/*
* 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
/*
* Convert DAS log to a vector of absolute time
* @param orderedtofs: tofs with abstimevec
*/
void ProcessDasNexusLog::convertToAbsoluteTime(
API::MatrixWorkspace_sptr ws, std::string logname,
std::vector<Kernel::DateAndTime> &abstimevec,
std::vector<double> &orderedtofs) {
// 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. Get converted
size_t numsamepulses = 0;
std::vector<double> tofs;
Kernel::DateAndTime prevtime(0);
for (size_t i = 0; i < times.size(); i++) {
Kernel::DateAndTime tnow = times[i];
if (tnow > prevtime) {
// (a) Process previous logs
std::sort(tofs.begin(), tofs.end());
for (size_t j = 0; j < tofs.size(); j++) {
Kernel::DateAndTime temptime =
prevtime + static_cast<int64_t>(tofs[j] * 100);
abstimevec.push_back(temptime);
orderedtofs.push_back(tofs[j]);
}
// (b) Clear
tofs.clear();
// (c) Update time
prevtime = tnow;
} else {
numsamepulses++;
}
// (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 (size_t j = 0; j < tofs.size(); j++) {
Kernel::DateAndTime temptime =
prevtime + static_cast<int64_t>(tofs[j] * 100);
abstimevec.push_back(temptime);
orderedtofs.push_back(tofs[j]);
}
} else {
throw std::runtime_error("Impossible for this to happen!");
}
return;
} // END Function
void AddSinglePointTimeSeriesProperty(API::LogManager &logManager,
const std::string &time,
const std::string &name,
const TYPE value) {
// create time series property and add single value
Kernel::TimeSeriesProperty<TYPE> *p =
new Kernel::TimeSeriesProperty<TYPE>(name);
p->addValue(time, value);
// add to log manager
logManager.addProperty(p);
}
/*
* Check log in workspace
*/
void ProcessDasNexusLog::checkLog(API::MatrixWorkspace_sptr ws,
std::string logname) {
// 1. Get log
Kernel::Property *log = ws->run().getProperty(logname);
if (!log) {
g_log.error() << "Log " << logname << " does not exist!" << std::endl;
throw std::invalid_argument("Non-exising log name");
}
Kernel::TimeSeriesProperty<double> *tslog =
dynamic_cast<Kernel::TimeSeriesProperty<double> *>(log);
if (!tslog) {
g_log.error() << "Log " << logname << " is not time series log"
<< std::endl;
throw std::invalid_argument("Log type error!");
}
// 2. Survey
std::vector<Kernel::DateAndTime> times = tslog->timesAsVector();
g_log.information() << "Entries of times = " << times.size() << std::endl;
size_t countsame = 0;
size_t countinverse = 0;
for (size_t i = 1; i < times.size(); i++) {
Kernel::DateAndTime tprev = times[i - 1];
Kernel::DateAndTime tpres = times[i];
if (tprev == tpres)
countsame++;
else if (tprev > tpres)
countinverse++;
}
// 3. Output
Kernel::DateAndTime t0(ws->run().getProperty("run_start")->value());
Kernel::time_duration dts = times[0] - t0;
Kernel::time_duration dtf = times[times.size() - 1] - t0;
size_t f = times.size() - 1;
g_log.information() << "Number of Equal Time Stamps = " << countsame
<< std::endl;
g_log.information() << "Number of Inverted Time Stamps = " << countinverse
<< std::endl;
g_log.information() << "Run Start = " << t0.totalNanoseconds() << std::endl;
g_log.information() << "First Log (Absolute Time, Relative Time): "
<< times[0].totalNanoseconds() << ", "
<< Kernel::DateAndTime::nanosecondsFromDuration(dts)
<< std::endl;
g_log.information() << "Last Log (Absolute Time, Relative Time): "
<< times[f].totalNanoseconds() << ", "
<< Kernel::DateAndTime::nanosecondsFromDuration(dtf)
<< std::endl;
return;
}
/** Creates a TimeSeriesProperty<bool> showing times when a particular period was active.
