public void TestChromelessImage()
{
var indexPropertiesFile = ConfigFile.Default <IndexPropertiesCollection>();
var indexProperties = ConfigFile.Deserialize <IndexPropertiesCollection>(indexPropertiesFile);
var indexSpectrograms = new Dictionary <string, double[, ]>(6);
var indexStatistics = new Dictionary <string, IndexDistributions.SpectralStats>();
var keys = (LDSpectrogramRGB.DefaultColorMap1 + "-" + LDSpectrogramRGB.DefaultColorMap2).Split('-');
foreach (var key in keys)
{
var matrix = new double[256, 60].Fill(indexProperties[key].DefaultValue);
indexSpectrograms.Add(key, matrix);
double[] array = DataTools.Matrix2Array(matrix);
indexStatistics.Add(key, IndexDistributions.GetModeAndOneTailedStandardDeviation(array, 300, IndexDistributions.UpperPercentileDefault));
}
var images = LDSpectrogramRGB.DrawSpectrogramsFromSpectralIndices(
inputDirectory: null,
outputDirectory: this.outputDirectory,
ldSpectrogramConfig: new LdSpectrogramConfig(),
indexPropertiesConfigPath: indexPropertiesFile,
indexGenerationData: new IndexGenerationData()
{
AnalysisStartOffset = 0.Seconds(),
FrameLength = 512,
IndexCalculationDuration = 60.0.Seconds(),
RecordingBasename = "RGB_TEST",
RecordingDuration = 60.0.Seconds(),
SampleRateResampled = 22050,
},
basename: "RGB_TEST",
analysisType: AcousticIndices.AnalysisName,
indexSpectrograms: indexSpectrograms,
summaryIndices: Enumerable
.Range(0, 60)
.Select((x) => new SummaryIndexValues(60.0.Seconds(), indexProperties))
.Cast <SummaryIndexBase>()
.ToArray(),
indexStatistics: indexStatistics,
imageChrome: ImageChrome.Without);
foreach (var(image, key) in images)
{
Assert.That.ImageIsSize(60, 256, image);
Assert.That.ImageRegionIsColor(Rectangle.FromLTRB(0, 0, 60, 256), Color.Black, (Bitmap)image);
}
}
/// <summary>
/// ONLY Use this concatenation method when you want to concatenate the files for a fixed single day.
/// The files to be concatenated must be somewhere in the subdirectory structure of the passed list of data directories
/// Read them into a dictionary
/// MOST RECENT METHOD TO CONCATENATE Spectral INDEX.CSV FILES - Early September 2015.
/// It is designed to deal with Yvonne's case where want to concatenate files distributed over arbitrary directories.
/// It only merges files for the passed fixed date. i.e only 24 hours
/// </summary>
public static void DrawSpectralIndexFiles(
Dictionary <string, double[, ]> dictionary,
LdSpectrogramConfig sgConfig,
IndexGenerationData indexGenerationData,
FileInfo indexPropertiesConfigFileInfo,
DirectoryInfo opDir,
SiteDescription siteDescription,
FileInfo sunriseDataFile = null,
List <GapsAndJoins> segmentErrors = null)
{
// derive new indices such as sqrt(PMN), NCDI etc -- main reason for this is to view what their distributions look like.
dictionary = IndexMatrices.AddDerivedIndices(dictionary);
// Calculate the index distribution statistics and write to a json file. Also save as png image
if (indexGenerationData.RecordingStartDate != null)
{
DateTimeOffset dto = (DateTimeOffset)indexGenerationData.RecordingStartDate;
string dateString = $"{dto.Year}{dto.Month:D2}{dto.Day:D2}";
string opFileStem = $"{siteDescription.SiteName}_{dateString}";
var indexDistributions = IndexDistributions.WriteSpectralIndexDistributionStatistics(dictionary, opDir, opFileStem);
//SummaryIndexBase[] summaryIndices = null;
string analysisType = "Towsey.Acoustic";
Tuple <Image, string>[] tuple = LDSpectrogramRGB.DrawSpectrogramsFromSpectralIndices(
opDir, // topLevelDirectories[0], // this should not be required but it is - because things have gotten complicated !
