diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb index ac10b41..1c0dd66 100644 --- a/tutorials/tutorial3/tutorial.ipynb +++ b/tutorials/tutorial3/tutorial.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d93daba0", "metadata": {}, "outputs": [], @@ -198,7 +198,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76717f5a37704e30bb3f1a38e7003ea1", + "model_id": "7d2872aa19ec4653bd9d42ba84fa29be", "version_major": 2, "version_minor": 0 }, @@ -212,7 +212,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0097048c29dd4be39a547512d9b169e1", + "model_id": "fbe1678f8dbd41a2aa2f2d5b9801da48", "version_major": 2, "version_minor": 0 }, @@ -226,7 +226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48d42d6a9b8d46b5b53bc2787d3c51a4", + "model_id": "bd787e0f3d1f4649a5e7b075ec14baa6", "version_major": 2, "version_minor": 0 }, @@ -240,7 +240,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7edc4f22087241bd9e203d5a897878c2", + "model_id": "425446a460604c8aa52bb4d9b6f48376", "version_major": 2, "version_minor": 0 }, @@ -254,7 +254,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "748747f9d9a84086876d753cf9add48b", + "model_id": "c95cc85ce5d14e19b9ef5d392e05c3d7", "version_major": 2, "version_minor": 0 }, @@ -268,7 +268,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60e94c2b432a4d4d9bb851ca3a53671c", + "model_id": "cbc03af4532e43f0b407092a96884077", "version_major": 2, "version_minor": 0 }, @@ -282,7 +282,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "27e15be1112340328cb3b9180f265cb0", + "model_id": "21c2ee1ecd694f87ad3e84432fa612f7", "version_major": 2, "version_minor": 0 }, @@ -296,7 +296,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c82f36a3fa5b4b1583f04ca2ade49eea", + "model_id": "46533167290c41cca3811fa6160d130f", "version_major": 2, "version_minor": 0 }, @@ -310,7 +310,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "6324e0c199e843c68455ea212f15f1db", + "model_id": "80fe10514fb04056b56335b4aff30255", "version_major": 2, "version_minor": 0 }, @@ -1514,7 +1514,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b9d012d9a3a84c50ae6d07e3f528676f", + "model_id": "bcd36f11ffe04fbcaecd279c74175c16", "version_major": 2, "version_minor": 0 }, @@ -1528,7 +1528,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "adb2211bbc8143db8e457b9e7a5a52a8", + "model_id": "5a1889c988654fd99805dff3ab5f5e84", "version_major": 2, "version_minor": 0 }, @@ -1542,7 +1542,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2aada9410cd9474686a4bdf04b361b92", + "model_id": "19aa82d7ca7f43c6b232ed15b2420688", "version_major": 2, "version_minor": 0 }, @@ -1556,7 +1556,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "104bc974638d438b8507540b70f08d66", + "model_id": "39b0a2b97a5f4b4e8da509fe786a10d2", "version_major": 2, "version_minor": 0 }, @@ -1570,7 +1570,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0be0e5967f744c7beb0867e40d7a571", + "model_id": "59cc6a178cd341e88e92b9658303d133", "version_major": 2, "version_minor": 0 }, @@ -1584,7 +1584,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b2c3216dee449a4a71c9cd083f8427b", + "model_id": "51aed4ee26724f10a6201000f0ed826f", "version_major": 2, "version_minor": 0 }, @@ -1598,7 +1598,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c8e8a7d76834babab88f4fb10753e47", + "model_id": "b9850a8f3d924bef9d19c7ac6b0ac387", "version_major": 2, "version_minor": 0 }, @@ -1612,7 +1612,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a17f4277dc65411e85c5da1789914e76", + "model_id": "6a5c95dbdc9546619807c69916991023", "version_major": 2, "version_minor": 0 }, @@ -1626,7 +1626,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b819f5a9fa58478583836f420472fc86", + "model_id": "ea2c042f92e841ce85eadd3154bec4cc", "version_major": 2, "version_minor": 0 }, @@ -1640,7 +1640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "de25821dd7bf498d956197c6be18afb9", + "model_id": "e3b747308af94a77bc46a17a3573a070", "version_major": 2, "version_minor": 0 }, @@ -1864,7 +1864,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fa4a91456ca4433daa8f44372be6fce3", + "model_id": "3a9cd8e41c74452d8ce856a0bd8006e2", "version_major": 2, "version_minor": 0 }, @@ -1878,7 +1878,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "901a9d8bff90475096d10bdca4f142b3", + "model_id": "61c9ff989b96498e888d652490805994", "version_major": 2, "version_minor": 0 }, @@ -1892,7 +1892,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0082865f3d641d1a8c856a07ecc5d58", + "model_id": "23e3995386924aa381663180b6d12b95", "version_major": 2, "version_minor": 0 }, @@ -1906,7 +1906,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1c60eff23ad45ae8c5d199f680ffca3", + "model_id": "e35b9e9a04bc4f1d9fee4283501ea2d7", "version_major": 2, "version_minor": 0 }, @@ -1920,7 +1920,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f98337f888e4eafaec01d2c44523ef4", + "model_id": "ff2fc9653dba4779ba3dafb69c372e3b", "version_major": 2, "version_minor": 0 }, @@ -1934,7 +1934,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a538e00a00c45af94d5b97fba5985bc", + "model_id": "e109c5edfb3748968bb0ddca906708f1", "version_major": 2, "version_minor": 0 }, @@ -1948,7 +1948,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7677d88da31e4750b3d789b658e31e68", + "model_id": "9d97fb7790414da5a98c342965a746d5", "version_major": 2, "version_minor": 0 }, @@ -1962,7 +1962,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ba4e84ad1ca47a49c4044550998c1ee", + "model_id": "7a27da467c604dc883ff14249f7dd5d7", "version_major": 2, "version_minor": 0 }, @@ -1976,7 +1976,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7571b260e139475f850c6a0f40087238", + "model_id": "ab868d74978b451da12317f3992c274c", "version_major": 2, "version_minor": 0 }, @@ -1990,7 +1990,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "94b16ccb456d45b0b4f66e2f245c6866", + "model_id": "801c3a26e126489ab971d6875cc7e8f9", "version_major": 2, "version_minor": 0 }, @@ -2004,7 +2004,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93a92afa7af44ae1b077f66c8cfc522d", + "model_id": "48564f6176b14937ae6dc07568dc7b40", "version_major": 2, "version_minor": 0 }, @@ -2018,7 +2018,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "336da4ab72d64e65bc746797b3536a05", + "model_id": "45449338007349b6a1bee2cae5a37681", "version_major": 2, "version_minor": 0 }, @@ -2032,7 +2032,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21aa656a0aff4e91ab6c65f4b3ad3b6f", + "model_id": "81ab7aa97cd24a6689bedbc87ebd434e", "version_major": 2, "version_minor": 0 }, @@ -2046,7 +2046,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58b413b35010476fa28c4a089a37c711", + "model_id": "7ed0a78187e24c14bdcc8c0022708d72", "version_major": 2, "version_minor": 0 }, @@ -2060,7 +2060,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "43ad13aae1a7466082ca12c8999c26a1", + "model_id": "e84a7643dd2d4c8386473ff87daef9c0", "version_major": 2, "version_minor": 0 }, @@ -2074,7 +2074,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4b6d00e2321458bbbfd00759922ddba", + "model_id": "33f2138ee75141ecafe83189d3a3cc14", "version_major": 2, "version_minor": 0 }, @@ -2088,7 +2088,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0be14af3a716469e88d3ab0205e19f0a", + "model_id": "ddf9c5db73dd44b3bed1885ce5f6a905", "version_major": 2, "version_minor": 0 }, @@ -2102,7 +2102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd9c5585a6eb448e95b0e884e456076e", + "model_id": "70a2c7349fbf47ea92db1c2024529354", "version_major": 2, "version_minor": 0 }, @@ -2116,7 +2116,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "32e907f9d35546f7a1b7999e474f1473", + "model_id": "784b734723f04d40bc66fd5fd5023a7f", "version_major": 2, "version_minor": 0 }, @@ -2130,7 +2130,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "efb02e14da594bc98b0118b14e81120e", + "model_id": "6bbbe1911d7648d8a3a87a2aa7b2728c", "version_major": 2, "version_minor": 0 }, @@ -2354,7 +2354,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c7390edfbf74097b55e885447ee0cfe", + "model_id": "94661e5c32904cf6baee575ce8aff370", "version_major": 2, "version_minor": 0 }, @@ -2368,7 +2368,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5df67923a38f46729f16f286b3b57f5c", + "model_id": "fda3a208c78840e2b335ff4744b89eea", "version_major": 2, "version_minor": 0 }, @@ -2382,7 +2382,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee76c1f873eb404195c659fb3993e7be", + "model_id": "ac10ffdcf89e49b09289c08bf3674ac4", "version_major": 2, "version_minor": 0 }, @@ -2396,7 +2396,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "332f3f8584f741cebdd9a9368e40b891", + "model_id": "b97c5dec132d4a76b38774f94adb33f7", "version_major": 2, "version_minor": 0 }, @@ -2410,7 +2410,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "59638040af26425a9a5d0d0e1ddc3fcf", + "model_id": "06ca539e83354404a4315f8f6f14ef99", "version_major": 2, "version_minor": 0 }, @@ -2494,7 +2494,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b134261fe3c42d58a5a2922b762c2b7", + "model_id": "51c9d1d3ef58442685a8a1089d9af10e", "version_major": 2, "version_minor": 0 }, @@ -2508,7 +2508,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6fd180277c154e72941f6dfbb2b82c98", + "model_id": "c9c6a3c742f74dd193d63c44ca6e49a8", "version_major": 2, "version_minor": 0 }, @@ -2522,7 +2522,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e77cfbae5d9f471caadfd63dc57472c4", + "model_id": "e9e85436d451475a81dd364348a67154", "version_major": 2, "version_minor": 0 }, @@ -2536,7 +2536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4ac168313f1c4537957780dd63402bc2", + "model_id": "30a4676ca0a34735a55df2770db50794", "version_major": 2, "version_minor": 0 }, @@ -2550,7 +2550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb0d552e61534616adc872f162a64b77", + "model_id": "c881bf45220b4181be7a716f3b566155", "version_major": 2, "version_minor": 0 }, @@ -2564,7 +2564,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6d569664a24411286322addb7aa31da", + "model_id": "859483f7f4f84dd0b7e8b5588ad15e3a", "version_major": 2, "version_minor": 0 }, @@ -2578,7 +2578,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac839add1f9f4deab0201f967e960834", + "model_id": "6238248d60114cc18a50850fcc5a377f", "version_major": 2, "version_minor": 0 }, @@ -2592,7 +2592,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "517382b614984c3480a7b886863643e2", + "model_id": "71d89009cfbf4ffeb8af2dceb57c3004", "version_major": 2, "version_minor": 0 }, @@ -2606,7 +2606,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b2346cb7d73342c699b96434062cf778", + "model_id": "3e0d236c6f6a4bd3b0568ba6be497582", "version_major": 2, "version_minor": 0 }, @@ -2620,7 +2620,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68d3c50bc4814aaca7be1c3fb0e7740a", + "model_id": "7f91e24122484b0caa16230816112265", "version_major": 2, "version_minor": 0 }, @@ -2634,7 +2634,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fa4e1cd455c6407aa6c2231d52be1027", + "model_id": "d03e2193cf2441d2ab5e3fa975ee5d5c", "version_major": 2, "version_minor": 0 }, @@ -2648,7 +2648,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e94af5954d2145d2b40f1a42b63ee2ef", + "model_id": "96e2c6265c8c46bfbb20aaab9c2104a3", "version_major": 2, "version_minor": 0 }, @@ -2662,7 +2662,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ca5a50a6c8e495096fa30a5039fba09", + "model_id": "0181c8387606434885875d617e01ef29", "version_major": 2, "version_minor": 0 }, @@ -2676,7 +2676,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec275191ca29482f9c4a4d7f7f08c2b1", + "model_id": "f0b26e15a13541b39d9e20f60af06b53", "version_major": 2, "version_minor": 0 }, @@ -2690,7 +2690,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8573eee7798c45ad87cd4f6d7459b61b", + "model_id": "d37a848f2df04e32a2dd412f5be76445", "version_major": 2, "version_minor": 0 }, @@ -2704,7 +2704,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17c2b628682148e2996fe880bd9b46d7", + "model_id": "f1f2579257094c4390e30ab2f16f9487", "version_major": 2, "version_minor": 0 }, @@ -2718,7 +2718,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19a4b53025ab460faac535c3340bde37", + "model_id": "e8941b8e00b0446c8b4bcb60e49dc0cc", "version_major": 2, "version_minor": 0 }, @@ -2732,7 +2732,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f33a7e53e1dd4a7aae708d657706838d", + "model_id": "0dfc749075bb4c02a07f3be38500988c", "version_major": 2, "version_minor": 0 }, @@ -2746,7 +2746,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f8d516f78a44763977b8f83c3e660f9", + "model_id": "003dfe7e1ba14c72b9162cd77ef54b5f", "version_major": 2, "version_minor": 0 }, @@ -2760,7 +2760,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "019140d6ead64812b3f726851f5ef19f", + "model_id": "f139ab33772449a4b088f5018714d1ad", "version_major": 2, "version_minor": 0 }, @@ -2774,7 +2774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4732045e70934ba2b816b2d7eb6f2d57", + "model_id": "72df687702bb4069a07e609c0514ae68", "version_major": 2, "version_minor": 0 }, @@ -2788,7 +2788,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57a5060681b848188ef63594b8d755b4", + "model_id": "8a9363ff30854ac0a03ac435457fde15", "version_major": 2, "version_minor": 0 }, @@ -2802,7 +2802,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "747f715bb0c74522926d51f4836f5e1c", + "model_id": "33ca4675fdc143378eae9beb90978083", "version_major": 2, "version_minor": 0 }, @@ -2816,7 +2816,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "081f275e092e4451afb0389cfb6a8331", + "model_id": "50ca3c706adb4d9f82adf622f9c4bc11", "version_major": 2, "version_minor": 0 }, @@ -2830,7 +2830,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "939754639d664cd48d52220b2cb5a956", + "model_id": "d1a8803021ce41a3a98d3d12591414bb", "version_major": 2, "version_minor": 0 }, @@ -2844,7 +2844,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bb392af3ff07484f989d4dcb078921c0", + "model_id": "1f7d80fa96364814a1347d75a6a21fba", "version_major": 2, "version_minor": 0 }, @@ -2858,7 +2858,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "237e86de6ea64c6d9a628bc9e4e0fd65", + "model_id": "b52bc001daeb4ebeb657ddc2108d29f6", "version_major": 2, "version_minor": 0 }, @@ -2872,7 +2872,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c01abac0e8249f59e6e33c04614500e", + "model_id": "c615dc776e6045158954476d32896c87", "version_major": 2, "version_minor": 0 }, @@ -2886,7 +2886,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "daf25cac4c8146548f25242b2e3ec907", + "model_id": "dfd1bcc43bcd48489d3c2c175dff6326", "version_major": 2, "version_minor": 0 }, @@ -2900,7 +2900,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "d7dd8e38e4024253a7bd16f6cd7fabea", + "model_id": "a0f12c5173ba4723a11ac47df885f2ef", "version_major": 2, "version_minor": 0 }, @@ -2984,7 +2984,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68fabc1f39944835805fc799999ec0a6", + "model_id": "a085a9f0f99849e99a4a593759e37393", "version_major": 2, "version_minor": 0 }, @@ -2998,7 +2998,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b9f781a7e7f749409c84fc6be9fed972", + "model_id": "b8c590b87f7a47a1bbfe1edc3e8cd82f", "version_major": 2, "version_minor": 0 }, @@ -3012,7 +3012,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1f255d940e8a450487c5f170a6ff9310", + "model_id": "13dd56e841ad470ebe03a00091d54ecd", "version_major": 2, "version_minor": 0 }, @@ -3026,7 +3026,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56f0283f45fc4aa6844b66134cf07a0e", + "model_id": "6d25415000be402482a5d8caa5a55762", "version_major": 2, "version_minor": 0 }, @@ -3040,7 +3040,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e58ea30936548de9d304552408f8aa1", + "model_id": "343e3d40c67f4294b499eea5cdbc54a3", "version_major": 2, "version_minor": 0 }, @@ -3054,7 +3054,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac5990eb87a14478a6a008d46b581197", + "model_id": "d9ae4494832d4d06bec599b60d5ffb93", "version_major": 2, "version_minor": 0 }, @@ -3068,7 +3068,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc0f0c608f9c4741bbade6132cdcecc9", + "model_id": "c497561c0c8940149d06667a658eb2f3", "version_major": 2, "version_minor": 0 }, @@ -3082,7 +3082,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c47e0f71c7f944e5895b7fa03582bd68", + "model_id": "69b5b006f56346cdb63f4972dee159e2", "version_major": 2, "version_minor": 0 }, @@ -3096,7 +3096,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3615023cecc14a05a06ae1db1cd84c8e", + "model_id": "0ae25f8b25a04174ad8a8aff19b16f0b", "version_major": 2, "version_minor": 0 }, @@ -3110,7 +3110,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "d193365a742143adaf6dcfd8c394b61d", + "model_id": "42a83b87e4e84245a1c2141fa901ee8d", "version_major": 2, "version_minor": 0 }, @@ -3194,7 +3194,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b00af8db6b19426196988544ec4ba72c", + "model_id": "84def8ee98c447e28700d4093bd54b66", "version_major": 2, "version_minor": 0 }, @@ -3208,7 +3208,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e910edd06c8430cbc51c22b12323f6c", + "model_id": "c81b40e69a594c46a9e856740853ebb0", "version_major": 2, "version_minor": 0 }, @@ -3222,7 +3222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa59ff696a4a4f2f9ce5130c4e402f8f", + "model_id": "9214766dfeae4e46bab299697f10eca6", "version_major": 2, "version_minor": 0 }, @@ -3236,7 +3236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "841bdd068bc1497a82f5c9e7642aa067", + "model_id": "7c22502fca7747d6b11d3a223d2cd33a", "version_major": 2, "version_minor": 0 }, @@ -3250,7 +3250,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3598a710b64406faecb808d7f2a94f4", + "model_id": "a95fe3af7b644317ab602aba52b9364b", "version_major": 2, "version_minor": 0 }, @@ -3264,7 +3264,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc4b25a12e744a299f5e659001117a61", + "model_id": "fe7766e6ca254536b852f0b5dd4ab841", "version_major": 2, "version_minor": 0 }, @@ -3278,7 +3278,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9891b1752fa44d11ae2756671bc960c8", + "model_id": "8932ae7754f44c4285a04eb617643c3a", "version_major": 2, "version_minor": 0 }, @@ -3292,7 +3292,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "879d1ad32aa244de8e797d5a4694c569", + "model_id": "c2179424d5954e08846a203fef3e4506", "version_major": 2, "version_minor": 0 }, @@ -3306,7 +3306,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7857094a8b3a4cd4b5b8e48707882abf", + "model_id": "9e17a8c1f1294d088b5273f9cd67e01e", "version_major": 2, "version_minor": 0 }, @@ -3320,7 +3320,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f6e24c60a3d046f9997b7d836d028b3d", + "model_id": "32765f5112dd4b95a37b46bace550333", "version_major": 2, "version_minor": 0 }, @@ -3334,7 +3334,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b105e60e8b64398902dbf4c59ba3bd1", + "model_id": "29ea004c8fe04e74b2c304a9c71a8974", "version_major": 2, "version_minor": 0 }, @@ -3348,7 +3348,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "573b9ecb1e774cd9801b83e8894d587a", + "model_id": "b0dc5b89baa74243a8ca682f235b7647", "version_major": 2, "version_minor": 0 }, @@ -3362,7 +3362,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04893e3acc8445db968d11d85ce56e8e", + "model_id": "9d588f40448d42af94f816f7aafa0c7c", "version_major": 2, "version_minor": 0 }, @@ -3376,7 +3376,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a669e1a414b947eaa91ef6469ca01f0d", + "model_id": "39e4eb93d2764163bd923e659afbc518", "version_major": 2, "version_minor": 0 }, @@ -3390,7 +3390,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2ad440dbb4b466c93b0d87748f9f17d", + "model_id": "823eb669a93b4461be10f14843221581", "version_major": 2, "version_minor": 0 }, @@ -3404,7 +3404,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "86f536391f844fa687687a52a8a094f5", + "model_id": "e2a3ecbdf2ea4f65a278c464b8898e49", "version_major": 2, "version_minor": 0 }, @@ -3418,7 +3418,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7b4fa7673024c40870b184f62ea3029", + "model_id": "2c15b5cc29e842709cdfc4cb93d736e5", "version_major": 2, "version_minor": 0 }, @@ -3432,7 +3432,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bae20062a4014ca9bace1a128815d798", + "model_id": "ae18e13b64894c1db6dd72dfcd6d5d3f", "version_major": 2, "version_minor": 0 }, @@ -3446,7 +3446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6679091c05524696bed37cc89fb52506", + "model_id": "36f69dd79133478da6edd591391b71b3", "version_major": 2, "version_minor": 0 }, @@ -3460,7 +3460,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c0535404564453da4750c01f1393fd8", + "model_id": "2889b92e2bab4320980ddc27b8b541d5", "version_major": 2, "version_minor": 0 }, @@ -3474,7 +3474,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f0c061ae6c54d84b50f9cf8a8da13a3", + "model_id": "aa522fd483094872b52da7e97c8620d3", "version_major": 2, "version_minor": 0 }, @@ -3488,7 +3488,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2135fcb7313349bf83a45f574faa55fa", + "model_id": "9b3f5db7aa6c456da61ae78d9d03447c", "version_major": 2, "version_minor": 0 }, @@ -3502,7 +3502,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73152e955c8d4969b4fc26eeda5e4fc6", + "model_id": "027412f38b99427891aaea6f6c17a001", "version_major": 2, "version_minor": 0 }, @@ -3516,7 +3516,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd87b21b6d3d4355beeeb68d722dca30", + "model_id": "dff0b64ab4d44d258460aa8f0b61567e", "version_major": 2, "version_minor": 0 }, @@ -3530,7 +3530,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9dad13d10ad84c0c97266f3cf6195018", + "model_id": "fd4555a85d24449ab23fcae189faa67e", "version_major": 2, "version_minor": 0 }, @@ -3544,7 +3544,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8954086ef7a0494e86346addd0dacff8", + "model_id": "b0ceeb465ff344f0b24f86fca38a8e4c", "version_major": 2, "version_minor": 0 }, @@ -3558,7 +3558,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2370525a5ef148d896ce27d4b264bfd7", + "model_id": "e6a7fb7f53d64fa281713ce0223e4578", "version_major": 2, "version_minor": 0 }, @@ -3572,7 +3572,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "729f9f68061a47df8989371fbade151d", + "model_id": "654911441b2a433e885d1e4c5f8a0c40", "version_major": 2, "version_minor": 0 }, @@ -3586,7 +3586,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "442c164cb41f4185bb4424f966e8c977", + "model_id": "83824141595a4bcf9451409fc27753c7", "version_major": 2, "version_minor": 0 }, @@ -3600,7 +3600,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "262f3c9f18994d9faf0df4ff3bf5e7f1", + "model_id": "383d197b3b0a457a837a1b241f0fa33d", "version_major": 2, "version_minor": 0 }, @@ -3614,7 +3614,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "adbbc79d219a48b7b0fea3fb736d620d", + "model_id": "137f9eca83a7428885a7529b2b3af6ee", "version_major": 2, "version_minor": 0 }, @@ -3628,7 +3628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "936f22a38dd64be891d162ef82726ddb", + "model_id": "e3383afa7a2d43b88f2d054755e87a52", "version_major": 2, "version_minor": 0 }, @@ -3642,7 +3642,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afde8727fcee47a59b87c2edea1b995b", + "model_id": "a46d16dda5c14046beb677871ba4a448", "version_major": 2, "version_minor": 0 }, @@ -3656,7 +3656,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edfbfbdc17ce44e1990b6c2424699f11", + "model_id": "e13538a5411341398328f4c83389d350", "version_major": 2, "version_minor": 0 }, @@ -3670,7 +3670,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57f6fa02ac1e41c3a291c08d548745be", + "model_id": "4ddd6f8aa6a2437684f030e7defa81a7", "version_major": 2, "version_minor": 0 }, @@ -3684,7 +3684,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c5b60961f3a4903a0aaf1ce8c76fb39", + "model_id": "3f4a6b44d4ab4dbb95d4c3cd0891077f", "version_major": 2, "version_minor": 0 }, @@ -3698,7 +3698,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "46a897d3d658406cafaa137095ae0721", + "model_id": "ec4c8ca5772b4f48bbda1f31579bb30f", "version_major": 2, "version_minor": 0 }, @@ -3712,7 +3712,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "431eaddba7514c4abd2af65c7ace539d", + "model_id": "fd8dd4e11b09490b816e7e2e4c535a51", "version_major": 2, "version_minor": 0 }, @@ -3726,7 +3726,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "03a20f70d3ed4fd8b08854c63de02d0a", + "model_id": "8520d9be5f6b49e0a7034d72802e7f72", "version_major": 2, "version_minor": 0 }, @@ -3740,7 +3740,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "502431786c004c73a8d846d7f6a508bd", + "model_id": "ce18e445e6d44e7aaeda7ef89db5f140", "version_major": 2, "version_minor": 0 }, @@ -3754,7 +3754,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "600fc3927c8845f9ac9259c77c5149d2", + "model_id": "604a7d32316642c1b1be2194606830bc", "version_major": 2, "version_minor": 0 }, @@ -3768,7 +3768,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a48403c3088f401d9ad5baf968337a0c", + "model_id": "472a2345356c4c7497a845e1255d5aec", "version_major": 2, "version_minor": 0 }, @@ -3782,7 +3782,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31057fa6c89e4984bcd9ac405e784888", + "model_id": "181da02f7096454d93b63841046aca9f", "version_major": 2, "version_minor": 0 }, @@ -3796,7 +3796,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a12ffa67b0ee4663a1aa2a84624f8e83", + "model_id": "d571464cb74d42afb26118eb78366014", "version_major": 2, "version_minor": 0 }, @@ -3810,7 +3810,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f73faf7dd5b45bc9a71775e3e25810a", + "model_id": "7bb0b883217948a1ba9d58da016ca8e6", "version_major": 2, "version_minor": 0 }, @@ -3824,7 +3824,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3431a2cb85d147ba92e5161db2d7a774", + "model_id": "ccd3586446054c52aee65b2fae8cc8a2", "version_major": 2, "version_minor": 0 }, @@ -3838,7 +3838,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd10c36b87c047af9865dfbc79dcd05a", + "model_id": "3ac41333f1694c36908a51e847be393f", "version_major": 2, "version_minor": 0 }, @@ -3852,7 +3852,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60d03091e16e4c0789052cbbf292da68", + "model_id": "bb85dece726d4b3b887c1a3b39c70add", "version_major": 2, "version_minor": 0 }, @@ -3866,7 +3866,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d9562b2ce854fd1b30b4a47b2f4f92e", + "model_id": "55bee6c410e8403eaba16fc4e499d6be", "version_major": 2, "version_minor": 0 }, @@ -3880,7 +3880,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "f5ad194befee445eae9c145bd5dfc4a8", + "model_id": "6e105cb39a2c48f68b05a62e3865d7af", "version_major": 2, "version_minor": 0 }, @@ -4524,7 +4524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f09bd8f9d40419d9d9ffc775120e3b5", + "model_id": "e8dae830b54646b6849af5992795cc43", "version_major": 2, "version_minor": 0 }, @@ -4538,7 +4538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a4a10cc29324ed6ae5dd62e7ffcdba7", + "model_id": "d93409e100a745518392c933af9a9c3c", "version_major": 2, "version_minor": 0 }, @@ -4552,7 +4552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c47dff3efc745bbbedf9e02b7094702", + "model_id": "daa62b0cda244d578ed945510be9c70a", "version_major": 2, "version_minor": 0 }, @@ -4566,7 +4566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7c36fec6ba534198ad53558cad531b31", + "model_id": "4f6c44d87ae5487dad213d946eef43d5", "version_major": 2, "version_minor": 0 }, @@ -4580,7 +4580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30dafd24bae244329e63d87bc0d8ff06", + "model_id": "9e8cc6b971dd4800988c5684b1763c5c", "version_major": 2, "version_minor": 0 }, @@ -4594,7 +4594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e11dd1750d64c83b2649526b869d740", + "model_id": "e33f97fbc68842ce8cdc40f64f07fbe2", "version_major": 2, "version_minor": 0 }, @@ -4608,7 +4608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0b82e6793624252a206bf9873d1c7a7", + "model_id": "a061b56fe0ea401592a60dfd1656b00d", "version_major": 2, "version_minor": 0 }, @@ -4622,7 +4622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a37d301ad69148bc917f367c9dd00327", + "model_id": "836bb8503af84a7daa39753e3f6ccdde", "version_major": 2, "version_minor": 0 }, @@ -4636,7 +4636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3aa98c858ed94a2196d3cd98de5e93f1", + "model_id": "c673ca8bb42542dfb5090accbe8acb0f", "version_major": 2, "version_minor": 0 }, @@ -4650,7 +4650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bb618bae1fed4ab790ed8b3fbaffeaf9", + "model_id": "4a956c0ac0f64558914eb61ba5bbc4b6", "version_major": 2, "version_minor": 0 }, @@ -4664,7 +4664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41da3ac9f7754339acaef57bf2d534af", + "model_id": "06d19b2b90be49049dbf85b4727b593c", "version_major": 2, "version_minor": 0 }, @@ -4678,7 +4678,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f86b227912a4ae7a1d8c11eef38eae0", + "model_id": "f3e13fcb30e54259987ef71599bb81b9", "version_major": 2, "version_minor": 0 }, @@ -4692,7 +4692,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30ef613f7c034d5498e1f52f5a91880a", + "model_id": "177ddfe2429d453580b82ff4b87c47b7", "version_major": 2, "version_minor": 0 }, @@ -4706,7 +4706,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30b9656aaaf6435593dffb3d99f4f43a", + "model_id": "e92a3994af99494eb92e57ecc1b2d934", "version_major": 2, "version_minor": 0 }, @@ -4720,7 +4720,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d91b5b95f2304102847be3c9688506f4", + "model_id": "64581eafef71419aa452e9c7cb69a49c", "version_major": 2, "version_minor": 0 }, @@ -4734,7 +4734,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9991387437ad4a03947219d07e622fa2", + "model_id": "ee9474cef3204db7bab6bf34c8ab5d11", "version_major": 2, "version_minor": 0 }, @@ -4748,7 +4748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e5babdbdd914dd9bda2a2c18fe6f312", + "model_id": "ae9125ddce59443ea2840c2ffbbec96c", "version_major": 2, "version_minor": 0 }, @@ -4762,7 +4762,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee7e77bd8e8d4356949c619503010953", + "model_id": "642f0af755da4baf99a1a98c117938b2", "version_major": 2, "version_minor": 0 }, @@ -4776,7 +4776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abe9a19cc482452fb668eb0d35bdbbff", + "model_id": "2d301fb2582d48fda60d396196f12c51", "version_major": 2, "version_minor": 0 }, @@ -4790,7 +4790,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31b77dbd516d4269a791f0dad03824fb", + "model_id": "c15d8dbf947d4ae1a94ee034e9ba59df", "version_major": 2, "version_minor": 0 }, @@ -4804,7 +4804,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15da6241ede84532858a35fa30e28b6d", + "model_id": "363408a0a6b943f197f48fbb2130c168", "version_major": 2, "version_minor": 0 }, @@ -4818,7 +4818,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f7f465d7db140ebaf66d8442f73a408", + "model_id": "8e80842a88af45dfba5b9340b3b3d308", "version_major": 2, "version_minor": 0 }, @@ -4832,7 +4832,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e97d253091364078a5c376b832d44449", + "model_id": "1ae2d64c7f3c458f8e00bd05cf0a9efb", "version_major": 2, "version_minor": 0 }, @@ -4846,7 +4846,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e937cf350a84da0bbe327f4d3aa63af", + "model_id": "045bdbc445ed458790ecc2f9fc391098", "version_major": 2, "version_minor": 0 }, @@ -4860,7 +4860,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "c2309689533e4cd1a928b6b8401b2fcd", + "model_id": "3cdc50e9331042a4b09e771c8ddc9311", "version_major": 2, "version_minor": 0 }, @@ -5014,7 +5014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "624004331b3a48b49ecf7b1d8aa7999e", + "model_id": "8a9891a5b69f41a2b2ac83b07c00a5ec", "version_major": 2, "version_minor": 0 }, @@ -5028,7 +5028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6bf6136fbc064534b29f60ad6cde4e7e", + "model_id": "93b27b1c2d15408ca4c246c74e8d13ca", "version_major": 2, "version_minor": 0 }, @@ -5042,7 +5042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f607e55ec2f4ffbac4e127e93f33405", + "model_id": "17bb694322a44520898b58f9494b1109", "version_major": 2, "version_minor": 0 }, @@ -5056,7 +5056,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5fdae6bf6c834f858e2d473f15517734", + "model_id": "d31c620888a04c83bf1e505d01b0d3b1", "version_major": 2, "version_minor": 0 }, @@ -5070,7 +5070,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "cf54931a1ffd4be788d079d40ac77b55", + "model_id": "73f1684428ce4454931fd2092474048b", "version_major": 2, "version_minor": 0 }, @@ -5154,7 +5154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c347e0965230462482985f273553b5f4", + "model_id": "ec7810da8b2c493da224904b6818b88e", "version_major": 2, "version_minor": 0 }, @@ -5168,7 +5168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1863fa2ab8bf474d92e6db6f552b328a", + "model_id": "878bcb00bb2d47b1baedabf303691182", "version_major": 2, "version_minor": 0 }, @@ -5182,7 +5182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "53a73f14615a4b8ab6042686557aa292", + "model_id": "8e1f212d58c549bd86f3b37cc2680389", "version_major": 2, "version_minor": 0 }, @@ -5196,7 +5196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "07c9852fd34840968cae97f2c16381bf", + "model_id": "db6931ee46294fc094ea47793a27e1ea", "version_major": 2, "version_minor": 0 }, @@ -5210,7 +5210,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fecb5a0fcfff44ed88cc8589895a77d9", + "model_id": "f632a65ce4684fe991548c9987dafe5a", "version_major": 2, "version_minor": 0 }, @@ -5224,7 +5224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0dfa8fb931f24a18972bfbd099ad46b9", + "model_id": "193a863743fb46aa9c27f2dce249756c", "version_major": 2, "version_minor": 0 }, @@ -5238,7 +5238,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62517ff24a20479f95733778bf8c9d65", + "model_id": "c1d8cdad3f0c4f64b321b658b819aa7c", "version_major": 2, "version_minor": 0 }, @@ -5252,7 +5252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61257a1841aa4619ade3891ec28cf4b0", + "model_id": "b1d0aebc303e4788a141c659c07792b2", "version_major": 2, "version_minor": 0 }, @@ -5266,7 +5266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05a697b7c666439f97fa3b652a1de559", + "model_id": "bedcbc16961540c9aadb40b84e4455a8", "version_major": 2, "version_minor": 0 }, @@ -5280,7 +5280,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35d16903427e4775b59631a1011c2336", + "model_id": "180837e503fd45628137bbfe1e40d46d", "version_major": 2, "version_minor": 0 }, @@ -5294,7 +5294,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e4b070a31434510952627fbecd49f19", + "model_id": "1f7c70f61c9d4a67b54e0f076619c4fb", "version_major": 2, "version_minor": 0 }, @@ -5308,7 +5308,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fba954740f484f3abb37ee09b33a716a", + "model_id": "dedcd0ecb453439bbe420bbffb57d8cb", "version_major": 2, "version_minor": 0 }, @@ -5322,7 +5322,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0435596a15644badac050f97b2c39cec", + "model_id": "70c434b289a749d088444fb5cdc4e4b0", "version_major": 2, "version_minor": 0 }, @@ -5336,7 +5336,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abc3ad7d017c41e0adb5f07436bde669", + "model_id": "53001010e3d947878223e52f028a24a9", "version_major": 2, "version_minor": 0 }, @@ -5350,7 +5350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "255b6590ecf84d2da09d3dd5b0a3cbef", + "model_id": "bd597fbc299e4ef2bb715f395236a86b", "version_major": 2, "version_minor": 0 }, @@ -5434,7 +5434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb533e6cb58e4809a94124bfaa4aab68", + "model_id": "9bec31bae8434c598dae36ab5fb52720", "version_major": 2, "version_minor": 0 }, @@ -5448,7 +5448,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3acbb4f252f24a4eab4582102f37d77e", + "model_id": "8924fcde08da413194504b0bacdfa419", "version_major": 2, "version_minor": 0 }, @@ -5462,7 +5462,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3f2e56be23b44e3b2497668fdbe7566", + "model_id": "4bb514686f84491c9ec5126c74bf6bd7", "version_major": 2, "version_minor": 0 }, @@ -5476,7 +5476,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0fbe9c8d51f74452bc8f1b4598989cb8", + "model_id": "fb8c9e9cf074453db3c5414ab4e29960", "version_major": 2, "version_minor": 0 }, @@ -5490,7 +5490,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1955118b68654cbfae84e29a32717b47", + "model_id": "cb8a1473d9d7486d89445456969316bb", "version_major": 2, "version_minor": 0 }, @@ -5504,7 +5504,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68a386614a094db5a7235c6bb0508b82", + "model_id": "20424719dfc54ef69f482f3dfa5b3b5b", "version_major": 2, "version_minor": 0 }, @@ -5518,7 +5518,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6fd88c5679344df8b0ac4df03a4576d8", + "model_id": "f7ff7da82d28416e864ab183218280e4", "version_major": 2, "version_minor": 0 }, @@ -5532,7 +5532,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d85e5ac2d6414d849499188957e280ec", + "model_id": "664537b276a6414e8d1271579601931e", "version_major": 2, "version_minor": 0 }, @@ -5546,7 +5546,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74e0be7c0cf045f08abbae31eafdcb1f", + "model_id": "69fb46cc0c6d4276a48a33825f2db255", "version_major": 2, "version_minor": 0 }, @@ -5560,7 +5560,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38b74eea46ab4713911a08b5f90bd45e", + "model_id": "0af380a1d6114cb08eaa69a5f343cf67", "version_major": 2, "version_minor": 0 }, @@ -5574,7 +5574,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2cc9fccb4f594581a19eed1ee3a94a53", + "model_id": "bec250ed22914a6e91ad2dd4295b85de", "version_major": 2, "version_minor": 0 }, @@ -5588,7 +5588,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f20390c4fd4484f8da007bd51dab66a", + "model_id": "e1e39f098c914dd8b9987a8f6184e67e", "version_major": 2, "version_minor": 0 }, @@ -5602,7 +5602,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7cab42f03ac4ed28467404887f04267", + "model_id": "f7cf58aa0b07470c9779c54a6b18d5c6", "version_major": 2, "version_minor": 0 }, @@ -5616,7 +5616,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9899d1c9f5ac4f089f65d58fe8e2ef08", + "model_id": "55dd379251db4a27a7a4d66298a86280", "version_major": 2, "version_minor": 0 }, @@ -5630,7 +5630,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24c2ca522ae94d8fae24089afd8771a4", + "model_id": "fea78d10f5cf4f3ca7e0de715b580215", "version_major": 2, "version_minor": 0 }, @@ -5644,7 +5644,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60fbca8e61194b19bf8eb50bbaf77d51", + "model_id": "d3bbc218500443fba98fc76acb9bbdc4", "version_major": 2, "version_minor": 0 }, @@ -5658,7 +5658,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e97378220c744a8baa50b3fbda38a2f5", + "model_id": "105a340670c44d92bd05c341a74ffe7c", "version_major": 2, "version_minor": 0 }, @@ -5672,7 +5672,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bcc444979ef54d508718d63d691596e1", + "model_id": "9b6761e0fb544c608ab6f928acc06fc7", "version_major": 2, "version_minor": 0 }, @@ -5686,7 +5686,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "185657239e394ecbb9bd308be3a2fa2f", + "model_id": "40cfd1c84f3747f0a957667b782e5cab", "version_major": 2, "version_minor": 0 }, @@ -5700,7 +5700,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a0741d159794a9697851cb998ab4671", + "model_id": "2a24bfd0ffd44332a11775600c2f7235", "version_major": 2, "version_minor": 0 }, @@ -5714,7 +5714,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a511f9b5da7f4ed4901a4762d44d0de9", + "model_id": "8a9be7fe242b4f4ba36b2086a61f35b4", "version_major": 2, "version_minor": 0 }, @@ -5728,7 +5728,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "69f7424d4c9649f18a88a52f68cab773", + "model_id": "9d9ebc4f64854d08836a20caee78bbf0", "version_major": 