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 get_data(self):
directory = self.get_dir()
shots = self.setup_dict['shots']
if type(shots) == type([]):
shots = ",".join(shots)
keys = " ".join([self.setup_dict['X'], self.setup_dict['Y']])
self.data, self.errmsg, self.raw_data = qrange.qrange(directory, shots, keys)
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'
self.raw_data = s + self.raw_data
self.sdata = None
if self.setup_dict['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)
def plotCam( KEY, axplot ):
A1dat = statdat.statdat(sigNoRes, K(args.XKEY), K(KEY))
A1res = statdat.statdat(sigRes, K(args.XKEY), K(KEY))
A1tof = statdat.statdat(bgdat, K(args.XKEY), K(KEY))
A1datU = unumpy.uarray(( A1dat[:,1].tolist(), A1dat[:,3].tolist() ))
try:
A1resU = unumpy.uarray(( A1res[:,1].tolist(), A1res[:,3].tolist() ))
plotres = True
except:
A1resU = unumpy.uarray(( [1.], [1.] ))
plotres = False
A1tofU = unumpy.uarray(( A1tof[:,1].tolist(), A1tof[:,3].tolist() ))
A1datU = A1datU[ np.argsort( A1dat[:,0] )]
try:
A1resU = A1resU[ np.argsort( A1res[:,0] )]
plotres = plotres and True
except:
A1resU = A1resU
plotres = False
A1tofU = A1tofU[ np.argsort( A1tof[:,0] )]
axplot.errorbar( A1dat[:,0][np.argsort(A1dat[:,0])], unumpy.nominal_values(A1datU/A1tofU),\
yerr = unumpy.std_devs(A1datU/A1tofU), \
capsize=0., elinewidth = 2.75*msscale ,\
fmt='.', ecolor='green', mec='green', \
mew=2.75*msscale, ms=11.0*msscale,\
marker='o', mfc='limegreen', \
label="signal/base")
if plotres and args.singleres:
tw = axplot.twinx()
tw.errorbar( A1dat[:,0][np.argsort(A1dat[:,0])], unumpy.nominal_values(A1datU/A1resU),\
yerr = unumpy.std_devs(A1datU/A1resU), \
capsize=0., elinewidth = 2.75*msscale ,\
fmt='.', ecolor='red', mec='red', \
mew=2.75*msscale, ms=11.0*msscale,\
marker='o', mfc='pink', \
label="signal/base")
axplot.grid()
axplot.set_xlabel(args.XKEY)
axplot.set_ylabel(KEY)
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 plotkey(
ax, gk, key, dat, base, marker="o", ms=5.0, mec="def", mfc="def", labelstr="def", save=False, raw=True, raw_offset=0
):
if mec == "def":
mec = gdat[gk]["ec"]
if mfc == "def":
mfc = gdat[gk]["fc"]
if labelstr == "def":
labelstr = gdat[gk]["label"]
if raw:
ax.plot(
fx(dat[:, K("DIMPLELATTICE:knob05")]) + raw_offset,
dat[:, K(key)],
".",
marker="o",
mec=mec,
ms=3,
mew=1.0,
color="gray",
alpha=0.5,
)
datS = statdat.statdat(dat, K("DIMPLELATTICE:knob05"), K(key))
alldat.append(datS)
ax.errorbar(
fx(datS[:, 0]),
datS[:, 1] / base,
yerr=datS[:, 3] / base,
capsize=0.0,
elinewidth=1.0,
fmt=".",
ecolor=mec,
mec=mec,
mew=1.0,
marker=marker,
mfc=mfc,
ms=ms,
label=labelstr,
)
if save:
fname = key.replace(":", "_") + "_" + labelstr[-3:] + ".rawdat"
X = np.transpose(np.vstack((fx(datS[:, 0]), datS[:, 1] / base, datS[:, 3] / base)))
np.savetxt(fname, X, fmt="%10.2f", delimiter="\t", newline="\n")
########## MAKE THE BIG PLOT AT THE BOTTOM ######
scale = (cols ** 0.5) / 3.0
msscale = (cols ** 0.5) / 3.5
if verbose:
print "rows = ", rows
print "cols = ", cols
print "mainplotrows = ", mainplotrows
if args.ratio == True:
ax = plt.subplot(gs[Yplots : Yplots + mainplotrows, : (cols + 1) / 2])
else:
ax = plt.subplot(gs[Yplots : Yplots + mainplotrows, :])
if args.ratio == True:
ax2 = plt.subplot(gs[Yplots : Yplots + mainplotrows, (cols + 1) / 2 :])
xdat = statdat.statdat(sgdat, K(args.XKEY), K(SIGNALKEY))
xdatNoRes = statdat.statdat(sigNoRes, K(args.XKEY), K(SIGNALKEY))
xdatRes = statdat.statdat(sigRes, K(args.XKEY), K(SIGNALKEY))
bxdat = statdat.statdat(bgdat, K(args.XKEY), K(SIGNALKEY))
ax.errorbar(
xdatNoRes[:, 0],
xdatNoRes[:, 1],