opDir,
sgConfig,
indexPropertiesConfigFileInfo,
indexGenerationData,
opFileStem,
analysisType,
dictionary,
null, //summaryIndices,
indexDistributions,
siteDescription,
sunriseDataFile,
segmentErrors,
ImageChrome.With);
}
}
public static void DrawSummaryIndexFiles(
Dictionary <string, double[]> dictionaryOfCsvColumns,
IndexGenerationData indexGenerationData,
FileInfo indexPropertiesConfigFileInfo,
DirectoryInfo opDir,
SiteDescription siteDescription,
FileInfo sunriseDatafile = null,
List <GapsAndJoins> erroneousSegments = null, // info if have fatal errors i.e. no signal
bool verbose = false)
{
var dto = (DateTimeOffset)indexGenerationData.RecordingStartDate;
string dateString = $"{dto.Year}{dto.Month:D2}{dto.Day:D2}";
string opFileStem = $"{siteDescription.SiteName}_{dateString}";
// Calculate the index distribution statistics and write to a json file. Also save as png image
var indexDistributions = IndexDistributions.WriteSummaryIndexDistributionStatistics(dictionaryOfCsvColumns, opDir, opFileStem);
var start = ((DateTimeOffset)indexGenerationData.RecordingStartDate).TimeOfDay;
string startTime = $"{start.Hours:d2}{start.Minutes:d2}h";
if (start.Hours == 0 && start.Minutes == 0)
{
startTime = "midnight";
}
string titletext =
$"SOURCE: \"{opFileStem}\". Starts at {startTime} {Meta.OrganizationTag}";
Bitmap tracksImage = IndexDisplay.DrawImageOfSummaryIndices(
IndexProperties.GetIndexProperties(indexPropertiesConfigFileInfo),
dictionaryOfCsvColumns,
titletext,
indexGenerationData.IndexCalculationDuration,
indexGenerationData.RecordingStartDate,
sunriseDatafile,
erroneousSegments,
verbose);
var imagePath = FilenameHelpers.AnalysisResultPath(opDir, opFileStem, SummaryIndicesStr, ImgFileExt);
tracksImage.Save(imagePath);
}
public void SummariseResults(AnalysisSettings settings, FileSegment inputFileSegment, EventBase[] events, SummaryIndexBase[] indices, SpectralIndexBase[] spectralIndices, AnalysisResult2[] results)
{
var acousticIndicesConfig = (AcousticIndicesConfig)settings.AnalysisAnalyzerSpecificConfiguration;
var sourceAudio = inputFileSegment.Source;
var resultsDirectory = AnalysisCoordinator.GetNamedDirectory(settings.AnalysisOutputDirectory, this);
bool tileOutput = acousticIndicesConfig.TileOutput;
var frameWidth = acousticIndicesConfig.FrameLength;
int sampleRate = AppConfigHelper.DefaultTargetSampleRate;
sampleRate = acousticIndicesConfig.ResampleRate ?? sampleRate;
// Gather settings for rendering false color spectrograms
var ldSpectrogramConfig = acousticIndicesConfig.LdSpectrogramConfig;
string basename = Path.GetFileNameWithoutExtension(sourceAudio.Name);
// output to disk (so other analyzers can use the data,
// only data - configuration settings that generated these indices
// this data can then be used by post-process analyses
/* NOTE: The value for FrameStep is used only when calculating a standard spectrogram
* FrameStep is NOT used when calculating Summary and Spectral indices.
