/*
* Write a certain number of log entries (from beginning) to file
*/
void ProcessDasNexusLog::writeLogtoFile(API::MatrixWorkspace_sptr ws,
std::string logname,
size_t numentriesoutput,
std::string outputfilename) {
// 1. Get log
Kernel::Property *log = ws->run().getProperty(logname);
Kernel::TimeSeriesProperty<double> *tslog =
dynamic_cast<Kernel::TimeSeriesProperty<double> *>(log);
if (!tslog)
throw std::runtime_error("Invalid time series log: it could not be cast "
"(interpreted) as a time series property");
std::vector<Kernel::DateAndTime> times = tslog->timesAsVector();
std::vector<double> values = tslog->valuesAsVector();
// 2. Write out
std::ofstream ofs;
ofs.open(outputfilename.c_str(), std::ios::out);
ofs << "# Absolute Time (nanosecond)\tPulse Time (nanosecond)\tTOF (ms)\n";
Kernel::DateAndTime prevtime(0);
std::vector<double> tofs;
for (size_t i = 0; i < numentriesoutput; i++) {
Kernel::DateAndTime tnow = times[i];
if (tnow > prevtime) {
// (a) Process previous logs
std::sort(tofs.begin(), tofs.end());
for (double tof : tofs) {
Kernel::DateAndTime temptime =
prevtime + static_cast<int64_t>(tof * 100);
ofs << temptime.totalNanoseconds() << "\t" << tnow.totalNanoseconds()
<< "\t" << tof * 0.1 << '\n';
}
// (b) Clear
tofs.clear();
// (c) Update time
prevtime = tnow;
}
// (d) Push the current value
tofs.push_back(values[i]);
} // ENDFOR
// Clear the last
if (!tofs.empty()) {
// (a) Process previous logs: note value is in unit of 100 nano-second
std::sort(tofs.begin(), tofs.end());
for (double tof : tofs) {
Kernel::DateAndTime temptime = prevtime + static_cast<int64_t>(tof * 100);
ofs << temptime.totalNanoseconds() << "\t" << prevtime.totalNanoseconds()
<< "\t" << tof * 0.1 << '\n';
}
} else {
throw std::runtime_error("Impossible for this to happen!");
}
ofs.close();
} // END Function
/** Set up an Event workspace
* @param numentries :: number of log entries to output
* @param times :: vector of Kernel::DateAndTime
* @param values :: vector of log value in double
*/
void ExportTimeSeriesLog::setupEventWorkspace(int numentries,
vector<DateAndTime> ×,
vector<double> values) {
Kernel::DateAndTime runstart(
m_dataWS->run().getProperty("run_start")->value());
// Get some stuff from the input workspace
const size_t numberOfSpectra = 1;
const int YLength = static_cast<int>(m_dataWS->blocksize());
// Make a brand new EventWorkspace
EventWorkspace_sptr outEventWS = boost::dynamic_pointer_cast<EventWorkspace>(
API::WorkspaceFactory::Instance().create(
"EventWorkspace", numberOfSpectra, YLength + 1, YLength));
// Copy geometry over.
API::WorkspaceFactory::Instance().initializeFromParent(m_dataWS, outEventWS,
false);
m_outWS = boost::dynamic_pointer_cast<MatrixWorkspace>(outEventWS);
if (!m_outWS)
throw runtime_error(
"Output workspace cannot be casted to a MatrixWorkspace.");
g_log.debug("[DBx336] An output workspace is generated.!");
// Create the output event list (empty)
EventList &outEL = outEventWS->getOrAddEventList(0);
outEL.switchTo(WEIGHTED_NOTIME);
// Allocate all the required memory
outEL.reserve(numentries);
outEL.clearDetectorIDs();
for (size_t i = 0; i < static_cast<size_t>(numentries); i++) {
Kernel::DateAndTime tnow = times[i];
int64_t dt = tnow.totalNanoseconds() - runstart.totalNanoseconds();
// convert to microseconds
double dtmsec = static_cast<double>(dt) / 1000.0;
outEL.addEventQuickly(WeightedEventNoTime(dtmsec, values[i], values[i]));
}
// Ensure thread-safety
outEventWS->sortAll(TOF_SORT, NULL);
// Now, create a default X-vector for histogramming, with just 2 bins.
Kernel::cow_ptr<MantidVec> axis;
MantidVec &xRef = axis.access();
xRef.resize(2);
std::vector<WeightedEventNoTime> &events = outEL.getWeightedEventsNoTime();
xRef[0] = events.begin()->tof();
xRef[1] = events.rbegin()->tof();
// Set the binning axis using this.
outEventWS->setX(0, axis);
return;
}
