From 05d4ae38756d2579b7da4c366b3a2c02fbd39b52 Mon Sep 17 00:00:00 2001 From: stephanbreimann Date: Tue, 10 Oct 2023 18:07:55 +0200 Subject: [PATCH] Change INFO to Overview inf load_dataset --- .../{INFO_benchmarks.xlsx => Overview.xlsx} | Bin .../__pycache__/_load_dataset.cpython-39.pyc | Bin 9125 -> 9182 bytes aaanalysis/data_handling/_load_dataset.py | 19 +++++++++--------- .../__pycache__/_aaclust_plot.cpython-39.pyc | Bin 10645 -> 10645 bytes docs/build/doctrees/environment.pickle | Bin 25748791 -> 25748791 bytes .../generated/aaanalysis.AAclustPlot.doctree | Bin 117444 -> 117444 bytes .../generated/aaanalysis.load_dataset.doctree | Bin 25334 -> 25370 bytes .../aaanalysis-plot_settings-2.pdf | Bin 15072 -> 15072 bytes .../aaanalysis-plot_get_cdict-1.pdf | Bin 13214 -> 13214 bytes .../aaanalysis-plot_gcfs-1.pdf | Bin 16804 -> 16804 bytes .../aaanalysis-plot_legend-1.pdf | Bin 34842 -> 34842 bytes .../aaanalysis-plot_get_cmap-1.pdf | Bin 13745 -> 13745 bytes .../aaanalysis-plot_get_clist-1.pdf | Bin 13712 -> 13712 bytes .../aaanalysis-plot_settings-1.pdf | Bin 10276 -> 10276 bytes .../generated/aaanalysis.AAclustPlot.html | 12 +++++------ .../generated/aaanalysis.load_dataset.html | 6 +++--- docs/build/html/searchindex.js | 2 +- .../generated/aaanalysis-plot_gcfs-1.pdf | Bin 16804 -> 16804 bytes .../generated/aaanalysis-plot_get_cdict-1.pdf | Bin 13214 -> 13214 bytes .../generated/aaanalysis-plot_get_clist-1.pdf | Bin 13712 -> 13712 bytes .../generated/aaanalysis-plot_get_cmap-1.pdf | Bin 13745 -> 13745 bytes .../generated/aaanalysis-plot_legend-1.pdf | Bin 34842 -> 34842 bytes .../generated/aaanalysis-plot_settings-1.pdf | Bin 10276 -> 10276 bytes .../generated/aaanalysis-plot_settings-2.pdf | Bin 15072 -> 15072 bytes .../unicode_data/13.0.0/codec-utf-8.json.gz | Bin 60 -> 60 bytes 25 files changed, 20 insertions(+), 19 deletions(-) rename aaanalysis/_data/benchmarks/{INFO_benchmarks.xlsx => Overview.xlsx} (100%) diff --git a/aaanalysis/_data/benchmarks/INFO_benchmarks.xlsx b/aaanalysis/_data/benchmarks/Overview.xlsx similarity index 100% rename from aaanalysis/_data/benchmarks/INFO_benchmarks.xlsx rename to aaanalysis/_data/benchmarks/Overview.xlsx diff --git a/aaanalysis/data_handling/__pycache__/_load_dataset.cpython-39.pyc b/aaanalysis/data_handling/__pycache__/_load_dataset.cpython-39.pyc index 6c407436c9d64ef367967cdc58fb6c663e0d3f0a..23bda7d7a14dec7c33a7aabaf379a8c73e687bd1 100644 GIT binary patch delta 490 zcmZ4Le$SmRk(ZZ?0SLSbRa52FHuAABG2WUi%#_5*;a`?oRF;`qKDml%hldVONjgIf zL##?IV+unJV;MscOAX@!h7^W{jKv@pP;?<<3Zo>$M5aQPUqrqk=P7g*#^UcXz`xzM>CM)xVb7%l<&}1x9 zoZP^p&gi#!1&SZ1G@qlOy;SG6rq_#c#pPn7r9o*p!K}WOAnHRd~>Db`r~D;mC&t^d2ciIA`-i zDII1`UU($P$`!L6g_t@`ZVTHf2s1$bE8A@d(_5jG?Jk(P`Iv$zBV+aEJBo7{83iU+ HD(e6M6t{>Q delta 420 zcmccTzSNyBk(ZZ?0SKOyil!b{-N?tn#Q1BnFjEpMi>IHP|KvKR9Y#t(>2!t~hFGau z#uSDc#xjN?mKw$d3@Ho?8B-V~8749nvIH||GTma-D`EtyUCB7Pj(HaAXOOXz67R+X`}QcfdlM|0{?xGFELC ORhq-d$Uk|LvJL>ytZ`ca diff --git a/aaanalysis/data_handling/_load_dataset.py b/aaanalysis/data_handling/_load_dataset.py index 3c58ba8c..1b58f13f 100644 --- a/aaanalysis/data_handling/_load_dataset.py +++ b/aaanalysis/data_handling/_load_dataset.py @@ -19,16 +19,17 @@ # I Helper Functions # Check functions -def check_name_of_dataset(name="INFO", folder_in=None): +def check_name_of_dataset(name="Overview", folder_in=None): """""" - if name == "INFO": + if name == "Overview": return - list_datasets = [x.split(".")