def plot_data_separate(wdir, data_idx, istep, idx, dpi, plot_with_factor):
if not plot_with_factor:
data_idx['plot-factor'] = np.ones(len(data_idx['value']))
_, _, eta_bins = find_eta_bins(data_idx)
figure = py.figure(figsize=[10.0, 5.0], dpi=500)
plot_temp = figure.add_subplot(1, 1, 1)
plot_colors = ['r']
# plot_colors = ['b', 'g', 'r', 'c', 'm', 'y']
plot_styles = ['-']
# plot_styles = ['-', '-.', '--', ':', '-', '--']
for i in xrange(len(eta_bins)):
pt = np.array(data_idx['pT'][eta_bins[i][0] - 1:eta_bins[i][1]])
value = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['value'][eta_bins[i][0] - 1 : eta_bins[i][1]])
alpha = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['alpha'][eta_bins[i][0] - 1 : eta_bins[i][1]])
# plot_temp.errorbar(pt, value, alpha, fmt = '%s.' % plot_colors[i])
plot_temp.errorbar(pt, value, alpha, fmt='%s.' % plot_colors[0])
theory = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['thy'][eta_bins[i][0] - 1 : eta_bins[i][1]])
# plot_temp.plot(pt, theory, '%s%s' % (plot_colors[i], plot_styles[i]))
plot_temp.plot(pt, theory, '%s%s' % (plot_colors[0], plot_styles[0]))
plot_temp.axhline(0.0, color='black', ls=':')
# legend = []
# for i in range(len(pdf_sets)):
# legend.append(Line2D([0], [0], linewidth = 1, color = 'k', linestyle = '%s' % plot_styles[i], label = pdf_sets[i]))
# plot_temp.legend(handles = legend)
plot_temp.set_xlabel(r'$p_T$', size=15)
plot_temp.set_ylabel(r'$A_{LL}$', size=15)
# plot_temp.set_yscale('log')
if data_idx['col'][0] == 'star':
if idx == 20001:
figure.savefig('%s/gallery/pjet-%s-2003-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
elif idx == 20002:
figure.savefig('%s/gallery/pjet-%s-2005-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
elif idx == 20003:
figure.savefig('%s/gallery/pjet-%s-2006-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
elif idx == 20004:
figure.savefig('%s/gallery/pjet-%s-2009-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
elif idx == 20006:
figure.savefig('%s/gallery/pjet-%s-2012-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
else:
figure.savefig('%s/gallery/pjet-%s-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
def plot_data(wdir, data, istep, dpi, plot_with_factor):
if len(data) == 4:
n_rows, n_columns = 2, 2
else:
print('number of dataset is different from 4')
print(
'you need to specify number of rows and columns for subplots in "plot_data"'
)
return
figure, ax = py.subplots(n_rows, n_columns)
plot_styles = ['-']
plot_colors = ['b', 'g', 'm', 'y', 'c', 'r']
# plot_styles = ['-', '-.', '--', ':', '-', '--']
plot_with_factor_input = plot_with_factor
count = 0
for idx in data:
i_row, i_column = count / n_columns, count % n_columns
data_idx = data[idx]
plot_with_factor = plot_with_factor_input
if not plot_with_factor:
data_idx['plot-factor'] = np.ones(len(data_idx['value']))
if all(_ == 1.0 for _ in data_idx['plot-factor']):
## this counts for the case where there is no plot factor from the experimental group
## value of plot factor is all set to 1.0 in such case
plot_with_factor = False
