diff --git a/00_aoi_caching_simulation/00-aoi_cache_simulation.ipynb b/00_aoi_caching_simulation/00-aoi_cache_simulation.ipynb index 6f59753..a650db5 100644 --- a/00_aoi_caching_simulation/00-aoi_cache_simulation.ipynb +++ b/00_aoi_caching_simulation/00-aoi_cache_simulation.ipynb @@ -26,7 +26,7 @@ "\n", "# Constants\n", "SEED = 42\n", - "ACCESS_COUNT_LIMIT = 5000 # Total time to run the simulation\n", + "ACCESS_COUNT_LIMIT = 10 # Total time to run the simulation\n", "EXPERIMENT_BASE_DIR = \"./experiments/\"\n", "TEMP_BASE_DIR = \"./.aoi_cache/\"\n", "\n", @@ -101,10 +101,10 @@ " \"No Refresh (3.0s ttl)\": (100, 100, 0, CacheType.TTL, 3),\n", " \"No Refresh (4.0s ttl)\": (100, 100, 0, CacheType.TTL, 4),\n", " \"No Refresh (5.0s ttl)\": (100, 100, 0, CacheType.TTL, 5),\n", - " \"No Refresh (3_000_000_000.0s ttl)\": (10, 10, 0, CacheType.TTL, 3*10**9),\n", + " \"No Refresh (tests ttl)\": (3, 3, 0, CacheType.TTL, 1),\n", "}\n", "\n", - "experiment_name = \"No Refresh (0.5s ttl)\"\n", + "experiment_name = \"No Refresh (tests ttl)\"\n", "config = configurations[experiment_name]\n", "\n", "DATABASE_OBJECTS = config[0]\n", @@ -163,7 +163,7 @@ " self.request_log = {i: [] for i in range(1, DATABASE_OBJECTS + 1)}\n", " self.hits = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track hits per object\n", " self.misses = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track misses per object\n", - " self.cumulative_age = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track cumulative age per object\n", + " self.cumulative_age = {i: [] for i in range(1, DATABASE_OBJECTS + 1)} # Track cumulative age per object\n", " self.access_count = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track access count per object\n", " self.next_refresh = {} # Track the next refresh time for each cached object\n", " self.object_start_time = {} # Used as helper variable to determine the starting time of an object in the cache\n", @@ -179,19 +179,23 @@ " # Cache hit: increment hit count and update cumulative age\n", " self.hits[obj_id] += 1\n", " self.access_count[obj_id] += 1\n", - " \n", - " self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n", "\n", + " age = env.now - self.initial_fetch[obj_id]\n", + " self.cumulative_age[obj_id].append(age)\n", + "\n", + " assert len(self.cumulative_age[obj_id]) == self.access_count[obj_id], \"Age values collected and object access count do not match.\"\n", + "\n", + " print(f\"[{env.now:.2f}] {obj_id} Hit: Current Age {age:.2f} (Average: {sum(self.cumulative_age[obj_id])/len(self.cumulative_age[obj_id]):.2f}) \")\n", " # Cache hit: Refresh database object on hit\n", " # self.initial_fetch[obj_id] = env.now\n", " else:\n", - " assert obj_id not in self.storage.keys(), \"Found object in cache on miss.\"\n", - " assert obj_id not in self.initial_fetch.keys(), \"Found age timer on miss.\"\n", - " assert obj_id not in self.object_start_time.keys(), \"Found cache time ratio timer on miss.\"\n", + " assert obj_id not in self.storage.keys(), \"Found object in cache on miss. It should've been deleted.\"\n", + " assert obj_id not in self.initial_fetch.keys(), \"Found age timer on miss. It should've been deleted.\"\n", + " assert obj_id not in self.object_start_time.keys(), \"Found cache time ratio timer on miss. It should've been deleted.\"\n", " # Cache miss: Add TTL if TTL-Cache\n", " # When full cache: If non-TTL-Cache: Evict. If TTL-Cache: Don't add to Cache.\n", " if self.cache_type == CacheType.TTL:\n", - " assert obj_id not in self.ttl.keys(), \"Found cache time ratio timer on miss.\"\n", + " assert obj_id not in self.ttl.keys(), \"Found cache time ratio timer on miss. It should've been deleted.\"\n", " self.ttl[obj_id] = env.now + CACHE_TTL\n", " else:\n", " if len(self.storage) == DATABASE_OBJECTS:\n", @@ -199,7 +203,7 @@ " self.evict_oldest()\n", " elif self.cache_type == CacheType.RANDOM_EVICTION:\n", " self.evict_random()\n", - " elif self.cache-type == CacheType.TTL:\n", + " elif self.cache_type == CacheType.TTL:\n", " return\n", " \n", " # Cache miss: increment miss count\n", @@ -211,10 +215,17 @@ " self.object_start_time[obj_id] = env.now\n", " \n", " self.initial_fetch[obj_id] = env.now\n", - " self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n", + " age = env.now - self.initial_fetch[obj_id]\n", + " assert age == 0, \"Initial age at miss is not 0\"\n", + " self.cumulative_age[obj_id].append(age)\n", + "\n", + " assert len(self.cumulative_age[obj_id]) == self.access_count[obj_id], \"Age values collected and object access count do not match.\"\n", + " \n", + " print(f\"[{env.now:.2f}] {obj_id} Miss: Average Age {sum(self.cumulative_age[obj_id])/len(self.cumulative_age[obj_id]):.2f} \")\n", " \n", " if MAX_REFRESH_RATE != 0:\n", " self.next_refresh[obj_id] = env.now + np.random.exponential(1/self.db.mu_values[obj_id]) # Schedule refresh\n", + " \n", " \n", " def evict_oldest(self):\n", " \"\"\"Remove the oldest item from the cache to make space.\"\"\"\n", @@ -245,16 +256,18 @@ " \n", " def check_expired(self):\n", " \"\"\"Increment age of each cached object.\"\"\"\n", - " if self.cache_type == CacheType.TTL:\n", - " for obj_id in list(self.ttl.keys()):\n", - " if self.ttl[obj_id] <= env.now:\n", - " # Remove object if its TTL expired\n", - " # print(f\"[{env.now:.2f}] Cache: Object {obj_id} expired\")\n", - " self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n", - " del self.storage[obj_id]\n", - " del self.ttl[obj_id]\n", - " del self.initial_fetch[obj_id]\n", - " del self.object_start_time[obj_id]\n", + " evicted_objects = []\n", + " for obj_id in list(self.ttl.keys()):\n", + " if self.ttl[obj_id] <= env.now:\n", + " # Remove object if its TTL expired\n", + " print(f\"[{env.now:.2f}] Cache: Object {obj_id} expired\")\n", + " evicted_objects.append(obj_id)\n", + " self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n", + " del self.storage[obj_id]\n", + " del self.ttl[obj_id]\n", + " del self.initial_fetch[obj_id]\n", + " del self.object_start_time[obj_id]\n", + " return evicted_objects\n", "\n", " \n", " def record_cache_state(self):\n", @@ -274,8 +287,34 @@ " \"\"\"Process that ages cache objects over time, removes expired items, and refreshes based on object-specific intervals.\"\"\"\n", " while True:\n", " if cache.cache_type == CacheType.TTL:\n", - " cache.check_expired() # Remove expired objects\n", + " if cache.storage:\n", + " obj_id, next_eviction = min(cache.ttl.items(), key=lambda x: x[1])\n", + " print(f\"[{env.now:.2f}] Waiting for next eviction...\")\n", + " yield env.timeout(next_eviction - env.now) # Wait till next request (subject to change when object has been hit)\n", "\n", + " \n", + " if next_eviction == cache.ttl[obj_id]:\n", + " print(f\"[{env.now:.2f}] Object {obj_id} needs to be evicted (At time: {next_eviction})\")\n", + " evicted_objects = cache.check_expired()\n", + " print(f\"[{env.now:.2f}] Evicted {len(evicted_objects)} object(s).\")\n", + " assert len(evicted_objects) != 0, \"There was no object to evict.\"\n", + " else:\n", + " print(f\"[{env.now:.2f}] Object TTL was extended.\")\n", + " evicted_objects = cache.check_expired()\n", + " assert len(evicted_objects) == 0, \"There would've been an object to evict.\"\n", + " continue\n", + " else:\n", + " obj_id, next_request = min(cache.db.next_request.items(), key=lambda x: x[1])\n", + " print(f\"[{env.now:.2f}] Waiting for next request...\")\n", + " yield env.timeout(next_request - env.now) # Wait till next request (subject to change when object has been hit)\n", + " \n", + " evicted_objects = cache.check_expired()\n", + " assert len(evicted_objects) == 0, \"There would've been an object to evict.\"\n", + " evicted_objects = cache.check_expired()\n", + " assert len(evicted_objects) == 0, \"There would've been an object to evict.\"\n", + " else:\n", + " yield env.timeout(0.05) # Run every 0.05 second\n", + " \n", " if MAX_REFRESH_RATE != 0:\n", " # Refresh objects based on their individual refresh intervals\n", " for obj_id in list(cache.storage.keys()):\n", @@ -285,8 +324,7 @@ " # Schedule the next refresh based on the object's mu\n", " cache.next_refresh[obj_id] = env.now + np.random.exponential(1/cache.db.mu_values[obj_id])\n", " \n", - " cache.record_cache_state() # Record cache state at each time step\n", - " yield env.timeout(0.05) # Run every second" + " cache.record_cache_state() # Record cache state at each time step" ] }, { @@ -353,20 +391,154 @@ "scrolled": true }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00] Waiting for next request...\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "Progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 4997/5000 [00:28<00:00, 173.02it/s]" + "Progress: 70%|████████████████████████████████████████████████████████████████▍ | 7/10 [00:00<00:00, 1560.05it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Simulation ended after 5157.967811921139 seconds.\n", - "CPU times: user 26.4 s, sys: 3.24 s, total: 29.6 s\n", - "Wall time: 28.9 s\n" + "[0.06] Waiting for next request...\n", + "[0.06] 1 Miss: Average Age 0.00 \n", + "[0.06] Waiting for next eviction...\n", + "[0.67] 2 Miss: Average Age 0.00 \n", + "[0.68] 2 Hit: Current Age 0.01 (Average: 0.00) \n", + "[0.92] 3 Miss: Average Age 0.00 \n", + "[1.06] Object 1 needs to be evicted (At time: 1.0598387686086808)\n", + "[1.06] Cache: Object 1 expired\n", + "[1.06] Evicted 1 object(s).\n", + "[1.06] Waiting for next eviction...\n", + "[1.29] 1 Miss: Average Age 0.00 \n", + "[1.53] 1 Hit: Current Age 0.24 (Average: 0.08) \n", + "[1.68] Object 2 needs to be evicted (At time: 1.6773433908263593)\n", + "[1.68] Cache: Object 2 expired\n", + "[1.68] Evicted 1 object(s).\n", + "[1.68] Waiting for next eviction...\n", + "[1.73] 1 Hit: Current Age 0.44 (Average: 0.17) \n", + "[1.85] 2 Miss: Average Age 0.00 \n", + "[1.92] Object 3 needs to be evicted (At time: 1.9190821536272646)\n", + "[1.92] Cache: Object 3 expired\n", + "[1.92] Evicted 1 object(s).\n", + "[1.92] Waiting for next eviction...\n", + "[1.93] 1 Hit: Current Age 0.64 (Average: 0.26) \n", + "[1.97] 2 Hit: Current Age 0.12 (Average: 0.03) \n", + "[2.15] 2 Hit: Current Age 0.31 (Average: 0.09) \n", + "[2.27] 2 Hit: Current Age 0.42 (Average: 0.14) \n", + "[2.58] 2 Hit: Current Age 0.74 (Average: 0.23) \n", + "[2.63] 2 Hit: Current Age 0.79 (Average: 0.30) \n", + "[2.68] 1 Hit: Current Age 1.39 (Average: 0.45) \n", + "[2.71] 3 Miss: Average Age 0.00 \n", + "[2.73] Object TTL was extended.\n", + "[2.73] Waiting for next eviction...\n", + "[2.75] 2 Hit: Current Age 0.90 (Average: 0.37) \n", + "[3.13] 1 Hit: Current Age 1.84 (Average: 0.65) \n", + "[3.26] 2 Hit: Current Age 1.42 (Average: 0.47) \n", + "[3.31] 3 Hit: Current Age 0.61 (Average: 0.20) \n", + "[3.36] 1 Hit: Current Age 2.06 (Average: 0.83) \n", + "[3.40] 1 Hit: Current Age 2.11 (Average: 0.97) \n", + "[3.50] 2 Hit: Current Age 1.66 (Average: 0.58) \n", + "[3.57] 2 Hit: Current Age 1.72 (Average: 0.67) \n", + "[3.59] 2 Hit: Current Age 1.74 (Average: 0.76) \n", + "[3.63] Object TTL was extended.\n", + "[3.63] Waiting for next eviction...