Crée une Désinence copiée sur la désinence d.

        :param d: Desinence à copier
        :type d: Desinence 
        :return: Désinence copiée sur la désinence d.
        :rtype: Desinence
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def clone(self, d):
        
        return Desinence(d.grq(), d.morphoNum(), d.numRad(), self)
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function(d) {
    return d.dcExtent || (d.dcExtent = [1, Math.ceil(later.D.extent(d)[1] /7)]);
  }
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def dframe(self):
        
        self.param.warning("The MultiDimensionalMapping.dframe method is "
                           "deprecated and should no longer be used. "
                           "Use a more specific subclass which does support "
                           "the dframe method instead, e.g. a HoloMap.")
        try:
            import pandas
        except ImportError:
            raise Exception("Cannot build a DataFrame without the pandas library.")
        labels = self.dimensions('key', True) + [self.group]
        return pandas.DataFrame(
            [dict(zip(labels, k + (v,))) for (k, v) in self.data.items()])
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def from_dict(cls, d):
        
        o = super(DistributionList, cls).from_dict(d)
        o.members = []
        if 'dlm' in d:
            o.members = [utils.get_content(member)
                         for member in utils.as_list(d["dlm"])]
        return o
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def from_dict(cls, d):
        

        name = PartitionIdentity._name_class(**d)

        if 'id' in d and 'revision' in d:
            # The vid should be constructed from the id and the revision
            on = (ObjectNumber.parse(d['id']).rev(d['revision']))
        elif 'vid' in d:
            on = ObjectNumber.parse(d['vid'])
        else:
            raise ValueError("Must have id and revision, or vid")

        try:
            return PartitionIdentity(name, on)
        except TypeError as e:
            raise TypeError(
                "Failed to make identity from \n{}\n: {}".format(
                    d,
                    e.message))
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def df(objs, labels=None):
    
    import pandas as pd
    from .objects import Object, DynamicObject
    if objs:
        objs = list(objs)
        obj = objs[0]
        if isinstance(obj, Object):
            df = pd.DataFrame.from_records(o.tuple() for o in objs)
            df.columns = obj.__class__.defaults
        elif isinstance(obj, DynamicObject):
            df = pd.DataFrame.from_records(o.__dict__ for o in objs)
        else:
            df = pd.DataFrame.from_records(objs)
        if isinstance(obj, tuple) and hasattr(obj, '_fields'):
            # assume it's a namedtuple
            df.columns = obj.__class__._fields
    else:
        df = None
    if labels:
        exclude = [label for label in df if label not in labels]
        df = df.drop(exclude, axis=1)
    return df
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def d(self):
        
        # TODO this will only work from a star or below. good thing?
        d = self.parent.d
        if ed_params.estimateMissingValues:
            if d is np.nan:
                d = self.estimateDistance()
                if d is not np.nan:
                    self.flags.addFlag('Estimated Distance')
            return d
        else:
            return np.nan
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def showDfs(d):
    
    if "metadata" in d:
        print("metadata")
    if "paleoData" in d:
        try:
            for k, v in d["paleoData"].items():
                print(k)
        except KeyError:
            pass
        except AttributeError:
            pass
    if "chronData" in d:
        try:
            for k, v in d["chronData"].items():
                print(k)
        except KeyError:
            pass
        except AttributeError:
            pass
    # print("Process Complete")
    return
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def from_dict(cls, d):
        
        result = OptionHandler(cls.new_instance(d["class"]))
        result.options = split_options(d["options"])
        return result
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def draw(self, d, i=None):
        '''
        
        '''
        if i is None:
            self._dominoes.append(d)
        else:
            self._dominoes.insert(i, d)
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def shrink(self, shrink):
        
        if isinstance(shrink, list):
            return self._shrink_list(shrink)
        if isinstance(shrink, dict):
            return self._shrink_dict(shrink)
        return shrink
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def dl(self, ds_type:DatasetType=DatasetType.Valid)->DeviceDataLoader:
        ""
        #TODO: refactor
        return (self.train_dl if ds_type == DatasetType.Train else
                self.test_dl if ds_type == DatasetType.Test else
                self.valid_dl if ds_type == DatasetType.Valid else
                self.single_dl if ds_type == DatasetType.Single else
                self.fix_dl)
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def from_dict(ddict):
        
        d = Definition(ddict['name'], ddict['line'], ddict['column'],
                       ddict['icon'], ddict['description'],
                       ddict['user_data'], ddict['path'])
        for child_dict in ddict['children']:
            d.children.append(Definition.from_dict(child_dict))
        return d
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def deserialize_dtype(d):
    
    if isinstance(d['descr'], six.string_types):
        return np.dtype(d['descr'])
    descr = []
    for col in d['descr']:
        col_descr = []
        for c in col:
            if isinstance(c, six.string_types):
                col_descr.append(str(c))
            elif type(c) is list:
                col_descr.append(tuple(c))
            else:
                col_descr.append(c)
        descr.append(tuple(col_descr))
    return np.dtype(descr)
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def record(self):
        # type: () -> bytes
        '''
        
        '''
        if not self._initialized:
            raise pycdlibexception.PyCdlibInternalError('PD record not yet initialized!')

        return b'PD' + struct.pack('=BB', RRPDRecord.length(self.padding),
                                   SU_ENTRY_VERSION) + self.padding
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def write_dfile(self):
        
        f_in = self.tempfiles.get_tempfile(prefix="bmds-", suffix=".(d)")
        with open(f_in, "w") as f:
            f.write(self.as_dfile())
        return f_in
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def store_extra_keys(self, d: Dict[str, Any]) -> None:
        
        new_dict = dict(self.extra_keys, **d)
        self.extra_keys = new_dict.copy()