if plot_with_factor:
## find the base of the plot factors
plot_factor = data_idx['plot-factor'][0]
for i in [2, 3, 5, 6, 7, 10]:
plot_factor_power = np.log(plot_factor) / np.log(i)
if abs(round(plot_factor_power) - plot_factor_power) < 1e-10:
plot_factor_base = i
break
_, absolute_eta, eta_bins = find_eta_bins(data_idx)
for i in xrange(len(eta_bins)):
eta_range = ''
if plot_with_factor:
if absolute_eta:
eta_min, eta_max = str(
data_idx['eta-abs-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-abs-max'][eta_bins[i][0] - 1])
plot_factor_power = round(
np.log(data_idx['plot-factor'][eta_bins[i][0] - 1]) /
np.log(plot_factor_base))
eta_range += r'$|\eta|~\in~[%s,~%s]~(\times %i^{%i})$' % (
eta_min, eta_max, plot_factor_base, plot_factor_power)
else:
eta_min, eta_max = str(
data_idx['eta-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-max'][eta_bins[i][0] - 1])
plot_factor_power = round(
np.log(data_idx['plot-factor'][eta_bins[i][0] - 1]) /
np.log(plot_factor_base))
eta_range += r'$\eta~\in~[%s,~%s]~(\times %i^{%i})$' % (
eta_min, eta_max, plot_factor_base, plot_factor_power)
else:
if absolute_eta:
eta_min, eta_max = str(
data_idx['eta-abs-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-abs-max'][eta_bins[i][0] - 1])
eta_range += r'$|\eta|~\in~[%s,~%s]$' % (eta_min, eta_max)
else:
eta_min, eta_max = str(
data_idx['eta-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-max'][eta_bins[i][0] - 1])
eta_range += r'$\eta~\in~[%s,~%s]$' % (eta_min, eta_max)
pt = np.array(data_idx['pT'][eta_bins[i][0] - 1:eta_bins[i][1]])
value = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['value'][eta_bins[i][0] - 1 : eta_bins[i][1]])
alpha = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['alpha'][eta_bins[i][0] - 1 : eta_bins[i][1]])
ax[i_row, i_column].errorbar(pt,
value,
alpha,
fmt='%s.' % plot_colors[i])
theory = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['thy'][eta_bins[i][0] - 1 : eta_bins[i][1]])
ax[i_row, i_column].plot(pt,
theory,
'%s%s' % (plot_colors[i], plot_styles[0]),
label=eta_range)
ax[i_row, i_column].axhline(0.0, color='black', ls=':')
ax[i_row, i_column].set_yscale('log')
ax[i_row, i_column].relim()
ax[i_row, i_column].autoscale_view()
if (idx != 10001) and (idx != 10002):
ax[i_row, i_column].legend(fontsize=10, loc='best')
if idx == 10001:
year = 'run II'
elif idx == 10002:
year = 'run II'
elif idx == 10003:
year = 'MB'
elif idx == 10004:
year = 'HT'
ax[i_row, i_column].title.set_text(
r'$\mathrm{%s~%s}$' %
(data_idx['col'][0].upper(), year.replace(' ', '~')))
count += 1
ax[1, 0].set_xlabel(r'$p_T~(\mathrm{GeV})$', size=15)
ax[1, 1].set_xlabel(r'$p_T~(\mathrm{GeV})$', size=15)
# plot_temp.xaxis.set_label_coords(1.05, 0.2)
ax[0, 0].set_ylabel(
r'$\frac{\mathrm{d}^2\sigma}{\mathrm{d}y \mathrm{d} p_T}~{\scriptstyle [\mathrm{nb} / (\mathrm{GeV} / c)]}$',
size=17)
## the units of D0 and CDF can be different
ax[1, 0].set_ylabel(
