diff --git a/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb b/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb index 9732abb..d4c0aae 100644 --- a/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb +++ b/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb @@ -25,8 +25,6 @@ "ACCESS_COUNT_LIMIT = 1000 # Total time to run the simulation\n", "EXPERIMENT_BASE_DIR = \"./experiments/\"\n", "TEMP_BASE_DIR = \"./.aoi_cache/\"\n", - "BASE_FILE = pd.read_csv(\"../calculated.csv\")\n", - "BASE_FILE.index += 1\n", "\n", "ZIPF_CONSTANT = 2 # Shape parameter for the Zipf distribution (controls skewness) Needs to be: 1< \n", "\n", @@ -88,6 +86,24 @@ { "cell_type": "code", "execution_count": 5, + "id": "ad6c68a4-2ebe-40fc-a391-5a4724e07bdf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EvictionStrategy.LRU\n" + ] + } + ], + "source": [ + "print(EvictionStrategy.LRU)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "00a944e4-842b-49ba-bb36-587d9c12fdf4", "metadata": {}, "outputs": [], @@ -102,7 +118,11 @@ " def __post_init__(self):\n", " if not hasattr(self, 'eviction_strategy') or self.eviction_strategy is None:\n", " raise ValueError(\"Eviction strategy must be defined in subclasses.\")\n", - " \n", + "\n", + " def __repr__(self):\n", + " db_object_count = self.db_objects if isinstance(self.db_objects, int) else len(self.db_objects)\n", + " return f\"[{self.__class__.__name__}] Database Object Count: {db_object_count}, Cache Size: {self.cache_size}, Eviction Strategy: {self.eviction_strategy}\"\n", + " \n", " def generate_objects(self):\n", " if isinstance(self.db_objects, int):\n", " self.db_objects = [\n", @@ -123,14 +143,18 @@ "@dataclass\n", "class TTLSimulation(SimulationConfig):\n", " eviction_strategy: EvictionStrategy = field(default=EvictionStrategy.TTL, init=False)\n", - " \n", + "\n", + " def __repr__(self):\n", + " return super().__repr__().replace(super().__class__.__name__, self.__class__.__name__)\n", + " \n", " def generate_objects(self, fixed_ttl):\n", " if isinstance(self.db_objects, int):\n", " self.db_objects = [\n", " DatabaseObject(id=i, data=f\"Generated Object {i}\", lambda_value=np.random.zipf(ZIPF_CONSTANT), mu_value=None, ttl=fixed_ttl) \n", " for i in range(self.db_objects)\n", " ]\n", - " \n", + "\n", + " \n", " def from_file(self, path: str, lambda_column_name: str, ttl_column_name: str):\n", " df = pd.read_csv(path)\n", " lambdas = df[lambda_column_name]\n", @@ -144,14 +168,26 @@ "@dataclass\n", "class LRUSimulation(SimulationConfig):\n", " eviction_strategy: EvictionStrategy = field(default=EvictionStrategy.LRU, init=False)\n", + " \n", + " def __repr__(self):\n", + " return super().__repr__().replace(super().__class__.__name__, self.__class__.__name__)\n", + " \n", "\n", "@dataclass\n", "class RandomEvictionSimulation(SimulationConfig):\n", " eviction_strategy: EvictionStrategy = field(default=EvictionStrategy.RANDOM_EVICTION, init=False)\n", "\n", + " \n", + " def __repr__(self):\n", + " return super().__repr__().replace(super().__class__.__name__, self.__class__.__name__)\n", + "\n", "@dataclass\n", "class RefreshSimulation(TTLSimulation):\n", + "\n", " \n", + " def __repr__(self):\n", + " return super().__repr__().replace(super().__class__.__name__, self.__class__.__name__)\n", + " \n", " def generate_objects(self, fixed_ttl, max_refresh_rate):\n", " if isinstance(self.db_objects, int):\n", " self.db_objects = [\n", @@ -173,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "5cea042f-e9fc-4a1e-9750-de212ca70601", "metadata": {}, "outputs": [], @@ -191,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "499bf543-b2c6-4e4d-afcc-0a6665ce3ae1", "metadata": {}, "outputs": [], @@ -336,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "687f5634-8edf-4337-b42f-bbb292d47f0f", "metadata": {}, "outputs": [], @@ -405,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "c8516830-9880-4d9e-a91b-000338baf9d6", "metadata": { "scrolled": true @@ -434,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "e269b607-16b9-46d0-8a97-7324f2002c72", "metadata": {}, "outputs": [], @@ -447,33 +483,33 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "33fdc5fd-1f39-4b51-b2c7-6ea6acf2b753", "metadata": {}, "outputs": [], "source": [ "# Simulate with a Cache that does lru, We'll have 100 Database Objects and a Cache Size of 10\n", "# We'll generate lambdas from a zipf distribution\n", - "# config = LRUSimulation(100, 10)\n", - "# config.from_file('./input/2024-12-13/input.csv', 'Lambda')" + "config = LRUSimulation(100, 10)\n", + "config.from_file('./input/2024-12-13/input.csv', 'Lambda')" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "6c391bfd-b294-4ff7-8b22-51777368a6b9", "metadata": {}, "outputs": [], "source": [ "# Simulate with a Cache that does Refreshes with TTL based eviction, We'll have 100 Database Objects and a Cache Size of 10\n", "# We'll generate lambdas from a zipf distribution. Each object will have a fixed ttl of 1 when its pulled into the cache. Mu for the refresh rate is 10\n", - "config = RefreshSimulation(100, 10)\n", - "config.from_file(path='./input/2024-12-13/output.csv', lambda_column_name='Lambda', ttl_column_name='TTL_2', mu_column_name='u_opt_2')" + "# config = RefreshSimulation(100, 10)\n", + "# config.from_file(path='./input/2024-12-13/output.csv', lambda_column_name='Lambda', ttl_column_name='TTL_2', mu_column_name='u_opt_2')" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "0a444c9d-53dd-4cab-b8f1-100ad3ab213a", "metadata": {}, "outputs": [], @@ -486,7 +522,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, + "id": "6ac338bd-2094-41d2-8e92-565d03422b87", + "metadata": {}, + "outputs": [], + "source": [ + "with open(f\"{TEMP_BASE_DIR}/simulation_config.txt\", 'w') as f:\n", + " f.write(str(config))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "id": "66f65699-a3c9-48c4-8f1f-b9d7834c026a", "metadata": { "scrolled": true @@ -496,16 +543,146 @@ "name": "stderr", "output_type": "stream", "text": [ - "Progress: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:53<00:00, 18.64it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation ended after 41546.30037303802 seconds.\n", - "CPU times: user 50.4 s, sys: 8.37 s, total: 58.7 s\n", - "Wall time: 53.6 s\n" + "IOPub message rate exceeded.██████████▉ | 149/1000 [00:07<00:44, 19.08it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.██████████████████████▍ | 220/1000 [00:09<00:35, 22.26it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.████████████████████████████▊ | 260/1000 [00:11<00:32, 23.10it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.███████████████████████████████████████████▏ | 349/1000 [00:13<00:24, 26.18it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.███████████████████████████████████████████████████▌ | 401/1000 [00:15<00:22, 26.67it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.████████████████████████████████████████████████████████████████▎ | 480/1000 [00:17<00:19, 27.16it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.██████████████████████████████████████████████████████████████████████▉ | 521/1000 [00:18<00:17, 27.82it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.███████████████████████████████████████████████████████████████████████████████████████▍ | 624/1000 [00:22<00:13, 27.69it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.██████████████████████████████████████████████████████████████████████████████████████████████████▌ | 693/1000 [00:24<00:10, 27.97it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.