/// <summary>
/// Adds Informations about the recording and the patient to the edfFile
/// </summary>
/// <param name="recDescription">Description of the recording</param>
/// <param name="patientInfo">Information about the patient</param>
/// <param name="birthDate">BirthDate of the patient</param>
/// <param name="gender">Gender of patient</param>
/// <param name="name">Name of patient</param>
public void addInformation(string recDescription, string patientInfo, DateTime birthDate, char gender, string name)
{
//Copy header info from the original signal
edfFile.FileInfo.Recording = recDescription;
edfFile.FileInfo.Patient = patientInfo;
edfFile.FileInfo.PatientBirthDate = DateTime.Today;
edfFile.FileInfo.PatientGender = gender;
edfFile.FileInfo.PatientName = name;
edfFile.FileInfo.StartDate = DateTime.Today;
edfFile.FileInfo.StartTime = DateTime.Now.TimeOfDay;
saveStart = DateTime.Now;
edfFile.CommitChanges();
}
/// <summary>
/// Prepares the EDF files.
/// Opens the input file, creates a new ouput file and sets the header information.
/// </summary>
/// <returns></returns>
private bool PrepareEDFFiles()
{
if (AppConf.CopyInputSignal)
{
_neuroLoopGain.NumOutputSignals = 15;
}
else
{
_neuroLoopGain.NumOutputSignals = 14;
}
double[] sFrecs = new double[_neuroLoopGain.NumOutputSignals];
// Calculate best value for output EDF datablock duration according to analysis parameters
try
{
//OPEN INPUT FILENAME
_neuroLoopGain.InputEDFFile = new EdfFile(InputEDFFileName, true, false, false, true);
_neuroLoopGain.InputSignalSelected = InputSignalSelected;
int numInputSignals = _neuroLoopGain.InputEDFFile.FileInfo.NrSignals;
_neuroLoopGain.InputBufferOffsets = new int[numInputSignals];
for (int k = 0; k < numInputSignals; k++)
_neuroLoopGain.InputBufferOffsets[k] = _neuroLoopGain.InputEDFFile.SignalInfo[k].BufferOffset;
//OUTPUT EDF
EdfFile outputEDF = new EdfFile(AppConf.OutputFileName, false, false, false, false);
//EdfFile outputEDF = new EdfFile(AppConf.OutputFileName, false, true, false, false);
EdfSignalInfoBase edfSignalInfo = _neuroLoopGain.InputEDFFile.SignalInfo[InputSignalSelected];
outputEDF.CreateNewFile(_neuroLoopGain.NumOutputSignals);
//PreCondition checked before: (1 / AppConf.SmoothTime) is between 1 and sFrecs[0]
double doubleValue = MathEx.RoundNearest(1 / AppConf.SmoothTime);
Range.EnsureRange(doubleValue, 0, _neuroLoopGain.InputSampleFrequency);
for (int k = 0; k < _neuroLoopGain.NumOutputSignals; k++)
{
sFrecs[k] = doubleValue;
}
if (AppConf.CopyInputSignal)
sFrecs[0] = _neuroLoopGain.InputSampleFrequency;
//TODO: check this
List<EdfDataBlockSizeCalculatorResult> results = new List<EdfDataBlockSizeCalculatorResult>();
double dataBlockDuration = EdfDataBlockSizeCalculator.Calculate(sFrecs, MaxEdFblockSize, results);
if (!MathEx.SameValue(dataBlockDuration, -1))
{
// Look for minimun possible error
double bestError = results[0].MaxRelativeError;
int bestIdx = 0;
for (int k = 1; k < results.Count; k++)
if (results[k].MaxRelativeError < bestError)
{
bestError = results[k].MaxRelativeError;
bestIdx = k;
}
else if (MathEx.SameValue(results[k].MaxRelativeError, bestError) && (results[k].Duration > results[bestIdx].Duration) && (results[k].Duration <= MaxBlockDuration))
