def gen_conf(wdir, tar, est):
print('Modifying config with new experimental data file...')
load_config('%s/input.py' % wdir)
istep = core.get_istep()
conf['steps'][istep]['datasets'] = {}
conf['steps'][istep]['datasets']['pidis'] = []
conf['datasets']['pidis']['filters'] = []
#--placeholder index
idx = 80000
conf['datasets']['pidis']['xlsx'][idx] = './%s/sim/all-%s-%s.xlsx' % (
wdir, tar, est)
conf['steps'][istep]['datasets']['pidis'].append(idx)
fdir = os.environ['FITPACK']
fn = [fdir + '/database/EIC/expdata/1000.xlsx']
conf['pidis grid'] = {}
if tar == 'p': conf['pidis grid']['overwrite'] = True
if tar == 'd': conf['pidis grid']['overwrite'] = False
conf['pidis grid']['xlsx'] = fn
conf['idis grid'] = {}
if tar == 'p': conf['idis grid']['overwrite'] = True
if tar == 'd': conf['idis grid']['overwrite'] = False
conf['idis grid']['xlsx'] = fn
return conf
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 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 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 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
def get_moments(wdir, flavors, x_1, x_2, Q2=None):
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
## 'conf' will be modified for each replica individually later in the loop over 'replicas'
## the reason for doing this is that 'fix parameters' has to be set correctly for each replica
if 'ppdf_smx' not in conf['steps'][istep]['active distributions']:
print('ppdf-smx-proton not in active distribution')
return
resman = RESMAN(nworkers=1, parallel=False, datasets=False)
parman = resman.parman
parman.order = replicas[0]['order'][istep]
## make sure 'parman' uses the same order for active distributions as all the replicas do
# print parman.order
ppdf = conf['ppdf_smx']
## setup kinematics
# X = 10.0 ** np.linspace(-3, -1, 100)
# X = np.append(X, np.linspace(0.1, 0.99, 100))
if Q2 == None: Q2 = conf['Q20']
print(
'\ngenerating momentus for polarized pdf_smx-proton from %s at Q2 = %f'
% (wdir, Q2))
## compute moments for all replicas
moments = {}
statistics = {}
n_replicas = len(replicas)
for i in range(n_replicas):
lprint('%d/%d' % (i + 1, n_replicas))
parman.order = copy.copy(replicas[i]['order'][istep])
core.mod_conf(istep, replicas[i])
parman.set_new_params(replicas[i]['params'][istep], initial=True)
for flavor in flavors:
if flavor not in moments: moments[flavor] = []
if flavor not in statistics: statistics[flavor] = {}
if flavor == 'up':
func = lambda x: (ppdf.get_xF(x, Q2, 'u') + ppdf.get_xF(
x, Q2, 'ub')) / x
elif flavor == 'dp':
func = lambda x: (ppdf.get_xF(x, Q2, 'd') + ppdf.get_xF(
x, Q2, 'sb')) / x
elif flavor == 'sp':
func = lambda x: (ppdf.get_xF(x, Q2, 's') + ppdf.get_xF(
x, Q2, 'sb')) / x
else:
func = lambda x: ppdf.get_xF(x, Q2, flavor) / x
moments[flavor].append(quad(func, x_1, x_2)[0])
for flavor in flavors:
statistics[flavor]['mean'] = np.mean(moments[flavor])
statistics[flavor]['standard_deviation'] = np.std(moments[flavor])
print
for flavor in flavors:
print flavor, ': ', statistics[flavor]
checkdir('%s/data' % wdir)
if Q2 == conf['Q20']:
save({
'Q2': Q2,
'moments': moments,
'statistics': statistics
}, '%s/data/ppdf_smx-moments-%s-to-%s-%d.dat' %
(wdir, x_1, x_2, istep))
else:
save({
