public void GetPsd(string path)
{
var psd = MatrixTools.GetColumnAverages(this.Data);
FileTools.WriteArray2File(psd, path + ".csv");
GraphsAndCharts.DrawGraph(psd, "Title", new FileInfo(path));
//GraphsAndCharts.DrawGraph("Title", psd, width, height, 4 new FileInfo(path));
//image.Save(path);
}
public void DrawLogPsd(string path)
{
var psd = MatrixTools.GetColumnAverages(this.Data);
var logPsd = DataTools.LogValues(psd);
FileTools.WriteArray2File(logPsd, path + ".csv");
GraphsAndCharts.DrawGraph(logPsd, "log PSD", new FileInfo(path));
//GraphsAndCharts.DrawGraph("Title", psd, width, height, 4 new FileInfo(path));
//image.Save(path, ImageFormat.Png);
}
public static Image CreateImageOfSpectralIndices(SpectralIndexValues spectralIndices)
{
var images = new List <Image>();
foreach (var key in Keys)
{
var spectrum = CachedSelectors[key](spectralIndices);
double[] normalisedIndex = DataTools.normalise(spectrum);
var image = GraphsAndCharts.DrawGraph(key, normalisedIndex, 100);
images.Add(image);
}
var combinedImage = ImageTools.CombineImagesVertically(images.ToArray());
return(combinedImage);
}
} // GetSpeciesRepresentations()
public static void DrawSpeciesImages(Arguments arguments, Output output)
{
LoggedConsole.WriteLine("2b. Draw feature representation of every species.");
int scalingFactor = 20;
int imageHeight = 100;
int speciesCount = arguments.SpeciesCount;
var keyArray = FEATURE_KEYS.Split(',');
int featureCount = keyArray.Length * output.ReducedSpectralLength;
// loop through all 50 species
for (int r = 0; r < speciesCount; r++)
{
double[] ipVector = MatrixTools.GetRow(output.SpeciesFeatureMatrix, r);
// now make images
var images = new List <Image>();
int featureID = 0;
foreach (string key in keyArray)
{
double[] vector = new double[output.ReducedSpectralLength];
int featureOffset = featureID * output.ReducedSpectralLength;
for (int c = 0; c < output.ReducedSpectralLength; c++)
{
vector[c] = ipVector[featureOffset + c];
}
featureID++;
vector = DataTools.Normalise2Probabilites(vector);
vector = DataTools.filterMovingAverage(vector, 3);
string label = string.Format("{0} {1} ({2})", r + 1, key, output.InstanceNumbersPerSpecies[r]);
Image image = GraphsAndCharts.DrawGraph(label, vector, output.ReducedSpectralLength, imageHeight, scalingFactor);
images.Add(image);
}
Image combinedImage = ImageTools.CombineImagesVertically(images);
string outputFileName = string.Format("Species{0}.SpectralFeatures.png", r + 1);
string path = Path.Combine(arguments.OutputDirectory.FullName, outputFileName);
combinedImage.Save(path);
} // loop through 50 species
}
/// <summary>
/// Do your analysis. This method is called once per segment (typically one-minute segments).