r'$\frac{\mathrm{d}^2\sigma}{2 \pi \mathrm{d}y \mathrm{d} p_T}~{\scriptstyle [\mathrm{nb} / (\mathrm{GeV} / c)]}$',
size=17)
## y labeling has to be more automatic
py.tight_layout()
figure.savefig('%s/gallery/jet-%d.png' % (wdir, istep), dpi=dpi)
def plot_data_on_theory(wdir, data, istep, dpi):
if (10003 in data) and (10004 in data):
nrows, ncols = 5, 3
else:
nrows, ncols = 4, 3
fig = py.figure(figsize=(ncols * 4.5, nrows * 3.0))
count = 0
ax = {}
eta_range = {}
for key in sorted(data):
_, absolute_eta, eta_bins = find_eta_bins(data[key])
for i in range(len(eta_bins)):
count += 1
if absolute_eta:
eta_min, eta_max = data[key]['eta-abs-min'][
eta_bins[i][0] -
1], data[key]['eta-abs-max'][eta_bins[i][0] - 1]
else:
eta_min, eta_max = data[key]['eta-min'][
eta_bins[i][0] - 1], data[key]['eta-max'][eta_bins[i][0] -
1]
ax[count] = py.subplot(nrows, ncols, count)
eta_range[count] = ''
theory = data[key]['thy'][eta_bins[i][0] - 1:eta_bins[i][1]]
pt = data[key]['pT'][eta_bins[i][0] - 1:eta_bins[i][1]]
value = data[key]['value'][eta_bins[i][0] - 1:eta_bins[i][1]]
alpha = data[key]['alpha'][eta_bins[i][0] - 1:eta_bins[i][1]]
if absolute_eta:
eta_range[count] += r'$|\eta|~\in~[%s,~%s]$' % (str(eta_min),
str(eta_max))
else:
eta_range[count] += r'$\eta~\in~[%s,~%s]$' % (str(eta_min),
str(eta_max))
if key == 10001:
ax[count].errorbar(pt,
value / theory,
alpha / theory,
color='firebrick',
marker='.',
linestyle='none',
label=r'$\mathrm{%s}$' %
data[key]['col'][0].upper())
elif key == 10002:
ax[count].errorbar(pt,
value / theory,
alpha / theory,
color='darkorange',
marker='v',
linestyle='none',
label=r'$\mathrm{%s}$' %
data[key]['col'][0].upper())
elif key == 10003:
ax[count].errorbar(pt,
value / theory,
alpha / theory,
color='olivedrab',
marker='s',
linestyle='none',
label=r'$\mathrm{%s}$' %
(data[key]['col'][0].upper() + '~MB'))
elif key == 10004:
ax[count].errorbar(pt,
value / theory,
alpha / theory,
color='darkcyan',
marker='*',
linestyle='none',
label=r'$\mathrm{%s}$' %
(data[key]['col'][0].upper() + '~HT'))
if key == 10002:
count += 1 ## make a white space to fill up a row for CDF
ax[1].legend(fontsize=20, loc='upper left')
ax[4].legend(fontsize=20, loc='upper center')
ax[7].legend(fontsize=20, loc='upper left')
ax[10].legend(fontsize=20, loc='upper center')
if (10003 in data) and (10004 in data):
ax[13].legend(fontsize=20, loc='upper center')
ax[14].legend(fontsize=20, loc='upper center')
minorLocator = MultipleLocator(0.05)
majorLocator = MultipleLocator(0.2)
for i in ax:
ax[i].axhline(1.0, color='black', ls=':')
ax[i].set_ylim(0.8, 1.2)
ax[i].yaxis.set_minor_locator(minorLocator)
ax[i].yaxis.set_major_locator(majorLocator)
ax[1].text(100.0, 0.85, eta_range[1], fontsize=22)
ax[2].text(50.0, 0.85, eta_range[2], fontsize=22)
ax[3].text(100.0, 0.85, eta_range[3], fontsize=22)
ax[4].text(100.0, 0.85, eta_range[4], fontsize=22)
ax[5].text(75.0, 0.85, eta_range[5], fontsize=22)
ax[6].text(75.0, 0.85, eta_range[6], fontsize=22)
ax[7].text(200.0, 0.85, eta_range[7], fontsize=22)
ax[8].text(100.0, 0.85, eta_range[8], fontsize=22)