[0] for x in os.listdir(folder_in) if "." in x] + list_datasets = [x.split(".")[0] for x in os.listdir(folder_in) + if "." in x and not x.startswith(".")] if name not in list_datasets: list_aa = [x for x in list_datasets if 'AA' in x] list_seq = [x for x in list_datasets if 'SEQ' in x] list_dom = [x for x in list_datasets if 'DOM' in x] - raise ValueError(f"'name' ({name}) is not valid." + raise ValueError(f"'name' ({name}) is not valid. Chose one of the following:" f"\n Amino acid datasets: {list_aa}" f"\n Sequence datasets: {list_seq}" f"\n Domain datasets: {list_dom}") @@ -119,7 +120,7 @@ def _get_aa_window(df_seq=None, aa_window_size=9): # II Main Functions -def load_dataset(name: str = "INFO", +def load_dataset(name: str = "Overview", n: Optional[int] = None, random: bool = False, non_canonical_aa: Literal["remove", "keep", "gap"] = "remove", @@ -131,7 +132,7 @@ def load_dataset(name: str = "INFO", Loads protein benchmarking datasets. The benchmarks are categorized into amino acid ('AA'), domain ('DOM'), and sequence ('SEQ') level datasets. - By default, an overview table is provided (``name='INFO'``). For in-depth details, refer to [Breimann23a]_. + By default, an overview table is provided (``name='Overview'``). For in-depth details, refer to [Breimann23a]_. Parameters ---------- @@ -159,7 +160,7 @@ def load_dataset(name: str = "INFO", ------- pandas.DataFrame A DataFrame of either the selected sequence dataset (``df_seq``) or - general info on all benchmark datasets (``df_info``). + overview on all benchmark datasets (``df_overview``). Notes ----- @@ -188,8 +189,8 @@ def load_dataset(name: str = "INFO", check_min_max_val(min_len=min_len, max_len=max_len) check_aa_window_size(aa_window_size=aa_window_size) # Load overview table - if name == "INFO": - return ut.read_excel_cached(FOLDER_BENCHMARKS + "INFO_benchmarks.xlsx") + if name == "Overview": + return ut.read_excel_cached(FOLDER_BENCHMARKS + "Overview.xlsx") df = ut.read_csv_cached(FOLDER_BENCHMARKS + name + ".tsv", sep="\t") # Filter data if min_len is not None: diff --git a/aaanalysis/feature_engineering/__pycache__/_aaclust_plot.cpython-39.pyc b/aaanalysis/feature_engineering/__pycache__/_aaclust_plot.cpython-39.pyc index a76b4131ee2f2be7442744bd381614b120761a89..3e58e4538c64c7b19cbd06ba8c2f96bc35068c02 100644 GIT binary patch delta 66 zcmbOlJT;g%k(ZZ?0SF#us-%A0$Scjq7`R!J?=3rH+-7mHWERF*o2#Y1Gcg|7Tr8W+ V!T5Ufb=575j1HT7)H#?KMFE&06iNU9 delta 66 zcmbOlJT;g%k(ZZ?0SJUMRZ>4~KsgrVgPPN6NCT& diff --git a/docs/build/doctrees/environment.pickle 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    -class aaanalysis.AAclustPlot(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]
    +class aaanalysis.AAclustPlot(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]

