text: """Class handling collections of RESQML properties for grids, wellbore frames, grid connection sets etc."""
  
  version = '1st December 2021'
  
  # Nexus is a registered trademark of the Halliburton Company
  
  import logging
  
  log = logging.getLogger(__name__)
  
  import numpy as np
  import numpy.ma as ma
  
  import resqpy.olio.uuid as bu
  import resqpy.olio.write_hdf5 as rwh5
  import resqpy.olio.xml_et as rqet
  from resqpy.olio.xml_namespaces import curly_namespace as ns
  
  from .string_lookup import StringLookup
  from .property_common import dtype_flavour, _cache_name, _cache_name_for_uuid, selective_version_of_collection
  import resqpy.property._collection_create_xml as pcxml
  import resqpy.property._collection_get_attributes as pcga
  import resqpy.property._collection_support as pcs
  import resqpy.property._collection_add_part as pcap
  
  
  class PropertyCollection():
      """Class for RESQML Property collection for any supporting representation (or mix of supporting representations).
  
      notes:
         this is a base class inherited by GridPropertyCollection and WellLogCollection (and others to follow), application
         code usually works with the derived classes;
         RESQML caters for three simple types of numerical property: Continuous (ie. real data, aka floating point);
         Discrete (ie. integer data, or boolean); Categorical (integer data, usually non-negative, with an associated
         look-up table to convert to a string); Points properties are for storing xyz values; resqpy does not currently
         support Comment properties
      """
  
      def __init__(self, support = None, property_set_root = None, realization = None):
          """Initialise an empty Property Collection, optionally populate properties from a supporting representation.
  
          arguments:
             support (optional): a grid.Grid object or a well.WellboreFrame object which belongs to a resqpy.Model which includes
                associated properties; if this argument is given, and property_set_root is None, the properties in the support's
                parent model which are for this representation (ie. have this grid or wellbore frame as the supporting representation)
                are added to this collection as part of the initialisation
             property_set_root (optional): if present, the collection is populated with the properties defined in the xml tree
                of the property set
             realization (integer, optional): if present, the single realisation (within an ensemble) that this collection is for;
                if None, then the collection is either covering a whole ensemble (individual properties can each be flagged with a
                realisation number), or is for properties that do not have multiple realizations
  
          note:
             at present, if the collection is being initialised from a property set, the support argument must also be specified;
             also for now, if not initialising from a property set, all properties related to the support are included, whether
             the relationship is supporting representation or some other relationship;
             the full handling of RESQML property sets and property series is still under development
  
          :meta common:
          """
  
          assert property_set_root is None or support is not None, \
              'support (grid, wellbore frame, blocked well, mesh, or grid connection set) must be specified when populating property collection from property set'
  
          self.dict = {}  # main dictionary of model property parts which are members of the collection
          # above is mapping from part_name to:
          # (realization, support, uuid, xml_node, continuous, count, indexable, prop_kind, facet_type, facet, citation_title,
          #   time_series_uuid, time_index, min, max, uom, string_lookup_uuid, property_kind_uuid, extra_metadata, null_value,
          #     const_value, points)
          #  0            1        2     3         4           5      6          7          8           9      10
          #   11                12          13   14   15   16                  17                  18              19
          #     20           21
          # note: grid is included to allow for super-collections covering more than one grid
          # todo: replace items 8 & 9 with a facet dictionary (to allow for multiple facets)
          self.model = None
          self.support = None
          self.support_root = None
          self.support_uuid = None
          self.property_set_root = None
          self.time_set_kind_attr = None
          self.has_single_property_kind_flag = None
          self.has_single_uom_flag = None
          self.has_single_indexable_element_flag = None
          self.has_multiple_realizations_flag = None
          self.parent_set_root = None
          self.realization = realization  # model realization number within an ensemble
          self.null_value = None
          self.imported_list = [
          ]  # list of (uuid, file_name, keyword, cached_name, discrete, uom, time_index, null_value,
          #                                   min_value, max_value, property_kind, facet_type, facet, realization,
          #                                   indexable_element, count, local_property_kind_uuid, const_value, points)
          self.guess_warning = False
          if support is not None:
              self.model = support.model
              self.set_support(support = support)
              assert self.model is not None
              # assert self.support_root is not None
              assert self.support_uuid is not None
              if property_set_root is None:
                  # todo: make more rigorous by looking up supporting representation node uuids
                  props_list = self.model.parts_list_of_type(type_of_interest = 'obj_DiscreteProperty')
                  discrete_props_list = self.model.parts_list_filtered_by_supporting_uuid(props_list, self.support_uuid)
                  self.add_parts_list_to_dict(discrete_props_list)
                  props_list = self.model.parts_list_of_type(type_of_interest = 'obj_CategoricalProperty')
                  categorical_props_list = self.model.parts_list_filtered_by_supporting_uuid(
                      props_list, self.support_uuid)
                  self.add_parts_list_to_dict(categorical_props_list)
                  props_list = self.model.parts_list_of_type(type_of_interest = 'obj_ContinuousProperty')
                  continuous_props_list = self.model.parts_list_filtered_by_supporting_uuid(props_list, self.support_uuid)
                  self.add_parts_list_to_dict(continuous_props_list)
                  props_list = self.model.parts_list_of_type(type_of_interest = 'obj_PointsProperty')
                  points_props_list = self.model.parts_list_filtered_by_supporting_uuid(props_list, self.support_uuid)
                  self.add_parts_list_to_dict(points_props_list)
              else:
                  self.populate_from_property_set(property_set_root)
  
      def set_support(self, support_uuid = None, support = None, model = None, modify_parts = True):
          """Sets the supporting object associated with this collection if not done so at initialisation.
  
          Does not load properties.
  
          Arguments:
             support_uuid: the uuid of the supporting representation which the properties in this collection are for
             support: a grid.Grid, unstructured.UnstructuredGrid (or derived class), well.WellboreFrame, well.BlockedWell,
                surface.Mesh, or fault.GridConnectionSet object which the properties in this collection are for
             model (model.Model object, optional): if present, the model associated with this collection is set to this;
                otherwise the model is assigned from the supporting object
             modify_parts (boolean, default True): if True, any parts already in this collection have their individual
                support_uuid set
          """
  
          # todo: check uuid's of individual parts' supports match that of support being set for whole collection
          model = pcs._set_support_model(self, model, support)
  
          if support_uuid is None and support is not None:
              support_uuid = support.uuid
  
          if support_uuid is None:
              pcs._set_support_uuid_none(self)
          else:
              pcs._set_support_uuid_notnone(self, support, support_uuid, model, modify_parts)
  
      def supporting_shape(self, indexable_element = None, direction = None):
          """Return the shape of the supporting representation with respect to the given indexable element
  
          arguments:
             indexable_element (string, optional): if None, a hard-coded default depending on the supporting representation class
                will be used
             direction (string, optional): must be passed if required for the combination of support class and indexable element;
                currently only used for Grid faces.
  
          returns:
             list of int, being required shape of numpy array, or None if not coded for
  
          note:
             individual property arrays will only match this shape if they have the same indexable element and a count of one
          """
  
          # when at global level was causing circular reference loading issues as grid imports this module
          import resqpy.fault as rqf
          import resqpy.grid as grr
          import resqpy.surface as rqs
          import resqpy.unstructured as rug
          import resqpy.well as rqw
  
          support = self.support
  
          if isinstance(support, grr.Grid):
              shape_list = pcga._supporting_shape_grid(support, indexable_element, direction)
  
          elif isinstance(support, rqw.WellboreFrame):
              shape_list = pcga._supporting_shape_wellboreframe(support, indexable_element)
  
          elif isinstance(support, rqw.BlockedWell):
              shape_list = pcga._supporting_shape_blockedwell(support, indexable_element)
  
          elif isinstance(support, rqs.Mesh):
              shape_list = pcga._supporting_shape_mesh(support, indexable_element)
  
          elif isinstance(support, rqf.GridConnectionSet):
              shape_list = pcga._supporting_shape_gridconnectionset(support, indexable_element)
  
          elif type(support) in [
                  rug.UnstructuredGrid, rug.HexaGrid, rug.TetraGrid, rug.PrismGrid, rug.VerticalPrismGrid, rug.PyramidGrid
          ]:
              shape_list, support = pcga._supporting_shape_other(support, indexable_element)
  
          else:
              raise Exception(f'unsupported support class {type(support)} for property')
  
          return shape_list
  
      def populate_from_property_set(self, property_set_root):
          """Populates this (newly created) collection based on xml members of property set."""
  
          assert property_set_root is not None, 'missing property set xml root'
          assert self.model is not None and self.support is not None, 'set support for collection before populating from property set'
  
          self.property_set_root = property_set_root
          self.time_set_kind_attr = rqet.find_tag_text(property_set_root, 'TimeSetKind')
          self.has_single_property_kind_flag = rqet.find_tag_bool(property_set_root, 'HasSinglePropertyKind')
          self.has_multiple_realizations_flag = rqet.find_tag_bool(property_set_root, 'HasMultipleRealizations')
          parent_set_ref_root = rqet.find_tag(property_set_root, 'ParentSet')  # at most one parent set handled here
          if parent_set_ref_root is not None:
              self.parent_set_root = self.model.referenced_node(parent_set_ref_root)
          # loop over properties in property set xml, adding parts to main dictionary
          for child in property_set_root:
              if rqet.stripped_of_prefix(child.tag) != 'Properties':
                  continue
              property_root = self.model.referenced_node(child)
              if property_root is None:
                  log.warning('property set member missing from resqml dataset')
                  continue
              self.add_part_to_dict(rqet.part_name_for_part_root(property_root))
  
      def set_realization(self, realization):
          """Sets the model realization number (within an ensemble) for this collection.
  
          argument:
             realization (non-negative integer): the realization number of the whole collection within an ensemble of
                     collections
  
          note:
             the resqml Property classes allow for a realization index to be associated with an individual property
             array; this module supports this by associating a realization number (equivalent to realization index) for
             each part (ie. for each property array); however, the collection can be given a realization number which is
             then applied to each member of the collection as it is added, if no part-specific realization number is
             provided
          """
  
          # the following assertion might need to be downgraded to a warning, to allow for reassignment of realization numbers
          assert self.realization is None
          self.realization = realization
  
      def add_part_to_dict(self, part, continuous = None, realization = None, trust_uom = True):
          """Add the named part to the dictionary for this collection.
  
          arguments:
             part (string): the name of a part (which exists in the support's parent model) to be added to this collection
             continuous (boolean, optional): whether the property is of a continuous (real) kind; if not None,
                      is checked against the property's type and an assertion error is raised if there is a mismatch;
                      should be None or True for Points properties
             realization (integer, optional): if present, must match this collection's realization number if that is
                      not None; if this argument is None then the part is assigned the realization number associated
                      with this collection as a whole; if the xml for the part includes a realization index then that
                      overrides these other sources to become the realization number
             trust_uom (boolean, default True): if True, the uom stored in the part's xml is used as the part's uom
                      in this collection; if False and the uom in the xml is an empty string or 'Euc', then the
                      part's uom in this collection is set to a guess based on the property kind and min & max values;
                      note that this guessed value is not used to overwrite the value in the xml
          """
  
          if part is None:
              return
          assert part not in self.dict
          uuid = self.model.uuid_for_part(part, is_rels = False)
          assert uuid is not None
          xml_node = self.model.root_for_part(part, is_rels = False)
          assert xml_node is not None
  
          realization = pcap._add_part_to_dict_get_realization(self, realization, xml_node)
          type, continuous, points, string_lookup_uuid, sl_ref_node = pcap._add_part_to_dict_get_type_details(
              self, part, continuous, xml_node)
          extra_metadata = rqet.load_metadata_from_xml(xml_node)
          citation_title = rqet.find_tag(rqet.find_tag(xml_node, 'Citation'), 'Title').text
          count, indexable = pcap._add_part_to_dict_get_count_and_indexable(xml_node)
          property_kind, property_kind_uuid, lpk_node = pcap._add_part_to_dict_get_property_kind(xml_node, citation_title)
          facet_type, facet = pcap._add_part_to_dict_get_facet(xml_node)
          time_series_uuid, time_index = pcap._add_part_to_dict_get_timeseries(xml_node)
          minimum, maximum = pcap._add_part_to_dict_get_minmax(xml_node)
          support_uuid = pcap._add_part_to_dict_get_support_uuid(self, part)
          uom = pcap._add_part_to_dict_get_uom(self, part, continuous, xml_node, trust_uom, property_kind, minimum,
                                               maximum, facet, facet_type)
          null_value, const_value = pcap._add_part_to_dict_get_null_constvalue_points(xml_node, continuous, points)
  
          self.dict[part] = (realization, support_uuid, uuid, xml_node, continuous, count, indexable, property_kind,
                             facet_type, facet, citation_title, time_series_uuid, time_index, minimum, maximum, uom,
                             string_lookup_uuid, property_kind_uuid, extra_metadata, null_value, const_value, points)
  
      def add_parts_list_to_dict(self, parts_list):
          """Add all the parts named in the parts list to the dictionary for this collection.
  
