lvjiameng commited on Feb 5

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ExampleCode/example1/Multi_band_analy.ipynb +0 -0
ExampleCode/example1/Sanomalous_sources.csv +323 -0
ExampleCode/example1/anomalies_with_fastspecfit_bpt.csv +314 -0
ExampleCode/example1/anomaly_metadata.csv +323 -0
ExampleCode/example1/dataset/SFDJA_image_catalog.csv +0 -0
ExampleCode/example1/features/FAJD_multimodal_features.h5 +3 -0
ExampleCode/example1/features/FAJD_multimodal_features_norm.h5 +3 -0
ExampleCode/example1/features/SFAJD_multimodal_features.h5 +3 -0
ExampleCode/example1/features/SFAJD_multimodal_features_norm.h5 +3 -0
ExampleCode/example1/features/clustered_sources_gmm_tsne.csv +0 -0
ExampleCode/example1/features/clustered_sources_gmm_umap.csv +0 -0
ExampleCode/example1/model/DesiEncoder.py +416 -0
ExampleCode/example1/model/JplusEncoder.py +171 -0
ExampleCode/example1/model/SpecEncoder.py +416 -0
ExampleCode/example1/model/__pycache__/DesiEncoder.cpython-310.pyc +0 -0
ExampleCode/example1/model/__pycache__/JplusEncoder.cpython-310.pyc +0 -0
ExampleCode/example1/model/checkpoint/DesiEncoder.pth +3 -0
ExampleCode/example1/model/checkpoint/JplusEncoder.pth +3 -0
ExampleCode/example2/Cot.ipynb +373 -0
ExampleCode/example2/image/239.5743316076613_32.91914641623415.jpg +0 -0
ExampleCode/example2/llm_result/239.5743316076613_32.91914641623415.txt +10 -0

ExampleCode/example1/Multi_band_analy.ipynb ADDED Viewed

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ExampleCode/example1/Sanomalous_sources.csv ADDED Viewed

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+226.28036,46.963696,-0.5923697736365776,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=705/001-000.hdf5,3780,226.2803680873732,46.963695730304295,0.0377137482793957,289.48236,96.03292,80.29495,30.222914,1.2453047,1.3990825,125.0
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+225.21576,47.30673,-0.5937442856152948,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=704/001-001.hdf5,4395,225.21575862763004,47.30672733710575,0.231386497408801,35.592216,18.119886,5.268182,3.386487,1.6014321,1.0757612,125.0
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+224.01189,36.243046,-0.6695349974551924,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-001.hdf5,1799,224.01188541773564,36.24304626196033,0.0472313120185995,129.3738,119.57966,26.353537,21.31139,1.4268224,0.32690594,91.21222
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+210.91286,35.773186,-0.5600545266906618,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=658/001-001.hdf5,800,210.91285383161784,35.773186728036734,0.0133403466239761,13.649347,2.275988,4.354769,9.20346,1.6012939,3.2976995,125.0
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+226.92117,46.89221,-0.6103942130253949,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=705/001-000.hdf5,1263,226.92118065891,46.892207161877565,0.0905692343984707,699.1391,164.21088,163.69133,178.81459,1.1030179,2.24335,362.03806
+219.89328,46.63121,-0.6413601162909434,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=663/001-002.hdf5,627,219.8932850479245,46.63120865207797,0.0375364890667122,1684.9369,813.02466,260.66617,90.362076,1.2437927,2.8132715,110.71867
+200.04846,33.09609,-0.6771650566707986,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=652/001-001.hdf5,630,200.04846720021123,33.09608833422207,0.0396385996701661,84.131096,44.45386,9.918953,8.187026,1.7141064,0.6740512,70.03848
+223.0503,46.521004,-0.5628563220725986,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=706/001-000.hdf5,1315,223.05029934664813,46.52100280670349,0.0721267166692352,2.0307114,13.6547785,0.0,11.176326,2.0280647,0.0,173.46309
+219.58582,47.816025,-0.617485136663874,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=706/001-001.hdf5,2490,219.5858130520594,47.81602561856626,0.2460078607801517,0.0,1.4027765,4.4766035,0.0,2.680429,2.5337577,157.76195
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+207.20651,31.754204,-0.6166484366615139,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-000.hdf5,1127,207.20651397910143,31.75420387753708,0.1065807729707902,11.271789,11.850342,0.0,0.0,1.8284445,0.87155294,170.73048
+207.21309,32.910126,-0.5813153566528981,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-000.hdf5,4324,207.21309367339447,32.91012618252359,0.1944679851553689,1.0747736,4.8003674,2.6386006,6.2366476,1.5625354,0.65093094,120.43932
+207.24332,31.675657,-0.5980202166751152,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-000.hdf5,1111,207.243312105534,31.675658178620367,0.0760450093763785,4.0224624,12.887316,0.0,7.8925886,2.0682714,0.4021392,216.6748
+212.29573,39.28846,-0.6444623863571558,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=659/001-001.hdf5,4668,212.29572729739496,39.28845866917107,0.3523514935677235,40.476967,13.044783,10.372715,7.89279,1.1488165,2.7687988,177.3769
+224.27069,47.335457,-0.5661041696964321,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=704/001-001.hdf5,4514,224.2706961343229,47.33545732165221,0.1360163461397858,0.0,12.464649,3.7781096,1.2228712,2.0113862,0.0,301.08035
+206.13358,33.488094,-0.6074852402469132,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-001.hdf5,129,206.13358107594075,33.48809456522228,0.2739931985305081,0.79417723,0.1487888,5.8457084,0.0,1.7899655,1.5389441,220.4389
+226.09256,47.6867,-0.6362475765912455,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=705/001-000.hdf5,4664,226.09255430835245,47.68669715018158,0.0933861996400477,184.60187,144.15887,32.058487,15.440179,1.811719,0.29084396,300.61115
+210.64293,35.302143,-0.5987487019556038,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=658/001-000.hdf5,1708,210.64292425767096,35.30214254064556,0.466394972343971,54.03332,18.863293,16.772726,8.307977,1.0934554,1.3408453,125.0
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+211.47302,33.170837,-0.6771737735875608,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=656/001-001.hdf5,1486,211.473027373856,33.1708379494616,0.0545837896154792,0.0,66.03611,0.0,0.0,2.0488505,0.39095822,266.29742
+211.6843,39.01213,-0.601324104715165,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=659/001-001.hdf5,3753,211.68428987765725,39.01212954702455,0.211787656312605,0.01942657,2.4512324,0.0,1.707608,1.8512852,0.5878976,182.66818
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+219.74295,47.307888,-0.6040367003693287,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=706/001-001.hdf5,1331,219.74294741930424,47.30788952630464,0.0270759705789607,307.4401,80.6204,88.83942,116.65541,1.2357608,0.41265604,133.1318
+206.14038,33.54464,-0.5599020531649982,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-001.hdf5,222,206.1403824454885,33.54463875626278,0.1662019101960731,269.41565,106.14157,54.2275,25.099771,1.4659518,3.5225248,125.0
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+222.43597,34.342525,-0.587221023224993,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,1188,222.43597798310344,34.342523897281595,0.340490227739321,115.31614,29.967829,34.649204,23.886856,1.1803927,3.504892,125.0
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+222.72176,36.17021,-0.5864638856708192,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-001.hdf5,865,222.7217498790792,36.17021351244886,0.1222176893819427,175.63924,75.940025,31.894058,4.6895742,1.3481951,0.62894213,157.79854
+222.73546,35.57169,-0.6601356727954886,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-001.hdf5,487,222.7354556328703,35.571688282728815,0.0039695336777407,3443.6672,151.33598,1137.5629,4294.6865,1.0020062,3.1881292,125.0
+223.05081,34.027065,-0.5774383871353495,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,1079,223.05080455271624,34.027063514016305,0.1620309838191107,65.19058,26.102282,16.987501,6.538011,1.3159517,1.4064438,65.37186
+223.44524,34.79861,-0.6067579467275153,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,3708,223.4452363851028,34.79861119735138,0.0770945828784734,7.858922,28.771578,0.0,10.71163,1.9498831,0.039399184,181.50952
+223.44724,36.1347,-0.5610555272001411,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-001.hdf5,1116,223.44723745611304,36.1347000781951,0.0731210143332696,205.15065,105.85612,48.582436,10.044661,1.6816188,1.3304148,166.3001
+223.70625,34.51449,-0.555503584624521,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,3656,223.70625149860203,34.51448932189829,0.2030341848228544,23.15143,6.185155,4.9168677,3.5135589,1.3334256,0.7558826,175.21295
+223.99643,35.66342,-0.6016213504029252,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,4756,223.9964274557889,35.66342324032763,0.0335815965404772,1.7743999,42.331783,0.3233424,36.214302,1.9824646,1.254335,143.1142
+219.32945,47.251503,-0.608815626493015,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=663/001-002.hdf5,2625,219.3294462582454,47.25150453486008,0.1225995221736021,11.084662,19.678726,1.3400545,7.8316894,1.989124,1.6353171,214.41988
+224.5197,47.406647,-0.6595209700694811,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=704/001-001.hdf5,4609,224.5197061619336,47.40664589577288,0.0848143174376725,24.303368,62.071705,4.255969,25.421356,2.273095,0.07493029,403.0216
+224.0696,46.916237,-0.608113270953753,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=704/001-001.hdf5,4032,224.06959449323315,46.91623619256161,0.0855187944154564,13.586229,39.610065,7.738013,12.804683,1.916908,0.502094,202.82097
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+211.44347,39.15259,-0.5702953258768438,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=659/001-001.hdf5,3686,211.4434711622688,39.152590230234246,0.1031131140504137,7.4845605,15.498243,0.0,0.0,1.6772119,0.3228938,183.57558
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+218.09491,47.11061,-0.6362163480872454,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=663/001-002.hdf5,1709,218.09490236960383,47.11061185946859,0.1086942674583146,25.940062,29.512754,15.613782,21.51171,1.964001,0.22049187,235.82353
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+223.06969,35.077347,-0.6195896885621592,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,3848,223.06968003069235,35.07734690562238,0.4227590622199453,31.929966,15.543224,3.8774934,4.424237,1.3776451,0.5248221,360.56583
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+224.81299,47.517773,-0.5779536529468127,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=704/001-001.hdf5,4686,224.8129919346501,47.51777144135956,0.0851662470778703,45.707638,43.13437,11.197289,21.72629,2.053551,0.0,183.23315
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+219.40982,47.672005,-0.5917026923590818,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=706/001-001.hdf5,2438,219.4098279806456,47.67200364275616,0.0944676187780445,32.995667,32.615368,5.8607965,7.4234166,1.8971497,0.25134394,181.4559
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+222.34369,47.35065,-0.5879728632814807,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=706/001-000.hdf5,3263,222.34368248831785,47.35064964242633,0.0999071807136972,0.75243735,32.346878,0.9037573,23.178295,2.0101779,0.9400376,218.99979
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+223.30116,35.969868,-0.5586866983049844,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-001.hdf5,182,223.30115513907,35.96986727040134,0.3674628680816229,0.0,1.389918,0.0,0.0,1.8981191,0.3340102,280.11584
+212.12698,36.495354,-0.6237574265822019,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=658/001-000.hdf5,3854,212.1269803985712,36.49535507613856,0.0352466235505702,65.05379,27.91922,19.133852,4.8374023,1.3267293,0.28137922,125.0
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+212.20108,38.540554,-0.5939662515907348,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=659/001-001.hdf5,4462,212.20108588225563,38.54055292090585,0.0451353453884809,230.41386,76.14302,55.541157,8.832134,1.2838078,0.96622896,70.85919
+219.80396,46.843464,-0.595464341439691,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=663/001-002.hdf5,979,219.8039581605784,46.84346306890006,0.1358284506131482,1.7759002,0.24040967,0.0,0.0,2.7820559,2.3690228,174.38004
+205.70216,33.306023,-0.558505411106937,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=653/001-000.hdf5,529,205.70215960251792,33.30602375467269,0.2803082382127252,39.887375,22.379267,7.4868894,1.9807751,1.6393282,1.2445171,192.72423
+223.61409,35.44088,-0.6368079425050536,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=574/001-000.hdf5,3914,223.61409079603712,35.44088127025764,0.0778410484414868,13.306026,54.100056,3.659013,3.9201136,2.0894976,0.6376175,310.91486
+205.78004,33.76482,-0.5941471438933146,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=653/001-000.hdf5,1063,205.78003962340856,33.76481875704611,0.1654175069687837,9.861464,9.286304,0.39036524,0.46077505,1.6748273,0.0,135.48343
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+219.44644,33.34785,-0.558589038219422,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=571/001-001.hdf5,4010,219.4464410391988,33.347851265519964,0.1650695086216278,0.0,5.1087046,0.0,0.0,2.1012168,0.03527971,320.60318
+218.0475,47.841133,-0.5645063302210299,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=669/001-000.hdf5,3628,218.0475054283721,47.84113262095771,0.2942295415873331,38.088425,22.34242,11.646282,32.820354,1.2801782,1.4459016,188.33478
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+205.8765,32.22188,-0.6105517207001179,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=646/001-001.hdf5,1129,205.87649247668529,32.22188182216297,0.2398050138166083,25.879347,53.64465,4.146173,17.096897,1.9438381,0.15102965,252.7436
+206.59183,31.800785,-0.5652848645046274,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-000.hdf5,3792,206.59183453206663,31.800784627149223,0.2282949010244794,10.015573,10.765384,3.0920367,0.08779311,2.0636342,2.8829436,229.82335
+219.68506,47.537968,-0.5771996032189316,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=706/001-001.hdf5,1165,219.6850660197225,47.53796808062533,0.0420239513877465,0.0,62.255276,9.690014,9.430833,1.8897711,0.95282644,215.8427
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+200.03183,32.856133,-0.7160977424000261,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=652/001-001.hdf5,301,200.03183573380787,32.85613284210982,0.0374133604904739,33.25536,144.10402,0.0,85.99707,2.01732,0.22207399,224.32664
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+207.03954,32.582333,-0.5662401829845286,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=647/001-000.hdf5,4277,207.0395312970765,32.58233251772263,0.2150068343093536,4.1546574,1.1920553,1.2519939,0.9764111,1.6171916,0.68595624,112.16352
+219.82018,46.754856,-0.5618618777518325,AstroM3/Dataset/catalogs/fastspecfit/data/healpix=663/001-002.hdf5,618,219.82017451084744,46.75485778726789,0.0761472565263532,128.16225,45.55345,21.298595,14.156609,1.604411,0.378113,121.82631
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ExampleCode/example1/anomaly_metadata.csv ADDED Viewed

	@@ -0,0 +1,323 @@

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ExampleCode/example1/model/DesiEncoder.py ADDED Viewed

	@@ -0,0 +1,416 @@

+import torch
+import torch.nn as nn
+from functools import partial
+from timm.models.vision_transformer import PatchEmbed, Block
+import torch.nn.functional as F
+import numpy as np
+import torch
+import numpy as np
+import skimage
+import numpy as np
+import cv2 as cv
+def sdss_rgb(imgs, bands, scales=None,
+             m=0.02):
+    """
+    Transformation from raw image data (nanomaggies) to the rgb values displayed
+    at the legacy viewer https://www.legacysurvey.org/viewer
+    Code copied from
+    https://github.com/legacysurvey/imagine/blob/master/map/views.py
+    """
+    rgbscales = {'u': (2, 1.5),  # 1.0,
+                 'g': (2, 2.5),
+                 'r': (1, 1.5),
+                 'i': (0, 1.0),
+                 'z': (0, 0.4),  # 0.3
+                 }
+    if scales is not None:
+        rgbscales.update(scales)
+    I = 0
+    for img, band in zip(imgs, bands):
+        plane, scale = rgbscales[band]
+        img = np.maximum(0, img * scale + m)
+        I = I + img
+    I /= len(bands)
+    Q = 20
+    fI = np.arcsinh(Q * I) / np.sqrt(Q)
+    I += (I == 0.) * 1e-6
+    H, W = I.shape
+    rgb = np.zeros((H, W, 3), np.float32)
+    for img, band in zip(imgs, bands):
+        plane, scale = rgbscales[band]
+        rgb[:, :, plane] = (img * scale + m) * fI / I
+    rgb = np.clip(rgb, 0, 1)
+    return rgb
+def dr2_rgb(rimgs, bands, **ignored):
+    return sdss_rgb(rimgs, bands, scales=dict(g=(2, 6.0), r=(1, 3.4), z=(0, 2.2)), m=0.03)
+# --------------------------------------------------------
+# 2D sine-cosine position embedding
+# References:
+# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
+# MoCo v3: https://github.com/facebookresearch/moco-v3
+# --------------------------------------------------------
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token:
+        pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+    return emb
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=float)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+# --------------------------------------------------------
+# Interpolate position embeddings for high-resolution
+# References:
+# DeiT: https://github.com/facebookresearch/deit
+# --------------------------------------------------------
+def interpolate_pos_embed(model, checkpoint_model):
+    if 'pos_embed' in checkpoint_model:
+        pos_embed_checkpoint = checkpoint_model['pos_embed']
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.patch_embed.num_patches
+        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            checkpoint_model['pos_embed'] = new_pos_embed
+class MaskedAutoEncoderViT(nn.Module):
+    """ Masked Autoencoder with VisionTransformer backbone
+    """
+    def __init__(self, img_size=224, patch_size=16, in_chans=3,
+                 embed_dim=1024, depth=24, num_heads=16,
+                 decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
+                 mlp_ratio=4.0, norm_layer=nn.LayerNorm, norm_pix_loss=False,lambda_consistency=1.0):
+        super().__init__()
+        # --------------------------------------------------------------------------
+        # MAE encoder specifics
+        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
+        num_patches = self.patch_embed.num_patches
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)   # fixed sin-cos embedding
+        self.blocks = nn.ModuleList([
+            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
+            for i in range(depth)])
+        self.norm = norm_layer(embed_dim)
+        # --------------------------------------------------------------------------
+        # --------------------------------------------------------------------------
+        # MAE decoder specifics
+        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
+        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False)   # fixed sin-cos embedding
+        self.decoder_blocks = nn.ModuleList([
+            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer)
+            for i in range(decoder_depth)
+        ])
+        self.decoder_norm = norm_layer(decoder_embed_dim)
+        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True)  # decoder to patch
+        # --------------------------------------------------------------------------
+        self.norm_pix_loss = norm_pix_loss
+        self.lambda_consistency = lambda_consistency
+        self.initialize_weights()
+    def initialize_weights(self):
+        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
+        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
+        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True)
+        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))
+        w = self.patch_embed.proj.weight.data
+        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        torch.nn.init.normal_(self.cls_token, std=.02)
+        torch.nn.init.normal_(self.mask_token, std=.02)
+        self.apply(self._init_weights)
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            torch.nn.init.xavier_uniform_(m.weight)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+    def random_masking(self, x, mask_ratio):
+        """
+        Perform per-sample random masking by per-sample shuffling.
+        Per-sample shuffling is done by argsort random noise.
+        x: [N, L, D], sequence
+        """
+        N, L, D = x.shape   # batch, length, dim
+        len_keep = int(L * (1 - mask_ratio))
+        noise = torch.rand(N, L, device=x.device)   # noise in [0, 1]
+        ids_shuffle = torch.argsort(noise, dim=1)   # ascend: small is keep, large is remove
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+        ids_keep = ids_shuffle[:, :len_keep]
+        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+        mask = torch.ones([N, L], device=x.device)
+        mask[:, :len_keep] = 0
+        mask = torch.gather(mask, dim=1, index=ids_restore)
+        return x_masked, mask, ids_restore
+    def patchify(self, imgs):
+        """
+        imgs: (N, 3, H, W)
+        x: (N, L, patch_size**2 *3)
+        """
+        p = self.patch_embed.patch_size[0]
+        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
+        h = w = imgs.shape[2] // p
+        x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
+        x = torch.einsum('nchpwq->nhwpqc', x)
+        x = x.reshape(shape=(imgs.shape[0], h*w, p**2*3))
+        return x
+    def unpatchify(self, x):
+        """
+        x: (N, L, patch_size**2 *3)
+        imgs: (N, 3, H, W)
+        """
+        p = self.patch_embed.patch_size[0]
+        h = w = int(x.shape[1]**0.5)
+        assert h *w == x.shape[1]
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
+        return imgs
+    def forward_encoder(self, x, mask_ratio):
+        x = self.patch_embed(x)
+        x = x + self.pos_embed[:, 1:, :]
+        x, mask, ids_restore = self.random_masking(x, mask_ratio)
+        cls_token = self.cls_token + self.pos_embed[:, :1, :]
+        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.norm(x)
+        return x, mask, ids_restore
+    def forward_decoder(self, x, ids_restore):
+        x = self.decoder_embed(x)
+        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
+        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)   # no cls token
+        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))   # unshuffle
+        x = torch.cat([x[:, :1, :], x_], dim=1)   # append cls token
+        x = x + self.decoder_pos_embed
+        for blk in self.decoder_blocks:
+            x = blk(x)
+        x = self.decoder_norm(x)
+        x = self.decoder_pred(x)
+        x = x[:, 1:, :]
+        return x
+    def forward_loss(self, imgs, pred, mask):
+        """
+        imgs: [N, 3, H, W]
+        pred: [N, L, p*p*3]
+        mask: [N, L], 0 is keep, 1 is move.
+        """
+        target = self.patchify(imgs)
+        if self.norm_pix_loss:
+            mean = target.mean(dim=-1, keepdim=True)
+            var = target.var(dim=-1, keepdim=True)
+            target = (target - mean) / (var + 1.e-6)**0.5
+        loss = (pred - target) ** 2
+        loss = loss.mean(dim=-1)   # [N, L], mean loss per patch
+        loss = (loss * mask).sum() / mask.sum()   # mean loss on removed patches
+        return loss
+    def consistency_loss(self, latent1, latent2, use_cls=False):
+        """
+        latent1, latent2: [N, L, D]
+        use_cls: 是否使用 cls_token，如果 False 就用平均 patch 特征
+        """
+        if use_cls:
+            z1 = latent1[:, 0]   # [N, D] cls token
+            z2 = latent2[:, 0]
+        else:
+            z1 = latent1[:, 1:].mean(dim=1)   # [N, D] 平均所有 patch 特征
+            z2 = latent2[:, 1:].mean(dim=1)
+        z1 = F.normalize(z1, dim=-1)
+        z2 = F.normalize(z2, dim=-1)
+        loss = 2 - 2 * (z1 * z2).sum(dim=-1).mean()
+        return loss
+    def forward(self, imgs, mask_ratio=0.75):
+        # --- 第一次 mask ---
+        latent1, mask1, ids_restore1 = self.forward_encoder(imgs, mask_ratio)
+        pred1 = self.forward_decoder(latent1, ids_restore1)
+        loss_recon1 = self.forward_loss(imgs, pred1, mask1)
+        # --- 第二次 mask ---
+        latent2, mask2, ids_restore2 = self.forward_encoder(imgs, mask_ratio)
+        pred2 = self.forward_decoder(latent2, ids_restore2)
+        loss_recon2 = self.forward_loss(imgs, pred2, mask2)
+        # --- 一致性损失 ---
+        loss_cons = self.consistency_loss(latent1, latent2)
+        # --- 总 loss ---
+        loss_total = (loss_recon1 + loss_recon2) / 2 + self.lambda_consistency * loss_cons
+        return loss_total, pred1, mask1
+    def forward_encoder_with_given_mask(self, x, given_patch_mask):
+        """
+        Forward encoder using a given patch-level mask.
+        Args:
+            x: (N, 3, H, W)
+            given_patch_mask: (N, L), 1 for masked patches, 0 for kept
+        Returns:
+            x: encoded tokens with cls token (N, len_keep + 1, embed_dim)
+            mask: (N, L), same as input
+            ids_restore: (N, L), mapping for unshuffling
+        """
+        x = self.patch_embed(x)  # (N, L, D)
+        x = x + self.pos_embed[:, 1:, :]  # (N, L, D)
+        N, L, D = x.shape
+        noise = torch.rand(N, L, device=x.device)
+        mask_float = given_patch_mask.float()
+        ids_shuffle = torch.argsort(mask_float * (noise.max() + 1) + noise, dim=1)  # (N, L)
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+        len_keep = L - given_patch_mask.sum(dim=1).max().int().item()
+        ids_keep = ids_shuffle[:, :len_keep]
+        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+        cls_token = self.cls_token + self.pos_embed[:, :1, :]
+        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
+        x = torch.cat((cls_tokens, x_masked), dim=1)
+        for blk in self.blocks:
+            x = blk(x)
+        x = self.norm(x)
+        return x, given_patch_mask, ids_restore
+    def forward_with_given_mask(self, imgs, given_patch_mask):
+        latent, mask, ids_restore = self.forward_encoder_with_given_mask(imgs, given_patch_mask)
+        pred = self.forward_decoder(latent, ids_restore)
+        loss = self.forward_loss(imgs, pred, mask)
+        return loss, pred, mask
+def mae_vit_base_patch16(**kwargs):
+    model = MaskedAutoEncoderViT(
+        patch_size=16, embed_dim=768, depth=12, num_heads=12,
+        decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16,
+        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
+    return model

