Matlab

found = client.searchDeclaredEventsByName('S');

found = client.searchDeclaredEventsByName('S');

found = client.searchDeclaredEventsByName('SHOT', 'pt');

found = client.searchDeclaredEventsByUniqueID('SHOT', 'pt');

found = client.searchDeclaredEventsByDescription('SHOT');

found = client.searchDeclaredEventsByDescription('SHOT', 'pt');

for i=1:1:size(found)
    found(i)
end

max = client.searchMaxEventNumber('0x0000')

min = client.searchMinEventNumber('0x0000')

NOTE: You can construct time with a resolution of picosseconds, just add to the example values for millis, micros, nanos and picos
NOTE 2: Date constructors have the months index to 0 (January is 0 and December is 11)

Search events in December 2005:

date_start = Date(2005, 11, 1);
date_end = Date(2005, 11, 31);
tstart = TimeStamp(date_start);
tend = TimeStamp(date_end);
eventsFound = client.searchEventsByEventTimeWindow(tstart, tend);
for i = 1:1:size(eventsFound)
    eventsFound(i)
end

Search events in the 22 December 2005 between 5pm and 6pm:

date_start = Date(2005, 11, 22);
date_end = Date(2005,11,22);
time_start = Time(17, 0, 0);
time_end = Time(18, 0, 0);
tstart = TimeStamp(date_start, time_start);
tend = TimeStamp(date_end, time_end);
eventsFound = client.searchEventsByEventTimeWindow(tstart, tend);
for i = 1:1:size(eventsFound)
    eventsFound(i)
end

parametersFound = client.searchParametersByName('DENS');

parametersFound = client.searchParametersByName('DENS', 'pt');

parametersFound = client.searchParametersByUniqueID('DENS');

parametersFound = client.searchParametersByDescription('current');

parametersFound = client.searchParametersByDescription('corrente', 'pt');

for i = 1:1:size(parametersFound)
    parametersFound(i)
end

This function returns the parameters unique identifiers where the data isn’t null for the selected event:

dataFound = client.searchDataByEvent('0x0000', 17898);
for i = 1:1:size(dataFound)
    dataFound (i)
end

NOTE: The unique identifiers are CASE-SENSITIVE

Data for only one parameter

dataStruct=client.getData('POST.PROCESSED.DENSITY','0x0000', 17898)
dataStruct=dataStruct(1);

Data for several parameters in the same event

dataStruct=client.getMultipleData({'POST.PROCESSED.DENSITY', 'POST.PROCESSED.IPLASMA'},'0x0000', 17898)
dataStructDens=dataStruct(1,1);
dataStructIP=dataStruct(2,1);
dens=dataStructDens.getData(); 
ip=dataStructIP.getData(); 

Data for several parameters in different events

dataStruct=client.getMultipleData({'POST.PROCESSED.DENSITY', 'POST.PROCESSED.IPLASMA'},{'0x0000','0x0000'}, [17898,17899])
dataStructDens=dataStruct(1,1);
dataStructIP=dataStruct(2,1);
dens=dataStructDens.getData(); 
ip=dataStructIP.getData(); 

Data for the same parameter in different events

dataStruct=client.getMultipleData('POST.PROCESSED.DENSITY',{'0x0000','0x0000'}, [17898,17899])
dataStructDens=dataStruct(1,1);
dataStructIP=dataStruct(2,1);
dens=dataStructDens.getData(); 
ip=dataStructIP.getData(); 

Data for the same parameter in different event numbers

dataStruct=client.getMultipleData('POST.PROCESSED.DENSITY', '0x0000', [17898,17899])

This data structure gives you information about:

• start time
• end time
• time of the event
• mime_type
• the parameter unique identifier

The following example shows how to calculate and plot the density at ISTTOK:
1. The data

dataStruct=client.getMultipleData({'CENTRAL.OS9_ADC_VME_I8.IF0CS',
'CENTRAL.OS9_ADC_VME_I8.IF0SN'},'0x0000', 11244);
cosine = dataStruct(1,1).getData;
isine = dataStruct(2,1).getData;

2. Calculate the phase

len = length(cosine)
cosavg = cosine - max(movavg(cosine, 10, 10))/2 - min(movavg(cosine, 10, 10))/2;
sinavg = isine - max(movavg(isine, 10, 10))/2 - min(movavg(isine, 10, 10))/2;
for j = 1:len
  phase(j) = atan2(double(sinavg(j)), double(cosavg(j)));
end

3. Unwrap

unwraped = unwrap(phase);

4. The start time

tstart = dataStruct(1,1).getTStart

5. The end time

tend = dataStruct(1,1).getTEnd

6. The time between samples is:

tbs= (tend.getTimeInMicros - tstart.getTimeInMicros)/len

7. The events

events = dataStruct(1,1).getEvents;

8. The event time (I’m assuming the event I want is at the index 0, but I should check first...)

tevent = events(1,1).getTimeStamp

9. The delay of the start time relative to the event time

delay = tstart.getTimeInMicros - tevent.getTimeInMicros

10. Create the time array

times = delay:tbs:delay+tbs*(len-1);

11. Normalize the data

dens = -7e17 * unwraped;
thold = dens(len-100:len);
density = dens-mean(thold);

11. Plot the chart

plot(times, density)