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@alexander-held alexander-held released this 08 Sep 22:54
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v0.3.0
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This release adds the possibility to access figures produced by cabinetry for further customization and introduces a list of breaking API changes. It also adds more features to the specification of systematic uncertainties, labels to kinematic distributions, caching for yield uncertainty calculations, and additional validation for cabinetry configurations. The minimum required pyhf version for this version of cabinetry is 0.6.3, and Python 3.9 is now officially supported.

The cabinetry.visualize API has changed in several ways. Model predictions are no longer automatically produced within visualize.data_mc, but now have to be produced via model_utils.prediction. An optional fit_results argument allows to create post-fit distributions, and if it is not set, pre-fit distributions are produced. Example:

old:

cabinetry.visualize.data_mc(model, data, fit_results=fit_results)

new:

model_pred = cabinetry.model_utils.prediction(model, fit_results=fit_results)
cabinetry.visualize.data_mc(model_pred, data)

The yield table functionality has been removed from visualize.data_mc and is instead available via tabulate.yields, which also takes a model prediction as input. See #267 for more information about these changes.

Functions in the visualize module now return the figures they produce. The following functions return a single figure: correlation_matrix, pulls, ranking, scan, limit. The functions data_mc and templates can create more than one figure, and therefore return a list of dictionaries containing figures and relevant associated information (region name, or region / sample / systematic name, respectively).

Users may modify, customize and save the returned figures. A new save_figure argument has been added to the visualize API to turn off automatic saving if desired.

Breaking changes:

  • Many functions in the public cabinetry API have been renamed. get_ prefixes have been removed from all function, and capitalization has been removed. Functionality from the contrib.visualize_matplotlib module has been split into visualize.plot_model, visualize.plot_results and visualize.utils (see #250). The following list shows changes other than the cases of removed get_ prefixes (see #263 for a full list).
    • histo.build_name -> hist.name
    • model_utils.build_Asimov_data -> model_utils.asimov_data
    • model_utils.calculate_stdev -> model_utils.yield_stdev
    • smooth.smooth_353QH_twice -> smooth.smooth_353qh_twice
    • visualize.data_MC_from_histograms -> visualize.data_mc_from_histograms
    • contrib.visualize_matplotlib.data_MC -> visualize.plot_model.data_mc
    • workspace.WorkspaceBuilder.get_NF_modifiers -> workspace.WorkspaceBuilder.normfactor_modifiers
    • WorkspaceBuilder.get_Normalization_modifier -> WorkspaceBuilder.normalization_modifier
  • The template order in visualize.plot_model.templates has changed, it is now original up / down template, then modified up/down template (see #246).
  • The method keyword argument has been removed from the visualize API, it previously only supported the default "matplotlib" (see #250).
  • The internal handling of nominal templates in cabinetry has changed. As a consequence of this, the "_Nominal" suffix is no longer used in histogram names (see #260).
  • In addition to this, the cabinetry.route.Router.register_template_builder API has changed: None is no longer accepted for region/sample/systematic, the corresponding behavior is now achieved via the default "*". The template can be set to None, in which case only nominal templates will be matched (see #260 and the documentation).
  • Both workspace.WorkspaceBuilder.get_yield_for_sample and workspace.WorkspaceBuilder.get_unc_for_sample have been removed, as the functionality is more conveniently achieved via histo.Histogram.from_config directly and only nominal templates were supported (see #262).
  • The order of keyword arguments in histo.Histogram.from_config changed: template and modified flipped places (see #262).
  • The visualization.data_mc API has changed, this function no longer takes the optional fit results argument and now requires a model prediction (which can be created via model_utils.prediction) instead of a model (see #267).
  • Yield tables have been removed from visualization.data_mc and moved to tabulate.yields (see #267).
  • The inference result containers in the fit module have been moved to a fit.results_containers submodule: FitResults, RankingResults, ScanResults, LimitResults, SignificanceResults (see #267).
  • The function model_utils.parameter_names was removed, it is now available via the model config in pyhf: model.config.par_names (see #248).
  • The with_aux keyword arguments in model_utils.model_and_data and model_utils.asimov_data have been renamed to include_auxdata for consistency with pyhf (see #248).
  • The test setup extra no longer includes all pyhf backends, they can now be installed via the new pyhf_backends setup extra (see #248).

Full list of changes:

  • chore: updating version to 0.3.0 (#279)
  • feat: Add MANIFEST.in (#278)
  • feat: add Python 3.9 support (#277)
  • fix: compatibility with pyhf 0.6.3 (parameter label API, auxdata) (#248)
  • docs: multiple minor docstring fixes (#274)
  • test: additional call argument formatting harmonization (#273)
  • style: apply optional formatting to magic commas and strings (#272)
  • feat: close single figures by default (#271)
  • feat: use uproot4 for file writing (#256)
  • refactor: correlation matrix and NLL access (#270)
  • test: harmonize call argument formatting (#269)
  • refactor: list conversions (#268)
  • feat: model prediction API and yield table changes (#267)
  • feat: yield uncertainty caching (#266)
  • feat: return figures from visualizations (#264)
  • feat: remove get prefixes from API and additional improvements (#263)
  • refactor: streamline histogram loading (#262)
  • build: list direct dependencies explicitly (#261)
  • refactor: improved nominal template handling (#260)
  • feat: validate uniqueness of names in config (#259)
  • fix: configs without systematics (#258)
  • feat: manual correlation of systematics (#257)
  • fix: add missing template settings to config schema (#255)
  • feat: variable overrides in systematic templates (#254)
  • test: install Ghostscript in nightly tests (#253)
  • refactor: use absolute imports (#252)
  • feat: make matplotlib core dependency and refactor visualization code (#250)
  • refactor: small string handling improvements (#249)
  • feat: labels for data/MC and template visualizations (#246)
  • test: increase integration test tolerance (#247)
  • build: PEP 517/518 support (#245)
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