ptspec: fix pT region for model and cocktail

Author: tschaume

pyana/examples/gp_ptspec.py

@@ -31,7 +31,7 @@ def getSubplotTitle(mn, mr):
 
 def gp_ptspec():
   """example for a 2D-panel plot (TODO)"""
-  fenergies = ['19', '27', '39', '62']#, '200']
+  fenergies = ['19', '27', '39', '62', '200']
   nen = len(fenergies)
   #mee_keys = ['pi0', 'LMR', 'omega', 'phi', 'IMR', 'jpsi']
   mee_keys = ['LMR', ]
@@ -55,7 +55,7 @@ def gp_ptspec():
         lmr_label = 'LMR: %g < M_{ee} < %g GeV/c^{2}' % (
           mee_range_split[0], mee_range_split[1]
         )
-    if energy == '200': continue
+    #if energy == '200': continue
     if mee_name not in mee_keys: continue
     mee_dict[mee_name] = mee_range
     data[filebase] = np.loadtxt(open(file_url, 'rb'))
@@ -64,12 +64,15 @@ def gp_ptspec():
         data[filebase] = data[filebase][:-1] # skip mT<0.4 point
     if energy == '200': data[filebase][:,(1,3,4)] /= 0.5
     # calculate average pT first
-    pTs = data[filebase][:,0]
-    probs = unp.uarray(data[filebase][:,1], data[filebase][:,3]) # dN/pT
+    mask = (data[filebase][:,0] > 0.4) & (data[filebase][:,0] < 2.)
+    avpt_data = data[filebase][mask]
+    pTs = avpt_data[:,0]
+    wghts = avpt_data[:,1]
+    probs = unp.uarray(avpt_data[:,1], avpt_data[:,3]) # dN/pT
     probs /= umath.fsum(probs) # probabilities
     avpt = umath.fsum(pTs*probs)
     logging.info(('%s: {} %g' % (
-        filebase, np.average(pTs[:-1], weights = data[filebase][:-1,1])
+        filebase, np.average(pTs, weights = wghts)
     )).format(avpt)) # TODO: syst. uncertainties
     # save datapoint for average pT and append to yvalsPt for yaxis range
     dp = [ float(getEnergy4Key(energy)), avpt.nominal_value, 0., avpt.std_dev, 0. ]
@@ -146,28 +149,30 @@ def gp_ptspec():
   #  arrow_bar = 0.002, layout = '3x2'
   #)
   #make plot for LMR spectra only
-  lmr_key = getSubplotTitle('LMR', '0.4-0.76') # 0.4 not for 200 GeV! skipping above!
+  lmr_key = getSubplotTitle('LMR', '0.4-0.76')
+  if energy == '200':
+      lmr_key = getSubplotTitle('LMR', '0.3-0.76')
   pseudo_point = np.array([[-1,0,0,0,0]])
   model_titles = ['cocktail + model', 'cocktail', 'in-medium', 'QGP']
   model_props = [
     'with lines lc %s lw 5 lt %d' % (default_colors[-2], i+1)
     for i in xrange(len(model_titles))
   ]
-  make_plot(
-    data = dpt_dict[lmr_key][0] + [ pseudo_point ] * len(model_titles),
-    properties = dpt_dict[lmr_key][1] + model_props,
-    titles = dpt_dict[lmr_key][2] + model_titles,
-    name = os.path.join(outDir, 'ptspecLMR'),
-    ylabel = '1/N@_{mb}^{evt} d^{2}N@_{ee}^{acc.}/dp_{T}dM_{ee} (c^3/GeV^2)',
-    xlabel = 'dielectron transverse momentum, p_{T} (GeV/c)',
-    ylog = True, xr = [0, 2.0], yr = [1e-8, 100],
-    lmargin = 0.15, bmargin = 0.08, rmargin = 0.98, tmargin = 0.84,
-    key = ['maxrows 4', 'samplen 0.7', 'width -2', 'at graph 1.,1.2'],
-    arrow_bar = 0.005, size = '10in,13in',
-    labels = {
-      'stat. errors only': [0.7,0.95,False], lmr_label: [0.05,0.03,False]
-    }
-  )
+  #make_plot(
+  #  data = dpt_dict[lmr_key][0] + [ pseudo_point ] * len(model_titles),
+  #  properties = dpt_dict[lmr_key][1] + model_props,
+  #  titles = dpt_dict[lmr_key][2] + model_titles,
+  #  name = os.path.join(outDir, 'ptspecLMR'),
+  #  ylabel = '1/N@_{mb}^{evt} d^{2}N@_{ee}^{acc.}/dp_{T}dM_{ee} (c^3/GeV^2)',
+  #  xlabel = 'dielectron transverse momentum, p_{T} (GeV/c)',
+  #  ylog = True, xr = [0, 2.0], yr = [1e-8, 100],
+  #  lmargin = 0.15, bmargin = 0.08, rmargin = 0.98, tmargin = 0.84,
+  #  key = ['maxrows 4', 'samplen 0.7', 'width -2', 'at graph 1.,1.2'],
+  #  arrow_bar = 0.005, size = '10in,13in',
+  #  labels = {
+  #    'stat. errors only': [0.7,0.95,False], lmr_label: [0.05,0.03,False]
+  #  }
+  #)
   # make mean pt plot
   yMinPt, yMaxPt = 0.95*min(yvalsPt), 1.05*max(yvalsPt)
   #make_plot(
@@ -202,16 +207,19 @@ def gp_ptspec():
   # make mean pt plot for LMR only
   make_plot(
     data = [
-      np.array(data_avpt['LMR_c']),
+      #np.array(data_avpt['LMR_c']),
       np.array(data_avpt['LMR_m']),
       np.array(data_avpt['LMR'])
     ],
     properties = [
+     #'with lines lt 2 lw 4 lc %s' % default_colors[0],
      'with lines lt 1 lw 4 lc %s' % default_colors[0],
-     'with lines lt 2 lw 4 lc %s' % default_colors[0],
      'lt 1 lw 4 ps 1.5 lc %s pt 18' % default_colors[0]
     ],
-    titles = [ 'cocktail', '+medium', getMeeLabel('data') ],
+    titles = [
+        #'cocktail',
+        'model', getMeeLabel('data')
+    ],
     name = os.path.join(outDir, 'meanPtLMR'),
     xlabel = '{/Symbol \326}s_{NN} (GeV)',
     ylabel = 'LMR {/Symbol \341}p_{T}{/Symbol \361} in STAR Acceptance (GeV/c)',