'Q2': Q2,
'moments': moments,
'statistics': statistics
}, '%s/data/ppdf_smx-moments-%s-to-%s-%d-%f.dat' %
(wdir, x_1, x_2, istep, Q2))
def gen_xf(wdir, flavors, Q2=None):
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
## 'conf' will be modified for each replica individually later in the loop over 'replicas'
## the reason for doing this is that 'fix parameters' has to be set correctly for each replica
if 'ppdf_smx' not in conf['steps'][istep]['active distributions']:
print('ppdf_smx-proton not in active distribution')
return
resman = RESMAN(nworkers=1, parallel=False, datasets=False)
parman = resman.parman
parman.order = replicas[0]['order'][istep]
## make sure 'parman' uses the same order for active distributions as all the replicas do
# print parman.order
ppdf = conf['ppdf_smx']
ppdfJAM = conf['ppdf']
## setup kinematics
xs = 10.0**np.linspace(-5, -1, 100)
xs = np.append(xs, np.linspace(0.1, 0.3, 100))
#xs = np.append(xs, np.linspace(0.1, 0.99, 100))
if Q2 == None: Q2 = conf['Q20']
print('\ngenerating polarized pdf-proton from %s at Q2 = %f' % (wdir, Q2))
## compute xf for all replicas
xfs = {}
n_replicas = len(replicas)
for i in range(n_replicas):
lprint('%d/%d' % (i + 1, n_replicas))
## filter
#flag=False
#params=replica['params'][istep]
#order=replica['order'][istep]
#for i in range(len(order)):
# if order[i][0]!=1:continue
# if order[i][1]!='ppdf':continue
# #if order[i][2]=='s1 a':
# # if params[i]<-0.9: flag=True
#if flag: continue
parman.order = copy.copy(replicas[i]['order'][istep])
core.mod_conf(istep, replicas[i])
parman.set_new_params(replicas[i]['params'][istep], initial=True)
x0 = conf['smx']['x0']
xsep = conf['smx']['xsep']
for flavor in flavors:
if flavor not in xfs: xfs[flavor] = []
if flavor == 'up':
#func = lambda x: x*ppdf.get_C(x, Q2)[1]
#func = lambda x: np.heaviside(xsep-x,1)*(x*ppdf.get_C(x, Q2)[1] + ppdfJAM.get_xF(xsep,xsep*Q2/x,flavor)) + np.heaviside(x-xsep,0)*ppdfJAM.get_xF(x,Q2,flavor)
func = lambda x: np.heaviside(xsep - x, 1) * (x * ppdf.get_C(
x, Q2)[1] + x * ppdf.get_C_NOevo(xsep, xsep * Q2 / x)[
1]) + np.heaviside(x - xsep, 0) * x * ppdf.get_C_NOevo(
x, Q2)[1]
elif flavor == 'dp':
#func = lambda x: x*ppdf.get_C(x, Q2)[2]
#func = lambda x: np.heaviside(xsep-x,1)*(x*ppdf.get_C(x, Q2)[2] + ppdfJAM.get_xF(xsep,xsep*Q2/x,flavor)) + np.heaviside(x-xsep,0)*ppdfJAM.get_xF(x,Q2,flavor)
func = lambda x: np.heaviside(xsep - x, 1) * (x * ppdf.get_C(
x, Q2)[2] + x * ppdf.get_C_NOevo(xsep, xsep * Q2 / x)[
2]) + np.heaviside(x - xsep, 0) * x * ppdf.get_C_NOevo(
x, Q2)[2]
elif flavor == 'sp':
#func = lambda x: x*ppdf.get_C(x, Q2)[3]
#func = lambda x: np.heaviside(xsep-x,1)*(x*ppdf.get_C(x, Q2)[3] + ppdfJAM.get_xF(xsep,xsep*Q2/x,flavor)) + np.heaviside(x-xsep,0)*ppdfJAM.get_xF(x,Q2,flavor)
func = lambda x: np.heaviside(xsep - x, 1) * (x * ppdf.get_C(
x, Q2)[3] + x * ppdf.get_C_NOevo(xsep, xsep * Q2 / x)[
3]) + np.heaviside(x - xsep, 0) * x * ppdf.get_C_NOevo(
x, Q2)[3]
elif flavor == 'g':
#func = lambda x: x*ppdf.get_C(x, Q2)[0]
#func = lambda x: np.heaviside(xsep-x,1)*(x*ppdf.get_C(x, Q2)[0] + ppdfJAM.get_xF(xsep,xsep*Q2/x,flavor)) + np.heaviside(x-xsep,0)*ppdfJAM.get_xF(x,Q2,flavor)
func = lambda x: np.heaviside(xsep - x, 1) * (x * ppdf.get_C(
