def get_data(self):
directory = self.get_dir()
shots = self.dic['shots']
shots.replace(' ', '') #remove all the spaces
# keys = " ".join([self.dic['X'], self.dic['Y']])
# self.data, errmsg, raw_data = qrange.qrange(directory, shots, keys)
keys = [self.dic['X'], self.dic['Y']]
# print 'Before qrange.'
self.data, errmsg, raw_data = qrange.qrange_eval(directory, shots, keys)
# print 'After qrange.'
s = ''
for i in range(self.data.shape[1]):
col = self.data[:,i]
s00 = numpy.mean(col)
s01 = stats.sem(col)
s02 = numpy.std(col)
s03 = numpy.max(col) - numpy.min(col)
s = s + "Mean = %10.6f\n" % s00
s = s + "Std. deviation = %10.6f\n" % s02
s = s + "Std. Error of the mean = %10.6f\n" % s01
s = s + "Pk-Pk = %10.6f\n" % s03
s = s+ '\n'
raw_data = s + raw_data
self.dic['data_str'] = raw_data
self.sdata = None
if self.dic['X'] == "SEQ:shot":
s = [ numpy.mean(self.data[:,1]), numpy.std(self.data[:,1]), stats.sem(self.data[:,1]),numpy.max(self.data[:,1]) - numpy.min(self.data[:,1]) ]
a = []
for val in s:
a.append( [val for i in range(self.data[:,1].size)])
self.sdata = numpy.c_[self.data[:,0], numpy.transpose(numpy.array(a))]
else:
self.sdata = statdat.statdat(self.data, 0, 1)
return
def getdata_(self):
""" Executes qrange to extract data from reports.
"""
#data, errmsg, rawdat = qrange.qrange(self.datadir, self.range, self.X + " " + self.Y)
data, errmsg, rawdat = qrange.qrange_eval(self.datadir, self.range, [self.X , self.Y])
msg=None
if data.shape == (0,):
msg = "Data set yields no valid points:\n\n RANGE = %s\n X = %s\n Y = %s\n" % (self.range, self.X, self.Y)
print msg
return data, msg, False
self.raw_data = rawdat
colnames = rawdat.split('\n')[0]
colnames = colnames[1:].split('\t')[1:]
#Get basic statistics for all columns in data
s = ''
for i in range(data.shape[1]):
col = data[:,i]
s00 = numpy.mean( col ) #mean
s02 = numpy.std( col ) #standard deviation
s03 = numpy.max( col ) - numpy.min( col ) # peak to peak value
s01 = stats.sem( col ) #standard error of the mean
#s = s + colnames[i] + ':\n'
s = s + "Mean = %10.6f\n" % s00
s = s + "Std. deviation = %10.6f\n" % s02
s = s + "Std. error of the mean = %10.6f\n" % s01
s = s + "Pk-Pk = %10.6f\n" % s03
s = s + '\n'
self.stat = s
#Get data that is reduced statistically. This means that if there is more
#than 1 point for a given value of X,then the average and error will be used
#The results of this operation are stored in a list called sdata:
#
# sdata = (mean, standard deviation, pk-pk, standard error of the mean)
sdata=None
if self.X == "SEQ:shot" and self.ST:
s = [ numpy.mean( data[:,1] ), \
numpy.std( data[:,1]), \
stats.sem( data[:,1]), \
numpy.max( data[:,1]) - numpy.min(data[:,1]) \
]
a = []
for val in s:
a.append( [ val for i in range( data[:,1].size ) ] )
sdata = numpy.c_[ data[:,0], numpy.transpose(numpy.array(a)) ]
elif self.ST:
import statdat
sdata = statdat.statdat( data, 0, 1)
return data, sdata, msg, True
def data_fetch( datakeys, gdat, **kwargs ):
save = kwargs.get('save', False)
fmt = kwargs.get('fmt', '%9.4g')
print "Fetching data..."
for k in gdat.keys():
try:
gdat[k]['data'] = np.loadtxt(k+'.dat')
print "Loaded %s succesfully." % k
except:
data, errmsg, rawdat = qrange.qrange_eval( gdat[k]['dir'], gdat[k]['shots'], datakeys)
if save:
np.savetxt(k+'.dat', data, fmt=fmt)
gdat[k]['data'] = data
print "Done."
