From fac37e9bd41f41b22a692743bcb60370f239d502 Mon Sep 17 00:00:00 2001 From: stephanbreimann Date: Mon, 2 Oct 2023 10:34:54 +0200 Subject: [PATCH] Update array-like docstring style (always manually) --- .../__pycache__/_decorators.cpython-39.pyc | Bin 3305 -> 3353 bytes aaanalysis/_utils/_decorators.py | 4 +- .../__pycache__/aaclust.cpython-39.pyc | Bin 18571 -> 18624 bytes aaanalysis/aaclust/aaclust.py | 2 +- docs/build/doctrees/environment.pickle | Bin 25729217 -> 25729176 bytes .../generated/aaanalysis.AAclust.doctree | Bin 98786 -> 119420 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 .../html/generated/aaanalysis.AAclust.html | 73 ++++++++++++++++-- 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 docs/source/conf.py | 2 +- .../1554069b354df67f/bbf538cf6444290f | Bin 65 -> 0 bytes .../1554069b354df67f/e8a6e421c4f49011 | Bin 65 -> 0 bytes .../7470545d53391772/15ec082caeff4c3e | Bin 40 -> 0 bytes .../7470545d53391772/7bb75c3a2a968769 | Bin 40 -> 0 bytes .../b39c61c841e0f341/50acabca6ddc291d | Bin 141 -> 0 bytes .../b39c61c841e0f341/83cf208e045a1f78 | Bin 57 -> 0 bytes .../unicode_data/13.0.0/codec-utf-8.json.gz | Bin 60 -> 60 bytes 30 files changed, 73 insertions(+), 10 deletions(-) delete mode 100644 tests/unit/aaclust_tests/.hypothesis/examples/1554069b354df67f/bbf538cf6444290f delete mode 100644 tests/unit/aaclust_tests/.hypothesis/examples/1554069b354df67f/e8a6e421c4f49011 delete mode 100644 tests/unit/aaclust_tests/.hypothesis/examples/7470545d53391772/15ec082caeff4c3e delete mode 100644 tests/unit/aaclust_tests/.hypothesis/examples/7470545d53391772/7bb75c3a2a968769 delete mode 100644 tests/unit/aaclust_tests/.hypothesis/examples/b39c61c841e0f341/50acabca6ddc291d delete mode 100644 tests/unit/aaclust_tests/.hypothesis/examples/b39c61c841e0f341/83cf208e045a1f78 diff --git a/aaanalysis/_utils/__pycache__/_decorators.cpython-39.pyc b/aaanalysis/_utils/__pycache__/_decorators.cpython-39.pyc index 348317a13c58374ecd27de50854f04c60b593476..62881529043f06b9209829743de889a618e75ead 100644 GIT binary patch delta 993 zcmbVKOKTKC5T2T6&&&Muq@hFR;z))YZGhWC+ zGzDvedi_2HYM0K2+jPNrXi$f`y9RapX49h{CA&tGDAuQeVuQl(Tg`xmw4(Tsnoo@+ z8oYnz?}2nSS=QzI2TuU1V!`}<#l=hx3)PSL2$-7@1v1*qSUWAYx@9YG^%BBj(X<|g zRdmo$`?_z%aKl4qH|utbHr0`d&tl}fje_%Ry^ti&dc}H&y=LbycvSqf>$RbKkgSL~ z=f$Kh2N+(-B7`9?@!8=!r;r@e0SK4=^*JmHIY$9D#jzg(HV}`vfv@=-6nFfYYGbQq zsl`VX^o(~#AKn^kpfopkNgv+8HYg3P$GIU!f>H6s{dNKG*V(027Hw(2?6lfiKxw0F zY*KvmR@}pS#CPv3nBte$n8Yl5CcBBzw-B_)0`6*Jl}uMr00UfzeRf;z1_vYJ1K9>w z5Pz?1{@qvtvLh_<4kHd5k%ji-= zXehwqIBG*c+yA4Ph=V^cA);^ze6bQPq!-nF(&9a)n~+wk-DA^=(-{(vUB&%1bb?KG z9gTT}la84d>QGcXz#>rzenx>|3K`xTL50~O!mzaSmGd*G3qR2}W~<6hDy)vadi1A6 zS($x~!=T4RKY;WfY~@9_*Xp!&l{#u@Do=`$WO<`QUzNpOoGyM^fo%~WP{C!z6Qo*m F{sPuquxJ1P delta 936 zcmaKqJx>%t7{_<^b@tVZLCNuAju?f_fd)7T5~HCoP}mq^Os-ijJHvTq**%{<1sfYk ztZ~%D!Vf@UVW^FjTKEn28~6n*tej`$$k}+w{%+@a`Oh=|o%a(z>tQDhJx%_8e5vyf z$Kf<-A2*w=O^un%I?