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...to carry pyhf fit information even if it's irrelevant for filling. It's to make a 1:1 correspondence between booking trees and fit models. This will make life easier for the physicist.
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It would be useful if there was a way to dump histogram (and book ?) configuration to, say, JSON and to have an ability to recreate the empty histogram from it.
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# Description
simple_broadcast does not work the same in different backends
# Expected Behavior
Arrays got broadcasted correctly in numpy
# Actual Behavior
Arrays did not get broadcast …
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# Description
Should we use `pipenv` to manage the installed package versions?
# Relevant Issues and Pull Requests
This came up as a result of #186.
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# Description
As outlined in the [command line options](http://pytest-benchmark.readthedocs.io/en/stable/usage.html#commandline-options) for `--benchmark-histogram`
>--benchmark-histogram=FILENAME…
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# Description
I would have expected this to work
```
spec = {
'channels': [
{
'name': 'singlechannel',
'samples': [
{
…
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# Description
The current way of importing backends is a little redundant:
```python
from pyhf.tensor.numpy_backend import numpy_backend
from pyhf.tensor.pytorch_backend import pytorch_backend…
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# Description
we want to be able to do e.g. `tf.Session().run(qmu)`
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Is it possible to plot normalised overlayed histograms using something like:
```python
histogram.overlay("x").marker("y", error=True, normed=True).to(canvas)
histogram.overlay("x").normalize().ma…
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# Description
If using a backend other than `numpy_backend` currently we have to manually set the optimizer. However, this should be done automatically when the backend is changed.
Otherwise thi…