* @param period :: The data period
* @return the times when requested period was active
*/
Kernel::Property* LoadRaw::createPeriodLog(int period)const
{
Kernel::TimeSeriesProperty<int>* periods = dynamic_cast< Kernel::TimeSeriesProperty<int>* >(m_perioids.get());
if(!periods) return 0;
std::ostringstream ostr;
ostr<<period;
Kernel::TimeSeriesProperty<bool>* p = new Kernel::TimeSeriesProperty<bool> ("period "+ostr.str());
std::map<Kernel::DateAndTime, int> pMap = periods->valueAsMap();
std::map<Kernel::DateAndTime, int>::const_iterator it = pMap.begin();
if (it->second != period)
p->addValue(it->first,false);
for(;it!=pMap.end();++it)
p->addValue(it->first, (it->second == period) );
return p;
}
/** Export part of designated log to an file in column format and a output file
* @param logname :: name of log to export
* @param numentries :: number of log entries to export
* @param outputeventws :: boolean. output workspace is event workspace if
* true.
*/
void ExportTimeSeriesLog::exportLog(string logname, int numentries,
bool outputeventws) {
// 1. Get log, time, and etc.
std::vector<Kernel::DateAndTime> times;
std::vector<double> values;
if (logname.size() > 0) {
// Log
Kernel::TimeSeriesProperty<double> *tlog =
dynamic_cast<Kernel::TimeSeriesProperty<double> *>(
m_dataWS->run().getProperty(logname));
if (!tlog) {
std::stringstream errmsg;
errmsg << "TimeSeriesProperty Log " << logname
<< " does not exist in workspace " << m_dataWS->getName();
g_log.error(errmsg.str());
throw std::invalid_argument(errmsg.str());
}
times = tlog->timesAsVector();
values = tlog->valuesAsVector();
} else {
throw std::runtime_error("Log name cannot be left empty.");
}
// 2. Determine number of export log
if (numentries == EMPTY_INT()) {
numentries = static_cast<int>(times.size());
} else if (numentries <= 0) {
stringstream errmsg;
errmsg << "For Export Log, NumberEntriesExport must be greater than 0. "
"Input = " << numentries;
g_log.error(errmsg.str());
throw std::runtime_error(errmsg.str());
} else if (static_cast<size_t>(numentries) > times.size()) {
numentries = static_cast<int>(times.size());
}
// 3. Create otuput workspace
if (outputeventws) {
setupEventWorkspace(numentries, times, values);
} else {
setupWorkspace2D(numentries, times, values);
}
return;
}
/// Run the LoadLog Child Algorithm
void LoadMuonNexus1::runLoadLog(DataObjects::Workspace2D_sptr localWorkspace) {
IAlgorithm_sptr loadLog = createChildAlgorithm("LoadMuonLog");
// Pass through the same input filename
loadLog->setPropertyValue("Filename", m_filename);
// Set the workspace property to be the same one filled above
loadLog->setProperty<MatrixWorkspace_sptr>("Workspace", localWorkspace);
// Now execute the Child Algorithm. Catch and log any error, but don't stop.
