def line_figure(self, wdir, Q2, dpi):
## this block may contain something not useful
load_config('%s/input.py' % wdir)
istep = core.get_istep()
# jar = load('%s/data/jar-%d.dat' % (wdir, istep))
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
self.cluster = labels['cluster']
self.cluster_average_chi2 = labels['cluster_average_chi2']
self.best_cluster = np.argmin(self.cluster_average_chi2)
nrows, ncols = 1, 1
fig = py.figure(figsize=(ncols * 5.0, nrows * 3.0))
ax = py.subplot(nrows, ncols, 1)
## polarized PDF
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
self.xf_data = load('%s/data/ppdf-%d.dat' % (wdir, istep))
else:
self.xf_data = load('%s/data/ppdf-%d-%f.dat' % (wdir, istep, Q2))
print '\nplotting polarized PDF line figure from %s at Q2 = %.2f' % (
wdir, Q2)
self.plot_lines(ax, 'g', 'r', all_cluster=2)
ax.set_ylim(-0.11, 0.3)
ax.set_yticks([-0.1, 0.0, 0.1, 0.2])
ax.set_yticklabels([r'$-0.1$', r'$0.0$', r'$0.1$', r'$0.2$'])
# ax.text(0.7, 0.8, r'\boldmath$x\Delta g$', color = 'k', transform = axs[3].transAxes, size = 28)
ax.title.set_text(
r'\boldmath$x\Delta g~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' % Q2)
legends = ax.legend(frameon=0, loc='best')
for line in legends.get_lines():
line.set_linewidth(1.0)
ax.semilogx()
ax.set_xlim(8e-3, 9e-1)
ax.set_xticks([1e-2, 1e-1])
ax.tick_params(axis='both',
which='both',
right=True,
top=True,
direction='in',
labelsize=20)
ax.set_xticklabels([r'', r''])
ax.axhline(0.0, color='k', linestyle='dashdot', linewidth=0.5)
ax.set_xticks([0.01, 0.1, 0.5, 0.8])
ax.set_xticklabels([r'$0.01$', r'$0.1$', r'$0.5$', r'$0.8$'])
# py.subplots_adjust(left = 0.12, bottom = 0.08, right = 0.99, top = 0.97, wspace = None, hspace = 0.2)
py.tight_layout()
if Q2 == conf['Q20']:
py.savefig('%s/gallery/ppdf-lines-pjet-%d.png' % (wdir, istep),
dpi=dpi)
else:
py.savefig('%s/gallery/ppdf-lines-pjet-%d-%f.png' %
(wdir, istep, Q2),
dpi=dpi)
def histogram_figure(self, wdir, Q2, dpi):
## plot histogram for parameters of all flavors
load_config('%s/input.py' % wdir)
istep = core.get_istep()
# jar = load('%s/data/jar-%d.dat' % (wdir, istep))
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
self.cluster = labels['cluster']
self.best_cluster = np.argmin(labels['cluster_average_chi2'])
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
self.ppdf_parameter_data = load('%s/data/ppdf-parameters-%d.dat' % (wdir, istep))
else:
self.ppdf_parameter_data = load('%s/data/ppdf-parameters-%d-%f.dat' % (wdir, istep, Q2))
shapes = self.ppdf_parameter_data.keys()
flavors = self.ppdf_parameter_data[shapes[0]].keys()
for _ in sorted(self.ppdf_parameter_data[shapes[0]][flavors[0]]):
print '\nplotting PPDF parameter %s histograms from %s' % (_, wdir)
if len(flavors) == 8:
n_rows, n_columns = 4, 2
figure = py.figure(figsize = (n_columns * 5.0, n_rows * 3.0))
axs = [py.subplot(n_rows, n_columns, count + 1) for count in range(8)]
sys.exit('please make a subplot configuration for your flavors')
elif len(flavors) == 7:
n_rows, n_columns = 4, 2
figure = py.figure(figsize = (n_columns * 5.0, n_rows * 3.0))
axs = [py.subplot(n_rows, n_columns, count + 1) for count in range(8) if count != 7]
else:
sys.exit('please make a subplot configuration for your flavors')
if sorted(flavors) == sorted(['g', 'up', 'ub', 'dp', 'db', 'sp', 'sb']):
self.ppdf_histogram(axs[0], 'up', _, r'\boldmath$u_+$')
self.ppdf_histogram(axs[1], 'ub', _, r'\boldmath$\overline{u}$')
self.ppdf_histogram(axs[2], 'dp', _, r'\boldmath$d_+$')
self.ppdf_histogram(axs[3], 'db', _, r'\boldmath$\overline{d}$')
self.ppdf_histogram(axs[4], 'sp', _, r'\boldmath$s_+$')
self.ppdf_histogram(axs[5], 'sb', _, r'\boldmath$\overline{s}$')
self.ppdf_histogram(axs[6], 'g', _, r'\boldmath$g$')
for ax in axs:
# ax.semilogx()
# ax.tick_params(axis = 'both', which = 'both', right = True, top = True, direction = 'in', labelsize = 20)
# ax.set_xticklabels([r'', r''])
ax.legend(frameon = 0, loc = 'best', fontsize = 17)
axs[0].title.set_text(r'$\mathrm{parameter}~%s~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV^2}$' % (_, Q2))
axs[1].title.set_text(r'$\mathrm{parameter}~%s~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV^2}$' % (_, Q2))
py.tight_layout()
if Q2 == conf['Q20']:
py.savefig('%s/gallery/ppdf-histogram-pjet-%s-%d.png' % (wdir, _, istep), dpi = dpi)
else:
py.savefig('%s/gallery/ppdf-histogram-pjet-%s-%d-%f.png' % (wdir, _, istep, Q2), dpi = dpi)
def make_figure(wdir,
task,
plot_with_factor=True,
only_best_cluster=True,
dpi=200):
## attribute 'plot_with_factor' is inherited from jet and not yet used
## it is kept there because someday it might be useful
if task == 1:
print('\nplotting PJET data from %s' % (wdir))
elif task == 2:
print('\nplotting PJET data over theory from %s' % (wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
core.mod_conf(istep, replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
if 'pjet' not in predictions['reactions']:
print('PJET is not in data file')
return
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
cluster = labels['cluster']
cluster_average_chi2 = labels['cluster_average_chi2']
best_cluster = np.argmin(cluster_average_chi2)
data = predictions['reactions']['pjet']
for idx in data:
predictions = copy.copy(data[idx]['prediction-rep'])
del data[idx]['prediction-rep']
del data[idx]['residuals-rep']
if only_best_cluster:
best_predictions = [
predictions[i] for i in range(len(predictions))
if cluster[i] == best_cluster
]
data[idx]['thy'] = np.mean(best_predictions, axis=0)
data[idx]['dthy'] = np.std(best_predictions, axis=0)**0.5
else:
all_predictions = [predictions[i] for i in range(len(predictions))]
data[idx]['thy'] = np.mean(all_predictions, axis=0)
data[idx]['dthy'] = np.std(all_predictions, axis=0)**0.5
if task == 1:
plot_data(wdir, data, istep, dpi, plot_with_factor)
# for idx in data:
# plot_data_separate(wdir, data[idx], istep, idx, dpi, plot_with_factor)
elif task == 2:
## this is copied from jet plot and not yet ready
plot_data_on_theory(wdir, data, istep, dpi)
return
def plot_chi2_dist_per_exp(wdir, istep):
#--needs revisions NS (09/02/19)
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
cluster, colors, nc, order = get_clusters(wdir, istep)
chi2 = labels['chi2dof']
data = load('%s/data/predictions-%d.dat' % (wdir, istep))
for reaction in data['reactions']:
for idx in data['reactions'][reaction]:
if reaction != 'sidis': continue
if idx != 2006: continue
res = np.array(data['reactions'][reaction][idx]['residuals-rep'])
chi2 = np.array([np.sum(r**2) / len(r) for r in res])
#chi2=np.array([_ for _ in chi2 if _<5])
Mean = np.average(chi2)
dchi = np.std(chi2)
chi2min = Mean - 2 * dchi
chi2max = Mean + 2 * dchi
R = (chi2min, chi2max)
nrows = 1
ncols = 1
#--plot labeled residuals
ax = py.subplot(nrows, ncols, 1)
ax.hist(chi2,
bins=30,
range=R,
histtype='step',
label='size=%d' % len(chi2))
for j in range(nc):
_chi2 = [chi2[i] for i in range(len(chi2)) if cluster[i] == j]
c = colors[j]
print c, np.mean(_chi2), reaction, idx
label = 'cluster id=%d size=%d' % (j, len(_chi2))
ax.hist(_chi2,
bins=30,
range=R,
histtype='step',
label=label,
color=c)
ax.set_xlabel('chi2/npts')
ax.set_ylabel('yiled')
ax.legend()
ax.text(0.1, 0.8, 'step %d' % istep, transform=ax.transAxes)
py.tight_layout()
checkdir('%s/gallery' % wdir)
py.savefig('%s/gallery/chi2-dist-%d-%s-%d.jpg' %
(wdir, istep, reaction, idx))
py.close()
def plot_obs(wdir, only_best_cluster=True, dpi=200):
print('\nplotting dy data from %s' % (wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
core.mod_conf(istep, replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
if 'dy' not in predictions['reactions']:
print('DY is not in data file')
return
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
cluster = labels['cluster']
cluster_average_chi2 = labels['cluster_average_chi2']
best_cluster = np.argmin(cluster_average_chi2)
data = predictions['reactions']['dy']
for idx in data:
predictions = copy.copy(data[idx]['prediction-rep'])
del data[idx]['prediction-rep']
del data[idx]['residuals-rep']
if only_best_cluster:
best_predictions = [
predictions[i] for i in range(len(predictions))
if cluster[i] == best_cluster
]
data[idx]['thy'] = np.mean(best_predictions, axis=0)
data[idx]['dthy'] = np.std(best_predictions, axis=0)**0.5
else:
all_predictions = [predictions[i] for i in range(len(predictions))]
data[idx]['thy'] = np.mean(all_predictions, axis=0)
data[idx]['dthy'] = np.std(all_predictions, axis=0)**0.5
Q2_bins = []
# Q2_bins.append([30, 40])
Q2_bins.append([35, 45]) ## label pp: 37 < Q2 < 42 pd: idem
Q2_bins.append([45, 52]) ## label pp: 47 < Q2 < 49 pd: idem
Q2_bins.append([52, 60]) ## label pp: 54 < Q2 < 58 pd: 55 < Q2 < 57
Q2_bins.append([60, 68]) ## label pp: 63 < Q2 < 66 pd: 62 < Q2 < 65
Q2_bins.append([68, 75]) ## label pp: 70 < Q2 < 73 pd: 70 < Q2 < 72
Q2_bins.append([100, 140]) ## label pp: 118 < Q2 < 131 pd: 124 < Q2 < 129
Q2_bins.append([140, 160]) ## label pp: 148 < Q2 < 156 pd: 145 < Q2 < 154
Q2_bins.append([160, 280]) ## label pp: 173 < Q2 < 222 pd: 73 < Q2 < 280
plot_ratio(wdir, data, istep, Q2_bins, dpi)
return
def get_replicas(wdir):
"""
load the msr files
"""
replicas=sorted(os.listdir('%s/msr-inspected'%wdir))
replicas=[load('%s/msr-inspected/%s'%(wdir,_)) for _ in replicas]
return replicas
def update_idis_tabs(wdir, tabs, tar):
load_config('%s/input.py' % wdir)
istep = core.get_istep()
data = load('%s/data/predictions-%d-sim.dat' % (wdir, istep))
blist = []
blist.append('thy')
blist.append('shift')
blist.append('residuals')
blist.append('prediction')
blist.append('N')
blist.append('Shift')
blist.append('W2')
blist.append('alpha')
blist.append('residuals-rep')
blist.append('r-residuals')
for _ in data['reactions']:
for idx in data['reactions'][_]:
print('update %s %d%s...' % (_, idx, tar))
tab = data['reactions'][_][idx]
for k in blist:
try:
del tab[k]
except:
continue
tab['value'] = np.mean(tab['prediction-rep'], axis=0)
for i in range(len(tab['prediction-rep'])):
tab['value%s' % (i + 1)] = tab['prediction-rep'][i]
del tab['prediction-rep']
df = pd.DataFrame(tab)
df.to_excel('%s/sim/%s%s.xlsx' % (wdir, idx, tar), index=False)
def get_chi2(wdir, kc):
istep = core.get_istep()
#replicas=core.get_replicas(wdir)
#core.mod_conf(istep,replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
data = predictions['reactions']
tab = {}
for reaction in data:
for idx in data[reaction]:
if reaction not in tab: tab[reaction] = {}
if idx not in tab[reaction]: tab[reaction][idx] = {}
value = data[reaction][idx]['value']
alpha = data[reaction][idx]['alpha']
thy = np.mean(data[reaction][idx]['prediction-rep'], axis=0)
col = data[reaction][idx]['col'][0]
res = (value - thy) / alpha
chi2 = np.sum(res**2)
npts = res.size
chi2_npts = chi2 / npts
tab[reaction][idx]['col'] = col
