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How to use remove_output_dir method in SeleniumLibrary

Best Python code snippet using SeleniumLibrary

web_services.py

Source:web_services.py

...132            for file in filePaths:133                z.write(file)134        zip_file.seek(0)135        print('{} file is created successfully!'.format(zip_filepath))136        self.remove_output_dir(output_path)137        return zip_file, zip_filepath138    def retrieve_file_paths(self, dir_name):139        # setup file paths variable140        file_paths = []141        # Read all directory, subdirectories and file lists142        for root, directories, files in os.walk(dir_name):143            for filename in files:144                # Create the full filepath by using os module.145                file_path = os.path.join(root, filename)146                file_paths.append(file_path)147        print("Filepaths:")148        print(file_paths)149        # return all paths150        return file_paths151    def remove_output_dir(self, output_path):152        shutil.rmtree(output_path)153    def remove_zip_file(self, zip_filepath, output_dir):154        os.remove(zip_filepath)...

multinest_sampler.py

Source:multinest_sampler.py

1__author__ = 'aymgal'2import os3import json4import shutil5import numpy as np6from lenstronomy.Sampling.Samplers.base_nested_sampler import NestedSampler7import lenstronomy.Util.sampling_util as utils8__all__ = ['MultiNestSampler']9class MultiNestSampler(NestedSampler):10    """11    Wrapper for nested sampling algorithm MultInest by F. Feroz & M. Hobson12    papers : arXiv:0704.3704, arXiv:0809.3437, arXiv:1306.214413    pymultinest doc : https://johannesbuchner.github.io/PyMultiNest/pymultinest.html14    """15    def __init__(self, likelihood_module, prior_type='uniform', 16                 prior_means=None, prior_sigmas=None, width_scale=1, sigma_scale=1,17                 output_dir=None, output_basename='-',18                 remove_output_dir=False, use_mpi=False):19        """20        :param likelihood_module: likelihood_module like in likelihood.py (should be callable)21        :param prior_type: 'uniform' of 'gaussian', for converting the unit hypercube to param cube22        :param prior_means: if prior_type is 'gaussian', mean for each param23        :param prior_sigmas: if prior_type is 'gaussian', std dev for each param24        :param width_scale: scale the widths of the parameters space by this factor25        :param sigma_scale: if prior_type is 'gaussian', scale the gaussian sigma by this factor26        :param output_dir: name of the folder that will contain output files27        :param output_basename: prefix for output files28        :param remove_output_dir: remove the output_dir folder after completion29        :param use_mpi: flag directly passed to MultInest sampler (NOT TESTED)30        """31        self._check_install()32        super(MultiNestSampler, self).__init__(likelihood_module, prior_type, 33                                               prior_means, prior_sigmas,34                                               width_scale, sigma_scale)35        # here we assume number of dimensons = number of parameters36        self.n_params = self.n_dims37        if output_dir is None:38            self._output_dir = 'multinest_out_default'39        else:40            self._output_dir = output_dir41        self._is_master = True42        self._use_mpi = use_mpi43        if self._use_mpi:44            from mpi4py import MPI45            self._comm = MPI.COMM_WORLD46            if self._comm.Get_rank() != 0:47                self._is_master = False48        else:49            self._comm = None50        if self._is_master:51            if os.path.exists(self._output_dir):52                shutil.rmtree(self._output_dir, ignore_errors=True)53            os.mkdir(self._output_dir)54        self.files_basename = os.path.join(self._output_dir, output_basename)55        # required for analysis : save parameter names in json file56        if self._is_master:57            with open(self.files_basename + 'params.json', 'w') as file:58                json.dump(self.param_names, file, indent=2)59        self._rm_output = remove_output_dir60        self._has_warned = False61    def prior(self, cube, ndim, nparams):62        """63        compute the mapping between the unit cube and parameter cube (in-place)64        :param cube: unit hypercube, sampled by the algorithm65        :param ndim: number of sampled parameters66        :param nparams: total number of parameters67        """68        cube_py = self._multinest2python(cube, ndim)69        if self.prior_type == 'gaussian':70            utils.cube2args_gaussian(cube_py, self.lowers, self.uppers, 71                                     self.means, self.sigmas, self.n_dims)72        elif self.prior_type == 'uniform':73            utils.cube2args_uniform(cube_py, self.lowers, self.uppers, self.n_dims)74        for i in range(self.n_dims):75            cube[i] = cube_py[i]76    def log_likelihood(self, args, ndim, nparams):77        """78        compute the log-likelihood given list of parameters79        :param args: parameter values80        :param ndim: number of sampled parameters81        :param nparams: total number of parameters82        :return: log-likelihood (from the likelihood module)83        """84        args_py = self._multinest2python(args, ndim)85        logL = self._ll(args_py)86        if not np.isfinite(logL):87            if not self._has_warned:88                print("WARNING : logL is not finite : return very low value instead")89            logL = -1e1590            self._has_warned = True91        return float(logL)92    def run(self, kwargs_run):93        """94        run the MultiNest nested sampler95        see https://johannesbuchner.github.io/PyMultiNest/pymultinest.html for content of kwargs_run96        :param kwargs_run: kwargs directly passed to pymultinest.run97        :return: samples, means, logZ, logZ_err, logL, stats98        """99        print("prior type :", self.prior_type)100        print("parameter names :", self.param_names)101        102        if self._pymultinest_installed:103            self._pymultinest.run(self.log_likelihood, self.prior, self.n_dims,104                                  outputfiles_basename=self.files_basename,105                                  resume=False, verbose=True,106                                  init_MPI=self._use_mpi, **kwargs_run)107            analyzer = self._Analyzer(self.n_dims, outputfiles_basename=self.files_basename)108            samples = analyzer.get_equal_weighted_posterior()[:, :-1]109            data  = analyzer.get_data()  # gets data from the *.txt output file110            stats = analyzer.get_stats()111        else:112            # in case MultiNest was not compiled properly, for unit tests113            samples = np.zeros((1, self.n_dims))114            data = np.zeros((self.n_dims, 3))115            stats = {116                'global evidence': np.zeros(self.n_dims),117                'global evidence error': np.zeros(self.n_dims),118                'modes': [{'mean': np.zeros(self.n_dims)}]119            }120        logL = -0.5 * data[:, 1]  # since the second data column is -2*logL121        logZ     = stats['global evidence']122        logZ_err = stats['global evidence error']123        means    = stats['modes'][0]['mean']  # or better to use stats['marginals'][:]['median'] ???124        print("MultiNest output files have been saved to {}*"125              .format(self.files_basename))126        if self._rm_output and self._is_master:127            shutil.rmtree(self._output_dir, ignore_errors=True)128            print("MultiNest output directory removed")129            130        return samples, means, logZ, logZ_err, logL, stats131    def _multinest2python(self, multinest_list, num_dims):132        """convert ctypes list to standard python list"""133        python_list = []134        for i in range(num_dims):135            python_list.append(multinest_list[i])136        return python_list137    def _check_install(self):138        try:139            import pymultinest140            from pymultinest.analyse import Analyzer141        except:142            print("Warning : MultiNest/pymultinest not properly installed (results might be unexpected). \143                    You can get it from : https://johannesbuchner.github.io/PyMultiNest/pymultinest.html")144            self._pymultinest_installed = False145        else:146            self._pymultinest_installed = True147            self._pymultinest = pymultinest...