ExampleCode/example1/model/JplusEncoder.py ADDED Viewed

	@@ -0,0 +1,171 @@

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models import create_model
+class MultiBand_Gate(nn.Module):
+    def __init__(self):
+        """
+        初始化门控类。
+        不需要指定输入大小，PSF 和 input_data 的特征在内部单独提取。
+        """
+        super(MultiBand_Gate, self).__init__()
+        # 提取 input_data 特征的卷积网络
+        self.input_encoder = nn.Sequential(
+            nn.Conv2d(1, 8, kernel_size=3, padding=1),  # 输入: (B, 1, 224, 224) -> (B, 8, 224, 224)
+            nn.InstanceNorm2d(8),
+            nn.LeakyReLU(),
+            nn.AdaptiveAvgPool2d((14, 14))             # 下采样为相同尺寸
+        )
+        # 合并后生成门控权重的网络
+        self.gate_network = nn.Sequential(
+            nn.Conv2d(8, 4, kernel_size=3, padding=1),  # 输入: (B, 16, 14, 14)
+            nn.InstanceNorm2d(4),
+            nn.LeakyReLU(),
+            nn.AdaptiveAvgPool2d((1, 1)),               # 全局平均池化 -> (B, 8, 1, 1)
+            nn.Flatten(),                                # -> (B, 8)
+            nn.Linear(4, 1),                             # -> (B, 1)
+        )
+    def forward(self, input_data):
+        """
+        前向传播函数。
+        参数:
+        - psf: 点扩散函数，形状为 (B, 1, 51, 51)
+        - input_data: 输入图像数据，形状为 (B, 1, 224, 224)
+        返回:
+        - gate_weight: 门控权重，形状为 (B, 1, 224, 224)
+        """
+        input_feat = self.input_encoder(input_data)       # -> (B, 8, 14, 14)
+        combined_feat = input_feat
+        gate_weight = self.gate_network(combined_feat)            # -> (B, 1, 224, 224)
+        return gate_weight
+class MultiTask_Gate(nn.Module):
+    def __init__(self, input_dim, num_experts, dropout_rate):
+        """
+        初始化多任务门控类。
+        参数:
+        - input_dim: 输入数据的维度。
+        - num_experts: 专家数量。
+        - dropout_rate: Dropout比率。
+        """
+        super(MultiTask_Gate, self).__init__()
+        # TODO：这里的输入可以插入qita特征
+        self.gate = nn.Sequential(
+            nn.Linear(input_dim, 128),
+            nn.LayerNorm(128),
+            nn.LeakyReLU(),
+            # nn.Dropout(dropout_rate),                                  # TODO：这里的dropout会导致模型不收敛, 目前不使用dropout
+            nn.Linear(128, num_experts),
+            nn.Softmax(dim=1)
+        )
+    def forward(self, x):
+        x = F.adaptive_avg_pool2d(x, (1, 1))[:,:,0,0]  # 全局平均池化
+        return self.gate(x)
+class MultiBand_MoE(nn.Module):
+    def __init__(self, model_name='vit_small_patch16_224', pretrained=False, multi_band_experts=12):
+        """
+        初始化多波段输入的Mixture of Experts (MoE)模型。
+        参数:
+        - model_name: 使用的基础模型名称。
+        - pretrained: 是否使用预训练权重。
+        - input_size: 输入数据的形状 (h, w)，即高度和宽度。
+        - multi_band_experts: 专家数量。
+        """
+        super(MultiBand_MoE, self).__init__()
+        self.backbone = create_model(model_name=model_name, pretrained=pretrained, num_classes=0)
+        self.backbone.patch_embed.proj = nn.Conv2d(multi_band_experts, 384, kernel_size=(16, 16), stride=(16, 16), bias=False)
+        self.multi_band_experts = nn.ModuleList([MultiBand_Gate() for _ in range(multi_band_experts)])
+    def forward(self, input_data):
+        """
+        前向传播函数。
+        参数:
+        - input_data: 输入数据，形状为 (batch_size, num_bands, h, w)。
+        - psf: 点扩散函数，形状为 (batch_size, num_bands, h, w)。
+        返回:
+        - output: 特征图像，形状为 (batch_size, num_bands, h, w)。
+        - multi_band_weights: 多波段权重，形状为 (batch_size, num_bands, 1, 1)。
+        """
+        multi_band_weights = torch.zeros((input_data.size(0), len(self.multi_band_experts), 1, 1)).to(input_data.device)
+        ################################################################################################################
+        for i in range(len(self.multi_band_experts)):
+            multi_band_weight = self.multi_band_experts[i](input_data[:, i:i+1, :, :])
+            # print("multi_band_weight.shape:", multi_band_weight.shape)
+            multi_band_weight = multi_band_weight.unsqueeze(1)
+            multi_band_weights[:, i, :, :] = multi_band_weight
+        multi_band_weights = F.softmax(multi_band_weights, dim=1)
+        output_data = input_data * multi_band_weights
+        ##################################################################################################################
+        # # 不增加 multi_band_weights
+        # output_data = input_data
+        feature = self.backbone.forward_features(output_data)
+        # TODO：这里的特征图像需要进行 reshape，不知道是否正确，先这样
+        feature = feature[:, 1:, :]
+        B, N, C = feature.shape
+        H = W = int(N ** 0.5)
+        feature = feature.reshape(B, H, W, C)
+        feature = feature.permute(0, 3, 1, 2)
+        return feature, multi_band_weights[:,:,0,0]
+class ReconTask_MoE(nn.Module):
+    def __init__(self, input_dim, num_experts, dropout_rate):
+        super(ReconTask_MoE, self).__init__()
+        self.gating = MultiTask_Gate(input_dim, num_experts, dropout_rate)
+        self.num_experts = num_experts
+        self.embeding_dim = int(384 / num_experts)  # 384是backbone的输出通道数
+        self.decoder = nn.Sequential(
+            nn.ConvTranspose2d(self.embeding_dim, 64, kernel_size=4, stride=2, padding=1),   # -> 56x56
+            nn.InstanceNorm2d(64),
+            nn.LeakyReLU(),
+            nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1),    # -> 112x112
+            nn.InstanceNorm2d(32),
+            nn.LeakyReLU(),
+            nn.ConvTranspose2d(32, 16, kernel_size=4, stride=2, padding=1),    # -> 112x112
+            nn.InstanceNorm2d(16),
+            nn.LeakyReLU(),
+            nn.ConvTranspose2d(16, 12, kernel_size=4, stride=2, padding=1),  # -> 224x224
+        )
+    def forward(self, x):
+        gate_weights = self.gating(x)
+        expert_outputs = [x[:, i * self.embeding_dim:(i + 1) * self.embeding_dim,:,:] for i in range(self.num_experts)]
+        b, c, h, w = expert_outputs[0].shape
+        output = sum(gate_weights[:, i].view(b, 1, 1, 1) * expert_outputs[i] for i in range(self.num_experts))
+        output = self.decoder(output)
+        return output, gate_weights

ExampleCode/example1/model/SpecEncoder.py ADDED Viewed

	@@ -0,0 +1,416 @@

+import os
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchinterp1d import interp1d
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import os
+from tqdm import tqdm
+from dataset import desi
+from scipy.ndimage import gaussian_filter1d
+from astropy.convolution import convolve, Gaussian1DKernel
+import numpy as np
+import logging
+class PatchEmbed1D(nn.Module):
+    def __init__(self, patch_size=16, d_model=512):
+        super().__init__()
+        self.patch_size = patch_size
+        self.proj = nn.Linear(patch_size, d_model)
+    def forward(self, x):
+        B, L = x.shape
+        pad_len = (math.ceil(L / self.patch_size) * self.patch_size) - L
+        if pad_len > 0:
+            x = F.pad(x, (0, pad_len))
+        x = x.view(B, -1, self.patch_size)
+        tokens = self.proj(x)
+        return tokens, L + pad_len
+class ReconTransformer(nn.Module):
+    def __init__(self,
+                 orig_length=7781,
+                 target_length=9780,
+                 patch_size=16,
+                 d_model=512,
+                 nhead=8,
+                 num_layers=6,
+                 dim_feedforward=2048,
+                 dropout=0.1,
+                 use_z_cond=True):
+        super().__init__()
+        self.orig_length = orig_length
+        self.target_length = target_length
+        self.patch_size = patch_size
+        self.d_model = d_model
+        self.use_z_cond = use_z_cond
+        self.patch_embed = PatchEmbed1D(patch_size=patch_size, d_model=d_model)
+        max_patches = math.ceil(orig_length / patch_size)
+        self.pos_embed = nn.Parameter(torch.randn(1, max_patches, d_model) * 0.02)
+        if use_z_cond:
+            self.z_proj = nn.Sequential(
+                nn.Linear(1, d_model),
+                nn.GELU(),
+                nn.Linear(d_model, d_model)
+            )
+        else:
+            self.z_proj = None
+        encoder_layer = nn.TransformerEncoderLayer(
+            d_model=d_model,
+            nhead=nhead,
+            dim_feedforward=dim_feedforward,
+            dropout=dropout,
+            activation='gelu',
+            batch_first=True
+        )
+        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
+        self.recon_head = nn.Linear(d_model, patch_size)
+        self.norm = nn.LayerNorm(d_model)
+        self._init_weights()
+        self.out_patches = math.ceil(self.target_length / patch_size)
+    def _init_weights(self):
+        nn.init.xavier_uniform_(self.recon_head.weight)
+        if self.recon_head.bias is not None:
+            nn.init.zeros_(self.recon_head.bias)
+    def forward(self, spec, z=None):
+        B, L = spec.shape
+        tokens, padded_len = self.patch_embed(spec)
+        N_in = tokens.shape[1]
+        pos = self.pos_embed[:, :N_in, :]
+        tokens = tokens + pos
+        if self.use_z_cond and z is not None:
+            if z.dim() == 1:
+                z = z.unsqueeze(-1)
+            z_emb = self.z_proj(z)
+            tokens = tokens + z_emb.unsqueeze(1)
+        tokens = self.norm(tokens)
+        enc_out = self.encoder(tokens)
+        N_out = self.out_patches
+        if N_out != N_in:
+            enc_out = F.interpolate(
+                enc_out.transpose(1, 2),
+                size=N_out,
+                mode="linear",
+                align_corners=False
+            ).transpose(1, 2)
+        patches = self.recon_head(enc_out)
+        recon = patches.reshape(B, N_out * self.patch_size)
+        recon = recon[:, :self.target_length]
+        if z is None:
+            z = torch.zeros(len(recon))
+        B = recon.size(0)
+        if z.dim() == 1:
+            z = z.unsqueeze(1)
+        return recon
+    def extract_features(self, spec, z=None, pool="mean"):
+        B, L = spec.shape
+        tokens, padded_len = self.patch_embed(spec)
+        N_in = tokens.shape[1]
+        pos = self.pos_embed[:, :N_in, :]
+        tokens = tokens + pos
+        if self.use_z_cond and z is not None:
+            if z.dim() == 1:
+                z = z.unsqueeze(-1)
+            z_emb = self.z_proj(z)
+            tokens = tokens + z_emb.unsqueeze(1)
+        tokens = self.norm(tokens)
+        enc_out = self.encoder(tokens)
+        if pool == "mean":
+            feat = enc_out.mean(dim=1)
+        elif pool == "cls":
+            feat = enc_out[:, 0, :]
+        elif pool == "flatten":
+            feat = enc_out.reshape(B, -1)
+            raise ValueError("Unknown pooling method")
+        return feat
+def to_rest_frame(spec_obs, z, wave_obs_min=3600.0, wave_obs_max=9824.0, n_obs=7781, n_rest=9780, device="cuda"):
+    B = spec_obs.size(0)
+    wave_obs = torch.linspace(wave_obs_min, wave_obs_max, n_obs,
+                              dtype=torch.float32, device=device)
+    wave_obs_batch = wave_obs.unsqueeze(0).expand(B, -1)
+    lmbda_min = wave_obs_min / (1.0 + 0.8)
+    lmbda_max = wave_obs_max / 1.0
+    wave_rest = torch.linspace(lmbda_min, lmbda_max, n_rest,
+                               dtype=torch.float32, device=device)
+    wave_rest_batch = wave_rest.unsqueeze(0).expand(B, -1)
+    wave_redshifted = wave_rest_batch * (1 + z.unsqueeze(-1))
+    spec_rest = interp1d(wave_obs_batch, spec_obs, wave_redshifted)
+    valid = (wave_redshifted >= wave_obs_min) & (wave_redshifted <= wave_obs_max)
+    spec_rest[~valid] = 0.0
+    return spec_rest, wave_rest
+def weighted_mse_loss(recon, target, weight):
+    diff = (recon - target) ** 2
+    weighted = diff * weight
+    denom = weight.sum()
+    if denom == 0:
+        return diff.mean()
+    return weighted.sum() / denom
+def consistency_loss(sf, sf_aug, individual=False):
+    batch_size, s_size = sf.shape
+    x = torch.sum((sf_aug - sf)**2/(0.5)**2,dim=1)/s_size
+    sim_loss = torch.sigmoid(x)-0.5
+    if individual:
+        return x, sim_loss
+    return sim_loss.sum()
+def train_model(desi, model, trainloader, validloader, device, epochs=20, lr=1e-4, weight_decay=1e-5,
+                save_path="best_recon_transformer.pth", plot_interval=1, fig_dir="figures",
+                log_interval=5000, log_file="training.log"):
+    # === 新增：配置 logging ===
+    os.makedirs(fig_dir, exist_ok=True)
+    os.makedirs(os.path.dirname(save_path) if os.path.dirname(save_path) else '.', exist_ok=True)
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.FileHandler(log_file, mode='w'),      # 写入文件（覆盖模式）
+            logging.StreamHandler()                       # 同时输出到控制台
+        ]
+    )
+    logger = logging.getLogger()
+    model.to(device)
+    optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
+    best_val_loss = float("inf")
+    fixed_valid_batch = None
+    iter_loss_accums, iter_mse_accums, iter_consistency_accums = [], [], []
+    train_losses, val_losses = [], []
+    for epoch in range(1, epochs + 1):
+        model.train()
+        train_loss, num_samples = 0.0, 0
+        iter_count = 0
+        iter_loss_accum = iter_mse_accum = iter_consistency_accum = 0.0
+        for spec, w, z, _, _, _, _, _ in tqdm(trainloader, desc=f"Epoch {epoch} [Train]", leave=False):
+            B = spec.size(0)
+            # Preprocessing: smoothing
+            flux_smooth = gaussian_filter1d(spec.cpu().detach().numpy(), sigma=5, axis=1)
+            kernel = Gaussian1DKernel(stddev=5, x_size=5)
+            flux_conv_batch = np.array([convolve(f, kernel) for f in flux_smooth])
+            spec = torch.tensor(flux_conv_batch, device=device, dtype=torch.float32)
+            spec, w, z = spec.to(device).float(), w.to(device).float(), z.to(device).float()
+            rest_spec = to_rest_frame(spec, z, device=device)[0]
+            w_spec = to_rest_frame(w, z, device=device)[0]
+            optimizer.zero_grad()
+            recon = model(spec, z)
+            mse_loss = weighted_mse_loss(recon, rest_spec, w_spec)
+            spec_aug, w_aug, z_aug = desi.augment_spectra(spec, w, z)
+            specf_aug = model.extract_features(spec_aug, z_aug)
+            specf = model.extract_features(spec, z)
+            consistency_loss_value = consistency_loss(specf, specf_aug)
+            loss = mse_loss + consistency_loss_value
+            # loss = mse_loss
+            loss.backward()
+            optimizer.step()
+            train_loss += loss.item() * B
+            num_samples += B
+            iter_count += 1
+            iter_loss_accum += loss.item()
+            iter_mse_accum += mse_loss.item()
+            iter_consistency_accum += consistency_loss_value.item()
+            if iter_count % log_interval == 0:
+                avg_iter_loss = iter_loss_accum / log_interval
+                avg_iter_mse = iter_mse_accum / log_interval
+                avg_iter_consistency = iter_consistency_accum / log_interval
+                iter_loss_accums.append(avg_iter_loss)
+                iter_mse_accums.append(avg_iter_mse)
+                iter_consistency_accums.append(avg_iter_consistency)
+                # === 替换 print 为 logger.info ===
+                logger.info(
+                    f"Epoch {epoch} Iter {iter_count}: "
+                    f"Avg Total={avg_iter_loss:.6f}, "
+                    f"MSE={avg_iter_mse:.6f}, "
+                    f"Consistency={avg_iter_consistency:.6f}"
+                )
+                # Plot loss curves
+                fig_loss, axes = plt.subplots(3, 1, figsize=(10, 12), sharex=True)
+                axes[0].plot(iter_loss_accums, color='blue')
+                axes[0].set_ylabel('Total Loss'); axes[0].grid(True)
+                axes[1].plot(iter_mse_accums, color='green')
+                axes[1].set_ylabel('MSE Loss'); axes[1].grid(True)
+                axes[2].plot(iter_consistency_accums, color='orange')
+                axes[2].set_ylabel('Consistency Loss')
+                axes[2].set_xlabel('Iteration'); axes[2].grid(True)
+                fig_loss.suptitle('Training Loss Components')
+                plt.tight_layout(rect=[0, 0, 1, 0.96])
+                fig_loss.savefig(os.path.join(fig_dir, "loss_curve_iter.png"))
+                plt.close(fig_loss)
+                # Plot reconstructions
+                for i in range(min(4, B)):
+                    plt.figure(figsize=(12, 3))
+                    plt.subplot(2,1,1)
+                    plt.plot(rest_spec[i].cpu().numpy(), label="Input", color='blue', linewidth=0.5, alpha=0.7)
+                    plt.plot(recon[i].detach().cpu().numpy(), label="Reconstructed", color='red', linewidth=0.5, alpha=0.7)
+                    plt.title(f"Epoch {epoch} Iter {iter_count} Sample {i} | z = {z[i].item():.3f}")
+                    plt.legend(); plt.grid(True); plt.tight_layout()
+                    plt.subplot(2,1,2)
+                    plt.plot(w_spec[i].detach().cpu().numpy(), label="w", color='black', linewidth=0.5, alpha=0.7)
+                    plt.legend(); plt.grid(True); plt.tight_layout()
+                    plt.savefig(os.path.join(fig_dir, f"recon_epoch{epoch}_iter{iter_count}_sample{i}.png"))
+                    plt.close()
+                iter_loss_accum = iter_mse_accum = iter_consistency_accum = 0.0
+        train_loss /= num_samples
+        train_losses.append(train_loss)
+        # Validation
+        model.eval()
+        val_loss, num_samples = 0.0, 0
+        with torch.no_grad():
+            for i, (spec, w, z, _, _, _, _, _) in enumerate(tqdm(validloader, desc=f"Epoch {epoch} [Valid]", leave=False)):
+                spec, w, z = spec.to(device).float(), w.to(device).float(), z.to(device).float()
+                rest_spec = to_rest_frame(spec, z, device=device)[0]
+                w_spec = to_rest_frame(w, z, device=device)[0]
+                recon = model(spec, z)
+                loss = weighted_mse_loss(recon, rest_spec, w_spec)
+                val_loss += loss.item() * spec.size(0)
+                num_samples += spec.size(0)
+                if fixed_valid_batch is None and i == 0:
+                    fixed_valid_batch = (rest_spec.cpu(), recon.cpu(), z.cpu())
+        val_loss /= num_samples
+        val_losses.append(val_loss)
+        # === 替换 print 为 logger.info ===
+        logger.info(f"Epoch {epoch}: Train Loss = {train_loss:.6f}, Val Loss = {val_loss:.6f}")
+        if val_loss < best_val_loss:
+            best_val_loss = val_loss
+            save_dict = model.module.state_dict() if isinstance(model, nn.DataParallel) else model.state_dict()
+            torch.save(save_dict, save_path)
+            logger.info(f" ✅ New best model saved to {save_path}")
+        # Plot validation reconstructions periodically
+        if epoch % plot_interval == 0 and fixed_valid_batch is not None:
+            spec_fixed, recon_fixed, z_fixed = fixed_valid_batch
+            B_vis = min(4, spec_fixed.size(0))
+            fig_recon, axes = plt.subplots(B_vis, 1, figsize=(12, 2 * B_vis))
+            if B_vis == 1: axes = [axes]
+            for i in range(B_vis):
+                ax = axes[i]
+                ax.plot(spec_fixed[i].numpy(), label="Input", color='blue', linewidth=0.5, alpha=0.7)
+                ax.plot(recon_fixed[i].numpy(), label="Reconstructed", color='red', linewidth=0.5, alpha=0.7)
+                ax.set_title(f"z = {z_fixed[i].item():.3f}")
+                ax.legend(); ax.grid(True)
+            plt.tight_layout()
+            plt.savefig(os.path.join(fig_dir, f"recon_epoch_{epoch:03d}.png"))
+            plt.close(fig_recon)
+    logger.info("\n🎉 Training completed.")
+    logger.info(f"Best Validation Loss: {best_val_loss:.6f}")
+if __name__ == "__main__":
+    device = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")
+    desi = desi.DESI()
+    CSV_PATH = '/home/data/multimodalUniverse/DESIspec_make/matched_sources_by_targetid_inner.csv'
+    trainloader = desi.get_data_loader(
+        csv_path=CSV_PATH,
+        which="train",
+        batch_size=64,
+        shuffle=True
+    )
+    validloader = desi.get_data_loader(
+        csv_path=CSV_PATH,
+        which="test",
+        batch_size=64,
+        shuffle=False
+    )
+    model = ReconTransformer(
+        orig_length=7781,
+        target_length=9780,
+        patch_size=4,
+        d_model=512,
+        nhead=8,
+        num_layers=6,
+        dim_feedforward=2048,
+        dropout=0.1,
+        use_z_cond=True
+    )
+    model.to(device)
+    train_model( desi, model=model,
+        trainloader=trainloader,
+        validloader=validloader,
+        device=device,
+        epochs=1000,
+        lr=1e-4,
+        weight_decay=1e-5,
+        save_path="./training_plots_reshift/best_recon_transformer_acc.pth",
+        plot_interval=1,
+        fig_dir="training_plots_reshift")