|YRsJCrqGv`QaJ2Fa{E2A!_heq3F9`j|~WBRrh2gmOoefsr) zkWg&tzZXoY8C&4WKX4EfVM|oueh2b|AM~?c+8e|a0#O+E{SXTjqSJ3iM3yWpwmbbk z&zb6sT?jDd?D4Yfba_S7&VY9dcmbEN`LeLBW^F_VgPz#1cBa%g1W*!u#7LF+Y?T`q zFgUIn5ZC^#`8yWI3<(q{(KSj;LLF**E*RpeJvNS6e9D@{D{YrB!t^657QL(DyZvfJ^UruIZS7np~P@`5x zFCV}a8B@Bk0}J?m1B=K$+{DE^qJhA3fwDo(q{cyiaJX_zLi$I7yW1*@GmaW3msMTf zsPu7?7Ws1yU7n{r!4K2RyC_|r*&4y7T*W@bD50+Rq<9<5JyhR!1%=#u2Py9ldPT10 kRt+PU?tHw8-rg(Nvy9&%xB=c9)1ZWsfKW4X82|tP diff --git a/aaanalysis/_utils/_decorators.py b/aaanalysis/_utils/_decorators.py index 333f8856..df31b6d9 100644 --- a/aaanalysis/_utils/_decorators.py +++ b/aaanalysis/_utils/_decorators.py @@ -5,7 +5,7 @@ import traceback from sklearn.exceptions import ConvergenceWarning import functools - +from functools import wraps # Catch Runtime class CatchRuntimeWarnings: @@ -36,6 +36,7 @@ def get_warnings(self): return self._warn_list def catch_runtime_warnings(func): + @wraps(func) def wrapper(*args, **kwargs): with CatchRuntimeWarnings() as crw: result = func(*args, **kwargs) @@ -55,6 +56,7 @@ def __init__(self, message, distinct_clusters): self.distinct_clusters = distinct_clusters def catch_convergence_warning(func): + @wraps(func) def wrapper(*args, **kwargs): with warnings.catch_warnings(record=True) as w: # Trigger the "always" behavior for ConvergenceWarning diff --git a/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc b/aaanalysis/aaclust/__pycache__/aaclust.cpython-39.pyc index 11b34382be31d40f6fb2cae5fc489a5758383d63..696e507e027321167acdc0454a84823ba1d2380a 100644 GIT binary patch delta 53 zcmeC4$ar8PBX1%vFBbz4u+&SXKH11CEXz1`v!bjr(`Hxsb=*vB?3=%8DX~lrus*SQ IrT!xc0D4mo!~g&Q delta 53 zcmX>wk+FLsBX1%vFBbz42v$m^uHVQjEXz1;v!bjr(`IM+_1sJs*fxLBQev6xXfbK? IJ^e=#0Dy!LO8@`> diff --git a/aaanalysis/aaclust/aaclust.py b/aaanalysis/aaclust/aaclust.py index bb82c5c6..1b1e8e98 100644 --- a/aaanalysis/aaclust/aaclust.py +++ b/aaanalysis/aaclust/aaclust.py @@ -187,7 +187,7 @@ def fit(self, clusters by maximizing the within-cluster Pearson correlation beyond the ``min_th`` threshold. The quality of clustering is either based on the minimum Pearson correlation of all members (``on_center=False``) or between the cluster center and its members (``on_center=True``), using either the 'min_cor_all' or 'min_cor_center' - function, respectively, as described in [Breimann23a]_. + function, respectively, as described in [Breimann23a]_. 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z^y3nlG}OLMr~gYMdPR~=Z-hFJ0pAgU{eScETRnr1-*EvEorTU_suEbN^bYU>d_tB>e?PT9q>Ve?YxhQH1f1TSc*LXA ztlS%I98PzRaTVPAjc1>F(^LA-QF405{HSW%>6rx~G(9t|Nl(uPqKZ?21&9UEc6x^R zmNKS%oNwv2Xo=(eHCMMq8-w$eHAd2Hf{#*uc5~)U$(lY&sR5XDRQf2TYDuRe!AB`A zIiy7qsV z;0rBR8yJX5Ii6%gm`%s4T!ABvUp0=`+VNnVA(at@x+UD8#0ojZOk?HtUr( zThB(D=H|D-vqA3%rriHQZ;z z(61mgeZwqlr+jV%LyO-q3j)@evbFQ0vkIFqA#(BRbcW84Hk3`DU}!z(uZ0(8Lw31Q z^T^;2#r#6^RaO9$NvI!_O{ie9R@ML9gjkWKYnvD;EH}JUqGFTx