    Bases: object

    Plot results of AAclust analysis.

    Dimensionality reduction is performed for visualization using decomposition models such as @@ -150,7 +150,7 @@

    aaanalysis.AAclustPlot
    -__init__(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]
    +__init__(model_class=<class 'sklearn.decomposition._pca.PCA'>, model_kwargs=None)[source]
    Parameters:
      @@ -187,7 +187,7 @@

      aaanalysis.AAclustPlot
      -static eval(data_eval, names=None, dict_xlims=None, figsize=(7, 6))[source]
      +static eval(data_eval, names=None, dict_xlims=None, figsize=(7, 6))[source]

      Evaluates and plots n_clusters and clustering metrics BIC, CH, and SC for the provided data.

      The clustering evaluation metrics (BIC, CH, and SC) are ranked by the average of their independent rankings.

      @@ -232,7 +232,7 @@

      aaanalysis.AAclustPlot
      -center(X, labels=None, component_x=1, component_y=2, ax=None, figsize=(7, 6), dot_alpha=0.75, dot_size=100, legend=True, palette=None)[source]
      +center(X, labels=None, component_x=1, component_y=2, ax=None, figsize=(7, 6), dot_alpha=0.75, dot_size=100, legend=True, palette=None)[source]

      PCA plot of clustering with centers highlighted :type palette: Optional[ListedColormap] :param palette: list of RGB tuples or matplotlib.colors.ListedColormap

      @@ -259,7 +259,7 @@

      aaanalysis.AAclustPlot
      -medoids(X, labels=None, component_x=1, component_y=2, metric='euclidean', ax=None, figsize=(7, 6), dot_alpha=0.75, dot_size=100, legend=True, palette=None, return_data=False)[source]
      +medoids(X, labels=None, component_x=1, component_y=2, metric='euclidean', ax=None, figsize=(7, 6), dot_alpha=0.75, dot_size=100, legend=True, palette=None, return_data=False)[source]

      PCA plot of clustering with medoids highlighted

      Parameters:
      @@ -283,7 +283,7 @@

      aaanalysis.AAclustPlot
      -static correlation(df_corr=None, labels=None, bar_position='left', bar_width=0.1, bar_spacing=0.1, bar_colors='gray', bar_ticklabel_pad=None, vmin=-1, vmax=1, cmap='viridis', **kwargs_heatmap)[source]
      +static correlation(df_corr=None, labels=None, bar_position='left', bar_width=0.1, bar_spacing=0.1, bar_colors='gray', bar_ticklabel_pad=None, vmin=-1, vmax=1, cmap='viridis', **kwargs_heatmap)[source]

      Heatmap for correlation matrix with colored sidebar to label clusters.

      Parameters:
      diff --git a/docs/build/html/generated/aaanalysis.load_dataset.html b/docs/build/html/generated/aaanalysis.load_dataset.html index 3700e967..013c84a6 100644 --- a/docs/build/html/generated/aaanalysis.load_dataset.html +++ b/docs/build/html/generated/aaanalysis.load_dataset.html @@ -126,10 +126,10 @@

      aaanalysis.load_dataset

      -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='Overview', n=None, random=False, non_canonical_aa='remove', min_len=None, max_len=None, aa_window_size=9)[source]

      Loads protein benchmarking datasets.

      The benchmarks are categorized into amino acid (‘AA’), domain (‘DOM’), and sequence (‘SEQ’) level datasets. -By default, an overview table is provided (name='INFO'). For in-depth details, refer to [Breimann23a].

      +By default, an overview table is provided (name='Overview'). For in-depth details, refer to [Breimann23a].

      Parameters:
        @@ -150,7 +150,7 @@

        aaanalysis.load_dataset
        Returns:

        A DataFrame of either the selected sequence dataset (df_seq) or -general info on all benchmark datasets (df_info).

        +overview on all benchmark datasets (df_overview).

        Return type:

        pandas.DataFrame

        diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index b015e542..f2dd6eae 100644 --- a/docs/build/html/searchindex.js +++ b/docs/build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "generated/aaanalysis.AAclust", "generated/aaanalysis.AAclustPlot", "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_clist", "generated/aaanalysis.plot_get_cmap", "generated/aaanalysis.plot_legend", "generated/aaanalysis.plot_settings", "generated/plotting_prelude", "generated/tutorial1_quick_start", "generated/tutorial1_slow_start", "generated/tutorial2a_data_loader", "generated/tutorial2b_scales_loader", "index", "index/CONTRIBUTING_COPY", "index/badges", "index/citations", "index/introduction", "index/overview", "index/references", "index/tables", "index/usage_principles", "index/usage_principles/aaontology", "index/usage_principles/data_flow_entry_points", "index/usage_principles/feature_identification", "index/usage_principles/pu_learning", "index/usage_principles/xai", "tutorials"], "filenames": ["api.rst", "generated/aaanalysis.AAclust.rst", "generated/aaanalysis.AAclustPlot.rst", "generated/aaanalysis.CPP.rst", "generated/aaanalysis.CPPPlot.rst", "generated/aaanalysis.SequenceFeature.rst", "generated/aaanalysis.dPULearn.rst", "generated/aaanalysis.load_dataset.rst", "generated/aaanalysis.load_scales.rst", "generated/aaanalysis.plot_gcfs.rst", "generated/aaanalysis.plot_get_cdict.rst", "generated/aaanalysis.plot_get_clist.rst", "generated/aaanalysis.plot_get_cmap.rst", "generated/aaanalysis.plot_legend.rst", "generated/aaanalysis.plot_settings.rst", "generated/plotting_prelude.rst", "generated/tutorial1_quick_start.rst", 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