def plot_nf(results_table):
x = 'NumFlavors'
df = results_table.sort(x)
for (process, bin_, oqcd), process_df in df.groupby(['Observable',
'Bin', 'PDF_OrderQCD'],
sort=False):
fig = plt.figure()
for (col, asn), pdf_df in process_df.groupby(['Collaboration',
'as_from_name'],
sort=False):
label = "%s(as: %s)"%(col, asn)
plt.errorbar(pdf_df[x], pdf_df['CV'],
yerr = np.array(pdf_df['Down68'],
pdf_df['Up68']),
label = label, linestyle='-', marker = 's')
plot_remarks(process_df, x)
plt.xlabel(r'$N_f$')
plt.ylabel(r'Value of observable')
xran = plotutils.extend_range(process_df[x].min(),
process_df[x].max())
plt.xlim(*xran)
plt.xticks(process_df[x].unique())
plt.legend()
plt.title("%s PDFs, %s" % (oqcd, process_label(process, bin_)), y=1.08)
plt.tight_layout()
plt.grid(axis='x')
yield (process, bin_, oqcd),fig
def plot_alphaS(results_table):
x = 'alpha_sMref'
df = results_table.sort(x)
for (process, nf, bin_), process_df in df.groupby(['Observable',
'NumFlavors', 'Bin'],
sort = False):
fig = plt.figure()
for (oqcd,col), col_df in process_df.groupby(['PDF_OrderQCD',
'Collaboration']):
label = "%s (%s)" % (col, oqcd)
plt.errorbar(col_df[x], col_df['CV'],
yerr = np.array(col_df['Down68'],
col_df['Up68']),
label = label, linestyle='-', marker = 's')
plot_remarks(process_df, x)
plt.xlabel(r'$\alpha_S(M_%s)$' % M_REF[nf])
plt.ylabel(r'Value of observable')
xran = plotutils.extend_range(process_df[x].min(),
process_df[x].max())
plt.xlim(*xran)
plt.legend()
plt.title("%s $N_f=$%d" % (process_label(process, bin_), nf), y = 1.08)
plt.tight_layout()
yield (process, nf, bin_),fig