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd7f04e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import glob\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import requests\n",
+    "from PIL import Image\n",
+    "from io import BytesIO\n",
+    "\n",
+    "# ======================\n",
+    "# 配置信息\n",
+    "# ======================\n",
+    "\n",
+    "cot_info = {\n",
+    "    \"allwise\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/allwise.json\",\n",
+    "    \"galex\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/Galex.json\",\n",
+    "    \"spec\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/catalogs/fastspecfit/fastspecfit_ra_dec_with_source.csv\",\n",
+    "    \"vla\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/vla.json\",\n",
+    "    \"vla_g\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/vla_g.json\",\n",
+    "    \"chandra\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/chandra.json\",\n",
+    "    \"desiimage\": \"/home/dell461/ljm/DATA_other/AstroM3/Dataset/semantics/desi.json\"\n",
+    "}\n",
+    "\n",
+    "DEFAULT_CATALOG_DIR = '/home/dell461/ljm/DATA_other/AstroM3/Dataset/catalogs/Parameters/'\n",
+    "DEFAULT_SEARCH_RADIUS_ARCSEC = 2.0\n",
+    "\n",
+    "# ======================\n",
+    "# 核心函数：交叉匹配\n",
+    "# ======================\n",
+    "\n",
+    "def cross_match_catalogs(\n",
+    "    target_ra: float,\n",
+    "    target_dec: float,\n",
+    "    catalog_dir: str = DEFAULT_CATALOG_DIR,\n",
+    "    search_radius_arcsec: float = DEFAULT_SEARCH_RADIUS_ARCSEC,\n",
+    "    ra_col_names: list = None,\n",
+    "    dec_col_names: list = None,\n",
+    "    verbose: bool = True\n",
+    "):\n",
+    "    if ra_col_names is None:\n",
+    "        ra_col_names = ['ra', 'raj2000', 'ra_deg', 'ra_degrees', 'alpha']\n",
+    "    if dec_col_names is None:\n",
+    "        dec_col_names = ['dec', 'dej2000', 'dec_deg', 'dec_degrees', 'delta']\n",
+    "\n",
+    "    ra_col_names = [name.lower().strip() for name in ra_col_names]\n",
+    "    dec_col_names = [name.lower().strip() for name in dec_col_names]\n",
+    "\n",
+    "    csv_pattern = os.path.join(catalog_dir, '*.csv') if os.path.isdir(catalog_dir) else catalog_dir\n",
+    "    csv_paths = glob.glob(csv_pattern)\n",
+    "\n",
+    "    if not csv_paths:\n",
+    "        if verbose:\n",
+    "            print(f\"⚠️ No CSV files found in: {csv_pattern}\")\n",
+    "        return []\n",
+    "\n",
+    "    all_matches = []\n",
+    "    if verbose:\n",
+    "        print(f\"🔍 Searching within {search_radius_arcsec} arcsec of RA={target_ra}, Dec={target_dec}\\n\")\n",
+    "\n",
+    "    for csv_path in sorted(csv_paths):\n",
+    "        filename = os.path.basename(csv_path)\n",
+    "        if verbose:\n",
+    "            print(f\"Processing: {filename}\")\n",
+    "\n",
+    "        try:\n",
+    "            df = pd.read_csv(csv_path)\n",
+    "        except Exception as e:\n",
+    "            if verbose:\n",
+    "                print(f\"  ❌ Failed to read {filename}: {e}\")\n",
+    "            continue\n",
+    "\n",
+    "        ra_col = dec_col = None\n",
+    "        for col in df.columns:\n",
+    "            col_clean = col.lower().strip()\n",
+    "            if col_clean in ra_col_names:\n",
+    "                ra_col = col\n",
+    "            if col_clean in dec_col_names:\n",
+    "                dec_col = col\n",
+    "\n",
+    "        if not ra_col or not dec_col:\n",
+    "            if verbose:\n",
+    "                print(f\"  ⚠️ RA/Dec columns not found. Skipping.\")\n",
+    "            continue\n",
+    "\n",
+    "        df[ra_col] = pd.to_numeric(df[ra_col], errors='coerce')\n",
+    "        df[dec_col] = pd.to_numeric(df[dec_col], errors='coerce')\n",
+    "        df = df.dropna(subset=[ra_col, dec_col])\n",
+    "\n",
+    "        if df.empty:\n",
+    "            if verbose:\n",
+    "                print(f\"  ⚠️ No valid numeric RA/Dec rows. Skipping.\")\n",
+    "            continue\n",
+    "\n",
+    "        dra = (df[ra_col] - target_ra) * np.cos(np.radians(target_dec))\n",
+    "        ddec = df[dec_col] - target_dec\n",
+    "        dist_deg = np.sqrt(dra**2 + ddec**2)\n",
+    "        dist_arcsec = dist_deg * 3600.0\n",
+    "\n",
+    "        mask = dist_arcsec <= search_radius_arcsec\n",
+    "        matched_rows = df[mask].copy()\n",
+    "        matched_rows['match_distance_arcsec'] = dist_arcsec[mask].values\n",
+    "\n",
+    "        if not matched_rows.empty:\n",
+    "            all_matches.append({\n",
+    "                'file': filename,\n",
+    "                'matches': matched_rows\n",
+    "            })\n",
+    "        else:\n",
+    "            if verbose:\n",
+    "                print(f\"  ❌ No match within {search_radius_arcsec} arcsec\")\n",
+    "\n",
+    "    if verbose:\n",
+    "        print(\"\\n\" + \"=\"*60)\n",
+    "        if all_matches:\n",
+    "            print(f\"✅ Total matched files: {len(all_matches)}\")\n",
+    "        else:\n",
+    "            print(\"❌ No matches found in any file.\")\n",
+    "\n",
+    "    return all_matches\n",
+    "\n",
+    "\n",
+    "# ======================\n",
+    "# 加载语义描述\n",
+    "# ======================\n",
+    "\n",
+    "def load_semantic_info(filename: str):\n",
+    "    basename = filename.lower()\n",
+    "    for key in cot_info:\n",
+    "        if key in basename:\n",
+    "            try:\n",
+    "                with open(cot_info[key], 'r', encoding='utf-8') as f:\n",
+    "                    return json.load(f)\n",
+    "            except Exception as e:\n",
+    "                print(f\"⚠️ Failed to load semantic info for {key}: {e}\")\n",
+    "                return {}\n",
+    "    return {}\n",
+    "\n",
+    "\n",
+    "# ======================\n",
+    "# 格式化用于 LLM 的输入\n",
+    "# ======================\n",
+    "\n",
+    "def format_matches_for_llm(matches):\n",
+    "    if not matches:\n",
+    "        return \"No observational parameters found within search radius.\"\n",
+    "\n",
+    "    sections = []\n",
+    "    for item in matches:\n",
+    "        filename = item['file']\n",
+    "        df = item['matches']\n",
+    "        row = df.iloc[0]  # 取第一个匹配源\n",
+    "\n",
+    "        semantic = load_semantic_info(filename)\n",
+    "        lines = [f\"[Source: {filename}]\"]\n",
+    "\n",
+    "        for col, val in row.items():\n",
+    "            if col == 'match_distance_arcsec':\n",
+    "                continue\n",
+    "            if pd.isna(val) or (isinstance(val, float) and np.isnan(val)):\n",
+    "                continue\n",
+    "\n",
+    "            if isinstance(val, float):\n",
+    "                if abs(val) < 1e-4 or abs(val) > 1e5:\n",
+    "                    val_str = f\"{val:.3e}\"\n",
+    "                else:\n",
+    "                    val_str = f\"{val:.4f}\"\n",
+    "            else:\n",
+    "                val_str = str(val)\n",
+    "\n",
+    "            desc = semantic.get(col, {}).get('description', '') if isinstance(semantic, dict) else ''\n",
+    "            if desc:\n",
+    "                lines.append(f\"  {col}: {val_str}  # {desc}\")\n",
+    "            else:\n",
+    "                lines.append(f\"  {col}: {val_str}\")\n",
+    "\n",
+    "        sections.append(\"\\n\".join(lines))\n",
+    "\n",
+    "    return \"\\n\\n\".join(sections).strip()\n",
+    "\n",
+    "\n",
+    "# ======================\n",
+    "# 调用 LLM 进行分类\n",
+    "# ======================\n",
+    "\n",
+    "def classify_galaxy_with_llm(ra, dec, param_text):\n",
+    "    prompt = f\"\"\"\n",
+    "You are a professional astronomer. Analyze the following multi-wavelength observational parameters \n",
+    "for a source at RA={ra}, Dec={dec}. Use the provided metadata (including units and descriptions) \n",
+    "to infer the most likely galaxy type (e.g., spiral, elliptical, starburst, AGN, quasar, etc.).\n",
+    "\n",
+    "Observational data:\n",
+    "{param_text}\n",
+    "\n",
+    "Provide a concise classification with reasoning based on physical indicators (e.g., X-ray luminosity, UV excess, radio morphology, IR colors), no more than 200 words.\n",
+    "\"\"\"\n",
+    "\n",
+    "    from openai import OpenAI\n",
+    "\n",
+    "    OPENAI_API_KEY = \"sk-c38a5fddbcb44147b0efa9d7d1ff5aec\"\n",
+    "    OPENAI_API_BASE = \"https://dashscope.aliyuncs.com/compatible-mode/v1\"\n",
+    "\n",
+    "    client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE)\n",
+    "\n",
+    "    try:\n",
+    "        response = client.chat.completions.create(\n",
+    "            model=\"qwen-plus\",\n",
+    "            messages=[\n",
+    "                {\"role\": \"system\", \"content\": \"You are a professional astronomer. Be precise, evidence-based, and concise.\"},\n",
+    "                {\"role\": \"user\", \"content\": prompt.strip()}\n",
+    "            ],\n",
+    "            temperature=0.7,\n",
+    "            max_tokens=512\n",
+    "        )\n",
+    "        return response.choices[0].message.content.strip()\n",
+    "    except Exception as e:\n",
+    "        return f\"❌ Error calling LLM: {e}\"\n",
+    "\n",
+    "\n",
+    "# ======================\n",
+    "# 下载并显示 DESI/Legacy Survey 图像\n",
+    "# ======================\n",
+    "\n",
+    "def fetch_and_show_desi_image(ra, dec, layer=\"ls-dr9\", pixscale=0.25, size=92, save_path=\"1.jpg\"):\n",
+    "    \"\"\"\n",
+    "    从 Legacy Survey 下载 cutout 图像并显示。\n",
+    "    \n",
+    "    Parameters:\n",
+    "        ra, dec: 天体坐标（度）\n",
+    "        layer: 图像图层，如 'ls-dr9'\n",
+    "        pixscale: 像素尺度（角秒/像素）\n",
+    "        size: 图像尺寸（像素）\n",
+    "        save_path: 可选，保存路径\n",
+    "    \"\"\"\n",
+    "    url = f\"https://www.legacysurvey.org/viewer/cutout.jpg?ra={ra}&dec={dec}&layer={layer}&pixscale={pixscale}&size={size}\"\n",
+    "    \n",
+    "    try:\n",
+    "        response = requests.get(url)\n",
+    "        response.raise_for_status()\n",
+    "        img = Image.open(BytesIO(response.content))\n",
+    "        \n",
+    "        plt.figure(figsize=(6, 6))\n",
+    "        plt.imshow(img)\n",
+    "        plt.title(f\"DESI/Legacy Survey Cutout\\nRA={ra:.5f}, Dec={dec:.5f}\", fontsize=12)\n",
+    "        plt.axis('off')\n",
+    "\n",
+    "        plt.savefig(save_path, dpi=150, bbox_inches='tight')\n",
+    "\n",
+    "        return img\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Failed to fetch or display image: {e}\")\n",
+    "        return None\n",
+    "\n",
+    "\n",
+    "# ======================\n",
+    "# 主流程函数\n",
+    "# ======================\n",
+    "\n",
+    "def analyze_galaxy(ra: float, dec: float, show_image: bool = True, **kwargs):\n",
+    "    \"\"\"\n",
+    "    完整分析流程：交叉匹配 → 格式化 → LLM 分类 → 可视化图像\n",
+    "    \n",
+    "    Parameters:\n",
+    "        ra, dec: 目标天体坐标（度）\n",
+    "        show_image: 是否显示 DESI 图像\n",
+    "        **kwargs: 传递给 cross_match_catalogs 的参数\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # 1. 交叉匹配\n",
+    "    matches = cross_match_catalogs(ra, dec, **kwargs)\n",
+    "\n",
+    "    # 2. 格式化参数\n",
+    "    param_text = format_matches_for_llm(matches)\n",
+    "\n",
+    "    # 3. LLM 分类\n",
+    "    result = classify_galaxy_with_llm(ra, dec, param_text)\n",
+    "\n",
+    "    fetch_and_show_desi_image(ra, dec,save_path=f\"/home/dell461/ljm/DATA_other/AstroM3/ExampleCode/example2/image/{ra}_{dec}.jpg\")\n",
+    "    \n",
+    "    with open(f\"/home/dell461/ljm/DATA_other/AstroM3/ExampleCode/example2/llm_result/{ra}_{dec}.txt\", \"w\") as f:\n",
+    "        f.write(result)  \n",
+    "\n",
+    "    return {\n",
+    "        \"matches\": matches,\n",
+    "        \"llm_result\": result,\n",
+    "        \"formatted_params\": param_text\n",
+    "    }\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d02a3ffd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 Searching within 2.0 arcsec of RA=239.5743316076613, Dec=32.91914641623415\n",
+      "\n",
+      "Processing: allwsie_classification_results.csv\n",
+      "  ❌ No match within 2.0 arcsec\n",
+      "Processing: chandraimage_classification_results.csv\n",
+      "  ❌ No match within 2.0 arcsec\n",
+      "Processing: desiimage_classification_results.csv\n",
+      "  ❌ No match within 2.0 arcsec\n",
+      "Processing: galex_classification_results.csv\n",
+      "  ❌ No match within 2.0 arcsec\n",
+      "Processing: vla_classification_result_g.csv\n",
+      "Processing: vla_classification_results.csv\n",
+      "\n",
+      "============================================================\n",
