3.1. Settings

The Settings class provides a general purpose data container for various kinds of information that need to be stored and processed by PLAMS environment. Other PLAMS objects (like for example Job, JobManager or GridRunner) have their own Settings instances that store data defining and adjusting their behavior. The global scope Settings instance (config) is used for global settings.

It should be stressed here that there are no different types of Settings in the sense that there are no special subclasses of Settings for job settings, global settings etc. Everything is stored in the same type of object and the possible role of particular Settings instance is determined only by its content.

3.1.1. Tree-like structure

The Settings class is based on the regular Python dictionary (built-in class dict, tutorial can be found here) and in many aspects works just like it:

>>> s = Settings()
>>> s['abc'] = 283
>>> s[147147] = 'some string'
>>> print(s['abc'])
283
>>> del s[147147]

The main difference is that data in Settings can be stored in multilevel fashion, whereas an ordinary dictionary is just a flat structure of key-value pairs. That means a sequence of keys can be used to store a value. In the example below s['a'] is itself a Settings instance with two key-value pairs inside:

>>> s = Settings()
>>> s['a']['b'] = 'AB'
>>> s['a']['c'] = 'AC'
>>> s['x']['y'] = 10
>>> s['x']['z'] = 13
>>> s['x']['foo'][123] = 'even deeper'
>>> s['x']['foo']['bar'] = 183
>>> print(s)
a:
  b:    AB
  c:    AC
x:
  foo:
      123:  even deeper
      bar:  183
  y:    10
  z:    13
>>> print(s['x'])
foo:
    123:    even deeper
    bar:    183
y:  10
z:  13

So now a value stored for each key can be either a “proper value” (string, number, list etc.) or another Settings instance that creates one more level in the data hierarchy. That way similar information can be arranged in subgroups that can be copied, moved and updated separately. It is convenient to think of a Settings object as a tree. The root of the tree is the top instance (s in the above example), “proper values” are stored in leaves (a leaf is a childless node) and internal nodes correspond to nested Settings instances (we will call them branches). Tree representation of s from the example above is illustrated on the following picture:

_images/set_tree.png

Tree-like structure could be also achieved with regular dictionaries, but in a rather cumbersome way:

>>> d = dict()
>>> d['a'] = dict()
>>> d['a']['b'] = dict()
>>> d['a']['b']['c'] = dict()
>>> d['a']['b']['c']['d'] = 'ABCD'
===========================
>>> s = Settings()
>>> s['a']['b']['c']['d'] = 'ABCD'

In the last line of the above example all intermediate Settings instances are created and inserted automatically. Such a behavior, however, has some downsides – every time you request a key that is not present in a particular Settings instance (for example as a result of a typo), a new empty instance is created and inserted as a value of this key. This is different from dictionaries where exception is raised in such a case:

>>> d = dict()
>>> d['foo'] = 'bar'
>>> x = d['fo']
KeyError: 'fo'
===========================
>>> s = Settings()
>>> s['foo'] = 'bar'
>>> x = s['fo']

>>> print(s)
fo:
foo:    bar

3.1.2. Dot notation

To avoid inconvenient punctuation, keys stored in Settings can be accessed using the dot notation in addition to the usual bracket notation. In other words s.abc works as a shortcut for s['abc']. Both notations can be used interchangeably:

>>> s.a.b = 'AB'
>>> s['a'].c = 'AC'
>>> s.x['y'] = 10
>>> s['x']['z'] = 13
>>> s['x'].foo[123] = 'even deeper'
>>> s.x.foo.bar = 183
>>> print(s)
a:
  b:    AB
  c:    AC
x:
  foo:
      123:  even deeper
      bar:  183
  y:    10
  z:    13

Due to internal limitation of the Python syntax parser, keys other than single word strings won’t work with that shortcut, for example:

>>> s.123.b.c = 12
SyntaxError: invalid syntax
>>> s.q we.r.t.y = 'aaa'
SyntaxError: invalid syntax
>>> s.5fr = True
SyntaxError: invalid syntax

In those cases one has to use the regular bracket notation:

>>> s[123].b.c = 12
>>> s['q we'].r.t.y = 'aaa'
>>> s['5fr'] = True

The dot shortcut does not work for keys which begin and end with two (or more) underscores (like '__key__'). This is done on purpose to ensure that Python magic methods work properly.

3.1.3. Global settings

Global settings (variables adjusting general behavior of PLAMS as well as default settings for various objects) are stored in a public Settings instance named config. This instance is created during initialization of PLAMS environment (see init()) and populated by executing plams_defaults. It is publicly visible from everywhere without a need of import so every time you wish to adjust some settings you can simply type in your script, for example:

config.job.pickle = False
config.sleepstep = 10

These changes are going to affect only the script they are called from. If you wish to permanently change some setting for all PLAMS executions, you can do it by editing plams_defaults. This file is located in utils subfolder and contains definitions of all config entries together with short explanations of their roles.

Technical

config is made visible from everywhere by being added to built-ins namespace in builtins module.

3.1.4. API

class Settings(*args, **kwargs)[source]

Automatic multi-level dictionary. Subclass of built-in dict.

The shortcut dot notation (s.basis instead of s['basis']) can be used for keys that:

  • are strings
  • don’t contain whitespaces
  • begin with a letter or underscore
  • don’t both begin and end with two or more underscores.