K = lambda key: datakeys.index(key)
return gdat, K
if __name__ == '__main__':
parser = argparse.ArgumentParser(__file__)
parser.add_argument('--range', action = "store", \
help="range of shots to be used.")
parser.add_argument('--onlyCD', action= "store_true", \
help="do only column density plot.")
parser.add_argument('--onlyAZ', action= "store_true", \
help="do only azimuthal average plot.")
parser.add_argument('--onlyAZAB', action= "store_true", \
help="do only azimuthal average and abel transform plot.")
args = parser.parse_args()
datakeys = ['SEQ:shot']
data, errmsg, rawdat = qrange.qrange_eval( os.getcwd() , args.range, datakeys)
print data
print type(data)
if not hasattr( data, '__iter__'):
#if not isinstance( data, list):
data = [ data ]
shots = [ int(i) for i in data ]
print shots
# As an example run this in 140103
# with --range 2959,2983,3016,3049
output = args.range
output = output.replace('-','m')
output = output.replace(':','-')
cdir = os.getcwd()
BraggKeys = [SIGNALKEY, ANDOR1KEY, ANDOR2KEY, "DIMPLELATTICE:imgdet", "SEQ:shot", "DIMPLELATTICE:force_lcr3"]
AllKeys = BraggKeys + [args.XKEY, args.BGKEY]
print "\nObtaining data from : ", AllKeys
def K(key):
try:
index = AllKeys.index(key)
return index
except Exception as e:
print e
exit()
data, errmsg, rawdat = qrange.qrange_eval(cdir, args.range, AllKeys)
# Deal with the backgrounds
if args.BGKEY == "DIMPLELATTICE:force_lcr3":
# This defines the value of the BGKEY that is used for background
bgdval = 0.0
# This defines the value of the BGKEY that is used for signal
sigval = 0.0
elif args.BGKEY == "DIMPLELATTICE:signal":
bgdval = 0.0
sigval = 1.0
elif args.BGKEY == "DIMPLELATTICE:tof":
bgdval = args.tofval
sigval = 0.0
# Remove everything that has DL:force_lcr3 == 0
cond1 = data[:, K("DIMPLELATTICE:force_lcr3")] != 0
"DIMPLELATTICE:image",
]
def K(key):
return datakeys.index(key)
print "Fetching data..."
for k in gdat.keys():
try:
gdat[k]["data"] = np.loadtxt(k + ".dat")
print "Loaded %s succesfully." % k
except:
print k
data, errmsg, rawdat = qrange.qrange_eval(gdat[k]["dir"], gdat[k]["shots"], datakeys)
np.savetxt(k + ".data", data)
gdat[k]["data"] = data
print "Done."
# Get figure started
from matplotlib import rc
rc("font", **{"family": "serif"})
figure = plt.figure(figsize=(12.0, 7.5))
gs = matplotlib.gridspec.GridSpec(2, 3, wspace=0.4, hspace=0.24, top=0.90, left=0.1, right=0.97, bottom=0.1)
figure.suptitle(r"U/t CURVE (We use the shorthand $X_{t}\equiv\frac{X}{X_{\mathrm{TOF}}}$)")
ax1 = plt.subplot(gs[0, 0])
axA1 = plt.subplot(gs[0, 1])
'label':'7$E_{r}$ July 29',\
'dir':'/lab/data/app3/2013/1307/130729/',\
'shots':'3243:3258,3259:3357,-3325,3364:3379',\
'ec':'blue','fc':'lightblue',\
}
datakeys = ['DIMPLELATTICE:imgdet', 'ANDOR1EIGEN:signal' , 'ANDOR2EIGEN:signal', 'HHHEIGEN:andor2norm', 'DIMPLELATTICE:force_lcr3' ]
print "Fetching data..."
for k in detdat.keys():
try:
detdat[k]['data'] = np.loadtxt(k+'.dat')
print "Loaded %s succesfully." % k
except:
print k
data, errmsg, rawdat = qrange.qrange_eval( detdat[k]['dir'], detdat[k]['shots'], datakeys)
np.savetxt(k+'.dat', data)
detdat[k]['data'] = data
print "Done."
# Get figure started
from matplotlib import rc
rc('font',**{'family':'serif'})
figure = plt.figure(figsize=(4.,3.))
gs = matplotlib.gridspec.GridSpec( 1,1)
figure.suptitle('')
ax1 = plt.subplot( gs[0,0] )
help="range of shots to be used.")
parser.add_argument('--output', action="store", \
help="optional path of png to save figure")
args = parser.parse_args()
rangestr = args.range.replace(':','to')
rangestr = rangestr.replace(',','_')
beams = ["GR1","GR2","GR3","IR1","IR2","IR3"]
data = {}
for beam in beams:
keys = ["SEQ:shot"]
for k in ["_Cx","_Cy","_Wx","_Wy"]:
keys.append("LATTICEWEBCAM:"+beam+k)
data[beam] = qrange.qrange_eval("./",args.range,keys)[0]
ZOOM = 1
pairs = [("GR1","IR1"),("GR2","IR2"),("GR3","IR3")]
fig = plt.figure(figsize=(16,12))
fig2 = plt.figure(figsize=(16,12))
gs = matplotlib.gridspec.GridSpec(3,3,wspace =0.5, hspace =0.6,top=0.9,left = 0.07,right=0.93,bottom=0.1)
gs2 = matplotlib.gridspec.GridSpec(2,3,wspace =0.5, hspace =0.5,top=0.9,left = 0.07,right=0.93,bottom=0.1)
for GR,IR in pairs:
if GR == "GR1":
#ax = fig2.add_subplot(231)
#ax2 = fig2.add_subplot(234)