{
// Try to get block durations as much as possible to avoid excesive disk reads
bestIdx = k;
}
if (!MathEx.SameValue(bestError, 0))
{
//TODO: error??, not valid SUSSsmoothingTime parameter (we're gonna miss some samples)
ApplicationError.Add("not valid SmoothTime parameter (we're gonna miss some samples)", DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("not valid SmoothTime parameter (we're gonna miss some samples). Error = " + bestError.ToString());
outputEDF.FileInfo.SampleRecDuration = results[bestIdx].Duration;
}
else
{
outputEDF.FileInfo.SampleRecDuration = results[bestIdx].Duration; // Duration in seconds of block record
AppConf.SmoothTime = 1.0 / sFrecs[1]; // Reset the value of SmoothTime to the one actually used by the system
}
}
else
{
ApplicationError.Add("failed to assign a data block duration for output EDF", DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("failed to assign a data block duration for output EDF");
}
_neuroLoopGain.OutputBufferOffsets = new int[15];
_neuroLoopGain.OutputBufferOffsets[0] = 0;
for (int k = 0; k < _neuroLoopGain.NumOutputSignals; k++)
{
outputEDF.SignalInfo[k].PreFilter = edfSignalInfo.PreFilter;
double nsamples = outputEDF.FileInfo.SampleRecDuration * sFrecs[k];
if (Math.Truncate(nsamples) < nsamples)
{
//TODO: Error?? the number of samples in a data block should be an integer number
ApplicationError.Add("not valid SmoothTime parameter: we are going miss some samples", DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("not valid SmoothTime parameter: we are going miss some samples");
/* It should effectively be an integer number, but if we admit some error to happen
* (bestError != 0), then we can work with the truncated value => we're gonna miss some
* sample during the process
*/
nsamples = Math.Truncate(nsamples);
}
outputEDF.SignalInfo[k].NrSamples = (int)nsamples;
//if (k == 0)
if ((k == 0) && AppConf.CopyInputSignal)
{
outputEDF.SignalInfo[k].DigiMax = edfSignalInfo.DigiMax;
outputEDF.SignalInfo[k].DigiMin = edfSignalInfo.DigiMin;
outputEDF.SignalInfo[k].PhysiMax = edfSignalInfo.PhysiMax;
outputEDF.SignalInfo[k].PhysiMin = edfSignalInfo.PhysiMin;
outputEDF.SignalInfo[k].PhysiDim = edfSignalInfo.PhysiDim;
}
else
{
outputEDF.SignalInfo[k].DigiMax = short.MaxValue;
outputEDF.SignalInfo[k].DigiMin = -short.MaxValue;
//if (k < 9)
if (k < _neuroLoopGain.NumOutputSignals - 6)
{
outputEDF.SignalInfo[k].PhysiDim = "Filtered";
outputEDF.SignalInfo[k].PhysiMax = short.MaxValue;
outputEDF.SignalInfo[k].PhysiMin = -short.MaxValue;
}
else
//if ((k >= 9) && (k < 12))
if ((k >= _neuroLoopGain.NumOutputSignals - 6) && (k < _neuroLoopGain.NumOutputSignals - 3))
{
outputEDF.SignalInfo[k].PhysiMax = short.MaxValue;
outputEDF.SignalInfo[k].PhysiMin = -short.MaxValue;
}
else
{
outputEDF.SignalInfo[k].PhysiDim = "%";
outputEDF.SignalInfo[k].PhysiMax = short.MaxValue / AppConf.MicGain;
outputEDF.SignalInfo[k].PhysiMin = -short.MaxValue / AppConf.MicGain;
}
if (k > 0)
{
if (AppConf.CopyInputSignal)
_neuroLoopGain.OutputBufferOffsets[k] = _neuroLoopGain.OutputBufferOffsets[k - 1] + outputEDF.SignalInfo[k - 1].NrSamples;
