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How to use _get_title method in pyatom

Best Python code snippet using pyatom_python

base.py

Source:base.py

...54        save_vars = self._get_save_vars(exclude=['ch_info_'])55        write_hdf5(56            fname,57            save_vars,58            title=_get_title(self.__class__, self.comment),59            overwrite=overwrite, slash='replace')60    def _get_title(self):61        return _get_title(self.__class__, self.comment)62    @classmethod63    def _read(cls, fname, comment='default'):64        return _read_container(cls, fname, comment=comment)65class BaseMarker(BaseContainer):66    """Base class for M/EEG markers"""67    def __init__(self, tmin, tmax, comment):68        BaseContainer.__init__(self, comment=comment)69        self.tmin = tmin70        self.tmax = tmax71    @property72    def _axis_map(self):73        raise NotImplementedError('This should be in every marker')74    def fit(self, epochs):75        self._fit(epochs)76        self.ch_info_ = epochs.info77        return self78    def transform(self, epochs):79        self._transform(epochs)80        return self81    def _get_title(self):82        return _get_title(self.__class__, self.comment)83    def _get_preserve_axis(self, targets):84        to_preserve = []85        if not isinstance(targets, list):86            targets = [targets]87        for elem in targets:88            if 'topography' == elem or 'channels' == elem:89                to_preserve.append('channels')90            elif 'times' == elem:91                to_preserve.append('times')92            elif 'epochs' == elem:93                to_preserve.append('epochs')94        if any(x not in self._axis_map.keys() for x in to_preserve):95            raise ValueError('Cannot reduce {} to {}'.format(96                self._get_title(), targets))97        return to_preserve98    def _reduce_to(self, reduction_func, target, picks):99        if not hasattr(self, 'data_'):100            raise ValueError('You did not fit me. Do it again after fitting '101                             'some data!')102        out, funcs = self._prepare_reduction(reduction_func, target, picks)103        for func in funcs:104            out = func(out, axis=0)105        return out106    def reduce_to_epochs(self, reduction_func, picks=None):107        """Reduce  marker to a single value per epoch.108        Parameters109        ----------110        reduction_func : list of dictionaries.111            Each dictionary should have two keys: 'axis' and 'function'.112            The marker is going to be reduced following the order of the list.113            Selecting the corresponding axis and applying the corresponding114            function.115        picks : dictionary of axis to array.116            Before appling the reduction function, the corresponding axis will117            be subselected by picks. A value of None indicates all the118            elements.119        Example:120            reduction_func = [121                {'axis': 'frequency', 'function': np.sum},122                {'axis': 'channels', 'function': np.mean},123                {'axis': 'epochs', 'function': np.mean}]124            picks = {'epochs': None, 'channels': np.arange(224)}125        Returns126        -------127        out : np.ndarray of float, shape(n_epochs,)128            The value of the marker for each epoch.129        """130        return self._reduce_to(131            reduction_func, target='epochs', picks=picks)132    def reduce_to_topo(self, reduction_func, picks=None):133        return self._reduce_to(134            reduction_func, target='topography', picks=picks)135    def reduce_to_scalar(self, reduction_func, picks=None):136        return self._reduce_to(reduction_func, target='scalar', picks=picks)137    def compress(self, reduction_func):138        if not hasattr(self, 'data_'):139            raise ValueError('You did not fit me. Do it again after fitting '140                             'some data!')141        if 'epochs' in self._axis_map:142            axis = self._axis_map['epochs']143            logger.info(144                'Compressing {} on axis {} (epochs)'.format(145                    self._get_title(), axis)146            )147            data = reduction_func(self.data_, axis=axis)148            # Keep dimension149            self.data_ = np.expand_dims(data, axis=axis)150    def _prepare_data(self, picks, target):151        data = self.data_152        to_preserve = self._get_preserve_axis(target)153        if picks is not None:154            if any([x not in self._axis_map for x in picks.keys()]):155                raise ValueError('Picking is not compatible for {}'.format(156                    self._get_title()))157            for axis, ax_picks in picks.items():158                if axis in to_preserve:159                    continue160                if ax_picks is not None:161                    this_axis = self._axis_map[axis]162                    data = (data.swapaxes(this_axis, 0)[ax_picks, ...]163                                .swapaxes(0, this_axis))164        return data165    def _prepare_reduction(self, reduction_func, target, picks,166                           return_axis=False):167        data = self._prepare_data(picks, target)168        _axis_map = self._axis_map169        funcs = list()170        axis_to_preserve = self._get_preserve_axis(target)171        if len(axis_to_preserve) > 0:172            removed_axis = []173            for this_axis_to_preserve in axis_to_preserve:174                removed_axis.append(_axis_map.pop(this_axis_to_preserve))175            if reduction_func is not None:176                reduction_func = [i for i in reduction_func177                                  if i['axis'] not in axis_to_preserve]178        permutation_list = list()179        permutation_axes = list()180        if reduction_func is None:181            for ax_name, remaining_axis in _axis_map.items():182                permutation_list.append(remaining_axis)183                permutation_axes.append(ax_name)184                funcs.append(np.mean)185        elif len(reduction_func) == len(_axis_map):186            for rec in reduction_func:187                this_axis = _axis_map.pop(rec['axis'])188                permutation_axes.append(rec['axis'])189                permutation_list.append(this_axis)190                funcs.append(rec['function'])191        else:192            raise ValueError('Run `python -c "import this"` to see '193                             'why we will not tolerate these things')194        if len(axis_to_preserve) > 0:195            permutation_list += removed_axis196        logger.info('Reduction order for {}: {}'.format(197            self._get_title(), permutation_axes))198        data = np.transpose(data, permutation_list)199        if return_axis is False:200            out = data, funcs201        else:202            out = data, funcs, permutation_axes203        return out204class BaseTimeLocked(BaseMarker):205    def __init__(self, tmin, tmax, comment):206        BaseMarker.__init__(self, tmin, tmax, comment)207    def fit(self, epochs):208        self.ch_info_ = epochs.info209        self.shape_ = epochs.get_data().shape210        self.epochs_ = epochs211        self.data_ = epochs.get_data()212        return self213    def compress(self, reduction_func):214        logger.info(215            'TimeLocked markers cannot be compressed '216            'epoch-wise ({})'.format(self._get_title()))217    def save(self, fname, overwrite=False):218        if not isinstance(fname, Path):219            fname = Path(fname)220        self._save_info(fname, overwrite=overwrite)221        save_vars = self._get_save_vars(222            exclude=['ch_info_', 'data_', 'epochs_'])223        has_epochs = False224        with h5py.File(fname, 'r') as h5fid:225            if 'nice/data/epochs' in h5fid:226                has_epochs = True227                logger.info('Epochs already present in HDF5 file, '228                            'will not be overwritten')229        if not has_epochs:230            epochs = self.epochs_231            logger.info('Writing epochs to HDF5 file')232            write_hdf5_mne_epochs(fname, epochs, overwrite=overwrite)233        write_hdf5(234            fname, save_vars, overwrite=overwrite,235            title=_get_title(self.__class__, self.comment), slash='replace')236    @classmethod237    def _read(cls, fname, epochs, comment='default'):238        return _read_time_locked(cls, fname=fname, epochs=epochs,239                                 comment=comment)240    def _get_title(self):241        return _get_title(self.__class__, self.comment)242class BaseDecoding(BaseMarker):243    def __init__(self, tmin, tmax, comment):244        BaseMarker.__init__(self, tmin, tmax, comment)245    def fit(self, epochs):246        self._fit(epochs)247        return self248    def _get_title(self):249        return _get_title(self.__class__, self.comment)250def _get_title(klass, comment):251    if issubclass(klass, BaseMarker):252        kind = 'marker'253    elif issubclass(klass, BaseContainer):254        kind = 'container'255    else:256        raise NotImplementedError('Oh no-- what is this?')257    return '/'.join([258        'nice', kind, klass.__name__, comment])259def _read_container(klass, fname, comment='default'):260    data = read_hdf5(fname,  _get_title(klass, comment), slash='replace')261    init_params = {k: v for k, v in data.items() if not k.endswith('_')}262    attrs = {k: v for k, v in data.items() if k.endswith('_')}263    file_info = read_hdf5(fname, title='nice/data/ch_info', slash='replace')264    if 'filename' in file_info:265        del(file_info['filename'])266    attrs['ch_info_'] = Info(file_info)267    out = klass(**init_params)268    for k, v in attrs.items():269        if k.endswith('_'):270            setattr(out, k, v)271    return out272def _check_epochs_consistency(info1, info2, shape1, shape2):273    if version.parse(mne.__version__) < version.parse('0.24'):274        from mne.epochs import _compare_epochs_infos...

