182 lines
7.9 KiB
Python
182 lines
7.9 KiB
Python
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import random
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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from odoo.tools import pycompat
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def Random(seed):
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""" Return a random number generator object with the given seed. """
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r = random.Random()
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r.seed(seed, version=2)
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return r
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def format_str(val, counter, values):
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""" Format the given value (with method ``format``) when it is a string. """
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if isinstance(val, str):
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return val.format(counter=counter, values=values)
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return val
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def chain_factories(field_factories, model_name):
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""" Instantiate a generator by calling all the field factories. """
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generator = root_factory()
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for (fname, field_factory) in field_factories:
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generator = field_factory(generator, fname, model_name)
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return generator
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def root_factory():
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""" Return a generator with empty values dictionaries (except for the flag ``__complete``). """
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yield {'__complete': False}
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while True:
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yield {'__complete': True}
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def randomize(vals, weights=None, seed=False, formatter=format_str, counter_offset=0):
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""" Return a factory for an iterator of values dicts with pseudo-randomly
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chosen values (among ``vals``) for a field.
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:param list vals: list in which a value will be chosen, depending on `weights`
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:param list weights: list of probabilistic weights
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:param seed: optional initialization of the random number generator
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:param function formatter: (val, counter, values) --> formatted_value
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:param int counter_offset:
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:returns: function of the form (iterator, field_name, model_name) -> values
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:rtype: function (iterator, str, str) -> dict
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"""
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def generate(iterator, field_name, model_name):
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r = Random('%s+field+%s' % (model_name, seed or field_name))
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for counter, values in enumerate(iterator):
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val = r.choices(vals, weights)[0]
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values[field_name] = formatter(val, counter + counter_offset, values)
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yield values
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return generate
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def cartesian(vals, weights=None, seed=False, formatter=format_str, then=None):
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""" Return a factory for an iterator of values dicts that combines all ``vals`` for
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the field with the other field values in input.
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:param list vals: list in which a value will be chosen, depending on `weights`
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:param list weights: list of probabilistic weights
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:param seed: optional initialization of the random number generator
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:param function formatter: (val, counter, values) --> formatted_value
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:param function then: if defined, factory used when vals has been consumed.
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:returns: function of the form (iterator, field_name, model_name) -> values
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:rtype: function (iterator, str, str) -> dict
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"""
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def generate(iterator, field_name, model_name):
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counter = 0
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for values in iterator:
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if values['__complete']:
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break # will consume and lose an element, (complete so a filling element). If it is a problem, use peekable instead.
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for val in vals:
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yield {**values, field_name: formatter(val, counter, values)}
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counter += 1
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factory = then or randomize(vals, weights, seed, formatter, counter)
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yield from factory(iterator, field_name, model_name)
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return generate
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def iterate(vals, weights=None, seed=False, formatter=format_str, then=None):
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""" Return a factory for an iterator of values dicts that picks a value among ``vals``
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for each input. Once all ``vals`` have been used once, resume as ``then`` or as a
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``randomize`` generator.
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:param list vals: list in which a value will be chosen, depending on `weights`
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:param list weights: list of probabilistic weights
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:param seed: optional initialization of the random number generator
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:param function formatter: (val, counter, values) --> formatted_value
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:param function then: if defined, factory used when vals has been consumed.
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:returns: function of the form (iterator, field_name, model_name) -> values
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:rtype: function (iterator, str, str) -> dict
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"""
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def generate(iterator, field_name, model_name):
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counter = 0
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for val in vals: # iteratable order is important, shortest first
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values = next(iterator)
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values[field_name] = formatter(val, counter, values)
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values['__complete'] = False
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yield values
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counter += 1
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factory = then or randomize(vals, weights, seed, formatter, counter)
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yield from factory(iterator, field_name, model_name)
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return generate
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def constant(val, formatter=format_str):
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""" Return a factory for an iterator of values dicts that sets the field
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to the given value in each input dict.
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:returns: function of the form (iterator, field_name, model_name) -> values
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:rtype: function (iterator, str, str) -> dict
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"""
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def generate(iterator, field_name, _):
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for counter, values in enumerate(iterator):
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values[field_name] = formatter(val, counter, values)
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yield values
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return generate
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def compute(function, seed=None):
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""" Return a factory for an iterator of values dicts that computes the field value
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as ``function(values, counter, random)``, where ``values`` is the other field values,
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``counter`` is an integer, and ``random`` is a pseudo-random number generator.
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:param callable function: (values, counter, random) --> field_values
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:param seed: optional initialization of the random number generator
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:returns: function of the form (iterator, field_name, model_name) -> values
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:rtype: function (iterator, str, str) -> dict
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"""
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def generate(iterator, field_name, model_name):
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r = Random('%s+field+%s' % (model_name, seed or field_name))
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for counter, values in enumerate(iterator):
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val = function(values=values, counter=counter, random=r)
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values[field_name] = val
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yield values
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return generate
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def randint(a, b, seed=None):
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""" Return a factory for an iterator of values dicts that sets the field
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to a random integer between a and b included in each input dict.
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:param int a: minimal random value
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:param int b: maximal random value
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:param int seed:
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:returns: function of the form (iterator, field_name, model_name) -> values
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:rtype: function (iterator, str, str) -> dict
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"""
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def get_rand_int(random=None, **kwargs):
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return random.randint(a, b)
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return compute(get_rand_int, seed=seed)
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def randfloat(a, b, seed=None):
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""" Return a factory for an iterator of values dicts that sets the field
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to a random float between a and b included in each input dict.
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"""
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def get_rand_float(random=None, **kwargs):
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return random.uniform(a, b)
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return compute(get_rand_float, seed=seed)
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def randdatetime(*, base_date=None, relative_before=None, relative_after=None, seed=None):
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""" Return a factory for an iterator of values dicts that sets the field
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to a random datetime between relative_before and relative_after, relatively to
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base_date
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:param datetime base_date: override the default base date if needed.
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:param relativedelta|timedelta relative_after: range up which we can go after the
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base date. If not set, defaults to 0, i.e. only in the past of reference.
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:param relativedelta|timedelta relative_before: range up which we can go before the
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base date. If not set, defaults to 0, i.e. only in the future of reference.
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:param seed:
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:return: iterator for random dates inside the defined range
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"""
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base_date = base_date or datetime(2020, 1, 1)
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seconds_before = relative_before and ((base_date + relative_before) - base_date).total_seconds() or 0
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seconds_after = relative_after and ((base_date + relative_after) - base_date).total_seconds() or 0
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def get_rand_datetime(random=None, **kwargs):
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return base_date + relativedelta(seconds=random.randint(int(seconds_before), int(seconds_after)))
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return compute(get_rand_datetime, seed=seed)
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