/// </summary>
/// <param name="audioRecording"></param>
/// <param name="configuration"></param>
/// <param name="segmentStartOffset"></param>
/// <param name="getSpectralIndexes"></param>
/// <param name="outputDirectory"></param>
/// <param name="imageWidth"></param>
/// <returns></returns>
public override RecognizerResults Recognize(AudioRecording audioRecording, Config configuration, TimeSpan segmentStartOffset, Lazy <IndexCalculateResult[]> getSpectralIndexes, DirectoryInfo outputDirectory, int?imageWidth)
{
const double minAmplitudeThreshold = 0.1;
const int percentile = 5;
const double scoreThreshold = 0.3;
const bool doFiltering = true;
const int windowWidth = 1024;
const int signalBuffer = windowWidth * 2;
//string path = @"C:\SensorNetworks\WavFiles\Freshwater\savedfortest.wav";
//audioRecording.Save(path); // this does not work
int sr = audioRecording.SampleRate;
int nyquist = audioRecording.Nyquist;
// Get a value from the config file - with a backup default
//int minHz = (int?)configuration[AnalysisKeys.MinHz] ?? 600;
// Get a value from the config file - with no default, throw an exception if value is not present
//int maxHz = ((int?)configuration[AnalysisKeys.MaxHz]).Value;
// Get a value from the config file - without a string accessor, as a double
//double someExampleSettingA = (double?)configuration.someExampleSettingA ?? 0.0;
// common properties
//string speciesName = (string)configuration[AnalysisKeys.SpeciesName] ?? "<no species>";
//string abbreviatedSpeciesName = (string)configuration[AnalysisKeys.AbbreviatedSpeciesName] ?? "<no.sp>";
// min score for an acceptable event
double eventThreshold = (double)configuration.GetDoubleOrNull(AnalysisKeys.EventThreshold);
// get samples
var samples = audioRecording.WavReader.Samples;
double[] bandPassFilteredSignal = null;
if (doFiltering)
{
// high pass filter
int windowLength = 71;
double[] highPassFilteredSignal;
DSP_IIRFilter.ApplyMovingAvHighPassFilter(samples, windowLength, out highPassFilteredSignal);
//DSP_IIRFilter filter2 = new DSP_IIRFilter("Chebyshev_Highpass_400");
//int order2 = filter2.order;
//filter2.ApplyIIRFilter(samples, out highPassFilteredSignal);
// Amplify 40dB and clip to +/-1.0;
double factor = 100; // equiv to 20dB
highPassFilteredSignal = DspFilters.AmplifyAndClip(highPassFilteredSignal, factor);
//low pass filter
string filterName = "Chebyshev_Lowpass_5000, scale*5";
DSP_IIRFilter filter = new DSP_IIRFilter(filterName);
int order = filter.order;
//System.LoggedConsole.WriteLine("\nTest " + filterName + ", order=" + order);
filter.ApplyIIRFilter(highPassFilteredSignal, out bandPassFilteredSignal);
}
else // do not filter because already filtered - using Chris's filtered recording
{
bandPassFilteredSignal = samples;
}
// calculate an amplitude threshold that is above Nth percentile of amplitudes in the subsample
int[] histogramOfAmplitudes;
double minAmplitude;
double maxAmplitude;
double binWidth;
int window = 66;
Histogram.GetHistogramOfWaveAmplitudes(bandPassFilteredSignal, window, out histogramOfAmplitudes, out minAmplitude, out maxAmplitude, out binWidth);
int percentileBin = Histogram.GetPercentileBin(histogramOfAmplitudes, percentile);
double amplitudeThreshold = (percentileBin + 1) * binWidth;
if (amplitudeThreshold < minAmplitudeThreshold)
{
amplitudeThreshold = minAmplitudeThreshold;
}
bool doAnalysisOfKnownExamples = true;
if (doAnalysisOfKnownExamples)
{
// go to fixed location to check
//1:02.07, 1:07.67, 1:12.27, 1:12.42, 1:12.59, 1:12.8, 1.34.3, 1:35.3, 1:40.16, 1:50.0, 2:05.9, 2:06.62, 2:17.57, 2:21.0
//2:26.33, 2:43.07, 2:43.15, 3:16.55, 3:35.09, 4:22.44, 4:29.9, 4:42.6, 4:51.48, 5:01.8, 5:21.15, 5:22.72, 5:32.37, 5.36.1,
//5:42.82, 6:03.5, 6:19.93, 6:21.55, 6:42.0, 6:42.15, 6:46.44, 7:12.17, 7:42.65, 7:45.86, 7:46.18, 7:52.38, 7:59.11, 8:10.63,
//8:14.4, 8:14.63, 8_15_240, 8_46_590, 8_56_590, 9_25_77, 9_28_94, 9_30_5, 9_43_9, 10_03_19, 10_24_26, 10_24_36, 10_38_8,