tab[reaction][idx]['chi2'] = chi2
tab[reaction][idx]['npts'] = npts
tab[reaction][idx]['chi2_npts'] = chi2_npts
return tab
def run(self, nruns=1):
if 'nruns' in conf:
nruns = conf['nruns']
# set outputdir
if 'args' in conf:
outputdir = '%s/mcdata' % conf['args'].config.split(
'/')[-1].replace('.py', '')
else:
outputdir = 'mcdata'
checkdir(outputdir)
# get exisiting runs
idx = [int(x.replace('.dat', '')) for x in os.listdir(outputdir)]
if len(idx) == 0:
fname = ['%s/%i.dat' % (outputdir, i) for i in range(nruns)]
nestout = [None for i in range(nruns)]
else:
fname = ['%s/%s' % (outputdir, _) for _ in os.listdir(outputdir)]
nestout = [
load('%s/%s' % (outputdir, _)) for _ in os.listdir(outputdir)
]
for i in range(nruns):
print 'run ', i
self.single_run(fname[i], nestout[i])
def get_MC_samples(self, mcpath):
F = os.listdir(mcpath)
data = []
for f in F:
data.append(load('%s/%s' % (mcpath, f)))
samples = []
likelihoods = []
n = 0
for d in data:
for p in d['samples']:
samples.append(p)
likelihoods.extend(d['l'])
n += len(d['active nll'])
samples = np.array(samples)
likelihoods = np.array(likelihoods)
I = np.argsort(likelihoods)[::-1]
x = np.array([((n - 1.) / n)**i for i in range(len(likelihoods) + 1)])
dx = (0.5 * (x[:-1] - x[1:]))[::-1]
x = x[::-1]
likelihoods = likelihoods[I]
samples = samples[I]
weights = np.array([likelihoods[i] * dx[i] for i in range(dx.size)])
weights /= np.sum(weights)
weights2 = np.array(
[weights[i] for i in range(weights.size) if weights[i] > 1e-4])
samples2 = np.array(
[samples[i] for i in range(len(weights)) if weights[i] > 1e-4])
weights2 /= np.sum(weights2)
print 'runs max likelihoods'
for d in data:
print d['active nll'][0]
print 'sample size=', weights.size
print 'sample2 size=', weights2.size
out = {}
out['samples'] = samples
out['weights'] = weights
out['samples2'] = samples2
out['weights2'] = weights2
out['order'] = conf['parman'].order
out['runs'] = {}
cnt = 0
for d in data:
out['runs'][cnt] = {
'samples': d['samples'],
'weights': d['weights']
}
cnt += 1
return out
def plot_obs(wdir, kc):
print('\nplotting ln data from %s' % (wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
core.mod_conf(istep, replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
data = predictions['reactions']['ln']
plot_H1(wdir, istep, data[1000])
plot_ZEUS(wdir, istep, data[2000])
def _gen_labels_by_chi2(wdir, istep):
print('\t by chi2 ...')
#--load data where residuals are computed
data = load('%s/data/predictions-%d.dat' % (wdir, istep))
res = np.array(data['res'])
#--compute chi2/npts
chi2dof = []
for row in res:
chi2dof.append(np.sum(row**2) / len(row))
chi2dof = np.array(chi2dof)
return chi2dof
def gen_exp_dict(self, wdir, istep):
#--needs revision NS (09/02/19)
data = load('%s/data/predictions-%d.dat' % (wdir, istep))
summary = []
for reaction in data['reactions']:
for idx in data['reactions'][reaction]:
tab = data['reactions'][reaction][idx]
col = tab['col'][0]
value = tab['value']
npts = len(value)
msg = 'reaction: %8s,'
msg += ' idx: %7d,'
msg += ' col: %10s,'
msg += ' npts: %5d,'
msg = msg % (reaction, idx, col[:10], npts)
if reaction == 'dy':
msg += ' rea: %s,' % tab['reaction'][0]
if reaction == 'sia':
msg += ' had: %s,' % tab['hadron'][0]
if reaction == 'idis':
msg += ' tar: %s,' % tab['target'][0]
msg += ' obs: %s,' % tab['obs'][0]
if reaction == 'sidis':
msg += ' tar: %1s,' % tab['target'][0][0]
msg += ' had: %5s,' % tab['hadron'][0]
msg += ' obs: %s,' % tab['obs'][0]
if reaction == 'pidis':
msg += ' tar: %s,' % tab['target'][0]
msg += ' obs: %s,' % tab['obs'][0]
if reaction == 'psidis':
msg += ' tar: %1s,' % tab['target'][0][0]
msg += ' had: %5s,' % tab['hadron'][0]
msg += ' obs: %s,' % tab['obs'][0]
if reaction == 'jet':
msg += ' rea: %s' % tab['reaction'][0]
msg += ' col: %s' % tab['col'][0]
if reaction == 'pjet':
msg += ' rea: %s' % tab['reaction'][0]
msg += ' col: %s' % tab['col'][0]
summary.append(msg)
summary = [_ + '\n' for _ in summary]
F = open('%s/data/exp-dict-%d.txt' % (wdir, istep), 'w')
F.writelines(summary)
F.close()
def update_tabs(wdir, tar, est, lum):
istep = core.get_istep()
data = load('%s/data/predictions-%d-all-%s-%s.dat' %
(wdir, istep, tar, est))
blist = []
blist.append('thy')
blist.append('shift')
blist.append('residuals')
blist.append('prediction')
blist.append('N')
blist.append('Shift')
blist.append('W2')
blist.append('alpha')
blist.append('residuals-rep')
blist.append('r-residuals')
blist.append('L')
blist.append('H')
#--placeholder index
idx = 80000
tab = data['reactions']['pidis'][idx]
#--delete unnecessary data
for k in blist:
try:
del tab[k]
except:
continue
#--save mean value
tab['value'] = np.mean(tab['prediction-rep'], axis=0)
#--save individual values
for i in range(len(tab['prediction-rep'])):
tab['value%s' % (i + 1)] = tab['prediction-rep'][i]
del tab['prediction-rep']
tab['stat_u'], tab['pole_u'], tab['polh_u'], tab['lum_u'], tab[
'syst_u'] = all_errors(wdir, tar, est, tab['value'], lum)
df = pd.DataFrame(tab)
filename = '%s/sim/all-%s-%s.xlsx' % (wdir, tar, est)
df.to_excel(filename, index=False)
print('Updating xlsx file and saving to %s' % filename)
def plot_obs(wdir, kc):
print('\nplotting dy-pion data from %s' % (wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
core.mod_conf(istep, replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
if 'dy-pion' not in predictions['reactions']: return
