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    Load protein benchmarking datasets.

    The benchmarks are categorized into amino acid (‘AA’), domain (‘DOM’), and sequence (‘SEQ’) level datasets. Use default settings (name='INFO') for an overview table. Detailed analysis is in [Breimann23a].

    @@ -149,11 +149,6 @@

    aaanalysis.load_dataset

    DataFrame

    -

    Examples

    >>> import aaanalysis as aa
    diff --git a/docs/build/html/generated/aaanalysis.load_scales.html b/docs/build/html/generated/aaanalysis.load_scales.html
    index 67fb3968..36bdd380 100644
    --- a/docs/build/html/generated/aaanalysis.load_scales.html
    +++ b/docs/build/html/generated/aaanalysis.load_scales.html
    @@ -120,7 +120,7 @@
     

    aaanalysis.load_scales

    -aaanalysis.load_scales(name='scales', just_aaindex=False, unclassified_in=True)[source]
    +aaanalysis.load_scales(name='scales', just_aaindex=False, unclassified_in=True)[source]

    Load amino acid scales, scale classification (AAontology), or scale evaluation.

    A thorough analysis of the residue and sequence datasets can be found in [Breimann23a].

    diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index 877333e5..370f01b4 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "generated/aaanalysis.AAclust", "generated/aaanalysis.CPP", "generated/aaanalysis.CPPPlot", "generated/aaanalysis.SequenceFeature", "generated/aaanalysis.dPULearn", "generated/aaanalysis.load_dataset", "generated/aaanalysis.load_scales", "generated/aaanalysis.plot_gcfs", "generated/aaanalysis.plot_get_cdict", "generated/aaanalysis.plot_get_cmap", "generated/aaanalysis.plot_set_legend", "generated/aaanalysis.plot_settings", "index", "index/CONTRIBUTING_COPY", "index/badges", "index/citations", "index/introduction", "index/overview", "index/references", "index/tables", "index/usage_principles/aaontology", "index/usage_principles/data_flow_entry_points", "index/usage_principles/feature_identification", "index/usage_principles/pu_learning", 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[[5, "aaanalysis.dPULearn.__init__"]], "dpulearn (class in aaanalysis)": [[5, "aaanalysis.dPULearn"]], "eval() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.eval"]], "fit() (aaanalysis.dpulearn method)": [[5, "aaanalysis.dPULearn.fit"]], "labels_ (aaanalysis.dpulearn attribute)": [[5, "aaanalysis.dPULearn.labels_"]], "load_dataset() (in module aaanalysis)": [[6, "aaanalysis.load_dataset"]], "load_scales() (in module aaanalysis)": [[7, "aaanalysis.load_scales"]], "plot_gcfs() (in module aaanalysis)": [[8, "aaanalysis.plot_gcfs"]], "plot_get_cdict() (in module aaanalysis)": [[9, "aaanalysis.plot_get_cdict"]], "plot_get_cmap() (in module aaanalysis)": [[10, "aaanalysis.plot_get_cmap"]], "plot_set_legend() (in module aaanalysis)": [[11, "aaanalysis.plot_set_legend"]], "plot_settings() (in module aaanalysis)": [[12, "aaanalysis.plot_settings"]]}}) \ No newline at end of file
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    -aaanalysis.load_dataset(name='INFO', n=None, random=False, non_canonical_aa='remove', min_len=None, max_len=None, aa_window_size=9)[source]
    +aaanalysis.load_dataset(name='INFO', n=None, random=False, non_canonical_aa='remove', min_len=None, max_len=None, aa_window_size=9)[source]

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