//10_41_08, 10_50_9, 11_05_13, 11_08_63, 11_44_66, 11_50_36, 11_51_2, 12_04_93, 12_10_05, 12_20_78, 12_27_0, 12_38_5,
//13_02_25, 13_08_18, 13_12_8, 13_25_24, 13_36_0, 13_50_4, 13_51_2, 13_57_87, 14_15_00, 15_09_74, 15_12_14, 15_25_79
//double[] times = { 2.2, 26.589, 29.62 };
//double[] times = { 2.2, 3.68, 10.83, 24.95, 26.589, 27.2, 29.62 };
//double[] times = { 2.2, 3.68, 10.83, 24.95, 26.589, 27.2, 29.62, 31.39, 62.1, 67.67, 72.27, 72.42, 72.59, 72.8, 94.3, 95.3,
// 100.16, 110.0, 125.9, 126.62, 137.57, 141.0, 146.33, 163.07, 163.17, 196.55, 215.09, 262.44, 269.9, 282.6,
// 291.48, 301.85, 321.18, 322.72, 332.37, 336.1, 342.82, 363.5, 379.93, 381.55, 402.0, 402.15, 406.44, 432.17,
// 462.65, 465.86, 466.18, 472.38, 479.14, 490.63, 494.4, 494.63, 495.240, 526.590, 536.590, 565.82, 568.94,
// 570.5, 583.9, 603.19, 624.26, 624.36, 638.8, 641.08, 650.9, 65.13, 68.63, 704.66,
// 710.36, 711.2, 724.93, 730.05, 740.78, 747.05, 758.5, 782.25, 788.18, 792.8,
// 805.24, 816.03, 830.4, 831.2, 837.87, 855.02, 909.74, 912.14, 925.81 };
var filePath = new FileInfo(@"C:\SensorNetworks\WavFiles\Freshwater\GruntSummaryRevisedAndEditedByMichael.csv");
List <CatFishCallData> data = Csv.ReadFromCsv <CatFishCallData>(filePath, true).ToList();
//var catFishCallDatas = data as IList<CatFishCallData> ?? data.ToList();
int count = data.Count();
var subSamplesDirectory = outputDirectory.CreateSubdirectory("testSubsamples_5000LPFilter");
//for (int t = 0; t < times.Length; t++)
foreach (var fishCall in data)
{
//Image bmp1 = IctalurusFurcatus.AnalyseLocation(bandPassFilteredSignal, sr, times[t], windowWidth);
// use following line where using time in seconds
//int location = (int)Math.Round(times[t] * sr); //assume location points to start of grunt
//double[] subsample = DataTools.Subarray(bandPassFilteredSignal, location - signalBuffer, 2 * signalBuffer);
// use following line where using sample
int location1 = fishCall.Sample / 2; //assume Chris's sample location points to centre of grunt. Divide by 2 because original recording was 44100.
int location = (int)Math.Round(fishCall.TimeSeconds * sr); //assume location points to centre of grunt
double[] subsample = DataTools.Subarray(bandPassFilteredSignal, location - signalBuffer, 2 * signalBuffer);
// calculate an amplitude threshold that is above 95th percentile of amplitudes in the subsample
//int[] histogramOfAmplitudes;
//double minAmplitude;
//double maxAmplitude;
//double binWidth;
//int window = 70;
//int percentile = 90;
//Histogram.GetHistogramOfWaveAmplitudes(subsample, window, out histogramOfAmplitudes, out minAmplitude, out maxAmplitude, out binWidth);
//int percentileBin = Histogram.GetPercentileBin(histogramOfAmplitudes, percentile);
//double amplitudeThreshold = (percentileBin + 1) * binWidth;
//if (amplitudeThreshold < minAmplitudeThreshold) amplitudeThreshold = minAmplitudeThreshold;
double[] scores1 = AnalyseWaveformAtLocation(subsample, amplitudeThreshold, scoreThreshold);
string title1 = $"scores={fishCall.Timehms}";
Image bmp1 = GraphsAndCharts.DrawGraph(title1, scores1, subsample.Length, 300, 1);
//bmp1.Save(path1.FullName);
string title2 = $"tStart={fishCall.Timehms}";
Image bmp2 = GraphsAndCharts.DrawWaveform(title2, subsample, 1);
var path1 = subSamplesDirectory.CombineFile($"scoresForTestSubsample_{fishCall.TimeSeconds}secs.png");
//var path2 = subSamplesDirectory.CombineFile($@"testSubsample_{times[t]}secs.wav.png");
Image[] imageList = { bmp2, bmp1 };
Image bmp3 = ImageTools.CombineImagesVertically(imageList);
bmp3.Save(path1.FullName);
//write wave form to txt file for later work in XLS
//var path3 = subSamplesDirectory.CombineFile($@"testSubsample_{times[t]}secs.wav.csv");
//signalBuffer = 800;
//double[] subsample2 = DataTools.Subarray(bandPassFilteredSignal, location - signalBuffer, 3 * signalBuffer);
//FileTools.WriteArray2File(subsample2, path3.FullName);
}
}
int signalLength = bandPassFilteredSignal.Length;