try {
loadLog->execute();
} catch (std::runtime_error &) {
g_log.error("Unable to successfully run LoadLog Child Algorithm");
} catch (std::logic_error &) {
g_log.error("Unable to successfully run LoadLog Child Algorithm");
}
if (!loadLog->isExecuted())
g_log.error("Unable to successfully run LoadLog Child Algorithm");
NXRoot root(m_filename);
try {
NXChar orientation = root.openNXChar("run/instrument/detector/orientation");
// some files have no data there
orientation.load();
if (orientation[0] == 't') {
Kernel::TimeSeriesProperty<double> *p =
new Kernel::TimeSeriesProperty<double>("fromNexus");
std::string start_time = root.getString("run/start_time");
p->addValue(start_time, -90.0);
localWorkspace->mutableRun().addLogData(p);
setProperty("MainFieldDirection", "Transverse");
} else {
setProperty("MainFieldDirection", "Longitudinal");
}
} catch (...) {
setProperty("MainFieldDirection", "Longitudinal");
}
auto &run = localWorkspace->mutableRun();
int n = static_cast<int>(m_numberOfPeriods);
ISISRunLogs runLogs(run, n);
runLogs.addStatusLog(run);
}
/** Write value from a RunInfo property (i.e., log) to a stream
*/
void writeLogValue(std::ostream &os, const Run &runinfo,
const std::string &name,
const std::string &defValue = "UNKNOWN") {
// Return without property exists
if (!runinfo.hasProperty(name)) {
os << defValue;
return;
}
// Get handler of property
Kernel::Property *prop = runinfo.getProperty(name);
// Return without a valid pointer to property
if (prop == NULL) {
os << defValue;
return;
}
// Get value
Kernel::TimeSeriesProperty<double> *log =
dynamic_cast<Kernel::TimeSeriesProperty<double> *>(prop);
if (log) {
// Time series to get mean
os << log->getStatistics().mean;
} else {
// None time series
os << prop->value();
}
// Unit
std::string units = prop->units();
if (!units.empty())
os << " " << units;
return;
}
/*
* Merge 2 TimeSeries log together for the third one
* @param ilogname1: name of log 1 to be merged
* @param ilogname2: name of log 2 to be merged
* @param ologname: name of the merged log to be added to workspace
*/
void Merge2WorkspaceLogs::mergeLogs(std::string ilogname1,
std::string ilogname2, std::string ologname,
bool resetlogvalue, double logvalue1,
double logvalue2) {
// 1. Get log
Kernel::TimeSeriesProperty<double> *p1 = getTimeSeriesLog(ilogname1);
Kernel::TimeSeriesProperty<double> *p2 = getTimeSeriesLog(ilogname2);
std::vector<Kernel::DateAndTime> times1 = p1->timesAsVector();
std::vector<Kernel::DateAndTime> times2 = p2->timesAsVector();
Kernel::TimeSeriesProperty<double> *rp =
new Kernel::TimeSeriesProperty<double>(ologname);
// 2. Merge
size_t index1 = 0;
size_t index2 = 0;
bool icont = true;
Kernel::DateAndTime tmptime;
double tmpvalue;
bool launch1 = true;
;
bool nocomparison = false;
std::cout << "Merging!!" << std::endl;
while (icont) {
// std::cout << "index1 = " << index1 << ", index2 = " << index2 << ",
// launch1 = " << launch1 << ", nocomparison = " << nocomparison <<
// std::endl;
// i. Determine which log to work on
if (!nocomparison) {
if (times1[index1] < times2[index2]) {
launch1 = true;
} else {
launch1 = false;
}
}
// ii. Retrieve data from source log
if (launch1) {
// Add log1
tmptime = times1[index1];
if (resetlogvalue) {
tmpvalue = logvalue1;
} else {
tmpvalue = p1->getSingleValue(tmptime);
}
} else {
// Add log 2
tmptime = times2[index2];
if (resetlogvalue) {
tmpvalue = logvalue2;
} else {
tmpvalue = p2->getSingleValue(tmptime);
}
}
// iii. Add log
rp->addValue(tmptime, tmpvalue);
// iv. Increase step
if (launch1) {
index1++;
} else {
index2++;
}
// v. Determine status
if (nocomparison) {
// no comparison case: transition to terminate while
if (launch1 && index1 >= times1.size()) {
icont = false;
} else if (!launch1 && index2 >= times2.size()) {
icont = false;
}
} else {
// still in comparison: transition to no-comparison
if (launch1 && index1 >= times1.size()) {
nocomparison = true;
launch1 = false;
} else if (!launch1 && index2 >= times2.size()) {
nocomparison = true;
launch1 = true;
}
} // ENDIFELSE nocomparison
} // ENDWHILE
// 3. Check and add new log
int newlogsize = rp->size();
if (size_t(newlogsize) != (times1.size() + times2.size())) {
g_log.error()
<< "Resulted log size is not equal to the sum of two source log sizes"
<< std::endl;
throw;
}
matrixWS->mutableRun().addProperty(rp);
return;
}
/**
* reads the .log stream and creates timeseries property and sets that to the run object
* @param logFileStream :: The stream of the log file (data).
* @param logFileName :: The name of the log file to load.