2, "version_minor": 0 }, @@ -5742,7 +5742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf471163b7bb4b0db0f308770d43c87c", + "model_id": "3c40507dcff84cf99cc791124133fcd0", "version_major": 2, "version_minor": 0 }, @@ -5756,7 +5756,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c53ac4a6f8749d383eab3ed5bd975d1", + "model_id": "439404d7fc6f491fb7af59c93631cd7c", "version_major": 2, "version_minor": 0 }, @@ -5770,7 +5770,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "7ec51e14df0a4fbbb1a2d8fac6a20ece", + "model_id": "837c0f1c16c74f6e84fe0aab12e51434", "version_major": 2, "version_minor": 0 }, @@ -6414,7 +6414,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51124700a4904b17bd0a2681fdb95ea6", + "model_id": "572caf8511d24c93b6a166191f5c90d5", "version_major": 2, "version_minor": 0 }, @@ -6428,7 +6428,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2446d9f730b24c7dbaf40a0fd35c363a", + "model_id": "80e08fd7f52b4998b2044c86f19a5ca6", "version_major": 2, "version_minor": 0 }, @@ -6442,7 +6442,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35bb24fc43d64a8dacc5c8b26a15ea81", + "model_id": "7b47a95d46054aa0bb6baf6e4abb286c", "version_major": 2, "version_minor": 0 }, @@ -6456,7 +6456,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d613813a1dbf44e0ae58dfd3629e889b", + "model_id": "f09419e728f147e692becad55223b75c", "version_major": 2, "version_minor": 0 }, @@ -6470,7 +6470,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b5119bb43214403babfe491651ebfa78", + "model_id": "95909e6b64504a678c35f78759e09fe0", "version_major": 2, "version_minor": 0 }, @@ -6484,7 +6484,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0da736aff71749afa760416379b3e4df", + "model_id": "181dd936ba6d4256961e68c2c7913732", "version_major": 2, "version_minor": 0 }, @@ -6498,7 +6498,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4db8e32833ec43399e9d9c036783bc2b", + "model_id": "12680541e80b41328fe0aec62aa580b7", "version_major": 2, "version_minor": 0 }, @@ -6512,7 +6512,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "26ecea04fa0646a385b6ae0095b84ce2", + "model_id": "a36165cc50414fbaad828077aeff23d6", "version_major": 2, "version_minor": 0 }, @@ -6526,7 +6526,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e74166f6d5574b83a57c7122be14f4d2", + "model_id": "79ccc180b8444cdcba2be147c1039f31", "version_major": 2, "version_minor": 0 }, @@ -6540,7 +6540,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "87dbbcf7304748c490a481826511c1c1", + "model_id": "ece3e81b89d349419c1588cd90ea3cc0", "version_major": 2, "version_minor": 0 }, @@ -6554,7 +6554,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df89fbcb70654f97bc0187df158e5ff9", + "model_id": "967d37595ed74558b486659464a1f94b", "version_major": 2, "version_minor": 0 }, @@ -6568,7 +6568,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41e82ac2b20f4fe0963f514278a98737", + "model_id": "c38659ae194e4cdc824d58122ffb3eb3", "version_major": 2, "version_minor": 0 }, @@ -6582,7 +6582,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "483a4ba84eea433b89d535a09c97ce77", + "model_id": "09b6042ef72f4a55b0381ff9012fa784", "version_major": 2, "version_minor": 0 }, @@ -6596,7 +6596,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a00f7ea479254e0da4e0dd2ad2da3f82", + "model_id": "51362657200f4568a9a717db43be1382", "version_major": 2, "version_minor": 0 }, @@ -6610,7 +6610,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "8196172355ce46afa6ef950e60c782a2", + "model_id": "384d7654966641f198762098f348707b", "version_major": 2, "version_minor": 0 }, @@ -8164,7 +8164,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ec92f675d6e49e1bc3fe7dad33c089d", + "model_id": "23b0af9379e041efb0dcbcc1bcafc102", "version_major": 2, "version_minor": 0 }, @@ -8178,7 +8178,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73e65e5180ed427a9cd22358ffa23798", + "model_id": "fe6f6008dd3243ce8e64065bb780e61a", "version_major": 2, "version_minor": 0 }, @@ -8192,7 +8192,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29ea12e97c474534b9caa25e234a85f2", + "model_id": "9bc1e140a8a24e5d9d7b8b69af778979", "version_major": 2, "version_minor": 0 }, @@ -8206,7 +8206,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe14a9e2237a4512a578bf14584dd228", + "model_id": "81c027d5cb1949338adb7e196ad4193f", "version_major": 2, "version_minor": 0 }, @@ -8220,7 +8220,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "5d5d0553056f43e0ae2a081c3bbb384d", + "model_id": "0120c110ce654a56a32bb2228fdd012f", "version_major": 2, "version_minor": 0 }, @@ -8304,7 +8304,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ac99bc59f6c447ab6e59e9ed7e87ab3", + "model_id": "b846ef980205495c85533bd24de3a7ff", "version_major": 2, "version_minor": 0 }, @@ -8318,7 +8318,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "584d9c5fc3d741239f4bd9da67962b3d", + "model_id": "430ab230f8d74494bcf62f321730611c", "version_major": 2, "version_minor": 0 }, @@ -8332,7 +8332,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f1bf7f38e2644629daf4dc8a87ed036", + "model_id": "0e6efc9ac05549a2adbf0680a2dfda0f", "version_major": 2, "version_minor": 0 }, @@ -8346,7 +8346,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0cc8e90e7dfb48dfa0e981f11df1d9b7", + "model_id": "fbfbea3e906a4cb0abefec6b11fcda0d", "version_major": 2, "version_minor": 0 }, @@ -8360,7 +8360,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec709fe01a814f27838c99cce40513a0", + "model_id": "98619c0c41244f4aa60ae3e960901b06", "version_major": 2, "version_minor": 0 }, @@ -8374,7 +8374,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d27ba811b83422c97879687b4c04861", + "model_id": "56ac2636887c4f3b9117a751fc6b5b97", "version_major": 2, "version_minor": 0 }, @@ -8388,7 +8388,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60d8e3b00e5e4071b588d228820bb03c", + "model_id": "d1bea164eba342a8ade9c91c990775ef", "version_major": 2, "version_minor": 0 }, @@ -8402,7 +8402,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10fbe9095d834418b95257d8aa05268e", + "model_id": "78c7a650bc7840e1ad2e8e401b9964ae", "version_major": 2, "version_minor": 0 }, @@ -8416,7 +8416,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ef12d11422c4a86b96e28f34099dfc7", + "model_id": "7365f306837f4beea44348992f702fcf", "version_major": 2, "version_minor": 0 }, @@ -8430,7 +8430,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "4fee48e8b61340d0a0d74a593e0b5f5b", + "model_id": "fad5656deff7420e8c810987c6761cd4", "version_major": 2, "version_minor": 0 }, @@ -9004,7 +9004,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34339c14908242769134bb3a50f47530", + "model_id": "da7f02fe300d409b879fb4727895a3ce", "version_major": 2, "version_minor": 0 }, @@ -9018,7 +9018,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c1dc3285a03441098f2969f15e47a2d", + "model_id": "c9816b04662f4e4894de0c00a1395805", "version_major": 2, "version_minor": 0 }, @@ -9032,7 +9032,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2f4e60f84d64804a3c83e7ffd2213ab", + "model_id": "198f7df4157f4b7a8a675836405641d4", "version_major": 2, "version_minor": 0 }, @@ -9046,7 +9046,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b366697b75ee467787178171cf8b1194", + "model_id": "549c59a8d45d4fefae02432c43c233ef", "version_major": 2, "version_minor": 0 }, @@ -9060,7 +9060,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20d8050e732448579cb3957900f67311", + "model_id": "21ac8e030a8c490e8177c4ad82dffe0e", "version_major": 2, "version_minor": 0 }, @@ -9074,7 +9074,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b2191aa03ca2439f8a6a4dda3bf58c3e", + "model_id": "f749a665d3b14a8faa85e16428dc147f", "version_major": 2, "version_minor": 0 }, @@ -9088,7 +9088,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f6de749f97545e2ab600061129ed6ae", + "model_id": "77196f7f0b664ce9b818e788d9f1680e", "version_major": 2, "version_minor": 0 }, @@ -9102,7 +9102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58421ab145e6450bb65fe5b049b64add", + "model_id": "b9d7f11d69d74e6c8c0fa84ccbad3157", "version_major": 2, "version_minor": 0 }, @@ -9116,7 +9116,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20b0ba33012d47fb920422efcf7e50b4", + "model_id": "fd547461582745f08b4058203ee9747a", "version_major": 2, "version_minor": 0 }, @@ -9130,7 +9130,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6635a8bd474441a5a78bcfb2d8bc756c", + "model_id": "8dd26ed22faf405c9290268cbb390f03", "version_major": 2, "version_minor": 0 }, @@ -9144,7 +9144,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "42042e8a297c4046a48012cd2af4b737", + "model_id": "34839f3dfc934f8a98e1a53cdec19fe5", "version_major": 2, "version_minor": 0 }, @@ -9158,7 +9158,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e1c4c21dbbb4199b572530ac1cab517", + "model_id": "fe48b7be5ca94bfdb119e9163b0d6503", "version_major": 2, "version_minor": 0 }, @@ -9172,7 +9172,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2a1b6d8e0f645e48f26b5d9643f2e6f", + "model_id": "fb12c141832743a0a3152718d899c508", "version_major": 2, "version_minor": 0 }, @@ -9186,7 +9186,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d7a81a9b4dc4139b744674ba394d971", + "model_id": "501f853e658546c484ec545270f5cb1f", "version_major": 2, "version_minor": 0 }, @@ -9200,7 +9200,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ceb7eee4ae06405ca4dde66aa63d73f6", + "model_id": "1de35abc007541779f9d3c2d43a44af5", "version_major": 2, "version_minor": 0 }, @@ -9214,7 +9214,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f22bea9c53204dba90d4620539a1c2ea", + "model_id": "83e938d06320497abe7d42d7fbb19138", "version_major": 2, "version_minor": 0 }, @@ -9228,7 +9228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e82ee3ba68aa4dd8ab8017a2456ac46f", + "model_id": "cc026e9391c84365b3e4153a065b53c6", "version_major": 2, "version_minor": 0 }, @@ -9242,7 +9242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b028aa23c29431faf44fad9924d7fd8", + "model_id": "66eb7e0f595b4517bf2b16ed001546a6", "version_major": 2, "version_minor": 0 }, @@ -9256,7 +9256,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "14db2e9dea9e455eab21c13db4562cbf", + "model_id": "74725bf9bdf44298b4f4c5df042126de", "version_major": 2, "version_minor": 0 }, @@ -9270,7 +9270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea7bf9f8172c45dc84e1282a684c9658", + "model_id": "f34ec206dd6b45fbaa67b2b7048ca70c", "version_major": 2, "version_minor": 0 }, @@ -9284,7 +9284,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c1b5141a7ca34f6e94550f8266ffb142", + "model_id": "22a1aab898d04595b8ae6a4298db5e6d", "version_major": 2, "version_minor": 0 }, @@ -9298,7 +9298,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d8b71ae7480d48c58ef2d19b0ccbd911", + "model_id": "48a857aef08d41769f578f4d333a1cfc", "version_major": 2, "version_minor": 0 }, @@ -9312,7 +9312,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "168076a0ad964f129d6c9a6fe0289c7d", + "model_id": "36f1991ea0784242bf07d17918e8ec8c", "version_major": 2, "version_minor": 0 }, @@ -9326,7 +9326,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a567a2c7a65a4b1795dadbcb69bf08b7", + "model_id": "75f975681b524a80955261b126836009", "version_major": 2, "version_minor": 0 }, @@ -9340,7 +9340,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "d3ab5b2bf6874332b5b3c38ec16a27c8", + "model_id": "3722989fa15647c7939b73ecf82e165a", "version_major": 2, "version_minor": 0 }, @@ -9424,7 +9424,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6cef83e757a49f9855db5ab8d17d5fb", + "model_id": "63e1dda71de4405c8d9d7f0df0dc193b", "version_major": 2, "version_minor": 0 }, @@ -9438,7 +9438,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "807495391add470d83db941b3e4653e7", + "model_id": "9c08033c418a44f7bc10f3f345d56463", "version_major": 2, "version_minor": 0 }, @@ -9452,7 +9452,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62daa4ecc7b940d6a5543ac044630b5b", + "model_id": "b0a8438c5a3c40b0b3de7557ae67a82a", "version_major": 2, "version_minor": 0 }, @@ -9466,7 +9466,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db34ec0b10ac4ac1959fc7f7bc223f0a", + "model_id": "4783b994cd1a4cb195eb31d0f6a723a1", "version_major": 2, "version_minor": 0 }, @@ -9480,7 +9480,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "e192be1ca78446e491f7000aca7eada6", + "model_id": "80d8fda591b84d3a8d8fd262acc60f26", "version_major": 2, "version_minor": 0 }, @@ -11034,7 +11034,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "acaa7071c93244488ab275c764a3140c", + "model_id": "aaea28cdaa2a43afa3dd2e0b2f1192c4", "version_major": 2, "version_minor": 0 }, @@ -11048,7 +11048,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1b112b8410c4a5dab907b7b6e9f621b", + "model_id": "88ceed75d1ce47e9832132de5836af84", "version_major": 2, "version_minor": 0 }, @@ -11062,7 +11062,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b2e9331cdb1463ea022381c37d2ea05", + "model_id": "bcdf7e585584465ab0f51a14aa12ce17", "version_major": 2, "version_minor": 0 }, @@ -11076,7 +11076,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1ccf8387b1e4fa6b9fae12f047634de", + "model_id": "2dbbe1df1d79480f90a7b0657ee695bd", "version_major": 2, "version_minor": 0 }, @@ -11090,7 +11090,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34f9cb38cfb54495b12e0bf55ed31bd1", + "model_id": "f34ae0a25ca1455a95d5cb60ce2e86d8", "version_major": 2, "version_minor": 0 }, @@ -11104,7 +11104,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1c37f0553144a48b17c3c19433a204d", + "model_id": "bf01260bff594f7ba717c113efbbc9a6", "version_major": 2, "version_minor": 0 }, @@ -11118,7 +11118,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afb04fbb53924602a3b5d44c3d83b12d", + "model_id": "a38f0199006f4bdc9aebd6c6582e5c8d", "version_major": 2, "version_minor": 0 }, @@ -11132,7 +11132,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6cdfb3c3a014a73b20c053689b403a1", + "model_id": "774e47ca4fc54c0b9c1748e95f55a78d", "version_major": 2, "version_minor": 0 }, @@ -11146,7 +11146,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3baaef7b60364ae5ae457c7e55d9a2ba", + "model_id": "3b640ddf6e8f40349ee3bc3c3a8b9da3", "version_major": 2, "version_minor": 0 }, @@ -11160,7 +11160,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8cb642e7332842179b2a539cc2f4f4c8", + "model_id": "46ec060d50f84f7184e9f87692032bba", "version_major": 2, "version_minor": 0 }, @@ -11174,7 +11174,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17e3f672d9d14565ab96ef8e7e976912", + "model_id": "a5b7dff3bc9b432bbff0c88f5c323d30", "version_major": 2, "version_minor": 0 }, @@ -11188,7 +11188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e05f6b4f28d4d68b7280d5daf3fda82", + "model_id": "9f312f6cae84477494d1e834d28662fb", "version_major": 2, "version_minor": 0 }, @@ -11202,7 +11202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a216514707044c9eaa1abe59d0fef7ec", + "model_id": "3f0e11cc3fe24b2f94b749d409255a67", "version_major": 2, "version_minor": 0 }, @@ -11216,7 +11216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a714ef1615642f987e720d5593d7d75", + "model_id": "eb32fe854f214eaeacc66fdfbc9dfecd", "version_major": 2, "version_minor": 0 }, @@ -11230,7 +11230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "579ec2c795304bb7a96aa4e8fcb22948", + "model_id": "5f6e70e263ce42729125cd164fae739f", "version_major": 2, "version_minor": 0 }, @@ -11244,7 +11244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff78c5a3784547979fd08c9d5f62cb85", + "model_id": "d59a20fcc0b649659d688a1771fd3458", "version_major": 2, "version_minor": 0 }, @@ -11258,7 +11258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c5afe068cd0482f8e9e8ed9d78c9eaf", + "model_id": "03eadeed87e74a3093cf9340c8d6a8c8", "version_major": 2, "version_minor": 0 }, @@ -11272,7 +11272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0307982f2e847beb75f3a89b24e58d0", + "model_id": "8891e0be2b144eafaef9d5a2b6766366", "version_major": 2, "version_minor": 0 }, @@ -11286,7 +11286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33e676ab4bcc497ebc43d1941d726253", + "model_id": "d63f55dfeb7f415e9e26ad7488d066b5", "version_major": 2, "version_minor": 0 }, @@ -11300,7 +11300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "6a726fb2d6bd4ff1af365d1bf583abc6", + "model_id": "cfb6c37bfbe34344a6748b888ab08d94", "version_major": 2, "version_minor": 0 }, @@ -12084,7 +12084,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf61c0440a494edea8c984f7af19c817", + "model_id": "cefd76e64c7b41c2b9985f8082550fc3", "version_major": 2, "version_minor": 0 }, @@ -12098,7 +12098,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fdea00aff8d44402abed65f7fd13bedd", + "model_id": "9f16e95539644350b8e075519788478a", "version_major": 2, "version_minor": 0 }, @@ -12112,7 +12112,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3345ba0768ee4272869c256fb6adccfd", + "model_id": "af658180ecc34e5a95e7380701e7a240", "version_major": 2, "version_minor": 0 }, @@ -12126,7 +12126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8513b98501548e086ac3a548dce4b8c", + "model_id": "83cca5f0585e4a5d9a8db2b3a302ce23", "version_major": 2, "version_minor": 0 }, @@ -12140,7 +12140,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "524cd4774e3c499486e565446cbfd52f", + "model_id": "ae7154a9af3241e0a65400afbfc99040", "version_major": 2, "version_minor": 0 }, @@ -12154,7 +12154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f96f2f05862481e8a41239453565c43", + "model_id": "f4858670726d43bc9b771bd0d9bc1839", "version_major": 2, "version_minor": 0 }, @@ -12168,7 +12168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f48bd3c4de14349b6bc2db8fc1407ce", + "model_id": "e9c0b5e0793a4ce8b8d9d787a848921f", "version_major": 2, "version_minor": 0 }, @@ -12182,7 +12182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24358efb295640bca90cc1b61cc659b8", + "model_id": "d949a06b95a24a0bae3d102e47d6f9a3", "version_major": 2, "version_minor": 0 }, @@ -12196,7 +12196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55769dc2eea6464184db27f446f8412c", + "model_id": "0de58651e6d2476ebcc48d13c743b5ce", "version_major": 2, "version_minor": 0 }, @@ -12210,7 +12210,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa38b4ba74b94329a58a296add376ac5", + "model_id": "ce65a142c0044ed5b926c5e4ed6e34ce", "version_major": 2, "version_minor": 0 }, @@ -12224,7 +12224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19f37af5f0844c06b6020d97719116ca", + "model_id": "b6ee772a607441b7a3bc490828a10631", "version_major": 2, "version_minor": 0 }, @@ -12238,7 +12238,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c6365f5e084e4a6cbc33ff60ecd793d9", + "model_id": "75823a21491e499c822615bcf91b4d99", "version_major": 2, "version_minor": 0 }, @@ -12252,7 +12252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce3b6f0b7b444cc7878481e80139e2ec", + "model_id": "b76aaced71f147378280e1e5de25b663", "version_major": 2, "version_minor": 0 }, @@ -12266,7 +12266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f6711a3986842ea93d7eb92013d1bf2", + "model_id": "0894bcb31263465494e9eb5d082d9aec", "version_major": 2, "version_minor": 0 }, @@ -12280,7 +12280,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "2cc98da83a6c4aeea931f2fd8efbf4d6", + "model_id": "c92a99c99e8c427c80e996154a3b4fe5", "version_major": 2, "version_minor": 0 }, @@ -12434,7 +12434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca6e916871af4707af2928605247bf03", + "model_id": "e5a09f10380c4198970f746bb713d5e0", "version_major": 2, "version_minor": 0 }, @@ -12448,7 +12448,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "07d687a4af524bb8869c602ecdd261c9", + "model_id": "83a042f9826049468cd586aba2794093", "version_major": 2, "version_minor": 0 }, @@ -12462,7 +12462,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2302ae9db26405ba0b50b670d3e225f", + "model_id": "b020e6b6b8a74f8ca26875b3254d6dec", "version_major": 2, "version_minor": 0 }, @@ -12476,7 +12476,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7b22a6f0f814e0f838316654227c247", + "model_id": "c007d471b8e445c38480fe91048e820f", "version_major": 2, "version_minor": 0 }, @@ -12490,7 +12490,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "61c0fe0a5fae4af7882943ecd8a2ef01", + "model_id": "f06bf2e795304cd1afa58c9f3651fd03", "version_major": 2, "version_minor": 0 }, @@ -12854,7 +12854,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0dd7e16ed524050941ef82ea0e336f5", + "model_id": "2e071e8518a74f8bad8362f49f1d2295", "version_major": 2, "version_minor": 0 }, @@ -12868,7 +12868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84a2fb661ba249428227197bda595380", + "model_id": "c14a360cdc6445d7ab38a1b021d09558", "version_major": 2, "version_minor": 0 }, @@ -12882,7 +12882,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf92c9d6865c4d15a14845b41050ea26", + "model_id": "07f0f7473c2d40409a41f1dbcea3be64", "version_major": 2, "version_minor": 0 }, @@ -12896,7 +12896,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "66374fe8e655473cb4596018682901be", + "model_id": "c0e5c10acc52450cbb22ab34ffefdd3f", "version_major": 2, "version_minor": 0 }, @@ -12910,7 +12910,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "f801a8e2240144d287f9a610b13c5fe9", + "model_id": "5d0de15ef88a443984f824a3d6091ff6", "version_major": 2, "version_minor": 0 }, @@ -13134,7 +13134,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8114e49baf354fa1a0aed22ede0d4ed3", + "model_id": "2adeefd4cace453db3cc2cf781422712", "version_major": 2, "version_minor": 0 }, @@ -13148,7 +13148,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a58d79e3133466fa1894f6e69d58ba7", + "model_id": "15a870c5c8cf4dab81f05582e6866345", "version_major": 2, "version_minor": 0 }, @@ -13162,7 +13162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "209d986b25414873aecf1718240c5bbd", + "model_id": "09d790b212584e36a87d6b220d7ba539", "version_major": 2, "version_minor": 0 }, @@ -13176,7 +13176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0283a891f16846eea0035c8050423118", + "model_id": "575b0bc5a182402ab62454c74d26510d", "version_major": 2, "version_minor": 0 }, @@ -13190,7 +13190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5680f36f3c9345c291126ec407ca32e1", + "model_id": "fc0e8b4373974fa8b9e4093ef6b3a073", "version_major": 2, "version_minor": 0 }, @@ -13204,7 +13204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a8f080113844ee8ae80f5980c8edc50", + "model_id": "12dc498e9c4a454286d85d076482082f", "version_major": 2, "version_minor": 0 }, @@ -13218,7 +13218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3df4cca35f9f4da09ac1d91399d0152f", + "model_id": "2e5bdd0eb6cc485e8d47a5ef68abd7d8", "version_major": 2, "version_minor": 0 }, @@ -13232,7 +13232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4165b82db9c84399835633c6b8f2eac3", + "model_id": "a72f33390d98487da41f4570efdfa0ab", "version_major": 2, "version_minor": 0 }, @@ -13246,7 +13246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aaf10fd2af1442b28e2ed2f2046cc563", + "model_id": "735bb389304b4933b0c50e9461a8ddd3", "version_major": 2, "version_minor": 0 }, @@ -13260,7 +13260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "524d9f63a7994aa2a39a7628cea29cfe", + "model_id": "a2f5e125d91c4a3eab6931ece0485b5c", "version_major": 2, "version_minor": 0 }, @@ -13274,7 +13274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62caf91a94bc4e5a95d9b3441452e3aa", + "model_id": "f45832a08a7e4bbfb1f87b1972b8d725", "version_major": 2, "version_minor": 0 }, @@ -13288,7 +13288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4961c5c57f98425fbfd1fa94d8528f9f", + "model_id": "9ef1133a121e45b69613f01d22e84c33", "version_major": 2, "version_minor": 0 }, @@ -13302,7 +13302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ff4e18bb5c04eff93f08b3da1380c27", + "model_id": "6933f16cafa442d4a72e3b9803c4428c", "version_major": 2, "version_minor": 0 }, @@ -13316,7 +13316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e41f918c31a047a9885edd336a731228", + "model_id": "a4367584cdb84923a8b969cf67a3da10", "version_major": 2, "version_minor": 0 }, @@ -13330,7 +13330,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff82ddbeeef94cb6a39825b3017be692", + "model_id": "4bffb19a623847e5b3eb88ffb46891fc", "version_major": 2, "version_minor": 0 }, @@ -13344,7 +13344,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2a74306686143688ec074ac0f04fa1c", + "model_id": "1908851b094d47a1a87d2c3937274c70", "version_major": 2, "version_minor": 0 }, @@ -13358,7 +13358,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c087ced9725c41cdadd3da542d139e26", + "model_id": "37abffbe7ef94cc99a89e438a24cd109", "version_major": 2, "version_minor": 0 }, @@ -13372,7 +13372,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bce85056b3a54ee3a453a671b3638ac3", + "model_id": "68d8eacbfe104c73b3698c50fa3792a1", "version_major": 2, "version_minor": 0 }, @@ -13386,7 +13386,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d43949a417ee4fd19b056bb56652c7bf", + "model_id": "c4f38c061e7b48f392c4633e64d8781d", "version_major": 2, "version_minor": 0 }, @@ -13400,7 +13400,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84a98626d7e84d93a4d516b22e5ec9f5", + "model_id": "74466e85138d4bde97622bc55eb43130", "version_major": 2, "version_minor": 0 }, @@ -13414,7 +13414,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5eaaa9c2c0f844e4ae31f8a7f0792eef", + "model_id": "c478059c6a0943cdb70a9eb7d9f09514", "version_major": 2, "version_minor": 0 }, @@ -13428,7 +13428,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "47087010c69740c8a14503cd673bde46", + "model_id": "de92e04f348c4edc97e6f02de9dc2ad0", "version_major": 2, "version_minor": 0 }, @@ -13442,7 +13442,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c0e89f5117c45358b1ab9ef6327f1fd", + "model_id": "fdc1532a7a0f4dd885026a167fe0b199", "version_major": 2, "version_minor": 0 }, @@ -13456,7 +13456,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eaece8ddb0b847959e378aad0bc87774", + "model_id": "de71ee16bad446e1814bb2081bad7adc", "version_major": 2, "version_minor": 0 }, @@ -13470,7 +13470,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3bd85a3670fb4368bb175c3a0e78543e", + "model_id": "f9e2d1f8708c4ef2b217d444262a8475", "version_major": 2, "version_minor": 0 }, @@ -13484,7 +13484,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d19abd8da21b48c38ad00ec927e63887", + "model_id": "fe8101223dcf499a93c21c2475bb3ba5", "version_major": 2, "version_minor": 0 }, @@ -13498,7 +13498,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "222d774d94a24719820b2af11fbdaed5", + "model_id": "4a0e4dfe81cc4af9863d7f9e4c3f7ba4", "version_major": 2, "version_minor": 0 }, @@ -13512,7 +13512,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "919ed3f4814e4dffa1fa56cc198d67ea", + "model_id": "5a9d4e99891447de9344c4476d21c376", "version_major": 2, "version_minor": 0 }, @@ -13526,7 +13526,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "03df3969ce4c4a6eac9a8361e0efe141", + "model_id": "add827b827504806b087622c35c8d3ed", "version_major": 2, "version_minor": 0 }, @@ -13540,7 +13540,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5354f1d623a4882aef0729afc2d9be3", + "model_id": "69f7566148b34cdf935ba1e8401a15b4", "version_major": 2, "version_minor": 0 }, @@ -13554,7 +13554,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "294717ffe85843d1a5e86487e1bb4d43", + "model_id": "ae49dd65596c4d41a6bc97ecddd63d8e", "version_major": 2, "version_minor": 0 }, @@ -13568,7 +13568,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8309de6109ec45aa9d0f717fb88b4bc8", + "model_id": "479460c87b6047acb1a24e22fa504c55", "version_major": 2, "version_minor": 0 }, @@ -13582,7 +13582,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c3b6f171f014df38520fb7e2ccd1ae6", + "model_id": "f0c2a0f7d8e846bb9907e2eea47eeb70", "version_major": 2, "version_minor": 0 }, @@ -13596,7 +13596,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17f194164e0742a5aabc5bcef9fb2549", + "model_id": "3049e985e6294939a75c77bb4bda2370", "version_major": 2, "version_minor": 0 }, @@ -13610,7 +13610,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05757849e05843ce906e5f21e66232ca", + "model_id": "5f941241cb1d47318bcc8dab6db36a23", "version_major": 2, "version_minor": 0 }, @@ -13624,7 +13624,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "beb5bebb257d40a28280b784f653572f", + "model_id": "9cb3fdf148824214a048669c5eb5da1b", "version_major": 2, "version_minor": 0 }, @@ -13638,7 +13638,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eaf0fa8582574c78b838175afdb4881e", + "model_id": "66b725ff7bd14c41a8872168dce916b1", "version_major": 2, "version_minor": 0 }, @@ -13652,7 +13652,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c0479b549a8d496f87f8b957155c50fa", + "model_id": "118ea10c680d48e69aa1cb8e5dd34d5c", "version_major": 2, "version_minor": 0 }, @@ -13666,7 +13666,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "012dd201537847f581ca0f732a791f46", + "model_id": "7a4bc641825146a697491b382dba06be", "version_major": 2, "version_minor": 0 }, @@ -13680,7 +13680,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aba599860fe1488690b690c2cb26beb9", + "model_id": "46b570f30148490984e4f30864757c3a", "version_major": 2, "version_minor": 0 }, @@ -13694,7 +13694,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74d097f95c814a538a533ae7ad1488af", + "model_id": "142af851333748be91f3d3942926fa5f", "version_major": 2, "version_minor": 0 }, @@ -13708,7 +13708,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "676a68acea5a4b7792c30ad6d9b6aa92", + "model_id": "dc137fa6fac0475fa84d9e96be9d15db", "version_major": 2, "version_minor": 0 }, @@ -13722,7 +13722,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16e8da0d3beb4a0eb77cbe3bf852d36f", + "model_id": "f1592897494a463989aaeb3b4164817e", "version_major": 2, "version_minor": 0 }, @@ -13736,7 +13736,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "86d66ce9f01b46408dab7fc01841b882", + "model_id": "3d3763f9a6d1419aaae7d98d08d79dc1", "version_major": 2, "version_minor": 0 }, @@ -13750,7 +13750,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ccb40b09b14a4f958de6b4ee29d91273", + "model_id": "1aa590b6ba544102bfffcdfc8f0c7dc5", "version_major": 2, "version_minor": 0 }, @@ -13764,7 +13764,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f5485dc811994ed9ac71085dbf9b256a", + "model_id": "59e6c165d2a14c578c013b444a3eb645", "version_major": 2, "version_minor": 0 }, @@ -13778,7 +13778,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8d8cc0953bd4ea0bb681238bb18f97c", + "model_id": "dfd9d5d4d5b14b078818d7de15b84e09", "version_major": 2, "version_minor": 0 }, @@ -13792,7 +13792,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fabdef21e94446308bed66bf07e6e460", + "model_id": "21e4a92c94fb4c3b870e0cda19ea6a0b", "version_major": 2, "version_minor": 0 }, @@ -13806,7 +13806,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c65eb3a60e546699b114fa7ac5285e1", + "model_id": "95da4ac693ee48a19199e5a24c7e0d98", "version_major": 2, "version_minor": 0 }, @@ -13820,7 +13820,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ea7a54a571f4237b4bc0ccf6ceadaa6", + "model_id": "9bfff26b4914470a9bce14963e50946e", "version_major": 2, "version_minor": 0 }, @@ -13834,7 +13834,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37de05daea53427083d502593bbf7e23", + "model_id": "42e00f3afcf34d01a3a1686c4c47f96e", "version_major": 2, "version_minor": 0 }, @@ -13848,7 +13848,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5cca1d8a012145fd96c140d79e96a56b", + "model_id": "5c983b0fec5f4c72b44e1d2d85e73fc1", "version_major": 2, "version_minor": 0 }, @@ -13862,7 +13862,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "639868cb043040a7b6f9697262b99f12", + "model_id": "aad5755148c54ff097bcd8fd4ca9d2f8", "version_major": 2, "version_minor": 0 }, @@ -13876,7 +13876,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "676a11e0c6024e9eb53428d6d68aac81", + "model_id": "a5d1079c3bdb4983817693ac09293fa4", "version_major": 2, "version_minor": 0 }, @@ -13890,7 +13890,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04d46b74a3f34fb6893a05006cd857c4", + "model_id": "160ca79522e34a71b79f1127095623a1", "version_major": 2, "version_minor": 0 }, @@ -13904,7 +13904,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "773482b67003493e9c6a024c0657269a", + "model_id": "8c4c1bf38665458ab354e447dc2b7567", "version_major": 2, "version_minor": 0 }, @@ -13918,7 +13918,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d6491b853c1436ba0a5bd6fe0d32070", + "model_id": "d67badc401d64672b0c6e4d24b7e9458", "version_major": 2, "version_minor": 0 }, @@ -13932,7 +13932,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e45badfe2ec443778643cf1ee07d1c88", + "model_id": "07032ddeba62474392bcb3a8eeb08b15", "version_major": 2, "version_minor": 0 }, @@ -13946,7 +13946,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4270a6e0493941eb9d91fb1c11568392", + "model_id": "8644dd378f8844e48fb1259580473ab1", "version_major": 2, "version_minor": 0 }, @@ -13960,7 +13960,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae11178261e04bd5b27d1be6a08ea4e7", + "model_id": "dbd5728d18fd4888b85b38b549551882", "version_major": 2, "version_minor": 0 }, @@ -13974,7 +13974,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3422b2abb0ee4787bdc3482bf7bbba48", + "model_id": "a0aa5a4b3bc44fb4bca933df11a8814a", "version_major": 2, "version_minor": 0 }, @@ -13988,7 +13988,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8315c3665b5a427fb3601e4a2824654d", + "model_id": "c23be7d561564c3d9c85e99f61a14479", "version_major": 2, "version_minor": 0 }, @@ -14002,7 +14002,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d8beb71c26b467ea3a4c7f88b22d6d1", + "model_id": "a6a17dd8ce504cb38d8b4f98697e3771", "version_major": 2, "version_minor": 0 }, @@ -14016,7 +14016,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de3263f4eeec4ac7bfcd233f1be4f94e", + "model_id": "d73573f21970407cac257d9ac46fcb25", "version_major": 2, "version_minor": 0 }, @@ -14030,7 +14030,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34b9db61596e40fb872dfa1336314f43", + "model_id": "90c0e5e941c743df8ed53d6c1ac4c7f3", "version_major": 2, "version_minor": 0 }, @@ -14044,7 +14044,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bd3a07b39e114f689372a7f3c06c0b0a", + "model_id": "18579ba38e5b42879a70b2ba386cfceb", "version_major": 2, "version_minor": 0 }, @@ -14058,7 +14058,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fe7d3f681ce4dd3b37fbe2164f76aa0", + "model_id": "3624b1b76d5b45daa32e29f767cf2ec3", "version_major": 2, "version_minor": 0 }, @@ -14072,7 +14072,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8aedf15345ef4522ac4f409a0bc6157e", + "model_id": "27539866b13d4f83a50719656f77a459", "version_major": 2, "version_minor": 0 }, @@ -14086,7 +14086,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1aa3a52e81cf426fa3052ece150c893f", + "model_id": "8afbcee70b4e40908390b09d2970cfec", "version_major": 2, "version_minor": 0 }, @@ -14100,7 +14100,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58c7bad0173e4a1dbd2b7fb92a07f171", + "model_id": "f9f61f6e80da47c5ae049ab8aaca38d3", "version_major": 2, "version_minor": 0 }, @@ -14114,7 +14114,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80dbcf9a93bc415dad1de914ab160d1c", + "model_id": "f837823bfb6e4c6cbf90240ce2545669", "version_major": 2, "version_minor": 0 }, @@ -14128,7 +14128,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f4edb40f42145c68b1dd898252d29c0", + "model_id": "ae98f935f86d4f658ee5ca6d5ddac078", "version_major": 2, "version_minor": 0 }, @@ -14142,7 +14142,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30a08692c05d4544831ba86a1596140c", + "model_id": "dbad564208c54e6eb36201fc51cc583a", "version_major": 2, "version_minor": 0 }, @@ -14156,7 +14156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ad765df20e34324b4e95b0a914fb1a9", + "model_id": "7746608dfe4a4d5d8f3d51e0f0ae8b39", "version_major": 2, "version_minor": 0 }, @@ -14170,7 +14170,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "03662b8d7ace4283a32943e17f0cb5f8", + "model_id": "c007a352170c49cdbdbc44afd5276d76", "version_major": 2, "version_minor": 0 }, @@ -14184,7 +14184,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c757b9e028e4408e9d752cf59899af84", + "model_id": "955d1e07572d41d783471d399a33185f", "version_major": 2, "version_minor": 0 }, @@ -14198,7 +14198,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64244f160a194b488c8a112efde1c30b", + "model_id": "d4ff06d09d25480286d310720320631e", "version_major": 2, "version_minor": 0 }, @@ -14212,7 +14212,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ede0d67e168640dfb1cd38d79bd2d571", + "model_id": "47a98fc19b43400cbc727d460090c315", "version_major": 2, "version_minor": 0 }, @@ -14235,7 +14235,7 @@ "# generate the data\n", "problem.discretise_domain(1000, 'random', domains=['phys_cond', 'time_cond', 'bound_cond1', 'bound_cond2', 'bound_cond3', 'bound_cond4'])\n", "\n", - "# crete the solver\n", + "# create the solver\n", "pinn = PINN(problem, HardMLP(len(problem.input_variables), len(problem.output_variables)))\n", "\n", "# create trainer and train\n", @@ -14248,7 +14248,7 @@ "id": "c2a5c405", "metadata": {}, "source": [ - "Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `Plotter` class of **PINA**." + "Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `matplotlib`. We plot the predicted output on the left side, the true solution at the center and the difference on the right side." ] }, { @@ -14256,19 +14256,60 @@ "execution_count": 5, "id": "c086c05f", "metadata": {}, + "outputs": [], + "source": [ + "def fixed_time_plot(fixed_variables, pinn):\n", + " #sample domain points and get values corresponding to fixed variables\n", + " pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", + " grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", + " fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", + " fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", + " fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", + " fixed_pts.labels = list(fixed_variables.keys())\n", + " pts = pts.append(fixed_pts).to(device=pinn.device)\n", + " predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", + " #get true solution\n", + " true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", + " pts = pts.cpu()\n", + " #plot prediction, true solution and difference\n", + " grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", + " fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", + " cb = getattr(ax[0], 'contourf')(*grids, predicted_output)\n", + " fig.colorbar(cb, ax=ax[0])\n", + " ax[0].title.set_text('Neural Network prediction')\n", + " cb = getattr(ax[1], 'contourf')(*grids, true_output)\n", + " fig.colorbar(cb, ax=ax[1])\n", + " ax[1].title.set_text('True solution')\n", + " cb = getattr(ax[2],'contourf')(*grids,(true_output - predicted_output))\n", + " fig.colorbar(cb, ax=ax[2])\n", + " ax[2].title.set_text('Residual')\n", + " plt.show(block=True)" + ] + }, + { + "cell_type": "markdown", + "id": "910c55d8", + "metadata": {}, + "source": [ + "Let's take a look at the results at different times, for example `0.0`, `0.5` and `1.0`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0265003f", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Plotting at t=0\n", - "Plotting at t=0.5\n", - "Plotting at t=1\n" + "Plotting at t=0\n" ] }, { "data": { - "image/png": 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" ] @@ -14276,9 +14317,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=0.5\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -14286,9 +14334,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=1.0\n" + ] + }, { "data": { - "image/png": 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hSr8PAPfff7/L36+//nr2+0REAkhNTUVkZCRatGiBAQMGoEGDBlixYgWaN2+OgoICbNiwAXfeeSdOnTrljG9//PEH0tLS8MsvvzgfvREeHo7du3fjl19+8fi3HflR5fjmuOvLVxVz5KKiIpw8eRI9evTAr7/+iqKiojotm4i8wwKkiqZMmYKLFy9W+yzIX375BYqi4PLLL0dkZKTLnz179uD48eM+/3arVq2qfFZeXo45c+bg8ssvR3BwMCIiIhAZGYkff/xRlc724YcfRnh4eLXPgnQEnBtuuKHK+q5du9aj9Y2Ojsbll1/uLDZ+8803uP7669G9e3ccO3YMv/76K7777juUl5c7C5AnTpzA2bNn0a5duyrLS0xMRHl5eZVnbrrbfg633norGjVqhKysLNhstlrbXNHll1/u8vc2bdrA39+/yjMTK//+iRMnUFhYiIULF1bZdo7E2LH9HM+lrFwwdLf+lR04cACxsbFo0qSJV+tVnd9++83tbwcFBaF169bO7x2aN29epd2NGzfGn3/+qUp7iIi8ddlll9XpDc+//PIL1qxZU6XvTk1NBYAaY1+PHj3Qv39/PPnkk4iIiMD//d//YdGiRS7P9P3tt98QGxuLRo0auczreARJ5X5Wa9X1+wCQkJBQpT2OZw1XxH6fiEgM8+bNQ3Z2NpYvX46bb74ZJ0+eRHBwMABg//79UBQFTzzxRJUY5xjM4IhxM2bMQGFhIf72t7+hQ4cOmDBhgsvz+9357bff4O/vX+VRT57kNDX57rvvkJqaigYNGiA8PByRkZHO5zuzAEmkLz4DUkWtW7fGvffei4ULF2LSpElVvi8vL4efnx+++OILt6PtGjZs6Pz/6kaflZWVuZ238uhHAHjmmWfwxBNP4L777sPMmTPRpEkT+Pv7Y+zYsR49CL82jlGQ06dPdzsK0vEb7733HmJiYqp87+lbz7p164b169fj3LlzyM3NxdSpU3HllVciPDwc33zzDfbs2YOGDRvi6quv9nld3G0/h/79+2PJkiX44IMP8M9//tPn3wCq36+Vf9+x7e69914MHTrU7TyO51/KrLpRp0otL1IgItJKTfHAncovTykvL8eNN96Ixx57zO30f/vb36pdlp+fH5YvX47Nmzfj888/R1ZWFu677z68+OKL2Lx5s8t5gi9qOrfQS3X9PhERGe+aa65xvgW7X79+6NatG+655x7s27fPmZ88+uijSEtLczt/27ZtAQDdu3fHgQMH8N///hdr167FW2+9hTlz5mDBggUYMWJEndvpaTw7cOAAevfujYSEBLz00kto0aIFgoKCsHr1asyZM0eVnJiIPMcCpMqmTJmC999/H7NmzaryXZs2baAoClq1alVjAgJcGg1Q+a3BwKUrQxVvK67J8uXL0atXL7z99tsunxcWFnr0AhVPjB07FnPnzsWTTz5Z5QU6jqtXUVFRzpEf1anpdt/rr78eixYtwocffoiysjJce+218Pf3R7du3ZwFyGuvvdaZ1ERGRqJ+/frYt29flWXt3bsX/v7+aNGihcfr+MILLyAwMBAPPPAAGjVqhHvuucfjeX/55ReX0Y379+9HeXm527e2VeR481tZWVmt265ly5bYtWsXFEVx2Y7u1r+yNm3aICsrCwUFBTWOgvT0duyWLVs6f7vicVpaWoqDBw/Wui5ERKJyF5dLS0urPHalTZs2OH36dJ36u65du6Jr1654+umnsXTpUgwaNAgffvghRowYgZYtW2LdunU4deqUyyjIvXv3AvirH65uHYBL5wEVY7a7UZO+9Ps33HCDy3f79u2rsT1ERCSugIAAPPvss+jVqxdee+013HfffQCAevXqeRTjmjRpguHDh2P48OE4ffo0unfvjunTp1dbgGzZsiXKy8tx4MABl1GP7nKamnLlij7//HOUlJRgxYoViIuLc35el0efEZHveAu2ytq0aYN7770Xb7zxBux2u8t3t99+OwICAvDkk09WGeGlKAr++OMPl+Vs3rwZpaWlzs9WrlxZ5dbhmgQEBFT5nWXLljmfzaEGxyjI//73v9i5c6fLd2lpabDZbHjmmWfcPsvqxIkTzv9v0KABALgNJI5bq2fNmoWOHTsiLCzM+fn69euxfft25zTApfXu06cP/vvf/7rc6pyfn4+lS5eiW7duXt1K7efnh4ULF2LAgAEYOnQoVqxY4fG88+bNc/n7q6++CgC1vuE5ICAA/fv3xyeffIJdu3ZV+b7itrv55ptx7NgxLF++3PnZ2bNnsXDhwlrb179/fyiKgieffLLKdxWPnQYNGrjdN5WlpqYiKCgIr7zyisv8b7/9NoqKiur8FjsiIqO0adOmyvMbFy5cWGW0xZ133omcnBxkZWVVWUZhYSEuXrxY7W/8+eefVeJ2p06dAMB5G/bNN9+MsrIyvPbaay7TzZkzB35+fjXGF8eFwYrrcebMGSxZsqTKtJ72+126dEFUVBQWLFjgcqv4F198gT179rDfJyKSWM+ePXHNNddg7ty5sNls6NmzJ9544w237zyomJ9UzGuBS3f6tW3b1iVOVOaIX6+88orL53Pnzq0ybZs2bVBUVORyW/fvv/+OTz/91GU6xwCVirG1qKgIixYtqrYdRKQdjoDUwL/+9S+899572LdvH6644grn523atMFTTz2FyZMn49ChQ+jXrx8aNWqEgwcP4tNPP8WoUaPw6KOPAgBGjBiB5cuXo2/fvrjzzjtx4MABvP/++1WeiVGTW265BTNmzMDw4cNx7bXX4qeffsIHH3zg8QhKTz388MOYM2cOfvjhB2chEQBsNhtef/11DB48GJ07d8bAgQMRGRmJvLw8rFq1Ctddd50zgUpKSgJw6aHDaWlpCAgIwMCBAwFcGsofExODffv2OV/iAlwa2j9x4kQAcClAAsBTTz2F7OxsdOvWDQ888AACAwPxxhtvoKSkBM8//7zX6+jv74/3338f/fr1w5133onVq1dXGenhzsGDB3Hbbbehb9++yMnJwfvvv4977rkHV111Va3zPvfcc/jyyy+RnJyMkSNHon379igoKMCOHTuwbt06FBQUALj0gpfXXnsNQ4YMQW5uLpo1a4b33nsP9evXr/U3evXqhcGDB+OVV17BL7/8gr59+6K8vBzffPMNevXqhTFjxgC4tH/WrVuHl156CbGxsWjVqhWSk5OrLC8yMhKTJ0/Gk08+ib59++K2227Dvn37MH/+fPz97393eeEMEZFMRowYgfvvvx/9+/fHjTfeiB9++AFZWVlV7iiYMGECVqxYgVtuuQXDhg1DUlISzpw5g59++gnLly/HoUOHqr0LYcmSJZg/fz7+8Y9/oE2bNjh16hTefPNN2Gw23HzzzQAuPZe4V69e+Ne//oVDhw7hqquuwtq1a/Hf//4XY8eOrfE8oU+fPoiLi0NGRgYmTJiAgIAAvPPOO87YXFFSUhJef/11PPXUU2jbti2ioqLcxr169eph1qxZGD58OHr06IG7774b+fn5ePnllxEfH49x48Z5u6mJiEggEyZMwB133IHFixdj3rx56NatGzp06ICRI0eidevWyM/PR05ODo4cOYIffvgBANC+fXv07NkTSUlJaNKkCbZv347ly5c7cwt3OnXqhLvvvhvz589HUVERrr32Wqxfvx779++vMu3AgQMxceJE/OMf/8BDDz2Es2fP4vXXX8ff/vY3l5et9unTB0FBQbj11lvxz3/+E6dPn8abb76JqKioal8cS0Qa0v/F2+axaNEiBYCybdu2Kt8NHTpUAaBcccUVVb775JNPlG7duikNGjRQGjRooCQkJCiZmZnKvn37XKZ78cUXlcsuu0wJDg5WrrvuOmX79u1Kjx49lB49ejin+fLLLxUAyrJly6r8zvnz55VHHnlEadasmRIaGqpcd911Sk5OTpVlHDx4UAGgLFq0qMb1rem3pk2bpgBQGjRo4Ha+tLQ0JSwsTAkJCVHatGmjDBs2TNm+fbtzmosXLyoPPvigEhkZqfj5+SmVD8077rhDAaB89NFHzs9KS0uV+vXrK0FBQcq5c+eq/O6OHTuUtLQ0pWHDhkr9+vWVXr16KZs2bXKZpqZ96FinEydOOD87e/as0qNHD6Vhw4bK5s2bq91Wjnl//vlnZcCAAUqjRo2Uxo0bK2PGjKnSVgBKZmam2+Xk5+crmZmZSosWLZR69eopMTExSu/evZWFCxe6TPfbb78pt912m1K/fn0lIiJCefjhh5U1a9YoAJQvv/zSOd3QoUOVli1busx78eJF5YUXXlASEhKUoKAgJTIyUrnpppuU3Nxc5zR79+5VunfvroSGhioAlKFDh7psv4MHD7os87XXXlMSEhKUevXqKdHR0cro0aOVP//802WaHj16uP334a6NRERqy8zMrBJrquuXFEVRysrKlIkTJyoRERFK/fr1lbS0NGX//v1Ky5YtnX2iw6lTp5TJkycrbdu2VYKCgpSIiAjl2muvVWbPnq2UlpZW26YdO3Yod999txIXF6cEBwcrUVFRyi233OISLx3LHzdunBIbG6vUq1dPufzyy5UXXnhBKS8vd5nOXdtyc3OV5ORkJSgoSImLi1Neeuklt3253W5X0tPTlUaNGikAnOcNjnOBirFFURTlo48+Uq6++molODhYadKkiTJo0CDlyJEjLtMMHTrU7XmCI2YSEZExasqJysrKlDZt2iht2rRRLl68qBw4cEAZMmSIEhMTo9SrV0+57LLLlFtuuUVZvny5c56nnnpKueaaa5Tw8HAlNDRUSUhIUJ5++mmXGOiu7z937pzy0EMPKU2bNlUaNGig3Hrrrcrhw4cVAMq0adNcpl27dq1y5ZVXKkFBQUq7du2U999/3+0yV6xYoXTs2FEJCQlR4uPjlVmzZinvvPNOlbhXOUcmIvX5KQrf9kCktunTp+PJJ5/EiRMnVHveJhERERERERGRjPgMSCIiIiIiIiIiItIMC5BERERERERERESkGRYgiYiIiIiIiIiISDNeFyC//vpr3HrrrYiNjYWfnx8+++yzWufZuHEjOnfujODgYLRt2xaLFy/2oalE8pg+fToUReHzH0ka8+bNQ3x8PEJCQpCcnIytW7fWOP2yZcuQkJCAkJAQdOjQAatXr3b5XlEUTJ06Fc2aNUNoaChSU1Pxyy+/uExTUFCAQYMGwWazITw8HBkZGTh9+rTLND/++COuv/56hISEoEWLFm7fYl9bW2rCmEZEZE5qx7WK7r//fvj5+WHu3Lkqt7ruGNeIiMzJDHHN6wLkmTNncNVVV2HevHkeTX/w4EGkp6ejV69e2LlzJ8aOHYsRI0YgKyvL68YSEZH6PvroI4wfPx7Tpk3Djh07cNVVVyEtLQ3Hjx93O/2mTZtw9913IyMjA99//z369euHfv36YdeuXc5pnn/+ebzyyitYsGABtmzZggYNGiAtLQ3nz593TjNo0CDs3r0b2dnZWLlyJb7++muMGjXK+X1xcTH69OmDli1bIjc3Fy+88AKmT5+OhQsXetWWmjCmERGZjxZxzeHTTz/F5s2bERsbq/Vq+IRxjYjIfEwT1+ryCm0AyqefflrjNI899phyxRVXuHx21113KWlpaXX5aSIiUsk111yjZGZmOv9eVlamxMbGKs8++6zb6e+8804lPT3d5bPk5GTln//8p6IoilJeXq7ExMQoL7zwgvP7wsJCJTg4WPn3v/+tKIqi/PzzzwoAZdu2bc5pvvjiC8XPz085evSooiiKMn/+fKVx48ZKSUmJc5qJEycq7dq187gt3mBMIyIyB7XjmsORI0eUyy67TNm1a5fSsmVLZc6cOaq3XU2Ma0RE5mCWuBaodYEzJycHqampLp+lpaVh7Nix1c5TUlKCkpIS59/Ly8tRUFCApk2bws/PT6umEhHVSFEUnDp1CrGxsfD3r/sjdM+fP4/S0lIVWlaVoihV+svg4GAEBwe7fFZaWorc3FxMnjzZ+Zm/vz9SU1ORk5Pjdtk5OTkYP368y2dpaWnO27wOHjwIu93u0veHhYUhOTkZOTk5GDhwIHJychAeHo4uXbo4p0lNTYW/vz+2bNmCf/zjH8jJyUH37t0RFBTk8juzZs3Cn3/+icaNG9faFrUxphGRmagZ10SIaYA2cQ241HcPHjwYEyZMwBVXXFGHtREL4xoRmQnj2iWixjXNC5B2ux3R0dEun0VHR6O4uBjnzp1DaGholXmeffZZPPnkk1o3jYjIJ4cPH0bz5s3rtIzz588jLq4BTpwoV6lVrho2bFjleYrTpk3D9OnTXT47efIkysrK3PbTe/fudbvs6vp1u93u/N7xWU3TREVFuXwfGBiIJk2auEzTqlWrKstwfNe4ceNa26I2xjQiMqO6xjVRYhqgTVwDgFmzZiEwMBAPPfSQj2shJsY1IjIjxjUx45rmBUhfTJ482aVaW1RUhLi4OGzcEomGDfnibiIyxunT5eiZfAKNGjWq87JKS0tx4kQ5Nm6JQsOG6o4WOH1aQc/k4zh8+DBsNpvzc3dX1Eh71cW0CetvQHADIcMwEVlEyZmLeKH3hjrHNbPHtNzcXLz88svYsWMHR/iBcY2IxMW45hmj4prmESImJgb5+fkun+Xn58Nms7m9ogZUP/S0YUN/NGzEAiQRGUvNTrphQz8N+rVLV+psNptLUHMnIiICAQEBbvvpmJgYt/NU1687pnf8Nz8/H82aNXOZplOnTs5pKj80+eLFiygoKHBZjrvfqfgbtbVFbWrGtOAGgQhpWE+TdhIReUOtuGZ0TAO0iWvffPMNjh8/jri4OOf3ZWVleOSRRzB37lwcOnTImxUSCuMaEZkR45qYcU3zal5KSgrWr1/v8ll2djZSUlK0/mkiIqpFUFAQkpKSXPrp8vJyrF+/vtp+urZ+vVWrVoiJiXGZpri4GFu2bHFOk5KSgsLCQuTm5jqn2bBhA8rLy5GcnOyc5uuvv8aFCxdcfqddu3Zo3LixR21RG2MaEZHYtIhrgwcPxo8//oidO3c6/8TGxmLChAnSvy2acY2ISGxmimtej4A8ffo09u/f7/z7wYMHsXPnTjRp0gRxcXGYPHkyjh49infffRcAcP/99+O1117DY489hvvuuw8bNmzAxx9/jFWrVqm3FkRE5LPx48dj6NCh6NKlC6655hrMnTsXZ86cwfDhwwEAQ4YMwWWXXYZnn30WAPDwww+jR48eePHFF5Geno4PP/wQ27dvx8KFCwFcuuI4duxYPPXUU7j88svRqlUrPPHEE4iNjUW/fv0AAImJiejbty9GjhyJBQsW4MKFCxgzZgwGDhyI2NhYAMA999yDJ598EhkZGZg4cSJ27dqFl19+GXPmzHG2vba21IYxjYjIfNSOa02bNkXTpk1dfqNevXqIiYlBu3bt9F25WjCuERGZj1nimtcFyO3bt6NXr17Ovzue/zF06FAsXrwYv//+O/Ly8pzft2rVCqtWrcK4cePw8ssvo3nz5njrrbeQlpamQvOJiKiu7rrrLpw4cQJTp06F3W5Hp06dsGbNGueDi/Py8lzeInfttddi6dKlmDJlCh5//HFcfvnl+Oyzz3DllVc6p3nsscdw5swZjBo1CoWFhejWrRvWrFmDkJAQ5zQffPABxowZg969e8Pf3x/9+/fHK6+84vw+LCwMa9euRWZmJpKSkhAREYGpU6di1KhRXrWlJoxpRETmo0VckwXjGhGR+ZglrvkpiqIY2gIPFBcXIywsDNt3R/MZkERkmNOnytHlinwUFRV59LyOmmjZr6nZTlKfY99P2dyHz8oiIkOdP30BT3VdW+d4wZhmbYxrRCQKxjWxsZpHREREREREREREmmEBkoiIiIiIiIiIiDTDAiQRERERERERERFphgVIIiIiIiIiIiIi0gwLkERERERERERERKQZFiCJiIiIiIiIiIhIMyxAEhERERERERERkWYCjW4AyWd5cWfdfmuAbYduv0VEREREREREROpjAZKc9CwsekqrNrGwSURERERERESkDxYgLUTEAqNRvN0WLFgSEREREREREfmGBUiTYZFRG7VtVxYoiYiIiIiIiIjcYwFSYiw2iqO6fcHCJBERERERERFZHQuQEmHBUT4sTBIRERERERGR1bEAKTAWHM3L3b5lUZKIiIiIiIiIzIgFSIGw4Ghtlfc/C5JEREREREREZAYsQBqMRUeqDguSRERERERERGQGLEAahIVH8lbFY4bFSCIi+WTbE4xuQo1ujNlrdBOIiEhCosU3xjMiMbEAqSMWHUktHB1JRGQs0ZItNfi6Tkz0iIjMwSyxjfGMSEwsQOqAhUfSmuMYYyGSiKjuzJKA6aW27cWEjojIeIxttfNkGzGmEfmOBUgNsfBIeuNt2kREnmEipp+atjUTOSIi9TC2aY8xjch3LEBqgIVHEgFHRRIRXcKETFzV7RsmcURE1WNcExNjGlHNWIBUEQuPJCKOiiQiK2FSZg6V9yOTNyKyMsY2ubnbf4xrZEUsQKrEisXHLHuipstPi9mj6fKtiKMiichsmJRZA5M3IrIKxjVr4IU2siIWIFVg9uKj1oVGNX+XRUvPsBBJRLJiYkYOFY8FJm5EJDPGNmJMIytgAbIOzFp4NKrgqIba2s4CpavlxZ1ZhCQi4TExo9pwJAkRyYRxjWrCmEZmxQKkj8xUfJS54Oit6tbVyoVJPiOSiETE5IzqgiNJiEg0jGvkK8Y0MgsWIH0ge/HRSgVHT1XeJlYtSLIYSURGYnJGWnAcV0zaiEhvjGukNhYjSWYsQHpJ1uIji47eYUGSz4kkIv0wQSM9MGkjIr0wrpEeeIGNZMMCpBdkKz6y6KgeKxckWYgkIq0wQSOjMGkjIi0wrpERGNNIFixAekiW4iOLjvqouJ2tUoxkIZKI1MDkjETCpI2I1MDYRiJgTCPRsQDpARmKjyw8GsdqxUgWIonIF0zOSGRM2ojIF4xtJCLGNBIVC5CSY+FRLFYqRrIQSUSeYoJGsmDSRkSeYFwjGTCmkWhYgKyFqKMfWXgUn2MfsRBJRFbFBI1kxaSNiNxhXCMZMaaRKFiArIGIxUcWHuVjlVGRy4s7swhJRACYoJF5ZNsTmLAREeMamQILkWQ0FiAlwcKjOZh9VCRHQxIRkzQyGyZsRNbGuEZmw4trZBQWIKshyuhHFh7NyQqFSBYhiayFCRqZHRM2ImthXCMz48U1MoK/0Q2g6rH4aH5Z9kTT7mdRivhEpD0maWQVPNaJrIH/1skqeKyTnjgC0g2jCydmLUhR9cw6IpK3ZBOZH09cyWo4aoTI3BjXyGo4wp/0whGQgmHx0drMuv+NLuoTkTaYpJGV8fgnMh/+uyar4rFPeuAIyEqMKpTIWHjKOxKhy+/ENT+py++IwsyjITkSksgceJJad4eORBrdBKf45ieMboK0OGqEyBwY19RjVHxjLKs7jvAnrbEAKQBZio96FRy9/V2zFijNWIhkEZJIfkzSPCNSgbE2tbWVSV3NWIQkkhvjmudEjm2etI3xzDOMa6QVFiArMGL0owzFR6MKj55y1z4zFSWz7IksQhKREJikVSVyMqaW6taRidxfmKwRyYlxzT2zxjbGM88xrpEWWIA0kMjFR9GLjrWp3H7ZC5IsQhKR0ZikmTch81Xl7WH1BI7JGpFcGNcuYWxzvw2sHtMAxjVSHwuQ/5/eox9FLD7KXnSsScV1k7UYabZbslmEJJKHVZM0JmXeYQLHZI1IFlaNawBjm6d4ke0SxjVSEwuQBhCt+GjmwqM7shcjzTQakkVIIhIJkzL1VdymVknemKwRic1qxUfGNnWwIElUdyxAQt/RjyIVH61WeHRH1mIki5BEpBezJ2pMzPRjpWIki5BEZCTGNu0xphF5jwVIHYlSfGTh0T3HdpGlEGmmIiQRicmsxUcmZsazQuLGhI1IPGaNawBjm5EY04g84290A6xChOJj3pEIFh894NhOMmwrEY4rNRjxBnryTUFBAQYNGgSbzYbw8HBkZGTg9OnTNc5z/vx5ZGZmomnTpmjYsCH69++P/Px8l2ny8vKQnp6O+vXrIyoqChMmTMDFixddptm4cSM6d+6M4OBgtG3bFosXL3b5/uuvv8att96K2NhY+Pn54bPPPqvSlmHDhsHPz8/lT9++fX3aFmZnxiTt0JFIJmgCcuwX7htS07x58xAfH4+QkBAkJydj69atNU6/bNkyJCQkICQkBB06dMDq1atdvlcUBVOnTkWzZs0QGhqK1NRU/PLLL1quAqnMzHGN/ac4zLxPzPhvSCZmiGuWL0DqUfgQoUgkQzFNRDIUIrPsiUIcY3XFIqQcBg0ahN27dyM7OxsrV67E119/jVGjRtU4z7hx4/D5559j2bJl+Oqrr3Ds2DHcfvvtzu/LysqQnp6O0tJSbNq0CUuWLMHixYsxdepU5zQHDx5Eeno6evXqhZ07d2Ls2LEYMWIEsrKynNOcOXMGV111FebNm1dje/r27Yvff//d+eff//63j1vDvMx0gmnmRMCMzLavzPRvSSYfffQRxo8fj2nTpmHHjh246qqrkJaWhuPHj7udftOmTbj77ruRkZGB77//Hv369UO/fv2wa9cu5zTPP/88XnnlFSxYsABbtmxBgwYNkJaWhvPnz+u1WlQHZvu3aLa+0qzMeA5itn9LsjBLXPNTFEXRbOkqKS4uRlhYGLbvjkbDRurWTLUuehhdGBK9eCYb0W/PNsMt2SI/D/L0qXJ0uSIfRUVFsNlsdVqWlv2amu2saM+ePWjfvj22bduGLl26AADWrFmDm2++GUeOHEFsbGyVeYqKihAZGYmlS5diwIABAIC9e/ciMTEROTk56Nq1K7744gvccsstOHbsGKKjowEACxYswMSJE3HixAkEBQVh4sSJWLVqlUvQHDhwIAoLC7FmzZoqv+vn54dPP/0U/fr1c/l82LBhKCwsdDs6Ui+OfT9lcx+ENKxnWDtqYoaTSzOd7FudGW5nE/W2tfOnL+CprmvrHC9Ei2nJycn4+9//jtdeew0AUF5ejhYtWuDBBx/EpEmTqkx/11134cyZM1i5cqXzs65du6JTp05YsGABFEVBbGwsHnnkETz66KMALsW36OhoLF68GAMHDlRhTeXFuKYPxjVzYEzTFuPaJaLGNUuPgDRz8VGGkXsyEn27Gl3wJrEUFxe7/CkpKanT8nJychAeHu4sPgJAamoq/P39sWXLFrfz5Obm4sKFC0hNTXV+lpCQgLi4OOTk5DiX26FDB2fxEQDS0tJQXFyM3bt3O6epuAzHNI5leGPjxo2IiopCu3btMHr0aPzxxx9eL8PMZE/SzDbSgMyxT2X/dyUCT2NaaWkpcnNzXWKGv78/UlNTq40ZtcWYgwcPwm63u0wTFhaG5ORkn+IQ6Uv2f39m6APpL2bYn7L/mxKFFeMaX0JjQiIXyMwi70iE8KMhZWW1t2J/duoqhCjqjhY4f/oCgLVo0aKFy+fTpk3D9OnTfV6u3W5HVFSUy2eBgYFo0qQJ7HZ7tfMEBQUhPDzc5fPo6GjnPHa73aX46Pje8V1N0xQXF+PcuXMIDQ31aB369u2L22+/Ha1atcKBAwfw+OOP46abbkJOTg4CAgI8WoaZyXxCKfvJPNXOsY/NMHrErESIaSdPnkRZWZnbmLF3r/tRO9XFmIoxyPFZddOQmBjXSFSyxzSrvJSGcU1dLEBqxKiRaCw+6kfUt2ab4e3YVitCauXw4cMuw/qDg4PdTjdp0iTMmjWrxmXt2SP3MeVQ8XaCDh06oGPHjmjTpg02btyI3r17G9gy8hUTNOuRNWmzSrKmFU9jGpHsGNesRdaYRnVnxbhm2QKklrdfG1F8ZOHROCIWIs1QhKS6s9lsHj1X5JFHHsGwYcNqnKZ169aIiYmp8qDjixcvoqCgADExMW7ni4mJQWlpKQoLC11GQebn5zvniYmJqfIWN8dbsitOU/nN2fn5+bDZbB6PfqxuvSIiIrB//37LFyBlGyXCBI0OHYlkwmYhnsa0iIgIBAQEuI0ZNcWqmqZ3/Dc/Px/NmjVzmaZTp07erAbpiHGNZCJjIZIX1urGinHN0s+ANAsWH8Ug2n6Q/e3YfCu2fiIjI5GQkFDjn6CgIKSkpKCwsBC5ubnOeTds2IDy8nIkJye7XXZSUhLq1auH9evXOz/bt28f8vLykJKSAgBISUnBTz/95FLczM7Ohs1mQ/v27Z3TVFyGYxrHMnx15MgR/PHHHy6B14pkStLM8OwkUo9sx4NM/9ZkFRQUhKSkJJeYUV5ejvXr11cbM2qLMa1atUJMTIzLNMXFxdiyZUud4xBpQ7Z/azL1Y6Qt2Y4F2f6tychMcY0FSJXpXfARrehldSK+pEb2QiSJIzExEX379sXIkSOxdetWfPfddxgzZgwGDhzofAP20aNHkZCQ4BzRGBYWhoyMDIwfPx5ffvklcnNzMXz4cKSkpKBr164AgD59+qB9+/YYPHgwfvjhB2RlZWHKlCnIzMx03opw//3349dff8Vjjz2GvXv3Yv78+fj4448xbtw4Z/tOnz6NnTt3YufOnQAuPVx5586dyMvLc34/YcIEbN68GYcOHcL69evxf//3f2jbti3S0tL02oxUB7KdlJN+ZCpEMlnT3vjx4/Hmm29iyZIl2LNnD0aPHo0zZ85g+PDhAIAhQ4Zg8uTJzukffvhhrFmzBi+++CL27t2L6dOnY/v27RgzZgwAwM/PD2PHjsVTTz2FFStW4KeffsKQIUMQGxuLfv36GbGKZBIy9V2kHx4XVJlZ4polb8HWamQVi4/kIOJLamS8LZvPghTPBx98gDFjxqB3797w9/dH//798corrzi/v3DhAvbt24ezZ886P5szZ45z2pKSEqSlpWH+/PnO7wMCArBy5UqMHj0aKSkpaNCgAYYOHYoZM2Y4p2nVqhVWrVqFcePG4eWXX0bz5s3x1ltvuRQOt2/fjl69ejn/Pn78eADA0KFDsXjxYgQEBODHH3/EkiVLUFhYiNjYWPTp0wczZ860xDNXqiNDMYQn4eQp3pZNAHDXXXfhxIkTmDp1Kux2Ozp16oQ1a9Y4H7afl5cHf/+/xmFce+21WLp0KaZMmYLHH38cl19+OT777DNceeWVzmkee+wxnDlzBqNGjUJhYSG6deuGNWvWICQkRPf1o5rJENcAxjaqnSy3ZfNWbO2ZJa75KYqiaLZ0lRQXFyMsLAzbd0ejYaO6D9o0QwFSxOJjcF6Qx9OWxJVq2BJxiFaEdJCtEClKEfL0qXJ0uSIfRUVFHj2voyaOfm3K5j4Iaaj+m9We6rpWlXaS+rTc974QPVFjgka+Ej1hEyFZUyteMKZZG+OadxjXyBeMaZ5hXBObJUdAakGv4qNohUdvio7ezmeGIqWIL6gB5BwNSUTqEzlJY4JGdcXRkETWI3JcAxjbyHeixzSOgiRP8BmQEhGp+BicF+Rz8dHb36j8R0Yi7TsHmZ4LyRfSEFkLEzRSi8jHkuiFEiJSD5/pR2rgcUSys1wBUotChh6FHFEKWCIUAWUtSIqyDyviC2qIrEvU4gdPrEltTNiIrIFxjaxC1GNK1H+DJA7LFSBlJELhSuRin0zFSBH2pTssQhKRCEQ9oSZzEPH4YrJGZG4i9jtkDjy2SEYsQNaR2Qs3shT2HGRoL4uQvuFt2ETqEbHowRNp0gOPMyJzYlwjKxLxGBPx3yKJgy+hEZyRxSrRC3k1cbRd1BfZ5B2JEO7FNABfTkNE+hPx5FlvRsRbUeOjHkR7kD8f3E9kPoxtf9Eyxlk5ljmIFtOIasICZB1oPWLMqOKjzIXHykQuRLII6b3lxZ0xwLbD6GYQkUqslKCJFlura4+I8VILTNiIzEO0EVdWim2AsfGtpt+2SjwDxItpvLBG1bFUAVKmWzhZfFSXqIVIFiGJSG+iJGpmT9Bkjafu2i1a7FSLaAkbEcmPsU0cVopnAGMayYHPgPSRlqMfjSg+yvDsRDWIuJ58JqR3ZLqQQETumTFBq/hCNNHiTF2Zed1EORZFuTBARL4TpT9Rk9n6f7OtT2VmPAbJXFiAFIxRxUerES3osAhJRHpgkUNdZk5iamK29WbCRiQvUeKamfoRs/XxNTHjuopyLIryb5PEwgKkxZmps/WFSOvPIiQRWYEoJ8a+MmOyUhdm2R4iHJdM1ojkJEL/UVdm6cvrwkzbwAzHJJkTC5A+0Kogo3cBygydqxrMEmi0JFoRkrdhE8lJ1hNiMyUlWpJ9G8l6fBJZlQhFe5n7Dca26plh28h8bJJ5WaYAKXrBQs/io+ydqVZE2CaijoIExCtCEpF3jE7UZDwRZrz0jcyJm4zHKREZQ8b+Qub+2SgybzOjj1Gjzz1JPJYpQKpF9iKMjB2nnkQILixCEpHZGH0C7A2ZEw0RcVt6h8kakRxkimsA+2K1yLgdZTtWydxYgBSAXgUn2TpLIxm9rUQuQopC9FHNRHSJLCe+MiYVMpFp+8pyzBJZmZHFepn6CJn6XpnItl1lOmbJ3FiANBiLj+LiNnOPoyCJ5MNRVdWTLYmQnSzbm8kaEclMlr5WdtzOteM5KFXEAqQXZC28sFP0nZHbTuRRkLL+WyAifYlexGF8NI4MSZtRxy+TNSJxyRDXRO9bzUiG7S76sUvWwAKkgfQoMIneEcrAyIDCImTNeBs2kbhEPtGVIVGwCu4LIvKWUUV6xjWqjej7QeRjmKyBBUgTE7nzkxG3JxHJyIhETdQTXNETAysTdb+IeiwTkb5E7gtE7T+tTOR9YsSxzJH95GCJAqQao6TUHu2l9cg2kTs9mRmxXTkKkoio7hgXxSdqgVjkwgMRWZeofSZdwv1DVJUlCpBEamIR0hWLkERUkWjFGiYA8uH+4mgRIpGIFtcA9pMyEXFfiXhMkzWwAGkAM49+bPSbUu0fMxExkFgVnwNJVD29ixiindCyr5aXaIVj0Y5tIqtiXBOrbyTPiLjf9D62eWGNABYgTcfo4mNt35upQKn3tuYoSCIiz4l2ok++EWk/ilaIICJrEak/JN9wH5LV+VSAnDdvHuLj4xESEoLk5GRs3bq1xunnzp2Ldu3aITQ0FC1atMC4ceNw/vx5nxpsBDWLK1oWkYzq0NQqIJqhKGllLEKSzKwW17QgSnFGxFEGVDdW3Z8cLUK+YkxThyhxDbBuP2hGIu1LkY5xsgavC5AfffQRxo8fj2nTpmHHjh246qqrkJaWhuPHj7udfunSpZg0aRKmTZuGPXv24O2338ZHH32Exx9/vM6Np78YWXzUevkiFyM5ClIMvA2b6oJxzTxEOqkndYmyb5mskegY08yFF9XMifuVrMrrAuRLL72EkSNHYvjw4Wjfvj0WLFiA+vXr45133nE7/aZNm3DdddfhnnvuQXx8PPr06YO777671itxZmS24pHeRUFRC5FGFCFFPZY4CpJkZNa4pufoKRGKMjyRNz8mbES1M2tMAxjXyHxE2Md6Husc2U9eFSBLS0uRm5uL1NTUvxbg74/U1FTk5OS4nefaa69Fbm6uM4j9+uuvWL16NW6++eY6NJsq0rvjMroQaPTvu8M3YxPJiXHNHEQ4gSf9GL2/RShMELnDmGYeRvdzpB/ua7KSQG8mPnnyJMrKyhAdHe3yeXR0NPbu3et2nnvuuQcnT55Et27doCgKLl68iPvvv7/GYf0lJSUoKSlx/r24uNibZqpKrRFdWhWLjCg+isLRllMt/QxuySXBeUEoiSvV9TfzjkQgrvlJXX+zNln2RKTF7DG6GUQe0SOuiRTTtGB0MYYn7tZkRMyt6NCRSMQ3P6H572TbE3BjjPu+iKgyK+ZqWmBcI71ZJaYRaf4W7I0bN+KZZ57B/PnzsWPHDvznP//BqlWrMHPmzGrnefbZZxEWFub806JFC59/38zPhrNy8bEiUdulF46EJNKXt3FNzZgmGiZpZCTuf6K6MzpXEw3jGhmF+56swKsCZEREBAICApCfn+/yeX5+PmJiYtzO88QTT2Dw4MEYMWIEOnTogH/84x945pln8Oyzz6K8vNztPJMnT0ZRUZHzz+HDh71pJmlA9CKfKLdlGxU4RCtCGvEsSDNfbCDt6BHXGNO0wRN1Aow9DowuVBBVZuZczQrPjmNcIyvENCv8W6bqeVWADAoKQlJSEtavX+/8rLy8HOvXr0dKSorbec6ePQt/f9efCQgIAAAoivuCUXBwMGw2m8sfmWlRHNKzcxKhsOcpEdrKIuQlfCENyUCPuGZETNPj5M7I4guTNKqIxwPRJczV6oZxjUTAY4HMzKtnQALA+PHjMXToUHTp0gXXXHMN5s6dizNnzmD48OEAgCFDhuCyyy7Ds88+CwC49dZb8dJLL+Hqq69GcnIy9u/fjyeeeAK33nqrM7iJigUUMQp63mr0myLMcyH1JuIzIYlEZ6W4ZgZmPzHXMu6aOTYa9fwsPjeLRMOYJh+zx7WK6hrjzBzHKmJMI7PyugB511134cSJE5g6dSrsdjs6deqENWvWOB92nJeX53IVbcqUKfDz88OUKVNw9OhRREZG4tZbb8XTTz+t3loITObRjzIWHx2MfkGNkQ8SZhGSyDuMa/IwU5JmRIx195tmSuaMfoi/VvgiGvIGY5pvjBr9aKa4VpFWMa665ZopljmYNaaRtfkp1Y2tF0hxcTHCwsKwfXc0Gjby7r05dXkunBojINUuQLL46D0jA5KRQUOUIqTeb8QeYNuh2bJPnypHlyvyUVRUVOfbjRz92pTNfRDSsJ5KLbzk/OkLeKrrWlXaSerTct87aH0LthGJmhmSNBliqxmSOL1jrx6jRbQqQKoVLxjTrE3ruGbWx4qYIa4B4sY2M8QzwJh8Uuu4puVFNcY1sWn+FmySj6hBxFdmWx9PifZMSCLShxkf7i1rkuZ4QZooL0rzhGztFQFfRkNE3pI1rjnIECtkaKMnZD9W3DHjuSp5hgVIDck4+lH2Dro6Rq2X0QFDhCIkn6VKZC56F1uM7kd9YYaEB5A3eZPxmCEi4zCueUbWmADI3XZA/2OGF9ZIKyxAVsOKRRNZO2RPsQhJRCQXo/tPb8ie3NRGtnVjskZEVHdmjG1mXCciWbAAKQmtT6St0gFbZT0rM7oIacWCPpEZschSldWSGJkSN5kK2LXh7WpE2uDoR/dk6efrSqb15IU1MgNTFyDr8gKaujK64EPVMyLIyHKyQUQkCtH7TZmSFq3IsA30PI6YrBFRTUSPa4Ac/boWZFlvGY4hopqYugBpFhz9qD4rrrPRRXG9RkEaeeGBiNQh8gm2LEmKnrhNiEgrWo7+1fOigchxDWA/7iDDdhD9WPIUR/ZbEwuQFid6B6slvdddhGBhdBGSiLRlhkRNhL7SHRmSEqOJun1EPaaIiIzG2OYet8slHNlPamMBUnBanjSzU7XmNmARkojIc0xCvCPq9tKrCMlkjYgqE/EiiKh9tWhE3U4iHlNEnmAB0o263irKAo9c9AwqVg8WfBkNkZysOvpRxKRDFqImbUREgHXjGsDY5gsRYxovrJGMWIAUGEc/klZYJCci0YiUpImYaMhKpO0o0jHmCz4vi4jqgrGt7rj9iOqGBUgLYsdZlRVHQbIISURUFWOk+kTapnrEYI4WIVKH7EV3Uc75AbH6YdmJtC1FOsaIPMECJNH/J1IwMTM9bsPmm7CJ1KNHMUWUE2jGAe1w5A0RicJqcY19r/pE2q4yX1iT/SIDeY8FSJWpNapMq45ElI6SxMBRkOStgoICDBo0CDabDeHh4cjIyMDp06drnOf8+fPIzMxE06ZN0bBhQ/Tv3x/5+fku0+Tl5SE9PR3169dHVFQUJkyYgIsXL7pMs3HjRnTu3BnBwcFo27YtFi9e7PL966+/jo4dO8Jms8FmsyElJQVffPGF122RFU/ifCdSImF2ImxnUQoDJAat4prDH3/8gebNm8PPzw+FhYUarAGReyL0t2bHbUyiET2msQBJVIFeQUSk5IdFSPLGoEGDsHv3bmRnZ2PlypX4+uuvMWrUqBrnGTduHD7//HMsW7YMX331FY4dO4bbb7/d+X1ZWRnS09NRWlqKTZs2YcmSJVi8eDGmTp3qnObgwYNIT09Hr169sHPnTowdOxYjRoxAVlaWc5rmzZvjueeeQ25uLrZv344bbrgB//d//4fdu3d73BbSn9H9IZMH/Vlhm/M2bHloEdcqysjIQMeOHbVoOgnK6LgGWKOfFYUI21qEY47EIHpMYwGyEhHe0svRj8bidtKeCP/OyHt79uzBmjVr8NZbbyE5ORndunXDq6++ig8//BDHjh1zO09RURHefvttvPTSS7jhhhuQlJSERYsWYdOmTdi8eTMAYO3atfj555/x/vvvo1OnTrjpppswc+ZMzJs3D6WlpQCABQsWoFWrVnjxxReRmJiIMWPGYMCAAZgzZ47zt2699VbcfPPNuPzyy/G3v/0NTz/9NBo2bOj8HU/aQq60LqIYfcLM/t44Rm97o489EoNWcc3h9ddfR2FhIR599FE9Voc8YIWLA0b3r1ZkhW1uhX87spMhprEAaRFW6BRlI1Lyw1GQ5ImcnByEh4ejS5cuzs9SU1Ph7++PLVu2uJ0nNzcXFy5cQGpqqvOzhIQExMXFIScnx7ncDh06IDo62jlNWloaiouLnaMXc3JyXJbhmMaxjMrKysrw4Ycf4syZM0hJSfG4LWQdjIvGM/rWd5HisKf4qAV1aRXXAODnn3/GjBkz8O6778LfnymXVRjZrxjdp1qd0dtfxphG6pIhpgX6PCdVwSLOX8L2n691mqK2ITq0xDeNflNwqqWf0c0gqpPi4mKXvwcHByM4ONjn5dntdkRFRbl8FhgYiCZNmsBut1c7T1BQEMLDw10+j46Ods5jt9tdio+O7x3f1TRNcXExzp07h9DQUADATz/9hJSUFJw/fx4NGzbEp59+ivbt23vcFtKP0UkaicOsMffQkUjENz9hdDNMQ+2YBmgX10pKSnD33XfjhRdeQFxcHH799dc6tZPkwLhGgHljGqlPllxNzZjGAqRgtAhcegckT4qPlacTsRhpteCRdyQCcc1P6vZ7WfZEpMXs0e33RLU+vx0CT9ctgars4pkSAGvRokULl8+nTZuG6dOnV5l+0qRJmDVrVo3L3LNHjn3Vrl077Ny5E0VFRVi+fDmGDh2Kr776ylmEJGKSJiajYm5wXhBK4kp1/12z+jL/b4bHNMD4uDZ58mQkJibi3nvv1ew3zE6L0b5mvYWUcU08jGnmIUJcM1NMYwGSVOVp8bG6+UQsRGpJtCChdxFSS8uLO2OAbYfRzTDU4cOHYbPZnH+v7oraI488gmHDhtW4rNatWyMmJgbHjx93+fzixYsoKChATEyM2/liYmJQWlqKwsJClytr+fn5znliYmKwdetWl/kcb16rOE3lt7Hl5+fDZrM5Rz8CQFBQENq2bQsASEpKwrZt2/Dyyy/jjTfe8Kgt9BctEzWjRokwSROb1S78kXc8jWmA8XFtw4YN+Omnn7B8+XIAgKJc6nsiIiLwr3/9C08++WSNbSP5MK5RZWYsQmoxsj/bnoAbY/aqukxZyJKrqRnTTFuAXF7c2egmCEHPoORr8bHyMkQqQjIZIpnZbDaXoFadyMhIREbWXmxKSUlBYWEhcnNzkZSUBOBSQCovL0dycrLbeZKSklCvXj2sX78e/fv3BwDs27cPeXl5zmczpqSk4Omnn8bx48edtw1kZ2fDZrM5Ry6mpKRg9erVLsvOzs52LqM65eXlKCkp8bgtsuJz4WrHJE0ORsRd2ZI1q/I0pgHGx7VPPvkE586dc86zbds23Hffffjmm2/Qpk0bj9aBqDaMa+JjLkk1kSVXUzOmmbYAqTc1nv8o84Nj1Sg+Vl6WSIVILVl5FCRvw5ZLYmIi+vbti5EjR2LBggW4cOECxowZg4EDByI2NhYAcPToUfTu3RvvvvsurrnmGoSFhSEjIwPjx49HkyZNYLPZ8OCDDyIlJQVdu3YFAPTp0wft27fH4MGD8fzzz8Nut2PKlCnIzMx0Xgm8//778dprr+Gxxx7Dfffdhw0bNuDjjz/GqlWrnO2bPHkybrrpJsTFxeHUqVNYunQpNm7ciKysLADwqC2kPSNiHZM0InJHq7hWOSE7efKk8/cqP2eL5Me4RjUx24U1EpcMMY0FSBPTKzCpWXysvFwRipB6BA0GCZLFBx98gDFjxqB3797w9/dH//798corrzi/v3DhAvbt24ezZ886P5szZ45z2pKSEqSlpWH+/PnO7wMCArBy5UqMHj0aKSkpaNCgAYYOHYoZM2Y4p2nVqhVWrVqFcePG4eWXX0bz5s3x1ltvIS0tzTnN8ePHMWTIEPz+++8ICwtDx44dkZWVhRtvvNHjttAlZnpOFpM0+TBZIz1pEddIPIxrZCQzjYTkyH6xiR7TWICsIMueaHQTqBJRipB6ECn5MdOzIEldTZo0wdKlS6v9Pj4+3vlcEIeQkBDMmzcP8+bNq3a+li1bVrnFurKePXvi+++/r/b7t99+u8b5PW0LaUfvUSJM0uRlpmSNxKZVXKuoZ8+eVZZB5iDzHWxEZD6ixzR/n+Yi1akdvGQf/aj3b9RGr+3JkxgiInWw+Cg/vfehVjFY7ZFXfOYrkXUxtsnLLDGNqC5YgCSf6VkYDNt/XohCpB5ECRZqPNfUExx5TERE1WGyTUR1ZZbbr9kfyo/7kKyOBUgV6FWo8ZQeHZtRxUAji5AMGERkBVolanpeXGF/bS7cn0TWIssoX8Y18oWe+1KWkf1kHSxAEgnIaqMgtbK8uLPRTSAinTFJo7oQJf4SETkwrpkP96krWS46UN3xJTQCkO1k1+hboY18MY2eD8UX6aU0WsuyJyItZo/RzSCSkgwnbXrFObOd0IcdKKnzMoraBKvQEuPJ/lIavjWUyFxky99EVznemSV2icBKOSWJjwVIk9E6+TK6+OhglbdjM2AQEVmDGsVGT5Yrc1KnVxGSsZfIPGS/VdQsF9a8jXG1TS9zLHOQ/cIakS9YgCRpsQipj7wjEYhrftKw3yci/WiRqHH0Y/W0Kjp685uyJXFM2IjIaIxrtdM6vskeyxxkjmkc2U++YAHSYGoGMKuMfqzIiCKkEYHC6CIkEZHIZErSjCg61sTRHlmTNyIiEoOR8a3ibzOeVcVckkTBl9DUkewv6TADEQujZsPjnIiobsIOlAhXfKzI0T6R2+igR8FZixFOst8KSkT6ke3CmkixQ6Z4Bsi1r4nqigXI/y/Lnmh0E4TGIp/xzP6wa/4bJDIfPfot0U/cZUqCHGRss9XI8PIpIjNiXLtEliKfDG0E5L2wRuQtFiBNQoZApSW9C6RGbW8GDiKiv4gc+2RJemoicvuZrBFRbTjqWH2yxjZZ201kNixAUq1kGf0oSztlxduwicyNiZo6zJbkiLw+Ihegicg3Vh/dK2q/JnIs8IbI6yHqvq8Jzx3JWyxAGohX1skXPG68s7y4s9FNILIkrfsqEU/URU1q1CBy0kZEpAcrxjXAnLFN1HXS+hhgHklGYwHSBLTsqGQbVahne408STAqeGg9CpLPgSQiGVmpOCfaesqWrHG0CBHJwuyxzezrJxurj362ChYgiYiIJGXVkzWRRolYMXlh0kZEpC6R4hpgrdgm2rqKdiwQqYkFyDow+zPxZBv96CBru73FIfREJCqr9E+iJS16E2X9mawRUWVqjza2UlwTpW/Xk5XWmyP7yUgsQEqOJ93GMnr7G3EyZPbCO5EVyXTyaHS/C1grUamNFbaDVQoPRGQMEeIaYI3+vDaibANRjgkitbEAaRDRT2ZlH0Uoe/uJiKgqEU7IRUlORCLCNhHh2CAikpUI/bgouC2ItMMCJJHkRC9me4svoiGSm9n6pIqYlFSP28ZzMo04JiJt45oIF0/Yf1clwjbR8tgw87kaic2UBcjlxZ2NboIuRAhYIuMoSO3wNmwishoRkhHRGb2NeF5EROQdo/ttkXHbEKnPlAVIqhsW7rwjQsLDq1hEZHZG9rVMQjxn1m0lYpzNticY3QQiIckyytjoHMKs/bWajN5GRh8jRGpjARK85dPMWEwlIqqZmomaiEWaujI6+ZCRkduMyRoRqcmMcQ1gbPMGt1XtZCn6k/FYgDSAyIGMBTt56X1c8TZsItKLUUUlJh2+47YjIqoeR/XLxYwX1kSuSZB5sQApKV7h95yViqoMJERE6mCCVndGbUMma0Ry4mMFtMfY5jtuO6K6YwHSRxz9RRWxIKwutR+LYJUXUxGZkRH9K5MM9XBbusfb1Yisi6P65WW2C2tEemMBkpysNFLQzDhCg4iMwL6HqmNEwsZkjYjqykxxjcVH9ZhpW4p2jHMUtPmxAEmWwOKqNjgSmMg4VjhJ4+hH8+B2JSI9cHRxVex/1cdtSuQbFiAlpEVCxgIdEZH1MFFzxYRCW2bYvqKNFiEiueh9Yc0M/a6o9N62oo/s5zkleYIFSCITYoJEROQdJmnmI3qyRkRERGQlLEDqjIUh42g9ytOqiQ5vwyYiLWKbnn0qi4/64bYmIhnIHtcA9rd6MMMoSNYnSE8sQBJvvzYp2YOJ2m/CJiIiMeiZsFn14iARWRuLj/rhtibyHAuQREREJBSOfiSRqXWBj8/LIiIiIithAVIyvJJfN1Yb7Sn7KEgiIi2x+GgcbnsishJeWDM3juwn8gwLkEQqsmpA4HMgiYjIW3olbGaOzdn2BKObQCQMtUYVy3wBn8VH43DbE9WOBUiLs9qIQD2YOdEhIqpM7URNrz6UiQIRERERHy1C+mEB0gcc7UUykfkqLhFphyeJJAIWgonI7HhhzTo4sp+oZixA6oiFIDHoMeqTQYGISFxM0qxH7bjMczoiEgnjmji4L4iqxwIkkUZEKkLKmihl2RONbgIR6UikfpP0w2SNiPg8UyIi82MBUiJqJ2Z8/qP2rJRMi/5oguXFnY1uApHpyHhxg8UuMXG/EJEIZHyuMftP8eixT6yUZ5J5sABJRERElsAkzdpETNb4LFYiIhKBjBeVST6WL0DyFk9r0nP0pygJD4MKERGJjAViIiLvsN8UF/eNb/g4BnOzfAGSSA+iFCGJiESldT/JRIDUxgt7ROIQcTQxz/9JazzGSDYsQFoUn/9IWhD9OZBEZsGrw2RWWheKmawRkVnwwpr4uI+IXLEASboI2nvE6CYYToSkh6M1iAgQc6SIlpgAEBFRbXieTFR3VjvHJO8EGt0AIitp9JuCUy39jG4GEVGdqZmoiXCBRnTeXMgrTWiuYUuIiKg2fKwIOYQdKEFRm2Cjm+GR4LwglMSVGt0MMjEWICVhhuQsaO8RJkVEREQeqMudAxXnlTHuypSsERGR72qLdTLGML1xgAvJhAVI0lzFwCJSETJs/3kUtQ3R/XeNDhKyXdnKsiciLWaP0c0gIknJNkpE7UeWyF6M1ILRcZiIqC5ki2sVeRvjKk8vaxzjhTWiS/gMSJ2I9EwRvoCGiHxVUFCAQYMGwWazITw8HBkZGTh9+nSN85w/fx6ZmZlo2rQpGjZsiP79+yM/P99lmry8PKSnp6N+/fqIiorChAkTcPHiRZdpNm7ciM6dOyM4OBht27bF4sWLq/3N5557Dn5+fhg7dqzL5z179oSfn5/Ln/vvv9+rbUCkhaC9RzR/XrIev6EWWRJskc7vyDdaxbVt27ahd+/eCA8PR+PGjZGWloYffvhBy1UhEpIj9qgRf9RcFpEZiR7TWIAk3TFgmOOW+urwTdjmNmjQIOzevRvZ2dlYuXIlvv76a4waNarGecaNG4fPP/8cy5Ytw1dffYVjx47h9ttvd35fVlaG9PR0lJaWYtOmTViyZAkWL16MqVOnOqc5ePAg0tPT0atXL+zcuRNjx47FiBEjkJWVVeX3tm3bhjfeeAMdO3Z0256RI0fi999/d/55/vnnfdwapAYt+0MZilhGJFJM3sTCB/YbS4u4dvr0afTt2xdxcXHYsmULvv32WzRq1AhpaWm4cOGC1qtEBjPzeb43tI41jGVEVYke03gLNpFBeAsYyWbPnj1Ys2YNtm3bhi5dugAAXn31Vdx8882YPXs2YmNjq8xTVFSEt99+G0uXLsUNN9wAAFi0aBESExOxefNmdO3aFWvXrsXPP/+MdevWITo6Gp06dcLMmTMxceJETJ8+HUFBQViwYAFatWqFF198EQCQmJiIb7/9FnPmzEFaWprz906fPo1BgwbhzTffxFNPPeV2PerXr4+YmBi1Nw+RV0RImkR6LIo7vGWNtKZVXNu7dy8KCgowY8YMtGjRAgAwbdo0dOzYEb/99hvatm2r30qSR2QZzSzLhTUjfk/keAZoG9OYVxIgR0zjCEjSVHUBSITEy8pkOckiseTk5CA8PNwZ0AAgNTUV/v7+2LJli9t5cnNzceHCBaSmpjo/S0hIQFxcHHJycpzL7dChA6Kjo53TpKWlobi4GLt373ZOU3EZjmkcy3DIzMxEenp6lWkr+uCDDxAREYErr7wSkydPxtmzZz3cAuTAPqRuRIqBIrVFTxyhRIB2ca1du3Zo2rQp3n77bZSWluLcuXN4++23kZiYiPj4eE3XichIRsYUjohUB8/x5CVDTOMISC/x9lJSE69WkZaKi4td/h4cHIzgYN+vvNrtdkRFRbl8FhgYiCZNmsBut1c7T1BQEMLDw10+j46Ods5jt9tdio+O7x3f1TRNcXExzp07h9DQUHz44YfYsWMHtm3bVu063HPPPWjZsiViY2Px448/YuLEidi3bx/+85//1L4BSCqijhIRMTmSZfQIWZvaMQ3QLq41atQIGzduRL9+/TBz5kwAwOWXX46srCwEBjL9UptVHmMgalwDxIptIo/u58h+qkiWXE3NmMYISIYROTgQ6eXIsabwD1X3bezl5y69aMoxRN5h2rRpmD59epXpJ02ahFmzZtW4zD17xH4T+eHDh/Hwww8jOzsbISHVb8+Kz0Dp0KEDmjVrht69e+PAgQNo06aNHk2lCqw2Ck2kBM0dEeMyk7XaZdsTcGPMXqObAQDIOxZheEwDjI9r586dQ0ZGBq677jr8+9//RllZGWbPno309HRs27YNoaGhmv02kd5EjG0ixjOSkwhxzUwxjQVIIiKTOnz4MGw2m/Pv1V1Re+SRRzBs2LAal9W6dWvExMTg+PHjLp9fvHgRBQUF1T5TMSYmBqWlpSgsLHS5spafn++cJyYmBlu3bnWZz/HmtYrTVH4bW35+Pmw2G0JDQ5Gbm4vjx4+jc+fOzu/Lysrw9ddf47XXXkNJSQkCAgKqtC85ORkAsH//fssUIK0wUkTEUSIiJmjuMGnzXnBeEEriSo1uhul5GtMA4+Pa0qVLcejQIeTk5MDf39/5WePGjfHf//4XAwcOrLFtJC9eWBOH1eKZSHfWHToSifjmJ4xuhvBkydXUjGksQFpM2P7zRjeBKjEqWGiZMOUdiUBc85OaLJs8Z7PZXIJadSIjIxEZWXtRKiUlBYWFhcjNzUVSUhIAYMOGDSgvL3cW8ipLSkpCvXr1sH79evTv3x8AsG/fPuTl5SElJcW53KeffhrHjx933jaQnZ0Nm82G9u3bO6dZvXq1y7Kzs7Ody+jduzd++uknl++HDx+OhIQETJw40W3xEQB27twJAGjWrFmt60/kK5ETNHeslrSRHDyNaYDxce3s2bPw9/eHn99f53eOv5eXl3u0DkQV8cKab0SMZxzZTw6y5GpqxjS+hEYCVruSpicWZIk8l5iYiL59+2LkyJHYunUrvvvuO4wZMwYDBw50vlXt6NGjSEhIcI5oDAsLQ0ZGBsaPH48vv/wSubm5GD58OFJSUtC1a1cAQJ8+fdC+fXsMHjwYP/zwA7KysjBlyhRkZmY6rwTef//9+PXXX/HYY49h7969mD9/Pj7++GOMGzcOwKVnk1x55ZUufxo0aICmTZviyiuvBAAcOHAAM2fORG5uLg4dOoQVK1ZgyJAh6N69Ozp27Kj35iSLkCFBc0ekdmuVePP8irSKazfeeCP+/PNPZGZmYs+ePdi9ezeGDx+OwMBA9OrVy7D1JVKLSDGiNjK1laguZIhpLECSoRgQLmESVLMse6LRTaD/74MPPkBCQgJ69+6Nm2++Gd26dcPChQud31+4cAH79u1zebP0nDlzcMstt6B///7o3r07YmJiXF76EhAQgJUrVyIgIAApKSm49957MWTIEMyYMcM5TatWrbBq1SpkZ2fjqquuwosvvoi33noLaWlpHrc9KCgI69atQ58+fZCQkIBHHnkE/fv3x+eff17HrUIiEWmUiOwxTvb2E3lCi7iWkJCAzz//HD/++CNSUlJw/fXX49ixY1izZg1H3AuIb/31joyxQcY2E/lC9JjGW7BJM+zoicynSZMmWLp0abXfx8fHQ1FcC+ohISGYN28e5s2bV+18LVu2rHKLdWU9e/bE999/73FbN27c6PL3Fi1a4KuvvvJ4fnJPrUTN7BdezBIDRbx9jUhNWsW1G2+8ETfeeKNq7STxaRXXeGFNHSLFM9Fvw+azjeUlekzjCEgiQRiRjPOKLxGR+mRO0EQlUgJOROrKticY3QTygBlimxnWoSZmv7hL8vOpADlv3jzEx8cjJCQEycnJVd5eWllhYSEyMzPRrFkzBAcH429/+1utI12IiNS2vLhz7RORJTGukVrMmNyYcZ1EY4W305N+GNNIbWaKA2ZaFyLZeF2A/OijjzB+/HhMmzYNO3bswFVXXYW0tLQqr/t2KC0txY033ohDhw5h+fLl2LdvH958801cdtlldW48mQODgDnlHYkwuglEHmFcMw+OktOOWWM1R4uQ2TCmmQvjmjZEiGnct2RFXj8D8qWXXsLIkSMxfPhwAMCCBQuwatUqvPPOO5g0aVKV6d955x0UFBRg06ZNqFevHoBL950TUVWNflNwqqVf7RMSkWoY10gtIiQ0WjL6+VkiPzOLz8siUTCmkdrMHtuISD9ejYAsLS1Fbm4uUlNT/1qAvz9SU1ORk5Pjdp4VK1YgJSUFmZmZiI6OxpVXXolnnnkGZWVl1f5OSUkJiouLXf4QkTb4HEiyMj3iGmOaNTBBIyKjMVcjtZk5tpl53YhE5VUB8uTJkygrK0N0dLTL59HR0bDb7W7n+fXXX7F8+XKUlZVh9erVeOKJJ/Diiy/iqaeeqvZ3nn32WYSFhTn/tGjRwptmUjXC9p83ugnkAd4ORqQfPeIaY1pVWvRzvJVJH0zYiMTFXM04Zjx/t0J/b8Z1NMOxyBdTmZfmb8EuLy9HVFQUFi5ciKSkJNx1113417/+hQULFlQ7z+TJk1FUVOT8c/jwYa2bSURE5BFv4xpjmvmZMYGpidXWl8jMZM/V6voCJ5HvBOKFNfPjPiar8eoZkBEREQgICEB+fr7L5/n5+YiJiXE7T7NmzVCvXj0EBAQ4P0tMTITdbkdpaSmCgqp2+sHBwQgOFvMZP0REZB56xDUzxTSREzWjsBinL5GfA0lkNOZqpBYrxTajn29MZCVejYAMCgpCUlIS1q9f7/ysvLwc69evR0pKitt5rrvuOuzfvx/l5eXOz/73v/+hWbNmbgMaEZlj6DyRDGSMa7wthURgpuSUMZfMQsaYRkRiqutF57qOTiZz8voW7PHjx+PNN9/EkiVLsGfPHowePRpnzpxxvmltyJAhmDx5snP60aNHo6CgAA8//DD+97//YdWqVXjmmWeQmZmp3loIjiNGamemREZGPEbJyqwU18x6MmjULUyMXUQkGivFNNKGFWObFdeZyAhe3YINAHfddRdOnDiBqVOnwm63o1OnTlizZo3zYcd5eXnw9/+rrtmiRQtkZWVh3Lhx6NixIy677DI8/PDDmDhxonprQURCyjsSgbjmJ1VZVpY9EWkxe1RZFlFFjGtEvuFta0TiYUwzBz4b0Dr4aBGyEq8LkAAwZswYjBkzxu13GzdurPJZSkoKNm/e7MtPERERaY5xTT9mud2VoyWMw2SNqGaMafoyS1wDrB3beFGNSHuavwWbiHxjppMZIiIyJysnq5XxcSZERCQC5pEkKhYgiYiIiLzAohsREZkNYxu3AZHWWIAkzXAIOxERaY3PySIiIjNhXCMis2IBkkhgeg6f561jRETkCzOMGOHtakRkZWbox9VixLZg0ZmsggVIIiIiIg8xSRMDkzUiIiIiubAASURERER1wsIsEcmGd/9QdRjTiLTBAiQRERHViokaExLS1qEjkUY3gYgsiLGNiPTCAiRZWlHbEKObUCs+l4qIzELt/oy34RIRkZF4nk5mxovPpDYWIIlIU3lHIoxuAhER6YCjaIiI6kbvC2vst6un97ZRe9+zOE4iYgGSNFWa0FzV6YiIiIiIiIiISC4sQBKRE4fZExG5x1Ei4uEt+ERERETyYAGSiIiIiFTBQi0REZkFYxqRuliAJJIAn+FBRESkLcZaIrnwzfF1w+IaEemNBUjSHJ/vSEREatPz9lsmaUREREREdcMCJBERERGRCvgsZSLyFZ9rS0RmxwIkEREREamGI0aJiMTGftpzem4rFqHJ7FiA1EFJXKnRTRAab9EmIiIiIiIiIjIvFiBJFywy1h0fjg9k2RONbgIRWQxHiYiNo0WIiIjExBdFUWUsQBIREZHmeBGFiIjMhHGNiMg7LEASkQs+QJ/InHgVmoiIiMg6WCQn0bAASboR8TbssP3njW4CERGR6fDWdX1l2xOMbgIRSYL9s/e4zYjUwQKkhRS1DTG6CVWIWJQkIiIiIiIiIiL1sABJuhKx4MhRkERE5A5HPBARmRMfOUREpD8WIIlIc3lHIoxuAhGZCN98TEREREQkFxYgyTAijoYUHR8kTERERERkLrywRkRWwAIk6U7EwiNvwyYiqh5vVSMiIiLSHovRZGYsQBIRERGR6vgMTSIisbBf9p1Vtx0vQpOaWIAkQ4g4CpKIiIisjY86ISIiItIGC5ASONXSz+gmWAJvwyYiIiIiIiIiUh8LkERERERERERERKQZFiCJiIiISFp8YD8RERGR+FiAtJiitiFGN0FovA2biIgA6z5snoiIiIhICyxAeimu+Umjm0BEdbC8uLPRTSAiIiIiIiKyFBYgiYiIiIiIiIiISDMsQBJRFcF5QUY3gYiIiIiIiIhMggVIIsk0+k0xuglkYQUFBRg0aBBsNhvCw8ORkZGB06dP1zjP+fPnkZmZiaZNm6Jhw4bo378/8vPzXabJy8tDeno66tevj6ioKEyYMAEXL150mWbjxo3o3LkzgoOD0bZtWyxevLjKbx09ehT33nsvmjZtitDQUHTo0AHbt293fq8oCqZOnYpmzZohNDQUqamp+OWXX3zfIEREJDVf4trChQvRs2dP2Gw2+Pn5obCw0OX7Q4cOISMjA61atUJoaCjatGmDadOmobS0VMM1ISIiqxM9prEASUREHhs0aBB2796N7OxsrFy5El9//TVGjRpV4zzjxo3D559/jmXLluGrr77CsWPHcPvttzu/LysrQ3p6OkpLS7Fp0yYsWbIEixcvxtSpU53THDx4EOnp6ejVqxd27tyJsWPHYsSIEcjKynJO8+eff+K6665DvXr18MUXX+Dnn3/Giy++iMaNGzunef755/HKK69gwYIF2LJlCxo0aIC0tDScP88XUBERWZEvce3s2bPo27cvHn/8cbff7927F+Xl5XjjjTewe/duzJkzBwsWLKh2eiIiIjWIHtMCvZ6DfFISVyrMba1FbUP4tucahO0/z7eFE7mxZ88erFmzBtu2bUOXLl0AAK+++ipuvvlmzJ49G7GxsVXmKSoqwttvv42lS5fihhtuAAAsWrQIiYmJ2Lx5M7p27Yq1a9fi559/xrp16xAdHY1OnTph5syZmDhxIqZPn46goCAsWLAArVq1wosvvggASExMxLfffos5c+YgLS0NADBr1iy0aNECixYtcv5+q1atnP+vKArmzp2LKVOm4P/+7/8AAO+++y6io6Px2WefYeDAgdpsOCIiEpIvcQ0Axo4dC+DSyHx3+vbti759+zr/3rp1a+zbtw+vv/46Zs+ereo6EBERAXLENI6AJCIyqeLiYpc/JSUldVpeTk4OwsPDnQENAFJTU+Hv748tW7a4nSc3NxcXLlxAamqq87OEhATExcUhJyfHudwOHTogOjraOU1aWhqKi4uxe/du5zQVl+GYxrEMAFixYgW6dOmCO+64A1FRUbj66qvx5ptvOr8/ePAg7Ha7y3LCwsKQnJzsshwiIhKP2jEN8C2u+aqoqAhNmjRRdZlERCQvEXI1X/ka0zgCUhKnWvrx2X9EJhR0OAgBIeqOji47Xw4AaNGihcvn06ZNw/Tp031ert1uR1RUlMtngYGBaNKkCex2e7XzBAUFITw83OXz6Oho5zx2u92l+Oj43vFdTdMUFxfj3LlzCA0Nxa+//orXX38d48ePx+OPP45t27bhoYceQlBQEIYOHepclrvlVNd+IiLynEwxDfAtrvli//79ePXVVzn6kYhIMjLFNRlimuULkGkxe5BlTzS6GUREqjt8+DBsNpvz78HBwW6nmzRpEmbNmlXjsvbs2aNq27RQXl6OLl264JlnngEAXH311di1axcWLFiAoUOHGtw6IiKqC09jGiBWXDt69Cj69u2LO+64AyNHjtTlN4mISHwy5mp1jWmWL0BaFZ8DSWR+NpvNJahV55FHHsGwYcNqnKZ169aIiYnB8ePHXT6/ePEiCgoKEBMT43a+mJgYlJaWorCw0GUUZH5+vnOemJgYbN261WU+x1uyK05T+c3Z+fn5sNlsCA0NBQA0a9YM7du3d5kmMTERn3zyicuy8vPz0axZM5fldOrUqcb1JyIiY3ka0wBt45o3jh07hl69euHaa6/FwoUL67w8IiIyDxFyNW+oEdNYgCQisrjIyEhERkbWOl1KSgoKCwuRm5uLpKQkAMCGDRtQXl6O5ORkt/MkJSWhXr16WL9+Pfr37w8A2LdvH/Ly8pCSkuJc7tNPP43jx487bxvIzs6GzWZzFhRTUlKwevVql2VnZ2c7lwEA1113Hfbt2+cyzf/+9z+0bNkSwKUX0sTExGD9+vXOgmNxcTG2bNmC0aNH17r+REQkBy3jmqeOHj2KXr16ISkpCYsWLYK/Px+9T0RE3jNTTGMkJCIijyQmJqJv374YOXIktm7diu+++w5jxozBwIEDnW9VO3r0KBISEpwjGsPCwpCRkYHx48fjyy+/RG5uLoYPH46UlBR07doVANCnTx+0b98egwcPxg8//ICsrCxMmTIFmZmZzlsR7r//fvz666947LHHsHfvXsyfPx8ff/wxxo0b52zfuHHjsHnzZjzzzDPYv38/li5dioULFyIzMxMA4Ofnh7Fjx+Kpp57CihUr8NNPP2HIkCGIjY1Fv379dNySREQkAl/iGnDpOVs7d+7E/v37AQA//fQTdu7ciYKCAuc8PXv2RFxcHGbPno0TJ07AbrfzecNERKQZGWIaR0BaGG/DJiJvffDBBxgzZgx69+4Nf39/9O/fH6+88orz+wsXLmDfvn04e/as87M5c+Y4py0pKUFaWhrmz5/v/D4gIAArV67E6NGjkZKSggYNGmDo0KGYMWOGc5pWrVph1apVGDduHF5++WU0b94cb731FtLS0pzT/P3vf8enn36KyZMnY8aMGWjVqhXmzp2LQYMGOad57LHHcObMGYwaNQqFhYXo1q0b1qxZg5CQEK02GRERCcyXuLZgwQI8+eSTzr93794dALBo0SIMGzYM2dnZ2L9/P/bv34/mzZu7/J6i8KWSRESkDdFjGguQRETksSZNmmDp0qXVfh8fH18lEIWEhGDevHmYN29etfO1bNmyyi3WlfXs2RPff/99jdPccsstuOWWW6r93s/PDzNmzHApbhIRkXX5EtemT59e45tKhw0bVuvzuoiIiNQmekzjLdhERERERERERESkGRYgJXKqpZ/qyyxqy9sOiYiIiIiIiIhIOyxA+iCu+Umjm0BEREQaKk1oXvtEJISiNsFGN4GIiIiIasECpI5K4kqNbgIREREREREREZGuWIAk3oZNRERERERERESaYQGSiIiIiIiIiIiINMMCJBERERERtHnhHxERyY/PhiaqOxYgCQBvwyYiourxGcbkCyZrRERiYb8sPtFerMZzQFITC5CS4ZV5IiIiIiIiIiKSCQuQ5MRRkJdwOxARERERkV5EG/VGRKQFFiCJiIhIKnolarxVjYiIiIhIHSxA6ozPUCAiIiIiIjIOczIiIv2xAEkuePux+PgcUCIiIiIiIiKSCQuQEmIBirTGq8JERFQXvH1dXzfG7DW6CUQkCfbP3pN1m7FuQKJhAdJHcc1PGt0EzVh5FKSV152IzC2++Qmjm0BERERERBbFAiSAtJg9RjeBiIjI1GS9Ci/rqAer4JtjicgossY1IiKjmLIAOcC2w+gmSI8jAcms2D8QERERERFpi3ffUGWmLEASEREREREREdWFnndCcFQ/mR0LkAZQ4wUfegz5t9ooSKutLxERkRZ42zoRkff0LD6xnyYiI7AASTViUY6IiETEUQIkIjUuMhMRERGZEQuQRERERDXgSBEiIvHw+XJERHJhAZJqZYVRkFZYRyIiIq3JXKzlG22JyEpk7q/1wm1EpC4WICXGE2Xr4T4nIiIiIiIiItmwAFkHcc1PGt0E3Zh5hKCZ142IiNTBURDi4XNAiYiIiOTBAqRBZHxIOQt1REREREREZHZ6X3jkRTWyAhYgydJYVNWHlUYLExFZFUeJEhHVjd5FKPbb5sVHd5GIWICUnN4dCwt2RETWpMbIfbVjFhM1IiIyEos8ZGYy3rVJYmMBkrxmliKkWdZDbQw0RETkLRZn6y6++Qmjm0BEFsT+uypuEyJtsABJPmHxjoiIiIzCZ2URUV3xojsRkb5YgPz/0mL26P6bagU9o4b+y1yElLHtvMWDiMh4HBVBREREROQ9FiDJcmQsPhIREYnKDEVZXuQjIlEYMcLbDP24WozYFhzVT1bBAmQdWf3tvrIV82RrL7kyYqQyEYmNiRoRERERkfhYgDQJI6/cy1LUk6WdREREsmAx9i98nhwRyYz9ObcBkdZYgCRVsLhHRESeMMutrkxSjMNb1YiIiKpnlnMtMh8WIA1mpqvlIhchRW6bJxhEiIhINCzCEhGZ6zzdyv26UevOi2pkJT4VIOfNm4f4+HiEhIQgOTkZW7du9Wi+Dz/8EH5+fujXr58vP0u1ECH4iVboK2obIlybiEg8jGtyM+rk3cqJGhGJizFNfixKEZEZeV2A/OijjzB+/HhMmzYNO3bswFVXXYW0tDQcP368xvkOHTqERx99FNdff73PjSU5iFLwE6UdMjHTiFwiT8kW126M2evzvPHNT6jYErIyFl+JxCRbTCPxWLF/t+I6ExnB6wLkSy+9hJEjR2L48OFo3749FixYgPr16+Odd96pdp6ysjIMGjQITz75JFq3bl2nBlPNRBgFCag38tCxHG+WxVGPROQNxjXP8SJFVUxa9KXFqCBRzp2I1MCYRkRqqOs5Hy96kzteFSBLS0uRm5uL1NTUvxbg74/U1FTk5ORUO9+MGTMQFRWFjIwMj36npKQExcXFLn9EFtf8pNFNEFZdCoGV561tWSw8EpG39IhrssU0otqw6EokJuZqpBYr9fNGritvtSer8aoAefLkSZSVlSE6Otrl8+joaNjtdrfzfPvtt3j77bfx5ptvevw7zz77LMLCwpx/WrRo4U0zpaP2iBLRruSrWRSsXGT0ZYSkbETbn95igZ5Epkdcs1pMM4qRJ/FWStSISFxWzNXqOspK5JH9RhenrBDbzLiOsueOQN0eN0Ri0/Qt2KdOncLgwYPx5ptvIiIiwuP5Jk+ejKKiIuefw4cPa9jKv6TF7NHld6zIl9uo1VweEZEafIlrRsU0kZnh5NiqzJisEVmVbLmayBjXiIhqF+jNxBEREQgICEB+fr7L5/n5+YiJiaky/YEDB3Do0CHceuutzs/Ky8sv/XBgIPbt24c2bdpUmS84OBjBwRyOXBenWvqh0W+K0c2owlE0DNt/3uCWEBHpE9cY06yhNKE5gvYeMboZmjK6+Gj0aCAi0TFXI7WZObYZHdOIrMirEZBBQUFISkrC+vXrnZ+Vl5dj/fr1SElJqTJ9QkICfvrpJ+zcudP557bbbkOvXr2wc+dO3oZmYTWNXuTIRiLSC+MaqYnJjHWJfBsnWQdjGmnBjLFNhHXiRTWyIq9GQALA+PHjMXToUHTp0gXXXHMN5s6dizNnzmD48OEAgCFDhuCyyy7Ds88+i5CQEFx55ZUu84eHhwNAlc/VNsC2A8uLO2v6G2oqiStFcF6QqssUdRSkg7vRkCw+utLzdg4mT2RVssQ1ql1Rm2CEHSgxtA1mHS0iQrKmBVFum+TbQkktjGnmIkJcMxuzxjMiGXhdgLzrrrtw4sQJTJ06FXa7HZ06dcKaNWucDzvOy8uDv7+mj5Ykk2HRkYiMxLhGVDMma0TyYEwjLZj14hoR6cvrAiQAjBkzBmPGjHH73caNG2ucd/Hixb78pPDimp9E3hHPH96sF9FHQRIRiYBxTX9mjk9mStREKT7yVjUiz8kW026M2Ytse4Luv6s2M8c1wByxzewxTZRR/UTV4eUvIkExgBCRiER/XIMohSpRkpy6MMM6EBHJTpS4BsgdF2Ruu95EP9cjebEAKRCt/qGzkEVEaikoKMCgQYNgs9kQHh6OjIwMnD59usZ5zp8/j8zMTDRt2hQNGzZE//79q7yhMy8vD+np6ahfvz6ioqIwYcIEXLx40WWajRs3onPnzggODkbbtm2rjNKIj4+Hn59flT+ZmZnOaXr27Fnl+/vvv79uG4WoGkx2iMTnS1xbuHAhevbsCZvNBj8/PxQWFrqdbtWqVUhOTkZoaCgaN26Mfv36qb8CVGcstnhHxtgmY5uJfCF6TGMBkog0E9f8pNFNIJUNGjQIu3fvRnZ2NlauXImvv/4ao0aNqnGecePG4fPPP8eyZcvw1Vdf4dixY7j99tud35eVlSE9PR2lpaXYtGkTlixZgsWLF2Pq1KnOaQ4ePIj09HTnmznHjh2LESNGICsryznNtm3b8Pvvvzv/ZGdnAwDuuOMOl/aMHDnSZbrnn39ejU1D5JasSY9I7eataqQlX+La2bNn0bdvXzz++OPVTvPJJ59g8ODBGD58OH744Qd89913uOeee9RuPpEhRIoRtRGtrSKNaCXzET2m+fQMSJKP2Z9JYjZ6J0UyXPlNi9ljdBMsb8+ePVizZg22bduGLl26AABeffVV3HzzzZg9ezZiY2OrzFNUVIS3334bS5cuxQ033AAAWLRoERITE7F582Z07doVa9euxc8//4x169YhOjoanTp1wsyZMzFx4kRMnz4dQUFBWLBgAVq1aoUXX3wRAJCYmIhvv/0Wc+bMQVpaGgAgMjLS5befe+45tGnTBj169HD5vH79+oiJiVF9+8givvkJHDoSWfuEEhPtraGyPTdLtGRNdDLEUHLPl7gGAGPHjgVQ/fMUL168iIcffhgvvPACMjIynJ+3b99e1faTdYgW1wA5YhvjGVmJDDGNIyArqUuRQ43RXlqexPJKP5G1FBcXu/wpKanbiWtOTg7Cw8OdAQ0AUlNT4e/vjy1btridJzc3FxcuXEBqaqrzs4SEBMTFxSEnJ8e53A4dOjjf0AkAaWlpKC4uxu7du53TVFyGYxrHMiorLS3F+++/j/vuuw9+fq593wcffICIiAhceeWVmDx5Ms6ePevFViA1WSkuyZAElSY0l6Kd5OrGmL1GN0EXasc0wLe45okdO3bg6NGj8Pf3x9VXX41mzZrhpptuwq5du+rcZhKbleIaIHZsE7ltWhDp2ItvfsLoJkhBhFzNE2rGNI6AJCIyUKPDCgKC1B2dXFZ6aXktWrRw+XzatGmYPn26z8u12+2Iiopy+SwwMBBNmjSB3W6vdp6goCCEh4e7fB4dHe2cx263uxQfHd87vqtpmuLiYpw7dw6hoaEu33322WcoLCzEsGHDXD6/55570LJlS8TGxuLHH3/ExIkTsW/fPvznP/+pfQMQ1ZHIo0VETdR4q5pcZIppgG9xzRO//vorAGD69Ol46aWXEB8fjxdffBE9e/bE//73PzRp0qRO7SZrEnEUJPBX/BAlvokazwDGNBnJFNdkiGksQFoMb8UWn0hXr0huhw8fhs1mc/49ONj9Sc+kSZMwa9asGpe1Z49ct8C//fbbuOmmm6rcalDxGSgdOnRAs2bN0Lt3bxw4cABt2rTRu5mkISZqnhM5WdMKY618PI1pgPFxrby8HADwr3/9C/379wdw6fEjzZs3x7Jly/DPf/5Ts9+2Iis8WkQGIlxks2I8I3nJkqupGdNYgLQgFiGJrMFms7kEteo88sgjVUYKVta6dWvExMTg+PHjLp9fvHgRBQUF1T5TMSYmBqWlpSgsLHQZBZmfn++cJyYmBlu3bnWZz/GW7IrTVH5zdn5+Pmw2W5XRj7/99hvWrVvn0ajG5ORkAMD+/ftZgPRCSVwpgvOCjG6G1Jio1Y4jRagiT2MaoG1c80SzZs0AuD4fKzg4GK1bt0ZeXp7PyyUSnVEX2USPZzLhc431I0Ku5gk1YxoLkAJiYkd6YpChyMjIKi9wcSclJQWFhYXIzc1FUlISAGDDhg0oLy93FvIqS0pKQr169bB+/XrnFbN9+/YhLy8PKSkpzuU+/fTTOH78uPO2gezsbNhsNmegS0lJwerVq12WnZ2d7VxGRYsWLUJUVBTS09NrXaedO3cC+Cuwkv60vCgm6ihIByNHQzJZIzPTMq55IikpCcHBwdi3bx+6desGALhw4QIOHTqEli1b+rxc0o6a+ZeV45qDXvFNplim5UU1juo3NzPFNL6ERmVqvIhGD+ykxMT9QiJLTExE3759MXLkSGzduhXfffcdxowZg4EDBzpvdT569CgSEhKcIxrDwsKQkZGB8ePH48svv0Rubi6GDx+OlJQUdO3aFQDQp08ftG/fHoMHD8YPP/yArKwsTJkyBZmZmc5bEe6//378+uuveOyxx7B3717Mnz8fH3/8McaNG+fSxvLycixatAhDhw5FYKDrNbYDBw5g5syZyM3NxaFDh7BixQoMGTIE3bt3R8eOHbXefETV0vPlL3zRjHj4sH7j+BLXgEvP2dq5cyf2798PAPjpp5+wc+dOFBQUALg0quX+++/HtGnTsHbtWuzbtw+jR48GANxxxx06ryWRcbSKOYxlRFXJENM4AtLCeCs2aUmWYjx554MPPsCYMWPQu3dv+Pv7o3///njllVec31+4cAH79u1zebP0nDlznNOWlJQgLS0N8+fPd34fEBCAlStXYvTo0UhJSUGDBg0wdOhQzJgxwzlNq1atsGrVKowbNw4vv/wymjdvjrfeegtpaWku7Vu3bh3y8vJw3333VWl7UFAQ1q1bh7lz5+LMmTNo0aIF+vfvjylTpqi5iYh8puWIEdkSNVlGivAuAvn5EtcWLFiAJ5980vn37t27A7g0At9xm9wLL7yAwMBADB48GOfOnUNycjI2bNiAxo0b67NiZFqyjIKsqGIM8jXGyRbHKuIjRUgvosc0FiAFxduwrYejH0kGTZo0wdKlS6v9Pj4+HoriemEjJCQE8+bNw7x586qdr2XLllVusa6sZ8+e+P7772ucpk+fPlV+36FFixb46quvapyfzMeqiVrl5RCRe77EtenTp9f6ptJ69eph9uzZmD17thrNJDKN6mKTI94xdhH5TvSYxgKkG2kxe5BlTzS6GbrgKEhrs9rIjQG2HUY3gYjcYCyqnrtEzF1R0kwJG0eKEJHsGNe8Z6Y4VpHWMY2DWEgmLEAKTK9RkAyQxmPgICLZyDRSX8ZRkDUxa5JGRESeMVtcIzFYbXAK6Y8vodGAjM++YwGMiMg6+NILEhVHihCRUVh8IbVZcUQ/zzGpJqYuQPJ2S+/wpJxElRazx+gmEAnlxpi9RjdBOlZMAoiIrMKKRQ/GNWL+TrIxdQHSDHglzvyMChw8tohIJDyJJtmSacZRIqoJ45q1yRbTRMGL7ObGAiS5YKAkNcj4GAIiMj8mA0REZCaMa0QkExYgNaJmAUbvK+wsQuqH25qIZMYRYKQWPZJoEWOuFW8bJSIyOxkLwzynIz2wAEluiXiSbjbcxkRErvToF2VMCoiISB9qF2EY10grzCVJRixAVkO0l14YcUWCnZp58QoXERGJgskzEfG5b3XDflQc3BdE1WMBkmrEIqQ2uF2JiIzD5MB61I67vJBHRERE5B0WIDWk9os4jDrZPdXSjwUz8hhfQEMkB1GfPadXvGERUgzcD0Rkdoxr1qHXPhA1Nxf13JLEwQIkeUzUjk42Rm9HjtogIjWxTyFfMVmuO962SkQkBpljGs/lSC8sQErG6M7B6OIZERGRWmROFshzPHchsga1Rl8ZnW/VBeMaEYmMBUjyGk/kfcdt5z3RXghFRNpjX2l+MifJMhcniMgYesY1mftXWem5zXmORDJjAbIGahQ+tHgenggnvnwupPdE2F4iHDtERCJhoqY/JmqX8FlZRETy43kEkedYgKQ6EfnEnvTHF9AQWZPsFzeYPBARkZkwrpmTFrm37OdwJBcWICUlUkfB0ZC14/YhIvKO3v0mkzV9cDsTkQy0yLUY18yH25jIOyxASkykIiTAIlt1RNkuoh0vRGQ83gJKetI7UeNIESKyOhbItMNt64rnlOQJ0xcgB9h2GN0ES92WytGQrrgtiIh8x9Ei5sFtS0R6EL0IwtzAHIyIaTx2yAxMX4A0O1GvxLMQSUSkrRtj9hrdBBeixiNvsVBmDjwHISK6hHGNqmOWczeSBwuQtVDjTdhaE7njsHIhUqT11uMYsdJIXyIyNyZr6uL2rEr0UVpEJHaO5S32w+rh6EftiHZxndTHAqROrF6ccRQirdJ5WmU9tabmBQARHsdARN4zqj9lsqYOM21HMxUjiMg4jGvy4jYkqhsWIE1CppNisxciRVs3mY4NIiKRMNGoG6O2n2hxmIg8w9FP2mNc8x1jGlHdsQBpIrIVmsxYiDTb+hCR+al5K6hWcYh9q3yY5BKRGZgxrrF/9p4Zt5maxzYfK0KeYgFSR1a/Dbs6ZkkszbIevuCxTURmZcakQ2tGbjMrx2IiIk8xtnmO24pIPSxAekCGF9E4yDYK0ixETXh4PBCRWXC0iBzMuq1EjKe8XZXIPVlGYxmdP5i1v1aT0dvI6GOESG0sQJqQiCfJtZG5c5W57URE5JmiNsGGJyKiM3r7yBKPZSmOEJH5Gd1vi8zM20bGegGZAwuQOtPrVlV2KvoQOdmR/RiQaeQxEf1Fy75HhD7XzAlJXXC7EJFZMa5ZjwjbRIRjg0htLECSMGTrZGVrr1b4/EcishoREhORiLA9tIzJsl/QIyKqjQj9uAh4twORtixRgBxg22F0E1xwFGT1ZCnqid5OGfc9ERlHpltCRel/maAwUSMiUoNIcc3KfbpI6y7TRTWZziHJeJYoQKpB1ttBWYhS16mWfsKcJBARicoqsUekZEVvIq074zIRVaZ2UcQqcQ0Qq3/Xi0jrzJhGZsYCpAXIFjBF7XRFbVdleu5v3n5NZCyrvgVXpP7YiiNGrLS+sp1DEZGcRIprgHVim1XWUwZWPae1GhYgDaJ34Ua2E2iRgjBHPRIRUW2skMCImKjJFp95qxoRVUfE/ky0Pl9NIq6b1seAbDUBMh8WIC2EHY73RDwRqIlZ9rGsjzwgor9o3R+J2D+LWKBTg6jrJeIxQETmZZbzbG+JGgN8Zbb1IZIJC5BeULsoYsTtqzIFTiMTC456rB1vvyYyFxlHZonaT5spuTHLevhCpnMmIrOw+m2YosY1QP7YJnr7Rd731ZHx3JGMFWh0A0h/JXGlCM4LMroZHqnYETf6TdH192TDRImIRCNTvNGCI9EJO1BicEu8J3KSBsgdr4lIH/HNT+DQkUijm2E6ssU20eMZoE9MY65IIuAISIMZNYpMxg7IMSpRiw5a9hGPMu5PIiI1yNB3iz7qoiKZ2iobNUeKWH2UGJFR9DjnliGuAeLHC9HbR2RFHAFpYTKPTKkuMHs7SlKWAC8a2W6/HmDbYXQTiEhDp1r66TJKvq5EHTUiW4LGkSJEZHayxDXANYYYHd9ki2cA81GyFhYgBRDX/CTyjkQY8tsyFyHdsWIHziSJiNSixe1qZoszahAhWZMxSQOsGeeJSCyMa9UzIr7JGs8A/WIa80UShWUKkANsO7C8uHOdl5MWswdZ9kQVWiQOR4fEQEoi4BuwicgXMo0Wqahy4qRVwiZzgqY3JmpE5iHzcyBljWsO7uJOXWMcY5k4+AIa8oVlCpCiM3IUpAOv5snHiCRJttuviczuxpi9yLYnGN0MIcierAHVJ1feJG1mTdA4+pGIrMYMca0is8YnX3D0I1kRC5ACYRGSvMFgQkSyYGypO6snbbIXHzlShMhc9IxrZitCkvwxTW18sZp18C3YPuAtokRERGLiSb356LlPeXGPiIi0xPMUsjIWIAXD21vJE0YlSDw+icxPq5FaevZbPLknIpKX2qOhGNfIqrQ6Rjmqn3zFAiRVwav/YjPz/uHoYiJSC5M1c+DoRyKiSxjX5Md9SFbHAqSAOMqMqmNkcsTjkojqSu8+jCf6cjPL/lN7pAiflUUkDsY18pTe+44X1UhELED6iCO1iMiKCgoKMGjQINhsNoSHhyMjIwOnT5+ucZ7z588jMzMTTZs2RcOGDdG/f3/k5+e7TJOXl4f09HTUr18fUVFRmDBhAi5evOgyzcaNG9G5c2cEBwejbdu2WLx4scv3p06dwtixY9GyZUuEhobi2muvxbZt21ymURQFU6dORbNmzRAaGorU1FT88ssvvm8QkzLTrTVM1uTERI304m1cKygowIMPPoh27dohNDQUcXFxeOihh1BUVOQynSdxjcgXjGvyMdM+M9M5ohmJHtNYgCS3eCIuHu4TEsGgQYOwe/duZGdnY+XKlfj6668xatSoGucZN24cPv/8cyxbtgxfffUVjh07httvv935fVlZGdLT01FaWopNmzZhyZIlWLx4MaZOneqc5uDBg0hPT0evXr2wc+dOjB07FiNGjEBWVpZzmhEjRiA7OxvvvfcefvrpJ/Tp0wepqak4evSoc5rnn38er7zyChYsWIAtW7agQYMGSEtLw/nz51XcSlQT9mVUGzMlaiQ+b+PasWPHcOzYMcyePRu7du3C4sWLsWbNGmRkZDin8SSukb60LJoYEdfYT8rDiH3Fcy3rEj2m+SmKovi0ZjoqLi5GWFgYtu+ORsNGvtdMlxd3VrFVQJY9UdXlVZR3JEKzZXsqOC/I6CbQ/2d0ENHr9mutRhYPsO1QZTmnT5WjyxX5KCoqgs1mq9OyHP1ap8FPIyAoRJX2OZSVnsfO9/6lSjsr2rNnD9q3b49t27ahS5cuAIA1a9bg5ptvxpEjRxAbG1tlnqKiIkRGRmLp0qUYMGAAAGDv3r1ITExETk4Ounbtii+++AK33HILjh07hujoaADAggULMHHiRJw4cQJBQUGYOHEiVq1ahV27djmXPXDgQBQWFmLNmjU4d+4cGjVqhP/+979IT093TpOUlISbbroJTz31FBRFQWxsLB555BE8+uijzvZFR0dj8eLFGDhwoGrbqiaOfT9lcx+ENKynyjKz7QmqLKeiQ0ciVV+mgxHxpdFvwp/uEMyXqGlR9FDzFuzzpy/gqa5r6xwvZIxpgG9xzZ1ly5bh3nvvxZkzZxAYGOhRXDMTxjXj8ibGNrEZVShmXLNmXJMhpnEEJJHgjC4+6kX04iMBOTk5CA8PdwY0AEhNTYW/vz+2bNnidp7c3FxcuHABqampzs8SEhIQFxeHnJwc53I7dOjgDGgAkJaWhuLiYuzevds5TcVlOKZxLOPixYsoKytDSIjrCUJoaCi+/fZbAJdGUdrtdpflhIWFITk52bkcWcn2TDiOFiF3uI9Ib77ENXccSWRgYKBzubXFNTIXo87X2W+Ky4zFRy3Idg4rMhliGguQghLhhR+ydV5mJMI+EOFYJN8UFxe7/CkpKanT8ux2O6Kiolw+CwwMRJMmTWC326udJygoCOHh4S6fR0dHO+ex2+0uAc3xveO7mqYpLi52jn5MSUnBzJkzcezYMZSVleH9999HTk4Ofv/9d5dluVtOde23MjM+44eJmriYqFFt1I5pgG9xrbKTJ09i5syZLre4eRLXiNTC2CYes+4TM54bGkmEXK0yrWNaoFdTS26AbYfqt2ETaYVJkTXYfj2PQJV74osXLz3PsEWLFi6fT5s2DdOnT68y/aRJkzBr1qwal7lnj/gv3nrvvfdw33334bLLLkNAQAA6d+6Mu+++G7m5uUY3jdwoiSs15Ja1Uy39eMuaYMyaqFmR7WAJAgPV3Z8XL15KyDyNaYB+ca24uBjp6elo3759tW0h39wYs1f127Djm5/Q9DZso+IawNgmEiNjGvNH9YkQ18wU0yxVgCTvGRlIrUyU4MHRj3I7fPiwy3NFgoOD3U73yCOPYNiwYTUuq3Xr1oiJicHx48ddPr948SIKCgoQExPjdr6YmBiUlpaisLDQZRRkfn6+c56YmBhs3brVZT7HW7IrTlP5zdn5+fmw2WwIDQ0FALRp0wZfffUVzpw5g+LiYjRr1gx33XUXWrdu7bKs/Px8NGvWzGU5nTp1qnH9yVyYqInDzIma6M/Jko2nMQ3QNq45nDp1Cn379kWjRo3w6aefol69v5596ElcI1IbY5vxeEGNvCFCruagV0xjAZJIMKIUH/Wk1fMfrc5ms3n0YOPIyEhERtY+IiAlJQWFhYXIzc1FUlISAGDDhg0oLy9HcnKy23mSkpJQr149rF+/Hv379wcA7Nu3D3l5eUhJSXEu9+mnn8bx48edtw1kZ2fDZrOhffv2zmlWr17tsuzs7GznMipq0KABGjRogD///BNZWVl4/vnnAQCtWrVCTEwM1q9f7yw4FhcXY8uWLRg9enSt629FZh8tAvAB/kZiokbe8DSmAdrGNeBS7EhLS0NwcDBWrFhR5fnDnsQ1MiejB2+wCGkco2OajBfVrE6EXA3QN6bxGZBUKysWxIwi0rbm6EeqLDExEX379sXIkSOxdetWfPfddxgzZgwGDhzofKva0aNHkZCQ4LxKFhYWhoyMDIwfPx5ffvklcnNzMXz4cKSkpKBr164AgD59+qB9+/YYPHgwfvjhB2RlZWHKlCnIzMx0Xgm8//778euvv+Kxxx7D3r17MX/+fHz88ccYN26cs31ZWVlYs2YNDh48iOzsbPTq1QsJCQkYPnw4AMDPzw9jx47FU089hRUrVuCnn37CkCFDEBsbi379+um4JUkkRicMVmX0dhcp3pJxfIlrxcXF6NOnD86cOYO3334bxcXFsNvtsNvtKCsrA+BZXCNjWKGIYnT/akXc5iQCGWIaR0AKLK75SeQdiTC6GaQTJkMkgw8++ABjxoxB79694e/vj/79++OVV15xfn/hwgXs27cPZ8+edX42Z84c57QlJSVIS0vD/Pnznd8HBARg5cqVGD16NFJSUtCgQQMMHToUM2bMcE7TqlUrrFq1CuPGjcPLL7+M5s2b46233kJaWppzmqKiIkyePBlHjhxBkyZN0L9/fzz99NMutxA89thjOHPmDEaNGoXCwkJ069YNa9asqXKlj/Rj9GgRgCNG9GSVJM0KRQ6z8Dau7dixw/k20bZt27os6+DBg4iPj/corpF5Ma5ZhygxjXkkOYge0/wURRG+ZyouLkZYWBi2745Gw0Z1G7Sp9ktosuyJqi6vMpEKkEYHUjMTLWjoPfpRy1uwB9h2qLas06fK0eWKfBQVFXl8G1h1HP1a9+ueQGCgusWvixfP4+vvZqrSTlKfY99P2dwHIQ3r1T6DF9R+YL+DlrdhO4gQY5isactKiZpWBUi1nwF5/vQFPNV1bZ3jhTOmdZuqTUz7dgZjmsC0imuMaepgbNMGY1rdafFcY8Y1sfEWbCKDiVZ81Buf/0hEojjV0k+YhMJsRNmuVo+5RDLR6qVLeoxQFqmvEaX/NRNuUyLfsABJHhMpkJqFiNvUTM9+VHP0IxFpS6T+kImFeqxY1OXt10QEiBfXrNYXa0G07SjSMUbkCRYgBSdaMYidnHq4LYlIFlYsqIiWZMhItO0ne9zVajQYEVmHaP2yTETbdnrFNCueA5J2WIAk0llJXKmwSZBoBW8ishYR+0bREg4ZiFi8FfHYIiLj6FVUEbHvEbGPFhm3F5F6WIAkr4kYSGUh8rYzovjI5z8SUWUi9pNMPjxn9e3EkSJEVJmIcQ1gf10bkWO/qMcUUW1YgJSAiKPS2Ol5j9uMiLSm5S2aehZWRO0vRU5GjCbythH1eCIiYzGuid13G0nkbaLnsaTlvxE+VsSaAo1uAJHZiXrCUZGIRW4iIlE5EpNGvykGt8R4IidpgHkSNSKrujFmL7LtCUY3w/QY18SPZ0RmwBGQ5DMZCmtGk2EbGVV85O3XRPLhaBFXVh45IsO6y3AMEZF1yNAnOfp20ft3Ncm0vryoRrJjAVISoo5QkyGQGkHkF80QEclCln5UpuSlLqyYmIqCt6oRaUPvIosscQ0wf2yTbf1kOnaIqsNbsKnOSuJKEZwXZHQzhCBbYBC1sK2GAbYdRjeBiFQgU4ypmMiY6TY2mRI0B73jMUeKEJGnZIprgLlim4zxDJAvxySqDkdAkiqs3inKOOLRyOIjb78m0o7WI6VYaPGM7KMFZW6/bPGYiIzFuOY5GWODjG2uyIiYpvW/CY7qty6OgJRIXPOTyDsSYXQzqiXb1Tw1MMkhItKe7PFFhtEjsiZmlZkxUSOyOjO+iEb2uAZUjRuixDezxDOAuSaZj08jIOfNm4f4+HiEhIQgOTkZW7durXbaN998E9dffz0aN26Mxo0bIzU1tcbpZcORXK6s0knKOOKxIjPfek3kC8Y17xhRcJG5z62o4kgMI0dkiNAGtZnlGCGqK8Y07zGu1Z0R8U2UmGomvKhGWvK6APnRRx9h/PjxmDZtGnbs2IGrrroKaWlpOH78uNvpN27ciLvvvhtffvklcnJy0KJFC/Tp0wdHjx6tc+OtSIbCkdmCqYOj6Cj7+hl9DLFoT6JhXJOH7P1vddwlUGolUVouWyRGHRt6JGq8VY28wZgmF7PGNYfqYpAn8agu85qB2Y8NsiavC5AvvfQSRo4cieHDh6N9+/ZYsGAB6tevj3feecft9B988AEeeOABdOrUCQkJCXjrrbdQXl6O9evX17nxJC6zdJhmKTo6GF18JBIR45pvjLpCbpb+2BOeJF9WTs4crHRMENWGMc13jGvGsHL8qo6ZL6qRtXlVgCwtLUVubi5SU1P/WoC/P1JTU5GTk+PRMs6ePYsLFy6gSZMm3rWUpCN64a5icbG6P2YiQvFRr9GPfAM2ecqscc3sI6bM1j+T74w8FpiokWjMGtOsgHGNHMx+LJj9HJVq5tVLaE6ePImysjJER0e7fB4dHY29ez07kCZOnIjY2FiXwFhZSUkJSkpKnH8vLi72ppmmJ/rLaCrT+yHLZu+0fSFC8ZFIRHrENTPHtPjmJ3DoSKQhv22GB/hT3TDeE7kyc66m14toGNfISLyoRmbn00tofPXcc8/hww8/xKeffoqQkJBqp3v22WcRFhbm/NOiRQsdW0la0GtEIZMRItKTJ3GNMU077POty+h9r1eixpEipCfmasYzum8j43DfkxV4VYCMiIhAQEAA8vPzXT7Pz89HTExMjfPOnj0bzz33HNauXYuOHTvWOO3kyZNRVFTk/HP48GFvmkkC07JjZaftniijH/nyGRKRHnHN7DHN6Cvm7Puth/ucyD3maupgXCM9ifDoL6OPebIOrwqQQUFBSEpKcnkoseMhxSkpKdXO9/zzz2PmzJlYs2YNunTpUuvvBAcHw2azufwh89CikzW60xaVKMVHIlHpEdeMimlWGjnFGGAdIuxrJmokKuZq5iFCUYq0Z7V9bKVzU3LP61uwx48fjzfffBNLlizBnj17MHr0aJw5cwbDhw8HAAwZMgSTJ092Tj9r1iw88cQTeOeddxAfHw+73Q673Y7Tp0+rtxYWZIbCkhqBlcG5eiIdIxz9SCJjXKs7EQoyjAfmx/1LVDszxzQ9ixcixDWA/Z6ZibJvRTnWyRq8egkNANx11104ceIEpk6dCrvdjk6dOmHNmjXOhx3n5eXB3/+vuubrr7+O0tJSDBgwwGU506ZNw/Tp0+vWekGkxexBlj3R6GZIy9H5evvQZVE6bRGJVHzUG9+ATd5iXDMXPsTfnESJ+XomahwpQr5gTDMfxjXzsWJMIwJ8KEACwJgxYzBmzBi3323cuNHl74cOHfLlJ8iCvClEitJpi0i04iNHP5IMGNfqzsg3h1bGZM08RIr3TNRIFoxp6mBcIy2IFNeI9KbrW7BJXaIVmtTiuI3OXefMW+yqF9f8pGmPCSLyjd4jqEQq0DBWyI/7kIiMJlpcY78oL9H2n97HNkf1E8ACJAmuYjFSpA5bNKIWHjn6kYiMxNghL9H2m0hFCCIrYxFDvP6Rasd9RnQJC5CSE7XwRPrhMUBEIhGxUMMTf3mIWDQ24phmkYVIHKLGNdH6SqpK1P0k4jFN1sACpEo40ouMIHLx0Yh/E3wBDZEYRDyxFTUJoL9w/xCRqESMawD7TZGJum9EPZbJGixXgDRjgULkIhRpQ/TnPbIgTyQOjqRyxUKkeETeJ0zUiMTDuOZK5D7Uirg/quK/WXKwXAGSSHYiFx6JiBxEL9wwOTCe6EmaUccwEzUiMckQ10TuU81Ohu0v+jFM5scCpEmwKGUNMuxnjn4kIgfRT3RlSBbMiNudiGQlelwD2MfqTZbtLcOxS+bHAiSRBES/5VoEZny8ApFajBxRJcMJryzJg+xk2s4yHLdEVsa4VjuZ+lwZybR9jTxmOaqfKmIBUkVGj/xigcp8ZCs8Gv1vgIioLmRKJmQi23ZlokZEZiJbHyw62banLAVzsgYWIIkEJVPhEWDxkYiqJ9vJryO5kCnBEJGM21C2Y5WIjCFjXyFjnywSbj+iumMB0mRkK1pRVbKNeiQiORg9skrGZA1gwuEtmYu3sh6jRFbFuOYbmftpvcm+rYw+Ro3+N0riYQFSZSKMAmPxSk4yFx6NPu75/EciORh9IlwXsichWjLDthHh2GSiRiQfEfqOujBD/602s2wT2Y9NMqdAoxtAZHWyFh0djC4+EpHnbozZi2x7gqFtiG9+AoeORBrahrqqmJQE5wUZ2BLjyJ6YERGpxQxxDbB2bDNbTGPxkUTFAqRJxTU/ibwjEUY3g2oge+ERYPGRiHxjlmQNqJq0mDlpM1uC5sBEjUheIlxYA8wV1wDzxzazxjNAnJjGUf3kjiVvwdb6dk1RijJmKHCZkcy3WouIt18TyUmUE2S1Vbx1S/YEx0zrUh1RjkMmakTyE6U/0YLs8UD29nvKzMcgmQNHQJocR0KKwYwFR1EK7UTkHVFGiwDmGzHijrtER8SRJGZOyKrDRI3IHBjX9FddzDA6vlkxljmIFNN4UY2qwwKkBbAIaRwzFh4BFh+JSD1WSdYqqi1B0iKBs3JS5g4TNSLSihXjmoMnscbXGMc4Vj2RYhpRTViA1EhazB5k2RONboYTi5D6MWvR0UGk4iNvvybyjUijRQBrJ2vuMMnSDpM0ItID41r1GOPUJVpc40U1qoklnwFpVWYvjBnF8UxHKzzbUaTiIxGZi2gn0GQ+Ih5jTNSI1CHivyUR+xwyFx5jJBuOgNSQaKMgAY6ErAuzFxdrw+IjkbmINgoS4IgR0g6TNCIygqPvYWwjNYka00S8EEBi4QhIC7J6Ic0bVhnZWBsRi4+8/ZrInOKbnxD2xJrkxOOJyBpELn6wHyK18FgimbEAaVEsqtWO2+cSEYuPRKQOJmtkZqIXs0X+90dE6hO5PyI5iHwMMaaRJ1iA1JjoxRsW2apicfYvoh6/HP1IZA0in2iT2EQ/dpioEWlD9H9bol8YITHxuCGzsGwBkgWMv7DY9hdui7+IWnwkInUxWSOz4fFCRKJjP0WekuFYEf1cksRh2QKknmQo5Fh91J/V17+itJg9UhyzZIyCggIMGjQINpsN4eHhyMjIwOnTp2uc5/z588jMzETTpk3RsGFD9O/fH/n5+S7T5OXlIT09HfXr10dUVBQmTJiAixcvOr///fffcc899+Bvf/sb/P39MXbsWLe/tWzZMiQkJCAkJAQdOnTA6tWrXb4fNmwY/Pz8XP707dvXt41BupLhBJyMJUuxmomaWLyNawUFBXjwwQfRrl07hIaGIi4uDg899BCKiorcTv/HH3+gefPm8PPzQ2FhoUZrQRXJ8m9Mlj6LjMHjg3whekxjAVInshR0rFaIs9r61kaG45Sjl401aNAg7N69G9nZ2Vi5ciW+/vprjBo1qsZ5xo0bh88//xzLli3DV199hWPHjuH22293fl9WVob09HSUlpZi06ZNWLJkCRYvXoypU6c6pykpKUFkZCSmTJmCq666yu3vbNq0CXfffTcyMjLw/fffo1+/fujXrx927drlMl3fvn3x+++/O//8+9//rsMWMQcmayQzHhdUF97GtWPHjuHYsWOYPXs2du3ahcWLF2PNmjXIyMhwO31GRgY6duyoVfOpGrLENYAX2KgqmY4Jmf6tWYHoMS3Q5znJ1BxFubwjEQa3RDssPLqSofhIxtqzZw/WrFmDbdu2oUuXLgCAV199FTfffDNmz56N2NjYKvMUFRXh7bffxtKlS3HDDTcAABYtWoTExERs3rwZXbt2xdq1a/Hzzz9j3bp1iI6ORqdOnTBz5kxMnDgR06dPR1BQEOLj4/Hyyy8DAN555x237Xv55ZfRt29fTJgwAQAwc+ZMZGdn47XXXsOCBQuc0wUHByMmJkbVbUP6cpyYHzoSaXBLyGgyJWkAEzXR+BLXrrzySnzyySfOv7dp0wZPP/007r33Xly8eBGBgX+lV6+//joKCwsxdepUfPHFF9qvEEmLcY0A+WIaiUWGmMYRkDqSscBjxhGCZlynupDplmuOfjRWTk4OwsPDnQENAFJTU+Hv748tW7a4nSc3NxcXLlxAamqq87OEhATExcUhJyfHudwOHTogOjraOU1aWhqKi4uxe/dur9pX8Xccy3H8jsPGjRsRFRWFdu3aYfTo0fjjjz88/g0zk7EwwpFv1sV9T2rwJa65U1RUBJvN5pKo/fzzz5gxYwbeffdd+Psz5TIC4xrJQtb9LuO/MTOTIaZxBKTO0mL2IMueaHQzvGaWEZEsPLqSpfBIvikuLnb5e3BwMIKDg31ent1uR1RUlMtngYGBaNKkCex2e7XzBAUFITw83OXz6Oho5zx2u92l+Oj43vGdN+1zt5yKy+jbty9uv/12tGrVCgcOHMDjjz+Om266CTk5OQgICPD4t8zqxpi9yLYnGN0Mr3HkyP9r796Do6rv/4+/SMgmILkhgZBOAgXkKqgFCREdWkkJNbVi6aigFFsqVdFWoCoIGjVekNLWUbGMlhb7q0jFilqkKAKpRSMCJjVCpCKhiBAwUhKQS26f3x98d2WTTchu9nbOeT5mMiObs7ufj0n2lfcrZ3edw4oDmhuDWvsEO9OkwHKtqaqqKhUWFno9xe3UqVOaNGmSfv3rXysrK0u7d+9u1zrhPOSaM5BpzhYNs1pToc40RxeQP0r6QC/VfCvSy7AUqxaRFI/erFg82vXsR9d/9qtjjCuotxnTWCtJyszM9Lq8oKBA999/f7Pj58yZo8cee6zV2ywvt973jC/XXXed57+HDh2qYcOGqW/fvioqKtLYsWMjuDIEAwObfVl5SJOcM6hFQ6ZJ4cu1mpoa5efna/DgwV5rmTt3rgYNGqQbbrih3feB9rHqH9bcyDV7snqmOUk05JqdMs3RBWSkWPUsyDOdWehFexlJ+ejNiuUjAvPZZ58pKSnJ8++W/qI2e/Zs3Xjjja3eVp8+fZSenq5Dhw55XV5fX6/Dhw+3+JqK6enpqq2t1ZEjR7zOgjx48KDnOunp6Xr//fe9rud+l2x/XqsxPT292btrn3k/Le2rW7du2rVrFwXk/7H6sCYxsNkJQxrc2pppUmhzze3o0aMaP368EhMTtWrVKsXFxXk+t2HDBpWVlemll16SJBljJEndunXTvHnz9MADD7R62wgucg3Rwi6Z5pQ/qoVaNMxqbuHKNArICLFDCekWrWUkxaM3KxePdj37MdSSkpK8Qq0laWlpSks7+y+0OTk5OnLkiLZt26bhw4dLOh1IjY2Nys7O9nmd4cOHKy4uTuvXr9fEiRMlSTt37tTevXuVk5Pjud2HH35Yhw4d8jxtYN26dUpKStLgwYPbtFf37axfv1533HGH57J169Z57seXffv26csvv1TPnj3bfD9OYIdhTWJgsyq7DGhuDGrB0dZMk0Kba9Lps0Ty8vIUHx+v1157TQkJCV6f/9vf/qYTJ054/r1lyxb99Kc/1b/+9S/17du3TXtAcNkt1ySyzSrINLQkGmY1KbyZRgEZQXYqId2aln6RKCQpHr1ZuXiUKB+jyaBBgzR+/HjddNNNWrJkierq6nTbbbfpuuuu87yr2ueff66xY8fqz3/+s0aOHKnk5GRNmzZNs2bNUteuXZWUlKTbb79dOTk5GjVqlCRp3LhxGjx4sKZMmaKFCxeqsrJS8+fP14wZM7z+ElhaWipJOnbsmL744guVlpbK5XJ5Sspf/vKXGjNmjH7zm98oPz9fK1as0NatW/XMM894rvfAAw9o4sSJSk9P16effqq77rpL/fr1U15eXhj/TyLcGNiswW5DmsSgFu0CybWamhqNGzdOx48f11/+8hfV1NR4XscrLS1NsbGxzQayqqoqz/01fU1kIFD8kS262THTEN2skGkUkAgpX2VgqEpJikdvVi8eEZ2ef/553XbbbRo7dqxiYmI0ceJEPfHEE57P19XVaefOnTp+/Ljnst/97neeY0+dOqW8vDw9/fTTns/HxsZq9erVuuWWW5STk6NzzjlHU6dO1YMPPuh13xdddJHnv7dt26bly5erV69e2rNnjyTpkksu0fLlyzV//nzdc889Ou+88/TKK6/o/PPP99zPhx9+qOeee05HjhxRRkaGxo0bp8LCwna/kYEd2eVskaYY2KKLnQc0ykdr8DfXPvjgA8+7ifbr18/rtioqKtS7d++wrR3+sXuuSWRbpNk50yRyzQqiPdM6GPcTuKNYTU2NkpOTtXV7D3VJDPwtv1sS6TeisdtZkIEKpJikdGzOTsVjtJ39eOxoo0YMOajq6uo2Pw2sJe7Htdy0aUF/YeP6xlq99cXSoKwTwef+2s9/b5wSusSd/QoRZsdhzReGtvCx+4AmWWdIO3msTg+NerPdeeHJtO4/C02mHfoDmRbFyLXoRK6FhxMyTSLXgsnJucYZkFHAjk/FDgRlYvvYqXiUoq98BJzKrmeMNMUZJKHllAENQPRzYq5JZFuwODHPrFI+IvpRQOp00RHpsyApIREIu5WOAKKTU4Y1N4a29nPigObGoAZEP6flmsQf2tqDTAOCgwIyilBCoi2cUDpy9iMQfZw4rLlRSLbOyYNZUwxqgHWQa97IttPItK+RaQg2Csgo4y6XKCLRlBOKR4nyEYhmTh7WzuTkwY3BrGUMaoD1kGtfa+nx3c75Rqa1jExDKFBA/p9oeBr2mTgbEk4pHM9E+QhEP4Y131obYqw4vDGU+YdBDbAucq11Vs43siwwZBpChQIyilFCOo8TS0c3ykfAOhjW/NPWASjUgxyDWGgwqAHWR64Fxt9caW/OkWOhR6YhlCggzxBtZ0FKPCXbCZxcOgKwLoa14GOwshaGNMBeyLXQI+eiG7mGUIuJ9ALQNpRU9pKXXu75AGc/AlbFL6pwKr73AXviZxtOxfc+woEzIJuIxrMg3Tgb0tooG32jfASszf0LK2eNwCkY0gB7I9fgJGQawokC0oIoIq2F4tE3ikfAXnjqGuyOIQ1wFnINdkeuIdx4CrYPVilGKLaiG0+xbplVfsYA+IdfZGFXfG8DzsTPPuzou+kf872NiKCAbIFVChJeSzA68fVomVV+tgAEhl9qYTd8PwPORq7BTvheRiTxFGwb4anZkUfx2DrKR8A5eA0tWB1DGoAzkWuwMjIN0YACshXR/IY0raGIDC9Kx7OjeASci9fQgtUwpAFoDbkGKyHTEE0oIM/CqiWk5F2MUUYGH8Vj21A+AuCsEVgBQxqAtiLXYAXkGqINBWQbWLmEdKOMDA5Kx7ajeATQFAMbohEDGoBAkWuINmQaohkFZBvZoYR0a1qiUUi2jtLRf5SPAFpz5i/HDG2IFIY0AMFCriHSyDRYAQWkH+xUQp6JQrI5SsfAUDwC8BdnjyDcGNIAhBK5hnAi02AlFJB+smsJeSZf5ZvdS0kKx/aheATQXgxsCCUGNADhxlmRCBUyDVZFARkAJ5SQTbVU0Fm1mKRwDA6KRwDBxsCGYGFAAxAtyDa0F5kGO6CADJATS0hfWivyoqWcpGwMLkpHAOHCwAZ/MaABiHZkG9qKTIPdUEC2g7uIoYj0zd/ir72FJUVjaFE8Aoikpr+EM7TBjQENgFVRRqIpMg12RgEZBJwNGRwUiNGJ4hFANKKQdC6GMwB2RK45E5kGJ6GADBJKSNgJpSMAq2FwsycGMwBO5evxj2yzNjINTkcBGUQ8JRtWRukIwE4Y3KyHwQwAWke2WQeZBjRHARkCFJGwCkpHAE7S0jDA8BZeDGUAEDxkW2SRaUDbUUCGEEUkohGlIwB4O9vwwBDnPwYyAIgssi04yDMgeCggw4AiEpFE4QgA7dPW4cMJwxyDGADYg9OzjTwDwo8CMowoIhEOFI4AEBnBHmaCMfQxYAEA2oMcARAsFJARcGZBRBmJ9qBsBAD7YugDAACAXVBARhhlJNqCohEAAAAAAFgVBWQUaVoyUUg6F4UjAAAAAACwCwrIKEYh6RwUjgAAAAAAwK4oIC2kpZKKYtJaKBsBAAAAAICTUEDaAMVkdKJoBAAAAAAAoIC0tbMVYBSU7UPBCAAAAAAAcHYUkA7mb4HmlMKSYhEAAAAAACB4KCDRZsEu5sJRaFImAgAAAAAARBYFJCKGchAAAAAAAMD+YiK9AAAAAAAAAAD2RQEJAAAAAAAAIGQoIAEAAAAAAACEDAUkAAAAAAAAgJChgAQAAAAAAAAQMhSQAAAAAAAAAEKGAhIAAAAAAABAyFBAAgAAAAAAAAiZgArIxYsXq3fv3kpISFB2drbef//9Vo9fuXKlBg4cqISEBA0dOlRr1qwJaLEAgMg6fPiwrr/+eiUlJSklJUXTpk3TsWPHWr3OyZMnNWPGDJ177rnq0qWLJk6cqIMHD3ods3fvXuXn56tz587q3r277rzzTtXX13s+f+DAAU2ePFn9+/dXTEyM7rjjjmb38+yzz+qyyy5TamqqUlNTlZub2yyfjDG677771LNnT3Xq1Em5ubn65JNPyDUAcCh/c+3w4cO6/fbbNWDAAHXq1ElZWVn6xS9+oerqaq/jtmzZorFjxyolJUWpqanKy8vTv//971BvRxKzGgA4VbRnmt8F5F//+lfNmjVLBQUF+uCDD3TBBRcoLy9Phw4d8nn8u+++q0mTJmnatGkqKSnRhAkTNGHCBH300Ud+LxYAEFnXX3+9tm/frnXr1mn16tV6++23NX369FavM3PmTP3973/XypUr9c9//lP79+/XD3/4Q8/nGxoalJ+fr9raWr377rt67rnntGzZMt13332eY06dOqW0tDTNnz9fF1xwgc/7KSoq0qRJk7Rx40YVFxcrMzNT48aN0+eff+45ZuHChXriiSe0ZMkSbd68Weecc44uvfRSzZw5k1wDAAfyN9f279+v/fv3a9GiRfroo4+0bNkyrV27VtOmTfMcc+zYMY0fP15ZWVnavHmzNm3apMTEROXl5amuri6k+2FWAwDnivZM62CMMf5cITs7WxdffLGeeuopSVJjY6MyMzN1++23a86cOc2Ov/baa/XVV19p9erVnstGjRqlCy+8UEuWLGnTfdbU1Cg5OVlbt/dQl0SeNQ4gMo4dbdSIIQdVXV2tpKSkdt2W+3EtN22aOsa4grTC0+oba/XWF0uDss4zlZeXa/DgwdqyZYtGjBghSVq7dq2uuOIK7du3TxkZGc2uU11drbS0NC1fvlw/+tGPJEkff/yxBg0apOLiYo0aNUr/+Mc/9P3vf1/79+9Xjx49JElLlizR3XffrS+++EIul/f/n29/+9u68MIL9fjjj7e63oaGBqWmpuqpp57Sj3/8YxljlJGRodmzZ+tXv/qVZ32pqan67ne/qzfeeENS6HPN/bWf/944JXSJO+vxABAqJ4/V6aFRb7Y7LzyZ1v1nocm0Q38IeqZJgeWaLytXrtQNN9ygr776Sh07dtTWrVt18cUXa+/evcrMzJQklZWVadiwYfrkk0/Ur1+/oO7jTJGc1cg1AJHm5FyzQqZ19GdDtbW12rZtm+bOneu5LCYmRrm5uSouLvZ5neLiYs2aNcvrsry8PL3yyist3s+pU6d06tQpz7/dp38eO9boz3IBIKjcj0F+/t2mVfWmVgryQ1u9qZV0OjjPFB8fr/j4+IBvt7i4WCkpKZ5Ak6Tc3FzFxMRo8+bNuvrqq5tdZ9u2baqrq1Nubq7nsoEDByorK8tTQBYXF2vo0KGe8lE6nRO33HKLtm/frosuuiig9R4/flx1dXXq2rWrJKmiokKVlZVea+nUqZOMMYqL+3pgCnautZRpp76q93k8AISL+3EoWLlmpUyTAss1X9xDZMeOp0erAQMG6Nxzz9XSpUt1zz33qKGhQUuXLtWgQYPUu3fvdq25NZGe1cg1AJHm5FyzQqb5VUBWVVWpoaHBa0iUpB49eujjjz/2eZ3Kykqfx1dWVrZ4P48++qgeeOCBZpd/O/sLf5YLACHx5ZdfKjk5uV234XK5lJ6erqLK/xekVXnr0qWL5y9UbgUFBbr//vsDvs3Kykp1797d67KOHTuqa9euLT6mV1ZWyuVyKSUlxevyM3OgpZxwfy5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" ] @@ -14298,82 +14353,12 @@ } ], "source": [ - "method='contourf'\n", - "# plotting at fixed time t = 0.0\n", "print('Plotting at t=0')\n", - "fixed_variables={'t': 0.0}\n", - "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", - "fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", - "fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", - "fixed_pts.labels = list(fixed_variables.keys())\n", - "pts = pts.append(fixed_pts)\n", - "pts = pts.to(device=pinn.device)\n", - "predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", - "true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", - "pts = pts.cpu()\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", - "cb = getattr(ax[0], method)(*grids, predicted_output)\n", - "fig.colorbar(cb, ax=ax[0])\n", - "ax[0].title.set_text('Neural Network prediction')\n", - "cb = getattr(ax[1], method)(*grids, true_output)\n", - "fig.colorbar(cb, ax=ax[1])\n", - "ax[1].title.set_text('True solution')\n", - "cb = getattr(ax[2],method)(*grids,(true_output - predicted_output))\n", - "fig.colorbar(cb, ax=ax[2])\n", - "ax[2].title.set_text('Residual')\n", - "# plotting at fixed time t = 0.5\n", + "fixed_time_plot(fixed_variables={'t':0.0},pinn=pinn)\n", "print('Plotting at t=0.5')\n", - "#plotter.plot(pinn, fixed_variables={'t': 0.5})\n", - "fixed_variables={'t': 0.5}\n", - "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", - "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", - "fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", - "fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", - "fixed_pts.labels = list(fixed_variables.keys())\n", - "pts = pts.append(fixed_pts)\n", - "pts = pts.to(device=pinn.device)\n", - "predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", - "true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", - "pts = pts.cpu()\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", - "cb = getattr(ax[0], method)(*grids, predicted_output)\n", - "fig.colorbar(cb, ax=ax[0])\n", - "ax[0].title.set_text('Neural Network prediction')\n", - "cb = getattr(ax[1], method)(*grids, true_output)\n", - "fig.colorbar(cb, ax=ax[1])\n", - "ax[1].title.set_text('True solution')\n", - "cb = getattr(ax[2],method)(*grids,(true_output - predicted_output))\n", - "fig.colorbar(cb, ax=ax[2])\n", - "ax[2].title.set_text('Residual')\n", - "# plotting at fixed time t = 1.\n", - "print('Plotting at t=1')\n", - "#plotter.plot(pinn, fixed_variables={'t': 1.0})\n", - "fixed_variables={'t': 1.0}\n", - "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", - "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", - "fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", - "fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", - "fixed_pts.labels = list(fixed_variables.keys())\n", - "pts = pts.append(fixed_pts)\n", - "pts = pts.to(device=pinn.device)\n", - "predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", - "true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", - "pts = pts.cpu()\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", - "cb = getattr(ax[0], method)(*grids, predicted_output)\n", - "fig.colorbar(cb, ax=ax[0])\n", - "ax[0].title.set_text('Neural Network prediction')\n", - "cb = getattr(ax[1], method)(*grids, true_output)\n", - "fig.colorbar(cb, ax=ax[1])\n", - "ax[1].title.set_text('True solution')\n", - "cb = getattr(ax[2],method)(*grids,(true_output - predicted_output))\n", - "fig.colorbar(cb, ax=ax[2])\n", - "ax[2].title.set_text('Residual')" + "fixed_time_plot(fixed_variables={'t':0.5},pinn=pinn)\n", + "print('Plotting at t=1.0')\n", + "fixed_time_plot(fixed_variables={'t':1.0},pinn=pinn)" ] }, { @@ -14381,7 +14366,7 @@ "id": "35e51649", "metadata": {}, "source": [ - "The results are not so great, and we can clearly see that as time progress the solution gets worse.... Can we do better?\n", + "The results are not so great, and we can clearly see that as time progresses the solution gets worse.... Can we do better?\n", "\n", "A valid option is to impose the initial condition as hard constraint as well. Specifically, our solution is written as:\n", "\n", @@ -14392,7 +14377,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "33e43412", "metadata": {}, "outputs": [], @@ -14425,7 +14410,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "f4bc6be2", "metadata": {}, "outputs": [ @@ -14441,7 +14426,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "689e6742159e465d83f030291d3e5c48", + "model_id": "bedd3bc14a07423d8bb066c0e0eae71c", "version_major": 2, "version_minor": 0 }, @@ -14455,7 +14440,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9eda1d06d1d84b96ab4f2645ac8fabe6", + "model_id": "601aac870d2c449fa2cd3a2e2e13ba99", "version_major": 2, "version_minor": 0 }, @@ -14469,7 +14454,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52b2e73e7d6c4251902e13df66352eb5", + "model_id": "30e7d1a2a8e5492a92aa88b763d75c1a", "version_major": 2, "version_minor": 0 }, @@ -14483,7 +14468,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f9481035286e4902855137337ed94f8e", + "model_id": "a119cd3fad7e44e3ab2b6dace0907acf", "version_major": 2, "version_minor": 0 }, @@ -14497,7 +14482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f78accaf0074659aefd3115d9d567da", + "model_id": "aac3c161828d496ead728cf162953850", "version_major": 2, "version_minor": 0 }, @@ -14511,7 +14496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "854d4977764a40bda72deb8e9c0fb016", + "model_id": "73e43690e8e84ee1988d65143274246e", "version_major": 2, "version_minor": 0 }, @@ -14525,7 +14510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "698934f06c8344dcbbc804eaf3297dcb", + "model_id": "366dcbbf8a894845b79ad3e170ddde42", "version_major": 2, "version_minor": 0 }, @@ -14539,7 +14524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "888cfa4ffb624615b83a40e29e199a37", + "model_id": "3f407c0649dd4db1a5f81a913de34eae", "version_major": 2, "version_minor": 0 }, @@ -14553,7 +14538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1e98aff63cd46db89dda070e3ea10e4", + "model_id": "d6178f4c455545ef8fb46a8fea48cd4f", "version_major": 2, "version_minor": 0 }, @@ -14567,7 +14552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0be5ed3cdac54ed08c2edc140491ccbf", + "model_id": "e1f20a67ab634da094d213c09d3978cd", "version_major": 2, "version_minor": 0 }, @@ -14581,7 +14566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c144a00e50540a8acd3d68676df8f7a", + "model_id": "7672f4ed9d0847b2a4afce5aac76e8c4", "version_major": 2, "version_minor": 0 }, @@ -14595,7 +14580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca033b0aa5284a7b87395ffee2b30704", + "model_id": "f8812742da96410bb246f19eda6c5b71", "version_major": 2, "version_minor": 0 }, @@ -14609,7 +14594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c0502b9d3f6642b8b3fc67ac23547d7f", + "model_id": "d8c378b76a494b3fb595d5cd08e57c94", "version_major": 2, "version_minor": 0 }, @@ -14623,7 +14608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97a0df9857cd4318983402ab8e78c607", + "model_id": "da45f1e7329c49dbab8278959ee67968", "version_major": 2, "version_minor": 0 }, @@ -14637,7 +14622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afe3c18f7dad4abba3b928f39f82d54a", + "model_id": "7639373e22284ba395ce96f18ab3e5a8", "version_major": 2, "version_minor": 0 }, @@ -14651,7 +14636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c7d285ca0fc4a6290782bd0a73de505", + "model_id": "00dc597732e7435f84aac65aa8bddf4a", "version_major": 2, "version_minor": 0 }, @@ -14665,7 +14650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c995839d90d348aeb131a98904779cb8", + "model_id": "82d9391737fc4db9a32184a2689c5e51", "version_major": 2, "version_minor": 0 }, @@ -14679,7 +14664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b2993134646e470b8d88356a556a0cf1", + "model_id": "98833f49580340c9be3f74d5c8b14a30", "version_major": 2, "version_minor": 0 }, @@ -14693,7 +14678,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0034743a00142d6a5c9c44e8f7c8801", + "model_id": "740e4fb3a5a84125b848d05bb90408ac", "version_major": 2, "version_minor": 0 }, @@ -14707,7 +14692,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e49ecb8fdc04160a7a9745cbbf63a7b", + "model_id": "6b6574b69dff409897b97190199c5ef1", "version_major": 2, "version_minor": 0 }, @@ -14721,7 +14706,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff541a930ad44ac49fad68452d1af1aa", + "model_id": "1bdd4c5f9ae54ec8ac7c116f1a056a65", "version_major": 2, "version_minor": 0 }, @@ -14735,7 +14720,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "90aada1dc79f48c0b9b33873494ffd98", + "model_id": "68a7d0ea9ea74ebd8567f64209cd6400", "version_major": 2, "version_minor": 0 }, @@ -14749,7 +14734,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "552d68347a9a4e54b2c708396680662a", + "model_id": "40cc37b6d4684ce8ae761972d1188f40", "version_major": 2, "version_minor": 0 }, @@ -14763,7 +14748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "89637888a1d74033913594980a4f6401", + "model_id": "46ba58637fab468d96eb06a0c1fb5609", "version_major": 2, "version_minor": 0 }, @@ -14777,7 +14762,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef222e209fa449b8b17d4544e0823fbb", + "model_id": "8374bcfaa1494bcf8ce0d7fafcba2b84", "version_major": 2, "version_minor": 0 }, @@ -14791,7 +14776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc4d600ef97240ba9c3475824009deac", + "model_id": "267bfdb40af5491aaea69d74702df363", "version_major": 2, "version_minor": 0 }, @@ -14805,7 +14790,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80ebdfcb09d64eac9fbd47225af4a1a7", + "model_id": "581fb4f92f344f1aa0c8d94de0bad98d", "version_major": 2, "version_minor": 0 }, @@ -14819,7 +14804,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9d92e40f67ca4c91818f99d9ddc76b73", + "model_id": "6051c8f78d4c480b8de183583d0f2920", "version_major": 2, "version_minor": 0 }, @@ -14833,7 +14818,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a289d56cbb334f2b8df590c5cc1d3adb", + "model_id": "9337c6a9da7a4feaa10f6d6b4d6c130c", "version_major": 2, "version_minor": 0 }, @@ -14847,7 +14832,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f0ac3c58e8334eae97079dbd297672d7", + "model_id": "458eb179245d4e9b8280145ed32fc7c9", "version_major": 2, "version_minor": 0 }, @@ -14861,7 +14846,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "535def4c38444906844ebe5af44de3c0", + "model_id": "294d2a8490864390bd71491c42054de7", "version_major": 2, "version_minor": 0 }, @@ -14875,7 +14860,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18174ed41b784b1193fa06fc01151adf", + "model_id": "2164def6a6234cbca811be405b9bc827", "version_major": 2, "version_minor": 0 }, @@ -14889,7 +14874,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c324ee995b3d4743b23d69dd533d4152", + "model_id": "78e4c15eba224a2fbcc2656f5c47340f", "version_major": 2, "version_minor": 0 }, @@ -14903,7 +14888,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "170db58345be449985bcec745db1cfb1", + "model_id": "8e9797e968574a7aa92fd10f08068402", "version_major": 2, "version_minor": 0 }, @@ -14917,7 +14902,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "105c4b5705014c85801428b2c4956f77", + "model_id": "1899e9a3db494818b6da387b68dfb4f2", "version_major": 2, "version_minor": 0 }, @@ -14931,7 +14916,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48ccc5a4e2744b83b1ae97a1d7878bec", + "model_id": "fb4006cee1484d53a3db78dbf5eac964", "version_major": 2, "version_minor": 0 }, @@ -14945,7 +14930,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d887fd206c794329a17dede198a4593d", + "model_id": "e4c6d6eacb2e4914acb9fb1972d15ab2", "version_major": 2, "version_minor": 0 }, @@ -14959,7 +14944,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df38a39e11374af78048c61dcd932412", + "model_id": "27c5cc7635e8474db5b18df16b36d0b0", "version_major": 2, "version_minor": 0 }, @@ -14973,7 +14958,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8549dd36adfa4a77b41c89b46fedc1ff", + "model_id": "1a8025da24444feaafac2d22e0aacf97", "version_major": 2, "version_minor": 0 }, @@ -14987,7 +14972,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ca2037d22dd459cbb0dc8fe2fbfb6b4", + "model_id": "0ea8f8df6b004638b58b1d53501694bb", "version_major": 2, "version_minor": 0 }, @@ -15001,7 +14986,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5130fa47a06548debcde6766b83a1b07", + "model_id": "730a42e6b03445acb9182cacebda3771", "version_major": 2, "version_minor": 0 }, @@ -15015,7 +15000,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc7285b79e1a4de6b9f8f45d2e3b033f", + "model_id": "76b45525617f416bae20116b3eca2dbb", "version_major": 2, "version_minor": 0 }, @@ -15029,7 +15014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f45fe65c33941beb8a1be2e2b4ceee8", + "model_id": "52f3e5f8acee4e2f94cc6a9c4bad6960", "version_major": 2, "version_minor": 0 }, @@ -15043,7 +15028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "552745e750a0463a8a3e98bf4aa1ca6d", + "model_id": "3e1fc3f3aeeb48c994ef8f1f8c8a578f", "version_major": 2, "version_minor": 0 }, @@ -15057,7 +15042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6dadae0762684325b59bf59d5a074fea", + "model_id": "b2a6610799d74fa2aed45d10d34a81bb", "version_major": 2, "version_minor": 0 }, @@ -15071,7 +15056,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99e313d77b904c9b92f8d7eeac41ea05", + "model_id": "2b18ce2b1f5f4e9e9649a38b623d8a04", "version_major": 2, "version_minor": 0 }, @@ -15085,7 +15070,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5f664c11a0649d496aa56d6d5827c69", + "model_id": "e078bf8f8a0841d182247980906b9cc2", "version_major": 2, "version_minor": 0 }, @@ -15099,7 +15084,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17e5b2d06767473ba8b766d35e7b7758", + "model_id": "418c67398c2a41359df17a7032786529", "version_major": 2, "version_minor": 0 }, @@ -15113,7 +15098,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d6301900a674b86a20d83f1241749d5", + "model_id": "49b446bef97245f1af871fc7bc314753", "version_major": 2, "version_minor": 0 }, @@ -15127,7 +15112,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "414fed9395494bb7b161f5b23d9cebf1", + "model_id": "d71cf9321f3d4a059ad4757d1e3d2e84", "version_major": 2, "version_minor": 0 }, @@ -15141,7 +15126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f87969cb34e141fd8a061fda57a76641", + "model_id": "65ea9b07c74b46f4afaecc6fdbac90f7", "version_major": 2, "version_minor": 0 }, @@ -15155,7 +15140,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c75adecab9f74273bf228f5d8923443b", + "model_id": "1b889e013e3f49aca91410d3de802a8b", "version_major": 2, "version_minor": 0 }, @@ -15169,7 +15154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7995fd17875543fcbde1c360d5c44fd2", + "model_id": "f47bde8ef42f46dabd58855206625ac8", "version_major": 2, "version_minor": 0 }, @@ -15183,7 +15168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eab58ad4aa0a428eb85fb37106ad419e", + "model_id": "0d321e034b414c9795e28bfb2d4ae341", "version_major": 2, "version_minor": 0 }, @@ -15197,7 +15182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fc588e0b9824a0cbaee2f37f13e8a61", + "model_id": "6dd4f19d53f741e4853a0a9a5f214c7c", "version_major": 2, "version_minor": 0 }, @@ -15211,7 +15196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "213cd44d69ff4cd88e5a27177950fc06", + "model_id": "4db264c214c74ca98557f8d4c0617d4c", "version_major": 2, "version_minor": 0 }, @@ -15225,7 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"533c6e32cce44421a100e681413833cd", + "model_id": "7740b37a54fe4bbeb1cc347c78a23e61", "version_major": 2, "version_minor": 0 }, @@ -15295,7 +15280,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e10f48edf2554f78bbff79298888536c", + "model_id": "f639d8d468144df69cefbbf1f05157e0", "version_major": 2, "version_minor": 0 }, @@ -15309,7 +15294,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b4d1aca8aa242aebb673fb047ac25ef", + "model_id": "7f412f5862454911b73933f3c257f835", "version_major": 2, "version_minor": 0 }, @@ -15323,7 +15308,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19f9a3fbe40d48eab2a52a6620e0ad19", + "model_id": "4042bee5fa6443c78ceec7a5f5c76619", "version_major": 2, "version_minor": 0 }, @@ -15337,7 +15322,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "857bb7f7803846569a63aa22e5e65d88", + "model_id": "e94efc5006284f94bf330c8e2d599131", "version_major": 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{ - "model_id": "36724ae02d8d428f9137e95a1499c295", + "model_id": "8bf738368c0a4b6eb38f239b3487bf07", "version_major": 2, "version_minor": 0 }, @@ -15421,7 +15406,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8fa1310f6194f47a4c450cc106d539e", + "model_id": "883058f0671f405baf130654ee173f72", "version_major": 2, "version_minor": 0 }, @@ -15435,7 +15420,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18612d3a7e564d27b23bab4d97516a37", + "model_id": "58f2049645984028a86e4d6ea49f810d", "version_major": 2, "version_minor": 0 }, @@ -15449,7 +15434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31e0937ef41d4b479077cb55ffb95624", + "model_id": "88cb1c8a905944e4b4e3ba08602b0b70", "version_major": 2, "version_minor": 0 }, @@ -15463,7 +15448,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52e255c6ecbe48d78db671a2282e2903", + "model_id": "c2d83617ece4488298f308c337f57f4a", 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"model_id": "72cdcbf4b6924a4ab9b8add0e68bbb9b", "version_major": 2, "version_minor": 0 }, @@ -15603,7 +15588,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96d2ebf4d09845a4a6006154f16df606", + "model_id": "5a199f2bae2e4fd69916f32d8ad3c527", "version_major": 2, "version_minor": 0 }, @@ -15617,7 +15602,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81b1b1454c1643458f661dd43f3a7453", + "model_id": "9081b994345e48b7965221200191cdac", "version_major": 2, "version_minor": 0 }, @@ -15631,7 +15616,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "12f266333188498581ec647cbf2e1129", + "model_id": "894786010c9e43769a7fee5532e723fa", "version_major": 2, "version_minor": 0 }, @@ -15645,7 +15630,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d64e8206e144103b8327a0f45e6b609", + "model_id": "843e731825644b3286b60e777c0f658e", "version_major": 2, "version_minor": 0 }, @@ -15659,7 +15644,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e33a97501ff485e881c6987babc5545", + "model_id": "9ab810b677ed479a958b0d9838b2c734", "version_major": 2, "version_minor": 0 }, @@ -15673,7 +15658,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a61da0942624213a2a0e155e31b3a6d", + "model_id": "73c817fbb0514525a9ba0741a20fbf9e", "version_major": 2, "version_minor": 0 }, @@ -15687,7 +15672,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c379c521eaa4e6c95216c0d211a9c5c", + "model_id": "e9f64bb67b6a4226b670ffd7571597c1", "version_major": 2, "version_minor": 0 }, @@ -15701,7 +15686,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca67ec0d836447f8838f470192864bd8", + "model_id": "d2e2990bb88c41f29de5852cdf8d8ec0", "version_major": 2, "version_minor": 0 }, @@ -15715,7 +15700,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e9882a95d2d4949a4c577fa8eed91c1", + "model_id": "72ec0e3e80854d3c9e08545a3b2f0aff", "version_major": 2, "version_minor": 0 }, @@ -15729,7 +15714,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "531023449d7647bea0739048a2f52cb2", + "model_id": "5ed5f34fec75462f981dfa8f3e1f9c24", "version_major": 2, "version_minor": 0 }, @@ -15743,7 +15728,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1648b93d39149ccabc99634b034b361", + "model_id": "7301fb1921aa402d83315674082e2e8a", "version_major": 2, "version_minor": 0 }, @@ -15757,7 +15742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ac1bc3863ef48b1ae7a8f825b1d8255", + "model_id": "6b726e142e594976a8292108f91bcd10", "version_major": 2, "version_minor": 0 }, @@ -15771,7 +15756,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81a9b1cece904cdf8e1b57d5c5fe6ff5", + "model_id": "bf7dc652c6d545e8a41007ae5b08f5d0", "version_major": 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{ - "model_id": "33fe03ce4f804149b3cd649d2a16e8ac", + "model_id": "48739614a9aa4e78a65e4d612591cfb1", "version_major": 2, "version_minor": 0 }, @@ -15855,7 +15840,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2223243e99942c48e1ff64254287967", + "model_id": "4ce2a095e92e404d90084f027787dbb5", "version_major": 2, "version_minor": 0 }, @@ -15869,7 +15854,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5ed581f0116a4a3fa9168a7400f035a6", + "model_id": "d8c3c8dc3cd3486fb4b33c861f21dce7", "version_major": 2, "version_minor": 0 }, @@ -15883,7 +15868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b563f5e177ad47b78710c68c371db89d", + "model_id": "0a365d8c91984574a20b6fbffd8e0593", "version_major": 2, "version_minor": 0 }, @@ -15897,7 +15882,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a67d72d8a0574ee4809d2a5895ea9de0", + "model_id": "3b7a0daca45c4a2c910fa8846b891966", 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"model_id": "5dfeb276584249c6b588da403b510c66", "version_major": 2, "version_minor": 0 }, @@ -16037,7 +16022,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cab3c7b88e58428db5cd1c7b4a0ef764", + "model_id": "a85507d40fae490cbff33f5df7a0b219", "version_major": 2, "version_minor": 0 }, @@ -16051,7 +16036,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca9a6e20d4b04341826f439c9deb0bce", + "model_id": "26a561bd07054aa09378e64090414780", "version_major": 2, "version_minor": 0 }, @@ -16065,7 +16050,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "311cba03863c4356b5550119afdd42ab", + "model_id": "487f4c78964944d994bf4529620b8878", "version_major": 2, "version_minor": 0 }, @@ -16079,7 +16064,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed951868836b4bc99ff208449c893ef7", + "model_id": "a1e24888964243d3a80e674172a5e148", "version_major": 2, "version_minor": 0 }, @@ -16093,7 +16078,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e782cc64bf8456ab37335142a3022c0", + "model_id": "769576dfbc724b8ea0071dfc7b4ebaa3", "version_major": 2, "version_minor": 0 }, @@ -16107,7 +16092,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "228fed715d624441ac23e76b1a7bc11e", + "model_id": "d91a0a6fd31e4940ba01f00665ad5a81", "version_major": 2, "version_minor": 0 }, @@ -16121,7 +16106,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35ec178b98724138991b72c571b742dd", + "model_id": "2333d8442f424fd3bbaaad9fe85094b5", "version_major": 2, "version_minor": 0 }, @@ -16135,7 +16120,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51ed26160c9b45298e4da6bf9a54c4f7", + "model_id": "b65c179d65fe42928e58a19e0e134cca", "version_major": 2, "version_minor": 0 }, @@ -16149,7 +16134,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1d4ccfacba84cd0a8f912ebda95f9ec", + "model_id": "d8b53626daf04ecf87f6900af04492a5", "version_major": 2, "version_minor": 0 }, @@ -16163,7 +16148,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ad5bd2356fe46cb920365d15c9b06bd", + "model_id": "57e0f1ef1a75488ba84afc2d3ec95108", "version_major": 2, "version_minor": 0 }, @@ -16177,7 +16162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc8f0b0c38344374925d2f3a1847ee5d", + "model_id": "a27f9b14d45b4e5e920659638e92e9a6", "version_major": 2, "version_minor": 0 }, @@ -16191,7 +16176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da87300c082d4ccf8bad570c7e4ca1ce", + "model_id": "8f2c9e41d6b5480bb8a316f078822e37", "version_major": 2, "version_minor": 0 }, @@ -16205,7 +16190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "66177276b25e481e8107cfa1e2de623a", + "model_id": "cd16b1fa4cb240a5ab96466f07d677d6", "version_major": 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{ - "model_id": "1959a35096dc441f85fd3c827bc7e101", + "model_id": "89430f0f0c3f4065bfd47a68914589cf", "version_major": 2, "version_minor": 0 }, @@ -16289,7 +16274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "665e52615db5464baaf25705a883b68d", + "model_id": "e650a2468d2741eca38633e75a58c9e7", "version_major": 