Source Code for Module timeside.analyzer.dc

 1  # -*- coding: utf-8 -*- 
 2  # 
 3  # Copyright (c) 2007-2009 Guillaume Pellerin <yomguy@parisson.com> 
 4   
 5  # This file is part of TimeSide. 
 6   
 7  # TimeSide is free software: you can redistribute it and/or modify 
 8  # it under the terms of the GNU General Public License as published by 
 9  # the Free Software Foundation, either version 2 of the License, or 
10  # (at your option) any later version. 
11   
12  # TimeSide is distributed in the hope that it will be useful, 
13  # but WITHOUT ANY WARRANTY; without even the implied warranty of 
14  # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the 
15  # GNU General Public License for more details. 
16   
17  # You should have received a copy of the GNU General Public License 
18  # along with TimeSide.  If not, see <http://www.gnu.org/licenses/>. 
19   
20  # Author: Guillaume Pellerin <yomguy@parisson.com> 
21   
22  from timeside.core import implements, interfacedoc 
23  from timeside.analyzer.core import Analyzer 
24  from timeside.api import IValueAnalyzer 
25  import numpy 
26   
27   
28 -class MeanDCShift(Analyzer): 
29      implements(IValueAnalyzer) 
30   
31      @interfacedoc 
32 -    def setup(self, channels=None, 
33                samplerate=None, 
34                blocksize=None, 
35                totalframes=None): 
36          super(MeanDCShift, self).setup( 
37              channels, samplerate, blocksize, totalframes) 
38          self.values = numpy.array([0]) 
39   
40      @staticmethod 
41      @interfacedoc 
42 -    def id(): 
43          return "mean_dc_shift" 
44   
45      @staticmethod 
46      @interfacedoc 
47 -    def name(): 
48          return "Mean DC shift" 
49   
50      @staticmethod 
51      @interfacedoc 
52 -    def unit(): 
53          return "%" 
54   
55 -    def process(self, frames, eod=False): 
56          if frames.size: 
57              self.values = numpy.append(self.values, numpy.mean(frames)) 
58          return frames, eod 
59   
60 -    def post_process(self): 
61          dc_result = self.new_result(data_mode='value', time_mode='global') 
62          dc_result.data_object.value = numpy.round( 
63              numpy.mean(100 * self.values), 3) 
64          self.process_pipe.results.add(dc_result) 
65