    +

    Applies AAclust algorithm to feature matrix (X).

    +

    AAclust determines the optimal number of clusters, k, without pre-specification. It partitions data (X) into +clusters by maximizing the within-cluster Pearson correlation beyond the min_th threshold. The quality of +clustering is either based on the minimum Pearson correlation of all members (on_center=False) or between +the cluster center and its members (on_center=True), using either the ‘min_cor_all’ or ‘min_cor_center’ +function, respectively, as described in [Breimann23a].

    +
    +
    Parameters:
    +
      +
    • X (array-like, shape (n_samples, n_features)) – Feature matrix. Rows correspond to scales and columns to amino acids.

    • +
    • n_clusters (Optional[int]) – Pre-defined number of clusters. If provided, k is not optimized. Must be 0 > n_clusters > n_samples.

    • +
    • min_th (float) – Pearson correlation threshold for clustering (between 0 and 1).

    • +
    • on_center (bool) – If True, min_th is applied to the cluster center. Otherwise, to all cluster members.

    • +
    • merge_metric (Optional[str]) –

      Metric used as similarity measure for optional cluster merging:

      +
      +
        +
      • None: No merging is performed

      • +
      • correlation: Pearson correlation

      • +
      • euclidean: Euclidean distance

      • +
      • manhattan: Manhattan distance

      • +
      • cosine: Cosine distance

      • +
      +
      +

    • +
    • names (Optional[List[str]]) – Sample names. If provided, sets AAclust.medoid_names_ attribute.

    • +
    +
    +
    Returns:
    +

    The fitted instance of the AAclust class, allowing direct attribute access.

    +
    +
    Return type:
    +

    AAclust

    +
    +
    +
    +

    Notes

    +
      +
    • Sets all attributes of the aanalysis.AAclust class.

    • +
    • +
      The AAclust algorithm consists of three main steps:
        +
      1. Estimate the lower bound of k.

      2. +
      3. Refine k (recursively) using the chosen quality metric.

      4. +
      5. Optionally, merge smaller clusters as directed by the merge_metric.

      6. +
      +
      +
      +
    • +
    • A representative scale (medoid) closest to each cluster center is selected for redundancy reduction.

    • +
    +
    +
    +

    See also

    + +
    +
    +

    Warning

    +
      +
    • All RuntimeWarnings during the AAclust algorithm are caught and bundled into one RuntimeWarning.

    • +
    +

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    @@ -328,7 +389,7 @@

    aaanalysis.AAclust
    Parameters:
      -
    • X (array-like, shape (n_samples, n_features)) – Feature matrix.

    • +
    • X (array-like, shape (n_samples, n_features)) – Feature matrix. Rows correspond to scales and columns to amino acids.

    • labels (array-like, shape (n_samples, )) – Cluster labels for each sample in X.

    @@ -344,7 +405,7 @@

    aaanalysis.AAclust

    Notes

    BIC was modified to align with the SC and CH, so that higher values signify better clustering -contrary to conventional BIC implementation favoring lower values. See [Breimann23a].

    +contrary to conventional BIC implementation favoring lower values. See [Breimann23a].

    See also

    diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js index bfce06b4..b5e7bb35 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_clist", "generated/aaanalysis.plot_get_cmap", "generated/aaanalysis.plot_legend", "generated/aaanalysis.plot_settings", "generated/plotting_prelude", "generated/tutorial1_quick_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", 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