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How to use cluster_manager method in localstack
Best Python code snippet using localstack_python
test_clustering_simple.py
Source:test_clustering_simple.py 
1import numpy as np2import unittest3import covid19sim.frozen.clustering.simple as clu4import covid19sim.frozen.message_utils as mu5from tests.utils import FakeHuman, generate_received_messages, generate_random_messages, Visit6never = 9999 # dirty macro to indicate a human will never get infected7class SimpleClusteringTests(unittest.TestCase):8 # note: we only ever build & test clusters for a single human, assuming it would also work for others9 def test_same_day_visit_clusters(self):10 n_trials = 10011 for _ in range(n_trials):12 # scenario: single day visits, 1 cluster per visit, not enough visits to overlap the clusters13 visits = [14 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=2),15 Visit(visitor_real_uid=2, visited_real_uid=0, exposition=False, timestamp=2),16 ]17 # we will cheat and hard-code some initial 4-bit uids to make sure there is no overlap18 humans = [19 FakeHuman(20 real_uid=idx,21 exposition_timestamp=never,22 visits_to_adopt=visits,23 # with only two visitors, there should never be cluster uid overlap at day 224 force_init_uid=np.uint8(idx),25 ) for idx in range(3)26 ]27 day2_uids = [h.rolling_uids[2] for h in humans]28 self.assertTrue(len(np.unique(day2_uids)) == 3)29 messages = generate_received_messages(humans)30 # now we need to get all messages sent to human 0 to do our actual clustering analysis31 h0_messages = messages[0]["received_messages"]32 self.assertEqual(len(h0_messages), 3) # three timesteps in book33 self.assertEqual(sum([len(msgs) for msgs in h0_messages.values()]), 2)34 self.assertEqual(len(h0_messages[2]), 2)35 h0_messages = [msg for msgs in h0_messages.values() for msg in msgs]36 cluster_manager = clu.SimplisticClusterManager(max_history_ticks_offset=never)37 cluster_manager.add_messages(h0_messages)38 self.assertEqual(len(cluster_manager.clusters), 2)39 self.assertEqual(cluster_manager.latest_refresh_timestamp, 2)40 expositions = cluster_manager._get_expositions_array()41 self.assertEqual(len(expositions), 2)42 self.assertEqual(sum(expositions), 0)43 embeddings = cluster_manager.get_embeddings_array()44 self.assertTrue((embeddings[:, 1] == 0).all()) # risk level45 self.assertTrue((embeddings[:, 2] == 1).all()) # message count46 self.assertTrue((embeddings[:, 3] == 0).all()) # timestamp offset47 def test_same_day_visit_clusters_overlap(self):48 # scenario: single day visits, and some visits will share the same cluster49 visits = [50 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=0),51 Visit(visitor_real_uid=2, visited_real_uid=0, exposition=False, timestamp=0),52 Visit(visitor_real_uid=3, visited_real_uid=0, exposition=False, timestamp=0),53 Visit(visitor_real_uid=4, visited_real_uid=0, exposition=False, timestamp=0),54 Visit(visitor_real_uid=5, visited_real_uid=0, exposition=False, timestamp=0),55 ]56 # we will cheat and hard-code some initial 4-bit uids to make sure there is overlap57 humans = [58 FakeHuman(59 real_uid=idx,60 exposition_timestamp=never,61 visits_to_adopt=visits,62 force_init_uid=np.uint8(max(idx - 1, 0) % 3),63 force_init_risk=np.uint8(7),64 ) for idx in range(6)65 ]66 day0_visitor_uids = [h.rolling_uids[0] for h in humans[1:]]67 self.assertTrue(len(np.unique(day0_visitor_uids)) == 3) # two visits will be overlapped68 messages = generate_received_messages(humans)69 h0_messages = messages[0]["received_messages"]70 self.assertEqual(len(h0_messages), 1) # single timestep in book71 self.assertEqual(len(h0_messages[0]), 5) # all 5 encounter messages in day 072 h0_messages = [msg for msgs in h0_messages.values() for msg in msgs]73 cluster_manager = clu.SimplisticClusterManager(max_history_ticks_offset=never)74 cluster_manager.add_messages(h0_messages)75 self.assertEqual(len(cluster_manager.clusters), 3)76 self.assertEqual(cluster_manager.latest_refresh_timestamp, 0)77 expositions = cluster_manager._get_expositions_array()78 self.assertTrue(len(expositions) == 3 and sum(expositions) == 0)79 embeddings = cluster_manager.get_embeddings_array()80 self.assertTrue((embeddings[:, 1] == 7).all()) # risk level81 self.assertTrue(np.logical_and(embeddings[:, 2] > 0, embeddings[:, 2] < 3).all())82 self.assertTrue((embeddings[:, 3] == 0).all()) # timestamp offset83 def test_cluster_risk_update(self):84 # scenario: single day visits, and some visits will share the same cluster85 visits = [86 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=0),87 ]88 humans = [89 FakeHuman(real_uid=0, exposition_timestamp=never, visits_to_adopt=visits,90 force_init_risk=np.uint8(0)),91 FakeHuman(real_uid=1, exposition_timestamp=never, visits_to_adopt=visits,92 force_init_risk=np.uint8(7)),93 ]94 messages = generate_received_messages(humans)95 h0_messages = messages[0]["received_messages"]96 self.assertEqual(len(h0_messages), 1) # single timestep in book97 self.assertEqual(len(h0_messages[0]), 1) # single encounter message in day 098 h0_messages = [msg for msgs in h0_messages.values() for msg in msgs]99 cluster_manager = clu.SimplisticClusterManager(max_history_ticks_offset=never)100 cluster_manager.add_messages(h0_messages)101 self.assertEqual(len(cluster_manager.clusters), 1)102 self.assertEqual(cluster_manager.clusters[0].risk_level, np.uint8(7))103 # we will add a manual update for one of the visits104 cluster_manager.add_messages([105 mu.create_update_message(h0_messages[0], np.uint8(9), np.uint64(1))106 ])107 self.assertEqual(len(cluster_manager.clusters), 1)108 # add a new encounter: it should not match the existing cluster due to diff risk109 cluster_manager.add_messages([110 mu.EncounterMessage(humans[1].rolling_uids[0], risk_level=np.uint8(1), encounter_time=0)111 ])112 self.assertEqual(len(cluster_manager.clusters), 2)113 self.assertEqual(cluster_manager.clusters[1].risk_level, np.uint8(1))114 # add a new encounter: it should match the existing cluster due to same risk115 new_encounter = \116 mu.EncounterMessage(humans[1].rolling_uids[0], risk_level=np.uint8(7), encounter_time=0)117 cluster_manager.add_messages([new_encounter])118 self.assertEqual(len(cluster_manager.clusters), 2)119 self.assertEqual(len(cluster_manager.clusters[0].messages), 2)120 # update one of the two encounters in the first cluster; average risk should change121 cluster_manager.add_messages([122 mu.create_update_message(new_encounter, np.uint8(13), np.uint64(1))123 ])124 self.assertEqual(len(cluster_manager.clusters), 2)125 self.assertEqual(len(cluster_manager.clusters[0].messages), 2)126 def test_cleanup_outdated_cluster(self):127 # scenario: a new encounter is added that is waaay outdated; it should not create a cluster128 visits = [129 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=2),130 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=5),131 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=8),132 ]133 humans = [134 FakeHuman(real_uid=0, exposition_timestamp=never, visits_to_adopt=visits),135 