Source code for hypothesis._settings

# coding=utf-8
# This file is part of Hypothesis, which may be found at
# Most of this work is copyright (C) 2013-2017 David R. MacIver
# (, but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# consult the git log if you need to determine who owns an individual
# contribution.
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at

"""A module controlling settings for Hypothesis to use in falsification.

Either an explicit settings object can be used or the default object on
this module can be modified.


from __future__ import division, print_function, absolute_import

import os
import inspect
import warnings
import threading
from enum import Enum, IntEnum, unique

import attr

from hypothesis.errors import InvalidArgument, HypothesisDeprecationWarning
from hypothesis.configuration import hypothesis_home_dir
from hypothesis.internal.compat import PYPY
from hypothesis.utils.conventions import UniqueIdentifier, not_set
from hypothesis.utils.dynamicvariables import DynamicVariable

__all__ = [

unlimited = UniqueIdentifier('unlimited')

all_settings = {}

_db_cache = {}

class settingsProperty(object):

    def __init__(self, name, show_default): = name
        self.show_default = show_default

    def __get__(self, obj, type=None):
        if obj is None:
            return self
                return obj.__dict__[]
            except KeyError:
                raise AttributeError(

    def __set__(self, obj, value):
        obj.__dict__[] = value

    def __delete__(self, obj):
        raise AttributeError('Cannot delete attribute %s' % (,))

    def __doc__(self):
        default = repr(getattr(settings.default, if \
            self.show_default else '(dynamically calculated)'
        return '\n'.join((
            'default value: %s' % (default,)

default_variable = DynamicVariable(None)

class settingsMeta(type):

    def __init__(self, *args, **kwargs):
        super(settingsMeta, self).__init__(*args, **kwargs)

    def default(self):
        v = default_variable.value
        if v is not None:
            return v
        if hasattr(settings, '_current_profile'):
            assert default_variable.value is not None
        return default_variable.value

    def default(self, value):
        raise AttributeError('Cannot assign settings.default')

    def _assign_default_internal(self, value):
        default_variable.value = value

