test: reduce overhead in feature_llmq_evo.py: only 2 regular nodes, only 4 evo nodes, less blocks generated

Author: knst

test/functional/feature_llmq_evo.py

@@ -49,8 +49,9 @@ def getmnlistdiff(self, baseBlockHash, blockHash):
 
 class LLMQEvoNodesTest(DashTestFramework):
     def set_test_params(self):
-        self.set_dash_test_params(4, 3, evo_count=5)
-        self.mn_rr_height = 400
+        # we just need a couple of regular nodes to be ensured that they are not included in platform quorum, 2 is enough
+        self.set_dash_test_params(3, 2, evo_count=4)
+        self.mn_rr_height = 320
 
     def run_test(self):
         # Connect all nodes to node1 so that we always have the whole network connected
@@ -66,7 +67,7 @@ def run_test(self):
         b_0 = self.nodes[0].getbestblockhash()
         self.test_getmnlistdiff(null_hash, b_0, {}, [], expectedUpdated)
 
-        self.test_masternode_count(expected_mns_count=3, expected_evo_count=0)
+        self.test_masternode_count(expected_mns_count=2, expected_evo_count=0)
         evo_protxhash_list = list()
         for i in range(self.evo_count):
             evo_info: MasternodeInfo = self.dynamically_add_masternode(evo=True)
@@ -77,7 +78,7 @@ def run_test(self):
             b_i = self.nodes[0].getbestblockhash()
             self.test_getmnlistdiff(null_hash, b_i, {}, [], expectedUpdated)
 
-            self.test_masternode_count(expected_mns_count=3, expected_evo_count=i+1)
+            self.test_masternode_count(expected_mns_count=2, expected_evo_count=i+1)
             self.dynamically_evo_update_service(evo_info)
 
         self.log.info("Test llmq_platform are formed only with EvoNodes")
@@ -93,7 +94,7 @@ def run_test(self):
         self.test_masternode_winners()
 
         self.activate_mn_rr()
-        self.log.info("Activated MN RewardReallocation, current height:" + str(self.nodes[0].getblockcount()))
+        self.log.info(f"Activated MN RewardReallocation, current height: {self.nodes[0].getblockcount()}")
 
         # Generate a few blocks to make EvoNode/MN analysis on a pure MN RewardReallocation window
         self.bump_mocktime(1)