+      "✅ Total matched files: 2\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9Kv33HTt2yCc/+Ulo37JlizzykY+UP/zDP5QzzzxTvv3tb8txxx0nzWZTtm/fLl//+tflAQ94gLz0pS+V2dlZ+bM/+zP567/+a5mZmZGnPe1psnXrVjnvvPNk48aNa/7c59RTT5VLL71UXvayl8nTn/50uf322+WNb3yjbNy4cY0h++hHP1qe97znyZve9CbZuXOnnHrqqRIEgXznO9+RRqMhf/Inf7L62l6vJ6effrq8973vlc2bN8vv/u7v7nN+9rKysiJbtmyRZzzjGfLYxz5WDjnkEBmNRvKVr3xF3vnOd8rhhx8uv/d7vyciIhs2bJDHPvaxq+e3efNm+fKXvyyXXnrpAY9zVx7/+MfLwQcfLC972ctkx44da34VvXfc3/CGN8hrXvMa+dnPfiZPeMITZGZmRnbu3Cnf+ta3pNlsrgZU7OXss8+WZz7zmWJZlrzsZS+bqh/HHnusvPe975WXvexlctRRR8lLX/pSud/97id5nst3vvMdef/73y/3v//9V8fyec97nrz2ta+Vc889V44//ni54YYb5F3vehes272PUd7//vdLu92WMAzlsMMOk7m5OXnta18r55xzjpx++uny7Gc/WxYXF+W8886TMAzlda973epnPO95z5Pzzz9fTjvtNHnRi14ki4uL8va3vx1+xd9ut2Xz5s1y+eWXy0knnSSzs7MyPz//Xx4SovyaUqsWptTOXit573++75t169aZ448/3rzlLW8xu3btgvfsz44WEfNv//ZvxhhjbrjhBvPKV77SHH300WZhYcG4rmtmZmbM8ccfby6++GLaj7129GmnnbZPO3nz5s37PPadjdcPfehD5mEPe5hpNpsmiiJzz3ve05x++unm29/+9uprqqoyb3rTm8zBBx9sfN83D3zgA81nP/tZc+SRR5qnPe1pa4771re+1WzZssUEQWAOP/xw84EPfGB1LO5MWZbm7/7u78z9739/4/u+6Xa75thjjzWf+cxn4Fy+8pWvGBExb33rW/c9SXciTVPzt3/7t+aJT3yiOfTQQ00QBCYMQ3P44YebV73qVWZxcXHN67dv326e/vSnm9nZWdPtds1pp51mvv3tb1M7utls7vfY55xzjhERc8ghh5iyLOlrLrvsMnPiiSeaTqdjgiAwmzdvNk9/+tPNl770JXouQRCYJzzhCVOd+5357ne/a57//OebQw891Pi+b5rNpnnwgx9szj333DVrNk1T86pXvcoccsghJooic/zxx5vvfve7YEcbY8zf//3fm8MOO8w4jgPj88EPftA88IEPXJ3PpzzlKeaHP/wh9Osf//EfzeGHH27CMDRHHHGEueSSS8CONsaYL33pS+bBD36wCYIA1q3yPwvLmF/RHxkqyq+ILMtk3bp18sY3vnHNN8f/Lm6++Wa5733vK6973evknHPO+S891itf+Up573vfK7fffvvq88f/KXzmM5+RJz/5yfK5z31OnvSkJ9XdHUWpBd2Elf/RfO9735OPfexj8ohHPEI6nY785Cc/kbe//e0yGAzkBz/4ATWnfxVce+21cuONN8qLX/xiefGLXyx///d//19ynF9HbrjhBrn11lvl7LPPlmazKf/xH/+xTylMUX7b0U1Y+R/NT3/6U3nJS14i3/ve96Tf70u325UTTjhB3vzmN//CpvI0WJYljUZDnvSkJ8mFF154wL8N/m3ihBNOkG984xvykIc8RP7xH/9R7nvf+9bdJUWpDd2EFUVRFKUm9E+UFEVRFKUmdBP+DebDH/7wahUWy7LEdV3ZuHGjPOtZz9pvsPz/+T//RyzLoulWvww33nij/Pmf/7kcddRR0uv1ZHZ2Vh75yEfSPyX60pe+JI973ONk06ZNEgSBrFu3Th7zmMfI5z//eXhtlmVy7rnnymGHHSa+78vmzZvl1a9+9dR/Z7lly5Y147T3v5e85CVrXnfGGWfQ1+3979prr6Wfb4yR4447TizLgr/1HY/H8qxnPUvuc5/7SLvdlmazKfe73/3kTW96k4zH4194TKZlbyWlvf/5vi8LCwvyyEc+Ul7zmtfA30P/unB3xu3SSy+VZz/72XKve91LoiiSLVu2yHOf+9y7VVzhn/7pn+TBD36whGEo8/Pz8pznPEduv/12eN1FF1202i/btvf7Z0Xf+ta35OSTT5Z2uy2tVktOPPHE1ZjSO/P1r39dzjrrLDnqqKMkCAKxLGu12Mdd2dfafOtb3zr1uSq/XujfCf8WcOGFF8p973tfSZJEvvGNb8ib3/xmueqqq+THP/4xTZv60Ic+JCIiP/zhD+Xf//3f5WEPe9ivpB9f/OIX5XOf+5w873nPk2OOOUaKopBLLrlEnvGMZ8h5550n55577uprFxcX5X73u5+cddZZsmHDBllaWpL3ve99csopp8jFF18sp5122uprn/3sZ8vnP/95Offcc+WYY46Ra665Rt70pjfJD3/4w6mDJx75yEfK3/7t365pu6t09drXvhY2ZhGR3/3d35UgCOSYY46hn/3ud79bfvrTn9J/y/NcjDHyZ3/2Z3LYYYeJbdvy1a9+Vd7whjfIV77yFfnSl770C43J3eUtb3mLnHjiiVKWpSwuLsq///u/y4c+9CH5u7/7O/nABz5wwLSq/27uzri97W1vkw0bNshrXvMaucc97iG33367vOUtb5GHPOQhcu211+63NKSIyD/8wz/Iy1/+cjnrrLPkrW99q2zdulVe+9rXyqMf/Wj5zne+s+Yauvjii2XHjh3y0Ic+VKqqWlPG8c5cd911ctxxx8lDH/pQufjii8UYI29/+9vlpJNOkquuumq1wpiIyJe//GX50pe+JA9+8IOl0+lA6cm78vSnP11e+cpXrmk79NBD9/se5deYev4ySvlVsK/KQ+edd94+K+Jcd911RkTMKaecYkTEvOhFL/qV9Wf37t1rqvjs5ZRTTjGNRsMkSbLf92dZZg466CDz6Ec/erVtXxWR3vKWtxgRMV/84hcP2K8DVV3aH3v/jvev/uqv6L/ffPPNptVqmUsvvdSIiPmjP/qjqT73Va96lRER85//+Z/7fR0bk7vD3kpKn/jEJ+DfFhcXzYMf/GDjuq65/vrrf6HP/++GjdudqzHt5Y477jCe55kXvvCF+/28JElMt9uFSl3f/OY3jYiYc845Z037nf8++pRTToG//93LySefbNavX2/G4/Fq22AwMPPz81CR6s6f+Td/8zdGRMzNN99MP/furDHlNwP9dfRvIXsjAlkm8gUXXCAiIm9961vlEY94hHz84x+nxeV/EfZmNt+Vhz70oTKZTGRpaWm/7/c8T3q9nrju//sFzd5f393170hPPfVUEfl5vvR/JRdccIFYliUveMEL6L//4R/+oTzucY+Tpz3taXfrc/cmkN35XBlsTH5VzM7Oyvnnny9FUcjf/d3frfm3m266SZ7znOfIunXrJAgCOfzww6EGsMjP6wq/8pWvlHvc4x6rv0J/0pOeJD/+8Y9/5f0V4ePGssc3bdokBx98MP2V8p35wQ9+ICsrK7C+jj32WJmdnYX1decUtf3xjW98Q0444QRpNBqrbe12W4477jj55je/uSaLetrPVH470dn/LWRvlZl73/vea9rjOJaPfexjqyXoXvCCF8hwOJRPfOIT8BllWa4pSbev/1g29F256qqrZGFhgd4sq6qSoihk27Zt8rrXvU5uvPHGNb9q25sPfNdCDHv//665wPviq1/9qrTbbfE8T4444gh5xzveIWVZ7vc9Kysr8slPflJOOukkOeyww+DfP/jBD8q3vvUtede73nXA4xtjpCgKGQwGcsUVV8g73vEOefazn01/jXigMflVcswxx8jGjRvlq1/96mrbDTfcIMccc4z84Ac/kHe84x3y2c9+Vk455RR5+ctfviZ6cjgcyqMe9Sg5//zz5cwzz5TPfOYz8r73vU/ufe97r9lkfpm1dHfG7c787Gc/k1tvvfWAv4re1/ra23bTTTetqZI1LVmW7bd4yPe///27/Zl7+ehHPypRFEkQBHLUUUfJhRde+At/lvJrQN1fxZVfnL2/jr722mtNnudmOByaK664wmzYsMEcd9xxJs/zNa+/6KKLjIiY973vfcaYnxdcb7Va9Fed+4uGvPN/BypM/oEPfMCIiHnnO99J//3kk09e/axOp2MuvfTSNf9+2WWXGRGBmMsLLrjAiIi5973vfaBhMi972cvMhz70IXP11Vebyy67zDz3uc81ImJOO+20/b7vve99rxER87GPfQz+bevWrabb7Zrzzz9/tU3286vCj33sY2vG7cwzz4T52cuBxuTusL9fR+/lYQ97mImiaM3xDz74YLOysrLmdX/8x39swjA0S0tLxhhj3vCGN6yJKd0Xv8xaujvjtpc8z80JJ5xgOp2Oue222/b72sXFRWPbNvza+qc//enqMbdt20bfu79fRz/oQQ8y9773vdf8qjnPc3OPe9zDiIj56Ec/St93oF9HP+c5zzH/9E//ZL761a+aT37yk+aJT3zifh+XKL/+6Cb8G8xdc5/3/nf44Yeb5eVleP3xxx9voigy/X5/te3MM880ImJuvPHGNa+9/vrrzXXXXXfA/+6444599u/zn/+88X3fPP3pT6fPio0x5sYbbzTf+ta3zOWXX26e8YxnGM/z1tyg0jQ197rXvcymTZvMF7/4RbO8vGy+8IUvmPXr1xvHccx973vfuzlqP+eP//iPjYiY//iP/9jna44++mgzNzdHn2Wfeuqp5rjjjltzXvvbhJeWlsx1111nrrzySvPmN7/ZdDod8+QnP5lmMB9oTO4O02zCD33oQ1c34TiOjeu65k/+5E9Mnudr/vv85z9vRMR8/vOfN8YYc+yxx071Q9Avs5buzrgZ8/Ms8NNPP904jmMuu+yyaYbIPO95zzOe55n3ve99ZnFx0Xzve98zD3vYw1YzpHfs2EHft79NeO8PiS996UvN1q1bzW233WZe+MIXrn7mxz/+cfq+A23CjFNPPdW4rktz3pVff3QT/g1m7yZ80UUXrd6oXvziFxsRgVD8m266yViWZZ7+9Keb5eXl1f8+97nPGRExf/mXf7nm9UVRwE2Y/bevm+EVV1xhwjA0p5xyiknTdOpzesITnmBmZmbWfO5NN91kHv7wh6/+kNFsNs073/lOMz8/b0466aS7MWL/j2uvvdaIiHnPe95D//173/ueERFz9tlnw7994hOfMK7rmmuvvXbNWMr/L7otLy+bLMv2e/yPf/zjRkSm+pbLxmRaptmEN2zYYO55z3saY37+Df9A31gvuugiY4wx97rXvcxjHvOYA/bhl11Ld2Z/41ZVlXnBC15gbNuG35zsj9FoZE477TRj27YREWPbtnn+859vnvzkJ5sgCPb5zXt/m7AxPy/40Wq1Vsft2GOPNf/rf/0vIyLma1/7Gn3PL7IJ7x2TvT8cKb9Z6Cb8G8y+7OizzjoLbryvfvWr93tj3bhxoymKYvX1v8yvEPduwCeffPIBjei7cu655+7z28fWrVvN9ddfb8bjsdm2bZsREXPeeefdrc/fy17reu+v5u/Ky1/+ciMi5vvf/z7824GqSImI+fSnP73f4//sZz8zItNVT9rfmByIA23C//7v/25EZPXXsePx2DiOY84444x9fmPds2ePMWb6b8K/qkcbxux73PZuwJZl0b8KmIalpSXzve99z+zevdsYY8x97nMfc+KJJ+7z9QfahI35uX39/e9/39xyyy3GGGP+8A//0DSbTTOZTOjrf5FNeO+v7K+44oqp36P8+qB/J/xbyNvf/nb51Kc+Jeeee6783u/9nhhj5B//8R/lnve8p3zwgx+E13/2s5+Vd7zjHfKFL3xh1Tr+zGc+s6Y4+b7YW0B+L1/84hflqU99qjzqUY+Syy67jMop+8IYI1dffbX0ej1aUeiggw6Sgw46SERE/uqv/kqazaa88IUvnPrz78xFF10kIiIPf/jD4d/SNJWPfOQj8tCHPpQGmpxxxhlywgknQPuJJ54oT33qU+Xss88+YBDKVVddJSIi97rXvfb7ugONyS/D0tKSvOQlLxHP8+RP//RPRUSk0WjIiSeeKN/5znfkgQ984D7rPYuIPPGJT5Rzzz1XrrzySnnMYx6zz9f9omuJwcbNGCMvetGL5MILL1yVxH4RZmZmVv8m+F/+5V/kJz/5ibztbW/7hT5rL0EQrK6F2267TS655BJ50YteJFEU/VKfe2cuvvhi8TxPjjrqqF/ZZyr/fegm/FvIzMyMvPrVr5ZXvepV8tGPflR6vZ5s27ZN3va2t9HN4/73v7+8613vkgsuuGB1E37AAx5wt4/79a9/XZ761KfKhg0b5JxzzpHvfve7a/79iCOOWC1w/pSnPEWOPPJIedCDHiRzc3Oybds2+fCHPyxXX321vPvd717zJyhvf/vbZcOGDXLooYfKzp075Z//+Z/lsssuk4svvnh1UxYRufXWW+We97ynPP/5z1/9U6yPfvSjcumll8opp5wimzdvln6/L5/4xCfk4x//uJxxxhly5JFHwnlcdtllsrS0JGeddRY9zy1btuwzKemggw5aM8bnn3++fO1rX5PHP/7xcsghh8h4PJavfe1r8g//8A/yiEc8Qp7ylKesvvbujMnrX/96Oe+88+Sqq66ic3pXbrrpJrn22mulqqrVsI4LLrhABoOBXHTRRWss4ne+853yqEc9Sh796EfLS1/6UtmyZYsMh0P56U9/Kp/5zGfkyiuvFBGRV7ziFXLJJZfIU57yFPnLv/xLeehDHypxHMvVV18tp556qpx44oki8outpbszbi9/+cvlggsukBe84AXygAc8YE2yWRAE8uAHP3j1/0866SS5+uqrpSiK1bZPfepTsm3bNjn88MMlSRL5yle+Iu985zvlJS95yZrjiPzcHL/hhhtERGTHjh0ymUxWE+GOOOIIOeKII0Tk53/69KlPfUqOPvpoCYJAvve978lb3/pW+Z3f+R154xvfuOYzd+/eLVdffbWI/D9r+gtf+IIsLCzIwsKCHH/88SIi8jd/8zdyww03yEknnSQHH3yw7Nq1Sy644AL54he/KK9//etlfn7+bo+z8mtAvV/ElV+Gff062pifCzaHHnqo+Z3f+R3z1Kc+1fi+v19x41nPepZxXfcX+pXnXg70a9qrrrpq9bVve9vbzDHHHGNmZmaM4zhmbm7OnHzyyeazn/0sfO55551n7nnPe5ogCEyv1zNPeMITzFe/+lV43c0332xE1hZIv+aaa8xJJ51kNmzYYDzPM41GwxxzzDHmPe95zz6fQT7ucY8zzWbTDAaDu3X+QsSsb3zjG+bUU081mzZtMr7vm0ajYY488kjzxje+cU2Qw90dk1e+8pXGsizzox/9aL992vvr6L3/ua5r5ubmzLHHHmvOOeec1V+T3pWbb77ZvOAFLzAHHXSQ8TzPLCwsmEc84hHmTW9605rXLS8vm7PPPtsceuihxvM8s27dOnPKKaeYH//4x9MM2T65O+O2v1933/XXxccff7y5623v05/+tHnQgx5kms2miaLIHH300eaCCy6gMuH+1vidf53+k5/8xBx33HFmdnbW+L5v7nWve5m/+qu/MqPRCD7zrnN05/+OP/741df9y7/8i3nUox5lFhYWjOu6pt1um0c/+tHU3ld+c9AqSoryG8hDH/pQ2bx5M/0bb0VRfnPQTVhRfsMYDAaysLAg3/3ud+Xwww+vuzuKovwS6CasKIqiKDWhsZWKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotSEbsKKoiiKUhNTJ2Yt3HcB2rrZBmjz8xa0VVEGbTvT/4S2dQsYy2f3PWjLl7EOrCN4jPke/ozhVljAPjf43hWvgLZ+hQXrJe1BU5Tjeexs43k0Y+xLu8J4v0TGeNg2Su1RJ8RjDLHPwW7+s1eVYURhPkOWyPoc+1Nuhza3wNeVVQfabo0x2rJw29B2/5VFaBuFONbbEjznzGBfIj+GNs/FOek0cLxmPVyDHbKOcvK3B7clOE87BjPQ1nKx/nLXxvUxO7sEbSvZT6FtW441kZsx1srd1Mb5MC7W+r19sAxtvteEtrkIzy0d4zhn5HpwJzhvB4V4LwrtBrTdOsL+9UO8rttzeL6+wXHubMcaxrcvDKAt2QxNYo/x8+ZHeK8cLfPaxfasA22uhfO+qcD3d0q899idWWjbPsL7TEHiRoMAP29rju/dE+F14w9xvO6Z4jhscXHNLBu8rm/CS0mGXVyDJsZ5d3YOoa0T4wW7LsL+RQ0cg6GF1/+Ptt6IHbwL+k1YURRFUWpCN2FFURRFqQndhBVFURSlJnQTVhRFUZSamFrMMhY+iJ7YKAtUAX6k6+BD+yYppzkhApI0etAUVChhCBEa4hQfslcRPsmPXSIuOCiEtCuUU2IHZYHUQcHJWPjeMsW2PMeH+2JjW8vDY7Qcch4u/pxVdnHeRERGRCLy1+F4+eg9SL5ClpJBGSLy8HX3EBQf8hIPMvZQtAnI+G/w8PziAgWTqsD3OqQvXoYyWVriYO2usH9lifJHz8Ux8Gxcb8vLd0DbyMfjNrr4eZOYXIdkfuctlJKaRGwbEMnG+HgR95dRdjN9XOfhDI7p4iwRA0Nc++mkD20zKbn+yQk7TTyGj46dRMu4XvrpTdBWjPEY1jKuoSLF8RsO8dwin9eMHg6xkyMbzyVo4fU6zHBsmjHeQ60SZUjL4HvzDK/rFrmWuuRemzTI2BDJaYeLayuOcU7cFMewsYTXXFbh+LkhWR8G12pqcF8qcyIMk71vGvSbsKIoiqLUhG7CiqIoilITugkriqIoSk3oJqwoiqIoNTH1k2THRYGjrLAtK3BfZyKFaeLD7qRECcBxUBZgklNAfpxYHOMD+olBISEm8lKXiGNNG487zsgxiLnUtIk0Rc4jJcewAxQh/IDIVTnKPSsYQiQjbBIREYtIWJ0ZlCGsEco3JIhM8oJIKySlaqbAwfaWcX3c3sDXBRaOQ4MIObHguE5I/+yyi31JMLlqTOSUjEgdjovnu6FJhDWfpBURWWtcoYwzSTBdyFSYZjcv+F5ZwQSjsaDEkm/ANe2EPWizXFxdBUkhGpc4BjmRv6SD12s6wdtWMiQCIkm4y0JcL1mJ89Yg12sxswfabCJHmSH22RG8tqqcHNcncyQig2WSMEaSyPZYeL+sSkzWmhvh+AdEUKsc7LfF0ufIfC5M8FxuJ2LWoIFzsttgZ6oBXiOdGF/XDXBfykNcM3YTj+s38fOsGOWvjPQv83HNTIN+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamFrNYklO7RPGhGqJcNc5X8MBENmo1UAKIiNBQGHygHsziA/9hHyURw1KSSpIGFJL0HiKOlDFKCk2S1LUxRbFiZOPnjUJsy1okJSkiUhc57tDBKU6JCCEi0mgQCSsh6TM7cY7LGMc1Z4JESsoHppg0Fa6g5BC5G/HzKiL42Ng/28P1Gwi+tyTJOkWF45pOsM2QdRQS+WOQYElG28Xxa8xiqlQ1wWPkJE0paKyHtoNIJc47UpSNhsQv8UpcR06KL+yE8/jegIhyRGj0hkT4IaU0Z3soPgUtvF6rBI+bE3lxRKS9gghEUuF7qxGOQTnCOW9GJCXNw2vBrm7H44qIa+FrwwDLEbrkvlCWeP+tYhTyClJ61Lg4Dk5ArFWyF6RDnON+G9dHQe5RY3JfNaStRdZl5KJE6AmJiyOpd5IS4SrDtsQl8qKQC2wK9JuwoiiKotSEbsKKoiiKUhO6CSuKoihKTegmrCiKoig1MbWY1SPbdZcIHClJgXJIglSZoajUHhMZh/ycMCEpSXaTSF05CgQtw5K/8BhjB6Wd1CFCmGCf51Psy4YRyhE7SCzXqEHkLxsFh2SCokaRYv+GBuUej5yHiEg7x89sEbkiHuP73QhFIIcIZeNkO7StkLFOOjiuXkpK7JF0pzJCCavj4TE8KmbhPOU+KedIynj6RByRCvvSJ+lCBbmWQot8HhFR0oIkmBF5qWnjddhatwBtY8E+VzmerzXA8xAi2TgRXnMtMi6dAYqUUY7HsFp4j6mamBTlkYQ1H91DGZEEqEkTz6Mb97B/Nh4jJSJPk4xLu43XemFQlBMR6XXxM8cu9jsga99UKGGFFqZPuR4R1BwiellESrLxWh85eH4rAUqT7QKPsb7C9WZCnGMht7KlFMel7OPEN2y8n3gkCask5VKNQ8og5nhu06DfhBVFURSlJnQTVhRFUZSa0E1YURRFUWpCN2FFURRFqYmpxayZnKSLOChSlCTxac6dgzYzJslEA1Iyqo3SSR6hYGKR0nJtC22XLqkUNiRixu4UH+SnMUnC6RMzgEhTI8GH9jkp0+gH2BbmKFa0JkQcK7AvK0Ric4jvIyLSSFCQ8Ehb4mO5v7KBgo/t44Ecci6xS1LW5lHmaZNEpSLEdWQ8Ik2Q0nSew5JwSGrYDMpa4TyKT40xrv24j22+i+8lV5eMmExC1rSN7p3E0U5oW5LDoK1DUpecZTzflSUU9Bo2nkduSIrehMg4ETkPIp1JggJRXuG4VCStKPXJ2iUumV3h6yLyeQERkmbaOAYZEYMckrblRfjeMRlTEZGqQ5LSxni/LIZ4fbljPGmXlKH1ibDp5SwlkAhIFbknk1KLVb4N2sIC+3KIh+tSFvC+s2eCgu/irj60OaTP3YhIqyS10ZB15BpSdjch1t8U6DdhRVEURakJ3YQVRVEUpSZ0E1YURVGUmtBNWFEURVFqYmoxy5AST313CdoSF82nluAD8I7BB+9LOZZ4M23SxYD87DAkCUasDKKD0tTEIRIWOmdijTEdx8uxf5MS37w7RInCIpWvGiWOX3uEskYrJmlPDWLoNFBmMCQ9SkSkTFHqmBgcw4mH5c0mOZ5MVBG5pUSBY0RSrwbEd3PXoaRDHCyxiLCSpUQwsXCsjUvmqcQ1k5FSZqaFgontoJTYSfAY8QqeW5Fhm7OA660i8qLjo7DyswSvufUZmaMhHreFpysFK4lJUoiSDKWktMAP9HyShEcS6Txy+adEHI0NjvOEpDN1Umyb6+N7R12UcVwbrxmHpC5NJniMFSLo7Ql60CYiQpavZAmOq7WMx27EOIamIslm5FbrG7w3ukSCFRfvE6XBa312gIJf0yZpdqRcYkaM0qIgZVAtPEa7iWPQmsP1tlzhdVNYuLYiIrsFE3KRTIF+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamFrPshQ3Q5mUknYUk5uTkIXYaoBSTGHxQHkX4sDudYKKPLyggTUi6UE7Keo1JEo5jUJoI3R60NYixktsorA3Xo8zUsfBnIG+EnzfahZLIIEWBwJtFOYLV+jJMrBCR1CaJT0Ruiysc15QIF40Aj91uoTw2Lsn5EenEXUC5pTnEc5mMUIxbTImE1cREn5CcW3M3KRtJ5I+VBklU6q6DNoeUtbSWcc1EPopeXg9llyzFvsyRRLWfxX1oG/fx3A4xeFvozeC8jUji2NjCdZl65Bom0tkGUvqOhK6J8XBdxeRWVpFyk4FL5K8Y+5yQEp6jmXlsi8maJIlZJDBLRhPsc1awa5inY7VIulMmOJ++h2vGxeEXk5JUP1yCUhLJiZUUzBK8Dg8iMqQ0cBzGRNZaHuI1Mt6DpR+7ROZrk9KoZZsInEQiLMm9yCblKsltbCr0m7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpN6CasKIqiKDUxtZj10wQf+M+SpJlmjE+ngw6KAbeMMOmkuQlTUpIJChJWH487IlJXwZK1KmxreiidxES4IB6VRF00F8IQ+3JLickz7RTtiDJGCWBPSpJ/miRZK0CZwR9uh7aACDAiIiskGSYNUCgrSJlHP8GlFDXwvSMiTUWkzOB9SPrXrl23Q5sMUSZp+lgOsuxthLYlUkItIFJNK8Zx8cncNXxc5ykRn4SIQEGLXIotvB5sMh+dEUqJhxQ9aLuDpNmZDq6ZxQznN6nwem10MYFrYoiEOexD23wP3ztLxrlBrtddBR5jTL5P2AHKlRGJqfNIcp1hslC0Gdp27NwBbV1SkrXd6UFbSa5rn9w/RUQ2byBiXIblKosOkeB2obwUZHh+TQ+vh4qUFO03ccD6i5h22HFJecMVnKeVAq+5vCIJckSQkgn2xSLJdWUD+zImpQdjlujloAA3KNg1oolZiqIoivIbhW7CiqIoilITugkriqIoSk3oJqwoiqIoNTG1mLVC4kA6pAyaNca21EEZJ3FRTolTFDM6+Pxbun4P2rbuwjQVZwMmIjkRSjvJCEUPlpzk5CjZxBPsoEVKI3Y9PC5xYiTLcFwGETFH1qGgY/VwOhfw4yRfJHUaRWRIkmYKFyUHq8Cf3SL0HiQxRDyzcB3ZTNYipf2cDilvxpK1SKKP7aNc0Q5QaApIaTSXlOfzfByDnEhdfoTjNyyI1IXLQxyXXEtLRPpJUXIqxiiJzG/A62too9jm9XCsbJcINaRkZ2FIbckWfp7J8bgFWVcZGfsJSXYjlQLFc3EdNALsi2Njn5Mx9q+KUbi0bHIdNvDzDJE1F1rkehvgcUVEyiUUwLwekT3X4XobTzAlsDXBPnoVKa2YoxC24pNzJtdmvA3vyTsKclzBzwsylAitEc57ZeG45m0sHzomkmg5wPU7b4hoS+LAsg6+bjJHFuEU6DdhRVEURakJ3YQVRVEUpSZ0E1YURVGUmtBNWFEURVFqYmoxa55IBBURKXbnKI6UezBNJVhHHnbb+LB7hSTmmBylk04DBY6iwpJxuUHRICXltebaKBqku+6Atv4ySVjZiJZSj5RfC0vsc0xqtwURyiRpgIKDTaSnTo+ICxYKGCIiS2P8TJfEhJWk9KPxUUpYKUkyVIDn3CaSU7si66MiZdBcFFl8j6QnkTJtoeDnxaPd0DZ0cR21ScLVUh/f6xQkRYuk7bDSaOkE59Ow68FBARF7LOLneNxZkjQnJPXKhHjcVHC9zKQ4v10HryW/JKXqBNdLQcphjh1sqwz2bzxCkbJKcB3MpPi61gjHflZwfj2SMud08HotHZLAl5H+kbUhIjIiY+1GWFpRKnJfMNgfz+D5VURGXVomYlyIn9d1SVnFGBO9FonkNJPgWmiURJDyetC2EuL5LlZkL9iDY90l0mqbyGkZuTYLl5TODIlAOwX6TVhRFEVRakI3YUVRFEWpCd2EFUVRFKUmdBNWFEVRlJqYWsyK99yGbdV6fGFIyhH2MTmll+D+70SYYCTtddBUlSgvzZB6ZJMERYqyRPnAkFSoksg9zgy+NyaV6goPZRzijUlm43mEM/jeDUROWckw2alM+9A2WociWvsgTJQREQnvQFPBKbDjNhFj7ArPZSUnog0R1FKS9LXkknSiEtdWZOFxO02cz3iIIkqeoL40jnfhMWZQ5onauFZtkrYTJ3genR5eN2WO14hF0qfaPVwfGSmNOCkwJckiKUQdMh/DPq6t2JAUshZKLO4KzkdzSEpsErGlT2Sh3WRdOTYRjcj1XxBJaWxQfPJIacQeWfeHFChh9iM8jzzA+LOclM3LtuFas4gsKCJibToE2iYOrkGPiKIL5D7dCEgZP8HrS0pyHQ7xnN0eSbMipTgdIhvaNkprhUXiBBtkbMh9uphgIp0hyYZChNxFEnqVE6FxUpDzmOD1Pw36TVhRFEVRakI3YUVRFEWpCd2EFUVRFKUmdBNWFEVRlJqYWszqdPGhcxFjmxstQJvt40Nsv40pJJ6PokFp96AtNSixxCE+yLddbGumeNwyQ4GDpfc0DkIRxSZlEAtyjEmGYxAz96CDAoyXo2jgDlCemZCydD8b4M9ZcwGW9RIR8Ru4HGZWUFBpkASkUYp9zNGpkSEpqzZpo2x0e4SDMxujkBN6aFK4ZN4nJY5NRtJxjItrIcApEcfgcScJzvuAlBR0fbxufNJmQjxGEuB5mIrYgUTSMyUma2UOzm9mkRKlJIXIIglrMsB17hY4zk1SWnKTj+vPTknqHUnMI36OBDZJ2/JwnDNSpnErkXEeVOF78xau3a0FmfMYxy+smGDGxSwJSInCFZJIF+NF12bClY9zl5HSmSEpPZjneA2TKp6SzeJ8HmyRJKwI+zchqW2LJd7zHJIgt8HBPockzcoQWzZrkH1ugq9r7cI2PyY3iinQb8KKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1GLWYDcmsaR7UEBos2goUr6ucshDbCJcmBiPKxaKKEmIQkhQodjSyLHNIeXcbp7gcScNUmZQ8LhejvJMXOAD/4mNw28RKSYtUMBwXRyrZoTC1Z4Yzy25gxW6E7lnA9O1fCI0uRWKFCYjKTpEPLEEBS4hpQyZDdUdo7RiMhQ4BkQSGw9wXF1S4m22ifJSt01Slhw8Xy/HsW55mJ5EXBKxuyj4VGTt796NKXCHzG6AtsZ6lKGWJrj2R2Sttruz0OYGOPZ7duA6ylJSOrOHwmUREQFR8LreRMtukrKKpPTlSomCjkdSvuImro1+hmNf7MYx8AL8vGQRk7BaOR7joI0HQVte4HyIiKyMUUa1x7jOI7IuU5KK1icS55gkVwUspYqIZwkRuMo2vvfQCtfCwMHPWyLrMiOlWrsk2SwipRFDshayJr5u5BHBt4XrLSLT1JmQsqBToN+EFUVRFKUmdBNWFEVRlJrQTVhRFEVRakI3YUVRFEWpianFrGhAyqA5PWzziahgUGgqh6T8WopPu5PJIrTNzqHs0iKpJgERiLySpHyRFJdOhX1Z2oV9cZtE1iJyWuKhhBG4KOPkRPRybZJa1USpyKlQrLLHOAbpkMhRIlKVKMaMSChS30IBbExKWGYlCk3tENvChEh6Ja6jKMbxWiaCX17gefQS/LxOhmPYXEdkssYI2hJSyqxJ+tKoiChj4dofpijelAOSlLYL12pK5KVxG1+XzPSxfwOUcWbJuswSkhBG0sBiHBaxE7yWdgd43eQezlvLxQ/cQK7rxhj7NyxJJB1JvWp5uJ4DCyWbgeA6mCxjelQ4wte1Gyi7OSEeYzTgYlY63IOfSYRGi5SD3U0SwSY2EQErXJezLo5r18V13ifi2Zhcr4XB62E4xuPmBR634+I6nyHz5Bm8blJyT8hJecNOB9eHQ9pyB/uX9/E8pkG/CSuKoihKTegmrCiKoig1oZuwoiiKotSEbsKKoiiKUhNTi1lzGT7wdxsoNFglPrAuSLJLr4MP8mNSymxngg/PmxbaQt0hft4cST+aEAljJylB1yXl+sIUpZ0uSZSpSJzKdpLO5BMBJh7he0MySwEZZ3sJxYD2BM+3UfByaYWDQsm4jT+nDUm5v6qJspwkRF4iqUitPdjvJhGBtlZ4LkskKY0sIzk4x/fOklKcyRA/b2sX128/QSFno7sR2so+jqnTJALiGNOnDvZ70Lbh4PtC284K1/kNpJymmG3Q1CpxfhsDFJpiIrE4RML0QzKmQsogdvB6mCwQ4WeC5evCZezLLEnMqkgC32iCcxlW+LrWCMWxcYFpZf0dmKzVC/H66jp4fewe4OctLqKAJSLiCK6Z9bNYNnZA0rF2udif0sf7m0/KLZYpKZdIylp6AzyuW5L7eYDXzbjCsbZIApdHBNWIlEEMZ1BQTXO875i4D22jW3bg523Czxt4uAbtgJQUnQL9JqwoiqIoNaGbsKIoiqLUhG7CiqIoilITugkriqIoSk1YxhiSiYRsOvRYaFsptkJbFmDyz3wPpRhvRJJYchQzCnz+LbcsoyxQzKEU4y2QlKQuijf2EAUJl4hBaYKCg9VFKaYiJbyOirG8me2jxLItwYf7Nxco91QtHKuIpAa52/F8G+56aBMRuYNIOjH5MW22jULZAkmQMksoreQV9juzcQwLwbmbhDdAWxqjRLTQxHSidVEP+1Jin3c7KO4MaJITSifrS5SNzAQvr1tW8Bj2RpyT3gyOS76I15c17ONxM3KMyRy0RfM9aIvJGBiPCGYWjn0+QMkp8vC4YQdll9LD+VgeYEqdnZI0JUMEM1KuzxAJqCAlN6PmJnydjce4g5QtTJbwPLbIOmibNSgaLdsoBomI9A/Bc07msI+B3APfezNKf6GDwlt3lpRvTHD8DSkHG0Z4Tyh9Uj5wkZRVDMjnzRPhaoWk7S3jGmz0sLTnCnGQx7txnW8g6YlCEriWurgHLYek/O1/9vHz7oJ+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamTswiISnSDsjDc1Kyq0HCeypS3jDP8cF7EKCgc2gXj2ELKaW3TEoojjGxpXSxgxNSSqu0yXCNiDmWopgxHKOsNergz0A7SDk846D0MGdwXAIHRYhJA6WiRLiL55PSeW0y8XMOthki6Qx9krJE0pNmSRrQjIVj/eNZlJf8FSIMkRJ2SUySl9q4Zg6eQYloV4rCxe7dmGxkCSu/hsdY6GB6UhiRpKkEz21XH0UgL8V13iaXdjSDbRkp8TYc4vlGAbk2fbxunArXtEfuHS5Zg+M+HteLcW1EHl4PjR6KXibE+eiPUDSKSULYjEVqMro4Vu31pJQeSaiKyX1i7OKcRzOYTCgiUrXw2OkynssIb2/SyMl92sZzZuVlS5bMZ+H4pwbXdGnhWpgNiFBa4hwXW7EvniESVhfnfeLg9dAfoZzmkVKQcYbz1PBwraJqLOKTa30a9JuwoiiKotSEbsKKoiiKUhO6CSuKoihKTegmrCiKoig1MbWYlaUo7cw0cA+fs1E2IJXvJHXxAf1SifKMkIfxYYrHjQb4QL0koteElBnMN2JfJqRsFivd5qR4cgFJ9EkW8VH+jgIFgp1tPN+AlB1zJ5jy45Hj2kSAK3KcSxGRVoxizLyNkkmDJGstkZVkSFm1wEKZp1ehhLHex3P5v+S48ySpZ4FIP2mGxkpC1kdEfJyZAk/OuD1oWyRCk2nifHZcHJeOwb64gmPVibAvDrvmYnzv7mQ7tIVWD9q6pOymW5LylSmOacHKFhIJyMlwXaZ9Is8wgcjDcxuQ/hUlucfkOPaTjMyb14e2rMTzXddFkc9v43EHGS6sCRHbZlv8e1FAgpxmR/j+jKyjqI3SVEYS7pgI2CDCYBhhklu/wH6vLBOpboDzvpGUHqzINTzI+9AWV3gvyxt4vo0W3rutCY7f8A5MTwwF59Ma4nG7Pt6LpkG/CSuKoihKTegmrCiKoig1oZuwoiiKotSEbsKKoiiKUhNTi1kdIelHGQo1S2OUK4SUfauI5FSSBC4RIgY1UHKqhviQPSBlqbwmPmRPIlJSjJxblRHBpCDWWYLvdQsUOFzyOsslCVyEcYVihUXK+nWwSZwE5QgRkTzFVB67jeM6ClBAyAzOU5uU9uuRBJ5SUHLYxcrpJZgmZEqS2uThz5bNCPuXhTh3TklS1pZICbURjv/6FkkSIldYbPC9K5M+tFkWrsu2g2JRSZK6JiOUbNxDsC+tFMclLPHaTHJcSJMW9i/v4OcNxtiXyS2Y/DXXxuvVn0NpJw/wGOMYx9TPcKwaCa7JIicl94jIJ3ML0GQMKdNK5s3tYFtKkvp2pZjEJiLSIdKUn5K0Lh+lpMJDQSq1cE0bwTEMfLxfBh6RZRNyb1zBvWCS4FjnG/C+H3TJXjDG++UKKfNqyPoI57GU5GA7Jo51FnD8XFK6cbgbJeIowetmGvSbsKIoiqLUhG7CiqIoilITugkriqIoSk3oJqwoiqIoNTG1mLWRlKBKSfLPzRN8UG6HKNRYNu7/rQYRfoYoFUxIktByhccNQ/y8dhfbElJKz7dISUFS1q9DxqWsUHASB8WFOUNSuUqckpSIN4mFfbYtlIrW2ySdiXyeiMgi+gyyJ8T3D0htOidDkaKV4wc6BY7DoqDAMfRwDA/yManHrfCcM5JEJkS4cknKT0DGJiDHaDVwnsomikW7KhRghhUp40d+HvbJ+nBJ/3wionk97EvfIoJUTpKrSArciBy38Em5xA5J2yLCpZVj/wpSPjBv47kZct9xWIreBNvY6wwRs6oCxcDMQrNtZRFlIWdE5q2N5VddD8c0xY8TEZFxitdNSlLCpElkuQhf1whRKGtX89BmiDwaT5agzUvxuOtJSVF7DtfMyMPEsj65F9ktbIwyFKn6JJ1stAeFt4TsLZ0G7lVpgvtN7GFfShvX/jToN2FFURRFqQndhBVFURSlJnQTVhRFUZSa0E1YURRFUWpiajHLZiXKfJQrKlJazu7gw+6qIHKFhT8TlCS5Kjb4oDzaiMeQJj4oz3osbQeFFYskTRnygD5zUK6IRyh1uD629Rwc/iDHMVjGw8pOFzs4IilTiYXHsG1Sr09EijbO3YSUnCwKHEO3JIlURCxaIaUMx0R4y4jbdkiFKUZOGwUTy8V5t5ZwjrukrFqzgeuymMFxseZQCLl9ERN4ikWUTlIyx84sHiNqknMj14ProfzVnetBm5fhuCwWeNxlkjRXWpgG1KxQbAn6uLYKUj4wJPNGwpQkJ+UmAyIphSnKX0bwdWOy9iuSnNZq4XWzK9sGbTFJn/MSvBYMKY06jskJV+RiFxE3xOQwK8L53DbBe4Bt43HCBo5XREoA5uSenBn8vMjG9dEjwmC/vAP7RxLkCpLA5Tu4Vmc6mESY7OlD22A33jtCD9cgzSu0yT20Q5LcSNnHadBvwoqiKIpSE7oJK4qiKEpN6CasKIqiKDWhm7CiKIqi1MTUYlbfoFyREcEkJcKVGaFsYGUoNDghtnVCFBoaAR53UqDJk7ikxFuMokxRkrKAgg/ZAx9FCJ9ICm6EktiISCJNm4htQ1Lqb4i6QBkRmcHB1y2TimCDffzsZXnY7zLG+WwXKGGERF7IfJwTQ1KRggLHYYbIPHmOc+cJimMLPqYTHdJBqeMQku5UNHA+f+igcLW1xJSvxMe5I86ZCCk5mfZxrAJSxrMiazUVlM4iIu2sK1BEuZX1hcU2BXhu6/0etC1YOH47c7z++w6Rhdq4WH1SqjJKSdKUwXvHiCS7ZW2SZkWks7BBEqBsLF9nkfQuq02E0IRcr0tE4BQia4mI28K0OFbSsRWQdCyflIPN8F6RZ9ifCZGmlkniW5HiPIUZXtdtYt+5RByLLLaP4BzbXVy/UYHrqFnh67o+Xg8BuTc6JKHRImVLRx4vEXsg9JuwoiiKotSEbsKKoiiKUhO6CSuKoihKTegmrCiKoig1Mb2Y5eJ+bVkoL0QuSg5BQRJHiJhVZSiYVA6m8uREwgqJDMWcmCzF9zouvrIgaU8WEanyBAWHkCRhJT6OX+Jg22BEUn4yUrbQw9c5IUm3IqUbc/I6EZE8QaGhiHEcfJekO5HSY0y+E1IWMCJSV9PDvix1UeBYIIlg9yixz79DpLo5Imttc3GsqzGKGTmRjUryM21AknUiIrtUMZFOhihIWUS8yXycz5iUGZzZjePSrciaIeKNkHVekeC1MetLQUpnkmAoUu1TbLI2SlIxLiHHyGycSy8iSX02Lt6ElG7sELkytvF1FikdGJCbURO7IhZJqBIRqRz8gCEpM1hWpN/kvV6CkmOSY4nCcoakf83iHOcYtiW+j9ewfQtKk4tDFMICImYFZL8Zp7hn5CTtrIrIuDpEEiUSsU3KfeYkZW2FXRBToN+EFUVRFKUmdBNWFEVRlJrQTVhRFEVRakI3YUVRFEWpCd2EFUVRFKUmprajC2IasvKJbRvVxfEiWnzzUQ/aZppoOA+Xsf7kcISfNxOiTZewaEwSZWdIDc+qRIu6Itao7xFbtURjr5mhJbtioXG6x8Y4RNaXdSFGrkURqdGa4/lWLlEZRWR5iOM121oHbSsTNCuHE6zXuYFYipLheCXE/vbXoUWZ9YiNP8K2dTmJWJzBvqQdnPcBqV/rebjQmxNS93mC50HKzYqQqMNmCz/PMCveJgY7mePxEpqaExvXIBkCCcl5+HM4fv0K18EemyjOTVJjPCNRgBNSI9slZqqH42eT6z8g0YyNIZ6bRf6aITNoAJcFtjU6C9AWl/h5lpB7kcFrpjuH615EJJjDNTgh12uygueXDvGaixNSd7iJazBs47mEDt6PrAZec05A/oJjfgO0jchfu0xKEkdJlHrXITXeSZxnUuKesXOENvhMiPNpF6TuOIm87Bfs73EOjH4TVhRFUZSa0E1YURRFUWpCN2FFURRFqQndhBVFURSlJqYWs4IChSGf1HLNXHxQbkhd38Jl0WL4sLvK58gxUE7ZTurPhm38PJvUXq1ifGjfdYiM0ybnW6HgsDzuY1+WScxhhONit1BImCdRgJsCYsXlKCTckRD5qCDyjIh4FamDOkBpIstJlCgRT4oRijFN4i6EDRTyMrI+ijGJdvRwjicWnvNPxtugbUxqt44DHANSIleqGOcuInVpxwWOgU1EQJsIg0UD+xKTiFVZwr50l/AYOxooUs0JkcSIUOOSuMxdLspffRKb6FskMpTIbuRykMEKyktjEvc420GhaaGBx3BHeP0nuMTFslBiaxEZZ0Lea8i14Hl4j2l2cfy8Nt5PREQMiV2N0HcTK8F7RWpwHDIyNuRSknTYx9dZeNIzpG65Ra7DlIieDon5rIi4l3XxIHmLxK4SkTXMsM0le9WI3H8tD++rXo7fX5sjHgd8IPSbsKIoiqLUhG7CiqIoilITugkriqIoSk3oJqwoiqIoNTG1mNUo0Zpo2KRGLpFdRh6+LibpJyPy3jRCIaEk0k5KkphCFx+UOyX5ucNgXzxSO9QjNUZzg22GvK5FxIqxjZKI8fC9s6QYaaMgKThjbLMKMn6k3q6IyJyPYlGUktQmH6WOfoRzssPC8xuTtKiDSDpRtYRCjiMoV+zp4nze1MC+sBq+doJzXKZENiKJVLFN6k2T85hl50aExj5ZM+MOjnNJ1tuci+exgQhSP7KJpOejRDR0UCJqkTXoEwGpmeHrbNLnysE2j0TwzWRoCzkjXAd2ges0bhDZjcyvnaLw0/QxVa4gCWbLfRRWUzJ+7TaOldsjyVptfm1m5Pz8EoUhlpo3JsLmxMI1aJN66cm2ZWhbJ/h53S6+1yby1yjCNbjg4uviGNd04uC9I2uRMSA1gTeRYS1JTeBbJzifToDi6OwY11HPImbbFOg3YUVRFEWpCd2EFUVRFKUmdBNWFEVRlJrQTVhRFEVRamJqMcsyKCo4LKVKSPmwkpSvM5je0yd138gze2mRUmZtkmrUSFCQCElKSubjzyKjMZ7HiJTws2wcQr/ClK8VFwUCEqYkLSKduSSxZYUkNsVjIguVJBHJcPnDyVGkcgoyhiTBqyDlw8IAhRfbxbF2iIQRLeFYL9so5GxP8fNKIqd0SHk5b0ySvwy2LZFyaQlJbZMhyhpRRWKNAuzf7goXgyGl80KDr5sN8Nzmu7g+Dgnx8/ZU2OdFklZUGJyjDkm4CsYkJYlIcVmIa3BArvVOB+87DZLsNopJGUkWYBShfFiQ9KOMJHVFHo79JCaSWBvXRmLhHNk+KblXYJuISEGky1myPgKShsfk0YqIsUy4rEgKVFWSkqlEoKsaOHdjbxHaeimJQEM/Sioq0OLcseS6UPDcUrI/BCm2eQWuXyFJhB4p2TkN+k1YURRFUWpCN2FFURRFqQndhBVFURSlJnQTVhRFUZSamFrMMs48tCUJKbuVolATEDGrFHzYXZJUGIdIU20LxQcvJXIPSQ1qzaNoMAzwQf7W5T60TUYoji2EOC4tG49xY3kHtDWJXNW28OF+FaLgsEdQbEnIz1TtAqWHXkaMMBHxeyQZKiAyCkkEkhiX0lyJ4+D62B8vR8mhCrGNyW1CxjqJydgsYp8zslbzHvYvIelOLUNkEiJrLGckZc3H9ZtaZO6GeN1ECV4j4mHb7i6OwaYc15ZL0o9KG49rSrzWHQuvh5LIM6wWZEHS8WIbxypo4XnkZJxHQ1I2s8Jxbkd4LcUljt/KCMWsgpQ8dR0seeiGKGZOSuxfQlKXSnItiIiEAxybnkvuqzkmXDWIL9QxuN78CMc6IklYXo5i3JAk81UFylq+wTGMSdnYCbm8oiHeY9o2Hrcglu5KiGvVIWbs/ZwZaPMmpIwnKZcYh7wM5YHQb8KKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1GKWT9JAMlL2Kc7xgbVLyqAFJL0nJwKHTYSrlKSaFEN8ndvAh/YmRAkgJ0JIRpKdxEGBy7WxzJVT4HuDWRRgmqQcnkcSluIBTlO/RKnDckhyUpuli+G4iIikASkH52MfhwVKDg2SsjSHLxN3ROaTlPFb9vBcshBFj3iCn5eNsc3KcW0NSTrZhKwFmySMzfs4T2WIx906whSy4XgPtLVIipafYJ99IkPmJB0r7uIct7bjhAQZKeM3IolPAc6vux77bMgY2DmJwipwTA0RwgYlKYdp4/Wf23g9BEQWdEYo40QOjouZI/eiEbldOnhd5xW+NyeCntvCcXZJOJOIiE+EtzTDtRAQobEZYX96JKXOzpbwuE02T3hPKcj1H5D5nOsdCm19UiJSSMlZj5QodMh1nZB0txxv3TLfxMbZJeyLS0TWsYvjsmJz4fVA6DdhRVEURakJ3YQVRVEUpSZ0E1YURVGUmtBNWFEURVFqYmoxKxBMF4p9FANGJE3JJVJXYBNZI0ZZq4zxYfyeFXzwbgSTcIYkKmaZyAIJKfUVNFCaCEiZsbJAES0VFBcOXYdtLilB5/XxdekAxZGQyFEyg+/N54m0w0q8iciu3X18LSlNVxChrCQ1JycVtrkZvrciqVdlhW3NnEgwI5RJRiHOcdDC9zo+jk3EUqAKkkxEks3iNp5bRkS0kIhoHQ+PkZF0IZvMsZCygC5ZHltJ6pXJSIm3CSlpR2Qj28M5spp4vixNzRmgxOJMsNMVSTXzSBJeRCQnv8LjmpKUS7VxXJpEUmxHKGEawTFYTlAmK12cy5l5TNYy+whdqlyc94zIXhOyzg251Xolvo5UtRTxWZodznE3xO9zTVIadeiRtZrinMw2SZlHD++1O0l507CB/euRe1aLjMvyDFmDxGP1SDnHgJSbnAb9JqwoiqIoNaGbsKIoiqLUhG7CiqIoilITugkriqIoSk1MLWaVk63QVkRoEaREzMjbuNebHIWGIEb5wJBknSFJMMm7PWjrk4f7UqHIE5L0qZmIPLUnD97TMZYOMz4edy7bgV2xUSarSGm0Vs7K/+G4jBwc+6UGyhEJSQgSEWnNoWzU2UNSajw0Fco2igr9DMfVJj/2dUocBzfDMZwh6ThjD48Rt7CtX2JpugYpLzfvkLQoIqwskcShiY9rJmzhuGy08dxaTbwebi1QSso6+N5wgh107sA0u9vJvDUD7LOJcB0UBtd5luLaiEmJt7LAMbWJvLhArrnJEI87HuN9p8TLQaoIE5HsNo5zm8ybVaJIVZI0wIZPSukJXnOui/PR9fE6HJPkPxGRkqzLcRPFrEUL+902OA5dt4d9JDJkStLJbJtIay4p9xnjOf9gBe+/v5Ni/w5pY4nY/5zDY+zwsS+HkTV90JikrFWkBOgh2BdScVI2LZKEu5vwep0G/SasKIqiKDWhm7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpNWMaYqWI+fqfxAGhz2ihmLHtYpq1ch58Xk6Sp5piU4itmoc24+FB8BxEpLFJ+bdbCh/FzpEwYqaQl4yYKRLeRNKvSwof7m/IFaMtWdkFbi8hVCUsN6qLAFZNUo5kIx8qe4FiJiJQJHqd7KE7eJEI5aMcyyihtC4/dIylGLUHzYTzYDW0/i46ANmeGyF8OCiFyB4pxC6SC2ro5nLsJSU9aSkn6FIkmsogbaHWI7EZSrwbbURxzDV4PkwD7vELW/kNuQ+FqhYhUyy4p3dbB/vUtnHNp4fwGFr53JkfJJlzGvuRLuCb9JknHmsP0qQEJF8tJypfjErkqx+t6sYkf6OxehLaDyf1Eujhvu4h02hjhuIiIHFbhDSnPUDbc3SBlGUlC3gLpolfiONxi45pZJN/dApJidl88hOS3k+2mjfMZbsJ7xzhASa8iIlrX6uEhEjzGAhmXlofHsEj83MoYx2DPMo7BVT+9EQ9yF/SbsKIoiqLUhG7CiqIoilITugkriqIoSk3oJqwoiqIoNTF1YtaoRyQCH9t6Dj5Qj/soEAhJP3JInFI/RwHGylFe2jjTg7bBkJS5y0gCDKlp5XrY5hA5Yp4kV0Wk7Fg1vBnaTBtlkmGAtkC8jKJRJ9sGbQUJ29ltoRDmRMRYEZGAJPBkyXbs4xAFDttDUSzwUPCx7D60LZOfBQfk82ZyImZM8Fx8kkhle7guqxLH9Y4RikDpCOc4cIno0cFxWSKpVxNBkWpUkPOYRynGS3GSZ4j8ZUjJyBUXBbOCyCm9Vg/aqgKvw2FC7gkklatHxJvJ8k5o67bxfEtS9rEKSRm+NrZ5Ft7erBzXmjPB8yBBUTK3E8fPS3CN+w38vGaXTBJJDRvk5F4pItsNykE2kTiLGF/XFryXBURQM6Q0YnMJ5TtbcD7bHhlrwbFJOnhcu417wdIE10ezheMV+njcfIRj2CJSnR3jmvaIlGgJKUdKpjPs8rSzA6HfhBVFURSlJnQTVhRFUZSa0E1YURRFUWpCN2FFURRFqYmpxaxqHT4837W7D20bLJQA1ksX2ixSsm+RpBCNLBQNFmbw88wI+9IkAtdYUAwYW9jWJOLNTImv88nDfXeCaU9lg6QuVSgLDUi5xCYph9droEQxGqAEtLtEmcltYnqPiMhyghJGI8XjmAHKC90WEcpKHJtihpQAtFFUWkpxaR5qoWiX9KFJJEVrInDx580qJGXLSEm8gpSS7FjYZk1wjqsSxy9p4jWSklJ1gYNpYJ6NMplPyjRGGa6j7RbGJNlE4PL7mFxlkwi5w9qYppaO8Xol7pfMtbBUXWFI6l0XRa+qgfObksqjORGNBJekOGTSwwQ/8H4VztGwheOy6OB5pLvx2jREbBOL1M0TkbSL6y0gEpzfJx85wmsujcj3rwxf1yCe2EEuJge21+E9OfZxHW3LcP1WFr4unMGxloBcw0RY84mAaApMJ5zpkOS1Aq+bktVfdbHPEpG2KdBvwoqiKIpSE7oJK4qiKEpN6CasKIqiKDWhm7CiKIqi1MT0YhaRP9wm2cMdlAXyZXx43iBl91qkzJ0V4OuyEQo6TSERJuSBekXqeqUJKc2VEEEiRyGpQRJqXAsFh9F8D9rae/C9Vh+lgsYGFAi8Fp5vO0KZpCKfJxb/2SspUFSySerQfBPbuh5KJjsF525nQUrdBdjvOdLFYpak/FREIlpBAanIyHttFEzcCPtsBEWbkpTiHCUoAlkRCogOkaEWPDxhdxcR4Mg1knnYP5+UGa08lHvaTZT+XJJCNhqg0ZST9CmfpM9ZCY5pbJM0pRLFwKiL8+aF2JaQ0oPxgJSMHOH4NTJcB5HBay6x8B6YNbEvkwrHLx5hX9yclPV09vG9qMC1xRLBel1MyBv1Uc5MBPvjEeHNJSl6UuJ8TohAu9sipThJKdlxQpKriCRWxaTkoY/j3yVOVzUkY0Ck0yzD9Zs7OM4rDp7v0Cai3RToN2FFURRFqQndhBVFURSlJnQTVhRFUZSa0E1YURRFUWpiajHLGuCDbcfDJ+CDDJNh4gQlgG6B+39aouzikBJZeYoP/B1SRi5L8POKEqWJiIhBERFgJsuL0BZ6RJiYx3HZE6Cgc4iHgsN6wQf+K9gk2yc4pp0OptbMuyhbTPbwklu9CJO0ciKZhSHOXUSO45A6ecvLuD42dnGsNzs4Tz8oSRpTiOPgG7Ksh6SNlQ/MUMiLbJRJ2kSkMoaUeAxQfLIEPy8akuQ1Iu61iPgYL+C5mR6Oy0ay9iXG9dtq4zHaNl4jt20j5eZIGlujheLTcNCHtizD67Vd4RpqFCjAVESQzBMiTZJbnk2ENanwGLe0SUpdhVLcCknRa7t4bc728J5gkzH4eX+w3SMpaxOSEjYhApfH7nlE9ixJylo/JqKdQaluF5Gh5kb4uoUe9iVZJiVnC5Y0R4Swsg9tGxp4ve4a470tH+PaL8g1N/JxnIdECJsG/SasKIqiKDWhm7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpNTC1mtUjptoKkuMRj8kA9aELbmEhJowk+KO/4+FA8DFByKNAfoKW+2iTFiSXA2C6KGTkRF5wGvm4ckoQVkgrTIIk5bVJiLG7hyaU+yj15TvrcZ2IWqecmIv49sC1poQTXt8lnom8hcUlKClbYRqoySlXh+aVDbEtyFKlsB8ehExIByRAJi8hfPkm4ankkeY2kBjFhxSHXUhajeNNq4lgVFVnoPiadFSmu6RmSBhYTiahKMBmq1UC5an4DSVjr4PotHTy3qIOSTblCih6Se4KpcE0GObmuyTi7IWkj6yCOca1ZM3g/kTH2pVxi64CsZ1KiNCLJcyIiDhEBS8Gx2bWC99/Kx3Vk23jvSQ2uLauJ45UTWbZMsC8RScdrYfekRRL4iiaO9coQ53gpw/XreyzFEMev4+Ca3jUkqXc+SamrcP1m7NqcAv0mrCiKoig1oZuwoiiKotSEbsKKoiiKUhO6CSuKoihKTUwtZjUijANpeShckUpQskwSrlibPYeiQuaSZKICH9B7IUoiMy3sczRESWT3Iia7LIVEzOiQhKU5lMSyAiUFN94NbbtcTMzZ7qCcErRxrJodPA9/gFJHnOEYxKTEo4iI5aKQE3bxXMYTPM4wJyXnyM94m0g6VttHkWI0wvHvpPh5RYlrMCDiWOATmUdIqTVS+dGQEKN8TEQqXL7iEYloUOCcjISkMZGxt0mCkZDynGYXXoimiSe3YRYTria78XpYWd4GbV2S+FThdMgiSdHLicTSauGb8wERPTM8