else
_neuroLoopGain.OutputBufferOffsets[k + 1] = _neuroLoopGain.OutputBufferOffsets[k] + outputEDF.SignalInfo[k].NrSamples;
}
}
outputEDF.SignalInfo[k].Reserved = edfSignalInfo.Reserved;
//if ((k > 0) && (k < 9))
if ((k >= _neuroLoopGain.NumOutputSignals - 14) && (k < _neuroLoopGain.NumOutputSignals - 6))
if (AppConf.CopyInputSignal)
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k] + " " + edfSignalInfo.PhysiDim + "**2/x";
else
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k + 1] + " " + edfSignalInfo.PhysiDim + "**2/x";
else
if (AppConf.CopyInputSignal)
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k];
else
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k + 1];
outputEDF.SignalInfo[k].TransducerType = edfSignalInfo.TransducerType;
outputEDF.SignalInfo[k].ThousandSeparator = edfSignalInfo.ThousandSeparator;
}
// Info for MC analysis
outputEDF.FileInfo.Recording = string.Format(CultureInfo.InvariantCulture,
"Startdate {0} NeuroLoop-gain analysis at {1:#.#}Hz of {2} in {3}",
_neuroLoopGain.InputEDFFile.FileInfo.StartDate.HasValue ? _neuroLoopGain.InputEDFFile.FileInfo.StartDate.Value.ToString("dd-MMM-yyyy", CultureInfo.InvariantCulture) : "X",
sFrecs[1],
_neuroLoopGain.InputEDFFile.SignalInfo[InputSignalSelected].SignalLabel, Path.GetFileName(InputEDFFileName));
//Copy header info from the original signal
outputEDF.FileInfo.Patient = _neuroLoopGain.InputEDFFile.FileInfo.Patient;
outputEDF.FileInfo.StartDate = _neuroLoopGain.InputEDFFile.FileInfo.StartDate;
outputEDF.FileInfo.StartTime = _neuroLoopGain.InputEDFFile.FileInfo.StartTime;
outputEDF.CommitChanges();
_neuroLoopGain.MCsignalsBlockSamples = outputEDF.SignalInfo[1].NrSamples;
_neuroLoopGain.OutputEDFFile = outputEDF;
}
catch (Exception e)
{
ApplicationError.Add("Error opening input and/or ouput files: " + e.Message, DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("Error opening input and/or ouput files: " + e.Message);
}
return !ApplicationError.Signaled;
}
/// <summary>
/// Prepares the EDF files.
/// Opens the input file, creates a new ouput file and sets the header information.
/// </summary>
/// <returns></returns>
private bool PrepareEDFFiles()
{
if (AppConf.CopyInputSignal)
{
_neuroLoopGain.NumOutputSignals = 15;
}
else
{
_neuroLoopGain.NumOutputSignals = 14;
}
double[] sFrecs = new double[_neuroLoopGain.NumOutputSignals];
// Calculate best value for output EDF datablock duration according to analysis parameters
try
{
//OPEN INPUT FILENAME
_neuroLoopGain.InputEDFFile = new EdfFile(InputEDFFileName, true, false, false, true);
_neuroLoopGain.InputSignalSelected = InputSignalSelected;
int numInputSignals = _neuroLoopGain.InputEDFFile.FileInfo.NrSignals;
_neuroLoopGain.InputBufferOffsets = new int[numInputSignals];
for (int k = 0; k < numInputSignals; k++)
{
_neuroLoopGain.InputBufferOffsets[k] = _neuroLoopGain.InputEDFFile.SignalInfo[k].BufferOffset;
}
//OUTPUT EDF
EdfFile outputEDF = new EdfFile(AppConf.OutputFileName, false, false, false, false);
//EdfFile outputEDF = new EdfFile(AppConf.OutputFileName, false, true, false, false);
