def compareGrid(specFlag, globals):
Te_MIN = globals.TE_MIN
Te_MAX = globals.TE_MAX
ne_MAX = globals.NE_MAX
ne_MIN = globals.NE_MIN
data = Data()
data.loadData(1110328120)
ps = data.getPolySegData(3,0)
fitPolySeg(ps)
print "Init density: ", ps.ne0, " Init temp: ", ps.Te0
maxProb = 0
ps.ne0 = -1
ps.Te0 = -1
gridPts = 10.0
gridsearch = zeros((gridPts,gridPts))
i = 0
for Te in logspace(log10(Te_MIN), log10(Te_MAX), gridPts):
j = 0
for ne in logspace(log10(ne_MIN), log10(ne_MAX), gridPts):
Nphotons = specPhotons(ne, Te, ps, specFlag)
prob = - calcModelProbability(ps, globals, Nphotons)
gridsearch[i,j] = prob
if prob > maxProb:
maxProb = prob
ps.ne0 = ne
ps.Te0 = Te
j += 1
i += 1
return gridsearch, maxProb, ps
def compareGrid(specFlag):
from ..globals import global_settings
#call global variables
Te_MIN;
Te_MAX;
NE_STEPS;
TE_STEPS;
# Saturation levels
ACQIRIS_MIN;
ACQIRIS_MAX;
STRUCK_MIN;
STRUCK_MAX;
# Laser wavelength
LASER_LAM;
# Logbook quality
LOGBOOK_TRUE;
LOGBOOK_UNSURE;
globals = init_globals()
Te_MIN = globals[0];Te_MAX = globals[1];
NE_STEPS = globals[2]; TE_STEPS = globals[3];
ACQUIRIS_MIN = globals[4]; ACQUIRIS_MAX = globals[5];
STRUCK_MIN = globals[6]; STRUCK_MAX = globals[7];
LASER_LAM = globals[8];
LOGBOOK_TRUE = globals[9]; LOGBOOK_UNSURE = globals[10]
#global_settings.init_globals()
data = Data()
data.loadData(1110328120)
ps = data.getPolySegData(3,0)
fitPolySeg(ps)
print "Init density: ", ps.ne0, " Init temp: ", ps.Te0
maxProb = 0
ps.ne0 = -1
ps.Te0 = -1
gridPts = 10.0
gridsearch = zeros((gridPts,gridPts))
i = 0
for Te in logspace(log10(Te_MIN), log10(Te_MAX), gridPts):
j = 0
for ne in logspace(log10(ne_MIN), log10(ne_MAX), gridPts):
Nphotons = specPhotons(ne, Te, ps, specFlag)
prob = - calcModelProbability(ps, Nphotons)
gridsearch[i,j] = prob
if prob > maxProb:
maxProb = prob
ps.ne0 = ne
ps.Te0 = Te
j += 1
i += 1
return gridsearch, maxProb, ps
def fitShot(shotNum, specFlag = "tsc", numProcs = None, burstLen = 0):
"""This function fits an entire shot. This version will use up to the
number of detected processors on the machine if the multiprocessing
module is installed. The multiprocessing module is a standard part of
python 2.6, and has been backported to 2.5:
http://code.google.com/p/python-multiprocessing/
Parameters:
shotNum -- The shot number to fit. For example 1070814040.
numProcs -- The number of processes to create. Defaults to the number
of CPUs in the machine.
burstLen -- For culling of extra Fast Thomson laser diode pulses
Default of 0 acts like old system, keeping all pulses
Any other value n will cause laser diode to skip pulses
~200 us after n pulses before looking for next burst
"""
from globals import init_globals
glob = init_globals()
Te_MIN = glob[0]
Te_MAX = glob[1]
NE_STEPS = glob[2]
TE_STEPS = glob[3]
ACQIRIS_MIN = glob[4]
ACQIRIS_MAX = glob[5]
STRUCK_MIN = glob[6]
STRUCK_MAX = glob[7]
LASER_LAM = glob[8]
LOGBOOK_TRUE = glob[9]
LOGBOOK_UNSURE = glob[10]
#Load data
from Data import Data
data = Data()
try:
data.loadData(shotNum)
except Exception, ex:
print "Failed to fit shot:", ex
return None
def fitShot(shotNum, specFlag = "tsc", numProcs = None, burstLen = 0):
"""This function fits an entire shot. This version will use up to the
number of detected processors on the machine if the multiprocessing
module is installed. The multiprocessing module is a standard part of
python 2.6, and has been backported to 2.5:
http://code.google.com/p/python-multiprocessing/
Parameters:
shotNum -- The shot number to fit. For example 1070814040.
numProcs -- The number of processes to create. Defaults to the number
of CPUs in the machine.
burstLen -- For culling of extra Fast Thomson laser diode pulses
Default of 0 acts like old system, keeping all pulses
Any other value n will cause laser diode to skip pulses
~200 us after n pulses before looking for next burst
"""
data = Data()
try:
data.loadData(shotNum)
except Exception, ex:
print "Failed to fit shot:", ex
return None
def test_compile():
# a function to call each spectral calculation one time to make sure they have all
# been compiled properly before using them on a fit (JDL has indicated that trying
# to run an inline function for the first time with multiple processors going plays
# havoc with the compiler)
data = Data()
data.loadData(1110328120)
ps = data.getPolySegData(3,0)
calcAmpOffset(ps)
calcVoltageFromRawData(ps)
calc_t0(ps)
calcTransMask(ps)
calcNumPhotons(ps)
filterChans(ps)
calcScatteringAngle(ps)
calcLambdaArray(ps)
ne = 45000.0
Te = 300.0
dist = cold2o_Spec(ps, Te)
NP_cold2o = ne * ps.calib.deltaLam * dot(ps.trans_Bayes, dist)
dist = selden_Spec(ps, Te)
NP_selden = ne * ps.calib.deltaLam * dot(ps.trans_Bayes, dist)
dist = selden_old(ps, Te)
NP_selden_old = (ne/1500000) * ps.calib.deltaLam * dot(ps.trans_Bayes, dist)
prob_cold2o = calcModelProbability(ps, NP_cold2o)
prob_selden = calcModelProbability(ps, NP_selden)
prob_selden_old = calcModelProbability(ps, NP_selden_old)
return prob_cold2o, prob_selden, prob_selden_old