x, Q2)[0] + x * ppdf.get_C_NOevo(xsep, xsep * Q2 / x)[
0]) + np.heaviside(x - xsep, 0) * x * ppdf.get_C_NOevo(
x, Q2)[0]
xfs[flavor].append([func(x) for x in xs])
print
checkdir('%s/data' % wdir)
if Q2 == conf['Q20']:
save({
'X': xs,
'Q2': Q2,
'XF': xfs
}, '%s/data/ppdf_smx-%d.dat' % (wdir, istep))
else:
save({
'X': xs,
'Q2': Q2,
'XF': xfs
}, '%s/data/ppdf_smx-%d-%f.dat' % (wdir, istep, Q2))
def get_parameters(wdir, Q2=None):
load_config('%s/input.py' % wdir)
istep = core.get_istep()
replicas = core.get_replicas(wdir)
## 'conf' will be modified for each replica individually later in the loop over 'replicas'
## the reason for doing this is that 'fix parameters' has to be set correctly for each replica
if 'ppdf_smx' not in conf['steps'][istep]['active distributions']:
print('ppdf_smx-proton not in active distribution')
return
resman = RESMAN(nworkers=1, parallel=False, datasets=False)
parman = resman.parman
parman.order = replicas[0]['order'][
istep] ## make sure 'parman' uses the same order as all the replicas do
# print parman.order
ppdf = conf['ppdf_smx']
## setup kinematics
if Q2 == None: Q2 = conf['Q20']
## get parameters for all flavors of PDF
print('\ngetting ppdf_smx-parameters from %s at Q2 = %.2f' % (wdir, Q2))
## check if any of the shapes are fixed
shape_1 = []
shape_2 = []
shape_3 = []
for parameter in conf['params']['ppdf_smx']:
if '1' in parameter:
shape_1.append(conf['params']['ppdf_smx'][parameter]['fixed'])
elif '2' in parameter:
shape_2.append(conf['params']['ppdf_smx'][parameter]['fixed'])
elif '3' in parameter:
shape_3.append(conf['params']['ppdf_smx'][parameter]['fixed'])
else:
print('there seems to be more than three shapes')
print(parameter)
sys.exit('please update script %s' % __file__)
fixed_shape_1 = False
fixed_shape_2 = False
fixed_shape_3 = False
if (len(shape_1) != 0) and all([_ == True for _ in shape_1]):
fixed_shape_1 = True
elif len(shape_1) == 0:
fixed_shape_1 = True
if (len(shape_2) != 0) and all([_ == True for _ in shape_2]):
fixed_shape_2 = True
elif len(shape_2) == 0:
fixed_shape_2 = True
if (len(shape_3) != 0) and all([_ == True for _ in shape_3]):
fixed_shape_3 = True
elif len(shape_3) == 0:
fixed_shape_3 = True
parameters = {}
if not fixed_shape_1: parameters[1] = {}
if not fixed_shape_2: parameters[2] = {}
if not fixed_shape_3: parameters[3] = {}
n_replicas = len(replicas)
for i in range(n_replicas):
lprint('%d/%d' % (i + 1, n_replicas))
parman.order = copy.copy(replicas[i]['order'][istep])
core.mod_conf(istep, replicas[i])
parman.set_new_params(replicas[i]['params'][istep], initial=True)
for flavor, value in ppdf.params.iteritems():
for j in parameters:
if str(j) in flavor:
flavor_key = flavor.replace(str(j), '')
if flavor_key not in parameters[j]:
parameters[j][flavor_key] = {
'n': [],
'a': [],
'b': [],
'c': [],
'd': []
}
parameters[j][flavor_key]['n'].append(value[0])
parameters[j][flavor_key]['a'].append(value[1])
parameters[j][flavor_key]['b'].append(value[2])
parameters[j][flavor_key]['c'].append(value[3])
parameters[j][flavor_key]['d'].append(value[4])
else:
pass
## remove c or d parameters if fixed in all flavors and shapes
## this is to avoid producing empty figures
shapes_fixed_c = []
shapes_fixed_d = []
