pyiron_base.storage.datacontainer.DataContainerBase#
- class pyiron_base.storage.datacontainer.DataContainerBase(*args, **kwargs)[source]#
Bases:
MutableMapping
,Lockable
,HasGroups
Mutable sequence with optional keys.
If no argument is given, the constructor creates a new empty DataContainerBase. If specified init maybe a Sequence, Set or Mapping and all recursive occurrences of these are also wrapped by DataContainerBase.
>>> pl = DataContainerBase([3, 2, 1, 0]) >>> pm = DataContainerBase({"foo": 24, "bar": 42})
Access can be like a normal list with integers or optionally with strings as keys.
>>> pl[0] 3 >>> pl[2] 1 >>> pm["foo"] 24
Keys do not have to be present for all elements.
>>> pl2 = DataContainerBase([1,2]) >>> pl2["end"] = 3 >>> pl2 DataContainerBase({0: 1, 1: 2, 'end': 3})
It is also allowed to set an item one past the length of the DataContainerBase, this is then equivalent to appending that element. This allows to use the update method also with other DataContainerBases
>>> pl[len(pl)] = -1 >>> pl DataContainerBase([3, 2, 1, 0, -1]) >>> pl.pop(-1) -1
Where strings are used they may also be used as attributes. Getting keys which clash with methods of DataContainerBase must be done with item access, but setting them works without overwriting the instance methods, but is not recommended for readability.
>>> pm.foo 24 >>> pm.tail = 23 >>> pm DataContainerBase({'foo': 24, 'bar': 42, 'tail': 23})
Keys and indices can be tuples to traverse nested DataContainerBases.
>>> pn = DataContainerBase({"foo": {"bar": [4, 2]}}) >>> pn["foo", "bar"] DataContainerBase([4, 2]) >>> pn["foo", "bar", 0] 4
Using keys with “/” in them is equivalent to the above after splitting the key.
>>> pn["foo/bar"] == pn["foo", "bar"] True >>> pn["foo/bar/0"] == pn["foo", "bar", 0] True
To make that work strings that are only decimal digits are automatically converted to integers before accessing the list and keys are restricted to not only contain digits on initialization.
>>> pl["0"] == pl[0] True >>> DataContainerBase({1: 42}) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "datacontainer.py", line 126, in __init__ raise ValueError( ValueError: keys in initializer must not be int or str of decimal digits or in correct order, is 1
When initializing from a dict, it may not have integers or decimal strings as keys unless they match their position in the insertion order. This is to avoid ambiguities in the final order of the DataContainerBase.
>>> DataContainerBase({0: "foo", 1: "bar", 2: 42}) DataContainerBase(['foo', 'bar', 42]) >>> DataContainerBase({0: "foo", 2: 42, 1: "bar"}) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "datacontainer.py", line 132, in __init__ raise ValueError( ValueError: keys in initializer must not be int or str of decimal digits or in correct order, is 2
Using keys is completely optional, DataContainerBase can always be treated as a list, with the exception that iter() iterates of the keys and indices. This is to correctly implement the MutableMapping protocol, to convert to a normal list and discard the keys use values().
>>> pm[0] 24 >>> pn["0/0/1"] 2 >>> list(pl) [0, 1, 2, 3] >>> list(pl.values()) [3, 2, 1, 0] >>> list(pl.keys()) [0, 1, 2, 3]
Implements
HasGroups
. Groups are nested data containers and nodes are everything else.>>> p = DataContainerBase({"a": 42, "b": [0, 1, 2]}) >>> p.list_groups() ['b'] >>> p.list_nodes() ['a']
Attention
Subclasses beware!
DataContainerBase require some careful treatment when creating subclasses.
1. Since DataContainerBases are expected to recursively instantiate themselves subclasses need to keep their __init__ compatible to the base class. That means being able to be instantiated without arguments, if arguments are given the first one (or `init) has to accept a Mapping or Iterable. Additional arguments may be added, but must be after init and must have a default.
2. Creating new instance attributes that don’t live in the container itself is possible, but you need to use object.__setattr__ the first time you define that attribute. Afterwards using normal assignment syntax works.
3. Subclasses should always be thought of as general data structures, if you want to subclass to have access to the HDF5 functionality or the way the DataContainerBase is shown in jupyter notebooks, but only have a fixed number of attributes it is better to create a new class that has an DataContainerBase as an attribute and dispatch to the
DataContainerBase.from_hdf()
,DataContainerBase.to_hdf()
andDataContainerBase._repr_json_()
methods.A few examples for subclasses
>>> class ExtendedContainer(DataContainerBase): ... def __init__(self, init=None, my_fancy_field=42, table_name=None): ... super().__init__(init=init, table_name=table_name) ... object.__setattr__(self, "my_fancy_field", my_fancy_field)
After defining it once like this you can access my_fancy_field as a normal attribute, but it will not be stored in the container itself and will not be stored in HDF5.
