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How to use _clean method in ATX

Best Python code snippet using ATX

test_container.py

Source:test_container.py

...6import os7import numpy as np8import pytest9import shutil10def _clean(path):11    if os.path.isdir(path):12        shutil.rmtree(path)13    if os.path.isfile(path):14        os.remove(path)15x = Input(shape=(10,), dtype='float32', name='input')16y = Dense(2)(x)17model = Model(x, y)18### ModelContainer19def test_model_container():20    model_container = ModelContainer()21    with pytest.raises(NotImplementedError):22        model_container.build()23    with pytest.raises(NotImplementedError):24        model_container.train()25    with pytest.raises(NotImplementedError):26        model_container.evaluate(None, None)27    with pytest.raises(NotImplementedError):28        model_container.save_model_json(None)29    with pytest.raises(NotImplementedError):30        model_container.load_model_from_json(None)31    with pytest.raises(NotImplementedError):32        model_container.save_model_weights(None)33    with pytest.raises(NotImplementedError):34        model_container.load_model_weights(None)35    with pytest.raises(NotImplementedError):36        model_container.check_if_model_exists(None)37    with pytest.raises(NotImplementedError):38        model_container.get_available_intermediate_outputs()39    with pytest.raises(NotImplementedError):40        model_container.get_intermediate_output(None)41### KerasModelContainer42def test_init():43    _clean('./model.json')44    model_container = KerasModelContainer(model)45    assert len(model_container.model.layers) == 246    assert model_container.model_name == "DCASEModelContainer"47    assert model_container.metrics == ['classification']48    model_container.save_model_json('./')49    model_container = KerasModelContainer(model_path='./')50    assert len(model_container.model.layers) == 251    _clean('./model.json')52def test_load_model_from_json():53    _clean('./model.json')54    model_container = KerasModelContainer(model)55    model_container.save_model_json('./')56    model_container = KerasModelContainer()57    model_container.load_model_from_json('./')58    assert len(model_container.model.layers) == 259    _clean('./model.json')60def test_save_model_from_json():61    _clean('./model.json')62    model_container = KerasModelContainer(model)63    model_container.save_model_json('./')64    assert os.path.exists('./model.json')65    _clean('./model.json')66def test_save_model_weights():67    weights_file = './best_weights.hdf5'68    _clean(weights_file)69    model_container = KerasModelContainer(model)70    model_container.save_model_weights('./')71    assert os.path.exists(weights_file)72    _clean(weights_file)73def test_save_model_weights():74    weights_file = './best_weights.hdf5'75    _clean(weights_file)76    model_container = KerasModelContainer(model)77    model_container.save_model_weights('./')78    weights = model_container.model.layers[1].get_weights()79    model_container.model.layers[1].set_weights([np.zeros((10,2)), np.zeros(2)])80    model_container.load_model_weights('./')81    new_weights = model_container.model.layers[1].get_weights()82    assert np.allclose(new_weights[0], weights[0])83    assert np.allclose(new_weights[1], weights[1])84    _clean(weights_file)85def test_check_if_model_exists():86    model_container = KerasModelContainer(model)87    model_file = './model.json'88    _clean(model_file)89    model_container.save_model_json('./')90    assert model_container.check_if_model_exists('./')91    92    x = Input(shape=(11,), dtype='float32', name='input')93    y = Dense(2)(x)94    new_model = Model(x, y)  95    model_container = KerasModelContainer(new_model)96    assert not model_container.check_if_model_exists('./')97    _clean(model_file)98    assert not model_container.check_if_model_exists('./')99def test_train():100    x = Input(shape=(4,), dtype='float32', name='input')101    y = Dense(2)(x)102    new_model = Model(x, y)  103    model_container = KerasModelContainer(new_model)104    X_train = np.concatenate((np.zeros((100, 4)), np.ones((100, 4))), axis=0)105    Y_train = np.zeros((200, 2))106    Y_train[:100, 0] = 1107    Y_train[100:, 1] = 1108    X_val = np.zeros((1, 4))109    Y_val = np.zeros((1, 2))110    Y_val[0, 0] = 1111    X_val2 = np.ones((1, 4))112    Y_val2 = np.zeros((1, 2))113    Y_val2[0, 1] = 1114    file_weights = './best_weights.hdf5'115    file_log = './training.log'116    _clean(file_weights)117    _clean(file_log)118    model_container.train((X_train, Y_train), ([X_val, X_val2], [Y_val, Y_val2]), epochs=3, label_list=['1', '2'])119    assert os.path.exists(file_weights)120    assert os.path.exists(file_log)121    _clean(file_weights)122    _clean(file_log)123    124    results = model_container.evaluate(([X_val, X_val2], [Y_val, Y_val2]), label_list=['1', '2'])125    assert results['classification'].results()['overall']['accuracy'] > 0.25126    # DataGenerator127    class ToyDataGenerator():128        def __init__(self, X_val, Y_val):129            self.X_val = X_val130            self.Y_val = Y_val131        def __len__(self):132            return 3133        def get_data_batch(self, index):134            return X_val, Y_val135        136        def shuffle_list(self):137            pass138    data_generator = ToyDataGenerator(X_train, Y_train)139    data_generator_val = ToyDataGenerator([X_val, X_val2], [Y_val, Y_val2])140    data_generator = KerasDataGenerator(data_generator)141    x = Input(shape=(4,), dtype='float32', name='input')142    y = Dense(2)(x)143    new_model = Model(x, y)  144    model_container = KerasModelContainer(new_model)145    model_container.train(146        data_generator,147        ([X_val, X_val2], [Y_val, Y_val2]),148        epochs=3,149        batch_size=None,150        label_list=['1', '2'])151    assert os.path.exists(file_weights)152    assert os.path.exists(file_log)153    _clean(file_weights)154    _clean(file_log)155    156    results = model_container.evaluate(([X_val, X_val2], [Y_val, Y_val2]), label_list=['1', '2'])157    assert results['classification'].results()['overall']['accuracy'] > 0.25158    # Other callbacks159    for metric in ["tagging", "sed"]:160        model_container = KerasModelContainer(new_model, metrics=[metric])161        file_weights = './best_weights.hdf5'162        file_log = './training.log'163        _clean(file_weights)164        _clean(file_log)165        model_container.train((X_train, Y_train), ([X_val, X_val2], [Y_val, Y_val2]), epochs=3, label_list=['1', '2'])166        #assert os.path.exists(file_weights)167        assert os.path.exists(file_log)168        _clean(file_weights)169        _clean(file_log)170        171        #results = model_container.evaluate(([X_val, X_val2], [Y_val, Y_val2]), label_list=['1', '2'])...