█████████████████████████████████████████████████████████████████████████████████████████████████████████ | 733/1000 [00:26<00:09, 28.08it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 796/1000 [00:28<00:07, 28.31it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 854/1000 [00:30<00:05, 28.39it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 920/1000 [00:31<00:02, 29.13it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 978/1000 [00:33<00:00, 29.28it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" ] } ], @@ -518,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "6f900c68-1f34-48d1-b346-ef6ea6911fa5", "metadata": {}, "outputs": [], @@ -531,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "3b6f7c1f-ea54-4496-bb9a-370cee2d2751", "metadata": { "scrolled": true @@ -541,106 +718,106 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object 0: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 1: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 2: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 3: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 4: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 5: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 6: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 7: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 8: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 9: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 10: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 11: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 12: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 13: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 14: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 15: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 16: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 17: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 18: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 19: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 20: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 21: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 22: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 23: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 24: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 25: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 26: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 27: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 28: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 29: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 30: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 31: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 32: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 33: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 34: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 35: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 36: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 37: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 38: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 39: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 40: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 41: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 42: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 43: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 44: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 45: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 46: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 47: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 48: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 49: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 50: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 51: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 52: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 53: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 54: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 55: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 56: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 57: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 58: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 59: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 60: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 61: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 62: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 63: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 64: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 65: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 66: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 67: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 68: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 69: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 70: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 71: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 72: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 73: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 74: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 75: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 76: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 77: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 78: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 79: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 80: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 81: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 82: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 83: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 84: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 85: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 86: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 87: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 88: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 89: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n", - "Object 90: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.55, Expected Age = 20367.51\n", - "Object 91: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.54, Expected Age = 20926.62\n", - "Object 92: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.51, Expected Age = 20502.64\n", - "Object 93: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.49, Expected Age = 20425.76\n", - "Object 94: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.46, Expected Age = 20902.52\n", - "Object 95: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.43, Expected Age = 20666.00\n", - "Object 96: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.39, Expected Age = 20772.20\n", - "Object 97: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.35, Expected Age = 20708.69\n", - "Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.30, Expected Age = 20858.00\n", - "Object 99: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.22, Expected Age = 20546.25\n" + "Object 0: Hit Rate = 0.03, Average Time spend in Cache: 0.03, Average Age = 0.02, Expected Age = 1.12\n", + "Object 1: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.78\n", + "Object 2: Hit Rate = 0.03, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 1.03\n", + "Object 3: Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.80\n", + "Object 4: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.70\n", + "Object 5: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.52\n", + "Object 6: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.40\n", + "Object 7: Hit Rate = 0.02, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.90\n", + "Object 8: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.31\n", + "Object 9: Hit Rate = 0.03, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.26\n", + "Object 10: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.56\n", + "Object 11: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.54\n", + "Object 12: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.39\n", + "Object 13: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.31\n", + "Object 14: Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.68\n", + "Object 15: Hit Rate = 0.03, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.15\n", + "Object 16: Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.70\n", + "Object 17: Hit Rate = 0.03, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 1.10\n", + "Object 18: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.52\n", + "Object 19: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.24\n", + "Object 20: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.40\n", + "Object 21: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.40\n", + "Object 22: Hit Rate = 0.04, Average Time spend in Cache: 0.04, Average Age = 0.03, Expected Age = 1.34\n", + "Object 23: Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.67\n", + "Object 24: Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.04, Expected Age = 1.48\n", + "Object 25: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.40\n", + "Object 26: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.03, Expected Age = 1.39\n", + "Object 27: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.03, Expected Age = 1.29\n", + "Object 28: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.28\n", + "Object 29: Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.05, Expected Age = 1.69\n", + "Object 30: Hit Rate = 0.06, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.70\n", + "Object 31: Hit Rate = 0.05, Average Time spend in Cache: 0.05, Average Age = 0.05, Expected Age = 1.58\n", + "Object 32: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.03, Expected Age = 1.26\n", + "Object 33: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.03, Expected Age = 1.28\n", + "Object 34: Hit Rate = 0.05, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.53\n", + "Object 35: Hit Rate = 0.06, Average Time spend in Cache: 0.05, Average Age = 0.05, Expected Age = 1.58\n", + "Object 36: Hit Rate = 0.05, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.40\n", + "Object 37: Hit Rate = 0.04, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.24\n", + "Object 38: Hit Rate = 0.05, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.28\n", + "Object 39: Hit Rate = 0.06, Average Time spend in Cache: 0.05, Average Age = 0.05, Expected Age = 1.53\n", + "Object 40: Hit Rate = 0.05, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.34\n", + "Object 41: Hit Rate = 0.05, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.28\n", + "Object 42: Hit Rate = 0.07, Average Time spend in Cache: 0.05, Average Age = 0.06, Expected Age = 1.72\n", + "Object 43: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.45\n", + "Object 44: Hit Rate = 0.06, Average Time spend in Cache: 0.05, Average Age = 0.04, Expected Age = 1.45\n", + "Object 45: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.42\n", + "Object 46: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.40\n", + "Object 47: Hit Rate = 0.05, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.21\n", + "Object 48: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.53\n", + "Object 49: Hit Rate = 0.05, Average Time spend in Cache: 0.06, Average Age = 0.04, Expected Age = 1.10\n", + "Object 50: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.32\n", + "Object 51: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.04, Expected Age = 1.32\n", + "Object 52: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.41\n", + "Object 53: Hit Rate = 0.06, Average Time spend in Cache: 0.06, Average Age = 0.05, Expected Age = 1.23\n", + "Object 54: Hit Rate = 0.06, Average Time spend in Cache: 0.07, Average Age = 0.05, Expected Age = 1.26\n", + "Object 55: Hit Rate = 0.06, Average Time spend in Cache: 0.07, Average Age = 0.05, Expected Age = 1.35\n", + "Object 56: Hit Rate = 0.06, Average Time spend in Cache: 0.07, Average Age = 0.06, Expected Age = 1.28\n", + "Object 57: Hit Rate = 0.07, Average Time spend in Cache: 0.07, Average Age = 0.06, Expected Age = 1.49\n", + "Object 58: Hit Rate = 0.06, Average Time spend in Cache: 0.07, Average Age = 0.05, Expected Age = 1.15\n", + "Object 59: Hit Rate = 0.07, Average Time spend in Cache: 0.07, Average Age = 0.07, Expected Age = 1.40\n", + "Object 60: Hit Rate = 0.07, Average Time spend in Cache: 0.07, Average Age = 0.06, Expected Age = 1.35\n", + "Object 61: Hit Rate = 0.07, Average Time spend in Cache: 0.07, Average Age = 0.06, Expected Age = 1.23\n", + "Object 62: Hit Rate = 0.07, Average Time spend in Cache: 0.07, Average Age = 0.06, Expected Age = 1.37\n", + "Object 63: Hit Rate = 0.08, Average Time spend in Cache: 0.07, Average Age = 0.07, Expected Age = 1.39\n", + "Object 64: Hit Rate = 0.08, Average Time spend in Cache: 0.08, Average Age = 0.07, Expected Age = 1.44\n", + "Object 65: Hit Rate = 0.09, Average Time spend in Cache: 0.08, Average Age = 0.07, Expected Age = 1.47\n", + "Object 66: Hit Rate = 0.09, Average Time spend in Cache: 0.08, Average Age = 0.07, Expected Age = 1.48\n", + "Object 67: Hit Rate = 0.08, Average Time spend in Cache: 0.08, Average Age = 0.07, Expected Age = 1.37\n", + "Object 68: Hit Rate = 0.09, Average Time spend in Cache: 0.09, Average Age = 0.08, Expected Age = 1.43\n", + "Object 69: Hit Rate = 0.09, Average Time spend in Cache: 0.09, Average Age = 0.08, Expected Age = 1.44\n", + "Object 70: Hit Rate = 0.09, Average Time spend in Cache: 0.09, Average Age = 0.08, Expected Age = 1.42\n", + "Object 71: Hit Rate = 0.09, Average Time spend in Cache: 0.09, Average Age = 0.07, Expected Age = 1.34\n", + "Object 72: Hit Rate = 0.10, Average Time spend in Cache: 0.10, Average Age = 0.08, Expected Age = 1.44\n", + "Object 73: Hit Rate = 0.09, Average Time spend in Cache: 0.10, Average Age = 0.07, Expected Age = 1.29\n", + "Object 74: Hit Rate = 0.10, Average Time spend in Cache: 0.10, Average Age = 0.08, Expected Age = 1.34\n", + "Object 75: Hit Rate = 0.10, Average Time spend in Cache: 0.11, Average Age = 0.09, Expected Age = 1.34\n", + "Object 76: Hit Rate = 0.10, Average Time spend in Cache: 0.11, Average Age = 0.09, Expected Age = 1.28\n", + "Object 77: Hit Rate = 0.11, Average Time spend in Cache: 0.11, Average Age = 0.09, Expected Age = 1.39\n", + "Object 78: Hit Rate = 0.12, Average Time spend in Cache: 0.11, Average Age = 0.11, Expected Age = 1.49\n", + "Object 79: Hit Rate = 0.12, Average Time spend in Cache: 0.12, Average Age = 0.11, Expected Age = 1.45\n", + "Object 80: Hit Rate = 0.12, Average Time spend in Cache: 0.12, Average Age = 0.10, Expected Age = 1.28\n", + "Object 81: Hit Rate = 0.13, Average Time spend in Cache: 0.13, Average Age = 0.12, Expected Age = 1.44\n", + "Object 82: Hit Rate = 0.12, Average Time spend in Cache: 0.13, Average Age = 0.11, Expected Age = 1.20\n", + "Object 83: Hit Rate = 0.15, Average Time spend in Cache: 0.14, Average Age = 0.13, Expected Age = 1.47\n", + "Object 84: Hit Rate = 0.16, Average Time spend in Cache: 0.15, Average Age = 0.14, Expected Age = 1.46\n", + "Object 85: Hit Rate = 0.16, Average Time spend in Cache: 0.15, Average Age = 0.14, Expected Age = 1.42\n", + "Object 86: Hit Rate = 0.16, Average Time spend in Cache: 0.16, Average Age = 0.15, Expected Age = 1.39\n", + "Object 87: Hit Rate = 0.17, Average Time spend in Cache: 0.17, Average Age = 0.16, Expected Age = 1.39\n", + "Object 88: Hit Rate = 0.17, Average Time spend in Cache: 0.18, Average Age = 0.16, Expected Age = 1.29\n", + "Object 89: Hit Rate = 0.19, Average Time spend in Cache: 0.19, Average Age = 0.18, Expected Age = 1.37\n", + "Object 90: Hit Rate = 0.21, Average Time spend in Cache: 0.21, Average Age = 0.21, Expected Age = 1.42\n", + "Object 91: Hit Rate = 0.22, Average Time spend in Cache: 0.22, Average Age = 0.22, Expected Age = 1.33\n", + "Object 92: Hit Rate = 0.24, Average Time spend in Cache: 0.25, Average Age = 0.25, Expected Age = 1.36\n", + "Object 93: Hit Rate = 0.27, Average Time spend in Cache: 0.27, Average Age = 0.28, Expected Age = 1.38\n", + "Object 94: Hit Rate = 0.28, Average Time spend in Cache: 0.29, Average Age = 0.30, Expected Age = 1.30\n", + "Object 95: Hit Rate = 0.33, Average Time spend in Cache: 0.33, Average Age = 0.37, Expected Age = 1.35\n", + "Object 96: Hit Rate = 0.38, Average Time spend in Cache: 0.38, Average Age = 0.44, Expected Age = 1.35\n", + "Object 97: Hit Rate = 0.45, Average Time spend in Cache: 0.45, Average Age = 0.58, Expected Age = 1.35\n", + "Object 98: Hit Rate = 0.57, Average Time spend in Cache: 0.57, Average Age = 0.92, Expected Age = 1.48\n", + "Object 99: Hit Rate = 0.77, Average Time spend in Cache: 0.77, Average Age = 1.97, Expected Age = 1.87\n" ] } ], @@ -684,7 +861,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "id": "b2d18372-cdba-4151-ae32-5bf45466bf94", "metadata": {}, "outputs": [], @@ -696,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "be7e67e7-4533-438a-ab65-ca813f48052a", "metadata": {}, "outputs": [], @@ -707,7 +884,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "80971714-44f1-47db-9e89-85be7c885bde", "metadata": {}, "outputs": [ @@ -738,8 +915,6 @@ " mu\n", " lambda\n", " hit_rate\n", - " expected_hit_rate\n", - " expected_hit_rate_delta\n", " avg_cache_time\n", " cache_time_delta\n", " avg_age\n", @@ -749,77 +924,67 @@ " \n", " \n", " 0\n", - " 1062\n", - " 0\n", - " 1062\n", - " 1.000000\n", + " 1000\n", + " 28\n", + " 972\n", + " None\n", " 0.0251\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.028000\n", + " 0.033253\n", + " -0.005253\n", + " 0.023204\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", " \n", " \n", " 1\n", - " 1088\n", - " 0\n", - " 1088\n", - " 1.000000\n", + " 1067\n", + " 48\n", + " 1019\n", + " None\n", " 0.0253\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.044986\n", + " 0.035666\n", + " 0.009320\n", + " 0.043221\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", " \n", " \n", " 2\n", - " 1000\n", - " 0\n", - " 1000\n", - " 1.000000\n", + " 1100\n", + " 29\n", + " 1071\n", + " None\n", " 0.0255\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.026364\n", + " 0.036409\n", + " -0.010046\n", + " 0.020289\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", " \n", " \n", " 3\n", - " 1044\n", - " 0\n", - " 1044\n", - " 1.000000\n", + " 1107\n", + " 51\n", + " 1056\n", + " None\n", " 0.0257\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.046070\n", + " 0.036398\n", + " 0.009673\n", + " 0.038728\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", " \n", " \n", " 4\n", - " 1129\n", - " 0\n", - " 1129\n", - " 1.000000\n", + " 1086\n", + " 48\n", + " 1038\n", + " None\n", " 0.0260\n", - " 0.000000\n", - " 0.0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 0.044199\n", + " 0.036152\n", + " 0.008047\n", + " 0.036428\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...