*/
var indexConfigData = new IndexGenerationData()
{
RecordingExtension = inputFileSegment.Source.Extension,
RecordingBasename = basename,
RecordingStartDate = inputFileSegment.TargetFileStartDate,
RecordingDuration = inputFileSegment.TargetFileDuration.Value,
SampleRateOriginal = inputFileSegment.TargetFileSampleRate.Value,
SampleRateResampled = sampleRate,
FrameLength = frameWidth,
FrameStep = settings.Configuration.GetIntOrNull(AnalysisKeys.FrameStep) ?? frameWidth,
IndexCalculationDuration = acousticIndicesConfig.IndexCalculationDurationTimeSpan,
BgNoiseNeighbourhood = acousticIndicesConfig.BgNoiseBuffer,
AnalysisStartOffset = inputFileSegment.SegmentStartOffset ?? TimeSpan.Zero,
MaximumSegmentDuration = settings.AnalysisMaxSegmentDuration,
BackgroundFilterCoeff = SpectrogramConstants.BACKGROUND_FILTER_COEFF,
LongDurationSpectrogramConfig = ldSpectrogramConfig,
};
var icdPath = FilenameHelpers.AnalysisResultPath(
resultsDirectory,
basename,
IndexGenerationData.FileNameFragment,
"json");
Json.Serialise(icdPath.ToFileInfo(), indexConfigData);
// gather spectra to form spectrograms. Assume same spectra in all analyzer results
// this is the most efficient way to do this
// gather up numbers and strings store in memory, write to disk one time
// this method also AUTOMATICALLY SORTS because it uses array indexing
var dictionaryOfSpectra = spectralIndices.ToTwoDimensionalArray(SpectralIndexValues.CachedSelectors, TwoDimensionalArray.Rotate90ClockWise);
// Calculate the index distribution statistics and write to a json file. Also save as png image
var indexDistributions = IndexDistributions.WriteSpectralIndexDistributionStatistics(dictionaryOfSpectra, resultsDirectory, basename);
// HACK: do not render false color spectrograms unless IndexCalculationDuration = 60.0 (the normal resolution)
if (acousticIndicesConfig.IndexCalculationDurationTimeSpan != 60.0.Seconds())
{
Log.Warn("False color spectrograms were not rendered");
}
else
{
FileInfo indicesPropertiesConfig = acousticIndicesConfig.IndexPropertiesConfig.ToFileInfo();
// Actually draw false color / long duration spectrograms
Tuple <Image <Rgb24>, string>[] images =
LDSpectrogramRGB.DrawSpectrogramsFromSpectralIndices(
inputDirectory: resultsDirectory,
outputDirectory: resultsDirectory,
ldSpectrogramConfig: ldSpectrogramConfig,
indexPropertiesConfigPath: indicesPropertiesConfig,
indexGenerationData: indexConfigData,
basename: basename,
analysisType: this.Identifier,
indexSpectrograms: dictionaryOfSpectra,
indexStatistics: indexDistributions,
imageChrome: (!tileOutput).ToImageChrome());
if (tileOutput)
{
Debug.Assert(images.Length == 2);
Log.Info("Tiling output at scale: " + acousticIndicesConfig.IndexCalculationDuration);
foreach (var image in images)
{
TileOutput(resultsDirectory, Path.GetFileNameWithoutExtension(sourceAudio.Name), image.Item2 + ".Tile", inputFileSegment, image.Item1);
}
}
}
}
public static void Execute(Arguments arguments)
{
if (arguments == null)
{
throw new NoDeveloperMethodException();
}
string date = "# DATE AND TIME: " + DateTime.Now;
LoggedConsole.WriteLine("# DRAW LONG DURATION SPECTROGRAMS DERIVED FROM CSV FILES OF SPECTRAL INDICES OBTAINED FROM AN AUDIO RECORDING");
LoggedConsole.WriteLine(date);
LoggedConsole.WriteLine("# Spectrogram Config file: " + arguments.FalseColourSpectrogramConfig);
LoggedConsole.WriteLine("# Index Properties Config file: " + arguments.IndexPropertiesConfig);
LoggedConsole.WriteLine();
(FileInfo indexGenerationDataFile, FileInfo indexDistributionsFile) =
ZoomParameters.CheckNeededFilesExist(arguments.InputDataDirectory.ToDirectoryInfo());
var indexGenerationData = Json.Deserialize <IndexGenerationData>(indexGenerationDataFile);
// spectral distribution statistics is required only when calcualting difference spectrograms.
