diff dml-cla/python/tuning_stats_byyear.py @ 0:718306e29690 tip

commiting public release

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dml-cla/python/tuning_stats_byyear.py	Tue Feb 09 21:05:06 2016 +0100
@@ -0,0 +1,125 @@
+# Part of DML (Digital Music Laboratory)
+# Copyright 2014-2015 Daniel Wolff, City University
+ 
+# 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
+
+# -*- coding: utf-8 -*-
+__author__='wolffd'
+__date__ ="$11-Mar-2015 12:47:23$"
+
+# this script derives standard statistics for tuning frequency, 
+# results are combined by year(range)
+# average
+# standard deviation
+
+# test JSON:
+#{ "module":"tuning_stats_byyear",
+#      "function":"per_file",
+#      "arguments": [[
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0001773XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "2015-12-14"},
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0002164XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "2015-12-14"},
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0001773XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "2015-12-14"},
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0002164XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "2015-12-14"},
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0001773XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "12.5.1993"},
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0001773XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "2015-12-14"},
+#      {"transcription": { "tag": "csv", "value":"D:\\mirg\\Pitch_Analysis\\026A-1CL0001773XX-0100A0_vamp_silvet_silvet_notes.csv"}, "date": "1907"}]]
+#}
+
+from aggregate import *
+import numpy
+import re
+
+# get code for single-collection tuning statistics
+from tuning_stats import fold_pitch, numpy_column, transcription_from_csv, \
+        transcription_from_n3,tuning_note,stats, weighted_stats
+
+# width of bins in years
+bin_width = 1
+
+# parsers for n3 / csv
+parser_table = { 'n3':transcription_from_n3, 
+                 'csv':transcription_from_csv }
+
+
+datematch = ''
+# gets statistics on a per-file basis and returns histograms 
+# over the file averages
+# inputs['filename']: filenames of fine-tune pitch extracted from recordings
+# inputs['date']: string containing year-... for each recording
+def per_file(inputs):
+    means = []
+    years = []
+
+    def accum(item):
+        # see if this has a valid date
+        y = parse_years(item['date'])
+        if y > 1000:
+
+            # get duration and normalised frequency for all tuning pitches (A3,A4,A5)
+            a_notes = [ (note[1],fold_pitch(note[2],note[3])) 
+                        for note in decode_tagged(parser_table,item['transcription']) 
+                        if tuning_note(note[3]) ]
+
+            if len(a_notes)==0:
+                print_status("No notes for "+str(item['filename']))
+            else:
+                # get frequency and duration columns
+                freq = numpy_column(a_notes,1)
+                dur = numpy_column(a_notes,0)
+                
+                # get mean values per clip now,
+                # then statistics over clips later
+                avg, std = weighted_stats(freq, weights = dur)
+                
+                # only append years if there is data
+                years.append(y)
+                means.append(avg) 
+        else:
+            print_status("No year found for "+str(item['filename']))
+
+    # get statistics per file      
+    st=for_each(inputs,accum)
+
+    # get year bins
+    years = numpy.array(years)
+    yearbins = range(numpy.min(years),numpy.max(years),bin_width)
+    #yearbinends = numpy.array(yearbins) + bin_width
+    
+    avg = []
+    std = []
+    
+    # foreach over the year-bundled outputs
+    for year in yearbins:
+       valid_idx = [i for (i, val) in enumerate(years) if val >= year and val < (year + bin_width)]
+       valid_means = [means[i] for i in valid_idx]
+       
+       # get statistics
+       y_avg,y_std = stats(numpy.array(valid_means,dtype=float))
+       avg.append(y_avg)
+       std.append(y_std)
+
+    return { 'result': { 'mean': avg, 'std-dev': std,'years': yearbins}, 
+             'stats' : st }
+
+# parses year from string
+# input: string containing 4-digit year
+def parse_years(date):
+    yearstr = re.search(r'[12]\d{3}', date)
+    if yearstr:
+        return int(yearstr.group(0))
+    else: 
+        return -1
+
+