\n", + "[4.21] 3 Hit: Current Age 1.51 (Average: 0.53) \n", + "[4.31] Object TTL was extended.\n", + "[4.31] Waiting for next eviction...\n", + "[4.34] 1 Hit: Current Age 3.05 (Average: 1.18) \n", + "[4.40] Object TTL was extended.\n", + "[4.40] Waiting for next eviction...\n", + "[4.58] 2 Hit: Current Age 2.73 (Average: 0.90) \n", + "[4.59] Object TTL was extended.\n", + "[4.59] Waiting for next eviction...\n", + "[4.70] 2 Hit: Current Age 2.86 (Average: 1.03) \n", + "[4.73] 2 Hit: Current Age 2.89 (Average: 1.14) \n", + "[5.12] 2 Hit: Current Age 3.27 (Average: 1.27) \n", + "[5.21] Object 3 needs to be evicted (At time: 5.211951106151475)\n", + "[5.21] Cache: Object 3 expired\n", + "[5.21] Evicted 1 object(s).\n", + "[5.21] Waiting for next eviction...\n", + "[5.31] 2 Hit: Current Age 3.47 (Average: 1.39) \n", + "[5.34] Object 1 needs to be evicted (At time: 5.33890464876055)\n", + "[5.34] Cache: Object 1 expired\n", + "[5.34] Evicted 1 object(s).\n", + "[5.34] Waiting for next eviction...\n", + "[5.36] 2 Hit: Current Age 3.51 (Average: 1.50) \n", + "[5.58] 2 Hit: Current Age 3.74 (Average: 1.62) \n", + "[5.60] 2 Hit: Current Age 3.75 (Average: 1.72) \n", + "[5.99] 1 Miss: Average Age 1.07 \n", + "[6.29] 1 Hit: Current Age 0.30 (Average: 1.01) \n", + "[6.31] Object TTL was extended.\n", + "[6.31] Waiting for next eviction...\n", + "[6.40] 2 Hit: Current Age 4.55 (Average: 1.85) \n", + "[6.52] 2 Hit: Current Age 4.67 (Average: 1.97) \n", + "[6.60] Object TTL was extended.\n", + "[6.60] Waiting for next eviction...\n", + "[6.76] 2 Hit: Current Age 4.92 (Average: 2.09) \n", + "[7.03] 2 Hit: Current Age 5.18 (Average: 2.22) \n", + "[7.10] 2 Hit: Current Age 5.25 (Average: 2.33) \n", + "[7.29] Object 1 needs to be evicted (At time: 7.290693998414112)\n", + "[7.29] Cache: Object 1 expired\n", + "[7.29] Evicted 1 object(s).\n", + "[7.29] Waiting for next eviction...\n", + "[7.38] 1 Miss: Average Age 0.93 \n", + "[7.58] 3 Miss: Average Age 0.42 \n", + "[8.10] Object 2 needs to be evicted (At time: 8.096464406708787)\n", + "[8.10] Cache: Object 2 expired\n", + "[8.10] Evicted 1 object(s).\n", + "[8.10] Waiting for next eviction...\n", + "[8.26] 2 Miss: Average Age 2.25 \n", + "[8.38] Object 1 needs to be evicted (At time: 8.376949796979092)\n", + "[8.38] Cache: Object 1 expired\n", + "[8.38] Evicted 1 object(s).\n", + "[8.38] Waiting for next eviction...\n", + "[8.58] Object 3 needs to be evicted (At time: 8.582581448612647)\n", + "[8.58] Cache: Object 3 expired\n", + "[8.58] Evicted 1 object(s).\n", + "[8.58] Waiting for next eviction...\n", + "[8.87] 1 Miss: Average Age 0.86 \n", + "[9.01] 2 Hit: Current Age 0.75 (Average: 2.19) \n", + "[9.26] Object TTL was extended.\n", + "[9.26] Waiting for next eviction...\n", + "[9.78] 1 Hit: Current Age 0.91 (Average: 0.87) \n", + "[9.86] 2 Hit: Current Age 1.60 (Average: 2.17) \n", + "[9.87] Object TTL was extended.\n", + "[9.87] Waiting for next eviction...\n", + "[9.87] 1 Hit: Current Age 1.00 (Average: 0.87) \n", + "[9.92] 1 Hit: Current Age 1.05 (Average: 0.88) \n", + "[9.93] 2 Hit: Current Age 1.67 (Average: 2.16) \n", + "[10.10] 2 Hit: Current Age 1.84 (Average: 2.14) \n", + "[10.20] 2 Hit: Current Age 1.94 (Average: 2.14) \n", + "[10.31] 1 Hit: Current Age 1.44 (Average: 0.92) \n", + "[10.39] 3 Miss: Average Age 0.35 \n", + "[10.72] 3 Hit: Current Age 0.33 (Average: 0.35) \n", + "[10.75] 1 Hit: Current Age 1.88 (Average: 0.97) \n", + "[10.78] Object TTL was extended.\n", + "[10.78] Waiting for next eviction...\n", + "[10.79] 2 Hit: Current Age 2.53 (Average: 2.15) \n", + "[10.91] 1 Hit: Current Age 2.04 (Average: 1.02) \n", + "[10.98] 1 Hit: Current Age 2.11 (Average: 1.07) \n", + "[11.20] Object TTL was extended.\n", + "[11.20] Waiting for next eviction...\n", + "[11.33] 2 Hit: Current Age 3.07 (Average: 2.18) \n", + "[11.50] 3 Hit: Current Age 1.11 (Average: 0.44) \n", + "[11.72] Object TTL was extended.\n", + "[11.72] Waiting for next eviction...\n", + "[11.72] 3 Hit: Current Age 1.33 (Average: 0.54) \n", + "[11.73] 3 Hit: Current Age 1.34 (Average: 0.62) \n", + "Simulation ended after 11.726962652128046 seconds.\n", + "CPU times: user 9.22 ms, sys: 2.17 ms, total: 11.4 ms\n", + "Wall time: 9.47 ms\n" ] } ], @@ -386,112 +558,17 @@ "cell_type": "code", "execution_count": 10, "id": "3b6f7c1f-ea54-4496-bb9a-370cee2d2751", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Object 1: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.48\n", - "Object 2: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.78, Average Age = 0.68, Expected Age = 0.68\n", - "Object 3: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.48\n", - "Object 4: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.48\n", - "Object 5: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.37, Expected Age = 0.54\n", - "Object 6: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 7: Hit Rate = 0.92, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.92, Average Age = 1.72, Expected Age = 1.15\n", - "Object 8: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 9: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 10: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 11: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.38, Expected Age = 0.55\n", - "Object 12: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 13: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.48\n", - "Object 14: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.48\n", - "Object 15: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.37, Expected Age = 0.54\n", - "Object 16: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.38, Expected Age = 0.56\n", - "Object 17: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.17, Expected Age = 0.50\n", - "Object 18: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.47\n", - "Object 19: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.77, Average Age = 0.66, Expected Age = 0.66\n", - "Object 20: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 21: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.50\n", - "Object 22: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.50\n", - "Object 23: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 24: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.38, Expected Age = 0.55\n", - "Object 25: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.48\n", - "Object 26: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.17, Expected Age = 0.50\n", - "Object 27: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 28: Hit Rate = 0.92, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.92, Average Age = 1.69, Expected Age = 1.17\n", - "Object 29: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.48\n", - "Object 30: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.48\n", - "Object 31: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.47\n", - "Object 32: Hit Rate = 0.87, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 1.10, Expected Age = 0.87\n", - "Object 33: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.49\n", - "Object 34: Hit Rate = 0.87, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.86, Average Age = 1.08, Expected Age = 0.86\n", - "Object 35: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 36: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.47\n", - "Object 37: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.50\n", - "Object 38: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.78, Average Age = 0.64, Expected Age = 0.65\n", - "Object 39: Hit Rate = 0.98, Expected Hit Rate = 0.98, Average Time spend in Cache: 0.98, Average Age = 5.52, Expected Age = 3.19\n", - "Object 40: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.46\n", - "Object 41: Hit Rate = 0.87, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 1.09, Expected Age = 0.87\n", - "Object 42: Hit Rate = 0.87, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.86, Average Age = 1.13, Expected Age = 0.87\n", - "Object 43: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.37, Expected Age = 0.53\n", - "Object 44: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.48\n", - "Object 45: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.48\n", - "Object 46: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.49\n", - "Object 47: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 12.37, Expected Age = 7.42\n", - "Object 48: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 49: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 50: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.46\n", - "Object 51: Hit Rate = 0.92, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.92, Average Age = 1.78, Expected Age = 1.18\n", - "Object 52: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 9.09, Expected Age = 4.90\n", - "Object 53: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 54: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 55: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.17, Expected Age = 0.49\n", - "Object 56: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.14, Expected Age = 0.46\n", - "Object 57: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.15, Expected Age = 0.48\n", - "Object 58: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 349.20, Expected Age = 171.99\n", - "Object 59: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.36, Expected Age = 0.54\n", - "Object 60: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.50\n", - "Object 61: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 1577.96, Expected Age = 1295.47\n", - "Object 62: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.47\n", - "Object 63: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 64: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.36, Expected Age = 0.53\n", - "Object 65: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.48\n", - "Object 66: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 14.76, Expected Age = 7.79\n", - "Object 67: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.15, Expected Age = 0.48\n", - "Object 68: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 2580.01, Expected Age = 2578.24\n", - "Object 69: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 70: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.47\n", - "Object 71: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.39, Expected Age = 0.55\n", - "Object 72: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.46\n", - "Object 73: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.47\n", - "Object 74: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.49\n", - "Object 75: Hit Rate = 0.79, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.78, Average Age = 0.68, Expected Age = 0.68\n", - "Object 76: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.39, Expected Age = 0.54\n", - "Object 77: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.35, Expected Age = 0.55\n", - "Object 78: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.78, Average Age = 0.67, Expected Age = 0.67\n", - "Object 