yerr=xdatNoRes[:, 3],
capsize=0.0,
elinewidth=2.75 * msscale,
fmt=".",
ecolor="blue",
mec="blue",
mew=2.75 * msscale,
ms=11.0 * msscale,
base = 0.656
base_err = 0.068
base1 = base
ax1.axvspan( 37.9/5.9-0.5, 37.9/5.9+0.5, facecolor='lightblue', alpha=0.6, linewidth=0)
ax1.axvspan( -37.9/5.9-0.5, -37.9/5.9+0.5, facecolor='lightblue', alpha=0.6, linewidth=0)
ax1.axhspan( (base-base_err)/base1, (base+base_err)/base1, facecolor='gray', alpha=0.6, linewidth=0)
for k in sorted(detdat.keys()):
dat = detdat[k]['data'][:,(0,3,4)]
# Remove baslines from the data
dat = dat[ dat[:,2] == -1. ]
dat = statdat.statdat( dat, 0, 1 )
ax1.errorbar( (dat[:,0]+131.)/5.9, dat[:,1]/base1, yerr=dat[:,3]/base1,\
capsize=0., elinewidth=1.,\
fmt='.', ecolor=detdat[k]['ec'], mec=detdat[k]['ec'],\
mew=1.0, marker='o', mfc=detdat[k]['fc'],\
#label=detdat[k]['label'])
label='$7E_{r}$\n$a_{s}=190a_{0}$')
#ax1.errorbar( dat[:,0], dat[:,1]/zero, yerr=dat[:,3]/zero, fmt='.', ecolor=edgecolor, mec=edgecolor, mew=1.0, marker='o', mfc=fillcolor)
ax1.grid()
ax1.set_xlabel('Detuning ($\Gamma$)')
ax1.set_ylabel('Bragg / Diffuse',ha='center',labelpad=20)
ax1.set_xlim(-15.,15.)
#ax1.xaxis.set_major_locator( matplotlib.ticker.MultipleLocator(0.1) )
#ax1.set_ylim(0.92, 1.48)
ax1.plot( d[:,0], d[:,1], 'o', color = colors[i], markersize=msize, markeredgewidth=0.3, label=Hleg)
ax1.plot( d[:,0], d[:,2], 'x', color = colors[i], markersize=msize, markeredgewidth=1.0, label=Vleg )
ax1.plot( HfitX, HfitY, linestyle = '-', color = colors[i])
ax1.plot( VfitX, VfitY, linestyle = '-', color = colors[i])
Hlegb = "%s\nX Position of Waist" %dat
Vlegb = "Y Position of Waist"
camPixSize = 4.65
msizeb= 8
if i == 0:
HposRefStat = statdat.statdat( d[:,[0,6]] , 0, 1)
HposRef = HposRefStat[:,1] * camPixSize
VposRefStat = statdat.statdat( d[:,[0,7]] , 0, 1)
VposRef = VposRefStat[:,1] * camPixSize
print HposRef
print VposRef
if i > 0:
HposStat = statdat.statdat( d[:,[0,6]] , 0, 1)
Hpos = HposStat[:,1] * camPixSize
VposStat = statdat.statdat( d[:,[0,7]] , 0, 1)
Vpos = VposStat[:,1] * camPixSize
ax1b.plot( HposStat[:,0], Hpos - HposRef, '->', alpha=0.3,color = colors[i], markersize=msizeb, markeredgewidth=0.3, label=Hlegb)
ax1b.plot( HposStat[:,0], Vpos - VposRef, '-<', alpha=0.3,color = colors[i], markersize=msizeb, markeredgewidth=1.0, label=Vlegb)
rangestr = args.range.replace(':','to')
rangestr = rangestr.replace(',','_')
shots = qrange.parse_range(args.range)
data = []
for s in shots:
report = ConfigObj( 'report'+s+'.INI')
line = [ qrange.evalstr( report, args.XKEY ) ]
for Y in args.YKEYS:
line.append( qrange.evalstr( report, Y) )
data.append(line)
dat = np.array(data)
#print dat
out = statdat.statdat( dat, 0, 1)[:,0]
header = '#\n# Column index\n# 0 %s\n' % args.XKEY
for i,Y in enumerate(args.YKEYS):
header = header + '# %d %s\n' % ( 3*i+1, Y )
header = header + '# %d %s\n' % ( 3*i+2, 'standard deviation' )
header = header + '# %d %s\n' % ( 3*i+3, 'standard error of the mean' )
header = header + '# %d %s\n' % ( 3*i+4, 'pk-pk' )
try:
out = np.c_[ out, statdat.statdat( dat, 0, i+1 )[:,1:] ]
except:
continue
print header,
format = '%8.3f'
def plotkey_relerr( ax, gdict, K, fx, xkey, ykey, dat, base, **kwargs):
try:
defmarker = gdict['marker']
except:
defmarker = 'o'
marker = kwargs.get( 'marker', defmarker)
ms = kwargs.get( 'ms', 5. )
mew = kwargs.get( 'mew', 1.0 )
mec = kwargs.get( 'mec', gdict['ec'])
mfc = kwargs.get( 'mfc', gdict['fc'])
save = kwargs.get( 'save', False )
exceptions = kwargs.get( 'exceptions', False)
raw = kwargs.get( 'raw', True )
raw_offset = kwargs.get( 'raw_offset', 0.)