for shape in parameters:
flavors_fixed_c = []
flavors_fixed_d = []
for flavor in parameters[shape]:
cs = parameters[shape][flavor]['c']
ds = parameters[shape][flavor]['d']
if all([_ == cs[0] for _ in cs]):
flavors_fixed_c.append(True)
else:
flavors_fixed_c.append(False)
if all([_ == ds[0] for _ in ds]):
flavors_fixed_d.append(True)
else:
flavors_fixed_d.append(False)
if all(flavors_fixed_c):
shapes_fixed_c.append(True)
else:
shapes_fixed_c.append(False)
if all(flavors_fixed_d):
shapes_fixed_d.append(True)
else:
shapes_fixed_d.append(False)
if all(shapes_fixed_c):
for shape in parameters:
for flavor in parameters[shape]:
del parameters[shape][flavor]['c']
print('parameter c is fixed for shape %s' % shape)
if all(shapes_fixed_d):
for shape in parameters:
for flavor in parameters[shape]:
del parameters[shape][flavor]['d']
print('parameter d is fixed for shape %s' % shape)
print
checkdir('%s/data' % wdir)
if Q2 == conf['Q20']:
save(parameters, '%s/data/ppdf_smx-parameters-%d.dat' % (wdir, istep))
else:
save(parameters,
'%s/data/ppdf_smx-parameters-%d-%f.dat' % (wdir, istep, Q2))
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 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_smx-%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_smx-%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_smx-steps-%d.png' % (wdirs[step_keys[-1]], istep), dpi = dpi)
else:
py.savefig('%s/gallery/ppdf_smx-steps-%d-%f.png' % (wdirs[step_keys[-1]], istep, Q2), dpi = dpi)
def g1_band_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)
ax1=py.subplot(nrows,ncols,1)
## polarized PDF
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
self.xf_data = load('%s/data/ppdf_smx-%d.dat' % (wdir, istep))
else:
self.xf_data = load('%s/data/ppdf_smx-%d-%f.dat' % (wdir, istep, Q2))
print '\nplotting g1 band figure from %s at Q2 = %.2f' % (wdir, Q2)
self.plot_g1_band(ax, 'b',alpha=1.)
p3=self.plot_g1_band(ax, 'r',central=True)
if Q2 == conf['Q20']:
self.xf_data = load('results/step_test1_smx_cut_0.1/data/ppdf_smx-%d.dat' % (istep))
else:
self.xf_data = load('results/step_test1_smx_cut_0.1/data/ppdf_smx-%d-%f.dat' % (istep, Q2))
print '\nplotting g1 band figure from %s at Q2 = %.2f' % (wdir, Q2)
self.plot_g1_band(ax1, 'b')
#self.plot_g1_band(ax1, 'b',central=True)
ax.set_ylim(-100, 10)
#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)
ax.title.set_text(r'\boldmath$g_1(x)~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' % Q2)
ax.semilogx()
ax1.semilogx()
ax.set_xlim(1e-5, 1e-1) #ax.set_xlim(1e-3, 9e-1)
#ax.set_xticks([1e-6,1e-5,1e-4,1e-3,1e-2, 1e-1])
#ax.xaxis.set_label_coords(0.95, -0.05)
ax.set_xlabel(r'\boldmath$x$',fontsize=18)
#ax.set_xticklabels([r'$10^{-6}$',r'$10^{-5}$',r'$10^{-4}$',r'$0.001$', r'$0.01$', r'$0.1$']) #ax.set_xticklabels([r'$0.01$', r'$0.1$', r'$0.5$', r'$0.8$'])
ax.tick_params(axis = 'both', which = 'both', right = True, top = True, direction = 'in')
#ax.set_xticklabels([r'', r''])
ax.axhline(0.0, color = 'k', linestyle = 'dashdot', linewidth = 0.5)
#axs3.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'$0.8$'])
#for line in legends.get_lines():
# line.set_linewidth(1.0)
p1 = ax1.fill(np.NaN, np.NaN, 'b', alpha=0.3)
p2 = ax.fill(np.NaN, np.NaN, 'b', alpha=1.)