D58InD6RMB0b15pNyvoxmczeQ1KmLDzGvEcWQkGSzohUuOISw1FESg/P2aR4P2r2sJShI0QizFagbULksZDIrRKTdbmC67xHUu8OWr8F2pbzPrQVRDIdT/AYQiTY5uwctA1TXNMpSccbVXiPCSuyZojMG5A+T4N+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamFrMcwYfsoxUUeWwf5QWflBm0SAk1myRw5QN8eN5dQJlkTMQWlySxBA4KXJ2KPKAnAsyKjULCiiHHFXxAHxGxwppDgWBAxKDhuA9tDVJC0TV43KKJP2c5DT7tCREzohGOjbcbZRmPyHJVQOQ7m6U24edFpPxlVuLctUOUTuYbKGZ5JR7DpJi2k5Jyf5WDY5iTn187IYpFEXZFSnLchJSMTIjg1+2gDCVDHOeIhKIVLn5eWZA0KxfHwAQkMU9wbexa6uN7yXzM9GagrSL3hJUlnLeiwtdFTOA0KNRUrFyqT2ScAM/NIs5UQZKsGmQdNDw830GO19v2gotZQkrxbexugLbOGNeHWcZ5H42IbdjGc24R4S0jaWLZGBec18ZEqjzCz3OCHrQtj7BsrJB58lmiF5H+Qh/HJSHSXxTh/hX6RNIjMl9FSudOg34TVhRFUZSa0E1YURRFUWpCN2FFURRFqQndhBVFURSlJqYWswoiiYxLfJDvepgGZDXxYfdG8tB+cusuaIuIbFRNULiYkBJqSzGmpMyS9BjH4LlZOT5490mlNfJ8XmIijlhE1moTMaNV4EH6KY5fWuB7qx6OQWsBRahszEtuDZew38Ee7PcD5zdhH5eWoG2Phe91/Xloi0hZtSjBlKX23Dpoa1kkPcnge7MhEV5KPG6jTcoCtnC9mRLHesXCuWtXOP6zRHLaSAS6mKyFUR/70iQpa+0eNMltpCRjSGRIKUlJOw/X4IgkUjUaOH4JOV9TYF+yMRGDXJScXNLl2RYR1ojIs3N5O7TFKRN0UCBKSSpcRoQ6JruNRvg6jxyDSZ0iIvEY1++OldugzSpR1mpbeK/1HVImk9zMJhMiYbr43vl1eG0WRJZdLvF6LUgJRbeBY1MJ9iVnMlSC17XFUvQE21yHCHkFtrGysaFP4uKmQL8JK4qiKEpN6CasKIqiKDWhm7CiKIqi1IRuwoqiKIpSE1OLWTn6VhLnxJAgJQr9PgoNPilz5ZO0IlZ1rzD4oNwx+F6HOA4TkqbikoQgh5S0ahiSPjUhwgURVjIfB9AjoozdxzHt2ShWGJKclJKyeSmRbDoNTBwTESmWcByESHDjERGampgINEcSmtIYxyYnaWymgSUirRjllCzrQ1sseFwhx/AiLDkXEonQtUkZRA/XdJ6Q+fRQ/miS/nVJWUVDUsOSCo/hN3BMow7OmzvGMc1TvJbIYSUl52EiXAcTh8g9RODMSSm4BkmzCz1SNi8j0iTpc16SVDkiJBmf3AYjIrs1UT7asQ3lKBLAJ9UEhaSlXVgecnYjjoGISEoS6YQIpV6E96iKpHoFRK6yChSfqpRIiRleSy6x5RpEzByS1EF2tQYuzonvYFtJpCkSjici5Lom8x5FeB3mpJRh6OO1lLJNcgr0m7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpN6CasKIqiKDUxtZg1IYk+psJUE2+MH2kv44PtpETJJiYPwJ0WKQtIynq1Sny83yKpPGNWLtFGCcMQmaFJ0mwCImYFFkoUCUlnKYlwtUdI2SyS6LPRoFSU2z1o2z3EMY0TbBMRsVKUR3JSevAm4ojMNBewrcJ+z9l4jF0WjvVN5OfDw5YwUc3NSRm6CKWJiqzfIsS5y4lVs0Dmbp2Ha3/sYWnPjJRzzLBJKiJ19BpoG82GREps4oSQsC0JMrw2xxmuhQGRvxwiZmUGP68a45ynKY6zS6TJdhvnjUlxWYltg0Efj0vubhlJcfKYNEkksTZJYgo6KGv1l7EMnwlwQja1Ua5sRDzNblRge3s9JtfdTu61uUtk2RDn0x+Qe/cI39sucfJa5J5HKljKCmkrMpwTQ8pkOuRaqkiSYzZh3y3x3AqSopWS0rQJK1toUPSKS7wnTIN+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamFrNKEj9lkVSTkqRoVcv4kJ0lwIzQNRKLGBzEC5KZMQoJUUUevJP+pU1MgCltkqKVk0SpMYpUFulhRGSXPeR8By2UAFoZHsMnZficIX5gMcCfsyZxHw8sIvM+ig9OA4+zI8BzyUOUW6oJyncbSjwXl0giIqQkJpE/mhaec9eag7YsxHkfhCvQlpPykkLEkWhCyu618NxWSEJQYpESdja2tUkiVdvDMXBCfF1C5JmExEq5OX6eW+E6b7ooorAynksTIj5W2L+wieITKx+akzyliUVEzxzngzhnYpGyjw0iYbo5SXu7bSe02TNMDCTH6GKZu4CIbcMUpS4RkfVbDoG23SSdcFyMsY8uOb8GmU+SHBhWeC6s3KohyVWjEiegdDFZL8tI6hUpB+sRMdZ28DwcUk43J2t6SPrXIBGNxsXPS8l143c0MUtRFEVRfqPQTVhRFEVRakI3YUVRFEWpCd2EFUVRFKUmphaz1vkou4xDFCniCMUWq4sP2QMLH5R3ZskD8AYpZZai8JNkmNTjEbnKsrEvIRF+PCJS2SRta0zSp/oklWediyJKmfShbbaNnzdn47i4Azy3yRDHoMHGucdEKJGuj+Ng+yTpiyUlEelnQGSZMkZxZM7HnwUfEGK/t/d60BaRhDZT4BoMMhSfoiYmFomHn5dnuN76ZA3OpiiszAi2ueRn3zkXx2+OlG5re7iOWkSeYwXibiRpYA4p7ekbUoqTyFCkoqhMSBJWK8A1FHYwLW7X8m48rsHjFiGTZ3B+bVLK0C+JSEWEMGuC0tOeMa5dv4fCVRaQEpTlEN9LRM/UYoX9RDIL+3PHaA++kFzvkmG/M0NEW5LqlUc4dzkZ60GB18MyET1tJlc1iag4pXDlOLhWK4PnQSoySloQsW1C7qFNPK5NpGRxMDFvGvSbsKIoiqLUhG7CiqIoilITugkriqIoSk3oJqwoiqIoNTG1mJXvIGlKPqmX5hNRZgZFBUPknlaID+hDkmCU5PjemDy0txrYv4CkwrRJSoqd4AP/siQiRYSizEqKxkrHwf41PDy30MOxikqW/EUSqsiYNojY5pISfiIiFUmBCVM8vy4pEZl6KJ7kpOSkG6AM5RJBJdyDaVYjIkjMkNJ+6QTfS6q5STFG+cMQSWxCrpKhi3MSVJje1UlwrHqkjOdBAZojG3wcq1aIQlOXpWgJCibJLKYVjV1cg30L3zsiCVI5ERoXDK7f0CIl8hwybwG+rk9kyISUm7MDXEMVSXHyydJvWkTMcvG92+bmoa0g5SYLB0WoyCcJfER6CohMKiKyfetWaGs3cL1V5GtVTpKhDEnhq8iaHnfwnlxaeB1mDh54bOE8NQY4Ac0GHiPwcBxI1UKpDBF3HTxfnyTmCSkPGQ9IKcgCx6Xbxuu6LPp4jCnQb8KKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1GLWGL0baXWJXEVcrczHh+IlKcNVGRRl3Iz8nFDgg/wqwgflWYOUmwqxzcTYl8wl5dIEjytWF5rsEUnWslF2aTZ60FbZKMqMxthnVpItJ+LSxCNJYsRREBFxMjI2pCRenJNEGiJ6zCyg3NIgKWtJjse4bUBSxyo8RkTkj5K05USCS4jpYWdE0ol60CZk/MckhcgtUdJh5T4DIle1iU3WIa+bs/Aa8XNcv1tauH6H5PIKSTrTNpJ0NiHl5kIiwBQkwcgiiUjr2ti/gpRBTVIierJ1QCK9DDlf4kyKT+5Z+UEboS2+4yfQtom4VTNtFBzHrBwmkT9FRA5Ncbwssn7jAF83ImPjkAgpx8fxXyJlHvd4uM7bEQ5iK8V7j0OkNc8h1yFJ/rKIpCdEOnMjkpTmkdc5eIxlcg9kUqJFEtBcIoRNg34TVhRFUZSa0E1YURRFUWpCN2FFURRFqQndhBVFURSlJqYXs8hLg4Q8KK8wDajyUPSIyQP1wEKBw0zwQfkwIWLGDPavrPC4ISndNkqWoW1CZKHMwsShRoAlHqOYJHqVWKbNWBtIGyYihSGeR0DEoOURCgTDAn/O2tTBPouIBBaW4hou4/j3S0zqMaSG3cKeJWgrih3QtjPEcd3auw+0/e7gZmjLSanAvuAc5z1SVo0IHNEEBZpOjvPkk3Sy2MI59tGTkcIhSUekumRGZL6sWoQ222A5PVvwGOGISGKk5KFv41gZB/sSC8puGRF5jCGl4CwcmKaNgxCVuP4Ckuxms+OSrxiWTdKjSJqV8XBcdpD33nsGr6WFEa770R07oc0n7w2JrCUisilHaapPyu5Jg5Q4dfDa9Mk9NCAlLLcKnvOE3EM9IlLNLOP6yAXlSqvC17nkunZIUpfl4ZpxXTxGQWTDilx0TZJSlyZ4rY9I2dh1G/DeMQ36TVhRFEVRakI3YUVRFEWpCd2EFUVRFKUmdBNWFEVRlJqYWsxanNsDbekQH+RvyvDh+QZ3HbRNSAmqeBEfdlslSgBlhvJXRkr7SQvbhqRUlTdAsaVLZJJ8EcUlK7wD2noNfLh/y6GboG053wZt4QTFm/kKBY7BCI+RN1E0aHgoKbRTFHRERNbZOA7buihN7GnfAm2WjfOU7sTlNbtyKLRtcPB1bhfH4ZYuClLlEKW6iJTES0pcW40cJbiZNpHOhrjeHCJ/lcUuaEsbONbDFhFgIhRRJiTBaJ6IeykpBbmuxDEYkoSlvMK+VBNcR767AG2ZwbW1M8FxXmniukp9PEaV4LVpkdKScw6OwaBCoWmFrIOqhwl3KzFJrlrEvtyr3A5t7iYUzG5ukfKGRORpELE19rBcoojIj1yUq2wP12qzwHNOY7xGihZKRLlHUswWcc04BVkzDTzuHQbvUdUc3k8OTXrQFgzxfIsuzsmuWfy8O0YoYfljXL8bbdyXIgvHqiIy6QJJQPNGU2+na9BvwoqiKIpSE7oJK4qiKEpN6CasKIqiKDWhm7CiKIqi1MTUT5J/J0ZRaWThQ/GVBkkmIXKQWzHRgJQj8/FhvG+wLRyQclgJKV9XksQWw0rL4dC0Q5LO0sG2uIPiQoOUuTM2ihBBgNJJUJAkoQDbTIWfVzKxjQg1IiJ7KiLGNfAz54lUY9v485zdwTkpXFJ2j8g3mwSPMUqxfJgV4ntLF+ezGuBasCcoNLlzRJZxcT7jDIWrtodylVeS9UFKGe62SKpRhm0dso5WBD9vWGD/igTPY0zGfruHa2Y3mbd4guvI9FFi81Lsc9XBsSoF58N2cPwcQxKu8NSEVOEU45ByiSRaKyP3jriD97Ym6fPBIQpTGXldWWDbcILHEBERUlrRIfOekpKTTXIvE1KGMo1RPC0C0u8Q584VPO5sgevINyg0tUgJW5uU58xJmdGKiLZdsmYCD88jXkbZuMzwHsPStkbkFuoPsX/ToN+EFUVRFKUmdBNWFEVRlJrQTVhRFEVRakI3YUVRFEWpianFrPtMUGi4o0kEE/SKpHDxQb41IPu/jSKPG+IxmhFKYt4KKa81IVISOYYQuapBElHKLo6BRfqShSgL5H18uO+QtKKIlHOzMian4bi4KX6elePYjyqUD0RE9pTY3vB6+MIBSbhyUa6YCEnWwRAdMTFKMB2yPhrzpKRYgWlHhgghIbF0/IxJWDgGiYfvDcmV45b4eV6K418Ri6iIibBW4XtZCcuRi2s6tbEvjRTHBdUUkTtI+cUBkZwKUkZuJiDpcxVeNyOS8hWT5LSM3CYMEW98Igv6EUmuIuVSSQCUGJIeJ01S4jEl5VItcu8oyFoj62CFpFuJiHjkVm1ZRFQiUmLYxpSwYoz9HgxWoC2LiADqYl+cGO9RbVJG1cdqn1KRBLTMI2NI5MVyhNdrSNZvg8iVE1aes8Lxd0l5yJRUnLTI9ToN+k1YURRFUWpCN2FFURRFqQndhBVFURSlJnQTVhRFUZSamFrMGif4ULwV4RNw18KPnNiYILNssTQlUlKsgcd1mkQCMigpuBXGmoQOSXEhJQBTkhSTFyiYNFIUElor+HB/NMKfd4Ic5YiWR1KNBMdlRBJgWImxlkVEL+KmiYi0mjh3NhF8JiPsj+vh2AgpvxaTNKalEabt7CEiyyYHz9nP8fMaRAgRD0UbQ2StOCYpUMQOKshYrZCrKcnxGO0U50RKlDp2EUkvCXCOQ5+UmyN9GRH5KyZrYYUkdY3IujSkbF5IrvVGiderQ66HnCS29RO8RsZEThNSmi9wmE1GjBrBQaia2L+QJJ1lCX7e4hDnbWKz0pIkdmlC+iwiTXItFeRCtjs9fB2R5QpyHJeU+6zIAplY2O+YzJMhqXcOSZBLIhxXQ1K+LJJ2ZhPpr8xQ9EwF+1wGeO+uyH0naOP4xTFJx2thucRp0G/CiqIoilITugkriqIoSk3oJqwoiqIoNaGbsKIoiqLUxNRi1n86+MB6IcOH9i2SdJRaKHUMiBgwIiJFgoFU4pKyYIaIN3YTH9rPeCiJ5DY+tN8zwJJseY7nMTtBMWM+WYK2VoGpMD5JzAobOCVMZkpJMk5OSptZRAjxLTKoIjLTImlCRIaySJmxpo9RWLlHSoA5OK59IssJOa7Zg3E7Bzko5DRtPL/ExnnKyOr3bByDkJTdzFOS7tZCKWmF1Dwj1dLEsohg0sDzWK5QOnEyPLcwQcnmJwUmJ60THL82Sf4px0SaMnhc38Z5axPRyyP3DibUOBG+d+zierFIuTmffMeIHGxziIRVCUngW8Q+Wzau+wER0SqHJOsNiFTIkrpExCPXQ2HIGBJZrj8gSW7kOO0ZkghY4LyPc1zA44isGZJE5pL0riQg4i5ZHx6RJtssjbHE945Iicg8RzGr6eIYNEnJ2ZKYjxWL6poC/SasKIqiKDWhm7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpNTC1mDdtEriDyQhlj+lFKypZZOas3RUpGkfJ8MSlR2CJCgksSW8oIf+7IShRM4j4KCTYRwkYeiRxawNet34kCQUJkt4KkdwkRplwLj5El2DYmooxFhJ+ffyiKMSGxK+ZImbEwQkFl6KKENSbJNY0AP6+KetBmL2ES2ZCUc3NJaT+frAVbSAoRke+8EN/bbKHYUpGfafsTXEdDsn5zkgw3R1LIbAdlkoIIJhVJP7o12gRtQpJ//Am5LRQspQrbUpJml+XkWkrwPlExqauNQmNl4/gNM/w8UklTQiLteSQVbljg50VE2rEbpExgTO5t5H4XEzm1jHAMREQmASmJWZH70Z4BtBmSUmXPzGBbD8c6NHjcZkHmmCQWLqdk3n18b9DA698tUKSqiOToEKnWIROfkeRAu8C2nofXcDvGca4yPMYww1KQ06DfhBVFURSlJnQTVhRFUZSa0E1YURRFUWpCN2FFURRFqYmpxaymR5J/LBR5lkg6lkNSfjoVHrpBSrdVORGpVlD4aRK5ymEpM6TPpUGxpZOQNLAWihRLbZQ6tq7DPs+RxKw+kZTSBn5eq0dSdMb4OtuQknukTFtOBDgRkYSUR2wmeJyIpI45RHKq8K1CXD6ZJQleUYkSTGith7Z+gek9O3KSYhbgemsS6WxMknUyF8clclHIyZdJFFaMJzwgyWaJj22lTa4bslYLIqeNXSJrEelkkfV5RGRDm5Qy7OIY2C5ew30Lr9e4wv5F5PrvkvQuIWllOSm5l/ukz2SdtkmqXIB+k0xIepfjY1uY4XzYKbnvhDgfA59cICJiGkTYJNKqRdZ+k5V+NNiflYykShHHbL5NZL6SlabEscmI8BrHmDBYEUGyRYS8sMS2ipQobTtEpKxwTAMydyERvXokrWw4UjFLURRFUX6j0E1YURRFUWpCN2FFURRFqQndhBVFURSlJqYWs1oTTCtKA1JmjMgfrkPKjMX4uhlBGcdJiEyS4sPzqIOviwUlhbIgJbKIh9Ii8lGvi22Jj+e2QtyKxY097N8Q+1dV+HkWKb8Wk8Ss0sL58As8RsMQyUNExCdSDXm/V6BUY01QrhgXKJ4lKSYRWYakehkia7iYrOMQETBjIhAr+2ZIYhlLgSOS3p5deD2wEmoZEYGsCMc59XBM+wbHKidtNlvnRKRab2P/Jg5+3mJIjDqyBgML+xyyMSDCYByhjJeS11kpjp+QpKnAIuINKY2ak1MLyfUwE2Dbd+Nt0FaQtWsLikt2A9u6HbzfFRmOqYiITUrnjUuUq4IZvEe1SSIVe+/KSh/aKiKE9YI57B9JHTNEgq0G5FqKUQ7MSUpddwOm8vkWnm9BpDpT4esGGSmXGpHkRXI9+A72zxviPWEa9JuwoiiKotSEbsKKoiiKUhO6CSuKoihKTegmrCiKoig1MbWYFZFkErdqQds4JdJOF6UEyyNyFUmAcSf4c8KogULDuMsSc1DMMMv4MD4s8YF/SZKr+kRsmXXxvd4OTIW5OcKH9odWeL5bfPy8UYpjsJ2Mcxqj1HWYjUJCl3yeiEifpI5tIyXxLJJ21M6IfLOEhkRG5DGPJAdNSJpY4KMQ4pMSai6R23ILP68gCUghSW0KiByUjnCs/Fm8HkqDffFIiplNxLFRhoJZ1cH16+fYv8jgvIfLOB9JiXO5ZOO55SGKVCw5qUjxumlEmBZXColiImlbgyGuP4/IVbaHtzLLwhe6RFjLxiin9XfjWJU+SYUiyVNhG48xYuvPkNRAH+9tIiItwTHMMzx2GuCxB02SYkjutZ2UCGUZjuGuPXgvcwMcmyjC66Hbwut1Qq6RFSKExWRoxi4el1T7lIRIsAOSlEaC+mSFlEucKXBcZro4R9Og34QVRVEUpSZ0E1YURVGUmtBNWFEURVFqQjdhRVEURamJqcUspzELbWWJSSxWjLLAiEg26xaIaEDStiaGpHKREoWpg8cgHpVUBRGIfCzJls3g62ySjhWReKzgDjyu9FAw8TNscxzsdBGSNCAP2xoNFCGaJMHIGeNxRURyYjQMSSpSs4nSxGiEIovr4/JKSYkyp43rKCXCS05SfiJSJnOuh3bFctyHtoQkf0X+DLQNh3gM30NRqSAlz+wm9sXNSOpVhfNkKpRJxjEeI2rjvLvEMLFHRERzyOvwEDIuiai4exHa5j28rucKXKvlAK+l3cM9eGBSXnO2g+cbBqQ85BgFonIFx75F0rvmN2Kf5+110JanfWibJFjSbkDugStjnPMZex7aRETEYmVesS0j1lpChM0GEYs6Do5DSVLMlsn9w0zIuZD7xDwpq7jQwuvfI9eNwe5JaZFrKSSyJhHMPIuUI3Tx8xKHyHc5EXzJfEyDfhNWFEVRlJrQTVhRFEVRakI3YUVRFEWpCd2EFUVRFKUmphazlnN8oJ4X+CA6RwdAykkf2rw5FB8GASlzleFDdstFc2QDkYB8kgqzx+B5jEgpM8/Cz/NIHTQrI+8lcs9ChtJJ0d8BbbtJekzZRaGmS9JojEHRKCkxdalvofAjIrKUk5SqDs5Tl6QTpR5JlSLjlZDSgySISCxiB7mC52cifJ3BLouf4zpyYmyrSF3LIS4ZsRySxjTBse66KJikKUn0YiUsiQxVTXAMMguP0c9wjg9roVzZIKltcymuab+Fx9hVsGQyFFb6IyK7LKG81GyQ8obEriyILCik1J9l8NzKHCWbmIR3xT5O+piMS0nudxWRnqIAxyUmIp8VkMUrImNyr3WJUFYNd0Fbs4UXWK9J5LYmft6QXDdVQcpz5jgndo4DO8mJSJWTbSjBgY3JdWNaeFyXfLcMSKJXzyPrwyeJj0RuDUi52oBIv9Og34QVRVEUpSZ0E1YURVGUmtBNWFEURVFqQjdhRVEURamJqcWsRZKE5RKhqeVgm1Oh5OCPh9A2buAD8ISkY/WINBEQQ8JhYgs6BZIRIcnr488nHinrx2pf2bMoZs0TqcjvHgxthghc+YiUHSMJNa1uDz+PlHPrDzBJSESk28G0o16LlHkkiUqGJBtlJBGsU+F4eaTEXkHSu6wGKbVGSo/1x308LimhaLsojgyJYCJdTFSLUxz/gzr4unF/N7SNUhS4oiaOfYOs/cWdKPPFRP6KDl6Pxy1IktCQmEVDvB6aRCKaCYigR1K+cgffGy6ggNQksqG1gmvDJuUSDUmKskj5RcvDa7Mi95OsxPuYYaKXi2MQhXgeLQfH3hdsy4jAJSIyIvcPm4y1RcoyhqScaUEOs4eUsEyJbBS6pJwmEfKKAq+RFSI5VimOQzkgCXJMzCI7WEWStYyH4xeS97YT3JdKclxrgtdcxuogToF+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamFrPcAKWEXoZt64io4JISeysJCisRETjmUiI+kAfvIx+lgpJIDpaHZsD6GCWMBSLyeBa+tyLyTNDG9/a7KISMRijoOAmW9eoFKFa45HUxKc1XsKSYOV4urdPA4+wmJfZuJ/JHluGxN5B+Hyq4FkqSorWTSES7x7hm2iWen0PKG1YkjakkyV85CRPzDM5xSVLb/nPHbdDmC45fN8C5C8l5+KTMoB+hKDdYQZFqUmJyUk6S1+ZIglzLx9cRF0fmZ1Amu2OAItWKR1LgfFwbJZF2QpZIV2FncpLi1CdSUVrh54VEqMmFvI6cR0FSlxKS3ick6cwhfTYFjp+IiG2Te0+J18i6OVxbNpEzRyS5ikmJERmH2RDXoE2kySERx1bIluOTMo0ekXndAF9nhTgnQ5uUQU2JfEsEv0CwzSH3opxc18suqZ07BfpNWFEURVFqQjdhRVEURakJ3YQVRVEUpSZ0E1YURVGUmphazOoQ4aprSDk3gw/ACyJc9BOSiERELy8hqTwReVC+DkWPAUm4iYho1C3wQT5LK7JI6k2WYcKKN9kJbYtNIpiR0mGBhdJDSkrkpQW22R0sVeeSNKByQpK/RKQipRDjHM+vCFGQKEgU2exsD9qiFSKPkbG2BftdJJj0lU5wTuZaKH8lJSmrRn4GLYREqi2hLOMREWV2HSZm2RYpoTjEMRjuJqlhpARdc2EdtPkxvteJcY6DDko7JmZ9wTKDKUlsahE5pSLXSGTjeWQF3idMSMqlEgmzImstJuUDJ0S8KXK8J6QkDWxIZJzYQzFw7GJfxmTOhYiLMy5J5crwXiQi0sXbghTsOBaO9YQJmw5ZW0TCciYoVyWTJWir2jhRcYTX9YT0uRXi6xoWXq82kSEnDq6jFZJElju4tppE8AuIJGazRDUSt5UQmXca9JuwoiiKotSEbsKKoiiKUhO6CSuKoihKTegmrCiKoig1MbWY1RiT0lLoecgeG183sfGB+tgh8swKSgl2jg+7HZIuNNfDFCh3jJ8XZEQCIrX5Rh4p+9YkkpPB/lkkvWu9g4M1GaM8U+QokzSIwGV7OAZJgccthAhmpASdiMiyoOxlQhyb9U0UkCZE9EjJvN+4jElOVY79mQiKGZuaPWhjc+cFmIBUWMTwIQE3ZYkSS0nElnKCiUMb51GMGyW4BvukjGe7g4lqQRNtnCxBEUVIqbWmj/Pupphw5ZKyhT6RnPwuWW8OS8fDeesRf6gkCVKjHMdqj2BbQkSZMMD5bQu2RQmujSap61dVRMwi31msHEWeZBmv66iB81sQYY0dV0TEI/cZ9g2KVNgUMTgOMx7efxvk/jayUfobxLh+3RL71yrIdVjiPUYMnnPlEOnP4LhmGUkdjPC4Dhl/n6QxjhaxVKgXkVKQEY6+zWLlpkC/CSuKoihKTegmrCiKoig1oZuwoiiKotSEbsKKoiiKUhNTi1nekDw8n0cpYRmficuIPLSPwvX4utEeaLNJqsyMi8JKtAMfireXiJQU4SmPQxRR+iRFxydJUYZ8XtxCwWHDzUSoKYiwRgSMMUkhCys8rmfh4GdEXFqsSF9ExPZQNnLRnxF/CV/XCnrQtktQ4Bh0iHiCXpF0XZSctrgoSKQkuWrbAPsXuCintFgqF5HTnB6+bty/A9omu3D9bjoIE66ElA/MDc7dJCUpS6ScZmduAdocH9/rmoOhLSKSWNjGCUkW8LjfnmyFtizGtXXvAIWwTQ7Oxy4H+/wzn5ThC1HaIaFrsmCRlD9yzZUFft7YQiGs6KKQGA5Iyb0cx6+d43o2EfbFBLwcnk/KsnpEBNpDylrKGI9TxdhWtPDek5FymiV5nVeSEoUYvCahQXnUWCRlkYhZeYmSWO7gtW45eJ8WkpiVDHAM3ALvMZZNJF0yH/Yv5mXpN2FFURRFqQvdhBVFURSlJnQTVhRFUZSa0E1YURRFUWpiajFrm92DtpkRvn1jhg/tywSfWJckGcqO8QG4ifBhfOXgw/1tFsof1jw+eG/hy8Ry8biFNPG4BXkz8yhGKLvcHOAD/xVSCtIz+LqFCl8XGGI9EBPCa7AkG2JCiUhIpAQzQYGmIOkzeRfFhyER7XwLxaeOhWliPkly+lEDU36IWyXePK7LleGN0BaSdKzNc5uhzQxxLQzGPWhrk4SwbEzK7pE0MJZq1CNpZ/1t26FtZQ6FoSUPB2ahdQu0NXqktKeH663q43zML6IUE870oG3SQbnn3wTL4WU+WfsJCk33uYOknxHu8LF/PybXTbfC4x4RHgRtVrAN2tIK1/MhszhvK+ZH0Db2cJ3aAa41EZGxoIzaJffLNkk7201K7GGxVZFkF6bZbZrH9dEm47qLSHBbZzdCWye/F7TZGX5eM8DzyHK8/o3Vg7ZZF0XFYgXTtvojLE05tHGdVxXen+bG2BcrJ9f1FOg3YUVRFEWpCd2EFUVRFKUmdBNWFEVRlJrQTVhRFEVRamL6xKwAU0hSUs6tyknSFCnjZ5OSZ46Qh92kLCCNJiEJUgWp6xWTNKUwws9zPDyPdEzOl0gPwQxKMT2HpLOwqmUZvk5K7N+wwPkoyesK8nNWRYQfERGvhfKHsfA4IyJNlSTVax2RdNjcFUs4Tz1B2WjHAFOMQhvXUYsIL3FGktLI+t1po8DhRUSWORQlvX6xCG3pCqZKdTpYdjNbRpmvWMIF0nLxWlqpcL15MyinSIQpX7mNc566TA7C/s318NrMWWm5DAW4LRUed2kHzscMcSGzBs7RIimDODK49rsOfuAsSUTKJygpDYh4Z7ooKe6JsS8OSQhbP4vvjUdEuBSRuMT2nCRm+V2cuzYRY9slik9LJc7nUoxirE+S6zpEKGvleE8wPl7XHinp6BNpNQhxDHOSCOgSMbZNbqs+K8XZm8NjWEQYznCOU9I2DfpNWFEURVFqQjdhRVEURakJ3YQVRVEUpSZ0E1YURVGUmphazKosfLKd5yhc+C75SB/3+sRFoamIUAJqNvFhvIvP7KWa4Oe5JGFpPEEJyMpIchWJwioMjoFtkxJ0BY7LLDlGK8G2coBtEyIGxD4e1xDBwYlxXFhpLhGRIYmfSiekj+jFiE/EhyDCz2u0UUqahJjgZY9ISTELhYtqQtYlkToqkuQmFa6tlAh+GVn7lottjSau3/EyZhP5Oc6dn+NxHZIq1+pugLahh59XkbKWO4hkk5EyfiGRXeZIic11GzHNajxAGSfp43EbMY59M8U+JznKWksz2OdsDj+vS4TQaBHXfjTGNoeUULxjGy58Zx6vuZwstW4Tx4qV/7QnfXyziBQh9nHokGMbXIMBmePIxmtp/UZcg7dN8DpcLoh4WpJ1lOK8D5uk5KGNbYasfd/Gc7PJte4WOE9tF9cWqW4oht47UFgrS5wPcvudCv0mrCiKoig1oZuwoiiKotSEbsKKoiiKUhO6CSuKoihKTUwtZpUs3omkrvguSWgi6VMTm6RZ+fikPGIC0ghlDSvHh+eGiAuW4MP9hkcSpEjaS1LgMVySKDUk5Q2XMpQFopykR6UodQ0tPG7aIMJaiBKFn+DYV6QEmohIEeNxbBvltjDENsfGflsVzpMXYR/dGZzjnIgUrQb2Lx5jSs0wJYleZE4swTH0iTAkKa4jEp4kwTyuhV4XE3gmCY5Ve4aUziTy19hDic0nJQCtIUoxqU+SxIaYcJWQZLiOi3NeNDF9yq7YeeB8OBFOiOfi+O1Ywr7syfG9rTaROn285qoShaRRRkQvtwdtjQamfCUxSXbycVy6BiWxdIjnlhERTUTECrCP6RDXvh1iHwu8lKRf4py0uriOQjIO1Qjv03mBfclKbJuMUVSMIkwOswweY5DgfBqHlHNs4n01Jol+oxQ/L45JUmJOZD6SlEi2r6nQb8KKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1GKW38Gn++keksBDymGFLsoulodPsYsY5YOcxM+YhCQs5di2soz9E5KiM9vCsm9ZjKXD0sluaAvmUERxvB60LZEH+S0iTFQ+SjvjHOUZm5hBfpOUoCM/ZpUkNUyES2HduXXQlgl2PBbsT8dD0WNI5IqdKSk9NotrphOgIFGS46akXGKZ4lK3cpR5zARf1ySpQe0Q19Fgzw5oiw7F8RsbnM/Cx/MtHbyWJhNcl26F8xaSUpetAKWdjEhTleC11DE4Lrvu2IN96eJ8ZBG+N2/gHI1X8HyTBNdauyLlQxdxDPJsCdoKIjlmpAzfKCEJYZtQTpvsxvXcIGMfjnGc7Q4R1gJi/IlIwQQwUvXQIvcKZx2WD1wa441hiUhwQYbrch1J+sqJVLfCUrRcPIYRItW6uD9MiCBlsYDGBs5dPMK9IHZwzUxKfG9FEhpZMGRLE7MURVEU5TcL3YQVRVEUpSZ0E1YURVGUmtBNWFEURVFqYmoxyzTIfh0QQYJIGO0Oig/MDSr6+HmlYFs3QkEiLVAmMR722Tgo2YxJYk6VYtqLS0oKWhm+rhriA//Uwqf7FumfTQKbbFJCMaxIiguZzbJFGkkqjIiIVeIYxpNd2NboQVtzAVOCSgslkZzIciUZ/+1jlLUaRK4QUlYxsFDqsJu4ZsQhg01KKOYlSkR5gK9LDWnD5SEuSU/KhzifASn36ZE0oJRIhKGNx2ijByQlSW1LybjYpMzorjGKWSEp8RZnuK5GFR53lOL59gJcVxttkuxk4TXXJ32ZBEQSJelzASkt6Ya4npvkvlj1UT6akJS/DetQlPNsPF8RkeU9+P4mk5cmeEHkQzyXYozzmZI12LCJENbEcQgtnJPA2QhtQ/92aHNSvB85RL5zyV5gk3qEhtwvE5KyZrskYZCsrVxwXCpS8nD63fQu/fjF3qYoiqIoyi+LbsKKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1D7XmNSfbLTQJGu1SP1ZF01IN0ab0U2JiUeiD8MIf3YoCzRnOw00DScJmtp9YhQ2HLTpOm0criTH8yjG+Hl+gNFspIqxRCQOMayI7YsfJ+4I+2c3sa0ksW4iIp6DVuEgW8QXNnH8u/MkEnEnGdcc39tpHQJttyyjkVjQmD5iTIbEWA3IUm+TuqgWzvt40oe2oU2O2yJWZoyWd7PANc3+XMAh680m11xJ6nWXGTHgSxxAy+5Bm4lIjCPJ5BuHJGbWQx08Idf6kESGZhkxtXMcPzvCxe8LXjeS4/VfEdM9MNjWJvXOiwrvgW4TjzskNnhF6u3GGY5BUfC/XBByfhkrak3qvqdLOF7tCq/XVokRoR0fz8UjtXldcl03Shz/lBj/oUPiY0lEZUDsaKvE4zok4rMluC9ZFp5vRf6KYmzwPuay+37F44APhH4TVhRFUZSa0E1YURRFUWpCN2FFURRFqQndhBVFURSlJqYWs7IcH053fPJAnXxiMsbINYtE6HVsfFDOIgjjBGWN0id1h4n6lI7IA/UUxYVOF8/NjVAWyCZ4In7VgzbPYUIInlvPw9qfUYgCxniAx2Wxn1ZJpB1Sg1dEpGpjezvEcRiR8c8WsZauNcI1Exj8vBmDkkhVkHqzBufJJvF2BU6TZAmZ9wmphz1BgYbNcUTqYfsNbKtInGpZEXGM/Dy8QtalITGCPVIPt5rgIIxJxKLrk76QeNax4LikJDp1MiECIomobHmkfnJFamQzUc5gWzrCOUrIvaPZQMEpIlG2jkvOd0SuGwtf5/hEsvPx3rY8IPIXLlMREZldmMHXkrjShj8LbckKXiNN9joHJUwvwvXb6OK5WEQyG+7G+z4p4SszXRS4EnKfTgtcWyzytrJxE2r4OH4Zkb9IQqU4pGgxriwRh0SdToN+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamfpLc9VFeiEg9y5zIGku7yQN/bw7a2qQG6mCRpPxEWM/SbZKajxU+3A+JEOL2UBypXJQKBhlJivFQcNjYXgdtu5Z3Q5tUKDNYhvxcZKEQEkQoR0SkpmdOxKyRxROzqhA/0yNt8yUexx3jZ2YVLi+ni3M8jnE+A5IqtZ2sVkOSoXwi5GSk7rMQuS0kMlQzZwlNJNGHHLc924O24QrKVX0ihFkklavh4yDkORHqKhy/PRUKXOloCdpmZjE1aJ7JX3tIPeYIr+uYXDdCUogOXo+CXoJekKyQ+rODPSjZzZDasAc38BjjFA+yTOrFBkO8XkuDa3d+rgdtxsExvYPUCLYD/r0o9/Be5pH15pBkLbuBa2ZERNugR2qeR3hvTAJyfRGZb9DBNd0weD8xBt/rBNgXmxwjd4k01SC1wwt83dIy1kq3SLLheIBrq00SwtbPYP3kadBvwoqiKIpSE7oJK4qiKEpN6CasKIqiKDWhm7CiKIqi1MTUYtZsgA/UGx6KCpMVFD2WByTZxUVpoje7AG1JhQ/KV4hkM9vDvjglnp5FhAubJPA4Hnmvg9JDTh74MxHFDjdA22SIyVNCRI82qdLmN/A8vALbXJIe0wyIUCMiE5uIYiUevFWi3OKQEmxBB4+TNlAIEbcPTYbIRlmKYkboYf9mmpg6lpF0soQk5jQcss4XUDZyibAytnFdGjIuEZkTE+FYGXShhIQ7SUEiwipSAnQhPAjatu7+CbRlZF2aEUmVc+fx8/qkxiZJFyNVM4XFFSUuCkl9j8hCRBZsTlBsG/VRslkpUczaTe6M9w9RuExZol9GrsMG9q8zi2stLpfxwCKyPEaJyA9w7ZPlIVKR/hDx1CVlCzPB904SnLyKvK4i96iELA+HlEYsK3ItBTifASn96hKh0STkfkkE1cEQx98qcQ06Nl6vSUZObgr0m7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpN6CasKIqiKDUxtZhlVlCocbr4YNuzUZpoNjHBJBnjw+5Jjg/ojYsSQK+Lokyjga+zEzy9lJSCK8gDdcfCn08sH88jI6X5khwFhzDcBG3DGOWICanx6Af4eT5JmclJiktWokBQkdJcIrzkXNNgmbGujQKd42N/vHX43n7jdmiLC5TR8gaKGQclOO/FLiI+EfmuUeA8rWQoxuQksWg5QvlmTErd2aS+WTjBuZsvUCaZJelihcFzI06MjEm5v5iUoJyPcY7WBSgbDSZ9PEgTlZ+ElGn0fFxDVo5tHhH+8hjnrQxIOTxSMjIkooyQBL5xQpLdHHLNRdgWxyh1jUiK04SUqpxBl1ECcs1YRBITEdnZR7m1T0pshmTNNMh9YZ6IhX6OY9MnHmV/go0e+T5HpkmyGXydIffLOMWURdvBD2yRJEcrJQcmZSibJa63qInryCYCnC9ESk7wOpwG/SasKIqiKDWhm7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpNTC1mOeQhdh6jnFIyyYkYKw5J4EpKkpLiorxA3C8pSUk7j0gsrqAQVlYoBhDfQmxShssinckFxyBa2g5tjk3KNBIRxbNwXHycDqlI2lNBHIWKlAkTEbFyFAuaASnZR+bEdVDqaBPJKfDxdYsWSUVr4Zy4JOFmMcfxGo9xWbdJspZH0s6yiJR+JIllKxWReYT0OcP15pNJCQuSAkVEpZyMQZ7iOI9JqlRJyha2u2gM2WTtSwsFmCwhgg67DkkVSc8iJe0E11+XJInNkFioPMPzzUhqWOHj2FukTGDoYKf7e26DttTp4edFWN50Mu5D23DwU2jrdPgteaGHxylyHJu4j/1uOTg2LQ/nvcqIjDrBsZEhWdM+ikqhYOTbwMU1aIQkcLnYF5ckr/lkPp0M11FR4D3GIvf9gqRjRT7eAAypBrt7gvfzadBvwoqiKIpSE7oJK4qiKEpN6CasKIqiKDWhm7CiKIqi1MTUYlZhEeGixAfgVolPrNMJPhRPDcoChcFEmqBBxKcVlGLGOQoJnQYmNrkuKetFRCWflCgzRKiZkPKBYmFbp9wNbSEp9VUQESoscJwjkoRVBni+Ex8FmNLG8RMRmWnhz2RzHWwLWSJYhWJGx5CSkyW+rkhwjuMKz69Pjls08fwykp6UktQgIQJHWOL4O8TCsCpiL8WY/BWS6mY5SbhKfbwUx8QOTIk8Uybk3EZ4jO2kNOJEUK6KWsR8JE2zDUxO23n7DmjzKiLeNMl108Dz6EU9aOuQUKl4Gdfa7gTvJ/02Sbgjd8GErJfZkFyHRFxySalVl5RkzTxy3+FVRqVNyqg6KYpnZYPIhkR4WyZLZjIg8lKKfeySkqc+Kx/qYcKgZ1BecmmZUTyPEdlvKgevObfEz/NCvIadJr4uJfdajyTmhQ6OQUTuJ9Og34QVRVEUpSZ0E1YURVGUmtBNWFEURVFqQjdhRVEURamJqcWspYyUzYpJuggpyeaTcn/MQBjmKImYCh922+QB/WRExIeQSDZNIhoJCg4REY0yIhA5RMJwHOyf38a++EREIQFL4sQotlgu9jl0Mamn6eF8lCTxSkSk0UUJw26TuKOMpONUKC8ERErySMqPn8xAm1uRsnYlro9gAcsb5iOUtQZLmKwVpGT9slJwJBnOGWObR0rB2aSM3zjAOYnbOJ9jG/tiSEpVSMo0OitDaEtmsX+diMy5jeNiZyRpiohBHR8FHZOjPFMISlNxTsrDpfh5skLKEfZxsZUpzrltY5+NjQJRmpLydQ08jyZZ9y6RtSySONjauB7ahhW53kRkeSeW9mtm2McgxOtrhUSgjYgoNiGlKdeRtdolaYdWB+eO7RndEc6dT2TNioz/hIiKsRDR1iGyXIOUKAxxLbC9qiSJj0nCJDYuvB4I/SasKIqiKDWhm7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpNTC1mJR5Jwhqh+BAP8KH4TPtgaAsjjO+xQ5RYLAtliCxG8akR4ucZUs4tDYg0BS0iLonRqVIcg7DE1zkNfN0OIkNFBRHHiNRhE+HHcfDnp4gEtjQd7F/YJLX5RGSQ7oK2Wyoc/67BAzmGjANLTytQNlpOUJAYksSisMT+lR5KXSsFSj82KZfY7OFxLRvPjYVtuTauNy8nCUZ4iciIJVx1cE5c9F/EZChcdRrE5ktQspknYz8rKCWxlKSClLkcjVEW6s2hKJeO8Do0gqJMNsLz2GFw/Y1YeUiSPtcmV3Y4IWlKJAGqIgJXaaF4U86SdT+D5zshZSmDIY5BMuRyj03uv/YsLpBdpMxganCtRkSuqiKUwoizJolDSrUG+N6QiEo9Uu0vtHCsZ52N0FY0cZ7GHkmfy/AgtPQrWeedBpZ4HIxQhkyXcV3aOTFRp0C/CSuKoihKTegmrCiKoig1oZuwoiiKotSEbsKKoiiKUhNTi1ksrWRdFx9i+zk+jLdIub+l3XuwMz1SIstDmcQlqUYOSYqJSUnBCUnlIsFEEiUocNgZHqPho1TgtHBYbwqJcDVCeSYiZR8LknizUqAIMdyD5RKbDkkDc0hdOhFJiLy0J0azqHTQ1uiScmmBi+O1K0GhoV9if0bEhjqESFOJwT73yXy6Ec6JF2JbP0HxaSwoxXR7WMavGqEQZuU4d1mOHWylJHGMlKUrHfy8FhEaK3KNtFMc+5RIjl4T5arRhJTnw6UlE4PXdRSiBNSReWgbVnjvuMXgfMRNPI+4g2U8/WVsa+3CdeWMceyFlFrMBOW5rdUytEU9ks5ESqMeZOEaOriNfRYRqWZxnd+R4XpbJgl5HYPX6wKZPMtB8XEkfWjLMxybjQbbeiRRcZ3g2rJSnJOKlI0tSDpZQUpxVqTkadQiyWYRrssdO/vQNuyTe2CCbTMtpvgeGP0mrCiKoig1oZuwoiiKotSEbsKKoiiKUhO6CSuKoihKTVjGEIODcOhDMfVqOEYJo78HH4rPzmH6yXAF9//cRUnE6mL35mZQ6vAHKKe4y6T0HZE/UiIk5YLSmaQoTTRIwM09evjeSQvP4/blrdA2JmkvHeaNWCRBxyWJYxHKJI7HBYJuE89vqY+yl2nicbwGfmZkUP4IKlLCMiECnUXkMSL9LacoDMUlKSVHSzriGI4rXL+srOICkcmaMa4jJo7dTlKvWF/mSSrXvEeS4YhgcnuC6yi7Deeo7eN5rOuQcqQWjktFJLuCzEd33QZom/Swz30Hr5GciIEzJcppFpm33QX2b0Jksq6PY9og6Xipuw7ahj7eYwY5jv2cj8lurd04zptbOFYiIomF47rdXYK2JcFxaFooZs2R8pflzm3QZgyKZ/56nLs4IGUoSRJZO8d74xxJqaomOMfDPl43Q1LKsOrgPYZUyZThCia+uSSNrWlwrwoqXB+kK3LzTpTd7op+E1YURVGUmtBNWFEURVFqQjdhRVEURakJ3YQVRVEUpSamTsyylvGBdZiiSLHRI6XCRvg6j5QZWynxybZvozjik8ShkCUTkaSToIk/d9g+DsPOPfjQPhA87roGKd02RDHju/HN0NYh5Ry7zR60TTKULfoFzkfLQ+ukS9oyl7t4/QLFh7yD728TkSUeYx+3TfrQVlrY5tgoObR8lD8yg2ur0yKpSKTs3mIfBZOMpMA12iiyVUTc2zXsQ1vPwf7NzHah7R42tqUxKSMXY/+Mi+s3IULThCQYbZwlaUUuriMnwvkoiBTjF/g6QxLk+oYcg0hnDZaEF+DrhiO8NtMC11+rRZLdbJyjlJQUnRBhjZXmDDOcj3aEEpZZwTGoMjzfPkn0ExFZrvCeMmniZ27cgClcg6343hVSMjEISNnT7iy0OV1SmnJC6qiS0pQbQlIikkiTi7M41osNnLtqiPPeHuN9f5aUUOwRIVTaeN9ZFjyPFYvsQSxCbgr0m7CiKIqi1IRuwoqiKIpSE7oJK4qiKEpN6CasKIqiKDUxtZi1YDDVpCRCje+Th+cFkWwsIhEFmHSSGBQIbFJ+LSxR9Oq1e9BmVfjQntUyXJjH90qO51GSFKdhjBKQexA+3G9ERDqzcVxiB8+tIClOGZmPlMhCDjmuiMieBEujZUS+mSSkVBgpM+a1UARyfJScqhSlHxFscwJMEyoT8nlEOgktkgLVRnHHiXCsxwNcb6MU5zMhpThjIvN1yNrvkMShxOA4j8mPzSV576yPqXJ2iWMQJygCjSa4ttJlXBttFz/PnUVRLraw080S20yKc2nwkpOMiJQTInX5RLzxyXwY0r+YlIeMmBhIZKY5B9sSG+87BYnCG7ooiYmIxOSatUN8/87bb4W2RoLrY66H6V8SkJKzOUnMIxLmrIP7w2FzuAbnUyJhEZk3J31JiJQopFyin6DkuKHEvcUngukykRyzCI87dLF/ppwqfBLQb8KKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1GJWh4hZg4oIK6Scm0/khSrHB9tzTRRltpNkopiUIywsTDpy2ySVx8O0IsvFPlt9FFYyC49retjnnKTtzBMxwCOJOVmBAoeToRzVI8k/BZFO+qQ0l5PxUoYhEYZckmKWk7Quy8LXNQ3Ou71CBJUE21JSvrEsUHgrElyDHinV6M2SlC+flDLso4BkD1H0mCdJThY53/GIJCCRBK4mSTaLiYgSe9jmkVS5mYokpc3iGnR34PnKBMWbRFAYym0835ZLxl5wMpNlnHO7iX3eNSRzbuN8hBZbMNhUBfg6m1xLMVmnE3LfCW0c0+V+H9oikiRWRTh+u2J8r4iIG+K4svKNIVkLoYPrvCTvLUnJPi/A+2rLxnveDJG/qm1YSnK5ScZ/jGOzocD7SYdIoim5hzokCW/i4z0rC/C9oxyvzQYpFeqQtTUaMMH0wOg3YUVRFEWpCd2EFUVRFKUmdBNWFEVRlJrQTVhRFEVRamJqMSst8IH6Ej7DlkVBUaZhkYQrBwWCBhGLSJiVFORHhyERDdwWymR7BlgGLV/GB+q2S9KxyGgNApQ1WvMoMwTfRZnEnSElwWwUEtwU3xs4KDPkpEzYkqDMkI3JxInIjI/CBakyJiuCwkWc4RhWJB2nUxBZy2Dpt9zCsZkMUCKKIhRjnBaO/4jIKRMirdlERIuI9OOS/glpKwyufRJ2JsukhF1KEpGElL+0UlLycICfF5MSdE7FxgA/z3VxnMdkbdgVjvNGD6/D5WUUrgYVzu/Iweu622AlO/HcygTPbUKuB4ec2/o2Jn/tISVZEyKYLg/xPGYXsCRgSdKxSo8sDhFxSTnTwJB5JyUdXSKFWT7eRH0X11uTjNccuffMhHjNWW08l++l26BtQ4rHOIjcJ1ySyjciMunuNhGGSfnWhIlt5Nx6JMWQpWNFZJ+bBv0mrCiKoig1oZuwoiiKotSEbsKKoiiKUhO6CSuKoihKTUwtZi1X+CB6QJJmTIRCw8jBB/SWjfv/4riPn0fStjwiYfhNPO7tO3bie0t8GN8gpbRWJihXdGaxNF9zHqWOpeXt0NZ1sH/pAAWpwqD0ZPuk/Bq6ApJb5HUVEYgMihAiXArp9rDfVYxCSLIHx2swQmmtEpS/fMGTmezBeWr3cJ4sVgbNkJSvESl5VpK0LZbyFeJaLUlCmB8QOYgkZlnEzLJJqUXmeVQZKaFGSvH5HZy3yQTlOSYH5W1S9jFHuaoiApdNShQWRPjJyTW8smsPtLUOQmmvFRHRiKx94ohKPCQyToiy26aDNkFbNsL7ycoSlvojQyB7SrwWWBpdh5RpFBGpSJJbl5TdbMxgicKkxOt9SMqRtokY2yJlGZ0YRdExSZrKUvw8t4cCV+Zi//opXjcOEVQrg9d1k6ytZgPv0zm5Dybk3hZPmFxJrmEfjzEN+k1YURRFUWpCN2FFURRFqQndhBVFURSlJnQTVhRFUZSamFrM2k4egDshPoj2bVLjiaS4MElkKCQZqoPygSEJRhFJgZojCSshScIqHHzwXjRY+S8cg3gnJnDNsCE4COUPGaC4EJQowEgTRYMxkbVy0j+PCCuBz0sZ7opRjLFIZFbDRbmim6MQsmijZDYkpd/WhT1o6xFpbdC/HftHpA4hqTfVGNeWjHBdpqR8oMyQEoBdbCti/LycJFe1iewSRiRxiIhFKZNESEk8Zw7X0SEkpW6lwa4vPK7bR7Gok+N7c/Jz/Z4JlgUckjqDzQ6mSvVIaT6T4jofk9pyLNWsQdaGpCjZ9Bfx/jRHRLSyIMddh+VS8xCvwwYZ+zYp1yci4pPF0CAlTncOlqAtbuLaism5hOxSYol7E1Ke08Wxtsh1c98Gjs3A4HWzs+xDWy543B4JGFtPrNU5Uhs1IaUb78jwvrNE7gkFkTBDFi84BfpNWFEURVFqQjdhRVEURakJ3YQVRVEUpSZ0E1YURVGUmpj6SfKISD/rfJSN7BiFhqaNh9kxIqXvIpQXMhI/4xt8KN4iJcWY+OTYpMxVgO8Nmvgg3yEShk2SbDrkgf8PGjdD27oWiigzDUzlGmd43DERMEoixdkN7IshApyIyNwsplklIyLVDHFcLUHhzQpRDnJImk3mYn9KG0vdRR4KFwmRJkYlyhUlSflpEAOp3cQxmAS4FsYkDcws41poxERobONYsZSvipQF9EkCWhbjml7Zg+tjtkFKwQn2j62OLMfWXgNTuUKDx9i5RMqHOriGDtm0Adr8EuU+PySpRoJz3p+Q9LkK11Bk4X0s2dPH445wTbIyrR5JnkpIwpJH5Ki4j30WEWnleA+d9PEeGhBJL5rH63BMSvu1ibDZIamIFim3WpFURJeUznxIjrLWLhLq1/NJUh/Zb2bJ/jCzQuZkiGO9a0JWOknlCmd60BaQ8pcOSbObBv0mrCiKoig1oZuwoiiKotSEbsKKoiiKUhO6CSuKoihKTUwtZoUkmSgeokTQJa9jB9lCJIwJkRyGJG0rclGucBN8KN7wUIARkgDjkrSXZNCHNt/H9zZJGb6AJB0ttPC9xqDcs31pK35ehsLEwRHKFkWEP1PFREQjHoSIiDSH+C8Tkiq1mwgNFil/t379PLSNF3dBW04kvZAkpfVyFDN2EyGkItKPPYPClZvj+dohrhmPVMmj642shdkuzlMm+N5hgYKUCYkUk5JSnAEmIq3sQolouImlcuH6SMd4XRsH52NH3Ie2yOAxwhylmFYb5zIlQl1FSkY6JJlMSOk718ZrLidlKcdEhgpW8LhFiMcISOlBn6SBuR6O35jIbmVFbh4ikhFBKvBwTXdCbBsSKWl2FsWigMiBLO1wPZFWfSLGNUhp1Htl+HmHkoTBipybIWtBiIyaEzltR4pzXJIUvfkurksyfGIRGbJr8yTCA6HfhBVFURSlJnQTVhRFUZSa0E1YURRFUWpCN2FFURRFqYmpxaw2eWg/MaTsU4mvG5OH9jMkDSgkSVMOKUdo2yjAjCx8yD4hRg1xocQZoaDjFiRti/SvIEMYByj3dFr4eSsrmLBUEsHEMyhRNCuUALISX1eSsl4uKYEmIuKTsTFjFClckiqTuTjHVoHj2ouIoOZhv1NSUowJTQzPxTkxJFnL+ERRI+XvAiL4uD72OXJIalhOSg+meG42k05IU8nKDJJyf3NE0IkzFOAc8oEkkEqGJS6OcY5ro7RZSh1+XoMIkqMJSSHzcKwCktgUELmSHTgl82sRWSvM8b6TNfF6bUZE5AuI/JXjdT0m0mTmoKAnIhLZRMJy8R7gxjiuHinV6Pt4nAZJDuyQHWK9g8fYRNrWEwV08yy2RST1qkHWvmNwbY0tXKw7Ovhmm81JC08uIPtNRO6Lbk7WjP2LfafVb8KKoiiKUhO6CSuKoihKTegmrCiKoig1oZuwoiiKotTE1GLWLCnnlpMknEWSesUEBCZhNYkkwuSerCLCBXnInhH5qzRESCAVrTwifxlSemxAkmxikrpUGJRYPAePMdMkY0COu1j2oc0mpRZDZwY/j8gHIiLLY/zMkMhLsxFKSSMbSx4OJljCziJlLdsuKYk5Rglj0sZ+e0SGmCXpREJkLSHrwyqYIEUSuIiQU5a4zosCxSKfSEnriNRlSOJbvyJlGomo5HgkLWqAc1QQockJcPxaFY6fbXC9sbSygpyvHaPtkqzgenEaeAzTwz63OzhWbYtcX6QkoCEJS0UT2/IK++c3cawSsoaWSFnFJMLzKAN+Sx6S8q2Wg6/1SAqcV2JbI8V56pGvZOssnLvNZM0cQfaHezRIQlsXjxsRgathkXHIyN6SExGNlJIsiXxXBHjcmyd4faXk3p1ZRKBjbVOg34QVRVEUpSZ0E1YURVGUmtBNWFEURVFqQjdhRVEURamJqcUs4i6IR2QBhzxQt1hKDXngL0TCIq6LpCSVyxBpxyJlFUsiJIiFckVAhJ8kx8ShCZHTnALHYHG0BG2HBVjmbq6BgsnSsA9tu1b2QFuDCDULJBknIclJIiJLJYonC41ZaOu1u/hmksK1PUPjLSGlx+wAxSy/JAljAZ5Li6yZgAl+JDEnJ3NckJKCNplj2yOfR0QlsvSl46Mw1LJw/RakZOeYSHopkQ3d/6+9u1lpGIjCADrBJkh/UMRufP+HcyPdtCnRYHyA+Raii7s5ZzkUQoZMLqEf94buQsMaRl2GrleHEGw77vsw3hhG1X2G+7hd+7DLfAnjK0PIpoW1r7Cp69Rf9yF0/hrvodtbeI8tYYzkc3i37UKO72PuA3DXWx9EG/enbm0KYavWWgtTHlvfg6u1U3i2UgxzDAHaw9bv9VNYewmh2rdw3dewh+0YNiycpTaGgxOehcd7v1/npf/duvQPwxzO/3voDPc9hE5dYV7tFM7cb/gSBoAiijAAFFGEAaCIIgwARYZt++O/yQDAv/gSBoAiijAAFFGEAaCIIgwARRRhACiiCANAEUUYAIoowgBQRBEGgCI/+gIvuSvNX/gAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ra, dec = 239.5743316076613, 32.91914641623415\n",
+    "result = analyze_galaxy(ra, dec, search_radius_arcsec=2.0, verbose=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "064a599b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

