def getValueFromChannel(Client, Channel, ShotNumber):
print("get Value from: " + Channel)
import matplotlib.pyplot as plt
from sdas.core.SDAStime import Date, Time, TimeStamp
import numpy as np
structArray = Client.getData(Channel, '0x0000', ShotNumber)
struct = structArray[0]
Value = struct.getData()
#TIME
tstart = struct.getTStart()
tend = struct.getTEnd()
#Calculate the time between samples
tbs = (tend.getTimeInMicros() - tstart.getTimeInMicros()) / (len(Value) *
1.0)
#Get the events associated with this data
events = struct.get('events')
tevent = TimeStamp(tstamp=events[0].get(
'tstamp')) #The delay of the start time relative to the event time
delay = tstart.getTimeInMicros() - tevent.getTimeInMicros()
#Finally create the time array
times = np.linspace(delay, delay + tbs * (len(Value) - 1), len(Value))
plt.plot(times, Value)
plt.xlabel('time Micros')
plt.ylabel(Channel)
plt.grid(True)
plt.show()
return Value, times
def load_sdas_data(self, channelID, shotnr):
dataStruct = self.client.getData(channelID, '0x0000', shotnr)
dataArray = dataStruct[0].getData()
len_d = len(dataArray)
tstart = dataStruct[0].getTStart()
tend = dataStruct[0].getTEnd()
tbs = (tend.getTimeInMicros() - tstart.getTimeInMicros()) * 1.0 / len_d
events = dataStruct[0].get('events')[0]
tevent = TimeStamp(tstamp=events.get('tstamp'))
delay = tstart.getTimeInMicros() - tevent.getTimeInMicros()
timeVector = np.linspace(delay, delay + tbs * (len_d - 1), len_d)
return [dataArray, timeVector]
def LoadSdasData(client, channelID, shotnr):
dataStruct=client.getData(channelID,'0x0000', shotnr);
dataArray=dataStruct.getData();
len_d=len(dataArray);
tstart = dataStruct.getTStart();
tend = dataStruct.getTEnd();
tbs= (tend.getTimeInMicros() - tstart.getTimeInMicros())*1.0/len_d;
events = dataStruct.get('events')[0];
tevent = TimeStamp(tstamp=events.get('tstamp'));
delay = tstart.getTimeInMicros() - tevent.getTimeInMicros();
timeVector = np.linspace(delay,delay+tbs*(len_d-1),len_d);
return [dataArray, timeVector]
def getSignal(sdasClient, channelID, shotNr):
signalStructArray = sdasClient.getData(channelID, '0x0000', shotNr)
signalStruct = signalStructArray[0]
signal = signalStruct.getData()
tstart = signalStruct.getTStart()
tend = signalStruct.getTEnd()
#Calculate the time between samples
tbs = (tend.getTimeInMicros() - tstart.getTimeInMicros()) / (len(signal) *
1.0)
#Get the events associated with this data
events = signalStruct.get('events')
tevent = TimeStamp(tstamp=events[0].get('tstamp'))
#The delay of the start time relative to the event time
delay = tstart.getTimeInMicros() - tevent.getTimeInMicros()
#Finally create the time array
times = np.linspace(delay, delay + tbs * (len(signal) - 1), len(signal))
return times, signal, tbs
def get_data(shot, channel):
info = client.getData(channel, '0x0000', shot)
data = info[0].getData()
t0 = TimeStamp(tstamp=info[0]['events'][0]['tstamp']).getTimeInMicros()
t1 = info[0].getTStart().getTimeInMicros()
t2 = info[0].getTEnd().getTimeInMicros()
dt = float(t2 - t1) / float(len(data))
time = np.arange(t1 - t0, t2 - t0, dt, dtype=data.dtype) * 1e-6
return data, time
#SAVES MIRNOV DATA IN A MATRIX
coilNr=0
data=[]
for coil in mirnv_corr:
coilNr+=1
structArray=client.getData(coil,'0x0000', shotnr)
struct=structArray[0]
data.append(struct.getData())
#TIME
tstart = struct.getTStart()
tend = struct.getTEnd()
#Calculate the time between samples
tbs = (tend.getTimeInMicros() - tstart.getTimeInMicros())/(len(data[coilNr-1])*1.0)
#Get the events associated with this data
events = struct.get('events')
tevent = TimeStamp(tstamp=events[0].get('tstamp'))
#The delay of the start time relative to the event time
delay = tstart.getTimeInMicros() - tevent.getTimeInMicros()
#Finally create the time array
times = np.linspace(delay,delay+tbs*(len(data[coilNr-1])-1),len(data[coilNr-1]))
#plt.plot(times, data[coilNr-1]);
#PLOTS ALL DATA FROM MIRNOVS
