, output_rdf_graph, transform, sample_count, input_f_files)
+
+        elif transform_type == rdflib.term.URIRef(u'http://dml.org/dml/cla#CollectionLevelSemitone'):
+
+            (semitone_histogram, sample_count) = find_cla_semitone_histogram(input_f_files)
+            output_rdf_graph = add_semitone_histogram_to_graph(semitone_histogram, output_rdf_graph, transform, sample_count, input_f_files)
+
+        elif transform_type == rdflib.term.URIRef(u'http://dml.org/dml/cla#CollectionLevelTonicNormSemitone'):
+
+            (tonic_norm_semitone_histogram, sample_count) = find_cla_tonic_norm_semitone_histogram(input_f_files, input_rdf_graph)
+            output_rdf_graph = add_tonic_norm_semitone_histogram_to_graph(tonic_norm_semitone_histogram, output_rdf_graph, transform, sample_count, input_f_files, input_rdf_graph)
+
+    return output_rdf_graph
+
+# Find all transforms, and their associated input files,
+# from rdf_graph
+def find_transforms_in_n3(rdf_graph):
+
+    qres = rdf_graph.query(
+        """prefix dml:     <http://dml.org/dml/cla#>
+            SELECT ?transform ?dml_input ?transform_type
+            WHERE {
+                ?transform a dml:Transform .
+                ?transform dml:input ?dml_input .
+                ?transform dml:type ?transform_type .
+            }""")
+
+    transforms = dict()
+    
+    for row in qres:
+
+        transform_bnode = row.transform
+        dml_input = row.dml_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()
+