def plot_data_vs_prediction(self, key_meas_, key_pred_, **kwargs):
x_m, y_m, [ey_lo_m, ey_hi_m
], ex_m = HEPData_plt.get_1D_distribution(self, key_meas_)
x_p, y_p, [ey_lo_p, ey_hi_p
], ex_p = HEPData_plt.get_1D_distribution(self, key_pred_)
fig = plt.figure(figsize=(5, 7))
ax1 = fig.add_subplot(211)
ax1.errorbar(x_p,
y_p,
yerr=[ey_lo_p, ey_hi_p],
xerr=ex_p,
c='r',
linestyle='None',
marker='+',
alpha=0.8,
label="Prediction")
ax1.errorbar(x_m,
y_m,
yerr=[ey_lo_m, ey_hi_m],
xerr=ex_m,
c='k',
linestyle='None',
alpha=1,
label="Data")
ax1.legend(loc=kwargs.get("legend_loc", "best"))
plt.ylabel(kwargs.get("ylabel", "observable"))
plt.title(kwargs.get("title", ""))
if "xlim" in kwargs:
ax1.axis(xmin=kwargs["xlim"][0], xmax=kwargs["xlim"][1])
if "ylim" in kwargs:
ax1.axis(ymin=kwargs["ylim"][0], ymax=kwargs["ylim"][1])
plt.grid()
ax2 = fig.add_subplot(212)
ax2.errorbar(x_p,
y_p / y_p,
yerr=[ey_lo_p / y_p, ey_hi_p / y_p],
xerr=ex_p,
c='r',
linestyle='None',
marker='+',
alpha=0.8)
ax2.errorbar(x_m,
y_m / y_p,
yerr=[ey_lo_m / y_p, ey_hi_m / y_p],
xerr=ex_m,
c='k',
linestyle='None',
alpha=1)
if "xlim" in kwargs:
ax2.axis(xmin=kwargs["xlim"][0], xmax=kwargs["xlim"][1])
plt.ylabel("Measured / prediction")
if "xlabel" in kwargs: plt.xlabel(kwargs["xlabel"])
plt.grid()
plt.show()
def plot_1D_distribution(self, key_, **kwargs):
x, y, [ey_lo, ey_hi], ex = HEPData_plt.get_1D_distribution(self, key_)
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(111)
ax.errorbar(x,
y,
yerr=[ey_lo, ey_hi],
xerr=ex,
c='k',
linestyle='None',
marker='+',
alpha=0.6,
label=kwargs.get("label", "label"))
ax.legend(loc=kwargs.get("legend_loc", "best"))
plt.xlabel(kwargs.get("xlabel", "observable"))
plt.ylabel(kwargs.get("ylabel", "observable"))
plt.title(kwargs.get("title", ""))
if "xlim" in kwargs:
ax.axis(xmin=kwargs["xlim"][0], xmax=kwargs["xlim"][1])
if "ylim" in kwargs:
ax.axis(ymin=kwargs["ylim"][0], ymax=kwargs["ylim"][1])
plt.grid()
plt.show()
def plot_1D_distribution(self, key_, **kwargs):
if self._distributions_1D[key_]._has_errors:
msg.error(
"Distribution_store.plot_1D_distribution",
"Key {0} had some errors when loading. Please clear them before plotting."
