Does anyone have an answer to this: https://stackoverflow.com/questions/63813448/writing-boost-histograms-with-uproot
It would have to be Uproot3, since Uproot4 doesn't write anything yet.
I just looked into it and found a physt example, which handles variances:
It creates classes with the right names and the right fields, which Uproot 3 recognizes when assigning to a key of an output file (in __setitem__). The recognition happens in
Considering how complicated this looks, it's a toss-up whether it's valuable to do it now, so that Uproot 3 will recognize and write boost-histogram and hist objects, or if it would be better to wait a month or two for me to add the file-writing to Uproot 4. The new interface would be more formal than this.
Maybe more than two months—I've claimed file-writing in Uproot 4 as a milestone for December 1, though.
Hist 2.0.0 is out! This is the result of the work @LovelyBuggies and I have been doing for Google Summer of Code 2020. Changes since Beta 1:
- Based on boost-histogram 0.11; now supports two way boost-histogram <-> hist conversion without metadata issues.
- mplhep is now used for all plotting. Return types changed;
figdropped, new figures only created if needed. - QuickConstruct was rewritten, uses
new.Reg(...).Double(); not as magical but clearer types and usage. - Plotting requirements are no longer required, use
pip install "hist[plot]"to request.
The following new features were added:
- Jupyter HTML repr's were added.
flow=Falseshortcut added.- Static type checker support for dependent projects.
See more details at https://github.com/scikit-hep/hist
2 replies
2 replies
@LovelyBuggies @henryiii I think I already asked Henry this (sorry) but can you remind me how one can take two hist objects with the same binning and add them? I see that in the example notebooks I have here https://github.com/matthewfeickert/heputils that just trying to fill a histogram
root_file = uproot.open("example.root")
mass_hists = [
heputils.convert.uproot_to_hist(root_file[key]) for key in root_file.keys()
]
stack_hist = mass_hists[0].copy()
stack_hist.reset()
for hist in mass_hists:
stack_hist.fill(hist)
stack_hist.plot1d()
mass_hists[0].plot1d()is apparently not the right way to do things.
sum(mass_hists[1:], mass_hists[0]) would add them all.
mass_hists[0] + mass_hists[1]
results in
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-7bab2e7a86b3> in <module>
12 # mass_hists[0]
13 # help(stack_hist)
---> 14 mass_hists[0] + mass_hists[1]
/srv/conda/envs/notebook/lib/python3.7/site-packages/boost_histogram/_internal/hist.py in __add__(self, other)
222 def __add__(self, other):
223 result = self.copy(deep=False)
--> 224 return result.__iadd__(other)
225
226 def __iadd__(self, other):
/srv/conda/envs/notebook/lib/python3.7/site-packages/boost_histogram/_internal/hist.py in __iadd__(self, other)
227 if isinstance(other, (int, float)) and other == 0:
228 return self
--> 229 self._compute_inplace_op("__iadd__", other)
230
231 # Addition may change the axes if they can grow
/srv/conda/envs/notebook/lib/python3.7/site-packages/boost_histogram/_internal/hist.py in _compute_inplace_op(self, name, other)
261 def _compute_inplace_op(self, name, other):
262 if isinstance(other, Histogram):
--> 263 getattr(self._hist, name)(other._hist)
264 elif isinstance(other, _histograms):
265 getattr(self._hist, name)(other)
ValueError: axes not mergableThis is all helpful and makes me appreciate the concept of metadata more. I guess the good news is that uproot will handle things in a more intelligent manner than I have been once some issues have been resolved, where what I have been (hackily) doing is the following https://github.com/matthewfeickert/heputils/blob/3dd0e858f002041a7ae15fc310d92ddb0ea4fe26/src/heputils/convert.py#L7-L32
If I just don't even attempt to handle the name then things work with the following:
import numpy as np
import uproot4 as uproot
import mplhep
import hist
from hist import Hist
import functools
import operator
mplhep.set_style("ATLAS")
def uproot_to_hist(uproot_hist):
values, edges = uproot_hist.to_numpy()
_hist = hist.Hist(
hist.axis.Regular(len(edges) - 1, edges[0], edges[-1]),
storage=hist.storage.Double(),
)
_hist[:] = values
return _hist
root_file = uproot.open("example.root")
mass_hists = [
uproot_to_hist(root_file[key]) for key in root_file.keys()
]
stack_hist = functools.reduce(operator.add, mass_hists)
stack_hist.plot1d()
for hist in mass_hists:
hist.plot1d()ax._ax.metadata!
__dict__ is not used.
It is explained at the end of the overview section in the Boost Histogram docs:
https://www.boost.org/doc/libs/develop/libs/histogram/doc/html/histogram/guide.html#histogram.guide.axis_guide.overview
It is something to add to the Rationale, too, I think.
https://www.boost.org/doc/libs/develop/libs/histogram/doc/html/histogram/rationale.html
Dear Hist/boost-histogram developers,
thank you for this great histogramming library, it is a pleasure to work with it :)
I have a question to you regarding fancy indexing on a StrCategory axis.
My (Hist) histogram looks as follows:
In [38]: h
Out[38]:
Hist(
StrCategory(['GluGluToHHTo2B2VTo2L2Nu_node_cHHH2p45'], growth=True),
StrCategory(['ee', 'mumu', 'emu'], growth=True),
StrCategory(['nominal'], growth=True),
Regular(40, 0, 200, name='MET', label='$p_{T}^{miss}$'),
storage=Weight()) # Sum: WeightedSum(value=2608.44, variance=47.3505) (WeightedSum(value=2775.4, variance=50.5706) with flow)Now I would like to "group" e.g. the "ee" and "emu" category together, which means that I'd like to do something like:
h[{"dataset": "GluGluToHHTo2B2VTo2L2Nu_node_cHHH2p45", "category": ["ee", "emu"], "systematic": "nominal"}]Unfortunately this does not work, as the ["ee", "emu"] is not a valid indexing operation.
Is there a way to do such a fancy indexing on StrCategory axes? In case this is not supported, is there a nice workaround?
(I am basically looking for something, which works similar to coffea.Hist.group method: https://github.com/CoffeaTeam/coffea/blob/master/coffea/hist/hist_tools.py#L1115)
Thank you already a lot in advance!
Best, Peter
Hist, if this can be handled a bit more conveniently. What do you think?np.sum(h[{"dataset": "GluGluToHHTo2B2VTo2L2Nu_node_cHHH2p45", "systematic": "nominal"}].view()[h.axes["category"].index(["ee", "emu"]), ...], axis=0)["ee", "emu”] would not cause the histogram to convert into a view.
I would like to fill a histogram with a very fine (regular) binning, e.g. 1000 bins from 0 to 1. After filling I would like to rebin into a variable binning of e.g.
[0, 5, 7, 8, 9.5, 1] (only 5 bins). As far as I can see, there is the rebin action, but unfortunately it only accepts a single value/factor. Is it possible to rebin a Regular/Variable axis to any arbitrary binning, as long as the binning is "valid"?