From e223d704c7a1a87eaea826c9e422502a8177429e Mon Sep 17 00:00:00 2001
From: Francesco Murdaca <fmurdaca@redhat.com>
Date: Tue, 29 Sep 2020 15:48:21 +0200
Subject: [PATCH] Adjust scaling issues and saving

Signed-off-by: Francesco Murdaca <fmurdaca@redhat.com>
---
 Pipfile                                       |    3 +-
 Pipfile.lock                                  |  765 ++++-
 ...Detection-using-TF-and-Deep-Learning.ipynb | 2823 +++--------------
 3 files changed, 1087 insertions(+), 2504 deletions(-)

diff --git a/Pipfile b/Pipfile
index 9944849..b8cdc46 100644
--- a/Pipfile
+++ b/Pipfile
@@ -4,12 +4,13 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
-tensorflow = "==2.2.0"
+tensorflow = "==2.3.0"
 pandas = "*"
 matplotlib = "*"
 numpy = "*"
 scikit-learn = "*"
 seaborn = "*"
+jupyter = "*"
 
 [dev-packages]
 
diff --git a/Pipfile.lock b/Pipfile.lock
index 9ee38e5..4d7aa79 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "882c51fb4e2b98c6d5c302b38903acca334cef38a59e3bab5e93060b85b49ea2"
+            "sha256": "c2726b817711057d17090eed0eaab9b0b497f2273b46f21818f25e0c5b959b09"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -23,6 +23,27 @@
             ],
             "version": "==0.10.0"
         },
+        "argon2-cffi": {
+            "hashes": [
+                "sha256:05a8ac07c7026542377e38389638a8a1e9b78f1cd8439cd7493b39f08dd75fbf",
+                "sha256:0bf066bc049332489bb2d75f69216416329d9dc65deee127152caeb16e5ce7d5",
+                "sha256:18dee20e25e4be86680b178b35ccfc5d495ebd5792cd00781548d50880fee5c5",
+                "sha256:392c3c2ef91d12da510cfb6f9bae52512a4552573a9e27600bdb800e05905d2b",
+                "sha256:57358570592c46c420300ec94f2ff3b32cbccd10d38bdc12dc6979c4a8484fbc",
+                "sha256:6678bb047373f52bcff02db8afab0d2a77d83bde61cfecea7c5c62e2335cb203",
+                "sha256:6ea92c980586931a816d61e4faf6c192b4abce89aa767ff6581e6ddc985ed003",
+                "sha256:77e909cc756ef81d6abb60524d259d959bab384832f0c651ed7dcb6e5ccdbb78",
+                "sha256:7d455c802727710e9dfa69b74ccaab04568386ca17b0ad36350b622cd34606fe",
+                "sha256:9bee3212ba4f560af397b6d7146848c32a800652301843df06b9e8f68f0f7361",
+                "sha256:9dfd5197852530294ecb5795c97a823839258dfd5eb9420233c7cfedec2058f2",
+                "sha256:b160416adc0f012fb1f12588a5e6954889510f82f698e23ed4f4fa57f12a0647",
+                "sha256:ba7209b608945b889457f949cc04c8e762bed4fe3fec88ae9a6b7765ae82e496",
+                "sha256:cc0e028b209a5483b6846053d5fd7165f460a1f14774d79e632e75e7ae64b82b",
+                "sha256:d8029b2d3e4b4cea770e9e5a0104dd8fa185c1724a0f01528ae4826a6d25f97d",
+                "sha256:da7f0445b71db6d3a72462e04f36544b0de871289b0bc8a7cc87c0f5ec7079fa"
+            ],
+            "version": "==20.1.0"
+        },
         "astunparse": {
             "hashes": [
                 "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872",
@@ -30,6 +51,37 @@
             ],
             "version": "==1.6.3"
         },
+        "async-generator": {
+            "hashes": [
+                "sha256:01c7bf666359b4967d2cda0000cc2e4af16a0ae098cbffcb8472fb9e8ad6585b",
+                "sha256:6ebb3d106c12920aaae42ccb6f787ef5eefdcdd166ea3d628fa8476abe712144"
+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==1.10"
+        },
+        "attrs": {
+            "hashes": [
+                "sha256:26b54ddbbb9ee1d34d5d3668dd37d6cf74990ab23c828c2888dccdceee395594",
+                "sha256:fce7fc47dfc976152e82d53ff92fa0407700c21acd20886a13777a0d20e655dc"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==20.2.0"
+        },
+        "backcall": {
+            "hashes": [
+                "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e",
+                "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"
+            ],
+            "version": "==0.2.0"
+        },
+        "bleach": {
+            "hashes": [
+                "sha256:52b5919b81842b1854196eaae5ca29679a2f2e378905c346d3ca8227c2c66080",
+                "sha256:9f8ccbeb6183c6e6cddea37592dfb0167485c1e3b13b3363bc325aa8bda3adbd"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+            "version": "==3.2.1"
+        },
         "cachetools": {
             "hashes": [
                 "sha256:513d4ff98dd27f85743a8dc0e92f55ddb1b49e060c2d5961512855cda2c01a98",
@@ -45,6 +97,47 @@
             ],
             "version": "==2020.6.20"
         },
+        "cffi": {
+            "hashes": [
+                "sha256:005f2bfe11b6745d726dbb07ace4d53f057de66e336ff92d61b8c7e9c8f4777d",
+                "sha256:09e96138280241bd355cd585148dec04dbbedb4f46128f340d696eaafc82dd7b",
+                "sha256:0b1ad452cc824665ddc682400b62c9e4f5b64736a2ba99110712fdee5f2505c4",
+                "sha256:0ef488305fdce2580c8b2708f22d7785ae222d9825d3094ab073e22e93dfe51f",
+                "sha256:15f351bed09897fbda218e4db5a3d5c06328862f6198d4fb385f3e14e19decb3",
+                "sha256:22399ff4870fb4c7ef19fff6eeb20a8bbf15571913c181c78cb361024d574579",
+                "sha256:23e5d2040367322824605bc29ae8ee9175200b92cb5483ac7d466927a9b3d537",
+                "sha256:2791f68edc5749024b4722500e86303a10d342527e1e3bcac47f35fbd25b764e",
+                "sha256:2f9674623ca39c9ebe38afa3da402e9326c245f0f5ceff0623dccdac15023e05",
+                "sha256:3363e77a6176afb8823b6e06db78c46dbc4c7813b00a41300a4873b6ba63b171",
+                "sha256:33c6cdc071ba5cd6d96769c8969a0531be2d08c2628a0143a10a7dcffa9719ca",
+                "sha256:3b8eaf915ddc0709779889c472e553f0d3e8b7bdf62dab764c8921b09bf94522",
+                "sha256:3cb3e1b9ec43256c4e0f8d2837267a70b0e1ca8c4f456685508ae6106b1f504c",
+                "sha256:3eeeb0405fd145e714f7633a5173318bd88d8bbfc3dd0a5751f8c4f70ae629bc",
+                "sha256:44f60519595eaca110f248e5017363d751b12782a6f2bd6a7041cba275215f5d",
+                "sha256:4d7c26bfc1ea9f92084a1d75e11999e97b62d63128bcc90c3624d07813c52808",
+                "sha256:529c4ed2e10437c205f38f3691a68be66c39197d01062618c55f74294a4a4828",
+                "sha256:6642f15ad963b5092d65aed022d033c77763515fdc07095208f15d3563003869",
+                "sha256:85ba797e1de5b48aa5a8427b6ba62cf69607c18c5d4eb747604b7302f1ec382d",
+                "sha256:8f0f1e499e4000c4c347a124fa6a27d37608ced4fe9f7d45070563b7c4c370c9",
+                "sha256:a624fae282e81ad2e4871bdb767e2c914d0539708c0f078b5b355258293c98b0",
+                "sha256:b0358e6fefc74a16f745afa366acc89f979040e0cbc4eec55ab26ad1f6a9bfbc",
+                "sha256:bbd2f4dfee1079f76943767fce837ade3087b578aeb9f69aec7857d5bf25db15",
+                "sha256:bf39a9e19ce7298f1bd6a9758fa99707e9e5b1ebe5e90f2c3913a47bc548747c",
+                "sha256:c11579638288e53fc94ad60022ff1b67865363e730ee41ad5e6f0a17188b327a",
+                "sha256:c150eaa3dadbb2b5339675b88d4573c1be3cb6f2c33a6c83387e10cc0bf05bd3",
+                "sha256:c53af463f4a40de78c58b8b2710ade243c81cbca641e34debf3396a9640d6ec1",
+                "sha256:cb763ceceae04803adcc4e2d80d611ef201c73da32d8f2722e9d0ab0c7f10768",
+                "sha256:cc75f58cdaf043fe6a7a6c04b3b5a0e694c6a9e24050967747251fb80d7bce0d",
+                "sha256:d80998ed59176e8cba74028762fbd9b9153b9afc71ea118e63bbf5d4d0f9552b",
+                "sha256:de31b5164d44ef4943db155b3e8e17929707cac1e5bd2f363e67a56e3af4af6e",
+                "sha256:e66399cf0fc07de4dce4f588fc25bfe84a6d1285cc544e67987d22663393926d",
+                "sha256:f0620511387790860b249b9241c2f13c3a80e21a73e0b861a2df24e9d6f56730",
+                "sha256:f4eae045e6ab2bb54ca279733fe4eb85f1effda392666308250714e01907f394",
+                "sha256:f92cdecb618e5fa4658aeb97d5eb3d2f47aa94ac6477c6daf0f306c5a3b9e6b1",
+                "sha256:f92f789e4f9241cd262ad7a555ca2c648a98178a953af117ef7fad46aa1d5591"
+            ],
+            "version": "==1.14.3"
+        },
         "chardet": {
             "hashes": [
                 "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae",
@@ -59,6 +152,29 @@
             ],
             "version": "==0.10.0"
         },
+        "decorator": {
+            "hashes": [
+                "sha256:41fa54c2a0cc4ba648be4fd43cff00aedf5b9465c9bf18d64325bc225f08f760",
+                "sha256:e3a62f0520172440ca0dcc823749319382e377f37f140a0b99ef45fecb84bfe7"
+            ],
+            "version": "==4.4.2"
+        },
+        "defusedxml": {
+            "hashes": [
+                "sha256:6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93",
+                "sha256:f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+            "version": "==0.6.0"
+        },
+        "entrypoints": {
+            "hashes": [
+                "sha256:589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19",
+                "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"
+            ],
+            "markers": "python_version >= '2.7'",
+            "version": "==0.3"
+        },
         "gast": {
             "hashes": [
                 "sha256:8f46f5be57ae6889a4e16e2ca113b1703ef17f2b0abceb83793eaba9e1351a45",
@@ -69,11 +185,11 @@
         },
         "google-auth": {
             "hashes": [
-                "sha256:bcbd9f970e7144fe933908aa286d7a12c44b7deb6d78a76871f0377a29d09789",
-                "sha256:f4d5093f13b1b1c0a434ab1dc851cd26a983f86a4d75c95239974e33ed406a87"
+                "sha256:31941bf019fb242c04d0de32845da10180788bfddb0de87d78c4bdf55555dda1",
+                "sha256:873051a6317294b083795cffc467bcd05b6df483ef542bfe0069ddbfbac0a096"
             ],
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
-            "version": "==1.21.1"
+            "version": "==1.21.3"
         },
         "google-auth-oauthlib": {
             "hashes": [
@@ -92,47 +208,47 @@
         },
         "grpcio": {
             "hashes": [
-                "sha256:013287f99c99b201aa8a5f6bc7918f616739b9be031db132d9e3b8453e95e151",
-                "sha256:0397616355760cd8282ed5ea34d51830ae4cb6613b7e5f66bed3be5d041b8b9a",
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-                "sha256:08a9b648dbe8852ff94b73a1c96da126834c3057ba2301d13e8c4adff334c482",
-                "sha256:0fa86ac4452602c79774783aa68979a1a7625ebb7eaabee2b6550b975b9d61e6",
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-                "sha256:c22b19abba63562a5a200e586b5bde39d26c8ec30c92e26d209d81182371693b",
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-                "sha256:d2c5e05c257859febd03f5d81b5015e1946d6bcf475c7bf63ee99cea8ab0d590",
-                "sha256:e64bddd09842ef508d72ca354319b0eb126205d951e8ac3128fe9869bd563552",
-                "sha256:e8c3264b0fd728aadf3f0324471843f65bd3b38872bdab2a477e31ffb685dd5b",
-                "sha256:ea849210e7362559f326cbe603d5b8d8bb1e556e86a7393b5a8847057de5b084",
-                "sha256:ebb2ca09fa17537e35508a29dcb05575d4d9401138a68e83d1c605d65e8a1770",
-                "sha256:ef9fce98b6fe03874c2a6576b02aec1a0df25742cd67d1d7b75a49e30aa74225",
-                "sha256:f04c59d186af3157dc8811114130aaeae92e90a65283733f41de94eed484e1f7",
-                "sha256:f5b0870b733bcb7b6bf05a02035e7aaf20f599d3802b390282d4c2309f825f1d"
-            ],
-            "version": "==1.31.0"
+                "sha256:01d3046fe980be25796d368f8fc5ff34b7cf5e1444f3789a017a7fe794465639",
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+                "sha256:14c0f017bfebbc18139551111ac58ecbde11f4bc375b73a53af38927d60308b6",
+                "sha256:182c64ade34c341398bf71ec0975613970feb175090760ab4f51d1e9a5424f05",
+                "sha256:1ada89326a364a299527c7962e5c362dbae58c67b283fe8383c4d952b26565d5",
+                "sha256:1ce6f5ff4f4a548c502d5237a071fa617115df58ea4b7bd41dac77c1ab126e9c",
+                "sha256:1d384a61f96a1fc6d5d3e0b62b0a859abc8d4c3f6d16daba51ebf253a3e7df5d",
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+                "sha256:28677f057e2ef11501860a7bc15de12091d40b95dd0fddab3c37ff1542e6b216",
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+                "sha256:6f7947dad606c509d067e5b91a92b250aa0530162ab99e4737090f6b17eb12c4",
+                "sha256:7cda998b7b551503beefc38db9be18c878cfb1596e1418647687575cdefa9273",
+                "sha256:99bac0e2c820bf446662365df65841f0c2a55b0e2c419db86eaf5d162ddae73e",
+                "sha256:9c0d8f2346c842088b8cbe3e14985b36e5191a34bf79279ba321a4bf69bd88b7",
+                "sha256:a8004b34f600a8a51785e46859cd88f3386ef67cccd1cfc7598e3d317608c643",
+                "sha256:ac7028d363d2395f3d755166d0161556a3f99500a5b44890421ccfaaf2aaeb08",
+                "sha256:be98e3198ec765d0a1e27f69d760f69374ded8a33b953dcfe790127731f7e690",
+                "sha256:c31e8a219650ddae1cd02f5a169e1bffe66a429a8255d3ab29e9363c73003b62",
+                "sha256:c4966d746dccb639ef93f13560acbe9630681c07f2b320b7ec03fe2c8f0a1f15",
+                "sha256:c58825a3d8634cd634d8f869afddd4d5742bdb59d594aea4cea17b8f39269a55",
+                "sha256:ce617e1c4a39131f8527964ac9e700eb199484937d7a0b3e52655a3ba50d5fb9",
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@@ -178,11 +294,57 @@
         },
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@@ -192,6 +354,53 @@
             "markers": "python_version >= '3.6'",
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         },
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@@ -232,70 +441,142 @@
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+                "sha256:ac792b385d81151bae2a5a8adb2b88261ceb4976dbfaaad9ce3a200e036753dc",
+                "sha256:b03b2c0badeb606d1232e5f78852c102c0a7989d3a534b3129e7856a52f3d161",
+                "sha256:b39321f1a74d1f9183bf1638a745b4fd6fe80efbb1f6b32b932a588b4bc7695f",
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+                "sha256:cd49930af1d1e49a812d987c2620ee63965b619257bd76eaaa95870ca08837cf",
+                "sha256:e15b382603c58f24265c9c931c9a45eebf44fe2e6b4eaedbb0d025ab3255228b",
+                "sha256:e91d31b34fc7c2c8f756b4e902f901f856ae53a93399368d9a0dc7be17ed2ca0",
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             "index": "pypi",
-            "version": "==1.19.1"
+            "version": "==1.18.5"
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             "hashes": [
@@ -313,27 +594,64 @@
             "markers": "python_version >= '3.5'",
             "version": "==3.3.0"
         },
+        "packaging": {
+            "hashes": [
+                "sha256:4357f74f47b9c12db93624a82154e9b120fa8293699949152b22065d556079f8",
+                "sha256:998416ba6962ae7fbd6596850b80e17859a5753ba17c32284f67bfff33784181"
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+            "version": "==20.4"
+        },
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             "index": "pypi",
-            "version": "==1.1.1"
+            "version": "==1.1.2"
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+            "version": "==0.7.1"
+        },
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+                "sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c"
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+            "markers": "sys_platform != 'win32'",
+            "version": "==4.8.0"
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+        "pickleshare": {
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+                "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"
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             "hashes": [
@@ -369,6 +687,21 @@
             "markers": "python_version >= '3.5'",
             "version": "==7.2.0"
         },
+        "prometheus-client": {
+            "hashes": [
+                "sha256:983c7ac4b47478720db338f1491ef67a100b474e3bc7dafcbaefb7d0b8f9b01c",
+                "sha256:c6e6b706833a6bd1fd51711299edee907857be10ece535126a158f911ee80915"
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+        "prompt-toolkit": {
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+                "sha256:83074ee28ad4ba6af190593d4d4c607ff525272a504eb159199b6dd9f950c950"
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+            "markers": "python_full_version >= '3.6.1'",
+            "version": "==3.0.7"
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         "protobuf": {
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                 "sha256:0bba42f439bf45c0f600c3c5993666fcb88e8441d011fad80a11df6f324eef33",
@@ -392,6 +725,14 @@
             ],
             "version": "==3.13.0"
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+        "ptyprocess": {
+            "hashes": [
+                "sha256:923f299cc5ad920c68f2bc0bc98b75b9f838b93b599941a6b63ddbc2476394c0",
+                "sha256:d7cc528d76e76342423ca640335bd3633420dc1366f258cb31d05e865ef5ca1f"
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+            "markers": "os_name != 'nt'",
+            "version": "==0.6.0"
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                 "sha256:014c0e9976956a08139dc0712ae195324a75e142284d5f87f1a87ee1b068a359",
@@ -428,6 +769,22 @@
             ],
             "version": "==0.2.8"
         },
+        "pycparser": {
+            "hashes": [
+                "sha256:2d475327684562c3a96cc71adf7dc8c4f0565175cf86b6d7a404ff4c771f15f0",
+                "sha256:7582ad22678f0fcd81102833f60ef8d0e57288b6b5fb00323d101be910e35705"
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+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==2.20"
+        },
+        "pygments": {
+            "hashes": [
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+                "sha256:926c3f319eda178d1bd90851e4317e6d8cdb5e292a3386aac9bd75eca29cf9c7"
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+            "markers": "python_version >= '3.5'",
+            "version": "==2.7.1"
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                 "sha256:c203ec8783bf771a155b207279b9bccb8dea02d8f0c9e5f8ead507bc3246ecc1",
@@ -436,6 +793,13 @@
             "markers": "python_version >= '2.6' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==2.4.7"
         },
+        "pyrsistent": {
+            "hashes": [
+                "sha256:2e636185d9eb976a18a8a8e96efce62f2905fea90041958d8cc2a189756ebf3e"
+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==0.17.3"
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         "python-dateutil": {
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                 "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c",
@@ -451,6 +815,54 @@
             ],
             "version": "==2020.1"
         },
+        "pyzmq": {
+            "hashes": [
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+                "sha256:fa411b1d8f371d3a49d31b0789eb6da2537dadbb2aef74a43aa99a78195c3f76"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==19.0.2"
+        },
+        "qtconsole": {
+            "hashes": [
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+            ],
+            "version": "==4.7.7"
+        },
+        "qtpy": {
+            "hashes": [
+                "sha256:2db72c44b55d0fe1407be8fba35c838ad0d6d3bb81f23007886dc1fc0f459c8d",
+                "sha256:fa0b8363b363e89b2a6f49eddc162a04c0699ae95e109a6be3bb145a913190ea"
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         "requests": {
             "hashes": [
                 "sha256:b3559a131db72c33ee969480840fff4bb6dd111de7dd27c8ee1f820f4f00231b",
@@ -521,16 +933,23 @@
                 "sha256:dc60bb302f48acf6da8ca4444cfa17d52c63c5415302a9ee77b3b21618090521",
                 "sha256:dee1bbf3a6c8f73b6b218cb28eed8dd13347ea2f87d572ce19b289d6fd3fbc59"
             ],
-            "markers": "python_version >= '3'",
+            "markers": "python_version >= '3.5'",
             "version": "==1.4.1"
         },
         "seaborn": {
             "hashes": [
-                "sha256:2d1a0c9d6bd1bc3cadb0364b8f06540f51322a670cf8438d0fde1c1c7317adc0",
-                "sha256:c901ce494541fb4714cfa7db79d0232dc3f4c4dfd3f273bacf17816084df5b53"
+                "sha256:390f8437b14f5ce845062f2865ad51656464c306d09bb97d7764c6cba1dd607c",
+                "sha256:62439a38482decdb263a8339f54ecb9823995ad8716abc830e91ca0753201e70"
             ],
             "index": "pypi",
-            "version": "==0.10.1"
+            "version": "==0.11.0"
+        },
+        "send2trash": {
+            "hashes": [
+                "sha256:60001cc07d707fe247c94f74ca6ac0d3255aabcb930529690897ca2a39db28b2",
+                "sha256:f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"
+            ],
+            "version": "==1.5.0"
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         "six": {
             "hashes": [
@@ -542,10 +961,10 @@
         },
         "tensorboard": {
             "hashes": [
-                "sha256:a3feb73e1221c0a512398ad2cd08570fb082d8a2ba364aa0562543ecbd3659ef"
+                "sha256:d34609ed83ff01dd5b49ef81031cfc9c166bba0dabd60197024f14df5e8eae5e"
             ],
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
-            "version": "==2.2.2"
+            "version": "==2.3.0"
         },
         "tensorboard-plugin-wit": {
             "hashes": [
@@ -555,27 +974,27 @@
         },
         "tensorflow": {
             "hashes": [
-                "sha256:267017724a49c367ca5df536e5f6d3d59643eaed946c82233d6b371e62b5ddc8",
-                "sha256:3ee8819732d8594913b7d22ded7b22e48a49aa015050d8dd8464eaa010ba2e41",
-                "sha256:572f69d2d0a3d3d83ebfb2c24e6d73d88b85a09f5da796974ef4a0ad83ff7cde",
-                "sha256:6735486ee9c3cb0807476e2b36ef7a4cd6c597cb24abf496e66b703360e1e54e",
-                "sha256:68ea22aee9c269a6a0c1061c141f1ec1cd1b1be7569390519c1bf4773f434a40",
-                "sha256:784ab8217e4b0eb4d121c28430c6cdc2ce56c02634a9720d84fb30598b338b8c",
-                "sha256:7ed67b47cdf6598a79583de5b57c595493eac2b8b6b3a828f912354716cb8149",
-                "sha256:8f364528f70d895b96a0de36c7c6002644bf4c5df1ee3fbfa775f5cee6571ad7",
-                "sha256:bbcfb04738099bd46822db91584db74703fdddacf4cd0a76acfc5e086956b5ba",
-                "sha256:c332c7fc5cfd54cb86d5da99787c9693e3a924848097c54df1b71ee595a39c93",
-                "sha256:dc5548562308acde7931f040e73d46ae31b398924cf675c3486fd3504e00a4af",
-                "sha256:f5f27528570fc0d7b90668be10c5dfd90d6ceb8fd2ed62d7d679554acb616bfe"
+                "sha256:0cfb0fbe875408cdbfc7677f12aa0b23656f3e6d8c5f568b3100450ec29262a7",
+                "sha256:2d9994157d6a222d9ffd956e99af4b5e46e47338428d2d197e325362283ec835",
+                "sha256:36a4ce9bbc9865385c1bb606fe34f0da96b0496ce3997e652d2b765a4382fe48",
+                "sha256:44c8d979b2d19ed56dbe6b03aef87616d6138a58fd80c43e7a758c90105e9adf",
+                "sha256:5c9f9a36d5b4d0ceb67b985486fe4cc6999a96e2bf89f3ba82ffd8317e5efadd",
+                "sha256:6f74ef59dc59cf8f2002738c65dffa591e2c332e9b1b4ced33ff8d39b6fb477c",
+                "sha256:797d6ca09d4f69570458180b7813dc12efe9166ba60454b0df7bed531bb5e4f4",
+                "sha256:92430b6e91f00f38a602c4f547bbbaca598a3a90376f90d5b2acd24bc18fa1d7",
+                "sha256:b1699903cf3a9f41c379d79ada2279a206a071b7e05671646d7b5e7fc37e2eae",
+                "sha256:bc9d761a857839344930eef86f0d6409840b1c9ada9cbe56b92287b2077ef752",
+                "sha256:c33a423eb1f39c4c6acc44c044a138979868f0d4c91e380c191bd8fddc7c2e9b",
+                "sha256:c6fad4e944e20199e963e158fe626352e349865ea4ca71655f5456193a6d3b9d"
             ],
             "index": "pypi",
-            "version": "==2.2.0"
+            "version": "==2.3.0"
         },
         "tensorflow-estimator": {
             "hashes": [
-                "sha256:d09dacdd127f2579cea8d5af21f4a918036b8ae246adc82f26b61f91cc247dc2"
+                "sha256:b75e034300ccb169403cf2695adf3368da68863aeb0c14c3760064c713d5c486"
             ],
-            "version": "==2.2.0"
+            "version": "==2.3.0"
         },
         "termcolor": {
             "hashes": [
@@ -583,6 +1002,21 @@
             ],
             "version": "==1.1.0"
         },
+        "terminado": {
+            "hashes": [
+                "sha256:3da72a155b807b01c9e8a5babd214e052a0a45a975751da3521a1c3381ce6d76",
+                "sha256:c55f025beb06c2e2669f7ba5a04f47bb3304c30c05842d4981d8f0fc9ab3b4e3"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==0.9.1"
+        },
+        "testpath": {
+            "hashes": [
+                "sha256:60e0a3261c149755f4399a1fff7d37523179a70fdc3abdf78de9fc2604aeec7e",
+                "sha256:bfcf9411ef4bf3db7579063e0546938b1edda3d69f4e1fb8756991f5951f85d4"
+            ],
+            "version": "==0.4.4"
+        },
         "threadpoolctl": {
             "hashes": [
                 "sha256:38b74ca20ff3bb42caca8b00055111d74159ee95c4370882bbff2b93d24da725",
@@ -591,6 +1025,28 @@
             "markers": "python_version >= '3.5'",
             "version": "==2.1.0"
         },
+        "tornado": {
+            "hashes": [
+                "sha256:0fe2d45ba43b00a41cd73f8be321a44936dc1aba233dee979f17a042b83eb6dc",
+                "sha256:22aed82c2ea340c3771e3babc5ef220272f6fd06b5108a53b4976d0d722bcd52",
+                "sha256:2c027eb2a393d964b22b5c154d1a23a5f8727db6fda837118a776b29e2b8ebc6",
+                "sha256:5217e601700f24e966ddab689f90b7ea4bd91ff3357c3600fa1045e26d68e55d",
+                "sha256:5618f72e947533832cbc3dec54e1dffc1747a5cb17d1fd91577ed14fa0dc081b",
+                "sha256:5f6a07e62e799be5d2330e68d808c8ac41d4a259b9cea61da4101b83cb5dc673",
+                "sha256:c58d56003daf1b616336781b26d184023ea4af13ae143d9dda65e31e534940b9",
+                "sha256:c952975c8ba74f546ae6de2e226ab3cc3cc11ae47baf607459a6728585bb542a",
+                "sha256:c98232a3ac391f5faea6821b53db8db461157baa788f5d6222a193e9456e1740"
+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==6.0.4"
+        },
+        "traitlets": {
+            "hashes": [
+                "sha256:70b4c6a1d9019d7b4f6846832288f86998aa3b9207c6821f3578a6a6a467fe44",
+                "sha256:d023ee369ddd2763310e4c3eae1ff649689440d4ae59d7485eb4cfbbe3e359f7"
+            ],
+            "version": "==4.3.3"
+        },
         "urllib3": {
             "hashes": [
                 "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a",
@@ -599,6 +1055,20 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' and python_version < '4'",
             "version": "==1.25.10"
         },
+        "wcwidth": {
+            "hashes": [
+                "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784",
+                "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"
+            ],
+            "version": "==0.2.5"
+        },
+        "webencodings": {
+            "hashes": [
+                "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78",
+                "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"
+            ],
+            "version": "==0.5.1"
+        },
         "werkzeug": {
             "hashes": [
                 "sha256:2de2a5db0baeae7b2d2664949077c2ac63fbd16d98da0ff71837f7d1dea3fd43",
@@ -615,6 +1085,13 @@
             "markers": "python_version >= '3'",
             "version": "==0.35.1"
         },
+        "widgetsnbextension": {
+            "hashes": [
+                "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7",
+                "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"
+            ],
+            "version": "==3.5.1"
+        },
         "wrapt": {
             "hashes": [
                 "sha256:b62ffa81fb85f4332a4f609cab4ac40709470da05643a082ec1eb88e6d9b97d7"
@@ -623,11 +1100,11 @@
         },
         "zipp": {
             "hashes": [
-                "sha256:aa36550ff0c0b7ef7fa639055d797116ee891440eac1a56f378e2d3179e0320b",
-                "sha256:c599e4d75c98f6798c509911d08a22e6c021d074469042177c8c86fb92eefd96"
+                "sha256:43f4fa8d8bb313e65d8323a3952ef8756bf40f9a5c3ea7334be23ee4ec8278b6",
+                "sha256:b52f22895f4cfce194bc8172f3819ee8de7540aa6d873535a8668b730b8b411f"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==3.1.0"
+            "version": "==3.2.0"
         }
     },
     "develop": {}
diff --git a/ml-models/anomaly-detection/Anomaly Detection-using-TF-and-Deep-Learning.ipynb b/ml-models/anomaly-detection/Anomaly Detection-using-TF-and-Deep-Learning.ipynb
index ac6cf97..eb5f6b9 100644
--- a/ml-models/anomaly-detection/Anomaly Detection-using-TF-and-Deep-Learning.ipynb	
+++ b/ml-models/anomaly-detection/Anomaly Detection-using-TF-and-Deep-Learning.ipynb	
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 674,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,17 +90,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 675,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Retrieve data\n",
-    "df = pd.read_csv('raw-data.csv')\n",
+    "df_initial = pd.read_csv('raw-data.csv')\n",
     "\n",
     "# Set time as index\n",
-    "df['time'] = pd.to_datetime(df['ts'],unit='ms')\n",
-    "df.set_index('time', inplace=True)\n",
-    "df.drop(columns=['ts'], inplace=True)"
+    "df_initial['time'] = pd.to_datetime(df_initial['ts'],unit='ms')\n",
+    "df_initial.set_index('time', inplace=True)\n",
+    "df_initial.drop(columns=['ts'], inplace=True)"
    ]
   },
   {
@@ -112,403 +112,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 676,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>value</th>\n",