[docs]class settings(settingsMeta('settings', (object,), {})): """A settings object controls a variety of parameters that are used in falsification. These may control both the falsification strategy and the details of the data that is generated. Default values are picked up from the settings.default object and changes made there will be picked up in newly created settings. """ _WHITELISTED_REAL_PROPERTIES = [ '_database', '_construction_complete', 'storage' ] __definitions_are_locked = False _profiles = {} def __getattr__(self, name): if name in all_settings: d = all_settings[name].default if inspect.isfunction(d): d = d() return d else: raise AttributeError('settings has no attribute %s' % (name,)) def __init__( self, parent=None, **kwargs ): self._construction_complete = False self._database = kwargs.pop('database', not_set) database_file = kwargs.get('database_file', not_set) deprecations = [] defaults = parent or settings.default if defaults is not None: for setting in all_settings.values(): if kwargs.get(, not_set) is not_set: kwargs[] = getattr(defaults, else: if kwargs[] != setting.future_default: if ( setting.deprecation_message is not None ): deprecations.append(setting) if setting.validator: kwargs[] = setting.validator( kwargs[]) if self._database is not_set and database_file is not_set: self._database = defaults.database for name, value in kwargs.items(): if name not in all_settings: raise InvalidArgument( 'Invalid argument %s' % (name,)) setattr(self, name, value) = threading.local() self._construction_complete = True for d in deprecations: note_deprecation(d.deprecation_message, self) def defaults_stack(self): try: return except AttributeError: = [] return def __call__(self, test): test._hypothesis_internal_use_settings = self return test @classmethod def define_setting( cls, name, description, default, options=None, deprecation=None, validator=None, show_default=True, future_default=not_set, deprecation_message=None, ): """Add a new setting. - name is the name of the property that will be used to access the setting. This must be a valid python identifier. - description will appear in the property's docstring - default is the default value. This may be a zero argument function in which case it is evaluated and its result is stored the first time it is accessed on any given settings object. """ if settings.__definitions_are_locked: from hypothesis.errors import InvalidState raise InvalidState( 'settings have been locked and may no longer be defined.' ) if options is not None: options = tuple(options) assert default in options if future_default is not_set: future_default = default all_settings[name] = Setting( name, description.strip(), default, options, validator, future_default, deprecation_message, ) setattr(settings, name, settingsProperty(name, show_default)) @classmethod def lock_further_definitions(cls): settings.__definitions_are_locked = True def __setattr__(self, name, value): if name in settings._WHITELISTED_REAL_PROPERTIES: return object.__setattr__(self, name, value) elif name == 'database': assert self._construction_complete raise AttributeError( 'settings objects are immutable and may not be assigned to' ' after construction.' ) elif name in all_settings: if self._construction_complete: raise AttributeError( 'settings objects are immutable and may not be assigned to' ' after construction.' ) else: setting = all_settings[name] if ( setting.options is not None and value not in setting.options ): raise InvalidArgument( 'Invalid %s, %r. Valid options: %r' % ( name, value, setting.options ) ) return object.__setattr__(self, name, value) else: raise AttributeError('No such setting %s' % (name,)) def __repr__(self): bits = [] for name in all_settings: value = getattr(self, name) bits.append('%s=%r' % (name, value)) bits.sort() return 'settings(%s)' % ', '.join(bits) @property def database(self): """An ExampleDatabase instance to use for storage of examples. May be None. If this was explicitly set at settings instantiation then that value will be used (even if it was None). If not and the database_file setting is not None this will be lazily loaded as an ExampleDatabase using that file the first time this property is accessed on a particular thread. """ if self._database is not_set and self.database_file is not None: from hypothesis.database import ExampleDatabase if self.database_file not in _db_cache: _db_cache[self.database_file] = ( ExampleDatabase(self.database_file)) return _db_cache[self.database_file] if self._database is not_set: self._database = None return self._database def __enter__(self): default_context_manager = default_variable.with_value(self) self.defaults_stack().append(default_context_manager) default_context_manager.__enter__() return self def __exit__(self, *args, **kwargs): default_context_manager = self.defaults_stack().pop() return default_context_manager.__exit__(*args, **kwargs) @staticmethod def register_profile(name, settings): """registers a collection of values to be used as a settings profile. These settings can be loaded in by name. Enable different defaults for different settings. - settings is a settings object """ settings._profiles[name] = settings @staticmethod def get_profile(name): """Return the profile with the given name. - name is a string representing the name of the profile to load A InvalidArgument exception will be thrown if the profile does not exist """ try: return settings._profiles[name] except KeyError: raise InvalidArgument( "Profile '{0}' has not been registered".format( name ) ) @staticmethod def load_profile(name): """Loads in the settings defined in the profile provided If the profile does not exist an InvalidArgument will be thrown. Any setting not defined in the profile will be the library defined default for that setting """ settings._current_profile = name settings._assign_default_internal(settings.get_profile(name))
@attr.s() class Setting(object): name = attr.ib() description = attr.ib() default = attr.ib() options = attr.ib() validator = attr.ib() future_default = attr.ib() deprecation_message = attr.ib() settings.define_setting( 'min_satisfying_examples', default=5, description=""" Raise Unsatisfiable for any tests which do not produce at least this many values that pass all assume() calls and which have not exhaustively covered the search space. """ ) settings.define_setting( 'max_examples', default=200, description=""" Once this many satisfying examples have been considered without finding any counter-example, falsification will terminate. """ ) settings.define_setting( 'max_iterations', default=1000, description=""" Once this many iterations of the example loop have run, including ones which failed to satisfy assumptions and ones which produced duplicates, falsification will terminate. """ ) settings.define_setting( 'max_mutations', default=10, description=""" Hypothesis will try this many variations on a single example before moving on to an entirely fresh start. If you've got hard to satisfy properties raising this might help, but you probably shouldn't