metadata.py

Source:metadata.py

...11        super(MetadataTab, self).__init__(app, figurepage, client)12        self.content = None13    def _is_useable(self):14        return True15    def _get_title(self, title):16        return Paragraph(17            text=title,18            css_classes=['table-title'])19    def _get_no_params(self):20        return Paragraph(text="No parameters", css_classes=['table-info'])21    def _get_parameter_table(self, params):22        tablegen = TableGenerator()23        params = get_params(params)24        if len(params) == 0:25            return self._get_no_params()26        else:27            for k, v in params.items():28                params[k] = paramval2str(k, v)29        return tablegen.get_table(params)30    def _get_values_table(self, values):31        tablegen = TableGenerator()32        if len(values) == 0:33            values[''] = ''34        return tablegen.get_table(values)35    def _get_strategy(self, strategy):36        columns = []37        childs = []38        childs.append(self._get_title(f'Strategy: {obj2label(strategy)}'))39        childs.append(self._get_parameter_table(strategy.params))40        for o in strategy.observers:41            childs.append(self._get_title(f'Observer: {obj2label(o)}'))42            childs.append(self._get_parameter_table(o.params))43        for a in strategy.analyzers:44            childs.append(self._get_title(f'Analyzer: {obj2label(a)}{" [Analysis Table]" if hasattr(a, "get_analysis_table") else ""}'))45            childs.append(self._get_parameter_table(a.params))46        columns.append(column(childs))47        return columns48    def _get_indicators(self, strategy):49        columns = []50        childs = []51        inds = strategy.getindicators()52        for i in inds:53            if isinstance(i, bt.IndicatorBase):54                childs.append(self._get_title(55                    f'Indicator: {obj2label(i)}@{obj2data(i)}'))56                childs.append(self._get_parameter_table(i.params))57        columns.append(column(childs))58        return columns59    def _get_datas(self, strategy):60        columns = []61        childs = []62        for data in strategy.datas:63            tabdata = {64                'DataName:': str(data._dataname).replace('|', '\\|'),65                'Timezone:': str(data._tz),66                'Live:': f'{"Yes" if data.islive() else "No"}',67                'Length:': len(data),68                'Granularity:': f'{data._compression} {bt.TimeFrame.getname(data._timeframe, data._compression)}',69            }70            # live trading does not have valid data parameters (other datas71            # might also not have)72            if not math.isinf(data.fromdate):73                tabdata['Time From:'] = str(bt.num2date(data.fromdate))74            if not math.isinf(data.todate):75                tabdata['Time To:'] = str(bt.num2date(data.todate))76            childs.append(self._get_title(f'Data Feed: {obj2label(data, True)}'))77            childs.append(self._get_values_table(tabdata))78        columns.append(column(childs))79        return columns80    def _get_metadata_columns(self, strategy):81        acolumns = []82        acolumns.extend(self._get_strategy(strategy))83        acolumns.extend(self._get_indicators(strategy))84        acolumns.extend(self._get_datas(strategy))85        return acolumns86    def _get_metadata_info(self):87        acolumns = self._get_metadata_columns(self._figurepage.strategy)88        info = gridplot(89            acolumns,90            ncols=self._app.scheme.metadata_tab_num_cols,...

export_MPDS.py

Source:export_MPDS.py

...26        for _ in range(12):27            basename.append(random.choice("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"))28        return "".join(basename)29    @classmethod30    def _get_title(cls, term):31        return cls.human_names.get(term, term.capitalize())32    @classmethod33    def save_plot(cls, data, columns, plottype, fmt='json', **kwargs):34        """35        Exports the data in the following formats for plotting:36        csv: for any external plotting application37        json: for a web-app at https://mpds.io/visavis38        """39        cls._verify_export_dir()40        plot = {"use_visavis_type": plottype, "payload": {}}41        if isinstance(data, pd.DataFrame):42            iter_data = data.iterrows43            pointers = columns44        else:45            iter_data = lambda: enumerate(data)46            pointers = range(len(data[0]))47        if fmt == 'csv':48            fmt_export = os.path.join(cls.export_dir, cls._gen_basename() + ".csv")49            f_export = open(fmt_export, "w")50            f_export.write("%s\n" % ",".join(map(str, columns)))51            for _, row in iter_data():52                f_export.write("%s\n" % ",".join([str(row[i]) for i in pointers]))53            f_export.close()54        else:55            fmt_export = os.path.join(cls.export_dir, cls._gen_basename() + ".json")56            f_export = open(fmt_export, "w")57            if plottype == 'bar':58                plot["payload"] = {"x": [], "y": [], "xtitle": cls._get_title(columns[0]), "ytitle": cls._get_title(columns[1])}59                for _, row in iter_data():60                    plot["payload"]["x"].append(row[pointers[0]])61                    plot["payload"]["y"].append(row[pointers[1]])62            elif plottype == 'plot3d':63                plot["payload"]["points"] = {"x": [], "y": [], "z": [], "labels": []}64                plot["payload"]["meshes"] = []65                plot["payload"]["xtitle"] = cls._get_title(columns[0])66                plot["payload"]["ytitle"] = cls._get_title(columns[1])67                plot["payload"]["ztitle"] = cls._get_title(columns[2])68                recent_mesh = 069                for _, row in iter_data():70                    plot["payload"]["points"]["x"].append(row[pointers[0]])71                    plot["payload"]["points"]["y"].append(row[pointers[1]])72                    plot["payload"]["points"]["z"].append(row[pointers[2]])73                    plot["payload"]["points"]["labels"].append(row[pointers[3]])74                    if row[4] != recent_mesh:75                        plot["payload"]["meshes"].append({"x": [], "y": [], "z": []})76                    recent_mesh = row[4]77                    if plot["payload"]["meshes"]:78                        plot["payload"]["meshes"][-1]["x"].append(row[pointers[0]])79                        plot["payload"]["meshes"][-1]["y"].append(row[pointers[1]])80                        plot["payload"]["meshes"][-1]["z"].append(row[pointers[2]])81            if kwargs:...

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