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fix: minor changes to docs (#125)
* changed tab titles on docs * changed funcx+pyhf typo * added ipython to docs/requirements for python syntax highlighting * rearranged to avoid warnings and fixed edit links * added docs badge in readme * added requirements.txt for event plotting notebook and got rid of unnecessary utils (removed tree printing) * added other documentation link in readme
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README.md

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# Analysis Grand Challenge (AGC)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7274936.svg)](https://doi.org/10.5281/zenodo.7274936)
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[![Documentation Status](https://readthedocs.org/projects/agc/badge/?version=latest)](https://agc.readthedocs.io/en/latest/?badge=latest)
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The Analysis Grand Challenge (AGC) is about performing the last steps in an analysis pipeline at scale to test workflows envisioned for the HL-LHC.
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This includes
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- [IRIS-HEP AGC Tools 2021 Workshop, Nov 3–4 2021](https://indico.cern.ch/e/agc-tools-workshop)
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- [IRIS-HEP AGC Tools 2022 Workshop, April 25–26 2022](https://indico.cern.ch/e/agc-tools-2)
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We also have a [dedicated webpage](https://iris-hep.org/grand-challenges.html).
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We also have a [dedicated webpage](https://iris-hep.org/grand-challenges.html) and a [website for documentation](https://agc.readthedocs.io/en/latest/?badge=latest).
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## AGC and IRIS-HEP
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docs/cms-open-data-ttbar

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../analyses/cms-open-data-ttbar/
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../analyses/cms-open-data-ttbar

docs/conf.py

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"**.ipynb_checkpoints",
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".DS_Store",
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".env",
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"atlas-open-data-hzz/alternative_implementations/*",
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"cms-open-data-ttbar/coffea.ipynb",
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"*/README.md",
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]
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# -- Options for HTML output -------------------------------------------------
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html_theme_options = {
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"home_page_in_toc": True,
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"repository_url": "https://github.com/iris-hep/analysis-grand-challenge",
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"repository_branch": "main/docs",
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"use_repository_button": True,
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"use_issues_button": True,
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"use_edit_page_button": True,
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"use_edit_page_button": False,
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}
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# Add any paths that contain custom static files (such as style sheets) here,

docs/event-plotting/plotEvents.ipynb

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docs/event-plotting/plotEvents.py

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# %% [markdown]
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# # Plot $t\bar{t}$ Events
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# ______
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#
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# This notebook allows one to visualize events, including jets and the genParticles they are matched to. Trees from events are also printed using the `printTrees` method in `utils`.
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import matplotlib.pyplot as plt
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import awkward as ak
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import particle
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from utils import printTrees
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from matplotlib.patches import Patch
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from matplotlib.lines import Line2D
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# %% tags=[]
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for iEvt in range(10):
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print("-----------------------------------------------------------------------------------------------")
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print("-----------------------------------------------------------------------------------------------")
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print(f" EVENT {iEvt} ")
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print("-----------------------------------------------------------------------------------------------")
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# print event tree from genParticles
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printTrees(selected_genpart_region[iEvt])
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print("-----------------------------------------------------------------------------------------------")
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# define mpl figure
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fig,axs = plt.subplots(1,2,figsize=(10,6),gridspec_kw={'width_ratios': [2.3, 1]})
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awkward==1.10.3
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coffea==0.7.21
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matplotlib==3.7.1
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numpy==1.23.5
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particle==0.21.0
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uproot==4.1.6

docs/event-plotting/utils.py

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docs/index.rst

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* **New user interfaces**: Complementary services that present the analyst with a notebook-based interface. Example software: Jupyter.
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* **Data access**: Services that provide quick access to the experiment’s official data sets, often allowing simple derivations and local caching for efficient access. Example software and services: ``Rucio``, ``ServiceX``, ``SkyHook``, ``iDDS``, ``RNTuple``.
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* **Event selection**: Systems/frameworks allowing analysts to process entire datasets, select desired events, and calculate derived quantities. Example software and services: ``coffea``, ``awkward-array``, ``func_adl``, ``RDataFrame``.
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Histogramming and summary statistics: Closely tied to the event selection, histogramming tools provide physicists with the ability to summarize the observed quantities in a dataset. Example software and services: ``coffea``, ``func_adl``, ``cabinetry``, ``hist``.
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* **Statistical model building and fitting**: Tools that translate specifications for event selection, summary statistics, and histogramming quantities into statistical models, leveraging the capabilities above, and perform fits and statistical analysis with the resulting models. Example software and services: ``cabinetry``, ``pyhf``, ``FuncX``+``pyhf`` fitting service
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* **Histogramming and summary statistics**: Closely tied to the event selection, histogramming tools provide physicists with the ability to summarize the observed quantities in a dataset. Example software and services: ``coffea``, ``func_adl``, ``cabinetry``, ``hist``.
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* **Statistical model building and fitting**: Tools that translate specifications for event selection, summary statistics, and histogramming quantities into statistical models, leveraging the capabilities above, and perform fits and statistical analysis with the resulting models. Example software and services: ``cabinetry``, ``pyhf``, ``FuncX`` + ``pyhf`` fitting service
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* **Reinterpretation / analysis preservation**: Standards for capturing the entire analysis workflow, and services to reuse the workflow which enables reinterpretation. Example software and services: ``REANA``, ``RECAST``.
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Acknowledgements
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.. toctree::
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:maxdepth: 2
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:titlesonly:
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:caption: CMS AGC
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:caption: ttbar with CMS Open Data
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:glob:
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cms-open-data-ttbar/ttbar_analysis_pipeline
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.. toctree::
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:maxdepth: 2
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:titlesonly:
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:caption: ATLAS AGC
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:caption: H>ZZ* with ATLAS Open Data
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:glob:
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atlas-open-data-hzz/HZZ_analysis_pipeline

docs/requirements.txt

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ipython>=8.12.0
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myst_parser>=1.0.0
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nbsphinx>=0.9.1
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pydata-sphinx-theme==0.13.1

docs/taskbackground.rst

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:width: 80%
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:alt: Example of a :math:`t\bar{t}` event in our signal region.
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The tree of the above event looks something like::
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The tree of the above event looks something like
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.. code-block:: text
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g
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├── t

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