// count number of 1000 sample segments
int blockLength = 1000;
int blockCount = signalLength / blockLength;
int[] indexOfMax = new int[blockCount];
double[] maxInBlock = new double[blockCount];
for (int i = 0; i < blockCount; i++)
{
double max = -2.0;
int blockStart = blockLength * i;
for (int s = 0; s < blockLength; s++)
{
double absValue = Math.Abs(bandPassFilteredSignal[blockStart + s]);
if (absValue > max)
{
max = absValue;
maxInBlock[i] = max;
indexOfMax[i] = blockStart + s;
}
}
}
// transfer max values to a list
var indexList = new List <int>();
for (int i = 1; i < blockCount - 1; i++)
{
// only find the blocks that contain a max value that is > neighbouring blocks
if (maxInBlock[i] > maxInBlock[i - 1] && maxInBlock[i] > maxInBlock[i + 1])
{
indexList.Add(indexOfMax[i]);
}
//ALTERNATIVELY
// look at max in each block
//indexList.Add(indexOfMax[i]);
}
// now process neighbourhood of each max
int binCount = windowWidth / 2;
FFT.WindowFunc wf = FFT.Hamming;
var fft = new FFT(windowWidth, wf);
int maxHz = 1000;
double hzPerBin = nyquist / (double)binCount;
int requiredBinCount = (int)Math.Round(maxHz / hzPerBin);
// init list of events
List <AcousticEvent> events = new List <AcousticEvent>();
double[] scores = new double[signalLength]; // init of score array
int id = 0;
foreach (int location in indexList)
{
//System.LoggedConsole.WriteLine("Location " + location + ", id=" + id);
int start = location - binCount;
if (start < 0)
{
continue;
}
int end = location + binCount;
if (end >= signalLength)
{
continue;
}
double[] subsampleWav = DataTools.Subarray(bandPassFilteredSignal, start, windowWidth);
var spectrum = fft.Invoke(subsampleWav);
// convert to power
spectrum = DataTools.SquareValues(spectrum);
spectrum = DataTools.filterMovingAverageOdd(spectrum, 3);
spectrum = DataTools.normalise(spectrum);
var subBandSpectrum = DataTools.Subarray(spectrum, 1, requiredBinCount); // ignore DC in bin zero.
// now do some tests on spectrum to determine if it is a candidate grunt
bool eventFound = false;
double[] scoreArray = CalculateScores(subBandSpectrum, windowWidth);
double score = scoreArray[0];
if (score > scoreThreshold)
{
eventFound = true;
}
if (eventFound)
{
for (int i = location - binCount; i < location + binCount; i++)
{
scores[location] = score;
}
var startTime = TimeSpan.FromSeconds((location - binCount) / (double)sr);
string startLabel = startTime.Minutes + "." + startTime.Seconds + "." + startTime.Milliseconds;
Image image4 = GraphsAndCharts.DrawWaveAndFft(subsampleWav, sr, startTime, spectrum, maxHz * 2, scoreArray);
var path4 = outputDirectory.CreateSubdirectory("subsamples").CombineFile($@"subsample_{location}_{startLabel}.png");
image4.Save(path4.FullName);
// have an event, store the data in the AcousticEvent class
double duration = 0.2;
int minFreq = 50;
int maxFreq = 1000;
var anEvent = new AcousticEvent(segmentStartOffset, startTime.TotalSeconds, duration, minFreq, maxFreq);
anEvent.Name = "grunt";
//anEvent.Name = DataTools.WriteArrayAsCsvLine(subBandSpectrum, "f4");
anEvent.Score = score;
events.Add(anEvent);
}
id++;
}
// make a spectrogram
var config = new SonogramConfig
{
NoiseReductionType = NoiseReductionType.Standard,
NoiseReductionParameter = configuration.GetDoubleOrNull(AnalysisKeys.NoiseBgThreshold) ?? 0.0,
};
var sonogram = (BaseSonogram) new SpectrogramStandard(config, audioRecording.WavReader);
//// when the value is accessed, the indices are calculated
//var indices = getSpectralIndexes.Value;
//// check if the indices have been calculated - you shouldn't actually need this
//if (getSpectralIndexes.IsValueCreated)
//{
// // then indices have been calculated before
//}
var plot = new Plot(this.DisplayName, scores, eventThreshold);
return(new RecognizerResults()
{
Events = events,
Hits = null,
//ScoreTrack = null,
Plots = plot.AsList(),
Sonogram = sonogram,
});
}
public void TestAnalyzeSr22050Recording()
{
int sampleRate = 22050;