2, "version_minor": 0 }, @@ -16303,7 +16288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0366204a5a541a8b0720905f65c020d", + "model_id": "772c139aa23f493fa0920a113bfba0db", "version_major": 2, "version_minor": 0 }, @@ -16317,7 +16302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "610f9f37c577476d978ad5cf1d25f33a", + "model_id": "85088a28082444b2a9be0de5abf6b161", "version_major": 2, "version_minor": 0 }, @@ -16331,7 +16316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95179fcf30f14806b17b3c6aa8a31149", + "model_id": "9d8964039baf46f0a04f8fc4673e4214", 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"model_id": "bed995f798ee418c9115fc8f4e8f6922", "version_major": 2, "version_minor": 0 }, @@ -16471,7 +16456,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "520a6c269ab54dd19b852ead4a8c3ad2", + "model_id": "e663ae55f224449aad8465f3cfc96c18", "version_major": 2, "version_minor": 0 }, @@ -16485,7 +16470,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee8660e7450147cfbdc6595fb31fdbc5", + "model_id": "890e5d4080784599a0e198d6e5c0b9a0", "version_major": 2, "version_minor": 0 }, @@ -16499,7 +16484,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c9b081bcdaa4011a41aca7085b74a18", + "model_id": "e05dce0ca9a64240a1d6e102a9f22b07", "version_major": 2, "version_minor": 0 }, @@ -16513,7 +16498,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e02f17fc07084a6898b5bb5cba730f17", + "model_id": "f5d2485a9ddf47ed9dec13cf2119b31f", "version_major": 2, "version_minor": 0 }, @@ -16527,7 +16512,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a26d6c30eb5f42b5b8c8a431adc21000", + "model_id": "caf132f21f7d449883b27df3a54b232e", "version_major": 2, "version_minor": 0 }, @@ -16541,7 +16526,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5b4b33881bbb408f906f2d4ed2c5746c", + "model_id": "202c1d002e7a44ab98b79990ee33c0c1", "version_major": 2, "version_minor": 0 }, @@ -16555,7 +16540,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "96290537342a456cab91e2d5b8dc4257", + "model_id": "8617d7eec58046d5b82dccfe500a7ae5", "version_major": 2, "version_minor": 0 }, @@ -16569,7 +16554,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7b98ea8d3aea42d2b89b5e232c16f363", + "model_id": "f8aef452c27a4d02948ba29a75e89efb", "version_major": 2, "version_minor": 0 }, @@ -16583,7 +16568,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d7b15262cad443fb0c143b0f135b9a4", + "model_id": "31283a34a08c4533892220eb627da6b4", "version_major": 2, "version_minor": 0 }, @@ -16597,7 +16582,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d07f629294bc4200b86e5c35f443a563", + "model_id": "8e005e98f15a4659b97d5cbd6bd9fca7", "version_major": 2, "version_minor": 0 }, @@ -16611,7 +16596,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c389f83c872b402fab44c73483a4fcba", + "model_id": "8fd3ce9002c7497ea334bb8496f4b80c", "version_major": 2, "version_minor": 0 }, @@ -16625,7 +16610,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd7dcec92ed841d3add7e3ad1b2f1d4f", + "model_id": "0a47ee5c2b0f4f22baf257faa5e936c9", "version_major": 2, "version_minor": 0 }, @@ -16639,7 +16624,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6989dcebac5f411aac838696dd3de0a6", + "model_id": "bae0a634ac3544f0a623fe5172c67f7e", "version_major": 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{ - "model_id": "31558551478a4fe492ef7f9f1e32bb9e", + "model_id": "3194dbf4bd6245819116c48df0d96c74", "version_major": 2, "version_minor": 0 }, @@ -16723,7 +16708,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74ed99282a6147e1a48e48347c32a17b", + "model_id": "73776778e0014eddb779f0f05a23d6c3", "version_major": 2, "version_minor": 0 }, @@ -16737,7 +16722,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1637cf148894338a36a8b5e2f32e0bd", + "model_id": "66cf413d252c42d4bdd762ceb5059ced", "version_major": 2, "version_minor": 0 }, @@ -16751,7 +16736,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "525e0300ebab452b91b942870c2ad04d", + "model_id": "0f5dd1de51384cd18e3ad663811972b5", "version_major": 2, "version_minor": 0 }, @@ -16765,7 +16750,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "364a94944f3f440ca85e8529fe734602", + "model_id": "9f18476ecfb2427ca81d6a3d9b0f9807", "version_major": 2, "version_minor": 0 }, @@ -16779,7 +16764,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e8557f4510b44a0aaaf4c8f72e09421", + "model_id": "3175557151b04df18a02b2aaad02032e", "version_major": 2, "version_minor": 0 }, @@ -16793,7 +16778,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fcd96e5e693e4ae5ab1dd45a1c006580", + "model_id": "810f694bf77c491cadb8b36533ffbeda", "version_major": 2, "version_minor": 0 }, @@ -16807,7 +16792,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37e5f237c65e4ee39d737ea05464dd17", + "model_id": "de90668269d94e1289da2fa941b86acd", "version_major": 2, "version_minor": 0 }, @@ -16821,7 +16806,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6381674f3c9847468acfd46b07cfcf13", + "model_id": "ec739a6695404349bcd5f978d87f97e4", "version_major": 2, "version_minor": 0 }, @@ -16835,7 +16820,7 @@ { "data": { 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"model_id": "3ac72faa486746c09f6a6e632a2fd982", "version_major": 2, "version_minor": 0 }, @@ -16905,7 +16890,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1b6b36664384361bce0a466eaa4ad2a", + "model_id": "509540ef72b540e0b2ca1bc6ad9d54a8", "version_major": 2, "version_minor": 0 }, @@ -16919,7 +16904,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b38a2044b614ca18f1a88b4ac9cdc81", + "model_id": "8826b7d2c1e6429d95a2d6e71d79d783", "version_major": 2, "version_minor": 0 }, @@ -16933,7 +16918,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dcad359cbf2c44008244a2e69c7e9153", + "model_id": "e3726bcd74c44d8285bbd09e53591bba", "version_major": 2, "version_minor": 0 }, @@ -16947,7 +16932,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5e038505d784d98ac6d32454d12cd68", + "model_id": "2450a9d082a943ea8c25bd469b248db7", "version_major": 2, "version_minor": 0 }, @@ -16961,7 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"b57b1175fb914008be488614efd82146", + "model_id": "4d70a7cb37a44af083bd245b5ade1682", "version_major": 2, "version_minor": 0 }, @@ -17031,7 +17016,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c4c5051053b4bd4bedeabbe4c6e25cb", + "model_id": "85de2e2481024897be1ca42b93b1bf8d", "version_major": 2, "version_minor": 0 }, @@ -17045,7 +17030,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b963f89b418f4f56a45533e0753dbb97", + "model_id": "77a9c54227e64dccae50d7f3a32f3378", "version_major": 2, "version_minor": 0 }, @@ -17059,7 +17044,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "23742e50b78f40f891f44e5c8fa66126", + "model_id": "6d270ec78b784b95863c4ffbbfd890f4", "version_major": 2, "version_minor": 0 }, @@ -17073,7 +17058,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7150813a0f1484d8a428a771a6bf384", + "model_id": "889482ab97ce4543bf66cfc611ab1d47", "version_major": 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{ - "model_id": "372628c1da7a4ac486108d4e05dceb25", + "model_id": "13c83bcee3ba4fefb79bf912d7906416", "version_major": 2, "version_minor": 0 }, @@ -17157,7 +17142,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d0f98a32f4f481c8109d612648b4f6e", + "model_id": "4f5a50ffddf1460c9c32d8fc55b70518", "version_major": 2, "version_minor": 0 }, @@ -17171,7 +17156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa285443444e4afa9647d332178d45f2", + "model_id": "a5a6f134fc6448deb2e7506109496a29", "version_major": 2, "version_minor": 0 }, @@ -17185,7 +17170,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f849625983624f909dd716c780593e9a", + "model_id": "2e790be27e8e459e855ec476d1a5b588", "version_major": 2, "version_minor": 0 }, @@ -17199,7 +17184,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b0170ab5cc14b46986fbbf8bf217ab4", + "model_id": "057b7226e9ab48cb89e99e34c29a9334", 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{ - "model_id": "3c0911dafa6a4fe293abb8ad14dc448f", + "model_id": "8678d56190584f36ad97263e4b46af79", "version_major": 2, "version_minor": 0 }, @@ -17591,7 +17576,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a00967381074a429711869e9ee028ae", + "model_id": "ad6406f07c4641b682f1f58a04c7c631", "version_major": 2, "version_minor": 0 }, @@ -17605,7 +17590,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "580b8628d6fb4c0995dfa791fb703c6e", + "model_id": "9a9433f09673445aa8744be50caf51c1", "version_major": 2, "version_minor": 0 }, @@ -17619,7 +17604,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "553a3a2c6a5d4099a9b48202e0df777d", + "model_id": "a136cec1818749e78e34c998436221db", "version_major": 2, "version_minor": 0 }, @@ -17633,7 +17618,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95bea3ef68c34ac99bef3196727b5466", + "model_id": "55d651c0aeac413890fbde98965dd1a2", "version_major": 2, "version_minor": 0 }, @@ -17647,7 +17632,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8a73145c4bf449286d1e7a7c3fba556", + "model_id": "85d776775dc647b5bf144fc3bcc41f6f", "version_major": 2, "version_minor": 0 }, @@ -17661,7 +17646,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c28cfea45144902822d9b87b43b4d16", + "model_id": "ff060948737e4b9f8e79051c99ec61d9", "version_major": 2, "version_minor": 0 }, @@ -17675,7 +17660,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d45fdba9e7241af8b77ede599be7ff9", + "model_id": "9670c40e38054b6a870d0f1899b0d91d", "version_major": 2, "version_minor": 0 }, @@ -17689,7 +17674,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de38a0e0b0c1478fab37ae5e620c9e17", + "model_id": "d4fa543422ac416b87f0d1dc009bec70", "version_major": 2, "version_minor": 0 }, @@ -17703,7 +17688,7 @@ { "data": { 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"model_id": "cd916eea060241f48ec4c650109e9f09", "version_major": 2, "version_minor": 0 }, @@ -17773,7 +17758,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83d17c49d4c4448b85f99fa5a7539eaf", + "model_id": "eacbab331a5947539ed578ed97a35bd5", "version_major": 2, "version_minor": 0 }, @@ -17787,7 +17772,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37b8df01b4f14c7cae23f456dba588bc", + "model_id": "b185a8d5a78c4366a2ddddfcd86adb66", "version_major": 2, "version_minor": 0 }, @@ -17801,7 +17786,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a04ee86edd044c58e7a8811ac6df082", + "model_id": "7e6ff3f74e4a40da8e1b56bfb76f8a27", "version_major": 2, "version_minor": 0 }, @@ -17815,7 +17800,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c86be78b8374d20893f3f74cea563de", + "model_id": "8df148f77fba4e3c8f16c8a36725aafd", "version_major": 2, "version_minor": 0 }, @@ -17829,7 +17814,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24bb124d97434c9ea92f1b51171ce87d", + "model_id": "4a7e412c0f024c778ae1b7e275ed065f", "version_major": 2, "version_minor": 0 }, @@ -17843,7 +17828,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e7f1a5ea401476f9f0639178c03003b", + "model_id": "56bc540b2bdd4e2780e3f23780309168", "version_major": 2, "version_minor": 0 }, @@ -17857,7 +17842,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "375870a8218e4135881166bbfa892932", + "model_id": "86b192e12ca54b57924a1beb6456f819", "version_major": 2, "version_minor": 0 }, @@ -17871,7 +17856,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "388068d4795e4658b4fe989925093045", + "model_id": "bed6ff84725345a6b596fdf87d2fd2f7", "version_major": 2, "version_minor": 0 }, @@ -17885,7 +17870,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "228a7aa4992d4a02b0fbcfbdcea49e90", + "model_id": "455eafdef42a494fa6e8862029f15f36", "version_major": 2, "version_minor": 0 }, @@ -17899,7 +17884,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6eb3f56e281f410bbbf17f0d0313621d", + "model_id": "ae2de45a2bc34e4c88b3b2e0f0e51a89", "version_major": 2, "version_minor": 0 }, @@ -17913,7 +17898,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac33808adbd142088c9f1de7db9a7661", + "model_id": "1639ff7b62e5494aaa8371e79d870c8e", "version_major": 2, "version_minor": 0 }, @@ -17927,7 +17912,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fcc7d3196ba94b6eb3230e59e0098667", + "model_id": "be43652934c44f9b896dc774225a0d9d", "version_major": 2, "version_minor": 0 }, @@ -17941,7 +17926,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8898c4ac2dce459095f0364e3f74e19f", + "model_id": "401da8cc0a6947be8a7041c2068b9b61", "version_major": 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"model_id": "6ebc16ba25074f81b2356424a92c1815", "version_major": 2, "version_minor": 0 }, @@ -18207,7 +18192,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f95407f9e27147ccb04bf5285799306c", + "model_id": "60c284f1571445be9b5739a5d6ee3fbf", "version_major": 2, "version_minor": 0 }, @@ -18221,7 +18206,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01ca78cc77e643d8a81ef0f066ef39f2", + "model_id": "8840a965ee2640869e3848d863b777e1", "version_major": 2, "version_minor": 0 }, @@ -18235,7 +18220,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7da3d984ec8646a1aa7c9d58c7fae01b", + "model_id": "9749e4353f234e37bc34ea7e461bac13", "version_major": 2, "version_minor": 0 }, @@ -18249,7 +18234,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "406ad9df9c02480abc4ad3c7e01ee805", + "model_id": "77d1efb39f3b48a2ae9bd4c834818b2a", "version_major": 2, "version_minor": 0 }, @@ -18263,7 +18248,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33cb2ad33529486d97220c6d3d609c42", + "model_id": "d6e73f8c82644100a5f1304dabe86376", "version_major": 2, "version_minor": 0 }, @@ -18277,7 +18262,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c17425bacf09498a8e02d942cc857fe2", + "model_id": "dc94a1c8f1ce4d8bbea5f6687fc3853e", "version_major": 2, "version_minor": 0 }, @@ -18291,7 +18276,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8a9b68fe2634aa2af5b5cfedc6f4d88", + "model_id": "67164d2f8d624bc3a12c49e82b232236", "version_major": 2, "version_minor": 0 }, @@ -18305,7 +18290,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "806d148616ba4e9dbda036ba24b11084", + "model_id": "828368ca1424417eb6382877a8d61c0b", "version_major": 2, "version_minor": 0 }, @@ -18319,7 +18304,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c075b0212b914549bcb267516273d8a7", + "model_id": "b796770fd44a4c0788710c3f501c0492", "version_major": 2, "version_minor": 0 }, @@ -18333,7 +18318,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb89a7b75b554f5b9addd634848d9d4c", + "model_id": "a8b22d3870af419bb93fb357e2f71135", "version_major": 2, "version_minor": 0 }, @@ -18347,7 +18332,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe99ead011b24d38bd7b910849caed7f", + "model_id": "dc79658772034e7680cb93e2dd2dff37", "version_major": 2, "version_minor": 0 }, @@ -18361,7 +18346,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3764a2627cd9435bb437f8658e457811", + "model_id": "89e41754ce2748919bc79f5a9b611e39", "version_major": 2, "version_minor": 0 }, @@ -18375,7 +18360,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c96086e652fc4bdea8dbbbd3942bcf0c", + "model_id": "76b297a0ba7040be8cc60139eb6ec4db", "version_major": 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{ - "model_id": "70a997e9a2da4510ae66a9d51a27dc71", + "model_id": "499f8cabafaf4f94ba2c20a4564cc00d", "version_major": 2, "version_minor": 0 }, @@ -18459,7 +18444,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ede48e67b17c48c79d503d015481f494", + "model_id": "813e03d5a7e4429e94f5c3f823be2b41", "version_major": 2, "version_minor": 0 }, @@ -18473,7 +18458,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc6b71f3fc274fdb84e66f8b1592b6c5", + "model_id": "532034b7d7f649f3bb90d0fe0768d04a", "version_major": 2, "version_minor": 0 }, @@ -18487,7 +18472,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "262a1a2d1a244622a8649ef63f931651", + "model_id": "865c47ddf4884ef891365ca118454ff3", "version_major": 2, "version_minor": 0 }, @@ -18501,7 +18486,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c98b368c84524573a314137a9f8aa4ea", + "model_id": "f41fd89e6070411f8a09d38a29d2d82f", "version_major": 2, "version_minor": 0 }, @@ -18515,7 +18500,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "154335e951eb4c8da3b480adcf6419df", + "model_id": "aa4006a92b404c9faf0aa1dae8c0ad93", "version_major": 2, "version_minor": 0 }, @@ -18529,7 +18514,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3884b644c97540a29cc020849f53cf4a", + "model_id": "e9175856b30349f58d9c2366d631d452", "version_major": 2, "version_minor": 0 }, @@ -18543,7 +18528,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4022759dca9f49699f42481c1efe7c45", + "model_id": "d353800b8aa84a3ab60906a4f2b556da", "version_major": 2, "version_minor": 0 }, @@ -18557,7 +18542,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1aea47022bf49d285bfd7a0cd2e7b03", + "model_id": "2099d4e036b0483c937da82d516ce676", "version_major": 2, "version_minor": 0 }, @@ -18571,7 +18556,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eff1ca12abe449459003a8c9109dee1b", + "model_id": "0bee9805867c4c7da26108c2e37ca4b8", "version_major": 2, "version_minor": 0 }, @@ -18585,7 +18570,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50100dca6a404754befc7252c16437a0", + "model_id": "0c4a6b6c2e9f4af1bbfa885fc28ac967", "version_major": 2, "version_minor": 0 }, @@ -18599,7 +18584,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d94069f7d96c41f0a06eaac02b4dcae4", + "model_id": "b029d963c40441598e54662c58906a31", "version_major": 2, "version_minor": 0 }, @@ -18613,7 +18598,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40a5f6e74778441f995a98d31821e321", + "model_id": "95084fc8d67e456aa27db6ff7c6c1433", "version_major": 2, "version_minor": 0 }, @@ -18627,7 +18612,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b8066896da148d4b00b2e6859ad3bbe", + "model_id": "174d5d790f0c4c73bae24363bc078bf6", "version_major": 2, "version_minor": 0 }, @@ -18641,7 +18626,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a9259e104f549879396d606d18947bf", + "model_id": "1b226371b9a542fcb5fe7b8d433dbd08", "version_major": 2, "version_minor": 0 }, @@ -18655,7 +18640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73c620f4109d4a02af280bf7b84a74e1", + "model_id": "a0d7dca599e94534a40d324e09a99dca", "version_major": 2, "version_minor": 0 }, @@ -18669,7 +18654,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "491ecb068b93480fb9824fa3cd4e77de", + "model_id": "db8f04286bf54a1589d3df4a69043bf9", "version_major": 2, "version_minor": 0 }, @@ -18683,7 +18668,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c9588af99b74a2387aa0254fc72e390", + "model_id": "05ecc684f998471dad69f6c296598007", "version_major": 2, "version_minor": 0 }, @@ -18697,7 +18682,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ad1dcc9062c4633b37787a18580a223", + "model_id": "0071ea6708104e3d94f55e6435b830dc", "version_major": 2, "version_minor": 0 }, @@ -18711,7 +18696,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81dd2a844c5e409d9c95d6f4696ecccb", + "model_id": "49bb3c6ed0d8475ea89f6e9cd15e6c96", "version_major": 2, "version_minor": 0 }, @@ -18725,7 +18710,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "92405c5b1e224e10aca943294fcf3733", + "model_id": "c443f2465dcb46f59e3f92aae27e803e", "version_major": 2, "version_minor": 0 }, @@ -18739,7 +18724,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bae7b607ff5544fa8f0a2bea2fe6eb7c", + "model_id": "001ee66be01748879224f361d4dadea9", "version_major": 2, "version_minor": 0 }, @@ -18753,7 +18738,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ecfcd633f6ae456e9be73979219bd44f", + "model_id": "7cc804917c304569b236bb7352a83482", "version_major": 2, "version_minor": 0 }, @@ -18767,7 +18752,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58856a26ce244783b08c79efdc30130b", + "model_id": "ff655b386b3e499a86003daa9f936a8c", "version_major": 2, "version_minor": 0 }, @@ -18781,7 +18766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "209be7bb114845aeb04f792d9f59fb3a", + "model_id": "cc6916b874604bdfb0e6cdafe59a5014", "version_major": 2, "version_minor": 0 }, @@ -18795,7 +18780,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f771abf53114bcfac9137837730825e", + "model_id": "d40f4e295228401a815bdb7381fd4655", "version_major": 2, "version_minor": 0 }, @@ -18809,7 +18794,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9d36f37cb1b24a5b97b3bfbd9c4323ce", + "model_id": "1872d1898186449a97a4f58218d2b263", "version_major": 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{ - "model_id": "be18e2fab87440e1ba52a1c085003df9", + "model_id": "ee92d9d05e444cdebdacb16d97c96fed", "version_major": 2, "version_minor": 0 }, @@ -18893,7 +18878,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4132cbfaff804f17b090dc756f274fb0", + "model_id": "d593c1d9f8aa40aaa4dd23ebf3c0fb0c", "version_major": 2, "version_minor": 0 }, @@ -18907,7 +18892,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "831e06682fb2459fb9731505a78c3bb0", + "model_id": "9137ca9dacd542f5a84130fe15f38e15", "version_major": 2, "version_minor": 0 }, @@ -18921,7 +18906,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9957419c63284732a22730f8e46a822a", + "model_id": "f781992b149e4464ad129f434be7a241", "version_major": 2, "version_minor": 0 }, @@ -18935,7 +18920,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1765e341b6e64831b71890b49ae2d824", + "model_id": "020d772871ed45f1ae3d4840284f3f3a", 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"model_id": "0a04624b9a4e4bd2871322e1c7ed7283", "version_major": 2, "version_minor": 0 }, @@ -19075,7 +19060,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3ec60e23c94402fbcace185adabae9a", + "model_id": "b0d53ff617164306aa15de025f1fb652", "version_major": 2, "version_minor": 0 }, @@ -19089,7 +19074,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdb3afa6b8e94e1b9224bb3b2006da86", + "model_id": "b0a3e63da0784e1ca0826378f122abf6", "version_major": 2, "version_minor": 0 }, @@ -19103,7 +19088,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3048bb1b2be345beb9327c0d83920915", + "model_id": "d3a0efa79c72440ea055f71f9d1e4878", "version_major": 2, "version_minor": 0 }, @@ -19117,7 +19102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cdfb0ac9f6d04d89bf9604a7d9875435", + "model_id": "2294a61ecdbb4df8891ea1f0dfc3228d", "version_major": 2, "version_minor": 0 }, @@ -19131,7 +19116,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1eed2515c40143fabff4d90ea4024cf8", + "model_id": "460e1c6f22b74931b9ba93bd4ae75d6f", "version_major": 2, "version_minor": 0 }, @@ -19145,7 +19130,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "23748a342aa6488dbee5143305b6c7d8", + "model_id": "4f462a771a484dfe9ce60c45c3d08ea0", "version_major": 2, "version_minor": 0 }, @@ -19159,7 +19144,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2d75c164bd244b4bad3893eef2d70e8", + "model_id": "0f18f6dbc5634cce9b1ad06e309d5562", "version_major": 2, "version_minor": 0 }, @@ -19173,7 +19158,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fe1d034177c496f88f065172c41d545", + "model_id": "2f9207f956a34c7495f66646115ff563", "version_major": 2, "version_minor": 0 }, @@ -19187,7 +19172,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af404596076d458b8337b0e5d3acb36e", + "model_id": "6b32ee492e844c9b93de0f31169c2b17", "version_major": 2, "version_minor": 0 }, @@ -19201,7 +19186,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f642da832ad4b96821c28c011857b77", + "model_id": "3e0aec09d5d74756be5a3acd89baa379", "version_major": 2, "version_minor": 0 }, @@ -19215,7 +19200,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ffd6d62bc1f441ab96b50e47bcc3e9be", + "model_id": "415de42b70844f40845a74d3f9a493bb", "version_major": 2, "version_minor": 0 }, @@ -19229,7 +19214,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "89c0697eeb4446feac79d31abc1381fa", + "model_id": "e77eefa18e5b4e17a66c868a170ac158", "version_major": 2, "version_minor": 0 }, @@ -19243,7 +19228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fcb9f364893642d2acbd49a2d5d9f174", + "model_id": "5e9d41326c2249c7b6335b347a65eb02", "version_major": 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{ - "model_id": "7b3b672c00734a6d8a2b71d072afb984", + "model_id": "5d258a88d6454e0fa14a02176c913fdd", "version_major": 2, "version_minor": 0 }, @@ -19327,7 +19312,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0473a6a1270d4b1ba17fd6845cf35218", + "model_id": "4ff3199385b641a5ab189432756d0aca", "version_major": 2, "version_minor": 0 }, @@ -19341,7 +19326,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "53782d7b68e945a0ba2104f48aed40d7", + "model_id": "be27faed83c9428d91903e7387030bdf", "version_major": 2, "version_minor": 0 }, @@ -19355,7 +19340,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3675e8fa01b4c42b26b821647d6b117", + "model_id": "786dcc71dce94a63b50481c73088bb6d", "version_major": 2, "version_minor": 0 }, @@ -19369,7 +19354,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0b771df30c141789e8830678266045c", + "model_id": "cfa6b071f8904c81b025704375c4418f", 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"model_id": "9ad10f1c9d684157b08f942b16e60c4e", "version_major": 2, "version_minor": 0 }, @@ -19509,7 +19494,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82d79a74c3424da198e89ffc07b16280", + "model_id": "c209486c59fb45e4a8d17d22f8e83760", "version_major": 2, "version_minor": 0 }, @@ -19523,7 +19508,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0268817a112c4987bb4f2b44986672f6", + "model_id": "d1f673d53197458a82672bef69e1d534", "version_major": 2, "version_minor": 0 }, @@ -19537,7 +19522,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c59bfa48bfbc47858e4471cc3260390e", + "model_id": "03dacea588214c9e9c0754d18a1f5488", "version_major": 2, "version_minor": 0 }, @@ -19551,7 +19536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e5195447cd84c349a9f12c12d432ba6", + "model_id": "5e62689fa628489fb9567666b9788581", "version_major": 2, "version_minor": 0 }, @@ -19565,7 +19550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e648cff6bae4b85baca4360acb2e202", + "model_id": "76fc62d0ad164c63bef2a200af667be6", "version_major": 2, "version_minor": 0 }, @@ -19579,7 +19564,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f71c1fcd9893488f981da7d690f5d410", + "model_id": "004d66e939d5443aa31e160a09f9a5aa", "version_major": 2, "version_minor": 0 }, @@ -19593,7 +19578,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aacdbeae50d54f9d950d5e4d33637083", + "model_id": "b08830c7187a4a319e29c120f81ab0b5", "version_major": 2, "version_minor": 0 }, @@ -19607,7 +19592,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83ebd7d73045413cb24916cb399adbf4", + "model_id": "2b65aa1511194dec96570093446edd32", "version_major": 2, "version_minor": 0 }, @@ -19621,7 +19606,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67858145d03243e7b5c0c0be31656a1b", + "model_id": "a6dc67589c8746ce8f6ade3aba9541db", "version_major": 2, "version_minor": 0 }, @@ -19635,7 +19620,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "32116d5d7bb44b74951501704d834492", + "model_id": "6712e6abce6b4f28871e7ac33b66b047", "version_major": 2, "version_minor": 0 }, @@ -19649,7 +19634,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d6a8fef8c881454fad41c0ba5844ebc0", + "model_id": "dbf7546a8dde463bb5ec38b467f97815", "version_major": 2, "version_minor": 0 }, @@ -19663,7 +19648,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68ac34f3df6041f08797c7a4aa3c40fe", + "model_id": "adb9406b42164859baeb325f83202441", "version_major": 2, "version_minor": 0 }, @@ -19677,7 +19662,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b6df67b6a444963aadd9877c86e580b", + "model_id": "e3556e1a7071479b838334ef80fcd0bc", "version_major": 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"f201d7661d6a4694a96c74cc5a7575a2", + "model_id": "08489efc07f54d0298424c310a7256a2", "version_major": 2, "version_minor": 0 }, @@ -20069,7 +20054,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ad89dc489314f36b40e1ebd64ad6afe", + "model_id": "fe9f9e3a4a544e8b94ec3796e3b6b3dd", "version_major": 2, "version_minor": 0 }, @@ -20083,7 +20068,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb1d17b1d7ef488891efb40c3147b712", + "model_id": "56f147c4f53f4d2aa72904dbea8d85e9", "version_major": 2, "version_minor": 0 }, @@ -20097,7 +20082,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6eb4f6317d1944d0a02690323c8e03c7", + "model_id": "42c70f63a2c1461e96adc3065abd5470", "version_major": 2, "version_minor": 0 }, @@ -20111,7 +20096,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3bf783176eb943ee930d7d4b68a2126c", + "model_id": "fd1ad2362104453b8034847b0e53ece0", "version_major": 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{ - "model_id": "9e88875b391e4ba1a660dc189cb95adc", + "model_id": "f095acf247614a2489f312a9a1a6cce1", "version_major": 2, "version_minor": 0 }, @@ -20195,7 +20180,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a0222adb96d43d4b5c905a43a488485", + "model_id": "b1ffa659d5514ea5b23cf8fad3f54386", "version_major": 2, "version_minor": 0 }, @@ -20209,7 +20194,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "09886aebcdbc4a40b4e658eedaac5ce0", + "model_id": "6ecb7c4dc66d44c09f674b55d3ddda57", "version_major": 2, "version_minor": 0 }, @@ -20223,7 +20208,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2784202a5e394f109f9ed00dc312fab2", + "model_id": "df8d7434d4db4adda9ab2f7b8fba659f", "version_major": 2, "version_minor": 0 }, @@ -20237,7 +20222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a2e0c3b8a9b40ce840ac69ba64a327c", + "model_id": "f9a97feb72c54f2face2bc89f5631ba9", 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"application/vnd.jupyter.widget-view+json": { - "model_id": "255d58d28e8242a5bca0071d95051623", + "model_id": "6441128563854914b02b4d0ae0b85cfd", "version_major": 2, "version_minor": 0 }, @@ -20321,7 +20306,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f842d157e8d74a69bac0662a6ccf96e4", + "model_id": "501917893d1f4d60b14454b8646e8e5d", "version_major": 2, "version_minor": 0 }, @@ -20335,7 +20320,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8485e20baccc4bda84f2ae8f7250eaf4", + "model_id": "74baea5a8c404392a037a000d2197bb5", "version_major": 2, "version_minor": 0 }, @@ -20349,7 +20334,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b0a331d75474b0882ddc829a321ccbb", + "model_id": "9b74242a7ada481498d18275f6ce0f4b", "version_major": 2, "version_minor": 0 }, @@ -20363,7 +20348,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cbb861ced2d444669d9d9d9599bf6a57", + "model_id": "7e559bd8eb044a69a98937117e40e60d", "version_major": 2, "version_minor": 0 }, @@ -20377,7 +20362,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e9052373d440476d83f4b33ed2154cfe", + "model_id": "c1e83131348449be904027bb29473a25", "version_major": 2, "version_minor": 0 }, @@ -20391,7 +20376,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83c2495989aa4b658e8f4cce00601b96", + "model_id": "2a74795ba65c4c959fbd24d519f848b6", "version_major": 2, "version_minor": 0 }, @@ -20405,7 +20390,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb1e9442daaf4867917f00e9bae898c6", + "model_id": "39f9091095a745388cd473b0ce9d39cf", "version_major": 2, "version_minor": 0 }, @@ -20419,7 +20404,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab5b4fc8950c45859ade38cde5a14a42", + "model_id": "8c1e9b19e08f44c19bf8290291930eb4", "version_major": 2, "version_minor": 0 }, @@ -20433,7 +20418,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f7ce7e8626745178f9b4eb792b3fa7d", + "model_id": "ffa74f154f22411db3c163d28d2c3ea5", "version_major": 2, "version_minor": 0 }, @@ -20447,7 +20432,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d0e77a3b5b6a4e82ba1dc83f788faaeb", + "model_id": "46c723973ab04395b0558fed62b5f87f", "version_major": 2, "version_minor": 0 }, @@ -20461,7 +20446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2b4191406994be9b13ff57224f7c11f", + "model_id": "38f398b537044999b56762030f9df4c9", "version_major": 2, "version_minor": 0 }, @@ -20475,7 +20460,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdbb602d97f842308ca407701a8d10a5", + "model_id": "8867b2b66c614e509876ba40d122f5ef", "version_major": 2, "version_minor": 0 }, @@ -20489,7 +20474,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "25d7f91434c14a04b571b8d849fa561d", + "model_id": "6724c94e99a84b898eb78a9279a424a6", "version_major": 2, "version_minor": 0 }, @@ -20503,7 +20488,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "855f5e4dbfe248dcb065f9e795efcd5e", + "model_id": "f36c632dee4342a69da7ba0ae2c9034d", "version_major": 2, "version_minor": 0 }, @@ -20517,7 +20502,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f42dfd4cc2104e799125acd79d44926e", + "model_id": "16f160fde4c547deb830eace5cffba5e", "version_major": 2, "version_minor": 0 }, @@ -20531,7 +20516,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15a6c07a91eb47108c9f9899484cd398", + "model_id": "9a8574b48f994c46a90d0e67b22b06e7", "version_major": 2, "version_minor": 0 }, @@ -20545,7 +20530,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5ff116b0ca2f44ea9733d3bfa8fc9f2a", + "model_id": "b2fcec3a1eae4261b43fc1d5a3f2701a", "version_major": 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{ - "model_id": "2ca44b0f71ef4490907b0b9732d6cb34", + "model_id": "4e2ce75cb5b04d8ebac7ae094031c7d8", "version_major": 2, "version_minor": 0 }, @@ -20629,7 +20614,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67f4085cd11d4c10bbabf1fede017c4c", + "model_id": "48e796e47ffc4265b9a14cc6eb299688", "version_major": 2, "version_minor": 0 }, @@ -20643,7 +20628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f299e66ea2294b0e901cda005331d7b7", + "model_id": "dda60f4f9261445184d4e280fc1a33d2", "version_major": 2, "version_minor": 0 }, @@ -20657,7 +20642,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9d306aef68ba4e82884d713f639410a7", + "model_id": "ebff5493b2bc4047bbcb7739bae6b6e4", "version_major": 2, "version_minor": 0 }, @@ -20671,7 +20656,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1d5d71d527f1443baf8f444473f9e79e", + "model_id": "143844834da342c1a809c5b3cf9e9e56", 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{ - "model_id": "ba1ebe79322e4dc6bec941473c50941d", + "model_id": "dde41da42c9f42f6aec439d106894af5", "version_major": 2, "version_minor": 0 }, @@ -21063,7 +21048,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "59dccaddfb9c4a36a136527b63d31540", + "model_id": "9415b246174248f7b25121450bbc7d8c", "version_major": 2, "version_minor": 0 }, @@ -21077,7 +21062,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f0246dd4f6d4f54b152400298528e6d", + "model_id": "c3b19660ffcc41b8a6f0d86645e2a9f6", "version_major": 2, "version_minor": 0 }, @@ -21091,7 +21076,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab8fc6f95b0a44ec952e3d1799894fbf", + "model_id": "639450be2ab54e2cb37c54bb98a8f42b", "version_major": 2, "version_minor": 0 }, @@ -21105,7 +21090,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31b94ebb4f794351acfeec8197048002", + "model_id": "ee60ead0f55149a29e7ea8142139f9fe", 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"model_id": "88d54b62fe3f4b899e6bdf4670f0dc4a", "version_major": 2, "version_minor": 0 }, @@ -21245,7 +21230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd4e95e5fcdd49388ec700b37a2a9e2c", + "model_id": "a7c5d7fd9ab4441d829a87f30acdc7e4", "version_major": 2, "version_minor": 0 }, @@ -21259,7 +21244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6cb15e0bb80b40708c4d64b978f1df72", + "model_id": "8cf350e163b14d1aa32994128ee97eba", "version_major": 2, "version_minor": 0 }, @@ -21273,7 +21258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16fdc82126df4e07ba61f941fbc0c2c0", + "model_id": "6abc9a8d97334774be3272329ad7a6c5", "version_major": 2, "version_minor": 0 }, @@ -21287,7 +21272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc1ccb82dc5448f181b9b7e357a77398", + "model_id": "de214047cc7f43bf8aa37419b2dc8339", "version_major": 2, "version_minor": 0 }, @@ -21301,7 +21286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc434bdebd9b41e7928a8160364918c9", + "model_id": "be9f6ba972e241939bbaa86a688c62e1", "version_major": 2, "version_minor": 0 }, @@ -21315,7 +21300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c51cf41d41264dd687cb6bea8e59c80d", + "model_id": "f95e3e649548426a98d70fe254f1ced0", "version_major": 2, "version_minor": 0 }, @@ -21329,7 +21314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93f7af0f848b419da520dc02b6b1efad", + "model_id": "b3b76b817b654ddc9cf2f0b8f51a662a", "version_major": 2, "version_minor": 0 }, @@ -21343,7 +21328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a31db8256564562842d2c469138af05", + "model_id": "873852d6b7b34e9497834e3c8f2c0e45", "version_major": 2, "version_minor": 0 }, @@ -21357,7 +21342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4da0a4248cba45f19d57f04092a3feae", + "model_id": "e694c496f5ed41ea88e2f25aa1a9e13e", "version_major": 2, "version_minor": 0 }, @@ -21371,7 +21356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd83210bff4e49c795e3b7e0bb1924a6", + "model_id": "194652325c684080b62821f3ebccebab", "version_major": 2, "version_minor": 0 }, @@ -21385,7 +21370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "20eff38e50024bb5a16818563df431d6", + "model_id": "75d473f5a6464dd58fb3703872addff2", "version_major": 2, "version_minor": 0 }, @@ -21399,7 +21384,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7bf8c4892f54bc487233b5dca5e4bb0", + "model_id": "dfc671f29c2145799a0dfdf1a142e31b", "version_major": 2, "version_minor": 0 }, @@ -21413,7 +21398,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2fd9f9a2d00943069f28d5cef45d1d1f", + "model_id": "8816a82000fc454691fd082245831b5c", "version_major": 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{ - "model_id": "dc39e5aa474147aca7890c8b24e9d7af", + "model_id": "f6abfb7848bd4f249447847df656cccf", "version_major": 2, "version_minor": 0 }, @@ -21497,7 +21482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d326398c971a4efd9f9bfb556ecb5b39", + "model_id": "3d6382c54c2540b3ac41701e5dd8ea95", "version_major": 2, "version_minor": 0 }, @@ -21511,7 +21496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8dc06e4b1554de78370320f4c71eba3", + "model_id": "827ddfcd23f54dd89e19c5fe78678407", "version_major": 2, "version_minor": 0 }, @@ -21525,7 +21510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f733889232cc4ed2b9f108f0d6d21331", + "model_id": "15535e42a1424c00a1b28bde3b9bd603", "version_major": 2, "version_minor": 0 }, @@ -21539,7 +21524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7648dbc9fc24f10aeb10425c3739975", + "model_id": "a8a3882e7eb3489691494a2bafd9ad99", "version_major": 2, "version_minor": 0 }, @@ -21553,7 +21538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76dea5ccd7914a51ad72e92e59624d64", + "model_id": "b55a9d0237d64e2ca7e48bd32186cbfc", "version_major": 2, "version_minor": 0 }, @@ -21567,7 +21552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dcd84619dbae4b9e8cbb7a9a2342a99f", + "model_id": "60817aee7a614369989b8622d35a2f6a", "version_major": 2, "version_minor": 0 }, @@ -21581,7 +21566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc99014b2a6548469ccdf27233615b37", + "model_id": "efb83e053edd472f987695a6352f0e0a", "version_major": 2, "version_minor": 0 }, @@ -21595,7 +21580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e461d40e3ed3447fadea726b5bf9c706", + "model_id": "a72ddd77a49b45078d55089c914164c3", "version_major": 