def data_mc_ratio(data, mc, label_data='Data', label_mc="MC",
y_label=None, figsize=None, ratio_range=(0, 2),
*args, **kwarg):
""" Perform data mc distributions
returns:
axes
"""
f, axes = plt.subplots(2, 1, gridspec_kw={"height_ratios": [3, 1]}, sharex=True, figsize=figsize)
ax0 = axes[0]
hm = b2plot.hist(mc, lw=2, ax=ax0, label=label_mc, *args, **kwarg)
hd = b2plot.errorhist(data, ax=ax0, label=label_data, color='black')
ax0.legend()
ax1 = axes[1]
ry, rye = ratio(hd[0], hm[0])
b2plot.errorbar(hd[1], ry, rye, ax=ax1, color='grey')
ax1.axhline(1, color='grey', lw=0.5, ls='--')
f.subplots_adjust(hspace=0.1)
ax1.set_ylim(*ratio_range)
b2plot.xlim()
if y_label is not None:
ax0.set_ylabel(y_label)
ax1.set_ylabel("Ratio")
ax1.yaxis.set_label_coords(-0.08, 0.5)
ax0.yaxis.set_label_coords(-0.08, 0.5)
return axes
def generate(self, cmd=""):
if not self.check_file_set(): return
if cmd == "":
tk.messagebox.showinfo("GUI Python", "There is nothing to build")
else:
gen = gn.Generator(self.FILENAME)
x = gen.x
if cmd == "S(0)":
y = gen.generate_peaks_without_extension()
elif cmd == "S(0)+B":
y = gen.generate_bkg_without_stat() + \
gen.generate_peaks_without_extension()
elif cmd == "S(1)+B":
y = gen.generate_peaks_with_custom_extension() + \
gen.generate_bkg_without_stat()
elif cmd == "S(2)+B":
if (self.enter_a.get() == "" or self.enter_b.get() == ""):
tk.messagebox.showinfo("GUI Python",
"Please enter A and B")
y = gen.generate_peaks_with_extension(float(self.enter_a.get()),
float(self.enter_b.get())) + \
gen.generate_bkg_without_stat()
elif cmd == "S(1)+B+D":
temp = gen.generate_peaks_with_custom_extension() + \
gen.generate_bkg_without_stat()
y = [gn.statistical_scatter(i) for i in temp]
elif cmd == "S(2)+B+D":
if (self.enter_a.get() == "" or self.enter_b.get() == ""):
tk.messagebox.showinfo("GUI Python",
"Please enter A and B")
temp = gen.generate_peaks_with_extension(float(self.enter_a.get()),
float(self.enter_b.get())) + \
gen.generate_bkg_without_stat()
y = [gn.statistical_scatter(i) for i in temp]
bp.hist(x, weights=y, bins=gen.sts.nbins, style=0)
plt.show()
return (x, y)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import b2plot
plt.style.use('belle2')
import numpy as np
import pandas as pd
df = {
'mass':
np.append(
np.random.random_sample(1000) * 7 - 3.5,
np.random.normal(0, 0.5, 1000))
}
df = pd.DataFrame(df)
b2plot.errorbar(df.sample(1500).mass, color='black', label='Data')
b2plot.hist(df.sample(1500).mass, fill=True, lw=2, style=1, label='MC10')
plt.legend()
b2plot.watermark(fontsize=20, px=.2)
b2plot.xlim()
b2plot.labels('E', "Events", "GeV", 1)
plt.savefig("example_hist_errorbar.pdf")
# -*- coding: utf-8 -*-
""" Stacked plot
In data science, often one wants to compare two different sets of data, like signal/background or prediction and
actual data.
In this very brief script we create two sets of data and compare them in one plot.
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import b2plot as bp
try:
plt.style.use('belle2')
except OSError:
print("Please install belle2 matplotlib style")
bp.hist(np.random.normal(0, 0.5, 1000), label="Pseudo Simulation")
bp.errorhist(np.random.normal(0, 0.5, 1000), label="Pseudo Data", color='black')
bp.labels("O", "Entries", "Unit")
plt.legend()
# bp.xlim()
# bp.labels('$\Delta M$', "Events", "GeV", 0)
bp.save("histogram2.png")
# -*- coding: utf-8 -*-
""" Stacked plot
In data science, often one wants to compare two different sets of data, like signal/background or prediction and
actual data.
In this very brief script we create two sets of data and compare them in one plot.
"""
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import b2plot as bp
try:
plt.style.use('belle2')
except OSError:
print("Please install belle2 matplotlib style")
data = np.random.normal(0, 0.5, 1000)
bp.hist(data)
bp.labels("O", "Entries", "Unit")
# bp.xlim()
# bp.labels('$\Delta M$', "Events", "GeV", 0)
bp.save("histogram.png")