EdfSignalInfoBase edfSignalInfo = _neuroLoopGain.InputEDFFile.SignalInfo[InputSignalSelected];
outputEDF.CreateNewFile(_neuroLoopGain.NumOutputSignals);
//PreCondition checked before: (1 / AppConf.SmoothTime) is between 1 and sFrecs[0]
double doubleValue = MathEx.RoundNearest(1 / AppConf.SmoothTime);
Range.EnsureRange(doubleValue, 0, _neuroLoopGain.InputSampleFrequency);
for (int k = 0; k < _neuroLoopGain.NumOutputSignals; k++)
{
sFrecs[k] = doubleValue;
}
if (AppConf.CopyInputSignal)
{
sFrecs[0] = _neuroLoopGain.InputSampleFrequency;
}
//TODO: check this
List <EdfDataBlockSizeCalculatorResult> results = new List <EdfDataBlockSizeCalculatorResult>();
double dataBlockDuration = EdfDataBlockSizeCalculator.Calculate(sFrecs, MaxEdFblockSize, results);
if (!MathEx.SameValue(dataBlockDuration, -1))
{
// Look for minimun possible error
double bestError = results[0].MaxRelativeError;
int bestIdx = 0;
for (int k = 1; k < results.Count; k++)
{
if (results[k].MaxRelativeError < bestError)
{
bestError = results[k].MaxRelativeError;
bestIdx = k;
}
else if (MathEx.SameValue(results[k].MaxRelativeError, bestError) && (results[k].Duration > results[bestIdx].Duration) && (results[k].Duration <= MaxBlockDuration))
{
// Try to get block durations as much as possible to avoid excesive disk reads
bestIdx = k;
}
}
if (!MathEx.SameValue(bestError, 0))
{
//TODO: error??, not valid SUSSsmoothingTime parameter (we're gonna miss some samples)
ApplicationError.Add("not valid SmoothTime parameter (we're gonna miss some samples)", DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("not valid SmoothTime parameter (we're gonna miss some samples). Error = " + bestError.ToString());
outputEDF.FileInfo.SampleRecDuration = results[bestIdx].Duration;
}
else
{
outputEDF.FileInfo.SampleRecDuration = results[bestIdx].Duration; // Duration in seconds of block record
AppConf.SmoothTime = 1.0 / sFrecs[1]; // Reset the value of SmoothTime to the one actually used by the system
}
}
else
{
ApplicationError.Add("failed to assign a data block duration for output EDF", DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("failed to assign a data block duration for output EDF");
}
_neuroLoopGain.OutputBufferOffsets = new int[15];
_neuroLoopGain.OutputBufferOffsets[0] = 0;
for (int k = 0; k < _neuroLoopGain.NumOutputSignals; k++)
{
outputEDF.SignalInfo[k].PreFilter = edfSignalInfo.PreFilter;
double nsamples = outputEDF.FileInfo.SampleRecDuration * sFrecs[k];
if (Math.Truncate(nsamples) < nsamples)
{
//TODO: Error?? the number of samples in a data block should be an integer number
ApplicationError.Add("not valid SmoothTime parameter: we are going miss some samples", DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("not valid SmoothTime parameter: we are going miss some samples");
/* It should effectively be an integer number, but if we admit some error to happen
* (bestError != 0), then we can work with the truncated value => we're gonna miss some
* sample during the process
*/
nsamples = Math.Truncate(nsamples);
}
outputEDF.SignalInfo[k].NrSamples = (int)nsamples;
//if (k == 0)
if ((k == 0) && AppConf.CopyInputSignal)
{
outputEDF.SignalInfo[k].DigiMax = edfSignalInfo.DigiMax;
