`samplespace.pyyaml_support` - YAML serialization support¶

When enabled, this module provides YAML serialization support for classes in samplespace.repeatablerandom, samplespace.distributions, and samplespace.algorithms.

The yaml module provided by PyYaml is required to use this module, and an exception will be thrown if it is not available.

Tip

Once the module is imported, enable_yaml_support() must be called to enable YAML support.

samplespace.pyyaml_support.enable_yaml_support(_force=False)¶: Add YAML serialization support. This function only needs to be called once per application.

Examples¶

Repeatable random sequence:

>>> import yaml
>>> from samplespace import RepeatableRandomSequence
>>> import samplespace.pyyaml_support
>>> samplespace.pyyaml_support.enable_yaml_support()
>>>
>>> rrs = RepeatableRandomSequence(seed=678)
>>> [rrs.randrange(10) for _ in range(5)]
[7, 7, 0, 8, 3]
>>>
>>> # Serialize the sequence as YAML
...
>>> as_yaml = yaml.dump(rrs)
>>> as_yaml
'!samplespace.rrs\nhash_input: s1enBV+SSXk=\nindex: 5\nseed: 678\n'
>>>
>>> # Generate some random values to compare against later
...
>>> [rrs.randrange(10) for _ in range(5)]
[0, 5, 1, 3, 9]
>>> [rrs.randrange(10) for _ in range(5)]
[7, 1, 1, 0, 5]
>>> [rrs.randrange(10) for _ in range(5)]
[2, 9, 1, 4, 8]
>>>
>>> # Restore the saved sequence
...
>>> rrs2 = yaml.load(as_yaml, Loader=yaml.FullLoader)
>>>
>>> # Verify that values match those at time of serialization
...
>>> [rrs2.randrange(10) for _ in range(5)]
[0, 5, 1, 3, 9]
>>> [rrs2.randrange(10) for _ in range(5)]
[7, 1, 1, 0, 5]
>>> [rrs2.randrange(10) for _ in range(5)]
[2, 9, 1, 4, 8]

Distributions:

>>> import yaml
>>> from samplespace import distributions
>>> import samplespace.pyyaml_support
>>> samplespace.pyyaml_support.enable_yaml_support()
>>>
>>> gamma = distributions.Gamma(5.0, 3.0)
>>> gamma_as_yaml = yaml.dump(gamma)
>>> print(gamma_as_yaml)
!samplespace.distribution
alpha: 5.0
beta: 3.0
distribution: gamma
>>> assert yaml.load(gamma_as_yaml, Loader=yaml.FullLoader) == gamma
>>>
>>> zipf = distributions.Zipf(0.9, 10)
>>> zipf_as_yaml = yaml.dump(zipf)
>>> print(zipf_as_yaml)
!samplespace.distribution
distribution: zipf
n: 10
s: 0.9
>>> assert yaml.load(zipf_as_yaml, Loader=yaml.FullLoader) == zipf
>>>
>>> wcat = distributions.WeightedCategorical(
...     population='abcde',
...     cum_weights=[0.1, 0.4, 0.6, 0.7, 1.1])
>>> wcat_as_yaml = yaml.dump(wcat)
>>> print(wcat_as_yaml)
!samplespace.distribution
distribution: weightedcategorical
items:
- - a
  - 0.1
- - b
  - 0.30000000000000004
- - c
  - 0.19999999999999996
- - d
  - 0.09999999999999998
- - e
  - 0.40000000000000013
>>> assert yaml.load(wcat_as_yaml, Loader=yaml.FullLoader) == wcat

Algorithms:

>>> import yaml
>>> from samplespace.algorithms import AliasTable
>>> import samplespace.pyyaml_support
>>> samplespace.pyyaml_support.enable_yaml_support()
>>>
>>> at = AliasTable.from_weights([0.1, 0.3, 0.5, 0.2, 0.6, 0.7])
>>> at.alias
[4, 5, 0, 5, 2, 4]
>>> at.probability
[0.25, 0.7499999999999998, 1.0, 0.5, 0.7499999999999991, 0.9999999999999996]
>>>
>>> at_as_yaml = yaml.dump(at)
>>> print(at_as_yaml)
!samplespace.aliastable
alias:
- 4
- 5
- 0
- 5
- 2
- 4
probability:
- 0.25
- 0.7499999999999998
- 1.0
- 0.5
- 0.7499999999999991
- 0.9999999999999996
>>> at2 = yaml.load(at_as_yaml, Loader=yaml.FullLoader)
>>> assert at2.alias == at.alias
>>> assert at2.probability == at.probability

samplespace.pyyaml_support - YAML serialization support¶

Examples¶

`samplespace.pyyaml_support` - YAML serialization support¶