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How to use _prepare_data method in autotest

Best Python code snippet using autotest_python

statics.py

Source:statics.py

...126        Returns127        -------128        return a dict of result mean.129        """130        return self._prepare_data(cols_dict, "mean")131    def get_median(self, cols_dict=None):132        medians = {}133        if cols_dict is None:134            cols_dict = self.cols_dict135        if self.medians is None:136            self.medians = self._get_quantile_median()137        for col_name in cols_dict:138            if col_name not in self.medians:139                LOGGER.warning("The column {}, has not set in selection parameters."140                               "median values is not available".format(col_name))141                continue142            medians[col_name] = self.medians[col_name]143        return medians144    def _get_quantile_median(self):145        bin_param = FeatureBinningParam(bin_num=2, cols=self.cols)146        binning_obj = QuantileBinning(bin_param)147        split_points = binning_obj.fit_split_points(self.data_instances)148        medians = {}149        for col_name, split_point in split_points.items():150            medians[col_name] = split_point[0]151        return medians152    def get_variance(self, cols_dict=None):153        return self._prepare_data(cols_dict, "variance")154    def get_std_variance(self, cols_dict=None):155        return self._prepare_data(cols_dict, "std_variance")156    def get_max(self, cols_dict=None):157        return self._prepare_data(cols_dict, "max_value")158    def get_min(self, cols_dict=None):159        return self._prepare_data(cols_dict, "min_value")160    def _prepare_data(self, cols_dict, data_type):161        """162        Return the specific static value(s) of the given column163        Parameters164        ----------165        cols_dict : dict166            Specify which column(s) need to apply statistic.167        data_type : str, "mean", "variance", "std_variance", "max_value" or "mim_value"168            Specify which type to show.169        Returns170        -------171        return a list of result result. The order is the same as cols.172        """173        if not self.finish_fit:174            self._static_sums()...

datasets.py

Source:datasets.py

...6    records, labels = physionet.read_challenge17_data(db_dir, verbose=verbose)7    if normalize:8        normalize = functools.partial(9            physionet.normalize_challenge17, inplace=True)10    data_set = _prepare_data(11        records,12        labels,13        normalize_fn=normalize,14        fs=fs,15        pad=pad,16        verbose=verbose)17    return data_set18def get_challenge20_data(db_dir, fs=None, pad=None, normalize=False, verbose=False):19    records, labels = physionet.read_challenge20_data(db_dir, verbose=verbose)20    if normalize:21        normalize = functools.partial(22            physionet.normalize_challenge20, inplace=True)23    data_set = _prepare_data(24        records,25        labels,26        normalize_fn=normalize,27        fs=fs,28        pad=pad,29        verbose=verbose)30    return data_set31def get_ptb_xl_data(db_dir, fs=None, pad=None, normalize=False,32                    category='rhythm', folds=None, verbose=False):33    records, labels = physionet.read_ptb_xl_data(34        db_dir=db_dir,35        fs='hr',36        category=category,37        remove_empty=True,38        folds=folds,39        verbose=verbose)40    if normalize:41        normalize = functools.partial(42            physionet.normalize_ptb_xl, inplace=True)43    data_set = _prepare_data(44        records,45        labels,46        normalize_fn=normalize,47        fs=fs,48        pad=pad,49        verbose=verbose)50    return data_set51def _prepare_data(records, labels, normalize_fn=None, fs=None, pad=None, verbose=False):52    x = _transform_records(53        records,54        fs=fs,55        pad=pad,56        normalize=normalize_fn,57        verbose=verbose)58    data_set = {'x': x,59                'y': labels.to_numpy(),60                'record_ids': labels.index.to_numpy(),61                'classes': labels.columns.to_numpy()}62    return data_set63def _transform_records(records, fs=None, pad=None, normalize=None, verbose=False):64    if not normalize:65        def normalize(signal): return signal...

test_team_members_filters.py

Source:test_team_members_filters.py

...7    """8    Test filter by role developer.9    :param user:10    """11    _prepare_data()12    queryset = TeamMembersFilterSet(13        data={KEY_ROLES: "DEVELOPER"},14        queryset=TeamMember.objects.all(),15    ).qs16    assert queryset.count() == 217def test_filter_by_role_leader(user):18    """19    Test filter by role leader.20    :param user:21    """22    _prepare_data()23    queryset = TeamMembersFilterSet(24        data={KEY_ROLES: "LEADER"},25        queryset=TeamMember.objects.all(),26    ).qs27    assert queryset.count() == 128def test_filter_by_role_watcher(user):29    """30    Test filter by role watcher.31    :param user:32    """33    _prepare_data()34    queryset = TeamMembersFilterSet(35        data={KEY_ROLES: "WATCHER"},36        queryset=TeamMember.objects.all(),37    ).qs38    assert queryset.count() == 139def test_filter_by_incorrect_role(user):40    """41    Test filter by incorrect role.42    :param user:43    """44    _prepare_data()45    queryset = TeamMembersFilterSet(46        data={KEY_ROLES: "incorrect value"},47        queryset=TeamMember.objects.all(),48    ).qs49    assert queryset.count() == 450def test_filter_by_none_role(user):51    """52    Test filter by none role.53    :param user:54    """55    _prepare_data()56    queryset = TeamMembersFilterSet(57        data={KEY_ROLES: None},58        queryset=TeamMember.objects.all(),59    ).qs60    assert queryset.count() == 461def _prepare_data():62    """Prepare data."""63    users = UserFactory.create_batch(2)64    teams = TeamFactory.create_batch(2)65    TeamMemberFactory.create(66        user=users[0],67        team=teams[0],68        roles=TeamMember.roles.LEADER,69    )70    TeamMemberFactory.create(71        user=users[1],72        team=teams[0],73        roles=TeamMember.roles.DEVELOPER,74    )75    TeamMemberFactory.create(...

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