\n", " \n", " \n", @@ -834,128 +999,103 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 95\n", - " 11404\n", - " 11403\n", - " 1\n", - " 2.344666\n", + " 11724\n", + " 3884\n", + " 7840\n", + " None\n", " 0.2759\n", - " 0.999912\n", - " 1.0\n", - " -0.000088\n", - " 0.999941\n", - " -0.000028\n", - " 0.428322\n", - " [0, 0.0891512597998787, 0.06415110059353868, 0...\n", + " 0.331286\n", + " 0.329118\n", + " 0.002168\n", + " 0.369984\n", + " [0, 0, 0, 0, 0, 0.689766350641932, 0, 0, 0, 0,...\n", " \n", " \n", " 96\n", - " 13706\n", - " 13705\n", - " 1\n", - " 2.563445\n", + " 14086\n", + " 5349\n", + " 8737\n", + " None\n", " 0.3299\n", - " 0.999927\n", - " 1.0\n", - " -0.000073\n", - " 0.999946\n", - " -0.000019\n", - " 0.386432\n", - " [0, 0.31426861097273395, 0.07673416342145734, ...\n", + " 0.379739\n", + " 0.382476\n", + " -0.002737\n", + " 0.440054\n", + " [0, 0, 0.6257414568279982, 0, 0.29622958616229...\n", " \n", " \n", " 97\n", - " 17197\n", - " 17196\n", - " 1\n", - " 2.876043\n", + " 17519\n", + " 7857\n", + " 9662\n", + " None\n", " 0.4152\n", - " 0.999942\n", - " 1.0\n", - " -0.000058\n", - " 0.999968\n", - " -0.000026\n", - " 0.349340\n", - " [0, 0.9920726875299433, 0.11549947514999914, 0...\n", + " 0.448485\n", + " 0.452053\n", + " -0.003568\n", + " 0.576042\n", + " [0, 0, 0, 0, 2.1522941477509043, 0, 0.29715894...\n", " \n", " \n", " 98\n", - " 23958\n", - " 23957\n", - " 1\n", - " 3.381806\n", + " 24674\n", + " 14117\n", + " 10557\n", + " None\n", " 0.5743\n", - " 0.999958\n", - " 1.0\n", - " -0.000042\n", - " 0.999999\n", - " -0.000041\n", - " 0.297864\n", - " [0, 0.055604529502096156, 0.05810636385222967,...\n", + " 0.572141\n", + " 0.571998\n", + " 0.000143\n", + " 0.919750\n", + " [0, 0.055604529502096156, 0.7127466308343264, ...\n", " \n", " \n", " 99\n", - " 41093\n", - " 41092\n", - " 1\n", - " 4.462294\n", + " 43025\n", + " 33051\n", + " 9974\n", + " None\n", " 1.0000\n", - " 0.999976\n", - " 1.0\n", - " -0.000024\n", - " 0.999997\n", - " -0.000022\n", - " 0.223745\n", - " [0, 0.1683508324120914, 0.24845511096226777, 0...\n", + " 0.768181\n", + " 0.768395\n", + " -0.000213\n", + " 1.966054\n", + " [0, 0.7104371771341902, 0.9702423340515842, 0,...\n", " \n", " \n", "\n", - "

100 rows × 12 columns

\n", + "

100 rows × 10 columns

\n", "" ], "text/plain": [ - " access_count hits misses mu lambda hit_rate \\\n", - "0 1062 0 1062 1.000000 0.0251 0.000000 \n", - "1 1088 0 1088 1.000000 0.0253 0.000000 \n", - "2 1000 0 1000 1.000000 0.0255 0.000000 \n", - "3 1044 0 1044 1.000000 0.0257 0.000000 \n", - "4 1129 0 1129 1.000000 0.0260 0.000000 \n", - ".. ... ... ... ... ... ... \n", - "95 11404 11403 1 2.344666 0.2759 0.999912 \n", - "96 13706 13705 1 2.563445 0.3299 0.999927 \n", - "97 17197 17196 1 2.876043 0.4152 0.999942 \n", - "98 23958 23957 1 3.381806 0.5743 0.999958 \n", - "99 41093 41092 1 4.462294 1.0000 0.999976 \n", - "\n", - " expected_hit_rate expected_hit_rate_delta avg_cache_time \\\n", - "0 0.0 0.000000 0.000000 \n", - "1 0.0 0.000000 0.000000 \n", - "2 0.0 0.000000 0.000000 \n", - "3 0.0 0.000000 0.000000 \n", - "4 0.0 0.000000 0.000000 \n", - ".. ... ... ... \n", - "95 1.0 -0.000088 0.999941 \n", - "96 1.0 -0.000073 0.999946 \n", - "97 1.0 -0.000058 0.999968 \n", - "98 1.0 -0.000042 0.999999 \n", - "99 1.0 -0.000024 0.999997 \n", + " access_count hits misses mu lambda hit_rate avg_cache_time \\\n", + "0 1000 28 972 None 0.0251 0.028000 0.033253 \n", + "1 1067 48 1019 None 0.0253 0.044986 0.035666 \n", + "2 1100 29 1071 None 0.0255 0.026364 0.036409 \n", + "3 1107 51 1056 None 0.0257 0.046070 0.036398 \n", + "4 1086 48 1038 None 0.0260 0.044199 0.036152 \n", + ".. ... ... ... ... ... ... ... \n", + "95 11724 3884 7840 None 0.2759 0.331286 0.329118 \n", + "96 14086 5349 8737 None 0.3299 0.379739 0.382476 \n", + "97 17519 7857 9662 None 0.4152 0.448485 0.452053 \n", + "98 24674 14117 10557 None 0.5743 0.572141 0.571998 \n", + "99 43025 33051 9974 None 1.0000 0.768181 0.768395 \n", "\n", " cache_time_delta avg_age \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.000000 \n", - "4 0.000000 0.000000 \n", + "0 -0.005253 0.023204 \n", + "1 0.009320 0.043221 \n", + "2 -0.010046 0.020289 \n", + "3 0.009673 0.038728 \n", + "4 0.008047 0.036428 \n", ".. ... ... \n", - "95 -0.000028 0.428322 \n", - "96 -0.000019 0.386432 \n", - "97 -0.000026 0.349340 \n", - "98 -0.000041 0.297864 \n", - "99 -0.000022 0.223745 \n", + "95 0.002168 0.369984 \n", + "96 -0.002737 0.440054 \n", + "97 -0.003568 0.576042 \n", + "98 0.000143 0.919750 \n", + "99 -0.000213 1.966054 \n", "\n", " ages \n", "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", @@ -964,16 +1104,16 @@ "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", ".. ... \n", - "95 [0, 0.0891512597998787, 0.06415110059353868, 0... \n", - "96 [0, 0.31426861097273395, 0.07673416342145734, ... \n", - "97 [0, 0.9920726875299433, 0.11549947514999914, 0... \n", - "98 [0, 0.055604529502096156, 0.05810636385222967,... \n", - "99 [0, 0.1683508324120914, 0.24845511096226777, 0... \n", + "95 [0, 0, 0, 0, 0, 0.689766350641932, 0, 0, 0, 0,... \n", + "96 [0, 0, 0.6257414568279982, 0, 0.29622958616229... \n", + "97 [0, 0, 0, 0, 2.1522941477509043, 0, 0.29715894... \n", + "98 [0, 0.055604529502096156, 0.7127466308343264, ... \n", + "99 [0, 0.7104371771341902, 0.9702423340515842, 0,... \n", "\n", - "[100 rows x 12 columns]" + "[100 rows x 10 columns]" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1016,13 +1156,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "id": "01f8f9ee-c278-4a22-8562-ba02e77f5ddd", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1050,7 +1190,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "f30a0497-9b2e-4ea9-8ebf-6687de19aaa9", "metadata": {}, "outputs": [ @@ -1090,13 +1230,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "id": "c192564b-d3c6-40e1-a614-f7a5ee787c4e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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o378/HBwcsHnzZiQnJ+O5555Dv379jOO6deuGoUOHGm+q0rdvXxQUFGDdunUIDQ1FbGysyXF79+4Nf39/LFiwACdPnkSLFi2QnJyM2NhY9O3bFxs2bKhwrUOGDMHUqVOxYMEC2Nvbm9RXok+fPtBqtQgNDYWfnx+KioqwY8cOnDp1Cs8//7wxzJdFlmWsWLHC7A0u+vfvjwkTJmD16tV4//33odFosHr1anTt2hUjRozAN998g7CwMOTn5yMpKQlHjx41hl4PDw98/fXXGDBgADp06IBnnnkGTZo0wbVr17B//374+/tj06ZNAICePXti+vTpmDt3Lho1aoSePXvCz88P169fx5kzZ/Dbb7/h7bffRrNmzZCXl4fHHnsMjRs3Rrt27dCgQQPk5OQgNjYWOp0Ob7zxBtRq9X2/RyVat26NzZs344UXXsCAAQMwZ84cdO7cGa6urrhx4wZ+//13nDhxAo0aNTI+p3Pnzhg3bhw+/fRTtGjRAv369YMQAv/9739x+fJljB8/Hp07dzaOL7lavmbNGrRr1w49e/ZEWloaNm7ciJ49e1b4ZjVleeKJJ7Bhwwb069cPvXr1gkajQatWrYx3OySi21hyvTsisj4l6zTf66tLly5CiFvr7S5ZskQEBgYKjUYjvLy8xPDhw0VaWlqZ68eWrNP866+/lnrdknWXU1JSSu0r61gl48+ePSvmz58vGjVqJFQqlfDz8xOzZs0SBQUFpY5TXFws5s2bJx555BGhUqnEI488Iv7zn/+IM2fO3HWd5n79+gl3d3dhb28v2rdvL9auXXvXNXbNuXjxolAoFAKAGDhwYJljPvvsM/HMM88IPz8/odFohJubm+jQoYP4/PPPRWFh4T2Pv23bNpP+3MvgwYMFALF69WrjNp1OJ1577TXj++Pq6ipCQkLEhx9+WOr5R48eFS+88ILw9PQUtra2wtvbW/Tq1UvExsaWGrtjxw7Ru3dv4e7uLmxtbYWXl5cICwsTc+fOFRcvXhRCCFFYWCjeffdd0aNHD1G/fn2hUqmEp6en6Ny5s1izZo3JDVnu5z260/Xr18XcuXNFaGiocHFxETY2NsLNzU107dpVfPLJJybrS5dYvny5aN++vbC3tzd+XyxfvrzM49+8eVOMHz9eeHp6CrVaLVq2bClWr159z3Wa79Y/Pz8/4efnZ7KtqKhITJ48WTRo0EDY2NiU+X1MRLdIQtzxNyoiIiIiIjLBOc1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcGbmzwgsizjypUrqFOnDiRJsnQ5RERERHQHIQSys7Ph4+NjvGvo3TA0PyBXrlyBr6+vpcsgIiIiIjMuXbqE+vXr33MMQ/MDUqdOHQC3mqDVasv9PFmWkZ6eDnd3d7O/8VDNxl5aF/bTurCf1oO9tC7V3U+9Xg9fX19jbrsXhuYHpGRKhlarrXBozs/Ph1ar5Q9/LcdeWhf207qwn9aDvbQulupneabS8ruLiIiIiMgMhmYiIiIiIjMYmomIiIiIzGBoJiIiIiIyg6GZiIiIiMgMhmYiIiIiIjMYmomIiIiIzGBoJiIiIiIyg6GZiIiIiMgMhmYiIiIiIjMYmomIiIiIzGBoJiIiIiIyg6GZiIiIiMgMhmYiIiIisjhZlnHy5EnjlyzLli7JhI2lCyAiIiKih1tCQgIWL1mKvy5cgX8DX5y/eAmN/XwQPWoEOnbsaOnyAPBKMxERERFZUEJCAiZPn4PTORr49xyBR8IHw7/nCCTnajB5+hwkJCRYukQADM1EREREZCGyLGPxkqXIc/JHq97D4OTdAEobWzh5N0DLp4ch39kfn33xZY2YqsHQTEREREQWkZSUhOTz/yCgfTgkSTLZJ0kS/IK743TKZSQlJVmowv9haCYiIiIii8jIyEBhsQEOdb3K3O/o5oXCYgMyMjKqubLSGJqJiIiIyCJcXFygslEi95quzP0513VQ2Sjh4uJSzZWVxtBMRERERBYRGBiIJv71cP7QTgghTPYJIXDhUByaBtRHYGCghSr8H4ZmIiIiIrIIhUKB6FEjoMk8j+Oxy5F59QIMRYXIvHoBx2OXQ5N5HmNGvgqFwvKR1fIVEBEREdFDq2PHjlgwdwaaOOTjwtYvcS5uDS5s/RJNHQuwYO6MGrNOM29uQkREREQW1bFjR4SGhuLkyZNIS0uDh4cHWrRoUSOuMJdgaCYiIiIii1MoFGjRooUxNNekwAzU4OkZ8+fPhyRJmDBhgnFbfn4+oqOj4ebmBkdHR/Tr1w+pqakmz7t48SIiIyNhb28PDw8PTJo0CcXFxSZj4uPj0bZtW6jVajRq1AgrV64s9fqLFy+Gv78/NBoNQkJCcODAgQdxmkRERERUC9TI0Hzw4EF88cUXaNmypcn2iRMnYvPmzVi/fj12796NK1eu4LnnnjPuNxgMiIyMRGFhIRISErBq1SqsXLkSM2bMMI5JSUlBZGQkunXrhmPHjmHChAl45ZVXsG3bNuOYdevWISYmBjNnzsSRI0fQqlUrREREIC0t7cGfPBERERHVODUuNOfk5GDw4MH48ssvTdbky8rKwldffYUPP/wQTzzxBNq1a4cVK1YgISEB+/btAwBs374dp06dwrfffovWrVujV69emDt3LhYvXozCwkIAwJIlSxAQEIAPPvgAzZo1w9ixY/H888/jo48+Mr7Whx9+iFdffRVDhw5F8+bNsWTJEtjb22P58uXV+2YQERERUY1Q4+Y0R0dHIzIyEuHh4Xj77beN2w8fPoyioiKEh4cbtzVt2hQNGjRAYmIiQkNDkZiYiKCgIHh6ehrHREREYPTo0UhKSkKbNm2QmJhocoySMSXTQAoLC3H48GFMnTrVuF+hUCA8PByJiYl3rbugoAAFBQXGx3q9HsCte6pX5H7psixDCFEj7rFO94e9tC7sp3VhP60He2ldqrufFXmdGhWa165diyNHjuDgwYOl9ul0OqhUKjg7O5ts9/T0hE6nM465PTCX7C/Zd68xer0eeXl5yMjIgMFgKHPM6dOn71r7vHnzMHv27FLb09PTkZ+ff9fn3UmWZWRlZUEIUeMmwFPFsJfWhf20Luyn9WAvrUt19zM7O7vcY2tMaL506RJee+017NixAxqNxtLlVNjUqVMRExNjfKzX6+Hr6wt3d3dotdpyH0eWZUiSBHd3d/7w13LspXVhP60L+2k92EvrUt39rEjmrDGh+fDhw0hLS0Pbtm2N2wwGA/bs2YNFixZh27ZtKCwsRGZmpsnV5tTUVHh5eQEAvLy8Sq1yUbK6xu1j7lxxIzU1FVqtFnZ2dlAqlVAqlWWOKTlGWdRqNdRqdantCoWiwk2XJKlSz6Oah720LuyndWE/rQd7aV2qs58VeY0a893VvXt3nDhxAseOHTN+BQcHY/DgwcZ/29raIi4uzvic5ORkXLx4EWFhYQCAsLAwnDhxwmSVix07dkCr1aJ58+bGMbcfo2RMyTFUKhXatWtnMkaWZcTFxRnHEBEREdHDpcZcaa5Tpw5atGhhss3BwQFubm7G7cOHD0dMTAxcXV2h1Woxbtw4hIWFITQ0FADQo0cPNG/eHEOGDMGCBQug0+kwbdo0REdHG68Cjxo1CosWLcLkyZMxbNgw7Nq1C99//z22bNlifN2YmBhERUUhODgYHTp0wMcff4zc3FwMHTq0mt4NIiIiIqpJakxoLo+PPvoICoUC/fr1Q0FBASIiIvDZZ58Z9yuVSsTGxmL06NEICwuDg4MDoqKiMGfOHOOYgIAAbNmyBRMnTsTChQtRv359LFu2DBEREcYx/fv3R3p6OmbMmAGdTofWrVtj69atpT4cSEREREQPB0kIISxdhDXS6/VwcnJCVlZWhT8IWFNvH0kVw15aF/bTurCf1oO9tC7V3c+K5DV+dxERERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkRo0KzZ9//jlatmwJrVYLrVaLsLAw/PLLL8b9Xbt2hSRJJl+jRo0yOcbFixcRGRkJe3t7eHh4YNKkSSguLjYZEx8fj7Zt20KtVqNRo0ZYuXJlqVoWL14Mf39/aDQahISE4MCBAw/knImIiIio5qtRobl+/fqYP38+Dh8+jEOHDuGJJ57As88+i6SkJOOYV199FVevXjV+LViwwLjPYDAgMjIShYWFSEhIwKpVq7By5UrMmDHDOCYlJQWRkZHo1q0bjh07hgkTJuCVV17Btm3bjGPWrVuHmJgYzJw5E0eOHEGrVq0QERGBtLS06nkjiIiIiKhGkYQQwtJF3Iurqyvee+89DB8+HF27dkXr1q3x8ccflzn2l19+wdNPP40rV67A09MTALBkyRK8+eabSE9Ph0qlwptvvoktW7bg5MmTxucNGDAAmZmZ2Lp1KwAgJCQE7du3x6JFiwAAsizD19cX48aNw5QpU8pVt16vh5OTE7KysqDVast9vrIsIy0tDR4eHlAoatTvNFRB7KV1YT+tC/tpPdhL61Ld/axIXrN54NVUksFgwPr165Gbm4uwsDDj9tWrV+Pbb7+Fl5cXevfujenTp8Pe3h4AkJiYiKCgIGNgBoCIiAiMHj0aSUlJaNOmDRITExEeHm7yWhEREZgwYQIAoLCwEIcPH8bUqVON+xUKBcLDw5GYmHjXegsKClBQUGB8rNfrAdxqvizL5T5vWZYhhKjQc6hmYi+tC/tpXdhP68FeWpfq7mdFXqfGheYTJ04gLCwM+fn5cHR0xMaNG9G8eXMAwKBBg+Dn5wcfHx8cP34cb775JpKTk/HDDz8AAHQ6nUlgBmB8rNPp7jlGr9cjLy8PGRkZMBgMZY45ffr0XeueN28eZs+eXWp7eno68vPzy33+siwjKysLQgj+xlzLsZfWhf20Luyn9WAvrUt19zM7O7vcY2tcaG7SpAmOHTuGrKwsbNiwAVFRUdi9ezeaN2+OESNGGMcFBQXB29sb3bt3x9mzZ9GwYUMLVg1MnToVMTExxsd6vR6+vr5wd3ev8PQMSZLg7u7OH/5ajr20LuyndWE/rQd7aV2qu58ajabcY2tcaFapVGjUqBEAoF27djh48CAWLlyIL774otTYkJAQAMCZM2fQsGFDeHl5lVrlIjU1FQDg5eVl/N+SbbeP0Wq1sLOzg1KphFKpLHNMyTHKolaroVarS21XKBQVbrokSZV6HtU87KV1YT+tC/tpPdhL61Kd/azIa9T47y5Zlk3mCt/u2LFjAABvb28AQFhYGE6cOGGyysWOHTug1WqNUzzCwsIQFxdncpwdO3YY502rVCq0a9fOZIwsy4iLizOZW01ERERED48adaV56tSp6NWrFxo0aIDs7GysWbMG8fHx2LZtG86ePYs1a9bgqaeegpubG44fP46JEyeic+fOaNmyJQCgR48eaN68OYYMGYIFCxZAp9Nh2rRpiI6ONl4FHjVqFBYtWoTJkydj2LBh2LVrF77//nts2bLFWEdMTAyioqIQHByMDh064OOPP0Zubi6GDh1qkfeFiIiIiCyrRoXmtLQ0vPTSS7h69SqcnJzQsmVLbNu2DU8++SQuXbqEnTt3GgOsr68v+vXrh2nTphmfr1QqERsbi9GjRyMsLAwODg6IiorCnDlzjGMCAgKwZcsWTJw4EQsXLkT9+vWxbNkyREREGMf0798f6enpmDFjBnQ6HVq3bo2tW7eW+nAgERERET0cavw6zbUV12km9tK6sJ/Whf20HuyldanJ6zTzu4uIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyIwaFZo///xztGzZElqtFlqtFmFhYfjll1+M+/Pz8xEdHQ03Nzc4OjqiX79+SE1NNTnGxYsXERkZCXt7e3h4eGDSpEkoLi42GRMfH4+2bdtCrVajUaNGWLlyZalaFi9eDH9/f2g0GoSEhODAgQMP5JyJiIiIqOarUaG5fv36mD9/Pg4fPoxDhw7hiSeewLPPPoukpCQAwMSJE7F582asX78eu3fvxpUrV/Dcc88Zn28wGBAZGYnCwkIkJCRg1apVWLlyJWbMmGEck5KSgsjISHTr1g3Hjh3DhAkT8Morr2Dbtm3GMevWrUNMTAxmzpyJI0eOoFWrVoiIiEBaWlr1vRlEREREVHOIGs7FxUUsW7ZMZGZmCltbW7F+/Xrjvj///FMAEImJiUIIIX7++WehUCiETqczjvn888+FVqsVBQUFQgghJk+eLAIDA01eo3///iIiIsL4uEOHDiI6Otr42GAwCB8fHzFv3rxy152VlSUAiKysrAqdr8FgEFevXhUGg6FCz6Oah720LuyndWE/rQd7aV2qu58VyWs2Fs7sd2UwGLB+/Xrk5uYiLCwMhw8fRlFREcLDw41jmjZtigYNGiAxMRGhoaFITExEUFAQPD09jWMiIiIwevRoJCUloU2bNkhMTDQ5RsmYCRMmAAAKCwtx+PBhTJ061bhfoVAgPDwciYmJd623oKAABQUFxsd6vR4AIMsyZFku93nLsgwhRIWeQzUTe2ld2E/rwn5aD/bSulR3PyvyOjUuNJ84cQJhYWHIz8+Ho6MjNm7ciObNm+PYsWNQqVRwdnY2Ge/p6QmdTgcA0Ol0JoG5ZH/JvnuN0ev1yMvLQ0ZGBgwGQ5ljTp8+fde6582bh9mzZ5fanp6ejvz8/PKdPG41LysrC0IIKBQ1avYMVRB7aV3YT+vCfloP9tK6VHc/s7Ozyz22xoXmJk2a4NixY8jKysKGDRsQFRWF3bt3W7oss6ZOnYqYmBjjY71eD19fX7i7u0Or1Zb7OLIsQ5IkuLu784e/lmMvrQv7aV3YT+vBXlqX6u6nRqMp99gaF5pVKhUaNWoEAGjXrh0OHjyIhQsXon///igsLERmZqbJ1ebU1FR4eXkBALy8vEqtclGyusbtY+5ccSM1NRVarRZ2dnZQKpVQKpVljik5RlnUajXUanWp7QqFosJNlySpUs+jmoe9tC7sp3VhP60He2ldqrOfFXmNGv/dJcsyCgoK0K5dO9ja2iIuLs64Lzk5GRcvXkRYWBgAICwsDCdOnDBZ5WLHjh3QarVo3ry5ccztxygZU3IMlUqFdu3amYyRZRlxcXHGMURERET0cKlRV5qnTp2KXr16oUGDBsjOzsaaNWsQHx+Pbdu2wcnJCcOHD0dMTAxcXV2h1Woxbtw4hIWFITQ0FADQo0cPNG/eHEOGDMGCBQug0+kwbdo0REdHG68Cjxo1CosWLcLkyZMxbNgw7Nq1C99//z22bNlirCMmJgZRUVEIDg5Ghw4d8PHHHyM3NxdDhw61yPtCRERERJZVo0JzWloaXnrpJVy9ehVOTk5o2bIltm3bhieffBIA8NFHH0GhUKBfv34oKChAREQEPvvsM+PzlUolYmNjMXr0aISFhcHBwQFRUVGYM2eOcUxAQAC2bNmCiRMnYuHChahfvz6WLVuGiIgI45j+/fsjPT0dM2bMgE6nQ+vWrbF169ZSHw4kIiIiooeDJIQQli7CGun1ejg5OSErK6vCHwRMS0uDh4cH52bVcuyldWE/rQv7aT3YS+tS3f2sSF7jdxcRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZwdBMRERERGRGpULzxYsXkZeXd9f9eXl5uHjxYqWLIiIiIiKqSSoVmgMCArBx48a77v/pp58QEBBQ6aKIiIiIiGqSSoVmIcQ99xcVFUGh4MwPIiIiIrIONuUdqNfrkZmZaXx8/fr1MqdgZGZmYu3atfD29q6SAomIiIiILK3cofmjjz7CnDlzAACSJGHChAmYMGFCmWOFEHj77berpEAiIiIiIksrd2ju0aMHHB0dIYTA5MmTMXDgQLRt29ZkjCRJcHBwQLt27RAcHFzlxRIRERERWUK5Q3NYWBjCwsIAALm5uejXrx9atGjxwAojIiIiIqopyh2abzdz5syqroOIiIiIqMaqVGgGgIyMDHz33Xc4d+4cMjIySq2oIUkSvvrqq/sukIiIiIjI0iq1Lty2bdvQoEEDjB07FsuWLcOuXbvw66+/lvqqqHnz5qF9+/aoU6cOPDw80KdPHyQnJ5uM6dq1KyRJMvkaNWqUyZiLFy8iMjIS9vb28PDwwKRJk1BcXGwyJj4+Hm3btoVarUajRo2wcuXKUvUsXrwY/v7+0Gg0CAkJwYEDByp8TkRERERU+1XqSvPrr78OLy8v/PDDDwgKCqqyYnbv3o3o6Gi0b98excXF+Pe//40ePXrg1KlTcHBwMI579dVXjSt5AIC9vb3x3waDAZGRkfDy8kJCQgKuXr2Kl156Cba2tvjPf/4DAEhJSUFkZCRGjRqF1atXIy4uDq+88gq8vb0REREBAFi3bh1iYmKwZMkShISE4OOPP0ZERASSk5Ph4eFRZedMRERERDWfJMzdqaQMGo0G7733HsaNG/cgajJKT0+Hh4cHdu/ejc6dOwO4daW5devW+Pjjj8t8zi+//IKnn34aV65cgaenJwBgyZIlePPNN5Geng6VSoU333wTW7ZswcmTJ43PGzBgADIzM7F161YAQEhICNq3b49FixYBAGRZhq+vL8aNG4cpU6aYrV2v18PJyQlZWVnQarXlPmdZlpGWlgYPDw/eIKaWYy+tC/tpXdhP68FeWpfq7mdF8lqlrjQ/+uijyM7OrlRxFZGVlQUAcHV1Ndm+evVqfPvtt/Dy8kLv3r0xffp049XmxMREBAUFGQMzAERERGD06NFISkpCmzZtkJiYiPDwcJNjRkREGNedLiwsxOHDhzF16lTjfoVCgfDwcCQmJpZZa0FBAQoKCoyP9Xo9gFvNl2W53OcsyzKEEBV6DtVM7KV1YT+tC/tpPdhL61Ld/azI61QqNL/99tuIjo7GoEGD4O/vX5lDmCXLMiZMmIDHHnvMZGm7QYMGwc/PDz4+Pjh+/DjefPNNJCcn44cffgAA6HQ6k8AMwPhYp9Pdc4xer0deXh4yMjJgMBjKHHP69Oky6503bx5mz55dant6ejry8/MrdN5ZWVkQQvA35lqOvbQu7Kd1YT+tB3tpXaq7nxW5CFyp0BwXFwd3d3c0a9YMTz75JHx9faFUKk3GSJKEhQsXVubwAIDo6GicPHkSe/fuNdk+YsQI47+DgoLg7e2N7t274+zZs2jYsGGlX+9+TZ06FTExMcbHer0evr6+cHd3r/D0DEmS4O7uzh/+Wo69tC7sp3VhP60He2ldqrufGo2m3GMrFZpL5vkCQGxsbJlj7ic0jx07FrGxsdizZw/q169/z7EhISEAgDNnzqBhw4bw8vIqtcpFamoqAMDLy8v4vyXbbh+j1WphZ2cHpVIJpVJZ5piSY9xJrVZDrVaX2q5QKCrcdEmSKvU8qnnYS+vCfloX9tN6sJfWpTr7WZHXqFQ1JfN07/VlMBgqfFwhBMaOHYuNGzdi165dCAgIMPucY8eOAQC8vb0B3Lpz4YkTJ5CWlmYcs2PHDmi1WjRv3tw4Ji4uzuQ4O3bsMN7xUKVSoV27diZjZFlGXFyccQwRERERPTwqfXOTByE6Ohpr1qzBjz/+iDp16hjnIDs5OcHOzg5nz57FmjVr8NRTT8HNzQ3Hjx/HxIkT0blzZ7Rs2RIA0KNHDzRv3hxDhgzBggULoNPpMG3aNERHRxuvBI8aNQqLFi3C5MmTMWzYMOzatQvff/89tmzZYqwlJiYGUVFRCA4ORocOHfDxxx8jNzcXQ4cOrf43hoiIiIgsqkaF5s8//xzArWXlbrdixQq8/PLLUKlU2LlzpzHA+vr6ol+/fpg2bZpxrFKpRGxsLEaPHo2wsDA4ODggKirKZF3ngIAAbNmyBRMnTsTChQtRv359LFu2zLhGMwD0798f6enpmDFjBnQ6HVq3bo2tW7eW+nAgEREREVm/Sq3TrFAoIEmS2XGVmaJhLbhOM7GX1oX9rFlkWUZSUhIyMjLg4uKCwMDACvWF/bQe7KV1sbp1mmfMmFEqNBsMBpw/fx6bNm1CkyZN8PTTT1fm0ERERPeUkJCAxUuWIvn8PygsNkBlo0QT/3qIHjUCHTt2tHR5RGSlKhWaZ82addd9V69eRWhoKBo3blzZmoiIiMqUkJCAydPnIM/JHwG9RsKhrhdyr+mQfGgnJk+fgwVzZzA4E9EDUeXXvb29vTFq1CjMnTu3qg9NREQPMVmWsXjJUuQ5+aNV72Fw8vaDja0aTt5+aPn0MOQ7++OzL77kneGI6IF4IJNFHBwckJKS8iAOTURED6mkpCQkn/8HAe3DS00RlCQJfsHdcTrlMpKSkixUIRFZsyoPzSdPnsQnn3zC6RlERFSlMjIyUFhsgEPdsm8y5ejmhcJiAzIyMqq5MiJ6GFRqTnNAQECZq2dkZmYiKysL9vb22LRp0/3WRkREZOTi4gKVjRK513Rw8vYrtT/nug4qGyVcXFwsUB0RWbtKheYuXbqU+acxFxcXNGzYEAMGDICrq2uVFEhERAQAgYGBaOJfD8mHdqLl08NM/jskhMCFQ3FoGlAfgYGBFqySiKxVpULzypUrq7gMIiKie1MoFIgeNQKTp8/B8djl8AvuDkc3L+Rc1+HCoThoMs9jzOszuFYvET0QVXJHwLy8PACAnZ1dVRyOiIioTB07dsSCuTNurdP8y1LjOs1NA+pjzOtcbo6IHpxKh+aLFy9i5syZ+Pnnn3Ht2jUAQN26dREZGYmZM2fCz6/0fDMiIqL71bFjR4SGht7XHQGJiCqqUqH59OnT6NSpEzIzM/Hkk0+iWbNmxu1ff/01Nm/ejL1796JJkyZVWiwRERFwa6pGUFCQpcsgoodIpULzlClToFAocPTo0VL/p3Xy5El0794dU6ZMwcaNG6ukSCIiIiIiS6rU37J2796N8ePHl/lbfosWLTB27FjEx8ffb21ERERERDVCpUJzUVHRPT/0Z29vj6KiokoXRURERERUk1QqNLdp0wbLli1DVlZWqX16vR5fffUV2rZte9/FERERERHVBJWa0zx79mz07NkTTZs2xdChQ423zE5OTsaqVatw/fp1LF68uEoLJSIiIiKylEqF5ieeeAI///wzJk2ahPnz55vsa926Nb755ht069atSgokIiIiIrK0Sq/THB4ejqNHj0Kn0+HChQsAAD8/P3h5eVVZcURERERENcF93xHQy8uLQZmIiIiIrFq5Pwj4999/Q6PRYPLkyfccN2nSJNjZ2SElJeW+iyMiIiIiqgnKHZo/+eQTeHl54Z133rnnuHfeeQdeXl745JNP7rs4IiIiIqKaoNyhefv27RgwYABsbW3vOU6lUmHAgAH45Zdf7rs4IiIiIqKaoNyh+eLFi2jSpEm5xj766KPGDwcSEREREdV25Q7NarUaOTk55Rqbm5sLlUpV6aKIiIiIiGqScofmpk2bYufOneUaGxcXh2bNmlW6KCIiIiKimqTcobl///6IjY3Fpk2b7jnuxx9/RGxsLPr373+/tRERERER1QjlDs1jxoxBmzZt8K9//QujR4/G77//Dr1eDyEE9Ho9fv/9d4wePRrPP/88WrVqhTFjxjzIuomIiIiIqk25b26iVquxbds2REVF4