Dictionary <string, IndexDistributions.SpectralStats> indexDistributionsData = null;
if (indexDistributionsFile != null && indexDistributionsFile.Exists)
{
indexDistributionsData = IndexDistributions.Deserialize(indexDistributionsFile);
}
// this config can be found in IndexGenerationData. If config argument not specified, simply take it from icd file
LdSpectrogramConfig config;
if (arguments.FalseColourSpectrogramConfig == null)
{
config = indexGenerationData.LongDurationSpectrogramConfig;
}
else
{
config = LdSpectrogramConfig.ReadYamlToConfig(arguments.FalseColourSpectrogramConfig.ToFileInfo());
}
FilenameHelpers.ParseAnalysisFileName(indexGenerationDataFile, out var originalBaseName, out var _, out var _);
// CHECK FOR ERROR SEGMENTS - get zero signal array
var input = arguments.InputDataDirectory.ToDirectoryInfo();
var csvFile = new FileInfo(Path.Combine(input.FullName, originalBaseName + "__Towsey.Acoustic.Indices.csv"));
//Dictionary<string, double[]> summaryIndices = CsvTools.ReadCSVFile2Dictionary(csvFile.FullName);
//var summaryIndices = Csv.ReadFromCsv<Dictionary<string, double[]>>(csvFile);
var summaryIndices = Csv.ReadFromCsv <SummaryIndexValues>(csvFile);
var indexErrors = GapsAndJoins.DataIntegrityCheckForZeroSignal(summaryIndices);
//config.IndexCalculationDuration = TimeSpan.FromSeconds(1.0);
//config.XAxisTicInterval = TimeSpan.FromSeconds(60.0);
//config.IndexCalculationDuration = TimeSpan.FromSeconds(60.0);
//config.XAxisTicInterval = TimeSpan.FromSeconds(3600.0);
LDSpectrogramRGB.DrawSpectrogramsFromSpectralIndices(
inputDirectory: input,
outputDirectory: arguments.OutputDirectory.ToDirectoryInfo(),
ldSpectrogramConfig: config,
indexPropertiesConfigPath: arguments.IndexPropertiesConfig.ToFileInfo(),
indexGenerationData: indexGenerationData,
basename: originalBaseName,
analysisType: AcousticIndices.TowseyAcoustic,
indexSpectrograms: null,
indexStatistics: indexDistributionsData,
segmentErrors: indexErrors,
imageChrome: false.ToImageChrome());
Log.Success("Draw Long Duration Spectrograms complete!");
}
public static Image <Rgb24> DrawDistanceSpectrogram(LDSpectrogramRGB cs1, LDSpectrogramRGB cs2)
{
string[] keys = cs1.ColorMap.Split('-');
string key = keys[0];
double[,] m1Red = cs1.GetNormalisedSpectrogramMatrix(key);
IndexDistributions.SpectralStats stats = IndexDistributions.GetModeAndOneTailedStandardDeviation(m1Red);
cs1.IndexStats.Add(key, stats);
m1Red = MatrixTools.Matrix2ZScores(m1Red, stats.Mode, stats.StandardDeviation);
////LoggedConsole.WriteLine("1.{0}: Min={1:f2} Max={2:f2} Mode={3:f2}+/-{4:f3} (SD=One-tailed)", key, dict["min"], dict["max"], dict["mode"], dict["sd"]);
key = keys[1];
double[,] m1Grn = cs1.GetNormalisedSpectrogramMatrix(key);
stats = IndexDistributions.GetModeAndOneTailedStandardDeviation(m1Grn);
cs1.IndexStats.Add(key, stats);
m1Grn = MatrixTools.Matrix2ZScores(m1Grn, stats.Mode, stats.StandardDeviation);
////LoggedConsole.WriteLine("1.{0}: Min={1:f2} Max={2:f2} Mode={3:f2}+/-{4:f3} (SD=One-tailed)", key, dict["min"], dict["max"], dict["mode"], dict["sd"]);