-       "      <th>label</th>\n",
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-       "    <tr>\n",
-       "      <th>time</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:13:54.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>18.340181</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2020-04-23 11:13:57.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>12.703972</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:13:59.618</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>17.647661</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:02.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>13.805114</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:04.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>16.874933</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:07.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>15.415206</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:09.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>16.180807</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:12.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>15.922729</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:14.618</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>15.407113</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:17.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>17.051560</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:19.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>15.324012</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:22.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>17.795010</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:24.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>13.470387</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:27.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>19.433830</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:29.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>11.702384</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:32.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>19.150144</td>\n",
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-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:34.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>11.176102</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:37.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>46.499027</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:39.617</th>\n",
-       "      <td>pump-1</td>\n",
-       "      <td>10.678349</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-04-23 11:14:42.999</th>\n",
-       "      <td>pump-2</td>\n",
-       "      <td>18.245524</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             id      value  label\n",
-       "time                                             \n",
-       "2020-04-23 11:13:54.617  pump-1  18.340181      0\n",
-       "2020-04-23 11:13:57.999  pump-2  12.703972      0\n",
-       "2020-04-23 11:13:59.618  pump-1  17.647661      0\n",
-       "2020-04-23 11:14:02.999  pump-2  13.805114      0\n",
-       "2020-04-23 11:14:04.617  pump-1  16.874933      0\n",
-       "2020-04-23 11:14:07.999  pump-2  15.415206      0\n",
-       "2020-04-23 11:14:09.617  pump-1  16.180807      0\n",
-       "2020-04-23 11:14:12.999  pump-2  15.922729      0\n",
-       "2020-04-23 11:14:14.618  pump-1  15.407113      0\n",
-       "2020-04-23 11:14:17.999  pump-2  17.051560      0\n",
-       "2020-04-23 11:14:19.617  pump-1  15.324012      0\n",
-       "2020-04-23 11:14:22.999  pump-2  17.795010      0\n",
-       "2020-04-23 11:14:24.617  pump-1  13.470387      0\n",
-       "2020-04-23 11:14:27.999  pump-2  19.433830      0\n",
-       "2020-04-23 11:14:29.617  pump-1  11.702384      0\n",
-       "2020-04-23 11:14:32.999  pump-2  19.150144      0\n",
-       "2020-04-23 11:14:34.617  pump-1  11.176102      0\n",
-       "2020-04-23 11:14:37.999  pump-2  46.499027      1\n",
-       "2020-04-23 11:14:39.617  pump-1  10.678349      0\n",
-       "2020-04-23 11:14:42.999  pump-2  18.245524      0"
-      ]
-     },
-     "execution_count": 676,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head(20)"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_initial.head(20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 677,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>3022.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>mean</th>\n",
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-       "      <td>0.027796</td>\n",
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-       "      <th>std</th>\n",
-       "      <td>4.539839</td>\n",
-       "      <td>0.164416</td>\n",
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-       "    <tr>\n",
-       "      <th>min</th>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>11.725275</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>13.975456</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>16.180843</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>48.423213</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             value        label\n",
-       "count  3022.000000  3022.000000\n",
-       "mean     14.433740     0.027796\n",
-       "std       4.539839     0.164416\n",
-       "min       8.089854     0.000000\n",
-       "25%      11.725275     0.000000\n",
-       "50%      13.975456     0.000000\n",
-       "75%      16.180843     0.000000\n",
-       "max      48.423213     1.000000"
-      ]
-     },
-     "execution_count": 677,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.describe()"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_initial.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 678,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='value'>"
-      ]
-     },
-     "execution_count": 678,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.distplot(df['value'].dropna())"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.distplot(df_initial['value'].dropna())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 679,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Data collected between **2020-04-23 11:13:54.617000** and **2020-04-23 13:19:47.999000**: **3022** samples from 2 pumps."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "Total time: **0 days 02:05:53.382000**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "Time between samples: **4.9986763732627395** s"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "There are **1511** samples for **pump-1**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "There are **1511** samples for **pump-2**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "printmd(f\"Data collected between **{min(df.index)}** and **{max(df.index)}**: **{df.shape[0]}** samples from 2 pumps.\")\n",
-    "total_time = max(df.index) - min(df.index)\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "printmd(f\"Data collected between **{min(df_initial.index)}** and **{max(df_initial.index)}**: **{df_initial.shape[0]}** samples from 2 pumps.\")\n",
+    "total_time = max(df_initial.index) - min(df_initial.index)\n",
     "printmd(f\"Total time: **{total_time}**\")\n",
     "\n",
-    "printmd(f\"Time between samples: **{(total_time.seconds/df.loc[(df['id'] == 'pump-1')].shape[0])}** s\")\n",
+    "printmd(f\"Time between samples: **{(total_time.seconds/df_initial.loc[(df_initial['id'] == 'pump-1')].shape[0])}** s\")\n",
     "\n",
-    "for pump in df['id'].unique():\n",
-    "    printmd(f\"There are **{df.loc[df['id'] == pump].shape[0]}** samples for **{pump}**\")"
+    "for pump in df_initial['id'].unique():\n",
+    "    printmd(f\"There are **{df_initial.loc[df_initial['id'] == pump].shape[0]}** samples for **{pump}**\")"
    ]
   },
   {
@@ -527,27 +169,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 680,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig= plt.figure(figsize=(20,10))\n",
     "\n",
-    "for pump in df['id'].unique():\n",
-    "    y = df.loc[(df['id'] == pump)]\n",
+    "for pump in df_initial['id'].unique():\n",
+    "    y = df_initial.loc[(df_initial['id'] == pump)]\n",
     "    df_time = [pd.to_datetime(t) for t in y.index]\n",
     "    plt.plot(df_time, y[\"value\"].values, label=pump)\n",
     "\n",
@@ -568,24 +197,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 681,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1 = df.loc[df['id'] == 'pump-1']\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df_initial.loc[df_initial['id'] == 'pump-1']\n",
     "df1 = df1.drop(columns=['id','label'])\n",
     "df1.plot(figsize=(20,5),  fontsize=12,subplots=True,  title = \"Pump 1\")\n",
     "plt.show()"
@@ -600,24 +216,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 682,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df2 = df.loc[df['id'] == 'pump-2']\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = df_initial.loc[df_initial['id'] == 'pump-2']\n",
     "df2 = df2.drop(columns=['id','label'])\n",
     "df2.plot(figsize=(20,5),  fontsize=12,subplots=True,  title = \"Pump 2\")\n",
     "plt.show()"
@@ -641,24 +244,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 683,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1 = df.loc[df['id'] == 'pump-1']\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df_initial.loc[df_initial['id'] == 'pump-1']\n",
     "df1 = df1.drop(columns=['id'])\n",
     "df1.plot(figsize=(20,10),  fontsize=12,subplots=True,  title = \"Pump 1\")\n",
     "plt.show()"
@@ -675,24 +265,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 684,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9b72bwdSIpfwqAOCxkdl1bkl9Y6wQEkJcD+ClAP4+/Dcp5aiU8ikpZUlKeQLAHwB4Q9T3SCk/IKW8SUp50/DwsGkzNh4cOsYwDMMwDMMwDLPuqNOnTkwsWqir5lUwDBmKh9CLAOwFcFoIMQrgfwJ4gxDioYhrJbGOzQdPBEwDcGRsHi96z3cxvZhf76YwDMM0BJ964DSePMfWT4ZhGBNKvDdjGAA0Zc0HAOyHExp2PYD3A/gKgFcIIV4shNgjHHYD+CsAX1ijtm5IVFJpnnOYRuC9330GJyeXcOeRzRsGyjAMU0/84ecex4/84z3r3QyGYZgNhbTotsOHDDH1jLFCSEq55IaGjUopRwEsAMhJKccB3ADgXgCL7s/HAfz2WjZ4o6HmGnYVZBimkRmdzfEpDwzDMAzD1AW8NWMYh9ThXFLKd0gpf9b9/HdSyp1Syg4p5W4p5W9LKefTN3PjI3naYRoI7u9MmB949+34qX/7/no3g2EYAF9/YhQ/9QHz8fifD5zGhflcDVrEMAxjFzbWM4wD5/exBE86DMM0Oo+cmVnvJjAMA+DXP34Q9x2fNCpzdmYZb/vc4/i1/zhYo1YxTOPy2YMjxseff/Du4/jNT2y+8TiXKyBXqL1HcYm4OTs2voCvPHZ+jVvDMOsHK4RqjIoZpU46DLMREXysnlUOj85h79u+gmcusEMmwzDJMcmhsVosAQAmF/jQAGbzYzO/zOhsDv/zM4/izf9xwKjcu75yCF99fLRGrVo/rn3HN/Gj/1y/edFe8rd34v/7ZNRZSky9wdvvZLBCqMZwDiFmo/IrHzuAX2dL8IbgS4+eA+CEgTAMwySFZROGicbm2Ci4ytYLc2YeQpuZI2MLNa+D5z8mjodPT+MLj5xd72ZYo3m9G8AwTH3y7UNj5LKcQ8guLNQwDEPBZOrgeYZpJNaju/NJVHRKJYn5lVX0trckLmNTVuX5c32gjqnXve9eAMBrr9+5hq2pX9hDyBI8ETCNAMsy64swXPnOzy5japHDPximUeFwdoaJxmbImF+n9SpripQSJycWrdT1f79xGNf92TcxlyskLrPZnjfDUGGFkCXYY4JpBLiXry+mAuxz3/0dPPud36pRaxiGqXdMpgz2XmAaic0qz5RKEh+8+ziW87VP2vyJ+0/jRX9zBw6emq55XV9+1EnyPLecXCHECvHND7/iZLBCyBIl7pAMwzAM0xA8cXYWx8drnwMjLSbGKhasmUZisyoLvvrEebzrK4fwnm88XfO6HjrtKILqdS60+YbZMYCpZziHkCXWw/WUYWzDBuT1xTRkjGGY2vDqf3JOyDn5Vz+yzi2pDEU04WmGaQTWQ2y3MbaWXM8gk9CqzYrVd8zbwHWBt9/JYA8hS3B/ZBiGYRimnqAIyyxgMxuNmaU8PnzPibo3ztZ588jUrbHK4vPepK+27mHPrGSwh5AlNuskzzA63M0ZhmHskXaDy8Iy0wj8wWcfwzefGsN1u/tw457+RGVYbg/y+YfPQkLidTfsMi5rQxFHqcNmWCD3p/WBn3syWCFkDe6RDMM0JvVulWWYjUraocUhY0wjMOMmGi4US4nLsLI0yO9++hEAICmEbGLijcQ5hDY/mzUX2FrDIWOW4KTSTCPA+4T1od6nF16PGaY2pB1aFGGZxzOzUTHpu5tWbt+s9+ViYoCyaazieXN94MeeDFYIWYInAoZhGhWe/himNqS1fvLYZBoBirGKqizIFYo4M7VEKmsTGwY8UedmQs4pvfnh/XcyWCFkCXYVZJjG48uPncNSfnW9m7HusMsuw9QGDhljmOpQhgl1aP3mJx7CrX/9XWLpzcV67H2MQsZSz5/Jv4DloPWBUxYkgxVCluD+yDCNxcOnp/GWTz6Md3zxyfVuyrrD8x/D1IbUGy6D4jyMmUaCum595/AFtzyPGJuQlH5pPSxNinN3WBf4sSeDFUKWYM0wwzQW8znHM+jcTG6dW7L+sIckw9SGtKKFiWzCG1xmo0Jyaks9tgyq2qRDa7OHjBnNnywHrQubdWytNakUQkKIS4UQOSHEx7Xf/bQQ4pQQYlEI8XkhxED6Zm58uEMyjQT3dw6r0KH2h288OYqf/eD9a9sYhmE8TIbmpk2yyzARpM7PxeFEVlEil1lS6XR1GjkI8SteF1gRl4y0HkLvBfCg+ocQ4ioA/wrg5wBsBbAE4H0p62AYZoNgEru92bG5+Ne7oEFt36/9x0Hc88zE2jaGYTYRNjet7HzPNBJpezuPLLuoZ2gypaVVFhjVxS95XSiV1rsFGwOyQkgI8SYAMwBu1379MwC+JKW8S0q5AODtAF4vhOhO1coNjTMD8ETANAIcUrD5OTuzjD/70pMoGrgLpBe6uF8xTBRs4WaY5JisRWnXnXoNx2SPCZ+0Xo9mIWPMesD9PRkkhZAQogfAnwN4a+hPVwF4VP1DSnkMQB7AZdQGbha4QzKNBDsKbV5+/7ZH8JHvncTBU9OJy6QVungjyjDR2MyBwSFjzEaFIpOk9hDihO3rgpnXjkVFHAsy6wI/9mRQPYTeCeBDUsqR0O+7AMyGfjcLoMxDSAjxZiHEASHEgfHxcWIzNgLOKsSCFNNI8ATsY1M5ZqMuE88ghU1LK8M0Eqk3GRbDKxhmI2E37NteZfWe6DktZp5Z6epipR+zWTBWCAkhrgfwUgB/H/HnBQA9od/1AJgPXyil/ICU8iYp5U3Dw8OmzdhwsGaYaQQ4hxAThc1cDAzTSFjNc8IDkWkg7IaMparKiPVQ7Nqs0WZdlLBAlpLtwgbFZDQTyrwIwF4Ap93NXxeAJiHEswB8HcB16kIhxMUAWgEcSdvQjYvU/s8wmxtWfDJRyJRJ/bhbMUw0NscWC9bMhseiRwd7j6wP9X7KGL9ru/CylQyKQugDAP5T+/f/hKMg+g0AWwDcJ4S4FcBDcPIMfU5KWeYh1Ghwh2QYZjNhJHSlFIF4I8ow0dgcWzwMmUaCE7avHTa9Ykwepc1TGjf5K65b+Lknw1ghJKVcgnOcPABACLEAICelHAcwLoT4dQCfADAI4NsAfmmN2rrB4S7JbH44ZMyHR7zPZhd2GWa9sLlpZZgNj4GIYvN0zPUwetgU18yUYxtHSWOSUpHloPWBDYrJoHgIBZBSviP0708C+GTa7908cFJphmFqS70ne01vhVujhjDMJiN9WIv5ppXV/syGxeIGvt6VBfW6rlpN9LwBkvKX3I6UyfDMS6Fe+3m9QT1ljEmMm0OIOyTTAKjFlfv7+mDj9BBKHTaPxmaYRsKmspWaA+PU5CIOnpoyLMUwawdl3Uq97vAJfiSoz50yP6VPyl/7kNtX/ePd2P9HX6UVZsB+sMlI7SHEJIOT7TIMU2vqVajksBaGqQ0b4djkF77nDgDAyb/6EeI3MIx90q9b5soC6l5BSmkcsm8jZIxSh9Vk3qlPkjOoi1jH4dGGT8ObCt5+J4M9hCzB/ZFpBJRAwqmEGB2bOQEYppFInefEoDyHjDFMcmymiqjXtBSUpTutZ5bNRPlmJ5rV6Uva5KQdG43y3lghVHNUDqHG6FAMw6wfNkLGFDbdsutV2GWYdcdqvo10dTHMemPz5C9Sfi6iNW0zbVo3gtcjpfwmekUbivQGyTVqSJ3DCqGaQwy6Z9aFP/nCE/ju4Qvr3YwNT6NMoJWwKqDV+fO2mYuBYRoJu/m5eCAyjUN67zuDa9OGjBHqssF6eIvbyOujMPJG4vlzXeAclslghZAlGqM7bXz+/b5T+KWPPrjezWA2EaYWvwdOTOE7h8dq1Jq1w+SubOZiYJhGInVSaaO6UlXFMGvChfkcZpbyNa8nbX83Uxako+73rAbts5ooP7WLpcGl9f6ONik218iNDCeVtgRPBEwjwTmEfEwtfj/5r/cBMEzAui5WuNpcGwVvRBkmGrtHNKeri2HWguf8xe3ICOD4u2ubpNzqkeQW8+bUOzZvpVRKV75ePbMYDYtr5EaGPYQswRZuZj0Zm8tZrW+zTaCrxRKW88X1bkY8df68Syk1OpspPwLDrCXpRwYnRWU2HqZLyrqcdmVw7aY3ehg8f6tJpVO+ZZteYAyN9OO4Md4cK4Qssekne6ZuOXhqGrf85e34r4MjNa9Lrfmbrbv/6r8fwJV/8nVSWWqSSFpd1qqyCs+fDBONzWOTbY/DXKGOlfDMhsSmR5zN/FyUtto8hMLIWyptVRbfsc0j7hkaNt/xRoYVQpbgiYBZL46MzQMAHjw5VfO6VC/fbP39u0+Pr3cTEmHlsVMsrZxDiGGqMp8r4DMHzhiVsXsij71xeOj8HK54+9fx1cfPW6uT2fzUq5LGrvKpvqHeipqfbD4JTslf/9jMSbWRYYWQJRqkPzEMAO7vTBA+ZYzZqCzni/jLrx6y4q3yts89jv/12cfw2MhM4jJWla3upTa8Hh8/OwsA+A6f+smsITbDuGwqCzbTEpn+mPDk5VOHp5l0ks30kjYQHDKWDFYI2aIx+hPDOHB/X5dHUK8hY5xUmtmo/Nvdx/GBu47jw987UfO6Lri53nKF5JlOUx+NTQgZ22weoEzjYJTzJa3XjsHCtVnz7FFEEuqtqBA4Ix2NzQTWdfqONjvpFYxr1JA6hxVClmgUDSPT2KjF38bCt5wv4gnXitzorMfsYjN2nudPZr0oFB3lTGG1PvugzVATHofMhqdOQyQ3q4cQpV1U+dF/X/YSPZuF3DrUqd1u05LeIFmvo2ttYYWQJdIebcgwGwHz5ZjOW297BK/+p3sws5S3UJs567Hom9Rp06KY2vW+MdZjhgFgNjbXIwGrzUT5DLMWqC5r00OIMraoVcpNtMewO6dZPGUs5TtmaKQ3SDYGrBCyRKN0qI1MvbrcrhU2b89GXQdPTQMwC6+wybp47Zhca7WB9oQuhqkF9aoDsavYlfbq5CHP1IB6TfScOvTToleMCTZDxrzyNbo2fV08qa0HfMpYMkgKISHEx4UQ54UQc0KII0KIX3F/v1cIIYUQC9p/b1/bJm8sPI1wo/SoDQy/ovTYDBmre5Q1fX1bEYvNd8QLMsPUhrTed2abVvvU6/zJbExM+nvq04lqdnFE8TpfI40UVhYTPdtMYF3v72izkl7+bIwX10ws924A/0NKuSKEuALAHUKIhwFMun/vk1KurkkLNwmN0Z02Npv9Hdm0cFu1QNXpjmE9rEFGIWMp6zK5P04KzTDVETR7eqo6Kadwc8gYs1GxuRSZnXbl/KSOrHpfYim5dsh1mVybWqFuUFe6qhgifOx8MkgeQlLKJ6WUK+qf7n/716xVm5BG0TDWC+PzK/jH248aau/5Ha0ZFp5lvb+tes8blvoVWUzOyR5nzHpjMy+FCTbDK3gcMhsduyFjBnW5I5Fapc3cSBRsKmlsKp/MBCGeP9cDmwrGjQw5h5AQ4n1CiCUAhwGcB/BV7c+nhBAjQoiPCCGG0jZyI6MMaTwP2OWttz2Cv/vWETx8ZiZxGfZiWDv4Wdb/IpJ2c2fzaFeeP5mNhNV8bWnL1/lGkmHWErOE7fa2knaVIPYHskn70ofq1etzZ9YDDhlLBlkhJKX8TQDdAG4F8DkAKwAmANwMYA+AG92/fyKqvBDizUKIA0KIA+Pj49Rm1D2cVX59WMoXAQBFk1hifktrRqNMoJXY7NZ0s5Axi7kYGGadsWmRtKrYTVUTw6w/m9VTxaYShILNXGU2lU8m86dJbiNmLbH3jjcyqU4Zk1IWpZT3ANgF4DeklAtSygNSylUp5RiAtwB4uRCiO6LsB6SUN0kpbxoeHk7TjA3BJt8bbgr4Ha0dVnMIWayrXq3pFAUctX1+4vDa16XY7Mo1ZnOxkRK2m8zWVu+L1U/MGqLyc9lMKm2kLLAY+6nkhXrNK0lVnPiH+JiXoUI53Y0zsNklrUKnUdaitTp2vhnROYTUU2z44+0bpUPVG0ZJdjf5K9psx857ddmripRHpG6FLouna6Rls49Npv4xGccbaa61uXlimPWmXr120lZW755+VuUFIyVNWjnI/FqeRu1iM1/mRsZYUSOE2CKEeJMQoksI0SSEeAWAnwJwuxDiFiHE5UKIjBBiEMA/ArhDSjm71g3fKCghslFczuoNI/fgRhn1FthYFvLkUNye63VzuJHcshtmRWbqlnrN0ZF6Q1PDqxmm3rCZfNnmnGEUwr0OGxKrXjtmgn8qbIbCMcB8roCnzs0ZlbG7Rm5cKJ47EsBvABgBMA3gbwD8rpTyiwAuBvB1APMAnoCTV+in1qapGxPfhbFRutTGhZV2Gws1pGwqn2yGSVEwyyNgTwC1eVoLw6w39aoEjsJkc8jjkGks0oaM1avyyT4m8obNnIM2DWO8D0zP//jYAbzqH+82Wrc4ZUEymk0LSCnHAbww5m+fAvCptI1imPWAJ+u1Y9OGjBHybVgNGTPy2klXV6mU/FqbQhfDrDcbSSFktHnicchsUHxv/fpU0qTPc2Jw7TqMY5shbTbzRDF2eeDEFADb6RFSFd8wNHxuH1s0SofayLD1cw0gCF1psen+vJmscGkbaHVBbhinXWYzQO6vBOWx1RwYqeuq/Tg+eGoaD52ernk9zMaCknDYpkyYdmyYefqtQ8iYwbV2E2ynq8qmgpHxsZuTqjEw9hBiaPCGZgPAr8gjfTjR5qRe3YNJp2sQ39J6WFpNvJEYZr3ZWB5C9kLGpKy9x+Qb/uVeAMDJv/qR2lbEbEhsruH1nnPQBmq82zih1XuGhJO/qNRrbrnNjs30DY0SPcIeQpbgDY1dKDLnZp+sqafkULxO7MyfTiV2cwjVtzXIzAqXsi6j8uwhxGxMKGuJ3ePZU5a3uEGmlLYZcstsXkiGjJR11msiZZvh7BRjVWp5oU7nNI5CSA9lHHPIWDJYIWSJBulPdQPleVMn63uPTeB/f/YxWuE6RX8UlMnQxgaeJmikg7II2dzPmCms7IV/2FU+Mcz6sqGUNITi1DmN83Uw643NXDtmddkzmqSVnShtteGJqOalevUeUZeyjpsOyRue5c9EsELIEo3iclYLPnH/KXzjydGa10N9Rz/9b/fj0wfOrHFr1hf9WRgJ8Z578Bo3qAL1ao33Fq6atKRynUnYSEksefpkNhJ257905SmJ8qlV8jhm1hujDbzNU8ZS1WQ3abORSOh5dNTm+wPlvPI233Hya9nTee2wGhbYIO+NcwhZojG6U234o/9+AoBZTgCamz+hkIaUEqKO/dup+YYpj2WzJnyTFk/WMkGGfiYrs5HcsnkGZdaHep//0s40Vq3pPI6ZdaZePQtseqqkDRkrSYmMoZRNCWmjYtNYZVLbehgJNxtCOM/RbN2yt0ZuZNhDyBLsIVT/2Nwg1zv6gkwKGbP4LOrVG8lmyJjXLIunjJk9i3R1baaxxWx+1iMMwWZd1DmNxzGz3thUgBopC1Ln2TO41qJ3sMJm/sV6Vfoxa4fd/tQYL5kVQpZokP60sbG4Qa53AkmlCQ/Ghkad4h68VnWu/cXpUH3P5LlbDRmz6HrPMGsJ7YCCdFgN/zC41mZyTl/5VL9et/XOiYlFfNNCuP1GwizUZAN539U4fyA5jQCB9MZZc8MdFZueKoyPmbe+PWXrRoZDxizRKB2q3qjXnCobiXpNKu3XZQ+bJxtQsCnsWvViSFecYaxCFfyV6sMor4/FhSu98olHsk1e/Dd3ADALt9/s2FXSJL92PZSt1O+nla9PeSH1nGbZU6We01LYhJJEnVxXgyxb7CFkiUbpUBsZm14M87kC9r7tK/jg3cdT1Vkr0i/+a9eWalhNKm1x82SCEkrq1VWaXXaZRiKtjsamh5DR/GnxvmyyslrEfx0c4XmmEbCY6LlevZcp41AvQrmtelXEbSzlU8ov2ERY9YZvkAfPCiFLsGWs/rGZ52RiIQ8A+Pj3T6WrtEYEcggRTMhWBSGLQ4uWuLH2Fh3Vd22Gf5idoGLP+skw682G8r6z6sVQnwP57791FL//mUfxrafG1rspTI2xu0Ymv9aqEoQwP0mqTOiVT35t+qTS9tQ0Nr3G63P2tIuSpq3Kn6lKbxxYIWSJerWMMT6pT0LaRLHEeuvMNPGyrHytUM/QpoeQ3YSUBnWhvj2EOByTaSjqfHMXLF+ba6OoVznowlwOADCXW13nljC1xijEMvW6ZW/TWusku1SZUOUAM9vAp8OqnEaoi2oi5FyK/uOu1yTlGxlWCNmiUXpUnWHT0kqZrOs1HjhgDSIs5Ha9dizWZRS3bF9RZaNOkoCX1tJarztJpmEwyutjcy2x6A5vc/O0HtTnasysJWYKUHuWjJJBktzIqggGSarXjplMaG4kTG+ctTdXUxTq1Cp5G6lhb4lsmAgfVghZojG6U/1htgilq2szae9lzOeq5bzQpc0ZMmYzt4cJ/nNPjk335U2+j2SYADZDJG3mK7IbysEwa4cyvtn0HrF7WqC54Y7qZUHJ32JDSbMeB6CYKe8Jghq1rk2KHzKWvIzqe1T7e1pl7UaBFUI1Zj08JhgfjjMlHptciv5ctZz706ZDh13lk0VruhHK4mfTjdaehxDPn3Re/vd34l1ffmq9m7HhMTn+nNpflcBqc90yWbmshnKkrMsEnl42Dja9R9InHDZfI6njmeLtQ74/ig7EqC578oLd3E0OrNhJD0WZSX3sjWLIICmEhBAfF0KcF0LMCSGOCCF+RfvbS4QQh4UQS0KI7woh9qxdczcu9Z4zZtNi0dJKUZzYgFKXPgGShBrDSt/55afwhUfOmhVysap8smlNN7GolVQZg+83bE9ZeatCF8+fVI6MLeCD95xY72Y0FGlDAuo1GW3ayup9HNdpBDejsbGSqBtcm6om2npM9SimKKxthNzSFOrpoHnQ02BFko/NlAWN8tipHkLvBrBXStkD4EcBvEsIcaMQYgjA5wC8HcAAgAMAPr0mLd2gUNzbSiWJ6//8m/j0g6dr0qZGwF8YkpdJneeEkkMoVY3JILnRyujPScuZatQ/dM8J/M5/PmJUxq/T3mxdr4ns1POmhn9QnqHVTWu64gxjFT3nFW1+qs8Njc3wNIaJwmbYYnqPOIubVoshYxQliA15geIJYlPu9w//qL0X2GbHprK1USAphKSUT0opV9Q/3f/2A3g9gCellJ+RUuYAvAPAdUKIK9aisRsZkw5ZKJUws1TA2z//ZM3a0yjUq6VgrepMVod5LXoJ2oJnXKUxvlBT+7q8OgkKF6rSj2Z5Igp4FKWhkcWPLTTM+nP30XF8+bFzpLI2Qjnq3ZBhc9Nqk3r3XGJ8qG+KlnuEWJkqb3Lteng+ETfVlDnDppLG5nMnhc8Rq2IPIR9Knj1yDqEGee7kHEJCiPcJIZYAHAZwHsBXAVwF4FF1jZRyEcAx9/cNiepG9Zt7ZHNjtjDY9xCyAUVhEvAeMSjnW2hsPov69BCyWZefuNGkBto79spYtNDU69hiNhY/96EH8JZPPlzzeqjWdEX9eiK6WNByr8eQ36whY5tJ4UVdC3xZnFKKiMU1knJbZt6ButejcVV2T/6ymIONEgpHz2XD+Mnha6uUDJanldtokBVCUsrfBNAN4FY4YWIrALoAzIYunXWvCyCEeLMQ4oAQ4sD4+Di1GZubTSacLOVX8e6vHkKuULRWp03Bul5Dxig3ljpkzOIEajeBdXLs5h8wr6sUeMeUPmJx05quOMNYhZpvQ1HrsR8snxxvziBWSQlptaGk2ezzy2ba0KRfS+pTJrRZl7qWHjJGUIIkLmF3Tkv93I1yh7qKCWJdJnlKNyuS0HdT12mtpvUl1SljUsqilPIeALsA/AaABQA9oct6AMxHlP2AlPImKeVNw8PDaZqxIaBM1puND9x1HP9613F89N6T1uq0a5WozbVpodxXIKk0oe9a9Q+yqRCqUwGP4pmlX0rrIwbXplaObc45kdmc6L2V0nXtegiZW1rJdREOKLCJyUlyG4l6lSmnFvP4hQ8/gKnFfOIy1FtRb5YSakLFqmK31oY7spFQBn4mK5P8+yvVaaUuysVkLzez+fOh09ObVnaiGGc5ZKwya3XsfDOcHEJPArhO/VII0an9viFZj0WoXikUHfV2YdWemrteF2SbKhPSgiAjPyYuZmMCVVXYDeNKfq1dpZiymiQvEwwLTF7Qy3NiUFnaZ9Eg6zFTx5gdO08bW3752ly7ZuWJgrXNMFPGp16f5UfvPYk7j4zjYwZGwvRymsm6tQ7edybfH7gX8/WYsql26k1ezpcJDcpYnNNsngab0sHS6L6++Og5vP599+ILj9By5tUrKmSs1ko/SezvGxljhZAQYosQ4k1CiC4hRJMQ4hUAfgrA7QD+G8DVQog3CCHaAPwJgMeklIfXttkbh7QT72ZiPaxvNhchSs4XG4+ElkPI/1yvIWNp44JpdSa/Nu04piREt5tU2uDa1Fa4zTknMhsH6ulEtLFlUdlKSQ5PrNRojbRoGdukIpdHvcqUFPHH5kl3VtMIECqjymmkkLGI8onKEeQ0+novtf+blKBDOQCA2q9Mnvvx8UXn58QirbI6h2KcpQ/H+pw/1xqKh5CEEx42AmAawN8A+F0p5RellOMA3gDgL9y/3QLgTWvU1g2NUUfkONE1w2aoXr2GBVJq0hdks7banzj52HlassJgWGDycuE6k12b1gqX/NrlfBF73/YVfObAmVR1MvXNXUfGreajM4FqXVRGE5O8FFZzlVmc09ZDBN+8SaXXuwVrR/rwY4NrU9VEq4sSjgUQFV3EuijGIJLBlIhdkdVcJkybGN2EzTal+acF1lYWp3rEbWSMFUJSynEp5QullH1Syh4p5TVSyn/T/v5tKeUVUsp2KeWLpJQn17TFGxSKFW6zDWSFzbFVr8KuicCfFlrCYO2zQblSygWPQv32J4seQsryZPD9wXdM6SP2vBhMBMQL8zkAwD9955mUtTL1yhNnZ/HzH34A7/zyU+vdlEj0oUGZC+0mo01+rXephZCxzRo6vx5spg0NtV94oc6EdZVObcexXoSSC4zqiWgULk65L/JzJ5xAZTN3E6GMTr16+tlEPQGK0cRE4R/0iEtebiOzVjmEmBhoOYQ2Z+9bD+ub3UR2ya+1qjBZD0HDYhe2WZdVDyHCtdT+ThPaTK5NK3RtzjmRoTG7XAAAnKhTd3gZ85lSfq2vLitt0bO1XpNKb/bZpd5lSqPWEW+FEu1oU9lK2bQG8wAmh6KYoPYh76ARK7KTuWFsPUIQU94eA6oRk3Zto8ifrBCqMZSNWqNoI21gtkja9OhwftrQkdGOPaa5S3r93cLK5VkK6lS5psYxVRFqEjqaNk6f1EdqbP0M1mVw7QaYP+94+gLuPDK+3s1Ycz5270kcG1+oeT31/o6D7uY19r7bQGOLIpBv1jAum9SrQojybq0qJur85C+qUYeSQ0h/FJTcQ2aeiPYmmvTzp7lCnapg4L0hzcmC8rypytaNDCuELGHTms5ouRjqVLCu15OxFFRBw4+RNq+Tis3RYuZinVIRR1C4UEMyKM/QpoBHCU+r543kL37kQfzChx9Y72bEUixJTC6sGJf50y8+ide/794atcpH9Seb75iajDZtUv+1vDYSixNovebZ2+xspieZursTxzGpLpvJ4QmyLlEfRFN0kQ+8oBirTK61p/RLCx+u4VOv+Tw3MqwQsoRNF0bGnzhtJr61IexKKY0XyLSulRShpl7DuNLXlfzatBY/St+lhmTUPmTM/PvTlq9jfVDd866vPIUb3/VtzOUKicuo/mRSJi02Tq4sESa1wKW1HlsWPVvTnvxltlGzx2Y3wm2mg0qo7yqtYoIC5QQqs++neTHI0E/Tuih5mKhKbso7qHWOo0BdhGdBdRDgvaEPpT+RwzE3+fqgYIWQJSjunPVs4U6DVY06wbPARl3+BGX2kn/mg/dj3x9+1agMabMP2iLkP4PN6SJES9xo8P3kupyfpASxhnX55c37O5XGWI7rh689PgoAWMitJi6zWYXVtMYFk3FCS3yb+NLo8oRrqaKJzXxFjE/dexZYSKlQIigm1sNrx+z7aeV9xQTRayd5VSQjYVAJUmsPIXv40jGtv9vcz9igWDI3cCsooZ/kcMzkxTY0rBCyxGZzlT54ahp3GebA8GI/LQwvZTm2GapX6wUZAO49Nml0PaUOpwytPM0rhvjcPSWIvfFCESapmyBaXQZlrHoI2fNiaBRrThJsPouNsG5RSHtyIi1k16Yhw/xaap02vQoZn3pV1lI8/KjyIy2s2t6DI3lyE8unDxmjrMdEJUjiUpblz1CdJtdS+6BRUzeAY8H+//NV/NanHiaVpYZwJy6TsvxGhBVCNSbtcdD1yhv+5V78vGkOjHWYmer++F4L0HJY6Bbu5PjWdIsWbosPk6LcMWke1bOAdIxsSgtIvW5aqTx8ehpfevRc7SuySNrnZnRMK0HYpaLqsLGkUMYxVaHulTGqK+WGhpAUlV6XwbXrIAiZeuxuFDaTspZsrPK8BGiCWtqQrqpVpZTTaCHmBmW0CswUGqq8QWWg1VVeOsG1qeV+8z5I7ReUttoIq07Dlx87b3S9mqJpSsnkUGXxjUzzejdgs+N1IwseQmdnlnFqchHP2z9EKl+PWNXep6qJlovBhgCaXjteW8uTzdxNaaEIGlRLBsmaTuzvJs/QD2sxqMviRlJhOrZe5yZDfs11Owi11Sf1buGm12URbxybC/5a8WTlvM1TnRsyiHWaeUrT6mDK2UT6oLI1Muk0T/FukyHFhKm4Vmv5M60ntw2vR180oc1pFNmk1sqCYHnza20YCW0ipcRqSaKlqfY+JiS52itL7YPJ69rIsIdQjaFNALS6XvK3d+Cn/+1+WuE6xab2PnXCTILVxAa0Y+ejPyety0Z/V9icq036iLqUHKdPErqSQ7X4Ucqk9wIz2YwzCrvzjL26bFIiLOL6s6h9Dgx9HNsZyNRXXa8hY5u063rUexgtdYNM8bSgKlvTylFrea1fKPJj4mLU9lG8l22cnEgxSKYdGZTnTleo08rVmk89cAaX/tHXcG5m2VqdJEM18fvrff5cK1ghVGP8ydBkI+lca+rqlytsoqMkXFK7qFtcGOo1zwnFQhNw2TUpRbA8pX/H9p5lrY9ap1qDSoR5RgY+p2urSV0UzATXxli8k0A+zbDG/WEjQZnTZHD3lBia913058TlDa5Nm0/Jpuco41OvG0nfoyN5GRnzuXo5c2UmtS5FreUg6klIqcPZa613Th0yZm64o0I6ZYz4/fUq23zx0bMAgJOTizWvi7RGpg3VS17VhoYVQjWGpLGu00G/HqTX3ttUTCS/1qaAZvPYT4oFJL33SLryRnUR3J6pihObR9zbdHumYFK+6OrF6zty3g5Wx4ZFe8R6KIHJJ/gR6qS7tpvXRtmoUR8/ZS60KTttVtmrXkNNKHO07uFD8hAiKmlqrWylEJhnCJouquct5RmST9YiPEWr+y2SnEad35PXZZP1mDYppzqTFc+bdF0IwwqhGiNDP5NQr4N+rbBpJTR6lqnXBfNF0samNa2ywOgdWFQWqFJ2lWsm15pvnqjWIMo8Qz1lLK1Qk/bUu2p4z5A1QpaV3PYGoqrJSg42ghIk9bHJFpVPlE21DQUDZf4kd0HCGrmRqHeZMu3JYUkopexPpDZS5AWDr6cqrGjPIq28QKuLdiiKeRkqpNPdDL4/OD/X+UC2CMlgavT96ZTBGxFWCNWYtJvCzQRFdE/7KGyGtVBOebCBv/ibPIu0ggbNCkfBajLbWgtCmpcFTflE6+8UZSbd2pK8XFT5amzW+fPuo+PY+7av4PTkUuIyNj2z1uOp21So2zwZxqoXg9H8Tq/HKW/+DK2GH2/SDddmsnBT+zvlCaRdt2p9ylhQYWVQzvtJlBdqbEAKlico1AlKGqPvJyqsSMYF7XO9K3Ztnmhm1ndTKoPr/LmvFawQqjGUiVcdz7hJT0Al5aQh12VRSWPzqHUTPCHeoAzVMqautJFAcK3Km9VlsjlxflIWLqD2yifqkbX+Ozbp79p9Ja/Ko17HFgCcn122suH63ENOnP6BU1OJy6RO2F6nSm6bpE0KTcsNQqvMVgJrG54F63FAgdnR2BuHeh2aFBmX6r1MUTCC2He9MgbXqr5r8kh0eYGWYNugrsAabq6kpRpMSYo8gpKG+v0kOc1EJqQaCZNfuiGhpVSwJ7NuRFghVGMo1vTNKlhTFn+bCYfXQ/lkQ+lHSahGtsIRhPi0liO7+UTMlTtU5Vit4/SDQo25pEFWMNZYsrY5fz50ehrPffd38JmDI9bqpG7GKdj0zjDCZlWEjWRa6yJViUyBNPapzyJ5TaQE1mmTqG9W2Wsz3RdVWaCuJXu21jqXjVK2Er+fpmyljWPasfMGZagKF4M6FBQlMrVfUPog9VQ9Rb06FlBlE+WFVOtk3vq1m2n+rISxQkgI0SqE+JAQ4pQQYl4I8YgQ4pXu3/YKIaQQYkH77+1r3+yNA00zWaPGbEDSu4BbrIvgPWIDf0NjXib8uWo5r0zyQsWUD6Ne96FeuyxYxigeQgGhhtBG6kaNJoCZ9ycbctDRsXkAwIGTyb12qJASsBLHRto5YzORtr+bQDp1Ka3yibSRNC8T/lwNb8xb7IObtAvX/dikrluUTaHRONYmQMoztBkyZtJ7UyvHaqxEDh5qQlHE1fq50+QZm3Iadcz/18ERfOfwGK2wAdT2+V6q5u+YnvcycbENTTOxzBkALwRwGsCrANwmhLhGu6ZPSrm6Bu3b8KTVCJPqlNJKsk0bkJ+EOprQYLZOO+brNYdQWk8aUtI8o2dh2CBVl/fT/rM0uZZuhTPfPFHnGZKAQt20Et4Xxctqs8yBabB56hJ1Tvvfn30Mnz5wBif/6kcSl1F9iPqKTdZI0uaJOI69MsR1ixYyVtv5Pe2GhqREguE7JmwYNhL1auGmzNGBtcQo5Mn9aVJXzGdK+aTXUryKjMt5MmHyMlSjjs0E1qo3UfL6mFAiTmoUZUZqDyHD63//M48CgNF6TMHmgRcy9DMJjZjM29hDSEq5KKV8h5TypJSyJKX8MoATAG5c++ZtAgiLkNWTtdYDkwk0ZUy/zVPGqDlVag3pyMXAhsakHEUJkrK/Wz3uOvm1pOOqiYIQJV+RfikpqXTymsibQkqZzbqpU5itJenqspEL7NMHztAKpoAyJqmbDFJ/N7iW4lVIPjEoos5qkE9OJHkj6eUNCqYosxGoe5nQAGoumzSeBablIr+g6qXm29ZgiHniYppyjPYsTNro3xVNyV1zpRppfjafc/W6KGU2G9Q5yQ8ZM6nLXcOJhpbN+g7CpM4hJITYCuAyAE9qvz4lhBgRQnxECDGUto6NTJocQlT7dt1agwh3tLFCxkzqSlWVEZTFP/Xx5xafhc3eTjtRgibgUdyybWxaPcu9yeKaehzTnmGtqdOp1sOmh5BNRZyqysYa6V9K3EgmLuVDz1eUrBxV4U9SBhOfBUWhntaavpkUJ0E2z43pd2LUN1zDEdW7LW2umarXUtZwonKM5rWjlzcppzbj5mXCnxOXr7FBMjjnmpSjPHeanGaTWq9xactTFHG6fLt514UgqRRCQogWAJ8A8DEp5WEAEwBuBrAHjsdQt/v3qLJvFkIcEEIcGB8fT9OMukb1I8rpRFTS5mSpJ9Leid2k0vbqMqFEWZBjPlct5wk1NOUThbpNKk2xBlEtT5SQMa0/UI4Xp24Ka71Rq1eFeGpISfnTVVmvOYTSrnGU+clsQ0Oryy9jUFfM5+RlCHMasV9QDGNG62pgTkteF2Xd2kjUu0hopiikKQsosnjg+wnP0MwjgdDfyYoJVWdyqCHmpNNWicqnqPJreW1UGZrsZF7GtC6ruM0yiQC1ud/yCxHr2kQK9UqQFUJCiAyA/wCQB/AWAJBSLkgpD0gpV6WUY+7vXy6E6A6Xl1J+QEp5k5TypuHhYWoz6h5Kp1WLCDUHRr3OGRRsapFthuqtx8ROtZqYCV0ExQTxWdA2JxI/9Ld34AuPnE1VZxIooXr0U8bUJ5qgYabccTfIhH7hlDeojFDIRAjf7Gyk+dOEtHmiSB5xFvq7p3Qi7miShlhTN3d+GdqzqLlVl+gx4ZfZnGwmmTCtpwpZmVnj/kQyIBH1VRTlmA5JCUKUgyhyK0VRaEJa72qqfLyJhnFqJQvJM4v4/Ztp/qwESSEkHCnsQwC2AniDlLIQc6l6jA17vL0/GVI2dzTqVotMYD0Sj5HrIrjsUsLoANP+pDbwBt8fqCt5Oc8tO3mR9JYCg9oKRYnj44v4/dsepdVl1Fhz4YR6qolNV2SaUKOVT16MVMZTFhDqMWU98lbbSPRM2TDYDdWzqehy6zT6fv1f5soTau6mpHMh9fHRThmj1UvZ3NlUdNnm60+MYnQ2Ryq7WWVCWjiRybVp+5NJXQ4UryKnLoriJHER+gaZJH/SBAbKXJ32vVIUcdT2UY0StjARh9YjvyE5913yqjY0VEXNvwC4EsBrpJTL6pdCiFuEEJcLITJCiEEA/wjgDinl7Bq0dUNC0/qn637FOl/8jVpncdJI+9jqvS66ZcygXET5atj0zEptlTC4lqIcC5QnbFqpG3iaF5iJAKrXa/5ETITkzbT50aEoj6mPIq0ls9bYDYVTGxrzDZdpXZRxTFHsBrx2jHKBlddZvYwuWJvMM8E6k0BWPilFXJ16F0op8esfP4if+Nd7SeU305xIlU1KpHFMqytcp8m1VGUByahjUBdVEUfyXg7IC4mL0RRdBKWaflWtFXFpldz1etgqeU5SJ0iTZNbk1VDf8UbGWCEkhNgD4NcAXA9gVAix4P73MwAuBvB1APMAngCwAuCn1q65G4ugUGg+gVLHcdqTuWqFmpioAh4Fmyd/2Q1PIwga1A0NIXmwTY84igcTlXo/Ct7sUdIsICQvsLSWVoNrPe87C4JQvcsJVj0sLT4LZfSwklS67INBGZha042rIuWYoCv8zTetVCUNaQNPDE/zy9eeldUijo7NG5VRz/DM1HLlC2OwMTb/5Y5jeMcXn6x+YQTUeYak3CGO45or1Qjto4ZIkmTCmM/Vy6lxbFAmrRLZ6Fno9SYtk1JOoyol613gMIC8T3UfAS38mLZH20SPvSLNpgWklKdQWQ77FL05mwuq9j7twlOvHkIU4d2mZSusETbOT0FQdFE3rZQNDWkTBLPFn1KGHtZivmFIOzZq76bqfzbbSBIsIMS6SP0pZYeyqZigjH1q6Get2UgKdRNUXeT5k+BxZjRPBRQuta2LotyhyybuT2L8McmLwYKywC9jXMSYP/zc4/jcQ2fx8Ntfhv7ObKIyaZOo2xia//frhwEA7/jRqxKX8YyEhLBKgBYGTz/BL3ldlDIEfRBZ8ewrJgzKUJUg3jgmyp+EuoyGS8hwn0mwlgfnXPMHT2xe3Ycu2ZD7KVDyeZLf8QamYXP72IC6B7LpPVLvpI0Xp4aaUOQvuydrmVzrKguI31/zxT/tszC41uaJZrQNjdbfCVYus2ehfzZ/XzbuS2Gk9Et9ApV5GZsCQ72uJek9/ezNn7QTqEy+X1tLCKc7EnVPpJAx0iljiUuE11Xz/kR97pT1zoZS8/7jUwCAhZXVxGU2q0xIUaRTQ51p+a/srVu00FS9rsTFSB4T+pU1z8FWoo5jgmwS8zlpIcr8RF3rKOG9NrGyByKEjKkXZlYlbQ3fyLBCqIaQ3QpTDmTTgfbw6WkUirXv8ZTbSmuhoeb1ISmfiBtkCkYTb0mVSf791FPGKAmsrW4kLVpaKYq4oJLGoC5vwTMR8NLNT9SxVWsrfNqE7SZjy2Z8PqWu9POMybUW67IY+pk6t0etQzkIczV1nqGYuEkbLuj3QlNKUkIS6lRvkt7rcW2aURdQPVX8UBNqxbUtQvNiSCmnmSirycpWc5kwoHwySrKtftIUJ0kfYdq9HaHbGpdbD0yeRWrvZaO+6/6kriXJq9rQsEKohgQHhwXNqSpvMFAOnZ/D6953L/7mG0+nqjMJaV1uaTkBqBYQ46oMF2Tz76eWpygLqJOhuQhv11sqtes9JZ69xps759rgT5MygOl9pdyoJWwkVfC3GyaVrq5aQ38WlDnDnpLbD/2svdJPXUkOuSX0XbIhI3EZwi4IunGB1i8oc5qZolDftNb2HQOOl8/KatGoDAWbY2spv4pX/9PdeHyk9mfBeO+IOL9TPEHoCdEJZUjKAuI8k7wqUpnUsriFdbUkKf3JfM4IPndz2clsTZCRn+uRes3Zmlo+rvPnvlawQqiGBE/ySF7OW/As5JeZWFgBADx5bo5WmQG+pZUmTNLCuJJfS00CTqlrPUI5zIQnqlBD2EhatfanrItgUaMIrQDNy8pG0jxvo2YUCkMRrP3PNgUNSnGrOYQszjPUDQMFyjumemiRrPDUTQbhIVITKSctRt1IUpJKE3VPRG8pvXzyurzyhtdf/affwOveSzv5ywSboc6PnJnBE2fn8K6vPJWqziTQbosmp6U9ap2SsJ0yjsmn1dZ6bMX+I2ld5mVMy/lKZKJSLen8mVbpR1WOEWRJG3nRKH3X6mEy3t7TvIxpuY0MK4RqCNUCnzqpdL0enap+kjfIhE2hRWu/1ZO1auwuGYyRJgoNCbHpIZTWskWLnaeO/doKu8Hyya+lKHYpQjJ1QbYpaNQ7qZWtFnMWUDxpqNBCbmnPgpQLjLgpTGzh1vpF7S2t+jg2X0vIuZtIRh3zMk+dt2BMSzmO63VOoyjvqQoXynpMURaQD8nwBOTkZaj9nTKO9bWg1ic8BcqTvLINvl9Gf05Sj1OG0L7EJcLzpzl2kzebK0Cp1NoDlD2EmDWFahlbDws3ebImKGlMTnpKbylIXiatMEmd5CmQkhUSkpuGP1evy/lpI+yGUhdNIaR9NikXUb4a+juiWZ4M6kq5USPPaSShK3ld3pHkFjwsFTaSSq/HKY316/Vo774opxkGvRjM66Ja7pPWRfUATbupNvOUlmXlTeqq9fxJhTIv2ezvNknr9W3WN9TYMqnLfDNOVhakNLSYPAuSUSem3qTlqM+d4uVP6Rfhz5VInR+W8PzC9Saty6Yuw4ZRXE2fpCTqhLXYtNxGhhVCNYQSJgHYtX6mhZZhP3kZuneGuRBPVeCF6zStiwLFa4ceTkQQaixuJE2geM9RBTxSH0y5kbRxQolqIjVJZNJidOunPcVEvZPea6c+lTTpQz/NBVe61dmgLsIBAJS5mq7kVvNM8jJUTz9fiK/9upWmDBWba6TJfdkMf6UlbKfKJsE6TcqY1EVWZpDKpFNMUOc0u7J4jZUgFLmfKjsRQm7JyieCcSEtRgr/1KfB0uTqxGWIa+RGhhVCNYSqYPBPyaFh4oGjsHEij5cwkxiGQJnjiesC0d3c5Np0UwzFy4p+4ppBOUIZq54F7oJlYqUl57GSgR/JiqQVhCzUlTZkLGlV1G6xWa3pCpvhx1aVNEbjON0aSdmcUJ8Fbd2q7dii5zgK/UxShDynqQ2NSV3EuTpFGSpWc98Rytt4FDSlX3l5s7oMykSUT1qPcV2EZ0FV0vjKJ/PxqJc3qYz83M2rohs/DesxrSutUrLW6xaVtPdFodYnSKd1ENiIsEKohlCtVTaP4fbKEJd/mqXA5PupdVHKpBMm7b5j82upYQi1XvCoz8KW8imtYtdkaFH7YNq6SEIX+b4oC7KJsiDxpTH1mvcRG1Z10rHzxLpoAl66Sc0kL5pnNCGHBSa/lnL8edByb/4MyXNi0k1rRJ1JoCiD6XNaefmkZQBTmUY9d3uSv4lRJ30ageTlqWOKAu3gBVp/Io0tquJUlSHMGcTmkbysKOu+Xt6kHFVeoJSjyMfhepPUY1pX6gTbyYt5RhObc5pR+9K2izh/Jh3/aVOIbERYIVRDZMznquVSdr6igWCddiNDmXipShqTDQMIEy/5hQWrTHit2tDY8MyiLJIpF7zkRdYgxCf5tRTvubSJG20ko/W3rESlH6GNdAVj0jIplWNEbHoJ1NqdO3WoSY2ujcKux4R537XiYSmDP5OV0TcMBGVr8qpIm7vgtfY2krXOMZEWE09pyrqlU6/bGV/pl7wM2WMi4lPyMsnHls0+GGgTRc6wMqcpmZAoB9mUTRIWI7fP+1n7Z0EZW2mp1xBziqybcju4IWGFUA2hD+S0A8VkgrJYl6eYoE2GpBMliM+dFjJW35OhjfwyFEGDvGCpd2xirU65OpptnsyVY2lDOSghbaZ1pQ9BTCpYR5evXs7enEaBqmxNWxcFShiXjbrS5oepdS4wslWXsEZSxsn65WFJXIz23ANJ+SnKJ3uif72GY3pYeBQ2cz36fTd5Gcp6HLiOPI4Ja2TimtLPaZS+S1dy166MU07/R8Iy5kUCdVEV6iTjbI3X1WCdtbk2ujxxbCUsI6kPfgPDCqFaQp2s0x4VXOdCjY2Tv0ib1ph6k9dZm2sjyxvlYTJXTFBDHhQ2lWNmi6T596cN/aS2r9aLf+pkr0ZlKII1dew7P21431GgKrn9MrW5NgqbYbAmXhCk5PCB525ejtotjOZqVabGRhPinpXkAWozB1vaUA4b1nQ1LZn04fTK1uTXWowYIyni0sqEVINk0lLUdRUw77uBNZIwzxCGo/vZpC5z5RNlL55WcQIkvy+qUYfy3NOmETB57pR1NarOJNhUPlHeF1XZupFhhVANSTuQyRualAolo7pqbGlNK8TTw3XMoQg1VOHLaMEjnFxDfRYU5VN6hZDFjSQhIbrZ/dGE3bQCHkXBSHPXr/2CbHNsUXoudV1Q4b1WvXYM1pL0eU6SX+sp/QzecnrjQu3XElJ4LyEYhhzWon4Sxwgp9xCxW5E8ii2I/pRxnFZHbTOvpAlpDXeUfmgz1KTWil1y8noZ/JmsDHHOIAxjypxBfe46Se+LatSRhIdB6YMAbWzZTF6fdnahhgUmf19af6/3k0bWCFYI1RDqQE67+JtMUKlzCBltGJyf5GM1CcIk1VJAWVDMFlfjrw9ACgussYXGqUuVsSnsJr82bSggSTlGXJBrvpGkCpOEQpQ8J1TJ364rMmWjla78RnDLrn1d6Z6bmaLLuCr6WkJZIwlzdXDs06T4Wh/DnTpUjzA/1e8pY+kalnaNrRUUownFo0Ovi6I4cepNWCYQtkiri7LVpfR3mzkHyZEBCefqtHJa+HPlMtpng7r8MrT+XutQPeo8Q5H7qXUpPwminTUx7CHErCnUmNa0i7/R6RUpuzrp+HMTV2miYsIvQ13wzOuq13wbaUP1aKfQ0OoywXdFrm1dwQ08rVxS9H5BsS5SnztFcCXHcCcsRhaELIZXpA9BJJQneHOZokrVazJvPym/yfcTx1bKkAeTQUkKawnUm3BDEyiTuCpS3w0qrGo9p+mfCbKJRc1JrQ9D0LHh7UOBdBISQUkD6AYaqnycdGyll/tpit3kePM70QOUonwiGyQTh3FFf65aLuZz5TLpZCe68dh87a/XkDGbqVHSr1uJq9rQGCuEhBCtQogPCSFOCSHmhRCPCCFeqf39JUKIw0KIJSHEd4UQe9a2yRsH4jhObaWqV2+k9InsaquYSJvslSq4Uqh5+FzM5+R1JS+Tur8btJAUMkZMVJp2c2fmgaN+Ut8x5b4SFyG5WKdtHxUjhXXqXDbm5W167ZD7LgGjU5cIwiT1uXshtyabp5jPScuR162EZahKU8oGlKqU9A0ZRMHfpC71bk3WrZSDy2QtSp1vw6DvUjwLqFAUE1RjlTe2iOsWZSNJlblIIWM19s6gKGmca4M/k9VF2MATZ12KsoD63GnGBdq+RI0pqwcaEccxqS7qOE6sYKTtPTcyFA+hZgBnALwQQC+APwZwmxBirxBiCMDnALwdwACAAwA+vUZt3XBQXVttDpS0lieK1ZR+xKh5uVrHcOvYdQE3X1BqLQgB2jJscxEyUj6le6+UZ2gjiTrlHYN6X95Pc0FIL18N6nP3k0onL6ND9ZiglDEK7xWqDK0uE9YnF1ht66LnzZGBn4nKpDRkkPMVEV4BKfwYydcgykZXL2k259KUBZTnTpWd/JAHk/aRqtLK21M+mZC2v5M8hIjjmLJuUYw6JtC9dsrLJy1jWhfpHcfUW7GM3i+IIcGkpNLJq/KVNAZlgvOnQV3eczcoY9FrJ+00Q5U/kz6PBkkbFKDZtICUchHAO7RffVkIcQLAjQAGATwppfwMAAgh3gFgQghxhZTycPrmbjDIE5RTkLqhsXl6BWWjRj0G0SzUzHwyTKsRpgknNCihSxRh3LScJ2iY1JTSgmymfDKvh7qh8b3Uai90UZQ01HDMtIpdmqXVpK50c1qtPXBKxPtSWLX4WdxI1loBSh/HwZ+J6rI5tgLK1qQbmujP1aD03bSKXaripNYnyaUNrzCS09IqWw2uLXrrFo1SSSKTSSa80vq7Xt5cyUBWFiRdt2I+Vy9nPk6CdZnLGXRjVfJylLFFU9KYl3Gujf6O5GXMFRNG86D2sCnyXb3mKrNbl14uYX8ihgVuZFLnEBJCbAVwGYAnAVwF4FH1N1d5dMz9fcNBnaxtWoOUUELt8BQh3kbIA2URSpuvyKSQr8ugKkNqO8nTT68gvGOiVYKi9KMI8dQwBK8MURBK+gzPzy7j/GyOUBdN0KB5nMnIzxXLEDa6QP0rQaghDwqzUBPjrw9Qr9bF1OFENG2rQRHanJHWck9LwEqri7JRo2xATZ5fYPNEqMtmziyj/IukEElaf08rf1LySlKV3JS+QVUWJA/j0hQnxLk6aRvpytZ0z4KkiCOOreRKGtqzsGusSn6tV0b7TDGM1WvEiM3Q/uDYopRpDI1QKoWQEKIFwCcAfMz1AOoCMBu6bBZAd0TZNwshDgghDoyPj6dpRt1Ct8KlHSgmdaWqquYujFSvHYrCJbjgJS7mQRJ2ic+fsimkhyFQnnviIuRFyNs8mSgL3OdmcroeRUmjlyMLQgnLvPzv79LKJ6+LLGion9SxlbQMpRD8/DJUD8uaJynX5meTumyGjCnseiMlv5bixUB97unDWigb5MRFSIYn+lpnvuki53yhrFsx9VaDlt8w8aUB1LRUa5kwrSKOSrj86GwOR8bmI69NK5sYbQq99iUuQpPhCZtP5/t1Wde8Lpq3Ke1aijGo1vIC/VQoXeZKOH9q9/LImRmMz68Y1pT8eVDnz7TKVgo2c4ealTdft6jz50aGrBASQmQA/AeAPIC3uL9eANATurQHQNlqIKX8gJTyJinlTcPDw9Rm1DVkS0bqgWJxAjByezavk+oCThPizZVP9FwR5mWC9Sa/lrbJMJ9AnWsJdZGEXYLwZHhtVF00IZ42HpM+l/ncqnEZpy7/MyknVeISRItfyk21idIvWG/yuijJjak5hChlrOYeSVdV5HOXUuJLj57Dymoxsl3hMv9yxzH84+1HI7+fbLmPKF+NwNcTNl10z6ek9dicP7XPBuUo6xZ1bFHuq95DxsjeI2lPGgrNiS/7+zsDRovAtSmNhKQ9IbE/1VrZql9615FkRvLgNGPen+jP3UReSF6HgjKOybJ44NCQZGX0yw6emsbNf/Ft44IkBSNhrjYb+8mvja7T5GWnm2foqRiSjmPa/LmRISmEhBACwIcAbAXwBillwf3TkwCu067rBLDf/X3D8RufeMj7XOsNsg4lZMzEmp52gyy9iUrim0+OVhRAyB5ChMmQIrhSw8woCquo8rUqE3wW5psnM8FfrzdZSepzp3gjBQU8i32wxosQOTEiYdNK6U/UDU36+TP+b98/PonTk0vatYT5M1AXoT9aUPh7ShDiscQUop773Ucn8Fufehj/79tBJY+vOAle/3+/fhh/960jkWsKNQcGJXQpbbgjVa5OWi54/ybjmLJRiy4fxYW5XNnvrORu8uoy2Ujak9PS5yozr4s6nsNrrDJYzC4Xyq4lrSWB8hQlCHFOTDy20smsAPCbn3go0fOnr5Hep+RlAvUa1OWVIRRCsvvKr5Zwespfl6nhvZQ5TRE2WlSri7THMLixIqG/08eGDPxMVhepKlJ5yjihPveNDNVD6F8AXAngNVLKZe33/w3gaiHEG4QQbQD+BMBjDZlQGsCjZ2a8zxQhnhjxUHMXdaqgocaUUkJ97YlRvPk/DuLD3ztRrYjbRsri7/z7+PgCfuVjB7CUX40tQ1kY0i7+1GmG9I4Nvj/43JOX8+L0iYsQLVFpbYX49dpI2nUBN98UkueMhEoGGfPZpC4KlcbWmz7wffzge77r/Zv2LGhCvMLEKymtkma9n/uMu4k8NbkYvLZUWdh96vxcxfbRvDkTFwmFtRiU8+qkbdSSC67EjaT+OWG5pJbWB05M4Tl/eTu++vh5APFeYJVIq0Q2MqbVwHC3sLKKuVyU4iTdumVSOq3nU1xbj0aEjdmcP2XoZ6IyJIW9cRGnXOjfZ6aWI69bi7pIXuOGSppUdVX4VxSfPnAGP/KP9xiU0L6dcF9RfXx2qXzcVq6LsMcgrP21zrEJEA3BxLooCmuKLEl1fNjIGCuEhBB7APwagOsBjAohFtz/fkZKOQ7gDQD+AsA0gFsAvGkN27thsSpYG+UQSrtBpihpnJ8LK45y5vGz4bRTcXUlb6MMTRp/9qWn8O1DY/jeM5MV6tLLm0/WNMVJ8jLBepNf6z8LkzL+xW+97VE8PhL/jgLlQnUmgbJRCyysyauK3nQu5SOtl5F1EYSaJPeUXy3hodPTQfdlgrrQbCOZTrCmugcn9hDSOobVMNiY8lFzAs1yT7svdSnVsyBtAuy1vDZpedUHwoJqNSXNgZNTZb8jnyJJmEApAqhThbkQTxFcAworan9a4zKHRx0l3veemXCuJaxbVKsuyTBW4SVdmM/h4j/8Ch44Ud4PhetOGCWn/e/PPoY3//uBst+nVUxQT3alEH4uTe6JY0fGFiLqcssQ5kEAWC1JvPtrh3B+NrnyhLqRpBgJ03hz3nm0ethYoH01D4O1t26tFs3mtBHdOwj0d5y4TESh6SQKIe1z0teVdC2ZyxXwzi8/hVzB8VTyjrgnjq0o5nMFfOupsdi/U1KImBJW3p+ZWqpaL2W9o86fGxljhZCU8pSUUkgp26SUXdp/n3D//m0p5RVSynYp5YuklCfXvNUbAOU++PsvuwzX7Oy14uaviLJcfe6hEXz/eLkyxGYOjLAQr57J1GK+ahnn+uRtDOc5UT/zq/E3rH//SoXr4srU+kjIYL3m/akkJb746Dm89p/vqWoJCH/9H3/+8YQNUz+qt++Js7P49IOnjTZq87kClvKrIatJ8mdRtrEsSbzwPXfguj/7ZpkXQlSbzASh5JuMv/raYbz+fffi6AVfaKZ0DbPNZ/Rnk3K1rotSptppMqvFEva+7Sv41zuPGdWlFNg6FItaUDgxL7fW3ne/+YmD+P3bHo38W1Rd+dUSrvuzb+IzB86E6qrclgdPTuE/Hzgd+/eo25pZctaGcoWQGlv+7/VrTkdY1ynCuH4tXUlDEchpQnzyTWt0+ep1mc+FSctkmxxxVK29vofl2vb3yHIlQpkK1z5wYgolCXzw7uOx10TJaccnFvHQ6RkUQoIZRU6jeDzr7aJKoeGx2pFtAgAcGy9XCFHCuPRrj4zO41/vPI6Pf/9U1XLqedQ6BPHf7vLfudHyELr2L79yCN+usPl2vl+bZwyq8uU04yLm5Qj9aWrRT9Kc5BlOhvYQtQ4nipqTppfi9zGRdVV5Ikv5VXz9idHE8ud7v/sMPnTPCXzm4Ejg2rX0enzrbY/iV//9AM7ORCtgqWuk4sJ8Dq997/div98p5/985sICbv3r7+KD98TPs0Co7xLecYPog9IfO89EM73oaIsHurIQgr5RoxA10N5626N40we+X/Z7ittetQ3Nw6enMR2h5AkL8corY3IhfiKNEiYfPDlV1WMlrHBR4XdjETkKwt8PIOB+WrkeogBaZbIulSR++1MP495jEzHlTepyfkoJPHRqGo+OzEYKZzrhxaqSB02wXPL2vfqf7sH//q/HAwJktb5/zTu+iRe954418xAanct59/bgyenIMpS6vnN4DEv5olu+eqmDp526L8z7/TPJQq6sQV77Ek4ey/liYNNBUXRRhfikxT5xf7zyoHJd7s+YTdTItCNs6MmHk2xaZyIsgL6wS9y0EhLLhoW26cU8fv+2Rz3lSbBMdL06X318FP/10AgWIxReUc2bWFjB7HIB/+uzjwV+X63v/cT778PbPvd47HVR7Ztyn3lYQR/l0TGvhdqcDlmMw99P8aSjejHQ1gWTMlq9CctQclmUfUfCgkmVT4vufOkrhMrLR7GUX8VyxFxLC51fm82T8nBobooP/I+qa3JhBfnVEo6GvGkq3UupJPH945Nl30fPb5j82mrliyXp5RAarZAfilqn2jjedSRaToqqiyovJC33sft85ZRZ/kX/2ut29WK5UMR7vvF0tUL+R8I8Q1UGk5TcBnVNaHuCJHWF9xtG3SnBGllWJOKyqPW3UrlqVb3nG0/j1z9+EA/qXoYVyiytOHPgatFs/tSpZuB60o3mmIrZs1G9TRWfOTCCR8/M4GP3nqxQzl8jT085Rtx7KkR+AKG+m7B3LK74snWD6INYIVQrJl0N92BnFkIIkoVbRGQqncsVyqxHZeVDdVWKbV3rkLH8agmve9+9+IWPPFBWTspgGbXBOj+7HDvpR1k/f+L99+E1/1xZYRO2BqmNc5RQEvsdCZ6NSbjTgZNT+NV/P4DVYqmq98jpqSV88dFzuDPmtAm9ruV80VsEItGeu/LGeuT0TMW2hts1UUFpFyxnLljrG+0kxS7Mr6T2UlOc1LyCnogJXQwu4tXreubCPH75owfw5Lm5svJxqO9NenypQlfUtbVkEi9cP/uh+/E33zzi129Qp7r2e89MRoZERFHQXcAT1vZ+zYNnLXNmKWVod1uL9ztdEIqTiSonRY0us7JaLPNKTLtRC9f1xUfP4b8eGsHff+tIWZlqG2Q9p9rdESEKUWXiLKFJ7yWuj0cJo0rQD3uRRinU9fdzJlIhpLfVoD95rveOsB3lKRZG//5f//jBRAlHAV2hTttIUt4BZSPpVpyQZBvJWbdfLannqzbwVRr4Q39zJ577V7e712q1xhT72uPn8cmQslm3OielkjJX9ddMhWzz4SW7VPLX5yfOBdeiSv3hvx8+izd94Pv4/CNnY8uYeUuVyya3HxrDs9/5LU/xVrG8VnBOG5Ojs+WyV1heODI2j71v+0pkyGfU96tQsSfOzWJyocrambA/RRRJXC48h5kpXPzPL7x8C950825MVVEyUJPXU+YZnST9IE1d4/Mr6G5tDpSvRNhDqPb5Zcp/ZxoyVg219j2je41X+AaloFYzTlLD3cFTU55MVO255V05bnwheh9FXksM0CNMcgVnEq2gdy9vV8JqL8zrXmqNoRJihVCNUAv7QGcrMiJZh/rQPSecE1JiLpVS4uV/dxc+dM+Jit8TXpROxITCAJWtXMWSjIzNrpQgVllsHovw4AlbP1XC0OmlQmDSiyoDOIO6mjIs3EY1wSnPoPMRQon//cFnsVwwE36qveIff/99+NZTYxibXwl47UShlAlRnlbheq/8k6/jrTEhH/q1UvqbuUdGZiq2NdwHF1ZWy7xRKpUzmT7HEnrF6P06eNR68rrCY+OUe2LUjt62WIVQWPlUKJYqbgZOTYbi2RO0S9WhC81J5gylTPvnn74BL3vWtsQP/uCp6WAbiQv5r0bku4hiZNp/JknqCitRkpQ5N7OM87PLZWM/jK8QavZ+l0Sxqysu86Gwlrj+cNWffAOv/IfgkcvU3EhxAl6LG2qjvMyCZaI/K85O+/O7nsTUV96Xl9GfwzhBaDo+EReaWf47tSGaCG32okJulUJoz2AHTk8tlXtMaBWsFiVu/evv4L9c9/pK6BuaX/jIA7j6T79RtUz4XlRenKrl3IKzywV85HsnEm1soowm1Th83knw25ltMto8LejzbpXJ5uHT05hdKiT2YFLywLj7rpN6MYzO5TCzVMD0Yj6R0u83PvEQ/s9/B0Ogq3nsvv3zT3jJrhWVrOlqMxH1fWrvEpa95nIFrLrfGV6LKj0DFer8ncNBha7UptCodvyf/348MqQt6tr/+/XDmFrM4/hEZc9iIPhc1DvNiBiFkFen81Mdt/6V0LMOlNGad24m5/3unipjTPXXJRNlRsK+qyhXXCeuKvDce9qa0d+ZxfRivuL41HPtJK0rVyjivPvcTAQ1vX1npssV7nHoxq4oL9QoJhZWMNzT6pavfn34uZvInxTPJ2rIGAJ1Vb50sMu5f93btdI7VmuHko2TzJ/L+SLe8C/34XXv/R6A6iFjedewcWEuWvlK9RpXrLh7jLi5NWzgUkrgSop3IHgCXNJxMj6/ghZX09Qg+iBWCNUKXyGURU9bS6KQm3d++Sn84+1HY92Xl/JFjM7lcLxKuE94oJ3UBPBKQnKYf7j9KJ777u+UhVlV8s6Iy8Pi1B0sM7tUQLbZ6YJxAnNY2I0KBahUTkrnnpUi6HyF2NTwo4jLbfSFR87i9e/7HqSUAaGr0mKiT3COkFxZAH3StRBOLereM+WLiXJT/eKj52LrVqWklF54XnUPofJ2ha0w0XVV3iBHMZZQCTKpxZUfGvVPEYp77memlvBzH7off/U1/5DDsrExuYhscwa3XjqMEzEbVb2ElMClf/Q1/M6nH4lt58h0sI8lETKUkKorLJMsQmpe6WvPQiDZglwqybJj0tcy8WAUpyaXMOQKOEnaGJ5zklg/n/dX38Fz3/2dipaxkekl/OVXnf6QCwgJ1YVCfQ73cttUGcerJYlj49UV8vnVEt70gftw37FKSe+jBTyVb+Gpc3OBsKlwu6KaOKLNh5HekxGFdME3qbCqP7uTceMsqq5FpRDKB0McS+q5+9cqRdXVO51wi3Krsf95cjGPM1PL+N//FQx7q9Su+dyqdyhBNcNE+E6+8UTlXCDhcg+fnsGffempiv0hikoh0TqHRuewtacVA13ZxJunxZVVPHluDl2u5b7S+y6WJF73vnvx0x/8fuIwiWnPYzjnrK2qTML23XbgDCnEB6juVfjpA2fwqVD+q0qbJxX6Wyk/Yri/60rP8EEblebMU+4YvP3QmCc3lJWJKP7J+0/jXV85VPb7qPfa3uLkAap0P1H1qnnyim09GJvLxeYC80L7RfUNmP6387PLGOpqRX9HS9WwMVV10jHi1qbVW71HlXs/0tbVnvYWDHRksVqSFT0SA3N2woX5odPTyBdL2DfUSUpl0dveUmb0qkRJAjfu6cdivoj/fvhs1etXiyVMLeWxpVsphKq3ssxwanBjMuZzxTKhC1ubM5Eh5ZXrqlybMkgfHvVP56v0KNTphMqI4htn4wt9/cnzbllXiVTF3q7CeeO8fKPmjnd/7RD+v08+VPb7qFYpRXrYAOSVCa0lqh3V3ps+byX1pBufX8GwK7M2iD6IFUK1Qm28BzuzGOzMVsyTEyYuCZsaJNUW5ahNr2Iutxq6VtVZ/j2fe8ixnh4I5VYJKkGCZfQNQnlMe3BDM7OcxzU7e7F/uBMfuOt4TA4MXfkEHB+PV24F2ug9Q4n5lVVvIquk1Ah/XdwE/zv/+QgeOj2DZy4shBI3xn51IIRhZjlf8bkDvoeQ/kyiLMEnEyzMurCrNnNPj81XdPuNalZVl2ytjePzK4mVDGNzyRII6ovQdw9f8OuMuf67T1/A3Ucn8P47j3ltD4+Nkell7Oxrx/a+Nkwu5iOTjutllAD2pQoKuHBCvGq6MSmlZz3VPfKS6NTOuXVt6WlNnKtsLlco63cUwTApS/lVXJhfwb6hjsR1hZUTSZ6hohihLFB85THf8qz30SSb1pllfywqoctXclduk06U8unohXl8//gU/tdnK3n6OT/DmyoVzlmSwJcePR9ZJlyvQikv21uaIjdKUfelu8afm9H7a/xLuueov1mLU7xGeggt+kml9bk/ymiilE7X7OwFUJ5HKBBq4rZbnYBUCVVOH9fVrMHhd//k+YSnNIaVfQkTlSrDStKN2uHz87h8Ww8EROJ5+uCpaayWJH7g4kG3rfHl1Lt48txcRQOSjlrrxudXcN/xyapGEwCBUOl/uP1owIs1yiiheyjoZcPGKp1cwQn7fOTMTOA7K92/sqBXkvvixvEV27px6PxcoH2VvJGOji3gyu09yAgRyLtW6blXMthE/a3dTQxdycPaL+9/Vh5CV2zvxmpJRhgXnZ/q/opu4UrPVr+XiYU8dva348rtPQE5NwopJZozAvO51cSeKkmVmQo9B2DSMt612ufe9hb0d2YB+PlIoxiZXvbGftKqHjgxhYwAnrN3wNBD1fm5d7AjMiQ3thwkbtzTj972FhwZm696/dRiHlICw91tbvnKrKwWMb8S3tckvy9KCHf4+/s7srHe/Irpxbzn0ZakrqhUH5XuS80fqh2+93J8Hd9+ypGjuz0lf/z8Nrtc8BVCMXuBcPvG5nL48D0nIlMLRHlmKbkvTmmrf3tJAuPuPVfL3zS9VECnO4fpuYEqcWE+h+FuR65uFBchVgjViJOTi+hqbUZfRwsGOrPVlTi6O7va0MQIDNW/K/jv89okFNbsVnLNU8LVg6F47jhBY7VYwuce8i0A4bwzYTf/maUC+tpb8M7XXo1zs7nII+HDLuC6xbSS668qVioBM+6C2pFtqjhxhCezuOd82dYuAI5XU9LFRBekZpYKVeN7lUJI3xBE5QRQHllqsotCF3anFvPYP9yJYkmWWSGDhcp/lUSpKeFYSxbzxYr9VBd2A5N/hWeorAdDXa34dz1xY0wZvf4vu4qAsGF/ejGPgc4stvU4wkfUQqd//0NuqFWljeTI9JJnRQeqWyQWVla9vmya5+TY+AKaMgJ7BjuQdN2Kei9mgqvZ4qg25nsHOxPXdW4m7GVV+XpdgRQVTqR47OwsdvW3449edSVyhZInSBYjxlYYXUE8Me8KXaX4cTwXCGuM/n71WSWR7W338xqFidsgTy3msWewA9fu6sXffPPpwPut5jFxamIRrc0ZXLOzN0YhVF5qRvt+/T3F9ddcoYi3uZ442eZMhZCxYPlD5+dweHQer752u/dv/1pVp3/9w67Xo1IIhTct+vefc+djFW5XiajbqrYGh8ucGF9MGP4VvEZfuyvV1ZltwnB3a0UPXZ2R6SXsG+wwOvBCrUk3XNTn1Fvh2tg+WKHQ7HIBz714ENt62vCHn3vc2xBVKwMAV+3owVK+GAhliBrHpwOGmYJ2rVIwxtcxn1sNhExVchJTclakAUpEt0+tr8+/ZAi5QimggIx7Bl9+7BwOjc7hJVdswcXDnYEQ0Kh5RqHfezj/YJS3VGuzI19EhX2F0edStTm9Zd8AAJR55JUZCd13nq/wcMOPYntPGwa7WquPSQDbep11PmkuSb0deh68OMLyNUXhArgKoQ5nLaikFB6ZXsKu/nY0ZUTiug6dn8PeoU70tDcbrfvq+/cMdmJkerly3koNKZ0u39/RkijPjgpH8z2EKl+vFGbX7e4L1JkUWsiYwy37BvCRX7wZfR0tgTEVxacPnMHEwgped8NOAMBivrJSMiqqpFLrlNFzOjRvxvWLUkl6h9bMr6xidrkQuDa8N9SVeXEhY+Hn9+HvnUChKDGzVB76WAqEtDo/lXE4SUhaSUpvvFUa+8WSU/9z9zuGjKc0OaIS4/MrGO5ucz3vExXZ8LBCqEYcPj+PK7Z1QwiBga4slgvFQALPMBNaOIyaWMJ9cJLoIXR+Ll4h5LvqBr9jfGHFU+iE49mjFEJSSvzlVw/jkTMz3t/CFlo1IaiJZmapgN72Fly5vQdA3CLt1zU2t4L/+P5Jr62Vj6v3N09qQd031OlMenFKsNC/46zAba779P0nphJbP/XNlqMQUmXKr70wl8PEghO/qm9Cg7mbnH+cnHCesZ4gN4x67ksrRayslnDrpcMAgKdH4ydG/V6Uu3icVSBc10UDjifImen48DxdMBgLbObjn6Hquz/9nN2xbQ3UsZhHT1szrtzeg8+5rsrhdz+zVEB/Rwu2ugqhSokvAd8ja0dfW2w7z04v47rdvVr52EvddjrPQln6kpYDHIXQRQMdaG1uQkaIRMqaqH5NFVyTMOLmplH9IonQVf4eKpfRT+Xxrczl1z0+Motrd/Vi2BU2leCRZBzrFkClnKjk6ad71Olu/1F1KbfwjgSK3TIvElep+fZXPwtTi/mANa5aUumnx+Zx6dYubO9rq3gKkI6ytnW3NYcUQtHtvv/EFOZXVvHPP30DXnjZcGzIWLh9n3/4LLJNGbzjR69CtikTUggFlWMTCyv48PdO4JVXb8OzL+oHAJyukMtLtbvSKVBx7QLiT1nx6wqWWcwXA0kq4+sK/jtJrg4JCSEE9g52JPIQWlxZxVxuFdt625ERIrGwe2Z6Cf0dLehxlZaVhrE+xwQTtscXmlkqYGtPK/7uJ6/DqcklT3FYaU5T68i+IUfZrMtRUeV0hVlQaeWWiWifvjF7SAu1ruS1o+5/eikfm1A8vBYpeUkpNCdjlGo6X3nsPLb3tOE3X7wfO3rbg+OxQsJhfW4Kj/uoUDgVvpLEQ0h/LuoZ//DV2/Hiy4fLQ9VDm1b13ExC7bb1tmGwMxsbZuKXA3b0tgMIhqnHUSpJ3PvMJHa4SqQkaR/CY9xUMdHb3oJffv4+3Lx3QPMQin8WZ6aWsas/uTEIcNbKy7c6exNKIuq9Q51YLcmK8l2gnHRCAfsSeNFcmMvhHV98Ct2tzXiOq0SsJpuo/ISvunqb1lbz+wp/rljGvfA3X3wJXnzFFkchVMVLZWwuh67WZvyw286x2cr9dXa5gBddPhyqN76FnoeQFzKm1sjo6//pO89geqmAlz9rKwBHbq10uIY6YeyqHT145MxMYF6L2s8USxKfvP80mjMChaL0TpH0v79c+XShiodQ2INpPMGe2FF0AbfsG0S2OYPHq+RPVTgKoVbjcbKRYYVQDZBS4tDoHK7Y3g3ACRsDKntY6BsgtViHx74SEEwVQmOzOU/I0BU2gD8Qw3Upy2RntqlskYuyPL3/zuP48PdO4Cdv2oWv/+6tAPwjAcPtktKxSo3N5bCttw19HS3INmeqhizcfXQchaLE7730MgDVhAb3J/wFdd9QJ0oSZe6lZYVc4r5fKSYePztb0cVSJ+AhtJzXypWXedLd+Dz7on7MLOUjvRDU58fcya2SslGVUvd95fZudGabKuY2UWV+/MZd+MpvvwDZpgyOxST+DpSTTlJXIPqkH4W+YdAXiopKNfcZ3rCnP7KtYaaWChjozOJ1N+zAo2dmcG5muez7Z5by6G3PegqhCxF9MOr7K7mdTizkve+r1D6vDW4o0g9ftS3w+ySL0LELi9g/7HisQVR2D1aoeehTv/oD+Ic3XZ+ojTomyiPAz/10sdvOJIJ1eONR7b6OaXnVfAtZsJ1L+VWcnlrCs7b3eNZHLwa9SgJWADhwahrX7erFzr52Lyl7pbAWfUMXd4yuui9lfat0ml9cXZOLeQx2ZnHFNme90UOyAh5nEc/wyNg8LtvajW09bRibKw/zjJrTZpby6OvIYmdfO87O6Mrc6Hbf+fQ4WpszeOmVW3HxUCdOTS5FbqbLvGomFrFnsANDXa24eLgzcPBAOIeQeo8/et0OtGebsKW7tUyZot+L6l/VklEC0WOjWj41/V6Uovd4hfnWrytaSVCtLgHgooHORNere9/R1+ZuJJON5zNTS9g90FF2ik0U+tp5NuHGcdrtVzfvG0BW89yqNPbVJuxiVyE0FRhn5dfra16UF1O1JOoPawqNuHlCSomZpQJ29LZBykohksHyj56ZwUUDHZ5yS7+XuLE1tZjHrv4OdGSbsaPPUQhFhcGGy+vzTDjnXVRd6rSwEzFJpeO8LCYW8mhpEuhpa8ZNewcwv7IaCFUPz2lTCWTc8GPf7iqE5nOrsco31abtfck9hB4+M4PRuRx+/nl7ASQ7VjxscDVZKSWA3QPt+JPXPAtNGYGBDmffEPcsCsUSTk0uYld/e2JPv1yhiJOTi7h0azeEMPR8cK99lmvAPZTQ08JRWCsPocrP8F/uPIbHz87iXa+7GkNdWb3aWJSR7hVXbcOXf+sFuHi408xopV27Ukjq9eQUUnNhf0e2qvfT1GIe/Z0tnlLyXMSBPTqzy04ERbDe6GtnlvKeXKXkO39vV15oYmEF//Sdo3jl1dvw6y/aD8BR+FcKMX/i3ByGurJ42yuvwOhcDu+/o3JC+rG5HOZzq7ja3XuGlYHhuvKrJUwu5pF1IwyiDrHRm7SwUsQh12t1Lrcame4B8MfPlp5WPGt7T+XICJecm4Nwe2+bkbJ1o8MKoRpwbtYZCJdvcybOgU5n81FpkdM3QOq6MsHfVRTN5VYrJrUMT/Kjczlcv7sPN1zUhy+UHU0a/R1qwbx6Z2+Z5SVq8b/32AQuGujAu19/LfYOdkII4PRkKOzDq9NJ8rxakk6oixDY1tMW6Z2hP4PvPTOB3vYWPP8Sx/Wvkiut7rk0pSmEgOjYXKdM8N9RE3ypJDGxsIKmjMDI9HKklTGKsbkculub0dbiJJ/zw1rKr1VWvut396Ek/WRx4bbO5wq4283NMZdbjbVYhvvRRQOduHi4K7CJDqOK/M+XX46Lh7tw7a5ePFDhKFivHIBd/cpDKH5zMhLzt0rP8OiFBezsa8elW7pCbY0uNLOUR39nFtfs7APgbMg866e7kk97HkLOGI32kojeFMd5mjkLeRbvfO1VuGigo+qGS204fvjqoEKomrA2u1zAsfEFXL6ty72l6ptbwFfG7R5o9xTF1GNak5w8pzbPV+1w5sMkVubzs8u4aKADP/Wci9DSVN1Co286J7x8UcFrlDvyjr52bHHft8r5UC2s5cJ8Do+cmcFLr9yK63f34VFXsa7KRY29CW1zoM+hUUKXGg/nZ5dj30VcXZMLKxjsbEV3WwuGurIBD5xgyFiw3OxSAWNzK7h8aze29LQhv1ry+mKlkNbpJUew3dnXniiH0OHROVy5vQdtLU3YN9SJfLFUFhIYVf701JKnXN7R1x4ZFqjaqdrd64ZZ7BnswNGQAlt/bCocp9I4VujNUvNEkvAUAPjtH7oEn/uN5wFA1Rwn4TYC5Zv1uLqEcNqWJHebylO2racNMAgZOzu9jN39HZ6HbqVy+uZZ3zjGNW21WMJ8bhV9HS1oacpgf2iOj8PzEBp2lShVvGr0sAd9g+JbuMvLqI3WYGcWD2sn+cWtt8uFIvLFkufhcGQsep0Ne+I8NjKD63b3YcA1IE7FeFnpqLEIOAq+xXwRc8vBU4YCN+iiH9DwcMhrp+QPLu93anx9//gU7nj6AsLoX6/f19SiMzcJITyvzOBcGFTEKW/ZSsbT8KPY1tvmncgUl29HNWlbbxuESJZ7Uc3JL7hkyPnuBOFO6ULGZGAN9zyEYuTcD959AnO5Vbz0yi1uLrDqdTxzYQElCcdDyGTww7+Xy7d1oykjkiuEpAoZy1ZNvHx+JodLtnThtdfvhBLSqs1npyYX0ZQR2Nnfjqt39qK1uclI0RXILVdFSaP45+8+A8AP8e7ryFZVGDqevK1e2GKlA24AZawMKoTi+pOS5a/Y1u0lb680p33hkXNYLUm89WWXefK00zfi5aBj4wu4bGs3XnDJEH70uh34f7cf8Yz4/t7Ov14ZhK/d1eveT/Ddhw/JUbLYla5hK6qv6G363jMTyBdL+LHrdwCID2dV42egM4tLtnR5URWVUIaVPYMdrud9Y8AKoRow4uXMcITZwa7Kmn4guJBMxiiEdKtOJU27Ljws54uYXS5gW28bXnLFFhwenQ8k1IubYNTgumZnL5bywXC3KA+hqcU8LtnShaaMQFtLE7b1tOFUmYeQX6dybb9owBHktvVEhyzozTs5uYRnX9SHQVfBVklo0F0Yp5eCCiE9OWygjDvs//y1V6GnrTnSvXV2uYBCUeLFl28BADw2MltWPorR2Ry29rahrz0biKeN9Cxw70sJxX7SWP/akpQ4cNI5LeIVV2312hZ5X1oVuwfaccu+AVw83FnRYq3uRaXKuXnfAB4fma3oieTUJdHV2oyBzmzgGOswdx2ZQGtzBjfvDXn7VFj8j4zN4/Jt3djuWlj8MtHXTy3mMdCR9TaVp6YW/b4rHWXGcqGI/s4sBjqzaGkSgQTXcd+/tac1VlGXXy1hYcXZ2Pzcc/fi1dduryqoqRDRy7Z24f+86gr0dVQPyQCAO56+gNWSxA9d4bx/IZIpdtT8MtCZ9UIN5xJ47Sj0KlZWSxWTkwOOpbsj24TdAx1oyohEQtfobA57Bjvw7tdfg+Gu1qrPQs+3MRGTQFwJL1t72jDc5eaMijgeOmpMPnnWEX5/YP8g9g11Ot5mJVnRs2BCmz/ijmeX0nlnSqGVK5Rix3GUErlQdKxqarO1b6gTJyajPYTCbTxywdkcKw8hwFeIesMk4r4cJWrW8UjQ3mVc3zs5sejNvcpa+GUtuXdU+6S7RnjrQ2/QYBB+7rPunN7X7qy1t146jEfOzISStPsVKGtiSaJq7gf9vi7f1gMhknvp/uaLL8Hl27qREdU3AE5lwX8mydkipRMyNtjVitWSrOqBp7x/d/S1Q6A8h0wUpZLEyPQydg20e/lkKo17/XRM3SIbJ2+ofFvKIq683SqVAfy1/eIhZ62cjFDy6KgEzABwfGLRu/eovDkK9Tyfd8kQjl5Y8LxQ4tqlNjLP3tOPpozA0VAiXbXl1/v7hbkczs3mcN2uXl8hFLiX6LqmFgve9Tv7nHVRyV2Vxr5SVu/sa8dXHz9fNaxvZjmPX3zeXlw00IG/+9aRsvboSiD9uyYX8l77vDDd+XLluPo+paiqFP4VlrO297Z7Mnbs6UTuz46WZly3q8873r4SSg7b2deObHMmsYfQLfsG8MxfvBK7+tuNFC5KsavoaWtGU0ZEyvrnZpbxD7cfwSuu2oofumJr4rVfpX+4emeP61VkoLByf7a1ZLB/uBNPnauuEJLSWSOFcJQm1TyEJhb8k52SKJ4BJ5H+zr52Lx+cSFTK58L8Ci5xZe0kxioAuP/4FF542bCXt6i/oyWQGzSKKdeTd7Azi2xTJpDKI0yp5ByG09uRDaQSiPt65T37Q1dswWpJ4vzsckXZ5K4j47hkSxcu3dqN7jbHuHNkbL7iPDA+v4JtPW0QQuCXnr8XUvo5NX2vRL+MCilURsfwuw8bxpTsfYXrSBG1xuptGp3LQQjgx9ycTHFGZjWO+zuy2DPQgdG5XFVDpr8/7XA87xvERYgVQjVAbU52uAu0ChmrtMjpnd/3EApeMxlxTRQyNGgAZxO0x03qqntuxFmeRudyaGkSuMwVzFQSVSC4iKjyKo+F4qKBjvIcDtoEpWtgAWBrb1uipKZbutuwo68dzRkRm4tCr0tKiemlPJoyArvdHCZxVoqSBFqaBH7+uXsx1NUa6YGkYlZf4Hop6cJenFVidrmAg6encdFAB/rcxHqVklhOLebR3daMy7Y6z/4Zd+MW3jCpI6NvdEOo4hZbvdzz9w8hkxHYP9yFc7PLsUK9V8ZdlG+9ZAirJVnxaFd1VLAQwO7+9tgJGgDuOjqO5+4fxLawcifm+kKxhOPji7hsq2Od0hfJSjlf+t2E0dnmDE5NLgW8AVQ/6OtogRACW7qT9UF1yk7UGFSbB5UQMokrtxI0e9uzePMP7sc3f/cHK96X4s6nxzHUlcUNrlAikEwMuuvIOHb1t6O9pQkDnVlkRPwxomEijwavIuRNunNDU0Zga3drQg+hHLa7ljSRIM/JuZllbZ7Nu20NXuMrhFrR096MbHPG25xUSyqt5vRd/e3o62hBSQIL+dWyDY2OCvkQAgHPglIoPG12uYDFfBHPdpP1xj2fKKHr/IxjDdw94Iyji4e6cOj8nNc3K+UQetrNW3TZtm5s6432kIsOoXFCe3b0tWNmqeAZGKKuzRWKODeb8xKKX72zF7deOoSPf/9U2bV6+8bnV7BcKGKvezLd9h7nFEAlyKlL1Vqkj2UAniL29kO+N0Nc6FG1vq/f147eNvS1twQ8LKLQH3VLUwZbe9oC4XXxdfkF9wx2YGFlFfMRiudwXQLwQiyq5VJR8/KWnlZcu6sPtx+6UBZKHubC/AryxRJ293dEenqEmV7Ko7XZ2Tg+cVbzEIq5Xs2BfW6YjMo3BsRvgvKrJbz3u8+gOSOwV4VZVfAQ+tZTY3jq/Bxu2TeA1uYM3vONp/FzH3rA27Q6dZVXpub05+wbCJx2F7vuuPeypbsVewY6AqGOOvpa9KhrWLp+dx86sk3INmcCBqmosaVkGyV33binH9nmDD5276my748yLmYE8Cu37sPjZ2fxx59/3PubF2ri/jtXKCJXKGG4uxVv/sGL8djIbMAQFv5+varJxbynrFEbfb3f6IY71S7AURDGbdrCz0KFjKn6oss4hTLC2TQ/OjJTVak7tejIjSrBc7V1DvBPJmpuyhh7FkjprHUKIQT6O7IB5arigRNTyBVK+F03fULSkLHHzs6ip60ZFw10IGMYMqbelYDwEktXY9r1hh/obEV/RwuW8sXYsD7Aka+H3PlFhfMm8RBS+whAGcaqNi1Q/tkX9SHbnKkaxgU4ivDlQtHzAAQcZcOqq8SJQ+2RMhmBbb1tFQ8MmFhcgZTO3nGbln4gbs555sICWpsznmx6empJUwgFyxSKJRw4OYXnutcCjjHy6dH5WMOYlE4CZ/VunrWjB9mmjLduRNV1ZmoJQgBX7YhTCGn7SCm9dA1XuqlWosZb+O572lo8Y8BIjMFF9xC6KEE6C8DPNbdnsNPZAjWGPogVQrVAucMri40SoCol5dUXJ09hEVYILax4HhuVEyr7n1XH3tXf7ilEdM+NSh5CW3va/FwbC9G5IqSUkFJ6eSwUO/vby8ICdKHr9NQSsq6gDDiC9vmZXJm1Mty8vk4n39DeoU4cvRB/hKVuTZ9adMKC1Ca9kieNctnt74x2AVWbhyu296CnrTkQlhD3LD/30AjG51fw2y+5FINdTvJD3VsqjHqWl291LMvKEhOcrJ1+1tIkcKmrOIrb2OgLqnJDVnHWcfkN1INXz+PmfQPobW/Bv993MjZWd35lFVI6k/SugfijSQvFEk5OLOKanb1lMdJxz/D4+CLyxZIXHnWTlkco1kNoyV+ALxrowKnJRV8BKnxPsX53E7ItRikZ/n6lgIlasDzljvudTtLWyqtJeDOLhJaxg6encdOeAWTcSSEjqruNn55cwvePT+Gnb7kIQgg0ZRzPgiQJwwH/WTxn7wDe8OxdAKp7SzibAmceCXt6RFEoljC+sBLwBKsaMjaz7HmfxIXcqgTSW1wr15buVj+HUMhaFeb87DKaMo7SULlxzyxWPi1weslJan7rpcMBxYR+LyUpPWXTTXsHvLqiUF1XV+Krzf1uN0zz55+3B7lCEa/6h7txYS4XOiY7+H1HxubR1dqMHb1tsTm0ou/LmU9VYnU1z0ddqyxtSrEDOMJkVGhTlHVxV7/TB5SbfTgJuFLyTIfG0N7BzjJvtHD7lOXy7Exl4VB/X0NdrRjubo09BSWM2tRs721L5Bmnt1CFqlQbL85G0t9wj89XHo/ffGoMN1zUh9bmJvz5a69CX0cL/vabT1cso4xIu/rbPZmgUpJstfm5bndf4KSmeCVK8P0pJUKlMicnF3Fqcgm/9Py96G1vQU9bcyBZcrjUe90wjx+/cRe+9ju34td+8GLcd3wSf/W1w96YilIazi4XIARwo5usXIWdxTlWzXr3ksVwd2uskkIfx4+emUFTRuCqHb2Ot1dntmpS6bllJ0xcrV9betrw2ut24NuHxpz7D8hpwbJjc47i4heftxc/fctF+MyBEa+fha9V8lJPewtec+0OtDQJfOnRc4Fr9OcWTio9GPIQ0uUUff5cyjsnHe13w/9i+33YSNjT6q0vUzGKWk+ZIZzQZSmr5+eaXFzx5IckOWLUvW3pVoYMM8+CkpRlQd/9HS2RnurqGe5058e2lson6CoeG5nBNbucPtaRbUaxJBOFfav2AY5Sbbi7tariGfBDuXf2tXuyZ6WwsYl530Oo2ZVr8quVn+HJyaWAQshEEbewsoqJhTz2DnU6c3QCpb2uYFCoeWsmNmQxuEfa1d+OUxX6n1o39wx2lKVIiOLcTA47+9s9T9yRqeVYZd8TZ2exmC/ilot9hdZlW7txfHwxcJKeXn5hZRUrqyXP6NDa3IQrt3d7imE1/vXufmZ6Cdt62rzw/LKQMf1zyTfYKQ/OqHkzPJ5621uwrbcNGREfXq3kwYHOrGdoqHb4wumpJXS3NqO/o8XoJM6NDiuEasDZmRwGO7PeaVQd2WZ0tzVXFCKnFvO4aKCjoufD5ELe69DVjtlTKOvUZVu7sdtdPPSNetyxySPuYB6KEDKDlifn+Pf8asmb8AFgoCNb5oqvJo2SdAb/1t5W7/juZ+3oQb5YKsv7EN4IKuHn0i1dgZOFwqhyJSkxvehYtHvdcIK4EAGV/E7VE2WZUQvxlu5W7A+3IWbWGJ93cg5dt6vXOwmksoeQI4i0Z5tw8XCXd0xiOPHtuZllbOttw/W7+tCUEbj7aLQbtF6HUooprfrxuCSR7k/1PFqaMvgfL9iHe49NRlr31X0CwFB3Frv7O3B2ZjnSA210NoeSdIWEjrBCKPKr8aibxFflA/qDH76iYpnlvGPVVP3F8VgKJpVW+QaUUmprT2vVsEUAmuKhvH/MhD2EIsqXlVkqoKu12XN5TmIZm1xYwanJJe8IaCCZAHrYPVnuefuHvN8Nd7Um9xByf77g0iH85E27vPZXwslx47yH7b3tVT2ExuZykBKeh1AmU/kZ5gpFTCzkcf3uPm8+AcqfxehcDu0tTehubQbgCLXROYQiFEIzOWzraUOTuzkAHIWit5GMHMfOpvj5+wdxfGIx8hjtkvSFZuXpF/d8olzA1UZdKfuv2tGLv/mJ6zA6l8M9z0yUKe91Dp2fw2VbuzzvOAAYDZ18UhYaUpKYyxW8pNKA7z0lI+pSeXOUoAo4Qly+WPJOLvLuT5vf/HnWaZeXd8FVqhRD69bMspOMUp2ImMkIDHRmA2HF4deqnnc1S7feroHOLLb1tseeguKXcRXqbnfc0VduIIkspzXy1kudMVptvEg4uUeU9baS99KJiUUcHp3Ha69z8i50t7Xgl1+wD3cfncDxCjnllMywe6AjcmMfZnLBWcNefe32YFtjxrEX8ueOLX2zFTf0laLiBy8b9sroBxQEk7dLHB2bxy8+by+u3tmLi4e78NaXX4a9gx3417uO+16FEbWdn1lGd2szLtnSheaM8NbjOO9qtQb0dbREnj6kvED0d/342VlcuqUL7e4pgwOd2ZCHUHldUxEb05397ZhdLmC1WIq19gPOXKjCP37uB/ZgtSTxvWcmIq9V73m4qxW9HS144WVb8OXHzsd6IClvy7lcAePzK56yZqAzCyHCIWP+nKYUQDe4ire4xM/6Y+/INqG1uclTIMalEVDvVZ/rog6Q0Jlc8DfwSU6RWlxZxWK+6I0PSjLacI77/s5spKf6xMIKss0Zby37gX2DuOvIREWZYT5XwKHz87hht/N8+6oYSMN43+wqn6eW8hVzmQK+sn1Xf7u3bsZ5Wi3lnec31O3KbO6aFiejAn4yZeWBCpgp4pTBfO+goxC6/8RkrMFToSsYFNXubdHdI6kyl27pwrELCxXDrFW7/vYnr8NbXnwJgPj+NLGwgqHOVmzvdWSUk5OLscaq+91TSG/Z53sIqdx+uld/ODk84Ct1AWDXQAfGQvJTMAx2xTk0yJWvwwqesPfy6JxzsrI6fCRKEVrmIOAeSLStpy02ImF60UlZ0NbS5PWT7x+fjLxWMTaXw/Y+Z37MCFE1z+BmgRVCNeDszLIXLqbYGpM0WaFcf3dq5cpPk1nxvEHCCqG40IAjY/MY6nJypAx0ZtGRbcIT53x332LEpDGfK+CRMzO4cW9/pHdT2JoeNUH2RbiH6m6F45olAPCttY+HXZFDc7PaaF+6pQsnJxcTJYg+PbWEXf3tnmU/7uhLZWl17qWyZWa4uxUXD3UFcpfELUKzywX0tjthSbv6OzA2t4JcoRRbxom7d57NZVu7Il3UlYfQjl7H8vKcvQP41lNjMc/CL6cE7n1DTuLvuDxCvnuwz2+/5FLs7GvHQTduOMyEJzi2YfdAOwpFGblxOqeFVKr2eG2NmXgfPTOD7rZm7zSZ63f34dE/fTl29rVHCvFKiFL9ZaCz1U3m7V8bDlPY2tMWqbQNf7/ypoha/FWfUblMkEC544Tg+Iox/xSf2CKeq64SnlVV1ZYtb4OuCVBD3ckVQur5CfjjvZorfdhKXM2yqDwY1RzqJMyMvzPVny4a6PCUSE5bg9eNzeWwtafV25Bt7W7z4tYD8ewR8qAzpzvfrd6Vcod3ype3b9pNaq4sZGqjHlY+qfbfsLsPGRFvGY8KTzsz5Xgu6ff9qmu2o6VJ4GgoSaR+j7cfGsODJ6fxwsucXGjZ5gwGO7NVQ8ZmlwuQ0hlXyjp9bqZcqabKKcF2j9bf1NgIb0QCIWMLvuId8D2gnh4Lhs+qIrNLzoksesjFUFewr4XH8TU7e9HanKmqENKf92BXFttiFMeBMu5P1Zqdfe04OblUlk+mvC7/s3K1r+YhdG4mh76OFj9kssJYVkqf67V54w3P3gUhgM8/fDaumDcmd/a1Y6DDCf9UytQoxuZWsLWnDbdeOhx7jY7nJdmu5mtNIRQz9r1E4m6ZLVpoBRAcx2dnlrGYL3ph2IBj5f7cbz4/WCak1Py5D92PzxwcwUuftRXZ5gyef8kQPv3gGXccBBVOimlv7clW9CwJJ7PVvRwGEngIKbkrYIjr9I1eeokoDyG1jikvPDU/hZNdq7E47G7UX3PddozO5fCgdshEeE6TUuK3PvkwCsUSXnqlk+OupSmDHb3tkacgSik9xacycsT1e/XcX3n1Nnztd5wTbbtbm9HSJGJPadQ9hFRi+LEqa54KdQaSJUTWjYWAmaeKamPYQ2gg5qj2cTfXjprvXnLlFozO5SqGfj54cgrFksRz9zuKAM+wkcDzyWsg4CmfpazuHTyieXoqWSwu8bdKS6H2Bb3tTm4bFdocRSDXi4uJIk7lBtwz2IEbLurH2NwK/uWOYxXLRHkIqcTucc9DhY+rsXrJ1m4srKzGKvtPTy15ibL7OrL4DfcksLg9xpQbmtnclMHVO3pw77FJXzYJyTPfPz6JS7Z0BZQ7an0+MV4+NgHN2Kvt2YY6s95aI7VxrJhYWMFQlxM+uXewA0+PBnNOhZXIdx4Zx7O293heOVHPMrwWqLl/Z3977GmWU4t5r6/3d2bx+mfvxIe+d6KizKunQOlua06sNN3osEJojZFS4pmx+cDiDjiL0FgFAUp1wKBCyP97seQoXtQR0+HBEhRk/M9Hxha8hGlCCFy/uw+fe+gsHnJzWkQlKr376AQKRYmXXrkVQ12OF49uTQlbnpTgooeMqZAZXWGjShVLbjyqNrnsHexEd2tz2ZGA4QlQbd5feY1jefyn7xxFJG6x1aLECTepadymxysi/VMe+jscy0x4AhpfWEFbSwZdrc3Yv6Uz8LdKOYT0iQvwQz2iPYT8DfRAp5+Ir3wjmfP6y817+/GMlvAy4lF49wUA7dkm7Ohtjz1pzHcPDooo1+/uw2NnZyLL+IJjq3e0ZtSzVkq0nf3t3kLq31f59xZLEvcem8R1u/q88CjAWQzivEemQ8Jyn5sDQDdoKUFdTfxbe9qwsLKKhVAcePi9eoqQiAVLtw4DflLuSgLKdOg0iUyEBTnMw6edEAOlSHWoHjJ2YmIJ/R0t3mlMgKGHkCZY91WxijnXO/PDgGvB7W1vwXyFE/EA/8QMf96qrOhSComd/e2e4kBvq2JywQ9dA5wjiM+7xzRXyrcBqJxGTp/u8wTpfGycPuAnNfeS4EfkhitJZzy0Nmcw3N2KLd1tVXMI6XWdnFzEjr42NGvHdLc0ZbBvqBNHxxaCXjvaU/zw905g90C7J2gCSiGaUxdH3pe+2VWCu1IMhA0Fqn0DndlA//ZC7pbCCiH/8/j8CoTwx9qewQ5cPNyJrz5+PvJZzCwVAkpVwMmpM17h6O7tvW3Y2d9eNZ+AXmzAzekwPr9SMRnzymoRTRnhjWWV2+HPvvRUfD3aA7xkSxe29jiu8JVOa8yvlnDw1DR+4OJB9LuKmrhNMeBv0HQ5Y2tPG67d1VfxFMkz00vY2tOKtpYmZDLCebYV5owL8yvY2tOKlqYMPvKLN3sbtjiFf1nIWKc/TuO9ipQSyekjf/wjVwb+rhdTYV4q5Fgx0Jn1QpTCdZ2ZWsbdRydww0V9eOdrrwYA/MaL9mNmqYAHTkyF8o7phgZfUdXXkcVsTLJZ/VGoEH29Xfq8GqWkVmN1SHtWan2fWswH5YXQDDrmWu8BoKu1Ga3NmbLca+qnbugBnBw8gBOyHNW+Ygm479gk7jwyjv/zqis9BQTgKGEfc719nXapZ6EphHZX85R0fr779dd4G1knzK61esgYnBDpjADGqyh1p7RQ5y3drTg3s1wxvEqNLU85bxgy5nioB+Wt/s5oheLEQt4L3wGAV1y9De0tTfj0g2div//Bk9NoaRKeZ2SfNw9XDzVz2ueQCYSnVpYbzs4sozPb5I2FSvUp+XFIU1Rcvq0bh8/HK4SUgWuv5oGKhIq4hZVVvP/OY7hsaxeu3NaDP3jF5bhkSxceOFnZe8RTxGrGzH2ux/3TMQp/ZaBUcv1lrmwTjohQnAwlyva8xmPapCsvX3rlVjxyZsaTvcP7hodPz5Qd5qL2qydiTihVhpWAQqirFXO5VaysFiPlIKUQAhzD5UOnZ0JKdL/+Q+fncOj8HN5w4y40N2XQ2x6dsyu8fKg+tau/Iz5kbCkfCEF+5dVOfsFKhhZdIbS1p61iePRmwlghJIR4ixDigBBiRQjxUe33e4UQUgixoP339jVt7Qbg5OQSzs3mPAFQsbU72vtAoTqgskIr/GN18yhJYFtPK3rbWyIUQkFtK+C4Qz42MoOb9vixou/6MUewUUdGKgFN36AdOj+HjIAXgjHcFUwEG04gOB3hIaSsAXp4lp8U1T1NQJv4M642PKxACE+AahK+cnsPnn/JEL5/InryVm0cncthuVD03BC397XFuu6XpL+B7+/MIr9aHtYwPu+0WwjhhV3pzyKK2eUCetzFd5enEIrOu+EliuzyLVOzywX3RCP/uqV8Eednl7HLFbT3DnWiJBF5spdehx6itXsgPoxB1RVWCF29sxdnppYr5lca7m71FA5RmnU9rtzzpFH3H7HkfeGRszgxsYifes5FZX9z8uaUlwlbcVRCQ12gm/Y8hJy2eictzYa9JPzv72ptdpJ+NmUiXblnQt+pFIyVBJTjE4tlLs9AZSXSw2emceX2bi/EAHD6biVPGkcYmA6E7wDO+xpfqH5cNRB0ve+rYvEDHKErv1ryBCGlDKh0qtkzFxbQkW3yQ8aqJJVWbuk7+9q95MpA+bOYXsoH+v/OvnYsuqcwxnnSAE5I2sj0kndqpO5qr4pFbdhUUnPVBydjTgs8O7OMnX3tEEI4SqrYHELuXB1Q+M/jcs3rQXHplm4cvRBOEun8vDCfw73HJvH6G3YFQpSHNO8tVSrcJfT+3dzkKMbVGI8KUTkxseg9N0Xw+UVvqsfnnTBDpegSQuDV1+7A/SemnNxIai1xr59aypfNJcNdrQFvmbIDCnrasLu/o6LCJVxuoDOLrb1tKMnKOQHH5lawpbvVU2C/+Iot+JFrt1esS72f33vpZfj2W1/o5cqrZCF//OwMlgtF/MDFTi4xtXGNY2R6Ca3NmcBmEgAuHuosOwRC5+z0MnZpytYt3W2xm0Hn5LsVDLuhOS++Ygu+8/svBFAh/GspDyGc/HNAUJaI21T7yfidMtfu6gv8XS+nnsnugWBfBBBQquv98d5jTgjVe378WnS6oTnKIHcuFA6tK4ceODGFPYMdaGtpQn+HEx65FHF4gyq/nC9iLrcaUAj1d2Q9r4LwvSiePDeHpozApVt9OUTNs1OL+dDY8svlCs6cp+oTQjjedCrBfmgC9DfqymLegu7WYAqEgPxZkt5G90fdI6EV1+7uxanJJe/deYrdEjDqznsXD3eiu63Z+3cYVZMI+dMMdmUThIzBy5sXdaKod72U3hwEAD989XYs5ov4SsTpiIoTbmiTkjVNk9GemlzyvIsUA53OxrjMMDkflJ972lrww1dvwzdjvMQBR+bc0dfupbLojZDRK6Hyy2SE8LzFKs2BxZLEXUfGcenWbidBdqfvWRuFHpqouHRLF05MLMbKJsfHFyFE0EOomhwEAPc+M4Gr//QbODm5iD/6kWchkxEQQuA5+wbw2MhsxRChqIiIgc4sdg+0B5SdwTIrgTL7XCX0Ke1EUJ3zrkygUDJh1DxQLDl7BtVXVbLrqPyIo3M5zC4X8Cw3T49CHbwSOKFUewYTmrFX4YUoL+S1SBO/TVOLvtLy2Rf1YXx+JTai4u6jzlz7smc53oQDHdnIHELh99qn7atG58pz0AKuHNahvyvXm6uCInR6yT+9cUt3KyuEKnAOwLsAfDjm731Syi73v3fSm7YxUULE8y8ZCvx+a28bLsznIicaKaWmEIo+UtvzwulqxUBntqJCSH3+h28fRXMmg5977h7vb+r7lRInnIsBcPIFbO/1tdPlR/4G652MmCCVcK5bgVW5fLGEqaV8QNusyoe9LsITgL6h2zPYEXu0ub4hAYD97iZY5fCJQko/vn9As7Tp6KFul5R5CEUvInOah5BSCCkvlHCR6SXnWHtVR19HFiXpxOPrz+Lo2AJKEt5mUFlIIk9e0+rQQ7Qq5XNRG/bO1qbA71X7oybIiQUnV1Jfe4s3UYdD+i7M5/Cx+07hogFHYA5b9aPW4XuOTmC4uxWvumZb2d9ETJmwFac3wptFjy0GnDxWgHOcu4567H/y6mfhrj94sSfcRCUQnFzMI+tulAFduRPdN+ZzBZyaXPLqdspU9xB6bGQW14U2QF2tzZjPrcbW9fH7T+Pw6Dxu3jsQ+P1wdysKxerHVTv3odroeKJ0tzVX9BCa8rwHVX+uLoQeG1/A/uEu7zk4LuDxz+LsjHP86LbetoCHUPj5OV4kWr6NPl85WynXzpGxeZSkn+zQDz31FUlxOT4GOrPePKdCMsJKkLNuQkjVpjhLl5+DzSmfX/VP3gtz7S5n4xUQwNybfMb1HLplX7AfDHVlNS8BGfipUMo/b1y1t2gKIf86VezkxFLQeough1DcMbfj8zlPoaB4jXty2FceP+8pxUpSoliSeOrcHC7ZGlTQKwVX3L0MdWWxpbs1cIJmFHqxwc7WWMWxTtjjA3DC3s7NLMduNlT7NCdIXLmtJ9bqDADfP+549TzHzQexf7gLz1TIBTQyvYyd/e1lnggXDXTg/Fwu9gSgycWVgBJpa0/8aYHOM0dgc+vnRYtu1zPjCwEFmr7Ox438OTfZc3dbc+Tf9bo8D6SQ0tC5l+hTfB46PY3BzqynBAKcPpNtzuDsTDAfnRqbc7kC7j02gR++ylmr/PBSv4/5IcFOeeVhp7djsDOLeVeZHr4Xhco7pNYvwPeInV7Mx3qNq36r1zfUlcVETDL+8fkV1xDiP+fhUJhx2Avh/KxzSu1AR/B5K+WbysOkz58Pn57BTldh4SRhr+wpKUK7l4FO/x7Kyzg/1bPf2tNaMeTx5OQSFlZWvbn1By4ewJbuVi/PUhTHJxbRkW3yQtKSHCihGJvLYWR62fPeUfR3ZN28bUGvZd37QnHVjh5MLeZjUyKMzi4HTqzqi/Dir8TI9BLaXZlNeYtVCk/97MEzODa+iDf/4MXevQDxHsVRSoeBzmysQhVwlKIXD3UGxoCAExlQia8+4Sj2PvyLN+OFl/lhrdfu7MV8brViwvGnR+chBAJer07ZPjx6ZjayjFJUKjlooMPJpxWnwBxfWPFCzYHKRkJHYQjPm204pFTU+6DytroipBDKZAR297cHjAJhj92MCHpFDWqGLqmtx4DyUPQ9ilSqk5MTeo4i//vvPz6J3QPtnhd2f8ReMNwmwJ9fd/a1o1iSkREJuvcUoIdKxif7n1nKe3PXcHdb1XxjmwVjhZCU8nNSys8DqOxX16CcnlpyLHshq+iO3jYnp0rEIrSYL2LFTTgWVgipAaYmy0E3H1B5DiGtTEnimQsL+NzDZ/Ert+4LLPxtLU3ozDZ5CXGVZlcXzM9MLwcs7eETUsKWp7D2G4gWhLx7mXcExvDEFZVAL85FEHAE7NnlAuYijuXViwnhHK0MqOSeMYKGllRatT9SIeS2+6KB4EYnbtMaCBnraw+cGhD2iPHz6zjvzPO00vKVAL6H1+XufSkPk5MRFoewhVuhTtWK2qDMuCck6aEogL8IRikPnDBA51SO3hh35IdOzWBiYQV/+bpr3PsL5RCKeIaPn53FtTt7yzYxQHycfthrLSqx3dRS0HJw2dZu3LinH585MBL4LvVeL9nSFcgpEGVh0D3InPa53xHRRgA47Fr/dYtNxG0GWM4XMZ9bLZsrhrtbkS+WYhU7j4/MoCPbhP/1isvLyqm2V0N3vQfg5siI31ArBYPyeKuWyPLBk1O4/8QUrtoRfB6VxLuz08vY2t3mHO9dIYdQmYeQlwMntLkLlVNClFIItTQ5yTz1pNLhbqsnNffyC3geQv51UjrtV8qpi4c6cWZqKTKxpZ+I3vl5cnIRqyXpzQE6L7nSCevQTzdTqI3W9nD/6fI9xaJCiQF/HdIVQkp5HD497ejYPEbncrjazYWj0L3EwuFzirAFHHCEysu2duHbh8YC3qaHzs9hYWU1UsG1slrylO/he+lpa/FCcyopHPVy/Z0t3vO+60j85lAl7dXZ1e/kVYuzNqp69PF/+bZunJpcwmLMccbfPz6Jy7d2e/PSJVWSlY6EPH0Ue4c6IGW0hykQtJg69+K46EfVozxHdLmjkoV7YWUVtx+6gFdc5Sv89XUnToE2u1xAT1tLIIxYR2/b9FIeXa3NAY84hd7P9JpG51awK6Q8E0JgZ1+7e2CCf62So46OzaNQlJ6HuB8mUz7fqfK+gkYL/QrlZ4t6bk+cnQ2FDPvr3WQoZEz/rDZN2wIKId9DKHzQSNRY1BPy6/evPo/OLjshj6F3o5RrKnehKnZhfgV3HBn3PIoqJW4PK3f0e4gLGVMGiG7XA21Ld+UwkANu+ORNbmiNEAL7h7sCHhRhjo8vurkZnZZ1tjaXhZ/HofIyhhVCKtRFfxa+90XwnVzsep3EJWEenct5YYKAFjK2nCxkTKVeEEJ43mJxHkKlksR7vvE0bt7b7ylH21qa0NaSiQ8Zm4/fR8QZkJ48N+sd8qFobW7Cfccn8cXQSXg633tmEj90xRa86PItgd+ruTGubzxxdhb/+eAZbOluDRxgATi5r87OLEcanD2vIvd9Njdl0N+RjTwAQEqJC3PBHKuep3mU4Skk6w5VUAg9Fdo36Ax1tWI1xkAzsbCCgc7gPat6nFOTg3JQOMRMyTfnYjyEHh2ZDUSyOIf6JM8hpN5blDFtOkYhFJfvadaVS3QPocnF6gnUNwO1yCF0SggxIoT4iBBiqPrlmwt1MkF486o0suoIcR2VT2ZnX3uZK7can0pzu6O3PXKwhDc0Ki/CS10XPJ2Brqy3cKpiAYXQ1FLA0r7Ntdb4eRuC9Ya9IgBt4xfhITTvLpJhISMqgV54/usLhDx1eO0No5e7ec9AYGJaWFmNViJJX8hQCSp1t2KVAFYJUtnmTEB4j9tSzCwXvMVXCIEfv3GX97ewrOtt1pSmXLOq6O/40Ogcsk2+4rG/owXdbc2RxynqVehWje2ukjLKNXNmOehNoejTFFRhnDCJtkA9s8tBgUgJEJe51vywQig84S+urOLY+ELZou8RE6c/tVQIWHG8RUBZZKQK6QlaeZ63fxDPjC8EJn/17Xr4XJQ3G+Aqxbp1y05lbx+V2+IybYGulkMoKqkh4PfZOGHmwvwK9g93lSn5vHwACY6RVZt+L7Syo6Xicby+h1AwZCxOKPzPB86gM9sUOEWu2ikPZ2eWPOVOn9a/9ee37Crd9T6tFGpnZ5YR9trROTw6j/aWpoBbep+bdD5sGfPu23tHLWhtbkJ3W7M3zvRLl/NFTCyseG1RoZ9RFkqvLrdrqlCiKA+h/cNd2N7bFumirZT7eiJqwBHe8qslzK+sxno+feupMWztafWed297izcXBIwSEvivh86iOSPKwkb8UIVQnhPt89mZHLb1BNcHAHjuxYN46NQMVlb9pPxqM/WckEJIWWOVUjLchzIZgf5OR2kUDg0O4pdrbW7Crv4O/OBlw7jtQHy+jrHZ4OYL8BWQccfc6+GYCrXJiwo1m17M48GTU4EcLfu3dGExX4z1rrgwn8PW7vLnqowbp6fKN7xSyjK3+90DHVhYWY0c+2rzuiViHowaxY+cdt7ny0KyisohVmldLbPS7/LXCf11z0bkmFLockjwlJxyLzXAMdacDZ1YqeQndUqfevdRiXtVKVVeJTfeFvIQAnwPgnCi5/lcAZOLeewPHUnth/HmA2Gs+thS72dbbzAfiJ/0PnhPF+ZXyuTSLaG8GnrzlKU+PL+oe+zINnl54nSPgmJJ4sXuBn17TwUPIW8NCoWMdcaHjHlh6u4Y3NpTOWTsodPT6GlrxiWad9i+4c5AjpUwJycXA96QW7pbq55GqFDJ3q/YFvTcUMr0R7Vk0U4uRFkmP6sUBscuRBgFSxJjsyuBOakj24SWJpE4qfSJiUUv1Kkj24zObFOsEWk+5xzn/oqrtgWUgpWSrE+4JxO2aPKJdzJwzElr52fLDQ5/+fprcOOefrz104/gSe0AHcXI9BJOTCyWRXEAunwb3Y8edvNmvf9nbyz7mzq59b5j5b4SU4vOKZidWoh/XH9dzBexXCgGU2p4CvXyNunOAoCTYD2rK9S1Mt94chRXbu/xQnN1Khlnx+fz5UphTW4Mywu+QsjPwyMEQvJIsH59vhjsjDY0hm8/nJs1nFg6VyhiMV8MyMo97S3IiPjDhcLJ+pVho9phKJuBtVQITQC4GcAeADcC6AbwibiLhRBvdnMRHRgfjz4ueyMyFdJGKq7c3gMhHBfHMMoqt3ugw5sAFWqAPXFuFt2tzbhooAMXDXTg+MRiSNsaLOMNyM5y4W+gI4spd1IOW7hzhSIuzK8EYu2397ZhKV/0FDlhIV5trHVB1rOMadaH8GY/bEHt73SOqo8LIwAQWCyU0iqsEArXowuayvMp6nQtKf2kfio0Sv/u8fkVzK+sBnKwfOJXbvHed9QGvlSSgZAxwBd0o8p4mzXXQ6hXU8DoV84sFQLJZIUQ2BFjVStJiWt29uIvX3dNQMNfKfRheqlQdiQ8UNlD6PzssjepN2teFDrhZLFhd//wInF8fNEN1ynf9ALxcfrTi3n0tbd49xv2+CpJ6d5jcLztGexEsSQD7z18hDRQxUOoq3zMxTkgqHCVwOapSpmoGHb9O+JylTkn/5S3zcRDKOzFMKCdNBHdViWsqJNDXDf1Cha/63b3Be6tu605UoGrODO17Clr9HEWDBkpTwQ52JlFa3PG9RDS7zH44Eeml3DRQEdAsB3sbA1Y4cMbNi+puVvfoHZqkP79Kom+UpDuqxD6Ga7ryNg8mjLCUxroCCHKckWpezw3m8NAZzbgZg/4OUKUB6deBgDuPDKO7z59AT9+467AuPJCxkLz9pPnZnHVjp4yS3Z3azOaMiIid5PzeXJhBRMLK5GKrpv3DWC5UMQD7tG5KhltS5MoW090C2b4XhRxocE6Jekk0v30m3/A+91Ne/pxdmY50mq4sLKK+ZXVMoXQbm9NiQ9ZBsrnGSBaAf8f3z+FXKGEn7nFz62mEiTHnR45uxytGNkdI1ADjgFntSSDCqGI9VERdUSxuq8oC7das3aHPJe+/dYX4n+8YF/F3Hzhe/noLz0Hv/ZCJ0RF91ibDnmD6ujztd4+lRg7zM6+dpyeWgqEpahyYyHvG5Wv4pkL82XXqv6uwhG2hJJKq3Y71wbboOoJK11am52Qnk89cDqwodKfYVTImMq/UyrJspNndQ9GxZbuVlyYWym7F1VXVMgk4Chh9w11aienBv+uNrXbetswvrASOb6iPOmcsq1YyhexlC/3ylFGVyXXDXe3YXIxPjH80bEFXLG9JzDn7xvsxMxSIVJZUCxJnJ1exh5NbjZJRjsyvYyhrtZATkDAUez3tDXjodMz3u+iEvyqe8s2ZXA8Yu2YWsojXyxhe8BrT6C3PZsoh1B+tYSR6WXvlFdAnRgaPW9OLgaVFIq+jmysssUJgwtfH516AAAedNcAdSqdYt9QJz70CzehvzOLd3zxybJy9z7jKGxeUEkhFPNMnh6bR3dbM67f3Vf2tyu2daOvoyVw+p5icrHcUWAgRiGk5+JUNGUEWpszkR5nag5WB7kIIQLPXQ2x4+MLeGxkNmCQ1ukPyZP6nDEe8W703IjhcHbPEOj20Wxzxs1FG+0hBASN/U7IWHky/nAZ5emk2hZex5XR5grN4NqUEeiLkd8BP4RX9xAC4uXqzcSaKYSklAtSygNSylUp5RiAtwB4uRAicicnpfyAlPImKeVNw8PJjibdCEwuBk+yUXS1NmPvYGekh5Aa0Lu1oxkVqv8/fnYOV+10FqhffsFelEoSn7j/lHddMJ5dlrko6ujxmcXQJkMJGno4ipcDIyIparGklGDBe+50E+/qE15YsNMT6QLAQEcLpAxq5yu58u8b7kQmQskWFjJ07xJ1TLdaTMLl1Po/2JlFe0tTwDJ7zBViLtasRs/bP4SH3v4yDHVlIzccC/lVlGTYM0dPfBu8/tyMs7lRirx+TbEWtnCHPXi29LRGHqW6tFLE1Tt78dPaxkFvR5QVxTkKPcJDqILF5vxMLtBverT8IgonUWOrp8gKu5SHn8eoJ/gGBVJFXJz+lHvkt6I3FDJWlDJyk7BvyBHodE8r5Y2ghxv0d7ZEbtLC7vXVEkRPLq6gr6MloOisdqJErIeQWrhiciNcmMuVHc0MJD8xBAAW3FwG7a4y4ZItXWUeVToTXux89ZCxldUinrmwUJbwcKCzNdbyu7JaxDntyGa9z+r9wlcI+eNQCOEJ7boSumxMzi6XJfsfck9m8y1jwTJhpd1gVysmPcWEf7ESHm903aWVlTdKqAyHjD09Oo+9gx1obW4quxYIJtrUy5+fWY603qt5fmIhX1YXAPzNN57G3qFO/NoL/ZPJ4nIIlUoSp6eWIpP4CuEob546Nxcq4/w8MuZYy6MUQs/fP4RuzRNVSj+fRtgrVwmJ6rlHJaxPkhhdSold/e24RTsoIrwm6qije8PPeM9gJ9pbmmKPhlaPWvd8iDuRDXC8Bq7Y1u3lZwB81/yoxOS5ghPGGPaqUffTnBGRXhnhExsBzTs3wnNJzSPhzWDccdAqjH5LhPKlUv6w6cV82b0MdGbxEzfuBhDsj9MJPYRUVfnVEqYW857Hq86tlw5jajGPbz016v1OzR9jczlkmzNeXRcPdeGmPf34m28e8TzZwl7ZY3M5tLVk0KMZR/TQL0Q8AxX2HrUu/t83XIvRuRzef6d/fLb+LMbmVtCZbfLCpwBHFlstSZyaWgrMaYViCednl8vG8ZbuViy71nf9XtRn51TG8mcHOPKTCmsK35dS0G7rbYOU0d6uUeME8Md7VE6ws6HT9ba4x6ZHKTSklHjGzWOno7x/oryEzs8uY7UkA3Pulu5WzCwVKp5MpnBCOcvfZSYjcN3uvkCyYnV/4Q16c1MGW3tbI5Nxf+ieEwAiwoS7Wysmk1c8cW4WxVB4srMGRssaUQYY59/lB+IookITKylo7js+iY5sE66LUM70dWTx6y/cjwdPTuOJ0MnF9x6bwFBXq2eECZcD4j2Ejowu4HI3SXaYTEZgz0AHzsXMoeFnMdTViomIkLEohZCSVaIMvofOz6MjG/RgDiiE3AGjZNooZRbgK68VgZCxCEOn8jDTD4dQ04Bar/Q5N5yuI87bR7UlXyyVKcDCS4HyDutqbUZzRpR5FX3xkXO4emePdzKiopJn+zu/fAiAb6B41o4e/MmrnxWp4N5s1PLYefXqGupo+8kF/2SCMLsiTtECHKGqI9uEgc5s2SZcQkJKicPn5/Cs7b3u93Rg90BHYNMqtf1YSTqCRGvIRVGh5yDyT3mIdvUDymMug6EB0tN+6wghsH9LFw5pJ6ToE0xf6OhroDxu3rn/eLpam3Htrr6yRH9hIUPfYG7tacPewQ7cH3E6mX7spxACuweCCV6Pe6dIRFvko+RWZdnQJ7uwtU1v77mZZWzrbStLruloy4PfHRaGA8dGuzgnvuQjLZ2Xb+vGVTt68M4vP1WWTDTqGGfA8dYQovyUqPlcAfMrqwEhsK+jpcyyE7Xo64SVO16+gxjh0rE6l/9+aiEfSGip+pYeKhnlzaeUlLrQF6VMGHAtXbogXHCTpevePmqRVpuBMCrENHxPQHzIWNSxp0DlkDHVD8KnmABAT7vjYpwkZOzQqIpBd8bU1Tt7kV8teSEAUW3VE3dX2uAeu+DkxLkypBAajMiZpjgztQwp/fcW5yHkCyjBZ+aEDuQQ5amiODeTKxOkVQJmpQeLOtEM8Pudbg3ULz1wahp7Bju8MdHb0YLXXr8DH7n3JO4NzWvhvD5HxuYjcwEoLgrlsVP1xm3WfIWQlhPA/VuuUMSh83N4xVXbAu7mve0tnrCuP7dC0bGYRymEAODHbtiBO4+M43xESNvTo/F5Dvo7s/jDV/nHiyuFUNSc4ru0lz93RdgTI4yUEsuFYsAFH/AF7ig38i89dg7NGYFbLw0aulqaMrhpbz++f7zyyZj6VsNXoEYo4CPe49YKXp9qzu6NUPRnMs6GI1Ih5I4dfcNw0UAHmjICj4+UGxMmFlbQ295Spqh08r2Vv4QLcyvobgsmLdbbFWloWVnFU+fnApZfvx7npz4m4wwcQDhkzCmj5sIoJdUrr96Ggc4svv6kphByy43O5bC1R8shlxH4p5++AV2tzfh/3z4SuFbd1+jcCrb1tJV5DwC+Mi5sDBr1QsvLx/ErrtqGV1693Tu5R78vwFFAhTc36lCDJ8/NaqGp0vOeDI9jv58541cfW05y8lJkrioAzqZ5xjkRSC+XEfBOY1Xr/fmIfCxx66Ln6bpQ3ofPzixjqMv3itzqrZXl104t5jGzVAh4cgO+11eU4USF+O4OeQjFXR9mZHopUiEEOIen6KE26v7CuWKA+NxIn3rgNPYNdXoheYrn7x/EAyemIr2qdO46Mg4hHIW8oqKHUCiJsqK/MxsbojaxUJ4XKepwGsWBk9O4cU9/wJim87obdgIA7gmto2dnlnHJls5IpU5ntgnNmegwOiklDo/OBcL7w+iGH50L8ytlCvK4U/GiFEKAL6uEOTw6h8u3dZd5MCvCYVxRHuxAVMiY81O6ESflHp+Oh9mslgtQzbnKSKTLYzv62nAuJhdt+FovVUbIUKOP/ddev8O7Z+egl/Iws+mlPC4a6Ch71/0R6UkAX8554027PQXwjr52/PIL9sXuQTYTlGPnm4UQbQCaADQJIdrc390ihLhcCJERQgwC+EcAd0gpo9Oub1LiQsYAZzMSZRk/M7XsJS8Mb/KdE6ZWsbJaCiz+4ckh4LUjZWwuI8ANGQudMqaElHHP+lCe4HDGc18Obp7CSbsUV+/owRNnZyNzD4W9gwBfCJrSJgFdEAqHBADA8y8ZxKMjs1jWTiEIC5BhxdN1u/tw6Hz5yS1S8xACHA2x7hJ/cmIRrc0ZzzVTJ+6oS/W+e7T32tMeHSa1slrEPc9MBBJF9rS58a6hHEIAyhQ2zskZQW8HtbhEabezzRn86q0XYzFfLMs9FOdin8kI9LSVe/6ojYQ+afZGeQjFbN4U4Xscm82hKSPKBAWvPTEeQmNzuUCCYbXQ631jPrda9gwHOrPoam0O5HCJUib0d7qnv2n3N+XmlNHv76VXbkVvews+ezA638jEwkqZ0KSGbGG1skIoPOa6Wp24/ijX1kr9QAhRdmpMHMrD8Qo3hE8lf37ibLnno6pXF4RamhwlddQGfHTOERbCgvFAV3ziX3Vs657QkfBAjIdQyAq2pafNDX2AVs7/vJwvYmoxX6bEVQlMiyU/l42O947cPjPUFR0yll8tlVmi3/7qZ+GigQ783m2PBEIa1P0XSxKlksSZ6eXIeVQR9hBS5aMSkgLOZiYjnJxJnvLJ/XDo/BxWSxLXaTlaAGduza+WkCsUyxTbYYu5zg9dsQUl6Se5dNrn/Dw5uYTObFOk8hJwEiArSlK6yewjQqM7nZNcvGS5Ef0nygihM7mYR65QKkvgPuQpm8rHzLefGsOtlw5Frok/cPEgDo/OeyEsOp5CSFuDvJOAIuSGsbkctoXWorYWx7B0PmLjECWk6+zoa4v0LFKCsz7/dbY240WXDeO/Hz5bFnYTFfqh7itKuROloPDKINqz656j4ygUJV5yZXmOxKgTzWaWo0OggeDzKAvjiuiDzU0ZbO1pQ67g37fybhuZXi6TU7b3tuPGvf2ecSmcb2MswnOzryOL5ozwFBbh56Y2VnHPLZxPSy8+GvG8L9vajZYmgafOzQVkQi+dQUi5o3J2qHvSZY5j7pHz4ZBVxe6Bds+LKGgkzHqhqFdu60G2OYO/+9aR2LCusIeQ8uaKWseeubAQUNao9xqVR0h5gu8PGf4GYsJSAGAk4jkN91T22FWUShJnZ6KTvQPOhnRmqeApbZSHUJQcFZW3aLVYwsxSAT963Y6ypOovvmIL8sUSvlXhuHrASV5/9Y7egJdgJZkhLkJhe4+T1y6s4CwUSzg3s1w2z/oeQuHcohKnJhfLlHY6A51ZdETIQ44BO3ptEUKgr6Ml0iNpbG4Fc7nVSCW0wjEUBes7Nr6Ax8/OlicM72zF7HKh7AAJL9yuM6wQaouU7Z4enS9rky5zqbnJP6k6en8aXq/08K+V1VLkGuuEjPt7E1XX7HIBndmmgLJuqCvo6R3uAz0BDyF3rC2F37vz8z0/fi3+4U03BNsfkVt3dnk1cr3b2tMW6d2qZP0or7NGgOK988cAlgG8DcDPup//GMDFAL4OYB7AEwBWAPzU2jRzY7CcL2IpX4wdcH3tLZGuiCPTfhLncOb6kpRaPKb/veEjw8NKmqnFlchwMed7XHfflVXPwq0W9KijHz0vlaXo44XDx/oprtnVi6nFvCc06BuGPYPli58SpPTNuF5XlIZ2z4CT80XP1q8EyBv39OMf3nR9ZD3j8ytlG0ynLv/57+wPHlE/Pu8cBRl1qolAtGJiLkIIDyvpVLm7j0xgajGPn7hpt/e3jKsMuTC3Uvb9UR5C4WehhIMoDyFAO5lCyzmxWixFKkv0esMLpn86mr+gRy2sUa6nOlEhY8Nd5Sc6BMqUfYcjbOqCuVrow4T7rRACW0JH0qrNYl+UBUMbz1HedW0tTfihK7bgnmcmIhUaTohpsA2tzU24Yls3vvbE+cgy04v5yGNPAUfBEXWS4QVPIRTfDx48ORUQ7KN46vwcdvW3e14i+4a6kG3K4OiFcgUr4Fo++4Jj3UlqH28ZCwseg51ZFIrlR+8CjqAP+ApmPS+O/ujU3BVWcm7tbqvoIaQ2XuGQsUE3RNTLSRXas0wv5gMWb3WaVakkyzZ3YQ+xoa5W/M+XX46xuRXcecTPr+d7CEnM5Zxca1HhyYrykDFnbDjef+XzdVdrM67Y1uOdsKPXqRQ34eTuuseXflunvDDo6E2Oeg8BAdF97pOLeQx1l4eAKXSFvMqXFzWnqJNconII7XDXkmo5hM5EWP4B38oazp+VXy3h1NRSbBL8192wE80ZgQ/fc7Lsb57iWcsjGGexzhWKmFzMxybujfIQqqYQ2tbbHlkurNxU/MRNu3BhfiXgiQJEH4kNOGtkZMiY61UThRDRHkLKoPPsi/rL/hb2sCyWpJtvKFoeEkLgxLtfhVv2DXjt+8zBEWQEYjecYeVSUUp86dFzOHhqukzBCwC73JPJSiXp59tQSZsjFDRNymNrRimEyj2EhrqykaemAcCloXAYtY7kCkUcHZsPnCILOMahi4e6cGRsPvDcfM+X4PX+qUHl7VPeorEKIS33o/5u9We6rbcNf/m6a3DvsUn8613HA+WjcvoBvswa9pCZXSrg4TMzAe8W9bzVoQ46yis5bATw5ooIGf7M9BKaMsLL/Qg4awvgJxqP48z0EgpFGSkTA/58p571xMIKss2ZQOisYkt3a9n9+x5+0Qrqy7d242+/Ga94A5xwo3AI71CXo9AIe5cD2qEKoTG3d6gTK6ulskiJ01NLWC3JQBJvwFnPW5szZZ7mU4t5LOaLseuLYmuEPDQVcZiITm97uWc74HjiANGhzIpBV+mhy21fe/w8AOBnbtkTaptSSgbbFxVu5d1L6NpcoYjppUKZMlGXffXcfG0tGXRERI0AUTmEnHLv/PJTaMoI3BxSMgP+oRK6bKLuISqcd3a54KUXKDPca9cruSYc8RAXLgo4z0sPA5NS5W8t7/fX7e7FmanlMuVdVDRAI0E5dv4dUkoR+u8dUspPSSn3SSk7pZTbpZQ/L6Ucrf6NmwdfsxvnIeR4TOiaUSklRiq41stS9LHu4SPDo5Q0cVrwLdrCGXdcYPDox5D7sjbZHTo/h/ncaqRg+oJLhiAE8HE315Fe7tpdfWXX7x/uQm97Cx7Qwrn0OeN3XnppWRk96bJXxi30kiu34LXX7ywrs6WnLeZ4bhkQMgY6s5jLrXoLZaVnmokJXVIKkfDkrh+RrMqpTdQNIe20UlCEJ9C+0ISrLGRjs7pCaCXwtzBKaNOPKp32NibRk6KeSNavJ5hMEyj3EJKuNb+Sh1D4GYY9fcJkIkL15nKrWC4Uy/pk9Klp5b9TCTMV04t59LQ1B07nivIsmI0JS3rOvgFMLOQ9y6PO5EK5KzEA/PIL9uHw6Dy+GWG5m3K9t6KUZMPdrRiPsCL5J/9EP8s33XwRzkwt4+6jlRP8PzPmxNArmjICO/vbPQtpGGduK/euiAq1iXOV9j0HywXx+45PYv9wZ5kwAwTnm1ml1IvwqlvMFzGnnYYX9nQBUOYV6HmIxHifTLkhKuodDXa2ehvT8LVRrv8vuXIL2loygRNL9Lna9xKLF1z2DAQ3ZBISS/ki8sVSrLL35r39gfBGz0oYkyhYbXrOzwZPXbrryDgywvckC+MphDTltZrfphbjw66BoGGgWJJOqEF39PW6xVa17xeeuwef/Y3nAXAUdqLCiSPKmFHWh1UC7pDL/+mpRRRLMjKsGHAU5i+6fBjfOVw+rtUmSZ/v4izWan6KMpJs740O/ZqpMq+rcmH55IuPnkNbS6YsfOqHrtiKgc4sPntwJPD7uDk+Kqm0c3JnLnZeEo6LUBmzywV0t0UfIx/2EDoxsQApgZ198euIEMILP15ZLeK2B8/gTc+5CHtiPPDC46dUkrjtwBls7WnFH77yyrLrd/a3I79awsSiL3MVpZMOYHQu+kS9bdp7LD98IhebVw8ALt0SHHeq/NefGMVcbjVSLtrW64QbqfcvpaMwaW9pKpv/1LHb6sQ8XTY5dmEB2eZMmbeHQs8/pfeHsMLix2/chSu3BxXUAJAvliAE0BTaFA50OvOt7rVSKJbwsr+/0znBTMsjMtSVxZbuVvzdt46UJUaPypkFAO3ZJrS3NPknlWqcnlrC9t62gEeEUvCciDkGXvGQe3JVXG4XJcco7z11cEWUwnxLTxvmc6uBvEVxOQcBZ/3+vZdditNTS7j98IXI+vOuAifsuavGeFTY09RCHu0tTWVJsuMOTVCGnfCpeYDa6AfrOOPOy3EeqApHlvPnwlVX7g/nPA3Wly3zSAJ85eHlFRRC6uh2Xe6dXMyju7W5bE5Uc0vYO39mqYCu1uayUDglq+h5dTyvn9C71ceeGmKTbkhenKElrLxTY/qxkVm84qqtkf2zz5XxwyHms8uFgMeP3sbpiEgTIKgQUv0kLDNHedF67Q+d/LtccGSdKAOIMiQ8rCVr19sWZzzY7DRUfp9a4226Ytx4e9tbUJJOomHFzFIBCyursfHDEjIyHjd8ZLi+sOZXSzjrnloQhbKO6JZx3UMonOS2p805FUYNFr2uf73rOHramvHGm32vFsXFw1142ZVb8cVHzgEICg23Xlqe4T+TEbh57wDu1xI+q7oee8fLy+KfgehTr9Q9RWmRgXh34VIpGDKmFlAljI9HHL+qiLNkxlllP/1rz8Uf/PDlgfZOLKygOVMeNrjVjQsvi7kNTVrKmqkEDMB3V47KhQAA3W0tGO5uDXgIqSR8cbHSA51ZHBtfCLi6qg2Kvuj1tmcxu+QnnJtdLiBfLJUtjM+/xE/WGiX4RgnLiqhNxmhE+BoQvREKL4JAeRz+9FKhTDj0PQv8fhen/Lt5r6P8e1h7L4AjnEwvFSKVjK+/YSf2D3fifd99puxv04vx4Q+OhTDCQ2iucj940eVOvpOoUxAVxZLEicnFMqFtV397pPutOrEwbL0Kuw4rxuedXCLh0698hVC5N8b9x6ciTwsBwiFjBXRkm8rymqi58MTkolbO/7uy0MeFDI3FKITC70gp/SYXy8dxlPKjpcmx2D+j5WbSrXC+kB8/NsKhsqWS30fj+s+Newe8JOpAMCdAW0um7PnpiVb15/ZfD43g1kuHY9cgpYgJhAd7lszogxkUev+YXnI8peLqGez081yoe3nds3d577MpI9DX3hJ74ojq12FLdEe2GR3ZpojwAPfggaH4UIYb9wzg5ORSWa4Jb94KyQ9RFmvvNMooD6HetsjEstU8hG7Y3YeV1RK++Og573fnZnO488g43vLiS8py/GSbM3jl1dvw3acvBLwEonKBAEC2KVPm5Xf30QmMzuXw3P2DZdcDznqsFCfhe4m7D4XqT3c87Si5o46ZDtbl5Dg6Pu7kMrslwiKuCFudlwtF3H9iCj9yzY6ycQf43ib6cfVSOveRK5QiQ78cBZ0KM/N/L6XE6GyuYk6LsJyiHt8jZ2bQ1dqM515c/ryVIUTVVSzJyPwkgON9t62nzUvWHPSqzGHvYEesV+/23jY0ZQROTS4FdH1RIeo7etswGpLTVDh7uE1NGYHBzmxAITQ2l8OF+RVct6s3YGhrbsrgg79wE4oliW9ouaDU9wMx8kJn9OlEZ6aWyuaIztZm7Oxrx9ELlRVCB09No6u1OdbzRM1Vai0aX1iJNCIA0SciTS5EKw0UL71yK7b2tOLzD5+N/Pv5WSdPX5lCqMJhFHGpM3wDZHCjr7zKwmF6gH+Ag47yXAvnyQvjeNX4ZWeWnVycAxU8QPraow8MuevIBHb2tUcan/y2lhsKZpYKkXOCUhienAw+i5nl8mT56l4ABOZ2tYaE18ud2rvS86JVWlfD70vtCZ3TFmP2tB3KQ0g5Jjg/wycrO9/v1B2VixYIKmF621uwraetzPO80t4unENoNkYeB3xP5/AhT3HeWY0CK4TWEGVN3BVjGfGUF9pk4wmcMZpuPSxBDwFTAzRKYPjCI2cxuZjHK64qj68H/E2hnm9G5RCamC8X5oQQgazsYcXH9Rf1x2pU9w11YnLRcaHUhbpLY1yxX3DJIE5NLuG4u0BUU+70VfAQiosyiksoKCEhtJCx/pBn1GRM7g0g/kjdSkK4qsvX4DveImEN/paeVoxpgpoi/J2XbOnC5Vu7AwL9hbkVV1CqHFqiHzd8/4kptDQJ3LC73B0fAH72lj04M7UcECAuzDuJRPXNWm+7c1LAsmutivMA+fAv3oz/98brAYRCH12X9TgrLaCE+CBqTIQ3VlGTfJTrsFKqqPcZlU/JP50ouPADwZAPwA83CucbUR5h4XAkwBFYX/qsrTh0fr7sBK9KecrikkpemF9BRpTHpSs6XQvWqZBwonNuZhn51VLg6FnAmbuijp+O9a6IiLMHHCEqyrNAtTnsjXF2ZhnLhWKktyEQPmUoOifWc/cPorU5gw/efVwr5xc8O7MMIcqVi+r5q/kz6pQx/R2pe3DcyYPXxnnMXbq1C0fH/M2EnkNICflRCs04VL43oHxDq7gplOdA3dfscqGsXwPO3JERSiGke5YAb/7Bi2Pb0uTmItOVfOr+HIVC5ftSp9yp5x83Hoa6fW80FaoTXhccQTI60emZqWUMdGbRGRGe4SQFDfZjpVjf9/+3997hkhzV+f9bN+e9G+7mpKxFYVfSSkgIiSAyJoONLTAYbNlgnHDCARtw4mv/nMEEG4zBmGBMNME2NphoQEJa5bxabd67N+dYvz96aqZn7vR0d/VMdVXN+3mefXb33j5zaqq66pw6daoqIkMIAK4sXJNcuToZFRCqtmJdun2xeiBhrMrtRnEBoWdfshXnbOrF5+4ojetqkeuSwm0ulTz94s2YXVzB9wuLOPNLwQp2tXf6SedvxFfvO122LfWLd57Euu7gIPVq7BjswcqqLLvcQX2XqO+hAgXqdfy/R0dw7qbeyDNainKFRR2VCVBra0ilLbnj6DgWl1fxpIjAlpqgHR2bKwu4qBv1qmVGbB8MjgWQUlZsaQ3aP+oWLyDw2T7389fjBfu3F2WAYBzc0Ls2mAKoQ4IXsBzyCe87ObXmkP/wd1KHHVcGxGudbdbW2oJLd6zDNx4aLpOr1oe3rFt7UUatBZGhijN0lM34xZsuWPOdL985iIu29OO/7yvPjBmfDbLP2qocVhy+kCXM0bG5qtkqlWN4JROzS/jyXadwzTkbIgNoxUDApNoytoihiPGxcl4ArL3goJK21hZcsWs97j8Vte1bne1XsbDTr+xy+Rg4Ob+E/77/TPF8wTBbB7rQ2dayxs947OwMNvV1lt18F5apDAoeKQSUohbSFeqs1fD5eQCwoUZgZKi/s9jvFA+dnsK3Hj6LV127p6a+kp0PBaEifI+tA13oaGspOyIDCN6Jar6qCiofHQsHhKqfCxSeg4YXWjbVCGZVvh+rUmJ2cTlyPAcCXze860WG/YUq53MCpWzjyjGj8gKkan1HSVSbDqo5qmo35Y9XsxNd7a1Y39O+5gB6JVOtvZoBBoTqiDKOOyIGqWoHRKpD+8ID27/+3HV4yoXBir26xQsoj/Cr1Qg1mFSuhl+yfQDPqHLgIlBK8z8TyhBaXZX42gNn8JV7TlU9jyF8KnvlYWCVe63DlA4dDW6UeMH+7Xjkj58Xmbb4jCcEZVaH3MUFd9QkpVqGkEC6DCEpsWbLGBAYkdXV4CynqPOhqgUmVLnaWkTVfbvF21AQngitrfuh/i6MzCysCQxUW8F6ykVDuOtY6SDv0wnO4NlasTf59sfHcMn2dWvSfRU37dscrPCNloz6man5NQdwVl4xrgJClc91trUWjVH41ToxEQQgajmXwUGl1TOEKlc1qk2Cqw38mwc6Mb+0iqlCam4QTKhu3MIrEmMR25J6OtrQ3d66JivmtseCjCF15Xgl+7YOYHFldc01t6NVrjANl322Iq0YKLwH/bXfg70be/BYjStoVbZK5eRl94YejM0urbneVR2cWy1DaHRmcc15RcMR50vt2dQDIYLtqWGiUuG//Es34JmFcSTsHFRzsrYMdOGVV+8qS9suewfH57C5v3NN+vaaLSOVGUIVTmD4Gum1ZwhVd7bOH+rD8fG54mGi4cyCqEOy45iIyRDaPthdVp9qbIqagHe0tWDn+h4cPjtTFujaOtAVm5Gxvqe94gyhwLaMzUZvzVXc8/Zn49Uh5zyqP2zq6yie86OKV7m4sCHixhFAne9X3b4FWx/L5e4+MYEdg91lN7FVolYn1ZkUilOT8+hub11z6cD6nnYcHp4p69OlLMi1ZVM/qzwPSGWHVabyK1paBHZt6MFoKDhWLfMzjMrsuaMQ3CqdA7a2PV64fweGpxbKbvh8bGQGF2zuW5N5plBbDiv7fnBrWMT3qLCrR0Zm15ypUw1lSx46PY3WFhG57Q9Y23/USvMlO6oHT/Zu7EWLAB4+PVUK7EqJe08EY+YTqgRdtg50YWF5tXhZgWJ6YRkTc0uxt97s3zWIv/3xK9DZ1lKsi7HZ2tmly6uyOJkdnlrAxNwS9kVs+9wZuka68gy1WgFRAHjB5dtw9/FJPHa2NO5WC+xv6e/CyMxiWQbayMxCZAB4T4UNGyme61f9/b1yz+Cac4Rq2ddqAaG5xRUMTy2sWfgAgoXPymzqMP9621GMzCzizc+8sOrvARTPC1JjftQZXUD1DJyobUVhLtzShyMjM2uCyEDpHLWoLWOV2TtfvPMkJuaW8PNPO3/NZ7W0COyouH4cCDLBo+ZN1TIe7z4xgXM29Va9mTDMlkIfUlvCRxPUxcVb+zE6s1g2P1AZTDdeWNumqe8Qzuwdi/A91DX1lf7deJVgClBKGjgWCiAVz62ssJdlW8ZCz0bNX4AgIBO+TVPK0AHmEe/buu52TC8sY7F4LlDB36qS5VTKklYBofLPqpwTnl/oO+HAnPp31e2S/cEZqqrdihlCEfZuc//aQ7qjfPhmgQGhOnJ8bA7re9ojB6lqVy4PT62dvF69d0MxMLIqg9Xg3tDVzUDpphW1F7dyQvIXP3qg6ioQULhmuq2lsF88+NmKlPh6YQ/xW5578RqZ9b0deGxkpuqhqLWi9OHDd1elRItYe3B2+Wf14LyhXvygMFlWuqKCO8U6Da2gqts/og5c3Bxx+8PkfLAyVK3s43O1tya0VAlMAKWJVLUBTE1M1HcciUjp3DLQCSnXlrdaGuq2dcH5SMrwnZ5aiDywU7G54sa6YJtPdJsKEWyzCK+qnykcuB1msOJ9VxkyVc+XKPwdHvyVsxi+VahaWSqr/djYXHDAY4XDXM0RrhoQKgZMg/IGK5Llz/V0tKKjraUsfVxtq6nc8gQExrDSkfz+Y6NY39NeNU0aiJ4Mjc7WyhCqfhhfrbRfxZ6NvTUzhO54fBxCrM3uu7xw89SvfOKOsp+ryXJlAHBTX2dZ5iMQTLzvODpeNRV+oKsdF23pLzvbBgif2VTervu2DeDSQkaDejeiMoSAYAtRmLIzhCbW3nwCrF11qnwHKzOEVNC8MpMGQOT5N2pSVQr6o/C3LG61igucvOa6UtAkvNWs1h75XWXp5sHf4zUyMvZu6g22f4S+V9zKrSpD+B1YlcHZCyurMvL9VrS0iDI7Eh0QCs5dmFtciTx/YH3Eqj8QTIZ2RmTvVjsL685j49i/q3o2jaK3sw1bB7rKtukCQUBo67quNbbi5mv34OTkPD7wzcPFn52cmEd/Zxv6qmQulc4cKR8D7j4+gT01tvIAay++KF6/HhEQ6uloQ39nW9HJP1tjAv70izejt6MVXwhlsD4+Oltz28fFW/sLweDySXutDCHlK6hD1KvdXlRVrmBLHh+dxfbBrsggFbA2C/SeExMY6GqrehMqEKxIn7OpF/edmioGwmUhA2dDb0fV+lWZoycn5suC51EZsFGEz9kbL5xtVo0hdQZhhe2IyhDaPtiNU5PB9fGVY1plFmklVxSy5MJBzmpbf7euWxt0GJtZihwfLtzSj8dCgY2oDArFzvU9GJlZLLt2fWx2MTKbZmOVsaJWlv8152zEwvIq/u/RkTW/A4DvHx7Fno09kYfQK9b1BNtGVwoBuyg/dMdgN7rbW8syK1RGRq0x/4It/ViVWDMmAcCDp6fR1b72TCgVVKkcA7/zyAg293cW/YJKtg+WMssUx8fnigf9V7J1YG3G493HJ8tu441CHd+hDpZWbVcrA0S1xT0nSgtcyt7G2aW9G3uwc303vhY6j6nWgfa7KrLzgUIfrbJ4OdTXiY62lrJsyajb3MIZrVIWsopnFiPPagPU1e3lNy6qDJqoY1CU76XmXOGM4mqHSofLHL49spod2zHYjdnFlYpzSIO/q5mwywrv2x1HAz9RzTuiFkCG+jvXZO2PzwZnX1Xz4ZsBBoTqyPHxucgoN1D9CsWJQuS6svMUV7lkcHZGpYHq6WjDloFOHC5MmisnJBfVuBpRCFG8nlJtFVtdlXjozDT27xqseuXey67cgftPTeGtn7t7zdaoalteFKUbyoKV8aitX2HO2dRXXJWodYgYAHS1t6Cj4haCajelhenpaEN/V9ua6HBlhk5pAFuK3KurUOnmldR0XCtuQ4naKqGi85XljUpDBUqTgTNVrrStJjOzuIKp+aAOR6ZrH+oKlBwUxZnJhTXGpvJ8p6gtY0DoINDQz9S5LlG3lQBBIKnSGVUOfWXKd7Wof7UsqNJe7XkcGZnB8fE5XFCxfUAIEVxzWbZXvLohBwLnqdJxeuj0FC7dsS4yW+7cTX3oaGvBncdKjoksbPuJckxU+1eeW3B6ciFyUqc4f3MfTk8u4N/vPFH19/9172lcVWV76JPO24RXX7sHh8/OlGWxVTsMHyhNFsP18d7/fQSLK6tlWR9hrtyzHnc8Pl620qrG0WrOVktF34rKEAKw5ir1cAbkifH5qpPJrvbW4rYloPzaZVkIvITH7HU97bhwSx++d3i0yhlCUedBFLYaVpxTpAI7Xe0tkVl8ire/6FJ88LUHC3LhlOjoFbCwA6XKOlnlkEjFlv61h94nmYCv72kvBhJU+YaL42x8ynb59ewRTl/oXVPfpdIGra9yaCkQvg66VoZQ+eHzR0fnIrcwhjl3qHfNORqnJ6rftvW0izYHAdHQGWS1zpBRPz81WZo4zC2u4NsPn8XTQwfrVmN9T/n5GcOT88FW0xpbLMJnN9Qa47s7WvHsS7fiM7cfxwOnpjC/tIJTk/NrDj8P09PRhr0be9cExYPrhKOydYO/1Rk9M4srNbOYw3JSSozPLcVuxaxcjLn3xCQu3jYQOZYDQWDlvpOTxX6yvCLx3UdHcFmEDVCHRldez64mkHEZQgohSmNatWxXhVrQqbwBKsqX3LE+uD7+VMUtjUDgw9Wisu0u3tpfNTCofJd/v/Nk8We1FkQu3NIPKUuHFJ+NuMJbofp2eFI+XiOLamNfx5obatW7WXmQNxCcldnT0brmnCIgeNd++PjYmuvIq7G+J+hjyo+O8m1bCplt4QyVE+PBQnXUAimA4s14j1Y5APvuExN4wraBNYHkrvZWDHS1lQXrpJT4v0dHcN15G6NviRzsKru9V0qJkxF2Fii95ypQOTK9gOPjc4kCQluKuwFKskD1DEbFvm0DEAJltyeWbp+qPS4IIfC0izbj2w+PFC+jGZtdjMxS2VElODYxV/3MoZYWgZ2D5ec1jswsorOtZc12KwD4we88A8+5ZCvUTdUrqzLyDElF+PutylCGaMT4XzkGKV3zS2uvqV/f0wEhSm0QHjKq9QH1PoSzydS71lsl6eKS7QPoaG3BDwvZquqG5yjfoPLyGCA6m6tZYECojhwfm6vpeKgXLbzCMD63WPVE+fBNGWOz1VdE9mzsxV3Hx8v2mO/a0I33vuqq2LLu3diLR4dnym4pe/D0NC6MONvnRw/uwquv3YOPfu/x4tknih2D0St8arKmDh6LDwcFab+Pj5avOEcFklS2SjiKXGuVUlHt8N3KVNzBUDDrbMzBfBBrV9aAIIuh2uCuyg4EbSyljEwFXh/abhKm2jtRaTzPTMUHAkqHjAfb0ibnl2seVgsEg7uakKvbwyr1VN4ANzy1gM6I61KLE/jQrPKRM9Po6WgtbnGsxkB3+5ptG0fH1h7wGJSnvL7aW6u/Uyr1++jYLD57+wkIAbz4irVnXGwZ6CxznmsFHTYWtkmFCW6Kif5uHW0tOLhnPb4TumlqamEZyzUyKK7aux47Brvx/oqrek9OzMVOIG5+4m5ctmMd/uRL968JWgxPLeDek5PFzMVKDuwaxPKqLNt6NTK9iI7WljWrP2orxmdDZ5UcPjuD/bsGcW2Vw04B4FlP2IKphWV86a7SxKDWrUkqOzJ8dkaUM1fpuKpv/u2Hz+Lw2Zmq2zmAckcjPCGaXljG0opcM6m85pwNuO2xUSyulNdtVFtWpuQrFSurEoeOjte8rS9MaZyRxeyPqDEJKA8IqQzSamcCKDb1B4eEh4NitRZGFOt7Osq3+q5KvO9/H0Vri4g8syZMOGs0umylVWxVvDUBocIZQpXv/OmpeSytyMirjTcVsv7U91aZXNWuHa/k3KFePFqRDh/cNlW9jx7YNYhDR8eLz5+cjA4IqTElfGDmvScnsbC8Wnb1djXW9XRgcn6p+J2Gpxewobf2VtPwNho1FkfZ3rc8J8g+/tj3H8exsVlIicjrthX7tvXjvtD2OiklJiIOXgVK7/uqlKVt/EkyhBCMF8GErPbEr3LMmVlciZ2gXry1H8fGSltAbz0yhsdHZ/HC/dXPT9pWzBCaKwu2qsl0rVvGwoS3s4/PRGcrVDskuNb2x/KDsst/V2uRECjvr8+9dCu+8ss3Vn1OXUP+zi/fj5VVWVwQiRrLLyxsDXygcB6Oyq6PCpyr7cyVWRdRn6+2IIXHrbuOTaCzraXqtsSu9lZcun1d1YOl3/mV+3F2ejHyUoQw6krtJL7t+Zv78EhI3w8eG8UVu2sHndQ4Wbl1dnVV4t4Tk5EZTMG5UyWZqYVlDE8tVD0/SLF9sBtnphaK2wAn5paq3gqrCAdGgdIhzOcn2AYa9m3V33EB7t7ONrz8yp34p+8+VsyYHptJnjlyYNcg5pZWijsqatnO7YPdmJhbKmbKBTZ6KTKAtHNDT/GYkYnZJXzh0AlsHqh+c9hQfyd6OlqDTJ+p2oEdRXlASNbM6gfWHgIupcT3CtlwB/eWv3OtLQLnbOrFvYUAatjXf2WVS4kqb9cDgP99cBjtrQJXVznwv7OtFfu29RftnpqfRNkJlSEUtsGHz84kym72FQaE6si7b74Sv/qsiyJ/P9TXid6O1rK0zInZ6hkk4RXuqDTfA7sG8eDpabznfx8pGuRfe9ZFeM6lW2PLevHWfjx4eqq493N6YRlnpxci99oLIfDsS4LPrUxxrDWpDW+7Cs7oiQ8J7d7Qg7mlFZydXiwOGrXE1lXcCnA2wSpA5e0DAHB2qjwg09Xeit6O4OyX4t7jiM/cv3MQ3z88WnZt993HJ/DDx8ciVz5KK5kSM4srWFherTo5VHVYGVCo9t6UVofnsbC8gtGZxditQsVDtifniw5BZQpqJYPd7RgrpNGq28MqM5FU+VSk/q7jE9i7sbfqO1By4ks/u/fkJC6ucsNJmAM71+GBU5OYCaWeHx2dqzqJqzyIMcox3rauG20tAo+PzuKhM1PYs6GnqvMdPlQTqL2tpjLVfGllFcPTC1XPAAlz/fmbcN/JyeKqylhMynNnWyt+5PJtuPNYafI4Nb+E8dmlyImtor+rHa990l4cH5/D7UfHy36nzju5PMIxVDfcPRxyREcKZ25Vtve+bQN4/uXb8C/fe7z4s8fOzmJvjYnhjRcMYef6bnyxSkCoWuZKOPtOXfdeKysmHBxU7+Cf/scD2L2hB69/8jlVZdaVZdKUfl5MS6/oy088ZyNmFldwT8VZS1GT7eL2v4oMoUPHJvC9w6N4/fXVy1VJeGvqqcl5DHS11dwOE54AKp21Mh3VVbvhrUZJMoTW3IImJb7+wBm86MD24vtUC1VtbS2iaso5UH4geemCgvJnNvR0YHF5FbOL5ednqEll1IUPlVsf485nCnPOpj5Mzi8X3+HVVYnTk/OR48HlOwcxMbdUnAycmpiLDB71dLSht6MVf//Nw8VzvVQZ41aIB7vbIWVpzD6TILNwQyhD6GxMhtfmgS5sHQjOhYk6dL6SfVsHcGRktjhpmltawdKKjM28VVfaA8kClOqWsYkaK/qKaluKog6UVijfYqkQED58dgbtrQLPjvDXNvV2or1V4MR4+QG3x8erHz4ehcqiXVoJzsWLsh3q52EbXKsfFlfwJ+bWnAdXK2ABlAfUamWN79rQg18tnK8zMr0QuyCiFidPhTJCak3+1fZYdd7d8fE5nJ1eiKyjsH+luOv4BPZtG1izqKvYsq6r6mLhl+46iadeNIQXH9gRWT7FYCF4XuuMLsUFm4Oz52YWlnFmah6Pnp2peWMeUP04CyDINJ5eWI4MCFXeADZaXDiNrnP13qgzzpQPFZ0hVJ7lU+zTCWyMGu+UbJKzFAHg5VfthJSlQGHUonw1Slv9pzA1vwwpo31NNS6pIO/M4gqWV2VkAGlb6LzP/7jnFE5OzOO3nrsvsixCCKyuxt82rCg7P1BKHB2dRWdbS+RC+O4NvWV1uboKfPfREfR0tFbNlL16zwbcemSs7OiRb7/l6XjuZdvWPFsaX0p9538fHMY152yItPdbBrqKNmh0ZgntrdG+wVB/JxZD50utxAQ/mwEGhOrIhVv6a95MIYTA+Zv7yiZNUY52eJUr6iDANz/zQvR3teHQ0fHQ1qokOTjAxdsGsLC89sDaWqm+Kp298myCWoNMactYsAIbMw4DQPG2hsdHZyNXdct1lF8FWjSaNRzZygyhucUVzCyurDnPY2NhW0Bx+0vEwP7HL7kMLQL4QeG2FSAw+AvLq3jr859QVUZ9o2ArR/StQeurZJYB1SeSQ32daBHB9gNVD3FnCKnfn56aT3QAIVDYMlaYNKjA2tpDpQvZYXOLGJtZxPcOj+KmfdW3LISDY+rv+05ORp5foLhiz3qsSuDQsXEAQX86O139gMfKdztqotPaIrBjfTeOjs7izGT02TvB4YhzZTdYRB5G2ddRuGGqsPI+tQAp4536/QWjqm6jUe1TyznZ1NeJpRVZvOI56paQajzzki3oaGspO+cDKE2Oo7YPqKyfR0Kp6rVuQ9u/cx2m5oPDUReWV3BiYq72bXItAhdt6S9byR2fXcRAV1vVfhDOsDwyEhx4XGtSGG5jKSXuOTGBQ0fH8fonnxO5KhiVIVTc119x4PM1Bac86jyJSno7g6vNK7eMKaImkpWEz+d67OwszonJYAkfarwqUQyWRAeEgjZWgav9uwbxgsvXOniVVPaVlcJ2nVoLDGFUoHiwZ23QsVi20E04xfPoqpwhBKwdX9/1Pw9jc38nrq5Y6Sx+dsXWx7hbvMKosUetwI7OLmJpRWJrxFitsmiOj89hbnEFZ6YWar7Pf/zSywCUtrMk3fKgzpBQB1CfiLneXH2mmgienV6IDTgO9nRgfHaxmK4ft2ChbMADhaB0reuEARQvcJicXy7eVhoXDAfU1qragf3id6jy+2tiJt3Vyrt/52DkhKWlRWDLQHCgbtm17uNzGOxpj90uqghuQA1tF404iL6/q22Nf1YrsLNzfTfaWwXuPzW1JrsuLpOirTWUKRzjEyqbc3pyoXS7YoRd6e5oRV9nsI1pcXkVd5+YrJlJuaniXJY/+dJ9aGsReOmV1YM0W0PbyYFggeb2x8fX3M5YLtOJUxW3Vs0vreDY2Bz27xysueClUNta445DAEpBvEeGp4tnb1U7BiJMZ1srejpai/1e8bUHgrNwbrxgqKpc5TksIwkWFCvrMG4c2FqRIVTKkIu3E6XjIUoZ83HjDVBarFBjzViNQ+wrOX9zH9paBO4/NVk6sy9yy1hQFrXIPl58PjpreKSQlfrg6Sl0trUUF+qrobbBnilmCMWM5WVnCAVB670beyPf0Y62loozByUOn53BBVv6qwZID+5dj/HZJdwemrNGnR21qa8TbS0CJwvtvbC8gofOTEfefgyo+VpQh+p2tyjfoJiBXTgn6b6Tk5hbis/09BkGhAxzwZb+slsNok6ULznx0dsdutpbcfXeDXh8tDQpTRJwAVBM6ay8zrXWat2WKil8AGo6f8VtQzPJzxAq7R2dw8ziMjraWmp+r8pDkc9OLxZvZoj8LoUModJVx9VTcVXgSB0qF3XYYHdHayEdstS2JyeCLQBRDnVpW4ssXZFY5V1Qzmm1q7oraWttwaa+YCuTMgK1DpIDSumz4UyouNWQ9QWnHgitPlQ4Kb0drWhrERidWcJtR8awsirxtIgzLCozhI6NzWFqfhlPqJF6DABX7l6P7vZWvOfrj0BKiX+77RgA4MYL1zowlVsTajlVuwtXqZ+eij6DaftgN+aXVjEys4jF5VUcGZmJvJlmqK8Ti4XteEDJuYlzalSZw6nLQPR7CKw9vE+dxxW3Eg8E2SFPu2gIX7zzZFlK7wOnprCpryNytbW/qx0bezuKK61A4BzGreQeH5vDu7/2CKQEzqlxeDgQ1PeJioysqHooBVtlMVh4oIYjEQ7MrUpZzOK8rsaqf9hpK7/lsfrke8tAF/Zu7MGh0JlQcQz1l1Zgwzf5CBG/Cq8In891+OwMzonZohPOEJJS4q+++iCA6BXZTaGtJht6O/C5n7++5gGmispFjsm5ZaysysRXvipTUstR3xjqC1E3lISzWBWLy6u47fExvPLqXZGXRKhAmOpfaQJCxWBSoW1Lt4ZVHw9K2WLzuO/UJKSsfjOV4rmXboMQpSuKxxKO6+qdHp8NsnMPn52ueYZb8JntxQWZs9MLsVsZ1eRW2ey45ysPV1fB8ajU/p6O4HDnR4ancejYOHZv6Kk5XiqEEMWDzeMmf9WyEqtdmR0n88RzaweRtq/rxomJeYQvGD0xHp0dVo2WFlE81wyIzlZoaRFr+l5lUDtMV3srrty9Ht966OyaDKEkKF8nzicsbfuZLwY4a23L3NQXnNf3hUMn8PCZ6cgMT2DtuSw/PDKGp+/bEpklEC4LAHz+0AksrqziZVftrPq8kqncZhYcwo+aN9mFUcciqOBJrcXOcLauup0rSTZN5dELAPC/Dwxj37aByHFpU19ncQwDQmf01MgQUu+Y0lUMckXYs77O4OD6U6GA0EBXW2x/U4R3A5yenI/1h4G1GVO1Frcq6Wxrxe6NPXh0eKYYYIsKwhb9oHEVEIqeBwDBWLlSuInzwTPTOG+or2a2U3BzYuiG35jF4XBmVzD+z9S81AUoP+NTbbmNCoA9+9Kt2NTXiXd84R4sr65CiOgkhtYWgW2DXUUb9vCZaaysymIGVjWCbdwLxVuha/kSxctjCmeC/con7kCLAA5G3PrbDDAgZJjzN/fhzNQCJudL222qXklY6CSLK6uYml+OdFB2re/GsdHZosOQJOACBKtuz7tsbWS5VhZBf2dwfXbldba16GwrbLuaKdwyluCNC19XHkSoe2pmPm1b11W2AnO2cH11LZnNA11l6YLDEUZp80AnzkwFGUIDXWvPegqjDo1UnBifqznhD58hVDrsde0A1tbagoGutsibcCpR+7prHfAZZqCrrRC4WUycITTY3Y6ZxRUsLq8WnZTKwIkQwQGH95+aLBq8qCvkK68KVvUYlyG0rrsdb3jqefjmQ2cxPL2Ar9xzCpdsH6iarlq5allrP3Vw2N98kCEUUX87QoHLR4ansbQicXFEedWWMxXQUA5l3Or79sFgFVZdpZvk2lS1Oqey2lR2T5IMISA4JPrM1ELZmVUPnJ6qeVB98PndFWcxRB9Ors6YODE+h4989zEAwY0stdixvny/fa299i2hDMtDRyfQ09Fac/vDjoqbteKcVGDtapqi1o0kb3r6BZGfV43NoYBQeIV5Q09HzbEojOpbc4tBJtbemAl+2aHSCDId9+9chxcdqH7WSTEgNL2QeEECWDsxVe9rkoAKUGrjWlu0utpb0dXegom5pdANJeWFVJPe8Ph6cmIOUkZvFwOCW022DHTi3V9/BEBpghN1+HaYoUImqrI7ajyIWr0OHzCuzkioFSzvaGvBtoGuYoB2dDZYJOmJySoJn/t2anIe80ursQGh9b0dmF9aLV6/HReoXN/TgbGZJZyemsf6nvaaC0pAKWtWbVH+v0dH0NYiah7Gq85RuePx8djsCEWLCLbKSRn/DlYeKp+Eaqv+tTLKgWBMfbziBr/j4/HnwYVRZyOVthtHf7dKPzPuLMEbLxzCPScm8csVN0wmYbAYEKr9XDEIMzWPQ8fG0d4qak4KVRBdbeuKO0h9R8FunZmcx4mJ+TWXDFQry6mJoN/ef2oSm/o6avopRZnQoqXKXEty3hgQjJVSBreAxS127tnYi7YWgT/+0v24v2D7k2XFdJRtGZNS4u4TE7hqz2CkzFB/J6YWlos3gEXdelWmp+KykeK5YxG3bQLBYnRpi1n0AdRVZQdKuwGq3YZbDdVXVRnHa5xBWI2NvaVDwIG1h6grhvqDLBjlH8ddlR4+V/Ch01PFM7OiUNtgh6cW0N/VFpu5d/O1u/FT1+8FEGxtfXx0NvaA+PA2ZxkTUB/oasdbf2QfDh2bwL/eeix2vnrB5n48VEiguL+w2H7x1ui+trG3IxjrZhdrnukJlNflzGKQffRT159T89ZL32FAyDBqe85IMa0t4gyhQsuMx6R679rQg6mF5eJAnMYhf+6l5Wn963vaI9OXgWCCv3VdF05UZAjFsW2wu3gwYpItbWGDcfjsTKxDWrkCc3am+m1dYdQKm1oZKp6mXzH539zfheHJhcJ5KLUNydV7N+D4+Bz+9dajAAqHBtcwXOFzokqrd9UHsA290VcjV6Juv0lyACGgrpsMDNjodPWboSoZ7C2tJJcykdbqUYehHhsL9iJHtUtlhtB9J6cgRHDWVRxqoj8yHaw6J3Wyam51LNwKNre0EulMKb2f/uHx4hk7+2rcyAKUUoPvOj5R3JpWi9YWgZ3re/D4aCFDaDY+Q0itzo1ML+Lo6Cz++r8fwo7B7kRnm4Q/e6JwKJ9KT75oS+3g3M71PcWA0OqqxNmpxcgJxY7Q2Q0Tc0v4haefH7uKGc4cPDE+h7uPT0QGO4vniADFrYe1VtJ+/wWX4BWFVV51CGNbi6g5MQwHNMLZVLWyuJ5fsVe+q722Cd61vgf3nZrE/NJKWdAp6YHSAIrpUo8Vts7Fjafh77ywvIKjY3O44YKhNbf2KVSfHp9dSrxlGVhr05JcCRxGaYrLRlpXuEp9ebV6Fq3SF54MJdlm2dPRhh+7ejcOHR3H0kpgf7raWxIdPFrabhZ8Z7XVMqoPDHS3oaOtBWcKh7uv626P7S/h/jg+E2w7j2sfNREZm10sZsnFZTGogM3IzEJwU2bMuzlYuDHpdI3tuGEGutshRMkX+sHhUVy+c11k5hYQjM2Hjk3gxMQ8Lo3JMlUIiGKmU9oMtyTBoWq2vdZZKwBw3uY+nJqcL2aWAvGXEVSiJoVjNRadFJW/i7MZP37N7mKZ0qJ0xU0KN/UFtxOdmVzAnUeD83pqBRGV/5P0CuldG4J+om7zPFAjgNhROE9FXWNeeTNtNYrnDoXq6IePj6G9VcSOxQr17jx0Zip2sbO9tQUvPLAdZ6cX8JW7TxW3xcXq6G4v2nwgCDxOzS/XnIBXXnyQZEGxcjvWyPQCuttba/bnbeu6cHJyHveemMRX7zud6uDfLf1BhtB0YZ5U65ISRVd7CzpaW4oHEwfZJslvnxosBNcmin2uuqzKgiltGVsqyldD+diHjo7j5MR87Hk3onD78fBUfOZm8PldxXnhifE5LK3I2EP/w36/2u1Qy2964f7t2NDbgTNTC7GXDF24pR+PDAfnWL3nfx/Buu72mmdNFnc7zATBuFrzGOX/D08tFG1LXIDNdxgQMkw4JVtKicm5parRY2UkVeAoKlCgzttR2yLSOOSVE/gkBy9u7u/E/NJq7HNhgq0eQQZPktJ1tbeis60FYzOLODIyExuhrryFIOoQ7jBX7B4EUDrPQ62kVhoatQJyvHB1Zy1ufuJuXLSlH5+67RhWVyVOTcxH7o8FSg7R8NRCMbU0qp0HezqK70IcKo337FTtg7DDbCh8/ujMIoSId4hV5sTpyQWcmZpHb0creqsEEw/sWo+x2SV899ER7Bjsjnw/K28Zu/fkBM7Z2FvTSSiWXd3CNr0Yewjqd97ydFx//say71CNsEGLChydO9SHV1+7B//03cfwzYfOokVET7aLN7KMBwdwfvb243jKhUORN7iE2bOxB4+dVRlCS+horX7NqKKUIbSIrz84jOVViXfffGXisWGwIl366Ogs5pdWY4NzO9d3BzfOrErcdXwi2I+9s7ozuam3Ex2tLbj/1BRWE6zIA6H99uNz+OStRzE6u4g3P7P6If7FbVKrwfO7Ysa2Db0d+KOXBOeuSFm6cbDW+Q5bKhwhxZ3HJ7Cpr6PqKm5lACjueuuXH9yJ8dkl/PudJ8t0JJlIK1Rd3FWY8NTaagQEGYOKx87OYmVV1gwKrO/pKPbfdBlC5W1eOow7XYZQ3AHAg93BgazqoOTKPle5xRKItgeVKCd7bGYx8oKIaqzrbkd7qygG7b967xlcvLU/cnuqEKJwTe48jo3NxWbNqrIfGy1lCCUJtIUneIcLV1CfG2N/w9toVHZuLTb0tmN2cQVHR2cjv2+Y1pbgJlEVrDk6Nht76Hj4fU2SEQAEi3DKt0nSjuGbyJJsJ6n2mXG2+bwqZ7MBwWGuSVGTwvEEiwmVdj/OD9jQ24FXX7sncVnCqPqIGzLaWluwsTc4h+fY+GxsEEVlSMdlCCh2ru/G6Mxi8fajuHdrU19ncWtU1M2wYbaHbD8Q+DhfvPMkbrhgqKrPVA3Vdx86Mx0bcAWAn3/a+QDSBQ8He8ovZ1EZGbWyn4ZC2aFAMIb2dNQOwvV3tkGIUECocPlELdRZWp+5PTgS4NeeHX15z5oyFjKEPv794BKLGy+Mv9VNCIF1Pe2YnFvCcmGrf5Jtpwq1LXY8QYB5R2grvApARb23anz+ZGHR+akX1c5+K50hNB97OUBYBog+CqKS8Pi6vCoxOR+duQ0EdasChnHB4Iu29mFpReJLd57Ew2em8fsveELkwhRQGk/PTi8Uzn2Krvf+zjZ0FhZZilv1IjK5mgUGhAxTTMkuXLW4uLJa81DpuFXTJ52/CTsGu/HOL98PIPmWMWDtFp+49GUgfotLNYqH7yJ5+dZ1t+O+U5NYWpGxZ4uoWwhUSm6tG3EU2we7cd5QL7750FkAwYpwX2fbGjk1GD5waio2fbqttQWX71yHR4ZngnNlVlZrGuPw4X/jM7UPk1vf0168EQ4AfvGm6K0nm/o7gi1j00FKfpKtJSoDaaSw7zbuFobwwd9BGm7176kc2ruPT9ZM81X7wZWT8MCpqZpp4WGUcTkyOoO5pZWaE4Dtg914/mXB1pcra2w5CBu0WpPvW248F0CQJTTY01Ezi6KzrQXHx+fw3UdGcHJiPvLgykr2bOjB46PBtoHRmQWs76290q/qY2RmEbc9Noqh/s6aafCVlM5UCdpCpZ1fmCAgtFi4Pe2/7z+DFgE89cKIQ8RbBIb6O4vX8SZZkQ9nCD06HFwPGrVtRr2+S6urODUxn/CWoeDv1dUgQ6hWCjtQniqtsncWl1fx9fvP4KaLt9S8TQ8I6uuvXnlFTR3XnbsR/Z1tuPv4RFmGUFLnDiiNuXccHUdPRyvOjcmgC2+3fLgwEa01CWtpEcWxMY39iQoIJXXKVHvFOerrChOd8dlFtIi1W7oGutrRIsrPEDo2Nhes3sbYu02hvpbE7igCpzgI3E/OL+EHR0bxrCdsqSkTnGe3gDOT0eeahdm6rgtnpoLzFMYSnoHRV9gWfmZqAScn5tHWImLfNXU9+uGzs5haWI5diVZ9/YHTU4nf4/U9HRibDSZnw1MLsWfohCfpUTa1knDfTBJICNuIJHXb09FadpshEB8QUn31kYpry2+4IH5SqxCFSeFYTLZCtd8leZ8rZRJnohbegyQLFVfsHsSX7z6Jo6NzsQHgTX2dwXk7UwsJFxqCcfzbD5/FQFdbrC3a2FdanAsCQrWf3zbQhZ6O1uJlMveenMSJiXk8r8rNSlGoecPU/PKa21KrEZ4vJPXZB3vayw6VVra5VtbEmgyh6YXYvtBSyLxVk/AkQbWtA10YnlrA8NQCdgx218xaqmRLfxeWViQ+ddsxXLJ9AFfsjvb7wqgyFs8BSrFlTG2LHSsGGqLfw+2D3WvPEKpxoycA/PDxcWxb17Xm2vdKWgrB4DNTCxhKkBkFlPqjatO4nRHhjKvJwq1qcdumS32/dllUtv+tR4LLeuKCtcUAZSHIU+sMNCEENg8E20vjLipoFhgQMkwpPX2x5kuoHE11lkrUYNTX2YZfekYpOJBmhbbSGavczlCNsCO2obcD//CTB2NldgwG18zOLi4nLt+67nY8VDg8Mm41XGUIqRXwJIdCAsANFwzhe4dHsLAc3Piwc/3aDBbleM8ursSeqwMEKd5npxfwnUeCQFOtINuejT1oEcFhaeNzS+jtaI1M7Q1PfD/5s9fhzYWrWKuhDjA+fHYm8cGzG3qDm9qSHp6n9tkeGZ3B8GR0Omp4NavWFgcVxDkzNY/llVUcG5tLnE6tDJbqK3Hvy49fswvf+s2nVT1nSBF+f2p93q4NPbhwc9DGcasiOwaDDJpP334M/V1teMa+2hNAxZ6NvZheWMbIzCJGZ5ZiHZOu9uDGkNGZRdxxdBxX7V6fKnNQfXe1uqUOtI7bOqK21xwbm8Ndx8Zx0daBmpP1jX0dxVsO45x8IEhnbm0ROD42V9hKGu0cqMyeUxPzWF6VxcMbaxG+nj3NVoBAJojW3HlsHFMLy3jaxdVvZQnzyZ+9LvZmIiGCbYXHQ7fZAfGOUflnBH8/dGYal25fFxvs3dDbgcfe+XycO9SLxeVVdLW3JFg1Lzh4iUu11qaNFBc/kjllCyvJsjnWFQ5LHZ1dxLru9jXfv6VFYLCnfEvu3ccnsGt9d83VSKA8O3F8bjFxQAgIAvenJufx2NlgK98lMen/m/uDAM/pyfnYmyOBYPKwvBrc3JY0Q0g5yYGeINsy7hakkv0dL+itrUeVQ8rkWx+D7IXFwlmEaxezonQA0Qe0VhL+lknqKmwjkh5aXRnsjMsQVD7CSMV28bgsvzAt6va0wjlStba3VX6PRIGx0Hd46ZU78Nmfvz5RuVTbr6zGZ5z/3FPOK26bi+tjatx+ZHg6UfnV+VyHjo3Hnq8GBP6Gao+RBHaipUXgvKHS7cLKN0wT1Au/j0l8