FakeHuman(real_uid=1, exposition_timestamp=never, visits_to_adopt=visits),136 ]137 messages = generate_received_messages(humans)138 h0_messages = messages[0]["received_messages"]139 self.assertEqual(len(h0_messages), 9)140 h0_messages = [msg for msgs in h0_messages.values() for msg in msgs]141 cluster_manager = clu.SimplisticClusterManager(max_history_ticks_offset=5)142 cluster_manager.add_messages(h0_messages)143 self.assertEqual(len(cluster_manager.clusters), 2)144 self.assertEqual(cluster_manager.clusters[0].first_update_time, np.uint8(5))145 self.assertEqual(cluster_manager.clusters[1].first_update_time, np.uint8(8))146 # new manually added encounters that are outdated should also be ignored147 cluster_manager.add_messages([148 mu.EncounterMessage(humans[1].rolling_uids[0], risk_level=np.uint8(1), encounter_time=0)149 ])150 self.assertEqual(len(cluster_manager.clusters), 2)151 self.assertEqual(cluster_manager.clusters[0].first_update_time, np.uint8(5))152 self.assertEqual(cluster_manager.clusters[1].first_update_time, np.uint8(8))153 def test_random_large_scale(self):154 n_trials = 10155 n_humans = 50156 n_visits = 2000157 n_expositions = 15158 max_timestamp = 10159 for _ in range(n_trials):160 h0_messages, visits = generate_random_messages(161 n_humans=n_humans,162 n_visits=n_visits,163 n_expositions=n_expositions,164 max_timestamp=max_timestamp,165 )166 cluster_manager = clu.SimplisticClusterManager(max_history_ticks_offset=never)167 cluster_manager.add_messages(h0_messages)168 self.assertLessEqual(169 len(cluster_manager.clusters),170 (mu.message_uid_mask + 1) * (mu.risk_level_mask + 1) * (max_timestamp + 1)171 )172 homogeneity_scores = cluster_manager._get_homogeneity_scores()173 for id in homogeneity_scores:174 self.assertLessEqual(homogeneity_scores[id], 1.0)175 min_homogeneity = 1 / sum([v.visited_real_uid == 0 for v in visits])176 self.assertLessEqual(min_homogeneity, homogeneity_scores[id])177if __name__ == "__main__":...
test_clustering_blind.py
Source:test_clustering_blind.py
1import numpy as np2import unittest3import covid19sim.frozen.clustering.blind as clu4import covid19sim.frozen.message_utils as mu5from tests.utils import FakeHuman, generate_received_messages, generate_random_messages, Visit6never = 9999 # dirty macro to indicate a human will never get infected7class BlindClusteringTests(unittest.TestCase):8 # note: we only ever build & test clusters for a single human, assuming it would also work for others9 def test_same_day_visit_clusters(self):10 n_trials = 10011 for _ in range(n_trials):12 # first scenario: single day visits, same risk for both visitors, should give 1 cluster13 visits = [14 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=2),15 Visit(visitor_real_uid=2, visited_real_uid=0, exposition=False, timestamp=2),16 ]17 humans = [FakeHuman(real_uid=idx, exposition_timestamp=never, visits_to_adopt=visits)18 for idx in range(3)]19 messages = generate_received_messages(humans)20 h0_messages = [msg for msgs in messages[0]["received_messages"].values() for msg in msgs]21 cluster_manager = clu.BlindClusterManager(max_history_ticks_offset=never)22 cluster_manager.add_messages(h0_messages)23 self.assertEqual(len(cluster_manager.clusters), 1)24 self.assertEqual(cluster_manager.latest_refresh_timestamp, 2)25 embeddings = cluster_manager.get_embeddings_array()26 self.assertEqual(len(embeddings), 1)27 self.assertTrue((embeddings[:, 1] == 0).all()) # risk level28 self.assertTrue((embeddings[:, 2] == 2).all()) # message count29 self.assertTrue((embeddings[:, 