ax[9].text(100.0, 0.85, eta_range[9], fontsize=22)
ax[10].text(100.0, 0.85, eta_range[10], fontsize=22)
ax[11].text(100.0, 0.85, eta_range[11], fontsize=22)
if (10003 in data) and (10004 in data):
ax[13].text(11.0, 0.9, eta_range[13], fontsize=22)
ax[14].text(20.0, 0.85, eta_range[14], fontsize=22)
ax[1].set_ylabel(r'\boldmath$\mathrm{data/theory}$', size=24)
ax[1].yaxis.set_label_coords(-0.17, 0.5)
ax[4].set_ylabel(r'\boldmath$\mathrm{data/theory}$', size=24)
ax[4].yaxis.set_label_coords(-0.17, 0.5)
ax[7].set_ylabel(r'\boldmath$\mathrm{data/theory}$', size=24)
ax[7].yaxis.set_label_coords(-0.17, 0.5)
ax[10].set_ylabel(r'\boldmath$\mathrm{data/theory}$', size=24)
ax[10].yaxis.set_label_coords(-0.17, 0.5)
if (10003 in data) and (10004 in data):
ax[13].set_ylabel(r'\boldmath$\mathrm{data/theory}$', size=24)
ax[13].yaxis.set_label_coords(-0.17, 0.5)
if (10003 in data) and (10004 in data):
ax[9].set_xlabel(r'\boldmath$p_{T}~(\mathrm{GeV})$', size=24)
ax[9].xaxis.set_label_coords(0.90, -0.12)
ax[13].set_xlabel(r'\boldmath$p_{T}~(\mathrm{GeV})$', size=24)
ax[13].xaxis.set_label_coords(0.90, -0.12)
ax[14].set_xlabel(r'\boldmath$p_{T}~(\mathrm{GeV})$', size=24)
ax[14].xaxis.set_label_coords(0.90, -0.12)
else:
ax[9].set_xlabel(r'\boldmath$p_{T}~(\mathrm{GeV})$', size=24)
ax[9].xaxis.set_label_coords(0.90, -0.12)
ax[10].set_xlabel(r'\boldmath$p_{T}~(\mathrm{GeV})$', size=24)
ax[10].xaxis.set_label_coords(0.90, -0.12)
ax[11].set_xlabel(r'\boldmath$p_{T}~(\mathrm{GeV})$', size=24)
ax[11].xaxis.set_label_coords(0.90, -0.12)
py.tight_layout()
# py.subplots_adjust(left = 0.08, bottom = 0.08, right = 0.99, top = 0.97, wspace = 0.2, hspace = 0.1)
py.savefig('%s/gallery/jet-ratio-%d.png' % (wdir, istep), dpi=dpi)
py.close()
def plot_data_separate(wdir, data_idx, istep, idx, dpi, plot_with_factor):
if not plot_with_factor:
data_idx['plot-factor'] = np.ones(len(data_idx['value']))
if all(_ == 1.0 for _ in data_idx['plot-factor']):
## this counts for the case where there is no plot factor from the experimental group
## value of plot factor is all set to 1.0 in such case
plot_with_factor = False
if plot_with_factor:
## find the base of the plot factors
plot_factor = data_idx['plot-factor'][0]
for i in [2, 3, 5, 6, 7, 10]:
plot_factor_power = np.log(plot_factor) / np.log(i)
if abs(round(plot_factor_power) - plot_factor_power) < 1e-10:
plot_factor_base = i
break
_, absolute_eta, eta_bins = find_eta_bins(data_idx)
figure = py.figure(figsize=[10.0, 5.0], dpi=500)
plot_temp = figure.add_subplot(1, 1, 1)
plot_styles = ['-']
plot_colors = ['b', 'g', 'm', 'y', 'c', 'r']
# plot_styles = ['-', '-.', '--', ':', '-', '--']
for i in xrange(len(eta_bins)):
eta_range = ''
if plot_with_factor:
if absolute_eta:
eta_min, eta_max = str(
data_idx['eta-abs-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-abs-max'][eta_bins[i][0] - 1])
plot_factor_power = round(
np.log(data_idx['plot-factor'][eta_bins[i][0] - 1]) /
np.log(plot_factor_base))