          argument:
             parts_list: a list of strings, each being the name of a part in the support's parent model
  
          note:
             the add_part_to_dict() function is called for each part in the list
          """
  
          for part in parts_list:
              self.add_part_to_dict(part)
  
      def remove_part_from_dict(self, part):
          """Remove the named part from the dictionary for this collection.
  
          argument:
             part (string): the name of a part which might be in this collection, to be removed
  
          note:
             if the part is not in the collection, no action is taken and no exception is raised
          """
  
          if part is None:
              return
          if part not in self.dict:
              return
          del self.dict[part]
  
      def remove_parts_list_from_dict(self, parts_list):
          """Remove all the parts named in the parts list from the dictionary for this collection.
  
          argument:
             parts_list: a list of strings, each being the name of a part which might be in the collection
  
          note:
             the remove_part_from_dict() function is called for each part in the list
          """
  
          for part in parts_list:
              self.remove_part_from_dict(part)
  
      def inherit_imported_list_from_other_collection(self, other, copy_cached_arrays = True, exclude_inactive = False):
          """Extends this collection's imported list with items from other's imported list.
  
          arguments:
             other: another PropertyCollection object with some imported arrays
             copy_cached_arrays (boolean, default True): if True, arrays cached with the other
                collection are copied and cached with this collection
             exclude_inactive (boolean, default False): if True, any item in the other imported list
                which has INACTIVE or ACTIVE as the keyword is excluded from the inheritance
  
          note:
             the imported list is a list of cached imported arrays with basic info for each array;
             it is used as a staging post before fully incorporating the imported arrays as parts
             of the support's parent model and writing the arrays to the hdf5 file
          """
  
          # note: does not inherit parts
          if exclude_inactive:
              other_list = []
              for imp in other.imported_list:
                  if imp[2].upper() not in ['INACTIVE', 'ACTIVE']:
                      other_list.append(imp)
          else:
              other_list = other.imported_list
          self.imported_list += other_list
          if copy_cached_arrays:
              for imp in other_list:
                  if imp[17] is not None:
                      continue  # constant array
                  cached_name = imp[3]
                  self.__dict__[cached_name] = other.__dict__[cached_name].copy()
  
      def inherit_parts_from_other_collection(self, other, ignore_clashes = False):
          """Adds all the parts in the other PropertyCollection to this one.
  
          Arguments:
             other: another PropertyCollection object related to the same support as this collection
             ignore_clashes (boolean, default False): if False, any part in other which is already in
                this collection will result in an assertion error; if True, such duplicates are
                simply skipped without modifying the existing part in this collection
          """
  
          assert self.support_uuid is None or other.support_uuid is None or bu.matching_uuids(
              self.support_uuid, other.support_uuid)
          if self.support_uuid is None and self.number_of_parts() == 0 and other.support_uuid is not None:
              self.set_support(support_uuid = other.support_uuid, support = other.support)
          if self.realization is not None and other.realization is not None:
              assert self.realization == other.realization
          for (part, info) in other.dict.items():
              if part in self.dict.keys():
                  if ignore_clashes:
                      continue
                  assert False, 'attempt to inherit a part which already exists in property collection: ' + part
              self.dict[part] = info
  
      def add_to_imported_list_sampling_other_collection(self, other, flattened_indices):
          """Makes cut down copies of parts from other collection, using indices, and adds to imported list.
  
          arguments:
            other (PropertyCollection): the source collection whose arrays will be sampled
            flattened_indices (1D numpy int array): the indices (in flattened space) of the elements to be copied
  
          notes:
            the values in flattened_indices refer to the source (other) array elements, after flattening;
            the size of flatted_indices must match the size of the target (self) supporting shape; where different
            indexable elements are at play, with different implicit sizes, make selective copies of other and
            call this method once for each group of differently sized properties; for very large collections
            it might also be necessary to divide the work into smaller groups to reduce memory usage;
            this method does not write to hdf5 nor create xml – use the usual methods for further processing
            of the imported list
          """
  
          source = 'sampled'
          if other.support is not None:
              source += ' from property for ' + str(other.support.title)
          for (part, info) in other.dict.items():
              target_shape = self.supporting_shape(indexable_element = other.indexable_for_part(part),
                                                   direction = pcga._part_direction(other, part))
              assert np.prod(target_shape) == flattened_indices.size
              a = other.cached_part_array_ref(part).flatten()[flattened_indices].reshape(target_shape)
              self.add_cached_array_to_imported_list(a,
                                                     source,
                                                     info[10],
                                                     discrete = not info[4],
                                                     uom = info[15],
                                                     time_index = info[12],
                                                     null_value = info[19],
                                                     property_kind = info[7],
                                                     local_property_kind_uuid = info[17],
                                                     facet_type = info[8],
                                                     facet = info[9],
                                                     realization = info[0],
                                                     indexable_element = info[6],
                                                     count = info[5],
                                                     const_value = info[20],
                                                     points = info[21])
  
      def inherit_parts_selectively_from_other_collection(
              self,
              other,
              realization = None,
              support_uuid = None,
              grid = None,  # for backward compatibility
              uuid = None,
              continuous = None,
              count = None,
              points = None,
              indexable = None,
              property_kind = None,
              facet_type = None,
              facet = None,
              citation_title = None,
              citation_title_match_starts_with = False,
              time_series_uuid = None,
              time_index = None,
              uom = None,
              string_lookup_uuid = None,
              categorical = None,
              ignore_clashes = False):
          """Adds those parts from the other PropertyCollection which match all arguments that are not None.
  
          arguments:
             other: another PropertyCollection object related to the same support as this collection
             citation_title_match_starts_with (boolean, default False): if True, any citation title that
                starts with the given citation_title argument will be deemed to have passed that filter
             ignore_clashes (boolean, default False): if False, any part in other which passes the filters
                yet is already in this collection will result in an assertion error; if True, such duplicates
                are simply skipped without modifying the existing part in this collection
  
          Other optional arguments (realization, grid, uuid, continuous, count, points, indexable, property_kind, facet_type,
          facet, citation_title, time_series_uuid, time_index, uom, string_lookup_uuid, categorical):
  
          For each of these arguments: if None, then all members of collection pass this filter;
          if not None then only those members with the given value pass this filter;
          finally, the filters for all the attributes must be passed for a given member (part)
          to be inherited.
  
          note:
             the grid argument is maintained for backward compatibility; it is treated synonymously with support
             which takes precendence; the categorical boolean argument can be used to filter only Categorical
             (or non-Categorical) properties
          """
  
          #      log.debug('inheriting parts selectively')
          pcs._set_support_and_model_from_collection(self, other, support_uuid, grid)
  
          if self.realization is not None and other.realization is not None:
              assert self.realization == other.realization
          if time_index is not None:
              assert time_index >= 0
  
          for (part, info) in other.dict.items():
              pcap._add_selected_part_from_other_dict(self, part, other, realization, support_uuid, uuid, continuous,
                                                      categorical, count, points, indexable, property_kind, facet_type,
                                                      facet, citation_title, citation_title_match_starts_with,
                                                      time_series_uuid, time_index, string_lookup_uuid, ignore_clashes)
  
      def inherit_similar_parts_for_time_series_from_other_collection(self,
                                                                      other,
                                                                      example_part,
                                                                      citation_title_match_starts_with = False,
                                                                      ignore_clashes = False):
          """Adds the example part from other collection and any other parts for the same property at different times.
  
          arguments:
             other: another PropertyCollection object related to the same grid as this collection, from which to inherit
             example_part (string): the part name of an example member of other (which has an associated time_series)
             citation_title_match_starts_with (booleam, default False): if True, any citation title that starts with
                the example's citation_title after removing trailing digits, will be deemed to have passed that filter
             ignore_clashes (boolean, default False): if False, any part in other which passes the filters
                yet is already in this collection will result in an assertion error; if True, such duplicates
                are simply skipped without modifying the existing part in this collection
  
          note:
             at present, the citation title must match (as well as the other identifying elements) for a part to be
             inherited
          """
  
          assert other is not None
          assert other.part_in_collection(example_part)
          time_series_uuid = other.time_series_uuid_for_part(example_part)
          assert time_series_uuid is not None
          title = other.citation_title_for_part(example_part)
          if citation_title_match_starts_with:
              while title and title[-1].isdigit():
                  title = title[:-1]
          self.inherit_parts_selectively_from_other_collection(
              other,
              realization = other.realization_for_part(example_part),
              support_uuid = other.support_uuid_for_part(example_part),
              continuous = other.continuous_for_part(example_part),
              points = other.points_for_part(example_part),
              indexable = other.indexable_for_part(example_part),
              property_kind = other.property_kind_for_part(example_part),
              facet_type = other.facet_type_for_part(example_part),
              facet = other.facet_for_part(example_part),
              citation_title = title,
              citation_title_match_starts_with = citation_title_match_starts_with,
              time_series_uuid = time_series_uuid,
              ignore_clashes = ignore_clashes)
  
      def inherit_similar_parts_for_facets_from_other_collection(self,
                                                                 other,
                                                                 example_part,
                                                                 citation_title_match_starts_with = False,
                                                                 ignore_clashes = False):
          """Adds the example part from other collection and any other parts for same property with different facets.
  
          arguments:
             other: another PropertyCollection object related to the same grid as this collection, from which to inherit
             example_part (string): the part name of an example member of other
             citation_title_match_starts_with (booleam, default False): if True, any citation title that starts with
                the example's citation_title after removing trailing digits, will be deemed to have passed that filter
             ignore_clashes (boolean, default False): if False, any part in other which passes the filters
                yet is already in this collection will result in an assertion error; if True, such duplicates
                are simply skipped without modifying the existing part in this collection
  
          note:
             at present, the citation title must match (as well as the other identifying elements) for a part to be
             inherited
          """
  
          # the following RESQML limitation could be reported as a warning here, instead of an assertion
          assert not other.points_for_part(example_part), 'facets not allowed for RESQML Points properties'
          assert other is not None
          assert other.part_in_collection(example_part)
          title = other.citation_title_for_part(example_part)
          if citation_title_match_starts_with:
              while title and title[-1].isdigit():
                  title = title[:-1]
          self.inherit_parts_selectively_from_other_collection(
              other,
              realization = other.realization_for_part(example_part),
              support_uuid = other.support_uuid_for_part(example_part),
              continuous = other.continuous_for_part(example_part),
              points = False,
              indexable = other.indexable_for_part(example_part),
              property_kind = other.property_kind_for_part(example_part),
              citation_title = title,
              time_series_uuid = other.time_series_uuid_for_part(example_part),
              time_index = other.time_index_for_part(example_part),
              ignore_clashes = ignore_clashes)
  
      def inherit_similar_parts_for_realizations_from_other_collection(self,
                                                                       other,
                                                                       example_part,
                                                                       citation_title_match_starts_with = False,
                                                                       ignore_clashes = False):
          """Add the example part from other collection and any other parts for same property with different realizations.
  
          arguments:
             other: another PropertyCollection object related to the same support as this collection, from which to inherit
             example_part (string): the part name of an example member of other
             citation_title_match_starts_with (booleam, default False): if True, any citation title that starts with
                the example's citation_title after removing trailing digits, will be deemed to have passed that filter
             ignore_clashes (boolean, default False): if False, any part in other which passes the filters
                yet is already in this collection will result in an assertion error; if True, such duplicates
                are simply skipped without modifying the existing part in this collection
  
          note:
             at present, the citation title must match (as well as the other identifying elements) for a part to be
             inherited
          """
  
          assert other is not None
          assert other.part_in_collection(example_part)
          title = other.citation_title_for_part(example_part)
          if citation_title_match_starts_with:
              while title and title[-1].isdigit():
                  title = title[:-1]
          self.inherit_parts_selectively_from_other_collection(
              other,
              realization = None,
              support_uuid = other.support_uuid_for_part(example_part),
              continuous = other.continuous_for_part(example_part),
              points = other.points_for_part(example_part),
              indexable = other.indexable_for_part(example_part),
              property_kind = other.property_kind_for_part(example_part),
              facet_type = other.facet_type_for_part(example_part),
              facet = other.facet_for_part(example_part),
              citation_title = title,
              citation_title_match_starts_with = citation_title_match_starts_with,
              time_series_uuid = other.time_series_uuid_for_part(example_part),
              time_index = other.time_index_for_part(example_part),
              ignore_clashes = ignore_clashes)
  
      def number_of_imports(self):
          """Returns the number of property arrays in the imported list for this collection.
  
          returns:
             count of number of cached property arrays in the imported list for this collection (non-negative integer)
  
          note:
             the importation list is cleared after creation of xml trees for the imported properties, so this
             function will return zero at that point, until a new list of imports is built up
          """
  
          return len(self.imported_list)
  
      def parts(self):
          """Return list of parts in this collection.
  
          returns:
             list of part names (strings) being the members of this collection; there is one part per property array
  
          :meta common:
          """
  
          return list(self.dict.keys())
  
      def uuids(self):
          """Return list of uuids in this collection.
  
          returns:
             list of uuids being the members of this collection; there is one uuid per property array
  
          :meta common:
          """
          return [self.model.uuid_for_part(p) for p in self.dict.keys()]
  
      def selective_parts_list(
              self,
              realization = None,
              support = None,  # maintained for backward compatibility
              support_uuid = None,
              grid = None,  # maintained for backward compatibility
              continuous = None,
              points = None,
              count = None,
              indexable = None,
              property_kind = None,
              facet_type = None,
              facet = None,
              citation_title = None,
              time_series_uuid = None,
              time_index = None,
              uom = None,
              string_lookup_uuid = None,
              categorical = None):
          """Returns a list of parts filtered by those arguments which are not None.
  