coilNr=0
for coil in data:
coilNr+=1
ax = plt.subplot(3, 4, coilNr)
plt.plot(times, coil)
plt.show()
def getTEnd(self):
return TimeStamp(tstamp=self.get("tend"))
def getTStart(self):
return TimeStamp(tstamp=self.get("tstart"))
# #print server.examples.getStateName(41) 'SHOT', 'pt'
# print client.searchDeclaredEventsByDescription('S', 'pt')
# except Error, v:
# print "ERROR xmlrpclib", v
# #print client.searchDeclaredEventsByName('SH')
# print client.searchMaxEventNumber()
# found=client.searchMinEventNumber('0x0000')
# #found=client.searchParametersByUniqueID('II')
# #found=client.searchDataByEvent('0x0000', 12050)
# print client.searchParametersByName('II', 'pt')
# found= client.searchDeclaredEventsByUniqueID('SHOT')
# for item in found:
# print 'item', item
if client.parameterExists('CENTRAL_OS9_ADC.IIG', '0x0000', 11244):
dS = client.getData('CENTRAL_OS9_ADC.IIGBT', '0x0000', 11244)
ic = dS.getData()
#print 'dS' , dS.get("tstart")
print len(ic), ic[1]
tstart = dS.getTStart()
tend = dS.getTEnd()
print tstart.getTimeInSeconds()
date_start = Date(2005, 11, 1)
date_end = Date(2005, 11, 31)
#t0=Time()
tstart = TimeStamp(date_start)
print tstart.getTimeInMicros()
tend = TimeStamp(date_end)
found = client.searchEventsByEventTimeWindow(tstart, tend)
for item in found:
print item
def getDataFromDatabase(ShotNumber):
from sdas.core.client.SDASClient import SDASClient
from sdas.core.SDAStime import Date, Time, TimeStamp
import numpy as np
import matplotlib.pyplot as plt
import sys
def StartSdas():
host = 'baco.ipfn.ist.utl.pt'
port = 8888
client = SDASClient(host, port)
return client
#CHANGE SHOT NUMBER HERE
print("Import data relative to the ShotNumber:" + str(ShotNumber))
shotnr = ShotNumber
client = StartSdas()
#numerical integrated mirnov coils signals
mirnv = [
'MARTE_NODE_IVO3.DataCollection.Channel_129',
'MARTE_NODE_IVO3.DataCollection.Channel_130',
'MARTE_NODE_IVO3.DataCollection.Channel_131',
'MARTE_NODE_IVO3.DataCollection.Channel_132',
'MARTE_NODE_IVO3.DataCollection.Channel_133',
'MARTE_NODE_IVO3.DataCollection.Channel_134',
'MARTE_NODE_IVO3.DataCollection.Channel_135',
'MARTE_NODE_IVO3.DataCollection.Channel_136',
'MARTE_NODE_IVO3.DataCollection.Channel_137',
'MARTE_NODE_IVO3.DataCollection.Channel_138',
'MARTE_NODE_IVO3.DataCollection.Channel_139',
'MARTE_NODE_IVO3.DataCollection.Channel_140'
]
# mirnv_raw=['MARTE_NODE_IVO3.DataCollection.Channel_145',
# 'MARTE_NODE_IVO3.DataCollection.Channel_146',
# 'MARTE_NODE_IVO3.DataCollection.Channel_147',
# 'MARTE_NODE_IVO3.DataCollection.Channel_148',
# 'MARTE_NODE_IVO3.DataCollection.Channel_149',
# 'MARTE_NODE_IVO3.DataCollection.Channel_150',
# 'MARTE_NODE_IVO3.DataCollection.Channel_151',
# 'MARTE_NODE_IVO3.DataCollection.Channel_152',
# 'MARTE_NODE_IVO3.DataCollection.Channel_153',
# 'MARTE_NODE_IVO3.DataCollection.Channel_154',
# 'MARTE_NODE_IVO3.DataCollection.Channel_155',
# 'MARTE_NODE_IVO3.DataCollection.Channel_156']
#numerical integrated mirnov coils signals with offset correction
mirnv_corr = [
'MARTE_NODE_IVO3.DataCollection.Channel_166',
'MARTE_NODE_IVO3.DataCollection.Channel_167',
'MARTE_NODE_IVO3.DataCollection.Channel_168',
'MARTE_NODE_IVO3.DataCollection.Channel_169',
'MARTE_NODE_IVO3.DataCollection.Channel_170',
'MARTE_NODE_IVO3.DataCollection.Channel_171',
'MARTE_NODE_IVO3.DataCollection.Channel_172',
'MARTE_NODE_IVO3.DataCollection.Channel_173',
'MARTE_NODE_IVO3.DataCollection.Channel_174',
'MARTE_NODE_IVO3.DataCollection.Channel_175',
'MARTE_NODE_IVO3.DataCollection.Channel_176',
'MARTE_NODE_IVO3.DataCollection.Channel_177'
]
#Calculated external flux on each minov coil through the SS modele
ext_flux = [
'MARTE_NODE_IVO3.DataCollection.Channel_214',
'MARTE_NODE_IVO3.DataCollection.Channel_215',
'MARTE_NODE_IVO3.DataCollection.Channel_216',
'MARTE_NODE_IVO3.DataCollection.Channel_217',
'MARTE_NODE_IVO3.DataCollection.Channel_218',
