cs.mappings 20210717

Facilities for mappings and objects associated with mappings.

Facilities for mappings and objects associated with mappings.

Latest release 20210717:

• New IndexedMapping: wrapper for another mapping providing LoadableMappingMixin stype .by_* attributes.
• Rename LoadableMappingMixin to IndexedSetMixin and make it abstract, rename .scan_mapping to .scan, .add_to_mapping to .add etc.

In particular named_column_tuple(column_names), a function returning a factory for namedtuples subclasses derived from the supplied column names, and named_column_tuples(rows), a function returning a namedtuple factory and an iterable of instances containing the row data. These are used by the csv_import and xl_import functions from cs.csvutils.

Class AttrableMapping(builtins.dict,AttrableMappingMixin)

A dict subclass using AttrableMappingMixin.

Class AttrableMappingMixin

Provides a __getattr__ which accesses the mapping value.

Method AttrableMappingMixin.__getattr__(self, attr)

Unknown attributes are obtained from the mapping entries.

Note that this first consults self.__dict__. For many classes that is redundants, but subclasses of dict at least seem not to consult that with attribute lookup, likely because a pure dict has no __dict__.

Class AttributableList(builtins.list)

An AttributableList maps unimplemented attributes onto the list members and returns you a new AttributableList with the results, ready for a further dereference.

Example:

>>> class C(object):
...   def __init__(self, i):
...     self.i = i
>>> Cs = [ C(1), C(2), C(3) ]
>>> AL = AttributableList( Cs )
>>> print(AL.i)
[1, 2, 3]

Method AttributableList.__init__(self, initlist=None, strict=False)

Initialise the list.

The optional parameter initlist initialises the list as for a normal list.

The optional parameter strict, if true, causes list elements lacking the attribute to raise an AttributeError. If false, list elements without the attribute are omitted from the results.

Function dicts_to_namedtuples(dicts, class_name, keys=None)

Scan an iterable of dicts, yield a sequence of namedtuples derived from them.

Parameters:

• dicts: the dicts to scan and convert, an iterable
• class_name: the name for the new namedtuple class
• keys: optional iterable of dict keys of interest; if omitted then the dicts are scanned in order to learn the keys

Note that if keys is not specified this generator prescans the dicts in order to learn their keys. As a consequence, all the dicts will be kept in memory and no namedtuples will be yielded until after that prescan completes.

Class FallbackDict(collections.defaultdict,builtins.dict)

A dictlike object that inherits from another dictlike object; this is a convenience subclass of defaultdict.

Class IndexedMapping(IndexedSetMixin)

Interface to a mapping with IndexedSetMixin style .by_* attributes.

Method IndexedMapping.__init__(self, mapping=None, pk='id')

Initialise the IndexedMapping.

Parameters:

• mapping: the mapping to wrap; a new dict will be made if not specified
• pk: the primary key of the mapping, default 'id'

Method IndexedMapping.add_backend(self, record)

Save record in the mapping.

Method IndexedMapping.scan(self)

The records from the mapping.

Class IndexedSetMixin

A base mixin to provide .by_* attributes which index records from an autoloaded backing store, which might be a file or might be another related data structure. The records are themselves key->value mappings, such as dicts.

The primary key name is provided by the .IndexedSetMixin__pk class attribute, to be provided by subclasses.

Note that this mixin keeps the entire loadable mapping in memory.

Note that this does not see subsequent changes to loaded records i.e. changing the value of some record[k] does not update the index associated with the .by_k attribute.

Subclasses must provide the following attributes and methods:

• IndexedSetMixin__pk: the name of the primary key; it is an error for multiple records to have the same primary key
• scan: a generator method to scan the backing store and yield records, used for the inital load of the mapping
• add_backend(record): add a new record to the backing store; this is called from the .add(record) method after indexing to persist the record in the backing store

See UUIDNDJSONMapping and UUIDedDict for an example subclass indexing records from a newline delimited JSON file.

Method IndexedSetMixin.__len__(self)

The length of the primary key mapping.

Method IndexedSetMixin.add(self, record, exists_ok=False)

Add a record to the mapping.

This indexes the record against the various by_* indices and then calls self.add_backend(record) to save the record to the backing store.

Method IndexedSetMixin.scan(self)

Scan the mapping records (themselves mappings) from the backing store, which might be a file or another related data structure. Yield each record as scanned.

Property IndexedSetMixin.scan_length

The number of records encountered during the backend scan.