          All arguments are optional.
  
          For each of these arguments: if None, then all members of collection pass this filter;
          if not None then only those members with the given value pass this filter;
          finally, the filters for all the attributes must be passed for a given member (part)
          to be included in the returned list of parts
  
          returns:
             list of part names (strings) of those parts which match any selection arguments which are not None
  
          note:
             the support and grid keyword arguments are maintained for backward compatibility;
             support_uuid takes precedence over support and both take precedence over grid
  
          :meta common:
          """
  
          if support is None:
              support = grid
          if support_uuid is None and support is not None:
              support_uuid = support.uuid
  
          temp_collection = selective_version_of_collection(self,
                                                            realization = realization,
                                                            support_uuid = support_uuid,
                                                            continuous = continuous,
                                                            points = points,
                                                            count = count,
                                                            indexable = indexable,
                                                            property_kind = property_kind,
                                                            facet_type = facet_type,
                                                            facet = facet,
                                                            citation_title = citation_title,
                                                            time_series_uuid = time_series_uuid,
                                                            time_index = time_index,
                                                            uom = uom,
                                                            categorical = categorical,
                                                            string_lookup_uuid = string_lookup_uuid)
          parts_list = temp_collection.parts()
          return parts_list
  
      def singleton(
              self,
              realization = None,
              support = None,  # for backward compatibility
              support_uuid = None,
              grid = None,  # for backward compatibility
              uuid = None,
              continuous = None,
              points = None,
              count = None,
              indexable = None,
              property_kind = None,
              facet_type = None,
              facet = None,
              citation_title = None,
              time_series_uuid = None,
              time_index = None,
              uom = None,
              string_lookup_uuid = None,
              categorical = None):
          """Returns a single part selected by those arguments which are not None.
  
          For each argument: if None, then all members of collection pass this filter;
          if not None then only those members with the given value pass this filter;
          finally, the filters for all the attributes must be passed for a given member (part)
          to be selected
  
          returns:
             part name (string) of the part which matches all selection arguments which are not None;
             returns None if no parts match; raises an assertion error if more than one part matches
  
          :meta common:
          """
  
          if support is None:
              support = grid
          if support_uuid is None and support is not None:
              support_uuid = support.uuid
  
          temp_collection = selective_version_of_collection(self,
                                                            realization = realization,
                                                            support_uuid = support_uuid,
                                                            uuid = uuid,
                                                            continuous = continuous,
                                                            points = points,
                                                            count = count,
                                                            indexable = indexable,
                                                            property_kind = property_kind,
                                                            facet_type = facet_type,
                                                            facet = facet,
                                                            citation_title = citation_title,
                                                            time_series_uuid = time_series_uuid,
                                                            time_index = time_index,
                                                            uom = uom,
                                                            string_lookup_uuid = string_lookup_uuid,
                                                            categorical = categorical)
          parts_list = temp_collection.parts()
          if len(parts_list) == 0:
              return None
          assert len(parts_list) == 1, 'More than one property part matches selection criteria'
          return parts_list[0]
  
      def single_array_ref(
              self,
              realization = None,
              support = None,  # for backward compatibility
              support_uuid = None,
              grid = None,  # for backward compatibility
              uuid = None,
              continuous = None,
              points = None,
              count = None,
              indexable = None,
              property_kind = None,
              facet_type = None,
              facet = None,
              citation_title = None,
              time_series_uuid = None,
              time_index = None,
              uom = None,
              string_lookup_uuid = None,
              categorical = None,
              dtype = None,
              masked = False,
              exclude_null = False):
          """Returns the array of data for a single part selected by those arguments which are not None.
  
          arguments:
             dtype (optional, default None): the element data type of the array to be accessed, eg 'float' or 'int';
                if None (recommended), the dtype of the returned numpy array matches that in the hdf5 dataset
             masked (boolean, optional, default False): if True, a masked array is returned instead of a simple
                numpy array; the mask is set to the inactive array attribute of the grid object
             exclude_null (boolean, default False): it True and masked is True, elements holding the null value
                will also be masked out
  
          Other optional arguments:
          realization, support, support_uuid, grid, continuous, points, count, indexable, property_kind, facet_type, facet,
          citation_title, time_series_uuid, time_index, uom, string_lookup_id, categorical:
  
          For each of these arguments: if None, then all members of collection pass this filter;
          if not None then only those members with the given value pass this filter;
          finally, the filters for all the attributes must be passed for a given member (part)
          to be selected
  
          returns:
             reference to a cached numpy array containing the actual property data for the part which matches all
             selection arguments which are not None
  
          notes:
             returns None if no parts match; raises an assertion error if more than one part matches;
             multiple calls will return the same cached array so calling code should copy if duplication is needed;
             support and grid arguments are for backward compatibilty: support_uuid takes precedence over support and
             both take precendence over grid
  
          :meta common:
          """
  
          if support is None:
              support = grid
          if support_uuid is None and support is not None:
              support_uuid = support.uuid
  
          part = self.singleton(realization = realization,
                                support_uuid = support_uuid,
                                uuid = uuid,
                                continuous = continuous,
                                points = points,
                                count = count,
                                indexable = indexable,
                                property_kind = property_kind,
                                facet_type = facet_type,
                                facet = facet,
                                citation_title = citation_title,
                                time_series_uuid = time_series_uuid,
                                time_index = time_index,
                                uom = uom,
                                string_lookup_uuid = string_lookup_uuid,
                                categorical = categorical)
          if part is None:
              return None
          return self.cached_part_array_ref(part, dtype = dtype, masked = masked, exclude_null = exclude_null)
  
      def number_of_parts(self):
          """Returns the number of parts (properties) in this collection.
  
          returns:
             count of the number of parts (members) in this collection; there is one part per property array (non-negative integer)
  
          :meta common:
          """
  
          return len(self.dict)
  
      def part_in_collection(self, part):
          """Returns True if named part is member of this collection; otherwise False.
  
          arguments:
             part (string): part name to be tested for membership of this collection
  
          returns:
             boolean
          """
  
          return part in self.dict
  
      # 'private' function for accessing an element from the tuple for the part
      # the main dictionary maps from part name to a tuple of information
      # this function simply extracts one element of the tuple in a way that returns None if the part is awol
      def element_for_part(self, part, index):
          """Returns a particular element by index from the tuple of metadata for the specified part.
  
          :meta private:
          """
  
          if part not in self.dict:
              return None
          return self.dict[part][index]
  
      # 'private' function for returning a list of unique values for an element from the tuples within the collection
      # excludes None from list
      def unique_element_list(self, index, sort_list = True):
          """Returns an optionally sorted list of unique values (excluding None) of an element identified by index.
  
          :meta private:
          """
  
          s = set()
          for _, t in self.dict.items():
              e = t[index]
              if e is not None:
                  s = s.union({e})
          result = list(s)
          if sort_list:
              result.sort()
          return result
  
      def part_str(self, part, include_citation_title = True):
          """Returns a human-readable string identifying the part.
  
          arguments:
             part (string): the part name for which a displayable string is required
             include_citation_title (boolean, default True): if True, the citation title for the part is
                included in parenthesis at the end of the returned string; otherwise it does not appear
  
          returns:
             a human readable string consisting of the property kind, the facet (if present), the
             time index (if applicable), and the citation title (if requested)
  
          note:
             the time index is labelled 'timestep' in the returned string; however, resqml differentiates
             between the simulator timestep number and a time index into a time series; at present this
             module conflates the two
  
          :meta common:
          """
  
          text = self.property_kind_for_part(part)
          facet = self.facet_for_part(part)
          if facet:
              text += ': ' + facet
          time_index = self.time_index_for_part(part)
          if time_index is not None:
              text += '; timestep: ' + str(time_index)
          if include_citation_title:
              title = self.citation_title_for_part(part)
              if title:
                  text += ' (' + title + ')'
          return text
  
      def part_filename(self, part):
          """Returns a string which can be used as the starting point of a filename relating to part.
  
          arguments:
             part (string): the part name for which a partial filename is required
  
          returns:
             a string suitable as the basis of a filename for the part (typically used when exporting)
          """
  
          text = self.property_kind_for_part(part).replace(' ', '_')
          facet = self.facet_for_part(part)
          if facet:
              text += '_' + facet  # could insert facet_type prior to this
          time_index = self.time_index_for_part(part)
          if time_index is not None:
              text += '_ts_' + str(time_index)
          return text
  
      def realization_for_part(self, part):
          """Returns realization number (within ensemble) that the property relates to.
  
          arguments:
             part (string): the part name for which the realization number (realization index) is required
  
          returns:
             integer or None
  
          :meta common:
          """
  
          return self.element_for_part(part, 0)
  
      def realization_list(self, sort_list = True):
          """Returns a list of unique realization numbers present in the collection."""
  
          return self.unique_element_list(0)
  
      def support_uuid_for_part(self, part):
          """Returns supporting representation object's uuid that the property relates to.
  
          arguments:
             part (string): the part name for which the related support object uuid is required
  
          returns:
             uuid.UUID object (or string representation thereof)
          """
  
          return self.element_for_part(part, 1)
  
      def grid_for_part(self, part):
          """Returns grid object that the property relates to.
  
          arguments:
             part (string): the part name for which the related grid object is required
  
          returns:
             grid.Grid object reference
  
          note:
             this method maintained for backward compatibility and kept in base PropertyClass
             for pragmatic reasons (rather than being method in GridPropertyCollection)
          """
  
          import resqpy.grid as grr
  
          support_uuid = self.support_uuid_for_part(part)
          if support_uuid is None:
              return None
          if bu.matching_uuids(self.support_uuid, support_uuid):
              return self.support
          assert self.model is not None
          part = self.model.part_for_uuid(support_uuid)
          assert part is not None and self.model.type_of_part(part) in [
              'obj_IjkGridRepresentation', 'obj_UnstructuredGridRepresentation'
          ]
          return grr.any_grid(self.model, uuid = support_uuid, find_properties = False)
  
      def uuid_for_part(self, part):
          """Returns UUID object for the property part.
  
          arguments:
             part (string): the part name for which the UUID is required
  
          returns:
             uuid.UUID object reference; use str(uuid_for_part()) to convert to string
  
          :meta common:
          """
  
          return self.element_for_part(part, 2)
  
      def node_for_part(self, part):
          """Returns the xml node for the property part.
  
          arguments:
             part (string): the part name for which the xml node is required
  
          returns:
             xml Element object reference for the main xml node for the part
          """
  
          return self.element_for_part(part, 3)
  
      def extra_metadata_for_part(self, part):
          """Returns the extra_metadata dictionary for the part.
  
          arguments:
             part (string): the part name for which the xml node is required
  
          returns:
             dictionary containing extra_metadata for part
          """
          try:
              meta = self.element_for_part(part, 18)
          except Exception:
              pass
              meta = {}
          return meta
  
      def null_value_for_part(self, part):
          """Returns the null value for the (discrete) property part; np.NaN for continuous parts.
  
          arguments:
             part (string): the part name for which the null value is required
  
          returns:
             int or np.NaN
          """
  
          if self.continuous_for_part(part):
              return np.NaN
          return self.element_for_part(part, 19)
  
      def continuous_for_part(self, part):
          """Returns True if the property is continuous (including points); False if it is discrete (or categorical).
  