labelstr = kwargs.get( 'labelstr', gdict['label'] )
xkey0 = xkey
ykey0 = ykey
xkey = K(xkey)
ykey = K(ykey)
try:
datS = statdat.statdat( dat, xkey, ykey )
Ystderr = datS[:,3]/base
Ystddev = datS[:,2]/base
X = datS[:,0]
Y = datS[:,1]/base
def meanY( x ):
try:
index = np.where( X == x )[0][0]
assert isinstance( index, int )
return Y[index]
except:
print "Error finding mean value of Y at X = ", x
raise
if raw:
datY = dat[:,ykey]/base
datX = dat[:,xkey]
datYnormed = np.array([ datY[i] / meanY( datX[i] ) \
for i in range(len(datX)) ] )
rawcolor = kwargs.pop('rawcolor', 'gray')
ax.plot( fx(datX)+raw_offset, datYnormed, '.',\
marker='.', ms=4.5,\
color=rawcolor, alpha=0.5)
ax.plot( fx(X), 100.*2.*Ystderr/Y, '.',\
mec=mec, mew=mew, marker=marker, mfc=mfc, ms=ms,\
label=labelstr)
#ax.plot( fx(X), 100.*Ystddev/Y, '.',\
# mec=mec, mew=mew, marker=marker, mfc=mfc, ms=ms,\
# label=labelstr)
if save:
fname = xkey0.replace(':','_') + '_' + labelstr[-3:] + 'RELERR.rawdat'
X = np.transpose(np.vstack( ( fx(datS[:,0]), datS[:,1]/base, datS[:,3]/base )))
np.savetxt( fname, X, fmt='%10.2f', delimiter='\t', newline='\n')
return fx(datS[:,0]), unumpy.uarray(( datS[:,1]/base, datS[:,3]/base ))
except:
print "Exception occured in plotkey %s vs %s"% (ykey0,xkey0)
if exceptions:
raise
return None
def plotkey( ax, gdict, K, fx, xkey, ykey, dat, base, **kwargs):
try:
defmarker = gdict['marker']
except:
defmarker = 'o'
marker = kwargs.get( 'marker', defmarker)
ms = kwargs.get( 'ms', 5. )
mew = kwargs.get( 'mew', 1.0 )
mec = kwargs.get( 'mec', gdict['ec'])
mfc = kwargs.get( 'mfc', gdict['fc'])
ew = kwargs.get( 'ew', 1.0 )
ecap = kwargs.get( 'ecap', 0. )
save = kwargs.get( 'save', False )
exceptions = kwargs.get( 'exceptions', False)
raw = kwargs.get( 'raw', True )
raw_offset = kwargs.get( 'raw_offset', 0.)
labelstr = kwargs.get( 'labelstr', gdict['label'] )
xkey0 = xkey
ykey0 = ykey
xkey = K(xkey)
ykey = K(ykey)
if kwargs.get('use_stddev', False):
error_index = 2 # standard deviation
else:
error_index = 3 # standard error
discard = kwargs.pop('discard',None)
if discard is not None:
if 'y>' in discard.keys():
dat = dat[ dat[:,ykey] < discard['y>'] ]
if 'y<' in discard.keys():
dat = dat[ dat[:,ykey] > discard['y<'] ]
try:
both_offset = kwargs.get('both_offset', 0.)