unCWZ9IMoXXdHZgo3AYMBBl/fZ21D28OXygABIuySYK84QOsA1sbc0RBT3vx1qu4ZytR/tv9p6aKC5lJUHYjSWZdJet7O7C4slo8ALuWzd052I3Tk/NYXI7fdtzb2VbMDt+1Pj5LtKOtJbiVMiZzPowqqsr6ipv3hBdgk2REAaUs4LgFJDVGqIsE4sZlZRcePjON5QQXVAz1Bdljpa16DAgRg4S3YxRfwioGTq3Gf69wjXmtFzW8sphmhTacrnrzE3fHph8C5Y7Yx2+5Fs+IWdUESiv780uriQ9CXdfdXjybJm6f/bZ1XXjGvs141/88jBPjc4lXam+4YBPml1Zx22NjODY2W3V7QNj41DooT6Ei2v/yvcfR3ipqrkZ0trVi94YePDI8HZveqFKAgfig36ZigGQqXUCokCGUdCV5Y28HHh+ZxZmp+cigSfi2iVpZGv2dbehqb8GZyQWcGA8m8XsSpsUPdgcr/CrFOW6SIYSIPWA5vMIRZ0jVKm/cqsiO9d04Nj6HHzw2ihsvGEp01ggQvmlsFmOz8enVQNDnjozMYnhqIdUBjED5wbJAsLrU09Fa8yBLoLS95tjYLI6NzWF3zK1m4XOxkhji1haBrQNdOD4eBITOreG8K0dJ3QCVJENIDZ23Hx3DXccnYre+hIPjKlZzx9FxALWzzxRJx+rthUDiqpS4+Ym78c3feFoxMy0J4fHivBSBJHWuwa8/++LYG11Un0sTeKw84Hgq4YRPobaAJbkifGJuCWMz0bfSrQ9tlxibWcQ3Hzpb8zpfhQpGHxmdwd3HJ1OlnB/cswHffWQE//vAMIDoA/cVG/o6cGpiHmOzS4nOwVBtcmoi2GaWpA8AwXh3enI+CDwlmEwOdAWTlDuPBwsycTYnbF+SLLIAhavtZ5ZiD98ufm5ojEwSbAbK+2OSQFXYXif9HpXjXFzAUfkIYfZs7El0Vbmit7MN04vLhdtqa2eXVk6gknyv8BbP11y3F3ti3mOFquPR0DalKMJjfZydVZ97bGwuUXaY8vHml1YTBQw2Fs4XnF1cDhYKE/hYF2wOAkJnJufxwW89hvOGelNt+w2/w0l8urbWFvQXgkJJ9Zy/uQ+LK6v4zO3HAQRBnjjfZ6i/E93trcUtjUdGZhMF1dZ1B9lIUkqcmZyP9fHVe/nwmenEPq0iHDxPc/aeshvFG4ZTBAvUs4+NzMQGkvbvGsSqBP7x24eD4y5i3ln1HeIymIHAR5lbWsHc0kri767GwaRHToT9y7HiTofadaVsQNwwpp5TwdS4ucm67nYM9rQX/bDYoy/6O3F2arG0VY9bxtIhhHiTEOJWIcSCEOJDFb+7SQhxvxBiVgjxNSGE3uZijxmoEhCq5tBu6uvExt6O4naNpPu+U/jjZfzRSy6LXTkGyidBSX2S8Ep60ih7uE7iAjFCCPzej1yCpdVVvP8bjya6JQQAnnjuRrS2CHzhzpM4MjJb9UaZsKGKW80DSlsrvnd4FJfvHER3zK0uwW0oMxidWaw5KQ6fpRPXxkVHayb+gE/F+t7gELyzU9E3Q1Wye2MPjozMFleSqzHQ3YaOgtNbK0MoOCMjWAE/UjhAudYNP2HUdbkqQyjJtaJxKMPQ39kWu89fOYRxQY3t67rx4KkpHB2dw76E2+EAFB3sIyMzGIu5SlPxhG0DuPPYOGYWV1I5MkDwnVtbRNFIni6s+sVN9ksHRc8VMu5qt1+4PyUNAuxY3407j01gemG55uGCqqT3FG5kSnLtrhACQgBfL0zQnxyzghsOFqtrl28/Oo4dg92J3sGkY/WOQkr5qgwODN21IX5lsFxPaKKbYnV1YTlYuU9yNbJy0pPcIlmtXIqdCbJyFEmveVdnY4zNLkaO4eHg5NcfPIPlVYnnXx6fMavGyj/50v2YXljGcy6NDyIpfubGc7G8KvGP33kMAGInoxt7O4o37CWZ5Kk2+d7hESwuryYeczYXtmecnpxPFHhSl02oDN24CVt4YpL0KufgkNbF4sG8cRlS4TEy6VYPlZ3b0dZSlmERxVMu3IRnPWELnn7x5sRnmqTJIFNUbvFMc6AuUJjYzgbbV+LqQtmKXRu68devPJBoa2J4EpXEh1SogHv42vIowvUWnyEUavsE55GFAzpxB+iqz5+aXy6e+5IkY+W8zX04OTGPD377MZyanMdvPOfiWJkw4SBY0sCu8keSZgi9cP927NnYgzd/8hC+dv8ZnJmajw0itLYIXLZjHe44Oo75pRWcnJiPDWwDwflbE3NLePD0NEZmFmNvAlTfZWZxRSMgVPr+cYs8YYb6O/HY2RkcKgQX0mwZU3Oyw2dnYoMjT794M67ZuwGfu+MERmfidzco3yIuiAaUtvMGcukCQsMJDvsGgvH+E7dci4u39pey+GK+g6qfOD+ms60V/V1tmF5YRmeCWzKB4IgFFRCqtWUMCMau8blFZggV0MkQOgHgDwF8MPxDIcQmAJ8G8FYAGwDcCuATWQvoG60tAgNdbWVbxqI6j9pu1N4qah4iC5Q6e5oMIQD40E9djX97w5MSPx92xJJOSsKDcFIHMFwnSQIxuzf24Jn7tuBDBec6iePV19mGneu78bHvP45VKYs3ZoQJOxRJyh6unyRbns4b6sPhszO44+h47LXebQVnK67ewwYzaXqtchoePTuTuI12DHbjoTNTwbk9kbc9ieIKQ5wjE5yRMY8jhSvWkzhnio19HZhZDK4+TWP0o1DvX5IDSdWkMO6d27G+G3OF61nT7IHftaEbLSLY8jMac3OCYt+2gWKG3WCKVGcgaLPB0B7/WmdEhenpCLLG7joWHCgdN3EJf4+k2zp2DHYXt5nVChiqicm7vvYw9u8arJlNFEaNn8+7bCtedKD2GU/hwIXaMnb/yUlcuiPhrUYJh+rtg92YmFvC1PySVsA/LJJkNbuSJAHi0paxdAV8ccVV9j/7lPMSyyYNCK3rbsfc0gpOTc5HLkgM9nTgu4+O4PGRWXzn4REM9rTj0u3x526pLWjTC8u4cEsfXl64qS4J2wa6IEQQuN860BW7eBDuL0nGJTWZ+8aDQYDzCduSnSOmDq9OujUNCN5RdYtb3PgULnvSBaINve0YnVnE6cKYFhcQCzv2cRklCjVeDXS1JfJtnnPpNrz/Jw/ig6+9OlHAGUierRSm8pyQnQm2v5bp7FGTntqLTupZIDgXJG78K8mUPjPN+KTez5GZ+IBQOCMqyaq/IknGnsowBpAou0mNobc/Pg4gfvtiuEy3Pz6GLQOdibIPw4QDbUkDgiqAkfQMoY62Fnzq556EjtYWfPaO44nt/oHdg7jnxGQxiyOJ37auux0Ts4v46n2nAQA37au9OyHc5kkyY8KUBYRSZAj93FPOQ4sQ+KMv3Qcg+dwFKNX92en4C26EEHjC9gHce3ISX3vgTM1DvIGSr5QkMBbO3ok7d61UnuDv4amFRNnoQLC4HrbDsRlChTqZXVyu+RxQ+p4bejsSjct7NvYWs43j6l4tFk3MLaGzLdkNoT6TOiAkpfy0lPKzAEYqfvVSAPdIKf9VSjkP4G0A9gsh0oXCm4D1vR0Yn1sqXvEY5dDuLRymPNgT3xHUICxrPrWWp160GVcl2N6g2FKWIZTM+ocnIUmj7KpOuttbYx1lxdteeMka+TjUquw152yoet5P+HrTJFvG+jrbiiuNSSLy5w0Fabrjs0u4em/t80SUYU9yTasiqQHcE5pcJ80Q2rauq3htcq0JihrQ45xmdXbF0bFZtLeKxAYMKBnr1haBge5kN3fUQhm0JJkeSnfcSkr4+8cF/8J0trXi4J4N+MrdpzA+u5QoOyCcpRGTWQAAdbpJREFUDaAzCQnfOHIm4bkAQOCsfu/wSOHfMRlCofc0aX/dHlrxquXo9odW+F9x1c7EWyxUps9FW5IFdT7389fjRy7fhoXlVZycmMPozGJsn1PjQ/ItY8H3XJXpA/5A+YQ47eoqkCyIpJ6ZLwQ8k/LOl12Ot4fG7QM1zvWqJHFAqGBzHh2eicyWe8H+IDD1z987gv87PIInnrMh0TujshMB4ILNyfu0klU3nu2K2V4JlE9IkowBKjD+tQeG0dHWkijTCwgyGWcWVzA5v5xoQgiU24y4oEN462lSW7O5vwsLy6v40p0n0V/YrlyL8Nb0pFkrKrMh7S2qaVCTuZdduRNfffNTEsnUI0NoPGWGUJpxJvyZaeRU9nLS21MVSc8QApKt+IcPxd+TcMsYAHziB0fR0daCgwn8Z2UT7jg6HmsX40gqr+pp27rk78tQfydeeGA7vv7AME5NJLP7+7b1B5cpPHAGQLKF0C39nTg1OY/vHR7FhVv6UgV4k2TGhAmfy5kmILR3U29Zlmi6Q6VDwZEE76Cah6ysSvzBiy+t+WxroY8lCYxtD7X95TFZWArVh6cXllMtICnbf/35G4tHZ0ShFgJWE0xYVX9LGpALByTjkgnW9bRjYTm4eKTZs4OA+p4hdAmAQ+o/UsoZAI8Ufk5CKANdOlS6+ku7dSDozHHZQUBpZeNkIY21UYQH7qSmP+1tHEApmp1m/hOOhiedYCrjGnelNpAsU0kIgfaCA5rEaQ+f6REXEFKD+8xC7ah6+JawpBPAPSEjnrSNwt+vVuBkU19wLXXcStXm/i4MTy4U9q53pTonQRmudd21z0hIiroRJskKuTL+qzHWLZwhldapf8YTNhczY5LcBhg2yGkcGcWG3g4MTy9ASonTkwuJzyHYub6neLNG3HcMT+rCgddahNPRt9dwdMP9X+f7X7Q12Vk7+3cN4snnB1vL/uRL9wcH2sesSqsgwEoSbwjlDnCaSwMUu0ITiKQrfuX642Uqrx9OSld7K17zpL3F/ye9ch5IHhB66oVDxSyLmYXqAasX7t+OHYPdeHR4BkdH5xLfRAOUgq97Y27DrIZyQtMEnoFkq/DhwPhPPWlv4vP7rjtvY/HfSTPrlA/T2iLQF7PFNjw+J7U1aiz57qMj+Kknn5N4W2Ea1EUJSVatdVHv6r5t/YkPhq/c8pPm3QRKt+ydHJ+LHcfVRC2NCQ1neaXZMqYWrpYTjoOKuP4eXuWvta24WlmSZQgFz956ZAw3nL8p0dXxanxcWF5NnE0WRXhRpBaqT6ZZWAOAy3euw8TcEhaWVxP5P+qd+tv/eRgbezsS+SfnDPVifmkV33hwGPsTLAKEbXiaoI5C+cFpF0ReckUpSy7pwjRQPqdL4n+ogFCLQILxM/g77jmgvK6SPA+Ub/tO46eeKGzn/dGDu2J997itXGFUf0seECr14bi6V77ag6enUgcafaSeVrUPwETFzyYAVB0dhBC3FM4iunV4eLiOxbCfdT1BhtD47BLaWqK3g6n9n+osh1q8uJDee0GNKyLrQUdbC37x6cEBx0kjquHBIelAoA5lnV1Mt+LcWVgNSHKNdZgkE96kA5Jyb5Ksriin8Jq9G2IdRFUnq7K2AxWseAVlTbp9Kuw0XJRwO1N45amW43DuUB/O39wXOyFZ39OBqYVlnByfT3z2kUJNWusV5RdC4LmXbkt00LpagYpzbJXRf95lW1MHrcI3kiUJXoYnETp1sntDL46MBAfl1toSWEnYgYjbIqgyqrYnTGkHyoNhtb5XWfqyxvePC86GeeU1u7Fv2wAeGZ7GqozX9xuF80YGYg5qVoQ/TydDKLxCqpMhlCSNWmW8xQxNsaRxyl5z3V4A8YHmXRt68M8//UQAwLWhYEcl63vb8YPHgkscLkowqVGo2znTTryA0mJJkkBd2LmNy0YEgjFs/851OLBrEL9S41bKSi4J3Wh5U8KbEFXZejpaU41tSds7vD30WQkustBBZYekjE+kQk1A0tRRuM9+5o1Pij3brJrO6YVlzCyuxB4qr8aaNIGd8HlLaQLWahEgSYZNmDTnMF13XrK62tjXga72lkR2LrwNLelNqOE2TLsYVEnSxZNd67uxY7A7VSADKPcZkwSq1TMLy6v4iSfuTmQvzg3dEJokcyX8joVv3E3KzU8MjoNIc5A3EGyFumT7QOogXthmJ3lf1fiWpHxpFkpbWwRuungzfjPFmVVhH+O3nptcTt00lySD7fyh5PZVbUNP2nZqsaRFlGeKV0O10/2npnBxinM9fSX73ooS0wAqe+oAgKlqD0sp3w/g/QBw8ODBBppg+1jf044jIzPF27CinAO1Aj6XIA3/yRdswn3veE7qwV+HNz/rIrzxaedr7bdMulp/WcwVvFG85Iod+PgPjiaeBN5wwSZ87PuP42CCCWDS1XU1KUpyzsO67nZ88zeelugmqF971oU4sGtdMSOhFkN9nRieWkg8AQw7gE+MuQpbsXVd+LykaEfz1599EX7x6RfEfp5qs0eGp2teCV8NFQCLuwkrDX/z41ckeq50XXntYWz7YDc+fsu1OJAwdTfMuUN9OHeoF6cm5hM5J+EV9DTXpSrO2dSDf/vhAl71ge9h27ouPO+y+AN2gZKzu667PTbgcWDXIDb0diSuZ6A8o67WpCrNIaRh/urHDuDibf2pz9rZub4b33woWNiIG+N+9Opd+NGrdyX+7HD7Zc1+S3tlb1KSBCmTkMZ+/fQN5+Knb0h229q2dd24/w+eU1wwqMb6ng7cfTw4hPzCFN/nx67ehcGejthzMKqhvm+ScVonu+vTb7weLSLdeyOEwMdvuRYdbS1lwcRapMnsCpO0vcOT6KSZNR9+3TVYSRGh3NDbgb0bexK/UzoMFrdkJZcJvxtps4PCOgHg/LitHD3q9h+9Q+vTjk/f++2bYidtlSQ9H3J6YTnx2L9v6wCkTDbZDtdnUtsa7ru6W8be+6orcbKQhZGEX3j6BXjt9XtT6wlnlSTxS8N+7qUJ/fbw9tVrEvjeYX/mnIS32IX55WdcgFdesyvxeUphPv+mJ2NpJd020vbWFvR3tmFqYTnRXOTcoV686MB2/EyCsee3nnsxlldWyxYJa/GB116d6DlFuO/rvKu7EgQ80wRFn3bxEG49Mopfuil+DgGUAkLrezpi+3N498olCc4M9J16BoTuAfAa9R8hRC+A8wo/JyHUlrHxuaWah6mqDKGk5zKYCAYpdA/fSmqgu9pb8ZPX7Ul11goA/MGLL8Wrrt2TOJr8vMu24bbffUaiCWCSVVkAkIUcoaS3XSW9TauttQXPuTTZxHxTfydwMl167Ydfdw0621oSr0CE67jWimJXe2ui90W9G8FhhikzhArtl0dk+SkXDqGnoxU/lcD5uvbc6AyFOH7ppgtwZGQ21QoRoHeGkLo6dmJuCR94zcHE76hyIJIY/F0bevDDtz4zVbmSpj2Ht22lCQi9+IpkB6lWsrG3o3j2iO7kOIr1ZRlCep+xa0M3jo7Oad1ylIRGbOGpN3FjkAoq93W2pcpaE0Kkul0sjNo2mDZDKClpMj3CpB2n1HuVNP7y56/Yj+8fHk39+UBy3+PGC4cSfz4QtOPXf/1pqWTSor5HmnbRCQSGKQsIxQTT1EQ2rY15/uXb8MU7T6YuW5qMjX97w3X4wqGT6GqPH2u++Rvp2vF3nr8v8burs9gQzo5+4rnJs0/DJPX9FN0dyc/fDBMOQCa5oSy8CJfkhjGglD2/e0NPah8/7bsJBH07zVlKYVpbBFpb0tfjYG87phaWE43b7a0t+OtXJlsY27m+B+979cHU5UmKqt4OTZueZHEjTRv+2NW78aMHd6W6xKinozVRIC48/35CzGHezUDqgJAQoq0g1wqgVQjRBWAZwGcA/JkQ4mUAvgjg9wDcKaW8v47l9YJ1PR2YnF/C8NRCzcFCZQj5tLcxzcThHS+qfbhaNdpbWxKvUijigkGff9P1xZXjJNx08RZ88a6TWnud64XKBEjjTKZ1oJUz96sptiPUIjw4p70pTG0ZS3ouSz3ZPNCFe9/xnIbrSXrrSyVJruqsJOzYJcmeU6hAUNa0+Fq85+YrY28OCq86mzgsMLxSnORmmzR0t7eio7UFiyurWlvGAODTb7gej43MpFrBf811e1I5bv/5Kzdian5Jp3hWUDwcektfXc4hS8LSSjBeJXGiu9pb8cvPuABPvzh9JlKjUXUnE86qX3bVTrwsxY1sQgics6k3cXaQrSgbl+b9Snr2UxThTM0kGYKDve2pA89//or9eOH+7bGHyWbhqj0bcNWeZLYobVasECLxuUnh9tAJ0jayjupB2G9NklETfpeT3gwrhMA3fv1pqW4M+9efuy7yUgAbWd/TESzCOFRmoHREiW7gMqnP8LM3novbjowlejZthuvuDT2JMg/DR4vs38UMIZ0Mod8F8Puh/78KwNullG8rBIPeBeCfAXwPwCuzF9E/1ve0Q0rg1sdG8YqrorcOdHe04k9ffnniLTw289mfvx6PFK6ldI3Ldw6m2sL05z+6H7/6rAtzvcLw4J4NODY6l9mZrEV7awsO/8nz6vZ54dW2tMG0DTkGhGzlq2++EfecmNSa3F64pR8v2L8dP3tjuu0T6tygHSmvRk7DcxNsXws7JUmzirIQPni53k6rECLIKFgBejr1xpSh/s7UfertKQPySQ4TtRk1hlyY8rawLBQzhBJOYH/5GfUJvtebYkCogTq+9mtPbeCnm0Fla+pm+ulw/uY+tLYI/MWP7k9kC/ZtHUi9taarvTX1Veo+kGax4X2vvgrdDlxrHT4kO63/mMbn3Z0weKRIc66fDahgg07QME8u3tqP333+vppz02r88+ufmGpB6Leety9t0RLzO8/flygLM5w9n3QHiM+krgEp5dsQXClf7XdfBcBr5mNQRmRVIvYgqx89mK5T2sqBXYNaZ6e4SFd765qrYk3zE0/cjZ8oHKTXSOq5kh4enJMeYqxQqwFZD7X1ifM39+N8zcltR1sL/jbF2T6Kno42vPOll+GJGbbG1RsT2R7rU94qkhZ1jhz3uTcOlVWQ5vygrLS1Bu+mye3ejaDk03AArsWgxhk9Wdm1oQcP/9FzE4+D73v1VcYy5FwnTUDI54DZH7z4UozNLOZdDKtQC0M6W/bzRAihdY5a2sPuG8kNFyTb7dDTEWTd+tw308CQWA6Ez7fYx32LhAAozxBKG1BTBy2bCIKR2rzymuZrgw2hrRhx29myoHvYvu387Y9fkfjGnkaxoTBZv7DBN3WG+asfO4APf/cILk54s6OtqIlxo86o8oXdG3rwszeem3p79vXnb8TDGTKs0265IMlIe5utK/zU9XvL5ilxvPraPQ0sjZusdzRDqJkQQlibdZsHDAjlQHhP6T7HHUFC6kV4Ip10L7pisKcDj/7x8xKfA0BIPVFbfq7YPah9kG8t1nW3Y2JuqbityTdesH973kXAUy8awq8968JMB7+nZc/GXrz1R55gTF+j6O9qx2899+LE19Q3K60tQmurxEd/+toGlIZkxaUzbdLw+y+4JO8iOM95m/sw1N+Z+hY9QvKCb2oOhCPGrh04RkijCO9X1zn7SOf2CeIvt7/1mamunc7CpdvX4Y9fchlesD/dTTBJ+a9fuREzi8lumyR69Ha24U1PT3a1LVnLzz7lvLyLQIhRXDgTiOTDzdfsxiuu2km/lDgDA0I5oG56eM11TLMkpJJGZFiQ5iPtTTNZaGkRDd2uuDnF9cyEEEIax2d//nrc/vgYt9eRSFpaBLo0rqsnJC9E0mtCG8nBgwflrbfemncxjHJmch5D/Z00KISEePjMFAa627G5nxNgQgghhBBCCMmKEOI2KeXBar9jhlBOcMWXkLXo3opFCCGEEEIIISQd6Q/qIIQQQgghhBBCCCFOw4AQIYQQQgghhBBCSJPBgBAhhBBCCCGEEEJIk8GAECGEEEIIIYQQQkiTwYAQIYQQQgghhBBCSJPBgBAhhBBCCCGEEEJIk8GAECGEEEIIIYQQQkiTwYAQIYQQQgghhBBCSJMhpJR5lwFCiGEAR/IuhyE2ATibdyFIQ0naxusATDS4LKQxsB/7D9vYf9jG7pLUfrKN/YdtbB/19m/Zxv7DNm48e6SUQ9V+YUVAqJkQQtwqpTyYdzlI40jaxkKI90spbzFRJlJf2I/9h23sP2xjd0lqP9nG/sM2to96+7dsY/9hG+cLt4wRkh9fyLsAhBBCiIPQfhJiL+yfhDgEA0KE5ISUkgaTEEIISQntJyH2wv5JiFswIGSe9+ddANJw2Mb+wzb2H7ax/7CN/Ydt7D9sY/9hG/sP2zhHeIYQIYQQQgghhBBCSJPBDCFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmgwEhQgghhBBCCCGEkCaDASFCCCGEEEIIIYSQJoMBIUIIIYQQQgghhJAmoy3vAgDApk2b5N69e/MuBiGEEEIIIYQQQog33HbbbWellEPVfmdFQGjv3r249dZb8y4GIYQQQgghhBBCiDcIIY5E/Y5bxgghhBBCCCGEEEKaDAaECCGEEEIIIYQQQpoMBoQIIYQQQgghhBBCmgwGhAghhBBCCCGEEEKajEQBISHEm4QQtwohFoQQH4p59leEEKeEEJNCiA8KITrrUlJCCCGEEEIIIYQQUheSZgidAPCHAD5Y6yEhxLMBvAXATQD2ADgXwNuzFJAQQgghhBBCCCGE1JdEASEp5aellJ8FMBLz6GsAfEBKeY+UcgzAHwB4baYSEkIIIYQQQgghhJC6Uu8zhC4BcCj0/0MAtgghNtZZjzssTANHv59e7thtwPxEOpmZs8Cpu9LreuxbwPJiOpnxo8DZh9PJSAk8+vXg7zQMPwhMHEsns7wAHPlOOhkAOHkImImLe1YwNwYc/2F6XY9/D1icSSczeRI4c196XY/+L7C6kk5m9FFg9HA6mdUV4PA30skAwOl7ganT6WRM962Td6bX5Wvfeuzb6WQAzb41zr6lWF0JdKXl9L3A1Kl0Mrp96/htQZulwXjfeiidjJTAI19L37fOPmSwb92p2bduS69Lp29NnTLYtw7b3bcWZxzoW98O3sU0mO5b40fTyfjct07fm14X+1YJrb41wr6lWF7M0LfOppPR7VtHv2+ubzlKvQNCfQDCMy317/7KB4UQtxTOJbp1eHi4zsWwiE+9DvjAM4OgQVJWloB/eDrwLz+WTtffXQe898npZE7eCXzo+cB//V46ub+6FHjXVelkbv8I8OEXAXd+Mp3cu68G/vKSdDJf/k3gH58LnLk/ndz7bgT+/qnpZD78YuDvn5ZOZuoU8MFnAZ99Qzq5v7gY+Ltr08k89F/Ah18IfOdv08n9zRXA3xxIJ/ON/w/4pxekdwDec13wTqXh33466Fuzo8llVleCvvXRV6Qs3/XA+25IJ+Nr3/rKbwEfel76Cd77bgTe/5R0Mh95iUbfOh30rc/8XDo5rb711aBvffuv08np9K1v/kWg65GvpZN7z3Xp2/jTtwR9K81EaHUV+HuNvvXeJ6fvW6fuCvrWf/5uOrm/uhR418F0Mrf/M/CRFwOHPp5O7l0HDfatGzT71tPTyej2rT+/KH3fevi/C33rr9LJ/c2B9H3rW39Z6Fv/k07uPdcBf/GEdDLFvpViIiRl0Fb//LJ0ut53Y/q+dfqe4B38j99JJ6fTt+74l0Lf+lg6uXcdTO8v/MfvBN8r7QTvfTcE9ZiGf35p+r41PRz0rU/fkk7uL/YF72Ea2LfK+funB+NhGt7/FI2+dW+hb/12OjmdvnXo40HfuuOj6eR0+tZ/FvrWqbvTyb3vBuC9Kevwoy8P2itNoGvmbPBepO5bT0jftxym3gGhaQADof+rf09VPiilfL+U8qCU8uDQ0FCdi2ERJ+8I/k4TEZarwd9po6AzZ9I9DwCzhYFzWGP1Li1jjwV/TzzeeF1nCkZ/fjy97HjK8qk2ToOKVOtkdKVl8njw9+gjjdc1UljFmE6Z7QMAKylX+4t9az65jG7fmk65UgX437fSBLkVEylXnk5oZActqb6lsXqXlqkTwd+2963V5XTPF/vWXAqhgoN2/NZ0uqZOpnseAGYLgSoTfWv8SPB32ndXB/atEuq9GDHRtwoZmWkzEgBApsyyOFlIol+aTaFDs28p25+GYt9KuZimg+pbaX0uHYp9K8UCkmIyZaaf8i/STFp1+5byadLAvhXSUWijtOOhjj1Q755OtmRajPatwvfR6VvKh0rKsR8Ef6fqW4X34eSh2s9Vkvb9c5x6B4TuAbA/9P/9AE5LKVPmWxJCCCEF0qY9E/OwjdyE7eYAbCMnYd+yH7aRo7Dd6k3Sa+fbhBBdAFoBtAohuoQQbVUe/TCA1wshniCEGATwuwA+VK/CEmI1NCz2wzZyE7abA7CNnIR9y37YRo7CdrMftpGTcEysO0kzhH4XwByCK+VfVfj37wohdgshpoUQuwFASvkVAH8K4GsAHgdwBMDv173UhBBCCCGEEEIIIUSbalk+a5BSvg3A2yJ+3Vfx7F8A+ItMpWp2GPl0EykBIfIuBakJ+5absN2sh3bLUdhu9sM2chKOifbDNnIUtlu9qfcZQqQu8EV3E7ab9dD4uwnbzQHYRk7CvmU/bCNHYbvZD9vISTgm1h0GhGyEL7qbsN0cgG3kJmw36+H45yhsN/thGzkJx0T7YRs5Ctut3jAgZCV80d2E7WY9NP5uwnZzALaRk7Bv2Q/byFHYbvbDNnISjol1hwEhG+GL7iZsNwdgG7kJ2816OP45CtvNfthGTsIx0X7YRo7Cdqs3DAhZCV90N2G7WQ+Nv5uw3RyAbeQk7Fv2wzZyFLab/bCN6obJcYpjYt1hQIgQQgghhBBCCCGkyWBAyEYY+XQTtpsDsI3chO1mPRz/HIXtZj9sIyfhmGg/bKP6IYRBZWy3esOAkJXwRXcTtpv10Pi7CdvNAdhGTsK+ZT9sI0dhu9kP28hJOCbWHQaEbIQvupuw3RyAbeQmbDfr4fjnKGw3+2EbOQnHRPthGzkK263eMCBkilSDDl90N2G7WQ+Nv5uw3RyAbeQk7Fv2wzZyFLab/bCNnIRjYt1hQMgYKV5evuj5kLXe2W75wGBrE8B2sx6Of47CdrMftpGTcEy0H7aRo7Dd6g0DQqbQGXRMDFS+XhNoa32TBmB5WxvVZUIH69t7XWQtvra1rf3Z9jpwQZcOtpfPKXT6Vv1LEa3L0/fe1kVCa+uAEAaECKkjHICth0bSTdhu+WCrY01KsG/4D9uYEEJKcEysOwwIGUNjy5iJK/xMXhNovS5uGXMSnXq3/l3U1WVCh4aSzH0jhby3bWvySlcFtzoXsbWtTfYtBX2T7LpsDbbmMs5YSh7HCJisfl/7WBpMtjHru47QN6k3DAiZwlbjT0rk4ViTOkDDYD8MtjoJ690B2LfcxKDdYhvrYdInZBvVEYPzLbabHkbHNLZREhgQMgYNg/3QMDgJDYP90LF2FE5arYd9y014nogDcNLqJGnqnQvB+cDxzzoYEDIFDYP90LF2FE5a7Yf17iQm7RbbWBPWu5tw0mo9nLQ6CtvNfhhstQ0GhIzBSav9cIByEk5a7YeOtaNw0mo97FtuQrvlAKx3J2GGUD7YWu/sh4lgQMgUJq92NdopU2C7Lg4ajmKyrS1/r0yo0vo+JvuW5eOMC7rIWnxta1ttpe114IIuHWwvn1Po9K36lyJaF997+guEBDAgZCXsyN7DwTofWO9uwnbLB2YxOICnwVZSglkM+cAJPImD7VYi1c1m9BdsgwEhY2hk7WhfG5hCl6/XIFp/7TwHs7qhU++676L1V9yb0GH5tfMmHQXrxzSD+D5ptbWt8+hbvHY+uy5bg622jzNGycEn5LXz2bF165Kv9a2Drz6hwzAgZApbjT8pwQHKf3yftFoLg61OQrvlAOxbbmLppJWUoE/oKLRb9kO7ZRsMCNkIjX9OcIByEk5a7YeOtaNw0mo97FtuQrvlAPQJncTWDCFSgnbLOhgQMgWNv/1wgHIUGn/7oWPtJLRbDsC+5Sa0W9ZDn9BRaLfsh3bLNhgQMgYPfrQf1puT8OBH+2EbEWIp7C+ENATaonyw9aZlQiyGASEb4QDlJoxYOwDr3U3YbvnALAbrYbDVTZh95wBcyCVxsN20YPaddTAgZAoaf/vhAOUonLTaD9ODnYR2ywHYt9yEdst66BM6Cu2W/dBu2QYDQsag8bcfDlBOwkmr/dCxdhTaLeth33IT2i0HoE/oJDxU2n5ot6yDASFT0PjbDwcoR6Hxtx861k5Cu+UA7FtuQrtlPfQJHYV2y35ot2yDASFj0PjngrUTGrZRkazvu7VtTIrQsXYU2i3rYd9yE9otB3DINyElmCFkP7Rb1sGAkClo/HPCUsPANirhVL2z3YpYO6axjYow2Oom1tY726h+0G5Zj1O+CSlh6/jpObRbTpMoICSE2CCE+IwQYkYIcUQI8RMRz3UKId4rhDgthBgVQnxBCLGjvkV2FY0XUnugsdQI+aqL5ItOW+u+HyZ16WBClfV1QF2kARitf8t1magL2/uWSRtiEtvL5xJa71X9ixGty/I+ZkKXreVySZfvNEldJs0QejeARQBbANwM4D1CiEuqPPdLAK4DcDmA7QDGAPxtHcrZXDTJy2cEawdgtnEJyyc/JAK2m/U4tcLNNtbCWhtHamLtarrnWOunsY2KOPW+ulTWBiNE8med8k2ag9iAkBCiF8DLALxVSjktpfwWgM8DeHWVx88B8B9SytNSynkAnwBQLXDUfOi8kGk6l64uXR065KLL0gkNB6gSedS79rtosB/rYEKV+j7WTkQNlsv68dOhtGzbx0STbW173zJRF7b3Ld02st038R5L673om+iqst03sTRY4JKNTIPJtlVY6y947pvUiSQZQhcCWJZSPhj62SFUD/R8AMD1QojtQogeBNlEX85eTB+w1Aj5jrWDBtuohEODdZMYhmRYOqYZPTfHclyqdwbUQ3ha7161m0P+An2TEtb6C+xbJQzWhbXvg4t4ard8HxMLJAkI9QGYrPjZBID+Ks8+BOAogOMFmX0A3lHtQ4UQtwghbhVC3Do8PJy8xK5iq2HgABV61OQAlVWVR+3mq2HwqY2qYeuYRsc6hEN1wUlrCWvfd4fep0bjqy3xqY2q4qu/4FG7MUPITawdp2i3kpAkIDQNYKDiZwMApqo8+24AnQA2AugF8GlEZAhJKd8vpTwopTw4NDSUvMTOYqth8PxFt3bQ4ABVwtd696mNqmHpmJa52j1qN6cmNCnwavyrBvuW/Thkw2m3SvjqL3g1Jrq0KG6pjcwFS+uCwdZEJAkIPQigTQhxQehn+wHcU+XZAwA+JKUclVIuIDhQ+hohxKbMJXUdWw2D9++5r4bBo4Zzqt5T4FETVcXWMY2OdQhf692nNqqCr/XuU99yym5ZaiNzwdK6YLC1hEv+Me1WCWttCe1WEmIDQlLKGQSZPu8QQvQKIa4H8CIAH6ny+A8A/KQQYp0Qoh3AGwGckFKerWeh3UTjhTLyEto6cOahqzk6vXfotLXuu2hSlw5Ghgzb64C6SAMwWv+Wt7V2Xdg6GTfoL1jfj20vn0No2UqTumzvY7q60jzrax2wH5N0JL12/o0AugGcAfAxAG+QUt4jhLhBCDEdeu7XAMwjOEtoGMDzALykjuVtDtiR64e1AzAj1iV8nWj51EbV8PX7efS9nMosYGarFtbaOFKTVFVJf6FuWJvRwTYq4VJduFTWRuOrv9AcbdyW5CEp5SiAF1f5+TcRHDqt/j+C4GYxUomOETJx7bxJR8H7a+dT4FJKbKPJY9Jq4tr5zLo0sPbaeU/Tg229trdI1rowqcvWiVoBa6+dz6HeTfgmtvctXbtl+7XzHrkW1bG13lXfMqBLYWsfc+kcVVvtVi7Xzjfs4SritraxuyTNECKZsdQI+W79rR00OECV4KTVTSwd0xhsLeFrvXs1/lXD13r3qd0csiW0WyV89Re8GhMN1gWDrXXE1nqn3UoCA0KmsNUIeWVEqmHpoMEBqoSv9e5737J1TKNjHcLXevepjarAvmU/TtkS2q0Svta7R+3m0qI47VYJa20J7VYSGBAyhq2GwfMXnY61A/ha7z61UTUsHdPoWJfwtd69Gv+q4Wu9+9RuDtkS2q0Svta7V2OiS4vitFslfK1339stgAEhU9h6Po33A1QKWBeOYrLd+I7owTYiJDG2+gsu6Wo0Pn2XML5+r1yg3csHWwOgbCMnaZIxkQEhY2gMUCaudjU5QOXieJqsC0astchaFzoBUF2dJnXpYEKVzvfKo41d0JUGLV2+rnDnMP4ZdQottVsmfRNf+5aOb8Ks8TpiuS3R7loagrb2MZdsuFf+gkmdnvsLDsKAkI00STTSCNYOwEwPLmHrRMslXXnga1161G6+pmV7Nf5lxFobR2pCfyEfrJ1Iso1KuFQXLpW10fjqLzRHGzMgZAodI8Rr5+ugy9JBgwNUiTzqndfOZ9DBa+eL2HptbxFP6z2P8Y/XzsOob2J739K1W977JrZjeb3z2nm3MuJstVu5XDvvab17PyYGMCBkDEuNkO2BBZPpnByg6oe1deGQrkbj1CTDUl2242u92z7+ZcbXevep3RyyJT7Zraz4Wu9ejYkG68Kp8dN2fK1339stgAEhU9hqGGwfoHwdNLwfoCytC6d0NRgGW+ugKwesrQuHdDUa9q066LIcp2yJR3YrM77Wu0ft5tKiuE92KyvW2hLarSQwIGQMWw2D7S+6r4OG5wOUtedaONTGDcdTZ9d7x9rSunBKV4Nxqi4s1ZUHvtoSn+wWg6110JUDttaFS+On7W1sqy3x3W7VCQaETGHrBNn2AcbWeiMWYbLd+I7owTYiJDG+2j36Jvbj6/fKBdq9ErbWha3lygH2/eo0Sb0wIGQMjQFK+yW0dDDU+j6a5SvqsnSw9z5ibemqiczYt0zq0iFV189YByYnd77qSkMu75Olq7p5rKYbzbKwXJe3vokBnTq6XMoab/T45FJmgZa/YFKXpbbEJRvuky5m3zU1DAjZCN/d+mGrwbPd6cqKtYO1r7p0cCko6asuHSydSDqlq8FYa3cc0pUH1talQ/5Cw3X5OpH0VZfvsN1K0F9wGQaETKFjhFy4dj6NPK92dVSXDrbWReFvF66d16mXNKqy9i1rJxmeThi06t2hurBWV4FU/djXCbxDvkkaTPoL9E0yfryndVH0FzKqTKXL5NXklta7r3ZLq209rQuX7HGOMCBkDA6GmgoyittaFw7p0sHXurBWl5YCc/JOOfEpYLBV69HsujhpdVNXGmi3/NelpSCjuK32waE21sHWurD2faj6AXUpRvTHu2RLLNXlMAwImcLbwZADlJ6ujKrSwEmr/7q0Pt4hB9RXXTr4WhfW6tJSkFHc1rpwyTfRwdLx3Sl/wXK7ZW0bu6RLB0vrwtfxU09BRnGTwbWGPVxFnAEhUlc8X1lrGKw3EoNTKzyW6mo01joKWXV51EZNhe3t5qvdszzYmgrby6eLr98rB2iLStjqO7GNSthevtxojnphQMgUOitjup3T1jOEdHRlrQNb68KlVQkdjJZPQ5etzkm5YINFso4vlrexE7rSqNKod19XnY2O1RoyttvVMnmNZ13wTdJg0l/Q8u9cep98ymLIwzcxqcvXYDB16bWt7eXLS1dzwICQMSydZBgdDPP4fJ8G67x06WCpA+qrrjw+39pJq0PfSwdbJ5JOpWXTbvmvSwdL68L2bVwmdbnkO5nMVPHJbtFfaIx8oz/fWn/B8nq3BAaEbMSll8/2slq7VcXzAcraDCZfdenASUb+unSwdCLplK4G41K2lK268oD+QnaYIaSJr7p0sL18YdhuWjh1BptH9V4DBoRMoWOEnLjaNYU8r513VJcOlteFC9fO6xgha6+dd8iJt3XSm0u9N+zhKuKWt7G1187noMsJ3yQFvHY+H11aH+9ScEvHXzCpi9fOO+UvpNHFa+dz0uUuDAgZg4OhpoKM4rbWhUO6dOCk1bAuLQUN/vywKk8nNAy2+q9L6+PZtzLrygNrx3dfdWkpyChua13YXu8ZsbUuOH6GPt7TurC93i2BASFT2Dpptb2j0LG2QJcOvtaFpbq0Pt5XZ9chXTr4WhfW6tJSkFHc1rpwyTdpML7aEt/tlq114ZSuBuPtojjtlv+63IUBIWOYfKEsn9CkwtZ6I9bgVNDQUl2Nxtd686mNmgrb281Xu0ffxH58/V45QFtUwta6sLVceWB7+XKjOeqFASFT6AxQup3T5GDYaF1Z68DWunApOq6D0fJp6NJua1t16YhkHV8sb2MndKVRZXD8LH2An7oaXZe229WsulzwTdJg0l/QqkNP3yc9BRnFLbcl2ipN+iY6WF7vvunSalvby5eXruaAASErcenltb2slho8DoYlfF2h8b3efa1Lr9rNocmT7UHuVFhqd5zSlQeW1qXt27hM6vLVd/JVlw62ly8M262Er/6C7fVeJxgQMoaGETJyO5HBjlK8JSeVgjQPV9FlaV3Yrsv3bCkTt+QY1aWlQE+Mt+TUUVcKWO/56FKk6ce+1oVLvkkaTPoL9E0y4mtdqL5lUpfBW8ZsrXdfx2qTtzqXPoC6HIYBIVNwMNST8XXQsF2X7eWjruwyvgf9bNXFendMl46Mr3Xhkm+ig6Xju6+6tD7e07pwSpcOltaFr+OnnoKM4rbWhe31bgcMCBmDg2EmGV2sHTRs12V7+airbjLaWDqm2a7L9vJRV3YZX+vCdt+EwVbHdOnI+FoXluvytW/5On7SN8lHl8MwIGQKDoZ6Mr4OGrbrsr181JVdxlcHz3pdtpePuuomo4u1dWG5bxKW0WovS8d373XZXj7q8rZv+Tp+Kl22l887Xe6SKCAkhNgghPiMEGJGCHFECPETNZ69UgjxDSHEtBDitBDil+pXXJcx+UIZ7CgNx9Z6I9Zg7UQtB122Gi7b682kLlvbqKnIIZBHXSl1sY3ywdfvlQO22yKTumwtn63lykMXfZMImqNe2hI+924AiwC2ADgA4ItCiENSynvCDwkhNgH4CoBfAfApAB0AdtattC6jM0Dpdk6Tg2Gjv1fWOkilS0/VGp0mlHmVxZCVRq+auKBLRybr+GIy8OyTroqV1qSHP3I1vX66bLVbOvK210VWXbqfr9O3bPVNbM/M0pbPaiM1MFnvWv6CSV26dWGpb+K9rjTo+Gn10pnkUYf8hSYhNkNICNEL4GUA3iqlnJZSfgvA5wG8usrjbwbwH1LKj0opF6SUU1LK++pb5GbApZeXq3haumwfDJ1KD04BdWWTqac8ddm/imfrpNXWcT2TjC6+6sqKR+3m6xkY3ge5U2C7LtvLlxe214u15XPIX3DpfcxAki1jFwJYllI+GPrZIQCXVHn2WgCjQojvCCHOCCG+IITYXY+Cuk8aI1T424WrXW3d08qrXTPqyhgQsr0uvLt2Poe+pTOmaePRhKFMJoU8r52vo64UWD9pzahK53tZ65to2i0tf0ET+iZrddlePpO6rL123qRPmBGjW8Zs12V7+VzQ1RwkCQj1AZis+NkEgP4qz+4E8BoAvwRgN4DDAD5W7UOFELcIIW4VQtw6PDycvMSuYqsRomNNXbEyDV5p9X3yZII8+patY5r1ujwPtvqqy/by+aaLdquJdNlavoyqrPcXdGU86luZx7SGPVxFnHbLfl3ukiQgNA1goOJnAwCmqjw7B+AzUsofSCnnAbwdwJOEEOsqH5RSvl9KeVBKeXBoaChtuR3E18GQA5Seroxw0kpd1XTZej6CUxOaFNCx1no0uy5OWt3UlQbarfrpatjDVcQtt1vUpanL077l1Pjpk93KqMrW7+UwSQJCDwJoE0JcEPrZfgD3VHn2TpTXXHPUYhJsnaTYWq4sMro0SRTYP2yfpJjUZes7bGu5suJTGzURtEX262Ib5YOv3ysXbPcXTOqytXy2lisPXez7VWmSMTE2ICSlnAHwaQDvEEL0CiGuB/AiAB+p8vg/AniJEOKAEKIdwFsBfEtKOVHPQrtJo1dNdHVlRWfgNTBY60THncqYSIHvWQw6kwxdnalep6y6Gvy9spbLZODZJ11hmYa/u7avOuuKm1zxs9yWGNWlREz4JlntToNX0036JiZX03Pxgyxd7c9sS9I8q8Z3XV0G/YVGB2uN2vBsquz9Xpb7aU75C81BkgwhAHgjgG4AZxCcCfQGKeU9QogbhBDT6iEp5f8A+G0AXyw8ez6An6hvkR3FaKdM8ywHqFx0WT9RM2j8fZ88GdHFSas7uhwKtvqqi3YrJJ/mWcu/l6+TVpe20HDLWB3lG6XLcrtlbb1lledCRulRT3U5TFuSh6SUowBeXOXn30Rw6HT4Z+8B8J56FK55cenla7RhyIqlumzfq+tShlAaqCubTD3lU+kyp8qvdnNo8mRtWjZtXD66smJ7u6XAqQmNju231Xey3BaY1GV7+fLC9nqxtnwO+QsuvY8ZSJohRDKjYYSsvdo1LG7phCaXq10b9nAVcYOrQba2cRZd3l07ryOTsW9568RbGmzNpd5T4G0b+z5p9ck30bRbJv0F66+dzwgzhLLp4rXzMFrvvo/V9BccC6jnBwNCpuBgSMfaJV3MEIr6AMt1GSyfrWOa9bo8day91aUj42tdmPRNsn6+7XaLujhpzUuXzsd72rcYbF0rowt1OQ0DQjbi7WDIAcodXZy0OqnL6PkIaVT56lj7HmylLk5ac9KVi91q2MNVxC23JUZ06cj4Whe26/K0b/k6fuayvcyjutDV5TAMCJnCVgfR1nJlkdGlSaLA/mGy3WzXZes7bHu9mdRlaxs1EbRF9utiG+WDr98rF2y3RSZ12Vo+W8uVB7aXLyeaZExkQMgYjV7tz6gr1cfrrnBrrLTqDlBadedpxNr3LAadSYauTp90ZS2XycCzT7rCMlptnEpZmoejdfqmy2SmihaW14WtvkmZjE4/NumbWOov2O4H2e476cprje9ZdWnWRaPbzXYb7pIuW8vnlL/QHDAgZApbO2UuExqP6kJbPgdduuWzPT3YVmc3l2BrKmUaMrry1FVdxvJgq6+6aLf81OXrpNWlLTQmfRMdvAo8m9Rl0G5ZXxcu6bK0fC75C00SUEp07TwxjUsvn+1lNVk+SweozCuStk9aU2C7LhPZRFl11UveJ10mVnW1ZByaPDVal5GsliwyuviqKysetZtTExqTvokO9E24HbOO2F4v1pbPvL+w1N6PY4cPY35+PpnYsz8Z/H12FRi7L135cqarqws7d+5Ee3t7YhkGhIyhYYR8u9rV5GqQ9Ve75rEKZ0KXjkwOdWHttfMO9S1vnXhLg62s92y6dOvd+0mr5b6JicxWXjufTZd2ZpZBu1UUt1yXyWvn0ynQ1KUjY7DefR+rdcsnpcY4b3ddHNt3C/r7+7F3716IJN/tRCFwtOkCoKNHo4z5IKXEyMgIjh07hnPOOSexHLeMmcKnwZCOtaO6DExadWS8r/c0H+9Q37J1TMsl2Kqp0yfH2npdnLQ6pcuk3aJvkk1XXvWuVS8e1XtmXexb1o+feQVbPfRN5vt2YePGjcmCQQ4jhMDGjRuTZ0IVYEDIGFkHuEbparBBrYucKWwvH6mKtSmxOeiyNdXa9nozqctk8IhEYNDuNXrSmhlbddlariwyLuDr98oB2qLsunQwmiGUArZRzgjvg0EKne/JgFDD0UgPNrlyX3w2xcujG7FWz1pfFwZ0QaPes+rybqVAYwtNZmfXZLBVR5etk9Y86t3kllvdumiwLu0xLcv2NAP17pLdStUGrvgLBnWZqHfbfROT/kJWW2Ctb5LDVj3rbaSJAEBWG5kGV3xCzWMErPVNNGRy8U10qV8/6evrq/n7xx57DJdeemmqz3zta1+LT33qU1mKBYABIQNk6JTaL7Gtg0ZGXY1ONzdpGLTKZ1CX0fTg5I9WFWy0419NvlG6XJi02j55ytq3fKr3rOOLli4TQUnb7VaU3kboymFC44RvkgaDujLZY5P1nkbEpL9gsB/nEgA1qctSu5V1fPHJN3HBJ9SSycM3IbVgQMgU1g9Qmp/v0wCV2a82OEBZuwqXx2Dtky6Tk1ZdXToyttd7Vl2WTloz+0EG7VajdXlrtyz3F7LqsnbSmvzRmjpNKPMqQygsbuukVUfeJbtlq43Ukbddl0F/IbNej/yFBjM9PY2bbroJV155JS677DJ87nOfK/5ueXkZN998M/bt24eXv/zlmJ2dBQDcdttteMpTnoKrrroKz372s3Hy5Mm6lom3jJGM5DHAmdClg+2DoYHymdSlg+26rHWMGyDvky5r2y2HwLO1umh33NKVVadH7WbRlofqH5+Xv2D7pDUFtusyauP0VOUC201TJmd/4ctvAU7dVVtscSr4u70HEK3xarZeBjz3nYlK1NXVhc985jMYGBjA2bNnce211+KFL3whAOCBBx7ABz7wAVx//fV43eteh7/7u7/DL/3SL+EXfuEX8LnPfQ5DQ0P4xCc+gd/5nd/BBz/4wUT6ksCAkDE0jBCvndfX5ePVrll1+ZohpGO4eO28vi6t68+TP5r5A5wKtloa2C19gD+6vJ20any8tq4cfBPf7JaXvokDdktLJod6N9G1fO1bRXFf7ZalvomOjO313mCklPjt3/5tfOMb30BLSwuOHz+O06dPAwB27dqF66+/HgDwqle9Cn/zN3+D5zznObj77rvxzGc+EwCwsrKCbdu21bVMDAiZwqfB0FvH2qUBylLDwElrNl0u9S1bx7TMujQepmOdHU5aNWU87Vu0W+7o8nbSqvHx2rroE1bXpYOlYxrtVnVdOmTVlSST58Ttwd+bLgQ6elPoi+ejH/0ohoeHcdttt6G9vR179+4tXhNfeUOYEAJSSlxyySX47ne/W9dyhOEZQsYwGaE06Fg3XM7WeiPWYO2kNQddJtODbfp8l3TZ2kZNBe2e/brYRvng6/fKAdqi7Lp0MJohlAK2ESkwMTGBzZs3o729HV/72tdw5MiR4u8ef/zxYuDnX/7lX/DkJz8ZF110EYaHh4s/X1pawj333FPXMjEgZAqdAcrEQKDlB2lGkXW+V166dDBqWDQe9jWLoeHbEEzq0l391FiF09Wls+JntI2zYmm96+iyPbPApC4X7JaOjEm7ZdQ38dRfyFqHWjrTPMsshqq6Go4s+0tbPtWzltot7/0F3c+3tY01ZIw2kX2Brptvvhm33norLrvsMnz4wx/GxRdfXPzdRRddhHe/+93Yt28fxsbG8IY3vAEdHR341Kc+hd/8zd/E/v37ceDAAXznO9+pa5m4ZcwYtg6GHKCyfb6mPNODI3RpYHTy1KBnizKctDqpy9ZJa95p2al0NezhKjK0W/lga71bPmm1fQuNt1vGaLeqfr6twVYdedt1+eoTOuWb1M/GTk9PAwA2bdoUuf3r/vvvr/rzAwcO4Bvf+Maan3/oQx+qS9mYIWQjFkYzI2EKpKauHAYobzOE0mC7Lt3ymdRVL3lb8ajdbM/a0dal8/G0O07pyqrTq3azZ0IToUBTV0Z/wfZJq7V4ZONyhz6lloxTvklzwICQKXSMkPZNSGmeNbkapCOjqUvrJiRbncGqH6Chy0C955EhpKPLxC05Orpc6lvWTmgYbK2qSwevdHHSWjddJm5p9NVu6dSdU8FWS9tYRyYX38SgLt/6lha0W9l1rRWv88NVxBnkqTcMCBnDUiPk/QBluWNtUhfr3QFdDvUtW8c0BlsjdDUY29vYhWCrjozt9a6rS0sng6356HLIblk7ac1hgsy+xWBrPXTpyDhlt5oDBoRM4dNgyAHKUV2ctFrfxi71LTrWJV0Mtjqgi5PW6uKWtnEudisFttsSk7oYbHVUF/uW/eNnVrtlQpeOTB71LiGbJDik8z0ZEPISgwNUw+VMdt7mGCi8w/aAkm75bNdl0+e7pMvWNmoqaPfs18V6ywdfv1cO0BZl16WD0QyhFBhtIx0Zv/t+1/RRjIyMaARL3KoXKSVGRkbQ1dWVSo63jJlCZ4AyMVjr6Ao/a72u5CKZB8NGR8d168LoCndGXVo0unwZdWlPgizVpbO6mEcbp/p4B+q94dttMuq0XVdetqTRkwCTdou+id7nZ9Vle9Y4sxjqIK9TFxl1aX9+o4O1lvsLRnW54JtoyOTgL+y87/04du5TMTw8nExu/Ezw9wiAtnTBlbzp6urCzp07U8kwIGQM2wco3ybIOjIOTWiYHqwnb/vkiZPWfHQ5FeS2dCLp0lY92i1zujIHW1Mpy6jL0nq3fQuNC76JjoztdiurLlvtlu/1rvv5traxlox5f6F9aQrnnHNOcrG3XRv8/Zp/B865IoU+N+GWMRvJI23PaOqoniqv0lSzOjLMENKEurLJ1FOeuqyf0Piky6iNs9Xu5KDLaOZtRjlr6zKHOnTBX7B90poKk++rSX8hD9/EJJa/I7a2m+3+gq4uh2FAyBQ6Rsjo1a66n2/palAu187bXu8mdVle7yavnU/18Q71LWsnNAy2VtWlg+26jAQLPJ+0Ntw30ayLXDJbNXWl+V7O+CYO1LuXvkmaj2+CetdqA8vHTxf8hWbPXs78DroHA0LGsNQI+TpAaclYPkAZrfewOCetmXT51rdccaxTfTwd64gP0NOlhaf17uOkNdXHa9aFr+OnloztdsukrpzaWAevfEIXdGnI0G5F6EoD7ZYPMCBkCpMRRqMZQg2W0x6fDH4vkjMm263Bk2ondJkslw621ltWOD7VDevtnqX+gkldbKN88PV75YLt9sE330RHxvY2MqnL4PjpEr5/vwIMCDUcjfTgzIOGji7d8vmqSwcdZzdFfnAzrHCnGng1ti5lHdgbnUarWxdauiL0NkRXDvWu27d8amPtldYs2wINbHXOvNpvqy2x3G5lnmQ0ut6j9DZCV9YV5AaPabq6Mq/2m/RNUpBH9p3JAKhPdos+YfXPt1WX0ew7U75J1vHdPRIFhIQQG4QQnxFCzAghjgghfiLm+Q4hxH1CiGP1KabLZOiUJla7fB2gbJ+0ZnVArU3ZzUFXw7chVNOZ5llOWvOZtLpQ75ZO1DLpMtC3TAaeGWxd+6wLNtwnXVmDY9YGnh3SpYVO+XQ/3+D7lArLbSR9k+y6jAZbDfkmTZIVFCbptfPvBrAIYAuAAwC+KIQ4JKW8J+L5XwcwDKA/cwm9gQOU/ZPWrDR4sPE2QyjyPyl11vnZeumydZLha7CVK61hIT1dWjIG29ipDCFbbaSOvI4uF+rCZBtryGSenDS43l3wF5ghtPZZW+2WdqaKhoxTPmFyMaMLGVoyOfgL6RRo6nKX2AwhIUQvgJcBeKuUclpK+S0Anwfw6ojnzwHwKgB/Us+CNhV5vHwmspFc0qWDtQOUyYlkDpPWVFgaIMwkY1pXveR90mVp+bIGW22dtJqc3NHGZdNlNPM2o5y1unKoQ20bnkpJRl0mg8iNxld/wXZ7mhVf69JkEFkHg8FWp95HfZJsGbsQwLKU8sHQzw4BuCTi+b8F8NsA5jKWzS90Xkij187buhqUly4dLB2gcpk8GWhjHRmly+i18y687412rDPCYKthXToyJietWesihZj1bZwRnfLx2nn9YCuvnXdUlw46vkmaj/e13iP/o/MBdXy2mritNtygrqzZyzoY9Qk1xB0kSUCoD8Bkxc8mUGU7mBDiJQBapZSfiftQIcQtQohbhRC3Dg8PJyqs21hqhOhYV9elAyethnVlkNGFk1Z3HOtUH++rY62pS0eGk1YLdOlgad+i3aquSwdr68J2XRmxdtJqe70b7Fu5vA8u2BKd9qLdcpkkAaFpAAMVPxsAMBX+QWFr2Z8C+MUkiqWU75dSHpRSHhwaGkoi4ja2GiGTE3BfdZGcMdluDZ5UO6HLZLl0sLXe8tBFqkJblJOuNM+yjXLB1++VC7R79utiG2XW5fuY4fv3K5DkUOkHAbQJIS6QUj5U+Nl+AJUHSl8AYC+Ab4ogJbYDwDohxCkA10opH6tLiZ3F5EqGyQG0wVHk3HTpkCYQp/PxDqwU5JHF0PDUVoO6ws/6qkuHVLoyfr7OpNXWemf23VqZNf9OKueCLh1Y70Z15ZIFlgZP/YU86l1rfNf8fF/9BdszhGytC6P1nnXOqoHJ7LsmITYgJKWcEUJ8GsA7hBA/jeCWsRcBeFLFo3cD2BX6/5MAvAvAlQhuHGtuOEC5pUsHpgcb1lVFvq7P1gvPJjSuTFq1P9+jes+c9mzppDXr5I52S09e6xVqgnr3KdhqNPDskK6GQ7uVXVcWGV1srQvL693W8a+qTHMEh5JsGQOANwLoBnAGwMcAvEFKeY8Q4gYhxDQASCmXpZSn1B8AowBWC/9faUjpncJ2I2TpoOHrpNXkACXX/KOBunKYtFo7efJ0QmP9pDXjO6QzQba13o1mCBlsYy0ZFxzrPOxWGjhpza5LQybz5Mlk+Sz1F6zPEMr4+b76C1p2Ic2z9Alz0WXUX8g4/pkMXuVIki1jkFKOAnhxlZ9/E8Gh09Vkvg5gZ4ayNS95vHy6Ok2ujPm0Cmd0gHJoItnw9nJp8tRo8phkuIDJdrN0opZZlw6WTu5o47LpMpp5m1HOqK6GPVxF3FYbbtI3yWHSmgqP7E5uuvLA17pscD8x6i/ofHxW38k9kmYIkazoGKE0V7tmjew6sUKjMwCYnNCkwGiGkKdtrCMjNfpWVl3e1TtXg9bI+RpstXbS6mu9O9K3jFw7X0U+VsaB8ZPXzuesKwV51LuRa+d1ZBxoYy0Zk+OnjowDvgkzhLyAASFjmHyhDHaUhsu5oIvkitGuZdCptFWXVtdyKdjqkS4Sge22yCT0TfSwvV118fV75QDtXkhGT1XDvxfbKLsu78cM379fAANCDacQ7k/lB2XsyD7tM81Llw62Ru+91aWx0po5tVVHl275bNeVBoMrrZnLZ3u9m1hpzZLF4ED2nbV2S0fGoN1yyYZr6Uouoq1LSyZj3zK52u9rxoQWttstE7p0ZAzarVza2ERd6KCpK3MWWBoM+SZZx3cHYUCo4WQYALQNl60DFCetpUcNTu581aXTT7yf0DigSwdf66LRk9ZcJk8G6iIXG5mGvGykDr7acEu/VyYbadCuGvUXUqCtS0cmD7ul+fnW2y1L7QLbOLsuo8FWQ75Jw4Nv9sGAkCkaPUC5MGi4pEuHhg+gJnVlNQwpRLKuLnKSkfLZeunyadKaVZfu51s6UdORMTq5y1gXtFt68r5OaIxOnnRk8vAJUynT05WLb+KT3XLJNzGhS0fGoN1ivYceNej3m/RNHIYBIWN4NMmI1FvPZ6vJeDRpzSVrJwVOZAhVk6/js9VkOHnipLWqLkvrPWuwteGTp6yTO01d1jrWGXUZC6ib9BccqHejgd20elLqMhl4zsUPMjhp9cmWNIMuHRlf6yKVnIZMHr6JyfI5CANCNtLwzlFNXlfOYFnzqJeG6co4obF2MLR94G30hKkecqZ0ZS2fwX5sfbtZqiu3iZqOjIXl05VzQZcOufgmDtSllcFWXV06Mi74Cz76JiZ1WWrj6iKXBdu/nwldBgO7OjJ5LFQ7CANCptAxQkaudjXYUVzSZSyF0da6iPxPA3Tl0MZp+lYzvO86xp+rQdQVpUtHpuHlC4t7Wu8+9S2nxs8UeB9spS4tGV/rwva+Zb3dckCXTjaS9XZLV5e7MCBkDJMvlMHBsOFyLugiueLrCrmtupjFkFGXnqpmcUrM4KstMqXLRPlsLVceukzq4ThTN6y3RdTl1XfJS5f3Y4bv3y+AAaFGoxM5zdqRGx3ZNaorQm9DdNkesXZgpSAPXUYnGSbLR116Mr7WhYauXLIYHMi+s9WW2G63rK+LCL0N0WXweyFj32p4+SL0xj+sL2O1riryiZ+11JY0hS4dmUaXL0pvI3Tl4S+k1Zvl2nmDdtVBGBBqOBk6pa7h8nawtlWXhoxTKbsmdaUgixNvq+PPyVO8fNJnra33PCaSmnImA7QmJ3e2Ota22y1vbbjlunTsam6BZ8v9BSOT1mryCZ+11ZZov062f6+M7epVGxv00/Kw/dr+QnPAgFCj4QDlvy4tGVcmdyZ0hcVtnKh5OsngpDXi8z3SZftELa/yMdiqJ+OUDU8uZv33yhqM8DLwbLOuKvKJn7XUljSFLh0ZX+vCUt9ERyaPYLCDMCDUcDhA1U9XGnR0ZdVr6QDluy4GW/3VpSPjVF0kF9ObqEXojX9YXyYVOU3uvLWRnLSa0xWltwG6MgcjLJ3cafkLkf9pgK6Mfctbu+WpLh0Zn/yFste9wd8ra7DV+mCwezAg1Gi0XnRDeuoh3/CO7KsukwNUVl0pxPL4XqkwJeOzrqzybDdjupghFKErBV7ZnYzkUT4X6tKUv6CLyfLZ7i9YOyn0yO7URc52XVl0+tZuOQRbbfUXHIYBoYaTwQjx2nk3dOnI+FoX1usq/M1r5zO87zpjWpTeRsj4OslwSJeODIPcmrrC4h71LV/HT++DrdSlJZNLkNuzetfR5ZTdslQXM4S8gAEhY5h8oQwOhg2Xc0EXyRVfV8i1XmEDE0lmMbiji0Tgqy0ypcvEu+hjvWWRM6WH40zdsN0+UJdf3yUvXd6PGb5/vwAGhBqNTuQ0i0xqOR2ZsK7kYma/l0FdxmQcWCnIQ1fDs1t0VwmzrlZRlzmZrI6QpfWeR/ZdGvJadbbVlthut3ytC9t16XSuvMpnu79gNCBvq12lLr3PzyITlnOhLhpcL75m3zkMA0INJ48ByqNBw9fvxfTg+ukyuRpkNNjq0YSGk9Z8dOU1UdORMVo+W22J5XZLS8aFurBcl5bpNzhpdclfMDlpTfVaWG5LvNWV/NE1elLrsr0uTPomEXoTy2g8bMR3chcGhBpN1kmhjoxPg4YLk3EtmRwGKBcmklpyBietWnK2Tu5cmDzpyLhSPl05FyZPDZQBQsXyzW4Z1FWUSfOsp3Vhuy6tYIRB38klf8HopNAjW+KUrjRklbG9LizVlTXYansw2EEYEGo4NhqResrnMcH2QJevK2NZJ2qNXjU1Omn1VFdW+YavBmXQoytnra6cJk86MlaWTxdL7U5m6JvUVy7px+dRh775JpZPCr2yOw7pyort38+ErmKQO5UCPV3GZLIGg92DAaFGk8kI6RquNGR0rK1doTGkq2wVLoUqH+vCNV06Mr7Whc4KMleDGGyN1KUjY2u9e9rGOjJ5TKp9Gz9pt6qIm6z3FCJ5fC9v691SXUbtlo5MVruVAu2xOge7YLvdchAGhExhe2S3UZ/fDLqIg+i2dYMNlxO6DJUra5C74TIu6CJVsd4W6amyc6VVU85ouXRkDOrS0mPwfSUR2G4fXLBFjf5etteb7XUN/8cM379fAQaEGk6WAcDk5E53AG1yXU6sOnuuSyu1VbN81BXSlVxES5dTdWGprjxWdW0v35p/p5EzpctSu+WjDU/9bL10NTg4llv5TAb90ujSkdGtw2ryCZ9ttF01rktHJiddOjLW1rtB38SV7LZUajL2fQdhQKjRcNKas67kInq6XBhAPdfV8ElGNflG6fJ0QqOly/by5aUrjUjWiZopGcO6rLdbDdZlvV3NS5elbZy17ze63l3yFxqti8FWd3Xp+P2+1kVmnzLu0Tx06ZavOWBAqOGYmrS6MGh4OGnNa0Jjva