double duration = 420; // signal duration in seconds = 7 minutes
int[] harmonics = { 500, 1000, 2000, 4000, 8000 };
var recording = DspFilters.GenerateTestRecording(sampleRate, duration, harmonics, WaveType.Cosine);
var recordingPath = this.outputDirectory.CombineFile("TemporaryRecording1.wav");
WavWriter.WriteWavFileViaFfmpeg(recordingPath, recording.WavReader);
// draw the signal as spectrogram just for debugging purposes
/*
* var fst = FreqScaleType.Linear;
* var freqScale = new FrequencyScale(fst);
* var sonoConfig = new SonogramConfig
* {
* WindowSize = 512,
* WindowOverlap = 0.0,
* SourceFName = recording.BaseName,
* NoiseReductionType = NoiseReductionType.Standard,
* NoiseReductionParameter = 2.0,
* };
* var sonogram = new SpectrogramStandard(sonoConfig, recording.WavReader);
* var image = sonogram.GetImageFullyAnnotated(sonogram.GetImage(), "SPECTROGRAM", freqScale.GridLineLocations);
* var outputImagePath = this.outputDirectory.CombineFile("Signal1_LinearFreqScale.png");
* image.Save(outputImagePath.FullName);
*/
var configPath = PathHelper.ResolveConfigFile("Towsey.Acoustic.yml");
var arguments = new AnalyseLongRecording.Arguments
{
Source = recordingPath,
Config = configPath.FullName,
Output = this.outputDirectory,
MixDownToMono = true,
Parallel = !Debugger.IsAttached,
};
AnalyseLongRecording.Execute(arguments);
var resultsDirectory = this.outputDirectory.Combine("Towsey.Acoustic");
var listOfFiles = resultsDirectory.EnumerateFiles().ToArray();
Assert.AreEqual(38, listOfFiles.Length);
var csvCount = listOfFiles.Count(f => f.Name.EndsWith(".csv"));
Assert.AreEqual(15, csvCount);
var jsonCount = listOfFiles.Count(f => f.Name.EndsWith(".json"));
Assert.AreEqual(2, jsonCount);
var pngCount = listOfFiles.Count(f => f.Name.EndsWith(".png"));
Assert.AreEqual(21, pngCount);
var twoMapsImagePath = resultsDirectory.CombineFile("TemporaryRecording1__2Maps.png");
var twoMapsImage = Image.Load <Rgb24>(twoMapsImagePath.FullName);
// image is 7 * 632
Assert.AreEqual(7, twoMapsImage.Width);
Assert.AreEqual(632, twoMapsImage.Height);
var bgnFile = resultsDirectory.CombineFile("TemporaryRecording1__Towsey.Acoustic.BGN.csv");
double[,] actualBgn = Csv.ReadMatrixFromCsv <double>(bgnFile, TwoDimensionalArray.None);
var expectedSpectrumFile = PathHelper.ResolveAsset("LongDuration", "BgnMatrix.LinearScale.csv");
// uncomment the following line when first produce the array
// bgnFile.CopyTo(expectedSpectrumFile.FullName);
// compare actual BGN file with expected file.
var expectedBgn = Csv.ReadMatrixFromCsv <double>(expectedSpectrumFile, TwoDimensionalArray.None);
CollectionAssert.That.AreEqual(expectedBgn, actualBgn, 0.000_000_001);
var array = MatrixTools.GetRow(actualBgn, 0);
Assert.AreEqual(7, expectedBgn.RowLength());
Assert.AreEqual(256, array.Length);
// draw array just to check peaks are in correct places - just for debugging purposes
var ldsBgnSpectrumFile = this.outputDirectory.CombineFile("Spectrum1.png");
GraphsAndCharts.DrawGraph(array, "LD BGN SPECTRUM Linear", ldsBgnSpectrumFile);
var generationData = Json.Deserialize <IndexGenerationData>(IndexGenerationData.FindFile(resultsDirectory));
Assert.AreEqual("TemporaryRecording1", generationData.RecordingBasename);
}
public void TestAnalyzeSr64000Recording()
{
int sampleRate = 64000;
double duration = 420; // signal duration in seconds = 7 minutes
int[] harmonics = { 500, 1000, 2000, 4000, 8000 };
var recording = DspFilters.GenerateTestRecording(sampleRate, duration, harmonics, WaveType.Cosine);
string recordingName = "TemporaryRecording2";
var recordingPath = this.outputDirectory.CombineFile(recordingName + ".wav");
WavWriter.WriteWavFileViaFfmpeg(recordingPath, recording.WavReader);
var fst = FreqScaleType.Linear125Octaves7Tones28Nyquist32000;
var freqScale = new FrequencyScale(fst);
/*
* // draw the signal as spectrogram just for debugging purposes
* // but can only draw a two minute spectrogram when sr=64000 - change duration above.