>>> e = ExtendedContainer({'foo': 1, 'bar': 5}, my_fancy_field=23) >>> e.my_fancy_field 23 >>> e ExtendedContainer({'foo': 1, 'bar': 5}) >>> e.my_fancy_field = 42 >>> e.my_fancy_field 42 >>> e ExtendedContainer({'foo': 1, 'bar': 5})
Be aware the
DataContainerBase
and its subclasses are recursive data structures, i.e. your fancy attribute will be available also on sub groups.>>> g = e.create_group('sub') >>> g.fnord = 23 >>> g.my_fancy_field 42 >>> e ExtendedContainer({'foo': 1, 'bar': 5, 'sub': ExtendedContainer({'fnord': 23})})
For that reason most of time you’ll actually want a class that uses a DataContainerBase for storage, but doesn’t derive from it.
>>> from pyiron_base.interfaces.object import HasStorage >>> class FancyClass(HasStorage): ... def __init__(self, foo): ... super().__init__() ... self.storage.foo = foo ... ... @property ... def foo(self): ... return self.storage.foo ... ... @foo.setter ... def foo(self, val): ... self.storage.foo = val ... ... def _repr_json_(self): ... return self.storage._repr_json_()
- __init__(init=None, table_name=None, wrap_blacklist=(), lock_method='warning')[source]#
Create new container.
- Parameters:
init (Sequence, Mapping) – initial data for the container, nested occurances of Sequence and Mapping are translated to nested containers
table_name (str) – default name of the data container in HDF5
wrap_blacklist (tuple of types) – any values in init that are instances of the given types are not wrapped in
DataContainerBase
Methods
__init__
([init, table_name, wrap_blacklist, ...])Create new container.
append
(val)Add new value to the container without a key.
clear
()Remove all items from DataContainerBase.
copy
()Returns deep copy of it self.
create_group
(name)Add a new empty subcontainer under the given key.
extend
(vals)Append vals to the end of this DataContainerBase.
get
(key[, default, create])If
key
exists, behave as generic, if not call create_group.groups
()Iterate over keys to nested containers.
has_keys
()Check if the container has keys set or not.
insert
(index, val[, key])Add a new element to the container at the specified position, with an optional key.
items
()keys
()list_all
()Returns dictionary of :method:`.list_groups()` and :method:`.list_nodes()`.
Return a list of names of all nested groups.
Return a list of names of all nested nodes.
lock
([method])Set
read_only
.mark
(index, key)Add a key to an existing item at index.
nodes
()Iterator over keys to terminal nodes.
pop
(k[,d])If key is not found, d is returned if given, otherwise KeyError is raised.
popitem
()as a 2-tuple; but raise KeyError if D is empty.
read
(file_name[, wrap])Parse file as dictionary and add its keys to this container.
search
(key[, stop_on_first_hit])Search for
key
in the Container hierarchy.setdefault
(k[,d])to_builtin
([stringify])Convert the container back to builtin dict's and list's recursively.
unlocked
()Unlock the object temporarily.
update
(init[, wrap, blacklist])Add all elements or key-value pairs from init to this container.
values
()write
(file_name)Writes the DataContainerBase to a text file.
Attributes
False if the object can currently be written to
- copy()[source]#
Returns deep copy of it self. A shallow copy can be obtained via the copy module.
- Returns:
deep copy of itself
- Return type:
>>> pl = DataContainerBase([[1,2,3]]) >>> pl.copy() == pl True >>> pl.copy() is pl False >>> all(a is not b for a, b in zip(pl.copy().values(), pl.values())) True
- create_group(name)[source]#
Add a new empty subcontainer under the given key.
- Parameters:
name (str) – key under which to store the new subcontainer in this container
- Returns:
the newly created subcontainer
- Return type:
- Raises:
ValueError – name already exists in container and is not a sub container
>>> pl = DataContainerBase({}) >>> pl.create_group("group_name") DataContainerBase([]) >>> list(pl.group_name) []
- extend(vals)[source]#
Append vals to the end of this DataContainerBase.
- Parameters:
vals (Sequence) – any python sequence to draw new elements from
- get(key, default=None, create=False)[source]#
If
key
exists, behave as generic, if not call create_group.- Parameters:
key (str) – key to search
default (optional) – return this instead if nothing found
create (bool, optional) – create empty container at key if nothing found
- Raises:
IndexError – if key is not in the container and neither
default
notcreate` are give –
- Returns:
element at
key
or new empty subcontainer- Return type:
object
- groups()[source]#
Iterate over keys to nested containers.