ax1.legend([p1[0],(p2[0],p3[0]),], [r'JAM-small$x$',r'JAM-small$x$+EIC'])
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/g1_smx-band-%d.png' % (wdir, istep), dpi = dpi)
else:
py.savefig('%s/gallery/g1_smx-band-%d-%f.png' % (wdir, istep, Q2), dpi = dpi)
def band_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))
axs0 = py.subplot(nrows, ncols, 1)
axs1 = py.subplot(nrows, ncols, 2)
axs2 = py.subplot(nrows, ncols, 3)
axs3 = py.subplot(nrows, ncols, 4)
## polarized PDF
if Q2 == None: Q2 = conf['Q20']
if Q2 == conf['Q20']:
self.xf_data = load('%s/data/ppdf_smx-%d.dat' % (wdir, istep))
else:
self.xf_data = load('%s/data/ppdf_smx-%d-%f.dat' % (wdir, istep, Q2))
print '\nplotting polarized PDF band figure from %s at Q2 = %.2f' % (wdir, Q2)
self.plot_band(axs0, 'up', 'r')
self.plot_band(axs1, 'dp', 'r')
self.plot_band(axs2, 'sp', 'r')
self.plot_band(axs3, 'g', 'r')
self.plot_band(axs0, 'up', 'r',central=True)
self.plot_band(axs1, 'dp', 'r',central=True)
self.plot_band(axs2, 'sp', 'r',central=True)
self.plot_band(axs3, 'g', 'r',central=True)
axs0.set_ylim(-0.5, 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)
axs0.title.set_text(r'\boldmath$x\Delta u^+(x)~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' % Q2)
#axs[1].set_ylim(-0.2, 0.2)
#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)
axs1.title.set_text(r'\boldmath$x\Delta d^+(x)~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' % Q2)
#axs[2].set_ylim(-0.1, 0.5)
#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)
axs2.title.set_text(r'\boldmath$x\Delta s^+(x)~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' % Q2)
#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)
axs3.title.set_text(r'\boldmath$x\Delta g(x)~\mathrm{at}~Q^2 = %.2f~\mathrm{GeV}^2$' % Q2)
for ax in [axs0,axs1,axs2,axs3]:
ax.semilogx()
ax.set_xlim(1e-5, 0.3) #ax.set_xlim(1e-3, 9e-1)
#ax.set_xticks([1e-6,1e-5,1e-4,1e-3,1e-2, 1e-1])
#ax.xaxis.set_label_coords(0.95, -0.05)
ax.set_xlabel(r'\boldmath$x$',fontsize=18)
#ax.set_xticklabels([r'$10^{-6}$',r'$10^{-5}$',r'$10^{-4}$',r'$0.001$', r'$0.01$', r'$0.1$']) #ax.set_xticklabels([r'$0.01$', r'$0.1$', r'$0.5$', r'$0.8$'])
ax.tick_params(axis = 'both', which = 'both', right = True, top = True, direction = 'in')
#ax.set_xticklabels([r'', r''])
axs0.axhline(0.0, color = 'k', linestyle = 'dashdot', linewidth = 0.5)
axs1.axhline(0.0, color = 'k', linestyle = 'dashdot', linewidth = 0.5)
axs2.axhline(0.0, color = 'k', linestyle = 'dashdot', linewidth = 0.5)
axs3.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 = axs0.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_smx-band-%d.png' % (wdir, istep), dpi = dpi)
else:
py.savefig('%s/gallery/ppdf_smx-band-%d-%f.png' % (wdir, istep, Q2), dpi = dpi)