ultimatepp/uppsrc/ScatterDraw/src.tpp/ScatterDraw_en-us.tpp

topic "1 ScatterDraw";
[0 $$1,0#96390100711032703541132217272105:end]
[i448;a25;kKO9;2 $$2,0#37138531426314131252341829483380:class]
[l288;2 $$3,0#27521748481378242620020725143825:desc]
[b42;2 $$4,4#13035079074754324216151401829390:normal]
[i448;a25;kKO9;2 $$5,0#37138531426314131252341829483370:item]
[H6;0 $$6,0#05600065144404261032431302351956:begin]
[l288;i1121;b17;O9;~~~.1408;2 $$7,0#10431211400427159095818037425705:param]
[ $$0,0#00000000000000000000000000000000:Default]
[{_}%EN-US 
[ {{10000@3 [s0; [*@(229)4 ScatterDraw]]}}&]
[s1; &]
[s2;:ScatterDraw`:`:class:%- [@(0.0.255)3 class][3 _][*3 ScatterDraw]&]
[s0; &]
[ {{4916:5084f0;g0;^ [s0; [2 
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hUCgUCoVCoVAoFAqFQqHUM954AzGM8vPgAZo1C/n7s+XjxiFDU2ioM3QoMvSuLQqFQqFQKAJw+bJSu197Da1apbTxmxbAqH51hrH060cVnEKhUCgUq7NkCSu48+apFA4ZgpYvN7NBquAUCoVCoVgbR0dWbQ8f1llhzBhU/epINHYsUrzjET39NBo/XlkIYTuXft+wgS3p21dFwcHFhQvKCrdvI9W3UVEoFAqFQjGHbt1YYdXzGgEunY7t7dvRf/8p5RiAyB2Mhw/RhAnozz+VhX361Cg4vkKAz5o1aOnSmgUpFAqFQrFNuLDXvA/Hc8+xX7299TmaPVvF6aRJKDCQ/erszH795puayk89xf7Pj8Hfe4+1XV2VX5OS2K/a3uNEoVAoFIpNMH8+mjnTzM/ixTXtDBliIChWU/AZM2p+gsgdSrp0UV+Efx8cjAMH2Kw7OIIPDvmh8xQKhUKhUCwBx8v47rZW1BR8zpyanwYM0K7+agqu+fn4Y6G6T6FQKBSKjdKhAyupQ4fqrKCm4O++W/MTVnBfX/VF1BR85UoB+0uhUCgUCkUJjoslEvXykhLlr7oUvFMntqR/f/UF+Qr+zz906BqFQqFQKFahc2dWZO/fR82bK0tatUL37iljZz0KjqrVHz9ulp6O9u1jDf5Y9K5dlSPYU1LYr9HRaPp09MEH1l4nCoVCoVBsgjff1HK3GqfWwViwQFmN/2QZBhRZc5T7oEEqcfeHH6rX4Q+lo1AoFAqFYgk+PuwU6BBuz5yJRoyoKV+0iM2WczYE7GoEB6PJk9kRbq+/jho0YEuKitAnn6jUadYMTZvG1oH/O3a03kpQKBQKhUKhUCgUCoVCoVAoFAqFQqFQKBQKhUKhUCgUCoVCoVAoFApFVCZNmnT37l1Gwa1bt9544w1fzdnWVBk5cmRZWRm2YalFixZZv5t1m7Cuk/PXMKU7mOItTO6yf2L6LzS4iH+Tfl7ZbUx15JndpvHnjJNXsFndrMN0Q2gQQk4a5aUIvYyQC0JdFR9NnlAsSzGeKoT6aBTCFh6PUITCbotQX20LwtHcz6o9o1BsjAsXLmDtfuutt9auXYvtBdxz4DqAOhvwG8EV9vvvv2/9ntZh0t46W36AyV74Z2TP2bHDPi7cwDT5mnGQGbhMKt3DpM84a6ovj8wWlScYR48AcztbV1kBhyJcKWmUn1aUA7/C0a5twX8RemDdrtU3jlZvUj6BisJ2CvuQtgrAWR3lFArFDECpQX8/UJ0o7cUXX1y+fLnxjUAL8+bNE7pr9Qd5auOy/Uz4k2/wC/0b97F3kVnDnUdGi8pjtqjgcQp1WKxaGKIo3Kywv0foG83FKKazE6FbGoV+ik3dUmFvQ+gfsTtFodgW9vb2IL7Hjh3TU+fVV19lquEH5sOHDy8tLcU2VXD9eBd0qjyuM/Lwb9wXouaAVsMK1jIl25mcpbelIUnKnxr18cxqpbSb9PVIbwaGV277oKoXwUgYuzP/E6Z4G/vJ/+iRX8WzuKbNKjhwFaHLqiXdeYGhLgV/XPHBdEWomeLDKD43EGrIq9kCoTvVP+0Qvvt1BrMVHI7m3tV2a8XWTkXonmLB+9XLYrIUGx9v6pMCd59CqQ9069YNxLdHjx66KsyePRsqPHjwYNq0adeuXQN7+/bt+CewlyxZwtlUwfXgkdmybC8TP2qj1l/zP2JKdjBle5jEV3bHDvsERLxwvVLuQdOTJhxQVlvNpL3xVcbs85VHmOY/Mkhin7fyAXyi+77f4LkVUBPC/MCW7GS4tqzgQzUS6ScQ+rva1qXgvyH0v2r7J55qLFEYV6t/qqr+CY71r3ihvQ0CCv6ntnKDCn6Al0XfXb09v0Po3Wobv2Qpp/rrouqEvMk3kyiU2gpjGVw7gwcPhq9+fn5avSQlJcGvBw4c4EqWLVsGJS1atMB9mDt3LtefeqngoKSlO5mSbeZ8IOh2cPfmmkqf+R2IOKhwypSTfpU9nXxqRCZv6T+Z7/3GfQ1q/TzUDOkygf1p+d2E0VtwefbCayD0Mf0XSsNStPa2YDVcJGxCdVDBr1afrs37ZPCailJNpOMU+qfVX3Up+Hc8gfhKcU+ca/MDRQupCvsaQl/ylsKiE2P5+ovFKcu2cxWvqS26q+lX8C2KJAZmo6J+WfXXVxRfWyhs2E1XeEu9qPipQMiNQaEQA5R0nrl89NFHXDtDhgxhdL/+8+mnn4ZfW7asyWwlJydDydSpU5FtKHjkM+9E9Xk/qvc8Mz4Nhqyyc3SpactOEvnsrIYfM2X72GC5ZCcTO3wN/qXhygexI9ZxFR29goo2MrHPf4xUFTx3+T9YoNWQpzbyLnzMK7cdtJP4Mpw4656Cv4rQAkVga8ZnJVzzqLZ2BqHfq22cQk+o/mqMgoPxNe+nREULyXAwKAzoag/FaOqe1YqTLdBGEIGhigsS87bzcrik5zW1TbHu03kV5igunExS8C2qddwVi7dShOFgzFLsvn6KrPsgRUl7a2wUCqXO0rdvXxDf/Px8rb/iCF2tkBNuW1Bwa2Dn4OxX2Ttr3m8g5cEdxiKFgqsl2EGsE15mhZuv4HnL7yS8tIVfza9xn8J1DATs5fuZisNM0WYm4aU6GYMLy/O8RDo/hY6MVvDveD+lKlpLrB4mp/kpsspK1HZ2IvSXRqGT6Qp+l/eTZ/XiTjo29RMCrwSFUrfp2LEjiC9E4lp/HThwIPzq4eHBL4SS2YpXhlMFtwg7ScE6Ju5FVrg1FTz/YyZ+5HqkqeCjt3J1pOGpJdvYtHxIp3He+R0lUnnOgquJY9hRCjau4GGKs/371ULwIe8nSxS8gcKotFav6xhmj2QzRsEdFcbTAneZQqmHqN0Z50hLS2vXrh381LNnT66wtLQUSsaMGYOogpuCS1CcZmH+J0zcyM+QQsHjRnxaUzkksXQXE/nsLKRXwb1y2lYcZVyC47mS3IXXE8ew46NtXMGBLxA6h1BnhRDwN73ZCp6MUKjCWGWtLtcxrKfg+NELRvHIOYVC0Q8ejn779u3KSmV40aRJkxs3bqxYsQJV63tREZspjI+Pv379OnwNDg5GVMFNwbe0e/FmxrfiWXt3H/jq5Bse2XN2xSHGu2EH+Jq37E7am1+DcOOfMuf8XLaXkUjZR8X1KLhHWrOyfUxk73fBdpD5hnV9vXgrk/ASOziaKji+bfoAoT9Uy7/X8VySQQXHA9uOKOzhCOHH+LMROohQocB9rxsYo+A3tC1oUMHxeLlVCnsyQv6Kr2kIrUfoMcG6T6HUH2bNmqU5Xn34cDhRocTERLVyPBAdIZWZVMFevHgxqf7XfjwzW5bvZ8ewlexgQMpLd7K3reNf/Az/mrvwr+It7NNk8D9E3+UHGZ/iJ/FP+auZpPF7ePa+mkYl9jmL/sJLgeJnL7hasEZZwTO7TZOvGCdPtRFeNkRo9Z3TFarl57TdXQ1D6AeEfqmu84ti6jaODEWdHIXtzHtCmfs0RLbIIYT+0yjEc7K1Vdi7tG3qUsXzd1zKbw/PBrwVXztVf/1FY/FOiEKpdUiCkP8KFPINCr+MQr9HnuMJ9AHC6tdff33BggUQSuMkOUdQUNDUqVM/+OCDGTNmJCXVDEdduXIlxO/YhoD9iSfoMBN92DlJA1sPi+w5J7rfgohn3pGn1NxQbbjifsyQld75naL7fxDZ81232JqHZiKfmRXYfHC1/Q5nYyQusrBuU2IGLArt8ip8DX9yWmBLdkiDNCwZLg/spXIxVqy2MlIRx6nNWjtGMRj7fd7nQ4Q8FOXjquuMU0zuzRGC0GeqD5gPVgy9nq+IEG1TvpFiWPtMjUKZYlvhZx0HIrRQdVOvRChaMZv6O9X1BysGnHO4KhZP55X0Rmiu4jmFqXQEAqW2Ih+Kov5FQXuQxwso5CsUcQN5vUa6TxQRYe+Dj/qMdC8oFAqFYjLOxcihQc3X4MMo4qru2pR6h+ZYdAqFQqHURdy6oijNO0yU+kvJZiZ58hHSvaBQKBSKpfivRuE0BqdQKBQKpa4RcR35zlcp8X4TeU1GXpOUH8/RKOxnFPodCvgddWjaqV1VO3Ho0KZj7uC4tu3biOaR7zp/YGLrTi31uG7ZsmVlZWV6