Stack¶
Build via Axes¶
A histogram stack holds multiple 1-D histograms into a stack, whose axes are required to match. The most common way to create one is with a categorical axes:
[1]:
from hist import Hist
import hist
import numpy as np
import matplotlib.pyplot as plt
ax = hist.axis.Regular(25, -5, 5, flow=False, name="x")
cax = hist.axis.StrCategory(["signal", "upper", "lower"], name="c")
full_hist = Hist(ax, cax)
full_hist.fill(x=np.random.normal(size=600), c="signal")
full_hist.fill(x=2 * np.random.normal(size=500) + 2, c="upper")
full_hist.fill(x=2 * np.random.normal(size=500) - 2, c="lower")
s = full_hist.stack("c")
You can build this directly with hist.Stack(h1, h2, h3), hist.Stack.from_iter([h1, h2, h3]), or hist.Stack.from_dict({"signal": h1, "lower": h2, "upper": h3}) as well.
HistStack has .plot() method which calls mplhep and plots the histograms in the stack:
[2]:
s.plot()
plt.legend()
plt.show()
For the “stacked” style of plot, you can pass arguments through to mplhep. For some reason, this reverses the graphical order, but we can easily undo that by applying a slicing operation to the stack:
[3]:
s[::-1].plot(stack=True, histtype="fill")
plt.legend()
plt.show()
We can use .show() to access histoprint and print the stacked histograms to the console.
Note: Histoprint currently supports only non-discrete axes. Hence, it supports only regular and variable axes at the moment.
[4]:
s.show(columns=120)
-5.00e+00 _ 1.45e+02 ╷
-4.60e+00 _═══════════
-4.20e+00 _════════════
-3.80e+00 _══════════════════
-3.40e+00 _══════════════════════
-3.00e+00 _│═════════════════════════════
-2.60e+00 _│══════════════════════════════
-2.20e+00 _█│││═══════════════════════
-1.80e+00 _█████│││═════════════════════════════════
-1.40e+00 _██████████████████████████│││════════════════════════════════════
-1.00e+00 _███████████████████████████████││││││││││═════════════════════════
-6.00e-01 _███████████████████████████████████████████████████││││││││││││════════════════════════════
-2.00e-01 _█████████████████████████████████████████████████████████████████████││││││││││││││││││════════════════════
2.00e-01 _████████████████████████████████████████████████████████████████████████████│││││││││││││││═════════════════
6.00e-01 _█████████████████████████████████████████████████████████││││││││││││││││││════════════
1.00e+00 _██████████████████████████████████████████████████████████│││││││││││││││││││││═════════
1.40e+00 _███████████████████████████████████││││││││││││││││││││││││══════
1.80e+00 _███████████████████████│││││││││││││││││││││││││││││││││═══════
2.20e+00 _███████│││││││││││││││││││││││││││═══
2.60e+00 _████││││││││││││││││││││││││││══
3.00e+00 _│││││││││││││││││││││││││││
3.40e+00 _│││││││││││││││││││││││││││││═
3.80e+00 _││││││││││││││││││││││
4.20e+00 _│││││││││││││││││││││
4.60e+00 _│││││││││
5.00e+00 _│││││││││││
█ signal │ upper ═ lower
Manipulations on a Stack¶
[5]:
h = Hist.new.Reg(50, -5, 5, name="x").StrCat(["good", "bad"], name="quality").Double()
h.fill(x=np.random.randn(100), quality=["good", "good", "good", "good", "bad"] * 20)
# Turn an existing axis into a stack
s = h.stack("quality")
s[::-1].plot(stack=True, histtype="fill")
plt.legend()
plt.show()
Histograms in a stack can have names. The names of histograms are the categories, which are corresponding profiled histograms:
[6]:
print(s[0].name)
s[0]
good
[6]:
Double() Σ=80.0
You can use those names in indexing, just like for axes (only when using string names):
[7]:
print(s["bad"].name)
s["bad"]
bad
[7]:
Double() Σ=20.0
You can scale a stack:
[8]:
(s * 5).plot()
plt.legend()
plt.show()
Or an item in the stack inplace:
[9]:
s["good"] *= 3
s.plot()
plt.legend()
plt.show()
You can project on a stack, as well, if the histograms are at least two dimensional. h.stack("x").project("y") is identical to h.project("y").stack("x").