* @param run :: The run information object
*/
void LoadLog::loadThreeColumnLogFile(std::ifstream& logFileStream, std::string logFileName, API::Run& run)
{
std::string str;
std::string propname;
Mantid::Kernel::TimeSeriesProperty<double>* logd = 0;
Mantid::Kernel::TimeSeriesProperty<std::string>* logs = 0;
std::map<std::string,Kernel::TimeSeriesProperty<double>*> dMap;
std::map<std::string,Kernel::TimeSeriesProperty<std::string>*> sMap;
typedef std::pair<std::string,Kernel::TimeSeriesProperty<double>* > dpair;
typedef std::pair<std::string,Kernel::TimeSeriesProperty<std::string>* > spair;
kind l_kind(LoadLog::empty);
bool isNumeric(false);
if (!logFileStream)
{
throw std::invalid_argument("Unable to open file " + m_filename);
}
while(Mantid::Kernel::Strings::extractToEOL(logFileStream,str))
{
if ( !isDateTimeString(str) )
{
throw std::invalid_argument("File" + logFileName + " is not a standard ISIS log file. Expected to be a file starting with DateTime String format.");
}
if (!Kernel::TimeSeriesProperty<double>::isTimeString(str) || (str[0]=='#'))
{ //if the line doesn't start with a time read the next line
continue;
}
std::stringstream line(str);
std::string timecolumn;
line >> timecolumn;
std::string blockcolumn;
line >> blockcolumn;
l_kind = classify(blockcolumn);
if ( LoadLog::string != l_kind )
{
throw std::invalid_argument("ISIS log file contains unrecognised second column entries:" + logFileName);
}
std::string valuecolumn;
line >> valuecolumn;
l_kind = classify(valuecolumn);
if ( LoadLog::string != l_kind && LoadLog::number != l_kind)
{
continue; //no value defined, just skip this entry
}
// column two in .log file is called block column
propname = stringToLower(blockcolumn);
//check if the data is numeric
std::istringstream istr(valuecolumn);
double dvalue;
istr >> dvalue;
isNumeric = !istr.fail();
if (isNumeric)
{
std::map<std::string,Kernel::TimeSeriesProperty<double>*>::iterator ditr = dMap.find(propname);
if(ditr != dMap.end())
{
Kernel::TimeSeriesProperty<double>* prop = ditr->second;
if (prop) prop->addValue(timecolumn,dvalue);
}
else
{
logd = new Kernel::TimeSeriesProperty<double>(propname);
logd->addValue(timecolumn,dvalue);
dMap.insert(dpair(propname,logd));
}
}
else
{
std::map<std::string,Kernel::TimeSeriesProperty<std::string>*>::iterator sitr = sMap.find(propname);
if(sitr != sMap.end())
{
Kernel::TimeSeriesProperty<std::string>* prop = sitr->second;
if (prop) prop->addValue(timecolumn,valuecolumn);
}
else
{
logs = new Kernel::TimeSeriesProperty<std::string>(propname);
logs->addValue(timecolumn,valuecolumn);
sMap.insert(spair(propname,logs));
}
}
}
try
{
std::map<std::string,Kernel::TimeSeriesProperty<double>*>::const_iterator itr = dMap.begin();
for(;itr != dMap.end(); ++itr)
{
run.addLogData(itr->second);
}
std::map<std::string,Kernel::TimeSeriesProperty<std::string>*>::const_iterator sitr = sMap.begin();
for(;sitr!=sMap.end();++sitr)
{
run.addLogData(sitr->second);
}
}
catch(std::invalid_argument &e)
{
g_log.warning() << e.what();
}
catch(Exception::ExistsError&e)
{
g_log.warning() << e.what();
}
}
/** Export part of designated log to an file in column format and a output file
* @brief ExportTimeSeriesLog::exportLog
* @param logname :: name of log to export
* @param timeunit :: unit of time for input start/stop time and output
* @param starttime :: relative start time of the output time series log
* @param stoptime :: relative stop time of the output time series log
* @param exportepoch :: flag to output time as epoch time/absolute time
* @param outputeventws :: boolean. output workspace is event workspace if
* @param numentries :: number of log entries to export
*/
void ExportTimeSeriesLog::exportLog(const std::string &logname,
const std::string timeunit,
const double &starttime,
const double &stoptime,
const bool exportepoch, bool outputeventws,
int numentries) {
// Get log, time, and etc.