          arguments:
             part (string): the part name for which the continuous versus discrete flag is required
  
          returns:
             True if the part is representing a continuous (or points) property, ie. the array elements are
             real numbers (float); False if the part is representing a discrete property or a categorical property,
             ie the array elements are integers (or boolean)
  
          note:
             RESQML differentiates between discrete and categorical properties; discrete properties are
             unbounded integers where the values have numerical significance (eg. could be added together),
             whilst categorical properties have an associated dictionary mapping from a finite set of integer
             key values onto strings (eg. {1: 'background', 2: 'channel sand', 3: 'mud drape'}); however, this
             module treats categorical properties as a special case of discrete properties
  
          :meta common:
          """
  
          return self.element_for_part(part, 4)
  
      def points_for_part(self, part):
          """Returns True if the property is a points property; False otherwise.
  
          arguments:
             part (string): the part name for which the points flag is required
  
          returns:
             True if the part is representing a points property, ie. the array has an extra dimension of extent 3
             covering the xyz axes; False if the part is representing a non-points property
          """
  
          return self.element_for_part(part, 21)
  
      def all_continuous(self):
          """Returns True if all the parts are for continuous (real) properties (includes points)."""
  
          unique_elements = self.unique_element_list(4, sort_list = False)
          if len(unique_elements) != 1:
              return False
          return unique_elements[0]
  
      def all_discrete(self):
          """Returns True if all the parts are for discrete or categorical (integer) properties."""
  
          unique_elements = self.unique_element_list(4, sort_list = False)
          if len(unique_elements) != 1:
              return False
          return not unique_elements[0]
  
      def count_for_part(self, part):
          """Returns the Count value for the property part; usually 1.
  
          arguments:
             part (string): the part name for which the count is required
  
          returns:
             integer reflecting the count attribute for the part (usually one); if greater than one,
             the array has an extra axis, cycling fastest, having this extent
  
          note:
             this mechanism allows a vector of values to be associated with a single indexable element
             in the supporting representation
          """
  
          return self.element_for_part(part, 5)
  
      def all_count_one(self):
          """Returns True if the low level Count value is 1 for all the parts in the collection."""
  
          unique_elements = self.unique_element_list(5, sort_list = False)
          if len(unique_elements) != 1:
              return False
          return unique_elements[0] == 1
  
      def indexable_for_part(self, part):
          """Returns the text of the IndexableElement for the property part; usually 'cells' for grid properties.
  
          arguments:
             part (string): the part name for which the indexable element is required
  
          returns:
             string, usually 'cells' when the supporting representation is a grid or 'nodes' when a wellbore frame
  
          note:
             see tail of Representations.xsd for overview of indexable elements usable for other object classes
  
          :meta common:
          """
  
          return self.element_for_part(part, 6)
  
      def unique_indexable_element_list(self, sort_list = False):
          """Returns a list of unique values for the IndexableElement of the property parts in the collection."""
  
          return self.unique_element_list(6, sort_list = sort_list)
  
      def property_kind_for_part(self, part):
          """Returns the resqml property kind for the property part.
  
          arguments:
             part (string): the part name for which the property kind is required
  
          returns:
             standard resqml property kind or local property kind for this part, as a string, eg. 'porosity'
  
          notes:
             see attributes of this module named supported_property_kind_list and supported_local_property_kind_list
             for the property kinds which this module can relate to simulator keywords (Nexus); however, other property
             kinds should be handled okay in a generic way;
             for bespoke (local) property kinds, this is the property kind title as stored in the xml reference node
  
          :meta common:
          """
  
          return self.element_for_part(part, 7)
  
      def property_kind_list(self, sort_list = True):
          """Returns a list of unique property kinds found amongst the parts of the collection."""
  
          return self.unique_element_list(7, sort_list = sort_list)
  
      def local_property_kind_uuid(self, part):
          """Returns the uuid of the bespoke (local) property kind for this part, or None for a standard property kind."""
  
          return self.element_for_part(part, 17)
  
      def facet_type_for_part(self, part):
          """If relevant, returns the resqml Facet Facet for the property part, eg. 'direction'; otherwise None.
  
          arguments:
             part (string): the part name for which the facet type is required
  
          returns:
             standard resqml facet type for this part (string), or None
  
          notes:
             resqml refers to Facet Facet and Facet Value; the equivalents in this module are facet_type and facet;
             the resqml standard allows a property to have any number of facets; this module currently limits a
             property to having at most one facet; the facet_type and facet should be either both None or both not None
  
          :meta common:
          """
  
          return self.element_for_part(part, 8)
  
      def facet_type_list(self, sort_list = True):
          """Returns a list of unique facet types found amongst the parts of the collection."""
  
          return self.unique_element_list(8, sort_list = sort_list)
  
      def facet_for_part(self, part):
          """If relevant, returns the resqml Facet Value for the property part, eg. 'I'; otherwise None.
  
          arguments:
             part (string): the part name for which the facet value is required
  
          returns:
             facet value for this part (string), for the facet type returned by the facet_type_for_part() function,
             or None
  
          see notes for facet_type_for_part()
  
          :meta common:
          """
  
          return self.element_for_part(part, 9)
  
      def facet_list(self, sort_list = True):
          """Returns a list of unique facet values found amongst the parts of the collection."""
  
          return self.unique_element_list(9, sort_list = sort_list)
  
      def citation_title_for_part(self, part):
          """Returns the citation title for the property part.
  
          arguments:
             part (string): the part name for which the citation title is required
  
          returns:
             citation title (string) for this part
  
          note:
             for simulation grid properties, the citation title is often a property keyword specific to a simulator
  
          :meta common:
          """
  
          return self.element_for_part(part, 10)
  
      def title_for_part(self, part):
          """Synonymous with citation_title_for_part()."""
  
          return self.citation_title_for_part(part)
  
      def titles(self):
          """Returns a list of citation titles for the parts in the collection."""
  
          return [self.citation_title_for_part(p) for p in self.parts()]
  
      def time_series_uuid_for_part(self, part):
          """If the property has an associated time series (is not static), returns the uuid for the time series.
  
          arguments:
             part (string): the part name for which the time series uuid is required
  
          returns:
             time series uuid (uuid.UUID) for this part
          """
  
          return self.element_for_part(part, 11)
  
      def time_series_uuid_list(self, sort_list = True):
          """Returns a list of unique time series uuids found amongst the parts of the collection."""
  
          return self.unique_element_list(11, sort_list = sort_list)
  
      def time_index_for_part(self, part):
          """If the property has an associated time series (is not static), returns the time index within the time series.
  
          arguments:
             part (string): the part name for which the time index is required
  
          returns:
             time index (integer) for this part
  
          :meta common:
          """
  
          return self.element_for_part(part, 12)
  
      def time_index_list(self, sort_list = True):
          """Returns a list of unique time indices found amongst the parts of the collection."""
  
          return self.unique_element_list(12, sort_list = sort_list)
  
      def minimum_value_for_part(self, part):
          """Returns the minimum value for the property part, as stored in the xml.
  
          arguments:
             part (string): the part name for which the minimum value is required
  
          returns:
             minimum value (as float or int) for this part, or None if metadata item is not set
  
          note:
             this method merely returns the minimum value recorded in the xml for the property, it does not check
             the array data
  
          :meta common:
          """
  
          mini = self.element_for_part(part, 13)
          if mini:
              if self.continuous_for_part(part):
                  mini = float(mini)
              else:
                  mini = int(mini)
          return mini
  
      def maximum_value_for_part(self, part):
          """Returns the maximum value for the property part, as stored in the xml.
  
          arguments:
             part (string): the part name for which the maximum value is required
  
          returns:
             maximum value (as float ir int) for this part, or None if metadata item is not set
  
          note:
             this method merely returns the maximum value recorded in the xml for the property, it does not check
             the array data
  
          :meta common:
          """
  
          maxi = self.element_for_part(part, 14)
          if maxi:
              if self.continuous_for_part(part):
                  maxi = float(maxi)
              else:
                  maxi = int(maxi)
          return maxi
  
      def patch_min_max_for_part(self, part, minimum = None, maximum = None, model = None):
          """Updates the minimum and/ox maximum values stored in the metadata, optionally updating xml tree too.
  
          arguments:
             part (str): the part name of the property
             minimum (float or int, optional): the new minimum value to be set in the metadata (unchanged if None)
             maximum (float or int, optional): the new maximum value to be set in the metadata (unchanged if None)
             model (model.Model, optional): if present and containing xml for the part, that xml is also patched
  
          notes:
             this method is rarely needed: only if a property array is being re-populated after being initialised
             with temporary values; the xml tree for the part in the model will only be updated where the minimum
             and/or maximum nodes already exist in the tree
          """
  
          info = list(self.dict[part])
          if minimum is not None:
              info[13] = minimum
          if maximum is not None:
              info[14] = maximum
          self.dict[part] = tuple(info)
          if model is not None:
              p_root = model.root_for_part(part)
              if p_root is not None:
                  if minimum is not None:
                      min_node = rqet.find_tag(p_root, 'MinimumValue')
                      if min_node is not None:
                          min_node.text = str(minimum)
                          model.set_modified()
                  if maximum is not None:
                      max_node = rqet.find_tag(p_root, 'MaximumValue')
                      if max_node is not None:
                          max_node.text = str(maximum)
                          model.set_modified()
  
      def uom_for_part(self, part):
          """Returns the resqml units of measure for the property part.
  
          arguments:
             part (string): the part name for which the units of measure is required
  
          returns:
             resqml units of measure (string) for this part
  
          :meta common:
          """
  
          # NB: this field is not set correctly in data sets generated by DGI
          return self.element_for_part(part, 15)
  
      def uom_list(self, sort_list = True):
          """Returns a list of unique units of measure found amongst the parts of the collection."""
  
          return self.unique_element_list(15, sort_list = sort_list)
  
      def string_lookup_uuid_for_part(self, part):
          """If the property has an associated string lookup (is categorical), return the uuid.
  
          arguments:
             part (string): the part name for which the string lookup uuid is required
  
          returns:
             string lookup uuid (uuid.UUID) for this part
          """
  
          return self.element_for_part(part, 16)
  
      def string_lookup_for_part(self, part):
          """Returns a StringLookup object for the part, if it has a string lookup uuid, otherwise None."""
  
          sl_uuid = self.string_lookup_uuid_for_part(part)
          if sl_uuid is None:
              return None
          sl_root = self.model.root_for_uuid(sl_uuid)
          assert sl_root is not None, 'string table lookup referenced by property is not present in model'
          return StringLookup(self.model, sl_root)
  
      def string_lookup_uuid_list(self, sort_list = True):
          """Returns a list of unique string lookup uuids found amongst the parts of the collection."""
  
          return self.unique_element_list(16, sort_list = sort_list)
  
      def part_is_categorical(self, part):
          """Returns True if the property is categorical (not conintuous and has an associated string lookup).
  
          :meta common:
          """
  
          return not self.continuous_for_part(part) and self.string_lookup_uuid_for_part(part) is not None
  
      def constant_value_for_part(self, part):
          """Returns the value (float or int) of a constant array part, or None for an hdf5 array.
  
          note:
             a constant array can optionally be expanded and written to the hdf5, in which case it will
             not have a constant value assigned when the dataset is read from file
          :meta common:
          """
  
          return self.element_for_part(part, 20)
  
      def override_min_max(self, part, min_value, max_value):
          """Sets the minimum and maximum values in the metadata for the part.
  
          arguments:
             part (string): the part name for which the minimum and maximum values are to be set
             min_value (float or int or string): the minimum value to be stored in the metadata
             max_value (float or int or string): the maximum value to be stored in the metadata
  
          note:
             this function is typically called if the existing min & max metadata is missing or
             distrusted; the min and max values passed in are typically the result of numpy
             min and max function calls (possibly skipping NaNs) on the property array or
             a version of it masked for inactive cells
          """
  
          if part not in self.dict:
              return
          property_part = list(self.dict[part])
          property_part[13] = min_value
          property_part[14] = max_value
          self.dict[part] = tuple(property_part)
  
      def establish_time_set_kind(self):
          """Re-evaulates the time set kind attribute.
  
          Based on all properties having same time index in the same time series.
          """
          self.time_set_kind_attr = 'single time'
          #  note: other option of 'equivalent times' not catered for in this code
          common_time_index = None
          common_time_series_uuid = None
          for part in self.parts():
              part_time_index = self.time_index_for_part(part)
              if part_time_index is None:
                  self.time_set_kind_attr = 'not a time set'
                  break
              if common_time_index is None:
                  common_time_index = part_time_index
              elif common_time_index != part_time_index:
                  self.time_set_kind_attr = 'not a time set'
                  break
              part_ts_uuid = self.time_series_uuid_for_part(part)
              if part_ts_uuid is None:
                  self.time_set_kind_attr = 'not a time set'
                  break
              if common_time_series_uuid is None:
                  common_time_series_uuid = part_ts_uuid
              elif not bu.matching_uuids(common_time_series_uuid, part_ts_uuid):
                  self.time_set_kind_attr = 'not a time set'
                  break
          return self.time_set_kind_attr
  
      def time_set_kind(self):
          """Returns the time set kind attribute.
  