outputEDF.SignalInfo[k].DigiMin = edfSignalInfo.DigiMin;
outputEDF.SignalInfo[k].PhysiMax = edfSignalInfo.PhysiMax;
outputEDF.SignalInfo[k].PhysiMin = edfSignalInfo.PhysiMin;
outputEDF.SignalInfo[k].PhysiDim = edfSignalInfo.PhysiDim;
}
else
{
outputEDF.SignalInfo[k].DigiMax = short.MaxValue;
outputEDF.SignalInfo[k].DigiMin = -short.MaxValue;
//if (k < 9)
if (k < _neuroLoopGain.NumOutputSignals - 6)
{
outputEDF.SignalInfo[k].PhysiDim = "Filtered";
outputEDF.SignalInfo[k].PhysiMax = short.MaxValue;
outputEDF.SignalInfo[k].PhysiMin = -short.MaxValue;
}
else
//if ((k >= 9) && (k < 12))
if ((k >= _neuroLoopGain.NumOutputSignals - 6) && (k < _neuroLoopGain.NumOutputSignals - 3))
{
outputEDF.SignalInfo[k].PhysiMax = short.MaxValue;
outputEDF.SignalInfo[k].PhysiMin = -short.MaxValue;
}
else
{
outputEDF.SignalInfo[k].PhysiDim = "%";
outputEDF.SignalInfo[k].PhysiMax = short.MaxValue / AppConf.MicGain;
outputEDF.SignalInfo[k].PhysiMin = -short.MaxValue / AppConf.MicGain;
}
if (k > 0)
{
if (AppConf.CopyInputSignal)
{
_neuroLoopGain.OutputBufferOffsets[k] = _neuroLoopGain.OutputBufferOffsets[k - 1] + outputEDF.SignalInfo[k - 1].NrSamples;
}
else
{
_neuroLoopGain.OutputBufferOffsets[k + 1] = _neuroLoopGain.OutputBufferOffsets[k] + outputEDF.SignalInfo[k].NrSamples;
}
}
}
outputEDF.SignalInfo[k].Reserved = edfSignalInfo.Reserved;
//if ((k > 0) && (k < 9))
if ((k >= _neuroLoopGain.NumOutputSignals - 14) && (k < _neuroLoopGain.NumOutputSignals - 6))
{
if (AppConf.CopyInputSignal)
{
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k] + " " + edfSignalInfo.PhysiDim + "**2/x";
}
else
{
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k + 1] + " " + edfSignalInfo.PhysiDim + "**2/x";
}
}
else
if (AppConf.CopyInputSignal)
{
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k];
}
else
{
outputEDF.SignalInfo[k].SignalLabel = _signalOutputLabels[k + 1];
}
outputEDF.SignalInfo[k].TransducerType = edfSignalInfo.TransducerType;
outputEDF.SignalInfo[k].ThousandSeparator = edfSignalInfo.ThousandSeparator;
}
// Info for MC analysis
outputEDF.FileInfo.Recording = string.Format(CultureInfo.InvariantCulture,
"Startdate {0} NeuroLoop-gain analysis at {1:#.#}Hz of {2} in {3}",
_neuroLoopGain.InputEDFFile.FileInfo.StartDate.HasValue ? _neuroLoopGain.InputEDFFile.FileInfo.StartDate.Value.ToString("dd-MMM-yyyy", CultureInfo.InvariantCulture) : "X",
sFrecs[1],
_neuroLoopGain.InputEDFFile.SignalInfo[InputSignalSelected].SignalLabel, Path.GetFileName(InputEDFFileName));
//Copy header info from the original signal
outputEDF.FileInfo.Patient = _neuroLoopGain.InputEDFFile.FileInfo.Patient;
outputEDF.FileInfo.StartDate = _neuroLoopGain.InputEDFFile.FileInfo.StartDate;
outputEDF.FileInfo.StartTime = _neuroLoopGain.InputEDFFile.FileInfo.StartTime;
outputEDF.CommitChanges();
_neuroLoopGain.MCsignalsBlockSamples = outputEDF.SignalInfo[1].NrSamples;
_neuroLoopGain.OutputEDFFile = outputEDF;
}
catch (Exception e)
{
ApplicationError.Add("Error opening input and/or ouput files: " + e.Message, DefaultErrorMessageId);
ErrorLogger.WriteErrorLog("Error opening input and/or ouput files: " + e.Message);
}
return(!ApplicationError.Signaled);
}