- Returns:
list of all keys to elements of
DataContainerBase
.- Return type:
list
- has_keys()[source]#
Check if the container has keys set or not.
- Returns:
True if there is at least one key set
- Return type:
bool
- insert(index, val, key=None)[source]#
Add a new element to the container at the specified position, with an optional key. If the key is already in the container it will be updated to point to the new element at the new index. If index is larger than container, append instead.
- Parameters:
index (int) – place val after this element
val – new element to add
key (str, optional) – optional key to mark the new element
- items() a set-like object providing a view on D's items #
- keys() a set-like object providing a view on D's keys #
- list_all()#
Returns dictionary of :method:`.list_groups()` and :method:`.list_nodes()`.
- Returns:
- results of :method:`.list_groups() under the key "groups"; results of :method:`.list_nodes()` und the
key “nodes”
- Return type:
dict
- list_groups()#
Return a list of names of all nested groups.
- Returns:
group names
- Return type:
list of str
- list_nodes()#
Return a list of names of all nested nodes.
- Returns:
node names
- Return type:
list of str
- lock(method: Literal['error', 'warning'] | None = None)#
Set
read_only
.Objects may be safely locked multiple times without further effect.
- Parameters:
method (str, either "error" or "warning") – if “error” raise an
Locked
exception if modification is attempted; if “warning” raise aLockedWarning
warning; default is “error” or the value passed to the constructor.- Raises:
ValueError – if method is not an allowed value
- mark(index, key)[source]#
Add a key to an existing item at index. If key already exists, it is overwritten.
- Parameters:
index (int) – index of the existing element to mark
key (str) – key for the existing element
- Raises:
IndexError – if index > len(self)
>>> pl = DataContainerBase([42]) >>> pl.mark(0, "head") >>> pl.head == 42 True
- nodes()[source]#
Iterator over keys to terminal nodes.
- Returns:
list of keys to normal values.
- Return type:
list
- pop(k[, d]) v, remove specified key and return the corresponding value. #
If key is not found, d is returned if given, otherwise KeyError is raised.
- popitem() (k, v), remove and return some (key, value) pair #
as a 2-tuple; but raise KeyError if D is empty.
- read(file_name, wrap=True)[source]#
Parse file as dictionary and add its keys to this container.
For supported file types, see
fileio.read()
.Errors during reading of the files generate a warning, but leave the container unchanged.
- Parameters:
file_name (str) – path to the input file
wrap (bool)
- Raises:
ValueError – if file extension doesn’t match one of the supported ones
- property read_only: bool#
False if the object can currently be written to
Setting this value will trigger
_on_lock()
and_on_unlock()
if it changes.- Type:
bool
- search(key, stop_on_first_hit=True)[source]#
Search for
key
in the Container hierarchy.This should be used if there is only one such item in the hierarchy.
If stop_on_first_hit is True the first item found is taken. Otherwise, a ValueError is raised if the key appears multiple times.
- Parameters:
key (str) – the key to look for
stop_on_first_hit (bool) – whether to stop on the first hit
- Raises:
TypeError – if key is not str
KeyError – if key is not found
ValueError – if stop_on_first_hit is False and key is found twice
- Returns:
element at
key
- Return type:
object
- setdefault(k[, d]) D.get(k,d), also set D[k]=d if k not in D #
- to_builtin(stringify=False)[source]#
Convert the container back to builtin dict’s and list’s recursively.
- Parameters:
stringify (bool, optional) – convert all non-recursive elements to str
- unlocked() _UnlockContext #
Unlock the object temporarily.
Context manager returns this object again and relocks it after the with statement finished.
Note
lock() vs. unlocked()
There is a small asymmetry between these two methods.
lock()
can only be done once (meaningfully), whileunlocked()
is a context manager and can be called multiple times.
- update(init, wrap=False, blacklist=(), **kwargs)[source]#
Add all elements or key-value pairs from init to this container. If wrap is not given, behaves as the generic method.
- Parameters:
init (Sequence, Set, Mapping) – container to draw new elements from
wrap (bool) – if True wrap all encountered Sequences and Mappings in
DataContainerBase
recursivelyblacklist (list of types) – when wrap is True, don’t wrap these types even if they’re instances of Sequence or Mapping
**kwargs – update from this mapping as well
- values() an object providing a view on D's values #
- write(file_name)[source]#
Writes the DataContainerBase to a text file.
For supported file types, see
fileio.write()
.- Parameters:
file_name (str) – the name of the file to be writen to.