          Based on all properties having same time index in the same time series.
          """
          if self.time_set_kind_attr is None:
              self.establish_time_set_kind()
          return self.time_set_kind_attr
  
      def establish_has_single_property_kind(self):
          """Re-evaluates the has single property kind attribute.
          
          Depends on whether all properties are of the same kind.
          """
          self.has_single_property_kind_flag = True
          common_property_kind = None
          for part in self.parts():
              part_kind = self.property_kind_for_part(part)
              if common_property_kind is None:
                  common_property_kind = part_kind
              elif part_kind != common_property_kind:
                  self.has_single_property_kind_flag = False
                  break
          return self.has_single_property_kind_flag
  
      def has_single_property_kind(self):
          """Return the has single property kind flag depending on whether all properties are of the same kind."""
  
          if self.has_single_property_kind_flag is None:
              self.establish_has_single_property_kind()
          return self.has_single_property_kind_flag
  
      def establish_has_single_indexable_element(self):
          """Re-evaluate the has single indexable element attribute.
  
          Depends on whether all properties have the same.
          """
          self.has_single_indexable_element_flag = True
          common_ie = None
          for part in self.parts():
              ie = self.indexable_for_part(part)
              if common_ie is None:
                  common_ie = ie
              elif ie != common_ie:
                  self.has_single_indexable_element_flag = False
                  break
          return self.has_single_indexable_element_flag
  
      def has_single_indexable_element(self):
          """Returns the has single indexable element flag depending on whether all properties have the same."""
  
          if self.has_single_indexable_element_flag is None:
              self.establish_has_single_indexable_element()
          return self.has_single_indexable_element_flag
  
      def establish_has_multiple_realizations(self):
          """Re-evaluates the has multiple realizations attribute.
  
          Based on whether properties belong to more than one realization.
          """
          self.has_multiple_realizations_flag = False
          common_realization = None
          for part in self.parts():
              part_realization = self.realization_for_part(part)
              if part_realization is None:
                  continue
              if common_realization is None:
                  common_realization = part_realization
                  continue
              if part_realization != common_realization:
                  self.has_multiple_realizations_flag = True
                  self.realization = None  # override single realization number applicable to whole collection
                  break
          if not self.has_multiple_realizations_flag and common_realization is not None:
              self.realization = common_realization
          return self.has_multiple_realizations_flag
  
      def has_multiple_realizations(self):
          """Returns the has multiple realizations flag based on whether properties belong to more than one realization.
  
          :meta common:
          """
  
          if self.has_multiple_realizations_flag is None:
              self.establish_has_multiple_realizations()
          return self.has_multiple_realizations_flag
  
      def establish_has_single_uom(self):
          """Re-evaluates the has single uom attribute depending on whether all properties have the same units of measure."""
  
          self.has_single_uom_flag = True
          common_uom = None
          for part in self.parts():
              part_uom = self.uom_for_part(part)
              if common_uom is None:
                  common_uom = part_uom
              elif part_uom != common_uom:
                  self.has_single_uom_flag = False
                  break
          if common_uom is None:
              self.has_single_uom_flag = True  # all uoms are None (probably discrete properties)
          return self.has_single_uom_flag
  
      def has_single_uom(self):
          """Returns the has single uom flag depending on whether all properties have the same units of measure."""
  
          if self.has_single_uom_flag is None:
              self.establish_has_single_uom()
          return self.has_single_uom_flag
  
      def assign_realization_numbers(self):
          """Assigns a distinct realization number to each property, after checking for compatibility.
  
          note:
             this method does not modify realization information in any established xml; it is intended primarily as
             a convenience to allow realization based processing of any collection of compatible properties
          """
  
          assert self.has_single_property_kind(), 'attempt to assign realization numbers to properties of differing kinds'
          assert self.has_single_indexable_element(
          ), 'attempt to assign realizations to properties of differing indexable elements'
          assert self.has_single_uom(
          ), 'attempt to assign realization numbers to properties with differing units of measure'
  
          new_dict = {}
          realization = 0
          for key, entry in self.dict.items():
              entry_list = list(entry)
              entry_list[0] = realization
              new_dict[key] = tuple(entry_list)
              realization += 1
          self.dict = new_dict
          self.has_multiple_realizations_flag = (realization > 1)
  
      def masked_array(self, simple_array, exclude_inactive = True, exclude_value = None, points = False):
          """Returns a masked version of simple_array, using inactive mask associated with support for this property collection.
  
          arguments:
             simple_array (numpy array): an unmasked numpy array with the same shape as property arrays for the support
                (and indexable element) associated with this collection
             exclude_inactive (boolean, default True): elements which are flagged as inactive in the supporting representation
                are masked out if this argument is True
             exclude_value (float or int, optional): if present, elements which match this value are masked out; if not None
                then usually set to np.NaN for continuous data or null_value_for_part() for discrete data
             points (boolean, default False): if True, the simple array is expected to have an extra dimension of extent 3,
                relative to the inactive attribute of the support
  
          returns:
             a masked version of the array, with the mask set to exclude cells which are inactive in the support
  
          notes:
             when requesting a reference to a cached copy of a property array (using other functions), a masked argument
             can be used to apply the inactive mask; this function is therefore rarely needed by calling code (it is used
             internally by this module); the simple_array need not be part of this collection
          """
  
          mask = self._masked_array_apply_inactive(exclude_inactive, points, simple_array)
          if exclude_value:
              null_mask = (simple_array == exclude_value)
              if mask is None:
                  mask = null_mask
              else:
                  mask = np.logical_or(mask, null_mask)
          if mask is None:
              mask = ma.nomask
          return ma.masked_array(simple_array, mask = mask)
  
      def _masked_array_apply_inactive(self, exclude_inactive, points, simple_array):
          mask = None
          if (exclude_inactive and self.support is not None and hasattr(self.support, 'inactive') and
                  self.support.inactive is not None):
              if not points:
                  if self.support.inactive.shape == simple_array.shape:
                      mask = self.support.inactive
              else:
                  assert simple_array.ndim > 1 and simple_array.shape[-1] == 3
                  if (self.support.inactive.ndim + 1 == simple_array.ndim and
                          self.support.inactive.shape == tuple(simple_array.shape[:-1])):
                      mask = np.empty(simple_array.shape, dtype = bool)
                      mask[:] = self.support.inactive[:, np.newaxis]
          return mask
  
      def h5_key_pair_for_part(self, part):
          """Return hdf5 key pair (ext uuid, internal path) for the part."""
  
          # note: this method does not currently support all the possible tag values for different instances
          # of the RESQML abstract arrays
  
          model = self.model
          part_node = self.node_for_part(part)
          if part_node is None:
              return None
          if self.points_for_part(part):
              patch_list = rqet.list_of_tag(part_node, 'PatchOfPoints')
              assert len(patch_list) == 1  # todo: handle more than one patch of points
              first_values_node = rqet.find_tag(patch_list[0], 'Points')
              tag = 'Coordinates'
          else:
              patch_list = rqet.list_of_tag(part_node, 'PatchOfValues')
              assert len(patch_list) == 1  # todo: handle more than one patch of values
              first_values_node = rqet.find_tag(patch_list[0], 'Values')
              tag = 'Values'
          if first_values_node is None:
              return None  # could treat as fatal error
          return model.h5_uuid_and_path_for_node(first_values_node, tag = tag)
  
      def cached_part_array_ref(self, part, dtype = None, masked = False, exclude_null = False):
          """Returns a numpy array containing the data for the property part; the array is cached in this collection.
  
          arguments:
             part (string): the part name for which the array reference is required
             dtype (optional, default None): the element data type of the array to be accessed, eg 'float' or 'int';
                if None (recommended), the dtype of the returned numpy array matches that in the hdf5 dataset
             masked (boolean, default False): if True, a masked array is returned instead of a simple numpy array;
                the mask is set to the inactive array attribute of the support object if present
             exclude_null (boolean, default False): if True, and masked is also True, then elements of the array
                holding the null value will also be masked out
  
          returns:
             reference to a cached numpy array containing the actual property data; multiple calls will return
             the same cached array so calling code should copy if duplication is needed
  
          notes:
             this function is the usual way to get at the actual property array; at present, the funtion only works
             if the entire array is stored as a single patch in the hdf5 file (resqml allows multiple patches per
             array); the masked functionality can be used to apply a common mask, stored in the supporting
             representation object with the attribute name 'inactive', to multiple properties (this will only work
             if the indexable element is set to the typical value for the class of supporting representation, eg.
             'cells' for grid objects); if exclude_null is set True then null value elements will also be masked out
             (as long as masked is True); however, it is recommended simply to use np.NaN values in floating point
             property arrays if the commonality is not needed
  
          :meta common:
          """
  
          model = self.model
          cached_array_name = _cache_name(part)
          if cached_array_name is None:
              return None
  
          if not hasattr(self, cached_array_name):
              pcga._cached_part_array_ref_get_array(self, part, dtype, model, cached_array_name)
  
          if masked:
              exclude_value = self.null_value_for_part(part) if exclude_null else None
              return self.masked_array(self.__dict__[cached_array_name], exclude_value = exclude_value)
          else:
              return self.__dict__[cached_array_name]
  
      def h5_slice(self, part, slice_tuple):
          """Returns a subset of the array for part, without loading the whole array.
  
          arguments:
             part (string): the part name for which the array slice is required
             slice_tuple (tuple of slice objects): each element should be constructed using the python built-in
                function slice()
  
          returns:
             numpy array that is a hyper-slice of the hdf5 array, with the same ndim as the source hdf5 array
  
          note:
             this method always fetches from the hdf5 file and does not attempt local caching; the whole array
             is not loaded; all axes continue to exist in the returned array, even where the sliced extent of
             an axis is 1
          """
  
          h5_key_pair = self.h5_key_pair_for_part(part)
          if h5_key_pair is None:
              return None
          return self.model.h5_array_slice(h5_key_pair, slice_tuple)
  
      def h5_overwrite_slice(self, part, slice_tuple, array_slice, update_cache = True):
          """Overwrites a subset of the array for part, in the hdf5 file.
  
          arguments:
             part (string): the part name for which the array slice is to be overwritten
             slice_tuple (tuple of slice objects): each element should be constructed using the python built-in
                function slice()
             array_slice (numpy array of shape to match slice_tuple): the data to be written
             update_cache (boolean, default True): if True and the part is currently cached within this
                PropertyCollection, then the cached array is also updated; if False, the part is uncached
  
          notes:
             this method naively writes the slice to hdf5 without using mpi to look after parallel writes;
             if a cached copy of the array is updated, this is in an unmasked form; if calling code has a
             reterence to a masked version of the array then the mask will not be updated by this method;
             if the part is not currently cached, this method will not cause it to become cached,
             regardless of the update_cache argument
          """
  
          h5_key_pair = self.h5_key_pair_for_part(part)
          assert h5_key_pair is not None
          self.model.h5_overwrite_array_slice(h5_key_pair, slice_tuple, array_slice)
          cached_array_name = _cache_name(part)
          if cached_array_name is None:
              return
          if hasattr(self, cached_array_name):
              if update_cache:
                  self.__dict__[cached_array_name][slice_tuple] = array_slice
              else:
                  delattr(self, cached_array_name)
  
      def shape_and_type_of_part(self, part):
          """Returns shape tuple and element type of cached or hdf5 array for part."""
  
          model = self.model
          cached_array_name = _cache_name(part)
          if cached_array_name is None:
              return None, None
  
          if hasattr(self, cached_array_name):
              return tuple(self.__dict__[cached_array_name].shape), self.__dict__[cached_array_name].dtype
  
          part_node = self.node_for_part(part)
          if part_node is None:
              return None, None
  
          if self.constant_value_for_part(part) is not None:
              assert not self.points_for_part(part), 'constant array not supported for points property'
              assert self.support is not None
              shape = self.supporting_shape(indexable_element = self.indexable_for_part(part),
                                            direction = pcga._part_direction(self, part))
              assert shape is not None
              return shape, (float if self.continuous_for_part(part) else int)
  
          h5_key_pair = self._shape_and_type_of_part_get_h5keypair(part, part_node, model)
          if h5_key_pair is None:
              return None, None
          return model.h5_array_shape_and_type(h5_key_pair)
  
      def _shape_and_type_of_part_get_h5keypair(self, part, part_node, model):
          if self.points_for_part(part):
              patch_list = rqet.list_of_tag(part_node, 'PatchOfpoints')
              assert len(patch_list) == 1  # todo: handle more than one patch of points
              return model.h5_uuid_and_path_for_node(rqet.find_tag(patch_list[0], 'Coordinates'))
          else:
              patch_list = rqet.list_of_tag(part_node, 'PatchOfValues')
              assert len(patch_list) == 1  # todo: handle more than one patch of values
              return model.h5_uuid_and_path_for_node(rqet.find_tag(patch_list[0], 'Values'))
  
      def facets_array_ref(self, use_32_bit = False, indexable_element = None):  # todo: add masked argument
          """Returns a +1D array of all parts with first axis being over facet values; Use facet_list() for lookup.
  