data = predictions['reactions']['dy-pion']
plot_E615(wdir, istep, data[10001])
plot_NA10(wdir, istep, data[10002], data[10003])
return
def plot_obs(wdir, kc):
print('\nplotting DY-pion (qT) from %s' % (wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
core.mod_conf(istep, replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
#print(predictions['reactions'].keys())
if 'pion_qT' not in predictions['reactions']: return
data = predictions['reactions']['pion_qT']
plot_E615_Q(wdir, istep, data[1001])
plot_E615_xF(wdir, istep, data[1002])
return
def get_msr_inspected(wdir):
print('\nget msr inspected (filtered msr files) using %s\n'%wdir)
replicas=sorted(os.listdir('%s/msr'%wdir))
X=[]
for i in range(len(replicas)):
lprint('progress: %d/%d'%(i+1,len(replicas)))
replica=load('%s/msr/%s'%(wdir,replicas[i]))
istep=sorted(replica['params'].keys())[-1] #--pick last step
params=replica['params'][istep]
#print params
#if params[2]<0.1: continue
if params is None: continue
data=replica['chi2']#[istep]
chi2,npts=0,0
for reaction in data:
#print
for idx in data[reaction]:
chi2+=data[reaction][idx]['chi2']
npts+=data[reaction][idx]['npts']
if chi2/npts-1<1000:
X.append(chi2/npts-1)
checkdir('%s/msr-inspected'%wdir)
cmd=['cp']
cmd.append('%s/msr/%s'%(wdir,replicas[i]))
cmd.append('%s/msr-inspected/%s'%(wdir,replicas[i]))
p = subprocess.Popen(cmd, stdout=subprocess.PIPE)
print()
print('original num. samples :%d'%len(replicas))
print('inspected num. samples :%d'%len(X))
nrows=1
ncols=1
#--plot labeled residuals
ax=py.subplot(nrows,ncols,1)
ax.hist(X,bins=10)
#py.tight_layout()
checkdir('%s/gallery'%wdir)
py.savefig('%s/gallery/chi2-dist-inspect.pdf'%(wdir))
py.close()
def gen_dist_from_fitpack(wdir,dist):
print('\ngenerating %s tables for benchmark using %s'%(dist,wdir))
load_config('%s/input.py'%wdir)
istep=core.get_istep()
core.mod_conf(istep) #--set conf as specified in istep
resman=RESMAN(nworkers=1,parallel=False,datasets=False)
parman=resman.parman
jar=load('%s/data/jar-%d.dat'%(wdir,istep))
replicas=jar['replicas']
X,Q2=gen_grid(dist)
qpd=conf[dist]
nx=len(X)
nQ2=len(Q2)
flavs=[-5,-4,-3,-2,-1,1,2,3,4,5,21]
fmap={-5:'bb',-4:'cb',-3:'sb',-2:'ub',-1:'db'}
fmap.update({5:'b',4:'c',3:'s',2:'u',1:'d'})
fmap.update({21:'g'})
#--gen qpd data per replica
checkdir('%s/benchmark'%wdir)
cnt=0
for par in replicas:
lprint('progress: %d/%d'%(cnt+1,len(replicas)))
parman.set_new_params(par)
data={}
for _ in Q2:
data[_]={}
for flav in flavs:
data[_][flav]=np.array([qpd.get_xF(x,_,fmap[flav]) for x in X])
save(data,'%s/benchmark/%s-%d.dat'%(wdir,dist,cnt))
cnt+=1
print
def print_individual_parameters(working_directory,
distribution,
norm,
index=0):
load_config('%s/input.py' % working_directory)
istep = core.get_istep()
jar = load('%s/data/jar-%d.dat' % (working_directory, istep))
if norm:
for i in range(len(jar['order'])):
if jar['order'][i][0] == 2:
print jar['order'][i][1].ljust(8), '%s' % str(
jar['order'][i][2]).ljust(
10), ':', jar['replicas'][index][i]
else:
for i in range(len(jar['order'])):
if (jar['order'][i][0] == 1) and (jar['order'][i][1]
== distribution):
print jar['order'][i][2].ljust(
13), ':', jar['replicas'][index][i]
def gen_report(self):
inputfile = conf['args'].config
nestfile = conf['args'].fname
nestout = load(nestfile)
weight = nestout['weights'][0]
q = nestout['samples'][0]
self.get_residuals(q)
report = conf['resman'].gen_report(verb=1, level=1)
save(report, 'report')
L = open(inputfile).readlines()
for i in range(len(L)):
if L[i].startswith('#'):
continue
if 'params' in L[i] and '<<' in L[i]:
l = L[i].split('=')[0].replace('conf', '').replace("'", '')
k, kk = l.replace('][',
'@').replace('[',
'').replace(']',
'').split('@')[1:]
left = L[i].split('<<')[0]
right = L[i].split('>>')[1]
L[i] = left + '<<%30.20e>>' % conf['params'][k.strip()][
kk.strip()]['value'] + right
if 'norm' in L[i] and '<<' in L[i]:
l = L[i].split('=')[0].replace('conf', '').replace("'", '')
dum1, k, dum2, kk = l.replace('][',
'@').replace('[', '').replace(
']', '').split('@')
left = L[i].split('<<')[0]
right = L[i].split('>>')[1]
value = conf['datasets'][k.strip()]['norm'][int(kk)]['value']
L[i] = left + '<<%30.20e>>' % value + right
F = open(inputfile, 'w')
F.writelines(L)
F.close()
def plot_chi2_dist(wdir, istep):
#--needs revisions NS (09/02/19)
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
chi2 = labels['chi2dof']
cluster, colors, nc, order = get_clusters(wdir, istep)
nrows = 1
ncols = 1
#--plot labeled residuals
ax = py.subplot(nrows, ncols, 1)
chi2 = [_ for _ in chi2 if _ < 2]
Mean = np.average(chi2)
dchi = np.std(chi2)
chi2min = Mean - 2 * dchi
chi2max = Mean + 2 * dchi
R = (chi2min, chi2max)
ax.hist(chi2,
bins=60,
range=R,
histtype='step',
label='size=%d' % len(chi2))
for j in range(nc):
chi2_ = [chi2[i] for i in range(len(chi2)) if cluster[i] == j]
c = colors[j]
print c, np.mean(chi2_), np.mean(chi2)
label = 'cluster id=%d size=%d' % (j, len(chi2_))
ax.hist(chi2_, bins=30, range=R, histtype='step', label=label, color=c)
mean = np.average(chi2_)
ax.axvline(mean, color=c, ls=':')
ax.set_xlabel('chi2/npts')
ax.set_ylabel('yiled')
ax.legend()
ax.text(0.1, 0.8, 'step %d' % istep, transform=ax.transAxes)
py.tight_layout()
checkdir('%s/gallery' % wdir)
py.savefig('%s/gallery/chi2-dist-%d.jpg' % (wdir, istep))
py.close()
def _gen_labels_by_echi2(wdir, istep):
print('\t by echi2 ...')