.format(key_), -1)
return
x, y, [ey_lo,
ey_hi], ex, labels, keys = HEPData_plt.get_1D_distribution(
self, key_)
x, y, [ey_lo_sys, ey_hi_sys
], ex, labels, sys_keys = HEPData_plt.get_1D_distribution(
self, key_, "sys")
x, y, [ey_lo_stat, ey_hi_stat
], ex, labels, stat_keys = HEPData_plt.get_1D_distribution(
self, key_, "stat")
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(111)
str_tot_legend = kwargs.get(
"label", "distribution") + " ( " + " + ".join(keys) + " )"
str_tot_legend = "\n".join([
str_tot_legend[120 * i:min(len(str_tot_legend), 120 * (i + 1))]
for i in range(int(len(str_tot_legend) / 120) + 1)
])
str_sys_legend = kwargs.get(
"label", "distribution") + " ( " + " + ".join(sys_keys) + " )"
str_sys_legend = "\n".join([
str_sys_legend[120 * i:min(len(str_sys_legend), 120 * (i + 1))]
for i in range(int(len(str_sys_legend) / 120) + 1)
])
if sum([np.fabs(x) for x in ey_hi_sys + ey_lo_sys]) > 0:
ax.errorbar(x,
y,
yerr=[ey_lo_sys, ey_hi_sys],
c='royalblue',
linewidth=18,
linestyle='None',
marker='None',
alpha=0.4,
label=str_tot_legend)
if sum([np.fabs(x) for x in ey_hi_stat + ey_lo_stat]) > 0:
ax.errorbar(x,
y,
yerr=[ey_lo_stat, ey_hi_stat],
c='indianred',
linewidth=6,
linestyle='None',
marker='None',
alpha=0.6,
label=kwargs.get("label", "distribution") +
" ( stat )")
ax.errorbar(x,
y,
yerr=[ey_lo, ey_hi],
xerr=ex,
c='k',
linewidth=2,
linestyle='None',
marker='+',
alpha=1,
label=str_sys_legend)
if labels:
ax.set_xticks(x)
ax.set_xticklabels(self._distributions_1D[key_]._bin_labels,
rotation=45)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.4, box.height])
if "legend_loc" in kwargs:
ax.legend(loc=kwargs.get("legend_loc", "best"))
else:
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.xlabel(
kwargs.get("xlabel", self._distributions_1D[key_]._indep_var))
plt.ylabel(kwargs.get("ylabel", self._distributions_1D[key_]._dep_var))
plt.title(kwargs.get("title", ""))
ax.axis(xlim=kwargs.get("xlim", [x[0], x[len(x) - 1]]))
ax.axis(ylim=kwargs.get("ylim", [x[0], x[len(x) - 1]]))
if kwargs.get("logy", False) is True: plt.yscale("log")
if kwargs.get("logx", False) is True: plt.xscale("log")
plt.grid()
plt.show()
def plot_data_vs_prediction(self, key_meas_, key_pred_, **kwargs):
x_m, y_m, [
ey_lo_m, ey_hi_m
], ex_m, labels, keys_meas = HEPData_plt.get_1D_distribution(
self, key_meas_)
x_p, y_p, [
ey_lo_p, ey_hi_p
], ex_p, labels, keys_pred = HEPData_plt.get_1D_distribution(
self, key_pred_)
fig = plt.figure(figsize=(15, 7))
ax1 = fig.add_subplot(211)
ax1.errorbar(x_p,
y_p,
yerr=[ey_lo_p, ey_hi_p],
xerr=ex_p,
c='r',
linestyle='None',
marker='+',
alpha=0.8,
label="Prediction ( " + " $\oplus$ ".join(keys_meas) +
" )")
ax1.errorbar(x_m,
y_m,
yerr=[ey_lo_m, ey_hi_m],
xerr=ex_m,
c='k',
linestyle='None',
alpha=1,
label="Data ( " + " $\oplus$ ".join(keys_pred) + " )")
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.4, box.height])
if "legend_loc" in kwargs:
ax1.legend(loc=kwargs.get("legend_loc", "best"))
else:
ax1.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.ylabel(kwargs.get("ylabel", "observable"))
plt.title(kwargs.get("title", ""))
ax.axis(xlim=kwargs.get("xlim", [x_m[0], x_m[len(x_m) - 1]]))
ax.axis(ylim=kwargs.get("ylim", [x_m[0], x_m[len(x_m) - 1]]))
if kwargs.get("logy", False) is True: plt.yscale("log")
if kwargs.get("logx", False) is True: plt.xscale("log")
plt.grid()
ax2 = fig.add_subplot(212)
ax2.errorbar(x_p,
y_p / y_p,
yerr=[ey_lo_p / y_p, ey_hi_p / y_p],
xerr=ex_p,
c='r',
linestyle='None',
marker='+',
alpha=0.8)
ax2.errorbar(x_m,
y_m / y_p,
yerr=[ey_lo_m / y_p, ey_hi_m / y_p],