YsvvsDSpUtLjRFCoGfPnvj666+hVqurtFAiIiIiIkup0B0B3dzcEBsbiwMHDuCnn37Cn3/+Cb1eD61Wi6ZNm6J3794IDQ19ULUSEREREVlEpW6j3aFDB3To0KGqayEiIiIiqpHKPaeZiIiIiOhhxdBMRERERGQGQzMRERERkRkMzUREREREZjA0ExERERGZUaWh+dy5c/jzzz+r8pBERERERBZXqdD8ySefYMCAASbbhg4dikcffRQtWrRAcHAw0tLSqqRAIiIiIiJLq1RoXrZsGTw9PY2Pt23bhlWrVmHEiBH49NNPce7cOcyePbvKiiQiIiIisqRK3dzkwoULaNasmfHx999/j4CAAHz++ecAAJ1Oh2+++aZqKiQiIiIisrBKXWkWQpg83r59O3r16mV87O/vD51Od3+VERERERHVEJUKzY0bN8bGjRsB3JqaceXKFZPQfPnyZTg7O1dJgUREREREllap6RlvvPEGBg0aBBcXF+Tm5qJZs2aIiIgw7t+1axdat25dVTUSEREREVlUpa40DxgwANu2bcPLL7+Mt956C7/++itsbG7l7xs3bsDV1RUjRoyo8HHnzZuH9u3bo06dOvDw8ECfPn2QnJxsMiY/Px/R0dFwc3ODo6Mj+vXrh9TUVJMxFy9eRGRkJOzt7eHh4YFJkyahuLjYZEx8fDzatm0LtVqNRo0aYeXKlaXqWbx4Mfz9/aHRaBASEoIDBw5U+JyIiIiIqPar9DrNTz75JD766CPMnDkT7u7uxu2urq744Ycf0Ldv3wofc/fu3YiOjsa+ffuwY8cOFBUVoUePHsjNzTWOmThxIjZv3oz169dj9+7duHLlCp577jnjfoPBgMjISBQWFiIhIQGrVq3CypUrMWPGDOOYlJQUREZGolu3bjh27BgmTJiAV155Bdu2bTOOWbduHWJiYjBz5kwcOXIErVq1QkREBJfSIyIiInoISeLOT/VV0s2bN7F27VoUFBTgqaeegp+f330fMz09HR4eHti9ezc6d+6MrKwsuLu7Y82aNXj++ecBAKdPn0azZs2QmJiI0NBQ/PLLL3j66adx5coV47J4S5YswZtvvon09HSoVCq8+eab2LJlC06ePGl8rQEDBiAzMxNbt24FAISEhKB9+/ZYtGgRAECWZfj6+mLcuHGYMmWK2dr1ej2cnJyQlZUFrVZb7nOWZRlpaWnw8PCAQsEbNtZm7KV1YT+tC/tpPdhL61Ld/axIXqvUnObhw4dj//79xtBZWFiI0NBQ42MnJyfs2rULbdq0qczhjbKysgDcunoNAIcPH0ZRURHCw8ONY5o2bYoGDRoYQ3NiYiKCgoJM1pGOiIjA6NGjkZSUhDZt2iAxMdHkGCVjJkyYYDyfw4cPY+rUqcb9CoUC4eHhSExMLLPWgoICFBQUGB/r9XoAt5ovy3K5z1mWZQghKvQcqpnYS+vCfloX9tN6sJfWpbr7WZHXqVRo/vXXX/Hiiy8aH69ZswYnT57E6tWr0apVK/Tr1w+zZ8/Gpk2bKnN4ALdOYsKECXjsscfQokULALfWf1apVKVW5vD09DQucafT6UwCc8n+kn33GqPX65GXl4eMjAwYDIYyx5w+fbrMeufNm1fmDV3S09ORn59fzrO+dd5ZWVkQQvA35lqOvbQu7Kd1YT+tB3tpXaq7n9nZ2eUeW6nQrNPp4O/vb3y8adMmBAcHY+DAgQCAV199Fe+9915lDm0UHR2NkydPYu/evfd1nOoydepUxMTEGB/r9Xr4+vrC3d29wtMzJEmCu7s7f/hrOfbSurCf1oX9tB7spXWp7n5qNJpyj61UaHZwcEBmZiYAoLi4GPHx8Rg3bpxxf506dYxTKypj7NixiI2NxZ49e1C/fn3jdi8vLxQWFiIzM9PkanNqaiq8vLyMY+5c5aJkdY3bx9y54kZqaiq0Wi3s7OygVCqhVCrLHFNyjDup1Wqo1epS2xUKRYWbLklSpZ5HNQ97aV3YT+vCfloP9tK6VGc/K/Ialaqmbdu2+PLLL3H06FG88847yM7ORu/evY37z549W2pqQ3kIITB27Fhs3LgRu3btQkBAgMn+du3awdbWFnFxccZtycnJuHjxIsLCwgAAYWFhOHHihMkqFzt27IBWq0Xz5s2NY24/RsmYkmOoVCq0a9fOZIwsy4iLizOOISIiIqKHR6WuNL/zzjuIiIhAcHAwhBB4/vnn0aFDB+P+jRs34rHHHqvwcaOjo7FmzRr8+OOPqFOnjnEOspOTE+zs7ODk5IThw4cjJiYGrq6u0Gq1GDduHMLCwhAaGgoA6NGjB5o3b44hQ4ZgwYIF0Ol0mDZtGqKjo41XgkeNGoVFixZh8uTJGDZsGHbt2oXvv/8eW7ZsMdYSExODqKgoBAcHo0OHDvj444+Rm5uLoUOHVuYtIyIiIqJarFKhOTg4GKdPn0ZCQgKcnZ3RpUsX477MzEyMGTPGZFt5ff755wCArl27mmxfsWIFXn75ZQDARx99BIVCgX79+qGgoAARERH47LPPjGOVSiViY2MxevRohIWFwcHBAVFRUZgzZ45xTEBAALZs2YKJEydi4cKFqF+/PpYtW2ZyV8P+/fsjPT0dM2bMgE6nQ+vWrbF169ZKXUEnIiIiotqtytZpJlNcp5nYS+vCfloX9tN6sJfWpSav01zpagwGA9auXYuRI0eib9++OHHiBIBbayv/8MMPpT5ER0RERERUW1UqNGdmZuKxxx7DoEGD8N133+Gnn35Ceno6AMDR0RHjx4/HwoULq7RQIiIiIiJLqVRonjJlCpKSkrBt2zacO3cOt8/wUCqVeP755/Hzzz9XWZFERERERJZUqdC8adMmjBs3Dk8++SQkSSq1v3Hjxjh//vz91kZEREREVCNUKjRnZWWVWkP5dkVFRSguLq50UURERERENUmlQnPDhg1x5MiRu+7fvn278UYiRERERES1XaVC8yuvvILly5dj3bp1xvnMkiShoKAAb731FrZu3YqRI0dWaaFERERERJZSqZubvPbaa0hKSsLAgQPh7OwMABg0aBCuX7+O4uJijBw5EsOHD6/KOomIiIiILKZSoVmSJHz55ZeIiorChg0b8Pfff0OWZTRs2BAvvPACOnfuXNV1EhERERFZTKVCc4lOnTqhU6dOVVULEREREVGNVKk5zSkpKdi8efNd92/evJlLzhERERGR1ajUleY33ngDer0evXv3LnP/4sWL4ezsjLVr195XcURERERENUGlrjQnJibiySefvOv+7t2747fffqt0UURERERENUmlQnNGRgbq1Klz1/2Ojo64fv16pYsiIiIiIqpJKhWaGzRogN9///2u+3/77TfUr1+/0kUREREREdUklQrNAwcOxHfffYdPPvkEsiwbtxsMBixcuBDr1q3DoEGDqqxIIiIiIiJLqtQHAadOnYq9e/diwoQJeOedd9CkSRMAQHJyMtLT09G1a1e89dZbVVooEREREZGlVCo0q9VqbN++HatWrcIPP/yAs2fPAgA6dOiAfv364aWXXoJCUamL2EREtZIsy0hKSkJGRgZcXFwQGBjI/x8kIrIilb65iUKhwNChQzF06NCqrIeIqNZJSEjA4iVLkXz+HxQWG6CyUaKJfz1EjxqBjh07Wro8IiKqApW6DHLjxg0cP378rvtPnDiBjIyMShdFRFRbJCQkYPL0OTido4F/r5FoM3QO/HuNRHKuBpOnz0FCQoKlSyQioipQqdA8ceJEjBgx4q77R44ciTfeeKPSRRER1QayLGPxkqXIc/JHq97D4OTtBxtbNZy8/dDy6WHId/bHZ198afKBaSIiqp0qFZp37dqFZ5555q77e/fujZ07d1a6KCKi2iApKQnJ5/9BQPtwSJJksk+SJPgFd8fplMtISkqyUIVERFRVKhWa09PTUbdu3bvud3NzQ1paWqWLIiKqDTIyMlBYbIBDXa8y9zu6eaGw2MDpakREVqBSodnb2xtHjx696/7Dhw/D3d290kUREdUGLi4uUNkokXtNV+b+nOs6qGyUcHFxqebKiIioqlUqNPfp0wdfffUVfvrpp1L7fvzxR6xYsQJ9+/a97+KIiGqywMBANPGvh/OHdkIIYbJPCIELh+LQNKA+AgMDLVQhERFVlUotOTdr1izs3LkTffv2RatWrdCiRQsAwMmTJ/HHH3+gWbNmmD17dpUWSkRU0ygUCkSPGoHJ0+fgeOxy+AV3h6ObF3Ku63DhUBw0mecx5vUZXK+ZiMgKVOr/yZ2cnLBv3z5MmzYNRUVF2LBhAzZs2ICioiJMnz4d+/fvh7OzcxWXSkRU83Ts2BEL5s5AE4d8XPhlKY6unIkLvyxFU8cCLJg7g+s0ExFZiUrf3MTBwQGzZ8++6xXlkrtiERFZu44dOyI0NJR3BCQismKVDs1lKSgowE8//YTVq1dj69atyM/Pr8rDExHVWAqFAkFBQZYug4iIHpD7Ds1CCMTFxWH16tXYuHEj9Ho93N3dMWjQoKqoj4iIiIjI4iodmg8fPozVq1dj7dq10Ol0kCQJAwYMwNixYxEaGlpqoX8iIiIiotqqQqH53LlzWL16NVavXo2///4b9erVw+DBg9GhQwf0798f/fr1Q1hY2IOqlYiIiIjIIsodmsPCwnDgwAHUrVsXzz//PJYtW4ZOnToBAM6ePfvACiQiIiIisrRyh+b9+/cjICAAH374ISIjI2FjU6WfISQiIiIiqrHKvR7SokWL4O3tjb59+8LLywsjR47Er7/+WuouWERERERE1qbcoXnMmDHYu3cvzp49iwkTJuC3335D9+7dUa9ePcyYMQOSJPHDf0RERERklSq88n5AQACmTZuGU6dO4eDBgxgwYADi4+MhhMCYMWMwYsQIxMbGco1mIiIiIrIa93W7qnbt2uHDDz/EpUuXsH37dkRERGDdunV45plnULdu3aqqkYiIiIjIoqrkHq8KhQLh4eFYuXIlUlNT8d1336F79+5VcWgiIiIiIourktB8O41Gg/79++PHH3+s6kMTEREREVlElYdmIiIiIiJrU6NC8549e9C7d2/4+PhAkiRs2rTJZP/LL79sXKWj5Ktnz54mY27cuIHBgwdDq9XC2dkZw4cPR05OjsmY48eP4/HHH4dGo4Gvry8WLFhQqpb169ejadOm0Gg0CAoKws8//1zl50tEREREtUONCs25ublo1aoVFi9efNcxPXv2xNWrV41f3333ncn+wYMHIykpCTt27EBsbCz27NmDESNGGPfr9Xr06NEDfn5+OHz4MN577z3MmjULS5cuNY5JSEjAwIEDMXz4cBw9ehR9+vRBnz59cPLkyao/aSIiIiKq8WrUbf169eqFXr163XOMWq2Gl5dXmfv+/PNPbN26FQcPHkRwcDAA4NNPP8VTTz2F999/Hz4+Pli9ejUKCwuxfPlyqFQqBAYG4tixY/jwww+N4XrhwoXo2bMnJk2aBACYO3cuduzYgUWLFmHJkiVVeMZEREREVBvUqNBcHvHx8fDw8ICLiwueeOIJvP3223BzcwMAJCYmwtnZ2RiYASA8PBwKhQL79+9H3759kZiYiM6dO0OlUhnHRERE4N1330VGRgZcXFyQmJiImJgYk9eNiIgoNV3kdgUFBSgoKDA+1uv1AABZliHLcrnPT5ZlCCEq9ByqmdhL68J+Whf203qwl9aluvtZkdepVaG5Z8+eeO655xAQEICzZ8/i3//+N3r16oXExEQolUrodDp4eHiYPMfGxgaurq7Q6XQAAJ1Oh4CAAJMxnp6exn0uLi7Q6XTGbbePKTlGWebNm4fZs2eX2p6enl6hG73IsoysrCwIIaBQ1KjZM1RB7KV1YT+tC/tpPdhL61Ld/czOzi732FoVmgcMGGD8d1BQEFq2bImGDRsiPj7e4utCT5061eTqtF6vh6+vL9zd3aHVast9HFmWIUkS3N3d+cNfy7GX1oX9tC7sp/VgL61LdfdTo9GUe2ytCs13euSRR1C3bl2cOXMG3bt3h5eXF9LS0kzGFBcX48aNG8Z50F5eXkhNTTUZU/LY3Ji7zaUGbs21VqvVpbYrFIoKN12SpEo9j2oe9tK6sJ/Whf20HuyldanOflbkNWr1d9fly5dx/fp1eHt7AwDCwsKQmZmJw4cPG8fs2rULsiwjJCTEOGbPnj0oKioyjtmxYweaNGkCFxcX45i4uDiT19qxYwfCwsIe9CkRERERUQ1Uo0JzTk4Ojh07hmPHjgEAUlJScOzYMVy8eBE5OTmYNGkS9u3bh/PnzyMuLg7PPvssGjVqhIiICABAs2bN0LNnT7z66qs4cOAAfv/9d4wdOxYDBgyAj48PAGDQoEFQqVQYPnw4kpKSsG7dOixcuNBkasVrr72GrVu34oMPPsDp06cxa9YsHDp0CGPHjq3294SIiIiILK9GheZDhw6hTZs2aNOmDQAgJiYGbdq0wYwZM6BUKnH8+HE888wzaNy4MYYPH4527drht99+M5kWsXr1ajRt2hTdu3fHU089hU6dOpmswezk5ITt27cjJSUF7dq1w+uvv44ZM2aYrOXcsWNHrFmzBkuXLkWrVq2wYcMGbNq0CS1atKi+N4OIiIiIagxJCCEsXYQ10uv1cHJyQlZWVoU/CJiWlgYPDw/Ozarl2Evrwn5aF/bTerCX1qW6+1mRvMbvLiIiIiIiMxiaiYiIiIjMqNVLzhGRdZBlGUlJSca7cgYGBvLPrEREVKMwNBORRSUkJGDxkqVIPv8PCosNUNko0cS/HqJHjUDHjh0tXR4REREATs8gIgtKSEjA5OlzcDpHA/9eI9Fm6Bz49xqJ5FwNJk+fg4SEBEuXSEREBIChmYgsRJZlLF6yFHlO/mjVexicvP1gY6uGk7cfWj49DPnO/vjsiy8hy7KlSyUiImJoJiLLSEpKQvL5fxDQPhySJJnskyQJfsHdcTrlMpKSkixUIRER0f8wNBORRWRkZKCw2ACHul5l7nd080JhsQEZGRnVXBkREVFpDM1EZBEuLi5Q2SiRe01X5v6c6zqobJRwcXGp5sqIiIhKY2gmIosIDAxEE/96OH9oJ+68MakQAhcOxaFpQH0EBgZaqEIiIqL/YWgmIotQKBSIHjUCmszzOB67HJlXz6O4MB+ZV2891mSex5iRr3K9ZiIiqhG4TjMRWUzHjh2xYO6MW+s0/7LUuE5z04D6GPP6DK7TTERENQZDMxFZVMeOHREaGso7AhIRUY3G0ExEFqdQKBAUFGTpMoiIiO6Kl3KIiIiIiMxgaCYiIiIiMoOhmYiIiIjIDIZmIiIiIiIzGJqJiIiIiMxgaCYiIiIiMoOhmYiIiIjIDIZmIiIiIiIzGJqJiIiIiMxgaCYiIiIiMoOhmYiIiIjIDIZmIiIiIiIzbCxdABFZnizLSEpKQkZGBlxcXBAYGAiFgr9TExERlWBoJnrIJSQkYPGSpUg+/w8Kiw1Q2SjRxL8eokeNQMeOHS1dHhERUY3AS0lED7GEhARMnj4Hp3M08O81Em2GzoF/r5FIztVg8vQ5SEhIsHSJRERENQJDM9FDSpZlLF6yFHlO/mjVexicvP1gY6uGk7cfWj49DPnO/vjsiy8hy7KlSyUiIrI4hmaih1RSUhKSz/+DgPbhkCTJZJ8kSfAL7o7TKZeRlJRkoQqJiIhqDoZmoodURkYGCosNcKjrVeZ+RzcvFBYbkJGRUc2VERER1TwMzUQPKRcXF6hslMi9pitzf851HVQ2Sri4uFRzZURERDUPQzPRQyowMBBN/Ovh/KGdEEKY7BNC4MKhODQNqI/AwEALVUhERFRzMDQTPaQUCgWiR42AJvM8jscuR+bV8yguzEfm1VuPNZnnMWbkq1yvmYiICFynmeih1rFjRyyYO+PWOs2/LDWu09w0oD7GvD6D6zQTERH9P4Zmoodcx44dERoayjsCEhER3QNDMxFBoVAgKCjI0mUQERHVWLyURERERERkBkMzEREREZEZDM1ERERERGZwTjNRLSPLMj+0R0REVM1q1H9p9+zZg969e8PHxweSJGHTpk0m+4UQmDFjBry9vWFnZ4fw8HD8/fffJmNu3LiBwYMHQ6vVwtnZGcOHD0dOTo7JmOPHj+Pxxx+HRqOBr68vFixYUKqW9evXo2nTptBoNAgKCsLPP/9c5edLVFEJCQkY8vIwDI2OwdgpszA0OgZDXh6GhIQES5dGRERk1WpUaM7NzUWrVq2wePHiMvcvWLAAn3zyCZYsWYL9+/fDwcEBERERyM/PN44ZPHgwkpKSsGPHDsTGxmLPnj0YMWKEcb9er0ePHj3g5+eHw4cP47333sOsWbOwdOlS45iEhAQMHDgQw4cPx9GjR9GnTx/06dMHJ0+efHAnT2RGQkICJk+fg9M5Gvj3Gok2Q+fAv9dIJOdqMHn6HAZnIiKiB0gSd94/t4aQJAkbN25Enz59ANy6yuzj44PXX38db7zxBgAgKysLnp6eWLlyJQYMGIA///wTzZs3x8GDBxEcHAwA2Lp1K5566ilcvnwZPj4++Pzzz/HWW29Bp9NBpVIBAKZMmYJNmzbh9OnTAID+/fsjNzcXsbGxxnpCQ0PRunVrLFmypMx6CwoKUFBQYHys1+vh6+uLjIwMaLXacp+3LMtIT0+Hu7s7/+Rey1VlL2VZxsvDX8XpHA1aPf0yJEky7hNC4PiWlWjqWIAVy5by++YB4c+mdWE/rQd7aV2qu596vR4uLi7Iysoym9dqzZzmlJQU6HQ6hIeHG7c5OTkhJCQEiYmJGDBgABITE+Hs7GwMzAAQHh4OhUKB/fv3o2/fvkhMTETnzp2NgRkAIiIi8O677xrniCYmJiImJsbk9SMiIkpNF7ndvHnzMHv27FLb09PTTa6EmyPLMrKysiCE4A9/LVeVvTx//jwMkg06dQ2Hvaqw1P6OXboj7fA2HDp0CP7+/vf1WlQ2/mxaF/bTerCX1qW6+5mdnV3usbUmNOt0OgCAp6enyXZPT0/jPp1OBw8PD5P9NjY2cHV1NRkTEBBQ6hgl+1xcXKDT6e75OmWZOnWqSdAuudLs7u5e4SvNkiTxN2YrUJW9TE5Oxp9/n4P6MQ/kFKlL7S9We+DPv8+hoKCg1M8AVQ3+bFoX9tN6sJfWpbr7qdFoyj221oTmmk6tVkOtLh1mFApFhZsuSVKlnkc1T1X10tXVFTZKBXKupcLJ26/U/uzrqbBRKuDq6srvmweIP5vWhf20HuyldanOflbkNWrNd5eXlxcAIDU11WR7amqqcZ+XlxfS0tJM9hcXF+PGjRsmY8o6xu2vcbcxJfuJqltgYCCa+NfD+UM7cefHEIQQuHAoDk0D6iMwMNBCFRIREVm3WhOaAwIC4OXlhbi4OOM2vV6P/fv3IywsDAAQFhaGzMxMHD582Dhm165dkGUZISEhxjF79uxBUVGRccyOHTvQpEkTuLi4GMfc/jolY0peh6i6KRQKRI8aAU3meRyPXY7Mq+dRXJiPzKu3Hmsyz2PMyFd5lYWIiOgBqVH/hc3JycGxY8dw7NgxALc+/Hfs2DFcvHgRkiRhwoQJePvtt/HTTz/hxIkTeOmll+Dj42NcYaNZs2bo2bMnXn31VRw4cAC///47xo4diwEDBsDHxwcAMGjQIKhUKgwfPhxJSUlYt24dFi5caDIf+bXXXsPWrVvxwQcf4PTp05g1axYOHTqEsWPHVvdbQmTUsWNHLJg7A00c8nHhl6U4unImLvyyFE0dC7Bg7gx07NjR0iUSERFZL1GD/PrrrwJAqa+oqCghhBCyLIvp06cLT09PoVarRffu3UVycrLJMa5fvy4GDhwoHB0dhVarFUOHDhXZ2dkmY/744w/RqVMnoVarRb169cT8+fNL1fL999+Lxo0bC5VKJQIDA8WWLVsqdC5ZWVkCgMjKyqrQ8wwGg7h69aowGAwVeh7VPA+qlwaDQRw/flzs3r1bHD9+nN8r1YQ/m9aF/bQe7KV1qe5+ViSv1dh1mms7vV4PJyencq37dztZlpGWlgYPDw/+qb2WYy+tC/tpXdhP68FeWpfq7mdF8hq/u4iIiIiIzGBoJiIiIiIyg+s0E1UzWZaRlJRkvANlYGAg/6RIRERUwzE0E1WjhIQELF6yFMnn/0FhsQEqGyWa+NdD9KgRXP2CiIioBuPlLaJqkpCQgMnT5+B0jgb+vUaizdA58O81Esm5GkyePgcJCQmWLpGIiIjugqGZqBrIsozFS5Yiz8kfrXoPg5O3H2xs1XDy9kPLp4ch39kfn33xJWRZtnSpREREVAaGZqJqkJSUhOTz/yCgfTgkSTLZJ0kS/IK743TKZSQlJVmoQiIiIroXhmaiapCRkYHCYgMc6nqVud/RzQuFxQZkZGRUc2VERERUHgzNRNXAxcUFKhslcq/pytyfc10HlY0SLi4u1VwZERERlQdDM1E1CAwMRBP/ejh/aCfuvAmnEAIXDsWhaUB9BAYGWqhCIiIiuheGZqJqoFAoED1qBDSZ53E8djkyr55HcWE+Mq/eeqzJPI8xI1/les1EREQ1FNdpJroPFblRSceOHbFg7oxb6zT/stS4TnPTgPoY8/oMrtNMRERUgzE0E1VSZW5U0rFjR4SGhvKOgERERLUMQzNRJZTcqCTPyR8BvUbCoa4Xcq/pkHxoJyZPn4MFc2cgNDS0zOcqFAoEBQVVc8VERER0P3h5i6iCeKMSIiKihw9DM1EFlfdGJadOnbJQhURERFTVGJqJKog3KiEiInr4cE4zURnutSrG7TcqcfL2K/Vc3qiEiIjI+jA0E93B3KoYJTcqST60Ey2fHmYyReP2G5U0b94c165ds+CZEBERUVXh9Ayi25SsinE6RwP/XiPRZugc+PcaieRcDSZPn4OEhATeqISIiOghxP+qE/2/iqyKUXKjkiYO+bjwy1IcXTkTF35ZiqaOBVgwlzcqISIisjacnkH0/4yrYvQaefdVMX5ZiqSkJAQFBfFGJURERA8Rhmai/1eZVTF4oxIiIqKHAy+JEf2/21fFKAtXxSAiInp4MTQT/b+SVTHOH9oJIYTJvttXxQgMDLRQhURERGQpDM30UJJlGSdOnMCePXtw4sQJyLLMVTGIiIjorjinmR465tZhXjB3xq39vyw17m8aUB9jXueqGERERA8rhmayanfe2S8rKwtTZr6NPCd/BPQaCYe6Xsi9pkPyoZ2YPH2Ocbk4ropBREREt2NoJqt15xVlW6UCN3SXYfdoCEJ7/+9OfiXrMB+PXY7PvvgSoaGhXBWDiIiITPDSGVmlsu7s59q2F67lA/naBsjIzDQZb1yHOeUykpKSLFM0ERER1Vi80kxW5847+5VcUVbZO0JdxxWScz2cv3ABLi7OkPC/m5iUtQ4zEREREcArzWSFjHf2ax9ucmc/lb0WCgVgI8nIuZmP7Oxsk+dxHWYiIiK6G4Zmsjp3u7OfS71HoHWui+zkfZBlGUVFRcZ9XIeZiIiI7oXTM8gq3L5KRnp6OlQ2CuRe08HJ2884RlIo0LTLs9j/3yUo0F9HnpcWxY4OyLmuw4VDcbfWYX59BlfJICIiolIYmqnWu9sqGTd/24zQf0WbTNHwaBQEN696KLhyGtf3rMbVYpnrMBMREZFZDM1Uq5WsknHnusu5277FlaPx2Aeg2eNPw9HNy3hFua4yH/OXL4OTkxPXYSYiIqJyYWimWutuq2Q4efuhY9S/kbDqP8g7sx8Xcv5BIa8oExER0X1gaKZay7hKRq+RJlMwgFvrLgdGDMb5n7/A1NdGw93dnVeUiYiIqNIYmqnWKfnQX3x8PLJzb8K+rmeZ4xzdvFBkkOHu7o7OnTtXc5VERERkTRiaqVa5/UN/+qwsXE1Lx8G98WjaOgSud6yvzHWXiYiIqKowNFONV3Jl+bfffsOyb9ZC8m6OgF4jYe/qgd1LZyHt9GHIGmc0b9rEGJy57jIRERFVpVo1uXPWrFmQJMnkq2nTpsb9+fn5iI6OhpubGxwdHdGvXz+kpqaaHOPixYuIjIyEvb09PDw8MGnSJBQXF5uMiY+PR9u2baFWq9GoUSOsXLmyOk6PypCQkIAhLw/Dy2Mm4q0583ChqA4Mvm1h0Ghhq7ZDUM/BUN28hmvH4nD66D4UFeYh8+p5HI9dfmvd5ZGvcg4zERER3bdalyYCAwNx9epV49fevXuN+yZOnIjNmzdj/fr12L17N65cuYLnnnvOuN9gMCAyMhKFhYVISEjAqlWrsHLlSsyYMcM4JiUlBZGRkejWrRuOHTuGCRMm4JVXXsG2bduq9Tzpf8vJnc7RwLVtL9i61Ufd9pHINSjxZ/JfuJGRAc9HW6LdM0PhUKzH5R0rsP+Lqbjwy1I0dSzAgrlcJYOIiIiqRq2bnmFjYwMvL69S27OysvDVV19hzZo1eOKJJwAAK1asQLNmzbBv3z6EhoZi+/btOHXqFHbu3AlPT0+0bt0ac+fOxZtvvolZs2ZBpVJhyZIlCAgIwAcffAAAaNasGfbu3YuPPvoIERER1XquDytZlnHixAnMnPM2Mu280b73UKT9dQxCSHBw94Nkq4I+9SLOX7gAFxdneD7aEl19G2Hf55Mw9uUB6Nq1K1fJICIioipV60Lz33//DR8fH2g0GoSFhWHevHlo0KABDh8+jKKiIoSHhxvHNm3aFA0aNEBiYiJCQ0ORmJiIoKAgeHr+b7WFiIgIjB49GklJSWjTpg0SExNNjlEyZsKECfesq6CgAAUFBcbHer0ewK0AKMtyuc9PlmUIISr0HGuSmJiIz5cuwx9JybhwRQe3x9vi+PHjcLYBlDYSivWpULvVh6NzXdxMv4icbD20dbS4mZEGJ2dndOnSxTiH2dLv4cPeS2vDfloX9tN6sJfWpbr7WZHXqVWhOSQkBCtXrkSTJk1w9epVzJ49G48//jhOnjwJnU4HlUoFZ2dnk+d4enpCp9MBAHQ6nUlgLtlfsu9eY/R6PfLy8mBnZ1dmbfPmzcPs2bNLbU9PT0d+fn65z1GWZWRlZUEI8VBdKZVlGbt378YPm3+GcK6P4J4t4XHqCFzatkFR3k1Ihny0CQqEyEmGi783RB1b3BQaeNoWwNEmH7m6E+ga2g5169ZFWlqapU8HwMPbS2vFfloX9tN6sJfWpbr7mZ2dXe6xtSo09+rVy/jvli1bIiQkBH5+fvj+++/vGmary9SpUxETE2N8rNfr4evrC3d3d2i12nIfR5ZlSJIEd3f3h+aHPzExEZ998SW279oN4dUcrm3bw+bCdaSf/hveDbKgcquP7OuXIBW7Ivvg71CcT4Xjox1QXJiHQskG10/HQpN5HmNnTytz6o6lPIy9tGbsp3VhP60He2ldqrufGo2m3GNrVWi+k7OzMxo3bowzZ87gySefRGFhITIzM02uNqemphqDlJeXFw4cOGByjJLVNW4fc+eKG6mpqdBqtfcM5mq1Gmq1utR2hUJR4aZLklSp59U2sixj9erVeO+Tz3HTzgOygxs8O/aFrdYDuRl2KBQKpP3xK+p1GwKNkztuFtxE4yf+hUsHd+By7CdQKSU4NaiHpgG+GBMzvUZ+6O9h6eXDgv20Luyn9WAvrUt19rMir1Grv7tycnJw9uxZeHt7o127drC1tUVcXJxxf3JyMi5evIiwsDAAQFhYGE6cOGHy5/sdO3ZAq9WiefPmxjG3H6NkTMkxqGokJCTgxaihmPDmNFyWnZGnbYCCYhkKx7qwVdvBycsfdZp3wc2LJ6HbvRpFWWkwFBVAobKDk4cPGvm4Ye7kcVix+CN8veKrGhmYiYiIyHrUqivNb7zxBnr37g0/Pz9cuXIFM2fOhFKpxMCBA+Hk5IThw4cjJiYGrq6u0Gq1GDduHMLCwhAaGgoA6NGjB5o3b44hQ4ZgwYIF0Ol0mDZtGqKjo41XiUeNGoVFixZh8uTJGDZsGHbt2oXvv/8eW7ZsseSpW407ry6jjju8HnsOxfk3kXlqLzKvXoCr76NQ2drCrVkYUJwPZepppG5fgsKcTNj7eKF180cx5qP3GJSJiIio2tSq0Hz58mUMHDgQ169fh7u7Ozp16oR9+/bB3d0dAPDRRx9BoVCgX79+KCgoQEREBD777DPj85VKJWJjYzF69GiEhYXBwcEBUVFRmDNnjnFMQEAAtmzZgokTJ2LhwoWoX78+li1bxuXmqkBCQgIWff4FtsXFQ3gHwk7bAAX/XITCsS7quNlB4+yB3L8SoXGrB1snJyhVamg8AtC8UzjOblsJH5scvP/ufAQFBfFPcERERFStJCGEsHQR1kiv18PJyQlZWVkV/iBgWloaPDw8rCoY7t27F+Mn/RsZSidkpv4D715jUJx/E//sXAmXLlFw9X0UhVf/wj/xa2DjHgCPtt2hcnCBPuUP1Ln5D5zzrta6m5VYay8fVuyndWE/rQd7aV2qu58VyWu16koz1U579+7FkKGvQO/UEGrXABT8cxkFSgfYe3qZXF12ahAI7y4DofttPdJ3foWim9nQSMXo1KUjokfVrsBMRERE1oWhmR6YkvnLc+e/j/Q8GfXCI6FQKKD/ax8KstIhFDZwbhWOtL3f43rCeijbdofarT6cW3aDKjUJdjkCkyeOw+DBg3n1gIiIiCyKoZkeiNvnLxc6ekO2VaBY4wJ7xzpQ13FF3tmDsAl+FrZ1A+DdZQB0v20wubr8eJeOiB41mVeXiYiIqEZgaKYqJcsyvvnmG7zz/kLk2XtCtneDe3BPpB382eTqcnrCBugPboLdI+2grevNq8tERERUozE0U5UomYqx8NPFOHHqNFSPtIdK2wD5qVcg120IWwdn06vLnQfg2uFfkBG/CpnF+bBXyry6TERERDUWQzPdl5Kw/Mmiz3Ay+QyKJRtIjnVRp00kFAoJN88eQkHWDWiadkLOkS3Gq8uu9QLg1r43UveugycEpk95g1eXiYiIqMZiaKZKS0hIwKw5byPx6AkUyApIGi3U7gGQ8/SwdasPKG1g6+iGvLMHoG3/LJyClcg7vQcZ8atwU61G8c0suNtJWL78S3Tq1MnSp0NERER0V7ysR5WSkJCAN96ahQN/noNNvUDY2NWBwk4L+0dDAACG7OuQJAUcWnZHUeoZ6A9shLC1g3u3l+DUpAOcXV3RyMcNXzMwExERUS3A0EwVJssyZs/9D/6+UYRCSQMbj0dhKC4GFDbQBLSFwt4JOad/AyQJap+mcAn9F4qzdMjYvQqX1s1F8el4dHzUE4s/eo+BmYiIiGoFhmYqN1mW8ccff+DVV1/Fb4eOAW7+kGxsofFuBElpAxiKYMhKh2PLHii8+heyEtah6Ppl2Lj7oU6rHrC1r4N6Tmp8+PZ0fLNyOT/wR0RERLUG5zRTuezduxeTp/wbf5xMQt7Nm7BxrQeFe0PI50/AoFDC1tkbhdcvIjf5NziFDYA2pB9yT+xERvwKQJYh8rLgpbXF8uXLeHWZiIiIah1eaSazvvjiCzzz/AAcPPkXChUaKOvUhULtAIWjM5QOzriZnACHoO6QFErc/GsfMn/7FgqVPZweGwD7hu1gq1bDt24dfMPATERERLUUrzTTPe3ZswdTZsxBrlBBYWcPW3c/GHIzobTTIu+vfXBs2QP6/RsAAHXaPIXcU/HITdqFm8l7oVAqYa+yQWir5pg57d+cjkFERES1FkMz3dXevXvR74UByCmSIKltoVDbw65RCHKPb4fdI+2Qe2o3AMC+aWfkpRxGwaUkyEV5AGT41XVE/389jxdeeAFBQUFcf5mIiIhqNYZmKtPevXsxOGoYMnILobBzgiQpAKUtNP6tkXfmAIoyrkAb2g85x3eg8OpfkGUDUJQH2+I8tGvZHHvif4WNDb+9iIiIyDow1ZAJWZaxatUqTHjjTeQWA5LGEZJaAxiKAUMx5OzrcGz5JPT7/n9KRnAfiIJcFKWdRfE/SXhEK+H9Be8yMBMREZFVYbIho4SEBLw2MQZHjicBtmoo7Z2hUDtAUjvCkJ0OufAmck//BqeO/aENfR45x7cjc/cKQDYAeXp4aW3x6fvLOHeZiIiIrA5D80NOlmUkJSUhPj4es/+zABlZ2ZDUjpCUNlDYO8PG3glQ2kC+mQHDzSzc/CsBwlAE+yaPwymsP/L+PoCiy3/A26EOli9ZxNUxiIiIyCoxND/EEhISsHjJUuz5PRGXL164dWXZrg4UansAEiTZALuAtsj9cw9s3XwhKZQounEFOX9sQ+6fv0FSKKFWSngsuCVXxyAiIiKrxtD8kEpISMCkabNx6uxFZKb+c2vdZbUdJKUNJE0dKO20KM74B0WZV1Hn/29UItlqoHT2gnwzCyjIQeMAX3z7zddo1aoVV8cgIiIiq8bQ/BAqLi7GiNHROKMHClIvQ2mrARRKSCp7QMKtK8yPtEP2UR1u/pUIYShCneBnIOdlo+BKMgqvnEZ9TSG+/GIR2rRpY+nTISIiInrgGJofIrIsY+XKlRg3bjzylXZQqOyhVNtBslFDFBfdusqsdoSccw3FmVfh3OVl6PdtQO6fe3Dzr0RICiVEcQHcHVVYvuJbTscgIiKihwZD80MiISEBAwcNwsXLVwEASkdHQC4GJCUkuzqQs29ALrgJlbMX5JsZxivMzl1eguFmJvL+3o+itLNo4K7F8i8W8wN/RERE9FBhaH4I7NmzBz2fikReQTGgtIHCVg3IMmCrAeR8SApb2Dh5wJBzHYW6M7D18EdxVhpyT+3GzeQEQJKAogK0DXwUH72/gFeYiYiI6KHDT29ZuUWLFqFL1yeQV1AEhUoDhdIWUKoghAwhKSEkBUR+NmxdvKG0d4IwFKHg4kkYcjMBKCDn6SGyr2FS9HAk7t3DwExEREQPJV5ptlKyLCMyMhJbt24HlDaQbNWApIBko7z1YT9751th2dkTxRlXUHglGbaej0AuyIMhNxMiPxvCUAi1rRIffDAfo0ePtvQpEREREVkMQ7OVkWUZX3/9NcaPfw3ZObmAQgEolZAUt8KyUNgCQoZCyLBx90dx5lUotR4oztDBkHIEkq0dIAREcT78fDzw9YrlnL9MREREDz1Oz7Aie/bsgW8DfwwdPgLZOTmA0gawUUFS2kJIEgQUQFE+bJ3cIednw5CVCpVnQ0g2aiidPQGVA+TCm1AWZuPN18bgTPJpBmYiIiIi8Eqz1Zg4cSI+XrwEkA23PrgnKSEpFICtBkJSAMUFUGocIAzFKM7UQensheKMqzCcPwbJVgMIARTnw8vNGd9/t5phmYiIiOg2DM1WIDg4GIePnQAMBkBtBxiKAKUtoFACShsohYDk6AL5ZiYkWzsI2YCi9AuAyg4QMuS8LMBQjJcGD8SKFSt4dz8iIiKiOzA013LBwcE4fPjYrakYCgkQBuPd/QQAqSgfSrcGMGSnQ2HvjGL9NWOYlgrzIeQiaB3tsHnjRnTu3NnSp0NERERUIzE012ILFy7E4cNHb33YT/7/q8tCAEpbSBCw0ThCCDsYsq5C6eQNQ851SBotROFNoOAmhKEY7YPbYt++fby6TERERHQPTEq1VHFxMd6aMeNWYJZwax6zjQqQBWAogijKh9LRDZLCBpLaAUXXLkCWDZDkQkgQgJDxwr/64cCBAwzMRERERGYwLdVSP/74I3Jzc3ErMSsAperWlWZJuvVhQCFQmHYOCgdnKOy0kBzrQjIUQxQWQBTlYcK4aKxbt87