#--load data where residuals are computed
data = load('%s/data/predictions-%d.dat' % (wdir, istep))
res = np.array(data['res'])
#--compute chi2/npts
echi2 = []
for row in res:
echi2.append(0) #np.sum(row**2)/len(row))
echi2 = np.array(echi2)
nexp = 0
for reaction in data['reactions']:
for idx in data['reactions'][reaction]:
nexp += 1
res = np.array(data['reactions'][reaction][idx]['residuals-rep'])
for i in range(len(res)):
echi2[i] += (np.sum(res[i]**2) / len(res[i]))
return echi2 / nexp
def get_clusters(wdir, istep, kc):
nc = kc.nc[istep]
hook = kc.hooks[istep]
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
cluster = labels['cluster']
chi2dof = labels['chi2dof']
echi2dof = labels['echi2dof']
#--get clusters idx ordering
echi2_means = [
np.mean([echi2dof[i] for i in range(len(echi2dof)) if cluster[i] == j])
for j in range(nc)
]
order = np.argsort(echi2_means)
clist = ['r', 'c', 'g', 'y']
colors = {order[i]: clist[i] for i in range(nc)}
return cluster, colors, nc, order
def gen_tables(wdir, dist, file_name, info, info_only=False):
print('\ngenerating LHAPDF tables for %s using %s' % (dist, wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
core.mod_conf(istep) #--set conf as specified in istep
resman = RESMAN(nworkers=1, parallel=False, datasets=False)
parman = resman.parman
jar = load('%s/data/jar-%d.dat' % (wdir, istep))
replicas = jar['replicas']
#--check order consistency
order = jar['order']
parman.order = order
#--create output dir
checkdir(wdir + '/data/')
checkdir(wdir + '/data/%s/' % file_name)
#--gen lhapdf_data_files
if info_only == False:
cnt = 0
for par in replicas:
lprint('progress: %d/%d' % (cnt + 1, len(replicas)))
parman.set_new_params(par)
X, Q2, table = _gen_table(dist)
gen_lhapdf_dat_file(X, Q2, table, wdir, file_name, cnt)
cnt += 1
print
#--gen_lhapdf_info_file
X, Q2 = gen_grid(dist)
nrep = len(replicas)
gen_lhapdf_info_file(X, Q2, nrep, wdir, file_name, info)
def run(self):
global conf
load_config(self.inputfile)
#--modify confs
if 'hooks' not in conf:
conf['hooks'] = {}
if self.msrhook != None:
conf['hooks']['msr'] = self.msrhook
if self.verbose == False:
conf['verbose'] = 1
else:
conf['verbose'] = self.verbose
#--backup conf(after mods)
conf_bkp = copy.deepcopy(conf)
#--decide if the input should be modified
output = True
if 'bootstrap' in conf and conf['bootstrap'] == True: output = False
if 'steps' in conf:
isteps = sorted(conf['steps'])
self.order = {}
self.params = {}
chi2 = {}
if self.prior != None:
prior = load(self.prior)
self.order = prior['order']
self.params = prior['params']
chi2 = prior['chi2']
prior_steps = sorted(self.order.keys())
isteps = [i for i in isteps if i not in prior_steps]
for i in isteps:
step = conf['steps'][i]
conf.update(copy.deepcopy(conf_bkp))
conf.update(self.get_conf(conf_bkp, step))
print
msg = '--step %d: ' % i
msg += 'npQCD objects ='
for _ in conf['params'].keys():
msg += _ + ' '
msg += 'datasets ='
for _ in conf['datasets'].keys():
msg += _ + ' '
print msg
par = self.get_par(i, step['dep'])
self.order[i] = self.parman.order[:]
self.params[i] = par[:]
chi2[i] = self.resman.get_chi2()
if output: self.gen_output(i)
#--store the results from steps
data = {'order': self.order, 'params': self.params, 'chi2': chi2}
if self.prior == None:
fname = '%s.msr' % id_generator(size=12)
else:
fname = self.prior.split('/')[-1]
save(data, fname)
#--run hooks
if 'msr' in conf['hooks']: #--ms==multi steps
cmd = conf['hooks']['msr'].replace('<<fname>>', fname)
os.system(cmd)
else:
par = self.get_par(0)
if output: self.gen_output(0)
#--run remining hooks if available
for _ in conf['hooks']:
if _ == 'msr': continue
os.system(conf['hooks'][_])
def steps_comparison_figure(self, wdirs, Q2, dpi):
step_keys = sorted(wdirs.keys())
load_config('%s/input.py' % wdirs[step_keys[-1]])
istep = core.get_istep()
nrows, ncols = 1, 1
fig = py.figure(figsize=(ncols * 5.0, nrows * 3.0))
ax = py.subplot(nrows, ncols, 1)
colors = ['y', 'r', 'b', 'g', 'm']
## PPDF
self.xf_data = {}
self.cluster = {}
self.best_cluster = {}
labels = {}
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
for step_key in step_keys:
self.xf_data[step_key] = load('%s/data/ppdf-%d.dat' %
(wdirs[step_key], step_key))
labels[step_key] = load('%s/data/labels-%d.dat' %
(wdirs[step_key], step_key))
self.cluster[step_key] = labels[step_key]['cluster']
self.best_cluster[step_key] = np.argmin(
labels[step_key]['cluster_average_chi2'])
else:
for step_key in step_keys:
self.xf_data[step_key] = load('%s/data/ppdf-%d-%f.dat' %
(wdirs[step_key], step_key, Q2))
labels[step_key] = load('%s/data/labels-%d.dat' %
(wdirs[step_key], step_key))
self.cluster[step_key] = labels[step_key]['cluster']
self.best_cluster[step_key] = np.argmin(
labels[step_key]['cluster_average_chi2'])
print '\nplotting PPDF band figure with following steps at Q2 = %.2f' % Q2
print[wdirs[_] for _ in wdirs]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
label = r'$\mathrm{step~%02d}$' % step
self.plot_band_steps(ax, step, 'g', color, alpha, label)
ax.set_ylim(-0.11, 0.3)
ax.set_yticks([-0.1, 0.0, 0.1, 0.2])