          arguments:
             use_32_bit (boolean, default False): if True, the resulting numpy array will use a 32 bit dtype; if False, 64 bit
             indexable_element (string, optional): the indexable element for the properties in the collection; if None, will
                be determined from the data
  
          returns:
             numpy array containing all the data in the collection, the first axis being over facet values and the rest of
             the axes matching the shape of the individual property arrays
  
          notes:
             the property collection should be constructed so as to hold a suitably coherent set of properties before
             calling this method;
             the facet_list() method will return the facet values that correspond to slices in the first axis of the
             resulting array
          """
          pcga._facet_array_ref_checks(self)
  
          #  could check that facet_type_list() has exactly one value
          facet_list = self.facet_list(sort_list = True)
          facet_count = len(facet_list)
          assert facet_count > 0, 'no facets found in property collection'
          assert self.number_of_parts() == facet_count, 'collection covers more than facet variability'
  
          continuous = self.all_continuous()
          if not continuous:
              assert self.all_discrete(), 'mixture of continuous and discrete properties in collection'
  
          if indexable_element is None:
              indexable_element = self.indexable_for_part(self.parts()[0])
  
          dtype = dtype_flavour(continuous, use_32_bit)
          shape_list = self.supporting_shape(indexable_element = indexable_element)
          shape_list.insert(0, facet_count)
  
          a = np.zeros(shape_list, dtype = dtype)
  
          for part in self.parts():
              facet_index = facet_list.index(self.facet_for_part(part))
              pa = self.cached_part_array_ref(part, dtype = dtype)
              a[facet_index] = pa
              self.uncache_part_array(part)
  
          return a
  
      def realizations_array_ref(self,
                                 use_32_bit = False,
                                 fill_missing = True,
                                 fill_value = None,
                                 indexable_element = None):
          """Returns a +1D array of all parts with first axis being over realizations.
  
          arguments:
             use_32_bit (boolean, default False): if True, the resulting numpy array will use a 32 bit dtype; if False, 64 bit
             fill_missing (boolean, default True): if True, the first axis of the resulting numpy array will range from 0 to
                the maximum realization number present and slices for any missing realizations will be filled with fill_value;
                if False, the extent of the first axis will only cpver the number pf realizations actually present (see also notes)
             fill_value (int or float, optional): the value to use for missing realization slices; if None, will default to
                np.NaN if data is continuous, -1 otherwise; irrelevant if fill_missing is False
             indexable_element (string, optional): the indexable element for the properties in the collection; if None, will
                be determined from the data
  
          returns:
             numpy array containing all the data in the collection, the first axis being over realizations and the rest of
             the axes matching the shape of the individual property arrays
  
          notes:
             the property collection should be constructed so as to hold a suitably coherent set of properties before
             calling this method;
             if fill_missing is False, the realization axis indices range from zero to the number of realizations present;
             if True, the realization axis indices range from zero to the maximum realization number and slices for missing
             realizations will be filled with the fill_value
  
          :meta common:
          """
          r_list, continuous = pcga._realizations_array_ref_initial_checks(self)
  
          if fill_value is None:
              fill_value = np.NaN if continuous else -1
          if indexable_element is None:
              indexable_element = self.indexable_for_part(self.parts()[0])
  
          r_extent = pcga._realizations_array_ref_get_r_extent(fill_missing, r_list)
          dtype = dtype_flavour(continuous, use_32_bit)
          # todo: handle direction dependent shapes
  
          shape_list = pcga._realizations_array_ref_get_shape_list(self, indexable_element, r_extent)
  
          a = np.full(shape_list, fill_value, dtype = dtype)
          if fill_missing:
              return pcga._realizations_array_ref_fill_missing(self, r_extent, dtype, a)
          else:
              return pcga._realizations_array_ref_not_fill_missing(self, r_list, dtype, a)
  
      def time_series_array_ref(self,
                                use_32_bit = False,
                                fill_missing = True,
                                fill_value = None,
                                indexable_element = None):
          """Returns a +1D array of all parts with first axis being over time indices.
  
          arguments:
             use_32_bit (boolean, default False): if True, the resulting numpy array will use a 32 bit dtype; if False, 64 bit
             fill_missing (boolean, default True): if True, the first axis of the resulting numpy array will range from 0 to
                the maximum time index present and slices for any missing indices will be filled with fill_value; if False,
                the extent of the first axis will only cpver the number pf time indices actually present (see also notes)
             fill_value (int or float, optional): the value to use for missing time index slices; if None, will default to
                np.NaN if data is continuous, -1 otherwise; irrelevant if fill_missing is False
             indexable_element (string, optional): the indexable element for the properties in the collection; if None, will
                be determined from the data
  
          returns:
             numpy array containing all the data in the collection, the first axis being over time indices and the rest of
             the axes matching the shape of the individual property arrays
  
          notes:
             the property collection should be constructed so as to hold a suitably coherent set of properties before
             calling this method;
             if fill_missing is False, the time axis indices range from zero to the number of time indices present,
             with the list of tine index values available by calling the method time_index_list(sort_list = True);
             if fill_missing is True, the time axis indices range from zero to the maximum time index and slices for
             missing time indices will be filled with the fill_value
  
          :meta common:
          """
          ti_list, continuous = pcga._time_array_ref_initial_checks(self)
  
          if fill_value is None:
              fill_value = np.NaN if continuous else -1
  
          if indexable_element is None:
              indexable_element = self.indexable_for_part(self.parts()[0])
  
          if fill_missing:
              ti_extent = ti_list[-1] + 1
          else:
              ti_extent = len(ti_list)
  
          dtype = dtype_flavour(continuous, use_32_bit)
          # todo: handle direction dependent shapes
          shape_list = self.supporting_shape(indexable_element = indexable_element)
          shape_list.insert(0, ti_extent)
          if self.points_for_part(self.parts()[0]):
              shape_list.append(3)
  
          a = np.full(shape_list, fill_value, dtype = dtype)
          if fill_missing:
              return pcga._time_array_ref_fill_missing(self, ti_extent, dtype, a)
          else:
              return pcga._time_array_ref_not_fill_missing(self, ti_list, dtype, a)
  
      def combobulated_face_array(self, resqml_a):
          """Returns a logically ordered copy of RESQML faces-per-cell property array resqml_a.
  
          argument:
             resqml_a (numpy array of shape (..., 6): a RESQML property array with indexable element faces per cell
  
          returns:
             numpy array of shape (..., 3, 2) where the 3 covers K,J,I and the 2 the -/+ face polarities being a resqpy logically
                arranged copy of resqml_a
  
          notes:
             this method is for properties of IJK grids only;
             RESQML documentation is not entirely clear about the required ordering of -I, +I, -J, +J faces;
             current implementation assumes count = 1 for the property;
             does not currently support points properties
          """
  
          assert resqml_a.shape[-1] == 6
  
          resqpy_a_shape = tuple(list(resqml_a.shape[:-1]) + [3, 2])
          resqpy_a = np.empty(resqpy_a_shape, dtype = resqml_a.dtype)
  
          for axis in range(3):
              for polarity in range(2):
                  resqpy_a[..., axis, polarity] = resqml_a[..., self.face_index_map[axis, polarity]]
  
          return resqpy_a
  
      def discombobulated_face_array(self, resqpy_a):
          """Return logical face property array a, re-ordered and reshaped regarding the six facial directions.
  
          argument:
             resqpy_a (numpy array of shape (..., 3, 2)): the penultimate array axis represents K,J,I and the final axis is -/+ face
                polarity; the resqpy logically arranged property array to be converted to illogical RESQML ordering and shape
  
          returns:
             numpy array of shape (..., 6) being a copy of resqpy_a with slices reordered before collapsing the last 2 axes into 1;
                ready to be stored as a RESQML property array with indexable element faces per cell
  
          notes:
             this method is for properties of IJK grids only;
             RESQML documentation is not entirely clear about the required ordering of -I, +I, -J, +J faces;
             current implementation assumes count = 1 for the property;
             does not currently support points properties
          """
  
          assert resqpy_a.ndim >= 2 and resqpy_a.shape[-2] == 3 and resqpy_a.shape[-1] == 2
  
          resqml_a_shape = tuple(list(resqpy_a.shape[:-2]) + [6])
          resqml_a = np.empty(resqml_a_shape, dtype = resqpy_a.dtype)
  
          for face in range(6):
              axis, polarity = self.face_index_inverse_map[face]
              resqml_a[..., face] = resqpy_a[..., axis, polarity]
  
          return resqml_a
  
      def cached_normalized_part_array_ref(self,
                                           part,
                                           masked = False,
                                           use_logarithm = False,
                                           discrete_cycle = None,
                                           trust_min_max = False):
          """DEPRECATED: replaced with normalized_part_array() method."""
  
          return self.normalized_part_array(part,
                                            masked = masked,
                                            use_logarithm = use_logarithm,
                                            discrete_cycle = discrete_cycle,
                                            trust_min_max = trust_min_max)
  
      def normalized_part_array(self,
                                part,
                                masked = False,
                                use_logarithm = False,
                                discrete_cycle = None,
                                trust_min_max = False,
                                fix_zero_at = None):
          """Return data normalised to between 0 and 1, along with min and max value.
  
          arguments:
             part (string): the part name for which the normalized array reference is required
             masked (boolean, optional, default False): if True, the masked version of the property array is used to
                determine the range of values to map onto the normalized range of 0 to 1 (the mask removes inactive cells
                from having any impact); if False, the values of inactive cells are included in the operation; the returned
                normalized array is masked or not depending on this argument
             use_logarithm (boolean, optional, default False): if False, the property values are linearly mapped to the
                normalized range; if True, the logarithm (base 10) of the property values are mapped to the normalized range
             discrete_cycle (positive integer, optional, default None): if a value is supplied and the property array
                contains integer data (discrete or categorical), the modulus of the property values are calculated
                against this value before conversion to floating point and mapping to the normalized range
             trust_min_max (boolean, optional, default False): if True, the minimum and maximum values from the property's
                metadata is used as the range of the property values; if False, the values are determined using numpy
                min and max operations
             fix_zero_at (float, optional): if present, a value between 0.0 and 1.0 (typically 0.0 or 0.5) to pin zero at
  
          returns:
             (normalized_array, min_value, max_value) where:
             normalized_array is a numpy array of floats, masked or unmasked depending on the masked argument, with values
             ranging between 0 and 1; in the case of a masked array the values for excluded cells are meaningless and may
             lie outside the range 0 to 1
             min_value and max_value: the property values that have been mapped to 0 and 1 respectively
  
          notes:
             this function is typically used to map property values onto the range required for colouring in;
             in case of failure, (None, None, None) is returned;
             if use_logarithm is True, the min_value and max_value returned are the log10 values, not the original
             property values;
             also, if use logarithm is True and the minimum property value is not greater than zero, then values less than
             0.0001 are set to 0.0001, prior to taking the logarithm;
             fix_zero_at is mutually incompatible with use_logarithm; to force the normalised data to have a true zero,
             set fix_zero_at to 0.0; for divergent data fixing zero at 0.5 will often be appropriate;
             fixing zero at 0.0 or 1.0 may result in normalised values being clipped;
             for floating point data, NaN values will be handled okay; if all data are NaN, (None, NaN, NaN) is returned;
             for integer data, null values are not currently supported (though the RESQML metadata can hold a null value);
             the masked argument is most applicable to properties for grid objects; note that NaN values are excluded when
             determining the min and max regardless of the value of the masked argument;
             not applicable to points properties
          """
  
          assert not self.points_for_part(part), 'property normalisation not available for points properties'
          assert fix_zero_at is None or not use_logarithm
  
          p_array = self.cached_part_array_ref(part, masked = masked)
          if p_array is None:
              return None, None, None
  
          min_value, max_value = pcga._normalized_part_array_get_minmax(self, trust_min_max, part, p_array, masked)
          if min_value is None or max_value is None:
              return None, min_value, max_value
  
          min_value, max_value, p_array = pcga._normalized_part_array_apply_discrete_cycle(
              discrete_cycle, p_array, min_value, max_value)
          min_value, max_value = pcga._normalized_part_array_nan_if_masked(min_value, max_value, masked)
  
          if min_value == np.nan or max_value == np.nan:
              return None, min_value, max_value
          if max_value < min_value:
              return None, min_value, max_value
  
          n_prop = p_array.astype(float)
          if use_logarithm:
              n_prop, min_value, max_value = pcga._normalized_part_array_use_logarithm(min_value, n_prop, masked)
              if min_value == np.nan or max_value == np.nan:
                  return None, min_value, max_value
  
          if fix_zero_at is not None:
              min_value, max_value, n_prop = pcga._normalized_part_array_fix_zero_at(min_value, max_value, n_prop,
                                                                                     fix_zero_at)
  
          if max_value == min_value:
              n_prop[:] = 0.5
              return n_prop, min_value, max_value
  
          return (n_prop - min_value) / (max_value - min_value), min_value, max_value
  
      def uncache_part_array(self, part):
          """Removes the cached copy of the array of data for the named property part.
  