79: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 150.39, Expected Age = 57.20\n", - "Object 80: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.46\n", - "Object 81: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.48\n", - "Object 82: Hit Rate = 0.92, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.92, Average Age = 1.79, Expected Age = 1.19\n", - "Object 83: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.37, Expected Age = 0.55\n", - "Object 84: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.15, Expected Age = 0.48\n", - "Object 85: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.48\n", - "Object 86: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.36, Expected Age = 0.54\n", - "Object 87: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.17, Expected Age = 0.49\n", - "Object 88: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.37, Expected Age = 0.54\n", - "Object 89: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.49\n", - "Object 90: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.48\n", - "Object 91: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.36, Expected Age = 0.54\n", - "Object 92: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.37, Expected Age = 0.55\n", - "Object 93: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.78, Average Age = 0.71, Expected Age = 0.67\n", - "Object 94: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.46\n", - "Object 95: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.64, Average Age = 0.37, Expected Age = 0.54\n", - "Object 96: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.48\n", - "Object 97: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.48\n", - "Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 2576.38, Expected Age = 2574.98\n", - "Object 99: Hit Rate = 0.87, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 1.16, Expected Age = 0.88\n", - "Object 100: Hit Rate = 0.63, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.37, Expected Age = 0.52\n" + "Object 1: Hit Rate = 0.76, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.87, Average Age = 1.07, Expected Age = 1.82\n", + "Object 2: Hit Rate = 0.91, Expected Hit Rate = 0.95, Average Time spend in Cache: 0.91, Average Age = 2.18, Expected Age = 1.80\n", + "Object 3: Hit Rate = 0.60, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.50, Average Age = 0.62, Expected Age = 0.94\n" ] } ], @@ -503,7 +580,7 @@ " hit_rate = cache.hits[obj_id] / max(1, cache.access_count[obj_id]) # Avoid division by zero\n", " expected_hit_rate = 1-math.exp(-db.lambda_values[obj_id]*CACHE_TTL)\n", " avg_cache_time = cache.cumulative_cache_time[obj_id] / max(1, simulation_end_time) # Only average over hits\n", - " avg_age = cache.cumulative_age[obj_id] / max(1, cache.access_count[obj_id])\n", + " avg_age = sum(cache.cumulative_age[obj_id]) / len(cache.cumulative_age[obj_id])\n", " expected_age = hit_rate / (db.lambda_values[obj_id] * (1 - pow(hit_rate,2)))\n", " print(f\"Object {obj_id}: Hit Rate = {hit_rate:.2f}, Expected Hit Rate = {expected_hit_rate:.2f}, Average Time spend in Cache: {avg_cache_time:.2f}, Average Age = {avg_age:.2f}, Expected Age = {expected_age:.2f}\")\n", " statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate, \"expected_hit_rate\": expected_hit_rate, \"avg_cache_time\":avg_cache_time, \"avg_age\": avg_age, \"expected_age\": expected_age})" @@ -523,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "80971714-44f1-47db-9e89-85be7c885bde", "metadata": {}, "outputs": [ @@ -561,234 +638,83 @@ " avg_age\n", " expected_age\n", " age_delta\n", + " age_delta in %\n", " \n", " \n", " \n", " \n", " 1\n", - " 5182\n", - " 2090\n", - " 3092\n", + " 21\n", + " 16\n", + " 5\n", " 0\n", " 1\n", - " 0.403319\n", - " 0.393469\n", - " 0.009850\n", - " 0.406136\n", - " -0.002817\n", - " 0.164532\n", - " 0.481671\n", - " -0.317139\n", + " 0.761905\n", + " 0.632121\n", + " 0.129784\n", + " 0.870220\n", + " -0.108316\n", + " 1.072219\n", + " 1.816216\n", + " -0.743997\n", + " -0.409641\n", " \n", " \n", " 2\n", - " 15535\n", - " 12175\n", - " 3360\n", + " 34\n", + " 31\n", + " 3\n", " 0\n", " 3\n", - " 0.783714\n", - " 0.776870\n", - " 0.006844\n", - " 0.782651\n", - " 0.001063\n", - " 0.675677\n", - " 0.677147\n", - " -0.001470\n", + " 0.911765\n", + " 0.950213\n", + " -0.038448\n", + " 0.914511\n", + " -0.002746\n", + " 2.177619\n", + " 1.801709\n", + " 0.375910\n", + " 0.208641\n", " \n", " \n", " 3\n", - " 5189\n", - " 2084\n", - " 3105\n", - " 0\n", - " 1\n", - " 0.401619\n", - " 0.393469\n", - " 0.008149\n", - " 0.406382\n", - " -0.004763\n", - " 0.158811\n", - " 0.478857\n", - " -0.320046\n", - " \n", - " \n", - " 4\n", - " 5180\n", - " 2077\n", - " 3103\n", - " 0\n", - " 1\n", - " 0.400965\n", - " 0.393469\n", - " 0.007496\n", - " 0.405551\n", - " -0.004586\n", - " 0.159370\n", - " 0.477779\n", - " -0.318409\n", - " \n", - " \n", - " 5\n", - " 10422\n", - " 6645\n", - " 3777\n", - " 0\n", - " 2\n", - " 0.637594\n", - " 0.632121\n", - " 0.005473\n", - " 0.643986\n", - " -0.006392\n", - " 0.367480\n", - " 0.537170\n", - " -0.169690\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 96\n", - " 5104\n", - " 2067\n", - " 3037\n", - " 0\n", - " 1\n", - " 0.404976\n", - " 0.393469\n", - " 0.011507\n", - " 0.398209\n", - " 0.006767\n", - " 0.157796\n", - " 0.484425\n", - " -0.326629\n", - " \n", - " \n", - " 97\n", - " 5116\n", - " 2067\n", - " 3049\n", - " 0\n", - " 1\n", - " 0.404027\n", - " 0.393469\n", - " 0.010557\n", - " 0.400947\n", - " 0.003080\n", - " 0.162748\n", - " 0.482845\n", - " -0.320097\n", - " \n", - " \n", - " 98\n", - " 190549\n", - " 190548\n", - " 1\n", - " 0\n", - " 37\n", - " 0.999995\n", - " 1.000000\n", - " -0.000005\n", - " 0.999993\n", - " 0.000002\n", - " 2576.381044\n", - " 2574.979730\n", - " 1.401314\n", - " \n", - " \n", - " 99\n", - " 20617\n", - " 17902\n", - " 2715\n", - " 0\n", + " 10\n", + " 6\n", " 4\n", - " 0.868313\n", - " 0.864665\n", - " 0.003648\n", - " 0.865881\n", - " 0.002431\n", - " 1.160598\n", - " 0.882312\n", - " 0.278286\n", - " \n", - " \n", - " 100\n", - " 10178\n", - " 6425\n", - " 3753\n", " 0\n", - " 2\n", - " 0.631264\n", + " 1\n", + " 0.600000\n", " 0.632121\n", - " -0.000857\n", - " 0.634451\n", - " -0.003187\n", - " 0.365712\n", - " 0.524736\n", - " -0.159023\n", + " -0.032121\n", + " 0.498486\n", + " 0.101514\n", + " 0.623042\n", + " 0.937500\n", + " -0.314458\n", + " -0.335422\n", " \n", " \n", "\n", - "

100 rows × 13 columns

\n", "" ], "text/plain": [ - " access_count hits misses mu lambda hit_rate expected_hit_rate \\\n", - "1 5182 2090 3092 0 1 0.403319 0.393469 \n", - "2 15535 12175 3360 0 3 0.783714 0.776870 \n", - "3 5189 2084 3105 0 1 0.401619 0.393469 \n", - "4 5180 2077 3103 0 1 0.400965 0.393469 \n", - "5 10422 6645 3777 0 2 0.637594 0.632121 \n", - ".. ... ... ... .. ... ... ... \n", - "96 5104 2067 3037 0 1 0.404976 0.393469 \n", - "97 5116 2067 3049 0 1 0.404027 0.393469 \n", - "98 190549 190548 1 0 37 0.999995 1.000000 \n", - "99 20617 17902 2715 0 4 0.868313 0.864665 \n", - "100 10178 6425 3753 0 2 0.631264 0.632121 \n", + " access_count hits misses mu lambda hit_rate expected_hit_rate \\\n", + "1 21 16 5 0 1 0.761905 0.632121 \n", + "2 34 31 3 0 3 0.911765 0.950213 \n", + "3 10 6 4 0 1 0.600000 0.632121 \n", "\n", - " expected_hit_rate_delta avg_cache_time cache_time_delta avg_age \\\n", - "1 0.009850 0.406136 -0.002817 0.164532 \n", - "2 0.006844 0.782651 0.001063 0.675677 \n", - "3 0.008149 0.406382 -0.004763 0.158811 \n", - "4 0.007496 0.405551 -0.004586 0.159370 \n", - "5 0.005473 0.643986 -0.006392 0.367480 \n", - ".. ... ... ... ... \n", - "96 0.011507 0.398209 0.006767 0.157796 \n", - "97 0.010557 0.400947 0.003080 0.162748 \n", - "98 -0.000005 0.999993 0.000002 2576.381044 \n", - "99 0.003648 0.865881 0.002431 1.160598 \n", - "100 -0.000857 0.634451 -0.003187 0.365712 \n", + " expected_hit_rate_delta avg_cache_time cache_time_delta avg_age \\\n", + "1 0.129784 0.870220 -0.108316 1.072219 \n", + "2 -0.038448 0.914511 -0.002746 2.177619 \n", + "3 -0.032121 0.498486 0.101514 0.623042 \n", "\n", - " expected_age age_delta \n", - "1 0.481671 -0.317139 \n", - "2 0.677147 -0.001470 \n", - "3 0.478857 -0.320046 \n", - "4 0.477779 -0.318409 \n", - "5 0.537170 -0.169690 \n", - ".. ... ... \n", - "96 0.484425 -0.326629 \n", - "97 0.482845 -0.320097 \n", - "98 2574.979730 1.401314 \n", - "99 0.882312 0.278286 \n", - "100 0.524736 -0.159023 \n", - "\n", - "[100 rows x 13 columns]" + " expected_age age_delta age_delta in % \n", + "1 1.816216 -0.743997 -0.409641 \n", + "2 1.801709 0.375910 0.208641 \n", + "3 0.937500 -0.314458 -0.335422 " ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -817,25 +743,27 @@ "expected_age.index = range(1,DATABASE_OBJECTS + 1)\n", "age_delta = pd.DataFrame((avg_age.to_numpy()-expected_age.to_numpy()), columns=['age_delta'])\n", "age_delta.index = range(1,DATABASE_OBJECTS + 1)\n", + "age_delta_p = pd.DataFrame(age_delta.to_numpy().T[0]/expected_age.to_numpy().T[0], columns=['age_delta in %'])\n", + "age_delta_p.index = range(1,DATABASE_OBJECTS + 1)\n", "\n", "merged = access_count.merge(hits, left_index=True, right_index=True).merge(misses, left_index=True, right_index=True) \\\n", " .merge(mu, left_index=True, right_index=True).merge(lmbda, left_index=True, right_index=True) \\\n", " .merge(hit_rate, left_index=True, right_index=True).merge(expected_hit_rate, left_index=True, right_index=True).merge(expected_hit_rate_delta, left_index=True, right_index=True) \\\n", " .merge(avg_cache_time, left_index=True, right_index=True).merge(cache_time_delta, left_index=True, right_index=True) \\\n", - " .merge(avg_age, left_index=True, right_index=True).merge(expected_age, left_index=True, right_index=True).merge(age_delta, left_index=True, right_index=True)\n", + " .merge(avg_age, left_index=True, right_index=True).merge(expected_age, left_index=True, right_index=True).merge(age_delta, left_index=True, right_index=True).merge(age_delta_p, left_index=True, right_index=True)\n", "merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\", index_label=\"obj_id\")\n", "merged" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "01f8f9ee-c278-4a22-8562-ba02e77f5ddd", "metadata": {}, "outputs": [ { "data": { - "image/png": 