ax.set_yticklabels([r'$-0.1$', r'$0.0$', r'$0.1$', r'$0.2$'])
# ax.text(0.1, 0.1, r'\boldmath$x\Delta g$', color = 'k', transform = ax.transAxes, size = 28)
ax.legend(frameon=0, loc='lower left', fontsize=7)
ax.semilogx()
ax.set_xlim(8e-3, 9e-1)
ax.set_xticks([1e-2, 1e-1])
ax.tick_params(axis='both',
which='both',
right=True,
top=True,
direction='in',
labelsize=20)
ax.set_xticklabels([r'', r''])
ax.axhline(0.0, color='k', linestyle='dashdot', linewidth=0.5)
ax.set_xticks([0.01, 0.1, 0.5])
ax.set_xticklabels([r'$0.01$', r'$0.1$', r'$0.5$'])
ax.set_ylabel(r'$x \Delta g$', size=15)
ax.set_xlabel(r'\boldmath$x$', size=15)
ax.xaxis.set_label_coords(1.05, 0.0)
py.tight_layout()
# py.subplots_adjust(left=0.05, bottom=0.04, right=0.99, top=0.99, wspace=0.17, hspace=0.04)
if Q2 == conf['Q20']:
py.savefig('%s/gallery/ppdf-steps-pjet-%d.png' %
(wdirs[step_keys[-1]], istep),
dpi=dpi)
else:
py.savefig('%s/gallery/ppdf-steps-pjet-%d-%f.png' %
(wdirs[step_keys[-1]], istep, Q2),
dpi=dpi)
def line_figure1(self, wdir, had):
# self.cluster, colors, nc, order = self.get_clusters(wdir, istep)
print 'Generating ff plot'
load_config('%s/input.py' % wdir)
istep = core.get_istep()
jar = load('%s/data/jar-%d.dat' % (wdir, istep))
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
self.cluster = labels['cluster']
n_replicas = len(self.cluster)
self.best_cluster = 0
nrows, ncols = 2, 3
fig = py.figure(figsize=(ncols * 5.0, nrows * 3.0))
# AX = [py.subplot(nrows, ncols, cnt) for cnt in range(1, 9)]
AX = [py.subplot(nrows, ncols, cnt + 1) for cnt in range(6)]
## FF
for i in had:
self.data = load('%s/data/ff%s-%d.dat' % (wdir, i, istep))
if i == 'pion':
c = 'r'
lab = r'$\pi$'
elif i == 'kaon':
c = 'b'
lab = r'$K$'
elif i == 'hadron':
c = 'k'
lab = r'$h$'
self.plot_lines(AX[0], 'u+ub', c, n_replicas, lab)
self.plot_lines(AX[1], 'd+db', c, n_replicas)
self.plot_lines(AX[2], 's+sb', c, n_replicas)
self.plot_lines(AX[3], 'c+cb', c, n_replicas)
self.plot_lines(AX[4], 'b+bb', c, n_replicas)
self.plot_lines(AX[5], 'g', c, n_replicas)
AX[0].text(0.9, 0.55, r'$u^+$', color='k', size=20)
AX[0].set_ylabel(r'$zD(z)$', size=20)
AX[0].legend(loc='upper center')
AX[1].text(0.9, 0.55, r'$d^+$', color='k', size=20)
AX[2].text(0.9, 0.55, r'$s^+$', color='k', size=20)
AX[3].text(0.9, 0.55, r'$c^+$', color='k', size=20)
AX[3].set_ylabel(r'$zD(z)$', size=20)
AX[3].set_xlabel(r'$z$', size=20, x=0.9, va='baseline')
AX[4].text(0.9, 0.55, r'$b^+$', color='k', size=20)
AX[4].set_xlabel(r'$z$', size=20, x=0.9, va='baseline')
AX[5].text(0.9, 0.55, r'$g$', color='k', size=20)
AX[5].set_xlabel(r'$z$', size=20, x=0.9, va='baseline')
for ax in AX:
ax.set_ylim(0.0, 0.6)
# ax.set_yticks([0.2,0.6,1.0,1.4])
ax.set_xlim(0.1, 1.0)
ax.set_xticks([0.2, 0.4, 0.6, 0.8])
py.subplots_adjust(left=0.12,
bottom=0.08,
right=0.99,
top=0.97,
wspace=None,
hspace=0.2)
py.tight_layout()
py.savefig('%s/gallery/ff-line-plots1-%d.png' % (wdir, istep),
dpi=1000)
def steps_comparison_figure(self, wdirs, Q2, dpi):
step_keys = sorted(wdirs.keys())
load_config('%s/input.py' % wdirs[step_keys[-1]])
istep = core.get_istep()
nrows, ncols = 2, 2
fig = py.figure(figsize=(ncols * 5.0, nrows * 3.0))
axs = [py.subplot(nrows, ncols, cnt + 1) for cnt in range(4)]
colors = ['k', 'r', 'g', 'Yellow', 'b']
## PPDF
self.xf_data = {}
self.cluster = {}
self.best_cluster = {}
labels = {}
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
for step_key in step_keys:
self.xf_data[step_key] = load('%s/data/ppdf-%d.dat' %
(wdirs[step_key], step_key))
labels[step_key] = load('%s/data/labels-%d.dat' %
(wdirs[step_key], step_key))
self.cluster[step_key] = labels[step_key]['cluster']
self.best_cluster[step_key] = np.argmin(
labels[step_key]['cluster_average_chi2'])
else:
for step_key in step_keys:
self.xf_data[step_key] = load('%s/data/ppdf-%d-%f.dat' %
(wdirs[step_key], step_key, Q2))
labels[step_key] = load('%s/data/labels-%d.dat' %
(wdirs[step_key], step_key))
self.cluster[step_key] = labels[step_key]['cluster']
self.best_cluster[step_key] = np.argmin(
labels[step_key]['cluster_average_chi2'])
print '\nplotting PPDF band figure with following steps at Q2 = %.2f' % Q2
print[wdirs[_] for _ in wdirs]
ax = axs[0]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
self.plot_band_steps(ax, step, 'uv', color, alpha)
self.plot_band_steps(ax, step, 'dv', color, alpha)
ax.set_ylim(0.0, 0.5)
ax.set_yticks([0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
ax.set_yticklabels([r'$0.1$', r'$0.2$', r'$0.3$', r'$0.4$', r'$0.5$'])
ax.text(0.6,
0.3,
r'\boldmath$x\Delta u^+$',
color='k',
transform=axs[0].transAxes,
size=28)
ax = axs[1]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
self.plot_band_steps(ax, step, 'd/u', color, alpha)
ax.set_ylim(-0.2, 0.0)
ax.set_yticks([-0.05, -0.1, -0.15])
ax.set_yticklabels([r'$-0.05$', r'$-0.10$', r'$-0.15$'])
ax.text(0.05,
0.15,
r'\boldmath$x\Delta d^+$',
color='k',
transform=axs[1].transAxes,
size=28)
ax = axs[2]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
self.plot_band_steps(ax, step, 'db+ub', color, alpha)
ax.set_ylim(-0.1, 0.05)
ax.set_yticks([-0.08, -0.04, 0.0, 0.04])