          argument:
             part (string): the part name for which the cached array is to be removed
  
          note:
             this function applies a python delattr() which will mark the array as no longer being in use
             here; however, actual freeing of the memory only happens when all other references to the
             array are released
          """
  
          cached_array_name = _cache_name(part)
          if cached_array_name is not None and hasattr(self, cached_array_name):
              delattr(self, cached_array_name)
  
      def add_cached_array_to_imported_list(self,
                                            cached_array,
                                            source_info,
                                            keyword,
                                            discrete = False,
                                            uom = None,
                                            time_index = None,
                                            null_value = None,
                                            property_kind = None,
                                            local_property_kind_uuid = None,
                                            facet_type = None,
                                            facet = None,
                                            realization = None,
                                            indexable_element = None,
                                            count = 1,
                                            const_value = None,
                                            points = False):
          """Caches array and adds to the list of imported properties (but not to the collection dict).
  
          arguments:
             cached_array: a numpy array to be added to the imported list for this collection (prior to being added
                as a part); for a constant array set cached_array to None (and use const_value)
             source_info (string): typically the name of a file from which the array has been read but can be any
                information regarding the source of the data
             keyword (string): this will be used as the citation title when a part is generated for the array
             discrete (boolean, optional, default False): if True, the array should contain integer (or boolean)
                data; if False, float
             uom (string, optional, default None): the resqml units of measure for the data
             time_index (integer, optional, default None): if not None, the time index to be used when creating
                a part for the array
             null_value (int or float, optional, default None): if present, this is used in the metadata to
                indicate that this value is to be interpreted as a null value wherever it appears in the data
             property_kind (string): resqml property kind, or None
             local_property_kind_uuid (uuid.UUID or string): uuid of local property kind, or None
             facet_type (string): resqml facet type, or None
             facet (string): resqml facet, or None
             realization (int): realization number, or None
             indexable_element (string, optional): the indexable element in the supporting representation
             count (int, default 1): the number of values per indexable element; if greater than one then this
                must be the fastest cycling axis in the cached array, ie last index
             const_value (int, float or bool, optional): the value with which a constant array is filled;
                required if cached_array is None, must be None otherwise
             points (bool, default False): if True, this is a points property with an extra dimension of extent 3
  
          returns:
             uuid of nascent property object
  
          note:
             the process of importing property arrays follows these steps:
             1. read (or generate) array of data into a numpy array in memory (cache)
             2. add to the imported list using this function (which also takes takes note of some metadata)
             3. write imported list arrays to hdf5 file
             4. create resqml xml nodes for imported list arrays and add as parts to model
             5. include newly added parts in collection
  
          :meta common:
          """
  
          assert (cached_array is not None and const_value is None) or (cached_array is None and const_value is not None)
          assert not points or not discrete
          assert count > 0
          if self.imported_list is None:
              self.imported_list = []
  
          uuid = bu.new_uuid()
          cached_name = _cache_name_for_uuid(uuid)
          if cached_array is not None:
              min_value, max_value = pcga._min_max_of_cached_array(self, cached_name, cached_array, null_value, discrete)
          else:
              if const_value == null_value or (not discrete and np.isnan(const_value)):
                  min_value = max_value = None
              else:
                  min_value = max_value = const_value
          self.imported_list.append((uuid, source_info, keyword, cached_name, discrete, uom, time_index, null_value,
                                     min_value, max_value, property_kind, facet_type, facet, realization,
                                     indexable_element, count, local_property_kind_uuid, const_value, points))
          return uuid
  
      def remove_cached_imported_arrays(self):
          """Removes any cached arrays that are mentioned in imported list."""
  
          for imported in self.imported_list:
              cached_name = imported[3]
              if hasattr(self, cached_name):
                  delattr(self, cached_name)
  
      def remove_cached_part_arrays(self):
          """Removes any cached arrays for parts of the collection."""
  
          for part in self.dict:
              self.uncache_part_array(part)
  
      def remove_all_cached_arrays(self):
          """Removes any cached arrays for parts or mentioned in imported list."""
  
          self.remove_cached_imported_arrays()
          self.remove_cached_part_arrays()
  
      def write_hdf5_for_imported_list(self, file_name = None, mode = 'a', expand_const_arrays = False, dtype = None):
          """Create or append to an hdf5 file, writing datasets for the imported arrays.
  
          arguments:
             file_name (str, optional): if present, this hdf5 filename will override the default
             mode (str, default 'a'): the mode to open the hdf5 file in, either 'a' (append), or 'w' (overwrite)
             expand_const_arrays (boolean, default False): if True, constant arrays will be written in full to
                the hdf5 file and the same argument should be used when creating the xml
             dtype (numpy dtype, optional): the required numpy element type to use when writing to hdf5;
                eg. np.float16, np.float32, np.float64, np.uint8, np.int16, np.int32, np.int64 etc.;
                defaults to the dtype of each individual numpy array in the imported list
  
          :meta common:
          """
  
          # NB: imported array data must all have been cached prior to calling this function
          assert self.imported_list is not None
          h5_reg = rwh5.H5Register(self.model)
          for ei, entry in enumerate(self.imported_list):
              if entry[17] is not None:  # array has constant value
                  if not expand_const_arrays:
                      continue  # constant array – handled entirely in xml
                  uuid = entry[0]
                  cached_name = _cache_name_for_uuid(uuid)
                  assert self.support is not None
                  #  note: will not handle direction dependent shapes
                  shape = self.supporting_shape(indexable_element = entry[14])
                  value = float(entry[17]) if isinstance(entry[17], str) else entry[17]
                  self.__dict__[cached_name] = np.full(shape, value, dtype = dtype)
              else:
                  uuid = entry[0]
                  cached_name = entry[3]
              tail = 'points_patch0' if entry[18] else 'values_patch0'
              h5_reg.register_dataset(uuid, tail, self.__dict__[cached_name], dtype = dtype)
          h5_reg.write(file = file_name, mode = mode)
  
      def write_hdf5_for_part(self, part, file_name = None, mode = 'a'):
          """Create or append to an hdf5 file, writing dataset for the specified part."""
  
          if self.constant_value_for_part(part) is not None:
              return
          h5_reg = rwh5.H5Register(self.model)
          a = self.cached_part_array_ref(part)
          tail = 'points_patch0' if self.points_for_part(part) else 'values_patch0'
          h5_reg.register_dataset(self.uuid_for_part(part), tail, a)
          h5_reg.write(file = file_name, mode = mode)
  
      def create_xml_for_imported_list_and_add_parts_to_model(self,
                                                              ext_uuid = None,
                                                              support_uuid = None,
                                                              time_series_uuid = None,
                                                              selected_time_indices_list = None,
                                                              string_lookup_uuid = None,
                                                              property_kind_uuid = None,
                                                              find_local_property_kinds = True,
                                                              expand_const_arrays = False,
                                                              extra_metadata = {}):
          """Add imported or generated grid property arrays as parts in parent model, creating xml.
          
          hdf5 should already have been written.
  
          arguments:
             ext_uuid: uuid for the hdf5 external part, which must be known to the model's hdf5 dictionary
             support_uuid (optional): the uuid of the supporting representation that the imported properties relate to
             time_series_uuid (optional): the uuid of the full or reduced time series for which any recurrent properties'
                timestep numbers can be used as a time index; in the case of a reduced series, the selected_time_indices_list
                argument must be passed and the properties timestep numbers are found in the list with the position yielding
                the time index for the reduced list; time_series_uuid should be present if there are any recurrent properties
                in the imported list
             selected_time_indices_list (list of int, optional): if time_series_uuid is for a reduced time series then this
                argument must be present and its length must match the number of timestamps in the reduced series; the values
                in the list are indices in the full time series
             string_lookup_uuid (optional): if present, the uuid of the string table lookup which any non-continuous
                properties relate to (ie. they are all taken to be categorical)
             property_kind_uuid (optional): if present, the uuid of the bespoke (local) property kind for all the
                property arrays in the imported list (except those with an individual local property kind uuid)
             find_local_property_kinds (boolean, default True): if True, local property kind uuids need not be provided as
                long as the property kinds are set to match the titles of the appropriate local property kind objects
             expand_const_arrays (boolean, default False): if True, the hdf5 write must also have been called with the
                same argument and the xml will treat the constant arrays as normal arrays
             extra_metadata (optional): if present, a dictionary of extra metadata to be added for the part
  
          returns:
             list of uuid.UUID, being the uuids of the newly added property parts
  
          notes:
             the imported list should have been built up, and associated hdf5 arrays written, before calling this method;
             the imported list is cleared as a deliberate side-effect of this method (so a new set of imports can be
             started hereafter);
             discrete and categorical properties cannot be mixed in the same import list - process as separate lists;
             all categorical properties in the import list must refer to the same string table lookup;
             when importing categorical properties, establish the xml for the string table lookup before calling this method;
             if importing properties of a bespoke (local) property kind, ensure the property kind objects exist as parts in
             the model before calling this method
  
          :meta common:
          """
  
          if self.imported_list is None:
              return []
          if ext_uuid is None:
              ext_uuid = self.model.h5_uuid()
          prop_parts_list = []
          uuid_list = []
          for attributes in self.imported_list:
              p_node = pcap._process_imported_property(self, attributes, property_kind_uuid, string_lookup_uuid,
                                                       time_series_uuid, ext_uuid, support_uuid,
                                                       selected_time_indices_list, find_local_property_kinds,
                                                       extra_metadata, expand_const_arrays)
              if p_node is not None:
                  prop_parts_list.append(rqet.part_name_for_part_root(p_node))
                  uuid_list.append(rqet.uuid_for_part_root(p_node))
  
          self.add_parts_list_to_dict(prop_parts_list)
          self.imported_list = []
          return uuid_list
  
      def create_xml(self,
                     ext_uuid,
                     property_array,
                     title,
                     property_kind,
                     support_uuid = None,
                     p_uuid = None,
                     facet_type = None,
                     facet = None,
                     discrete = False,
                     time_series_uuid = None,
                     time_index = None,
                     uom = None,
                     null_value = None,
                     originator = None,
                     source = None,
                     add_as_part = True,
                     add_relationships = True,
                     add_min_max = True,
                     min_value = None,
                     max_value = None,
                     realization = None,
                     string_lookup_uuid = None,
                     property_kind_uuid = None,
                     find_local_property_kinds = True,
                     indexable_element = None,
                     count = 1,
                     points = False,
                     extra_metadata = {},
                     const_value = None,
                     expand_const_arrays = False):
          """Create a property xml node for a single property related to a given supporting representation node.
  