evqzzz67adMmXXPCb9myZevWrZMnT05KSiooKIBF2rZtq891605Zz0fC/8KvVTVVbdtUaeuDMa5btWoFq5CZmdm1a9ddu3Zxr3fX5K+//tq+fXtFRUVubi4s0qZNG11ttm/bobRXdtMnytu3bS/A6pkCuC5/Nq9x9xI9rlu3bg39z8rKqqqq2r17N6zy/fv3ta7ygQMHNm/ePG/evMTExLKysqZNm+p3XdmjED5gWGHN9CHCMVbrXFe1b/Z4o+I+Ge3biL21wXXLLs0K+6e2b9NRZM+2uKPbtYNTzYgRIwjJqdgEH0URfyA7DwPV2FHr36IIzWkNrQz/fr3I2EcZqODm5ta5c+eHDx9ygjVnzpwPeMCZ/OTJk2pn+KeeesqgayftLxIRA4Ou7ezsHnvsMfycPubBgwezZ8/+QJV33nmHq/DFF188+eSTBpoNQMhFfxWr4aOYIFsvcLly8eJF/q6cNWvWRx99hGcqGD9+PKzytm3b+BXGjh1bUlJioF1PxYcEtfkYsxaOSBJMyLUdkoQZrmUNbHFHIwRX3cR8i4j3TBT50KjtHP4bCv0GRWh9vZ/tIZfLV65ciU/UN27cWLZsmcFF3n33XfziVMyQIUNycnJE6KqAdOjQgVsFUO0lS5bANYz+RSAUBWnj1rp9+7r3poi///6b6/+iRYsCAgzPWjN69OidO3dyS/Xt21eEflIoFA6IxEl3wep4T0fRDHJKNaoyKQX3aIrsHMR2ipGVI4lUSznIED4zr1271oxmMzIyzpw5g1sAQ2sd7w5mNCwMulw/evQI93nLli2urq6mNhsbG3v69GlY/IcfftB66eKWiRwJxUfSZOQUqf0nnGBZvHhxYGCgGS2//PLLu3btghZ69+6ttYJLLPshQi08xqyNgy9yzyXk2gO5E3ohjQ3uaGQDCg7yHQXynWZsfVIKLk1UvPmPBC5xyM5RpcTJyQmr2H//CTB2f9q0adDUd999p/mTa6blzZuJVtd4rfnz1ZvHq6++ipvS/PtyCkP2hm7lWAnHIOTgraUcy7fBPINB5s+fD+1cu3ZN8ycHf/ZDhNp2jImAxB05RxBy7YKcCb3W3QZ3NKrvCh64A4X/gSR+JixCs+iOjo5wHr53717Xrl2FajMpKUmXiNcesOYK2OC6deu0injtoby8HCfPLZdvzMiRIxnFuwAEaY1CoeinNp9eLCfyAQr7hR2ZFnZB+Ym4hjxH6VuEWBa9Sa3IomP5FlbIMFpFvJYkvlq2bGmltf7000/VRNwto7Zk0YcOHYrXWij5xnh5eWmKOM2iiwnhLDqhF9LY4I5G9V3BvWcgr4mK58WqP97TkbSZvkWIZdGTyGfRnZ2drSRkGCzi3377LVdSSxJfVl3r9evX80WccBbdR2lj+Z4xY4Y1HHl7e6uJOM2iiwnNotuO6/qt4GZgy1l0nDy3qgss4roGthHBqvKNwZF4VVWVVb0Yz5AhQ6A/M2dqvkBDMDRFnEKhCA5VcDVsM4sOvP8eOwype/fu1nb37LPPgqPi4mKwvchln7DrGzduWFu+MTgS79SuM4ommUVHMvT8kOHWlm8MFnHYwmA7RhPLohM/xsSHzaITeoKTYBbdBnc0ogqugQ1m0e2jkJOrA5xsQWXE8Xjo0CFwV5hXhMjNY4MC0MRxk5jq17WLwH///ffXnzfYOVVk4jhUR+LPZtHxk27ieMzNzQV3gwcORo7ksuhZZPwSdM1m0SMJuZaSy6Lb3o5GVME1sM0sOpxm7969K6bHvXv3ihP86qI4vwQ68NJLL4nmMTo6Gjxu3LBJNI+ajBrxEvQhLc3opyst5uLFiw8ePBDNHYViU1AFV8MGs+j4MV4R8ud8sJwtXbpUTKd8/vzzz/v374vsdPXq1bDWjz/+uMh+MY5B7KXa999+L6bTgoICcDryneeQj+HK1sCL3PR4pFzbk8ui2xPMotvejkZUwTWwwSw6nGA//vhjIn6//15UNeHAU9bs3LlTfNd///33/v37xfcLjBo5SuQAHHPxwv8Y5oEdIQV3I5fhJOXaNrPoNrijEVVwDWwtiw7xIJzVe/ToIb7rXr16geumTZuK73rmzJngGr+tQ2RWrVoFrkNCQkT2iy9aiEyqg8Pwoc8NFd81hVK/oQquBrEsemNkZy+2U2DhwoUE70eTCv8fPny4Z88e8f0CpaWlsNZyuVxkv5mZmeA3OTlZZL+Y8+fPk5qRzwaTq4Sz6AVkXNvgjkZUwTUglkVPJpNFh7P66mOG3zhmJXbs2AEdkEq1vVjFarRq1QqcknqvblRUFHjfvHmzyH7xW2ZkMjLj4GfNmkUkgY9sMrlKOItO6AETG9zRiCq4BjaVRcdP7H68bDWpDrRr2V78DEBFRQU4dbE3+b1jQnHrxu1Lly6J6dHR0fHBgwdfnPpSTKd8EhMSYZtHR0eT6gCFUi