4UZNXF1SD7JzSctNZRlwOTJx2sXfHz0R7bXj7qKhfR7ceVOtM866tv0mBdtttV6squy/byGdelIZM12GpjMNhxGBBqNF5OWiP0Jtbl02CYdYBKIVImbnDyZO3Am9GJ52qQ5bpMli9KbyN05TSRNDl5avRquqnyhZ/1yW7ZXj7qqhRKr8v28oXlvNJlu12lrvro0pHxtS6y+iY6Mgb9hayLXY7AgFDDafQAk0WmHvImy2qhLhcGKO91pcCUjLYuPVVGv1dWeS/bzVZdDk2eTJYvFRbanVxksshlwfa6NKAr8+TE0vI55Zs0UIa68tOVVd7272dCl7Egsu3+jNswINRobF9p9XKFxqQu28tHXdl12V4+w7pycax9nWQ0uS6j5Yv8TwN0uTRptbQufB0/abc812WyfGFxG+vCAV3W2y0HdGXOEPKo3h2GASFT2BoRtjXi7JIukjO67aYjZ0rGZl2sN3d0kar4aot8WuG2tVx56DKph35QHbHdPrigSwcb/RmPdfk+Zvj+/QowINRwNCKTZc9qrEo0OrJrVFeU3kboyuoUWhqx9n01XXeSoZN91+g0WurKJhN+1tq6iNDbCF3Wr+rmVD6vbCT9hfx1pcGUjTRYFy75CyazGLyyW9SVSSb8rK3+Qtn3Si5WVT7+YQ05k/5ChF6PSRQQEkJsEEJ8RggxI4Q4IoT4iYjnfl0IcbcQYkoIcVgI8ev1La6DeDlAUVfoQQ2ZkJxXk7ucdHHypKkrSm8jdLHec9GVOUvA4OTJ5OTOVsea/kJGXRF6G6LL4PfSsZG2ly8sp60rhVgeQW6fbAl1ZZQJP+tTXWjqcslfaBLaEj73bgCLALYAOADgi0KIQ1LKeyqeEwB+EsCdAM4D8J9CiKNSyo/XqbwOwgHKa122r1Y5pysFnDzVUVdyMda7Q7qsD+zmVD4GWzVkws/aWu+e6tKyx7aXLyznkS5fbYn262T796Jvkosul/yFJgkOxWYICSF6AbwMwFullNNSym8B+DyAV1c+K6X8UynlD6WUy1LKBwB8DsD19S60U3CAqqOu5GLmVhddGKBs1xUWt9GJ93SSYb0u28vngC6XJk8my+etjaS/UPp3cjHrv5eOPba9fGE5k/6CyUmrT7aEujLKhJ/VLV8adHTp6vXcX9CuQ7dIsmXsQgDLUsoHQz87BOCSWkJCCAHgBgCVWURNhqkXKaseE4OGh7pcGKC815UCUzI+68oqz3YLyenIeDp5Mlm+VFhod3KRMa0rq7xH9ZJ5cmKyfGlUeOibOGF3qKtu8rZ/PxO6mCHkBUkCQn0AJit+NgGgP0bubYXP/8dqvxRC3CKEuFUIcevw8HCCYriOpY6GT05TbrpIrthuXG0vX1o5rXKlF/Gu3vLQReqIC7bIwmCBtpyt5cqKKV0u1IXn2G4fqMuv75KXLo4ZXpAkIDQNYKDiZwMApqIEhBBvQnCW0POllAvVnpFSvl9KeVBKeXBoaChped2DKeCe63IhYp1VVwqxPL5Xwx1f3dVZnfJl/S66ukx+L9vr3SNdTq2mGyyflbZEV1eE3iR6UuuyvS481aVjI20vX1jOK130F7LritLbCF30TTLror/gBUkCQg8CaBNCXBD62X5EbAUTQrwOwFsA3CSlPJa9iK5j+wClI+OrETI5QOnIWD5Y56WLwVY/dWmXT0fG8rrQ1WV08hQW17BbJid3NtoSo7o4aXVKl5aNtL18YTlbdUX+J4Gein8nlbPVllBXhExyVV7WRZR8/MMacnn5C81BbEBISjkD4NMA3iGE6BVCXA/gRQA+UvmsEOJmAH8M4JlSykfrXVinsXbyZHKA0pGxXVdOA5SVg3VIzqQuaydPOjKeTmi8nbTqyHOilkmmTM4zx5rB1rXPUle03qTPNXrS6pK/YHLSmgofbbgLuqL0Jn3Op7ow6ZtE/qeGTNYx04Dv5DBJMoQA4I0AugGcAfAxAG+QUt4jhLhBCDEdeu4PAWwE8AMhxHThz3vrW2SH0H6hXJo8Nbmu3CZcKTC6Cqcjk3Hg5WqQ4YmaIV2+Tu5M1rtLkyed4U17xc9CW2JUlwuTVh0Z2+tdU5eWjTRY77YHnk3qst2uhp/1VldyEfomOenK7C/Y6M+4TVuSh6SUowBeXOXn30Rw6LT6/zl1K5kPZO1caeScmLT6qMuFAcpDXQy2NoEuFyatluvKvMplcPJkcnJn7UTNkC7r7appXToyGScMqUQsb2Pb/QWjulywJToy9fC5TOmy1SfUkbG9LtI+q0RM+gs6Mlnb2D2SZggRLbJOaEzoyipvsqwW6tKNHPu4MmZSV+Z6b7CMz7qyypsqq+3lM6Ir60QyhSqXVvxSYaHdyUXGtK6s8iad+AbXi8nAro6MCxM1rSqgb5Jdl45MThNwY75JehGrdbmSIZQKXV3uwoCQMWx1wHx1JvNwXEkuGDWuvjptug65ZTLURRJhuy3SxcJggbacreXKIGO0D7vwvnqOyfa2NViQRcaELp++S5mcjgzHjOr4/v0CGBBqJLorrUwBd0uXloypiLqvunRXTHXksupqdPny0pUGXV06Mqz3koiGrszZgSZ1mbQlyUWst1tZy5cG1ns2meBhc7p06j2zv2BSlwO+iTFdHtlI7Sw6+oTZdWnI5OWbNHysdhcGhBqKyQEqSj7ps74NUIYmrXlNaKzXlYKshsFWJz6XCY1JXZbXexp8rfeskwzbdelOJK23W7b7C+Ck1eREUsseG6wLp3wTg5NW222kT4uzrPccdWnIuOSbNAkMCDUSpwao5CJu6Wr0YFiHAUpHhqtwEf9ulFwWXT5NaEzqqkMbN/ukNa9VONsD6lbaEoO6nPJNWO9aY5r1geeQnHe6qsinerbJ7VZe/oKWjI114YAuX30Th2FAqKGYnLR6OmjYrqseA1RiBy/yPwl1NXgANamLExr/dRlt4wj5pLpsrXfrV+HC4gYndzbaEqO66Jvko6seExodLJzclcl5pIu+ST66WO9u6crDX0gll7F8DsKAUCPxdoCK0NsQXbYPhiYnTxl1ebUKV48JjY6MjZMMX3Vx0ppZV26rcDoyBid3VtoSg7oYbM1Hl275dGykE5M7H3XRbuWjy2S9R8knfdanetfUlYu/kEJOV5fDMCDUUHRfokYPSvWUN1lWC3W5MED5vgqXhoavRGTVpafK6PfKKm99G/hUvowTGtt16ZYvFRbanVxkTOvKKu9RvWSenFhaPqd8kwbKaOvSU2X/98rBnzGp1/a6TCtnLIjsgm/iLgwIGcPkC9VoJ9lSpyk3XSRXbDeutpcvrRzrzR1dJALaouy6Gv29fGwjX9uVVMV2u2J7+QDNbmahP+OzLu9pjnphQKiRGE3LzqjLp7Rso7pciFjrRO/D4o3WlbEurE9FbnT5XNOlI+NTG0fpbYCuXDIRTepKIxOlN6Eun+wWfZM669KRabCNNFkXeWX6WanLV7tluy7Wu1O6rPdNouT9hQGhhsIBKruuKL2N0KVRLm8nT5br4oTGf11s4+y6yiY0OjIGJ08mJ3de2UhOnuL1NkKXwbrQsscmJ3eR/4mRo2+SSc5Xu0V/ISQTobchunT8BU1defgLurqaBAaEGom3AxR1hR7UkNGVy2nyZFSXhoytTrz1E8kovY3QxUlrLrqsX4XLaXLnk2PNyZM7unTLp2OPcws82+gv1ENXs9stXegvrH3WUn+Bvkl2XQ7DgFBD4QDllq4ovbUec2CAsn4VLvI/CfRU/LsRcrZPMuqiK7mY9ZMntnFYSEOXwUlrXhNJG22Jti5OnvQ+v566GlwXWvbY8sldmZxHumi38tHFendLl7e+ibswINRIdF8iHbGsL6x2WTXkXKgXrwYoU4O1SV0mJmfU1Rh5H+vFpMOQxlHTkGuKIHcKjNo4Q7psL1895L2ql4yTE2vLp+MvhMXpm1BXFpm89FrqL2jL5RBstVWXwzAgZApbHQ2vnKacdJGc8dVRsVWXqX7iW73loYtUxVdb5JMu67+LIT26mKwLEoHtdsX28pnQ5dN3qYecjirPxwzPv56CAaGGohth1JHLGs1sdPnCz3qky4WIdS6r6QbrItUkI0pvI3RprEhSVzaZvHTZWu/epmVnrItU2G63ovQmfc5W38T2ejdYF1q232Bd2O4vmNSVm91Krsp6u5WHn2ZSl7X1rmuPdWQc8k2aJCLEgFAj4QBVIacjY3CA0tKVsXxGdJmaSJrU5cKERkfG10lrHvVuQpfl9W408Bz5nwboylgX1tpIQ7o4ac1Hl7Y/Y7lPaL2/oKsrLO6Tb2K53cqlLnR1pcHHetfUZdQ3yaqrOWBAqKE0g2FIg6EBSnfQMKkrl4lag8tnUheDrRVyOjK6jrUhXU5MWlPqWaOrwW3s0iqcyRVJnxxrBlsd0pXVrurq0sGEv2BoImlSV9YA6Bq9dZYJP8tga0WXZL0b0xWlt/aDVf+ZTs6Qb+IgDAg1Ek5a89Flst5zmzylUGX9KlzGuuBqkDldJoOtTRFQb7AuX1fhsk7uGGwN/Tu5qlwmarYGW03WhY6NNDq5s91fMKlLd3yP+oxG6LLcbuXip+nq0tHrU72nfVaJuOSb6L5bbsGAkDFsHwx9GqAMTiRdGKC8X4Wz1IlnsLWOupKrYhuXCWnoMjhpzWtyZ6Pd0l6FtNxG2l7vRnVptrGWPTZYF9r+go6MwYmat75Jxd8+6HKi3jXeV9vrPfystf5C+CMM6nIMBoQaScNf2DroyipvsqxW6tKctLo0ebJWlw46n1+PiZpPurLiY71YWr5csnZ0del8rwY747pyVtoqx3QV5XXldMpqUpdBG2nSrmr5Cw3WZTIzy1Zb4L2uerzjBvVq9Ulb2y2rPTblLxjQ5TAMCJnCVgfMR2fSZPmIw/hkkE3p8um7NIMuUhWvggWe6vLpu2SR0YV+kAXYbldsL58JXT59l7x0eU6TjIkMCDWUenRknYlagyO71usyWO8uRKwzr6ZbqIvpwdXlUnWtnOrCR12NrnddXUYzC3R06ZYvq7Nru92iLj99kyj5uEdzamOd8dN2f4G+SciWNLh8JnW5VO8mdZloYy2ZnPwFrUw/nfHdPRgQaiRGB40I+TS6dGRsnDyZrPco+fiHNeQyDoZGJpKmdLk0oYHB7+VAXRhd8fOl3jV1uTR50tXli2Pt0iTDpC5OWqFlS4z6QVn9Bct9J11dRjNVPLJb3vsLJnU1uHyu+QuNlHEcBoQaikuDBjhp9X4wbHD5TOpyLdjqy+TJpYmkSV22TlpdmjxZP1HzyG5Fytfz2WoyNta7SV0G/QWn/CDbfSdNXbRb9E3y1mVr+VzyF5okOMSAUCNxadAwqcv28qWSc2GAMjWRNKnL5OTJJV22l8+0Lh0ZG+tdU5f1k6fI/zRAF+2Wk7o4aYWWjSzrIpb7Qbb7TqnlqsinetYXf8GkLpf8hTRynta79b6Jbr27CwNCDcWlQSOtXBZdHtVFXQaopDKR/0moq8EDqEldLk1oTOqyvXzW6oqQT6rL1kmr9ZOnnHQ1u91yShcnrdmDEbb7QZZO7nz3TWy1W777C6l0eVrvZTY8hVhmXY32TdyFASFCCCGEEEIIIYQkpBmCJc3wHRkQaiwurRSY1GV7+VLJObSabiRVOocsBq/Sg6Pkkz5r6Qq397oszWLIbTVdR8bylXsn7JaOjOWr6cxigJaNdMkPst13qvhnYhnaLYO6XKp3k7os9Vl9zfRzGAaEGoqvk1bbByiDdZHXAGVjWrZJXS4FQE3qsr18gKWTVsvrXVdXbpMnG3VFyCd+1iO75ZQuTlr17LFLfpDtvpOmLgZb66BLR8Zyf8GkLlvLZ72/oDt+ugsDQo3EpUHDpK5UMhF6k+hJrUvDCLk0edKNctu+4merE+/l5MmlujCpS9dhaLAulyZPtk/UbLervuripLVcgY1tHCUf/7C+TCo5FyaSttst23W5VO9p5bLo0i2fjoxP/kLyj/eFRAEhIcQGIcRnhBAzQogjQoifiHhOCCH+nxBipPDn/wkhRH2L7BK+DlA0DKXHXBoMG1w+k7pcmtCY1OVEsNVDXbZOWssmNDoynKhZ71jbbiN9DbaarAste+ySH6RbFzoy9E2st1u++wsmddlaPpf8Bd3FdMdoS/jcuwEsAtgC4ACALwohDkkp76l47hYALwawH0Ft/heAwwDeW4/COodLg4ZJXdaWL+IzkgpZO0BlnUg2Wld1tckftNSJt37yxElr/XQ1unyauqxfhXNoosZgq5/+gkldJidPJtvYet8kJz/NK7uliykb6VK9m9Rlaflc8heM9JP8iQ0ICSF6AbwMwKVSymkA3xJCfB7AqwG8peLx1wD4cynlsYLsnwP4GTRLQGhuHJCrpf/Pj5f+vbIIzI4m+5yVxfLPSCK3MFn69/J8cl2rK6V/z40CS13xMovTpX8vzSbXVXS6VpLLLM2W601UF1Olf68uJde1PF/69/xEMrn5idK/U7Xxcunfc2NAa3u8TPh7Lc2laOPCOylXk8sszpT/O20bry6naOO50r8XEtb73Hjp3ytp2jjUt+bGE7ZxqG8tafat2VGgvTteJtzGi2n61mpJp07fWphK37dS1ftC6d86fWs5S9/qiJfJ3LeknX1La0wbL/07TRuvaPStBYN9azHcxhp9S9tuJexbYbuaym5l7Fup7NZS6d9zY0BbZ7yMbt+Sqo1T9K0lzb6FjH1rYbKxfWs5R58w/D0jdZnsWyF/IXHfCr0XJvtWKrtV2bcS+OLadqvQxqns1nT5vxvqE4b7VsI2zssnXF5IUe8h32R2tPz9isKoTxj2xRPWe1hmJc34aXvfCrXx7GjwGUnmaQ4jZEzUWwhxBYBvSyl7Qj/7NQBPkVK+oOLZCQDPklJ+r/D/gwC+JqXsr6Xj4MGD8tZbb9X8Chbxd9cBZ+7NuxSEEEIIIYQQQgjJwuv/C9h1Td6lyIwQ4jYp5cFqv0uyZawPwGTFzyYAVAvy9BV+F36uTwghZEXkSQhxC4ItZti9e3eCYjjAk98crKiEae0Ahi4CTt2V7rO2XAqMPJxsdUbR0Qes2wkM359O17YDwOm7yleG4ujdBLT3AuNHUigSwPYrgBO3I1UK3vpzCitwI8llWtqCOjx5R4ryAdi8Dxh/vHxFKY62LmDjecDpyh2UMWy9HBi+rzx6HUfXOqB3KHg30rD9CuDkofIMtjgGtgerO1Mnk8uIFmDb/kIbp2DTBcDU6fKofBytHcDQxcCpO9Pp0upbvcC6Xeb6VkcfMPZYCkUm+1YrsOUy9i0F+1aJLZcAI4/Y27d6NgKd/f71raGLgYmjdvetvs3A2YfS6WLfKsG+VWL93kIGg4996/7ybMs4OgeA/i3m+hYATJ5ILmO0b7UDQ/s87VsDwNjhFIrYt8rI2rcGPYlT1EA3Q+hXATw1IkPomVLK7xf+fxWArzdNhhAhhBBCCCGEEEKIJdTKEEpyy9iDANqEEBeEfrYfQLXw3D2F38U9RwghhBBCCCGEEEJyIjYgJKWcAfBpAO8QQvQKIa4H8CIAH6ny+IcBvFkIsUMIsR3ArwL4UB3LSwghhBBCCCGEEEIykiRDCADeCKAbwBkAHwPwBinlPUKIG4QQoaPn8T4AXwBwF4C7AXyx8DNCCCGEEEIIIYQQYglJDpWGlHIUwIur/PybCA6SVv+XAH6j8IcQQgghhBBCCCGEWEjSDCFCCCGEEEIIIYQQ4gkMCBFCCCGEEEIIIYQ0GQwIEUIIIYQQQgghhDQZDAgRQgghhBBCCCGENBkiOAc650IIMQzgSN7lMMQmAGfzLgRpKEnbeB2AiQaXhTQG9mP/YRv7D9vYXZLaT7ax/7CN7aPe/i3b2H/Yxo1nj5RyqNovrAgINRNCiFullAfzLgdpHEnbWAjxfinlLSbKROoL+7H/sI39h23sLkntJ9vYf9jG9lFv/5Zt7D9s43zhljFC8uMLeReAEEIIcRDaT0Lshf2TEIdgQIiQnJBS0mASQgghKaH9JMRe2D8JcQsGhMzz/rwLQBoO29h/2Mb+wzb2H7ax/7CN/Ydt7D9sY/9hG+cIzxAihBBCCCGEEEIIaTKYIUQIIYQQQgghhBDSZDAg1ACEECLvMhBCCCHNDu0xIYQQkj+0x/bCgFAdEUK8RgixQ3IfnrcIITYKIdhvPEYI8VIhxFDe5SCNQwjRk3cZSGOhPfYf2mP/oT32H9pj/6E9th8a0joghLhBCHEIwNsBDDAC6h9CiOuEEPcC+DiATwghzsu7TKS+FPrxPQB+D0Bf3uUh9UcIcb0Q4g4AHxVC/D0dUf+gPfYf2mP/oT32H9pj/6E9dgcGhDIghOgQQrwbwH8C+KCUcq+U8j5GQP1CCLEDwHsB/AOAnwWwDsCfCCGek2vBSF0QQnQKIT4K4AsA/kFKeUBKeTjvcpH6IoS4EMA/AfgwgD8FcB2AvxZCHMy1YKQu0B43B7THfkN73BzQHvsN7bF7MCCUASnlIoALAbxPSvnXACCE+BEhxIVCiNZ8S0fqyPkAJhA4J48C+BkAxwD8Alc03EdKuYDAGflLKeVfAoAQ4mlCiK35lozUg9CK1JUAHpRS/oWU8rsAXgmgB8BrhRCduRWQ1AXa46aB9thjaI/9hva4OaA9dg8GhFIihGgr/K0GrD8FcJkQ4j1CiMcA/AaALwP4ByHEJfmUkmRBCHGNEGKbEKKr8KN1AC6QUk4CgJTyCIB/AyAROKPEMQrnEuwP/egtAF4ihHibEOJRAH8I4BtCiD8XQmzIp5QkC0KIK4UQG1Gycz0ALlO/l1LeDeDfAewA8ELzJSRZoT32H9pj/6E99h/aY/+hPXYbBoQSIoS4pLAP8pOFHy0DgJTyvwA8BOCJAH5OSnkjgB8HMAjgR7li5Q4Fg/V9AB8A8C4Afw4AUsrPA1gRQvx86PFDAL4P4EohxDrjhSXaCCGuB/ApAB9RP5NSfhLAKQA3A7gFwI0A3gjgWQBezINL3UEIcb4Q4i4AnwDwGQB/VvjVZwHMCiF+IvT4fwEYA3A5VyXdgfbYf2iPmwPaY7+hPfYf2mM/4KCaACHEAQD/iODF/hEhxPVSypXQitXvAXiNlPIrQog2KeX3AXwTwFOllLP5lJqkQQjxcgQG6gtSyssQGKarQ/uZ/xTArwoh2gFASjkN4CiAcwBMmy8xSUsoVXkUQVvvEkK8KfTI6wC8Qkr5VQCi8PdXALxcSrlqtLBEi0Ib/yKAr0opLwDwtwBuFEK8W0o5isBheb0QogMApJRnATwM4EmFrQrEcmiP/Yf22H9oj/2H9th/aI/9gQGhZIwCeDeA1xf+/gcAkFLOCyGElPKMlPKuCpkzABZDnYLYzeMAni6l/IPC/68CsB3BKhUQHHx3FoW2L/AQgj7ENnaA0GF2BxCsQv0kgHeqlSgp5Qkp5R0VYsMAplUqLLGeVgDXAngAAKSU/4rg4NmfE0I8CYED2opgC4LifgALyikl1kN77D+0x55De9wU0B77D+2xJzAgVIPQCsZxAB+TUk4AeAeATUKInyv8rjX0fJeUclkI8SMA3grgE1LKeaOFJqkIpR7fJqV8WAixpZDeegMCY/VCIcTvSCnHETgszxFCfEII8VuF3/8HAEa53eIhAIsAvlr49zsBQAjRV/i7LdSPfwrAp6SUy3kVlqSiF8AjQKlvSylvA/D3AP6s4Jj8IYA3CSH+Wgjxiwi2o3y5cAgisRTaY/+hPW5KaI/9hfbYU2iP/UPwBrjqFCKbMvx/IFjVEEL8LIA/lVKuC/2+FcCTAfw1gI0Afk1K+QnDxSYpqGzjws/6AJyvVqaEEJcjSG98qZTyvwv73fcDuAmBY/Ixw8UmKYho458B8Awp5Y+J4GC7OwH8AIGT8ikABxHsc98C4JcLq1rEUqqM1e8GsAlB250s/GwIwWTjeVLK7wghXoDglpMbAXxASvkvORSdJIT22H9oj/2H9th/aI/9h/bYT5ghhGAFQgixJ/yzwovdJoR4XfhnhX/+A4DDQoi/KcgPSClXANwO4P9JKXfxZbeLFG08LaW8QwS0AXgQwYGVTy38/ttSyr+TUr6MzqddJG1jAPcBuEMI0Q3gpwHMANgtpfxAYZXjcQB/JaXcQefTLoQQ7UKIF4TbudDG7UKIWwo/+nMEk4hnhFKSFxEcOntRQeYLUsq3SylvovNpFzFtTHvsASnamPbYUZK2MWiPnaXQlj8phLiqIihAe+wJMW1Me+wRTR8QEkL8JIL9y78jhNha+JlKhbsJwPMK0U31vCi83K8F8AYhxF8COC2EeLKUcpJOiX0kbePQzwAAhbTkzsKf/zBYZJKSlP34KgC/AuAOBIeQvgzA1sIqFaSUD0kpP2yw+CQBIrhV6DiAXwfwBSHEewo/bwHwdADPEkJ0SCkfRXDI4atRuL62MLFYh8ApIZaSoI1pjx0naRvTHrtLyn5Me+wgItjedRzAywG8F8EtYsrvoj32gARtTHvsE1LKpv0D4EcAfA1BaurXALyo4veihuxLAKwC+A6Ap+T9Xfinfm2M4ErETQCeB+BeBIPg+ry/C//Up40LbftVAC8M/eynAVyS93fhn6rt24Lg6uHbAFxd+Nm1hfH3CYX/t1XItAJ4O4IbSz4E4DCALwHYUGtc5x932rhCnvbY8j+6bUx77M4fzbGa9tixPwB+DUEA79rC/38EwYHfOwr/b614nvbYsT9p27hClvbYwT9NeYaQEKJVBtfiDSLYw/ovQogPAhAA/lBK+UjF851SygUhRIuUclUI8XQEBuwNUsr3mf8GJA7dNi78+wkAfgvA9QDeKaV8v+HikwRkbOM1ZxkQ+whlCTwLQLeU8rNCiHYp5ZIQ4j8BfFBK+fHQ8+E2bkdwg82TAIxKKT9iuPgkAbptTHvsDhn7Me2xA2RsY9pjBwiNudsALEsph4UQewF8HMEB0q+TUv4g9HyXLBwcTHvsBrptTHvsPk0VEBJCvFBK+fnCv9XLq/6+BMHex38A8BFZ44R7NRE1VGySgjq28RVSSqazWkgd27hFSrlqqNgkBeE2Lvx/nQzSzNX/OxCsNr5CSvm9PMpIslGvNqY9tpc6tjHtsaXUsY1pjy2lso1DP78EwNcBfAHAVxAEbR9GEPybMVpIkol6tTHtsbs0xRlCQojnCCGOAPiIEOLm8O8Kk8hWKeU9AP4TwR7X/RXyvUKIQ0KInyrI8GW3jDq28esLMnQ+LaMB/ZjOp2VUa+PCRGGi8G9R2LN+LoL05WOidFU1hBB94TYm9lHvNqY9to86tjHtsaU0oB/THltGVBur3xf8rX1SytdJKT8J4PMAfgHAJYVn+4UQd9Ae20u925j22F28zxASQuwC8GYAywCmAVwH4OVSymkhgjTVUHbBBgCfAfBZKeVfCiHOA3C68OwOKeXx/L4JiYJt7D9sY/9J2Mbq75cCuEVK+RwlK6U8KoQQALazje2Ebew/bGP/YRv7T8o27kBw0dSSEOI0gN+VUv4929hu2MYkTDNkCJ0A8EkAf4Qg3W0JwC+GHyhMItullKMA/gLAK4UQDwC4FcDmwjN82e2Fbew/bGP/SdLGagXjGQA+LYLbiL4C4DtCiA0ygG1sL2xj/2Eb+w/b2H8St7GUcrEQKDgI4D4A31S/ZxtbDduYFPE+IFRIX/s/KeU4gEMAvgzguUKICwtRz9bCc0sFkacCuLrw3BYZXJlILIZt7D9sY/9J2sZCiG4EbftGACMAHgdwXiEQSCyGbew/bGP/YRv7T4I2bgMAIcQWIcQVQoj/h2C7/tcBPJBTsUkK2MYkjDcBIRGcYK/+Xfa9Qmlv8wD+B8GVh79a+N1KSO4XAFyBYL/kL8saB9IS87CN/Ydt7D91aGMBoB/AGQRXG9/CNrYLtrH/sI39h23sPxnaeLnw2EYEWSX7ATxJSvm2UHYYsQC2MUmC82cICSH2I0h3OwLgpJTyD2OebwHwYwBuAfArACYA7JfBFZltoQ5ALIFt7D9sY/+pQxtPArhYSvklIcR5UspHGl1mkg62sf+wjf2Hbew/dW7jISnlcKPLTNLBNiZpcDZDSAjRJoR4N4KI5m2Fv39XCPGawu9FFRkhg5sMPo4g3e17AB4E0Ft4hKejWwTb2H/Yxv5TxzZ+AMAGAOAEwy7Yxv7DNvYftrH/1LmN1wMAAwV2wTYmOrTlXYAM7EApK+CYEKIPwO0oGaE1qU+hn70VQQT0fQB+U0o5GSVDcoVt7D9sY/+pexsT62Ab+w/b2H/Yxv7DNvYftjFJjVMZQkKIdaH9jycA/GHhZX8pgEcAXAJgQQjx1BqfsRfAAIDrpJRv4MtuF2xj/2Eb+w/b2H/Yxv7DNvYftrH/sI39h21MsuLEGUJCiAsAvAvBlXiLAN4spXws9PtfBTAG4J8BXIAgRe7HpZSfMV9aogPb2H/Yxv7DNvYftrH/sI39h23sP2xj/2Ebk3phfUBICPF6AG8D8FEAHwbwdwCOA3gTgInCnkf1bJuUclkI8WcAniylvC6HIpOUsI39h23sP2xj/2Eb+w/b2H/Yxv7DNvYftjGpJy5sGTsHwDuklG+RUt4L4GYALwEwWPGyCwRXXALANIBhIUSn8dISHdjG/sM29h+2sf+wjf2Hbew/bGP/YRv7D9uY1A0XDpV+L4AFACi8wHMA7gewLvxQ4UCsJSHEkwG8AsCfSSkXDJeV6ME29h+2sf+wjf2Hbew/bGP/YRv7D9vYf9jGpG5YHxCSUh4DilfiLQghnoCg3PeoZ4QQWwA8BcDPArgMwG9JKf8pj/KS9LCN/Ydt7D9sY/9hG/sP29h/2Mb+wzb2H7YxqSfWB4QUsnTY0VMBPCilXAr97rQQYhTAp6WUN+VRPpIdtrH/sI39h23sP2xj/2Eb+w/b2H/Yxv7DNib1wJmAkBCiVUq5AuAaAF8p/OwNAC4C8HYp5VcBfDXHIpKMsI39h23sP2xj/2Eb+w/b2H/Yxv7DNvYftjGpB84EhKSUK0KINgAbAGwWQnwDwF4Ar5NSjuVaOFIX2Mb+wzb2H7ax/7CN/Ydt7D9sY/9hG/sP25jUA+uvnQ8jhLgMwCEApwH8uZTy/8u5SKTOsI39h23sP2xj/2Eb+w/b2H/Yxv7DNvYftjHJimsBoQ4AbwLwd1LK+bzLQ+oP29h/2Mb+wzb2H7ax/7CN/Ydt7D9sY/9hG5OsOBUQIoQQQgghhBBCCCHZacm7AIQQQgghhBBCCCHELAwIEUIIIYQQQgghhDQZDAgRQgghhBBCCCGENBkMCBFCCCGEEEIIIYQ0GQwIEUIIIYQQQgghhDQZDAgRQgghhBBCCCGENBkMCBFCCCGk6RFCDAoh3lj493YhxKfyLhMhhBBCSCMRUsq8y0AIIYQQkitCiL0A/l1KeWneZSGEEEIIMUFb3gUghBBCCLGAdwI4TwhxB4CHAOyTUl4qhHgtgBcD6AVwAYD/D0AHgFcDWADwPCnlqBDiPADvBjAEYBbAz0gp7zf9JQghhBBCksItY4QQQgghwFsAPCKlPADg1yt+dymAlwK4GsAfAZiVUl4B4LsAfrLwzPsB/IKU8ioAvwbg70wUmhBCCCFEF2YIEUIIIYTU5mtSyikAU0KICQBfKPz8LgCXCyH6ADwJwL8KIZRMp/liEkIIIYQkhwEhQgghhJDaLIT+vRr6/yoCX6oFwHghu4gQQgghxAm4ZYwQQgghBJgC0K8jKKWcBHBYCPEKABAB++tZOEIIIYSQesOAECGEEEKaHinlCIBvCyHuBvBnGh9xM4DXCyEOAbgHwIvqWT5CCCGEkHrDa+cJIYQQQgghhBBCmgxmCBFCCCGEEEIIIYQ0GQwIEUIIIYQQQgghhDQZDAgRQgghhBBCCCGENBkMCBFCCCGEEEIIIYQ0GQwIEUIIIYQQQgghhDQZDAgRQgghhBBCCCGENBkMCBFCCCGEEEIIIYQ0GQwIEUIIIYQQQgghhDQZ/z+lF2WN8E/1swAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df2 = df.loc[df['id'] == 'pump-2']\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = df_initial.loc[df_initial['id'] == 'pump-2']\n",
     "df2 = df2.drop(columns=['id'])\n",
     "df2.plot(figsize=(20,10),  fontsize=12,subplots=True, title = \"Pump 2\")\n",
     "plt.show()"
@@ -708,22 +285,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 685,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df21 = df2.head(100)\n",
     "df21.plot(figsize=(20,10), fontsize=12,subplots=True, title = \"Pump 2 - 100 Values\")\n",
@@ -746,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 686,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -814,27 +378,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 687,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Convert data for:  pump-1\n",
-      "Convert data for:  pump-2\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "length = 5 # Episode lenght\n",
     "\n",
     "df_epis = create_empty_df(length)\n",
     "\n",
-    "for id in df.id.unique():\n",
+    "for id in df_initial.id.unique():\n",
     "    print(\"Convert data for: \", id)\n",
     "    \n",
-    "    df2 = df.loc[df['id'] == id]\n",
+    "    df2 = df_initial.loc[df_initial['id'] == id]\n",
     "\n",
     "    epi = []\n",
     "    for index, row in df2.iterrows():\n",
@@ -843,8 +398,7 @@
     "        if len(epi) == length :\n",
     "            df_row = create_df(epi,row['label'] )\n",
     "            df_epis = df_epis.append(df_row, ignore_index=True)\n",
-    "            del(epi[0])\n",
-    "\n"
+    "            del(epi[0])"
    ]
   },
   {
@@ -856,417 +410,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 688,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "data": {
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-       "    .dataframe thead th {\n",
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-       "      <th>F2</th>\n",
-       "      <th>F3</th>\n",
-       "      <th>F4</th>\n",
-       "      <th>F5</th>\n",
-       "      <th>L</th>\n",
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-       "    <tr>\n",
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-       "      <td>17.647661</td>\n",
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-       "      <td>15.407113</td>\n",
-       "      <td>15.324012</td>\n",
-       "      <td>13.470387</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.180807</td>\n",
-       "      <td>15.407113</td>\n",
-       "      <td>15.324012</td>\n",
-       "      <td>13.470387</td>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
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-       "      <td>15.407113</td>\n",
-       "      <td>15.324012</td>\n",
-       "      <td>13.470387</td>\n",
-       "      <td>11.702384</td>\n",
-       "      <td>11.176102</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>15.324012</td>\n",
-       "      <td>13.470387</td>\n",
-       "      <td>11.702384</td>\n",
-       "      <td>11.176102</td>\n",
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-       "      <td>13.470387</td>\n",
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-       "    <tr>\n",
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-       "      <td>11.702384</td>\n",
-       "      <td>11.176102</td>\n",
-       "      <td>10.678349</td>\n",
-       "      <td>9.831242</td>\n",
-       "      <td>11.555063</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>11.176102</td>\n",
-       "      <td>10.678349</td>\n",
-       "      <td>9.831242</td>\n",
-       "      <td>11.555063</td>\n",
-       "      <td>13.197594</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10.678349</td>\n",
-       "      <td>9.831242</td>\n",
-       "      <td>11.555063</td>\n",
-       "      <td>13.197594</td>\n",
-       "      <td>14.337077</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>9.831242</td>\n",
-       "      <td>11.555063</td>\n",
-       "      <td>13.197594</td>\n",
-       "      <td>14.337077</td>\n",
-       "      <td>15.020920</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>11.555063</td>\n",
-       "      <td>13.197594</td>\n",
-       "      <td>14.337077</td>\n",
-       "      <td>15.020920</td>\n",
-       "      <td>15.802650</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>13.197594</td>\n",
-       "      <td>14.337077</td>\n",
-       "      <td>15.020920</td>\n",
-       "      <td>15.802650</td>\n",
-       "      <td>17.343995</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>14.337077</td>\n",
-       "      <td>15.020920</td>\n",
-       "      <td>15.802650</td>\n",
-       "      <td>17.343995</td>\n",
-       "      <td>16.413220</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>15.020920</td>\n",
-       "      <td>15.802650</td>\n",
-       "      <td>17.343995</td>\n",
-       "      <td>16.413220</td>\n",
-       "      <td>15.711286</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>15.802650</td>\n",
-       "      <td>17.343995</td>\n",
-       "      <td>16.413220</td>\n",
-       "      <td>15.711286</td>\n",
-       "      <td>14.035776</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>17.343995</td>\n",
-       "      <td>16.413220</td>\n",
-       "      <td>15.711286</td>\n",
-       "      <td>14.035776</td>\n",
-       "      <td>12.314211</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>16.413220</td>\n",
-       "      <td>15.711286</td>\n",
-       "      <td>14.035776</td>\n",
-       "      <td>12.314211</td>\n",
-       "      <td>11.173016</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>15.711286</td>\n",
-       "      <td>14.035776</td>\n",
-       "      <td>12.314211</td>\n",
-       "      <td>11.173016</td>\n",
-       "      <td>10.768019</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>14.035776</td>\n",
-       "      <td>12.314211</td>\n",
-       "      <td>11.173016</td>\n",
-       "      <td>10.768019</td>\n",
-       "      <td>9.691997</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           F1         F2         F3         F4         F5  L\n",
-       "0   18.340181  17.647661  16.874933  16.180807  15.407113  0\n",
-       "1   17.647661  16.874933  16.180807  15.407113  15.324012  0\n",
-       "2   16.874933  16.180807  15.407113  15.324012  13.470387  0\n",
-       "3   16.180807  15.407113  15.324012  13.470387  11.702384  0\n",
-       "4   15.407113  15.324012  13.470387  11.702384  11.176102  0\n",
-       "5   15.324012  13.470387  11.702384  11.176102  10.678349  0\n",
-       "6   13.470387  11.702384  11.176102  10.678349   9.831242  0\n",
-       "7   11.702384  11.176102  10.678349   9.831242  11.555063  0\n",
-       "8   11.176102  10.678349   9.831242  11.555063  13.197594  0\n",
-       "9   10.678349   9.831242  11.555063  13.197594  14.337077  0\n",
-       "10   9.831242  11.555063  13.197594  14.337077  15.020920  0\n",
-       "11  11.555063  13.197594  14.337077  15.020920  15.802650  0\n",
-       "12  13.197594  14.337077  15.020920  15.802650  17.343995  0\n",
-       "13  14.337077  15.020920  15.802650  17.343995  16.413220  0\n",
-       "14  15.020920  15.802650  17.343995  16.413220  15.711286  0\n",
-       "15  15.802650  17.343995  16.413220  15.711286  14.035776  0\n",
-       "16  17.343995  16.413220  15.711286  14.035776  12.314211  0\n",
-       "17  16.413220  15.711286  14.035776  12.314211  11.173016  0\n",
-       "18  15.711286  14.035776  12.314211  11.173016  10.768019  0\n",
-       "19  14.035776  12.314211  11.173016  10.768019   9.691997  0"
-      ]
-     },
-     "execution_count": 688,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df_epis.head(20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 689,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>F1</th>\n",
-       "      <th>F2</th>\n",
-       "      <th>F3</th>\n",
-       "      <th>F4</th>\n",
-       "      <th>F5</th>\n",
-       "      <th>L</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>3014.000000</td>\n",
-       "      <td>3014.000000</td>\n",
-       "      <td>3014.000000</td>\n",
-       "      <td>3014.000000</td>\n",
-       "      <td>3014.000000</td>\n",
-       "      <td>3014.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>14.419974</td>\n",
-       "      <td>14.421438</td>\n",
-       "      <td>14.430688</td>\n",
-       "      <td>14.430566</td>\n",
-       "      <td>14.429951</td>\n",
-       "      <td>0.027870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>4.514179</td>\n",
-       "      <td>4.514390</td>\n",
-       "      <td>4.544588</td>\n",
-       "      <td>4.544510</td>\n",
-       "      <td>4.544357</td>\n",
-       "      <td>0.164627</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>8.089854</td>\n",
-       "      <td>8.089854</td>\n",
-       "      <td>8.089854</td>\n",
-       "      <td>8.089854</td>\n",
-       "      <td>8.089854</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>11.724137</td>\n",
-       "      <td>11.724137</td>\n",
-       "      <td>11.724137</td>\n",
-       "      <td>11.724137</td>\n",
-       "      <td>11.724137</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>13.961320</td>\n",
-       "      <td>13.966907</td>\n",
-       "      <td>13.971367</td>\n",
-       "      <td>13.971367</td>\n",
-       "      <td>13.971367</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>16.179648</td>\n",
-       "      <td>16.180626</td>\n",
-       "      <td>16.180626</td>\n",
-       "      <td>16.180626</td>\n",
-       "      <td>16.179648</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>48.423213</td>\n",
-       "      <td>48.423213</td>\n",
-       "      <td>48.423213</td>\n",
-       "      <td>48.423213</td>\n",
-       "      <td>48.423213</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                F1           F2           F3           F4           F5  \\\n",
-       "count  3014.000000  3014.000000  3014.000000  3014.000000  3014.000000   \n",
-       "mean     14.419974    14.421438    14.430688    14.430566    14.429951   \n",
-       "std       4.514179     4.514390     4.544588     4.544510     4.544357   \n",
-       "min       8.089854     8.089854     8.089854     8.089854     8.089854   \n",
-       "25%      11.724137    11.724137    11.724137    11.724137    11.724137   \n",
-       "50%      13.961320    13.966907    13.971367    13.971367    13.971367   \n",
-       "75%      16.179648    16.180626    16.180626    16.180626    16.179648   \n",
-       "max      48.423213    48.423213    48.423213    48.423213    48.423213   \n",
-       "\n",
-       "                 L  \n",
-       "count  3014.000000  \n",
-       "mean      0.027870  \n",
-       "std       0.164627  \n",
-       "min       0.000000  \n",
-       "25%       0.000000  \n",
-       "50%       0.000000  \n",
-       "75%       0.000000  \n",
-       "max       1.000000  "
-      ]
-     },
-     "execution_count": 689,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df_epis.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 690,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of episodes: 3014\n",
-      "Number of features: 5\n",
-      "Number of episodes with anomaly: 84\n",
-      "Number of episodes witManipulatehout anomaly: 2930\n",
-      "Anomaly rate in dataset: 2.79%\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Calculate number of episodes\n",
     "n_episodes = df_epis.shape[0]\n",
@@ -1300,15 +466,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 691,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "factor = 5 # Number of copies\n",
     "dfr = df_epis.copy()\n",
+    "\n",
+    "increase_factor = 4.5\n",
+    "\n",
     "for i in range(1,factor):\n",
     "\n",
-    "    f = 0.5 + ((i - 1 ) * 0.5 / (factor-1)) # vary the anomaly by a factor \n",
+    "    f = increase_factor + ((i - 1 ) * 0.5 / (factor-1)) # vary the anomaly by a factor \n",
     "    #print(i,f)\n",
     "    \n",
     "    dfi = df_epis.copy()\n",
@@ -1320,21 +489,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 692,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of episodes: 15070\n",
-      "Number of features: 5\n",
-      "Number of episodes with anomaly: 420\n",
-      "Number of episodes without anomaly: 14650\n",
-      "Anomaly rate in dataset: 2.79%\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Calculate number of episodes\n",
     "n_episodes = df_epis.shape[0]\n",
@@ -1368,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 693,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1399,28 +556,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 694,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Feature columns:\n",
-      "['F1', 'F2', 'F3', 'F4', 'F5']\n",
-      "\n",
-      "Target column: L\n",
-      "\n",
-      "Feature values:\n",
-      "          F1         F2         F3         F4         F5\n",
-      "0  18.340181  17.647661  16.874933  16.180807  15.407113\n",
-      "1  17.647661  16.874933  16.180807  15.407113  15.324012\n",
-      "2  16.874933  16.180807  15.407113  15.324012  13.470387\n",
-      "3  16.180807  15.407113  15.324012  13.470387  11.702384\n",
-      "4  15.407113  15.324012  13.470387  11.702384  11.176102\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Extract feature columns\n",
     "feature_cols = list(df_epis.columns[:-1])\n",
@@ -1457,58 +595,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 695,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Training set has 10096 samples."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "Testing set has 4974 samples."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "Anomaly rate of the training set: 2.76%"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "Anomaly rate of the testing set: 2.83%"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split( X_all, y_all, test_size=0.33, random_state=42)\n",
     "\n",
@@ -1543,7 +632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 784,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1609,82 +698,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 799,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "DecisionTreeClassifier: \n",
-      "\n",
-      "Training a DecisionTreeClassifier using a training set size of 10096. . .\n",
-      "Trained model in 0.0345 seconds\n",
-      "Made predictions in 0.0023 seconds.\n",
-      "F1 score for training set: 1.0000.\n",
-      "Made predictions in 0.0019 seconds.\n",
-      "F1 score for test set: 0.9783.\n",
-      "\n",
-      "SVC: \n",
-      "\n",
-      "Training a SVC using a training set size of 10096. . .\n",
-      "Trained model in 0.0486 seconds\n",
-      "Made predictions in 0.0227 seconds.\n",
-      "F1 score for training set: 0.9472.\n",
-      "Made predictions in 0.0106 seconds.\n",
-      "F1 score for test set: 0.9278.\n",
-      "\n",
-      "GaussianNB: \n",
-      "\n",
-      "Training a GaussianNB using a training set size of 10096. . .\n",
-      "Trained model in 0.0027 seconds\n",
-      "Made predictions in 0.0017 seconds.\n",
-      "F1 score for training set: 0.8423.\n",
-      "Made predictions in 0.0015 seconds.\n",
-      "F1 score for test set: 0.7845.\n",
-      "\n",
-      "Report for DecisionTreeClassifier:\n",
-      "\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      1.00      1.00      4833\n",
