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‎CHANGES.md‎

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# v3.11.0, 2018-01-09
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A number of strategies from Axelrod's original second tournament, new tests and
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minor documentation changes.
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- Implemented GraaskampKatzen (k60r)
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https://github.com/Axelrod-Python/Axelrod/pull/1144
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- Implemented Weiner (k41r)
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https://github.com/Axelrod-Python/Axelrod/pull/1145
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- Implemented Harrington (k75r)
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https://github.com/Axelrod-Python/Axelrod/pull/1146
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- Implemented MoreTidemanAndChieruzzi (k84r)
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https://github.com/Axelrod-Python/Axelrod/pull/1147
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- Implemented Getzler (k35r) strategy
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https://github.com/Axelrod-Python/Axelrod/pull/1151
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- Implemented Leyvraz (k86r) strategy
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https://github.com/Axelrod-Python/Axelrod/pull/1153
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- Implemented White (k72r) strategy
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https://github.com/Axelrod-Python/Axelrod/pull/1154
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- Implemented Black (k83r) strategy
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https://github.com/Axelrod-Python/Axelrod/pull/1155
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- Add a test for memory depth
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https://github.com/Axelrod-Python/Axelrod/pull/1157
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- Fix implementation of TidemanAndChieruzzi
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https://github.com/Axelrod-Python/Axelrod/pull/1152
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- Modify implementation of strategy
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https://github.com/Axelrod-Python/Axelrod/pull/1143
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- Update python version requirements
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https://github.com/Axelrod-Python/Axelrod/pull/1158
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- Add citations section to README
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https://github.com/Axelrod-Python/Axelrod/pull/1148
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https://github.com/Axelrod-Python/Axelrod/pull/1150
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https://github.com/Axelrod-Python/Axelrod/compare/v3.11.0...v3.10.0
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# v3.10.0, 2017-11-27
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One new strategy

‎README.rst‎

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@@ -74,7 +74,7 @@ a peer reviewed paper introducing the library (22 authors).
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Installation
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------------
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The library requires Python 3.4 or greater. It will not run on Python 2.
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The library requires Python 3.5 or greater.
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The simplest way to install is::
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@@ -127,7 +127,7 @@ Publications
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* Vincent Knight, Owen Campbell, Marc Harper, Karol Langner et al. `An Open Framework for the Reproducible Study of the Iterated Prisoner’s Dilemma. <https://openresearchsoftware.metajnl.com/articles/10.5334/jors.125/>`_ Journal of Open Research Software 4, no. 1 (2016). (`ArXiv Preprint <https://arxiv.org/abs/1604.00896>`_)
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Contributors
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Contributors
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------------
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The library has had many awesome contributions from many `great

‎axelrod/strategies/axelrod_first.py‎

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@@ -529,7 +529,7 @@ class SteinAndRapoport(Player):
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name = 'Stein and Rapoport'
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classifier = {
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'memory_depth': 15,
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': {"length"},
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'long_run_time': False,

‎axelrod/strategies/geller.py‎

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@@ -40,7 +40,7 @@ class Geller(Player):
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name = 'Geller'
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classifier = {
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'memory_depth': -1,
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'memory_depth': float("inf"),
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'stochastic': True,
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'makes_use_of': set(),
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'long_run_time': False,
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"""
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name = 'Geller Cooperator'
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classifier = {
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'memory_depth': -1,
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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"""
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name = 'Geller Defector'
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classifier = {
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'memory_depth': -1,
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,

‎axelrod/strategies/mindreader.py‎

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@@ -22,7 +22,7 @@ class MindReader(Player):
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name = 'Mind Reader'
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classifier = {
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'memory_depth': -10,
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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name = 'Protected Mind Reader'
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classifier = {
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'memory_depth': -10,
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,
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name = 'Mirror Mind Reader'
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classifier = {
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'memory_depth': -10,
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,

‎axelrod/strategies/titfortat.py‎

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@@ -114,7 +114,7 @@ class DynamicTwoTitsForTat(Player):
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name = 'Dynamic Two Tits For Tat'
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classifier = {
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'memory_depth': 2, # Long memory, memory-2
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'memory_depth': float("inf"),
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'stochastic': True,
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'makes_use_of': set(),
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'long_run_time': False,
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def strategy(opponent):
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# First move
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if not opponent.history:
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# Make sure we cooporate first turn
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# Make sure we cooperate first turn
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return C
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if D in opponent.history[-2:]:
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# Probability of cooperating regardless
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"""
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super().__init__()
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self.p = p
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self.act_random = False
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if p in [0, 1]:
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self.classifier['stochastic'] = False
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"""This is the actual strategy"""
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if not self.history:
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return C
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if len(opponent.history) % 2 == 0:
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if self.act_random:
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self.act_random = False
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return random_choice(self.p)
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self.act_random = True
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return opponent.history[-1]

‎axelrod/tests/strategies/test_axelrod_first.py‎

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@@ -370,7 +370,7 @@ class TestSteinAndRapoport(TestPlayer):
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name = "Stein and Rapoport: 0.05: (D, D)"
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player = axelrod.SteinAndRapoport
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expected_classifier = {
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'memory_depth': 15,
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'memory_depth': float("inf"),
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'long_run_time': False,
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'stochastic': False,
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'makes_use_of': {'length'},
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# check the fresh start condition: least 20 rounds since the last ‘fresh start’
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opponent = axelrod.Cycler('CCCCD')
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actions = [(C, C), (C, C), (C, C), (C, C), (C, D), (D, C), (C, C),
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(C, C), (C, C), (C, D), (D, C), (D, C), (C, C), (C, C),
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(C, D), (D, C), (D, C), (D, C), (C, C), (C, D), (D, C),
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(D, C), (D, C), (C, C), (C, D), (D, C), (C, C), (C, C),
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opponent = axelrod.Cycler('CCCCD')
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actions = [(C, C), (C, C), (C, C), (C, C), (C, D), (D, C), (C, C),
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(C, C), (C, C), (C, D), (D, C), (D, C), (C, C), (C, C),
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(C, D), (D, C), (D, C), (D, C), (C, C), (C, D), (D, C),
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(D, C), (D, C), (C, C), (C, D), (D, C), (C, C), (C, C),
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(C, C), (C, D), (D, C), (D, C), (C, C), (C, C), (C, D)]
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self.versus_test(opponent, expected_actions=actions,
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match_attributes={'length': 35},

‎axelrod/tests/strategies/test_geller.py‎

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@@ -11,7 +11,7 @@ class TestGeller(TestPlayer):
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name = "Geller"
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player = axelrod.Geller
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expected_classifier = {
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'memory_depth': -1,
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'memory_depth': float("inf"),
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'stochastic': True,
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'makes_use_of': set(),
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name = "Geller Cooperator"
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expected_classifier = {
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'memory_depth': float("inf"),
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'memory_depth': float("inf"),
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'makes_use_of': set(),
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'long_run_time': False,

‎axelrod/tests/strategies/test_meta.py‎

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def test_strategy(self):
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actions = [(C, C), (C, D), (D, C), (C, D), (D, C)]
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actions = [(C, C), (C, D), (D, C), (D, D), (D, C)]
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self.versus_test(opponent=axelrod.Alternator(),
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‎axelrod/tests/strategies/test_mindreader.py‎

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'memory_depth': float("inf"),
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player = axelrod.ProtectedMindReader
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'memory_depth': float("inf"),
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'memory_depth': float("inf"),
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'stochastic': False,
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'makes_use_of': set(),
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'long_run_time': False,

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