smrf_test_case.py

Source:smrf_test_case.py

...59        cls.create_output_dir()60        cls.configure()61    @classmethod62    def tearDownClass(cls):63        cls.remove_output_dir()64        delattr(cls, 'output_dir')65    @classmethod66    def create_output_dir(cls):67        folder = os.path.join(cls._base_config.cfg['output']['out_location'])68        # Remove any potential files to ensure fresh run69        if os.path.isdir(folder):70            shutil.rmtree(folder)71        os.makedirs(folder)72        cls.output_dir = Path(folder)73    @classmethod74    def remove_output_dir(cls):75        if hasattr(cls, 'output_dir') and \76                os.path.exists(cls.output_dir):77            shutil.rmtree(cls.output_dir)78    @classmethod79    def thread_config(cls):80        config = cls.base_config_copy()81        config.raw_cfg['system'].update(cls.THREAD_CONFIG)82        config.apply_recipes()83        config = cast_all_variables(config, config.mcfg)84        return config85    @staticmethod86    def can_i_run_smrf(config):87        """88        Test whether a config is possible to run...

lakes_test_case.py

Source:lakes_test_case.py

...27        cls.load_base_config()28        cls.create_output_dir()29    @classmethod30    def tearDownClass(cls):31        cls.remove_output_dir()32    def tearDown(self):33        logging.shutdown()34    @classmethod35    def create_output_dir(cls):36        folder = os.path.join(37            cls._base_config.cfg['generate_topo']['output_folder'])38        # Remove any potential files to ensure fresh run39        if os.path.isdir(folder):40            shutil.rmtree(folder)41        os.makedirs(folder)42        cls.output_dir = Path(folder)43    @classmethod44    def remove_output_dir(cls):45        if hasattr(cls, 'output_dir') and \46                os.path.exists(cls.output_dir):47            shutil.rmtree(cls.output_dir)48    @staticmethod49    def assert_gold_equal(gold, not_gold, error_msg):50        """Compare two arrays51        Arguments:52            gold {array} -- gold array53            not_gold {array} -- not gold array54            error_msg {str} -- error message to display55        """56        if os.getenv('NOT_ON_GOLD_HOST') is None:57            try:58                np.testing.assert_array_equal(...

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