Class JSONableMappingMixin

Provide .from_json(), .as_json() and .append_ndjson() methods, and __str__=as_json and a __repr__.

Method JSONableMappingMixin.__str__(self)

The dict transcribed as JSON.

If the instance's class has json_default or json_separators these are used for the default and separators parameters of the json.dumps() call. Note that the default value of separators is (',',':') which produces the most compact JSON form.

Method JSONableMappingMixin.append_ndjson(arg, *a, **kw)

Append this object to f, a file or filename, as NDJSON.

Method JSONableMappingMixin.as_json(self)

The dict transcribed as JSON.

If the instance's class has json_default or json_separators these are used for the default and separators parameters of the json.dumps() call. Note that the default value of separators is (',',':') which produces the most compact JSON form.

Method JSONableMappingMixin.from_json(js)

Prepare an dict from JSON text.

If the class has json_object_hook or json_object_pairs_hook attributes these are used as the object_hook and object_pairs_hook parameters respectively of the json.loads() call.

Class MappingChain

A mapping interface to a sequence of mappings.

It does not support __setitem__ at present; that is expected to be managed via the backing mappings.

Method MappingChain.__init__(self, mappings=None, get_mappings=None)

Initialise the MappingChain.

Parameters:

• mappings: initial sequence of mappings, default None.
• get_mappings: callable to obtain the initial sequence of

Exactly one of mappings or get_mappings must be provided.

Method MappingChain.__getitem__(self, key)

Return the first value for key found in the mappings. Raise KeyError if the key in not found in any mapping.

Method MappingChain.get(self, key, default=None)

Get the value associated with key, return default if missing.

Method MappingChain.keys(self)

Return the union of the keys in the mappings.

Class MethodicalList(AttributableList,builtins.list)

A MethodicalList subclasses a list and maps unimplemented attributes into a callable which calls the corresponding method on each list members and returns you a new MethodicalList with the results, ready for a further dereference.

Example:

>>> n = 1
>>> class C(object):
...   def __init__(self):
...     global n
...     self.n = n
...     n += 1
...   def x(self):
...     return self.n
...
>>> Cs=[ C(), C(), C() ]
>>> ML = MethodicalList( Cs )
>>> print(ML.x())
[1, 2, 3]

Method MethodicalList.__init__(self, initlist=None, strict=False)

Initialise the list.

The optional parameter initlist initialises the list as for a normal list.

The optional parameter strict, if true, causes list elements lacking the attribute to raise an AttributeError. If false, list elements without the attribute are omitted from the results.

Function named_column_tuples(rows, class_name=None, column_names=None, computed=None, preprocess=None, mixin=None)

Process an iterable of data rows, usually with the first row being column names. Return a generated namedtuple factory and an iterable of instances of the namedtuples for each row.

Parameters:

• rows: an iterable of rows, each an iterable of data values.
• class_name: option class name for the namedtuple class
• column_names: optional iterable of column names used as the basis for the namedtuple. If this is not provided then the first row from rows is taken to be the column names.
• computed: optional mapping of str to functions of self
• preprocess: optional callable to modify CSV rows before they are converted into the namedtuple. It receives a context object an the data row. It should return the row (possibly modified), or None to drop the row.
• mixin: an optional mixin class for the generated namedtuple subclass to provide extra methods or properties

The context object passed to preprocess has the following attributes:

• .cls: attribute with the generated namedtuple subclass; this is useful for obtaining things like the column names or column indices; this is None when preprocessing the header row, if any
• .index: attribute with the row's enumeration, which counts from 0
• .previous: the previously accepted row's namedtuple, or None if there is no previous row

Rows may be flat iterables in the same order as the column names or mappings keyed on the column names.

If the column names contain empty strings they are dropped and the corresponding data row entries are also dropped. This is very common with spreadsheet exports with unused padding columns.

Typical human readable column headings, also common in speadsheet exports, are lowercased and have runs of whitespace or punctuation turned into single underscores; trailing underscores then get dropped.