key = keys[2];
double[,] m1Blu = cs1.GetNormalisedSpectrogramMatrix(key);
stats = IndexDistributions.GetModeAndOneTailedStandardDeviation(m1Blu);
cs1.IndexStats.Add(key, stats);
m1Blu = MatrixTools.Matrix2ZScores(m1Blu, stats.Mode, stats.StandardDeviation);
////LoggedConsole.WriteLine("1.{0}: Min={1:f2} Max={2:f2} Mode={3:f2}+/-{4:f3} (SD=One-tailed)", key, dict["min"], dict["max"], dict["mode"], dict["sd"]);
key = keys[0];
double[,] m2Red = cs2.GetNormalisedSpectrogramMatrix(key);
stats = IndexDistributions.GetModeAndOneTailedStandardDeviation(m2Red);
cs2.IndexStats.Add(key, stats);
m2Red = MatrixTools.Matrix2ZScores(m2Red, stats.Mode, stats.StandardDeviation);
////LoggedConsole.WriteLine("2.{0}: Min={1:f2} Max={2:f2} Mode={3:f2}+/-{4:f3} (SD=One-tailed)", key, dict["min"], dict["max"], dict["mode"], dict["sd"]);
key = keys[1];
double[,] m2Grn = cs2.GetNormalisedSpectrogramMatrix(key);
stats = IndexDistributions.GetModeAndOneTailedStandardDeviation(m2Grn);
cs2.IndexStats.Add(key, stats);
m2Grn = MatrixTools.Matrix2ZScores(m2Grn, stats.Mode, stats.StandardDeviation);
////LoggedConsole.WriteLine("2.{0}: Min={1:f2} Max={2:f2} Mode={3:f2}+/-{4:f3} (SD=One-tailed)", key, dict["min"], dict["max"], dict["mode"], dict["sd"]);
key = keys[2];
double[,] m2Blu = cs2.GetNormalisedSpectrogramMatrix(key);
stats = IndexDistributions.GetModeAndOneTailedStandardDeviation(m2Blu);
cs2.IndexStats.Add(key, stats);
m2Blu = MatrixTools.Matrix2ZScores(m2Blu, stats.Mode, stats.StandardDeviation);
////LoggedConsole.WriteLine("2.{0}: Min={1:f2} Max={2:f2} Mode={3:f2}+/-{4:f3} (SD=One-tailed)", key, dict["min"], dict["max"], dict["mode"], dict["sd"]);
var v1 = new double[3];
double[] mode1 =
{
cs1.IndexStats[keys[0]].Mode, cs1.IndexStats[keys[1]].Mode,
cs1.IndexStats[keys[2]].Mode,
};
double[] stDv1 =
{
cs1.IndexStats[keys[0]].StandardDeviation, cs1.IndexStats[keys[1]].StandardDeviation,
cs1.IndexStats[keys[2]].StandardDeviation,
};
LoggedConsole.WriteLine(
"1: avACI={0:f3}+/-{1:f3}; avTEN={2:f3}+/-{3:f3}; avCVR={4:f3}+/-{5:f3}",
mode1[0],
stDv1[0],
mode1[1],
stDv1[1],
mode1[2],
stDv1[2]);
var v2 = new double[3];
double[] mode2 =
{
cs2.IndexStats[keys[0]].Mode, cs2.IndexStats[keys[1]].Mode,
cs2.IndexStats[keys[2]].Mode,
};
double[] stDv2 =
{
cs2.IndexStats[keys[0]].StandardDeviation, cs2.IndexStats[keys[1]].StandardDeviation,
cs2.IndexStats[keys[2]].StandardDeviation,
};
LoggedConsole.WriteLine(
"2: avACI={0:f3}+/-{1:f3}; avTEN={2:f3}+/-{3:f3}; avCVR={4:f3}+/-{5:f3}",
mode2[0],
stDv2[0],
mode2[1],
stDv2[1],
mode2[2],
stDv2[2]);
// assume all matrices are normalised and of the same dimensions
int rows = m1Red.GetLength(0); // number of rows
int cols = m1Red.GetLength(1); // number
var d12Matrix = new double[rows, cols];
var d11Matrix = new double[rows, cols];
var d22Matrix = new double[rows, cols];
for (int row = 0; row < rows; row++)
{
for (int col = 0; col < cols; col++)
{
v1[0] = m1Red[row, col];
v1[1] = m1Grn[row, col];
v1[2] = m1Blu[row, col];
v2[0] = m2Red[row, col];
v2[1] = m2Grn[row, col];
v2[2] = m2Blu[row, col];