eta_range += r'$|\eta|~\in~[%s,~%s]~(\times %i^{%i})$' % (
eta_min, eta_max, plot_factor_base, plot_factor_power)
else:
eta_min, eta_max = str(
data_idx['eta-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-max'][eta_bins[i][0] - 1])
plot_factor_power = round(
np.log(data_idx['plot-factor'][eta_bins[i][0] - 1]) /
np.log(plot_factor_base))
eta_range += r'$\eta~\in~[%s,~%s]~(\times %i^{%i})$' % (
eta_min, eta_max, plot_factor_base, plot_factor_power)
else:
if absolute_eta:
eta_min, eta_max = str(
data_idx['eta-abs-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-abs-max'][eta_bins[i][0] - 1])
eta_range += r'$|\eta|~\in~[%s,~%s]$' % (eta_min, eta_max)
else:
eta_min, eta_max = str(
data_idx['eta-min'][eta_bins[i][0] - 1]), str(
data_idx['eta-max'][eta_bins[i][0] - 1])
eta_range += r'$\eta~\in~[%s,~%s]$' % (eta_min, eta_max)
pt = np.array(data_idx['pT'][eta_bins[i][0] - 1:eta_bins[i][1]])
value = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['value'][eta_bins[i][0] - 1 : eta_bins[i][1]])
alpha = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['alpha'][eta_bins[i][0] - 1 : eta_bins[i][1]])
plot_temp.errorbar(pt, value, alpha, fmt='%s.' % plot_colors[i])
theory = np.array(data_idx['plot-factor'][eta_bins[i][0] - 1 : eta_bins[i][1]]) * \
np.array(data_idx['thy'][eta_bins[i][0] - 1 : eta_bins[i][1]])
plot_temp.plot(pt,
theory,
'%s%s' % (plot_colors[i], plot_styles[0]),
label=eta_range)
plot_temp.legend(fontsize=10, loc='best')
plot_temp.set_xlabel(r'$p_T~(\mathrm{GeV})$', size=15)
# plot_temp.xaxis.set_label_coords(1.05, 0.2)
if data_idx['obs'][0].replace('<',
'').replace('>',
'') == 'd2_sigma_over_d_y_d_pt':
if data_idx['units'][0] == 'nb':
plot_temp.set_ylabel(
r'$\frac{\mathrm{d}^2\sigma}{\mathrm{d}y \mathrm{d} p_T}~{\scriptstyle [\mathrm{nb} / (\mathrm{GeV} / c)]}$',
size=17)
elif data_idx['units'][0] == 'pb':
plot_temp.set_ylabel(
r'$\frac{\mathrm{d}^2\sigma}{\mathrm{d}y \mathrm{d} p_T}~{\scriptstyle [\mathrm{pb} / (\mathrm{GeV} / c)]}$',
size=17)
elif data_idx['obs'][0].replace('<', '').replace(
'>', '') == 'd2_sigma_over_2_pi_d_y_d_pt':
if data_idx['units'][0] == 'nb':
plot_temp.set_ylabel(
r'$\frac{\mathrm{d}^2\sigma}{2 \pi \mathrm{d}y \mathrm{d} p_T}~{\scriptstyle [\mathrm{nb} / (\mathrm{GeV} / c)]}$',
size=17)
elif data_idx['units'][0] == 'pb':
plot_temp.set_ylabel(
r'$\frac{\mathrm{d}^2\sigma}{2 \pi \mathrm{d}y \mathrm{d} p_T}~{\scriptstyle [\mathrm{pb} / (\mathrm{GeV} / c)]}$',
size=17)
plot_temp.set_yscale('log')
if data_idx['col'][0] == 'star':
if idx == 10003:
plot_temp.set_title(r'$\mathrm{STAR~MB}$')
py.tight_layout()
figure.savefig('%s/gallery/jet-%s-MB-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
elif idx == 10004:
plot_temp.set_title(r'$\mathrm{STAR~HT}$')
py.tight_layout()
figure.savefig('%s/gallery/jet-%s-HT-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)
else:
plot_temp.set_title(r'$\mathrm{%s}$' % data_idx['col'][0].upper())
py.tight_layout()
figure.savefig('%s/gallery/jet-%s-%d.png' %
(wdir, data_idx['col'][0], istep),
dpi=dpi)