ax.set_yticklabels([r'$-0.08$', r'$-0.04$', r'$0.00$', r'$0.04$'])
ax.text(0.1,
0.7,
r'\boldmath$x\Delta s^+$',
color='k',
transform=axs[2].transAxes,
size=28)
ax = axs[3]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
self.plot_band_steps(ax, step, 'db-ub', color, alpha)
ax.set_ylim(-0.11, 0.3)
ax.set_yticks([-0.1, 0.0, 0.1, 0.2])
ax.set_yticklabels([r'$-0.1$', r'$0.0$', r'$0.1$', r'$0.2$'])
ax.text(0.7,
0.8,
r'\boldmath$x\Delta g$',
color='k',
transform=axs[3].transAxes,
size=28)
ax = axs[4]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
self.plot_band_steps(ax, step, 's+sb', color, alpha)
ax.text(0.6,
0.8,
r'\boldmath$x(s\!+\!\overline{s})$',
color='k',
transform=ax.transAxes,
size=28)
ax.set_ylim(0.0, 0.48)
ax.set_yticks([0.0, 0.1, 0.2, 0.3, 0.4])
ax.set_yticklabels([r'$0$', r'', r'$0.2$', r'', r'$0.4$'])
ax = axs[5]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
self.plot_band_steps(ax, step, 'rs', color, alpha)
ax.text(0.05,
0.78,
r'\boldmath$R_s$',
color='k',
transform=ax.transAxes,
size=30)
ax.set_ylim(0.0, 1.6)
ax.set_yticks([0.0, 0.5, 1.0, 1.5])
ax.set_yticklabels([r'$0$', r'$0.5$', r'$1$', r'$1.5$'])
ax = axs[6]
for i in range(len(step_keys)):
step = step_keys[i]
# alpha = (1.0 / len(step_keys)) * (len(step_keys) - i)
alpha = 0.5
color = colors[i]
label = r'$\mathrm{step~%02d}$' % step
self.plot_band_steps(ax, step, 'g', color, alpha, label)
ax.text(0.8,
0.8,
r'\boldmath$xg$',
color='k',
transform=ax.transAxes,
size=31)
ax.set_ylim(0.0, 2.5)
ax.set_yticks([0.0, 0.5, 1.0, 1.5, 2.0])
ax.legend(frameon=0,
loc='best',
bbox_to_anchor=[1.87, 0.9],
fontsize=20)
for ax in axs:
ax.semilogx()
ax.set_xlim(8e-3, 9e-1)
ax.set_xticks([1e-2, 1e-1])
# ax.xaxis.set_label_coords(0.95, -0.05)
ax.tick_params(axis='both',
which='both',
right=True,
top=True,
direction='in',
labelsize=20)
ax.set_xticklabels([r'', r''])
ax.xaxis.set_label_coords(0.7, -0.05)
ax.legend(frameon=0, loc='best')
axs[2].axhline(0.0, color='k', linestyle='dashdot', linewidth=0.5)
axs[3].axhline(0.0, color='k', linestyle='dashdot', linewidth=0.5)
for i in range(len(axs) - 1, len(axs) - 1 - ncols, -1):
axs[i].set_xlabel(r'\boldmath$x$', size=30)
# axs[i].text(0.84, -0.095, r'$0.5$', color = 'k', transform = ax.transAxes, size = 20)
axs[i].set_xticks([0.01, 0.1, 0.5, 0.8])
axs[i].set_xticklabels([r'$0.01$', r'$0.1$', r'$0.5$', r'$0.8$'])
py.tight_layout()
py.subplots_adjust(left=0.05,
bottom=0.04,
right=0.99,
top=0.99,
wspace=0.17,
hspace=0.04)
if Q2 == conf['Q20']:
py.savefig('%s/gallery/ppdf-steps-%d.png' %
(wdirs[step_keys[-1]], istep),
dpi=dpi)
else:
py.savefig('%s/gallery/ppdf-steps-%d-%f.png' %
(wdirs[step_keys[-1]], istep, Q2),
dpi=dpi)
def line_figure(self, wdir, Q2, dpi):
## this block may contain something not useful
load_config('%s/input.py' % wdir)
istep = core.get_istep()
# jar = load('%s/data/jar-%d.dat' % (wdir, istep))
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
self.cluster = labels['cluster']
self.cluster_average_chi2 = labels['cluster_average_chi2']
self.best_cluster = np.argmin(self.cluster_average_chi2)
nrows, ncols = 2, 2
fig = py.figure(figsize=(ncols * 5.0, nrows * 3.0))
axs = [py.subplot(nrows, ncols, cnt + 1) for cnt in range(4)]
## polarized PDF
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
self.xf_data = load('%s/data/ppdf-%d.dat' % (wdir, istep))
else:
self.xf_data = load('%s/data/ppdf-%d-%f.dat' % (wdir, istep, Q2))
print '\nplotting polarized PDF line figure from %s at Q2 = %.2f' % (
wdir, Q2)
self.plot_lines(axs[0], 'up', 'r', all_cluster=2)
self.plot_lines(axs[1], 'dp', 'r', all_cluster=2)
self.plot_lines(axs[2], 'sp', 'r', all_cluster=2)
self.plot_lines(axs[3], 'g', 'r', all_cluster=2)
axs[0].set_ylim(0.0, 0.5)
axs[0].set_yticks([0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
axs[0].set_yticklabels(
[r'$0.1$', r'$0.2$', r'$0.3$', r'$0.4$', r'$0.5$'])
# axs[0].text(0.6, 0.3, r'\boldmath$x\Delta u^+$', color = 'k', transform = axs[0].transAxes, size = 28)
axs[0].title.set_text(
r'\boldmath$x\Delta u^+~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' %
Q2)
axs[1].set_ylim(-0.2, 0.0)
axs[1].set_yticks([-0.05, -0.1, -0.15])
axs[1].set_yticklabels([r'$-0.05$', r'$-0.10$', r'$-0.15$'])
# axs[1].text(0.05, 0.15, r'\boldmath$x\Delta d^+$', color = 'k', transform = axs[1].transAxes, size = 28)
axs[1].title.set_text(
r'\boldmath$x\Delta d^+~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' %
Q2)
axs[2].set_ylim(-0.1, 0.05)
axs[2].set_yticks([-0.08, -0.04, 0.0, 0.04])
axs[2].set_yticklabels([r'$-0.08$', r'$-0.04$', r'$0.00$', r'$0.04$'])
# axs[2].text(0.1, 0.7, r'\boldmath$x\Delta s^+$', color = 'k', transform = axs[2].transAxes, size = 28)
axs[2].title.set_text(r'\boldmath$x\Delta s^+$')
axs[3].set_ylim(-0.11, 0.3)
axs[3].set_yticks([-0.1, 0.0, 0.1, 0.2])
axs[3].set_yticklabels([r'$-0.1$', r'$0.0$', r'$0.1$', r'$0.2$'])
# axs[3].text(0.7, 0.8, r'\boldmath$x\Delta g$', color = 'k', transform = axs[3].transAxes, size = 28)
axs[3].title.set_text(r'\boldmath$x\Delta g$')
for ax in axs:
ax.semilogx()
ax.set_xlim(8e-3, 9e-1)
ax.set_xticks([1e-2, 1e-1])