d12Matrix[row, col] = DataTools.EuclideanDistance(v1, v2);
d11Matrix[row, col] = (v1[0] + v1[1] + v1[2]) / 3; // get average of the normalised values
d22Matrix[row, col] = (v2[0] + v2[1] + v2[2]) / 3;
// following lines are for debugging purposes
// if ((row == 150) && (col == 1100))
// {
// LoggedConsole.WriteLine("V1={0:f3}, {1:f3}, {2:f3}", v1[0], v1[1], v1[2]);
// LoggedConsole.WriteLine("V2={0:f3}, {1:f3}, {2:f3}", v2[0], v2[1], v2[2]);
// LoggedConsole.WriteLine("EDist12={0:f4}; ED11={1:f4}; ED22={2:f4}", d12Matrix[row, col], d11Matrix[row, col], d22Matrix[row, col]);
// }
}
}
double[] array = DataTools.Matrix2Array(d12Matrix);
NormalDist.AverageAndSD(array, out var avDist, out var sdDist);
for (int row = 0; row < rows; row++)
{
for (int col = 0; col < cols; col++)
{
d12Matrix[row, col] = (d12Matrix[row, col] - avDist) / sdDist;
}
}
double zScore;
Dictionary <string, Color> colourChart = GetDifferenceColourChart();
Color colour;
var bmp = new Image <Rgb24>(cols, rows);
for (int row = 0; row < rows; row++)
{
for (int col = 0; col < cols; col++)
{
zScore = d12Matrix[row, col];
if (d11Matrix[row, col] >= d22Matrix[row, col])
{
if (zScore > 3.08)
{
colour = colourChart["+99.9%"];
}
// 99.9% conf
else
{
if (zScore > 2.33)
{
colour = colourChart["+99.0%"];
}
// 99.0% conf
else
{
if (zScore > 1.65)
{
colour = colourChart["+95.0%"];
}
// 95% conf
else
{
if (zScore < 0.0)
{
colour = colourChart["NoValue"];
}
else
{
// v = Convert.ToInt32(zScore * MaxRGBValue);
// colour = Color.FromRgb(v, 0, v);
colour = colourChart["+NotSig"];
}
}
}
}
// if() else
bmp[col, row] = colour;
}
else
{
if (zScore > 3.08)
{
colour = colourChart["-99.9%"];
}
// 99.9% conf
else
{
if (zScore > 2.33)
{
colour = colourChart["-99.0%"];
}
// 99.0% conf
else
{
if (zScore > 1.65)
{
colour = colourChart["-95.0%"];
}
// 95% conf
else
{
if (zScore < 0.0)
{
colour = colourChart["NoValue"];
}
else
{
// v = Convert.ToInt32(zScore * MaxRGBValue);
// if()
// colour = Color.FromRgb(0, v, v);
colour = colourChart["-NotSig"];
}
}
}
}
// if() else
bmp[col, row] = colour;
}
}
// all rows
}
// all rows
return(bmp);
}
public override void SummariseResults(
AnalysisSettings analysisSettings,
FileSegment inputFileSegment,
EventBase[] events,
SummaryIndexBase[] indices,
SpectralIndexBase[] spectralIndices,
AnalysisResult2[] results)
{
// below is example of how to access values in ContentDescription config file.
//sampleRate = analysisSettings.Configuration.GetIntOrNull(AnalysisKeys.ResampleRate) ?? sampleRate;
var cdConfiguration = (CdConfig)analysisSettings.Configuration;
var ldSpectrogramConfig = cdConfiguration.LdSpectrogramConfig;
//var cdConfigFile = analysisSettings.ConfigFile;
//var configDirectory = cdConfigFile.DirectoryName ?? throw new ArgumentNullException(nameof(cdConfigFile), "Null value");
var sourceAudio = inputFileSegment.Source;
string basename = Path.GetFileNameWithoutExtension(sourceAudio.Name);
var resultsDirectory = AnalysisCoordinator.GetNamedDirectory(analysisSettings.AnalysisOutputDirectory, this);
// check for null values - this was recommended by ReSharper!