xerr=ex_m,
c='k',
linestyle='None',
alpha=1)
box = ax2.get_position()
ax2.set_position([box.x0, box.y0, box.width * 0.4, box.height])
ax.axis(xlim=kwargs.get("xlim", [x_m[0], x_m[len(x_m) - 1]]))
plt.ylabel("Measured / prediction")
if "xlabel" in kwargs: plt.xlabel(kwargs["xlabel"])
if labels:
ax2.set_xticks(x)
ax2.set_xticklabels(self._distributions_1D[key_]._bin_labels,
rotation=45)
plt.grid()
plt.show()
def plot_ratio_1D(table_num_, table_den_, **kwargs):
x_n, y_n, [ex_lo_n, ex_hi_n], [
ey_lo_n, ey_hi_n
], labels, keys_num = plotter.get_1D_distribution(table_num_)
x_d, y_d, [ex_lo_d, ex_hi_d], [
ey_lo_d, ey_hi_d
], labels, keys_den = plotter.get_1D_distribution(table_den_)
chi2 = hlp.get_chi2(y_n, ey_lo_n, ey_hi_n, y_d, ey_lo_d, ey_hi_d)
for i in range(len(x_n)):
if x_n[i] == x_d[i]: continue
msg.error("plot_ratio_1D", "Arguments do not have the same binning")
raise ValueError(
"Ratio of distributions with bin centres at {0} and {1}", x_n, x_d)
fig = plotter.plt.figure(figsize=(10, 10))
ax1 = fig.add_subplot(211)
legend_char_width = 53
str_num_legend = "TABLE 1 ( " + " + ".join(keys_num) + " )"
str_num_legend = "\n".join([
str_num_legend[legend_char_width *
i:min(len(str_num_legend), legend_char_width * (i + 1))]
for i in range(int(len(str_num_legend) / legend_char_width) + 1)
])
str_den_legend = "TABLE 2 ( " + " + ".join(keys_den) + " )"
str_den_legend = "\n".join([
str_den_legend[legend_char_width *
i:min(len(str_den_legend), legend_char_width * (i + 1))]
for i in range(int(len(str_den_legend) / legend_char_width) + 1)
])
ax1.errorbar(x_n,
y_n,
yerr=[ey_lo_n, ey_hi_n],
xerr=[ex_lo_n, ex_hi_n],
c='k',
linewidth=2,
linestyle='None',
alpha=0.8,
label=str_num_legend)
ax1.errorbar(x_d,
y_d,
yerr=[ey_lo_d, ey_hi_d],
xerr=[ex_lo_d, ex_hi_d],
c='r',
linewidth=4,
linestyle='None',
alpha=0.4,
label=str_den_legend)
ax1.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plotter.plt.subplots_adjust(left=0.1, right=0.5, top=0.95, bottom=0.4)
plotter.plt.ylabel("Values")
plotter.plt.ylabel("Values")
xlim = kwargs.get(
"xlim", [x_d[0] - np.fabs(ex_lo_n[0]), x_d[-1] + np.fabs(ex_hi_n[-1])])
ymin, ymax = ax1.get_ylim()
ax1.axis(xmin=xlim[0], xmax=xlim[1])
try:
plotter.plt.text(
xlim[0], 1.19 * ymax - 0.19 * ymin,
("$\\bf{TABLE}$ $\\bf{1:}$ " + table_num_._dep_var._name).replace(
"\\\\", "\\").replace(r"\text{", r"{\rm "))
plotter.plt.text(
xlim[0], 1.08 * ymax - 0.08 * ymin,
("$\\bf{TABLE}$ $\\bf{2:}$ " + table_den_._dep_var._name).replace(
"\\\\", "\\").replace(r"\text{", r"{\rm "))
except:
msg.warning(
"plot_ratio_1D",
"could not render observable name - no title given to plot")
plotter.plt.grid()
ax2 = fig.add_subplot(212)
ax2.errorbar(x_n,
y_n / y_d,
yerr=[ey_lo_n / y_d, ey_hi_n / y_d],
xerr=[ex_lo_n, ex_hi_n],
c='k',
linewidth=2,
linestyle='None',
alpha=0.8)
ax2.errorbar(x_d,
y_d / y_d,
yerr=[ey_lo_d / y_d, ey_hi_d / y_d],
xerr=[ex_lo_d, ex_hi_d],
c='r',
linewidth=4,
linestyle='None',
alpha=0.4)
box = ax2.get_position()
ax2.set_position([box.x0, box.y0, box.width * 0.4, box.height])
ax2.axis(xmin=xlim[0], xmax=xlim[1])
plotter.plt.ylabel("Ratio $\\bf{vs.}$ TABLE 2")
try:
plotter.plt.xlabel(
kwargs.get(
"xlabel", table_den_._indep_vars[0].name().replace(
"\\\\", "\\").replace(r"\text{", r"{\rm ")))
except:
plotter.plt.xlabel("<error reading xlabel")
plotter.plt.subplots_adjust(left=0.1, right=0.5, top=0.92, bottom=0.4)
plotter.plt.grid()
if kwargs.get("show", False):
plotter.plt.show()
if kwargs.get("save", False):
fig.savefig(plotter.document, format='pdf')
plotter.plt.close(fig)
return chi2, y_n / y_d