std::vector<Kernel::DateAndTime> times;
std::vector<double> values;
if (logname.size() > 0) {
// Log
Kernel::TimeSeriesProperty<double> *tlog =
dynamic_cast<Kernel::TimeSeriesProperty<double> *>(
m_inputWS->run().getProperty(logname));
if (!tlog) {
std::stringstream errmsg;
errmsg << "TimeSeriesProperty Log " << logname
<< " does not exist in workspace " << m_inputWS->getName();
g_log.error(errmsg.str());
throw std::invalid_argument(errmsg.str());
}
times = tlog->timesAsVector();
values = tlog->valuesAsVector();
} else {
throw std::runtime_error("Log name cannot be left empty.");
}
// Get start time, stop time and unit factor
double timeunitfactor = 1.;
if (timeunit.compare("Seconds") == 0)
timeunitfactor = 1.E-9;
// Get index for start time
size_t i_start = 0;
size_t i_stop = times.size() - 1;
// Rule out the case that start time is behind last log entry
bool i_start_cal = false;
if (starttime != EMPTY_DBL()) {
int64_t timerangens =
times.back().totalNanoseconds() - times.front().totalNanoseconds();
double timerange = static_cast<double>(timerangens) * timeunitfactor;
g_log.debug() << "Time range is " << timerange << ", Start time is "
<< starttime << "\n";
if (timerange < starttime) {
i_start = times.size() - 1;
i_start_cal = true;
}
}
if ((!i_start_cal) && (starttime != EMPTY_DBL() || stoptime != EMPTY_DBL())) {
bool export_partial = calculateTimeSeriesRangeByTime(
times, starttime, i_start, stoptime, i_stop, timeunitfactor);
if (!export_partial)
throw std::runtime_error(
"Unable to find proton_charge for run start time. "
"Failed to get partial time series.");
}
// Determine number of export log
if (numentries == EMPTY_INT()) {
numentries = static_cast<int>(times.size());
} else if (numentries <= 0) {
stringstream errmsg;
errmsg << "For Export Log, NumberEntriesExport must be greater than 0. "
"Input = " << numentries;
g_log.error(errmsg.str());
throw std::runtime_error(errmsg.str());
} else if (static_cast<size_t>(numentries) > times.size()) {
numentries = static_cast<int>(times.size());
}
// Create otuput workspace
if (outputeventws) {
setupEventWorkspace(i_start, i_stop, numentries, times, values,
exportepoch);
} else {
setupWorkspace2D(i_start, i_stop, numentries, times, values, exportepoch,
timeunitfactor);
}
return;
}
/*
* Add and check log from processed absolute time stamps
*/
void ProcessDasNexusLog::addLog(API::MatrixWorkspace_sptr ws,
std::vector<Kernel::DateAndTime> timevec,
double unifylogvalue, std::string logname,
std::vector<Kernel::DateAndTime> pulsetimes,
std::vector<double> orderedtofs, bool docheck) {
// 1. Do some static
g_log.notice() << "Vector size = " << timevec.size() << std::endl;
double sum1dtms = 0.0; // sum(dt^2)
double sum2dtms = 0.0; // sum(dt^2)
size_t numinvert = 0;
size_t numsame = 0;
size_t numnormal = 0;
double maxdtms = 0;
double mindtms = 1.0E20;
size_t numdtabove10p = 0;
size_t numdtbelow10p = 0;
double sampledtms = 0.00832646 * 1.0E6;
double dtmsA10p = sampledtms * 1.1;
double dtmsB10p = sampledtms / 1.0;
for (size_t i = 1; i < timevec.size(); i++) {
int64_t dtns =
timevec[i].totalNanoseconds() - timevec[i - 1].totalNanoseconds();
double dtms = static_cast<double>(dtns) * 1.0E-3;
sum1dtms += dtms;
sum2dtms += dtms * dtms;
if (dtns == 0)
numsame++;
else if (dtns < 0)
numinvert++;
else
numnormal++;
if (dtms > maxdtms)
maxdtms = dtms;
if (dtms < mindtms)
mindtms = dtms;
if (dtms > dtmsA10p)
numdtabove10p++;
else if (dtms < dtmsB10p)
numdtbelow10p++;
} // ENDFOR
double dt = sum1dtms / static_cast<double>(timevec.size()) * 1.0E-6;
double stddt =
sqrt(sum2dtms / static_cast<double>(timevec.size()) * 1.0E-12 - dt * dt);