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iXtd39xkea3IQxn5joz83kPIT2KH7xOmzbS33x9Nmq73aeg77ZOipnWWJ9P7n2wL+TfA3opGe5tGJsxqTnQxJ0q+On6yLlrYnOxlxEW376MbDxuqK5cNVW5pn+v3/O3xcLMmK2b5jazS+vp/6F2QnNR121JXm6eK9hyU7GQkXS9FQUZT65zVakfqJkl3fveGQMUndfrz1hDwjWm6XY+MG2Q/0sSs/O77BDa0VPTs48YK97J/DfUYPDPh3b6qONw7IT8p2V81p8v89ob7UdJkfHTw6qnUXp+BLRakiXLnoZtO8ISAgEb1Jw4B8/eCg0a4EMc4bWmH6+YV7tev2HtDHbSUR45v0pq602a0D4rLei5e2a1RtSVzWjcSyFVx17bXX6pprrvH/d+ONN+rmm2/WjTfeGPD3Nddc42jjd955pz788ENt375d77//vu688041NnZ3ruXk5Oj666/XZ599pq+++kp33323KisTF+CC5Hj38sVRN4bGO3jDQ5IybDwJKC/sanjfcMjYqNIUjcKcDA0syTX97rcnTQ3729YEBledtWhI5IV6gWOmDTb9PDvDusI2cXCpcnkrxVRLhXVjZmaL/YpL44CCkM6DNR0tUaaqZ3BSSR1d108DCntvx2FPN6WxLOkPpaKRk2n/LaAF7RWO7ulkCU7jzJbyqNdlFpRR08+8PI83ux2Sg/u713EZ79FIOyw6OYzcCDKf3ktHl9h47ryEbOeq/UZaPowPVpybqaMt6lmpoL7M3n70NQwgBzcM7p+vQybWRf37XBdHv6gqztHhk+t10ITe93JMtO2j+e2VOmC89flJ1oNPn+/vN1J3nTBFz5/ToXcvX5zUtESyem6zMtMD64jvXr5Yh00aFJftDRuYWkHL0RhT535QR6q4fPmIsN9npCf2ydSdxwXODrFoeFVCt9+TOe2DjrfDJ9e7ur5T57fqubM7XF1nvBXl9uyRaYpy7AfgpPXwtxv6F1iPjDmypiRxCdnN45Fmt3X3BaVbHN8lI6qj2wANqADGfptwfWbGl4xjnVmGU9B77TNqoPYaGeW9GcSqr/6IKfWaYaOP+8gp9a6koydK8QmqHDlscnzaaYe5XFdD8tiqcR5xxBHxTgcAg4y0tKgfUCayadVX3uy3OhNhd79vHBokmNNbLtWnXe3revv56am710eKNr8+trtIQT01r+jLOGdwQyqVP1zT1qzbwql0BlNbXz1U4a6RSNfPLm5KWPB4UifPjteI7qnC6/VaBpikqlS5NhLN6+155XK49KbCaUxzeXYxyrVAdvPPHnZZI0nSdwemc5sBSBTH4fwffPCBfvOb3+jvf/+7JKm1tVXLli3TwIEDI/wSsC/aipPTAtTWdpJQictI92jHziiDq6h1pgwqdOacTG0XcV197Hp3eux27uIiBJKtj2VTcUW52jP09NNkvGX7Wj3Drt4eHIzE8HhS5x7jgVdsUinYIWUl8Pj0hnOxc1eyU5A8bvaX9EZpHo92pshF3tuDq3Z5e97Uz6lxZcRfZ5TPDFJJql1awekhL44vY/YZPhg7AYlBj5fOhZISen7J1C1FqppIYY5isG+44QY1NjZqzZo1+vnPf66f//znWr16tRobG3XDDTfEK43o5ZzkUyfOaoy8kAN1hilLTp2XOtOJnbN4qLw2j0wsUykgNl9802n9ZZwLYDenwYiH0jBD9VrNXb1sdFeQbnaG/aJpT5eGfO0pnL45lZ+dekOiB0+JYcfUprI4pATRcNK4OLCHTq3jkVQYxb1z1NSuqc32H1cT8PmJM5scrSfRQ0jnuFieHBqn6W3iyWrqnN44lHe8+ps6hlQo3zAV8sTBpfHZUAyWjLA/xU1FUU4cU9I7NA6wnuIZsCvclHN22G0vG1mNwMF9b4+xneaRNKKmWJK075gai1+EiqYtEKu9R9lrM9aWxmcq5/H1pSrOsz/VUzSM0yuNrivx/z25IbntqGiDcXvrFM12JPr5YGtF99RMPWFq1HVx7ru1M+WPz6Qk319mhlS5Ny3okKpCW0HQLRXUC93gpC90567ACFSvUi9YKZLmMNdNKrzI4TQvbi4Pfx9MbkxsflGwu09rSoK36zZjkNs+o2Mb3OOoqfUxpgapan57paTufuhRtSVJS0tfjvPa0Yte8u9Fu4I4sV1ru++++7Rq1SqdfPLJ+uCDD7R582Zt3rxZH3zwgU466SStXr1a999/fzzTCuj0Ba16dN1MPX9Oh549a25M67p1xXh/RVOSTp7j7OFnvPz86InaY1hl2AfYL547Tz8/eqIeWTtDFy8dFvCdsfwOfsBrx0vnzfP/vWpOk/5y1lw9um6m4/UEe3TdDF2y97DICyZQeWF2TL8vyc3S25cudCk1znxvvxFJ2a6P8fI8YWZo0GN2RppuP2pCyOePnzbbtLPnoTUz9L39RkqSNp47L+T7YCW7O6fHDuqnjKCHJMmuxF6w51A9vX6OK+tqGJAf8G+nb07Fcwj3B1ZP1zmLhzj+XUmedeCdldtWhF5LidBWWRh5IQdG2mjcPXbqTG04dWbETt2bjzSfAz4ZjIHKPqvnNmtOm3kgZTJcvPcwVRdbPzi949iJ/r89HukZh3WM6c39/ffDxUFlnZOHFCNqinXekqG664TJjrYvSTX9Ij8QfOWC+XrqzO786bv7DA/IJ/564QLH2zVa0F6pDafGXmcIx+08/udHTzT9/LwlQ3XcjAZ3N2YhmvpaNMzKkPkWAc9OnLmwVc+e3aEH10zXn8+YrdrdeULwg1GrPDVSn4UbHWPXHTja9rK5We4GsL98/nzL7waW2H+Qb3xYnkw3HzlOhTndbahXLrDev3h6y0Ed/LULF+gnh45xdfuVYYJxFg6rDPnsoAm1CQva/M781HlpKJw5beUJfxA41KQd8oODRoe97//wnVlxTJH7/nJ2bP0k4QSX8XedMFl/+M4sR8EIM5oH6LFTZ+r+VdPdTp6pm44Yp2sPHK1+NoKbHlk7U7Na7e+LHbNbB6i2NE/ZGel6ev0crZvXoufP6TBd1kmZ4DO1qUzPn9OhJ07vrt/1L8jWhlNn6p6Tp2nMoJJokx5RlkkgQJWhvu2R9UjKke794HawJP3qeOf142C/dljHvv2oCbbKm58eNjbiMk+vnxO2TuCWPzrIs67ab6T65Wfp6fVz9JsTJ+vSvYdHvd0J9dbB9c+sn6vnz+nQzw6PrQ171wmTdZLLL90G++mhY233gVaX5OjJMwP7fv7v5Kn6zYlTHG1zz5HV+r+Tp5q2q5363copum/VND1x+uyoft9cXqBH183U0+vnqKwgO+LIVY+um6FDJqbOCzapEJQTjV8eO0kvnWc/f0gzOTFut5OjKZOcKM7N1DPrQ+ssbtZjnAQbRjOaqrFucc8p0/SbE63LmEFlgeVarG2ov5w1V3ceN0nXHTgq4PPHT5utjbufmz1x+mzL/g4rpflZAfXlZWNin61oz5HVeuL02Xp47Qz/Z3YPt3G5dosX4+xaOiq2fbF6CXPl7PiUS6ka8HzLkeP12Kkzdfmy6OsMdhhfHIhkcP+u+2tOW4UeO3Wm/vf4SfFKVkRuj3pnp683VWQl4SWaeDEb2fqVC+brRRvPLdE32A6u+t73vqczzzxT3//+91VV1f3mb1VVla6++mqdccYZuvLKK+OSSMDH4/GoqbxA/QuyY367tLww8PdWleh4DgObb9KJ6xuhJVxzsF9+lqY191dzRWFog8rwzyUjnI/qYwx8KMnLUnlRjpos3r5w0u5oKi/0V3RSRawdF155lZFukY3GuS6RSsOdml3HUldHU7C6MvNj3lRe4H/AX5CdoaKc8KPGGPe+vn9wAFJyHTl1sKqK41PxTaHTriFVRWp1OfjIiuV9FmeZLm/XTqxbdUmurRFBMpwOYxZHZiPpmT0QSaYFQys0MEyDNDvDuA8ex6O+NZUX+Mvj4HqDk066NI9HaWkejQ/zcCJcGiIpyslUtaGTNPgSL3BhtLv4jmgTubPcaX+62X3u2X0emmLcF7vxrQ0JGgXI7FJ0I5/zeDwqyM5QW2WRavp1l/PBU6U0VySmzDATz2DjSIpzrR/oD7KoF5kpCbOeeAq+bkrysgLuxKKc5KTLybWbn53hesBvdqb19s3qu1npaY7OdywG9+8bI0i41U6vChN8LSnl2rCRBPdzOBFp5E7jfefxdNWfojk+DQMKNLTavdFVwpk7xP69n5OZrtp+7t6nxnKxqjhXq+Y2q3+B+Ute/fKd56dVxbnqX5AdEiDYOKBAw3ePLBYvDSbnPngkn2in3AyOycrJTHPl4ZJVf4SVwf3zbZU3ka6bguwMVRXnhq0T+MSaswX3j4RTtDs9VcW5Gjuo1DRgw64BYV5erCzOUf+CbHUMrQgbnBxJc3lB3KeTTU/z2GpX+QQHoIyoKdHYQf0cbXNw/3yNqClxpZ6QnZGu9upi1ZbmRfVCab/8LDWVF/j7tCJNfdhUnrz6vZmeGVrVNfJvrC94uP38wiowya0RYTzyqNKkDlaSm+XaeYxlFESneU1OZrqGVtkvd2NtQ5UX5WhSQ1lIf1xdWZ7KCrKVn52h2tI8x/n6oLI8/wvNkjv9Ow3981VbmqcWQ3+A3es10mnIc/nFqHDKLIJ90uPUP5tq+atP+8AiNQwoMA2yd1O0bbCGAQVBfbw9WzL7spzqofHNpsz2pSgnU/3CzNaDvsV2DvjCCy/osMMOs/z+sMMO0wsvvOBKooB4N5ZThVl549t3NwqjVDuMKZacmIU9R96e+8aUU9Z7aX7Gzabw6G3XhluCj0tfyRtTRbwPd9/IIVKHVeeNG+fZuO5Y1rcjaHh/JyJ1fJvpicWU27dlKmSriUpCvLZjtV63+oCSeZnG+9ykwvUXjWgflvcZJoeHI5a6Ajuse+hN6ZJI12lfODrJzN+i2XSqnZOAu8ljPqWGnTZtvM5DPF+eRN/jdv9MKlav7LQxU6nvM4WSElfB+xmPcxD3dorF+tM87p3HWHYhmt/21Ladkdeb+LpQuOMWqdxOhXu+F5z2lESdredJgdvRNfR5IRLbwVU7d+5UZqZ1RHVmZqZ27tzpSqKARBWdyc4iwzdSo0tdjuGNhXjPDes4cjrF6kTh3ji3I9LhzTEZzcVN2XF+QyAWTkd9CVYU4Y3OcMe+NwUgBe+L0z2LVz3Qd+1F09BJxtmxGl0t0YLfqPpq+w7LZXvPVZwiHBzQrAznR7/ExlQzdnTuiP6mDRnI0k5HuKT87NS4P+zo6uhzd51mdbG8OJffwRJVbJleE24EF1rsQF5QXWDztm9Nl8uMMHR4ZhLf1It3XS4vK/bR4hItI82jzKC3c3tC3cvtJH75jXUZ/vhbn4Z8Rt+cueDpvZ0we2EjErO321NpROCU5zH+Gd1xy0mRenkw36iwiXzTPfgazo+iTDC7fJOZ32QHlZtW0wJGZPKzaF4kCF2Hw2Qk4wF/ArMkVwMyeklWGk3Z4hNtkRavvj03zq6dAVlSqYqTyFFskin4mnF7WnPJvRdlrFi1uT0ej+no6FFxsA+FORkBI8T3lOqh2yPuB3OjnDAfXKD773DtAeM9bVb3/Loz+c+je8q14rZwfdpu6El9lUZ99XqQele7muAqRGK79G1vb9fvf/97y+9/97vfqb293ZVEoY8xfZMt9LNT5jS5vFmvrY4SY1qCh3sOrvsdP6Mh4N/3njIt7LpbKqyHVo22D2ptR4v/b7MCbWRtiQ6eWGdrXcYk3HjYWLVWFGqcYXjrUTUlttZTv3to66woKvyRjqEk3XHsRLVUFGj+UGfTfZwws1Gn79GqETXFGl1X4v/c7rDBka6fE2eZz7ndXF5ge973tjDTrv32pKn+v8NdS4n0093XyY8OHh3y3W1HTbD8XfAwxT89bGzAvycODpwiy3jsgxt6xjWdu2RohBSntuA7ON6dG80Rhr8/cHytWioK9MvjQucu33tUtSY3lAXkQVYmNTif8mzF1HpNaSzT/xw9wfH1ftPhY2wt9535kdMei+/u0z0fvUcevfDe5wHfLxs90PRBvlXgwXkuXN9HTqmPeR2J0mpjSrGR4aY/sXn/zGgeYDNF0lmL2tReXaTDJg0y3cywgdZ5fX+TIcVjGbnKuOXgPDTcsbt82Qi1VRbqugNHSZJKTYY4jtd1EnzPtVUW6jcnTvZ3Fl+6zzDH6/TKqxkt9s9hTma6lo0eqAXtFbpy+QgNqSrSuXv27LLDikfSxUvbNdRmHSRW5yweEvBvq6kPFg6r0vj6fpb1uHlDKwLqn9HyXeN2XLhXu9qri7RqbrP/M7O6jV23HDne//dBE7rq4cvH1OisRUNClm2pKNAle4de+3YDmI4Lao+EE66e6d+u4e9ZrQM0rLpYe42q1thB/WJun5XlZ+nq/UeGXWZEmHw9uG1mtHJ2Vz38+Jldx8PO0Zsw2H795L9fmQcLStLr/9ka8ll+dkbcX3zxSUafplkdanLQ9GBmwk0j5UR6mkfnLB4SUO6WmZRnly8fYfpbN9x85DhX1uMTabrCaK2a26wxdSUhU3lctmy4xS+6xTpSZ0P/fJ1tku+ZWT6mJuSzfUYP1IiaYp2xR5utdZhdA1bm7S6DTna538nJcbpi39DrMxI3Ao4OGFerHx08Wm2VhbpvVWD/yx3HTtT8oRW2p0lbMbXe/7dHHtPgKjspDn6g4fVKFUWB+YXTF2huO2qC42Bgswcr1x4wKuSzeOe7s1vt122l7rIvHtoqC7VHe6Xt5YOnyIsleMlntaF+FsyqblNXGnnKvenN/f0BlnbzKqm7T/JXx0+2tXxwWg6f3NWWdOPYGH3yxfaY15Gdka5lYwa6kJpuVlOjusHY59KTOJ26zdee8LVfz1k81FE+NKSqSBtOnamRYab4K83P1pIRVXGZHnloVZFOW9AqSbrhkMD+uvQ0j1Z3WN/jPpHah0dNHewoEPy2FRPUbpiyOM3jUYdhauGWigLdadIXamX/caH1mGDHzWgIO83i4hFVWtPRrBE1xZbnKtqpD8MFdQb2uUe1eusV7mY8M9PD9MGdF6Fvxo1+AjvOWmSv7hmJWXn+w4Oc9zMMrSqKa93DTh36xfc2W34XXEcbUlWk79ms566b16K2ykJdtNR5f6BbLl7aHjbIM1w/it3Tkoz++O/vN1JtlYVaNtp5uR7czxxsZssADRsY3ynJpfjc82bt4ET13aDnsh3psHLlSp199tm64YYbtGNHd1Tqjh07dP311+ucc87RSSedFJdEAk+dOUenzm8N+Ty4gu80z3MagRocMX35ssBKwfEzA4Np6srydLmNTkoz0bwZcM7iIQEd1MEjM504q1G/Xzk1qsbm/PZKPbR2hloNlQc7jb93L1+sP5422/L7x06dqVcumG/6u3cvXxxQKI+uK9G7ly8OWXZKY389vHambjy8u0P78mXDIz4wKsnN0kmzmvR/J09TgWF0hf85eqKtQK3gjo/g4LqSPPOKaGVxjh5YPV03HRG5A/7BNTN04Pha0++GVhf5j9PDa2dGXJdTkR52+Rgv1QW7r5O2ytAHtzN3P+y2c2m3VwdWxvKy0k3PfSR7jqjy/12Y0/NGhwjmtDM40uK3BwW8RRqhY0pT1702pi60InvtgaP1y+MmRewE8XikS6PIg87fs113HDtJ05sHmF7vK2c36t3LF+sHJo3SSCOh+Rw4ITDwNPjwXWzysNuuGw8bq0Fl3WWWxyPtDIqjudrQUW88dzcbHsobHTVtcNTp8blgr8DA+Iw0jxYOs99ZHo36sjzT8xTJQ2tnRFxmtMm1KTkbXSHDQSDwcTMadd+q6ZZzrp+9yLojaGpTaCfYjhhab8ZrZkHQAw+rDlGvV6rvn68H18zQ0lFdjWuzzrkL9mrXITYDs504eU5gfvHgmhkaO6hUb16yUO9evlgLh1VZ/DK8uW3ljpa/+oBR+ulh47T/+Fo9sHp62IANSdpzZHVU6UoFh02u1/2rpydkW1XF4Y+jT1ZGmu46YYpuPHycaSdvRnqa7YdV4Swebv96OmJKve5bNT0gCHLJiOjPe8OA7nvw/D2H6t3LF+uq/UeaBk/ct2q6DjUEbEby7uWLA4JwixzUdx5cM0Pj6+13Tt26YoLS0jzKyUzXb06cYto+c2LjufO0zCSAwujXJ0yx/K4xzMP+0xa06d3LF2v9QvsPRz2SrjnAXv3XKa+82hWUx1+1X3TbevfyxWEfxMRi71HRXefB+bkk7Tu2JiTI0sjr9cY06pmxbK8ozNYx0xt07ynd+dtwk8A8s/IwXHCVk/bHnLYKy+XNzld5hMCyp9fPNQ3iiIbxAce6eS26+6SpyjbUd969fLH/QW0wY7+E1emyU3s5aEKdHvvOLFVHKGMlacmIKl1l0hYtyM7Q/508zfIlpmDn72XvBdCr9hvpDzYzCzI32m9saJ4VLgjUSbfO4P75tq65dfO6gxlNB6V0eFstH1ujJSOq9eCaGWqvLvb3Mbx7+WJNaeyvGw8fp0fX2etvKDa0u3Z5vZZ9bpHSaDWd4GsXLvD/207bwGh8fT/nI0EH/fsnh47R3iYPpNx8wGnWbjHbps+7ly/W+UEPn9cvHGJ6LfnOq080LY8H18zQTw4bGxBg3TjAOvgimpcsI1k7r8Wyn+zBNebXhdmLSUdNHaxH13Uvf+Nh3X10x85o0PdNyunAUVS6+Pokx9XbC9I2BiFKUqHFCwixqulnry4eydX7j9KtK8z7JKJx7hL7dTOn6vvna/1Cd4IgzDgJurOjujgnqr5O32wBvvZrcNkaLgBRkh5YPV2NAwr0+5VTw2zfqx8dPCaqwI9wpjX11/2rp6u8qKsdtGh4VUjgSpnJi2g+aZ6uvOzXJ1q3EfYZPVDn7RkYcLZ4eJV+drh5P/wbF++hkbUlAc85PJKqS7rbag+vnalJEV4cMG7v8Mn1YZeVpLMWDdHvdp+D4If7g8rydP3BY7Smo0X/d/I0/X7lVNMXTY39V/0cjKj+kEVembB4AsPBCjeSdU2/8IGxbgSZ+4QLJJw7pMLWsYkU+H3LisB++HcvX2zZv2S2Z/uNrdG7ly+Oe59OeprHMhDWTv/q2nmBL948sHq69htnXm4HWzZmoB5cM0O1hkDk6w/uDsIMV+dwS6R+M6u6hhMX7NUeVf5vxc7LqfuOrdGDa2aoxkbAubGvYtmYgSH9zMHCDaoQLV+91dhv9usTp4TUZ4NFyhYm1JeqtrS73DRrB0c7ap+b5xSpzXbP6xFHHKFXX31VJ598stavX6/GxkZ5vV7985//1JdffqlVq1bpyCOPjGNS0ZfYrRdFPcS4S7+PddjrcFuPJmXBeX6sFUyzX/eVoF07hy6kjHV4uJ2+mZSqrN6uc7WDMWhltis4veMQ+7m9O07PUcgoYVEkyJPCs6ZHSlcsw9uaXbF2r2OzxZKVF/fkMiAZ1124bN7sqx07YwiuiuI3br8d3VfYvnfjnA6n4jQrYNzvLVenrOkBYj2esQRppiK36pN2g3jiVUvxeqWdfexa9iq+05MZy7BYgrRimZqwJzMLeomU3/bkI+V230vqCD0rycxqjMGKO3ZZB1dFYtVXZ/zU8YtH8sRcpiRiGtzo2tjRcW3aQ4/90tO9bbqznnDrTHSe5/a96+Yx6tn5pHt60YxHSWPnGIbLUaJ+1uHwZ4k+13bu/4SU797AXiJXBq4y+ayn3Urh0pu6vdyxsqqLRb4qYnkmaVbXSkbe21vPqhTFvtnMCFKljznN44m574VpARGJo2E8vv/972vffffVL3/5S7311luSpJkzZ+rAAw/UpEn2h8UEIrFbKYk1k7PzeyeFjeO34MJsPppdC94ft6Y4MEpmueJ0206Wj2a/Qqejc3a8U30e4lROXrjTFZDuHl4PCunci/M5SdQ5T0SndMD27C4XIV2xPHwze1gVLsC3F13GCWV1Cj2ecNe3u9ej8Tpyeq3HMi1guOspVRq4iZCIeorbm0hUZ1yi8143uZH0nrL/VukMF/TQdQ11fe/05ZFU7wwO35Htznp84pl9eL3e2F/sMegJl3O4UWvc8E1n+DLT7qYT8cKLaZB93LcaXjSXo1X+5P4LIO6s0f6LDMk+G84Yj4475WPs6/Axtpd27vKGjNRrVzzOSVd7wNnOBqfD6mFhssvSZLy4Z7+N7d42Y7ks7LW8I+sBxW98uNx/HU89oY7kE69DF/IusCe+fepuC1d/jDZow6Pw/UWhn3kiHoPgMiXZZYGTMi7srrl87iNfSzZfgonz4Q1X93BzhCy7Uj4vC/cyaQxpNzsPyciPoo7jTPXzFgW7hz9e58npIU3zSDvDfO+VN2J+nWp1G6Qex2PzTpo0Sdddd53uv/9+3X///br22msJrILrxtSVBPw7z2JIzeBMbmRNiely1tvp52j5YJGGWI6U8YebzmHiYHvDSRsZp5ySQudHdqNsjzTVXjhlJsOJOimnnC47JsIcvCVhhsu10yDKzYpumrnhu6c6rDSZCiaVGIfeNQ6/GW64XjuiqZs0Bw19bLz3x+4+z77K65TGrimtgqfa6R112/juRaQhru1M4yGF5uFG05v7x2Uv3Kj0BqcrOI+utTFsrpWKosDrcfjAYo0IUwYYp0StKArNO82mkok0vYwdkxvDXwMTdpdNkYa6NuN72DJ2UOTyLctkajA7hlaFTknqEy5fDy4v3RLuOUd2RugxND54dzqP/IY3Prb8zqq+Y3bfWCW5NYbyP5wJu6fPaDAZ2jsnaHpjO7f5mLp+ce/MCHedGfWzmB44WF1Z9HlLrIZW29uXcApsTkNnZ1tTdudBxnyvtjQvaYFRZnVXN4V7ESK4LB1i87oLN52BmVQPvgx37ocbykpXeN2bQidYU3mBBgXd67Ec+Xh19rm6Wm/iAojmmEwHa/f6SNYLLxOiaO9Hy1e/M07vMWxgaJ5itpxbnBzmIVVddQ4340UmDbau4w50cN87vabD5bHRPoRtGNDdNm6tiL1+5mY92DjNfE5mmiYM7qqDGqes9Xgi94X4jlrl7jaUr50aME1lFOkzuw7DXWfBZ89sOl8pfN+SJE2M0M42Mh5Dn0hTVtdF3U6NPpc2Ts07tbHMsrw2m6I1WsGvN8aqKcz0wpL59eLkXCabm8EAbtQX211odyRapHvbDt9UbfEu9wuyu/PZ4BcDpzX1j3q9/VzuKzGrn9UHPc8Ie+06uKyNi452+AxoWHVxxOvejdH9rX47zqTfzCo1vut0gs2pScMJ3obTtqX5OiOMjBpLVuViXTFcX2Vwn4evv665vMC0L6unCxeQmbu7njIgTB908D0dzMkUlsFc7wOwEG0Z2lzuft+pr9/IrbbRNzvChR6FiuV5cDJMjaHM8yG4CpG4P/E54JBZBev0Pdp06rwWrelo1jUHjFSJxcMp45sMp85r0anzW0yXs3L24iFaN69FG06d6SzRu01uKNPxMxtsL3/ukqEBnUqnLWjVunktumTv0Dlxr1g+wvZDlP89bpLOXjREC9orJEk/PWysrlw+QrWlefrlse4GPx4ysU6n79Gq36+cKilwPt+y/Cyt6bCe131w/3xdvmx4wDzJ8eKRdNaiNstj6Ds+/uXDdahZFKYluc4qgrNaB+g781t0ypyuY9RSUajv7jM8wq/s+8FBo0M+u/ukKTphZqNWzW3Wr0+YrDUdzSEPdqyMry/VhXu163+OnqCjpw32f241L3swNx9THGwy97HPuUuGat28Fj26rus+vuaAUVrb0aJfHT85IBEej0e/OGZixG2VutRxYGeua6esrtN7T5mmrPS0mOcdP31BqwaV5enEWY06YFytpjYFdhyOt9lQ/8lhYwP+feq8Fq2b16LKohxdsXxE2PstOJjCqc4doa9H254KyLDY+Pp+On2PtoDvrQKPfrdyqtZ2mJc/h00apIuWtvs7ch5cM13fmd+iVXObdehE6+t65ewmfWd+ix5YPV1tlUW6dJ9huvnIcbrlyPG6dJ9hpnnbb06cEmkXdeD48HPcX3vAqLDff3fv4Vrb0RIw97zd9ubDa2do3bwWnb/X0LDLXbXfSD126kyt7WjRytmNpsvMaSvXGYbzM7AkV1csH649hnXPAR+cJxrTuUfQXPH1/fN1xfLhujHo2jVz3IwGjakr0QOGY2Al+Nqb0limhgH5umDPoco1CVDrNEwL+ONDI6fFruNm2K+rWDl4Qlf5/7uVU3XZsuH6yaFjdNqC1pjXe/0hY7Smo1k/Pzo0f87LyjAt28I5Z8mQmNPkY9UJeMz0waafB6sqztH39h2hGw4xr/f46m/zh1YEfH7lviMirvtHB4/WNQeMtJUOM/eeMk2nLWjV0dMG65fHTvKXHyfMbNSj62bqhzaO+5FT6nX1/iNtPYSf3txfx0wfrDsilMNX7d9Vhv/6hCm60+L4RCuaeomv7vr/Dh9nvk6PtH5hm46aMsj2Oo31ysz00DLPl86fHDZWazqa9bPDx+m0Ba06apq9627pqIEB+aMTZp1m0Qa22clPfX57UmD59Zez5mr9wjb9+oTJpsvfftQESdLJc5psb8PubozbXf/1bcPKSMPD4uB8ynjN3n3SFJ2xR5v2HVurRcOqAtqC4UyJEOxsNL25v2sPLK0OU7ig6gPGmdctgkeuui3CMbVy0xHjdNmy4Xpg9XSdOKtRK2c36tF1ge2RtfO662EPrO6qa508p0n3njJN6WkeLR1VbZn/uDHac0MUD56+u/dw3XHsRJ27JHy9KJixDji92V7H8ff2Ham1HS0B99pJs0LvH7PljKyOlFlpWV6YHbZvQAos705b0KrfnDjFXzZJ0qPrZurIKfXd23d4qoz51+XLu+qwvzmxO19pqyzUkVPqNdlBsIRV38Cj62Zq3Txn/VFW9Yz7Vk0LW8daNLxSi0dUaXbrAB1i0p4ImM7HZBPGMuLSfYYFBGuFE/wih9m6czLT/XWUkrwsfXefruN+zynTApZrry7WJXsP0y0rxptuy5d33HXCZK3taNE1EdooZn5z4uSQh3dml9CL5863bMv59vGWFeN18d7DAl6A8Xl6/RxVFOWEra98z0bd7roDR+m6A0cFBEhI0s8OH6dxJu3wUbUl/ut5ZssAnbtkaEg+d5dFOeoTy4OjPUdU+9vu5+/ZbrrMqfNatGZu4LGNLbDYEFznMD8w/va3J03R6Xu06oCgtnG4dR40oU4dQ8p11X4jbS1vVXdMJLuH6OYjDWmNZlR/w9/7jB5ousyqOU26+ciu+93qugsXRHjF8tC+0zUdzVo4rNJk6ch891mkPpT/WzlNx81o0FFTB/v7HH2MbaAsQ53+dyunaq4h6PsXx0zSmo5m0zZhpHvQSbnSLz9L1xwwUj86eHRAG2NIVZGuPWCUHl03Q4PK8gKu4XB8aQs+L1ZJ9nhkmZf6nDqvqy8s2LyhFTpn8RDdeVzXMwzjy3aD++cHPDeJttp21NT6gFE7jXUoY7Dzfaum6TvzW3T8zAbXHq477atdMbU+Yr+Z0f+tnKa1HS263OQ+ccrrDayfLB5epfUL2wLqJWYvdtaW5ur7+43UuUuGqro4x/SeNTLmn05Oqa8fIx7CHfP+BdkB18MNh3b1Y91+9AQtHl4VsOxPDxur/cfVaP3CNv3k0DGqj/JlOqeX+o8t+p0kaemo6qjS4PPQmhlqrSjU5cuGqzCnq2412BBAtXRUtaY2laljSLk6hpRrVuuAsOu7YvkIre1o0d420+XxBLbxUpGv3b98bI3r635kd3/6i+fO1+GTzfuenLy0UZhtr0/Cd7+tmBraF2Vsj/vu53Ajdd94qLO+XTus+uWv3n+UFo8IvC+Dy6fgOlxwX4XbsVVnLWrrelaJXiO6YVeAOMvPztApJpXtYMZKjZ3lg39bmJNpWqk3CpjeJ6iQ8ng8WjFlsH76p3/u/nfob40Z8dHTBuvZf/5XD//tI0ld+7lqbrOe/ed/Q7bbLz9LD6yervoz74u4LxMbygLenlpgeGgcaRQSpzLS0wI6Yg+ZOEiHTAws1K999C3L3x84oU7fmgQ/2OKwVVOYk6lVc5p04i9eCPlu/6BOlOBVR/McKdJv9miv1IFBQUIHT6zTz5/5l/72n63ONxhkr5HVWvXLFwM+G1PXL2DEknH1pVqzuzLhu7YGluTqg81fm67ziN2d2e9++pX/s+DO10REcgePYmPsGAu+j0vzs7R6d0f+x198E/A7O5HrAwqy9dlX38aSXEnxeZPCqhNh2MBi/f3ShTrtrpf1j0++Ml/Ihoz0NP3ptNkBn/muE7OgM6tLvrww8I3eE2c1KiM9zX+ewlX4hw8s1nPvfu4g1YFimVbNmK7Llg1XvqGxsW+YxlFhToZWdzTrmkf/HvLdjJYBmmfoaG+rLFJbZfdDsV8dP1n7//TpkN/lZKbr5Dnd13VwPmvGzshaI2tLdOdz/zb9bs+R1RFHaSnOy/TfX041DCiIWOZK8jdEV3c066Y/v2O6THqaRyfOatQVD74hqasz7oDxdfpy+w7/MoPDvCn1w4NHq/nsBwI+O2C8dbCb0VmLwgfvGK/u4HJhYEmu7tgd9HzhPa+F/HaHYe6UAYXZGllbopf/vdlWusKmyWqaMQfrMJb/xlHdvvfQmzGkrGs/14TphDWWbXbKm8KczLiO8Te+vp/pqGNW9htXq10WHQzB9Tcf4xung/vn651PQ/P1JSO6OqHW/u/L/s8OnlinO559T5LUMaRCj77+kWW6hg0s9j8gnNxYpg2nzgr4vqm8QKcE1SmCLRpeZfsN7JWzm5Sdka4pTf21dFS1fv/Sh6bLGcvw2tK8iCMquiVcXSi47hbs+JmNuvel93XzU/+yXCaa0UnKC3P890bH0Apt2dZp63fB+aMTZqNlROOek6c5GqlidF0/ZaZ7/AGm5UU5On5mV3Bt8P0zoqZYM1oGuJreYEcYgjnsCK6DG7Pc4Lr4cTMa9P2Hu+oL4a4KJ6NRVhTlaNHwKq2/+1Xbv7FiVV68dtEelu3S0gLzFxO8Csy3Z7aE72S3MndIdz3K6sUZYz11SFWRf7lhA4v1j+8uCrt+N4KrzKbkmj+0wt/uN1Ocl6kpjf01pbG/Lr73b7a28+7li3Xizzfq9d1tx6byAj3x1qcRf9fPkLf6ZJu80GC2nJGTNnJVcVceFq5vwPjG+8rZXXWMsYaROxsGFOiCvdp161Pv2t+wgbHNWJLXtW/G9uEvj53kyogcXm/XuVg1t1lXP/L3kO+caq8uVnt1sWUdy+PxxPTCmm9kMEk6yGb9V5LmDinXL/9i3o4w8tVRpO7jbubQSdbtG1/WX1uaF3XbY+ygUv3yuEna49onJHVdv8GjENxx7ER/+8asLeerLc9uDR0dT+pqg1QVdwUeLB9bY3rPD6kqsnWdLR0VGpSyoL1CHRZBW8fPaPCPsuHxeAJeiPMZX1+qETXFeuX9LRG3b5SXla5t34Yf1SAtzaM3Ll7o/3dw9lBVnOO4n9aMcfQMt7qeRtf1czyKzWXLnAUtzAsTbJeo0RDsBMiPqSvRnLbutFpNx2Y3zavnNuu3L34Q8vmajpaI01ceO32wLrjHvDw8YHydzvhNYD1nTUeLLvi/0Da1HX+9cIEk6ZvO8Nd5XVleQPv/T3//xP/36rlNOvf3Xduf1TrAf/+Pqi3RaXu0+keWHliSG7a9G84pc5pCypVw9hnd1Zfy4z/+w/+Z7+FzWUF2SH+fm4pywuWlUn1ZnmWe4PF4dMx08xfCVs9t1oyWATrnd3+VFMO0gB5PQL1gTUezaR3KVwZLkfOc0GkBzdnpHzbeY6vmNKsox/4L3XVl0ZeVZozNr7Q0j46f2ai3P/7CXy85d8nQkL6CYdXF/n5TX3kUfM9asf9CrMffjxFrP5QZJ8fc2FYPtqC9MuDZ3Nsff+lv/8XTwuFVai4v0Fsffxny3d6jB1r2wZjxBo1A3FpZqIfWWr9sf92BoYEz+Vnp+sqiLuFrc1z1sP3z6Gvj/fuzbbZ/Y1d1cY4+3BL4DMlpVhPc7neTsT/9oqXDdPvT1n1Pdti55zyewH5Do3lDK9RkGKHL9wKzsQ4R3Bae1RL9aFJW6Q3XN3D9wWPU0P9N/fCxtyVJlcWB/SvBa5zZMkBTm8r05Ntdz+mt+nGjddwM85fH0XMxchV6tFSfxgLmoh3u1cnZjnX064A3KCzW5fTq69FzLodJvOOpEaLoSQp+6BHPOz+VzlNI0F+Eh7MhAZ4R1u/kYW80582/neAOhzgcY1/qvt0Zw9WRhHOfStdbItm9nmIaHTwFjm24jj+z+6/T5cZbNJI1/VpP4SQrdKO+E+1D/0Scxp52qcQzvVYPosy2HZdpEeK4b8k+zW6dNzt5m5O2Zbgl7eYTYddhOyXuTp3m5uXn9YbvlEx+idfFjWmTzKYWjFeeY7y+Ej1dQbTT2CF5zC6RnlDXs2orGD+1sxvG1XjkMWkvh19Jb5gSJNweJmP33Dqm8biKE5XHJeoWtLOZ4NMR6+kJN6pRtL8N+xuTCyphZZVhp8KmPfWz3JTmlTfgEMZSb4t3d0ssL7X1gKI5rHDBk2b5vlu728MPW+L0gvpMPJnlDT2hvhytmJ+bWqwvUr9YKjE7v8bypSftC5KD4Cr0aCnwDDIsu+VUby6sI4lXOeW0Me3GKYg2kCXStvvw5eGXkRY8clX8tuXGAxYpPh06PfVacJLsWM+t2bSAdgU8/A4JbIuPHnpKky6qh7/GoFnXUhJhmw6XN45cleht9wS2Ax9SLB+PJTlmD+yBeHEvkMn5b6zK/0S2kz7c/E3khWxIdBM12QEv1i+ieFO+vS65E5xm9kApXufFWBbG8vKD3Qt1y9eGUfOszrVJOmw9SLSXhJRiVhdJ5aLa7BIxXq7JSLudbdp5oBFNn4/T3zi5w3rTM5j4XhbuHCjH0wLGYRtu3z+u348OAxC7/h3byFWWSbET5N4D7qFwAeWBy9k7man80rjTtJlNd24UbT3bjZkmutdlvk+WddkEnh5bwZAJSJBX3ojbMTte4frSIwV6p3A1Kql29ISGVILE+xoxq3e6+QJTLOze9oms01u9rLDD8MK72QxPqS7DcNK5/RAJwVXo0eaHGWbZLcZs/5jpg0O/D3hYa+eN6FAtFQUmn8bXaheG545WtEWpozbE7o0Yh/o+bPew83bmrLd1LoMSVGgYPrY4r+vv2tLuuendnNrGzfK9v8VUHkbTdk+nl59lf/oV3/D4ktRgmF9+fL29KYSMCnO6pmc7YFzXdI52hzsuye3et1Pn2xuGOy1NKspJzVlzg6ezlKSKIvvTxritzWL41UjC1aft1rWD5xj33Y6+qYKiaQSF+4kvXRMcXr+ROiWaDcPoRsN3HILnDrcSlzd8TU7auAjHyW4+MLO163wG35O+TTb078pb9hzZNZd6rmGKqDJD3rbL69Vhk+olSRNtTmMWLbORD9squ87zPmMGmi7nEzyE9FFT6yV1X9dOHDuja1j9jiGhdaXC3VNeTjOZKjXcFJjhBM9nbyWW6Zci1QN8ZdT0oP2a3hzd8NNOO5Tdqpca841ZbfbP/d4m08m4bdWc7qmhndTLWiu687pkdam40ZkTfC+eMse6PmKcpsw49VWkaSWt33iOrva3aHjXEO3GupiVI02mw1u7u958UITpEWNldzsrdueLPhkmecqy0fbuheDywGpqSCf2ibBt3/ROAxxM+xdxm2Pcu/enhxmuf3RdiennVu0mr7d7WiSz+mpVcU7IZ8lgvOcinT8r1YZ98U2du//4Gv9UiCum1Gtum/m0YuGMN6mzGPPeZWO6yuyafl3truJc+9OZ2JVtmJ7dKhv11euMZfyHQfeT0zq0mVTokl8+JrSeFJwvGcXSbvfVH3NMpnA047unjPUR02Aww5F0UjbG65nu8TNCp4KyaivkGer6JXnd1/tIw5TV4TgOyImwz3aOidl0fanCKv3RTC8Vr2dmp8yOru4pSbNbnbWhnI5sFonV9NmzLKaZjOSEmYFTytSV5vn/NsuH7Lw46CunfHVcq6mRR9RGntZMspdPm5Vtknm71crAkq5yb69RXdOQ+voFJGn27vZTvzz3y0Rj/4SvzVmQnaFwub3xNAS3F0+a1aRwom2/zB3SdY3Z6e/tSkfs0xWdtiC0T2rx8O4+gm8dvkiWtbv+Mb6+NOAYrjBco8sc1oGdTgfqqxc2lReYfr5yduD5sxt8Z6amX/f9vPUb8+ngj9tdXrrR71BZZF4PD54Orlv33plNc2hWr/fdpwuHVYZ8l5nevb4Ki7T4+OqX4wbZP38HTQjtPw/H12YOx5cH79Eeuuz+47rqh6tMnrcF38cF2faeOTzw1022louHlbObdPLu6zva9lG4y7559z1lds/0LwhtNzodudQps/I6US8wme1vJMfPNJ9G1U3h+u2t8rTWyu5+P7vT4CVuNE+Pvw/88Mn1/vbHgvbu/NTYlo3meb2x39Po9D1aHa8Lqc9WTj569GjblbkXXnghpgSh74mlg8ZqXlWp68HhF9t3OFrfGxfvoXte/lANAwq0/MdPhXy/37hanfbrVyx/75VX96+arkU/eEKS/cKhJC9Lz6yfG/BQON7WdDTrug1vJWx7yVJZnKMnz5yjwpwMFWRl6PDJg0IaRW7JTPfo+XM65PV2Pzh7ZO1M/eOTL1WcmxnQUDJysxKxcFil48p3VkbkztrB/fP1xOmzAx4U+lncxLlZ6frL2XP1wedfB9yr1SW5enTdDHVc/bit9E1v7u9/A+q7y4br2BmD1TjA3jnMykjTs2fN1buffuWvKJ8yp8k/37KZNI9HT6+fq8+++lbTr/yDre1Eq6WiQLNay3Xj4/+MuOyl+wzTbJNOuIXD7AU0mDFeewdPdP7QtDTfOu+687hJOvDGZ0K20/Xv2C/6C/Zs12GTBmneNYHXUVN5gZ44fbbS0zyacvljEdczqCxP//rvtpB0WRVNPz9molrOeSDqdAcrzsuMKf+/YM92HT55kOU98X8nT1VxbqZmfu+PEdflRlZ03YGjNK6+1N+JYqW6JFdPnjlHJ/18o15+f4vlco0DCkzzHl8j9/7V07Vpyzeq392Zmp7m0cZzOrTLK2UZ3pz0eqU9hlXqsVNnqqZfXlTX4ImzGk0f/oTj69D+3cqpAens2odQlUEdUktHDdSwgcWqK81T89ld193pe7Tqygff9C/zl7PmKjM9TaMvfiTgt/uNrdGYun6qLwste545qyuPqy0N/W5qU389dupMzbnqT7b3U5LpQ+M5beVqKi/w53FPnD5bZQVZ2vr1DtfrPDccMsb/UMB4nEfWFOu2FRP0z0+/1MLrnlDn7jepfnLoGE1vDv/AxU4ddfXcZq2c3aT3Ptum+rI8NZ3dnT/4rlOn15txs3YDMNuriwI6hOLRP3HBnkN1xJR6/SBMGRrs5fPm6+vOnepnuIejLQNePHee9v/p03rr4y8tlzl3yVANrSpSW2VhyD1h5ZUL5mvhtU/YWvamI8b570Wpu4M7mO9a98nJ7KoTpXs8IQGGL583Xy+897lW3PqcrTRIXZ3X/9li7+3vo6cN1ukL2lRZnKO2cx8Mu6wvz5lruP9PmtWoBe0Vaugf35dB7GzngHG12md0YGDDi+fN09ZvdmjHzl0qL8zRB5u3abDNtB43o0GX7jPMdp3UjkXDq/S/z/3b8vu2yiI9ftps9S/M0v2vhtbZz140REdOrdd37nrZ9PdmwTOTGsr0zqdf+f9tdYcN7p8fsJyZcMf/7hOn6OG/faTj/2ej6fdvXLyHnnjrUx17+/OSugJFh1Z372+wvCzr7qj9xtboro3vh02rW4z3pNWDcJ+BJbm687hJIW2EPMNDkv89fpI++PxrNQwo0PTmAXrvs21qHFCgsYP6acMbHztK28CSXDWVF+htQ75nLCNG1pbosVNnqrokV598sd28rWbFZlZsJ8/21euKcjI0/IKHJSmkjlEdoW7olnXzWnT1I393ZV0DCrP1yRfbJXXVcUfUlGhw/8BA1UfXzQzbvxCuLhGpnuGrP15wz2v645ufREzvY6fO0idfbFedse7nYkBUvIKrztijTT81tIf/fMZsy76TjPQ0f10/x1CXPGRina7ab6Q6rrauv3pk1i6NKekRHT+zIeoXF6T4j5piFbh97PQGfff+NyR1BTEncvSM78xv0dhBpSrNz1Kap+uho6/u6TTQfNHwKv3Bxr0TLz8/eqLGX/qofwTAgSW5+t/jJ1le35EcNbVeP/nTPyRJG06dqYEludq05Rt17txl2h9gvLxfPn++Nrz+ke5/9T969PXusqh598PDm44Y5y+vzJQX5uipM+fohj++rZ8/817Ad05H0zH2FaR5pN+eNFX9C7M1sCRXz53dIY9H2rztW3/9LDi4e9HwSl213yhJ0thBpfrjd2YFtKObygv9dfGh5z0Usv17T5nm/9uYB9y/aro2b/tW7QOLte3bHab1FGP/RH52hv/v1Xe+aLm/xvNwwyFj9O5/t+2uk31perzvOHaiDv5/z1quz46WisKQ9kg4py1o1ZSGfnrn5Wd07sau/XZyWg+ZWKf9xwUGsxw2aZDO33Oo7nv1P/ZXZPDCufO0edu3qumXpy3buoON9hxZ7f/7e/uO1EmzGm3X5Y3PCP6+6Qv/51ZFwYTBodeXJH1/v5FaObsx5PxZlSl23i/rl99dx7fKcvcfV6uxg0pN+3icyjW8PN1aUag3P/oizNKB+hdka+ygftr4r8/9n+WYPFt4dN3M0HrJbmkej144d5527NrV9fzgrLl686MvVF2Sq3N++1c9/c//+pd9aXd7z6ou+fL58zXywq76Z3t1kS7Yq13j60t13IxG7fvjp/Tfr74N+c2SEVW695Wua3PZ6IG6ct8RAd8vGz1Qd7/4QcBnB02o1YTB5sf/smUjdNyMBlvPK169YL4eem1TxIDSr7bvCNi3RMnPSve/yDujpb/ttrUdvv6hkrzQvGnjOR3a6fUG1O8SJdpy2a492iv14Gvmz+v+eNosDTs/sKw6c2GbLn+gqx5mlq+cuUeb9htbEzbvm9xQFnAfWZnSWKan/tG93BOnz5bHE/6YhKRp979L8rL06LqZ+nbHLg2tLtKV+47Q6SbP0k9b0Kp9Rg9UVkaaplz2mOPgW7uCk3nLivH+uo7X69W4+uD7ufsXJWECtP964QK99dEXGlSWrx07d2nHLq/e3PRFyMuYi0dU6YBxtVG9MI3UZyu4au+99/b//c033+iGG27Q0KFDNXnyZEnSM888o9dee00nnXRSXBIJWAnXgIumkyQnM137jauN6a3lXAcj+xgFV9TjrScMxRgs2jf2jQ33ZosIYrcER5vnZKarvdreG15uiOdpNXsIH0l5YY7KC0Ov7SYHo/WUGR4SpKd5HP1W6noDxvgWTKS39zyS8rMzlG/zTZJYeL1SbT97DxoGl0UecSKWtyqyIgzhbcUq7zK+iROc37hxmaaleSzv59rSwM6XcOrL8ruDq8Is5zu2doIRnYol/0+LcE9kZ6RrkOHasTVFi8MTZFy8IDsjYmCVz8CSXFv3mVne40tjTmZ6QCCNJJXtzoe/MLzt5xviuWF3R8fOKB4QVBblmHYA2GGWTrPjbFatCe6caQr6d7nFW34ej8fyYV+kPK7BZgBrJBlpnoC3iH3nMtwD9WhVl+SavgFYW5rnv0+67uOug7xHDIGpRg0D8pWVkRa3wG27WbOd6ytWzRWFjuuPxXmZKlZgp0S0aeuXn6XyouywwVWZ6R5NbnQ2UmhRjv232q2muwi+d83yLbP6kNR1jCK9rRur4OvDKDjtoQ8JnNe9omFnO43loftRmJMZMHqsk7R6PM6Wt7VOG8uYPWDwWdBeGXFaFclsdI3IYs0WPB6P6ZvyxjJ5XsCIOV3C7a8Vt6bptrct+8vmZ6eb10sMf2dnpPvL0cz0NP89ZTZ1oB3VJbmBwVVB96xvW9G01WwxbC/cHgTX/9ycxscJN+vqpXlZ/uCq7Iy0kMAqKXQ0Czf56o92H3DnZqXbut9SrSso+N6I9ICrzOINfzvnwvm0gLEFFTXGOTA5XqLpL3TrsupfkB1Ql9u8zfBQPCA/iv+FHHFawAhpyMpI06CyPL2y+2WikbXFsT3ANWyuODfTtI0ZsHjQ8svG1Oilf28OWMbXLDaWV1aqS3ID6lxuyEhPCxh5zjeyp7FvNfgo15bmBfS9mx2D4DKxsihHm7Z2vZhgLCeM5zArw6Mpu0e1CDcSpHHdvr/tlnkZ6d3tRqs6qJO2SThO6gUej0cTB5fqv69Ht60Wk3Zi44B8ZUTZzyh19S1FGuEnmj5is/6qcHme2fXldLtuPYMJ18cT23q7//Z6IwfWeTz2RqCNVC8xvhRQXpTj79tqLM8PCAoJbu8FM96vlUU5/hF3BvfP19DqIj3x1qchvzEex8bygpBr1bwf0vr4O7kmPB6P7f6o4txM9S/I0qdfhgaIma/b6gtbP5fUdWx812y4fYqmhmTWP+RjVb9LFjenBUwLkxXmmQSTtVWGv5bM+k6Ck2tnim1JKg8aUTuaNqVx28Z7pLrY/PnA4P75CXvxxshY1zG7n9OC8kIrBdkZISMhmu1PUU4mgVW9mK0nG+eff77/72OOOUarVq3SxRdfHLLMv/9t/YYmEA/hHo66V3GN4bcpMXB99BLT6Wa/KubkrZlEJD2Rc6+nsngfhkQf5kTmHV6F3z87+x6uspyqQZTxSFZIx3cU2wjuUOirnO672TR48WZnO8YHs9EEU0Wzza7luhd0+nA41gc4yWS1q4naI6uA+7iXUWE2EO39YFxnRrheGOO2Qv6dmvm/lNigCbuiP1ddJysR13nYay0B24cT0V8Rka7FZOV14VgleVcMZa/NrM8VbtSX45mtBa/a6QghsTLWTQJGeXUhGU6PvZ3lIz4QjDLH7Kltg1RMdjLrKE4DU52d99CFU71un4zr2s5D+4B/u3S9WG033tVSJ+3QSPvq9FiYLZ/sMiWEjc0nql8rXociVfvlInFyOOJ+HUV5CMMd+oBpxqJbvcn2oq8nJftW9InUTy115S2pel3b7rdLYF0k2vvDrN8kIYc9Bc+tMUXxuFdMdzlF+rft7m/wYtH+LhpW+YFlP3Gi8ruABxaRFzeOaJ3AQVvRQznusrrrrrt0+OGHh3x+6KGH6je/+Y0riQLsygzzVmJRbvxHnbHDzpvHieKbqi7ZjAWuxxOf6mzMb6xaBFkUGt6aMc4PHss2X/9P+KF3nQyHGs3QqblxGEGkJ7M797kbvF6v7RGjgqcR8jEGV8XS/snODJ8Opw3ncA/Pw9310Q7/mx10HI2bt/u2iTFdwR3hOWGOT7hGQUZ6ajVKnY5QVmjjfsg35CHpCXoampsZOV3GeybcObK6t4JFcybDnX+zeypVyulomNV3dnkTVw+yOsU5MRxTO2kPd46j7TAwdr4F1zWshIwQmICsJ9pTmxflKK92ON1tXx73/ufRjVrrVn+LnXzIWK4W5gTmgeHe5LXKl+3mfanEbEqHWMRSZlnd35kZaa7Ura2mTrVzzVmNEpCXHfu956Q+GMv9Yef6dCsvcXIrWJ13O1PdRnvHBe+nW/mO3eBdY8eyk32wk87MJORDxk1GfJiRIoExbpeb0QY5B7eH8l3IU9xiZ488Hk/Ivkdqq0U6VtGOXG9XuPW7UY7bqWcW5Ngr09zqVwo7labh73DtczMZFscrPYZKs522fr6D4+LkerKTarNzF+t0S9kuj+RtZ312RsiJxHj6nZQDdoUrLXpejTs8s+nfwj2fSQZf/hCuf8WY18S77WznvjP2PSS6emTMe6INBHL6K7dGM49UR7L63tgHHu1MDvHmuy6NzykSMWVeKuZZ8e67MCuLEhWAZ9a+diN4MSfMveF237NVHSsZQaOx1FOMh93uyF/xSgtSn+Ozm5ubqyeffDLk8yeffFI5OYmd1gxYPqZGY+pKtHpus/+z7+4zXEOrivSr4ydrenP/gOWHVBX5/442f3x47QxJ8g8JH66o83ikPUdWadygfjp5dlN0G3TBmQvbNKKmWCum1Zt+P7quRC0VBfqfoycEfB7tMZrUUBr2+/Q0T9e8wEMq1NA/X4U5mVo6qlpl+VlqqSjQXSdMNv2dnfScNKtR4wb10x7DKqNJuimPR7r9qAlqqSjQ7UdP0NqOFo2qLdEB4+skSWs6mjWqtkT7ja2NsCZzZm+yNZUX6JK9h0mSTpnT7B8aO5Iz9mhTS0XXkJbHzWgIu+zV+49UW2WhLlk6TD86eLTaKgt1xfLhDlPfs62b13UuL92n61jnZ6X7/5akGw4Zo9aKQt2/arr/s8qiHE1pLPMfZyeyM9J0xO55y6WuxufeowdqTF2JVhnyMaPDJw/S9Ob+Grd7WOML92pXe7V5XhbNdG7nLB6iYQOLdOLMRmc/jmBIVaHmtJXrwPGh90W4dB41bbCGD7Q/leZZi9o0fGCxjp5ufb3nZKZrXP9dGliSo9aKQi0bM9B0ObN0XbDnULVXF1meHyvHTBusyQ1lmtGc2OFfrztwlOnnh0ys08yWAdpn9EBN2H0tnbN4SMT1Pbi7zLPaVltloa7Yd4SOmjpYUxrLNNXhdFzBgqdWDXbpPsM0tKpIZ+zRGnFdOZnpWjZmoOYPrVBN0PSb6Wke7Tu2RvOGVgTNr97tf4+bFHifO7jB9h9Xo7lt5WoOM2S7cW2XLRuuIVVFOmOPNlvrP2VOV53i0El1/s8eXdd1rupcmhbIqnEc7ISZjZpQX6r5Q0PL3S1ff6slIxJTDwquI1yyd+i14vQh6ZLddbiVs7vzR99lEG6/fT7fFnsgSGFOpvYZPVALh1XqtydNMa2vmXGjg/jivYep1TD9qm+Vh03qKpdG1/aLar2r5zarvbpIF+w5NPZEhhFuitIZLQO6juUxE2Pahm/ajljf1m6pKNDctnIdMM66Llmcl6m9RlZr8Ygq3XXClIDvbjlyvForCnXLkeP9n/ny/RGGMtVXRmRlpOl/j5sUU5rt8D0M23DqTH96YukXfeS1j1xJV1N5gWmZ1b8guqlfJQWUg2ctatOQqqKo6tW+e3fd/BYNrSrS+UH3ie9SCx2trtus1sC6xw2HjFFbZaGuPWCUrTT49uU3J3blObcfFSHPschwzN72NCujlo6q9v/d1UYsjzg1kST94piJtvNEox8eNDqgfHcyxZHZnT60qkjr5rc4SkPHkHLby56zZKiGVBXpyuUjutLgUi/12EH9NL25vw43tE3MeKN9kcNGOic2lGlqU5mOnFLvYMX2mT2YKCvI1pIRVdpzZHXU0z2vX2ivvhaJ3eN5zpLoy0uz66WtslBzLdpp4QS3h763X1d/wvf2HaHRQf1y4SRyQIRII1UZ6zJHTqnX1KYyLR9TI0ma01Zu2ea/fHfd/axF7lwLwS5b1tWvuX6heXtt4uBSzW6zmY+YHPDjZzRowuBSzR0SOtWrz21HTVBrRaFuWzEh4vQ0Ulffih3GK9JWn4rF9XLSrCYNG1ikcyPcH8dMG6xJDaUhZaPPoLI8zR9aoeVjamy/FGKnHeBzmYO6wPl7DvW3Cduri3Tx0vaA7wNG2raxvu/tO0JtlYX6wUGj/Z+dOKtRwwZG3zd+1LTBtpe18zD8pDBtxO/vzmO+Mz+w7R/NA2+Pp7sf2k4dI1apGJwQC2Mbw9eW8xldV+LPN80sGdE1/dlPDh0T9fbtlhtFORld9bJ5XdfMiqn1GlFTHLGfJR6jMHk80sVL23enJzB/LDIJfDTmh2716fhY1bN8zwK+v9/IwC8iZAyxHC5fvnTBXrH1BVy1O3+4aOmwsMudt2d7Vz1636569GkLWjWypliHThqk78xv0ciaYh08sS7kd15Jd+7uE/zV8ebPqeyKturuC/D+8aFj/Z9Z1bWM+byP7/xeFXR+z9xdjy0vzFZJXmg7KJUGrjp2elcZHq7OM6LG/BnC93afcyuD++erIDtDK2c36qhpgzWipth/bKTA42D2jM3Yhr0tqL180xHjAvrSjKY2hfab7z/OOg+1PxpboEuWDguovxnXc9CEWo2qLdGp81oCfhdc77BrzCBnfYNWfbOHTQrfLjXzk0PHqrWiUOctGaqWigL9MoZ+LmP9IpbgqvOjfJaEnsVxiPCaNWt04okn6oUXXtCECV2ZxrPPPqubb75Z5557rusJRO8XS99gTma67j5pasBnB0+s81eK/ufoibr20b/r2kffkiQ9sHq66s+8z9E2ghttLRWFevfyxabLer2hBVl2Rrp+feIU0+UT5YSZjTohTODEyJoSXbBXdIWnmYMnDtIz//ws7DLfC6rYXXdgaCUwGqfbfDDt1IyWAXq4ZaYkaXRdP63u6C4c13S0aE2Hsw71cH5w0GjtNbK7glaan6Xnzu6QpIjXb0VRjh5eO9PWdpaNqdGy3Q3gurI8LRlRreffDX/ezMQ7Cj2e6181t9lf0TlkYmgFbtHwKi0a3tURYHbf281P3rh4j4C3S257+l9df3gD87EfbHgr5LfBDcUjptTriCn1/m2HG6bUTv37mOkNOiZMYFK0PB6PbjZ0wAR8F+Z3RTkZuueUabaP7XEzGnXcjMiBYYc179KiRTOUmZmp/3v5Q939wgeSrDslfdfdkVMH68ipkToQQ9cSy0OQWCwdNVAPvbZJ97+6SVJ3g+XSfbo7dX9lEcBq1mALF5ywdNRALR3VFah2nktBEvuMrg77/SETB5neq1au3n+U5XchHUhBJjaU6eG1M/3XopP+hSv3Db/uYAdNqNNBE0I7dMx4PB6dOr9VpwZ1MjeVW9dPonHA+Fr94tn3Ii53ZpgHjBVFOcrJdK8eFL44CPz20EmDdGgUDXMjYx3u+j/8Q1J3x2u4/fYJDuoLZ9+xNfr1xvdDPk/zeHSNISDCqowPDbSIvUfssEmDdNikQSH58cV7h+/AjKSsIFv3GYKWnXBSJ2gYkK8PNpuPSHXa/FYNt+iEi0asVRWPx6ObLMpMI2OnqfF+H15TrIeCgmGN+b6PMd+OJ1958vT6uSHpaTn7AX27c1dU63WrSnjO4iGa1RraQdsQ4WHb25cuVNPZD5h+ZywHq4pz9cDqyNe4WdCDL48pL8zR/bvX8cc3P9Gf/v5JwG/CPQQKfjhsrM9GTJMC98VOu8IqJWb7t9fIav3oD28HfDZ/aKV+/9KHkrrbiLc8+U7E7Y6u62e73WO058hq7TkyfH3DrrbKQv95isR4yr67zP7D9oElgdeTmyPm/c/RkQNMA6fNcXfksvQ0j35xjLuBnsbOeqvn+j86OPoHvJJU6+ihp/WRsFumDSzJVXVxjj7c8o2D7Vpvw+ORrTInWFlBtvYaWa3/e7nrfm0cUKAH13SVPfuFCQ5OJcb78P5V05VmuEiMfWJX7R++Ln/ghDodGFR3d7PfIlLb4H9jfNC7flHkl2xmtgzQzJaugKTB/fP1xqbwI67bfSHQWDYY21o+YafxMhzjfvlZuveUrrzx4nv/ZvmbSO1yj8ejGw8fF3aZYHbaAT5OAnlq+uX58/sTZ3X1cZz7+9ccpc2owXCP+pTkdR237v4kZxdukYOAZDslRmm+9fr2HVujfcdaP3B2wuv1hvRDS+4FQQXXeQLKzhQKVHDCuEvGl9dLC7IcwtMLaQAAWYhJREFUteV+dPAY/ehgN1Nm7bDJg3Tagu77szAnU/938rTEbDyI1ysdNrleh02uD/muoih0gAqPx+Nqf47RBXu164K92jXlsg0BdYklI6q1ZERondgsV3Br6rL9xtVGrDPYWdfysTVabiN/CK5Hr5zdpJW7gzpPntOsk+dYBz9M2t0nGCsnuezImmK9/P4WSd3HfEhVkem14TGsey+Tto3x/P7hzY/9nxufE465+BEHqQuUiFGJzl4cvgz/20ULLEdB229crU779SuWv/3Dd2YF/Ds4rzCOXmoWXDW7tdzfhvXVl3zmDqnQ3CEVhv7k7nWZvaR45b4j9avnQ/sDpfDHubY0V//+zLzPq64sTw+umeFPg3F/8rIy9LuVXc+i1tz5ov9zs/zKTpqsRgF1Wv5dvPcwVRbn6HsPvWn7N3sMq/QPshEuANxWUoxB7DFc3yumDtaKiM+S0NM5Dq4688wz1dDQoOuuu04///nPJUlDhgzRLbfcov3339/1BAKximbI83i2e1JlDm07Uq0BmKxDl2KHIeWkynQJPZEbQ4y69fY6os/zUu0URBtU4R8Ng0zPlOvHpQ8c52innYlGou5DJ6PuuBHgZHdq0ZBDHY9D3wOuWeNlkMi8OdXKgVSWCmVMPN5Od4tZygKHp09YUmxzcjjNlu2LbYlYyodE5zfRXnPB6UzUXWfcbiLrIamq791d4Rnz/76Y96SCSHlKSN4RMFpTYs9ZqtUX4pGaeJYpfekOCz8tYGpdR9FI9r1gd+uOgsBpwJnyeiPnCx5P6DmhTLUW7aUW6Wo2DYJzmN+Y9W+leo6VqOzIuBk3nt/415ukA2wVABXNngX/xmqf3Dps0R4zpz+L1zlH7xTV5Lb7778/gVToMRI9TzXi10BJZMNnVyo+ueijevOZcGPfYrotkpU/ht1uYhMVMNWJYdtOjmtvvkYTLdmdduG43Sma6p2sbpwKt09nuHqAnfvQE/BuX3Ti9aDWuGvGFNrdXiJiq3qaRHaGUA7YlwrBDtG2KRJxns0Oj/GjeDy48Hhiq09alWdm96DZkmbNruRfJeZCRqZwcNzcKvcTnt9EOy1gCog1v7E6v07WmuyHCmbb76vPCkIeBPfR45BsTutnAWUg58zPrWPhzgt/Vp+nzgmz6mcIDLiMXrICiuOJYJn4SVZ9yk5/m3f3/5xKlds9VdJh5CSfDVgyARdKKvfBJpsnsBGe2G1H85sIP0qzN/NxQqTifSoF1QlSNI1IHVHdUps3b9bPfvYznXXWWfrss64ppF544QV98MEHriYOfU9xblTxfmFZVRLM5hQ2X4H9bdmJ2jXObZ8qmisCh6n2pXtw//wkpMba6LrQYTPjZcvXnf6/Bw9IreMgxa+hbrdOPaeta7jTYQOLHA1zngriWTeymkLN6rhObepv/oUDxkZa8Hze/QvCD80/IML3Utc0fZI0ob40itSZC/dwqSw/y7XtWDGeJ+Pc7NG+FesbGn/k7nUZ5zRPiiiHsR1S1VU+Da0KLKfG7p47vanc/Xt9RE1JwL+bUjg/qS21P8WbHdEez6ri0CHkU1XwtRSr6pJcTRxsnheV2sg73OggDr5mwy5bWxzyWV5W1xSxxbnW9VDjfWtnv6TQ+m59WerVXdwQ6fjbfQBnux1g08CSnnNf2tFe3XXvDh8Yeg3HakpjmST70wcZxT3/i/SWtuHvSQ3u1YsiGWY4D75ApOCaVPA0ZY272y/j6wPbT756ndHUxtjqo1ZtpbrS0M8bTco+s/sn1foyfVmsr07k1vqiMdxGf8Ko2hJJ7tRJfetyKrjMtVueuSndxTftKg35T1WYabODtVRYn4NWB+en2sE2jYL7emI1tDr1+rOi5XZdwMl1EQ1fu9Xt+nW8jLPqP4iQwQeXZ8apbn316GCj60okhbav/vafreE3FoFV/05vMtDBNOZWBpV1n7NCQz0jXgH10bSjrQK93EphuDqz08PQLwnlpZkhld15TTTHqa7MyRS64dkNAHFyrONd1wxXxiTrof243e2CnEzrR8Jer/m0ZZ6Av+MbkOOkfhQ3Lp6kBgfPlYz17sqi8O3labufKVQb6qhjBpWYLltRaN6ONutfG2Vy/lNJvmEawEh1fV9baGRNYB5dkB35GXTAC9hOEpgkNf3C57lWZfKQKOqVdgOKyy2u4eoE9Z+Z9T2EY6xju91OQO/jOJLllVdeUUdHh4qLi/Xuu+/qmGOOUWlpqe6++2699957uv322+ORTvQR/+/QMa6vM7jg+OlhY7VlW2dIgz1RTpjZqMz0NM1qHRB54Tj77UlT9PQ//6sDgua6/sUxE/XaB1tD5gtOlg2nztR9r/xHK6bWJ2yb+YZK1qJhVXHd1vTm/nrirU8d/SbZlboTZjYqKz1di0dUqqF/gT7+YnvIw5tIHlwzXYuue0J3nTAl7HKp9LablfqyPO0zukbLxw60tfzquc26+8X3deFe7TFv23h0Dpk0SF99u9PfwBrcP19/efcz0999d5/hag7T2e9z7ynT9dsXP9ARUwbFnFYzR00drP3H1+jfn32tz77arvrdQZ2DyvL0r/9ui3q94S6bsYP66aKl7aovy9eEwaXKzUxXx9AKx90D7dVFOnJKvf9hy/87fJzu+Mt7OnB8XdTpdkNAp4eNnbr3lGn6098/0VHT6iVJx85okMfj8ZcBPz50jO549j0dML42zFqis3pus/Kz0tW/MFubt3Vq+dga17cRqzuOmajXN33hv6/css/ogfr0S/t55y0rxuv9z78OeMieqnzXlFtzzP/P0RP0j4+/1Pj6Ul1/yBiNu+RR/3c/O3ycPvlye9wDfR9cM12PvPaRjpneEHa5W1eM16Ovf6SKwhzTZS9bNlzvfrpN+4y2V17YDSbx3er/e9wkvfL+Fk0YXKof/eFtSV2dbUdNi/1cpMJoa6vnNqsgO13ff/jvEZc1e0PUqh1QUZStj7Zujzpd4wb1U2vxLr25JYVeB4zBzUeO1y//8p4OnmCvPHPyIOP7+43U7U//S8vG2LsHjMa49KKF1QOa4GCQ362cqqf+8an6F2SrIDtDGelp/s+Om96gpvIC9ctz9wGY2X22cnaTfvhY1/3sqxcH78KUxjKdt2So/2HEz4+ZqP997t86ZGJg/c1Xr5vVOkB3PPue8rMzdNLsRn++6qTefdaiNnm90pLhgW0lXz60oL0i5Dd7jqjWpi3fBAQpjR1Uqgv3ak+5F3uMNqybqXtN2qNuPTf2lZv/+PhL3f1i+JcWT5nT7K+7WrnxsLGu1UlP36NNZQXZWjisMuDzSAHLvkvpZ4eP06dfbleDC+W00+PtRmzV7UdN0Lv//SrgRa9RtSW6aGm7BoUJZB5SVaQlI6p02GTrNtTBE+p03u9fs5WO0bXFeu7dz+0nfLf9x9Xq/lf/o6f+8V/tdGF07qOmDtbOXd6Y+ooSWZswy1N9dYFID6GcMl4XR9z8F1u/OX5GgwqyMzS/vTLisj/b3dY8yGbZ7JOM2tvAklxdsnSY6XeRrkJfELZPTma6fnTwaHXu3KUSizL3p4eN1S+f/XdIe/Wfn3xlO81mxg7q52r5FEtd+ojJg1yd1vIXx0zU3z/6QpMbyiIvHMG8oRU6e9EQDa8pVllBtq7ef6SyM9K17lcvaYeLswL85sQpeu7dz2y3oxLp5DlNyslM09wh1mWzXQNLcnXlviP8L/EZnbFHmybGOcD/tydM0lPvfK4jptSbfh+pqvjLYyfptQ+3aHZruWtpctrHlUx2njslax8uWjpMDf0LtHRUteUyXklnLGzTgMJsLRpuXjbFO/0HT6jTl9t3aEqML4CkCrP2T7BH183Q/a9u0tHTBuv2p/8lSRpaFb7v79oDRunnz7wX8Czi7MVDVVWcq0VB7bOh1UW6dJ9hqi4ODKi9dJ/hai4v1P+9/IH+sbvM/M6CFkf7ZyVej3P65WfpmgNGKjM9TdkZ5kHXPrcdNcHfrzHhuxv8n9+3alrE7XgMXTtm++LoPkjAPT+lsUynLWjVn9/6VBeYPG+yCq6Kpm5vt8+gpaJQ391nuCp2B1mdOGSnBjS0a+wg63LMzeeAS4ZXadUvX5RkL+D76GmD9dBrmzSipjhsOw+QogiuWrdunY488khdeeWVKizsfii7aNEiHXzwwa4mDn2DL7scVOD1v23kpuAOtQU2Oi0ccVg45mSma+XsJnfTEKXRdf1MR4Oa0tg/pgqs25WnxgEFWjW32d2VBgl+yGIsyNPiPLek8U285LO3r4U5mVrd0X1OTpjZ6HhLbZVF+udlix3/LhWVFWQHHI9gwZ1oa+e1aO28yI0VOxVK48PjzPQ02/nLwRPtdcrWleWF3bdoGG+3k+c0qTQ/S22VgW9KtFYUxhRcZbU9n8Mn1xvS0LV/xgcOdvKxPdortZ8hOLW8KEdrOtxphMbC6TC2wwYWBwTsZGcEllPlhfHbr9ysdJ0S5/w9VlOa+muKy4FVUtcbVk7yTjc7J+Mt+JqK1fTmAZre3NXQ71+QrbysdG37dqckhX247Ka2yqKQfMrMrNZyzQpzropzMx3lqU47Lic2lGliQ5lefX+L/7On1s91tpIUlpuVrpPnNNsKrjLL/6zaAf3ysmIKrpKk6ZVevbkl8nKpxqyYqIixPAv38LBfflbU9YpET08yqrYkZNQe42cHJCiYOiezu9PYcpoyjycgiLKqONf0HBrrdSN370e0HZhjB5WaPiTw5UNm0tI8Ot6k7At+gJcKz82Mo6s1WLRHo233Bu+fr9z881ufRgyuyslM99ddrbhZJ83PzvDv+/YdO23/znds3Cyn0x0Wim6MmjKjZYBmKPRhg7EtYWZYdVHEdlmGg36AaIMy0tM8+p+jJ+q+V/6jlXe8ENU6jLIy7Lc3U5XrfYIGka6LYE7aQqnS1rRj1dwmFVu88R+pzDELgF4ywjoQQOpqr5rVLdyY8s4qwCQasdRjQkaYinHXpjb1d2UkdanrnB07o/ulkmVjul6YWvurl1xZv8/YQf2iHkHSelrAWFLULbhsjnW9+48zf7HtxFnO+16dGjawSKPrA+txTvZncmOZJjfGHrRnJdxtnYxpe4PFs4yJlZ2+CK/XqwJD3c8n0uhhbh7OjPQ0nTSrZ9c1jKzaP0ZN5YVaNdfZiF1mzyLMzp1P8Es3Uvc18eoHm/3BVXlZ9kIGkjl16D6j7b2Ya9avMaAw21bgjPGKj7k+Yfi5m+1cY/vA4/Fo5ewmy3q61eNNN+r24bIH3/Onzs5OtZV4tWhSUB9KHBv+xme6dsqxrIyul+kAOxw/0X/uued0/PHHh3w+cOBAbdq0yZVEAW6KtUPN6c9T/Q0KmAs+bS6+XOV42/H6TU+V+uNWmUtUXtADBvYKKxEjk9k9Fb3lvuot+wFY6en5XiTGTirbD1O58UO48UANqSXe9fOecMnEI4l2p1pB/KX6qXByj8TnWrWxXUMiYz2eseyD2/sf677E+X2xHiHV76++IpF9bb2tLtjjLuHedfijRt4Tu952DFNhRGgrVrdtT5hVArDDbp3Y+EzZ1avfRoYWjzzPzcEjEp0buHE8els5guRzHFyVnZ2trVtD5yz/+9//rgEDUmMKMcAomoyTzBbJbDRw+fU8kc5ZPPOUcNdqquZlCUlWFLdwbwmmTdV0AYhfJ0RwB2088oGelrf0tgdq6H3MrtBI95l/WsCUaDH07nssFY5wTxaP9rTTF+fSkxhR5Pbux7onPa0MR2px8/pJZP3Mjakw3RRL2Z0a5X7vk4gA895w7npMIH5PSWcqizLb7G1N7162Ozb0nXvHbp5szE56Q3ChGyP6+gQfj55QzvWENKJncRxctddee+miiy5SZ2enpK7K1XvvvaczzjhDy5cvdz2BQKwS3QAoL8zx/52ZFvt0b4PKuufoHlnj3hQ78TR+cNe8uRk9+PXIg3cPlTouyiGnnThoQuBwmHaGuTabzrHXcrn+uqC9a2oK33zP8RJNpS07oyvPGFrVNfXUsdMbwi0uKfybn3uPHmj9ZYqwSv6BE7qGQQ+ejseu/OzuKXRK8rJs/cbudHrlu6eJmTskMdORxaIXtP+AXmVYtXldzjhalfG+jVSNHbK7vFg+Jv75fWuFs2Hqk+3IqV1TpE2Lw7SeZuoKemaG+9HWb2JeR/AUp/FqfsW7TOsJZ9Bf7+u5zSxbZrR0vbhXmGNvWop4CHcdd+yuAx49fbD1QmHWZ9VH4cvTU1WWYRo7Y7+HUceQrqlxj55m/9hEUlvaNR3WouFVlssMMKmfD03C8fRtc5nL5fJX39qfktFMcW53e6ggifeVlaN2l9mnzIltWpIVU+slSatTfNpxX1/EvARNbx0r3xRlkfokc3dPYzu5wbrutXZe1/Q8B5hMe+brq3HLqghTqCaasb5/3O5p9CJN81aa33XvzmoNfKG9J9RZfOf6wPGh59rOteKGJSO6yo0TZkbuWxtYkhtxGft6b0UtGX1MgVNfubPOZExp5svzndQfE2WP3dMZHjfD/F6pKu6+P9I9Hu03LnBatmROEeem6uKu+m0qT++YKnz9LHlZ6abfr9k9ZeF+Y8NM4ZfA57a+8uCk2famVzXmO2b53vj6ruef4R5/+upNwfdLJEOqirRoePh7UpKO3122LRwW+Xp1I7hqfH3X88jgZ5luFnm+KeV9bTu3EHsLtzluUV911VXad999VV5erq+//lozZ87Upk2bNHnyZF166aXxSCMQk1jje5zN1+1Vbla6/nL2XGWkpbky3OJDa2boo63faJdXqi4x78BMNQNLcvXnM2arODcz2UmxLbiAnTe0Qo+dOlM1/fLMf+Cijt3bKsnL0tedO1UdpkH/ygXz9cU3O1RZHJ9roS9UNNqri/X4abNdr6SFY/ewbjx3njZv+1aVRTl697/b1Dgg8hzg4d78nNRQpj9+Z5Yqi3P0/ufbdOPj/9Svnn/fZmrix04Q05y2rvtiYL/oOrgy0tP0/Dkd8nq7g9acCNcx8MfTZunTL75VXVn884do9IHbGH1cT+y4e/n8+fpq+w7HZU+k+/m3J03Rf7Z8o8H9I5cX0Xr5vPna1rlD/fLtBaqmir1GVqu9ukh1pYnJq4uzpMe/M0MlBT2jvu5T0y9X73/+dUzrOGRincbU9dOiHzzhUqrMuXXvp/Kbp26+UdqTNQwo0BOnz/Y/VE41Pzl0jO26uhOl+Vl6ev0c5WWlXvCL1DWdxAvnztOOXbuUa/Eg5SeHjtW7/92mpvIC17b78JqZ+viLbzSozPp4/+m0Wfrvl9+qtjRPL58/X9u+3aGygsS193x+u3KKPtzsfrlcnBvbNZFlaA9lpsf+EqDbzlk8RAdNqI36uvG1L89dPFQHT6hz9fqLhz9+Z7Y++WJ7QtqTbhQrs1rLbbXNnzunQ5u3fRu2H+34GQ3qGFKuhv6B5yg3M13XHzwm9sRKemb9XL3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" ] @@ -863,13 +791,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "f30a0497-9b2e-4ea9-8ebf-6687de19aaa9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -903,13 +831,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "c192564b-d3c6-40e1-a614-f7a5ee787c4e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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p0iV9/vnnMsaocePGbu80UJhatmypxo0bq0+fPipXrpzWrFmj/fv36/7771fv3r0d89q1a6chQ4Y4PoihV69eys7O1scff6yWLVtq7dq1Ttvt3r27qlatqldffVXJyclq0KCB9u/fr7Vr16pXr15avnz5Ddc6cOBATZ48Wa+++qrKli3rVF+enj17KigoSC1btlRsbKxycnL0+eef64cfftADDzzgCNiu2O12JSQkXPem+H369NG4ceO0aNEizZo1S/7+/lq0aJHatm2r4cOHa+HChYqPj9eFCxe0b98+7dq1yxFEK1WqpA8//FB9+/ZV8+bN9ec//1m1a9fWr7/+qu3bt6tq1apatWqVJKlr1656/vnnNX36dNWoUUNdu3ZVbGyszpw5o4MHD+qrr77SSy+9pLp16+r8+fO66667VKtWLTVt2lRVqlTRuXPntHbtWqWkpGjChAny8/O76R7ladKkidasWaMHH3xQffv21bRp09S6dWuFhobqt99+0zfffKO9e/eqRo0ajse0bt1aY8aM0VtvvaUGDRqod+/eMsbo008/1S+//KKxY8eqdevWjvl5Z5UXL16spk2bqmvXrkpNTdXKlSvVtWvXG/6AC1fat2+v5cuXq3fv3urWrZv8/f3VuHFjx6eWAbhCSd77C0DxyLuP7LW+2rRpY4y5fD/Qd955x9SvX9/4+/ubyMhIM2zYMJOamury/pZ595HdvHlzvv3m3Rf28OHD+da52lbe/EOHDpmZM2eaGjVqGF9fXxMbG2tefPFFk52dnW87ly5dMjNmzDDVqlUzvr6+plq1aub//t//aw4ePOj2PrK9e/c24eHhpmzZsubOO+80S5cudXsP0Os5duyY8fLyMpJMv379XM55++23zZ///GcTGxtr/P39TVhYmGnevLmZM2eOuXjx4jW3v379eqfvz7UMGDDASDKLFi1yjKWkpJgnnnjC0Z/Q0FDTokUL8/e//z3f43ft2mUefPBBExERYcqUKWOioqJMt27dzNq1a/PN/fzzz0337t1NeHi4KVOmjImMjDTx8fFm+vTp5tixY8YYYy5evGheeeUV07lzZ1O5cmXj6+trIiIiTOvWrc3ixYudPsThZnp0tTNnzpjp06ebli1bmpCQEOPj42PCwsJM27ZtzZtvvul0/9s88+bNM3feeacpW7as47iYN2+ey+3//vvvZuzYsSYiIsL4+fmZRo0amUWLFl3zPrLuvn+xsbEmNjbWaSwnJ8dMnDjRVKlSxfj4+Lg8jgFcZjPmqr/lAAAAABbANbIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALKlUfSDCnDlzNGfOHB05ckSSVL9+fb3wwgvq1q2bJKlt27ZKSkpyesyIESP0zjvvFHgfdrtdJ06cUPny5WWz2QqtdgAAABQOY4wyMzMVHR3t+ARAV0rVfWTXrFkjb29v1axZU8YYLViwQH/961+1a9cu1a9fX23btlWtWrU0bdo0x2PKli2roKCgAu/jl19+UUxMTFGUDwAAgEJ0/PhxVa5c2e36UnVG9uqP33v55Zc1Z84cbdu2TfXr15d0ObhGRkZ6vI/y5ctLutyYGwnAnrLb7Tp9+rTCw8Ov+RvFHxG9cY2+uEZf3KM3rtEX9+iNa/TFveLuTUZGhmJiYhy5zZ1SFWSvlJubq2XLlikrK0vx8fGO8UWLFumjjz5SZGSkunfvrueff15ly5Z1u53s7GxlZ2c7ljMzMyVJgYGBCgwMLLon8L/sdrvOnz+vwMBAfiiuQm9coy+u0Rf36I1r9MU9euMafXGvuHtjt9sl6bqXgZa6ILt3717Fx8frwoULCgwM1MqVK1WvXj1JUv/+/RUbG6vo6Gjt2bNHzzzzjPbv368VK1a43d6MGTM0derUfOOnT5/WhQsXiux55LHb7UpPT5cxhh+Kq9Ab1+iLa/TFPXrjGn1xj964Rl/cK+7e5J14vJ5SdY2sJF28eFHHjh1Tenq6li9frg8++EBJSUmOMHulL774Qh06dNDBgwdVvXp1l9u7+oxs3qnqs2fPcmlBCaM3rtEX1+iLe/TGNfriHr1xjb64VxKXFoSEhCg9Pf2aea3UnZH19fVVjRo1JElNmzbVd999pzfeeEPvvvtuvrktWrSQpGsGWT8/P/n5+eUb9/LyKraD1GazFev+rITeuEZfXKMv7tEb1+iLe/TGNfriXnH2pqD7KPXfJbvd7nRG9Uq7d++WJEVFRRVjRQAAACgNStUZ2cmTJ6tbt26qUqWKMjMztXjxYiUmJmr9+vU6dOiQFi9erHvvvVdhYWHas2ePnnzySbVu3VqNGjUq6dIBAABQzEpVkE1NTdWgQYN08uRJBQcHq1GjRlq/fr06deqk48ePa+PGjXr99deVlZWlmJgY9e7dW88991xJlw0AAIASUKqC7Ny5c92ui4mJyfepXgAAAPjjKvXXyAIAAACuEGQBAABgSQRZAAAAWBJBFgAAAJZEkAUAAIAlEWQBAABgSQRZAAAAuGW325WcnOz4stvtJV2SQ6m6jywAAABKjy1btmj2O+/pP0dPqGqVGB05dly1YqM1euRwtWrVqqTL44wsAAAA8tuyZYsmPj9NP53zV9Wuw1Wt4wBV7Tpc+7P8NfH5adqyZUtJl0iQBQAAgDO73a7Z77yn88FV1bj7UAVHVZG3TxkFR1VRoz8N1YUKVfX2u++X+GUGBFkAAAA42bdvn/Yf+a/i7uwom83mtM5msym2WQf9dPgX7du3r4QqvIwgCwAAACdnz57VxUu5Klcx0uX6wLBIXbyUq7NnzxZzZc4IsgAAAHASEhIiXx9vZf2a4nL9uTMp8vXxVkhISDFX5owgCwAAACf169dX7aq36cj3G2WMcVpnjNHR7zepTlxl1a9fv4QqvIwgCwAAACdeXl4aPXK4/NOOaM/aeUo7eVS5OReVdvKo9qydJ/+0Ixo14lF5eZVslCTIAgAAIJ9WrVrp1ekvqHa5Czq67n39vGmxjq57X3UCs/Xq9BdKxX1k+UAEAAAAuNSqVSu1bNlSycnJSk1NVaVKldSgQYMSPxObhyALAAAAt7y8vNSgQQNHkC0tIVbi0gIAAABYFEEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCWVqiA7Z84cNWrUSEFBQQoKClJ8fLz+9a9/OdZfuHBBo0ePVlhYmAIDA9W7d2+dOnWqBCsGAABASSlVQbZy5cqaOXOmduzYoe+//17t27dXjx49tG/fPknSk08+qTVr1mjZsmVKSkrSiRMndP/995dw1QAAACgJPiVdwJW6d+/utPzyyy9rzpw52rZtmypXrqy5c+dq8eLFat++vSQpISFBdevW1bZt29SyZcuSKBkAAAAlpFQF2Svl5uZq2bJlysrKUnx8vHbs2KGcnBx17NjRMadOnTqqUqWKtm7d6jbIZmdnKzs727GckZEhSbLb7bLb7UX7JP53P8aYYtmX1dAb1+iLa/TFPXrjGn1xj964Rl/cK+7eFHQ/pS7I7t27V/Hx8bpw4YICAwO1cuVK1atXT7t375avr68qVKjgND8iIkIpKSlutzdjxgxNnTo13/jp06d14cKFwi4/H7vdrvT0dBlj5OVVqq7kKHH0xjX64hp9cY/euEZf3KM3rtEX94q7N5mZmQWaV+qCbO3atbV7926lp6dr+fLlGjx4sJKSkjze3uTJkzV+/HjHckZGhmJiYhQeHq6goKDCKPma7Ha7bDabwsPD+aG4Cr1xjb64Rl/cozeu0Rf36I1r9MW94u6Nv79/geaVuiDr6+urGjVqSJKaNm2q7777Tm+88Yb69OmjixcvKi0tzems7KlTpxQZGel2e35+fvLz88s37uXlVWwHqc1mK9b9WQm9cY2+uEZf3KM3rtEX9+iNa/TFveLsTUH3Ueq/S3a7XdnZ2WratKnKlCmjTZs2Odbt379fx44dU3x8fAlWCAAAgJJQqs7ITp48Wd26dVOVKlWUmZmpxYsXKzExUevXr1dwcLCGDRum8ePHKzQ0VEFBQRozZozi4+O5YwEAAMAfUKkKsqmpqRo0aJBOnjyp4OBgNWrUSOvXr1enTp0kSa+99pq8vLzUu3dvZWdnq0uXLnr77bdLuGoAAACUhFIVZOfOnXvN9f7+/po9e7Zmz55dTBUBAACgtCr118gCAAAArhBkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWVKqC7IwZM3TnnXeqfPnyqlSpknr27Kn9+/c7zWnbtq1sNpvT18iRI0uoYgAAAJSUUhVkk5KSNHr0aG3btk2ff/65cnJy1LlzZ2VlZTnNe/TRR3Xy5EnH16uvvlpCFQMAAKCk+HjyoGPHjik8PFwBAQEu158/f16nT59WlSpVbmi769atc1qeP3++KlWqpB07dqh169aO8bJlyyoyMvLGCwcAAMAtw6MgGxcXp4ULF6p///4u1//zn/9U//79lZube1PFpaenS5JCQ0OdxhctWqSPPvpIkZGR6t69u55//nmVLVvW5Tays7OVnZ3tWM7IyJAk2e122e32m6qvIOx2u4wxxbIvq6E3rtEX1+iLe/TGNfriHr1xjb64V9y9Keh+PAqyxphrrs/JyZGX181dtWC32zVu3DjdddddatCggWO8f//+io2NVXR0tPbs2aNnnnlG+/fv14oVK1xuZ8aMGZo6dWq+8dOnT+vChQs3VWNB2O12paenyxhz0z251dAb1+iLa/TFPXrjGn1xj964Rl/cK+7eZGZmFmhegYNsRkaG0tLSHMtnzpzRsWPH8s1LS0vT0qVLFRUVVdBNuzR69GglJyfr66+/dhofPny4498NGzZUVFSUOnTooEOHDql69er5tjN58mSNHz/e6XnExMQoPDxcQUFBN1VjQdjtdtlsNoWHh/NDcRV64xp9cY2+uEdvXKMv7tEb1+iLe8XdG39//wLNK3CQfe211zRt2jRJks1m07hx4zRu3DiXc40xeumllwq66Xwef/xxrV27Vl9++aUqV658zbktWrSQJB08eNBlkPXz85Ofn1++cS8vr2I7SG02W7Huz0rojWv0xTX64h69cY2+uEdvXKMv7hVnbwq6jwIH2c6dOyswMFDGGE2cOFH9+vXTHXfc4TTHZrOpXLlyatq0qZo1a3ZjFetyAB4zZoxWrlypxMRExcXFXfcxu3fvlqSbPgMMAAAAaylwkI2Pj1d8fLwkKSsrS71793a6drUwjB49WosXL9bq1atVvnx5paSkSJKCg4MVEBCgQ4cOafHixbr33nsVFhamPXv26Mknn1Tr1q3VqFGjQq0FAAAApZtHb/aaMmVKYdchSZozZ46kyx96cKWEhAQ9/PDD8vX11caNG/X6668rKytLMTEx6t27t5577rkiqQcAAACll0dBVpLOnj2rJUuW6Oeff9bZs2fz3cnAZrNp7ty5N7TN690NISYmRklJSTdcKwAAAG49HgXZ9evX64EHHlBWVpaCgoIUEhKSb47NZrvp4gAAAAB3PAqyTz31lCIjI7VixQo1bNiwsGsCAAAArsuj+yccPHhQY8eOJcQCAACgxHgUZGvWrFngT1wAAAAAioJHQfall17S22+/rSNHjhRyOQAAAEDBeHSN7KZNmxQeHq66deuqU6dOiomJkbe3t9Mcm82mN954o1CKBAAAAK7mUZD9xz/+4fj32rVrXc4hyAIAAKAoeRRk7XZ7YdcBAAAA3BCPrpEFAAAAShpBFgAAAJbk0aUFXl5eBfrkrtzcXE82DwAAAFyXR0H2hRdeyBdkc3NzdeTIEa1atUq1a9fWn/70p0IpEAAAAHDFoyD74osvul138uRJtWzZUrVq1fK0JgAAAOC6Cv0a2aioKI0cOVLTp08v7E0DAAAADkXyZq9y5crp8OHDRbFpAAAAQFIRBNnk5GS9+eabXFoAAACAIuXRNbJxcXEu71qQlpam9PR0lS1bVqtWrbrZ2gAAAAC3PAqybdq0yRdkbTabQkJCVL16dfXt21ehoaGFUiAAAADgikdBdv78+YVcBgAAAHBjCuUa2fPnz+v8+fOFsSkAAACgQDwOsseOHdOQIUMUERGhwMBABQYGKiIiQkOHDtXRo0cLs0YAAAAgH48uLfjpp5909913Ky0tTZ06dVLdunUd4x9++KHWrFmjr7/+WrVr1y7UYgEAAIA8HgXZSZMmycvLS7t27VLDhg2d1iUnJ6tDhw6aNGmSVq5cWShFAgAAAFfz6NKCpKQkjR07Nl+IlaQGDRro8ccfV2Ji4s3WBgAAALjlUZDNyclRQECA2/Vly5ZVTk6Ox0UBAAAA1+NRkL399tv1wQcfKD09Pd+6jIwMzZ07V3fcccdNFwcAAAC449E1slOnTlXXrl1Vp04dDRkyxPFxtPv379eCBQt05swZzZ49u1ALBQAAAK7kUZBt3769PvvsMz399NOaOXOm07omTZpo4cKFateuXaEUCAAAALjiUZCVpI4dO2rXrl1KSUlx3Dc2NjZWkZGRhVYcAAAA4I7HQTZPZGQk4RUAAADFrsBv9jpw4ID8/f01ceLEa857+umnFRAQoMOHD990cQAAAIA7BQ6yb775piIjI/Xyyy9fc97LL7+syMhIvfnmmzddHAAAAOBOgYPshg0b1LdvX5UpU+aa83x9fdW3b1/961//uuniAAAAAHcKHGSPHTum2rVrF2huzZo1HW8AAwAAAIpCgYOsn5+fzp07V6C5WVlZ8vX19bgoAAAA4HoKHGTr1KmjjRs3Fmjupk2bVLduXY+LAgAAAK6nwEG2T58+Wrt2rVatWnXNeatXr9batWvVp0+fm60NAAAAcKvAQXbUqFG6/fbb9Ze//EWPPfaYvvnmG2VkZMgYo4yMDH3zzTd67LHH9MADD6hx48YaNWpUUdYNAACAP7gCfyCCn5+f1q9fr8GDB+vdd9/Ve++9l2+OMUZdu3bVhx9+KD8/v0ItFAAAALjSDX2yV1hYmNauXatvv/1W//znP/Xjjz8qIyNDQUFBqlOnjrp3766WLVsWVa0AAACAg0cfUdu8eXM1b968sGsBAAAACqzA18gCAAAApQlBFgAAAJZEkAUAAIAlEWQBAABgSQRZAAAAWFKhBtmff/5ZP/74Y2FuEgAAAHDJoyD75ptvqm/fvk5jQ4YMUc2aNdWgQQM1a9ZMqamphVIgAAAA4IpHQfaDDz5QRESEY3n9+vVasGCBhg8frrfeeks///yzpk6dWmhFAgAAAFfz6AMRjh49qrp16zqWP/nkE8XFxWnOnDmSpJSUFC1cuLBwKgQAAABc8OiMrDHGaXnDhg3q1q2bY7lq1apKSUm5ucoAAACAa/AoyNaqVUsrV66UdPmyghMnTjgF2V9++UUVKlQolAIBAAAAVzy6tGDChAnq37+/QkJClJWVpbp166pLly6O9V988YWaNGlSWDUCAAAA+XgUZPv27auwsDB99tlnqlChgkaNGiUfn8ub+u233xQaGqqBAwcWaqEAAADAlTwKspLUqVMnderUKd94aGioVqxYcVNFAQAAANfjcZC92u+//66lS5cqOztb9957r2JjYwtr0wAAAEA+HgXZYcOGafv27UpOTpYkXbx4US1btnQsBwcH64svvtDtt99eeJUCAAAAV/DorgWbN2/W/fff71hevHixkpOTtWjRIiUnJysyMpIPRAAAAECR8ijIpqSkqGrVqo7lVatWqVmzZurXr5/q1aunRx99VNu3by+sGi3LbrcrOTnZ8WW320u6JAAAgFuGR5cWlCtXTmlpaZKkS5cuKTExUWPGjHGsL1++vNLT0wulQKvasmWLZr/znv5z9ISqVonRkWPHVSs2WqNHDlerVq1KujwAAADL8+iM7B133KH3339fu3bt0ssvv6zMzEx1797dsf7QoUOKiIgotCKtZsuWLZr4/DT9dM5fVbsOV7WOA1S163Dtz/LXxOenacuWLSVdIgAAgOV5FGRffvllpaamqlmzZpo6dap69+6t5s2bO9avXLlSd911V6EVaSV2u12z33lP54OrqnH3oQqOqiJvnzIKjqqiRn8aqgsVqurtd9/nMgMAAICb5NGlBc2aNdNPP/2kLVu2qEKFCmrTpo1jXVpamkaNGuU09keyb98+7T/yX8V1GyGbzSbJONbZbDbFNuugn/71nvbt26eGDRuWXKEAAAAW5/F9ZMPDw9WjR4984xUqVNATTzxxU0VZ2dmzZ3XxUq7KVYx0uT4wLFIXL+Xq7NmzxVwZAADArcWjSwskKTc3V0uXLtWIESPUq1cv7d27V5KUnp6uFStW6NSpU4VWpJWEhITI18dbWb+muFx/7kyKfH28FRISUsyVAQAA3Fo8CrJpaWm666671L9/fy1ZskT//Oc/dfr0aUlSYGCgxo4dqzfeeKNQC7WK+vXrq3bV23Tk+40yxjitM8bo6PebVCeusurXr19CFQIAANwaPAqykyZN0r59+7R+/Xr9/PPPToHN29tbDzzwgD777LNCK9JKvLy8NHrkcPmnHdGetfOUdvKocnMuKu3kUe1ZO0/+aUc0asSj8vLy+GQ4AAAA5GGQXbVqlcaMGaNOnTr97xuanNWqVUtHjhy52dosq1WrVnp1+guqXe6Cjq57Xz9vWqyj695XncBsvTr9Be4jCwAAUAg8erNXenq64uLi3K7PycnRpUuXPC7qVtCqVSu1bNlSycnJSk1NVaVKldSgQQPOxAIAABQSj4Js9erVtXPnTrfrN2zYoHr16nlc1K3Cy8tLDRo0cARZQiwAAEDh8ShZPfLII5o3b54+/vhjx/WxNptN2dnZevbZZ7Vu3TqNGDGiUAsFAAAAruTRGdknnnhC+/btU79+/VShQgVJUv/+/XXmzBldunRJI0aM0LBhwwqzTgAAAMCJR0HWZrPp/fff1+DBg7V8+XIdOHBAdrtd1atX14MPPqjWrVsXdp0AAACAE48/2UuS7r77bt19992FVQsAAABQYB5dI3v48GGtWbPG7fo1a9b8oW+/BQAAgKLn0RnZCRMmKCMjQ927d3e5fvbs2apQoYKWLl16U8UBAAAA7nh0Rnbr1q3q1KmT2/UdOnTQV1995XFRAAAAwPV4FGTPnj2r8uXLu10fGBioM2fO3PB2Z8yYoTvvvFPly5dXpUqV1LNnT+3fv99pzoULFzR69GiFhYUpMDBQvXv31qlTp254XwAAALA2j4JslSpV9M0337hd/9VXX6ly5co3vN2kpCSNHj1a27Zt0+eff66cnBx17txZWVlZjjlPPvmk1qxZo2XLlikpKUknTpzQ/fff78nTAAAAgIV5dI1sv379NH36dDVv3lyPP/644xOrcnNz9Y9//EMff/yxnn322Rve7rp165yW58+fr0qVKmnHjh1q3bq10tPTNXfuXC1evFjt27eXJCUkJKhu3bratm2bWrZs6cnTAQAAgAV5FGQnT56sr7/+WuPGjdPLL7+s2rVrS5L279+v06dPq23bth4F2aulp6dLkkJDQyVJO3bsUE5Ojjp27OiYU6dOHVWpUkVbt251GWSzs7OVnZ3tWM7IyJAk2e122e32m67xeux2u4wxxbIvq6E3rtEX1+iLe/TGNfriHr1xjb64V9y9Keh+PAqyfn5+2rBhgxYsWKAVK1bo0KFDkqTmzZurd+/eGjRokOMsrafsdrvGjRunu+66