Sp0FERERUK3B6Ri115coVQEi35idLEmCjBgpvwsbZA8VZqYAQkIsKUPDPaSjUDpAk3LoLoFyEgS/8Cx999JGlT4GIiIio1uCV5lrKx8cHktIWgAQYioHiAijstJDzsmHj5AlhKAZkGZAUkAtyYLipBwrzsOjjD7FmzRpLl09ERERUq/BKcy317LPPwsO9LlJTU29Ny5CLgeJCwEZ1a4UMTR2guODWl6EYXl6euHTpEmxs2HIiIiKiiuKV5lrKxsYGM6dNhUICJKUSUNhAzsuGnKcHbDRAcf6tEC0b8Npr43H16lUGZiIiIqJKYoqqxUaPHo2UlBR8+MliGCAAtQNQkAcUZQAAHLR1EPvjj+jatatlCyUiIiKq5XiluZZbsGABdm3/BR1DO8C5jgPquDijnn8jDBj8Irb/8gsDMxEREVEV4JVmK9C5c2f8Fr8LSUlJyMjIgIuLCwIDA7n+MhEREVEVYaoyY/HixfD394dGo0FISAgOHDhg6ZLKpFAoEBQUhM6dOyMoKIiBmYiIiKgKMVndw7p16xATE4OZM2fiyJEjaNWqFSIiIpCWlmbp0oiIiIioGjE038OHH36IV199FUOHDkXz5s2xZMkS2NvbY/ny5ZYujYiIiIiqEec030VhYSEOHz6MqVOnGrcpFAqEh4cjMTGx1PiCggIUFBQYH+v1egCALMuQZbncryvLMoQQFXoO1UzspXVhP60L+2k92EvrUt39rMjrMDTfxbVr12AwGODp6Wmy3dPTE6dPny41ft68eZg9e3ap7enp6cjPzy/368qyjKysLAghOC+5lmMvrQv7aV3YT+vBXlqX6u5ndnZ2uccyNFeRqVOnIiYmxvhYr9fD19cX7u7u0Gq15T6OLMuQJAnu7u784a/l2Evrwn5aF/bTerCX1qW6+6nRaMo9lqH5LurWrQulUnnrNtW3SU1NhZeXV6nxarUaarW61HaFQlHhpkuSVKnnUc3DXloX9tO6sJ/Wg720LtXZz4q8Br+77kKlUqFdu3aIi4szbpNlGXFxcQgLC7NgZURERERU3Xil+R5iYmIQFRWF4OBgdOjQAR9//DFyc3MxdOhQS5dGRERERNWIofke+vfvj/T0dMyYMQM6nQ6tW7fG1q1bS304kIiIiIisG0OzGWPHjsXYsWMtXQYRERERWRDnNBMRERERmcHQTERERERkBkMzEREREZEZnNP8gAghAPzvdtrlJcsysrOzodFouN5kLcdeWhf207qwn9aDvbQu1d3PkpxWktvuhaH5ASm5LaOvr6+FKyEiIiKie8nOzoaTk9M9x0iiPNGaKkyWZVy5cgV16tSBJEnlfl7J7bcvXbpUodtvU83DXloX9tO6sJ/Wg720LtXdTyEEsrOz4ePjY/bKNq80PyAKhQL169ev9PO1Wi1/+K0Ee2ld2E/rwn5aD/bSulRnP81dYS7ByT9ERERERGYwNBMRERERmcHQXMOo1WrMnDkTarXa0qXQfWIvrQv7aV3YT+vBXlqXmtxPfhCQiIiIiMgMXmkmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGi2gMWLF8Pf3x8ajQYhISE4cODAPcevX78eTZs2hUajQVBQEH7++edqqpTMqUgvv/zySzz++ONwcXGBi4sLwsPDzfaeqldFfzZLrF27FpIkoU+fPg+2QKqQivYzMzMT0dHR8Pb2hlqtRuPGjfn/tzVERXv58ccfo0mTJrCzs4Ovry8mTpyI/Pz8aqqW7mXPnj3o3bs3fHx8IEkSNm3aZPY58fHxaNu2LdRqNRo1aoSVK1c+8DrLJKharV27VqhUKrF8+XKRlJQkXn31VeHs7CxSU1PLHP/7778LpVIpFixYIE6dOiWmTZsmbG1txYkTJ6q5crpTRXs5aNAgsXjxYnH06FHx559/ipdfflk4OTmJy5cvV3PlVJaK9rNESkqKqFevnnj88cfFs88+Wz3FklkV7WdBQYEIDg4WTz31lNi7d69ISUkR8fHx4tixY9VcOd2por1cvXq1UKvVYvXq1SIlJUVs27ZNeHt7i4kTJ1Zz5VSWn3/+Wbz11lvihx9+EADExo0b7zn+3Llzwt7eXsTExIhTp06JTz/9VCiVSrF169bqKfg2DM3VrEOHDiI6Otr42GAwCB8fHzFv3rwyx7/wwgsiMjLSZFtISIgYOXLkA62TzKtoL+9UXFws6tSpI1atWvWgSqQKqEw/i4uLRceOHcWyZctEVFQUQ3MNUtF+fv755+KRRx4RhYWF1VUilVNFexkdHS2eeOIJk20xMTHisccee6B1UsWVJzRPnjxZBAYGmmzr37+/iIiIeICVlY3TM6pRYWEhDh8+jPDwcOM2hUKB8PBwJCYmlvmcxMREk/EAEBERcdfxVD0q08s73bx5E0VFRXB1dX1QZVI5Vbafc+bMgYeHB4YPH14dZVI5VaafP/30E8LCwhAdHQ1PT0+0aNEC//nPf2AwGKqrbCpDZXrZsWNHHD582DiF49y5c/j555/x1FNPVUvNVLVqUg6yqfZXfIhdu3YNBoMBnp6eJts9PT1x+vTpMp+j0+nKHK/T6R5YnWReZXp5pzfffBM+Pj6l/s+Aql9l+rl371589dVXOHbsWDVUSBVRmX6eO3cOu3btwuDBg/Hzzz/jzJkzGDNmDIqKijBz5szqKJvKUJleDho0CNeuXUOnTp0ghEBxcTFGjRqFf//739VRMlWxu+UgvV6PvLw82NnZVVstvNJMZAHz58/H2rVrsXHjRmg0GkuXQxWUnZ2NIUOG4Msvv0TdunUtXQ5VAVmW4eHhgaVLl6Jdu3bo378/3nrrLSxZssTSpVEFxcfH4z//+Q8+++wzHDlyBD/88AO2bNmCuXPnWro0quV4pbka1a1bF0qlEqmpqSbbU1NT4eXlVeZzvLy8KjSeqkdlelni/fffx/z587Fz5060bNnyQZZJ5VTRfp49exbnz59H7969jdtkWQYA2NjYIDk5GQ0bNnywRdNdVebn09vbG7a2tlAqlcZtzZo1g06nQ2FhIVQq1QOtmcpWmV5Onz4dQ4YMwSuvvAIACAoKQm5uLkaMGIG33noLCgWvF9Ymd8tBWq22Wq8yA7zSXK1UKhXatWuHuLg44zZZlhEXF4ewsLAynxMWFmYyHgB27Nhx1/FUPSrTSwBYsGAB5s6di61btyI4OLg6SqVyqGg/mzZtihMnTuDYsWPGr2eeeQbdunXDsWPH4OvrW53l0x0q8/P52GOP4cyZM8ZffgDgr7/+gre3NwOzBVWmlzdv3iwVjEt+GRJCPLhi6YGoUTmo2j96+JBbu3atUKvVYuXKleLUqVNixIgRwtnZWeh0OiGEEEOGDBFTpkwxjv/999+FjY2NeP/998Wff/4pZs6cySXnaoiK9nL+/PlCpVKJDRs2iKtXrxq/srOzLXUKdJuK9vNOXD2jZqloPy9evCjq1Kkjxo4dK5KTk0VsbKzw8PAQb7/9tqVOgf5fRXs5c+ZMUadOHfHdd9+Jc+fOie3bt4uGDRuKF154wVKnQLfJzs4WR48eFUePHhUAxIcffiiOHj0qLly4IIQQYsqUKWLIkCHG8SVLzk2aNEn8+eefYvHixVxy7mHy6aefigYNGgiVSiU6dOgg9u3bZ9zXpUsXERUVZTL++++/F40bNxYqlUoEBgaKLVu2VHPFdDcV6aWfn58AUOpr5syZ1V84lamiP5u3Y2iueSraz4SEBBESEiLUarV45JFHxDvvvCOKi4uruWoqS0V6WVRUJGbNmiUaNmwoNBqN8PX1FWPGjBEZGRnVXziV8uuvv5b538KSHkZFRYkuXbqUek7r1q2FSqUSjzzyiFixYkW11y2EEJIQ/FsFEREREdG9cE4zEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzEREREZEZDM1ERERERGYwNBMRERERmcHQTERERERkBkMzERFVir+/P55++ulKP3/lypWQJAnnz5+vuqKIiB4QhmYiolqkJGgeOnTI0qUQET1UGJqJiIiIiMxgaCYiIiIiMoOhmYjIihQWFmLGjBlo164dnJyc4ODggMcffxy//vqrybjz589DkiS8//77WLx4MR555BHY29ujR48euHTpEoQQmDt3LurXrw87Ozs8++yzuHHjRpmvuX37drRu3RoajQbNmzfHDz/8UGpMUlISnnjiCdjZ2aF+/fp4++23IctyqXE//vgjIiMj4ePjA7VajYYNG2Lu3LkwGAxV8wYREVWSJIQQli6CiIjKZ+XKlRg6dCgOHjyI4ODgUvuvXbuGli1bYuDAgXj00UeRnZ2Nr776CufOncOBAwfQunVrALdCc0BAAFq3bo3CwkK88soruHHjBhYsWIC2bdviiSeeQHx8PAYMGIAzZ87g008/xcsvv4zly5cbX8vf3x9qtRppaWkYNWoUPDw8sGLFCiQlJWHr1q148sknAQA6nQ4tW7ZEcXExXnvtNTg4OGDp0qWws7PD8ePHkZKSAn9/fwBA3759oVKp0L59ezg6OmLXrl1Yv3493njjDbz33nsP/P0lIrorQUREtcaKFSsEAHHw4MEy9xcXF4uCggKTbRkZGcLT01MMGzbMuC0lJUUAEO7u7iIzM9O4ferUqQKAaNWqlSgqKjJuHzhwoFCpVCI/P9+4zc/PTwAQ//3vf43bsrKyhLe3t2jTpo1x24QJEwQAsX//fuO2tLQ04eTkJACIlJQU4/abN2+WOqeRI0cKe3t7k9cmIqpunJ5BRGRFlEolVCoVAECWZdy4cQPFxcUIDg7GkSNHSo3/17/+BScnJ+PjkJAQAMCLL74IGxsbk+2FhYX4559/TJ7v4+ODvn37Gh9rtVq89NJLOHr0KHQ6HQDg559/RmhoKDp06GAc5+7ujsGDB5eqx87Ozvjv7OxsXLt2DY8//jhu3ryJ06dPV+i9ICKqSgzNRERWZtWqVWjZsiU0Gg3c3Nzg7u6OLVu2ICsrq9TYBg0amDwuCdC+vr5lbs/IyDDZ3qhRI0iSZLKtcePGAGBcf/nChQt49NFHS712kyZNSm1LSkpC37594eTkBK1WC3d3d7z44osAUGb9RETVxcb8ECIiqi2+/fZbvPzyy+jTpw8mTZoEDw8PKJVKzJs3D2fPni01XqlUlnmcu20XD/BjMJmZmejSpQu0Wi3mzJmDhg0bQqPR4MiRI3jzzTfL/OAgEVF1YWgmIrIiGzZswCOPPIIffvjB5ArwzJkzH8jrnTlzBkIIk9f666+/AMD44T4/Pz/8/fffpZ6bnJxs8jg+Ph7Xr1/HDz/8gM6dOxu3p6SkPIDKiYgqhtMziIisSMkV4tuvCO/fvx+JiYkP5PWuXLmCjRs3Gh/r9Xp8/fXXaN26Nby8vAAATz31FPbt24cDBw4Yx6Wnp2P16tVmay8sLMRnn332QGonIqoIXmkmIqqFli9fjq1bt5ba3rVrV/zwww/o27cvIiMjkZKSgiVLlqB58+bIycmp8joaN26M4cOH4+DBg/D09MTy5cuRmpqKFStWGMdMnjwZ33zzDXr27Gmy5Jyfnx+OHz9uHNexY0e4uLggKioK48ePhyRJ+Oabbx7olBAiovJiaCYiqoU+//zzMrdfvHgROTk5+OKLL7Bt2zY0b94c3377LdavX4/4+Pgqr+PRRx/Fp59+ikmTJiE5ORkBAQFYt24dIiIijGO8vb3x66+/Yty4cZg/fz7c3NwwatQo+Pj4YPjw4cZxbm5uiI2Nxeuvv45p06bBxcUFL774Irp3725yPCIiS+DNTYiIiIiIzOCcZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyAyGZiIiIiIiMxiaiYiIiIjMYGgmIiIiIjKDoZmIiIiIyIz/A0wytBjtYbbrAAAAAElFTkSuQmCC", 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", 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" ] @@ -1121,13 +1261,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "id": "00a12eea-c805-4209-9143-48fa65619873", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1160,7 +1300,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "id": "adbfeb40-76bd-4224-ac45-65c7b2b2cb7b", "metadata": {}, "outputs": [], @@ -1175,7 +1315,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "id": "1f550686-3463-4e50-be83-ceafb27512b0", "metadata": {}, "outputs": [], @@ -1193,88 +1333,27 @@ " print(\"The mu is: \", db.lambda_values[object_id])" ] }, - { - "cell_type": "code", - "execution_count": 26, - "id": "5b6dac2e-8596-4e7c-97d8-aaf9632e4154", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 90.000000\n", - "mean 0.050767\n", - "std 0.027965\n", - "min 0.025100\n", - "25% 0.030700\n", - "50% 0.040200\n", - "75% 0.060625\n", - "max 0.146900\n", - "Name: lambda, dtype: float64" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged[merged['hits']==0]['lambda'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "29393374-e379-42c8-8333-abfecc18e828", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 10.000000\n", - "mean 0.356500\n", - "std 0.259982\n", - "min 0.158500\n", - "25% 0.194825\n", - "50% 0.257200\n", - "75% 0.393875\n", - "max 1.000000\n", - "Name: lambda, dtype: float64" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged[merged['hits']>0]['lambda'].describe()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "3f314883-98ec-4a59-ba80-11cf753a423e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "__main__.SimulationConfig" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SimulationConfig.mro" - ] - }, { "cell_type": "code", "execution_count": 28, + "id": "135f4a26-a666-4fd5-8f71-1f62abd4bb81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LRUSimulation] Database Object Count: 100, Cache Size: 10, Eviction Strategy: EvictionStrategy.LRU\n" + ] + } + ], + "source": [ + "print(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, "id": "b47990b1-0231-43ac-8bc5-8340abe4a8b3", "metadata": {}, "outputs": [], @@ -1287,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "db83cad4-7cc6-4702-ae3a-d1af30a561d2", "metadata": {}, "outputs": [], diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-results/details.csv b/00_aoi_caching_simulation/results/2024-12-13/LRUResults/details.csv similarity index 100% rename from 00_aoi_caching_simulation/input/2024-12-13/lru-results/details.csv rename to 00_aoi_caching_simulation/results/2024-12-13/LRUResults/details.csv diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-results/hit_age.csv b/00_aoi_caching_simulation/results/2024-12-13/LRUResults/hit_age.csv similarity index 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