Basic example:

>>> data1 = [
...   ('a', 'b', 'c'),
...   (1, 11, "one"),
...   (2, 22, "two"),
... ]
>>> cls, rows = named_column_tuples(data1)
>>> print(list(rows))
[NamedRow(a=1, b=11, c='one'), NamedRow(a=2, b=22, c='two')]

Human readable column headings:

>>> data1 = [
...   ('Index', 'Value Found', 'Descriptive Text'),
...   (1, 11, "one"),
...   (2, 22, "two"),
... ]
>>> cls, rows = named_column_tuples(data1)
>>> print(list(rows))
[NamedRow(index=1, value_found=11, descriptive_text='one'), NamedRow(index=2, value_found=22, descriptive_text='two')]

Rows which are mappings:

>>> data1 = [
...   ('a', 'b', 'c'),
...   (1, 11, "one"),
...   {'a': 2, 'c': "two", 'b': 22},
... ]
>>> cls, rows = named_column_tuples(data1)
>>> print(list(rows))
[NamedRow(a=1, b=11, c='one'), NamedRow(a=2, b=22, c='two')]

CSV export with unused padding columns:

>>> data1 = [
...   ('a', 'b', 'c', '', ''),
...   (1, 11, "one"),
...   {'a': 2, 'c': "two", 'b': 22},
...   [3, 11, "three", '', 'dropped'],
... ]
>>> cls, rows = named_column_tuples(data1, 'CSV_Row')
>>> print(list(rows))
[CSV_Row(a=1, b=11, c='one'), CSV_Row(a=2, b=22, c='two'), CSV_Row(a=3, b=11, c='three')]

A mixin class providing a test1 method and a test2 property:

>>> class Mixin(object):
...   def test1(self):
...     return "test1"
...   @property
...   def test2(self):
...     return "test2"
>>> data1 = [
...   ('a', 'b', 'c'),
...   (1, 11, "one"),
...   {'a': 2, 'c': "two", 'b': 22},
... ]
>>> cls, rows = named_column_tuples(data1, mixin=Mixin)
>>> rows = list(rows)
>>> rows[0].test1()
'test1'
>>> rows[0].test2
'test2'

Function named_row_tuple(*column_names, **kw)

Return a namedtuple subclass factory derived from column_names.

Parameters:

• column_names: an iterable of str, such as the heading columns of a CSV export
• class_name: optional keyword parameter specifying the class name
• computed: optional keyword parameter providing a mapping of str to functions of self; these strings are available via __getitem__
• mixin: an optional mixin class for the generated namedtuple subclass to provide extra methods or properties

The tuple's attributes are computed by converting all runs of nonalphanumerics (as defined by the re module's "\W" sequence) to an underscore, lowercasing and then stripping leading and trailing underscores.

In addition to the normal numeric indices, the tuple may also be indexed by the attribute names or the column names.

The new class has the following additional attributes:

• attributes_: the attribute names of each tuple in order
• names_: the originating name strings
• name_attributes_: the computed attribute names corresponding to the names; there may be empty strings in this list
• attr_of_: a mapping of column name to attribute name
• name_of_: a mapping of attribute name to column name
• index_of_: a mapping of column names and attributes their tuple indices

Examples:

>>> T = named_row_tuple('Column 1', '', 'Column 3', ' Column 4', 'Column 5 ', '', '', class_name='Example')
>>> T.attributes_
['column_1', 'column_3', 'column_4', 'column_5']
>>> row = T('val1', 'dropped', 'val3', 4, 5, 6, 7)
>>> row
Example(column_1='val1', column_3='val3', column_4=4, column_5=5)

Class PrefixedMappingProxy

A proxy for another mapping operating on keys commencing with a prefix.

Method PrefixedMappingProxy.get(self, k, default=None)

Return the value for key k or default.

Method PrefixedMappingProxy.keys(self)

Yield the post-prefix suffix of the keys in self.mapping.

Class SeenSet

A set-like collection with optional backing store file.

Method SeenSet.add(self, s, foreign=False)

Add the value s to the set.

Parameters:

• s: the value to add
• foreign: default False: whether the value came from an outside source, usually a third party addition to the backing file; this prevents appending the value to the backing file.

Class SeqMapUC_Attrs

A wrapper for a mapping from keys (matching the regular expression ^[A-Z][A-Z_0-9]*$) to tuples.

Attributes matching such a key return the first element of the sequence (and requires the sequence to have exactly on element). An attribute FOOs or FOOes (ending in a literal 's' or 'es', a plural) returns the sequence (FOO must be a key of the mapping).

Class StackableValues

A collection of named stackable values with the latest value available as an attribute.

DEPRECATED: I now recommend my cs.context.stackattrs context manager for most uses; it may be applied to any object instead of requiring use of this class.