-      "           1       1.00      0.96      0.98       141\n",
-      "\n",
-      "    accuracy                           1.00      4974\n",
-      "   macro avg       1.00      0.98      0.99      4974\n",
-      "weighted avg       1.00      1.00      1.00      4974\n",
-      "\n",
-      "----------------------------------------------------\n",
-      "\n",
-      "Report for SVC:\n",
-      "\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       1.00      1.00      1.00      4833\n",
-      "           1       1.00      0.87      0.93       141\n",
-      "\n",
-      "    accuracy                           1.00      4974\n",
-      "   macro avg       1.00      0.93      0.96      4974\n",
-      "weighted avg       1.00      1.00      1.00      4974\n",
-      "\n",
-      "----------------------------------------------------\n",
-      "\n",
-      "Report for GaussianNB:\n",
-      "\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.99      1.00      0.99      4833\n",
-      "           1       1.00      0.65      0.78       141\n",
-      "\n",
-      "    accuracy                           0.99      4974\n",
-      "   macro avg       0.99      0.82      0.89      4974\n",
-      "weighted avg       0.99      0.99      0.99      4974\n",
-      "\n",
-      "----------------------------------------------------\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_random_seed = 42\n",
     "\n",
@@ -1736,10 +752,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 698,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "# Create Keras callbacks\n",
     "losses = []\n",
     "\n",
@@ -1762,10 +779,6 @@
     "        keys = list(logs.keys())\n",
     "        print(\"Start epoch {} of training; got log keys: {}\".format(epoch + 1, keys))\n",
     "\n",
-    "    def on_epoch_end(self, epoch, logs=None):\n",
-    "        keys = list(logs.keys())\n",
-    "        print(\"End epoch {} of training; got log keys: {}\".format(epoch + 1, keys))\n",
-    "\n",
     "    def on_test_begin(self, logs=None):\n",
     "        keys = list(logs.keys())\n",
     "        print(\"Start testing; got log keys: {}\".format(keys))\n",
@@ -1780,12 +793,39 @@
     "\n",
     "    def on_predict_end(self, logs=None):\n",
     "        keys = list(logs.keys())\n",
-    "        print(\"Stop predicting; got log keys: {}\".format(keys))"
+    "        print(\"Stop predicting; got log keys: {}\".format(keys))\n",
+    "        \n",
+    "\n",
+    "class SaveCallback(keras.callbacks.Callback):\n",
+    "    def __init__(self, model_name, model):\n",
+    "        super().__init__()\n",
+    "        self.best_loss = np.inf\n",
+    "        self.filedir = f\"{model_name}_models/\"\n",
+    "        self.model_ = model\n",
+    "\n",
+    "    def on_epoch_end(self, epoch, logs=None):\n",
+    "        keys = list(logs.keys())\n",
+    "        if 'val_loss' in keys:\n",
+    "            if logs['val_loss'] < self.best_loss:\n",
+    "                savepath = self.filedir + 'epoch' + str(epoch + 1)\n",
+    "                print('\\n' + 'Val_loss improved from ' + str(self.best_loss) + ' to ' + str(logs['val_loss']) + '. Saving model to ' + savepath + '/' + 'ckpt...')\n",
+    "                self.best_loss = logs['val_loss']\n",
+    "                if not os.path.exists(savepath):\n",
+    "                    os.makedirs(savepath)\n",
+    "                self.model_.save_weights(savepath + '/' + 'epoch' + str(epoch + 1) + 'ckpt')\n",
+    "            else:\n",
+    "                print('\\n' + 'Val_loss did not improve from ' + str(self.best_loss))\n",
+    "                savepath = self.filedir + 'epoch' + str(epoch + 1)\n",
+    "                print('\\n' + 'Val_loss improved from ' + str(self.best_loss) + ' to ' + str(logs['val_loss']) + '. Saving model to ' + savepath + '/' + 'ckpt...')\n",
+    "                self.best_loss = logs['val_loss']\n",
+    "                if not os.path.exists(savepath):\n",
+    "                    os.makedirs(savepath)\n",
+    "                self.model_.save_weights(savepath + '/' + 'epoch' + str(epoch + 1) + 'n' + 'ckpt')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 699,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1793,39 +833,39 @@
     "\n",
     "def get_model(X):\n",
     "    \"\"\"Get model.\"\"\"\n",
-    "    model = keras.Sequential()\n",
+    "    new_model = keras.Sequential()\n",
     "\n",
-    "    model.add(keras.layers.LSTM(units=16,\n",
+    "    new_model.add(keras.layers.LSTM(units=16,\n",
     "                                input_shape=(X.shape[1],X.shape[2]),\n",
     "                                activation='relu', return_sequences=True))\n",
-    "    model.add(keras.layers.LSTM(units=4, activation='relu'))\n",
-    "    model.add(keras.layers.RepeatVector(n=X.shape[1]))\n",
-    "    model.add(keras.layers.LSTM(units=4, activation='relu', return_sequences=True))\n",
-    "    model.add(keras.layers.LSTM(units=16, activation='relu', return_sequences=True))\n",
-    "    model.add(\n",
+    "    new_model.add(keras.layers.LSTM(units=4, activation='relu'))\n",
+    "    new_model.add(keras.layers.RepeatVector(n=X.shape[1]))\n",
+    "    new_model.add(keras.layers.LSTM(units=4, activation='relu', return_sequences=True))\n",
+    "    new_model.add(keras.layers.LSTM(units=16, activation='relu', return_sequences=True))\n",
+    "    new_model.add(\n",
     "      keras.layers.TimeDistributed(\n",
     "        keras.layers.Dense(units=X.shape[2])\n",
     "      )\n",
     "    )\n",
-    "\n",
-    "    model.compile(loss='mae', optimizer='adam')\n",
     "    \n",
-    "    model.summary()\n",
+    "    new_model.summary()\n",
     "\n",
-    "    return model"
+    "    return new_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 700,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Create helper functions\n",
     "def train(x_train_data, tot_epochs, selected_batch_size):\n",
     "    \"\"\"Train model.\"\"\"\n",
-    "    model = get_model(X=x_train_data)\n",
-    "    history = model.fit(\n",
+    "    new_model_ = get_model(X=x_train_data)\n",
+    "\n",
+    "    new_model_.compile(loss='mae', optimizer='adam')\n",
+    "    history = new_model_.fit(\n",
     "        x_train_data,\n",
     "        x_train_data,\n",
     "        epochs=tot_epochs,\n",
@@ -1833,51 +873,51 @@
     "        validation_split=0.05,\n",
     "        verbose=1,\n",
     "        shuffle=False,\n",
-    "        callbacks=[CustomCallback()]\n",
+    "        callbacks=[CustomCallback(), SaveCallback(model_name=\"lstm_autoencoder\", model=new_model_)]\n",
     "    )\n",
     "    \n",
-    "    return history, model\n",
+    "    return history, new_model_\n",
     "\n",
-    "def predict(model, data):\n",
+    "def predict_results(newmodel, data):\n",
     "    \"\"\"Predict value.\"\"\"\n",
-    "    predicted_value =  model.predict(data)\n"
+    "    predicted_value =  newmodel.predict(data)\n",
+    "    return predicted_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Deep Learning Model: LSTM Autoencoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Separate data in healthy and anomalies"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 701,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of healthy samples is 2938\n",
-      "Total number of samples with anomalies is 84 -> 2.78%\n"
-     ]
-    }
-   ],
-   "source": [
-    "healthy_data = df.loc[(df['label'] == 0)]\n",
-    "anomalies_data = df.loc[(df['label'] == 1)]\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "healthy_data = df_initial.loc[(df_initial['label'] == 0)]\n",
+    "anomalies_data = df_initial.loc[(df_initial['label'] == 1)]\n",
     "\n",
     "print(f\"Total number of healthy samples is {healthy_data.shape[0]}\")\n",
-    "percentage_anomalies = anomalies_data.shape[0]/df.shape[0]*100\n",
+    "percentage_anomalies = anomalies_data.shape[0]/df_initial.shape[0]*100\n",
     "print(f\"Total number of samples with anomalies is {anomalies_data.shape[0]} -> {percentage_anomalies:.2f}%\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 702,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# X_all_healthy = pd.DataFrame(healthy_data['value'].values)\n",
-    "# y_all_healthy = pd.DataFrame(healthy_data['label'].values)\n",
-    "\n",
-    "# X_all_anomalies = pd.DataFrame(anomalies_data['value'].values)\n",
-    "# y_all_anomalies = pd.DataFrame(anomalies_data['label'].values)\n",
-    "\n",
     "df_epis_healthy = df_epis[df_epis['L'] == 0]\n",
     "df_epis_anomalies = df_epis[df_epis['L'] == 1]\n",
     "\n",
@@ -1889,44 +929,12 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Deep Learning Model: LSTM Autoencoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 703,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "**Training healthy** set has **12452** samples."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "**Testing healthy** set has **2198** samples."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "X_train_healthy, X_test_healthy, y_train_healthy, y_test_healthy = train_test_split( X_all_healthy, y_all_healthy, test_size=0.15, random_state=42)\n",
+    "X_train_healthy, X_test_healthy, y_train_healthy, y_test_healthy = train_test_split(X_all_healthy, y_all_healthy, test_size=0.15, random_state=42)\n",
     "\n",
     "# Show the results of the split\n",
     "printmd(\"**Training healthy** set has **{}** samples.\".format(X_train_healthy.shape[0]))\n",
@@ -1944,40 +952,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 704,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using normalized/scaled data!\n"
-     ]
-    }
-   ],
-   "source": [
-    "def scaleData(data, use_normalized):\n",
-    "    # normalize features\n",
-    "    if use_normalized:\n",
-    "        scaler = MinMaxScaler(feature_range=(0, 1))\n",
-    "        return pd.DataFrame(scaler.fit_transform(data))\n",
-    "    \n",
-    "    return data\n",
-    "\n",
-    "use_normalized = True\n",
-    "\n",
-    "if use_normalized:\n",
-    "    print (\"Using normalized/scaled data!\")\n",
-    "\n",
-    "X_train_scaled = scaleData(X_train, use_normalized=use_normalized)\n",
-    "X_test_scaled = scaleData(X_test, use_normalized=use_normalized)\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_scaler = MinMaxScaler(feature_range=(0, 1))\n",
     "\n",
-    "X_train_healthy_scaled = scaleData(X_train_healthy, use_normalized=use_normalized)\n",
-    "X_test_healthy_scaled = scaleData(X_test_healthy, use_normalized=use_normalized)\n",
+    "# Fit scaling with train and the transform test and validation\n",
+    "my_scaler.fit(X_train_healthy)\n",
     "\n",
-    "X_all_anomalies_scaled = scaleData(X_all_anomalies, use_normalized=use_normalized)"
+    "X_train_healthy_scaled = pd.DataFrame(my_scaler.transform(X_train_healthy))\n",
+    "X_test_healthy_scaled = pd.DataFrame(my_scaler.transform(X_test_healthy))\n",
+    "X_all_anomalies_scaled = pd.DataFrame(my_scaler.transform(X_all_anomalies))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Store set used to fit scaling to be reused in production\n",
+    "X_train_healthy.to_csv('sensor-training-data-for-scaling.csv', index = False, header=True, float_format='%.2f')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot both together to compare\n",
+    "fig, ax=plt.subplots(1,2)\n",
+    "sns.distplot(X_train_healthy, ax=ax[0])\n",
+    "ax[0].set_title(\"Original Data\")\n",
+    "sns.distplot(X_train_healthy_scaled, ax=ax[1])\n",
+    "ax[1].set_title(\"Scaled data\")"
    ]
   },
   {
@@ -1989,19 +999,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 705,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set healthy shape: (12452, 1, 5)\n",
-      "Test set healthy shape: (2198, 1, 5)\n",
-      "Set anomalies shape: (420, 1, 5)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "x_train_healthy = X_train_healthy_scaled.values.reshape(X_train_healthy_scaled.shape[0], 1, X_train_healthy_scaled.shape[1])\n",
     "print (\"Training set healthy shape: {}\".format(x_train_healthy.shape))\n",
@@ -2013,6 +1013,45 @@
     "print (\"Set anomalies shape: {}\".format(x_all_anomalies.shape))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limit = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "for index, row in X_train_healthy_scaled.iterrows():\n",
+    "    row.plot()\n",
+    "    count += 1\n",
+    "    if count == limit:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "for index, row in X_all_anomalies_scaled.iterrows():\n",
+    "    row.plot()\n",
+    "    count += 1\n",
+    "    if count == limit:\n",
+    "        break"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2022,670 +1061,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 706,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_16\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "lstm_69 (LSTM)               (None, 1, 16)             1408      \n",
-      "_________________________________________________________________\n",
-      "lstm_70 (LSTM)               (None, 4)                 336       \n",
-      "_________________________________________________________________\n",
-      "repeat_vector_15 (RepeatVect (None, 1, 4)              0         \n",
-      "_________________________________________________________________\n",
-      "lstm_71 (LSTM)               (None, 1, 4)              144       \n",
-      "_________________________________________________________________\n",
-      "lstm_72 (LSTM)               (None, 1, 16)             1344      \n",
-      "_________________________________________________________________\n",
-      "time_distributed_15 (TimeDis (None, 1, 5)              85        \n",
-      "=================================================================\n",
-      "Total params: 3,317\n",
-      "Trainable params: 3,317\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n",
-      "Starting training; got log keys: []\n",
-      "Start epoch 1 of training; got log keys: []\n",
-      "Epoch 1/100\n",
-      "11820/11829 [============================>.] - ETA: 0s - loss: 0.0417Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 1 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 22s 2ms/step - loss: 0.0417 - val_loss: 0.0226\n",
-      "Start epoch 2 of training; got log keys: []\n",
-      "Epoch 2/100\n",
-      "11820/11829 [============================>.] - ETA: 0s - loss: 0.0241Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 2 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0241 - val_loss: 0.0223\n",
-      "Start epoch 3 of training; got log keys: []\n",
-      "Epoch 3/100\n",
-      "11802/11829 [============================>.] - ETA: 0s - loss: 0.0233Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 3 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0232 - val_loss: 0.0220\n",
-      "Start epoch 4 of training; got log keys: []\n",
-      "Epoch 4/100\n",
-      "11821/11829 [============================>.] - ETA: 0s - loss: 0.0210Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 4 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0210 - val_loss: 0.0195\n",
-      "Start epoch 5 of training; got log keys: []\n",
-      "Epoch 5/100\n",
-      "11812/11829 [============================>.] - ETA: 0s - loss: 0.0202Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 5 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0202 - val_loss: 0.0189\n",
-      "Start epoch 6 of training; got log keys: []\n",
-      "Epoch 6/100\n",
-      "11819/11829 [============================>.] - ETA: 0s - loss: 0.0200Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 6 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0200 - val_loss: 0.0187\n",
-      "Start epoch 7 of training; got log keys: []\n",
-      "Epoch 7/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0198Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 7 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0198 - val_loss: 0.0180\n",
-      "Start epoch 8 of training; got log keys: []\n",
-      "Epoch 8/100\n",
-      "11819/11829 [============================>.] - ETA: 0s - loss: 0.0195Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 8 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0195 - val_loss: 0.0186\n",
-      "Start epoch 9 of training; got log keys: []\n",
-      "Epoch 9/100\n",
-      "11823/11829 [============================>.] - ETA: 0s - loss: 0.0194Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 9 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0194 - val_loss: 0.0174\n",
-      "Start epoch 10 of training; got log keys: []\n",
-      "Epoch 10/100\n",
-      "11819/11829 [============================>.] - ETA: 0s - loss: 0.0194Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 10 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0194 - val_loss: 0.0180\n",
-      "Start epoch 11 of training; got log keys: []\n",
-      "Epoch 11/100\n",
-      "11829/11829 [==============================] - ETA: 0s - loss: 0.0193Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 11 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0193 - val_loss: 0.0180\n",
-      "Start epoch 12 of training; got log keys: []\n",
-      "Epoch 12/100\n",
-      "11805/11829 [============================>.] - ETA: 0s - loss: 0.0193Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 12 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 22s 2ms/step - loss: 0.0192 - val_loss: 0.0181\n",
-      "Start epoch 13 of training; got log keys: []\n",
-      "Epoch 13/100\n",
-      "11813/11829 [============================>.] - ETA: 0s - loss: 0.0193Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 13 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0193 - val_loss: 0.0183\n",
-      "Start epoch 14 of training; got log keys: []\n",
-      "Epoch 14/100\n",
-      "11828/11829 [============================>.] - ETA: 0s - loss: 0.0192Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 14 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0192 - val_loss: 0.0176\n",
-      "Start epoch 15 of training; got log keys: []\n",
-      "Epoch 15/100\n",
-      "11810/11829 [============================>.] - ETA: 0s - loss: 0.0191Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 15 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0191 - val_loss: 0.0179\n",
-      "Start epoch 16 of training; got log keys: []\n",
-      "Epoch 16/100\n",
-      "11828/11829 [============================>.] - ETA: 0s - loss: 0.0191Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 16 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0191 - val_loss: 0.0175\n",
-      "Start epoch 17 of training; got log keys: []\n",
-      "Epoch 17/100\n",
-      "11829/11829 [==============================] - ETA: 0s - loss: 0.0190Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 17 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0190 - val_loss: 0.0177\n",
-      "Start epoch 18 of training; got log keys: []\n",
-      "Epoch 18/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0190Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 18 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0190 - val_loss: 0.0170\n",
-      "Start epoch 19 of training; got log keys: []\n",
-      "Epoch 19/100\n",
-      "11811/11829 [============================>.] - ETA: 0s - loss: 0.0189Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 19 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0189 - val_loss: 0.0174\n",
-      "Start epoch 20 of training; got log keys: []\n",
-      "Epoch 20/100\n",
-      "11809/11829 [============================>.] - ETA: 0s - loss: 0.0189Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 20 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0188 - val_loss: 0.0179\n",
-      "Start epoch 21 of training; got log keys: []\n",
-      "Epoch 21/100\n",
-      "11826/11829 [============================>.] - ETA: 0s - loss: 0.0188Start testing; got log keys: []\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stop testing; got log keys: []\n",
-      "End epoch 21 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 29s 2ms/step - loss: 0.0188 - val_loss: 0.0170\n",
-      "Start epoch 22 of training; got log keys: []\n",
-      "Epoch 22/100\n",
-      "11815/11829 [============================>.] - ETA: 0s - loss: 0.0188Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 22 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0188 - val_loss: 0.0169\n",
-      "Start epoch 23 of training; got log keys: []\n",
-      "Epoch 23/100\n",
-      "11823/11829 [============================>.] - ETA: 0s - loss: 0.0187Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 23 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0187 - val_loss: 0.0170\n",
-      "Start epoch 24 of training; got log keys: []\n",
-      "Epoch 24/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0188Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 24 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0187 - val_loss: 0.0180\n",
-      "Start epoch 25 of training; got log keys: []\n",
-      "Epoch 25/100\n",
-      "11823/11829 [============================>.] - ETA: 0s - loss: 0.0187Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 25 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0187 - val_loss: 0.0172\n",
-      "Start epoch 26 of training; got log keys: []\n",
-      "Epoch 26/100\n",
-      "11817/11829 [============================>.] - ETA: 0s - loss: 0.0187Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 26 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0187 - val_loss: 0.0179\n",
-      "Start epoch 27 of training; got log keys: []\n",
-      "Epoch 27/100\n",
-      "11808/11829 [============================>.] - ETA: 0s - loss: 0.0186Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 27 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0186 - val_loss: 0.0179\n",
-      "Start epoch 28 of training; got log keys: []\n",
-      "Epoch 28/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0186Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 28 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0186 - val_loss: 0.0182\n",
-      "Start epoch 29 of training; got log keys: []\n",
-      "Epoch 29/100\n",
-      "11815/11829 [============================>.] - ETA: 0s - loss: 0.0186Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 29 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 30s 3ms/step - loss: 0.0186 - val_loss: 0.0182\n",
-      "Start epoch 30 of training; got log keys: []\n",
-      "Epoch 30/100\n",
-      "11812/11829 [============================>.] - ETA: 0s - loss: 0.0186Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 30 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 30s 3ms/step - loss: 0.0186 - val_loss: 0.0175\n",
-      "Start epoch 31 of training; got log keys: []\n",
-      "Epoch 31/100\n",
-      "11812/11829 [============================>.] - ETA: 0s - loss: 0.0185Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 31 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0185 - val_loss: 0.0171\n",
-      "Start epoch 32 of training; got log keys: []\n",
-      "Epoch 32/100\n",
-      "11806/11829 [============================>.] - ETA: 0s - loss: 0.0185Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 32 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0185 - val_loss: 0.0171\n",
-      "Start epoch 33 of training; got log keys: []\n",
-      "Epoch 33/100\n",
-      "11820/11829 [============================>.] - ETA: 0s - loss: 0.0185Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 33 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0185 - val_loss: 0.0173\n",
-      "Start epoch 34 of training; got log keys: []\n",
-      "Epoch 34/100\n",
-      "11827/11829 [============================>.] - ETA: 0s - loss: 0.0185Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 34 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0185 - val_loss: 0.0175\n",
-      "Start epoch 35 of training; got log keys: []\n",
-      "Epoch 35/100\n",
-      "11811/11829 [============================>.] - ETA: 0s - loss: 0.0185Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 35 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0184 - val_loss: 0.0169\n",
-      "Start epoch 36 of training; got log keys: []\n",
-      "Epoch 36/100\n",
-      "11818/11829 [============================>.] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 36 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0184 - val_loss: 0.0171\n",
-      "Start epoch 37 of training; got log keys: []\n",
-      "Epoch 37/100\n",
-      "11808/11829 [============================>.] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 37 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0184 - val_loss: 0.0171\n",
-      "Start epoch 38 of training; got log keys: []\n",
-      "Epoch 38/100\n",
-      "11822/11829 [============================>.] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 38 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0184 - val_loss: 0.0171\n",
-      "Start epoch 39 of training; got log keys: []\n",
-      "Epoch 39/100\n",
-      "11817/11829 [============================>.] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 39 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0184 - val_loss: 0.0169\n",
-      "Start epoch 40 of training; got log keys: []\n",
-      "Epoch 40/100\n",
-      "11829/11829 [==============================] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 40 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 29s 2ms/step - loss: 0.0184 - val_loss: 0.0171\n",
-      "Start epoch 41 of training; got log keys: []\n",
-      "Epoch 41/100\n",
-      "11818/11829 [============================>.] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 41 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0184 - val_loss: 0.0168\n",
-      "Start epoch 42 of training; got log keys: []\n",
-      "Epoch 42/100\n",
-      "11813/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 42 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 30s 3ms/step - loss: 0.0183 - val_loss: 0.0171\n",
-      "Start epoch 43 of training; got log keys: []\n",
-      "Epoch 43/100\n",
-      "11822/11829 [============================>.] - ETA: 0s - loss: 0.0184Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 43 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0184 - val_loss: 0.0169\n",
-      "Start epoch 44 of training; got log keys: []\n",
-      "Epoch 44/100\n",
-      "11807/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 44 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0183 - val_loss: 0.0173\n",
-      "Start epoch 45 of training; got log keys: []\n",
-      "Epoch 45/100\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11826/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 45 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0183 - val_loss: 0.0171\n",
-      "Start epoch 46 of training; got log keys: []\n",
-      "Epoch 46/100\n",
-      "11821/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 46 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0183 - val_loss: 0.0169\n",
-      "Start epoch 47 of training; got log keys: []\n",
-      "Epoch 47/100\n",
-      "11805/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 47 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0183 - val_loss: 0.0177\n",
-      "Start epoch 48 of training; got log keys: []\n",
-      "Epoch 48/100\n",
-      "11826/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 48 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0183 - val_loss: 0.0169\n",
-      "Start epoch 49 of training; got log keys: []\n",
-      "Epoch 49/100\n",
-      "11808/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 49 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0183 - val_loss: 0.0174\n",
-      "Start epoch 50 of training; got log keys: []\n",
-      "Epoch 50/100\n",
-      "11809/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 50 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0183 - val_loss: 0.0167\n",
-      "Start epoch 51 of training; got log keys: []\n",
-      "Epoch 51/100\n",
-      "11811/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 51 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 36s 3ms/step - loss: 0.0183 - val_loss: 0.0175\n",
-      "Start epoch 52 of training; got log keys: []\n",
-      "Epoch 52/100\n",
-      "11820/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 52 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 33s 3ms/step - loss: 0.0183 - val_loss: 0.0168\n",
-      "Start epoch 53 of training; got log keys: []\n",
-      "Epoch 53/100\n",
-      "11809/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 53 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 32s 3ms/step - loss: 0.0182 - val_loss: 0.0172\n",
-      "Start epoch 54 of training; got log keys: []\n",
-      "Epoch 54/100\n",
-      "11804/11829 [============================>.] - ETA: 0s - loss: 0.0183Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 54 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0182 - val_loss: 0.0169\n",