          arguments:
             ext_uuid (uuid.UUID): the uuid of the hdf5 external part
             property_array (numpy array): the actual property array (used to populate xml min & max values);
                may be None if min_value and max_value are passed or add_min_max is False
             title (string): used for the citation Title text for the property; often set to a simulator keyword for
                grid properties
             property_kind (string): the resqml property kind of the property; in the case of a bespoke (local)
                property kind, this is used as the title in the local property kind reference and the
                property_kind_uuid argument must also be passed or find_local_property_kinds set True
             support_uuid (uuid.UUID, optional): if None, the support for the collection is used
             p_uuid (uuid.UUID, optional): if None, a new uuid is generated for the property; otherwise this
                uuid is used
             facet_type (string, optional): if present, a resqml facet type whose value is supplied in the facet argument
             facet (string, optional): required if facet_type is supplied; the value of the facet
             discrete (boolean, default False): if True, a discrete or categorical property node is created (depending
                on whether string_lookup_uuid is None or present); if False (default), a continuous property node is created
             time_series_uuid (uuid.UUID, optional): if present, the uuid of the time series that this (recurrent)
                property relates to
             time_index (int, optional): if time_series_uuid is not None, this argument is required and provides
                the time index into the time series for this property array
             uom (string): the resqml unit of measure for the property (only used for continuous properties)
             null_value (optional): the value that is to be interpreted as null if it appears in the property array
             originator (string, optional): the name of the human being who created the property object;
                default is to use the login name
             source (string, optional): if present, an extra metadata node is added as a child to the property
                node, with this string indicating the source of the property data
             add_as_part (boolean, default True): if True, the newly created xml node is added as a part
                in the model
             add_relationships (boolean, default True): if True, relationship xml parts are created relating the
                new property part to: the support, the hdf5 external part; and the time series part (if applicable)
             add_min_max (boolean, default True): if True, min and max values are included as children in the
                property node
             min_value (optional): if present and add_min_max is True, this is used as the minimum value (otherwise it
                is calculated from the property array)
             max_value (optional): if present and add_min_max is True, this is used as the maximum value (otherwise it
                is calculated from the property array)
             realization (int, optional): if present, is used as the realization number in the property node; if None,
                no realization child is created
             string_lookup_uuid (optional): if present, and discrete is True, a categorical property node is created
                which refers to this string table lookup
             property_kind_uuid (optional): if present, the property kind is a local property kind; must be None for a
                standard property kind
             find_local_property_kinds (boolean, default True): if True and property_kind is not in standard supported
                property kind list and property_kind_uuid is None, the citation titles of PropertyKind objects in the
                model are compared with property_kind and if a match is found, that local property kind is used
             indexable_element (string, optional): if present, is used as the indexable element in the property node;
                if None, 'cells' are used for grid properties and 'nodes' for wellbore frame properties
             count (int, default 1): the number of values per indexable element; if greater than one then this axis
                must cycle fastest in the array, ie. be the last index
             points (bool, default False): if True, this is a points property
             extra_metadata (dictionary, optional): if present, adds extra metadata in the xml
             const_value (float or int, optional): if present, create xml for a constant array filled with this value
             expand_const_arrays (boolean, default False): if True, the hdf5 write must also have been called with the
                same argument and the xml will treat a constant array as a normal array
  
          returns:
             the newly created property xml node
  
          notes:
             this function doesn't write the actual array data to the hdf5 file: that should be done
             before calling this function;
             this code (and elsewhere) only supports at most one facet per property, though the RESQML standard
             allows for multiple facets;
             RESQML does not allow facets for points properties;
             if the xml has not been created for the support object, then xml will not be created for relationships
             between the properties and the supporting representation
          """
  
          #      log.debug('creating property node for ' + title)
          # currently assumes discrete properties to be 32 bit integers and continuous to be 64 bit reals
          # also assumes property_kind is one of the standard resqml property kinds; todo: allow local p kind node as optional arg
          support_root, support_uuid, ext_uuid = pcxml._create_xml_get_basics(self, discrete, points, const_value,
                                                                              facet_type, null_value, support_uuid,
                                                                              ext_uuid)
  
          support_type = self.model.type_of_part(self.model.part_for_uuid(support_uuid))
          indexable_element = pcga._get_indexable_element(indexable_element, support_type)
          direction = None if facet_type is None or facet_type != 'direction' else facet
  
          if self.support is not None:
              pcxml._check_shape_list(self, indexable_element, direction, property_array, points, count)
  
          # todo: assertions:
          #    numpy data type matches discrete flag (and assumptions about precision)
          #    uom are valid units for property_kind
          assert property_kind, 'missing property kind when creating xml for property'
  
          pcga._get_property_type_details(self, discrete, string_lookup_uuid, points)
          p_node, p_uuid = pcxml._create_xml_get_p_node(self, p_uuid)
  
          pcxml._create_xml_add_basics_to_p_node(self, p_node, title, originator, extra_metadata, source, count,
                                                 indexable_element)
  
          pcxml._create_xml_realization_node(realization, p_node)
          related_time_series_node = pcxml._create_xml_time_series_node(self, time_series_uuid, time_index, p_node,
                                                                        support_uuid, support_type, support_root)
          pcxml._create_xml_property_kind(self, p_node, find_local_property_kinds, property_kind, uom, discrete,
                                          property_kind_uuid)
          pcxml._create_xml_patch_node(self, p_node, points, const_value, indexable_element, direction, p_uuid, ext_uuid,
                                       expand_const_arrays)
          pcxml._create_xml_facet_node(facet_type, facet, p_node)
  
          pcxml._create_xml_property_min_max(self, property_array, const_value, discrete, add_min_max, p_node, min_value,
                                             max_value)
  
          if discrete:
              sl_root = pcxml._create_xml_lookup_node(self, p_node, string_lookup_uuid)
          else:  # continuous
              pcxml._create_xml_uom_node(self, p_node, uom, property_kind, min_value, max_value, facet_type, facet, title)
              sl_root = None
          pcxml._create_xml_add_as_part(self, add_as_part, p_uuid, p_node, add_relationships, support_root,
                                        property_kind_uuid, related_time_series_node, sl_root, discrete,
                                        string_lookup_uuid, const_value, ext_uuid)
  
          return p_node
  
      def create_property_set_xml(self,
                                  title,
                                  ps_uuid = None,
                                  originator = None,
                                  add_as_part = True,
                                  add_relationships = True):
          """Creates an xml node for a property set to represent this collection of properties.
  
          arguments:
             title (string): to be used as citation title
             ps_uuid (string, optional): if present, used as the uuid for the property set, otherwise a new uuid is generated
             originator (string, optional): if present, used as the citation creator (otherwise login name is used)
             add_as_part (boolean, default True): if True, the property set is added to the model as a part
             add_relationships (boolean, default True): if True, the relationships to the member properties are added
  
          note:
             xml for individual properties should exist before calling this method
          """
  
          assert self.model is not None, 'cannot create xml for property set as model is not set'
          assert self.number_of_parts() > 0, 'cannot create xml for property set as no parts in collection'
  
          ps_node = self.model.new_obj_node('PropertySet')
          if ps_uuid is None:
              ps_uuid = bu.uuid_from_string(ps_node.attrib['uuid'])
          else:
              ps_node.attrib['uuid'] = str(ps_uuid)
  
          self.model.create_citation(root = ps_node, title = title, originator = originator)
  
          tsk_node = rqet.SubElement(ps_node, ns['resqml2'] + 'TimeSetKind')
          tsk_node.set(ns['xsi'] + 'type', ns['resqml2'] + 'TimeSetKind')
          tsk_node.text = self.time_set_kind()
  
          hspk_node = rqet.SubElement(ps_node, ns['resqml2'] + 'HasSinglePropertyKind')
          hspk_node.set(ns['xsi'] + 'type', ns['xsd'] + 'boolean')
          hspk_node.text = str(self.has_single_property_kind()).lower()
  
          hmr_node = rqet.SubElement(ps_node, ns['resqml2'] + 'HasMultipleRealizations')
          hmr_node.set(ns['xsi'] + 'type', ns['xsd'] + 'boolean')
          hmr_node.text = str(self.has_multiple_realizations()).lower()
  
          parent_set_ref_node = None
          if self.parent_set_root is not None:
              parent_set_ref_node = self.model.create_ref_node('ParentSet',
                                                               self.model.title_for_root(self.parent_set_root),
                                                               self.parent_set_root.attrib['uuid'],
                                                               content_type = 'obj_PropertySet',
                                                               root = ps_node)
  
          prop_node_list = []
          for part in self.parts():
              part_root = self.model.root_for_part(part)
              self.model.create_ref_node('Properties',
                                         self.model.title_for_root(part_root),
                                         part_root.attrib['uuid'],
                                         content_type = self.model.type_of_part(part),
                                         root = ps_node)
              if add_as_part and add_relationships:
                  prop_node_list.append(part_root)
  
          if add_as_part:
              pcxml._create_property_set_xml_add_as_part(self, ps_node, ps_uuid, add_relationships, parent_set_ref_node,
                                                         prop_node_list)
  
          return ps_node
  
      def basic_static_property_parts(self,
                                      realization = None,
                                      share_perm_parts = False,
                                      perm_k_mode = None,
                                      perm_k_ratio = 1.0):
          """Returns five parts: net to gross ratio, porosity, permeability rock I, J & K; each returned part may be None.
  
          arguments:
             realization: (int, optional): if present, only properties with the given realization are considered; if None,
                all properties in the collection are considered
             share_perm_parts (boolean, default False): if True, the permeability I part will also be returned for J and/or K
                if no other properties are found for those directions; if False, None will be returned for such parts
             perm_k_mode (string, optional): if present, indicates what action to take when no K direction permeability is
                found; valid values are:
                'none': same as None, perm K part return value will be None
                'shared': if share_perm_parts is True, then perm I value will be used for perm K, else same as None
                'ratio': multiply IJ permeability by the perm_k_ratio argument
                'ntg': multiply IJ permeability by ntg and by perm_k_ratio
                'ntg squared': multiply IJ permeability by square of ntg and by perm_k_ratio
             perm_k_ratio (float, default 1.0): a Kv:Kh ratio, typically in the range zero to one, applied if generating
                a K permeability array (perm_k_mode is 'ratio', 'ntg' or 'ntg squared' and no existing K permeability found);
                ignored otherwise
  
          returns:
             tuple of 5 strings being part names for: net to gross ratio, porosity, perm i, perm j, perm k (respectively);
             any of the returned elements may be None if no appropriate property was identified
  
          note:
             if generating a K permeability array, the data is appended to the hdf5 file and the xml is created, however
             the epc re-write must be carried out by the calling code
  
          :meta common:
          """
  
          perm_i_part = perm_j_part = perm_k_part = None
  
          ntg_part = pcga._find_single_part(self, 'net to gross ratio', realization)
          poro_part = pcga._find_single_part(self, 'porosity', realization)
  
          perms = selective_version_of_collection(self, realization = realization, property_kind = 'permeability rock')
          if perms is None or perms.number_of_parts() == 0:
              log.error('no rock permeabilities present')
          else:
              perm_i_part, perm_j_part, perm_k_part = pcga._get_single_perm_ijk_parts(self, perms, share_perm_parts,
                                                                                      perm_k_mode, perm_k_ratio, ntg_part)
  
          return ntg_part, poro_part, perm_i_part, perm_j_part, perm_k_part
  
      def basic_static_property_parts_dict(self,
                                           realization = None,
                                           share_perm_parts = False,
                                           perm_k_mode = None,
                                           perm_k_ratio = 1.0):
          """Same as basic_static_property_parts() method but returning a dictionary with 5 items.
  
          note:
             returned dictionary contains following keys: 'NTG', 'PORO', 'PERMI', 'PERMJ', 'PERMK'
          """
  
          five_parts = self.basic_static_property_parts(realization = realization,
                                                        share_perm_parts = share_perm_parts,
                                                        perm_k_mode = perm_k_mode,
                                                        perm_k_ratio = perm_k_ratio)
          return {
              'NTG': five_parts[0],
              'PORO': five_parts[1],
              'PERMI': five_parts[2],
              'PERMJ': five_parts[3],
              'PERMK': five_parts[4]
          }
  
      def basic_static_property_uuids(self,
                                      realization = None,
                                      share_perm_parts = False,
                                      perm_k_mode = None,
                                      perm_k_ratio = 1.0):
          """Returns five uuids: net to gross ratio, porosity, permeability rock I, J & K; each returned uuid may be None.
  
          note:
             see basic_static_property_parts() method for argument documentation
  
          :meta common:
          """
  
          five_parts = self.basic_static_property_parts(realization = realization,
                                                        share_perm_parts = share_perm_parts,
                                                        perm_k_mode = perm_k_mode,
                                                        perm_k_ratio = perm_k_ratio)
          uuid_list = []
          for part in five_parts:
              if part is None:
                  uuid_list.append(None)
              else:
                  uuid_list.append(rqet.uuid_in_part_name(part))
          return tuple(uuid_list)
  
      def basic_static_property_uuids_dict(self,
                                           realization = None,
                                           share_perm_parts = False,
                                           perm_k_mode = None,
                                           perm_k_ratio = 1.0):
          """Same as basic_static_property_uuids() method but returning a dictionary with 5 items.
  
          note:
             returned dictionary contains following keys: 'NTG', 'PORO', 'PERMI', 'PERMJ', 'PERMK'
          """
  
          five_uuids = self.basic_static_property_uuids(realization = realization,
                                                        share_perm_parts = share_perm_parts,
                                                        perm_k_mode = perm_k_mode,
                                                        perm_k_ratio = perm_k_ratio)
          return {
              'NTG': five_uuids[0],
              'PORO': five_uuids[1],
              'PERMI': five_uuids[2],
              'PERMJ': five_uuids[3],
              'PERMK': five_uuids[4]
          }
  