touch this dial unless you really know what you're doing. """ ) settings.define_setting( 'buffer_size', default=8 * 1024, description=""" The size of the underlying data used to generate examples. If you need to generate really large examples you may want to increase this, but it will make your tests slower. """ ) settings.define_setting( 'max_shrinks', default=500, description=""" Once this many successful shrinks have been performed, Hypothesis will assume something has gone a bit wrong and give up rather than continuing to try to shrink the example. """ ) def _validate_timeout(n): if n is unlimited: return -1 else: return n settings.define_setting( 'timeout', default=60, description=""" Once this many seconds have passed, falsify will terminate even if it has not found many examples. This is a soft rather than a hard limit - Hypothesis won't e.g. interrupt execution of the called function to stop it. If this value is <= 0 then no timeout will be applied. Note: This setting is deprecated. In future Hypothesis will be removing the timeout feature. """, deprecation_message=""" The timeout feature will go away in a future version of Hypothesis. To get the future behaviour set timeout=hypothesis.unlimited instead (which will remain valid for a further deprecation period after this setting has gone away). """, future_default=unlimited, validator=_validate_timeout ) settings.define_setting( 'derandomize', default=False, description=""" If this is True then hypothesis will run in deterministic mode where each falsification uses a random number generator that is seeded based on the hypothesis to falsify, which will be consistent across multiple runs. This has the advantage that it will eliminate any randomness from your tests, which may be preferable for some situations . It does have the disadvantage of making your tests less likely to find novel breakages. """ ) settings.define_setting( 'strict', default=os.getenv('HYPOTHESIS_STRICT_MODE') == 'true', description=""" If set to True, anything that would cause Hypothesis to issue a warning will instead raise an error. Note that new warnings may be added at any time, so running with strict set to True means that new Hypothesis releases may validly break your code. You can enable this setting temporarily by setting the HYPOTHESIS_STRICT_MODE environment variable to the string 'true'. """, deprecation_message=""" Strict mode is deprecated and will go away in a future version of Hypothesis. To get the same behaviour, use warnings.simplefilter('error', HypothesisDeprecationWarning). """, future_default=False, ) settings.define_setting( 'database_file', default=lambda: ( os.getenv('HYPOTHESIS_DATABASE_FILE') or os.path.join(hypothesis_home_dir(), 'examples') ), show_default=False, description=""" database: An instance of hypothesis.database.ExampleDatabase that will be used to save examples to and load previous examples from. May be None in which case no storage will be used. """ ) @unique class Phase(IntEnum): explicit = 0 reuse = 1 generate = 2 shrink = 3 @unique class HealthCheck(Enum): exception_in_generation = 0 data_too_large = 1 filter_too_much = 2 too_slow = 3 random_module = 4 return_value = 5 hung_test = 6 @unique class Statistics(IntEnum): never = 0 interesting = 1 always = 2 class Verbosity(object): def __repr__(self): return 'Verbosity.%s' % (,) def __init__(self, name, level): = name self.level = level def __eq__(self, other): return isinstance(other, Verbosity) and ( self.level == other.level ) def __ne__(self, other): return not self.__eq__(other) def __hash__(self): return self.level def __lt__(self, other): return self.level < other.level def __le__(self, other): return self.level <= other.level def __gt__(self, other): return self.level > other.level def __ge__(self, other): return self.level >= other.level @classmethod def by_name(cls, key): result = getattr(cls, key, None) if isinstance(result, Verbosity): return result raise InvalidArgument('No such verbosity level %r' % (key,)) Verbosity.quiet = Verbosity('quiet', 0) Verbosity.normal = Verbosity('normal', 1) Verbosity.verbose = Verbosity('verbose', 2) Verbosity.debug = Verbosity('debug', 3) Verbosity.all = [ Verbosity.quiet, Verbosity.normal, Verbosity.verbose, Verbosity.debug ] ENVIRONMENT_VERBOSITY_OVERRIDE = os.getenv('HYPOTHESIS_VERBOSITY_LEVEL') if ENVIRONMENT_VERBOSITY_OVERRIDE: # pragma: no cover DEFAULT_VERBOSITY = Verbosity.by_name(ENVIRONMENT_VERBOSITY_OVERRIDE) else: DEFAULT_VERBOSITY = Verbosity.normal settings.define_setting( 'verbosity', options=Verbosity.all, default=DEFAULT_VERBOSITY, description='Control the verbosity level of Hypothesis messages', ) def _validate_phases(phases): if phases is None: return tuple(Phase) phases = tuple(phases) for a in phases: if not isinstance(a, Phase): raise InvalidArgument('%r is not a valid phase' % (a,)) return phases settings.define_setting( 'phases', default=tuple(Phase), description=( 'Control which phases should be run. ' + 'See :ref:`the full documentation for more details <phases>`' ), validator=_validate_phases, ) settings.define_setting( name='stateful_step_count', default=50, description=""" Number of steps to run a stateful program for before giving up on it breaking. """ ) settings.define_setting( 'perform_health_check', default=True, description=u""" If set to True, Hypothesis will run a preliminary health check before attempting to actually execute your test. """ ) settings.define_setting( 'suppress_health_check', default=[], description="""A list of health checks to disable""" ) settings.define_setting( 'report_statistics', default=Statistics.interesting, description=u""" If set to True, Hypothesis will run a preliminary health check before attempting to actually execute your test. """ ) settings.define_setting( 'deadline', default=not_set, description=u""" If set, a time in milliseconds (which may be a float to express smaller units of time) that a test is not allowed to exceed. Tests which take longer than that will be converted into errors. Set this to None to disable this behaviour entirely. In future this will default to 200. For now, a HypothesisDeprecationWarning will be emitted if you exceed that default deadline and have not explicitly set a deadline yourself. """ ) settings.define_setting( 'use_coverage', default=not PYPY, show_default=False, description=""" Whether to use coverage information to improve Hypothesis's ability to find bugs. You should generally leave this turned on unless your code performs poorly when run under coverage. Note: This is turned on by default except on pypy, where coverage performance is sufficiently poor as to make this unusable. """ ) settings.lock_further_definitions() settings.register_profile('default', settings()) settings.load_profile('default') assert settings.default is not None def note_deprecation(message, s=None): if s is None: s = settings.default assert s is not None verbosity = s.verbosity warning = HypothesisDeprecationWarning(message) if verbosity > Verbosity.quiet: warnings.warn(warning, stacklevel=3)