Note that names conflicting with methods are not available as attributes and must be accessed via __getitem__. As a matter of practice, in addition to the mapping methods, avoid names which are verbs or which begin with an underscore.

Example:

>>> S = StackableValues()
>>> print(S)
StackableValues()
>>> S.push('x', 1)
>>> print(S)
StackableValues(x=1)
>>> print(S.x)
1
>>> S.push('x', 2)
1
>>> print(S.x)
2
>>> S.x = 3
>>> print(S.x)
3
>>> S.pop('x')
3
>>> print(S.x)
1
>>> with S.stack(x=4):
...   print(S.x)
...
4
>>> print(S.x)
1
>>> S.update(x=5)
{'x': 1}

Method StackableValues.__getattr__(self, attr)

Convenience: present the top value of key attr as an attribute.

Note that attributes push, pop and the mapping method names are shadowed by the instance methods and should be accessed with the traditional [] key dereference.

Method StackableValues.__getitem__(self, key)

Return the top value for key or raise KeyError.

Method StackableValues.__setattr__(self, attr, value)

For nonunderscore attributes, replace the top element of the stack.

Method StackableValues.get(self, key, default=None)

Get the top value for key, or default.

Method StackableValues.items(self)

Mapping method returning an iterable of (name, value) tuples.

Method StackableValues.keys(self)

Mapping method returning a list of the names.

Method StackableValues.pop(self, key)

Pop and return the latest value for key.

Method StackableValues.push(self, key, value)

Push a new value for key. Return the previous value or None if this is the first value for key.

Method StackableValues.stack(self, *a, **kw)

Context manager which saves and restores the current state. Any parameters are passed to update() after the save but before the yield.

Method StackableValues.update(self, *ms, **kw)

Update the mapping like dict.update method. Return a mapping with the preupdate values of the updated keys.

Method StackableValues.values(self)

Mapping method returning an iterable of the values.

Class UC_Sequence(builtins.list)

A tuple-of-nodes on which .ATTRs indirection can be done, yielding another tuple-of-nodes or tuple-of-values.

Method UC_Sequence.__init__(self, Ns)

Initialise from an iterable sequence.

Class UUIDedDict(builtins.dict,JSONableMappingMixin,AttrableMappingMixin)

A handy dict subtype providing the basis for mapping classes indexed by UUIDs.

The 'uuid' attribute is always a UUID instance.

Method UUIDedDict.__init__(self, _d=None, **kw)

Initialise the UUIDedDict, generating a 'uuid' key value if omitted.

Property UUIDedDict.uuid

A UUID from self['uuid'].

This does a sanity check that the stored value is a UUID, but primarily exists to support the setter, which promotes str to UUID, thus also validating UUID strings.

Release Log

Release 20210717:

• New IndexedMapping: wrapper for another mapping providing LoadableMappingMixin stype .by_* attributes.
• Rename LoadableMappingMixin to IndexedSetMixin and make it abstract, rename .scan_mapping to .scan, .add_to_mapping to .add etc.

Release 20210306: StackableValues: fix typo, make deprecation overt.

Release 20210123: AttrableMappingMixin.getattr: some bugfixes.

Release 20201228: New PrefixedMappingProxy presenting the keys of another mapping commencing with a prefix.

Release 20201102:

• StackableValues is obsolete, add recommendation for cs.context.stackattrs to the docstring.
• New AttrableMappingMixin with a getattr which looks up unknown attributes as keys.
• New JSONableMappingMixin with methods for JSON actions: from_json, as_json, append_ndjson and a str and repr.
• New LoadableMappingMixin to load .by_* attributes on demand.
• New AttrableMapping(dict, AttrableMappingMixin).

Release 20200130: New dicts_to_namedtuples function to yield namedtuples from an iterable of dicts.

Release 20191120: named_row_tuple: support None in a column name, as from Excel unfilled heading row entries

Release 20190617:

• StackableValues.push now returns the previous value.
• StackableValues.update has a signature like dict.update.
• StackableValues.pop removes entries when their stack becomes empty.
• StackableValues.stack: clean implementation of save/restore.
• StackableValues: avoid infinite recursion through ._fallback.
• StackableValues.keys now returns a list of the nonempty keys.
• Update doctests.

Release 20190103: Documentation update.

Release 20181231:

• Bugfix for mapping of column names to row indices.
• New subclass._fallback method for when a stack is empty.

Release 20180720: Initial PyPI release specificly for named_column_tuple and named_column_tuples.