# ax.xaxis.set_label_coords(0.95, -0.05)
ax.tick_params(axis='both',
which='both',
right=True,
top=True,
direction='in',
labelsize=20)
ax.set_xticklabels([r'', r''])
axs[2].axhline(0.0, color='k', linestyle='dashdot', linewidth=0.5)
axs[3].axhline(0.0, color='k', linestyle='dashdot', linewidth=0.5)
# for i in range(len(axs)):
# if (i % ncols) == 0: axs[i].set_ylabel(r'$xf(x)$', size = 20)
for i in range(len(axs) - 1, len(axs) - 1 - ncols, -1):
axs[i].set_xlabel(r'\boldmath$x$', size=30)
# axs[i].text(0.84, -0.095, r'$0.5$', color = 'k', transform = ax.transAxes, size = 20)
axs[i].set_xticks([0.01, 0.1, 0.5, 0.8])
axs[i].set_xticklabels([r'$0.01$', r'$0.1$', r'$0.5$', r'$0.8$'])
legends = axs[0].legend(frameon=0, loc='best')
for line in legends.get_lines():
line.set_linewidth(1.0)
py.subplots_adjust(left=0.12,
bottom=0.08,
right=0.99,
top=0.97,
wspace=None,
hspace=0.2)
py.tight_layout()
if Q2 == conf['Q20']:
py.savefig('%s/gallery/ppdf-lines-%d.png' % (wdir, istep), dpi=dpi)
else:
py.savefig('%s/gallery/ppdf-lines-%d-%f.png' % (wdir, istep, Q2),
dpi=dpi)
def make_figure(wdir, task, only_best_cluster=True, dpi=200):
if task == 1:
print('\nplotting PIDIS data from %s' % (wdir))
elif task == 2:
print('\nplotting PIDIS data over theory from %s' % (wdir))
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
core.mod_conf(istep, replicas[0]) #--set conf as specified in istep
predictions = load('%s/data/predictions-%d.dat' % (wdir, istep))
if 'pidis' not in predictions['reactions']:
print('PIDIS is not in data file')
return
labels = load('%s/data/labels-%d.dat' % (wdir, istep))
cluster = labels['cluster']
cluster_average_chi2 = labels['cluster_average_chi2']
best_cluster = np.argmin(cluster_average_chi2)
data = predictions['reactions']['pidis']
for idx in data:
predictions = copy.copy(data[idx]['prediction-rep'])
del data[idx]['prediction-rep']
del data[idx]['residuals-rep']
if only_best_cluster:
best_predictions = [
predictions[i] for i in range(len(predictions))
if cluster[i] == best_cluster
]
data[idx]['thy'] = np.mean(best_predictions, axis=0)
data[idx]['dthy'] = np.std(best_predictions, axis=0)
else:
all_predictions = [predictions[i] for i in range(len(predictions))]
data[idx]['thy'] = np.mean(all_predictions, axis=0)
data[idx]['dthy'] = np.std(all_predictions, axis=0)
maps = generate_maps()
subset_bins = pick_subsets(data)
# log_list = []
if task == 1:
indices = {}
for idx in data:
observable = data[idx]['obs'][0]
target = data[idx]['target'][0]
collaboration = data[idx]['col'][0]
if (observable == 'A1') or (observable == 'Apa'):
if target == 'p':
if collaboration.startswith('JLab') != True:
if 'Apa_proton' not in indices:
indices['Apa_proton'] = []
indices['Apa_proton'].append(idx)
elif collaboration.startswith('JLabHB(EG1DVCS)') == True:
if 'Apa_proton_DVCS' not in indices:
indices['Apa_proton_DVCS'] = []
indices['Apa_proton_DVCS'].append(idx)
elif collaboration.startswith('JLabHB(EG1b)') == True:
if 'Apa_proton_EG1b' not in indices:
indices['Apa_proton_EG1b'] = []
indices['Apa_proton_EG1b'].append(idx)
elif target == 'd':
if collaboration.startswith('JLab') != True:
if 'Apa_deuteron' not in indices:
indices['Apa_deuteron'] = []
indices['Apa_deuteron'].append(idx)
elif collaboration.startswith('JLabHB(EG1DVCS)') == True:
if 'Apa_deuteron_DVCS' not in indices:
indices['Apa_deuteron_DVCS'] = []
indices['Apa_deuteron_DVCS'].append(idx)
elif collaboration.startswith('JLabHB(EG1b)') == True:
if 'Apa_deuteron_EG1b' not in indices:
indices['Apa_deuteron_EG1b'] = []
indices['Apa_deuteron_EG1b'].append(idx)
elif (observable == 'A2') or (observable == 'Ape') or (observable
== 'Atpe'):
if target == 'p':
if collaboration.startswith('JLab') != True:
if 'Ape_proton' not in indices:
indices['Ape_proton'] = []
indices['Ape_proton'].append(idx)
elif target == 'd':
if collaboration.startswith('JLab') != True:
if 'Ape_deuteron' not in indices:
indices['Ape_deuteron'] = []
indices['Ape_deuteron'].append(idx)
if target == 'h':
if 'helium' not in indices:
indices['helium'] = []
indices['helium'].append(idx)
for key in indices:
print(key)
if key == 'Apa_proton':
data_Apa_proton(wdir, data, indices[key], subset_bins, maps,
istep, dpi)
elif key == 'Apa_proton_DVCS':
data_Apa_proton_DVCS(wdir, data, indices[key], subset_bins,
maps, istep, dpi)
elif key == 'Apa_proton_EG1b':
data_Apa_proton_EG1b(wdir, data, indices[key], subset_bins,
maps, istep, dpi)
elif key == 'Apa_deuteron':
data_Apa_deuteron(wdir, data, indices[key], subset_bins, maps,
istep, dpi)
elif key == 'Apa_deuteron_DVCS':
data_Apa_deuteron_DVCS(wdir, data, indices[key], subset_bins,
maps, istep, dpi)
elif key == 'Apa_deuteron_EG1b':
data_Apa_deuteron_EG1b(wdir, data, indices[key], subset_bins,
maps, istep, dpi)
elif key == 'Ape_proton':
data_Ape_proton(wdir, data, indices[key], subset_bins, maps,
istep, dpi)
elif key == 'Ape_deuteron':
data_Ape_deuteron(wdir, data, indices[key], subset_bins, maps,
istep, dpi)
elif key == 'helium':
data_helium(wdir, data, indices[key], subset_bins, maps, istep,
dpi)
elif task == 2:
plot_data_on_theory(wdir, data, istep, dpi)
return
