Allow explicit construction of FeatureFlags

Author: DRMacIver

hypothesis-python/src/hypothesis/searchstrategy/featureflags.py

@@ -40,10 +40,16 @@ class FeatureFlags(object):
     required disabled features.
     """
 
-    def __init__(self, data):
+    def __init__(self, data=None, enabled=(), disabled=()):
         self.__data = data
         self.__decisions = {}
 
+        for f in enabled:
+            self.__decisions[f] = 0
+
+        for f in disabled:
+            self.__decisions[f] = 255
+
         # In the original swarm testing paper they turn features on or off
         # uniformly at random. Instead we decide the probability with which to
         # enable features up front. This can allow for scenarios where all or
@@ -54,12 +60,17 @@ def __init__(self, data):
         # score >= that value. In particular when self.__baseline is 0, all
         # features will be enabled. This is so that we shrink in the direction
         # of more features being enabled.
-        self.__baseline = data.draw_bits(8)
+        if self.__data is not None:
+            self.__baseline = data.draw_bits(8)
+        else:
+            # If data is None we're in example mode so all that matters is the
+            # enabled/disabled lists above. We set this up so that
+            self.__baseline = 1
 
     def is_enabled(self, name):
         """Tests whether the feature named ``name`` should be enabled on this
         test run."""
-        if self.__data.frozen:
+        if self.__data is None or self.__data.frozen:
             # Feature set objects might hang around after data generation has
             # finished. If this happens then we just report all new features as
             # enabled, because that's our shrinking direction and they have no

hypothesis-python/tests/cover/test_feature_flags.py

@@ -17,8 +17,9 @@
 
 from __future__ import absolute_import, division, print_function
 
+from hypothesis import given, strategies as st
 from hypothesis.internal.compat import hrange
-from hypothesis.searchstrategy.featureflags import FeatureStrategy
+from hypothesis.searchstrategy.featureflags import FeatureFlags, FeatureStrategy
 from tests.common.debug import find_any, minimal
 
 STRAT = FeatureStrategy()
@@ -55,3 +56,29 @@ def test_marks_unknown_features_as_enabled():
     x = find_any(STRAT, lambda v: True)
 
     assert x.is_enabled("fish")
+
+
+def test_by_default_all_enabled():
+    f = FeatureFlags()
+
+    assert f.is_enabled("foo")
+
+
+@given(st.data())
+def test_repr_can_be_evalled(data):
+    flags = data.draw(STRAT)
+
+    features = data.draw(st.lists(st.text(), unique=True))
+
+    for f in features:
+        flags.is_enabled(f)
+
+    flags2 = eval(repr(flags))
+
+    for f in features:
+        assert flags2.is_enabled(f) == flags.is_enabled(f)
+
+    more_features = data.draw(st.lists(st.text().filter(lambda s: s not in features)))
+
+    for f in more_features:
+        assert flags2.is_enabled(f)