Hm. Okay I'll poke around at this later tonight, but does that mean that I need to make a second histogram to keep track of the uncertainties on my first histogram? For instance,
import hist
from hist import Hist
import uproot4 as uproot
root_file = uproot.open("example.root")
values, edges = root_file["data"].to_numpy()
_hist = Hist(
hist.axis.Regular(len(edges) - 1, edges[0], edges[-1], name=None),
storage=hist.storage.Double(),
)
_hist[:] = values
_hist.view()giving
array([ 0., 0., 0., 0., 1., 16., 95., 272., 459., 591., 674.,
658., 623., 531., 456., 389., 321., 254., 194., 161., 120., 95.,
74., 60., 53., 38., 27., 19., 20., 15., 11., 10., 14.,
5., 6., 3., 7., 2., 6., 1., 3., 2., 3., 0.,
0., 1., 0., 0., 0., 0.])if I wanted to now get the uncertainties on this histogram (so that I could then add them in quadrature while summing this "data" histogram with others to get the uncertainty on the resultant summed histogram) I'm not quite clear how I would do so.
import boost_histogram as bh
import numpy as np
bins = [1, 2, 3]
yields = np.asarray([3, 4])
stdev = np.asarray([1, 2])
h = bh.Histogram(
bh.axis.Variable(bins, underflow=False, overflow=False),
storage=bh.storage.Weight(),
)
h[...] = np.stack([yields, stdev ** 2], axis=-1)
print(f"yields: {h.view().value}, stdev: {np.sqrt(h.view().variance)}")
Currently, weighted histograms don’t plot or interact nicely with the rest of the Scikit-HEP ecosystem, but a Protocol to allow this is in the works and should be rougly ready by the end of the month.
@henryiii That's great and would be quite helpful to be able to use as defaults for https://github.com/matthewfeickert/heputils/blob/master/src/heputils/convert.py
Is there an Issue open at the moment that I could follow for this? I realize I'm also making a lot of requests without volunteering to help on anything, so apologies there, but maybe by 2021 I'll understand enough that I can start making some more helpful Issues or easy PRs.
I don’t think it’s really a replacement for gitter; it’s more of a replacement for a) stack overflow, and b) opening issues for help instead of a bug. It’s also all in one place - GitHub, so less fragmented. It’s really just a bit more orginisation on issues, with some minor added features. An issue closes - you aren’t supposed to care about the issue anymore when it’s fixed. But if it was opened as a discussion, or moved to a discussion, in the Q&A category, then it just gets an answer, but remains easy to find, so it becomes less likely to become a recurring quesiton. The “showcase” topic looks great - people can post thier most intersting and exciting examples of boost-histogram.
And while it’s new to me, other members of our comunity have been trying them out already (the PyHF repo has had them on for over a month, for example).
What is that one place you describe, gitter? To me gitter does not seem suited for focused discussions - the history is hard to read, even more so when one comes back after a month and tries to find a topic discussed previously.
Yes, I was referring to Gitter as the place to communicate with users.
- Gitter for issues that require some back-and-fourth for a short amount of time and are likely very user-specific (e.g.: my compilation fails and I don't know how to fix it), if that gets lost in the history it is probably ok since other users may not have the same question
- Discussions for questions that lots of users may have (and then in the future find there answered, like StackOverflow), or questions that take a long time to answer (to have one consistent thread there instead of being spread out in Gitter between other topics). I also think the "Show and tell" category could be quite nice, and a message like that would disappear quickly in Gitter. And I personally like the idea of everything in one place (repository) a lot.
My histogram is having a Regular and a strCategory axis and I want to show the cumulative sum of the categories, but with the different components stacked on top of each other.
I found out I can just use np.cumsum(axis=<category axis>) to calculate the cumulative sum and create a new histogram from that. In had also managed to draw a stacked histogram by plotting multiple plt.bar with different bottom=... arguments taken from the cumulative sums. But I dislike that visualy, because I want to have a black outline for my components, but no outlines for the individual bins, as seen in the screenshot.
Hi, I used to be able to modify histogram contents like this
import boost_histogram as bh
import numpy as np
bins = [0, 1, 2]
hist = bh.Histogram(bh.axis.Variable(bins), storage=bh.storage.Weight())
yields = [3, 4]
var = [0.1, 0.2]
hist[...] = np.stack([yields, var], axis=-1)
hist.view().value /= 2That worked until boost-histogram version 0.11.1. In the current master it does not anymore, I think scikit-hep/boost-histogram#475 changes the behavior:
Traceback (most recent call last):
File "test.py", line 10, in <module>
hist.view().value /= 2
File "[...]/boost_histogram/_internal/view.py", line 57, in fset
self[name] = value
File "[...]/boost_histogram/_internal/view.py", line 49, in __setitem__
raise ValueError("Needs matching ndarray or n+1 dim array")
ValueError: Needs matching ndarray or n+1 dim arrayIs there another way to achieve this now?