* duration = 120; // if drawing sonogram, then set signal duration = 2 minutes
* var sonogram = OctaveFreqScale.ConvertRecordingToOctaveScaleSonogram(recording, fst);
* var sonogramImage = sonogram.GetImageFullyAnnotated(sonogram.GetImage(), "SPECTROGRAM", freqScale.GridLineLocations);
* var outputImagePath = this.outputDirectory.CombineFile("SignalSpectrogram_OctaveFreqScale.png");
* sonogramImage.Save(outputImagePath.FullName);
*/
// Now need to rewrite the config file with new parameter settings
var configPath = PathHelper.ResolveConfigFile("Towsey.Acoustic.yml");
// Convert the Config config to IndexCalculateConfig class and merge in the unnecesary parameters.
//Config configuration = Yaml.Deserialise(configPath);
//IndexCalculateConfig config = IndexCalculateConfig.GetConfig(configuration, false);
// because of difficulties in dealing with Config config files, just edit the text file!!!!!
var configLines = File.ReadAllLines(configPath.FullName);
configLines[configLines.IndexOf(x => x.StartsWith("IndexCalculationDuration: "))] = "IndexCalculationDuration: 15.0";
//configLines[configLines.IndexOf(x => x.StartsWith("BgNoiseBuffer: "))] = "BgNoiseBuffer: 5.0";
configLines[configLines.IndexOf(x => x.StartsWith("FrequencyScale: Linear"))] = "FrequencyScale: " + fst;
// the is the only octave scale currently functioning for IndexCalculate class
configLines[configLines.IndexOf(x => x.StartsWith("FrameLength"))] = $"FrameLength: {freqScale.WindowSize}";
configLines[configLines.IndexOf(x => x.StartsWith("ResampleRate: "))] = "ResampleRate: 64000";
// write the edited Config file to temporary output directory
var newConfigPath = this.outputDirectory.CombineFile("Towsey.Acoustic.yml");
File.WriteAllLines(newConfigPath.FullName, configLines);
PathHelper.ResolveConfigFile("IndexPropertiesConfig.yml").CopyTo(this.outputDirectory.CombineFile("IndexPropertiesConfig.yml").FullName);
var arguments = new AnalyseLongRecording.Arguments
{
Source = recordingPath,
Config = newConfigPath.FullName,
Output = this.outputDirectory,
MixDownToMono = true,
Parallel = !Debugger.IsAttached,
};
AnalyseLongRecording.Execute(arguments);
var resultsDirectory = this.outputDirectory.Combine("Towsey.Acoustic");
var listOfFiles = resultsDirectory.EnumerateFiles().ToArray();
Assert.AreEqual(19, listOfFiles.Length);
var csvCount = listOfFiles.Count(f => f.Name.EndsWith(".csv"));
Assert.AreEqual(15, csvCount);
var jsonCount = listOfFiles.Count(f => f.Name.EndsWith(".json"));
Assert.AreEqual(2, jsonCount);
var pngCount = listOfFiles.Count(f => f.Name.EndsWith(".png"));
Assert.AreEqual(2, pngCount);
var bgnFile = resultsDirectory.CombineFile(recordingName + "__Towsey.Acoustic.BGN.csv");
double[,] actualBgn = Csv.ReadMatrixFromCsv <double>(bgnFile, TwoDimensionalArray.None);
var expectedSpectrumFile = PathHelper.ResolveAsset("LongDuration", "BgnMatrix.OctaveScale.csv");
// uncomment the following line when first produce the array
// bgnFile.CopyTo(expectedSpectrumFile.FullName);
// compare actual BGN file with expected file.