3] == 0).all()) # timestamp offset30 # 2nd scenario: single day visits, diff risk for both visitors, should give 2 cluster31 humans = [FakeHuman(real_uid=idx, exposition_timestamp=never,32 visits_to_adopt=visits, force_init_risk=mu.RiskLevelType(idx))33 for idx in range(3)]34 messages = generate_received_messages(humans, minimum_risk_level_for_updates=1)35 h0_messages = [msg for msgs in messages[0]["received_messages"].values() for msg in msgs]36 cluster_manager = clu.BlindClusterManager(max_history_ticks_offset=never)37 cluster_manager.add_messages(h0_messages)38 self.assertEqual(len(cluster_manager.clusters), 2)39 self.assertEqual(cluster_manager.latest_refresh_timestamp, 2)40 expositions = cluster_manager._get_expositions_array()41 self.assertTrue(len(expositions) == 2 and sum(expositions) == 0)42 embeddings = cluster_manager.get_embeddings_array()43 self.assertEqual(len(embeddings), 2)44 self.assertTrue((embeddings[:, 1] > 0).all()) # risk level45 self.assertTrue((embeddings[:, 2] == 1).all()) # message count46 self.assertTrue((embeddings[:, 3] == 0).all()) # timestamp offset47 def test_cluster_risk_update(self):48 # scenario: single encounter that gets updated (should remain at 1 cluster)49 visits = [50 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=0),51 ]52 humans = [53 FakeHuman(real_uid=0, exposition_timestamp=never, visits_to_adopt=visits,54 force_init_risk=np.uint8(0)),55 FakeHuman(real_uid=1, exposition_timestamp=never, visits_to_adopt=visits,56 force_init_risk=np.uint8(7)),57 ]58 messages = generate_received_messages(humans)59 h0_messages = [msg for msgs in messages[0]["received_messages"].values() for msg in msgs]60 cluster_manager = clu.BlindClusterManager(max_history_ticks_offset=never)61 cluster_manager.add_messages(h0_messages)62 self.assertEqual(len(cluster_manager.clusters), 1)63 self.assertEqual(cluster_manager.clusters[0].risk_level, np.uint8(7))64 cluster_manager.add_messages([65 mu.create_update_message(h0_messages[0], np.uint8(9), np.uint64(1))66 ])67 self.assertEqual(len(cluster_manager.clusters), 1)68 self.assertEqual(cluster_manager.clusters[0].risk_level, np.uint8(9))69 # add a new encounter: it should not match the existing cluster due to diff risk70 cluster_manager.add_messages([71 mu.EncounterMessage(mu.create_new_uid(), risk_level=np.uint8(1), encounter_time=0)72 ])73 self.assertEqual(len(cluster_manager.clusters), 2)74 self.assertEqual(cluster_manager.clusters[1].risk_level, np.uint8(1))75 # add a new encounter: it should match the existing cluster due to same risk76 new_encounter = \77 mu.EncounterMessage(mu.create_new_uid(), risk_level=np.uint8(9), encounter_time=0)78 cluster_manager.add_messages([new_encounter])79 self.assertEqual(len(cluster_manager.clusters), 2)80 self.assertEqual(cluster_manager.clusters[0].risk_level, np.uint8(9))81 self.assertEqual(len(cluster_manager.clusters[0].messages), 2)82 # update one of the two encounters in the first cluster; it should get split83 cluster_manager.add_messages([84 mu.create_update_message(new_encounter, np.uint8(13), np.uint64(1))85 ])86 self.assertEqual(len(cluster_manager.clusters), 3)87 self.assertEqual(cluster_manager.clusters[0].risk_level, np.uint8(9))88 self.assertEqual(cluster_manager.clusters[1].risk_level, np.uint8(13))89 self.assertEqual(cluster_manager.clusters[2].risk_level, np.uint8(1))90 self.assertTrue(all([len(c.messages) == 1 for c in cluster_manager.clusters]))91 def test_cleanup_outdated_cluster(self):92 # scenario: a new encounter is added that is