run_GATK_CNV.smk

Source:run_GATK_CNV.smk

1# wildcard_constraints:2#     seqID = config["seqID"]["sequencerun"]3rule collectReadCounts:4    input:5        bam="Results/{sample}_{seqID}/Data/{sample}_{seqID}-dedup.bam",  #lambda wildcards: config["samples"][wildcards.sample],6        bai="Results/{sample}_{seqID}/Data/{sample}_{seqID}-dedup.bam.bai",7        interval=config["CNV"]["interval"],  #"/projects/wp4/nobackup/workspace/arielle_test/CNV/bedFiles/TM_TE-annotated_closest-noduplicates.preprocessed.interval_list" #Better version? Should be same as other intervallist.8    output:9        "CNV/{sample}_{seqID}/{sample}_{seqID}.counts.hdf5",10    params:11        mergingRule="OVERLAPPING_ONLY",12    log:13        "logs/CNV/{sample}_{seqID}.collectReadCounts.log",14    container:15        config["singularitys"]["gatk4"]16    shell:17        "(gatk --java-options '-Xmx4g' CollectReadCounts -I {input.bam} -L {input.interval} "18        "--interval-merging-rule {params.mergingRule} -O {output} ) &> {log}"19rule denoiseReadCounts:20    input:21        hdf5PoN=config["CNV"]["PoN"],22        hdf5Tumor="CNV/{sample}_{seqID}/{sample}_{seqID}.counts.hdf5",23    output:24        stdCopyRatio="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.standardizedCR.tsv",25        denoisedCopyRatio="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.denoisedCR.tsv",26    log:27        "logs/CNV/{sample}_{seqID}-denoise.log",28    container:29        config["singularitys"]["gatk4"]30    shell:31        "(gatk --java-options '-Xmx4g' DenoiseReadCounts -I {input.hdf5Tumor} --count-panel-of-normals {input.hdf5PoN} "32        "--standardized-copy-ratios {output.stdCopyRatio} --denoised-copy-ratios {output.denoisedCopyRatio} ) &> {log}"33rule collectAllelicCounts:34    input:35        intervalList=config["CNV"]["interval"],  #Better version? Should be same as other intervallist. Also should be in config.36        bam="Results/{sample}_{seqID}/Data/{sample}_{seqID}-dedup.bam",  #lambda wildcards: config["samples"][wildcards.sample],37        bai="Results/{sample}_{seqID}/Data/{sample}_{seqID}-dedup.bam.bai",38        ref=config["reference"]["ref"],39    output:40        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.allelicCounts.tsv",41    log:42        "logs/CNV/{sample}_{seqID}_allelicCounts.log",43    container:44        config["singularitys"]["gatk4"]45    shell:46        "(gatk --java-options '-Xmx4g' CollectAllelicCounts -L {input.intervalList} -I {input.bam} -R {input.ref} "47        "-O {output} ) &> {log}"48rule modelSegments:49    input:50        denoisedCopyRatio="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.denoisedCR.tsv",51        allelicCounts="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.allelicCounts.tsv",52    output:53        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelBegin.seg",54        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelFinal.seg",55        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.cr.seg",56        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelBegin.af.param",57        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelBegin.cr.param",58        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelFinal.af.param",59        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelFinal.cr.param",60        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.hets.tsv",61    params:62        outDir="CNV/{sample}_{seqID}/",63        outPrefix="{sample}_{seqID}_clean",64    log:65        "logs/CNV/{sample}_{seqID}_modelSegments.log",66    container:67        config["singularitys"]["gatk4"]68    shell:69        "(gatk --java-options '-Xmx4g' ModelSegments --denoised-copy-ratios {input.denoisedCopyRatio} "70        "--allelic-counts {input.allelicCounts} --output {params.outDir} --output-prefix {params.outPrefix} ) &> {log}"71rule callCopyRatioSegments:72    input:73        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.cr.seg",74    output:75        "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.calledCNVs.seg",76    log:77        "logs/CNV/{sample}_{seqID}_calledCRSegments.log",78    container:79        config["singularitys"]["gatk4"]80    