var expectedBgn = Csv.ReadMatrixFromCsv <double>(expectedSpectrumFile, TwoDimensionalArray.None);
CollectionAssert.That.AreEqual(expectedBgn, actualBgn, 0.000_000_001);
var array = MatrixTools.GetRow(actualBgn, 0);
Assert.AreEqual(28, actualBgn.RowLength());
Assert.AreEqual(256, array.Length);
// draw array just to check peaks are in correct places - just for debugging purposes
var ldsBgnSpectrumFile = this.outputDirectory.CombineFile("Spectrum2.png");
GraphsAndCharts.DrawGraph(array, "LD BGN SPECTRUM Octave", ldsBgnSpectrumFile);
// ##########################################
// SECOND part of test is to create the LD spectrograms because they are not created when IndexCalcDuration < 60 seconds
// first read in the index generation data
var icdPath = resultsDirectory.CombineFile(recordingName + "__IndexGenerationData.json");
var indexConfigData = Json.Deserialize <IndexGenerationData>(icdPath);
var indexPropertiesConfig = PathHelper.ResolveConfigFile("IndexPropertiesConfig.yml");
var ldSpectrogramConfigFile = PathHelper.ResolveConfigFile("SpectrogramFalseColourConfig.yml");
var ldSpectrogramConfig = LdSpectrogramConfig.ReadYamlToConfig(ldSpectrogramConfigFile);
ldSpectrogramConfig.FreqScale = fst.ToString();
// finally read in the dictionary of spectra
string analysisType = "Towsey.Acoustic";
var keys = LDSpectrogramRGB.GetArrayOfAvailableKeys();
var dictionaryOfSpectra = IndexMatrices.ReadSpectralIndices(resultsDirectory, recordingName, analysisType, keys);
LDSpectrogramRGB.DrawSpectrogramsFromSpectralIndices(
inputDirectory: resultsDirectory,
outputDirectory: resultsDirectory,
ldSpectrogramConfig: ldSpectrogramConfig,
indexPropertiesConfigPath: indexPropertiesConfig,
indexGenerationData: indexConfigData,
basename: recordingName,
analysisType: analysisType,
indexSpectrograms: dictionaryOfSpectra);
// test number of images - should now be 23
listOfFiles = resultsDirectory.EnumerateFiles().ToArray();
pngCount = listOfFiles.Count(f => f.Name.EndsWith(".png"));
Assert.AreEqual(22, pngCount);
var twoMapsImagePath = resultsDirectory.CombineFile(recordingName + "__2Maps.png");
var twoMapsImage = Image.Load <Rgb24>(twoMapsImagePath.FullName);
// image is (7*4) * 652
Assert.AreEqual(28, twoMapsImage.Width);
Assert.AreEqual(652, twoMapsImage.Height);
}
public static Image CreateImageOfSpectralIndices(SpectralIndexValues spectralIndices)
{
string[] keys = { "ACI", "BGN", "CVR", "ENT", "EVN", "PMN", "POW", "RHZ", "RNG", "RPS", "RVT", "R3D", "SPT" };
var images = new List <Image>();
foreach (var key in keys)
{
double[] normalisedIndex = null;
switch (key)
{
case "ACI":
normalisedIndex = DataTools.normalise(spectralIndices.ACI);
break;
case "BGN":
normalisedIndex = DataTools.normalise(spectralIndices.BGN);
break;
case "CVR":
normalisedIndex = DataTools.normalise(spectralIndices.CVR);
break;
case "ENT":
normalisedIndex = DataTools.normalise(spectralIndices.ENT);
break;
case "EVN":
normalisedIndex = DataTools.normalise(spectralIndices.EVN);
break;
case "PMN":
normalisedIndex = DataTools.normalise(spectralIndices.PMN);
break;
case "POW":
normalisedIndex = DataTools.normalise(spectralIndices.POW);
break;
case "RHZ":
normalisedIndex = DataTools.normalise(spectralIndices.RHZ);
break;
case "RNG":
normalisedIndex = DataTools.normalise(spectralIndices.RNG);
break;
case "RPS":
normalisedIndex = DataTools.normalise(spectralIndices.RPS);
break;
case "RVT":
normalisedIndex = DataTools.normalise(spectralIndices.RVT);
break;
case "R3D":
normalisedIndex = DataTools.normalise(spectralIndices.R3D);
break;
case "SPT":
normalisedIndex = DataTools.normalise(spectralIndices.SPT);
break;
default:
break;
}
var image = GraphsAndCharts.DrawGraph(key, normalisedIndex, 100);
images.Add(image);
}
var combinedImage = ImageTools.CombineImagesVertically(images.ToArray());
return(combinedImage);
}