waaay outdated; it should not create a cluster93 visits = [94 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=2),95 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=5),96 Visit(visitor_real_uid=1, visited_real_uid=0, exposition=False, timestamp=8),97 ]98 humans = [99 FakeHuman(real_uid=0, exposition_timestamp=never, visits_to_adopt=visits),100 FakeHuman(real_uid=1, exposition_timestamp=never, visits_to_adopt=visits),101 ]102 messages = generate_received_messages(humans)103 h0_messages = [msg for msgs in messages[0]["received_messages"].values() for msg in msgs]104 cluster_manager = clu.BlindClusterManager(max_history_ticks_offset=5)105 cluster_manager.add_messages(h0_messages)106 self.assertEqual(len(cluster_manager.clusters), 2)107 self.assertEqual(cluster_manager.clusters[0].first_update_time, np.uint8(5))108 self.assertEqual(cluster_manager.clusters[1].first_update_time, np.uint8(8))109 # new manually added encounters that are outdated should also be ignored110 cluster_manager.add_messages([111 mu.EncounterMessage(mu.create_new_uid(), risk_level=np.uint8(1), encounter_time=0)112 ])113 self.assertEqual(len(cluster_manager.clusters), 2)114 self.assertEqual(cluster_manager.clusters[0].first_update_time, np.uint8(5))115 self.assertEqual(cluster_manager.clusters[1].first_update_time, np.uint8(8))116 def test_random_large_scale(self):117 n_trials = 25118 n_humans = 50119 n_visits = 2000120 n_expositions = 15121 max_timestamp = 10122 for _ in range(n_trials):123 h0_messages, visits = generate_random_messages(124 n_humans=n_humans,125 n_visits=n_visits,126 n_expositions=n_expositions,127 max_timestamp=max_timestamp,128 )129 cluster_manager = clu.BlindClusterManager(max_history_ticks_offset=never)130 cluster_manager.add_messages(h0_messages)131 self.assertLessEqual(132 len(cluster_manager.clusters),133 (mu.message_uid_mask + 1) * (mu.risk_level_mask + 1) * (max_timestamp + 1)134 )135 homogeneity_scores = cluster_manager._get_homogeneity_scores()136 for id in homogeneity_scores:137 self.assertLessEqual(homogeneity_scores[id], 1.0)138 min_homogeneity = 1 / sum([v.visited_real_uid == 0 for v in visits])139 self.assertLessEqual(min_homogeneity, homogeneity_scores[id])140if __name__ == "__main__":...
__main__.py
Source:__main__.py
1from lib.KubeManage import KubernetesManagment2from lib.util import *3from docs.__docs__ import *4import subprocess5################################################################################6############## Master Values #################7################################################################################8sys.path.insert(0, os.path.abspath('.'))9#Before we load the menu, we need to do some checks10# The .env needs to be reloaded in the case of other alterations11#12# Where the terminal is located when you run the file13PWD = os.path.realpath(".")14#PWD_LIST = os.listdir(PWD)15# ohh look a global list16global PROJECT_ROOT17try:18 PROJECT_ROOT = Path(os.path.dirname(__file__))19except:20 PROJECT_ROOT = Path(os.path.dirname(PWD))21debuggreen(f"PROJECT_ROOT: {PROJECT_ROOT}")22os.environ["PROJECT_ROOT"] = str(PROJECT_ROOT.absolute())23global BIN_ROOT24BIN_ROOT=Path(PROJECT_ROOT,'bin')25os.environ["BIN_ROOT"] = str(BIN_ROOT.absolute())26debuggreen(f"BIN_ROOT: {BIN_ROOT}")27global LIB_ROOT28LIB_ROOT = Path(PROJECT_ROOT,'lib')29os.environ["LIB_ROOT"] = str(LIB_ROOT.absolute())30debuggreen(f"LIB_ROOT: {LIB_ROOT}")31global YAML_REPO32YAML_REPO = Path(LIB_ROOT,"yaml_repo")33debuggreen(f"YAML_REPO: {YAML_REPO}")34global KUBECONFIGPATH35KUBECONFIGPATH = Path(YAML_REPO,'kubeconfig.yml')36os.environ["KUBECONFIGPATH"] = str(KUBECONFIGPATH.absolute())37debuggreen(f"KUBECONFIGPATH: {KUBECONFIGPATH}")38CLUSTER_ROLE_CONFIG = Path(YAML_REPO,"cluster_role_config.yml")39CLUSTER_ROLE_CONFIG_BINDING = Path(YAML_REPO,"cluster_role_config_binding.yml")40CLUSTER_DASHBOARD = Path(YAML_REPO,"kubernetes_dashboard.yml")41#debuggreen(f"CLUSTER_ROLE_CONFIG :{CLUSTER_ROLE_CONFIG}")42#debuggreen(f"CLUSTER_ROLE_CONFIG :{CLUSTER_ROLE_CONFIG}")43list_of_critical_vars = ["PROJECT_ROOT","LIB_ROOT","BIN_ROOT","KUBECONFIGPATH","CLUSTER_ROLE_CONFIG","CLUSTER_ROLE_CONFIG_BINDING"]44###############################################################################45## Docker Information ##46###############################################################################47class KindWrapper():48 def __init__(self,kind_bin_path:Path):49 self.binary_path = str(kind_bin_path.absolute())50 51 def create_cluster(self,namespace:str,kubeconfig_path:Path):52 """53 creates a cluster with given config54 """55 56def showenv():57 '''58 Checks shell env vars for required data59 '''60 for thing in os.getenv():61 if thing in list_of_critical_vars:62 greenprint(f"[+] {thing}")63def setpath():64 '''65 Establishes paths for tooling (kind, docker, etc...)66 '''67 sys.path.insert(0,str(BIN_ROOT))68def create_certificate_authority():69 """70 Creates a certificate authority using a shell script71 """72 greenprint("[+] Generating CA data using script 'init_ca.sh'")73 subprocess.Popen(f"sh -c {PROJECT_ROOT}/init_ca.sh")74# create your own custom scripts with ease!75def establish_host(cluster_reference:KubernetesManagment):76 """77 Performs setup of a cluster, use this before creating a cluster78 """79 #setpath()80 #cluster_reference.create_service_account("cluster_admin")81 #cluster_reference82 83def init_manager() -> KubernetesManagment:84 """85 Creates an instance of KubernetesManagment class86 """87 ClusterManager = KubernetesManagment(kubeconfig=KUBECONFIGPATH,88 PROJECT_ROOT=PROJECT_ROOT,89 LIB_ROOT=LIB_ROOT,90 BIN_ROOT=BIN_ROOT,91 YAML_REPO=YAML_REPO,92 CLUSTER_ROLE_CONFIG=CLUSTER_ROLE_CONFIG,93 CLUSTER_ROLE_CONFIG_BINDING=CLUSTER_ROLE_CONFIG_BINDING94 )95 return ClusterManager96def init_dashboard(cluster_manager:KubernetesManagment):97 '''98 Deploys the kubernetes dashboard at99 http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/100 '''101 try:102 #apply dashboard config103 clustermanager.kubectl_apply_config(CLUSTER_DASHBOARD)104 cluster_manager.make_thefucking_dashboard_fucking_work()105 # init proxy service106 cluster_manager.cluster.kubectl("proxy")107 greenprint("[+] Dashboard is running on http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy/")108 except:109 errorlogger("Cannot deploy dashboard, check the log file")110def createsandbox(cluster_manager:KubernetesManagment):111 '''112 Creates the sandbox, using kubernetes/docker113 '''114 # set environment to read from kube config directory115 #setenv({"KUBECONFIG":KUBECONFIGPATH})116 #TODO: make this117 cluster_manager.init_client()118 cluster_manager.create_cluster_role()119 cluster_manager.create_cluster_role_binding()120 cluster_manager.create_cluster()121def runsandbox(composefile):122 '''123 run a sandbox124 Args:125 composefile (str): composefile to use126 '''127 #subprocess.Popen(["docker-compose", "up", composefile])128if __name__ == "__main__":129 clustermanager = init_manager()130 init_dashboard(cluster_manager=clustermanager)...
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