shell:81        "(gatk CallCopyRatioSegments --input {input} --output {output} ) &> {log}"82rule plotModeledSegments:83    input:84        denoisedCopyRatio="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.denoisedCR.tsv",85        allelicCounts="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.hets.tsv",86        segments="CNV/{sample}_{seqID}/{sample}_{seqID}_clean.modelFinal.seg",  #Vad hander om man anv'nder Results/{sample}/Reports/{sample}_clean.calledCNVs.seg ist'llet?87        refDict=config["reference"]["ref"][:-5] + "dict",88    output:89        "CNV/{sample}_{seqID}_clean.calledCNVs.modeled.png",90    params:91        outDir="CNV/",92        outPrefix="{sample}_{seqID}_clean.calledCNVs",  #--minimum-contig-length 4670998393        pointSize=2.0,94    log:95        "logs/CNV/{sample}_{seqID}_plotSegments.log",96    container:97        config["singularitys"]["gatk4"]98    shell:99        "(gatk PlotModeledSegments --denoised-copy-ratios {input.denoisedCopyRatio} --allelic-counts {input.allelicCounts} "100        "--segments {input.segments} --sequence-dictionary {input.refDict} --point-size-allele-fraction {params.pointSize} "101        "--point-size-copy-ratio {params.pointSize} --output {params.outDir} --output-prefix {params.outPrefix} ) &> {log} "102#103# rule Filter_cnv:104#     input:105#         gatkSeg = "CNV/{sample}_{seqID}/{sample}_{seqID}_clean.calledCNVs.seg",106#         bedfile = config["CNV"]["bedPoN"],107#         png = "CNV/{sample}_{seqID}_clean.calledCNVs.modeled.png", #"Results/{sample}_{seqID}/Reports/{sample}_{seqID}_clean.calledCNVs.modeled.png",108#         cytoCoord = config["CNV"]["cyto"]109#     output:110#         relevant_cnvs = "CNV/{sample}_{seqID}_clean.calledCNV-relevant_cnv-GATK4.xlsx" #"Results/{sample}_{seqID}/Reports/{sample}_{seqID}_clean.calledCNV-relevant_cnv-GATK4.xlsx"111#         # cnv_done = "Tumor/{sample}_cnv_done.txt"112#     params:113#         outdir = "CNV/"#"Results/{sample}_{seqID}/Reports",114#     container:115#         config["singularitys"]["python"]116#     log:117#         "logs/Tumor/{sample}_{seqID}_relevant_cnvs-gatk4.log"118#     shell:...

elasticsearch.py

Source:elasticsearch.py

...24    def _generate_semver(cls, v, values) -> str:25        return f"v{values['major']}.{values['minor']}"26    @validator("document")27    def _cleanup_document(cls, v: _document.Document) -> dict:28        def _clean(d: Any) -> Any:29            if not d:30                return31            if isinstance(d, _document.PublicationReference):32                return d.dict(exclude_none=True, exclude_unset=True)33            if isinstance(d, str):34                return " ".join(re.findall(r"\w+", d))35            if isinstance(d, list):36                return [_clean(_d) for _d in d]37            if isinstance(d, _document.AlgorithmVariable):38                return [_clean(d.name), _clean(d.unit)]39            if isinstance(d, _document.DataAccessUrl):40                return [_clean(d.url), _clean(d.description)]41            if isinstance(d, _document.TextLeaf):42                return _clean(d.text)43            # Skipping equation indexing because those44            # are all latex code45            if isinstance(d, _document.EquationNode):46                return47            if isinstance(d, _document.EquationInlineNode):48                return49            if any(50                isinstance(d, i)51                for i in (52                    _document.SectionWrapper,53                    _document.DivWrapperNode,54                    _document.BaseNode,55                )56            ):57                return _clean(d.children)58            raise Exception("Unhandled Node! ", d)59        return {60            field: _clean(getattr(v, field))61            for field in v.__fields__62            if field not in ["version_description"]63        }64class ElasticsearchAtbd(BaseModel):65    """Elasticsearch document representing an ATBD"""66    id: str67    title: str68    alias: Optional[str]69    version: ElasticsearchAtbdVersion70    class Config:71        """Config."""72        title = "ElasticsearchAtbdVersion"...

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