Warning

As of PLAMS v1.1 strings used as keys do NOT get lowercased, they are used as is.

Iteration follows lexicographical order (via sorted() function)

Methods for displaying content (__str__() and __repr__()) are overridden to recursively show nested instances in easy-readable format.

Regular dictionaries (also multi-level ones) used as values (or passed to the constructor) are automatically transformed to Settings instances:

>>> s = Settings({'a': {1: 'a1', 2: 'a2'}, 'b': {1: 'b1', 2: 'b2'}})
>>> s.a[3] = {'x': {12: 'q', 34: 'w'}, 'y': 7}
>>> print(s)
a:
  1:    a1
  2:    a2
  3:
    x:
      12:   q
      34:   w
    y:  7
b:
  1:    b1
  2:    b2
__missing__(name)[source]

When requested key is not present, add it with an empty Settings instance as a value.

This method is essential for automatic insertions in deeper levels. Without it things like:

>>> s = Settings()
>>> s.a.b.c = 12

will not work.

__setitem__(name, value)[source]

Like regular __setitem__, but if the value is a dict, convert it to Settings.

__getattr__(name)[source]

If name is not a magic method, redirect it to __getitem__.

__setattr__(name, value)[source]

If name is not a magic method, redirect it to __setitem__.

__delattr__(name)[source]

If name is not a magic method, redirect it to __delitem__.

_str(indent)[source]

Print contents with indent spaces of indentation. Recursively used for printing nested Settings instances with proper indentation.

__iter__()[source]

Iteration through keys follows lexicographical order.

copy()[source]

Return a new instance that is a copy of this one. Nested Settings instances are copied recursively, not linked.

In practice this method works as a shallow copy: all “proper values” (leaf nodes) in the returned copy point to the same objects as the original instance (unless they are immutable, like int or tuple). However, nested Settings instances (internal nodes) are copied in a deep-copy fashion. In other words, copying a Settings instance creates a brand new “tree skeleton” and populates its leaf nodes with values taken directly from the original instance.

This behavior is illustrated by the following example:

>>> s = Settings()
>>> s.a = 'string'
>>> s.b = ['l','i','s','t']
>>> s.x.y = 12
>>> s.x.z = {'s','e','t'}
>>> c = s.copy()
>>> s.a += 'word'
>>> s.b += [3]
>>> s.x.u = 'new'
>>> s.x.y += 10
>>> s.x.z.add(1)
>>> print(c)
a:  string
b:  ['l', 'i', 's', 't', 3]
x:
  y:    12
  z:    set([1, 's', 'e', 't'])
>>> print(s)
a:  stringword
b:  ['l', 'i', 's', 't', 3]
x:
  u:    new
  y:    22
  z:    set([1, 's', 'e', 't'])

This method is also used when copy.copy() is called.

soft_update(other)[source]

Update this instance with data from other, but do not overwrite existing keys. Nested Settings instances are soft-updated recursively.

In the following example s and o are previously prepared Settings instances:

>>> print(s)
a:  AA
b:  BB
x:
  y1:   XY1
  y2:   XY2
>>> print(o)
a:  O_AA
c:  O_CC
x:
  y1:   O_XY1
  y3:   O_XY3
>>> s.soft_update(o)
>>> print(s)
a:  AA        #original value s.a not overwritten by o.a
b:  BB
c:  O_CC
x:
  y1:   XY1   #original value s.x.y1 not overwritten by o.x.y1
  y2:   XY2
  y3:   O_XY3

Other can also be a regular dictionary. Of course in that case only top level keys are updated.

Shortcut A += B can be used instead of A.soft_update(B).

update(other)[source]

Update this instance with data from other, overwriting existing keys. Nested Settings instances are updated recursively.

In the following example s and o are previously prepared Settings instances:

>>> print(s)
a:  AA
b:  BB
x:
  y1:   XY1
  y2:   XY2
>>> print(o)
a:  O_AA
c:  O_CC
x:
  y1:   O_XY1
  y3:   O_XY3
>>> s.update(o)
>>> print(s)
a:  O_AA        #original value s.a overwritten by o.a
b:  BB
c:  O_CC
x:
  y1:   O_XY1   #original value s.x.y1 overwritten by o.x.y1
  y2:   XY2
  y3:   O_XY3

Other can also be a regular dictionary. Of course in that case only top level keys are updated.

merge(other)[source]

Return new instance of Settings that is a copy of this instance soft-updated with other.

Shortcut A + B can be used instead of A.merge(B).

find_case(key)[source]

Check if this instance contains a key consisting of the same letters as key, but possibly with different case. If found, return such a key. If not, return key.

When Settings are used in case-insensitive contexts, this helps preventing multiple occurences of the same key with different case:

>>> s = Settings()
>>> s.system.key1 = value1
>>> s.System.key2 = value2
>>> print(s)
System:
    key2:    value2
system:
    key1:    value1
>>> t = Settings()
>>> t.system.key1 = value1
>>> t[t.find_case('System')].key2 = value2
>>> print(t)
system:
    key1:    value1
    key2:    value2
as_dict()[source]

Return a copy of this instance with all Settings replaced by the regular Python dict.

Methods update() and soft_update() are complementary. Given two Settings instances A and B, the command A.update(B) would result in A being exactly the same as B would be after B.soft_update(A).