yf = kwargs.get( 'yf', None)
if yf is not None:
ydict = kwargs.get('yf_kwargs', {} )
yf_usex = kwargs.get('yf_usex', False)
if yf_usex is True:
ydict['x'] = fx(xc)
if raw:
rawcolor = kwargs.pop('rawcolor', 'gray')
rawalpha = kwargs.pop('rawalpha', 0.5)
rawms = kwargs.pop('rawalpha', 4.5)
xraw = fx(dat[:, xkey] ) + raw_offset + both_offset
yraw = dat[:,ykey]/base
if yf is not None:
yraw = yf( yraw, **ydict )
ax.plot( xraw, yraw , '.',
marker='.', ms=rawms,\
color=rawcolor, alpha=rawalpha)
datS = statdat.statdat( dat, xkey, ykey, **kwargs)
xplot = fx(datS[:,0])
yplot = datS[:,1]/base
yploterr = datS[:,error_index]/base
if yf is not None:
yunc = unumpy.uarray(( yplot, yploterr ))
yunc = yf( yunc, **ydict )
yplot = unumpy.nominal_values( yunc )
yploterr = unumpy.std_devs( yunc )
ax.errorbar( xplot+both_offset, yplot, yerr=yploterr,\
capsize=ecap, elinewidth=ew,\
fmt='.', ecolor=mec, mec=mec,\
mew=mew, marker=marker, mfc=mfc, ms=ms,\
label=labelstr)
guide = kwargs.get( 'guide', False)
if guide:
guide_color = kwargs.get('guide_color', mec)
f = interp1d( xplot, yplot, kind='linear')
xnew = np.linspace( xplot.min(), xplot.max(), 120)
ax.plot( xnew, f(xnew), color=guide_color, zorder = 1.1 )
if save:
fname = xkey0.replace(':','_') + '_' + labelstr[-3:] + '.rawdat'
X = np.transpose(np.vstack( ( xplot, yplot, yploterr )))
np.savetxt( fname, X, fmt='%10.2f', delimiter='\t', newline='\n')
if kwargs.get('return_raw', False):
return xraw, yraw
return xplot, unumpy.uarray(( yplot, yploterr ))
except:
print "Exception occured in plotkey %s vs %s"% (ykey0,xkey0)
if exceptions:
raise
def plotCam( KEY, axplot ):
A1dat = statdat.statdat(sigNoRes, K(args.XKEY), K(KEY))
A1res = statdat.statdat(sigRes, K(args.XKEY), K(KEY))
A1tof = statdat.statdat(bgdat, K(args.XKEY), K(KEY))
A1datU = unumpy.uarray(( A1dat[:,1].tolist(), A1dat[:,3].tolist() ))
try:
A1resU = unumpy.uarray(( A1res[:,1].tolist(), A1res[:,3].tolist() ))
plotres = True
except:
A1resU = unumpy.uarray(( [1.], [1.] ))
plotres = False
try:
A1tofU = unumpy.uarray(( A1tof[:,1].tolist(), A1tof[:,3].tolist() ))
plottof = True
except:
A1tofU = np.ones_like(A1datU)
plottof = False
A1datU = A1datU[ np.argsort( A1dat[:,0] )]
try:
A1resU = A1resU[ np.argsort( A1res[:,0] )]
plotres = plotres and True
except:
A1resU = A1resU
plotres = False
if plottof == True:
A1tofU = A1tofU[ np.argsort( A1tof[:,0] )]
xvals = A1dat[:,0][np.argsort(A1dat[:,0])]
yunc = A1datU/A1tofU
yvals = unumpy.nominal_values(yunc)
yerrvals = unumpy.std_devs(yunc)
axplot.errorbar( xvals, yvals, yerr=yerrvals,\
capsize=0., elinewidth = 2.75*msscale ,\
fmt='.', ecolor='green', mec='green', \
mew=2.75*msscale, ms=11.0*msscale,\
marker='o', mfc='limegreen', \
label="signal/base")
if plotres and args.singleres:
tw = axplot.twinx()
tw.errorbar( A1dat[:,0][np.argsort(A1dat[:,0])], unumpy.nominal_values(A1datU/A1resU),\
yerr = unumpy.std_devs(A1datU/A1resU), \
capsize=0., elinewidth = 2.75*msscale ,\
fmt='.', ecolor='red', mec='red', \
mew=2.75*msscale, ms=11.0*msscale,\
marker='o', mfc='pink', \
label="signal/base")
axplot.grid()
axplot.set_xlabel(args.XKEY)
if 'ANDOR1' in KEY:
axplot.set_ylabel("A1/A1_tof")
print "A1/A1tof:\n\t", print_uarray(yunc)
elif 'ANDOR2' in KEY:
axplot.set_ylabel("A2/A2_tof")
print "A2/A2tof:\n\t", print_uarray(yunc)
else:
axplot.set_ylabel(KEY)