Sw0aNJAkpaSkyNfX1/FpYnkiIiKUkpLicjszZszQ1KlT842fPn1aFy5cuKkaC8Jutys9PV3GmJvuya2G3rhGX1yjL+7RG9foi3v0xjX64l5x9yYzM7NA8zz+QAQvLy8NGTJEQ4YM8XQT1zR69GglJyfr66+/vqntTJ48WePHj3csZ2RkKCYmRuHh4QoKCrrZMq/LbrfLZrMpPDycH4qr0BvX6Itr9MU9euMafXGP3rhGX9wr7t74+/sXaJ5HQfa3337TL7/8okaNGrlcv3fvXlWuXFkhISGebF6PP/641q5dqy+//NLpTWORkZG6ePGi0tLSnM7Knjp1SpGRkS635efnJz8/v3zjXl5exXaQ2my2Yt2fldAb1+iLa/TFPXrjGn1xj964Rl/cK87eFHQfHlXy5JNPavjw4W7XjxgxQhMmTLjh7Rpj9Pjjj2vlypX64osvFBcX57S+adOmKlOmjDZt2uQY279/v44dO6b4+Pgb3h8AAACsy6Mzsl988YUee+wxt+u7d++ud95554a3O3r0aC1evFirV69W+fLlHde9BgcHKyAgQMHBwRo2bJjGjx+v0NBQBQUFacyYMYqPj+eOBQAAAH8wHgXZ06dPq2LFim7Xh4WFKTU19Ya3O2fOHElS27ZtncYTEhL08MMPS5Jee+01eXl5qXfv3srOzlaXLl309ttv3/C+AAAAYG0eBdmoqCjt2rXL7fodO3YoPDz8hrdrjLnuHH9/f82ePVuzZ8++4e0DAADg1uHRNbI9e/bU3Llz9c9//jPfutWrVyshIUG9evW66eIAAAAAdzw6I/viiy9q48aN6tWrlxo3buy4z2tycrL+/e9/q27dui7v3QoAAAAUFo/OyAYHB2vbtm167rnnlJOTo+XLl2v58uXKycnR888/r+3bt+f70AIAAACgMHn8gQjlypXT1KlT3Z55PXv2rMf3kQUAAACup1DvaJudna1ly5apZ8+eioqKKsxNAwAAAE48PiObxxijTZs2adGiRVq5cqUyMjIUHh6u/v37F0Z9AAAAgEseB9kdO3Zo0aJFWrp0qVJSUmSz2dS3b189/vjjatmypWw2W2HWCQAAADi5oSD7888/a9GiRVq0aJEOHDig2267TQMGDFDz5s3Vp08f9e7dm4+KBQAAQLEocJCNj4/Xt99+q4oVK+qBBx7QBx98oLvvvluSdOjQoSIrEAAAAHClwEF2+/btiouL09///nfdd9998vG56ctrAQAAAI8V+K4F//jHPxQVFaVevXopMjJSI0aM0ObNmwv0sbIAAABAYStwkB01apS+/vprHTp0SOPGjdNXX32lDh066LbbbtMLL7wgm83GG7wAAABQbG74PrJxcXF67rnn9MMPP+i7775T3759lZiYKGOMRo0apeHDh2vt2rW6cOFCUdQLAAAASLrJD0Ro2rSp/v73v+v48ePasGGDunTpoo8//lh//vOfVbFixcKqEQAAAMinUD7Zy8vLSx07dtT8+fN16tQpLVmyRB06dCiMTQMAAAAuFepH1EqSv7+/+vTpo9WrVxf2pgEAAACHQg+yAAAAQHEgyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALIkgCwAAAEsiyAIAAMCSCLIAAACwJIIsAAAALKlUBdkvv/xS3bt3V3R0tGw2m1atWuW0/uGHH5bNZnP66tq1a8kUCwAAgBJVqoJsVlaWGjdurNmzZ7ud07VrV508edLxtWTJkmKsEAAAAKWFT0kXcKVu3bqpW7du15zj5+enyMjIYqoIAAAApVWpCrIFkZiYqEqVKikkJETt27fXSy+9pLCwMLfzs7OzlZ2d7VjOyMiQJNntdtnt9iKv1263yxhTLPuyGnrjGn1xjb64R29coy/u0RvX6It7xd2bgu7HUkG2a9euuv/++xUXF6dDhw7p//yf/6Nu3bpp69at8vb2dvmYGTNmaOrUqfnGT58+rQsXLhR1ybLb7UpPT5cxRl5epepKjhJHb1yjL67RF/fojWv0xT164xp9ca+4e5OZmVmgeZYKsn379nX8u2HDhmrUqJGqV6+uxMREdejQweVjJk+erPHjxzuWMzIyFBMTo/DwcAUFBRV5zXa7XTabTeHh4fxQXIXeuEZfXKMv7tEb1+iLe/TGNfriXnH3xt/fv0DzLBVkr1atWjVVrFhRBw8edBtk/fz85Ofnl2/cy8ur2A5Sm81WrPuzEnrjGn1xjb64R29coy/u0RvX6It7xdmbgu7D0t+lX375RWfOnFFUVFRJlwIAAIBiVqrOyJ47d04HDx50LB8+fFi7d+9WaGioQkNDNXXqVPXu3VuRkZE6dOiQJk6cqBo1aqhLly4lWDUAAABKQqkKst9//73atWvnWM67tnXw4MGaM2eO9uzZowULFigtLU3R0dHq3Lmzpk+f7vLSAQAAANzaSlWQbdu2rYwxbtevX7++GKsBAABAaWbpa2QBAADwx0WQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBFkAAABYEkG2CNntdiUnJzu+7HZ7SZcEAABwyyhVQfbLL79U9+7dFR0dLZvNplWrVjmtN8bohRdeUFRUlAICAtSxY0cdOHCgZIq9ji1btmjgw0M1bMwEvbtgiYaNmaCBDw/Vli1bSro0AACAW0KpCrJZWVlq3LixZs+e7XL9q6++qjfffFPvvPOOtm/frnLlyqlLly66cOFCMVd6bVu2bNHE56fpp3P+qtp1uKp1HKCqXYdrf5a/Jj4/jTALAABQCHxKuoArdevWTd26dXO5zhij119/Xc8995x69OghSfrwww8VERGhVatWqW/fvsVZqlt2u12z33lP54OrqnH3ofKySd4+2QqOqqJGfxqqPWvn6e1331fLli3l5VWqfo8AAACwlFIVZK/l8OHDSklJUceOHR1jwcHBatGihbZu3eo2yGZnZys7O9uxnJGRIely4CyKa1aTk5P1n6MnVK3rcHnZJJuMJCObjLxsNlVt1kH7172v5ORkNWjQoND3byV2u13GGK4dvgp9cY2+uEdvXKMv7tEb1+iLe8Xdm4LuxzJBNiUlRZIUERHhNB4REeFY58qMGTM0derUfOOnT58ukksSUlNTVbVKjKpFh8rbJ1s2GQV758imy3E2LCpUtioxSk1NVWpqaqHv30rsdrvS09NljOHs9BXoi2v0xT164xp9cY/euEZf3Cvu3mRmZhZonmWCrKcmT56s8ePHO5YzMjIUExOj8PBwBQUFFfr+KlWqpCPHjksnflNwVBXZZGQknc7xk5FNaSdTdPTYcVWqVEmVKlUq9P1bid1ul81mU3h4OC8YV6AvrtEX9+iNa/TFPXrjGn1xr7h74+/vX6B5lgmykZGRkqRTp04pKirKMX7q1Ck1adLE7eP8/Pzk5+eXb9zLy6tIvhENGjRQrdho7f9+oxr9aai8bDZdvsDAJruRjny/SXWq3qYGDRrwQyLJZrMV2ffCyuiLa/TFPXrjGn1xj964Rl/cK87eFHQflvkuxcXFKTIyUps2bXKMZWRkaPv27YqPjy/Bypx5eXlp9Mjh8k87oj1r5ynt5FHl5lxU2smj2rN2nvzTjmjUiEf5AQEAALhJpeqM7Llz53Tw4EHH8uHDh7V7926FhoaqSpUqGjdunF566SXVrFlTcXFxev755xUdHa2ePXuWXNEutGrVSq9Of0Gz33lP/1n3vmxVYnT02HHVqXqbRj31glq1alXSJQIAAFheqQqy33//vdq1a+dYzru2dfDgwZo/f74mTpyorKwsDR8+XGlpabr77ru1bt26Al9HUZxatWqlli1bKjk5WampqapUqRKXEwAAABSiUhVk27ZtK2OM2/U2m03Tpk3TtGnTirEqz3l5ealBgwaOIEuIBQAAKDwkKwAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFgSQRYAAACWRJAFAACAJRFkAQAAYEkEWQAAAFiST0kXUNyMMZKkjIyMYtmf3W5XZmam/P395eXF7w1Xojeu0RfX6It79MY1+uIevXGNvrhX3L3Jy2l5uc2dP1yQzczMlCTFxMSUcCUAAAC4lszMTAUHB7tdbzPXi7q3GLvdrhMnTqh8+fKy2WxFvr+MjAzFxMTo+PHjCgoKKvL9WQm9cY2+uEZf3KM3rtEX9+iNa/TFveLujTFGmZmZio6OvuYZ4D/cGVkvLy9Vrly52PcbFBTED4Ub9MY1+uIafXGP3rhGX9yjN67RF/eKszfXOhObhwtAAAAAYEkEWQAAAFgSQbaI+fn5acqUKfLz8yvpUkodeuMafXGNvrhHb1yjL+7RG9foi3ultTd/uDd7AQAA4NbAGVkAAABYEkEWAAAAlkSQBQAAgCURZAEAAGBJBNkb8OWXX6p79+6Kjo6WzWbTqlWrrvuYxMRE3XHHHfLz81ONGjU0f/78fHNmz56tqlWryt/fXy1atNC3335b+MUXsRvtzYoVK9SpUyeFh4crKChI8fHxWr9+vdOcF198UTabzemrTp06RfgsCt+N9iUxMTHfc7bZbEpJSXGa90c8Zh5++GGXvalfv75jzq1wzMyYMUN33nmnypcvr0qVKqlnz57av3//dR+3bNky1alTR/7+/mrYsKE+++wzp/XGGL3wwguKiopSQECAOnbsqAMHDhTV0yh0nvTl/fff1z333KOQkBCFhISoY8eO+X5WXB1XXbt2LcqnUqg86cv8+fPzPWd/f3+nOVY/XiTPetO2bVuXrzP33XefY47Vj5k5c+aoUaNGjg82iI+P17/+9a9rPqY0v74QZG9AVlaWGjdurNmzZxdo/uHDh3XfffepXbt22r17t8aNG6dHHnnEKbB9/PHHGj9+vKZMmaKdO3eqcePG6tKli1JTU4vqaRSJG+3Nl19+qU6dOumzzz7Tjh071K5dO3Xv3l27du1ymle/fn2dPHnS8fX1118XRflF5kb7kmf//v1Oz7tSpUqOdX/UY+aNN95w6snx48cVGhqqv/zlL07zrH7MJCUlafTo0dq2bZs+//xz5eTkqHPnzsrKynL7mC1btqhfv34aNmyYdu3apZ49e6pnz55KTk52zHn11Vf15ptv6p133tH27dtVrlw5denSRRcuXCiOp3XTPOlLYmKi+vXrp82bN2vr1q2KiYlR586d9d///tdpXteuXZ2OmSVLlhT10yk0nvRFuvzpTFc+56NHjzqtt/rxInnWmxUrVjj1JTk5Wd7e3vleZ6x8zFSuXFkzZ87Ujh079P3336t9+/bq0aOH9u3b53J+qX99MfCIJLNy5cprzpk4caKpX7++01ifPn1Mly5dHMvNmzc3o0ePdizn5uaa6OhoM2PGjEKttzgVpDeu1KtXz0ydOtWxPGXKFNO4cePCK6yEFaQvmzdvNpLM2bNn3c7hmLls5cqVxmazmSNHjjjGbrVjxhhjUlNTjSSTlJTkds6DDz5o7rvvPqexFi1amBEjRhhjjLHb7SYyMtL89a9/daxPS0szfn5+ZsmSJUVTeBErSF+udunSJVO+fHmzYMECx9jgwYNNjx49iqDCklGQviQkJJjg4GC362/F48UYz46Z1157zZQvX96cO3fOMXarHTPGGBMSEmI++OADl+tK++sLZ2SL0NatW9WxY0ensS5dumjr1q2SpIsXL2rHjh1Oc7y8vNSxY0fHnD8Ku92uzMxMhYaGOo0fOHBA0dHRqlatmgYMGKBjx46VUIXFq0mTJoqKilKnTp30zTffOMY5Zv6/uXPnqmPHjoqNjXUav9WOmfT0dEnK97Nxpeu91hw+fFgpKSlOc4KDg9WiRQvLHjcF6cvVfv/9d+Xk5OR7TGJioipVqqTatWvrscce05kzZwq11uJU0L6cO3dOsbGxiomJyXc27lY8XiTPjpm5c+eqb9++KleunNP4rXLM5ObmaunSpcrKylJ8fLzLOaX99YUgW4RSUlIUERHhNBYREaGMjAydP39ev/76q3Jzc13OufqayFvdrFmzdO7cOT344IOOsRYtWmj+/Plat26d5syZo8OHD+uee+5RZmZmCVZatKKiovTOO+/o008/1aeffqqYmBi1bdtWO3fulCSOmf914sQJ/etf/9IjjzziNH6rHTN2u13jxo3TXXfdpQYNGrid5+61Ju+YyPvvrXLcFLQvV3vmmWcUHR3t9D/crl276sMPP9SmTZv0yiuvKCkpSd26dVNubm5RlF6kCtqX2rVra968eVq9erU++ugj2e12tWrVSr/88oukW+94kTw7Zr799lslJyfne525FY6ZvXv3KjAwUH5+fho5cqRWrlypevXquZxb2l9ffIp8D8B1LF68WFOnTtXq1audrgXt1q2b49+NGjVSixYtFBsbq08++UTDhg0riVKLXO3atVW7dm3HcqtWrXTo0CG99tprWrhwYQlWVrosWLBAFSpUUM+ePZ3Gb7VjZvTo0UpOTrbcdb5FzZO+zJw5U0uXLlViYqLTG5v69u3r+HfDhg3VqFEjVa9eXYmJierQoUOh1l3UCtqX+Ph4p7NvrVq1Ut26dfXuu+9q+vTpRV1mifDkmJk7d64aNmyo5s2bO43fCsdM7dq1tXv3bqWnp2v58uUaPHiwkpKS3IbZ0owzskUoMjJSp06dcho7deqUgoKCFBAQoIoVK8rb29vlnMjIyOIstcQsXbpUjzzyiD755JN8f7q4WoUKFVSrVi0dPHiwmKorHZo3b+54zhwzl98dO2/ePA0cOFC+vr7XnGvlY+bxxx/X2rVrtXnzZlWuXPmac9291uQdE3n/vRWOmxvpS55Zs2Zp5syZ2rBhgxo1anTNudWqVVPFihUtd8x40pc8ZcqU0e233+54zrfS8SJ51pusrCwtXbq0QL8AW/GY8fX1VY0aNdS0aVPNmDFDjRs31htvvOFybml/fSHIFqH4+Hht2rTJaezzzz93/Cbs6+urpk2bOs2x2+3atGmT22tVbiVLlizRkCFDtGTJEqdbm7hz7tw5HTp0SFFRUcVQXemxe/dux3P+ox8z0uV3Ih88eLBA/4Ox4jFjjNHjjz+ulStX6osvvlBcXNx1H3O915q4uDhFRkY6zcnIyND27dstc9x40hfp8rupp0+frnXr1qlZs2bXnf/LL7/ozJkzljlmPO3LlXJzc7V3717Hc74Vjhfp5nqzbNkyZWdn66GHHrruXKsdM67Y7XZlZ2e7XFfqX1+K/O1kt5DMzEyza9cus2vXLiPJ/P3vfze7du0yR48eNcYYM2nSJDNw4EDH/J9//tmULVvWPP300+bHH380s2fPNt7e3mbdunWOOUuXLjV+fn5m/vz55ocffjDDhw83FSpUMCkpKcX+/G7GjfZm0aJFxsfHx8yePducPHnS8ZWWluaY89RTT5nExERz+PBh880335iOHTuaihUrmtTU1GJ/fp660b689tprZtWqVebAgQNm79695oknnjBeXl5m48aNjjl/1GMmz0MPPWRatGjhcpu3wjHz2GOPmeDgYJOYmOj0s/H777875gwcONBMmjTJsfzNN98YHx8fM2vWLPPjjz+aKVOmmDJlypi9e/c65sycOdNUqFDBrF692uzZs8f06NHDxMXFmfPnzxfr8/OUJ32ZOXOm8fX1NcuXL3d6TGZmpjHm8jE4YcIEs3XrVnP48GGzceNGc8cdd5iaNWuaCxcuFPtz9IQnfZk6dapZv369OXTokNmxY4fp27ev8ff3N/v27XPMsfrxYoxnvclz9913mz59+uQbvxWOmUmTJpmkpCRz+PBhs2fPHjNp0iRjs9nMhg0bjDHWe30hyN6AvFsjXf01ePBgY8zlW3K0adMm32OaNGlifH19TbVq1UxCQkK+7b711lumSpUqxtfX1zRv3txs27at6J9MIbvR3rRp0+aa8425fKuyqKgo4+vra2677TbTp08fc/DgweJ9YjfpRvvyyiuvmOrVqxt/f38TGhpq2rZta7744ot82/0jHjPGXL6lS0BAgHnvvfdcbvNWOGZc9USS02tHmzZtnH5WjDHmk08+MbVq1TK+vr6mfv365n/+53+c1tvtdvP888+biIgI4+fnZzp06GD2799fDM+ocHjSl9jYWJePmTJlijHGmN9//9107tzZhIeHmzJlypjY2Fjz6KOPWuqXQk/6Mm7cOMfrR0REhLn33nvNzp07nbZr9ePFGM9/ln766ScjyRHsrnQrHDNDhw41sbGxxtfX14SHh5sOHTo4PVervb7YjDGmkE7uAgAAAMWGa2QBAABgSQRZAAAAWBJBFgAAAJZEkAUAAIAlEWQBAABgSQRZAAAAWBJBFgAAAJZEkAUAAIAlEWQB4BZTtWpV/elPf/L48fPnz5fNZtORI0cKrygAKAIEWQAoBHnh7/vvvy/pUgDgD4MgCwAAAEsiyAIAAMCSCLIAUAwuXryoF154QU2bNlVwcLDKlSune+65R5s3b3aad+TIEdlsNs2aNUuzZ89WtWrVVLZsWXXu3FnHjx+XMUbTp09X5cqVFRAQoB49eui3335zuc8NGzaoSZMm8vf3V7169bRixYp8c/bt26f27dsrICBAlStX1ksvvSS73Z5v3urVq3XfffcpOjpafn5+ql69uqZPn67c3NzCaRAAeMBmjDElXQQAWN38+fM1ZMgQfffdd2rWrFm+9b/++qsaNWqkfv36qWbNmsrMzNTcuXP1888/69tvv1WTJk0kXQ6ycXFxatKkiS5evKhHHnlEv/32m1599VXdcccdat++vRITE9W3b18dPHhQb731lh5++GHNmzfPsa+qVavKz89PqampGjlypCpVqqSEhATt27dP69atU6dOnSRJKSkpatSokS5duqQnnnhC5cqV03vvvaeAgADt2bNHhw8fVtWqVSVJvXr1kq+vr+68804FBgbqiy++0LJlyzRhwgT99a9/LfL+AoBLBgBw0xISEowk891337lcf+nSJZOdne00dvbsWRMREWGGDh3qGDt8+LCRZMLDw01aWppjfPLkyUaSady4scnJyXGM9+vXz/j6+poLFy44xmJjY40k8+mnnzrG0tPTTVRUlLn99tsdY+PGjTOSzPbt2x1jqampJjg42Egyhw8fdoz//vvv+Z7TiBEjTNmyZZ32DQDFiUsLAKAYeHt7y9fXV5Jkt9v122+/6dKlS2rWrJl27tyZb/5f/vIXBQcHO5ZbtGghSXrooYfk4+PjNH7x4kX997//dXp8dHS0evXq5VgOCgrSoEGDtGvXLqWkpEiSPvvsM7Vs2VLNmzd3zAsPD9eAAQPy1RMQEOD4d2Zmpn799Vfdc889+v333/XTTz/dUC8AoLAQZAGgmCxYsECNGjWSv7+/wsLCFB4erv/5n/9Renp6vrlVqlRxWs4LtTExMS7Hz5496zReo0YN2Ww2p7FatWpJkuP+sEePHlXNmjXz7bt27dr5xvbt26devXopODhYQUFBCg8P10MPPSRJLusHgOLgc/0pAICb9dFHH+nhhx9Wz5499fTTT6tSpUry9vbWjBkzdOjQoXzzvb29XW7H3bgpwrc7pKWlqU2bNgoKCtK0adNUvXp1+fv7a+fOnXrmmWdcvjkMAIoDQRYAisHy5ctVrVo1rVixwulM6ZQpU4pkfwcPHpQxxmlf//nPfyTJ8Qau2NhYHThwIN9j9+/f77ScmJioM2fOaMWKFWrdurVj/PDhw0VQOQAUHJcWAEAxyDuTeuWZ0+3bt2vr1q1Fsr8TJ05o5cqVjuWMjAx9+OGHatKkiSIjIyVJ9957r7Zt26Zvv/3WMe/06dNatGjRdWu/ePGi3n777SKpHQAKijOyAFCI5s2bp3Xr1uUbb9u2rVasWKFevXrpvvvu0+HDh/XOO++oXr16OnfuXKHXUatWLQ0bNkzfffedIiIiNG/ePJ06dUoJCQmOORMnTtTChQvVtWtXp9tvxcbGas+ePY55rVq1UkhIiAYPHqyxY8fKZrNp4cKFRXo5AwAUBEEWAArRnDlzXI4fO3ZM586d07vvvqv169erXr16+uijj7Rs2TIlJiYWeh01a9bUW2+9paefflr79+9XXFycPv74Y3Xp0sUxJyoqSps3b9aYMWM0c+ZMhYWFaeTIkYqOjtawYcMc88LCwrR27Vo99dRTeu655xQSEqKHHnpIHTp0cNoeABQ3PhABAAAAlsQ1sgAAALAkgiwAAAAsiSALAAAASyLIAgAAwJIIsgAAALAkgiwAAAAsiSALAAAASyLIAgAAwJIIsgAAALAkgiwAAAAsiSALAAAASyLIAgAAwJL+H7PAGYfqJ5AHAAAAAElFTkSuQmCC", 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" ] @@ -934,13 +862,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "00a12eea-c805-4209-9143-48fa65619873", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -973,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "adbfeb40-76bd-4224-ac45-65c7b2b2cb7b", "metadata": {}, "outputs": [], @@ -988,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "1f550686-3463-4e50-be83-ceafb27512b0", "metadata": {}, "outputs": [], @@ -1008,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "b47990b1-0231-43ac-8bc5-8340abe4a8b3", "metadata": {}, "outputs": [], @@ -1021,7 +949,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "db83cad4-7cc6-4702-ae3a-d1af30a561d2", "metadata": {}, "outputs": [],