ExampleCode/example2/image/239.5743316076613_32.91914641623415.jpg ADDED Viewed

ExampleCode/example2/llm_result/239.5743316076613_32.91914641623415.txt ADDED Viewed

	@@ -0,0 +1,10 @@

+This source is a **low-luminosity radio galaxy of FR I type**, hosted by an **elliptical galaxy** (likely massive, gas-poor), with no evidence of strong star formation or quasar-like activity.
+Key evidence:
+- Radio morphology is classified as *FR I (Complex)* in both files, confirmed by high-resolution VLA imaging showing edge-darkened, collimated jets that fade toward the lobes—characteristic of sub-relativistic, turbulent jets decelerating in a dense ambient medium.
+- The *Peak Inside* and *Zero Peak Offset* (Peak_Offset_Arcsec = 0) indicate the radio core coincides precisely with the optical/near-IR host position—ruling out significant jet misalignment or double-lobe displacement typical of FR II or restarted sources.
+- DC_Maj = 12.5″ (deconvolved major axis) at BMAJ = 2.9″ beam implies a physical size ~30–50 kpc at typical FR I redshifts (z ≈ 0.05–0.1), consistent with nearby ellipticals.
+- Low Flux_Ratio (0.0833 in second file) reflects minimal lobe-to-core flux—another FR I hallmark, distinguishing it from FR II (high lobe/core ratio) or starbursts (diffuse, unstructured emission).
+- SNR = 7.1 is sufficient for robust morphological classification but too low for a bright quasar or blazar; absence of UV/X-ray/IR multiwavelength data here does not contradict this, as FR I nuclei are typically radiatively inefficient (ADIOS or ADAF accretion), lacking strong thermal disk emission or dust-obscured starburst signatures.
+No indication of spiral structure, starburst IR colors, or broad emission lines—thus, **elliptical-hosted FR I radio galaxy** is the most physically consistent classification.