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Create test implementations of new commondata format #1436
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Let me see whether I can make any of the above a little more concrete. For the time being, let's focus on the dataset currently called
My understanding is that this is the first file that a developer would write when implementing a new dataset (or migrating an old dataset to the new format). I think that it should be as simple (minimal) as possible. Let's call it
Here I already have some questions:
I understand that these files should be the output of a small python script (for each dataset) that would eventually replace the current filter files in buildmaster. The file KINEMATICS_NMCPD.json``` { "kinematics_avg": [ { "x": [0.0015, 0.0015, 0.0015, 0.0015, 0.0015, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.003, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.008, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0125, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.0175, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.035, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.07, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 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0.0013434, 0.0021582, 0.0020877, 0.002106, 0.0023172 ] } ] } ``` Some remarks.
|
May I suggest to use a Like the following: <details><summary>ciao come va?</summary>
<pre>
bene grazie
</pre>
</details> Very long output makes the end of answer (and the rest of the conversation) a bit harder to access... |
I reply to things I'm aware of, but @cschwan's answer would be better than mine.
|
As discussed, I think the YAML format may be more adequate for this.
I would think that very similarly to now from the point of view of the input information required (?)
At this stage, I would say "all of it" and in the same format.
All?
As @alecandido was saying, having these in a folder might be a good idea.
I think inventing them (on a case by case basis) might be better than complicating the format with undefined support.
Would need to be thought, but once everything is in a folder, it would be a matter of adding the files plus various kinds of information in the runcard, such as what are the variants and what is the default variant. For example (haven't really thought this too much): variants:
atlas_correlation_choice:
exp_uncertainties_data: EXP_UNC_ATLAS.json
default_variant: None # Ignore the above by default.
Yes, we should just have n kinematics. The external uncertainties would be a file with the same format and added to the runcard. That begs the question if we should have yet another piece of configuration for what the defaults are (as discussed elsewhere). |
@Zaharid Thanks for the detailed replies, that I'm going to use to work further on the prototype. |
@enocera I'm available for both helping with the prototype and acting as a pressure element, just let me know. |
@scarlehoff Thanks - this is appreciated. I'll open a PR soon-ish and we will start from there. |
@scarlehoff You've got my blessing to piss me off. |
Superseded by #1709 |
To make things in #1416 more concrete we agreed to try and produce test implementations of the (still fuzzy) current version of the idea for a couple of datasets.
The datasets should be one DIS and one hadronic and one of them should involve multiple (COMPOUND) fktables.
These should have:
The uncertainties should support a way of specifying correlated systematics across datasets. Probably also some way to deal with additive vs multiplicative.
Regarding formats, I would think for this a text format of some sort is more adequate (easier to read and exchange around) and also one that trivially round trips with pandas (ie better than csv), so maybe something like json.
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