if (inputFileSegment.TargetFileDuration == null || inputFileSegment.TargetFileSampleRate == null)
{
throw new NullReferenceException();
}
// output config data to disk so other analyzers can use the data,
// Should contain data only - i.e. the configuration settings that generated these indices
// this data can then be used by later analysis processes.
var indexConfigData = new IndexGenerationData()
{
RecordingExtension = inputFileSegment.Source.Extension,
RecordingBasename = basename,
RecordingStartDate = inputFileSegment.TargetFileStartDate,
RecordingDuration = inputFileSegment.TargetFileDuration.Value,
SampleRateOriginal = inputFileSegment.TargetFileSampleRate.Value,
SampleRateResampled = ContentSignatures.SampleRate,
FrameLength = ContentSignatures.FrameSize,
FrameStep = ContentSignatures.FrameSize,
IndexCalculationDuration = TimeSpan.FromSeconds(ContentSignatures.IndexCalculationDurationInSeconds),
BgNoiseNeighbourhood = TimeSpan.FromSeconds(5), // default value for content description
AnalysisStartOffset = inputFileSegment.SegmentStartOffset ?? TimeSpan.Zero,
MaximumSegmentDuration = analysisSettings.AnalysisMaxSegmentDuration,
BackgroundFilterCoeff = SpectrogramConstants.BACKGROUND_FILTER_COEFF,
LongDurationSpectrogramConfig = ldSpectrogramConfig,
};
var icdPath = FilenameHelpers.AnalysisResultPath(
resultsDirectory,
basename,
IndexGenerationData.FileNameFragment,
"json");
Json.Serialise(icdPath.ToFileInfo(), indexConfigData);
// gather spectra to form spectrograms. Assume same spectra in all analyzer results
var dictionaryOfSpectra = spectralIndices.ToTwoDimensionalArray(SpectralIndexValuesForContentDescription.CachedSelectors, TwoDimensionalArray.Rotate90ClockWise);
// Calculate the index distribution statistics and write to a json file. Also save as png image
// The following method returns var indexDistributions =, but we have no use for them.
IndexDistributions.WriteSpectralIndexDistributionStatistics(dictionaryOfSpectra, resultsDirectory, basename);
// Draw ldfc spectrograms and return path to 2maps image.
string ldfcSpectrogramPath =
DrawSpectrogramsFromSpectralIndices(
ldSpectrogramConfig,
outputDirectory: resultsDirectory,
indexGenerationData: indexConfigData,
basename: basename,
indexSpectrograms: dictionaryOfSpectra);
// Gather the content description results into an array of DescriptionResult and then convert to dictionary
var allContentDescriptionResults = results.Select(x => (DescriptionResult)x.MiscellaneousResults[nameof(DescriptionResult)]);
var contentDictionary = DataProcessing.ConvertResultsToDictionaryOfArrays(allContentDescriptionResults.ToList());
// Write the results to a csv file
var filePath = Path.Combine(resultsDirectory.FullName, "AcousticSignatures.csv");
// TODO: fix this so it writes header and a column of content description values.
//Csv.WriteToCsv(new FileInfo(filePath), contentDictionary);
FileTools.WriteDictionaryAsCsvFile(contentDictionary, filePath);
// prepare graphical plots of the acoustic signatures.
var contentPlots = GetPlots(contentDictionary);
var images = GraphsAndCharts.DrawPlotDistributions(contentPlots);
var plotsImage = ImageTools.CombineImagesVertically(images);
plotsImage.Save(Path.Combine(resultsDirectory.FullName, "DistributionsOfContentScores.png"));
// Attach content description plots to LDFC spectrogram and write to file
var ldfcSpectrogram = Image.Load <Rgb24>(ldfcSpectrogramPath);
var image = ContentVisualization.DrawLdfcSpectrogramWithContentScoreTracks(ldfcSpectrogram, contentPlots);
var path3 = Path.Combine(resultsDirectory.FullName, basename + ".ContentDescription.png");
image.Save(path3);
}