'MARTE_NODE_IVO3.DataCollection.Channel_219',
'MARTE_NODE_IVO3.DataCollection.Channel_220',
'MARTE_NODE_IVO3.DataCollection.Channel_221',
'MARTE_NODE_IVO3.DataCollection.Channel_222',
'MARTE_NODE_IVO3.DataCollection.Channel_223',
'MARTE_NODE_IVO3.DataCollection.Channel_224',
'MARTE_NODE_IVO3.DataCollection.Channel_225'
]
#Minov Coils signals with the effect from the external fluxes subtracted
mirnv_corr_flux = [
'MARTE_NODE_IVO3.DataCollection.Channel_202',
'MARTE_NODE_IVO3.DataCollection.Channel_203',
'MARTE_NODE_IVO3.DataCollection.Channel_204',
'MARTE_NODE_IVO3.DataCollection.Channel_205',
'MARTE_NODE_IVO3.DataCollection.Channel_206',
'MARTE_NODE_IVO3.DataCollection.Channel_207',
'MARTE_NODE_IVO3.DataCollection.Channel_208',
'MARTE_NODE_IVO3.DataCollection.Channel_209',
'MARTE_NODE_IVO3.DataCollection.Channel_210',
'MARTE_NODE_IVO3.DataCollection.Channel_211',
'MARTE_NODE_IVO3.DataCollection.Channel_212',
'MARTE_NODE_IVO3.DataCollection.Channel_213'
]
#Measured currents applied by the Primary, Horizontal and Vertical PowerSupply
prim = 'MARTE_NODE_IVO3.DataCollection.Channel_093'
hor = 'MARTE_NODE_IVO3.DataCollection.Channel_091'
vert = 'MARTE_NODE_IVO3.DataCollection.Channel_092'
#plasma current measured by the Rogowski coil
Ip_rog = 'MARTE_NODE_IVO3.DataCollection.Channel_088'
Ip_rog_value = getValueFromChannel(client, Ip_rog, shotnr)
#chopper =' MARTE_NODE_IVO3.DataCollection.Channel_141'
#Plasma current reconstructed by the mirnov coils without the correction from external fluxes
Ip_magn = 'MARTE_NODE_IVO3.DataCollection.Channel_085'
Ip_magn_value = getValueFromChannel(client, Ip_magn, shotnr)
#Plasma current reconstructed by the mirnov coils without the correction from external fluxes
Ip_magn_corr = 'MARTE_NODE_IVO3.DataCollection.Channel_228'
Ip_magn_corr_value = getValueFromChannel(client, Ip_magn_corr, shotnr)
#SAVES MIRNOV DATA IN A MATRIX
coilNr = 0
data = []
for coil in mirnv_corr:
coilNr += 1
structArray = client.getData(coil, '0x0000', shotnr)
struct = structArray[0]
data.append(struct.getData())
#TIME
tstart = struct.getTStart()
tend = struct.getTEnd()
#Calculate the time between samples
tbs = (tend.getTimeInMicros() -
tstart.getTimeInMicros()) / (len(data[coilNr - 1]) * 1.0)
#Get the events associated with this data
events = struct.get('events')
tevent = TimeStamp(tstamp=events[0].get('tstamp'))
#The delay of the start time relative to the event time
delay = tstart.getTimeInMicros() - tevent.getTimeInMicros()
#Finally create the time array
times = np.linspace(delay, delay + tbs * (len(data[coilNr - 1]) - 1),
len(data[coilNr - 1]))
#plt.plot(times, data[coilNr-1]);
#PLOTS ALL DATA FROM MIRNOVS
coilNr = 0
for coil in data:
coilNr += 1
#plt.title('Coil' + str(coilNr))
ax = plt.subplot(4, 3, coilNr)
ax.set_title('coil' + str(coilNr))
plt.plot(times, coil)
plt.grid(True)
plt.show()
return [Ip_magn_value, Ip_magn_corr_value, Ip_rog_value]
# print client.searchDeclaredEventsByDescription('S', 'pt')
# except Error, v:
# print "ERROR xmlrpclib", v
# #print client.searchDeclaredEventsByName('SH')
# print client.searchMaxEventNumber()
# found=client.searchMinEventNumber('0x0000')
# #found=client.searchParametersByUniqueID('II')
# #found=client.searchDataByEvent('0x0000', 12050)
# print client.searchParametersByName('II', 'pt')
# found= client.searchDeclaredEventsByUniqueID('SHOT')
# for item in found:
# print 'item', item
if client.parameterExists('CENTRAL_OS9_ADC.IIG','0x0000', 11244):
dS=client.getData('CENTRAL_OS9_ADC.IIGBT','0x0000', 11244)
ic=dS.getData()
#print 'dS' , dS.get("tstart")
print len(ic), ic[1]
tstart = dS.getTStart()
tend = dS.getTEnd()
print tstart.getTimeInSeconds()
date_start = Date(2005, 11, 1)
date_end = Date(2005, 11, 31)
#t0=Time()
tstart = TimeStamp(date_start)
print tstart.getTimeInMicros()
tend = TimeStamp(date_end)
found=client.searchEventsByEventTimeWindow(tstart, tend)
for item in found:
print item