g_log.notice() << "Normal dt = " << numnormal << std::endl;
g_log.notice() << "Zero dt = " << numsame << std::endl;
g_log.notice() << "Negative dt = " << numinvert << std::endl;
g_log.notice() << "Avg d(T) = " << dt << " seconds +/- " << stddt
<< ", Frequency = " << 1.0 / dt << std::endl;
g_log.notice() << "d(T) (unit ms) is in range [" << mindtms << ", " << maxdtms
<< "]" << std::endl;
g_log.notice() << "Number of d(T) 10% larger than average = "
<< numdtabove10p << std::endl;
g_log.notice() << "Number of d(T) 10% smaller than average = "
<< numdtbelow10p << std::endl;
g_log.notice() << "Size of timevec, pulsestimes, orderedtofs = "
<< timevec.size() << ", " << pulsetimes.size() << ", "
<< orderedtofs.size() << std::endl;
if (docheck) {
exportErrorLog(ws, timevec, pulsetimes, orderedtofs, 1 / (0.5 * 240.1));
calDistributions(timevec, 1 / (0.5 * 240.1));
}
// 2. Add log
Kernel::TimeSeriesProperty<double> *newlog =
new Kernel::TimeSeriesProperty<double>(logname);
for (size_t i = 0; i < timevec.size(); i++) {
newlog->addValue(timevec[i], unifylogvalue);
}
ws->mutableRun().addProperty(newlog, true);
return;
}
/** Executes the algorithm. Reading in the file and creating and populating
* the output workspace
*
* @throw Exception::FileError If the Nexus file cannot be found/opened
* @throw std::invalid_argument If the optional properties are set to invalid
*values
*/
void LoadNexusLogs::exec() {
std::string filename = getPropertyValue("Filename");
MatrixWorkspace_sptr workspace = getProperty("Workspace");
// Find the entry name to use (normally "entry" for SNS, "raw_data_1" for
// ISIS)
std::string entry_name = LoadTOFRawNexus::getEntryName(filename);
::NeXus::File file(filename);
// Find the root entry
try {
file.openGroup(entry_name, "NXentry");
} catch (::NeXus::Exception &) {
throw std::invalid_argument("Unknown NeXus file format found in file '" +
filename + "'");
}
/// Use frequency start for Monitor19 and Special1_19 logs with "No Time" for
/// SNAP
try {
file.openPath("DASlogs");
try {
file.openGroup("frequency", "NXlog");
try {
file.openData("time");
//----- Start time is an ISO8601 string date and time. ------
try {
file.getAttr("start", freqStart);
} catch (::NeXus::Exception &) {
// Some logs have "offset" instead of start
try {
file.getAttr("offset", freqStart);
} catch (::NeXus::Exception &) {
g_log.warning() << "Log entry has no start time indicated.\n";
file.closeData();
throw;
}
}
file.closeData();
} catch (::NeXus::Exception &) {
// No time. This is not an SNS SNAP file
}
file.closeGroup();
} catch (::NeXus::Exception &) {
// No time. This is not an SNS frequency group
}
file.closeGroup();
} catch (::NeXus::Exception &) {
// No time. This is not an SNS group
}
// print out the entry level fields
std::map<std::string, std::string> entries = file.getEntries();
std::map<std::string, std::string>::const_iterator iend = entries.end();
for (std::map<std::string, std::string>::const_iterator it = entries.begin();
it != iend; ++it) {
std::string group_name(it->first);
std::string group_class(it->second);
if (group_name == "DASlogs" || group_class == "IXrunlog" ||
group_class == "IXselog" || group_name == "framelog") {
loadLogs(file, group_name, group_class, workspace);
}
if (group_class == "IXperiods") {
loadNPeriods(file, workspace);
}
}
// If there's measurement information, load that info as logs.
loadAndApplyMeasurementInfo(&file, *workspace);
// Freddie Akeroyd 12/10/2011
// current ISIS implementation contains an additional indirection between
// collected frames via an
// "event_frame_number" array in NXevent_data (which eliminates frames with no
// events).