-      "Start epoch 55 of training; got log keys: []\n",
-      "Epoch 55/100\n",
-      "11807/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 55 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0170\n",
-      "Start epoch 56 of training; got log keys: []\n",
-      "Epoch 56/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 56 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0182 - val_loss: 0.0171\n",
-      "Start epoch 57 of training; got log keys: []\n",
-      "Epoch 57/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 57 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0182 - val_loss: 0.0168\n",
-      "Start epoch 58 of training; got log keys: []\n",
-      "Epoch 58/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 58 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 33s 3ms/step - loss: 0.0182 - val_loss: 0.0165\n",
-      "Start epoch 59 of training; got log keys: []\n",
-      "Epoch 59/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 59 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 29s 2ms/step - loss: 0.0182 - val_loss: 0.0173\n",
-      "Start epoch 60 of training; got log keys: []\n",
-      "Epoch 60/100\n",
-      "11812/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 60 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0182 - val_loss: 0.0170\n",
-      "Start epoch 61 of training; got log keys: []\n",
-      "Epoch 61/100\n",
-      "11829/11829 [==============================] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 61 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0182 - val_loss: 0.0170\n",
-      "Start epoch 62 of training; got log keys: []\n",
-      "Epoch 62/100\n",
-      "11810/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 62 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0173\n",
-      "Start epoch 63 of training; got log keys: []\n",
-      "Epoch 63/100\n",
-      "11810/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 63 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0166\n",
-      "Start epoch 64 of training; got log keys: []\n",
-      "Epoch 64/100\n",
-      "11815/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 64 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 22s 2ms/step - loss: 0.0182 - val_loss: 0.0168\n",
-      "Start epoch 65 of training; got log keys: []\n",
-      "Epoch 65/100\n",
-      "11821/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 65 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0170\n",
-      "Start epoch 66 of training; got log keys: []\n",
-      "Epoch 66/100\n",
-      "11813/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 66 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0171\n",
-      "Start epoch 67 of training; got log keys: []\n",
-      "Epoch 67/100\n",
-      "11813/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 67 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0172\n",
-      "Start epoch 68 of training; got log keys: []\n",
-      "Epoch 68/100\n",
-      "11828/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 68 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0182 - val_loss: 0.0167\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Start epoch 69 of training; got log keys: []\n",
-      "Epoch 69/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 69 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0181 - val_loss: 0.0167\n",
-      "Start epoch 70 of training; got log keys: []\n",
-      "Epoch 70/100\n",
-      "11823/11829 [============================>.] - ETA: 0s - loss: 0.0182Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 70 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0181 - val_loss: 0.0167\n",
-      "Start epoch 71 of training; got log keys: []\n",
-      "Epoch 71/100\n",
-      "11811/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 71 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0181 - val_loss: 0.0166\n",
-      "Start epoch 72 of training; got log keys: []\n",
-      "Epoch 72/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 72 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0181 - val_loss: 0.0168\n",
-      "Start epoch 73 of training; got log keys: []\n",
-      "Epoch 73/100\n",
-      "11825/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 73 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0181 - val_loss: 0.0168\n",
-      "Start epoch 74 of training; got log keys: []\n",
-      "Epoch 74/100\n",
-      "11826/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 74 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 24s 2ms/step - loss: 0.0181 - val_loss: 0.0176\n",
-      "Start epoch 75 of training; got log keys: []\n",
-      "Epoch 75/100\n",
-      "11826/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 75 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0181 - val_loss: 0.0167\n",
-      "Start epoch 76 of training; got log keys: []\n",
-      "Epoch 76/100\n",
-      "11820/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 76 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 22s 2ms/step - loss: 0.0181 - val_loss: 0.0172\n",
-      "Start epoch 77 of training; got log keys: []\n",
-      "Epoch 77/100\n",
-      "11808/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 77 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0181 - val_loss: 0.0167\n",
-      "Start epoch 78 of training; got log keys: []\n",
-      "Epoch 78/100\n",
-      "11829/11829 [==============================] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 78 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0181 - val_loss: 0.0168\n",
-      "Start epoch 79 of training; got log keys: []\n",
-      "Epoch 79/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 79 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 38s 3ms/step - loss: 0.0181 - val_loss: 0.0166\n",
-      "Start epoch 80 of training; got log keys: []\n",
-      "Epoch 80/100\n",
-      "11817/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 80 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 33s 3ms/step - loss: 0.0181 - val_loss: 0.0173\n",
-      "Start epoch 81 of training; got log keys: []\n",
-      "Epoch 81/100\n",
-      "11815/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 81 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 32s 3ms/step - loss: 0.0181 - val_loss: 0.0167\n",
-      "Start epoch 82 of training; got log keys: []\n",
-      "Epoch 82/100\n",
-      "11811/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 82 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 33s 3ms/step - loss: 0.0180 - val_loss: 0.0171\n",
-      "Start epoch 83 of training; got log keys: []\n",
-      "Epoch 83/100\n",
-      "11820/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 83 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 34s 3ms/step - loss: 0.0181 - val_loss: 0.0171\n",
-      "Start epoch 84 of training; got log keys: []\n",
-      "Epoch 84/100\n",
-      "11823/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 84 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 30s 3ms/step - loss: 0.0181 - val_loss: 0.0164\n",
-      "Start epoch 85 of training; got log keys: []\n",
-      "Epoch 85/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 85 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0181 - val_loss: 0.0171\n",
-      "Start epoch 86 of training; got log keys: []\n",
-      "Epoch 86/100\n",
-      "11803/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 86 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 23s 2ms/step - loss: 0.0180 - val_loss: 0.0170\n",
-      "Start epoch 87 of training; got log keys: []\n",
-      "Epoch 87/100\n",
-      "11819/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 87 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0180 - val_loss: 0.0173\n",
-      "Start epoch 88 of training; got log keys: []\n",
-      "Epoch 88/100\n",
-      "11819/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 88 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0180 - val_loss: 0.0165\n",
-      "Start epoch 89 of training; got log keys: []\n",
-      "Epoch 89/100\n",
-      "11821/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 89 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0180 - val_loss: 0.0165\n",
-      "Start epoch 90 of training; got log keys: []\n",
-      "Epoch 90/100\n",
-      "11827/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 90 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 31s 3ms/step - loss: 0.0180 - val_loss: 0.0171\n",
-      "Start epoch 91 of training; got log keys: []\n",
-      "Epoch 91/100\n",
-      "11818/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 91 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0180 - val_loss: 0.0165\n",
-      "Start epoch 92 of training; got log keys: []\n",
-      "Epoch 92/100\n",
-      "11810/11829 [============================>.] - ETA: 0s - loss: 0.0181Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 92 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0180 - val_loss: 0.0168\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Start epoch 93 of training; got log keys: []\n",
-      "Epoch 93/100\n",
-      "11826/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 93 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 27s 2ms/step - loss: 0.0180 - val_loss: 0.0164\n",
-      "Start epoch 94 of training; got log keys: []\n",
-      "Epoch 94/100\n",
-      "11819/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 94 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 35s 3ms/step - loss: 0.0180 - val_loss: 0.0174\n",
-      "Start epoch 95 of training; got log keys: []\n",
-      "Epoch 95/100\n",
-      "11816/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 95 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0180 - val_loss: 0.0166\n",
-      "Start epoch 96 of training; got log keys: []\n",
-      "Epoch 96/100\n",
-      "11825/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 96 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0180 - val_loss: 0.0172\n",
-      "Start epoch 97 of training; got log keys: []\n",
-      "Epoch 97/100\n",
-      "11821/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 97 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 25s 2ms/step - loss: 0.0180 - val_loss: 0.0166\n",
-      "Start epoch 98 of training; got log keys: []\n",
-      "Epoch 98/100\n",
-      "11806/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 98 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 32s 3ms/step - loss: 0.0180 - val_loss: 0.0166\n",
-      "Start epoch 99 of training; got log keys: []\n",
-      "Epoch 99/100\n",
-      "11821/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 99 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 26s 2ms/step - loss: 0.0180 - val_loss: 0.0165\n",
-      "Start epoch 100 of training; got log keys: []\n",
-      "Epoch 100/100\n",
-      "11814/11829 [============================>.] - ETA: 0s - loss: 0.0180Start testing; got log keys: []\n",
-      "Stop testing; got log keys: []\n",
-      "End epoch 100 of training; got log keys: ['loss', 'val_loss']\n",
-      "11829/11829 [==============================] - 28s 2ms/step - loss: 0.0180 - val_loss: 0.0164\n",
-      "Stop training; got log keys: []\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "from pathlib import Path\n",
+    "import datetime\n",
+    "now = datetime.datetime.utcnow()\n",
+    "current_path = Path().cwd()\n",
+    "\n",
     "tot_epochs = 100\n",
     "selected_batch_size = 1\n",
     "\n",
-    "history, lstm_autoencoder = train_model(\n",
-    "    x_train=x_train_healthy,\n",
+    "history, lstm_autoencoder = train(\n",
+    "    x_train_data=x_train_healthy,\n",
     "    tot_epochs=tot_epochs,\n",
     "    selected_batch_size=selected_batch_size\n",
     ")"
@@ -2700,22 +1091,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 720,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "metric = \"loss\"\n",
     "plt.figure()\n",
@@ -2738,33 +1116,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 721,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1280x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_train_pred = lstm_autoencoder.predict(x_train_healthy)\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train_pred = predict_results(lstm_autoencoder, x_train_healthy)\n",
     "x_train_pred_ = x_train_pred.reshape(x_train_pred.shape[0], x_train_pred.shape[2])\n",
     "x_train_healthy_ = x_train_healthy.reshape(x_train_healthy.shape[0], x_train_healthy.shape[2])\n",
     "train_mae_loss = np.mean(np.abs(x_train_pred_ - x_train_healthy_), axis=1)\n",
@@ -2793,42 +1149,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 792,
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "results_summary = []\n",
+    "from numpy import arange, mean\n",
+    "\n",
+    "for threshold_ in arange(min(train_mae_loss), max(train_mae_loss), mean(train_mae_loss)):\n",
+    "    x_test_pred = predict_results(lstm_autoencoder, x_test_healthy)\n",
+    "\n",
+    "    x_test_pred_ = x_test_pred.reshape(x_test_pred.shape[0], x_test_pred.shape[2])\n",
+    "    x_test_healthy_ = x_test_healthy.reshape(x_test_healthy.shape[0], x_test_healthy.shape[2])\n",
+    "\n",
+    "    test_mae_loss = np.mean(np.abs(x_test_pred_ - x_test_healthy_), axis=1)\n",
+    "\n",
+    "    scored_test = {}\n",
+    "    scored_test['Loss_mae'] = test_mae_loss\n",
+    "    scored_test['Threshold'] = threshold_\n",
+    "    scored_t = pd.DataFrame(scored_test)\n",
+    "    scored_t['Healthy_pred'] = scored_t['Loss_mae'] < scored_t['Threshold']\n",
+    "    scored_t['Healthy_pred_int'] = scored_t['Healthy_pred'].astype(int)\n",
+    "\n",
+    "    scored_t['Target'] = y_test_healthy.values\n",
+    "    no_anomalies = scored_t[scored_t['Healthy_pred_int'] == 0]\n",
+    "    percentage_identified = no_anomalies.shape[0]/scored_t.shape[0]\n",
+    "    \n",
+    "    start = time()\n",
+    "    x_all_pred = predict_results(lstm_autoencoder, x_all_anomalies)\n",
+    "    end = time()\n",
+    "\n",
+    "    x_all_pred_ = x_all_pred.reshape(x_all_pred.shape[0], x_all_pred.shape[2])\n",
+    "    x_all_anomalies_ = x_all_anomalies.reshape(x_all_anomalies.shape[0], x_all_anomalies.shape[2])\n",
+    "\n",
+    "    all_mae_loss = np.mean(np.abs(x_all_pred_ - x_all_anomalies_), axis=1)\n",
+    "\n",
+    "    scored_test = {}\n",
+    "    scored_test['Loss_mae'] = all_mae_loss\n",
+    "    scored_test['Threshold'] = threshold_\n",
+    "    scored_a = pd.DataFrame(scored_test)\n",
+    "    scored_a['Anomaly_pred'] = scored_a['Loss_mae'] > scored_a['Threshold']\n",
+    "    scored_a['Anomaly_pred_int'] = scored_a['Anomaly_pred'].astype(int)\n",
+    "\n",
+    "    scored_a['Target'] = y_all_anomalies.values\n",
+    "\n",
+    "    with_anomalies = scored_a[scored_a['Anomaly_pred_int'] == 1]\n",
+    "    percentage_identified_val_set = with_anomalies.shape[0]/scored_a.shape[0]\n",
+    "\n",
+    "    f1_score_ = f1_score(scored_a['Target'], scored_a['Anomaly_pred_int'])\n",
+    "\n",
+    "    result_summary = {\n",
+    "        \"threshold\": threshold_,\n",
+    "        \"percentage_anomalies_test\": percentage_identified*100,\n",
+    "        \"percentage_anomalies_val\": percentage_identified_val_set*100,\n",
+    "        \"f1_score\": f1_score_\n",
+    "    }\n",
+    "    \n",
+    "    results_summary.append(result_summary)\n",
+    "\n",
+    "pd.DataFrame(results_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "threshold = 0.030"
+    "threshold = 0.136306"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 793,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1280x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x_test_pred = lstm_autoencoder.predict(x_test_healthy)\n",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_pred = predict_results(lstm_autoencoder, x_test_healthy)\n",
     "\n",
     "x_test_pred_ = x_test_pred.reshape(x_test_pred.shape[0], x_test_pred.shape[2])\n",
     "x_test_healthy_ = x_test_healthy.reshape(x_test_healthy.shape[0], x_test_healthy.shape[2])\n",
@@ -2858,125 +1257,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 794,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Percentage of anomalies in healthy data identified: 7.87%\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "no_anomalies = scored_t[scored_t['Healthy_pred_int'] == 0]\n",
     "percentage_identified = no_anomalies.shape[0]/scored_t.shape[0]\n",
     "print(f\"Percentage of anomalies in healthy data identified: {percentage_identified*100:.2f}%\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict anomalies"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 795,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Made predictions in 0.0407 seconds.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Loss_mae</th>\n",
-       "      <th>Threshold</th>\n",
-       "      <th>Anomaly_pred</th>\n",
-       "      <th>Anomaly_pred_int</th>\n",
-       "      <th>Target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.103365</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.339458</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.084965</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.276226</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.273821</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Loss_mae  Threshold  Anomaly_pred  Anomaly_pred_int  Target\n",
-       "0  0.103365       0.03          True                 1       1\n",
-       "1  0.339458       0.03          True                 1       1\n",
-       "2  0.084965       0.03          True                 1       1\n",
-       "3  0.276226       0.03          True                 1       1\n",
-       "4  0.273821       0.03          True                 1       1"
-      ]
-     },
-     "execution_count": 795,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "start = time()\n",
-    "x_all_pred = lstm_autoencoder.predict(x_all_anomalies)\n",
+    "x_all_pred = predict_results(lstm_autoencoder, x_all_anomalies)\n",
     "end = time()\n",
     "\n",
     "# Print and return results\n",
@@ -3000,145 +1304,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 796,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Loss_mae</th>\n",
-       "      <th>Threshold</th>\n",
-       "      <th>Anomaly_pred_int</th>\n",
-       "      <th>Target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>420.000000</td>\n",
-       "      <td>4.200000e+02</td>\n",
-       "      <td>420.000000</td>\n",
-       "      <td>420.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.263620</td>\n",
-       "      <td>3.000000e-02</td>\n",
-       "      <td>0.978571</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.129270</td>\n",
-       "      <td>1.042075e-17</td>\n",
-       "      <td>0.144981</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>0.015536</td>\n",
-       "      <td>3.000000e-02</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>0.149017</td>\n",
-       "      <td>3.000000e-02</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.278706</td>\n",
-       "      <td>3.000000e-02</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.371289</td>\n",
-       "      <td>3.000000e-02</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>0.499539</td>\n",
-       "      <td>3.000000e-02</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Loss_mae     Threshold  Anomaly_pred_int  Target\n",
-       "count  420.000000  4.200000e+02        420.000000   420.0\n",
-       "mean     0.263620  3.000000e-02          0.978571     1.0\n",
-       "std      0.129270  1.042075e-17          0.144981     0.0\n",
-       "min      0.015536  3.000000e-02          0.000000     1.0\n",
-       "25%      0.149017  3.000000e-02          1.000000     1.0\n",
-       "50%      0.278706  3.000000e-02          1.000000     1.0\n",
-       "75%      0.371289  3.000000e-02          1.000000     1.0\n",
-       "max      0.499539  3.000000e-02          1.000000     1.0"
-      ]
-     },
-     "execution_count": 796,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "scored_a.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 797,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1280x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.figure(figsize=(16,9), dpi=80)\n",
@@ -3147,64 +1324,14 @@
     "sns.distplot(all_mae_loss,\n",
     "             bins = 20, \n",
     "             kde= True,\n",
-    "            color = 'blue');\n",
-    "# plt.xlim([0.0,.5])"
+    "            color = 'blue');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 800,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Percentage of anomalies identified in anomalies set: **97.86%**"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-09-03 11:22:22,272 [1305416] WARNING  py.warnings:99: [JupyterRequire] /home/fmurdaca/.local/share/virtualenvs/manuela-dev-SY9YZsQk/lib64/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.00      0.00      0.00         0\n",
-      "           1       1.00      0.98      0.99       420\n",
-      "\n",
-      "    accuracy                           0.98       420\n",
-      "   macro avg       0.50      0.49      0.49       420\n",
-      "weighted avg       1.00      0.98      0.99       420\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "F1 score for anomalies set: **0.9892**."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "with_anomalies = scored_a[scored_a['Anomaly_pred_int'] == 1]\n",
     "percentage_identified = with_anomalies.shape[0]/scored_a.shape[0]\n",
@@ -3228,77 +1355,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 801,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Model</th>\n",
-       "      <th>Model type</th>\n",
-       "      <th>F1 score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>LSTM Autoencoder</td>\n",
-       "      <td>Deep Learning model</td>\n",
-       "      <td>0.989170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>DecisionTreeClassifier</td>\n",
-       "      <td>Statistical model</td>\n",
-       "      <td>0.978261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>SVC</td>\n",
-       "      <td>Statistical model</td>\n",
-       "      <td>0.927757</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>GaussianNB</td>\n",
-       "      <td>Statistical model</td>\n",
-       "      <td>0.784483</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                    Model           Model type  F1 score\n",
-       "3        LSTM Autoencoder  Deep Learning model  0.989170\n",
-       "0  DecisionTreeClassifier    Statistical model  0.978261\n",
-       "1                     SVC    Statistical model  0.927757\n",
-       "2              GaussianNB    Statistical model  0.784483"
-      ]
-     },
-     "execution_count": 801,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "results = pd.DataFrame(summary_results)\n",
     "results.sort_values(by=['F1 score'], ascending=False)"
@@ -3319,59 +1378,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 803,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2020-09-03 11:22:50,162 [1305416] WARNING  py.warnings:99: [JupyterRequire] /home/fmurdaca/.local/share/virtualenvs/manuela-dev-SY9YZsQk/lib64/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
-      "  _warn_prf(average, modifier, msg_start, len(result))\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Percentage of anomalies identified: 96.19%\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "           0       0.00      0.00      0.00         0\n",
-      "           1       1.00      0.96      0.98       420\n",
-      "\n",
-      "    accuracy                           0.96       420\n",
-      "   macro avg       0.50      0.48      0.49       420\n",
-      "weighted avg       1.00      0.96      0.98       420\n",
-      "\n",
-      "Score is: 0.9806.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from joblib import dump, load\n",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# from joblib import dump, load\n",
+    "\n",
+    "# tf.keras.models.save_model(\n",
+    "#     lstm_autoencoder, \"file_name\", overwrite=True, include_optimizer=True, save_format=None,\n",
+    "#     signatures=None, options=None\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Validate that the model can be loaded\n",
+    "\n",
+    "# load the model from disk\n",
+    "# loaded_model = tf.keras.models.load_model(file_name)\n",
+    "\n",
     "from pathlib import Path\n",
     "\n",
-    "current_path = Path().cwd()\n",
+    "current_path = Path.cwd()\n",
+    "print(current_path)\n",
+    "# Create a new model instance\n",
+    "loaded_model = get_model(X=x_train_healthy)\n",
+    "# Restore the weights\n",
+    "epoch_selected = \"epoch100\"\n",
+    "checkpoint_selected = \"epoch100ckpt\"\n",
+    "path_to_weights = current_path.joinpath(\"lstm_autoencoder_models\", epoch_selected, checkpoint_selected)\n",
+    "print(path_to_weights)\n",
+    "loaded_model.load_weights(current_path.joinpath(\"lstm_autoencoder_models\", epoch_selected, checkpoint_selected))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_summary = []\n",
+    "from numpy import arange\n",
     "\n",
-    "filepath = current_path.joinpath('ml-models/anomaly-detection/lstm_autoencoder_model')\n",
+    "for threshold_ in arange(min(train_mae_loss), max(train_mae_loss), mean(train_mae_loss)):\n",
+    "    x_test_pred = predict_results(loaded_model, x_test_healthy)\n",
     "\n",
-    "tf.keras.models.save_model(\n",
-    "    lstm_autoencoder, filepath, overwrite=True, include_optimizer=True, save_format=None,\n",
-    "    signatures=None, options=None\n",
-    ")\n",
+    "    x_test_pred_ = x_test_pred.reshape(x_test_pred.shape[0], x_test_pred.shape[2])\n",
+    "    x_test_healthy_ = x_test_healthy.reshape(x_test_healthy.shape[0], x_test_healthy.shape[2])\n",
     "\n",
-    "# Validate that the model can be loaded\n",
+    "    test_mae_loss = np.mean(np.abs(x_test_pred_ - x_test_healthy_), axis=1)\n",
     "\n",
-    "# load the model from disk\n",
-    "loaded_model = tf.keras.models.load_model(filepath)\n",
-    "x_all_pred = loaded_model.predict(x_all_anomalies)\n",
+    "    scored_test = {}\n",
+    "    scored_test['Loss_mae'] = test_mae_loss\n",
+    "    scored_test['Threshold'] = threshold_\n",
+    "    scored_tt = pd.DataFrame(scored_test)\n",
+    "    scored_tt['Healthy_pred'] = scored_tt['Loss_mae'] < scored_tt['Threshold']\n",
+    "    scored_tt['Healthy_pred_int'] = scored_tt['Healthy_pred'].astype(int)\n",
+    "\n",
+    "    scored_tt['Target'] = y_test_healthy.values\n",
+    "    no_anomalies = scored_tt[scored_tt['Healthy_pred_int'] == 0]\n",
+    "    percentage_identified = no_anomalies.shape[0]/scored_t.shape[0]\n",
+    "\n",
+    "    \n",
+    "    x_all_pred = predict_results(loaded_model, x_all_anomalies)\n",
+    "    all_mae_loss = np.mean(np.abs(x_all_pred - x_all_anomalies), axis=1)\n",
+    "\n",
+    "    scored_test_aa = {}\n",
+    "    scored_test_aa['Loss_mae'] = all_mae_loss[:, 0]\n",
+    "    scored_test_aa['Threshold'] = threshold_\n",
+    "\n",
+    "    scored_aa = pd.DataFrame(scored_test_aa)\n",
+    "    scored_aa['Anomaly_pred'] = scored_aa['Loss_mae'] > scored_aa['Threshold']\n",
+    "    scored_aa['Anomaly_pred_int'] = scored_aa['Anomaly_pred'].astype(int)\n",
+    "\n",
+    "    scored_aa['Target'] = y_all_anomalies.values\n",
+    "\n",
+    "    with_anomalies = scored_aa[scored_aa['Anomaly_pred_int'] == 1]\n",
+    "    percentage_identified_val_set = with_anomalies.shape[0]/scored_aa.shape[0]\n",
+    "\n",
+    "    f1_score_ = f1_score(scored_aa['Target'], scored_aa['Anomaly_pred_int'])\n",
+    "\n",
+    "    result_summary = {\n",
+    "        \"threshold\": threshold_,\n",
+    "        \"percentage_anomalies_test\": percentage_identified*100,\n",
+    "        \"percentage_anomalies_val\": percentage_identified_val_set*100,\n",
+    "        \"f1_score\": f1_score_\n",
+    "    }\n",
+    "    \n",
+    "    results_summary.append(result_summary)\n",
+    "\n",
+    "pd.DataFrame(results_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_all_pred = predict_results(loaded_model, x_all_anomalies)\n",
     "all_mae_loss = np.mean(np.abs(x_all_pred - x_all_anomalies), axis=1)\n",
     "\n",
     "scored_test = {}\n",
     "scored_test['Loss_mae'] = all_mae_loss[:, 0]\n",
     "scored_test['Threshold'] = threshold\n",
+    "\n",
     "scored_a = pd.DataFrame(scored_test)\n",
     "scored_a['Anomaly_pred'] = scored_a['Loss_mae'] > scored_a['Threshold']\n",
     "scored_a['Anomaly_pred_int'] = scored_a['Anomaly_pred'].astype(int)\n",
@@ -3398,7 +1515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 718,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3423,21 +1540,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 719,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Initializing...\n",
-      "Load modelfile: model.joblib\n",
-      "Features types:  <class 'numpy.ndarray'> <class 'numpy.float32'>\n",
-      "Predict features:  [[16.1  15.4  15.32 13.47 17.7 ]]\n",
-      "Prediction:  [1]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "p = AnomalyDetection()\n",
     "    \n",