2, "version_minor": 0 }, @@ -21609,7 +21594,7 @@ { "data": { 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"model_id": "cd893a93f27640ffa428f70a60f7d62c", "version_major": 2, "version_minor": 0 }, @@ -21679,7 +21664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f738bf54e754ed98ffcc9eaf731e02b", + "model_id": "de4355e6a50f478383409865b7a95ba0", "version_major": 2, "version_minor": 0 }, @@ -21693,7 +21678,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "89901234c8ff4c919afc08498894ca22", + "model_id": "26c49ad31b1a43dcb22d27e1abb346c0", "version_major": 2, "version_minor": 0 }, @@ -21707,7 +21692,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1b10722eca542e1ab1f246621819fb1", + "model_id": "63c6a7dcf0ac42f0bec666c87f30dbc8", "version_major": 2, "version_minor": 0 }, @@ -21721,7 +21706,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ad75cd043c6496ea96fdb830646e43b", + "model_id": "7585d426f4e04673972c65496e442989", "version_major": 2, "version_minor": 0 }, @@ -21735,7 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"f5e4099b932e4388b9046d303a6aeb9a", + "model_id": "48e76bae210c42dd93d7be51fbe6b38e", "version_major": 2, "version_minor": 0 }, @@ -22239,7 +22224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5cfa0830d8684673be043cb90d63e455", + "model_id": "b1fcaf0819434cf9b2eb5facff3cd307", "version_major": 2, "version_minor": 0 }, @@ -22253,7 +22238,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b2f90fde40064754908b0c5af2d82064", + "model_id": "928f551145d74eb48667628130269199", "version_major": 2, "version_minor": 0 }, @@ -22267,7 +22252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed7934d26f0e4daebb8711fcd015b45d", + "model_id": "49acd32ed428452a88b58b98be4007b6", "version_major": 2, "version_minor": 0 }, @@ -22281,7 +22266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79ddde38982446d7a450c19443bcd0c7", + "model_id": "97d3a69ce440449780315f9160aa240e", "version_major": 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{ - "model_id": "9f6ad77f04ab485d9b0e4491475a6ef0", + "model_id": "6381d433e530407088b40b19cd0e90be", "version_major": 2, "version_minor": 0 }, @@ -22365,7 +22350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f570e1e45b54934a22c868b0b41cbd7", + "model_id": "af20ecbb69f249f08ba4cced5da05a60", "version_major": 2, "version_minor": 0 }, @@ -22379,7 +22364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51d2e550434f451b96cd776a0ce4a4a8", + "model_id": "eaf03fc4565a421582a6b93e92948e1e", "version_major": 2, "version_minor": 0 }, @@ -22393,7 +22378,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a699d31cff9741e3add61299dc648d5a", + "model_id": "6ceb7e75efc94848862b3f4c598c47e1", "version_major": 2, "version_minor": 0 }, @@ -22407,7 +22392,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "42717def3ad343208923dd1bce76cf0a", + "model_id": "005b8b43b6794dff8f4b9ea2c927ad2e", 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{ - "model_id": "81449188e2fd42c9aae16f7ec1343f0f", + "model_id": "c4ec5c6e1681408d92c79344f9288276", "version_major": 2, "version_minor": 0 }, @@ -22799,7 +22784,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "adfed624c3304f49bb585350cebc13d2", + "model_id": "b3a19f33e639446ab0c785ab5a2e0bdf", "version_major": 2, "version_minor": 0 }, @@ -22813,7 +22798,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3b988feda784679ba9847d55c84fd5b", + "model_id": "0ac5f68ac7f047819e60b9f3d54fd880", "version_major": 2, "version_minor": 0 }, @@ -22827,7 +22812,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c0fb0b1c221c46bdab120f1f27334c0b", + "model_id": "15f4973c486440f5a75e87d4767c65e0", "version_major": 2, "version_minor": 0 }, @@ -22841,7 +22826,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78a19f75b9ed42b5b28dad88ea3cd4a1", + "model_id": "8af1618400ab4430ac1e58e6c94b84a1", 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{ - "model_id": "269ed24024ca4ff283355f0a5e140641", + "model_id": "52b5896d5b844c038002e6246192397b", "version_major": 2, "version_minor": 0 }, @@ -23233,7 +23218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fa0f22a805842b7a9f311894bca71b3", + "model_id": "ca10723b76524a22bf7a2b21a4e927fe", "version_major": 2, "version_minor": 0 }, @@ -23247,7 +23232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b679c5a9be04a88a7fc3002d7069aa6", + "model_id": "6217bbcf886d41c7a4bdd716e2a72fb3", "version_major": 2, "version_minor": 0 }, @@ -23261,7 +23246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f0c76e14a954c8d9f7449dfce6adbad", + "model_id": "3123f6c2539541078d4122c1db11be71", "version_major": 2, "version_minor": 0 }, @@ -23275,7 +23260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "94fb35fbdb394ce4a462989bd9c35d80", + "model_id": "f3eb7a03d20346da87db99a18a780714", "version_major": 2, "version_minor": 0 }, @@ -23289,7 +23274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2cb375eaf1344b088517a45db34450bd", + "model_id": "1488dafbc18a4778a2c1a1c4da0c4924", "version_major": 2, "version_minor": 0 }, @@ -23303,7 +23288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8966fc1b8ada46bfbf265fb8035038d3", + "model_id": "c945833c9e77478e91bf7af4e1327c77", "version_major": 2, "version_minor": 0 }, @@ -23317,7 +23302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "163aadaa011243dcb9bc9143bf10f817", + "model_id": "8d26cb3727014658a2bf5b14dad7b98f", "version_major": 2, "version_minor": 0 }, @@ -23331,7 +23316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3349465d836484ab3cbf0deaba8d522", + "model_id": "0f1e89521bba4760948d5ef0c3e52b30", "version_major": 2, "version_minor": 0 }, @@ -23345,7 +23330,7 @@ { "data": { 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"model_id": "33cb7a4511574f4b824c05df15b5c0a5", "version_major": 2, "version_minor": 0 }, @@ -23415,7 +23400,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0464b807ccad4a91acaca37534ab43e5", + "model_id": "cb4b00ac1d4946a78d8a2657710601c7", "version_major": 2, "version_minor": 0 }, @@ -23429,7 +23414,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79b57f753eb446268cb5794f2e573733", + "model_id": "02cf0f22a2ea417cb93621bfcb0bc9b5", "version_major": 2, "version_minor": 0 }, @@ -23443,7 +23428,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "315076d676d54bfe8a855daa5e9a8af2", + "model_id": "149d67a49fb4472aba8b5a7c688f52a8", "version_major": 2, "version_minor": 0 }, @@ -23457,7 +23442,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0c935f1b58c24435892ebabf508f4074", + "model_id": "ea3b6527582045e7994d3efde138606c", "version_major": 2, "version_minor": 0 }, @@ -23471,7 +23456,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bcd1d26f94bd4d5090a68c63d219379c", + "model_id": "1eb9b3d4b0e24908998059a0f4cfeebe", "version_major": 2, "version_minor": 0 }, @@ -23485,7 +23470,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "36cd44595ca64f04a064cc50d404ad66", + "model_id": "140316e644d142a7ae0391bc86ef2ad5", "version_major": 2, "version_minor": 0 }, @@ -23499,7 +23484,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1107aa53520f4b1489ce37efab907c60", + "model_id": "4d818252c2ab47eabe2057cb630c5760", "version_major": 2, "version_minor": 0 }, @@ -23513,7 +23498,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4602e678104d41288e150732ddfa370d", + "model_id": "02ae574d0bab4574ba5bd920f8a96f89", "version_major": 2, "version_minor": 0 }, @@ -23527,7 +23512,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93801473cea44b51adcbe42ef0938c35", + "model_id": "89912d0efc2e4e28af411e5467b23711", "version_major": 2, "version_minor": 0 }, @@ -23541,7 +23526,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88b5205b2fb84b16baed1d5fbccbb884", + "model_id": "6600d3f8931b4b2e9f9f7972ea919c86", "version_major": 2, "version_minor": 0 }, @@ -23555,7 +23540,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8cdcf046bd294a1da275d27152700147", + "model_id": "7129383e2d8f4b6fb9596c3f3faf63f8", "version_major": 2, "version_minor": 0 }, @@ -23569,7 +23554,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bc46b4458277480dbdb87990cd8b97a7", + "model_id": "c4ba295d00b94586a95de8c62f22d7b2", "version_major": 2, "version_minor": 0 }, @@ -23583,7 +23568,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e169f4db48724193b8e83a14d1df4da5", + "model_id": "8c0fdbaaaa5a430da6bbca542ff2a06a", "version_major": 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{ - "model_id": "9fb498f4266b433195ea6e9fb6bcf20e", + "model_id": "6d3ee120c66f4d6ca312d11f9a46d382", "version_major": 2, "version_minor": 0 }, @@ -23667,7 +23652,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1e7942c66cd4fdd92ca94b68c277422", + "model_id": "998463c7bf324b238a4479549d4e2181", "version_major": 2, "version_minor": 0 }, @@ -23681,7 +23666,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdf4930e02eb42c7a7efa591732ed033", + "model_id": "90d568166c4c4ae69d6b91147149094b", "version_major": 2, "version_minor": 0 }, @@ -23695,7 +23680,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a820e521cce4829835bacb508dc0889", + "model_id": "4f25e7b818ee45b5b50e740cc72e0caf", "version_major": 2, "version_minor": 0 }, @@ -23709,7 +23694,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18066f2f48c943e4a56fdef8dfae4791", + "model_id": "4b66d9c883374ef09f72581e639f07f4", "version_major": 2, "version_minor": 0 }, @@ -23723,7 +23708,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8575a7a82ee846a8a5f4c185ce1fe189", + "model_id": "b9aa1763b00c4564bb5aabf61c5612c7", "version_major": 2, "version_minor": 0 }, @@ -23737,7 +23722,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2cc659a2c6a94dffb7667f521add6e8e", + "model_id": "f5c1931e3e3d4fb5ae1e7dbcfaa7f547", "version_major": 2, "version_minor": 0 }, @@ -23751,7 +23736,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6fe5d7721c2742c094050a35384c08f6", + "model_id": "bf7bb94eacbc41a483246cffa0ab1148", "version_major": 2, "version_minor": 0 }, @@ -23765,7 +23750,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b4e61b0458449fd955816bef6322b91", + "model_id": "3167f000d8a842889e2e15dcd5bd4e90", "version_major": 2, "version_minor": 0 }, @@ -23779,7 +23764,7 @@ { "data": { 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"model_id": "fc833ea561b44c1293c7bd366081c609", "version_major": 2, "version_minor": 0 }, @@ -23849,7 +23834,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c19461662fa4de196d7a6374c3123f3", + "model_id": "f00a9c130af94f378bc2395b61a86936", "version_major": 2, "version_minor": 0 }, @@ -23863,7 +23848,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe5e1959ba374fc7a1ba96921fbb9198", + "model_id": "f1ec091d85c841ba869a1b4148f0e361", "version_major": 2, "version_minor": 0 }, @@ -23877,7 +23862,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6abb6ea9405f40b8b211cd9a7fa53054", + "model_id": "442409b7b543493692f4df45daedd41d", "version_major": 2, "version_minor": 0 }, @@ -23891,7 +23876,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e911c3e72f2489a8fce9ab85cf7fd2e", + "model_id": "d328f25d4a7f43dcb76c6b43c9766c1e", "version_major": 2, "version_minor": 0 }, @@ -23905,7 +23890,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e8e634c8853418183da5d8c5864ae28", + "model_id": "5fcbee5e5e104b5f975daea44e649021", "version_major": 2, "version_minor": 0 }, @@ -23919,7 +23904,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa39961c47304ad1b6501f9f7423dde8", + "model_id": "688b1167c8ac4284ba57bf1e249c5bba", "version_major": 2, "version_minor": 0 }, @@ -23933,7 +23918,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e660957a60044828e21745a7c13771a", + "model_id": "4fc221440fd341ad810f05ad398422cd", "version_major": 2, "version_minor": 0 }, @@ -23947,7 +23932,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d00ec8602ca24baf9c6e24a1c6a6a4ed", + "model_id": "23a6fff5908b4bdcb696057160572353", "version_major": 2, "version_minor": 0 }, @@ -23961,7 +23946,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b7cfe5a19d045e985b7110e6cb163cd", + "model_id": "b0988c1a5856429fae75e6470c892ce3", "version_major": 2, "version_minor": 0 }, @@ -23975,7 +23960,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b774623fd38e4c6492089ed8e42db1e6", + "model_id": "2b07b520ff004e988e8727a9729ebda6", "version_major": 2, "version_minor": 0 }, @@ -23989,7 +23974,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "321246ee350f49378bee06f8dede7f06", + "model_id": "3b3b668e040d4773a1a4a40d656d506b", "version_major": 2, "version_minor": 0 }, @@ -24003,7 +23988,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b04490f97f8a4074832491ac90985cd8", + "model_id": "077da953f1ac4a0782abd6de796e2444", "version_major": 2, "version_minor": 0 }, @@ -24017,7 +24002,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41ffdb1d7a204a4e88efa4d2129fd4c7", + "model_id": "d99313f861e94bec8dbdb7ac870f9f99", "version_major": 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{ - "model_id": "fdc99ba1c0744c75a02a47837e98de77", + "model_id": "1aeca993f22545428fd832013dbe0591", "version_major": 2, "version_minor": 0 }, @@ -24101,7 +24086,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99a58cd12f704a9eb47b0b65b9c675ce", + "model_id": "6c6b9a3994b54a6684aedff5fb984589", "version_major": 2, "version_minor": 0 }, @@ -24115,7 +24100,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e15847435594476f8e9c41245745a186", + "model_id": "2bdbf2288ed243f4bba81e63db4003dd", "version_major": 2, "version_minor": 0 }, @@ -24129,7 +24114,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "640907f2372a410ab0331f175c177330", + "model_id": "ecc342e1c06b488a886d6f320a6f3fc0", "version_major": 2, "version_minor": 0 }, @@ -24143,7 +24128,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a5b48ac7fb0a47558eb6874ed5d0c46f", + "model_id": "54df3d9806e64e13b264e24b54ef224b", "version_major": 2, "version_minor": 0 }, @@ -24157,7 +24142,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "589b1a4ae5af41ebb7518049a53fef04", + "model_id": "cdb304e86d9e4c8b9b2621ca522d5b5b", "version_major": 2, "version_minor": 0 }, @@ -24171,7 +24156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da092374eae842be813f06bd66b5b5c7", + "model_id": "102504d901be4434a0b09c78398a1688", "version_major": 2, "version_minor": 0 }, @@ -24185,7 +24170,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fdd620aba7d949d987056f05070f1801", + "model_id": "27ec6a4e63454d709bd023b6fffcf6c1", "version_major": 2, "version_minor": 0 }, @@ -24199,7 +24184,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "744b6034d00d4c3c91570fd05e391f23", + "model_id": "9cc15c9ac5a048b18ec660c360110eaa", "version_major": 2, "version_minor": 0 }, @@ -24213,7 +24198,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8fa2f2a5404e481ead5c516cb87f7555", + "model_id": "c93438503dd34b63a0314cb1248f84a8", "version_major": 2, "version_minor": 0 }, @@ -24227,7 +24212,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e25077018bc408ba047645d61e6b535", + "model_id": "82a1b5199ffa4926a41e1ab8d6ff496b", "version_major": 2, "version_minor": 0 }, @@ -24241,7 +24226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9aeaf82d9b3e477ca74e35df5c0de983", + "model_id": "76e5757c3bb54a55a501eb674d0a3e13", "version_major": 2, "version_minor": 0 }, @@ -24255,7 +24240,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c13cba54e7a54ba4a93f2fad8fd13e78", + "model_id": "a6fc7f15240a4f44b868061b40e4f38c", "version_major": 2, "version_minor": 0 }, @@ -24269,7 +24254,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78f9a96bc82741a19641879d451e13f5", + "model_id": "abd7991eef4c4f1d9849361a9fe6549f", "version_major": 2, "version_minor": 0 }, @@ -24283,7 +24268,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e6590e357bf40389b68920b9072f852", + "model_id": "feaf06eb238b41559c889dfb83b1f12d", "version_major": 2, "version_minor": 0 }, @@ -24297,7 +24282,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2226dfd76ee74b1e853f54015cc4c33c", + "model_id": "d17c07f19b6f4acd9c44f276ebdccac2", "version_major": 2, "version_minor": 0 }, @@ -24311,7 +24296,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80d4f49dbe79450ebe12c5e15e193d26", + "model_id": "6651325a41204ed6ad935f2b7a4fe870", "version_major": 2, "version_minor": 0 }, @@ -24325,7 +24310,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c441516a792d42328142519d5132a6e7", + "model_id": "ef0fd7b91c47455dab6b33e760375d54", "version_major": 2, "version_minor": 0 }, @@ -24339,7 +24324,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4060f2bcd1164579a97c9ef03cde96f3", + "model_id": "5f1ce7fae2414706b28950900880ff97", "version_major": 2, "version_minor": 0 }, @@ -24353,7 +24338,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e6ed7bf1a0ec4ed5a2820225ff0da48f", + "model_id": "3a42665ca79c4de2a31d8a08bd287567", "version_major": 2, "version_minor": 0 }, @@ -24367,7 +24352,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "43969bedc79f4a7d805cd992ee5fa8ab", + "model_id": "f09fe9e1d84b48a4be71128968155f57", "version_major": 2, "version_minor": 0 }, @@ -24381,7 +24366,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "54c9960bd54f4163b9089068e8de8c1a", + "model_id": "c9f4c39e9ed245728acb346974d3c5f7", "version_major": 2, "version_minor": 0 }, @@ -24395,7 +24380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b15073f405c4681b92fa2a241947af5", + "model_id": "f0c832adce4549278d29a9fa14bb8bc4", "version_major": 2, "version_minor": 0 }, @@ -24409,7 +24394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fed6e81088e94c06a3d1bb15207e07c4", + "model_id": "370484b8ceeb4dd39764e7d6e50ea4a8", "version_major": 2, "version_minor": 0 }, @@ -24423,7 +24408,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f04301662d74428bb85f4cccd1d501b", + "model_id": "0062ccd3ec7d47d7ba2df94a3f2ec5c5", "version_major": 2, "version_minor": 0 }, @@ -24437,7 +24422,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf3d2f500a504f0cb80789aa6120847a", + "model_id": "72e1f0212d55452ba0e4d205d82f834f", "version_major": 2, "version_minor": 0 }, @@ -24451,7 +24436,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15c1262ab08842e39f76d17278bce138", + "model_id": "05c1685449b04f1da56f9d7dc367265b", "version_major": 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{ - "model_id": "460e8d1d427b44da80622f5bfabf46f9", + "model_id": "06cf9b157fdd4f5298c3fbb495a591dd", "version_major": 2, "version_minor": 0 }, @@ -24535,7 +24520,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a18e5604db54ec1a0530817c2965ea7", + "model_id": "163d613f322c48089bda1adfaecfb8e3", "version_major": 2, "version_minor": 0 }, @@ -24549,7 +24534,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ebf9341ffb241c18df22e7e10c5752b", + "model_id": "5b60756ee8524c28a867a419041751dc", "version_major": 2, "version_minor": 0 }, @@ -24563,7 +24548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "11413497cfbb49e88a61bc2fa33f365a", + "model_id": "c6396e09bde34ff39fac65c26a3d314d", "version_major": 2, "version_minor": 0 }, @@ -24577,7 +24562,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52f0539f4cb84ffab8138e430705da91", + "model_id": "26316cb2ade04097bcb12a65c2bce1a1", 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"model_id": "d1cd6cf2fe164b3eb8f07899dcaf0a84", "version_major": 2, "version_minor": 0 }, @@ -24717,7 +24702,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "98177256ccef4d5a9a6a88a8776a9803", + "model_id": "d249bdc5906f4489a29ca4e514dc15d1", "version_major": 2, "version_minor": 0 }, @@ -24731,7 +24716,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb1a425647e7411cb758a0c0c8c8abce", + "model_id": "18366a76cc5f434fb5a186fc74fd95cd", "version_major": 2, "version_minor": 0 }, @@ -24745,7 +24730,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c35c0fdf82d64b628e24e2324267fef0", + "model_id": "0a738a789f684f348fb62c9aed190fd4", "version_major": 2, "version_minor": 0 }, @@ -24759,7 +24744,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41ce9ca6b5c64a6ebd0e69106c163bd9", + "model_id": "d8fb53ba37a14f4bae6a13d9132b5e44", "version_major": 2, "version_minor": 0 }, @@ -24773,7 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"95444d4af2ab4c28b567846f4acd29ac", + "model_id": "a5be1f386c814a9c9196df0b4cf3b2c7", "version_major": 2, "version_minor": 0 }, @@ -24843,7 +24828,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "13303387ca2d4d329540142051c81be6", + "model_id": "f492cedd663848e3ac2b4d5b5d162c88", "version_major": 2, "version_minor": 0 }, @@ -24857,7 +24842,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "341ad9086f924d35aaf7e571778cd9bd", + "model_id": "01c20ed6696848089084f2b3a270f3ac", "version_major": 2, "version_minor": 0 }, @@ -24871,7 +24856,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2526204a1d984dd781f181a500b55f29", + "model_id": "4c3cadafdfc344348369e6328f015d19", "version_major": 2, "version_minor": 0 }, @@ -24885,7 +24870,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a02b38b00c474634afe2ba5462225f00", + "model_id": "ed182c98b68b4c31b5a623a2b0ba3760", "version_major": 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{ - "model_id": "ade11b6850714e6d9187a25e5cedcdb5", + "model_id": "16038242a7db45b5a5bedb9308901049", "version_major": 2, "version_minor": 0 }, @@ -24969,7 +24954,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02872993401142ecb91ce790b5484d30", + "model_id": "c697bfa3da054cd5ae0bdb2af7200576", "version_major": 2, "version_minor": 0 }, @@ -24983,7 +24968,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a7e4619a01d6464fbe8921b118938f0d", + "model_id": "d89b940d03d34b45ac93b58462ec4f2a", "version_major": 2, "version_minor": 0 }, @@ -24997,7 +24982,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2978252246943978d3a05a3374032ba", + "model_id": "3c5b89bdb16e49d4a4513c45dd4c4516", "version_major": 2, "version_minor": 0 }, @@ -25011,7 +24996,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67dd7e8ce0954267b2c52f55402df4d4", + "model_id": "6a1a9c56a82141a786628b0baaaaa23f", "version_major": 2, "version_minor": 0 }, @@ -25025,7 +25010,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "deadb5b271f84aab9cdd466a239929dd", + "model_id": "b902d38104664d40bcd1c7bb3d7fd4eb", "version_major": 2, "version_minor": 0 }, @@ -25039,7 +25024,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8eed4503bba940cbbb9465bed3285339", + "model_id": "05a586511ca841edab8016c0ec414879", "version_major": 2, "version_minor": 0 }, @@ -25053,7 +25038,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "65c83cdda6804ac2be50d1444fec16f5", + "model_id": "c17687304f7543c8b24797e2f1223de6", "version_major": 2, "version_minor": 0 }, @@ -25067,7 +25052,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61106d528ea74daba99fa26ec7ad02bc", + "model_id": "95f8e4e542de42369ef8bba0a7fa5ed7", "version_major": 2, "version_minor": 0 }, @@ -25081,7 +25066,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7421ae5b04824c558dad6cc556eff26e", + "model_id": "0d00fceab7664d8bb9e1faa71c5d5afc", "version_major": 2, "version_minor": 0 }, @@ -25095,7 +25080,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1081309bd9e84718b47e0fcab8d1609c", + "model_id": "5162a14ddce5430db021e9ef172730cf", "version_major": 2, "version_minor": 0 }, @@ -25109,7 +25094,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0228d95656974fe6a27f8c16e5b02af1", + "model_id": "11ca61b579a54601a7ec2831878899fa", "version_major": 2, "version_minor": 0 }, @@ -25123,7 +25108,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "11881107239c423f8a845be9168256fe", + "model_id": "afc76b677a3743e79c34578da92146e9", "version_major": 2, "version_minor": 0 }, @@ -25137,7 +25122,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "12ee6c840f0b4eef96bff17e31a7aa48", + "model_id": "603fa68c4e444564a4d09fd5a46c5403", "version_major": 2, "version_minor": 0 }, @@ -25151,7 +25136,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cfb25da50bb144d39aef6b323e378dc2", + "model_id": "144e5aaf274f46c8b89f55085b8d32ed", "version_major": 2, "version_minor": 0 }, @@ -25165,7 +25150,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17ce6628a6c542239f7573c6cbb76c2c", + "model_id": "be84cd1080d141db96cc82d51f64be18", "version_major": 2, "version_minor": 0 }, @@ -25179,7 +25164,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "994f4214d9b24b93a2e88af22b8dea9c", + "model_id": "5e210e9b25c348689aa6a3012842d3c8", "version_major": 2, "version_minor": 0 }, @@ -25193,7 +25178,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd226af4b7f14eda959e58080e39d3b8", + "model_id": "94d08b28f134495eabc21150bed44ee9", "version_major": 2, "version_minor": 0 }, @@ -25207,7 +25192,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf6d52376272484e8e4372b116e9dcd6", + "model_id": "7874599fbac448e9a2012ae9d9503411", "version_major": 2, "version_minor": 0 }, @@ -25221,7 +25206,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "397a6132734d4922a88b92a04aeb4b39", + "model_id": "a0a6f5d9720a486ab54bc8e73b773a46", "version_major": 2, "version_minor": 0 }, @@ -25235,7 +25220,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4ae9f0da5294303a3cfff56be9bd6f5", + "model_id": "fe990b76a3df48718c77250612abf757", "version_major": 2, "version_minor": 0 }, @@ -25249,7 +25234,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "169446eb65d74534b6d6f1f01297e439", + "model_id": "0d2ed2fd398142c4af7d08aad5cc7208", "version_major": 2, "version_minor": 0 }, @@ -25263,7 +25248,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91988122fd6046509eb1f5424279cc0c", + "model_id": "e123c02e30d64e83982ae7273a64eef9", "version_major": 2, "version_minor": 0 }, @@ -25277,7 +25262,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdfea6ff121b450aa71ad04d08847980", + "model_id": "0f905509bdb249a8a0d5dad010e5b4a5", "version_major": 2, "version_minor": 0 }, @@ -25291,7 +25276,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6677dcf735d348238f6fce1ca15064f5", + "model_id": "3f10fbfdaa9046bea3a65fc8e0206b1f", "version_major": 2, "version_minor": 0 }, @@ -25305,7 +25290,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3753302bbff44c32bc5c02d2f066dc9b", + "model_id": "06ee6b284795475fa74e3671018a9acc", "version_major": 2, "version_minor": 0 }, @@ -25319,7 +25304,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ea551336552486eb50cf6f434962c09", + "model_id": "0c1d5d96fcb441f0a683bd96c882146e", "version_major": 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{ - "model_id": "1b72b9adcefa4e2390e04c5d53dab0a5", + "model_id": "d9fe28a846d5487888328c302c38e729", "version_major": 2, "version_minor": 0 }, @@ -25403,7 +25388,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "83592484214449c48ebdf2040d18b1c9", + "model_id": "7a866caea16f48a3b9f878b0681768e8", "version_major": 2, "version_minor": 0 }, @@ -25417,7 +25402,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5352911fe3f342e1abfdef56097487cc", + "model_id": "ada774b773c84c16aa9a6baf79579fe2", "version_major": 2, "version_minor": 0 }, @@ -25431,7 +25416,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f3c35f06b874daa8c55d1988dc38529", + "model_id": "3395e8d97d864bf892cf3c39eb3bd792", "version_major": 2, "version_minor": 0 }, @@ -25445,7 +25430,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a7918e323dda408babbed892acea4c9f", + "model_id": "f4067c17087d402fbbaf99c37af1d3f5", 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"model_id": "19628d2ab56a4017bc6ad444ac6b967e", "version_major": 2, "version_minor": 0 }, @@ -25585,7 +25570,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "74a41eb2f18e4c8998776baa9fee0191", + "model_id": "df6c2760c5f14bde89e58d44ab698ada", "version_major": 2, "version_minor": 0 }, @@ -25599,7 +25584,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9c21ccda4ec4940a6e9279b808e25ea", + "model_id": "634204e20fdb4e6394ae665de0f849e6", "version_major": 2, "version_minor": 0 }, @@ -25613,7 +25598,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "442cc36d12dc423898c259287f540f96", + "model_id": "c2bae19ad9154a27a58965eb20053332", "version_major": 2, "version_minor": 0 }, @@ -25627,7 +25612,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df6d7f2df58e43d7a27c8939dadb3d39", + "model_id": "e63cb194819a4ee0b903108371ba42f2", "version_major": 2, "version_minor": 0 }, @@ -25641,7 +25626,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6228c391f98240c2a456fd0bdc0a6c32", + "model_id": "9935479584384dfba39c3d094f499d0f", "version_major": 2, "version_minor": 0 }, @@ -25655,7 +25640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9131c360c9fe49789c8658827f1cd95e", + "model_id": "84b4274431a1485c8f1fa4d1e2ee9d69", "version_major": 2, "version_minor": 0 }, @@ -25669,7 +25654,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57fc0a10609947a0b673ffcd0c663f4b", + "model_id": "dd8b67d99e60408da1d0d3dbc8400f9b", "version_major": 2, "version_minor": 0 }, @@ -25683,7 +25668,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f652deddf1cf41ab93f845aaa6c20275", + "model_id": "2408f5b4b7ad41cfada87b1ad11a7dab", "version_major": 2, "version_minor": 0 }, @@ -25697,7 +25682,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06b0ad6d1fa34024bf6325a3518af52c", + "model_id": "2fbedaf8e7144e06893a057137d027f3", "version_major": 2, "version_minor": 0 }, @@ -25711,7 +25696,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bc6627354a444bcb7c3e3d99f65bba7", + "model_id": "27c6c2df22274c0daae0f73d470508d9", "version_major": 2, "version_minor": 0 }, @@ -25725,7 +25710,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "28174b37056c45b69304b1838a1020ed", + "model_id": "4755efa867064ab1b3c017d4d9efa2ee", "version_major": 2, "version_minor": 0 }, @@ -25739,7 +25724,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8eadc158ff8c4286adcd74908726da7c", + "model_id": "c0025ae21bcb46a4b9faafc8844d4b0f", "version_major": 2, "version_minor": 0 }, @@ -25753,7 +25738,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6fa1458e04764b91adffd9dc693d3dc2", + "model_id": "4af491d678b242e0a4473aee35323819", "version_major": 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{ - "model_id": "af8bba8a20c84b33b9f3cf57930c3a82", + "model_id": "7a8eae37cb3148fa80ece2493c70d8bd", "version_major": 2, "version_minor": 0 }, @@ -25837,7 +25822,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71a7cd0788094e1a9de485dfac440724", + "model_id": "953d3965f6604d4283777f66cd67dd68", "version_major": 2, "version_minor": 0 }, @@ -25851,7 +25836,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a5d68dd5ddb74cd087e4517a183cf77b", + "model_id": "6b5e0e8e98d4431fbb38ac7ce9a2ea32", "version_major": 2, "version_minor": 0 }, @@ -25865,7 +25850,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f73d2bd657443b4a994fda357ea6b51", + "model_id": "e5ab6e2129e04c52b93b86e3925db485", "version_major": 2, "version_minor": 0 }, @@ -25879,7 +25864,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed03948e0997484f9d3d56770d3cf41c", + "model_id": "54672953829e413bafe2cb23f3c5c618", "version_major": 2, "version_minor": 0 }, @@ -25893,7 +25878,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8af26383db242878be11b44ef7c2d5d", + "model_id": "418c8e90f1c343b88e3dc33cdb6ced40", "version_major": 2, "version_minor": 0 }, @@ -25907,7 +25892,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3366952fa7594d75af474b960a155355", + "model_id": "5a24d19836a34eaaad38a8ee01f03a3b", "version_major": 2, "version_minor": 0 }, @@ -25921,7 +25906,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "89c6d83c5faa47e9b197d76adadb3795", + "model_id": "aa4f0aa5acc64282a4aaaa6f5d730565", "version_major": 2, "version_minor": 0 }, @@ -25935,7 +25920,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "75af45b337d64d91a62ef2e940fe32f0", + "model_id": "782bbf09775444a98e470b60d806f599", "version_major": 2, "version_minor": 0 }, @@ -25949,7 +25934,7 @@ { "data": { 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"model_id": "2de53678670f44109677beb829d1fe6e", "version_major": 2, "version_minor": 0 }, @@ -26019,7 +26004,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c39a8d049eb42a3a1c2ea6e20d70ab1", + "model_id": "adbda9d293fe4c13982cb775b5f8985d", "version_major": 2, "version_minor": 0 }, @@ -26033,7 +26018,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "17ab180135834eb1a0ea647225d3268b", + "model_id": "0ab11acd6a4a4c7a85fccc12346e34f3", "version_major": 2, "version_minor": 0 }, @@ -26047,7 +26032,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "877f180072d54f59b9d1909a0ef5e80a", + "model_id": "937ea80f0ad940eab12983555d5138c5", "version_major": 2, "version_minor": 0 }, @@ -26061,7 +26046,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "803157a689fe49b9a1bba36e19cc86aa", + "model_id": "598fd3b8bf2c45f08eb67e495e036e58", "version_major": 2, "version_minor": 0 }, @@ -26075,7 +26060,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60a009c721e147939b992e7a4c37d52b", + "model_id": "c28839ba132f4cb5bb441f3a5bbaf6e4", "version_major": 2, "version_minor": 0 }, @@ -26089,7 +26074,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "14ef44a28e0f4a8882f462407681f042", + "model_id": "59acd9bd2e0d4e9ca2e4af43918d8ace", "version_major": 2, "version_minor": 0 }, @@ -26103,7 +26088,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1aea5748bda4fc197e70cd4459d450f", + "model_id": "63362a28645d4723be622d2bdd3e55e7", "version_major": 2, "version_minor": 0 }, @@ -26117,7 +26102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "87f2dd4b5e894ec289e20b5490789514", + "model_id": "76a9553b95a649e98609d6edec2aafcb", "version_major": 2, "version_minor": 0 }, @@ -26131,7 +26116,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a6de3d0d85db46c7b34f75b26894d9e1", + "model_id": "267b3e2bab2548319ea0cb3a4f5c78b5", "version_major": 2, "version_minor": 0 }, @@ -26145,7 +26130,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f575459493446d2a28f82a28d94dacf", + "model_id": "c217a0fef847480885e6359652321585", "version_major": 2, "version_minor": 0 }, @@ -26159,7 +26144,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0a2e42b902894b44b198af52e0647664", + "model_id": "a4784c6f1cff43afb516f8f092a81bd4", "version_major": 2, "version_minor": 0 }, @@ -26173,7 +26158,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c6a9f197ab4540ca9cdaaf5c82c0d5b2", + "model_id": "0c412156bf824bbaaa61fab9bc23f2c2", "version_major": 2, "version_minor": 0 }, @@ -26187,7 +26172,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "08a7d30fe322424e90e34c2ff0aaa9e7", + "model_id": "9a7ac00a7cb44b7d8e1efbf2a3700e46", "version_major": 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{ - "model_id": "5756d30009a240d7aee14803c5ee7867", + "model_id": "5a4d89ca167f4f3db2d2aacfb122cc4c", "version_major": 2, "version_minor": 0 }, @@ -26271,7 +26256,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f76064493b346c4aa26226614e1619c", + "model_id": "2857d14272634eef861e92a52881e656", "version_major": 2, "version_minor": 0 }, @@ -26285,7 +26270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4597dff9fbb441889a3a7c3d0c05487b", + "model_id": "0a2ac871278941ecb7ff5df479177366", "version_major": 2, "version_minor": 0 }, @@ -26299,7 +26284,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40193fa5354f46f2964cbc7c8d14443c", + "model_id": "9573c518402b4a92983e419835f58131", "version_major": 2, "version_minor": 0 }, @@ -26313,7 +26298,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca0b3adfca944dda819804e571d58600", + "model_id": "194f4f19ff214aeeabf60c3f7e5e7309", 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"model_id": "359c1b912a8b4d2bb21c26c4d4e0e23e", "version_major": 2, "version_minor": 0 }, @@ -26453,7 +26438,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4038ac3605924b0aa1b0a953676f57b7", + "model_id": "f49ba45be8844ffb889c771a83a7fc58", "version_major": 2, "version_minor": 0 }, @@ -26467,7 +26452,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6eacf365cc30469dab6b6108e50639e3", + "model_id": "fc55123848da410c87d25f7f12cb1a09", "version_major": 2, "version_minor": 0 }, @@ -26481,7 +26466,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "28b10e3381394b21b4bff270e9d89c72", + "model_id": "d99b3f7a9ce347559048a13ea42c6875", "version_major": 2, "version_minor": 0 }, @@ -26495,7 +26480,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "54e069900085450cbe472b4d198cade5", + "model_id": "56264252d1c34dc280a48dc4f9f5c86e", "version_major": 2, "version_minor": 0 }, @@ -26509,7 +26494,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "471af5b57b024984b2e219b244c7db80", + "model_id": "d070910420f04b5499f500be439981cd", "version_major": 2, "version_minor": 0 }, @@ -26523,7 +26508,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef347a660ca249749e6094936d9eb657", + "model_id": "19c4c2e63b154c7591b71eb214cd5553", "version_major": 2, "version_minor": 0 }, @@ -26537,7 +26522,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4309b47ec3864ce38fa700113c3f3095", + "model_id": "b6c70e6eb2bc483295163bbfe50af9a3", "version_major": 2, "version_minor": 0 }, @@ -26551,7 +26536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82c3fc079f254d8aaf7380d5d9cff6c9", + "model_id": "7a33fde54a6d49d786155fcfc93b50b1", "version_major": 2, "version_minor": 0 }, @@ -26565,7 +26550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b784c8bbcca348d9945aeb231ac92a7e", + "model_id": "d1cc8b9cfcbd4b16b679e54ae0b15cc8", "version_major": 2, "version_minor": 0 }, @@ -26579,7 +26564,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e26c8ebd4a854b5d96c321105f7d33f1", + "model_id": "5086ef91d33a4175ab7ac38d3a1fef89", "version_major": 2, "version_minor": 0 }, @@ -26593,7 +26578,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68d3fdda245e422b96e0f6f640eeb3b9", + "model_id": "5f2b693d960c4f909352726c778dca08", "version_major": 2, "version_minor": 0 }, @@ -26607,7 +26592,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15072f9fd8a849cabbbe484e4cf95d90", + "model_id": "577f3da864134ab58f5a3d09221705b2", "version_major": 2, "version_minor": 0 }, @@ -26621,7 +26606,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4013db59aeaf480ea9ad31fdc41e3e8f", + "model_id": "f19b403dae91432781111183492588ae", "version_major": 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{ - "model_id": "350858f1d9fb4ecc8b7ca1832ec8ba47", + "model_id": "1aa0629723304dcfa41e871fb301b815", "version_major": 2, "version_minor": 0 }, @@ -26705,7 +26690,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c338fc52791749e1bd04ec94d496ecfb", + "model_id": "b59709d907684acd998fd0ce106cc2b7", "version_major": 2, "version_minor": 0 }, @@ -26719,7 +26704,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d005ca1cddc847c3a9d00a9f770e825a", + "model_id": "44396dbdc0a74fd48b2a3461e61df15e", "version_major": 2, "version_minor": 0 }, @@ -26733,7 +26718,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3426b969f5fd4458801e1e5d2776e900", + "model_id": "e5144eed59e448dd8c6a5a3e87a7c60c", "version_major": 2, "version_minor": 0 }, @@ -26747,7 +26732,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0cd5508345f4f40bf581c95af190312", + "model_id": "70e0c687bd0340aabae76a171ce99451", "version_major": 2, "version_minor": 0 }, @@ -26761,7 +26746,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b0eb4bcffdd4b88800015ebefe1c81f", + "model_id": "2ec50f07bb1547c69e1465de0dd16e9b", "version_major": 2, "version_minor": 0 }, @@ -26775,7 +26760,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e0ef885a2a344ffb70c8a4f5f50559e", + "model_id": "e9072458f70247469fa7f21de8d9c219", "version_major": 2, "version_minor": 0 }, @@ -26789,7 +26774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91961f19e3534f4a8cf2eccb2de4aa36", + "model_id": "efea950a6c9141cea3b6ae089dfce6d5", "version_major": 2, "version_minor": 0 }, @@ -26803,7 +26788,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c57d22c50474eee8e6bb9392c553f4a", + "model_id": "63c8031901254322940a1bbfdf28be71", "version_major": 2, "version_minor": 0 }, @@ -26817,7 +26802,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3ee948c1fa946d28fda77add67e7fd0", + "model_id": "579a2f39f3ec41bda44e573368846411", "version_major": 2, "version_minor": 0 }, @@ -26831,7 +26816,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "330332f99de543b7ba9ad827c74ca005", + "model_id": "4f4d8716d8c0471b866c0b9f76066955", "version_major": 2, "version_minor": 0 }, @@ -26845,7 +26830,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af2cf06ef24041e5a79ac665f625182a", + "model_id": "493342885749445a81bd7fff573c93f3", "version_major": 2, "version_minor": 0 }, @@ -26859,7 +26844,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "831737fa676e4c40bb7eb6ba91ead096", + "model_id": "53497915c06c40eab4326679b96f9a23", "version_major": 2, "version_minor": 0 }, @@ -26873,7 +26858,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6711bc6ecc2b42939e351932259120f5", + "model_id": "e9468ca1cb474072bab62a7e61c2bd78", "version_major": 2, "version_minor": 0 }, @@ -26887,7 +26872,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ddb45c237b4141748e42716b7a06be88", + "model_id": "f6dd4c63b8ff49a0a7aa74372d29a96a", "version_major": 2, "version_minor": 0 }, @@ -26901,7 +26886,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2dd25a8183e4461d9f42e4b7652644d1", + "model_id": "a99e9d4a243540429fece45ee77d1c9a", "version_major": 2, "version_minor": 0 }, @@ -26915,7 +26900,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ef2be213c634bd9b574a6fb72c4c2cc", + "model_id": "cb0d4709a4984543a91aa1943e8ae025", "version_major": 2, "version_minor": 0 }, @@ -26929,7 +26914,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "661fe9cfcd4a4012b25944d824e9432d", + "model_id": "76928e0b73ff4db7ac16dd5d21087197", "version_major": 2, "version_minor": 0 }, @@ -26943,7 +26928,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80730f0d3c354f9e98da12c72c437d10", + "model_id": "d77afb9d79b14a00a9cb5c24d280696e", "version_major": 2, "version_minor": 0 }, @@ -26957,7 +26942,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15b36bab22284a3c81456f0ffee50436", + "model_id": "633b1066e5ae459da57d1838ca6f654a", "version_major": 2, "version_minor": 0 }, @@ -26971,7 +26956,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "470a4b49dc3e438fb6713637089f96d5", + "model_id": "cc38dc2f58434b3d851d0b97a7bbe37a", "version_major": 2, "version_minor": 0 }, @@ -26985,7 +26970,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0aeaf88bd82742aea84da1118a037c00", + "model_id": "92d163360b14452c8697f0874801f535", "version_major": 2, "version_minor": 0 }, @@ -26999,7 +26984,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a972dc8985a349ca989ebfa48fcc4b80", + "model_id": "87ab3fd97a2c4e5fba94dceb07c0f429", "version_major": 2, "version_minor": 0 }, @@ -27013,7 +26998,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8f1a5a867ef46cb89384c4b144572fe", + "model_id": "8ce0e50bc6404eb2811ce7fc715d66c6", "version_major": 2, "version_minor": 0 }, @@ -27027,7 +27012,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d90f1b6009954c48b92d19f8b7e2b72c", + "model_id": "fba98a7c6445429f9d63ca8560cf411b", "version_major": 2, "version_minor": 0 }, @@ -27041,7 +27026,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "86149902740a44259805509544a9ce8d", + "model_id": "6755328eb731470ca1f49be8ff541ee6", "version_major": 2, "version_minor": 0 }, @@ -27055,7 +27040,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fbda67e3f174f82898585ec1f16d822", + "model_id": "f77ea2ba27114cc5864807a95dbc06b6", "version_major": 2, "version_minor": 0 }, @@ -27069,7 +27054,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8c1d9dd94714999af37733c4f6530fd", + "model_id": "aa702917828d49a486abea393ccaa36d", "version_major": 2, "version_minor": 0 }, @@ -27083,7 +27068,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "417140f3dc4d427e9adb71a23fe0c5a4", + "model_id": "d439dc6fb86e46bca30f0caa7ebe3f5b", "version_major": 2, "version_minor": 0 }, @@ -27097,7 +27082,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e3839c01605452dace114fec505f15b", + "model_id": "fb3719f706394116baa8e6cff6d88487", "version_major": 2, "version_minor": 0 }, @@ -27111,7 +27096,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c69f20a813c471f858dde9131a010e3", + "model_id": "aada311e61d146018e0d1c60d1243fc1", "version_major": 2, "version_minor": 0 }, @@ -27125,7 +27110,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d66bcd402c94c4c9845d50e1b6b9a12", + "model_id": "0c9c2b7eff3b49ab8b9d47e1103e23d5", "version_major": 2, "version_minor": 0 }, @@ -27139,7 +27124,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "daa405b90f7d434d88281acb8a29e81c", + "model_id": "5b453a1591814178b8780955d99bc60b", "version_major": 2, "version_minor": 0 }, @@ -27153,7 +27138,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "124e7612a004481ab3e43d03675e7a45", + "model_id": "8222ffa8e7134ac2bbb29b1bb2e209e6", "version_major": 2, "version_minor": 0 }, @@ -27167,7 +27152,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6cde477651544de8bc5eef65f568703f", + "model_id": "f9d082c0879a418d85b3a6816acb4f39", "version_major": 2, "version_minor": 0 }, @@ -27181,7 +27166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b99974f6c6e483f94076241781720a0", + "model_id": "62e3e86553564afca3bcfd63c305b7f6", 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"b27d6d876aaa461ba525c2354a41d5ee", + "model_id": "0fbec195a52446029258a468dad4876d", "version_major": 2, "version_minor": 0 }, @@ -27447,7 +27432,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "94bd2bf700e5421e9f9a0978c3f88993", + "model_id": "e055fb0c30c84492932b260178d5e119", "version_major": 2, "version_minor": 0 }, @@ -27461,7 +27446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f83c494de1ab417697b945aaaf7c0805", + "model_id": "6144814bfe6b40a6a3c3cea305abe7ef", "version_major": 2, "version_minor": 0 }, @@ -27475,7 +27460,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a460c7feb693444f8be6b1bbd271941d", + "model_id": "468e9c8b8760457fa488702486fe2f89", "version_major": 2, "version_minor": 0 }, @@ -27489,7 +27474,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d208a2b55f442aa890d730bfe884cd9", + "model_id": "e6a20ee628f14c00bb11dfb3f540d4d7", "version_major": 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{ - "model_id": "b6a2b73b938a47fc851857d7f1cbec8a", + "model_id": "2cc464e8135d41d9b8d0bf5220ecd084", "version_major": 2, "version_minor": 0 }, @@ -27573,7 +27558,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f63e05b520d4d0c8208dcfbce18bc00", + "model_id": "76c35da852c84312bb48d58ac07c4a97", "version_major": 2, "version_minor": 0 }, @@ -27587,7 +27572,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "539d1bc716634be0bec4c2ebcf87cc79", + "model_id": "fffab2778e6946ed949aba9272a9df48", "version_major": 2, "version_minor": 0 }, @@ -27601,7 +27586,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3a1b3b6ddb9241baa68739284643300b", + "model_id": "9bda7da47c8541cd85801011c7097c94", "version_major": 2, "version_minor": 0 }, @@ -27615,7 +27600,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9fd901b95acb4e25b670ba7f5f271bdf", + "model_id": "d76f5060f42e4efa89c1a6f584dd67bb", "version_major": 2, "version_minor": 0 }, @@ -27629,7 +27614,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1e7067d07fb1416697f399e2b45e7a6e", + "model_id": "f5a7482d264a4939bc3927923318af6e", "version_major": 2, "version_minor": 0 }, @@ -27643,7 +27628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d52a6123dc3241afbedb211a85b91a4c", + "model_id": "91bcda959abb46bebad5a3947b30a737", "version_major": 2, "version_minor": 0 }, @@ -27657,7 +27642,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "815a5031366647de8b9fe2253731b311", + "model_id": "5989c98a68a64d3ab984b6cd01445afc", "version_major": 2, "version_minor": 0 }, @@ -27671,7 +27656,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "65a57f208f3749799d6bdb1ae03eaad6", + "model_id": "581e80686b8a401fa38942046dfba052", "version_major": 2, "version_minor": 0 }, @@ -27685,7 +27670,7 @@ { "data": { 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"model_id": "eb38025b44c74504b01138515827e625", "version_major": 2, "version_minor": 0 }, @@ -27755,7 +27740,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2fca2d25f0b4b5780d407e2857e2037", + "model_id": "58fd525b46ec49f8965b029e6246b727", "version_major": 2, "version_minor": 0 }, @@ -27769,7 +27754,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c3c07408568c405196f289eed49a12f4", + "model_id": "e9519ab756ba4015aaec0b1b1eefb94c", "version_major": 2, "version_minor": 0 }, @@ -27783,7 +27768,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bfd0c5fe25248e9b92d66d1e59d6ea9", + "model_id": "5e2c5e07fbc047c4bcde67c6f692e2df", "version_major": 2, "version_minor": 0 }, @@ -27797,7 +27782,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7b460c19851a4507b920483510ef2794", + "model_id": "4392175cbd6b4867bf619621b9695400", "version_major": 2, "version_minor": 0 }, @@ -27811,7 +27796,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "624e68b1a1894e30ae7dd322e81e8e2d", + "model_id": "06a85ace6b0c416696e06275a39cceac", "version_major": 2, "version_minor": 0 }, @@ -27825,7 +27810,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae2f818a342b49858376d1c7158e49a5", + "model_id": "9bd6588dd0b84a02adf5a3d1e46fe786", "version_major": 2, "version_minor": 0 }, @@ -27839,7 +27824,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1acc4997b0f74a85adea4bcd456629b0", + "model_id": "b01c604bdcc248c1897f91068a8d3214", "version_major": 2, "version_minor": 0 }, @@ -27853,7 +27838,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f094ff67bdb47a5a962acf6ffadc1bf", + "model_id": "4087cf4175314940bea6d13576a9f11a", "version_major": 2, "version_minor": 0 }, @@ -27867,7 +27852,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7eff804fddfe4ddebcd685238726cf35", + "model_id": "6ad1a683eea3477dbf82f9f2a0b060bf", "version_major": 2, "version_minor": 0 }, @@ -27881,7 +27866,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d02517707ef4ca297cc51b25d81d0a0", + "model_id": "242320985bda4b5db21867976c310bfe", "version_major": 2, "version_minor": 0 }, @@ -27895,7 +27880,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31e3a71cad794a29b551d9d8d4f68cca", + "model_id": "699ee0865c9a4c3990a4a3825a2c3dc3", "version_major": 2, "version_minor": 0 }, @@ -27909,7 +27894,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "67a3d6f9e90d4dce9d86377111446b12", + "model_id": "44cac5db95454806bcf8175137a018a9", "version_major": 2, "version_minor": 0 }, @@ -27923,7 +27908,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1949491886c46c19e2106400916ae74", + "model_id": "dfb93fe6d2f648cf8be9006940d59d1f", "version_major": 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{ - "model_id": "8374fa00fa3748cba3490e43020e0625", + "model_id": "248eb3255e7f4094b239d197e9a538a7", "version_major": 2, "version_minor": 0 }, @@ -28007,7 +27992,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf21fb55d1084e4288bdd59cec6a2eed", + "model_id": "06b27105fad2447c8fcf5ce69acb9db8", "version_major": 2, "version_minor": 0 }, @@ -28021,7 +28006,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "25b9a4169e3649d4a41924e5137e5656", + "model_id": "b719de1aee154cb0a41b18a93a160136", "version_major": 2, "version_minor": 0 }, @@ -28035,7 +28020,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d6123bc9c26e44f29c1a0fcf46665a17", + "model_id": "05a20aed8986475e85392e24792f510d", "version_major": 2, "version_minor": 0 }, @@ -28049,7 +28034,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e76854c9e1b14525994485f2a68cb0c4", + "model_id": "c6644a7c3a3d4d0e9a9d11951e29faab", 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"model_id": "d54733d5df8540f3907fe4179c57646f", "version_major": 2, "version_minor": 0 }, @@ -28189,7 +28174,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93905cdf8ae04a45a1e870e9591d606a", + "model_id": "1653c4a2137347cea0a35fd32364791a", "version_major": 2, "version_minor": 0 }, @@ -28203,7 +28188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "14adc93deb084874af36c23d1a7f27b4", + "model_id": "236eaba1d0dd41b8af99a4b9a5fae4a1", "version_major": 2, "version_minor": 0 }, @@ -28217,7 +28202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b984f201ae0b49019da687bb671043a5", + "model_id": "0aae7f45b98a4397a5ad5d64a06504d0", "version_major": 2, "version_minor": 0 }, @@ -28231,7 +28216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5e29e8b4c76342e9be75e1ea4c83a807", + "model_id": "777c6db9f14c47a3b6d3775d05fe4653", "version_major": 2, "version_minor": 0 }, @@ -28245,7 +28230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c6db9c3915d4f46af05c09507eb3e48", + "model_id": "9e614430d2e845a382929877a64c73ed", "version_major": 2, "version_minor": 0 }, @@ -28259,7 +28244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18cb43bb3eb340b5b61a2cc73263e44c", + "model_id": "3c1e1bd3ae5447888ccab232858b9873", "version_major": 2, "version_minor": 0 }, @@ -28273,7 +28258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "518860fa8f24464482b167f618107f4e", + "model_id": "11f10500df514c7b8436b483d1b4e8cb", "version_major": 2, "version_minor": 0 }, @@ -28287,7 +28272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f4a98ea19304cfbbfb063769c19df30", + "model_id": "345867062f9e49e2b40567cf2ed6a2d6", "version_major": 2, "version_minor": 0 }, @@ -28301,7 +28286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "880033eae3ca4198a26ccdcd7af55f3f", + "model_id": "f34debc6820b4f50a8e2ca988c5d6a7d", "version_major": 2, "version_minor": 0 }, @@ -28315,7 +28300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "13ecbef3b9a449178ba8f5ade860b14a", + "model_id": "cee0d2a2619946bfbb62246e862cd7b6", "version_major": 2, "version_minor": 0 }, @@ -28329,7 +28314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "28a6fee9881444df8cf05052e9c83d1f", + "model_id": "4cf6e4d55e7e430eac96855b564fa95a", "version_major": 2, "version_minor": 0 }, @@ -28343,7 +28328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "348bd730b8554698a8c3e17fb69fd74c", + "model_id": "636414fb58c34a05b4f57464f4f7598c", "version_major": 2, "version_minor": 0 }, @@ -28357,7 +28342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0fed4c4d2e3a45b3904cd05da32b34f0", + "model_id": "fd592c422d214af0aec1184bbaa66a23", "version_major": 2, "version_minor": 0 }, @@ -28371,7 +28356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4fdec262a96425890fc16db3547e21d", + "model_id": "2bf216ee9f3c4f7d8a9f3fe0fa2f05aa", "version_major": 2, "version_minor": 0 }, @@ -28385,7 +28370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8acf989a42f44d52b71ff56d0de536a8", + "model_id": "6257f1aaa9a74b34a0f81eaecca5b27d", "version_major": 2, "version_minor": 0 }, @@ -28399,7 +28384,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f491b0b81ec24da58ea476ae5e8a868a", + "model_id": "3a568ca856e7429984b261add4144935", "version_major": 2, "version_minor": 0 }, @@ -28413,7 +28398,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a61a512e051e44a49fb3db77601c96ad", + "model_id": "12040c1ced6340088acc5f70e7ed87f4", "version_major": 2, "version_minor": 0 }, @@ -28427,7 +28412,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc3f7de2ce194f908e64a22464d2027a", + "model_id": "31ac5e48be9046f9a909d34c391d8fa4", "version_major": 2, "version_minor": 0 }, @@ -28441,7 +28426,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7645ffea3c94107b4373b5dfd319128", + "model_id": "b76e4defc3bc43bda9804c93f19dce61", "version_major": 2, "version_minor": 0 }, @@ -28455,7 +28440,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "738c97edacce442f9d5e1cca0ec8bb95", + "model_id": "060e35d1a47a4581b2f4b85b8e7a39e0", "version_major": 2, "version_minor": 0 }, @@ -28496,7 +28481,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "019767e5", "metadata": {}, "outputs": [ @@ -28504,9 +28489,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Plotting at t=0\n", - "Plotting at t=0.5\n", - "Plotting at t=1\n" + "Plotting at t=0\n" ] }, { @@ -28519,9 +28502,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=0.5\n" + ] + }, { "data": { - "image/png": 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" ] @@ -28529,9 +28519,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Plotting at t=1.0\n" + ] + }, { "data": { - "image/png": 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Kba+KFStWaMeOHbr55puD5mLs37+/UlJSShz3JenGG28M+vd5553HcR8ADJCWlqbExES1atVKl112merUqaN3331XLVu21O7du/Xxxx/riiuu0N69ewPx7ddff1V6erq+//77wHQYDRo00Lp16/T9999X+L2L86Pj41vxaK2qOjZHzs3N1a5du9SzZ0/9+OOPys3NrdZrA/gdxUUbjR8/XkeOHCl17sXvv/9elmXp5JNPVmJiYtBj/fr12rFjR5Xf+8QTTyyxrKioSE888YROPvlkxcXFqXHjxkpMTNR//vMfWw6kt912mxo0aFDq3IvFweSPf/xjic/74YcfVujzNm3aVCeffHKgkPj555/rvPPO0/nnn6/t27frxx9/1JdffqmioqJAcXHnzp06cOCA2rdvX+L1TjnlFBUVFZWY4zLU9is2YMAA1atXTx988IESEhLKbfOxTj755KB/t2vXTtHR0SXmKDz+/Xfu3Kk9e/Zo1qxZJbZdcdJbvP2K54E8vhgY6vMfb9OmTWrevLkaNWpUqc9Vmp9++inke8fGxqpt27aBvxdr2bJliXY3bNhQv/32my3tAYDKatGiRbXuhPz9999r4cKFJY7daWlpklRm7OvZs6cuvfRSTZw4UY0bN9b//M//aPbs2UFz6P70009q3ry56tWrF/Tc4mlBjj/Ohltpx31JSklJKdGe4rl9j8VxHwDMMH36dC1atEhvvvmmLrroIu3atUtxcXGSpB9++EGWZen+++8vEeOKOyoUx7gHH3xQe/bs0R/+8Ad16tRJd999d9B8+aH89NNPio6OLjH9UkVymrJ8+eWXSktLU506ddSgQQMlJiYG5lOmuOgfn332mQYMGKDmzZsrKipK77zzTljfr3j+z2MfKSkpYX1PpzHnoo3atm2ra665RrNmzdLYsWNL/L2oqEhRUVF6//33Q/aSq1u3buD/S+s1VlhYGPK5x/dalKRHH31U999/v6677jo99NBDatSokaKjo3X77bdXaFL58hT3XpwwYULI3ovF7/HKK68oKSmpxN8renewHj16aPHixTp48KBWrlypBx54QB07dlSDBg30+eefa/369apbt67OOOOMKn+WUNuv2KWXXqqXXnpJr732mv7yl79U+T2k0r/X49+/eNtdc801GjZsWMjnFM836Wal9Ra1yrkpAQCES1nxIJTjb0RSVFSkPn366H//939Drv+HP/yh1NeKiorSm2++qaVLl+rf//63PvjgA1133XV6/PHHtXTp0qDzhKoo69wiUko77gMAnHf22WcH7hY9cOBA9ejRQ1dffbU2btwYyE/uuusupaenh3z+SSedJEk6//zztWnTJv3rX//Shx9+qL///e964oknNHPmTF1//fXVbmdF49mmTZvUu3dvpaSkaOrUqWrVqpViY2O1YMECPfHEE7bkxHCH/fv36/TTT9d1111X4oax4XLqqafqo48+Cvzb63dH9/anc8D48eP16quvavLkySX+1q5dO1mWpRNPPLHM5EI6ehX/+LvrSkev6Bw71Lcsb775pi644AK98MILQcv37NlToZuRVMTtt9+uadOmaeLEiSVuRlN81alJkyaBHhulKWsI7nnnnafZs2fr9ddfV2Fhobp3767o6Gj16NEjUFzs3r17IGFJTExU7dq1tXHjxhKvtWHDBkVHR6tVq1YV/ox/+9vfFBMTo5tvvln16tXT1VdfXeHnfv/990G9En/44QcVFRWFvLvZsYrvkFZYWFjutmvTpo3Wrl0ry7KCtmOoz3+8du3a6YMPPtDu3bvL7L1Y0SHSbdq0Cbz3sftpQUGBNm/eXO5nAQBThYrLBQUFJaZCadeunfbt21et490555yjc845R4888ojmzp2rwYMH6/XXX9f111+vNm3a6KOPPtLevXuDei9u2LBB0u/H4dI+g3T0PODYmB2qt2NVjvt//OMfg/62cePGMtsDADBXjRo1NGnSJF1wwQV65plndN1110mSatasWaEY16hRIw0fPlzDhw/Xvn37dP7552vChAmlFhfbtGmjoqIibdq0Kai3Yqicpqxc+Vj//ve/lZ+fr3fffVetW7cOLK/OdGRwpwsvvLDMG4bm5+frvvvu0//93/9pz5496tixoyZPnqxevXpV+T1jYmJCdrLyKoZF26xdu3a65ppr9Nxzzyk7Ozvob3/+859Vo0YNTZw4sUTPLMuy9Ouvvwa9ztKlS1VQUBBYNn/+/BLDectSo0aNEu/zxhtvBObCsENx78V//etfWr16ddDf0tPTlZCQoEcffTTk3FE7d+4M/H+dOnUkKWSQKB7uPHnyZJ122mmqX79+YPnixYu1YsWKwDrS0c/dt29f/etf/woafpyTk6O5c+eqR48elRreHBUVpVmzZumyyy7TsGHD9O6771b4udOnTw/699NPPy1J5d4JuUaNGrr00kv11ltvae3atSX+fuy2u+iii7R9+3a9+eabgWUHDhzQrFmzym3fpZdeKsuyNHHixBJ/O3bfqVOnTsjv5nhpaWmKjY3VU089FfT8F154Qbm5udW+2xsAOKVdu3Yl5kucNWtWiV4SV1xxhTIzM/XBBx+UeI09e/boyJEjpb7Hb7/9ViJud+7cWZICQ6MvuugiFRYW6plnngla74knnlBUVFSZ8aX4ot+xn2P//v166aWXSqxb0eN+165d1aRJE82cOTNo+Pb777+v9evXc9wHABfr1auXzj77bE2bNk0JCQnq1auXnnvuuZD3GDg2Pzk2r5WOjtA76aSTguLE8Yrj11NPPRW0fNq0aSXWbdeunXJzc4OGWv/yyy96++23g9Yr7nxybGzNzc3V7NmzS20H/Gn06NHKzMzU66+/rv/85z+6/PLL1a9fv0rNG3q877//Xs2bN1fbtm01ePBgZWVl2dhi89BzMQzuu+8+vfLKK9q4caNOPfXUwPJ27drp4Ycf1rhx47RlyxYNHDhQ9erV0+bNm/X222/rhhtu0F133SVJuv766/Xmm2+qX79+uuKKK7Rp0ya9+uqrJeagKMvFF1+sBx98UMOHD1f37t21Zs0avfbaaxXu+VhRt912m5544gl9++23gSKhJCUkJGjGjBkaMmSIzjzzTF111VVKTExUVlaW3nvvPZ177rmB5KhLly6Sjk7gm56erho1auiqq66SdLR7fVJSkjZu3Bi4IYp0tLv9PffcI0lBxUVJevjhh7Vo0SL16NFDN998s2JiYvTcc88pPz9ff/3rXyv9GaOjo/Xqq69q4MCBuuKKK7RgwYISPTRC2bx5sy655BL169dPmZmZevXVV3X11Vfr9NNPL/e5jz32mD755BOlpqZq5MiR6tChg3bv3q1Vq1bpo48+0u7duyUdvVnKM888o6FDh2rlypVq1qyZXnnlFdWuXbvc97jgggs0ZMgQPfXUU/r+++/Vr18/FRUV6fPPP9cFF1yg0aNHSzr6/Xz00UeaOnWqmjdvrhNPPFGpqaklXi8xMVHjxo3TxIkT1a9fP11yySXauHGjnn32WZ111llBN28BADe5/vrrdeONN+rSSy9Vnz599O233+qDDz4oMRLg7rvv1rvvvquLL75Y1157rbp06aL9+/drzZo1evPNN7Vly5ZSRw+89NJLevbZZ/WnP/1J7dq10969e/X8888rISFBF110kaSj8wBfcMEFuu+++7Rlyxadfvrp+vDDD/Wvf/1Lt99+e5nnCX379lXr1q01YsQI3X333apRo4ZefPHFQGw+VpcuXTRjxgw9/PDDOumkk9SkSZOQca9mzZqaPHmyhg8frp49e2rQoEHKycnRk08+qeTkZN1xxx2V3dQAAIPcfffduvzyyzVnzhxNnz5dPXr0UKdOnTRy5Ei1bdtWOTk5yszM1M8//6xvv/1WktShQwf16tVLXbp0UaNGjbRixQq9+eabgdwilM6dO2vQoEF69tlnlZubq+7du2vx4sX64YcfSqx71VVX6Z577tGf/vQn3XrrrTpw4IBmzJihP/zhD0E3Lu3bt69iY2M1YMAA/eUvf9G+ffv0/PPPq0mTJqXehBX+k5WVpdmzZysrK0vNmzeXdHT4/8KFCzV79mw9+uijlX7N1NRUzZkzR+3bt9cvv/yiiRMn6rzzztPatWtLzJvtGZG/QbV3zJ4925JkLV++vMTfhg0bZkmyTj311BJ/e+utt6wePXpYderUserUqWOlpKRYo0aNsjZu3Bi03uOPP261aNHCiouLs84991xrxYoVVs+ePa2ePXsG1vnkk08sSdYbb7xR4n0OHTpk3XnnnVazZs2sWrVqWeeee66VmZlZ4jU2b95sSbJmz55d5uct670yMjIsSVadOnVCPi89Pd2qX7++FR8fb7Vr18669tprrRUrVgTWOXLkiHXLLbdYiYmJVlRUlHX8rnn55Zdbkqx58+YFlhUUFFi1a9e2YmNjrYMHD5Z431WrVlnp6elW3bp1rdq1a1sXXHCB9dVXXwWtU9Z3WPyZdu7cGVh24MABq2fPnlbdunWtpUuXlrqtip/73XffWZdddplVr149q2HDhtbo0aNLtFWSNWrUqJCvk5OTY40aNcpq1aqVVbNmTSspKcnq3bu3NWvWrKD1fvrpJ+uSSy6xateubTVu3Ni67bbbrIULF1qSrE8++SSw3rBhw6w2bdoEPffIkSPW3/72NyslJcWKjY21EhMTrQsvvNBauXJlYJ0NGzZY559/vlWrVi1LkjVs2LCg7bd58+ag13zmmWeslJQUq2bNmlbTpk2tm266yfrtt9+C1unZs2fI30eoNgKA3UaNGlUi1pR2XLIsyyosLLTuueceq3Hjxlbt2rWt9PR064cffrDatGkTOCYW27t3rzVu3DjrpJNOsmJjY63GjRtb3bt3t6ZMmWIVFBSU2qZVq1ZZgwYNslq3bm3FxcVZTZo0sS6++OKgeFn8+nfccYfVvHlzq2bNmtbJJ59s/e1vf7OKioqC1gvVtpUrV1qpqalWbGys1bp1a2vq1Kkhj+XZ2dlW//79rXr16lmSAucNxecCx8YWy7KsefPmWWeccYYVFxdnNWrUyBo8eLD1888/B60zbNiwkOcJxTETAOCMsnKiwsJCq127dla7du2sI0eOWJs2bbKGDh1qJSUlWTVr1rRatGhhXXzxxdabb74ZeM7DDz9snX322VaDBg2sWrVqWSkpKdYjjzwSFANDHfsPHjxo3XrrrdYJJ5xg1alTxxowYIC1detWS5KVkZERtO6HH35odezY0YqNjbXat29vvfrqqyFf891337VOO+00Kz4+3kpOTrYmT55svfjiiyXi3vE5MrxLkvX2228H/j1//vxALePYR0xMjHXFFVdYlmVZ69evtySV+bjnnntKfc/ffvvNSkhIsP7+97+H++M5JsqyuHMCYLcJEyZo4sSJ2rlzp23zWwIAAAAAgKqLiorS22+/rYEDB0qS5s2bp8GDB2vdunUlbjxXt25dJSUlqaCgQD/++GOZr3vCCScoMTGx1L+fddZZSktL06RJk6r9GUzEsGgAAAAAAAD4zhlnnKHCwkLt2LGjxHRrxWJjY5WSklLl99i3b582bdqkIUOGVPk1TEdxEQAAAAAAAJ60b9++oPk7N2/erNWrV6tRo0b6wx/+oMGDB2vo0KF6/PHHdcYZZ2jnzp1avHixTjvttCrdnO6uu+7SgAED1KZNG23fvl0ZGRmqUaOGBg0aZOfHMgrFRQAAAAAAAHjSihUrdMEFFwT+PWbMGEnSsGHDNGfOHM2ePVsPP/yw7rzzTm3btk2NGzfWOeeco4svvrhK7/fzzz9r0KBB+vXXX5WYmKgePXpo6dKlZQ6bdrvoyj7hs88+04ABA9S8eXNFRUXpnXfeKfc5S5Ys0Zlnnqm4uDiddNJJmjNnThWaCrjHhAkTZFkW8y3CNaZPn67k5GTFx8crNTVVy5YtK3P9N954QykpKYqPj1enTp20YMGCUte98cYbFRUVpWnTptnc6uojpgGANxHXiGsAUKxXr16yLKvEo/h4V7NmTU2cOFGbN29WQUGBtm/frn/+85/q1KlTld7v9ddf1/bt25Wfn6+ff/5Zr7/+utq1a2fLZ3nssccUFRWl22+/vcz1pk2bpvbt26tWrVpq1aqV7rjjDh06dMiWNoRS6eLi/v37dfrpp2v69OkVWn/z5s3q37+/LrjgAq1evVq33367rr/+en3wwQeVbiwAwH7z5s3TmDFjlJGRoVWrVun0009Xenq6duzYEXL9r776SoMGDdKIESP0zTffaODAgRo4cKDWrl1bYt23335bS5cuVfPmzcP9MaqEmAYA3kNcI64BgBctX75czz33nE477bQy15s7d67Gjh2rjIwMrV+/Xi+88ILmzZune++9N2xtq9bdoo+/y04o99xzj957772g4HzVVVdpz549WrhwYVXfGgBgk9TUVJ111ll65plnJElFRUVq1aqVbrnlFo0dO7bE+ldeeaX279+v+fPnB5adc8456ty5s2bOnBlYtm3bNqWmpuqDDz5Q//79dfvtt5d7hc1JxDQA8Abi2lHENQDwjn379unMM8/Us88+q4cfflidO3cutQf96NGjtX79ei1evDiw7M4779TXX3+tL774IiztC/uci5mZmUpLSwtalp6eXmYgzs/PV35+fuDfRUVF2r17t0444QRFRUWFq6kAUCbLsrR37141b95c0dGV7vhdwqFDh1RQUGBDy0qyLKvE8TIuLk5xcXFBywoKCrRy5UqNGzcusCw6OlppaWnKzMwM+dqZmZmBeUqKpaenBw29Kioq0pAhQ3T33Xfr1FNPreanMQcxDYCX2BnXTIhpEnGtsohrALzEi3Gt2KhRo9S/f3+lpaXp4YcfLvP1u3fvrldffVXLli3T2WefrR9//FELFiwI692qw15czM7OVtOmTYOWNW3aVHl5eTp48KBq1apV4jmTJk3SxIkTw900AKiSrVu3qmXLltV6jUOHDql16zraubPIplYFq1u3rvbt2xe0LCMjQxMmTAhatmvXLhUWFoY8Tm/YsCHka5d2XM/Ozg78e/LkyYqJidGtt95ajU9hHmIaAC+qblwzJaZJxLXKIq4B8CI74lqr1nW0y4C4Jh2dw3HVqlVavnx5hV7/6quv1q5du9SjRw9ZlqUjR47oxhtvDOuwaCPvFj1u3Ligq4e5ublq3bq17l78R8XVMbLJAHwgf/8R/a33x6pXr161X6ugoEA7dxZpyddNVLeuvVf59+2z1Ct1h7Zu3aqEhITA8rKuhNlp5cqVevLJJ7Vq1Sp6MIiYBsBcdsU1L8c0ibh2vNLiWq+mwxUTHetgy8LrYIdmTjcBQDmOHDmkrz99zJa4tmtnkT5YmqQ6das/Yu1Y+/cVKf2c7ArHta1bt+q2227TokWLFB8fX6H3WLJkiR599FE9++yzSk1N1Q8//KDbbrtNDz30kO6//35bPsfxwp7VJCUlKScnJ2hZTk6OEhISQl4Jk0rvDhpXJ0bxdWuGpZ0AUFF2JhZ160apbj17A5Z09ApbQkJCUMAKpXHjxqpRo0bI43RSUlLI55R2XC9e//PPP9eOHTvUunXrwN8LCwt15513atq0adqyZUtlP5AxiGkAvMiuuOZ0TJOIa5VlZ1yLiY71bHHxYMcWZvbKARCSXXGtTt3oMMS1oyoa11auXKkdO3bozDPPDCwrLCzUZ599pmeeeUb5+fmqUaNG0HPuv/9+DRkyRNdff70kqVOnTtq/f79uuOEG3XfffbZM8XW88GylY3Tr1i1oEklJWrRokbp16xbutwYAlCM2NlZdunQJOk4XFRVp8eLFpR6nyzuuDxkyRP/5z3+0evXqwKN58+a6++67XX/3SWIaAJiNuFY5xLXyHezYwukmAPCx3r17a82aNUExqGvXrho8eLBWr15dorAoSQcOHChRQCxerxr3dC5TpS/A7Nu3Tz/88EPg35s3b9bq1avVqFEjtW7dWuPGjdO2bdv08ssvS5JuvPFGPfPMM/rf//1fXXfddfr444/1j3/8Q++99559nwIAUGVjxozRsGHD1LVrV5199tmaNm2a9u/fr+HDh0uShg4dqhYtWmjSpEmSpNtuu009e/bU448/rv79++v111/XihUrNGvWLEnSCSecoBNOOCHoPWrWrKmkpCS1b98+sh+uHMQ0APAe4hpxzS4UFgE4rV69eurYsWPQsjp16uiEE04ILD8+rg0YMEBTp07VGWecERgWff/992vAgAEhi5F2qHRxccWKFbrgggsC/y6eb2PYsGGaM2eOfvnlF2VlZQX+fuKJJ+q9997THXfcoSeffFItW7bU3//+d6Wnp9vQfABAdV155ZXauXOnHnjgAWVnZ6tz585auHBhYIL3rKysoCtf3bt319y5czV+/Hjde++9Ovnkk/XOO++UCHpuQEwDAO8hrhHXAMBPjo9r48ePV1RUlMaPH69t27YpMTFRAwYM0COPPBK2NkRZ4eoTaaO8vDzVr19f45f2ZX4qAI45tO+wHj7nQ+Xm5lZofoyyFB/XVqxravs8Hvv2FqnrqTm2tBP2I6YBMIVdcY2Y5m/F339as794as5Fei0C7nPkyCF9uXiCbXHti7XNwxLXenTc7rm4FvY5FwEAAAAAcAsKiwBQORQXAQAAAAAAAFQJxUUAAAAAAESvRQCoCoqLAAAAAADfo7AIAFVDcREAAAAAAABAlVBcBAAAAAD4Gr0WAaDqKC4CAAAAAHyLwiIAVA/FRQAAAACAL1FYBIDqo7gIAAAAAAAAoEooLgIAAAAAfIdeiwBgD4qLAAAAAABfobAIAPahuAgAAAAA8A0KiwBgL4qLAAAAAAAAAKqE4iIAAAAAwBfotQgA9qO4CAAAAADwPAqLABAeMU43AAAAAACAYx1fCKy1dptDLQEAlIfiIoy1KDvF6SaUq0/SBqebAABwCdPjGjENgMkOdmxRrQIjvRYBIHwoLiIiTE+oqqoqn4vkDQDcz4txraqfibgGwG6lFQKrWmCksAgA4UVxEbbwYpIVLhXZViRqAOAcYlrllLe9iGkAKqO8QmBlC4wUFgEg/CguosJItiKntG1NggYA9iCmRU5Z25q4BuBYFS0EVneINADAXhQXUQIJl7koOgJA5RHXzEVcA1BVFSkw0msRTvmtfWyZf2+4sSBCLQEig+Kiz5FweUOo75HEDIAfEde84fjvkZgGeF9VCoHFzwlVZKSwiHApr3BYmdegyAivoLjoMyRd/kFiBsAPiGv+wEU0wNuqWwhkmDSqyo5CoV3vT6ERbkZx0QdIvCBRbATgDcQ0FDt2XyCmAe5lVw/DYwuM9FpEMaeLh5VBb0a4GcVFDyLxQkWQlAFwC+IaysMFNMCd7C4CUlT0LzcVEctDkRFuRHHRI0i8UB0UGgGYhriG6iCuAYB3eamQWBaKjHATiosuR/IFu5GQAXAKMQ3hULxfEdMAs9DLEBXhl0JiWX5rH0uBEcajuOhCJF+IFBIyAJFAXEMkcPEMAMxHMTE0ejHCdBQXXYTkC06hyAggHIhrcApxDQDMQDGxcigywlQUF12A5AumIBkDUF3ENJiEuAYAkUdBsfoYKg3TUFw0GAkYTEUyBqCyiGkwGXENAMKLgqL9KDDCJBQXDUQCBrcgGQNQEcQ1uAVxDQDsQ0Ex/BgmDVNQXDQIyRfcimQMQCjENbgVcQ0AqoaCojPoxQinUVw0BAkYvIBkDIBETIN3LMpOIaYBQDkoKJqBAiOcFO10A/xuUXYKSRg8h30a8C9+//AaztUAILTf2sdSWDQM3wecQnHRQZyowsvYvwF/oQADr2P/BoCjKCqaje8HTmBYtAM4OYVfMEwa8AfiGvyCYdIA/IpilfswTBqRRM/FCCMBgx+x3wPexe8bfkMvXQB+Qi84d+O7Q6RQXIwgTkThZ+z/gPfwu4afsf8D1VNr7Tanm4AyUFT0Dr5HRALDoiOEE9Dq2fJzotNNCEhuudPpJrgWw8kAbyCmVR9xzRuIawC8hkKUNzFEGuFGcTECSMLKZ1KSVZ7y2kqSVjYSMcDdiGkVQ1zzD+IaAC+gqAigOiguhhlJWDA3JVtVVdpnJDn7HTd6AdyJmFYScQ0SBUYA7kZh0R/ovYhworgYRn5PwvyQcFVGqO1BYgbALfwe0yTi2vGO3x5+j2kUGAG4DUVF/6HAiHChuBgmfkzCSLoqz++JGYkY4A5+jGkSca2yuIhGXAPgDhQV/Y0CI8KB4mIY+CkJI/Gylx+LjSRiAExBTLPfsdvUDzFNIq4BMBuFRUgUGGE/ios283phkcQrsvySlJGIAeYirsEufolpEnENgHkoKgIIJ4qLNvJyAkby5TyvJ2UkYoB5vBrXiGnO83pMk4hrAMxBYTFy9rYrKrGs3qZoB1pSPnovwk5m7uUu5MUEbMvPiYEHzOLV78WLvyO3mD59upKTkxUfH6/U1FQtW7aszPXfeOMNpaSkKD4+Xp06ddKCBQuC/m5Zlh544AE1a9ZMtWrVUlpamr7//vtwfgTYzIu/R68eO92O8w2EA3ENOFo8orAYPnvbFZV4lLaeqdg/YBeKiwjCCb67ePH78mJBw3Tz5s3TmDFjlJGRoVWrVun0009Xenq6duzYEXL9r776SoMGDdKIESP0zTffaODAgRo4cKDWrl0bWOevf/2rnnrqKc2cOVNff/216tSpo/T0dB06dChSHwvV4KXfoRePk17mte/KS78lNyGuucPBji2cboKnUTSyV0ULiWU931TsK7ADxUUbeOHE0Wsn837Ed4iqmjp1qkaOHKnhw4erQ4cOmjlzpmrXrq0XX3wx5PpPPvmk+vXrp7vvvlunnHKKHnroIZ155pl65plnJB3t3TFt2jSNHz9e//M//6PTTjtNL7/8srZv36533nkngp8MfsYx0d28VBT2wnmi2xDX4Gf0Vqy6UAXEqhQSy3p9wKsoLlaT208YvXLijt954Tt1++/KFHl5eUGP/Pz8EusUFBRo5cqVSktLCyyLjo5WWlqaMjMzQ75uZmZm0PqSlJ6eHlh/8+bNys7ODlqnfv36Sk1NLfU1YQ63//68cAxEMC98p27/XZmgIjFNIq65Bb0Ww4OiYsWEs4BY0fc2DfsOqosbuviU20/SUb7i79itE+X7ZSL8d/aernirpq2veWjfYUkfqlWrVkHLMzIyNGHChKBlu3btUmFhoZo2bRq0vGnTptqwIfT2z87ODrl+dnZ24O/Fy0pbB2ZycwGEuOZ9bo9rfuB0TJOIa25AYTE8KA6FZmIhTzraLtNu9MINXlAdFBerwY1JGMmX/5CM+dfWrVuVkJAQ+HdcXJyDrQHCg7jmP26Na365aBYuxDRvoLBoP4qKwUwtJoZiYoERqCr25CpyW2HRC0OKUD1u3Afc9jszTUJCQtAjVCLWuHFj1ahRQzk5OUHLc3JylJSUFPJ1k5KSyly/+L+VeU04z22/Nzce02Avvn9/qUhMk4hrJqOwaD8Ki0dFemiznUxrM/sUqoriog9w8o1jkZDjWLGxserSpYsWL14cWFZUVKTFixerW7duIZ/TrVu3oPUladGiRYH1TzzxRCUlJQWtk5eXp6+//rrU14Sz3FRY5BiGY7ltf3DTb82tiGtmorBoPz8XgZyYKzGcvPAZAIqLVeCWE0O3nXAjstyyb7jl9+ZmY8aM0fPPP6+XXnpJ69ev10033aT9+/dr+PDhkqShQ4dq3LhxgfVvu+02LVy4UI8//rg2bNigCRMmaMWKFRo9erQkKSoqSrfffrsefvhhvfvuu1qzZo2GDh2q5s2ba+DAgU58RHiEW45biDw3nfMQ18KPuGYWCov282Nh0UvFxFBM+lx+3L/c5LHHHgvEpbK88cYbSklJUXx8vDp16qQFCxaEtV3MuehRbjnBhrPcMm8V81SF15VXXqmdO3fqgQceUHZ2tjp37qyFCxcGJq7PyspSdPTv16K6d++uuXPnavz48br33nt18skn65133lHHjh0D6/zv//6v9u/frxtuuEF79uxRjx49tHDhQsXHx0f886Fsbih0ENNQUVt+TjQ+piH8iGvmoLBoP78VfkwquoUbczCiPMuXL9dzzz2n0047rcz1vvrqKw0aNEiTJk3SxRdfrLlz52rgwIFatWpVUGyzU5RlWVZYXtlGeXl5ql+/vsYv7av4uvbega6yTE/CSMBQVaYnYyYUFw/tO6yHz/lQubm5QZPKV0U4j2t2thP2MymmScQ1eBdxrXx2xQtimr8Vf/9pzf6imOjfC18UFu3lp6KinwqKoZhSYHTjnaOPHDmkLxdPsC2ufbG2uerWs/f72Le3SD06bq90G/ft26czzzxTzz77rB5++GF17txZ06ZNC7nulVdeqf3792v+/PmBZeecc446d+6smTNnVvcjhGTGXgtbkIChOkzff0wvgABuZPLvyk3DXGEm9h/AORQW7eWXwqKXhz1XBtvAH/Ly8oIe+fn5Za4/atQo9e/fX2lpaeW+dmZmZon10tPTlZmZWa02l4Vh0ZVgehIGVJdbhkkD8DZiGuxi8jBppvyAV1FYtJfXC4sU0kIzYYj0b+1jXdl70U6v70lV3BF7e+Tn7zss6W21atUqaHlGRoYmTJgQuh2vv65Vq1Zp+fLlFXqP7OzswFQgxZo2bars7OyqNLlCKC66HAkYwsHUZIxEDLCPqRfMiGuwGxfOgMihsGgvLxcWKSrC77Zu3Ro0LDouLq7U9W677TYtWrTI6Hl+GRZdQSYmYSRgCCf2LwCRxnEH4WTi/mXi+SVQVQc7NHO6CZ7i1cIiQ58rzoTt5NX90AQJCQlBj9KKiytXrtSOHTt05plnKiYmRjExMfr000/11FNPKSYmRoWFhSWek5SUpJycnKBlOTk5SkpKCstnkSguupaJJ8jwHvYzwJtMLGhwvEEksJ8BcAMvFnQoKlYN2wy9e/fWmjVrtHr16sCja9euGjx4sFavXq0aNWqUeE63bt20ePHioGWLFi1St27dwtZOhkVXgGlJmN9PjOOyIh9s81v7d64J04ZIMzQa8Ba/xzSJuBZpxDUAJvNaYZHiWPU5Pf8icy86q169eurYsWPQsjp16uiEE04ILB86dKhatGihSZMmSZJuu+029ezZU48//rj69++v119/XStWrNCsWbPC1k6Kiy7jpyTMiWSrNKW1xS/JmWmJGABvIKY5h7hGXANgHi8VFikq2svpAiPMlpWVpejo3/eP7t27a+7cuRo/frzuvfdenXzyyXrnnXdKFCntRHGxHCb1WvRyEmZa0lVRodrt1cTMpESMXh5A1ZkS17wc0yTimhuYFNcAwCuFRYqK3kTvRbMsWbKkzH9L0uWXX67LL788Mg0SxUXX8FoS5takqyKO/2xeSspIxADYwWsxTSKuuZUpcY2LZoC/UVhERdB7ESZjz3QBryRhcVmxgYefeO1zm7I/mtL7CnATfjf28trxvaK89rlNiWsA4FbcrCVy2M4wFcXFMpiQhLn9hNdrCUh1eWV7uH2/BOActx8/vHIct4tXtocJ+6UJ550AIs/NvRYpKvqLm/dVhB/Dog1mwoluVbg9wYiUY7eTG4eYmTKUDIB7ENe8rXg7uTGmScQ1AJHn5mINRUXnMDwaJmKPNJQbEzAv9Fxwilu3ndP7Kb08gIpz+vfi9PGiKtx6bHaam3szunE/BeBObi0s0lvRDE59B27dbxF+FBdL4WQS5rYTW7cmECZy47Z02/4KIPLcdJxwc2HMRGzLynH6IgCAyHBrgYaiIoDSMCwaVUayED5uGzLNUDIApXFLYZGYFl5uGjJNTAMQTm4sLFJUDK3uibkllu3bXD9i78/waJiEPdEwbkjC6IUQWWzrstHLAygfv5PSEdMiyy3b2w3nYwDch8Kiu9U9MTfoUdo6keTE9+PG/RjhR3ExBKeSMNNPZN2SEHiRG7a96fsvgMgz/bhg+nHVy4hrpeNiAABTUFhUucXE0p4D+A3Dog1hcgJm+sm/n5g+rIyhZACKEddQEabHNQCwi5t6e/m9qGhHcbDuibkRGyLN8GiYgOIiykQCZiaSsWCLslPUJ2mD080AjORELyhTC4vENHPFZcUaGdO4aAbADhQWzUQPw6r7rX2sGm40L27DOZS3j0MSdpQbhivBzETZxP0ZAEw8XiKYqecexDUA1UFh0RwVmTPR7veLFK9/dzAfxUWHmXjCauKJPUpnYjJm4n4NIDJM+/2beIxE2fi+mHcR8AoKi86LZDGxtPcH/IDiIgJIwNyN7w7A8SJdoDCxsAh3Mu2cxLR9GwDssrddkacKi5HunVgRkWpHpL9HNxXPEX4UFx1k0omqSSfwqDqTvsdI79/08gBwLJOOh6g6k75Hk87bAJjPDYUXLxYVTWVy2wA7VKm4OH36dCUnJys+Pl6pqalatmxZmetPmzZN7du3V61atdSqVSvdcccdOnToUJUaHE5+LU6YdOKO6jOptweJGNzCq3Etkkz5vZt0DIQ9/Pp9+vW8FNVHTHMehcXIMK2Hogm88L3CnSpdXJw3b57GjBmjjIwMrVq1SqeffrrS09O1Y8eOkOvPnTtXY8eOVUZGhtavX68XXnhB8+bN07333lvtxruZCUkYCZi38d0CFUNc8w6Oe95lyndrwvkbUBZiGirC7QUotxYU3djm8rihkI7IqHRxcerUqRo5cqSGDx+uDh06aObMmapdu7ZefPHFkOt/9dVXOvfcc3X11VcrOTlZffv21aBBg8q9guZlJpyYmnKSjvAy4Xs2YX8HyuLVuBbJXk8m/M5NON4hvLgoCpTPqzHNTUwvtri1sOiVXoqRaL9bv2O4W6WKiwUFBVq5cqXS0tJ+f4HoaKWlpSkzMzPkc7p3766VK1cGAtSPP/6oBQsW6KKLLqpGs1EdnJj7i5++b4aQobKIa97gp+McnP++TSimA6EQ05xHYdF+XigoAn4QU5mVd+3apcLCQjVt2jRoedOmTbVhw4aQz7n66qu1a9cu9ejRQ5Zl6ciRI7rxxhvL7Gqfn5+v/Pz8wL/z8vIq08wqiVRRwukTUqdPyOGMuKxY5bcucOz9t/ycqOSWOx17f6A0kYhrTsS0SCKuwQl+iWuLslPUJyn0sQg4npdzNdOZXlSU3FdY9HJBse6Judq3uX5Y32NvuyLV28T9exE5Yd/blixZokcffVTPPvusVq1apX/+859677339NBDD5X6nEmTJql+/fqBR6tWrcLdTF8gAfM3vn/AHpWNa16OaRQW4SS+f6D6yNWqj8KivfzSU9FLn9ENvwGEX6WKi40bN1aNGjWUk5MTtDwnJ0dJSUkhn3P//fdryJAhuv7669WpUyf96U9/0qOPPqpJkyapqCj0QW7cuHHKzc0NPLZu3VqZZhrLySSME3BIzu4HThchgFAiEde8GtOcRlyDRFwDjkWuFnluKKq4pbDol6JiJLnlu4c3VKq4GBsbqy5dumjx4sWBZUVFRVq8eLG6desW8jkHDhxQdHTw29SoUUOSZFlWyOfExcUpISEh6IGqIwHDsby+PzDvIiojEnHNiZgWid8BF8xgCvYH4ChytciisGgPPxcV/fq54U2VmnNRksaMGaNhw4apa9euOvvsszVt2jTt379fw4cPlyQNHTpULVq00KRJkyRJAwYM0NSpU3XGGWcoNTVVP/zwg+6//34NGDAgELic5uUkzOsn3PV+Cn3SY4e9baLC9tpOc2quKuZehIm8GNe8jLhWdcQ1+xHXYBpiWmRQWLQHxbXwz7/I3IuIlEoXF6+88krt3LlTDzzwgLKzs9W5c2ctXLgwMHFwVlZW0NWv8ePHKyoqSuPHj9e2bduUmJioAQMG6JFHHrHvUyAkryVg4Uy4Kvp+XkrMnJ4MHzAFcc09vBTXIh3TSntP4pr5uKkLKoOYFn4UFquPoqL3/NY+Vg03ei8Go+KirNL6uxskLy9P9evX1/ilfRVft6btrx/unotO9Fr0QgLmROJVWV5IypxIxMLdyyNcSdihfYf18DkfKjc3t9pDgMJ5XLOznbBfuGOaRFwzFXEtMiId1yLRc9H0uEZM87fi7//c3hMUExPvdHPCisJi9VBULF04ey9GqueiCcXFI0cO6cvFE2yLa6O/+JPibI5r+fsO65keb3surlW65yLM5+YEzA2J17GOb68bkzKv9vQA/MqL8466Na65LaZJwW12Y0xzAkOjAX+gsFg9FBadw9BoRAJ7GBxX7ycr8HA7t36OSCfu4e715MXiCmCKSPdadGNh0a2x4Hhujc9u3GcAmI3CYtX5+YYtlcE2gtv5vrjotaFjbjmhdmvCUlFe/3wAEAluiWmS94/7bvtsXrtoBsA5FBarjoJZ5YRze0ViH3HDbwXh4/viope4IQlzW3JiB7d8ZjfsPwCcRQGlJLcc4+3ipiKql+IaPfIBlIbCIgATUFwMo0gmYaafQLslEQknN2yDSO5HFCkAlIW4Zj43bAPiGoDqML0nlomFRYZBVw/bDm5FcRFh5YbEI9JM3yamJ/QAvM/k45Dpx3AnsE0AeJHphUUTURgzm4nFaHgHxUUPMDEJI9EoH9uIXh6A3cI5dDJSv1cTY5rEMbsiTN0+pu5TAMzlhsKiaYUivxQW+7beGHiEi1+2JbyF4mKY+DkJMzW5MJWJ28vE/aqymJ8KgB0oKlaOqdsrUnGNi2aA+1FYrBw/DIMuraAYzgKjW7nh94PwiHG6AU5ye/HBtAKQicmEWxRvu71tohxuye/ismKV37rA6WYAMIBfL5gR16rOxLgGAF5gWmHRq5wuHNY9MVf7Nte3/XX3titSvU30MYP92KvCwI9XrUnA7OHH7ejH3wuAkkwqLJra+86NTNqOJu1jVeH2i+KAG5je64rCYvhVdsiz00VIwBQUF13KlBNkEjD7mbRNTdnPACBSTDn+eolJ2zQScY2LZgC8zmuFxerOoxiuAqPXtjO8zdfDolE9JiULXlTvJ8uI4WQMjwb8LRKFElMuZBDXwodh0gBMR6/FivFCwcvvvQ0ZGo1wYI+ymV+SMBKwyPDLdqaXh3vs3r1bgwcPVkJCgho0aKARI0Zo3759ZT7n0KFDGjVqlE444QTVrVtXl156qXJyckKu++uvv6ply5aKiorSnj17wvAJvIshk1VnUo9xrzNhO5twHgVzENfgBiYUFt1+45Zw3+XZ7wVLgOKiy5hwQmxCYuAnJiS9Jux3VUGxxX6DBw/WunXrtGjRIs2fP1+fffaZbrjhhjKfc8cdd+jf//633njjDX366afavn27/vznP4dcd8SIETrttNPC0XQYyunji9PHVz/ywzbnopl7ENcgmd1r0ZTCohuFu6AYCW7d9vAf3xYXKTpUnglFLj9j28Np69ev18KFC/X3v/9dqamp6tGjh55++mm9/vrr2r59e8jn5Obm6oUXXtDUqVP1xz/+UV26dNHs2bP11VdfaenSpUHrzpgxQ3v27NFdd90ViY+DCgh3gYTCon85ve2d3vdgBuIaUD63FbecLCi6qYhpQtEa3uLb4mI4eDkJczoJwFFOfg/h3v/o5WG+zMxMNWjQQF27dg0sS0tLU3R0tL7++uuQz1m5cqUOHz6stLS0wLKUlBS1bt1amZmZgWXfffedHnzwQb388suKjiY0IfyIa85z+qKlGwuMXBy3F3ENEr0Wy+KmwqIpPRTD0QY3fQ/wLyIdykUCZha+D1RUXl5e0CM/P79ar5edna0mTZoELYuJiVGjRo2UnZ1d6nNiY2PVoEGDoOVNmzYNPCc/P1+DBg3S3/72N7Vu3bpabYR7cMEMxbz6fXDRzF52xzSJuAazUVisGFOKioDfcbdol3AqCfPqCb/bOXUnae4cbb9Pcv6gmH1xtr7mkf35kj5Uq1atgpZnZGRowoQJJdYfO3asJk+eXOZrrl+/3sYWBhs3bpxOOeUUXXPNNWF7D6AYcc1MxDVvMCGmScQ1VJypvRYpLJbP5IJi39Yb9WFWe1tfs+6Judq3ub6trxkuv7WPVcONxFa/obiIUpGAmc2LidiWnxOV3HJnWF7bj7Zu3aqEhITAv+PiQid8d955p6699toyX6tt27ZKSkrSjh07gpYfOXJEu3fvVlJSUsjnJSUlqaCgQHv27Anq5ZGTkxN4zscff6w1a9bozTfflCRZ1tFjT+PGjXXfffdp4sSJZbYN4RHOXldcMEMoTsU1uENFY5pEXIO7UVgsm8lFRcDPKC7axGtJGAmYO5CIoSwJCQlBiVhpEhMTlZhY/jGsW7du2rNnj1auXKkuXbpIOppAFRUVKTU1NeRzunTpopo1a2rx4sW69NJLJUkbN25UVlaWunXrJkl66623dPDgwcBzli9fruuuu06ff/652rVrV267wDxsFUFccwcn4hoXzdyhojFNIq6hYkzttegkkwuLbisqhqP3ImAyiosogQTMXSgwlm1Rdor6JG1wuhmecMopp6hfv34aOXKkZs6cqcOHD2v06NG66qqr1Lx5c0nStm3b1Lt3b7388ss6++yzVb9+fY0YMUJjxoxRo0aNlJCQoFtuuUXdunXTOeecI0klEq1du3YF3u/4Oa3gflwwA2AK4hpM42SvRVMLi24rKoaTm4ZGw3+4oYvhIp2EkYC5U6S/NzfeYRP2eO2115SSkqLevXvroosuUo8ePTRr1qzA3w8fPqyNGzfqwIEDgWVPPPGELr74Yl166aU6//zzlZSUpH/+859ONB8V5KUbURDX3MeJ74y45l/ENZiCwmIwL9yoxfT2Oz0EH97iy56Ldg8f81ISBvfySg9GhpCZrVGjRpo7d26pf09OTg7MLVUsPj5e06dP1/Tp0yv0Hr169SrxGvAGLpihorwS02A+4po/MST6d6YWFgG4Cz0XDUYSBpPRywOAyYhp7ueVXvl2X4RmjlXAe+hBdpQXeisez2ufBygNxUVIIgnzCr5HAICXENcA2M20XosMhz6KIhzgbhQXDRXJXmGcuHtLJL9Pt/TyAFBx4fr9EddQVXyfALyKwqI3eysez87PZ8r3BhyP4mI1ub0Iwgm7N/G9AvArjn+oDqb8AOAHphSovF5UBPzElzd0gTfV35Rf7dfIbRdnQ0vMEKnJ8OOyYpXfuiDs7wMgNDfMvxapgo3XCovEtd+5/QYv3KwMMINJQ6Kd6rVIYRFAOFBcNBBJWMXYkXRV5DW9kpj52aLsFPVJ2uB0MwCgVOGIaaFe180xjYtmALzCzzdwoagIeBPDon3KjYXF+pvygx5OvK/buPF7BuCccEz1wQWz0jkd04hrAPzEpF6LTnG616KfC4t+/uyonhkzZui0005TQkKCEhIS1K1bN73//vulrv/888/rvPPOU8OGDdWwYUOlpaVp2bJlYW+n74qLpg8fY66fkkxKgNyYkEUiEQvHfuv2+UwBRI6bCk6mxRGT2gIAfuDXXosU14CqadmypR577DGtXLlSK1as0B//+Ef9z//8j9atWxdy/SVLlmjQoEH65JNPlJmZqVatWqlv377atm1bWNvJsOhqcGvxww1JmBsSneI2unmIGQAgMkyPa8e2z/S4Fonh0eEYGs28iwCc5FSvRYqKQPUMGDAg6N+PPPKIZsyYoaVLl+rUU08tsf5rr70W9O+///3veuutt7R48WINHTo0bO30Xc9FvzO9sOjGHhRuaLNbey8CcLdIHBeIa/ZzY5v9xvSROIBpTBkS7bdeixQWw8POQrHf9kmT5OXlBT3y88s/9yosLNTrr7+u/fv3q1u3bhV6nwMHDujw4cNq1KhRdZtcJnouGsTPxRkvJDH0ZASAyDK5sOiVuGZqTHNr70UA/uVkEceJXosUFuEFH/98smrUtvdcqPDA0XPEVq1aBS3PyMjQhAkTQj5nzZo16tatmw4dOqS6devq7bffVocOHSr0fvfcc4+aN2+utLS0arW7PBQXfcTUJMwLCdixTC0yRuoum3ZiCBkQOW6d6sM0xLTIcWNcAwA/oLAYWt/WG/VhVnunmwFDbN26VQkJCYF/x8WVfq7Vvn17rV69Wrm5uXrzzTc1bNgwffrpp+UWGB977DG9/vrrWrJkieLj421reygUF6uIJKz6vJaAHc/EHh/hTsTo5QGgWLh745t4wczLcc3kIiMAlMWEIdF+GnrqpcLikIZfSZJe+a27wy2BFxXf/bkiYmNjddJJJ0mSunTpouXLl+vJJ5/Uc889V+pzpkyZoscee0wfffSRTjvtNFvaXBaKi4bwWxLm5QTsWCRjAOB9folpknkXztx20Ywe+QAizakbubhZcVERMFVRUVGZczT+9a9/1SOPPKIPPvhAXbt2jUibuKGLD5hUWPTrJPEmfWaT9gcA1ePXmzqYdBwz6fgeKX6N5QDch16LkeWFXouhCosUG+GkcePG6bPPPtOWLVu0Zs0ajRs3TkuWLNHgwYMlSUOHDtW4ceMC60+ePFn333+/XnzxRSUnJys7O1vZ2dnat29fWNtJz0VEjN8TEdN6e4QLQ6MB+OUGZcQ1M+Iacy8CQGiR7LXo9sIiBUSYaseOHRo6dKh++eUX1a9fX6eddpo++OAD9enTR5KUlZWl6Ojf+w3OmDFDBQUFuuyyy4Jep6wbxtiB4qIBwpmEmdK7w+8JWDFThkm7KRGzawjZouwU9UnaYEOLAO9x0zzCJsQ1YtrvTCkwhhMXzQBUlZ96LboZhUWY7IUXXijz70uWLAn695YtW8LXmDIwLLoK3JSEmYAkrCS2CQBUHoVFM5mwTUzYNwCYxYQh0U6i12L5hjT8KuKFRbduK6A8viou+m1uKqdPtJmTqWxOb5tw7h9+GRIJoCQv//6dPm6bjG1TcVykBvzBL70W3Vosq2xRkd6NQNl8VVw0kVeTMJKMimE7AYA7cLwun9PbyOmLqgBQzOnCIneILpsXCoV8xzANxUWPcvIE2+nkwm2c3F4kYgDcgrjmDl7dViZeDPbbiBygMvw+JDpS3NZr0Ylh0IBfcEMX2MqrSUW4+WFCfABmsnOIpokFmOoirlWekzHNTTcsA+BN9Fo0DwVFIPzouehBTvXuIAGrHqe2X7j2FzuLDMxPBfgbcc192HYAEHncxCUYPRWByKG4WEn08EA4kYwBgBk4HlcfF80AOMHJIdFO9lqkx2KwcBQVKVQCpaO4CFuQhNnHiW3J3IsATOXE8YmYZh+2ZWj0yAfgZqb3WqQICEQexUWPIQkDADiBXl0oDRfNACC86LX4OwqLgDMoLjrEK0kYhcXw8Mp29cp+DpjID3eK5YKZd7BdAUSCH4dEU1h0H9N7fgJVQXHRQyKdhJEohFekty+9PAD/YWhmMOJaeHlh+3LRDADMRa9FwDkUFwGDeSERs4sdRRA/9PQCUDUcb72Hi2YAIoVeiwD8juIiqoQkzJtIxABURTh6c0XyeERMixy2NQDYg8JiMHotAs6iuFgJJg8fIwnzLrdvb4aQAQCOFcm4xkUzwD+cmm/RqV6LTmG+QACh+Ka4aNJwSDcXW9xe6HIrtjsAP+GCGUxm13mcyRetAZiNXovOoYckEJpviotexlV52In9CYBfUFh0DtsegBc40WuRwmJJFPwA51FcRIWRCDiL7Q8A8JJIxTUvXzQzaWQO4CSnhkTDf175rbvTTQCMRHERcBG3FhjdPBUAgLLZ/fuOVCHIrcdTAIB/0WsRgKkoLkYYSRjcwMu9PAAcxXxvMAHnFwDcym83cjEVQ6IBM1BcRLk48TeLn78PiiEA7ODn46hf2X3RjB75AAAAv6O4WEEUNQAACC96TfsTxV4A1eHEfIv0WgSAYBQXUSZO+M0Uie+FJB9AedzYe4u4Zia+FwAoG/MtAjAZxUUXo/gDN3FjEQKAt1DA8jcTz5sYGQO4D70WzcF8i4A5KC5GkNuKKyRhZuP7AQB4CXENAADAnSguAiiVib08AHhTuI83FK5gN7ddNAa8yIn5FgEAJVFcdCmSMEh8T4BfLcpOcboJQFiEO65x0QxAdTAkGgBCo7gIAIDP+G2eNy7EAADcjJu5mOGV37o73QTAWDFONwDm8UISFrvh5wqvW5DSMowtCb/6m/KV2y7O6WZEzJafE5XccqfTzQB8z84hofQmK5+f4hoAAOXhZi6AWSguwhMqk3SV91ySsmD1frK0t02ULa8VlxWr/NYFtrxWVS3KTlGfpA2OtgEAymNXXHNjTPPbRTMAVcN8i3CrD7PaO90EwHYUFyvAjuFj9PAIj+okX+W9ppsSMhIxAAjNbb3x7Y5rbi80hoOdF80AwE/6tt7odBMAGIo5FxHELUlY7Iafw1JYjPR7AG6ze/duDR48WAkJCWrQoIFGjBihffv2lfmcQ4cOadSoUTrhhBNUt25dXXrppcrJyQlaZ/ny5erdu7caNGighg0bKj09Xd9++204PwpgFOJaMLecj3DHaPcjrgEAUH0UF+EqTiRGbknG3JKIwd0GDx6sdevWadGiRZo/f74+++wz3XDDDWU+54477tC///1vvfHGG/r000+1fft2/fnPfw78fd++ferXr59at26tr7/+Wl988YXq1aun9PR0HT58ONwfCQ4LZ298NxwXiWvw2w2WTENcg+m4mQsAN/BFcXFRdorTTXAF05MwpxMhPydjDMWHJK1fv14LFy7U3//+d6WmpqpHjx56+umn9frrr2v79u0hn5Obm6sXXnhBU6dO1R//+Ed16dJFs2fP1ldffaWlS5dKkjZs2KDdu3frwQcfVPv27XXqqacqIyNDOTk5+umnnyL5EYGIMSGmOP3+5TH9vATuR1xDZextV+R0ExzHXIEASuOL4qKX+LHIY0ICdiyT2gJEUmZmpho0aKCuXbsGlqWlpSk6Olpff/11yOesXLlShw8fVlpaWmBZSkqKWrdurczMTElS+/btdcIJJ+iFF15QQUGBDh48qBdeeEGnnHKKkpOTw/qZUDUMBa0ek+KISW2JJD+eT6Ek4pp7cTMXRNorv3V3uglB9m2u73QTgCAUF2E0U5MeU9vlhl4eFCUiJy8vL+iRn1+9/SM7O1tNmjQJWhYTE6NGjRopOzu71OfExsaqQYMGQcubNm0aeE69evW0ZMkSvfrqq6pVq5bq1q2rhQsX6v3331dMDPcdQ9WYejw0MX6YdhEPCMXumCYR1wBEHr0/4VVEtwhwQzHFxCTM9EQndsPP3HkT1Za1vbGia8Xb+ppFBw9Jklq1ahW0PCMjQxMmTCix/tixYzV58uQyX3P9+vW2te94Bw8e1IgRI3Tuuefq//7v/1RYWKgpU6aof//+Wr58uWrVqhW294az/NZ7jLhWefU35Su3XZzTzTDaouwU9Una4HQzJJkR0yTiGhAu3C0aFdFwY4HTTYADKC7CSKYnYMVMTMTCpd5Plva2iXK6GaiErVu3KiEhIfDvuLjQCfqdd96pa6+9tszXatu2rZKSkrRjx46g5UeOHNHu3buVlJQU8nlJSUkqKCjQnj17gnp55OTkBJ4zd+5cbdmyRZmZmYqOjg4sa9iwof71r3/pqquuKu+johL8cPMILphVnZ/iml3ismKV35pEKtwqGtMk4hoAAJFGcdFFwtXDw7QkzC0JWDHTEjF6eaBYQkJCUCJWmsTERCUmll9w6tatm/bs2aOVK1eqS5cukqSPP/5YRUVFSk1NDfmcLl26qGbNmlq8eLEuvfRSSdLGjRuVlZWlbt26SZIOHDig6OhoRUX9Xrwu/ndREZOnw/2Ia0D1VTSmScQ1eAd3ika41dvETHmwB3sSjOK2BKyYW9vtVn7oeWWiU045Rf369dPIkSO1bNkyffnllxo9erSuuuoqNW/eXJK0bds2paSkaNmyZZKk+vXra8SIERozZow++eQTrVy5UsOHD1e3bt10zjnnSJL69Omj3377TaNGjdL69eu1bt06DR8+XDExMbrgggsc+7yAHdwaH0xqd7gugvptWD5KIq4BqAjTbuYCmIjiIoxhUiJTFW5vP1ARr732mlJSUtS7d29ddNFF6tGjh2bNmhX4++HDh7Vx40YdOHAgsOyJJ57QxRdfrEsvvVTnn3++kpKS9M9//jPw95SUFP373//Wf/7zH3Xr1k3nnXeetm/froULF6pZs2YR/XxwP5N647s9Lri9/UBFENcARIpdN3PhTtEwEcOiy+H1HlKmJGFeSWBMGUrG0GiES6NGjTR37txS/56cnCzLCu4NFB8fr+nTp2v69OmlPq9Pnz7q06ePbe1E+Nh1kzKv9xojrgHuQFwDKoabuQAoCz0X4TivJGDFvPZ5jmVXMcANd1AHgKrychxwiikXQwEAAFBSlYqL06dPV3JysuLj45WamhqYg6Q0e/bs0ahRo9SsWTPFxcXpD3/4gxYsWFClBvuV13t4eA2JJeAuxDXYxYvHfy9+JtN4faQMIouYBqAiuJkL7FTpvWnevHkaM2aMMjIytGrVKp1++ulKT0/Xjh07Qq5fUFCgPn36aMuWLXrzzTe1ceNGPf/882rRokW1G+8G9NAqGwlL+NDLA6gY4pp3cNwLH6/Gay7ewmuIaeGxtx13+fYrbuYCVEyl51ycOnWqRo4cqeHDh0uSZs6cqffee08vvviixo4dW2L9F198Ubt379ZXX32lmjVrSjo6dwmc53QS5tVEpRjzVAHuQFyDXYhr4WXyfMJxWbHKb13gdDMAYpqH1D0x1+kmBDDfYvX55WYuDTcSC/2qUj0XCwoKtHLlSqWlpf3+AtHRSktLU2ZmZsjnvPvuu+rWrZtGjRqlpk2bqmPHjnr00UdVWFhY6vvk5+crLy8v6AG4kdcTTcDtIhHXiGn+wPEegNPI1QAATqlUcXHXrl0qLCxU06ZNg5Y3bdpU2dnZIZ/z448/6s0331RhYaEWLFig+++/X48//rgefvjhUt9n0qRJql+/fuDRqlWryjQTLkASBsAEkYhrxLSSwjEU1ene+H5B/AbMRa4GAHBK2GfwLCoqUpMmTTRr1ix16dJFV155pe677z7NnDmz1OeMGzdOubm5gcfWrVvD3UzfcTIJ81ti4uTnDcf3zPxU8LvKxjVimvcR1wC4FbkaUDovz7fIzVxgt0rNudi4cWPVqFFDOTk5QctzcnKUlJQU8jnNmjVTzZo1VaNGjcCyU045RdnZ2SooKFBsbMkbnsTFxSkuzsw5dZxAMQcAwiMScc1LMY2blJVEoS2yTJ53EXAauRrCgfkWzWH6fIvwt0qVq2NjY9WlSxctXrw4sKyoqEiLFy9Wt27dQj7n3HPP1Q8//KCiot/vsPXf//5XzZo1Cxms4G1+TcL8+rkB07kxri3KTgn7ewDl8VJc4yIuvMKNMQ3wA7tu5gKYrNJ9YceMGaPnn39eL730ktavX6+bbrpJ+/fvD9yRbOjQoRo3blxg/Ztuukm7d+/Wbbfdpv/+979677339Oijj2rUqFH2fQrABbyUiNmBHlAwhZ/i2pafE51uQlg4NdUHx3UApvFDTPutPeeQgIm4U7S/VWpYtCRdeeWV2rlzpx544AFlZ2erc+fOWrhwYWDi4KysLEVH/16zbNWqlT744APdcccdOu2009SiRQvddtttuueee+z7FHAFkjBnMIQMKBtxDaia2A0/qyClpdPNAHAMYhpgDy/PtwiEQ6WLi5I0evRojR49OuTflixZUmJZt27dtHTp0qq8lauZ2jOLO2o6h0TMPlt+TlRyy51ONwMeQVyLHK8MQeWCmXO4aAaUjZgGoCzczAXhwF6FiCAJ8xavFAcAANVDfP+dqReVAcCL3NKz0K75FrmZC0xHcRGA73BDDADVQUENABBOdU/MdboJkrhTNICKo7hoOC/0ECMJC+bE9mAoPACv4vgGAADs5JZekSbhZi7hM2nSJJ111lmqV6+emjRpooEDB2rjxooX/l9//XVFRUVp4MCB4WukKC6WyYt31SQJAwAAdvLCRUQvXMwFAJjFriHRdmK+Rff59NNPNWrUKC1dulSLFi3S4cOH1bdvX+3fv7/c527ZskV33XWXzjvvvLC3s0o3dAFQPdzYBQDcyQuFNC/gpi4AAL9gvkV/W7hwYdC/58yZoyZNmmjlypU6//zzS31eYWGhBg8erIkTJ+rzzz/Xnj17wtpOytYIK5IwAAC8j3gPAABQcXl5eUGP/PyKjTLNzT06J2ujRo3KXO/BBx9UkyZNNGLEiGq3tSLouQgAAMrFnXApoCG8tvycqOSWO51uBgC4xiu/ddeQhl/Z/ppAsf0/JSg6Pt7W1yw6dEiS1KpVq6DlGRkZmjBhQtnPLSrS7bffrnPPPVcdO3Ysdb0vvvhCL7zwglavXl3d5lYYxUWEDUlY2RgaDcBv7J7XjnmEAQCAieyab9HOIdHhmm+Rm7lUzdatW5WQkBD4d1xc+dO9jBo1SmvXrtUXX3xR6jp79+7VkCFD9Pzzz6tx48a2tLUiKC4CPmH3/FT1frK0t01UtV4jLitW+a0JRgDgBVw0A+BFe9sVOd0EAB6UkJAQVFwsz+jRozV//nx99tlnatmy9POtTZs2acuWLRowYEBgWVHR0eNYTEyMNm7cqHbt2lW94aWguOgj9PAAAAAAAJSnb+uNTjch4hgSDRNZlqVbbrlFb7/9tpYsWaITTzyxzPVTUlK0Zs2aoGXjx4/X3r179eSTT5YYjm0XzxcXF2WnON2EKrN7+BjMQy8PAHAHpvowD3eMBgCYyK4h0YB0dCj03Llz9a9//Uv16tVTdna2JKl+/fqqVauWJGno0KFq0aKFJk2apPj4+BLzMTZo0ECSypynsbq4W3SY+H3ie5IwAAD8h/gPAIgkL/c2ZL5FSNKMGTOUm5urXr16qVmzZoHHvHnzAutkZWXpl19+cbCVPui5CAAAAJTHjrmEAQDu5OUiJdzNssof0bpkyZIy/z5nzhx7GlMGei4CDqOXBwBUXiTnEeY4DQAAAJSO4iJsRxJmLm7qAwBA+Ph9WhwA8DvmW4RfUVwEAACAbbjICACIpOoOaTZxSDTzLcJtKC76BD3WAAAAAAAAYDeKiwAAAKWgF57ZuHgKADAFQ6LhZxQXAQOQvALwuno/lX+nOwAAgEhiSDRgD/Y02IoiGQCYacvPiU43AQAAICxMLBJ6GfMt4ngUFwFUGT2RAAChcLExshZlpzjdBABwHTsLkgyJht9RXAQAAAAAAHCYnUOigUiiuAgAABACve8AAIBbMd8iIom9zVAMN/UfklgAqBjuEAwAAKqDIdFVx3yLCIXiIuAzJOUAAAAAYBaGRMPNKC7CNvS8Q6Rx91sgMuKyYp1uAgAAgC38cGdphkQj0tjjAAAAYDsuOgIAIsmJoqHfhkQDpaG46AMMgwUAACgfc14DAJzgliHRzLeI0lBcBAAAAAAAnueHG7kwJBpOYK8DAAAAAAAAUCUUFwEAAOBaTP8CAKgIU2/kwpBoeAHFRQCO4i60AEzEzUgAAHCfSBUQGRINBGPPAwAAAAAAnmVqr0XAKyguAgahpwwAAAAAmMvOXosMiYZXUFwEAAAAAACeRK9FIPwoLgIAKmz37t0aPHiwEhIS1KBBA40YMUL79u0r8zmzZs1Sr169lJCQoKioKO3Zsyfo71u2bNGIESN04oknqlatWmrXrp0yMjJUUMAVUgBAeBHXAHgF8y3CSex9AIAKGzx4sNatW6dFixZp/vz5+uyzz3TDDTeU+ZwDBw6oX79+uvfee0P+fcOGDSoqKtJzzz2ndevW6YknntDMmTNLXR8AALsQ1wDvCWdPRT8OiQYqIsbpBgAA3GH9+vVauHChli9frq5du0qSnn76aV100UWaMmWKmjdvHvJ5t99+uyRpyZIlIf/er18/9evXL/Dvtm3bauPGjZoxY4amTJli62cAAKAYcQ3wPr8MiQ5nr0XmW0RF0HMRtuBGJIB58vLygh75+fnVer3MzEw1aNAgkIBJUlpamqKjo/X1119Xt7lBcnNz1ahRI1tfEwDgXnbHNIm4Bnid3YVFei0CpaPnIgA4KHZrrGrEx9r6moWHiiRJrVq1ClqekZGhCRMmVPl1s7Oz1aRJk6BlMTExatSokbKzs6v8usf74Ycf9PTTT9O7AwBcxk0xTSKuAV7mlx6LgCkoLgKAR23dulUJCQmBf8fFxYVcb+zYsZo8eXKZr7V+/Xpb21aabdu2qV+/frr88ss1cuTIiLwnAMB8FY1pEnENgL8wJBomoLgIWxSktGRoNGCYhISEoESsNHfeeaeuvfbaMtdp27atkpKStGPHjqDlR44c0e7du5WUlFSdpkqStm/frgsuuEDdu3fXrFmzqv16AADvqGhMk4hrAOzHkGigbBQXAcDnEhMTlZiYWO563bp10549e7Ry5Up16dJFkvTxxx+rqKhIqamp1WrDtm3bdMEFF6hLly6aPXu2oqOZEhgAUDXENQAAIosoBwCokFNOOUX9+vXTyJEjtWzZMn355ZcaPXq0rrrqqsAdNbdt26aUlBQtW7Ys8Lzs7GytXr1aP/zwgyRpzZo1Wr16tXbv3h14Tq9evdS6dWtNmTJFO3fuVHZ2tq3zXQEAcDziGoCKMLnXIkOiYQp6LgIAKuy1117T6NGj1bt3b0VHR+vSSy/VU089Ffj74cOHtXHjRh04cCCwbObMmZo4cWLg3+eff74kafbs2br22mu1aNEi/fDDD/rhhx/UsmXLoPezLCvMnwgA4GfENSC0D7Paq2/rjU43A4BLUFwEAFRYo0aNNHfu3FL/npycXCJxmjBhQpl39Lz22mvLnRsLAIBwIK4BKIudvRYBL2NYNGCQgpSW5a8EAAAAAHAVhkTDyyguAnBUfmsCFwAAAACz0GsRqDiKiwAAAMehJ7l75LaLc7oJAACUiV6L8DqKiwAAAAAAGMLuQhQqj16L7lTru1+cboJvUVwEAAAAAACAa9Vau83pJvgaxUUfYLgQAAAAAADls7vXIkOi4QcUFwEAAABJe9tEOd0EADAGQ4PhFvRadB7FRQAAUCbu6o6q4KY4AAC38XOvRaA62DNhG5IId/DSMPnkljudbgIAAAAAuJ5bh0TTa9EMFBcNxbAc/6E4CwAAAADOML3XIkqisGgOiosAAMBVItUDm4s+AACGocIU7IswGXsnAAAAAADwLb/3WnTjkGh6LZqF4iIAAAAAACiBO0abgV6LwSgsmoc9FECVMTcoACAUhpRHVp+kDU43AYDN3Nbzzc3otei+XoswD8VF2IpkAgDMxN3VAQAA4Hb0WjQTxUXAABRlAXidW3s6c3w2W6Ru7gMA8CY3DPtmSPTvKCyai73UJzj5BgAAAAAgfBgSDb+iuAgAAAAAAEJyQ+8+r6LX4u/otWg29lTYjiFk5qIHKwAg3DgPAAC4ATdyodci7ENxEXAYSRgAVB4XS2Ci/NYkakC4URCBX9Br8Xf0WjQfeysAAEAZuAgEAADK4rZei25CYdEdKC6GCVeuAQCA37i5EOvWO5oD8C6TClZem3fR9M8T7l6L9ACG3TxfXOyTtMHpJlSZm09y3ZxcAAAAAABQESYVgb2GXovSZ599pgEDBqh58+aKiorSO++8U+5z8vPzdd9996lNmzaKi4tTcnKyXnzxxbC2Myasrw6gTBRhAcAdClJaKnbDz043A8dg3k0AACqPXovusn//fp1++um67rrr9Oc//7lCz7niiiuUk5OjF154QSeddJJ++eUXFRUVhbWdFBd9JLddnOpvyne6GfAIO3rWMn0AAAAATFdvU7T2tgtvYo7IsXNIdDh6LXIjl6PotXjUhRdeqAsvvLDC6y9cuFCffvqpfvzxRzVq1EiSlJycHKbW/Y69FmFDrzyz0MMDABBOxH0A8DbT5ylE+dzSa9EPhcW8vLygR36+PR3B3n33XXXt2lV//etf1aJFC/3hD3/QXXfdpYMHD9ry+qWh5yLgEJIwAG6S37pAcVmx1XqNvW2iVO8ny6YWRb5HPkOjAQBAMXotel/dH6NVI87e76Qw/+jrtWrVKmh5RkaGJkyYUO3X//HHH/XFF18oPj5eb7/9tnbt2qWbb75Zv/76q2bPnl3t1y8NxUUAAABUCxfMqi+55U6nmwDAQPs211fdE3OdboZn0PvSfH7otShJW7duVUJCQuDfcXH2jDQsKipSVFSUXnvtNdWvf7QAPnXqVF122WV69tlnVatWLVve53iUxRFWJBuhsV0AAKg6pvoAAGdQnDvKjb0W3TAk2i+FRUlKSEgIethVXGzWrJlatGgRKCxK0imnnCLLsvTzz+EbgUNx0Wc4GQcAoOq4OAQAgL+Fo7AI2OXcc8/V9u3btW/fvsCy//73v4qOjlbLluE7j6W4WAaGp9iDRMx5FJUBAOHihTi/t02U000AAISZyb0u6bXor16LlbFv3z6tXr1aq1evliRt3rxZq1evVlZWliRp3LhxGjp0aGD9q6++WieccIKGDx+u7777Tp999pnuvvtuXXfddWEbEi1RXDQeJ7ve44UkzO36JG1wugkAbOLExROO4wAAPzO5SBdu9FpEpK1YsUJnnHGGzjjjDEnSmDFjdMYZZ+iBBx6QJP3yyy+BQqMk1a1bV4sWLdKePXvUtWtXDR48WAMGDNBTTz0V1nZyQxcAlUbRGwAgUWg9Vn5r83uFAHAnburibfRapNdiWXr16iXLskr9+5w5c0osS0lJ0aJFi8LYqpLouYiIIPk4iu0AwO+8cnGC47lzmOoDAFBZdvS2pNdieFBY9AaKi2Fk6hVsTsrhBcyJCgDOosAKwE/C3bvMbfw8NNpO9FqEV1RpT54+fbqSk5MVHx+v1NRULVu2rELPe/311xUVFaWBAwdW5W3hcn5PQpz6/BSTgfIR19zNqeOc3+MaADMR0wC4Bb0WvaPSxcV58+ZpzJgxysjI0KpVq3T66acrPT1dO3bsKPN5W7Zs0V133aXzzjuvyo0F4B2m9uyF/7gtrlXnhkT0+IVdKKwCZnJbTAMiwdQh0X7vDUth0VsqvTdPnTpVI0eO1PDhw9WhQwfNnDlTtWvX1osvvljqcwoLCzV48GBNnDhRbdu2rVaD4W5+TUb8+rkBNyCuVRwXBUri+B5Z4eil6pV5QAGJmOZlJs73x9BoszEkGpFUqeJiQUGBVq5cqbS0tN9fIDpaaWlpyszMLPV5Dz74oJo0aaIRI0ZU6H3y8/OVl5cX9PCzcJz0OjlUlkQMgCkiEdeIafAa4jhgJnI1wD3otUivRa+p1B69a9cuFRYWqmnTpkHLmzZtquzs7JDP+eKLL/TCCy/o+eefr/D7TJo0SfXr1w88WrVqVZlmAkZxMgmjhwdQtkjENWJaZHDRDIDfkavBCab3XjR1SHS40WsRkRbWcvnevXs1ZMgQPf/882rcuHGFnzdu3Djl5uYGHlu3bg1jK+EEvyRifvmcgF9UJa4R00riIoV7EdcA73BzrkbhBG5Gr0V6LXpRTGVWbty4sWrUqKGcnJyg5Tk5OUpKSiqx/qZNm7RlyxYNGDAgsKyoqOjoG8fEaOPGjWrXrl2J58XFxSkujjvcAgDCKxJxjZjmDwUpLRW74WenmxFWThcWneydCrgBuRqc8mFWe/VtvdHpZpRgeq/KcDG9+H6wYwsKjB5UqZJ5bGysunTposWLFweWFRUVafHixerWrVuJ9VNSUrRmzRqtXr068Ljkkkt0wQUXaPXq1XShd5jTJ+lOJynh5vTnc/r7BdyAuAY7OX3ch3O42RFMQEwLP6d7nLlxeK7b2b3Nnd6HgHCpVM9FSRozZoyGDRumrl276uyzz9a0adO0f/9+DR8+XJI0dOhQtWjRQpMmTVJ8fLw6duwY9PwGDRpIUonlXpXfukBxWbFON8NYXu3pQYIJuAdxzTty28Wp/qZ8R9tAXHMXU4boJ7fc6XQT4BHENDjF1N6LfmN6r0V4V6WLi1deeaV27typBx54QNnZ2ercubMWLlwYmDg4KytL0dFU4+22t02U6v1kOd2MsPBqIgbAHYhrQNm8WlgEvIiYBhxl4pBoei3CyypdXJSk0aNHa/To0SH/tmTJkjKfO2fOnKq8pWOSW+7Ulp8TnW5G2JjQy0PyVoHRy0mYXT08GD4G0/gprpmCi2buYEpMY6oPoOKIaYA93DYM3U29Fpl30XsonQM2IgkD4HWmXxww5fhnSjyoDi98BgDwGpMLXqb0FjSlHcei1yK8jj0cxnB7EuP29gMVsXv3bg0ePFgJCQlq0KCBRowYoX379pX5nFmzZqlXr15KSEhQVFSU9uzZE3K99957T6mpqapVq5YaNmyogQMH2v8BgAgiLgDmI64B9jKxsOe0SBQW3dRrEd5EcRHG9PKQ3JuIubXdbsXE984ZPHiw1q1bp0WLFmn+/Pn67LPPdMMNN5T5nAMHDqhfv3669957S13nrbfe0pAhQzR8+HB9++23+vLLL3X11Vfb3Xwg4twaH0xqd7jOU0y5mQucRVwDEIrJPUQBE1VpzkU4w8vzUx3LbXNVmZSASSRhCJ/169dr4cKFWr58ubp27SpJevrpp3XRRRdpypQpat68ecjn3X777ZJKn+fpyJEjuu222/S3v/1NI0aMCCzv0KGDre3HUV6fS1gyZz7hYsQ1bzN9qgCUjriGqqi3KVp72xU52oZ9m+ur7om5jrahLE7eOdq0npP0Wiwd8y56Cz0XI4CTzspzS2LjlnbCn/Ly8oIe+fnVK7ZkZmaqQYMGgQRMktLS0hQdHa2vv/66yq+7atUqbdu2TdHR0TrjjDPUrFkzXXjhhVq7dm212gvz+emihRviRUFKS1e0E8H6JG1wugkRYXdMk4hrAADYhZ6LkGReLw/p90TM1N4eJGCwQ72tlmrE2tsjubDg6Ou1atUqaHlGRoYmTJhQ5dfNzs5WkyZNgpbFxMSoUaNGys7OrvLr/vjjj5KkCRMmaOrUqUpOTtbjjz+uXr166b///a8aNWpU5dcGTGJyD0ZTY5pJU7egfG6KaRJxDQgnJ3ov2tVr0a4h0fRahJ/QcxHGMy3hMblnhxuSMHryRs7WrVuVm5sbeIwbNy7kemPHjlVUVFSZjw0bwtczpqjo6NCi++67T5deeqm6dOmi2bNnKyoqSm+88UbY3hfeZurx0MQYYlp7IsFPvWa9oqIxTSKuAQAQab7oudgnaYMWZac43Qxb+GXexeOZ0tvDjwkY3CshIUEJCQnlrnfnnXfq2muvLXOdtm3bKikpSTt27AhafuTIEe3evVtJSUlVbmezZs0kBc9FFRcXp7Zt2yorK6vKr4vwyW9doLisWKeb4WomxDXTY5qpBWI4o6IxTSKuwR9Mn3dRcnbuRT/wQq9F5l30Dl8UF1ExJg6NPpaTw6RNT8Ck8CZh9PDwtsTERCUmln+Dj27dumnPnj1auXKlunTpIkn6+OOPVVRUpNTU1Cq/f5cuXRQXF6eNGzeqR48ekqTDhw9ry5YtatOmTZVfF+4QzotmxLXy3xvwIuKaPzTcWKDf2nOhy3SRKjD6cUg0YBL2eLhOJIeUmTh8DXDKKaecon79+mnkyJFatmyZvvzyS40ePVpXXXVV4I6a27ZtU0pKipYtWxZ4XnZ2tlavXq0ffvhBkrRmzRqtXr1au3fvlnS0N8qNN96ojIwMffjhh9q4caNuuukmSdLll18e4U8JRB5xzd+SW+50ugm+RVxDVVE4gtO80GsR3kLPxQpIbrlTW34u/+qnF5jey+NY4ezxQeIFhPbaa69p9OjR6t27t6Kjo3XppZfqqaeeCvz98OHD2rhxow4cOBBYNnPmTE2cODHw7/PPP1+SNHv27MCwtb/97W+KiYnRkCFDdPDgQaWmpurjjz9Ww4YNI/PBAAMQ137nlt74zCPsfsQ1uJkbhkZL4e+9aFqvRcCPKC5GiJ3zU/l13sXSHJswVSchc1vidSzmpUKkNGrUSHPnzi3178nJybKs4OPThAkTyr2jZ82aNTVlyhRNmTLFjmYCAW66aFaMuAZEDnENiAw/zb8Y7p6tXuu1yLyL3kBxESW4MRErFiqRCpWYkXBVnGk9PKo7fKxPUvjuDgmgarhoVjo/xjUumAEAKsquXotu4bXCIryD4iI8z0sJVygkYQCc4KY7Rrv5olkoXo9rAICKc8vQaMns3osMiQaqh5loERIFKwDwLm4gAVOF+/zDzt74APyNm7pUjZ09DU3stRjO/YJeizAZR0SX4uQYEkkY4FcM7688LpoBAGAG04qC9Fp03sGOLZxuAqqJ4mIEue2OgiRiAIBI4WIG3Hbe4bbzOgDe5cbiWHULjKYVKMONXoswHcVFwKVIwgCgctx23AQAwMu8ViBkqDz8jL3fxSLRy4NEDABQGi4awC6RON8wsXcs858CQOXZWZR0Q69Pei3CDSguVhAnfzCJX5MwAN7GRTMAQGU4XXQxqaeaG4pkoXit9yKqjnkX3c2coyGMRSIGAADChfMMAPC3yhQYTe21GK5Cs9MFdKCiKC5GmN1DyOhd5j9+TsLoQQzADn4+jvqV3edLTAkAwERu7b0o0YMRcDuKi6gQEjEzROp7IAkDvM/UYn2kLpoR18zA9wAAKFZegdHUAiS9Fu3D0Gj3orgIAACqjIsHqCoKi9XXJ2mD000AAFtFqoDo5l6egIkoLnoAvTz8ge0PAPbiuOoPTCEDIJxMuqmL5N2imam9FsPFj70W4W5mHQnDiCu79iARc0YktztJGACncRzyPjefT9BbFwDCK9yFRDfcyAVwG34JDuCkFAAAM7i5yOVWXDA7ytR5TwG4n9d6L/qt16LfMe+iO1FcrASTTwIjefJMIhZZbk/CKKYD3uf23zlxDQAAs4SroOi1witgCoqLqBISschgO//OjuI+0yMA7hHpHmccbyOD7QzAbibMTWfi0FgvFNE+zGpvdK9FE793wCn8GjyERAwAUBkm98iH90T6vIHe+AAAAJFBcdEhnJyiPF5IwgCgOrho5h1sWwCIPC/0XrSTm7bHb+1jnW6Co5h30X0oLqJaSBbCwyvblSI6ED6mDfP3yu/dK8dfv+OCGYBIY4isv/B9A8H4RXiMEyfTJGL2cmJ7koQBwO+Ia/Zie5bElAQAIsVNvfXCie0AhBfFxUriZDA0Egd7sB0BIJhTFz84HtvDS9vRK71zAQDVQ69FoCR+FQ4K10kqiZg7sf0AwCwcl6vHqe1Hb3wACOb3Xnt+//xuxbyL7kJxEbYiEasaJ7dbuJIwO4vn9BgGwsfO35fXLpqh6jgfAOBH9GiDnfx+Uxe4C0c/j3IyESOhqBy2FwCYi2N05XnxghkAszXcyLQF5fFr7z27PzcFZCA0fhkO8+r8PSRjFeP0diIJA+AWXDRzB69uKxPP10y7YzwAAPAviose5nThyKsJhl28vH1MTMIAoDpy28V5+rhtB6e3j9PnPRXFVB+At5ncs81vvRf99nm9iHkX3cPcI18Y2HWFl5PCinM60TCVCdvFLUkYAHcJ58UFE45bJhy/TcR2AQB4ncmFY8Bp/Do8jkTMPGyPyqGYD8A0HMeDmbA9wnm+Q298AF7il9584fice9sV2f6agFdQXDSAH05aTUg8TGDKdvBjEsbcVEDp3FTEN+GimWTO8dxJDBUHgNBM7+HmlwKjF3DHaLiF2Uc92MKkRMyvSYifPzsA/zH1IoPd/HxcN+mzm3KeA8BZ3DEaxSieegvzLh41ffp0JScnKz4+XqmpqVq2bFmZ60+bNk3t27dXrVq11KpVK91xxx06dOhQ2NpHcRERZ1JCEgmmfV6SMMA7/Noj16TjmB8vHvnp8/qlUA7AfyjAAe4xb948jRkzRhkZGVq1apVOP/10paena8eOHSHXnzt3rsaOHauMjAytX79eL7zwgubNm6d77703bG2kuFhFdg8hC/fJq0mJmOSPZMzEzxju/YAkDIBfmXa8Dwc/xjW7uWkKAgDVY/rQaMmbBUYvfiZg6tSpGjlypIYPH64OHTpo5syZql27tl588cWQ63/11Vc699xzdfXVVys5OVl9+/bVoEGDyu3tWB3mH/HgaaYlKXbx6ueKNJIwwL38dtFMMrP4ZgdTP5eJ+wAAAEBF5OXlBT3y8/NDrldQUKCVK1cqLS0tsCw6OlppaWnKzMwM+Zzu3btr5cqVgWLijz/+qAULFuiiiy6y/4P8fzFhe2UYZ2+bKNX7yXK6GSUUJyz1N4X+MbmJiclXMZIwAGVJbrlTW35OdLoZlUJcCz+T41q40RsfgB/s21xfdU/MdboZtqDXoncd7NhCtdZui/j7NvihQDEx9vbJO3Lk6PlFq1atgpZnZGRowoQJJdbftWuXCgsL1bRp06DlTZs21YYNoadIuvrqq7Vr1y716NFDlmXpyJEjuvHGGxkW7ReROIk1ucBkas+IinBz2+1CEgbgeH4/Lrg5NpjedpPPZwCgmBuGRkveKMp54TOUhjtGe9PWrVuVm5sbeIwbN862116yZIkeffRRPfvss1q1apX++c9/6r333tNDDz1k23scj56LMI6benyYnHgdiyQMgFeZ2nvxWMQ1SPZO9eHXmzkBAOAVCQkJSkhIKHe9xo0bq0aNGsrJyQlanpOTo6SkpJDPuf/++zVkyBBdf/31kqROnTpp//79uuGGG3TfffcpOtr+Cx/uuJRiIztPxsIxH5zfey8eq7jXhGmJjqntKo1bvm8AqCq3HOdMjR3EtZL83usWcKuGG8377dJ7Mfzc3HagPLGxserSpYsWL14cWFZUVKTFixerW7duIZ9z4MCBEgXEGjVqSJIsKzwX5em56FNu6OlxrGMTHqd6frgl6XJCOJIwbuYCRF445l3Mb12guCyG8xyLmFZ1bikkA4AbeWn+RXiPU/MummDMmDEaNmyYunbtqrPPPlvTpk3T/v37NXz4cEnS0KFD1aJFC02aNEmSNGDAAE2dOlVnnHGGUlNT9cMPP+j+++/XgAEDAkVGu1FchOtEKilza+J1LJKwoxg+Bnif2y6aFTs+1oQrrnkhpkUKvRYB+JnbCoz0WoQfXHnlldq5c6ceeOABZWdnq3Pnzlq4cGHgJi9ZWVlBPRXHjx+vqKgojR8/Xtu2bVNiYqIGDBigRx55JGxtpLhooEj18nBrInas0pKlyiRnXk24IlVYJAkDnNUnaYMWZac43QwjENfKfg2344IZALeqtylae9sVOd0MAC42evRojR49OuTflixZEvTvmJgYZWRkKCMjIwIt+//vGbF38qhwDCGLJC8kYqF4NbGqKBIwACZhaHT1EdfcHdeY6gOAm7il9yK9FgFzuGN2WR+KZG8wt5+ww3tIwgBUFTHNeyL5ndIbH3A/E2/qIrnnxi7FKNzBRAc7tnC6CSiFu45wAMpFEgagOsJV3OeiGQAA7mJygdHktoXDb+0ZAQKzUVw0GIkYKovvEQCO4njoDVwwA+Albuu9KPmviAegatx3dDOQV4Zwkoi5W6S/P5IwAJUV6eMGcc3dvPL92X2e2Cdpg62vBwDlocAIkzA02ky+LC5yUlY6r5zI+42XvjevFOu9avfu3Ro8eLASEhLUoEEDjRgxQvv27Stz/VtuuUXt27dXrVq11Lp1a916663KzQ2eJDwrK0v9+/dX7dq11aRJE9199906cuRIuD8OSuGl36GXjo9+wgUzRApxDagYCowAyuLL4qKbOHGySyLmLk58XyRh/jV48GCtW7dOixYt0vz58/XZZ5/phhtuKHX97du3a/v27ZoyZYrWrl2rOXPmaOHChRoxYkRgncLCQvXv318FBQX66quv9NJLL2nOnDl64IEHIvGREGEcP1AezkMQScQ1bzL1pi6SO4dGF6PACKA0MU43AGba2yZK9X6ynG4GUG30VLbP+vXrtXDhQi1fvlxdu3aVJD399NO66KKLNGXKFDVv3rzEczp27Ki33nor8O927drpkUce0TXXXKMjR44oJiZGH374ob777jt99NFHatq0qTp37qyHHnpI99xzjyZMmKDYWCawRvUQ09zDaxfMvNQL2IuIa0Dl7dtcX3VPzC1/RQC+4t7LJoYJ58mjU7086DlgPq8lYTBbZmamGjRoEEjAJCktLU3R0dH6+uuvK/w6ubm5SkhIUExMTOB1O3XqpKZNmwbWSU9PV15entatW2ffB/AwtxXR6ZWPUPiOEGnENTjFzb0XJed7MDr9/nAe8y6ax91HNYQdJ/rm8uJ3Qw8Pe+Xl5QU98vPzq/V62dnZatKkSdCymJgYNWrUSNnZ2RV6jV27dumhhx4KGnKWnZ0dlIBJCvy7oq8L+3nx9+jF46ZXOPXdcMHMPeyOaRJxDagOCnwAjsWwaJfIb12guCxnhlAwnMwsTibHJGH2S9icr5gYe7/TI0eOJlytWrUKWp6RkaEJEyaUWH/s2LGaPHlyma+5fv36arcrLy9P/fv3V4cOHUK2A/7iVFwjppmHoq93mBDTJOIajmq4sUC/tTd3CHq9TdHa267I6WZUC0OkARSjuGij5JY7teXnxLC9PgVGkIChMrZu3aqEhITAv+Pi4kKud+edd+raa68t87Xatm2rpKQk7dixI2j5kSNHtHv3biUlJZX5/L1796pfv36qV6+e3n77bdWsWTPwt6SkJC1btixo/ZycnMDfALsR08zh5Qtm4ej967YpEOxU0ZgmEdfgHhQYAXiFb4uLfZI2aFF2itPNcBWSMWc5XVh0YxLmdwkJCUGJWGkSExOVmFj+hZFu3bppz549Wrlypbp06SJJ+vjjj1VUVKTU1NRSn5eXl6f09HTFxcXp3XffVXx8fInXfeSRR7Rjx47A8LRFixYpISFBHTp0KLddCB+vXzSTRFxzkNNxDe5S0ZgmEdeASKPAGBm/tY81+k7okXawYwvVWrvN6Wbg/2PORZdxelgqiYAz2O4wwSmnnKJ+/fpp5MiRWrZsmb788kuNHj1aV111VeCOmtu2bVNKSkqgx0ZeXp769u2r/fv364UXXlBeXp6ys7OVnZ2twsJCSVLfvn3VoUMHDRkyRN9++60++OADjR8/XqNGjSqzZwpgB46vznB6uzt9PgUzENdgArff3KXYvs31IzIPI3M9AmbyxpHMIH7ofeV0QuA3JmxvtyZhfh4+Fi6vvfaaUlJS1Lt3b1100UXq0aOHZs2aFfj74cOHtXHjRh04cECStGrVKn399ddas2aNTjrpJDVr1izw2Lp1qySpRo0amj9/vmrUqKFu3brpmmuu0dChQ/Xggw868hkRWSYcX0w4zvrF3jZRvtjefjgf9ArimrfRyyvyKP4B/uTbYdFu5uQwsmIMJ4sMExKwSCT+JGHu0ahRI82dO7fUvycnJ8uyfj8u9OrVK+jfpWnTpo0WLFhgSxv9KlzTfYR7aLRkTlwjpoWXCTFNMqOgDXMQ12ACL8y9CMDf6LmIajElUfAav/TsAACTcOwNH1O2K4VFAAC842DHFk43Af8fxcUwiEQvLJNOjk1JGLzCpO1p0n4GwNtMOt6YdBx2Oz8WbOmND6AqvDL3Yjgx5BowF0cwFzMtEfNb8mA307ZhpPYvkjDAfH78nZp2THYj07afSedNVcE8wkDVuGneRQqMANzK10cvTtLsRzJWNWwzADCz+MPxufJMPBcwcd8CALgLxV+gdPw6wiRSvTxMPVk2LakwlYkJmOSNXotcPADcycS4Zuqx2kR+305+7OULwF4UsEJjSDRKw7yLZuDI5QEmJmISyVhZTN42pu5PAMoXzqJ6JIsmph6HTD52O83kbWPq/gQApaHACMBtOGp5hMknziYnHJHGtgAA9+NY/jvTt0Ukz4/otQiYy03zLqIkei0C5qO4GEacZAYzPQEJJ7d8dpIwAGWh92Iwtxzbw8ENn90N+xAAlIbei0DFMTTaeb4/YnlpXja3nES7ISGxi5s+q1v2HwD+4ZbjkpuO9dVR/Dn98FlN46XzVQAVR4ERgFtwtAqzSPfOcksiJnk3SXHj54r0fhPu3wVJGOAdxDXnufEzeS2uAW5Q67tfnG5Cmdw6NNrvBUaGRAPuEON0A2C//NYFisuKdboZlXJs0lLvJ8vBllSN25KuY7kpcQdQvj5JG7QoOyVsr5/ccqe2/JwYttf3CuKac4hrAAC7+b3IC5SH4mIEOJGIubHAWMwtCZmbEy8AcBM3xzTJHXHNKzHNicIivRYBhFu9TdHa267I6WZEHL0WURkHO7ZQzf9sdroZvlWl8vv06dOVnJys+Ph4paamatmyZaWu+/zzz+u8885Tw4YN1bBhQ6WlpZW5PuzjhSv3xw4xc3JYlintsBtJGHAUca1ynPgdeyGmSebEExPaYDev7CNAdTkZ02qt3Vbl50aCW4dGS+7qOUdREPCfSh+h5s2bpzFjxigjI0OrVq3S6aefrvT0dO3YsSPk+kuWLNGgQYP0ySefKDMzU61atVLfvn21bZs5gScS87NRULFPqMTMrsQonK9tGpIw4CgvxjWv8upxi7hWfU7tG5E4v2MeYVQGMc3b3FRgrC4KlIC7VHpY9NSpUzVy5EgNHz5ckjRz5ky99957evHFFzV27NgS67/22mtB//773/+ut956S4sXL9bQoUOr2GxUlNuHklWGF5OlcCEJA35HXKsap+ZeJK7heF4tOgNVQUzzPr8OkQYq4mCHZpLZ95byrEpd+igoKNDKlSuVlpb2+wtERystLU2ZmZkVeo0DBw7o8OHDatSoUeVa6gFO9V7kpBvHYn8AfufVuOb1IjvHMRRzcl9gVApMY0pMY2h0+Hm9B6NpvRa9vr0BO1TqV7Jr1y4VFhaqadOmQcubNm2q7OzsCr3GPffco+bNmwcFvePl5+crLy8v6IHqIRGDRBIGHC8Scc3LMc3J3zVxDewDQDByNX8xveBlWoEQQHhF9Ij02GOP6fXXX9fbb7+t+Pj4UtebNGmS6tevH3i0atUqgq0MLxIxOIXvH7BfReKal2Oa0ziu+ZfT332kzue83gsZZiFXcx/TC4xVQVEScKdKHY0aN26sGjVqKCcnJ2h5Tk6OkpKSynzulClT9Nhjj+nDDz/UaaedVua648aNU25ubuCxdevWyjSzSvxy8pbfusDxE3JEntPfOb0WYapIxDUnYlokOf37dvr4hsjjOwdCMylXY2g0qoLCIqqr1ndMuOiUShUXY2Nj1aVLFy1evDiwrKioSIsXL1a3bt1Kfd5f//pXPfTQQ1q4cKG6du1a7vvExcUpISEh6OElTidiEifmfuG3YrJfLhLAPpGIa07FND/9Hvx0nPM7E75rE87jgFDI1fzJi70XTcL2dYdaa7cZf1HD6yr9SxkzZoyef/55vfTSS1q/fr1uuukm7d+/P3BHsqFDh2rcuHGB9SdPnqz7779fL774opKTk5Wdna3s7Gzt27fPvk+BKjHhBB3hY8r3SxIG0xHXqs+E37nfLqb4Ed8vUD5iWsXRezH8KtMTkV6LqCqKimaIqewTrrzySu3cuVMPPPCAsrOz1blzZy1cuDAwcXBWVpaio3+vWc6YMUMFBQW67LLLgl4nIyNDEyZMqF7rXSy55U5t+TnR6WYov3WB4rJinW4GbGZKAmZCwQEoD3HNW4hr3uTHuOan3sewj0kxrdbabTrYsUW1XgP+QGERVUFR0SyVLi5K0ujRozV69OiQf1uyZEnQv7ds2VKVt3BEn6QNWpSd4nQzIo5EzFtMScAAN/FqXIskUy6aScQ1LzEppnHBDG5BTKu4hhsL9Ft74kU47dtcX3VPzC3z76ZiSLS5KCyah1+Lg0w6SWU4mfuZ9h1Gev+mhwdQUqR/F6bFNbgb3yEAeJvJhUWYibkVzUVx0WEmJWISJ/JuxfcGACWZdtEFFWfa92ba+RrgFhQBANiBoqL5KC4eh95PJGNuYup3RRIG+JeJv38Tj5MIzcS45sQ+zfkoEDnc2CX8QvVQNL3XoolDov24r1JUdA/zfjE+ZGIiJpGMmc7U74ckDICJcc3EohWC8f0AgD+YXliE8ygqug/FRUOYmIhJJGMmMvk7MXU/BvyM4nswk4+hfmXyd0JcA6qPAgGkowXF4gdQFo4Z7kRxERVi8om/X/AdAHAT04syHE+dZ3pcc2of5oIAEHl+HG6K0pk4JNoP6K3obvxqQnDqpM70REwyPxHwKjdsc5IwAMczPa4R05zBdgf8h4JBeFEMg5tRVPQGjkKGMT0RK0ZiEBlu2c5u2W8Bv3KyCO+G44NbjrVu56bt7Ib9FgBgPwq1kUNR0VtinG4ASkpuuVNbfk50uhkVUpwkxGXFOtwS73BL4lWMBAyAVxDTwoO4VnH0xgcAeB0FRW+iLF8Kp0/u3FawcVNvBFOxDSvP6d8pgIpxa0zjmFw9btyGbttXATcxvaDAvIswudeiF/ZPeip6m7m/HrgSCVnluXl7kYQB7uF0Md6txws3H6Od4ObzALfuowAgmV0Yg79RVPQHhkUbzE3Do0NheFnp3Jh0HY8kDEBluTmuHXvcJq4FI6bZw+kLAADgZxRnw4Oion/wCyqDCSd5JpzsVpebezHYyUvbwYT90oTfJ+A2JvxuTDh+VJeXjudVxTYAUBUUGgD/4PfuL/RcdAE39/Q43vFJiJd7f3g14fJCYQCAs4hr7kRcA+B1DTcW6Lf27j2O19sUrb3tipxuhuvQa9F+FBb9h+JiOfokbdCi7BSnm+GpROxYXkrKvJp0HYsEDIBdiGvmI65Fjgm9ioFIqbV2mw52bOF0MwDX8MLNXOB9FBddxKuJ2LFCJTImJmZ+SLiOZ0oCJpGEAdVhykUzibhmEuIaABzl9t6LqBx6LdqPXov+xC/JZfx4InzsvE6hHl55T5P5cb8DEBl+PL4Q15xn0n7HBTMAdqJYVnFsK7jJ9OnTlZycrPj4eKWmpmrZsmVlrv/GG28oJSVF8fHx6tSpkxYsWBDW9tFzsQJM6uUh+aOnR2X4MSmKJJMSMIkkDLADcc1sxLXwMS2mAX7F0GjAm+i1GB7z5s3TmDFjNHPmTKWmpmratGlKT0/Xxo0b1aRJkxLrf/XVVxo0aJAmTZqkiy++WHPnztXAgQO1atUqdezYMSxtpFTvUsktd3KCjLBjHwMQKRxvEG4m7mNcMAPM5PY57uiRVz63bCO374uwx9SpUzVy5EgNHz5cHTp00MyZM1W7dm29+OKLIdd/8skn1a9fP91999065ZRT9NBDD+nMM8/UM888E7Y2uuMXZQBTT/5MPFGG+5lavDb1dwi4kYm/JxOPO/AG9i0AQDG3FBbhbXl5eUGP/Pz8kOsVFBRo5cqVSktLCyyLjo5WWlqaMjMzQz4nMzMzaH1JSk9PL3V9OzAs2gMYTgY7kYABcFLxMYi4BrsQ1wAzMTQ6vOptitbedkVON8M4FBbDx4tDomt994tiou29wdORoqO9UVu1ahW0PCMjQxMmTCix/q5du1RYWKimTZsGLW/atKk2bAjdWSA7Ozvk+tnZ2dVoedkoLlaCaXNUHYsCI+xgcgJmYi8rwO2Ia/Ayk2OaRFwDTMbdor2JwiJMsnXrViUkJAT+HRcX52Brqo9fl4eYOpQV5mPfAWAijkuoKtP3HQqLwFFe7OlkEopp7uam+Rb5LVdeQkJC0KO04mLjxo1Vo0YN5eTkBC3PyclRUlJSyOckJSVVan07cLSpJDecDJp+Qg2zuGF/ccPvDnAr039fXPxAZbG/AMDvKDAexXaAW8XGxqpLly5avHhxYFlRUZEWL16sbt26hXxOt27dgtaXpEWLFpW6vh34hXkUyRjKwz6Cqti9e7cGDx6shIQENWjQQCNGjNC+ffvKXP+WW25R+/btVatWLbVu3Vq33nqrcnNzQ67/66+/qmXLloqKitKePXvC9CngRhyvUB63xDXTC/p+Q1wDvM+NhUV6LeJYY8aM0fPPP6+XXnpJ69ev10033aT9+/dr+PDhkqShQ4dq3LhxgfVvu+02LVy4UI8//rg2bNigCRMmaMWKFRo9enTY2ui+X5kB3HRS6IaTbESem/YLN/3e/GDw4MFat26dFi1apPnz5+uzzz7TDTfcUOr627dv1/bt2zVlyhStXbtWc+bM0cKFCzVixIiQ648YMUKnnXZauJqPUrjld+aW4hEii/0C1UFccx6FifBzY3HNLn7+7PCOK6+8UlOmTNEDDzygzp07a/Xq1Vq4cGHgpi1ZWVn65ZdfAut3795dc+fO1axZs3T66afrzTff1DvvvKOOHTuGrY3c0KWKTJ4E/3jceRPFSL5QHevXr9fChQu1fPlyde3aVZL09NNP66KLLtKUKVPUvHnzEs/p2LGj3nrrrcC/27Vrp0ceeUTXXHONjhw5opiY38PQjBkztGfPHj3wwAN6//33w/+B4FrENRRzW1xzSyHfL4hrKE3DjQWeu6mLH+8eTWEx/Lg4EDmjR48utefhkiVLSiy7/PLLdfnll4e5Vb/j1+YjXNn3L7d+9yRhZsnMzFSDBg0CCZgkpaWlKTo6Wl9//XWFXyc3N1cJCQlBCdh3332nBx98UC+//LKiowlNTnDj782txzZUH9897EBcg9/4pdhWb1O0qz+rm4ZEA8Xc+4szgBsTMYkTcj9x83ft1t+XSfLy8oIe+fn51Xq97OxsNWnSJGhZTEyMGjVqpOzs7Aq9xq5du/TQQw8FDTnLz8/XoEGD9Le//U2tW7euVhtRPW793bn5WIfKcfN37dbflynsjmkScc0k9H6KHDcX3SrC65/PJPxucSyGRVeTm4ZHH49hZd7l1sTLj2L/u10x0fYOu4kuOnq1s1WrVkHLMzIyNGHChBLrjx07VpMnTy7zNdevX1/tduXl5al///7q0KFDUDvGjRunU045Rddcc0213wP+RlzzLrfHNb8UFk2IaRJxDSiPV4dIe6GwSK9FuBXFRZCMeYjbk69ifknCwm3r1q1KSEgI/DsuLi7kenfeeaeuvfbaMl+rbdu2SkpK0o4dO4KWHzlyRLt371ZSUlKZz9+7d6/69eunevXq6e2331bNmjUDf/v444+1Zs0avfnmm5Iky7IkSY0bN9Z9992niRMnlvnasJebL5oVI655h1fiGqqvojFNIq65Va2123SwYwunmxHgxXkXj+W1AqMXCouAm1FctIEXEjGJZMzNvJR8UVi0T0JCQlAiVprExEQlJpb/u+/WrZv27NmjlStXqkuXLpKOJlBFRUVKTU0t9Xl5eXlKT09XXFyc3n33XcXHxwf9/a233tLBgwcD/16+fLmuu+46ff7552rXrl257YL9iGtwkpdimkRcs0tFY5pEXIN9KDCaz0tFRTf1WmRINI5HcdEmXknEpOCTehIyc3kt+ZJIwEx3yimnqF+/fho5cqRmzpypw4cPa/To0brqqqsCd9Tctm2bevfurZdffllnn3228vLy1LdvXx04cECvvvpqYK4s6WjyV6NGjRKJ1q5duwLv16BBg4h+RngTcc0diGuINOIaKoICo7m8VFgE3I7ioo28VGAsRkJmFi8mXnCX1157TaNHj1bv3r0VHR2tSy+9VE899VTg74cPH9bGjRt14MABSdKqVasCd9w86aSTgl5r8+bNSk5OjljbUTlejGkSvRlN4+W4RmHRHYhrZjFtaLRfuK3A6MWiIr0W4XYUF1FhFBqd4eXE61gkYe7QqFEjzZ07t9S/JycnB+aWkqRevXoF/bsiqvIchIdXC4xSyWMrcS1y/BDXiGnuQVxDRXi996LkngKjFwuLgBdQXLSZlxOxY1FoDC8/JF7HIgkDzEVcgx38FtcAeI9fCoySjCwyermoSK9FeAHFxTDwSyJWjN4f1efnpIvCImA+4hpxrbKIawCqyuSh0X4oMEpm9WL0clFRcldhESgLxcUw8VsidiySsvL5Oek6FgkY4B7Etd8R14IR035HXAO8z08FxlAiVXT0elHRjei1iLJQXAwjPydixyot6fBLckbSFRoJGOA+xLWjQh3XiWkgrgH2Mbn3ouSfAmMoZRX97Cg8+qmoSK9FeAnFxTAjESudl4qOJFuVQwIGuBdxLbSy4gBxzfuIa4D9KDC6T1XnbPRTQbGY2wqL9FpEeSguRgCJWOVUNKGJRLJGcmU/EjDA/YhrlWNKXCOmhQdxDfAvCoyhVXTORj8WFSX3FRaBiqC4GCEkYvYjSXIfEjDAO4hr9iOuuQsxDQg/03svShQYS1NagdGvBcViFBbhVf7+ZUdYn6QNnIjCt9j3Ae/hdw2/Yt8HIscNwzEpGIVWb1N0iYefuXU/ccNvEM7z96/bIZyQwk8oqgPexm8cfsP+DiCUhhsLXFs8Qvixb8DrKC46hBNT+AH7OeAf/N7hdRTSAee4qecURSQcz837hJt+e3AWxUUHcZIKr2LfBvyJ3z28in0bQGW4uZgEe7EvwC8oLhqAE1Z4Cfsz4G9cXIDXsD8DZnBbDyqGScPt37/bfnNwFneLNkTxiSt33oRbkXwBOBZxDW5HXAPM44a7Rx+Pu0n7k9sLi0BlUVw0DMkY3IbkC0BZ+iRtIKbBVYhrAOxWXGiiyOgPXigs0msRlUVx0VAUGeEGJGAAKoKYBjcgpgHu4Mbei8UoMnqbF4qKQFVRXDQcCRlMRAIGoCqIaTARMQ1wHzcXGCWGSnuRlwqL9FpEVVBcdAkSMjiN5AuAXY49nhDX4BTiGgAn0YvRG7xUVASqg+Kiy1BkRKSRfAEIJ+IaIo24BniD23svFqPI6F4UFoHfUVx0KXp9IJxIvABEGkVGhBNxDYDpKDK6h5eLigyJRlVRXPQACo2wC8kXAKcR02AXYhrgfV7pvXisYwtXFBrN4uWiIlBdFBc9hqQMlUHiBcBkxDRUFnEN8B8vFhiL0ZvRDH4pKtJrEdVBcdHDSMoQCokXADc6/thFXEMx4hoAr6M3ozP8UlQE7EBx0SdIyvyLpAuAFxHX/Iu4BuB4Xu69eDwKjeHl14IivRZRXRQXfYqkzLtIugD4EXHNm4hpAFA6Co328WtREbALxUVIKv3kneTMbCRdABBaqOMjMc1sxDQA1eGn3ouhUGisPAqKR9FrEXaguIgyUXQ0AwkXAFQfMc0MxDQA4eL3AmOx44tmFBt/R0ERCA+Ki6iSshIDkrTKI9ECAOeUdwwmrlUecQ0AzOHnYiPFxLLRaxF2obgI21U0ofBDskZyBQDuR1z7HXENgOnovVi+UAU3rxQcKSYCzqC4CMfYnaDYldSROAEAqsLEuEZMA+BHFBgrz80FRwqKgPMoLsIzSKAAAF5CXAMAOKmiRbtIFyEpJtqDIdGwE8VFAAAAAICn0HsxcsJ9p2qKiYD5KC4CAAAAAIBqKy4EVrXISCExMui1CLtRXAQAAAAAeA69F51T3h2qKSIC3kJxEQAAAADgSRQYzUAx0Rz0WkQ4RDvdAAAAAAAAAADuRHERAAAAAOBZ9NQCjuK3gHChuAgAAAAAAACgSiguAgAAAAA8jR5b8Dt+AwgniosAAAAAAM+juAIA4UFxEQAAAAAAwKMorCPcKC4CAAAAAHyBIgsA2I/iIgAAAADANygwAoC9KC4CAAAAAAB4EMV0RALFRQAAAACAr1BwAQD7UFwEAAAAAADwGIroiBSKiwAAAAAA36HwAgD2oLgIAAAAAPAlCozwKvZtRFKViovTp09XcnKy4uPjlZqaqmXLlpW5/htvvKGUlBTFx8erU6dOWrBgQZUaCwBw1u7duzV48GAlJCSoQYMGGjFihPbt21fm+rfccovat2+vWrVqqXXr1rr11luVm5sbtN7y5cvVu3dvNWjQQA0bNlR6erq+/fbbcH+cAOIaAPiTF+MaMQ0A/KmyMU2SsrOzNWTIECUlJalOnTo688wz9dZbb1X6vStdXJw3b57GjBmjjIwMrVq1SqeffrrS09O1Y8eOkOt/9dVXGjRokEaMGKFvvvlGAwcO1MCBA7V27dpKNxYA4KzBgwdr3bp1WrRokebPn6/PPvtMN9xwQ6nrb9++Xdu3b9eUKVO0du1azZkzRwsXLtSIESMC6+zbt0/9+vVT69at9fXXX+uLL75QvXr1lJ6ersOHD4f9MxHXAMC/vBbXiGlVQw8veA37tD9VNqZJ0tChQ7Vx40a9++67WrNmjf785z/riiuu0DfffFOp946yLMuqzBNSU1N11lln6ZlnnpEkFRUVqVWrVrrllls0duzYEutfeeWV2r9/v+bPnx9Yds4556hz586aOXNmhd4zLy9P9evX1/ilfRVft2ZlmgsAtjm077AePudD5ebmKiEhoVqvVXxcS2tyvWKiY21q4VFHigr00Y6/29LOY61fv14dOnTQ8uXL1bVrV0nSwoULddFFF+nnn39W8+bNK/Q6b7zxhq655hrt379fMTExWrFihc466yxlZWWpVatWkqQ1a9botNNO0/fff6+TTjrJts8QSqTjGjENgCnsimtujGmSN+Oak7laWrO/2P79R9rBji2cbgJgC78WF48UFeijX56zL66F4bhmVxuPV9WYVrduXc2YMUNDhgwJLDvhhBM0efJkXX/99RV+/5jKNLagoEArV67UuHHjAsuio6OVlpamzMzMkM/JzMzUmDFjgpalp6frnXfeKfV98vPzlZ+fH/h38TCD/P1HKtNcALBV8TGoktdkynTEKpCKbHu5319TR4PiseLi4hQXF1fl183MzFSDBg0CwUqS0tLSFB0dra+//lp/+tOfKvQ6xYE0JuZoCGrfvr1OOOEEvfDCC7r33ntVWFioF154QaeccoqSk5Or3N6KiERcI6YBMJXdcc1NMU3yXlxzOlc7UlRQjdab4ciRQ043AbCFF36PVVH8uf0Y16oa07p376558+apf//+atCggf7xj3/o0KFD6tWrV6Xev1LFxV27dqmwsFBNmzYNWt60aVNt2LAh5HOys7NDrp+dnV3q+0yaNEkTJ04ssfxvvT+uTHMBICx+/fVX1a9fv1qvERsbq6SkJC3JftmmVgWrW7duoLdEsYyMDE2YMKHKr5mdna0mTZoELYuJiVGjRo3KPKYfa9euXXrooYeCuuf/v/buPybq+o8D+BMO7g4WvwwRbGgDfxVhP3CykxjLselklusPSIths6jEP9JWUeauRRk51tqMatkPalOZNTGXzF8oSw2pEWwYlw45M5ewWSEsJDx4ff/4zvt+0YP4fOA+d/d5Px8bf/jh/eFeT358nrv3nXcxMTFobGzEqlWrUFFRAQCYO3cuDh065L2j5i9G9Bo7jYiC3WR7LRQ7DTBfrwX6vlpjz+c6pg4ylwM9ABFNhanrNf9c14LpvtqePXtQVFSE22+/HREREYiOjkZdXZ3mZ9n7916bTq+88sqoR9B6e3sxe/ZsXLx4cdJ36INZX18fUlNT8dtvv035f/sIJqrkBNTJqkrOq1evYtasWZg2bdqkv5bdbofb7cbQkH8eVRQRhIWFjTo21iNh5eXleOedd8b9ei6Xa9Iz9fX1oaCgAHffffeo4rx27RrWrVuHnJwc7N69G8PDw6iqqkJBQQF+/PFHREVFTfq2A0nVTgPUuTYwp/moknWqei2YOg1gr/mbqr2mynUBUCcrc5qPGXvN3522ZcsW9Pb24ujRo0hMTMS+fftQWFiIEydOIDMzc8JfR9PmYmJiIiwWC3p6ekYd7+npQXJyss9zkpOTNa0Hxn46aFxcnOn/GAAgNjaWOU1Glayq5AwP1/xeWD7Z7XbY7fYp+VqT8cILL2Dt2rXjrklLS0NycvItLwjv8Xjw559/jntNB4D+/n4sX74cMTExqKurQ2Tk/15rcNeuXbhw4QKampq839tdu3YhISEB33zzDR577DF9wSbAiF5TvdMAda4NzGk+qmSdil4Llk4D1O013lczhirXBUCdrMxpPmbqNX922vnz5/H+++/jzJkzyMjIAADce++9OHHiBKqrqyf82ruAxs1Fq9WKrKwsNDQ0YNWqVQD++yLBDQ0N2LBhg89zHA4HGhoa8Pzzz3uPHTlyBA6HQ8tNExGRn0yfPh3Tp0//13UOhwO9vb1oaWlBVlYWAODYsWMYGRlBdnb2mOf19fVh2bJlsNls2L9//y0lPTAwgPDw8FGP3t3498jIFL/IyU3Ya0RE5qNqr7HTiIjMx5+dNjAwAODWzViLxaK9r0Sj2tpasdlsUlNTIx0dHVJaWirx8fHS3d0tIiLFxcVSXl7uXX/q1CmJiIiQqqoqcblc4nQ6JTIyUtrb2yd8m1evXhUAcvXqVa3jhhTmNB9VsjKnOpYvXy7333+/NDc3y8mTJ2Xu3LmyevVq7+cvXbok8+fPl+bmZhH57/csOztbMjMzpbOzUy5fvuz98Hg8IiLicrnEZrPJc889Jx0dHXLmzBl54oknJC4uTn7//Xe/ZzK611T6PVIlK3OajypZVck5HrP1Gu+r+Y8qOUXUycqc5qNSVl+0dtrQ0JDMmTNHcnNzpbm5WTo7O6WqqkrCwsLkwIEDmm5b8+aiiMj27dtl1qxZYrVaZfHixXL69Gnv5/Ly8qSkpGTU+j179si8efPEarVKRkaG5iEHBwfF6XTK4OCgnnFDBnOajypZmVMdf/zxh6xevVpuu+02iY2NlSeffFL6+/u9n3e73QJAjh8/LiIix48fFwA+P9xut/e8w4cPS05OjsTFxUlCQoIsXbpUmpqaDMtlZK+p9HukSlbmNB9VsqqSczxm7DXeV/MPVXKKqJOVOc1Hpay+aO00EZFz587Jo48+KklJSRIdHS0LFy6UL7/8UvNth4lM0Xt0ExERERERERERkVKm5l0JiIiIiIiIiIiISDncXCQiIiIiIiIiIiJduLlIREREREREREREunBzkYiIiIiIiIiIiHQJms3F6upq3HnnnbDb7cjOzsYPP/ww7vqvvvoKCxYsgN1uR2ZmJurr6w2adHK05NyxYwdyc3ORkJCAhIQE5Ofn/+v3JVho/XneUFtbi7CwMKxatcq/A04hrVl7e3tRVlaGlJQU2Gw2zJs3LyR+f7XmfO+99zB//nxERUUhNTUVGzduxODgoEHT6vPdd99h5cqVmDlzJsLCwrBv375/PaexsREPPPAAbDYb5syZg5qaGr/PScFPlU4D2Gv/JtR6TZVOA9hrY2GvkS+q9JoqnQaw18YSqr3GTvONnWagyb/Z9eTV1taK1WqVzz77TH7++Wd5+umnJT4+Xnp6enyuP3XqlFgsFtm2bZt0dHTIa6+9JpGRkdLe3m7w5NpozblmzRqprq6W1tZWcblcsnbtWomLi5NLly4ZPLk2WnPe4Ha75Y477pDc3Fx55JFHjBl2krRm/eeff2TRokWyYsUKOXnypLjdbmlsbJS2tjaDJ9dGa86dO3eKzWaTnTt3itvtlkOHDklKSops3LjR4Mm1qa+vl82bN8vevXsFgNTV1Y27vqurS6Kjo2XTpk3S0dEh27dvF4vFIgcPHjRmYApKqnSaCHvNbL2mSqeJsNfGwl4jX1TpNVU6TYS9ZrZeY6f5xk4zVlBsLi5evFjKysq8/x4eHpaZM2fK22+/7XN9YWGhFBQUjDqWnZ0tzzzzjF/nnCytOW/m8XgkJiZGvvjiC3+NOCX05PR4PLJkyRL55JNPpKSkJCTKSkR71g8//FDS0tJkaGjIqBGnhNacZWVlsnTp0lHHNm3aJDk5OX6dcypNpLBeeuklycjIGHWsqKhIli1b5sfJKNip0mki7DWz9ZoqnSbCXhsLe418UaXXVOk0Efaa2XqNneYbO81YAf9v0UNDQ2hpaUF+fr73WHh4OPLz89HU1OTznKamplHrAWDZsmVjrg8GenLebGBgANevX8e0adP8Neak6c35xhtvICkpCevWrTNizCmhJ+v+/fvhcDhQVlaGGTNm4J577sHWrVsxPDxs1Nia6cm5ZMkStLS0eJ+O39XVhfr6eqxYscKQmY0Sitci8i9VOg1gr5mt11TpNIC9Np5QvR6R/6jSa6p0GsBeM1uvsdPGForXolAWEegBrly5guHhYcyYMWPU8RkzZuCXX37xeU53d7fP9d3d3X6bc7L05LzZyy+/jJkzZ97yBxJM9OQ8efIkPv30U7S1tRkw4dTRk7WrqwvHjh3D448/jvr6enR2dmL9+vW4fv06nE6nEWNrpifnmjVrcOXKFTz44IMQEXg8Hjz77LN49dVXjRjZMGNdi/r6+nDt2jVERUUFaDIKFFU6DWCvma3XVOk0gL02HvYa3UyVXlOl0wD2mtl6jZ02NnaasQL+zEWamMrKStTW1qKurg52uz3Q40yZ/v5+FBcXY8eOHUhMTAz0OH43MjKCpKQkfPzxx8jKykJRURE2b96Mjz76KNCjTanGxkZs3boVH3zwAX766Sfs3bsXBw4cQEVFRaBHI6IgwV4Lfap0GsBeI6LxmbXTAPaaGXuNnUb+EPBnLiYmJsJisaCnp2fU8Z6eHiQnJ/s8Jzk5WdP6YKAn5w1VVVWorKzE0aNHsXDhQn+OOWlac54/fx4XLlzAypUrvcdGRkYAABERETh79izS09P9O7ROen6mKSkpiIyMhMVi8R6766670N3djaGhIVitVr/OrIeenFu2bEFxcTGeeuopAEBmZib+/vtvlJaWYvPmzQgPN8fjGmNdi2JjY/lImKJU6TSAvWa2XlOl0wD22njYa3QzVXpNlU4D2Gtm6zV22tjYacYK+G+N1WpFVlYWGhoavMdGRkbQ0NAAh8Ph8xyHwzFqPQAcOXJkzPXBQE9OANi2bRsqKipw8OBBLFq0yIhRJ0VrzgULFqC9vR1tbW3ej4cffhgPPfQQ2trakJqaauT4muj5mebk5KCzs9NbyABw7tw5pKSkBGVZAfpyDgwM3FJKN0paRPw3rMFC8VpE/qVKpwHsNbP1miqdBrDXxhOq1yPyH1V6TZVOA9hrZus1dtrYQvFaFNIC+W4yN9TW1orNZpOamhrp6OiQ0tJSiY+Pl+7ubhERKS4ulvLycu/6U6dOSUREhFRVVYnL5RKn0ymRkZHS3t4eqAgTojVnZWWlWK1W+frrr+Xy5cvej/7+/kBFmBCtOW8WKu8+JqI968WLFyUmJkY2bNggZ8+elW+//VaSkpLkzTffDFSECdGa0+l0SkxMjOzevVu6urrk8OHDkp6eLoWFhYGKMCH9/f3S2toqra2tAkDeffddaW1tlV9//VVERMrLy6W4uNi7vqurS6Kjo+XFF18Ul8sl1dXVYrFY5ODBg4GKQEFAlU4TYa+ZrddU6TQR9hp7jbRQpddU6TQR9prZeo2dxk4LBkGxuSgisn37dpk1a5ZYrVZZvHixnD592vu5vLw8KSkpGbV+z549Mm/ePLFarZKRkSEHDhwweGJ9tOScPXu2ALjlw+l0Gj+4Rlp/nv8vVMrqBq1Zv//+e8nOzhabzSZpaWny1ltvicfjMXhq7bTkvH79urz++uuSnp4udrtdUlNTZf369fLXX38ZP7gGx48f9/k3dyNbSUmJ5OXl3XLOfffdJ1arVdLS0uTzzz83fG4KPqp0mgh7TcRcvaZKp4mw10TYazRxqvSaKp0mwl4TMVevsdPYaYEWJmKi570SERERERERERGRYQL+motEREREREREREQUmri5SERERERERERERLpwc5GIiIiIiIiIiIh04eYiERERERERERER6cLNRSIiIiIiIiIiItKFm4tERERERERERESkCzcXiYiIiIiIiIiISBduLhIREREREREREZEu3FwkIiIiIiIiIiIiXbi5SERERERERERERLpwc5GIiIiIiIiIiIh04eYiERERERERERER6fIf7CgNMivM5nAAAAAASUVORK5CYII=", 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vvjrk9uuvv16WZemNN96Ie+zx+OSTT7R161b95je/CTl34bBhw1RQUFBvvS9Jv/rVr0J+P/XUU1nvA4ABioqKlJubqy5duui8885TixYt9Morr6hz587asWOHFi9erAsuuEC7du0K1rfvv/9excXF+vrrr4OnnGjTpo2+/PJLff3111E/d6A/qlvfAkdRxat2j1xeXq7t27fr9NNP13//+1+Vl5cntGzAqwgKbXTzzTfr4MGDEc9V+PXXX8uyLB199NHKzc0N+Vm1apW2bt0a93MfeeSR9W6rqanRfffdp6OPPlpZWVlq166dcnNz9e9//9uWleI111yjNm3aRDxXYaAwnHHGGfVe71tvvRXV6+3QoYOOPvroYCj4/vvv69RTT9Vpp52mLVu26L///a8++OAD1dTUBIPCbdu2ae/everZs2e95fXq1Us1NTX1zgkZ7v0LOOecc9SqVSu9+eabysnJaXTMtR199NEhv/fo0UPp6en1zulX9/m3bdumnTt36tFHH6333gUa2MD7FzhvYt1gL9zrr2vt2rXq2LGj2rZtG9PriuTbb78N+9yZmZnq3r178N8DOnfuXG/chx12mP73v//ZMh4AiFWnTp0SuqLv119/rQULFtRbdxcVFUlSg7Xv9NNP14gRIzRt2jS1a9dO/+///T/NnTs35Jyz3377rTp27KhWrVqFPDZw6o2669lki7Tel6SCgoJ64wmcC7c21vsAYIZZs2Zp4cKFeuGFF3T22Wdr+/btysrKkiR98803sixLf/jDH+rVuMCkg0CNu/XWW7Vz50796Ec/0nHHHacbbrgh5Pzy4Xz77bdKT0+vd4qjaHqahnzwwQcqKipSixYt1KZNG+Xm5gbPP0xQ6B/vvfeezjnnHHXs2FFpaWl6+eWXk/p8gfNl1v4pKChI6nPaiXMU2qh79+665JJL9Oijj4a91HtNTY3S0tL0xhtvhJ291rJly+D/R5rNVV1dHfaxdWcTStKdd96pP/zhD7r88st12223qW3btkpPT9e1114b1QnVGxOYVTh16tSwswoDz/GXv/xFeXl59f492qtcDRgwQIsWLdK+ffu0fPly3XLLLTr22GPVpk0bvf/++1q1apVatmypH//4x3G/lnDvX8CIESP05JNP6plnntEvf/nLuJ9Divy51n3+wHt3ySWXaPTo0WEfEzg/o5tFmsVpNXJCfgBIlobqQTh1L8JRU1OjIUOG6He/+13Y+//oRz+KuKy0tDS98MILWrp0qf7xj3/ozTff1OWXX6577rlHS5cuDdlOiEdD2xapEmm9DwBw3oknnhi86vHw4cM1YMAAXXzxxVqzZk2wP/ntb3+r4uLisI8/6qijJEmnnXaa1q5dq7///e9666239Oc//1n33Xef5syZoyuuuCLhcUZbz9auXavBgweroKBA9957r7p06aLMzEy9/vrruu+++2zpieEOe/bs0QknnKDLL7+83sVSk+WYY47R22+/HfzdTVf5ds9IXeLmm2/W008/rRkzZtT7tx49esiyLB155JENNgrSob3rda8SKx3a01L7cNqGvPDCCxo0aJAef/zxkNt37twZ1YU4onHttdfq/vvv17Rp0+pdiCWwN6h9+/bBmRSRNHSY66mnnqq5c+fq2WefVXV1tU4++WSlp6drwIABwaDw5JNPDjYfubm5at68udasWVNvWatXr1Z6erq6dOkS9Wu8++67lZGRod/85jdq1aqVLr744qgf+/XXX4fMFvzmm29UU1MT9ipdtQWu9FVdXd3oe9etWzd98cUXsiwr5H0M9/rr6tGjh958803t2LGjwVmF0R6G3K1bt+Bz1/6eVlVVad26dY2+FgAwVbi6XFVVVe90Iz169NDu3bsTWt+ddNJJOumkk3THHXdo/vz5GjVqlJ599lldccUV6tatm95++23t2rUrZFbh6tWrJf2wHo70GqRD2wG1a3a4WYjxrPfPOOOMkH9bs2ZNg+MBAJirSZMmmj59ugYNGqSHHnpIl19+uSSpadOmUdW4tm3bauzYsRo7dqx2796t0047TVOnTo0YFHbr1k01NTVau3ZtyCzCcD1NQ71ybf/4xz9UWVmpV155RV27dg3ensgpv+BOZ511VoMXy6ysrNTvf/97/d///Z927typY489VjNmzNDAgQPjfs6MjIywE6bcgEOPbdajRw9dcskleuSRR1RaWhryb7/4xS/UpEkTTZs2rd6MKcuy9P3334csZ+nSpaqqqgre9uqrr9Y7ZLYhTZo0qfc8zz//fPDcEXYIzCr8+9//rpUrV4b8W3FxsXJycnTnnXeGPdfStm3bgv/fokULSQq7wg8cUjxjxgwdf/zxat26dfD2RYsW6ZNPPgneRzr0us8880z9/e9/DznEt6ysTPPnz9eAAQNiOoQ4LS1Njz76qM477zyNHj1ar7zyStSPnTVrVsjvDz74oCQ1ekXfJk2aaMSIEXrxxRf1xRdf1Pv32u/d2WefrS1btuiFF14I3rZ37149+uijjY5vxIgRsixL06ZNq/dvtb87LVq0CPvZ1FVUVKTMzEw98MADIY9//PHHVV5envBVywDAKT169Kh3fsFHH3203uyFCy64QEuWLNGbb75Zbxk7d+7UwYMHIz7H//73v3p1u0+fPpIUPPz47LPPVnV1tR566KGQ+913331KS0trsL4EduDVfh179uzRk08+We++0a73+/Xrp/bt22vOnDkhh0i/8cYbWrVqFet9AHCxgQMH6sQTT9T999+vnJwcDRw4UI888kjYc/LX7k9q97XSoSPnjjrqqJA6UVegfj3wwAMht99///317tujRw+Vl5eHHM783Xff6aWXXgq5X2AiSe3aWl5errlz50YcB/xp4sSJWrJkiZ599ln9+9//1vnnn6+hQ4fGdJ7Nur7++mt17NhR3bt316hRo7RhwwYbR5xczChMgt///vf6y1/+ojVr1uiYY44J3t6jRw/dfvvtmjJlitavX6/hw4erVatWWrdunV566SVdeeWV+u1vfyvp0KXhX3jhBQ0dOlQXXHCB1q5dq6effrreORsa8rOf/Uy33nqrxo4dq5NPPlmff/65nnnmmahnJEbrmmuu0X333afPPvssGPhJUk5OjmbPnq1LL71UP/nJTzRy5Ejl5uZqw4YNeu2113TKKacEG52+fftKOnTy2uLiYjVp0kQjR46UdGgKe15entasWRO8GIh0aEr7jTfeKEkhQaEk3X777Vq4cKEGDBig3/zmN8rIyNAjjzyiyspK/fGPf4z5Naanp+vpp5/W8OHDdcEFF+j111+vN3MinHXr1uncc8/V0KFDtWTJEj399NO6+OKLdcIJJzT62LvuukvvvPOOCgsLNX78ePXu3Vs7duzQihUr9Pbbb2vHjh2SDl0o5KGHHtJll12m5cuX64gjjtBf/vIXNW/evNHnGDRokC699FI98MAD+vrrrzV06FDV1NTo/fff16BBgzRx4kRJhz6ft99+W/fee686duyoI488UoWFhfWWl5ubqylTpmjatGkaOnSozj33XK1Zs0YPP/ywfvrTn4ZcuAQA3OSKK67Qr371K40YMUJDhgzRZ599pjfffLPeDP0bbrhBr7zyin72s59pzJgx6tu3r/bs2aPPP/9cL7zwgtavXx9xVv+TTz6phx9+WD//+c/Vo0cP7dq1S4899phycnJ09tlnSzp03txBgwbp97//vdavX68TTjhBb731lv7+97/r2muvbXA74cwzz1TXrl01btw43XDDDWrSpImeeOKJYG2urW/fvpo9e7Zuv/12HXXUUWrfvn3Yute0aVPNmDFDY8eO1emnn66LLrpIZWVl+tOf/qT8/Hxdd911sb7VAACD3HDDDTr//PM1b948zZo1SwMGDNBxxx2n8ePHq3v37iorK9OSJUu0adMmffbZZ5Kk3r17a+DAgerbt6/atm2rTz75RC+88EKwtwinT58+uuiii/Twww+rvLxcJ598shYtWqRvvvmm3n1HjhypG2+8UT//+c919dVXa+/evZo9e7Z+9KMfhVy088wzz1RmZqbOOecc/fKXv9Tu3bv12GOPqX379hEvQAr/2bBhg+bOnasNGzaoY8eOkg4dYr9gwQLNnTtXd955Z8zLLCws1Lx589SzZ0999913mjZtmk499VR98cUX9c4zbaTUX2jZOwKXkV+2bFm9fxs9erQlyTrmmGPq/duLL75oDRgwwGrRooXVokULq6CgwJowYYK1Zs2akPvdc889VqdOnaysrCzrlFNOsT755JN6l21/5513LEnW888/X+959u/fb11//fXWEUccYTVr1sw65ZRTrCVLltRbxrp16yxJ1ty5cxt8vQ09V+BS9C1atAj7uOLiYqt169ZWdna21aNHD2vMmDHWJ598ErzPwYMHrauuusrKzc210tLS6l3W/vzzz7ckWc8991zwtqqqKqt58+ZWZmamtW/fvnrPu2LFCqu4uNhq2bKl1bx5c2vQoEHWhx9+GHKfhj7DwGvatm1b8La9e/dap59+utWyZUtr6dKlEd+rwGO/+uor67zzzrNatWplHXbYYdbEiRPrjVWSNWHChLDLKSsrsyZMmGB16dLFatq0qZWXl2cNHjzYevTRR0Pu9+2331rnnnuu1bx5c6tdu3bWNddcYy1YsMCSZL3zzjvB+40ePdrq1q1byGMPHjxo3X333VZBQYGVmZlp5ebmWmeddZa1fPny4H1Wr15tnXbaaVazZs0sSdbo0aND3r9169aFLPOhhx6yCgoKrKZNm1odOnSwfv3rX1v/+9//Qu5z+umnh/37CDdGALDbhAkT6tWaSOsly7Ks6upq68Ybb7TatWtnNW/e3CouLra++eYbq1u3bsF1YsCuXbusKVOmWEcddZSVmZlptWvXzjr55JOtmTNnWlVVVRHHtGLFCuuiiy6yunbtamVlZVnt27e3fvazn4XUy8Dyr7vuOqtjx45W06ZNraOPPtq6++67rZqampD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" ] @@ -28541,81 +28538,12 @@ } ], "source": [ - "# plotting at fixed time t = 0.0\n", "print('Plotting at t=0')\n", - "fixed_variables={'t': 0.0}\n", - "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", - "fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", - "fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", - "fixed_pts.labels = list(fixed_variables.keys())\n", - "pts = pts.append(fixed_pts)\n", - "pts = pts.to(device=pinn.device)\n", - "predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", - "true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", - "pts = pts.cpu()\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", - "cb = getattr(ax[0], method)(*grids, predicted_output)\n", - "fig.colorbar(cb, ax=ax[0])\n", - "ax[0].title.set_text('Neural Network prediction')\n", - "cb = getattr(ax[1], method)(*grids, true_output)\n", - "fig.colorbar(cb, ax=ax[1])\n", - "ax[1].title.set_text('True solution')\n", - "cb = getattr(ax[2],method)(*grids,(true_output - predicted_output))\n", - "fig.colorbar(cb, ax=ax[2])\n", - "ax[2].title.set_text('Residual')\n", - "# plotting at fixed time t = 0.5\n", + "fixed_time_plot(fixed_variables={'t':0.0},pinn=pinn)\n", "print('Plotting at t=0.5')\n", - "#plotter.plot(pinn, fixed_variables={'t': 0.5})\n", - "fixed_variables={'t': 0.5}\n", - "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", - "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", - "fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", - "fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", - "fixed_pts.labels = list(fixed_variables.keys())\n", - "pts = pts.append(fixed_pts)\n", - "pts = pts.to(device=pinn.device)\n", - "predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", - "true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", - "pts = pts.cpu()\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", - "cb = getattr(ax[0], method)(*grids, predicted_output)\n", - "fig.colorbar(cb, ax=ax[0])\n", - "ax[0].title.set_text('Neural Network prediction')\n", - "cb = getattr(ax[1], method)(*grids, true_output)\n", - "fig.colorbar(cb, ax=ax[1])\n", - "ax[1].title.set_text('True solution')\n", - "cb = getattr(ax[2],method)(*grids,(true_output - predicted_output))\n", - "fig.colorbar(cb, ax=ax[2])\n", - "ax[2].title.set_text('Residual')\n", - "# plotting at fixed time t = 1.\n", - "print('Plotting at t=1')\n", - "#plotter.plot(pinn, fixed_variables={'t': 1.0})\n", - "fixed_variables={'t': 1.0}\n", - "pts = pinn.problem.spatial_domain.sample(256, 'grid', variables=['x','y'])\n", - "fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))\n", - "fixed_pts *= torch.tensor(list(fixed_variables.values()))\n", - "fixed_pts = fixed_pts.as_subclass(LabelTensor)\n", - "fixed_pts.labels = list(fixed_variables.keys())\n", - "pts = pts.append(fixed_pts)\n", - "pts = pts.to(device=pinn.device)\n", - "predicted_output = pinn.forward(pts).extract('u').as_subclass(torch.Tensor).cpu().detach().reshape(256,256)\n", - "true_output = pinn.problem.truth_solution(pts).cpu().detach().reshape(256,256)\n", - "pts = pts.cpu()\n", - "grids = [p_.reshape(256, 256) for p_ in pts.extract(['x','y']).T]\n", - "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))\n", - "cb = getattr(ax[0], method)(*grids, predicted_output)\n", - "fig.colorbar(cb, ax=ax[0])\n", - "ax[0].title.set_text('Neural Network prediction')\n", - "cb = getattr(ax[1], method)(*grids, true_output)\n", - "fig.colorbar(cb, ax=ax[1])\n", - "ax[1].title.set_text('True solution')\n", - "cb = getattr(ax[2],method)(*grids,(true_output - predicted_output))\n", - "fig.colorbar(cb, ax=ax[2])\n", - "ax[2].title.set_text('Residual')" + "fixed_time_plot(fixed_variables={'t':0.5},pinn=pinn)\n", + "print('Plotting at t=1.0')\n", + "fixed_time_plot(fixed_variables={'t':1.0},pinn=pinn)\n" ] }, {