// the proton_log is for all frames and so is longer than the event_index
// array, so we need to
// filter the proton_charge log based on event_frame_number
// This difference will be removed in future for compatibility with SNS, but
// the code below will allow current SANS2D files to load
if (workspace->mutableRun().hasProperty("proton_log")) {
std::vector<int> event_frame_number;
this->getLogger().notice()
<< "Using old ISIS proton_log and event_frame_number indirection..."
<< std::endl;
try {
// Find the bank/name corresponding to the first event data entry, i.e.
// one with type NXevent_data.
file.openPath("/" + entry_name);
std::map<std::string, std::string> entries = file.getEntries();
std::map<std::string, std::string>::const_iterator it = entries.begin();
std::string eventEntry;
for (; it != entries.end(); ++it) {
if (it->second == "NXevent_data") {
eventEntry = it->first;
break;
}
}
this->getLogger().debug()
<< "Opening"
<< " /" + entry_name + "/" + eventEntry + "/event_frame_number"
<< " to find the event_frame_number\n";
file.openPath("/" + entry_name + "/" + eventEntry +
"/event_frame_number");
file.getData(event_frame_number);
} catch (const ::NeXus::Exception &) {
this->getLogger().warning() << "Unable to load event_frame_number - "
"filtering events by time will not work "
<< std::endl;
}
file.openPath("/" + entry_name);
if (!event_frame_number.empty()) // ISIS indirection - see above comments
{
Kernel::TimeSeriesProperty<double> *plog =
dynamic_cast<Kernel::TimeSeriesProperty<double> *>(
workspace->mutableRun().getProperty("proton_log"));
if (!plog)
throw std::runtime_error(
"Could not cast (interpret) proton_log as a time "
"series property. Cannot continue.");
Kernel::TimeSeriesProperty<double> *pcharge =
new Kernel::TimeSeriesProperty<double>("proton_charge");
std::vector<double> pval;
std::vector<Mantid::Kernel::DateAndTime> ptime;
pval.reserve(event_frame_number.size());
ptime.reserve(event_frame_number.size());
std::vector<Mantid::Kernel::DateAndTime> plogt = plog->timesAsVector();
std::vector<double> plogv = plog->valuesAsVector();
for (auto number : event_frame_number) {
ptime.push_back(plogt[number]);
pval.push_back(plogv[number]);
}
pcharge->create(ptime, pval);
pcharge->setUnits("uAh");
workspace->mutableRun().addProperty(pcharge, true);
}
}
try {
// Read the start and end time strings
file.openData("start_time");
Kernel::DateAndTime start(file.getStrData());
file.closeData();
file.openData("end_time");
Kernel::DateAndTime end(file.getStrData());
file.closeData();
workspace->mutableRun().setStartAndEndTime(start, end);
} catch (::NeXus::Exception &) {
}
if (!workspace->run().hasProperty("gd_prtn_chrg")) {
// Try pulling it from the main proton_charge entry first
try {
file.openData("proton_charge");
std::vector<double> values;
file.getDataCoerce(values);
std::string units;
file.getAttr("units", units);
double charge = values.front();
if (units.find("picoCoulomb") != std::string::npos) {
charge *= 1.e-06 / 3600.;
}
workspace->mutableRun().setProtonCharge(charge);
} catch (::NeXus::Exception &) {
// Try and integrate the proton logs
try {
// Use the DAS logs to integrate the proton charge (if any).
workspace->mutableRun().getProtonCharge();
} catch (Exception::NotFoundError &) {
// Ignore not found property error.
}
}
}
// Close the file
file.close();
}