+hCq4GKQWXk8ii9+/fH86rXpUSRGgYfOnYFOjAihUrxHR67dq1e7fu+3UicdMCaIDaPNMM1joiQryJL/Gd6OyOSYjQQ9mBDfygAyeOnxDftRe5Mxwp1ySz6HJyWXTb29GIKrgGNpVF79ixI5xXneKIDYNHnoh5yKxZs0ZMn/fv31/78Tok9lCyanxRi85NRE5ojxgxAjxmNE5i55MhxK93zt2+eVsiEftQc8sW2SF51zaaRbe9HY2ogmtgU1l0OKvfunWLeB+WLFkipsfbt28TeY6MIyI0EtZ627Ztonm8evXqqVOnRHOnlez0HFjrnBxCwSGFUh+hCq6GTWXR4Yw6b9Z7KKnm7aJiU4h2bN4l5mA25TC2oS9KKsRxqI5rOvv/75euHDhwQDSnFy9ePHb4OPJGToReWYVCUV77NDYPkJEhsmcbTK6yWXRCUSHNoosMVXA1bCqLDmfUlctWIT/l20UJEI9aN28jpoJXVVWBO2d7FycCw6JZnMLZ/2/dvCVmDA4KfuLoCeRQ83ZRsfFAuU0ziCi4DSZX2Sx6FCHXNIsuLlTB1bCdLHr37t3hjNqrVy+y3ejSpYuYCt62bVtw5+3tLY47XXz00UfQjdDQUHHcsQp+gsAoMj5pSWQUnEKpx1AFV4NYFr2R2Fn0nj174ie5XIuIZdEdG6POVQQU3NPN272VOA7VYbPoHig/jx0cHh4eLo5TUPDjR04gH5JZ9PyOZBTcBpOrNItuO66pgqtBLIueInYWvW/fvljBHWPJZdETUOf2BBTcS+btlC6OQ3XYLLoTysnKEfOBMpvOopMbOkfKtY1m0W1vRyOq4BrYThY9J4cVkXnz5pHthm1m0X/55RfohpubmzjuaBadQqmXUAVXw3ay6Ag/yfXBUpRMLoveiFwWvaU4DtXBY9GvX70u5hNtoOBHDh5FnspxdAQIRVltkshk0atEdkjeNeEsej4Z1za4oxFVcA1sJ4uOFAo+f94C5Euz6OKBNfSP3/8Q8x2jf/755++XrjgEITsv0XyqIkWDx/WDLd+wYUORPdtgclUiI5pFjyXj2gZ3NKIKroHtZNGRQsGJT/TRvHlz6Iarq0izlHfo0AHcOTgQmkZWgaPE6dGjR2JueTwVfEJMkmgeNTn//c8//PADwQ5QKPUPquBq2FQWHc+LLi9jx1aRIQPduHrr3r17ojlMTGRfsbFi8SpUKJpPVWJR1bMtoA8xMTGi+czPzweP6W3jUIBoPlVxR+cfnD379bfie7bB5Kq9L7EnlO3lyI1m0UWk7io46KxskL4K7s+i0O9R6Lc1n4jrhpu1qSx679692eHowcTmRQ+ocIcOrF+/XkynEP/++O05h1QxffLwQwVNcsR8GBwoKSlhH17LCkRkXi7K8s3lz3/68bz4fm0wuUqz6Lbjui4quLQ58l+DIq4i+RB91dz7sJLt9Rryfkv58V1ouHGbyqLn5ubCiX3mG++Q6kBVq3bijwzv1q0b+z4XibOYTvn8+P05MW8cAGFhYeBx97Y9onlUIyUpFTrw3nvvkeoAhVIvqYsK3oBBEbdQ2AUkH6yvmntvFPXQ5MaJZdErCWTRkeJW+Nm/TxNwrODmzZtiptAx8fHxsNarVq0S2S9GFuME3o8cPiKy319/vvj5zUOI0CtVY2JiYK0TExPFd+3VVnyfhF3b+yC3LEKuCWbRbW9Ho7qp4HaK2CXybyR/Tl+1uqXg0kQyqWx8K5yAY4TcXNzA9bpPRE2hY/57+Oinn34S3y/QqKIxexM8SuxpL1YsWQl+/QP8RPaLOXz4MHgPCCBwG94tV3yf1ZBKZcuQM6HHBu0ckAuhLLprJhm/iOgx1r5NB2K+LSPqrmEFh1DdpYjNuruUIa3Rh6w3cn+65uPaFoX9zCp4+GXklqZ88EdWqpyL0oARqWqU1BjORhiOAdUlir96WTHPiFY1ikw0YlQM90LkUm0gf9S/zwA4tebm5qr9VGM0UDUKjDDyeUasdgOloGFvsY8XRbb2lURW/xSnMBqaaMSrGnmGjWfHKBPpzhnIJYEtgb9BqaaRqGrkmGgkqRrZCAWj0SNfBtdxFRG4hPtJzXDNVrzshm9k8YwUHUamTiOimQ/43bx+K+x0aWr1T9jI0Ge4ahppqka6PsNZ0eebN2+eO3eOLUlTPhFvwMhQNVLVDal+I1PVSDHOyNJhJPOMbFUjSTlmTNNAfujo/uNecm/HON5POapGoolGrqqRoG7AweyuMODkgw044OGwN2w0VDXiTDTylYayG7HVJXqMBjyjwAijUNWIUTfgFKrrJzj1wQlQxYg20ShWNaJ4RomqEWnYcOIbpapGhAmGXTAqGkRqQI+lGFRw2QAUcYMNwyPvs0boGWXwzsc+GNkH1nwgCg67qFDwq8gOto/iTqm9J7Jzsa4hcUWeimm67T3MbUeqanhoMSQaBlKsIJzYFy1apKuOlQw7GfxDI95ghzLU9FCxg+zlVjeSMhPY97KtWIU3vpAuZEjiptMA/rp+4+SJkwgZqCwR2gAWLVnEXjIlhSFH1Tru2gx3YQxg4IBB4LespEzYlpWGmzZDpjTsnNi/LAgMuRLBDXsdxobtn7I3DhKSkIPOOjWGK3sICWLARoDzgLxE+YcmYMv6DA/WgNORcxh7vQEbHJdAH0QzfB6rPp3qrmOn1fBUNVxMM6Bx9uxtZ+bilhjgtOVjTVHdxKCCu5Qj+UDWsA9l75iHX0FBRkyhYVNj0TEnTpwglUhH5K4fHz16hKNCMSnIZ19oMmnSJJH9Yho2bAjeuz3eTWS/33/3w//u/CKyUw4iGU53d/YhizVr1hDwTXYsurMyOSY+JMeik8uit6tqT8y3ZRhUcDV85qHIO4ar2dRYdEyPJ5+Bs01SEsnpPsTnqcefhrVu1qyZmE5HjhzJ3oz2JfVUNrp+7a9bt26J6TEuLg5Wee4swtPviwxoN3vf3zuIdEco9Zy6OJINY6qCywYaNbDN1sais6Sir374kkgYTnAMJ8pDzL/MxYsXRXOYmZnJatn2N5FI7zPRIBS169sS+tC9e3fRfPbq1Qs85lSlohDRfKog/jEmk8lglVevXINITatCdiy62PPmKqFj0esWoOCy/iolDlHItQo5Vg/ydVaddCvkS/YBNIMQy6KnEsuio0iUmpUM55xp06aJ7NktT2SHPOLR4526wlq7uLiI4/CHH34Ad84R7ARlRHAMZv+/dePW33//LZrThw8fXv7td/bFpoQSD+IfY6tXr4YdHegb5Eho7n2JDLlEE3ItJZdFJ3cyIei6Liq4a1vknIsi/kDeM5BLBXKolmyPwSiKQZ4vKr9GPUKBW5H7M8itMwragyL/QW5PGm7cBrPomDNnztjayx/xg+GLFy8Wwdfzzz8PviZPniyCL/3gtPaOHTtE8HXkyBF27FxkpAi+agk4AF+7di3pjlBsgrqo4A0YNh8ecRVF3kbRINkTlOUeQxRfRyu/+q1iB5ZH3kNRD1DYrwamYOUglkWvIJZFZx+LqB6UfvbsWTFde7UR05sW19u2bRPn9gF4+eqrr5Ai2eIYKIJDLUgTlA9ILlu2DPrj4+NjVXdPPPEEeFmwYAFSPMXjLN408CqIfIzhADwoKEh81xw2mkUnfTIhQl1UcDsZ+1yY8uOGEPdmTAfFM0r8F2U6Kasho9+eSUrB2ednCWXRpUnsQzfAiBEj4OSTnJwsmmviiS8ID2GVT5+27qx0o0eP5mYkc45E9oRe8ekUihx8WQO0G/pj1TeFhYaGgoulS5fir3DRQuq6RcxjrFmzZrDWGzZsEN81H5pFtx3XdVHBrYrNZtExX35JZkgbQbKysmCVly9fbqX209PTof233nrLSu2bR9OmTa06pO2XX36B9t3dCd3yJ4FUKoVV3rRpE+mOUGwIquBq2GAW3b1YOfkDAAE4nIX27t0rjmtPctknvuvdu3fDWpeUlFjD0c2bN/kXRa5ks+hhNV+hY7dv37aGIzz+vKioiCshmEUX7RjDI0lw/lxk12qwWXRCU4wSzKLXkpOJyFAFV8OWs+iY8vJyOBHt27dPBNe1J/G1Z88eayQf7ty5A816eHhwJYSz6Lxp0RMSEqwxpA3nz5csWcIvrPdZ9HXr1mnegSKZRSd0vUSz6CJDFVwNG8+iY7CIT58+nXRHxCMiIgJW+dSpUwK2+fPPP0Obnp6eArYpLKtWrYIeCnsSuHjxoq3lz/Ht788++4x0Ryg2B1VwNWw0i67xSDROCVr7dZCera3avGmu8Q3xZcuWCdL+c889p/XxMYJZdBfVLDpm/fr1Aor4pUuXoLWwMHU3JLPoVj7G8IgCrakMUoc34Sw6oYC0Vp1MRIMquBq2mEVPVsmic+AZSKwq4u6Ebpnpco1z6cXFxRY2PnjwYGhn1qxZmj/Vniw6h1AijuU7PFzLiy0dg9gPEax6jOHoW9edCFKHN8ksuqvy9X/iU9tOJuJAFVwNmkXn8/XXX8MJKiEhgXRHxOPQoUMWijiOvrXKd61lw4YNFoq4Hvmur+iJvikUcaAKrgaxLHo5uSx6kZYsOiYzM/Py5cvWi8RrZ+Lr8OHDZou4nugbU9uy6ByWROIG5ds5WvnCevGx0jFmjHzTLLqY1M6TibWhCq4GsSx6eq3LonNYL51eaxNf5kXiBuUb1cosOgd+o5ap5wRjou96lkU3MvomeHhLCB1jNIsuMlTB1aBZdK18//33Igxsq1WYGokbI9+1nMDAQLyjq6qqjFzEZpPnO3futKoX18gM74YdvHKquI9ndmvfsqfzVzN5K+8b+Cy/n7tESzksG9plgmdmC++CLnYOeq/aKXUEquBq0Cy6LqwRidfyxBcWcfjfYM1BgwYZKd+uqciBVBY9Xl8WnQPfE8ezuOshNTUVT7xmjHzXmyy6Sfe+TXINku2VW+VX3oPV6OV3SrYzlceZymMqn0YnmbQ3v4kbuT5+1Eadnxc3Jo7bmDxtY/xItfLPEl/ZBS2wTR1nCtYyeSvuNlz1MOu9S/LkCq/cdh7pzczcKDxoFl1kqIKrQbPoesABmoAD22p/4mv+/PkGJ07H0ffs2bONadA5ilwWPcxAFp3j7bffhjX67rvvkpKStFbo2pV9MSsQERFhTIP1I4tuavSt3zUr2Tkg2c+AZOcu+wdLtkKjvwbBjRm4BEns7d08+R8Hma9Rju10XiU6yP3spXInn7Ck8fviR22Ke2Ftw49Zp+C6bB904+/8T5iwxyeBphu5jmrQLLrIUAVXg2bR9YMj8e3bt5PuiHjk5ORgtSotLdX89ddffzVevusQ+CWkWjPqWL5XrFhBpGOkOHnypOXJc2l4ql9lL22SvSlm4GIkcTBWo4XDzt7Rwd3HzsE5pNPYhJe3JY7dCV0CNc9bfgfCcwjM7RydRe4SxXiogqtBSsFltT6LzvHhhx/ic3tKSoqFrj1bWdiASK4LCwsvXLgAq9yrVy+ucMiQIXg7jB8/3vimXFNqexadD54s9LnnnuNKXnjhBSiBY8Ckdkhm0S0+xuBi5osvvoC1njt3rkkLyhtX9yGrVdKEA3kr74NqNzqlkOyXNsUMWGxn7wBBsaX904Adi55h/uISZ7eQjmPhoqLhR4+gtwXrmKx5v0FUbkya3V6G3HLNd20JdeVkIixUwdWgWXRjaN68+e+//255MO6eb8nSFmGG64MHD8IqHz16FFW/ewtO7AEBASY1Uiey6HzwU2awpllZWd9++y3Yq1atMrURkll0y44xnDkHUlNTTVpQGpIZ0qdJ4ph9+Z+wsXbJNiZuxKesajuacrlsFhI5cmkgSEP2Tr7hiWN35IOUKwLzhh8+DNWbY2ez6IRmj6hbJxOhoAquBs2iG4+AwXhdoUGDBniVb9y4MXDgQNLdEYnc3FymmvT0dNLdEY/+/fvDKm/bts34RVzDU/0rezf88D8It8v3MvB/wuit8qQyiZPU8MK1FTt7R4mTa0iHl5MnHFBK+aqHltwupwgFVXA1iGXRy8hl0QtNy6LzgWD8ypUrpp7lOOpc4gsHoZh169ZFR5ucGq5bWXSgW7dueH337dsH/588edKMoYx1LosOl2d4rY3PMrkExSZNOFC6S5Enn3E28pkF3u2c7ezl5ri3DAuz6PqRuMhAypMUUl6+X13K7ZxpFl1UqIKrQSyLnkEui55ichZdDS4Yh7O9SQvWocTX+PHj8TqOHTsWvr7//vv4644dO1q2bGl8O3Uoi56amorv/8J1S4MGbFo2MzMTrzX/zrgx1KEsOqfd586dM3I2gICmAxp++G/JDqZ0JxPZay6Eq7iclJYJlkXXi71UFqwi5Q9Cu0zyyq1AhN7FV4dOJgJCFVwNmkU3jxYtWuD5V83Q8VoOp92ff/45/663RCLBz5phevfuTbCTgjN8+HC8Xm+++abaT2vXroXyM2fO1LMJ8wcMGGCydjfpV7CGHU+ePuNsdL8PkB2hq3Ci2EvlwR3GYCkv28vkLn4Q/uRUJCGUUrQxqIKrQbPoltC6dWs8Sde///67adMmzbdMquFpQvAqMAZd5+TknD9/Hp/ST58+HR+v/TFXNze38vJy/CpwY3SczaKbNvZNMIzJokPcjV9nAyxfvlxXNfwmVuDBgwfDhg0z6JpkFt2IY4wfd5eUlBisr3gorCfW7ozZ52VJZWa7tgb23lbMouvzK5WHPvZy+pwDjY4zhRuYsG5T7OwdRPNem08m1oMquBo0i2457du337ZtGz4lfvbZZ6GhobpquhcI6dck9LjOzs7+8ccfcf937dpl5DR0lZWVeID6o0eP9u3bFxOj/f2ObBbd27wuWwqbRffX/lNubi4IMcgxXuudO3fCBYz+1uzs7N59992vvvoKL3L8+PFmzXQ+bUQyi657R0OH8fMFxsfdLoGxSa/uxze7M2b/JEvSMkWAMa6tijhZdF04hCEnr4iU1443Os0+VO7WQKQ52mrnycTaUAVXg2bRhcLJyWnVqlXcoK8WLVqQ7pEBioqKxowZc+/ePdxnk57y5mjatOmOHTu4+PSZZ55JTk4WuqdC0qRJk1OnTnG3CWbOnGlqC4WFhaD43N2EAQMGZGYSejOWccTFxR05coTrMOyv8vJyg0sptXs3e7M7qtdcibOb9Xtat3H0Cilcz1QcZjLnXnSPJXeruF5jIwruXIQcooyqSbPowsLX8YcPH27YsIEv5XJyqu5c/RwMCPfYsWM54T527NjkyZMtbNzBwWHJkiV4ElqsETXxKRyHhIb6OEJcVh3+g3DjUWqYqVOnymQySxqXSqUQyHOrfP/+/UGDBmVlZSl/DmaTD0RwLFcaaWlpffr0gY5xeYaSkhJXV1djGkmedESp3b3nGX+H19ay6EjbjC7B7UYVrME6/qtVdZxm0esrkiAUeRd5jjaqMs2iWwO5XF5VVbV9+3ZOytevX9+sUXOkPdMsBs1HFL384hi+cE+cONHDw0NYL+7u7txot//++2/X9t0telZklacJ68VI7ENQi26NvzhdI9y7d+8GkXVxEfICDq4E5syZw2XXQTEH9BvYsBWxqLzb622ffaonJ9xnzpyBPWJvb6wK+zfpCwJUsoOJ6vOeqaOzSGbRYwm51jGjC9bxyhNM6OOTkMQqZzqaRa+XhH6Lwi+h8IvIY6hR9WkW3aqARIKUc3nmW9f+/vfffzt06NCyZUtdQ8WEAuJiONoh4j5//vzNmzf5wu3pad2oGHS8UaNG/FQzCEr37t3btm1rVb8cjRs3ZiPuf2uEOzMzE6JmqzqFyza+lN/9+97AgQPFSbDjcBsuFDnhPnv2LAi3SXkG0O781exYtczZP7uLdTO3fhP22MTGXygGuXV9Hdk7ku5OfaDeK7j/R8hnJgq/XNsVXFZKLoteYMUsui5Aytu2bnv4x30//fgTp2t//fUXJ+hxcXEWunB0dISrBZDsH388//vvt//5R+nl7l1m3Zrt4+dMdrQXO53tKfPyzXNdsXXxd2e/r+7MXRAaUPMmTZq0atUKpMeS9uHPubkCkOwhQ4ZAy3CtApsU+9p/ck9OcZbURezJweQy+dzFs8+cVw5xv379+q1bt6BX/fv3h37iDlt4IsIrXlJScvjw4du3b2NHcPGwb+f+1q8V+3roGMCnA4+0pgVrsXaflyeVm90rkll0QjPnsVl0vUMgnf0iU14/0eg0k7v8jluMgdGSJkGz6PWYiL9ru4JLU4hl0dmEmx0Z10ih0kF+wRAPrv1w3bZN23/6qUbQ8ameA4t706ZNm2tQWVn58ssvg2BduXLr8mX2c+nSDU6yFWximCODBjV46in/9u2laYmoyUhUUYKaNUPNmys/7duLscaS6oH5fn5+5UWV+3bth/CQ31FurUHc9+7dC6rEX1OI5YcOHQprekuVGzducErNceLEia2fbTu07whEvs5Iyu5lge8SGA1cnUqR3N3j3bfn7t6+d9u2baeOfa7WW1gpWAv+St25c+fUqVPFxcX8LQCi36tXL24LwCUKF2hjNq/fMn/uB3K5cjI0iSl3hF0CGySN31u2l8mc84ssttzClXbLtrABM5HIkXMkGdcQhrgY8eiGk3dowWqm8ggDUbmdRJgnzkjNn4OIZtE7tO5EzLeI6FJw7zeR12TkNUn58XgRhf3EKnjYb8irKZIq3o3s85hCXrUaXXQbnZFU8SYE787IVZfRqcbAT3+wJWkaRrqq0VGbkaFqdOAZmUrDTWF4tVc3PJor26n5qR3PyDJkVCnPVNqNHFWjrbohb42ccLxQiJDiaeXkfsF+pdK1H6/bvXv3zmq2bt3KF3etLF584N9/dzDMzurPUYZpwDABDCNlGGTM559/0I0b6O5dtGUDavM66vQqatMYoQJlxxzLkESxUzxbsMP/tBvNdRvNlIZHI+Sm+JN3LEFIsd+Te4Y06BggdXRdtmzZnj178Cpv3LhR15oeP358+/btO1U5vO9I2YiMqPa+vm4BcU8GJHYNYZuGDivuS7qVK99i7NGYTfioGI20GWVKQ64w5JxRieTlRhgVKoasCLkp1h3Bvlb8gdjloKTuIdDV2McDojv75GTkHTlyRG2N9GwBiK/hMkC54ocPF+YVR3bwiX8yUObgieCKVHFkupew64gUCa4ao4mqUaI0XHNRxnuHS3cyJTuZ6H4LkEIR5LhOMfJoWm00UzHci9jdqsuAtUaKkaLKkkL2YNBptNBhFPCMlqpGPs9opWLAn7+ypKFuI49ntDZk5CLPNrqNttWGokH40/Zqq2FUqZbkopgXJzY+zeR/8q/yp+zqOlns6cWAkckzFNfeEIlggyvhG3ACZDcLZ2TwjI6qRro2o5OqkabbSOUZnbUbIA0+2EhhJUOn8ZgOI1lpOMaj7FERyAao/TE4/MGSyqK7ZhFz7WniWPSAgBCZLAA+YMInLCygT5+A9u0D7twBmQ79/nsVOd69G23ahHbtQpMmIZkMlq35+Huh+GfY/7kSb282DD98GO3ciTZuVGnn9nX09w20bRsqykNtLU6XwV+f8bOqent7BwYGBqii5/l6/UBc5mTmopbiFMJ+zABC6aCgILUtEBJiQlvGHGPOATEpE0+yw9X6vm8n3P1ZuOwhgr23MvoQH4mTgSy6Go4+oYWfMoVrmJCOYyx0ja/EiGDqeUxAbDwG14TYWPRMcmPRU60+Fl0XOCY1CdDZ7GwEYn31KnrwQCmyDx+i9evR/v1o8mRWrAMDkaHZ4JCz3vOMry8KClLK+pEjKpoOcTp8tm9HxcWoqsrk/jtHk5vRJVznjC7WxjGY/RDB4DHmkdqo/CD7vi3XSIFH2ZlxeAsC4bHoRk2AxFvExT112ucVh5is9y5Z4prU1ibrmt4HV4OORa+FFBWhV15hk9v80HjLFnTsGIqJYfXayqOqWUDT4dOsGSvoENrzBf3vv1lBb9zY6n2gCE7yxENl+5nEsTtsc0rz2kNIx7EFa5mijUxIB0uDcZuCKrgadCy6mOjPPoFwjx3L6iPWyoMH0dGjgkm2hYkvTtChS5yg37/PJtsb6c2dSpPJzYseRyyL7hxFbEYXXTva2T86dcrJ0l1MdP8PRHZtbezc2On3iWBwLLoeeMH4b2YsTjCVTdC17Si4vI9RNWkWXUy0Zp8KC1nhvn1bKYsQaE+YwMqlCK7NxsODzbfv3WtYymkWXWS07mh5SuPyA4rMeZQVn0+3anLVJSTRM6OFR1oT9U9KE5+K1tIEMvGAGVl0NUI6vVKwjsn/mDF1QZpFr5eEnEVB+1HYryjkKxRyGskHGKhPs+gEAaW+dUspgsePW0W4rY2mlO/ciSIjSXeLwiN54kHQ7sSxO2v/GzC9stuAKLvHFyaM3pK79O+cRX/hT/aCq8Vb2CnOKo9p+xxl8pb9l7PwT64+fPJW3AtuN0qeVMaqfHozWaK+d7KQxc7RuWijOSJug9R7BfdfjfwWId/3ke8C5L8KuXU2UJ9YFr2EXBY9n1gW3b16+Oj48SrC7ednddceVk58eXqyUr5vn3K9ICTHOi5pQLPoosLf0c7+USk4cz5gociujUEakgTy6lPQBdQW9Dd36W2lIh9np2QHEU8at0f5eXV/7POrJS7ujl7B6h+PANeohIy5JxJf2cPVT3xld/KkI404xT/Ovsg7e/6VnEXX81bcDWk/WqgZy+1lyFWIp+DNEHFr/0XXTtf1XsFNhVgWPYtcFj2NWBZdVoTGjVZq3BdfICvPq6qCe5FIjlxc0JIlynXcugnFV4rkVxMbzaJX72h5aiM2c76XcYvK0ruE8K7145nZElQbQmwcVrPvHH/nx8SxO1NeO+aR2cJB7ufkHeIoN2HP2bkiB21b28kvAqu8vatnWLcpyRMPJY4DZT8MHllB/+BqxqxzIOWWhOdsFl2gB9nsHJyLPmPgE9LhZWPqi/YXXatcUwVXg2bRxcHODo0bp9S1L78UVbuJADq+dGm1jm9GETYxDUMtAuS74gibORdqBjALcQmO9ynulrf8Tu6SWxAUs6o9+3yDIavspTInX7EPDrhICOv2OoTqeB74sn1M7pKbwcIF5mYjcXFPe+NLuKoJbjeKbE9qLVTB1aBZdGsjkdTkzM9eQXENxHPNx6M5AacuzmjFp9U6vlVsHXeJRY62l0V3K0SyOFa+QaFEdq15jLkExYFw5y69XbyVaXSKjbWTJx/xzGgBcbGAfu29lTM6morE2c3B3Ses62upU06ClJfvZ9JnfCtLKDZ+lhuhsuh8wrpNafy5YREn8hdN3DVVcDVoFt2qpKejmzeVcXdcNEKWDVu1BBmpxJcMycLQksVKHd+1SzzPThHksugh7IcIIf0ale8jIN9I9RiTJ5fHv7ixZIdCuBXhtoO7tZ5JYGd0sfS9QMjBwz+0y/jCT9kOF6xlQjqONcq1cFl0PuFPKES8Sp+IE/uLJuqaKrgaNItuPX7+WSlbMeReC157kEqVefVHj9BTT5HuTT1FntKobDcZ+cY4+4YnjNmet/wuOxptFxMzaCkEuaQ6YwYSJ6lLcHza9K8hJM//mEl/50fXCFNeFSMc1SL+IhHvtRaq4GrQLLo1GDFCqd2vvsreAeewwcSXNEllLHpZGfrtN3bL7N+PwsOt65rNohMKhJ0jCbwtS66YLjV9Lhn59it7KnfJzZJt7DDy5AkHPTNbWC/oVsPsLLoepGEpyZOPFKxmyg8x6TO/c43Q/vpSa2TROZQi3k67iNvgyQRRBdeAlIK7kcuiu1ozi56RgX74gVWoM2eQl8YbPWww8eUcg+w1grDGjZVXODt3WtE1m0Un9CCb+Fl0eWplxWEmcfQeqRWnbNGOk09Y0vh9oDUZs8/HDv1Q4uQqcgckHkhqcRZdO3Z2YY9NLPyUfSQtpN1LWly7WvelKuFPTIUNG6RNxG3wZIKogmtAs+hCYW+PXn5ZKUyTJpHuTa3H1VWZVP/vP5pUtxR5SiOQ76Rxe8V3nTh2BwTdZfsZeRq5d2VZn/CnZzT+At8fF3sac6WI03S6AqrgahDLohfXtyz6nTusHk2bpq8OfskyEUi5ZrPouoeTlZWhS5fY7XbkiPCubSSLLnH1LD/IJI7Zgb+KtqNlCSX5H7M585jByyTObqzrpiK5VsMaWXRNpKHJaW9+0+gkU7COCWo1TOnamll0jvBur1eeYGTJFfxCGzyZIKrgGhDLomeTy6KnC59Fx+/9bGDoSTG4biEFKdcu2rLoauCk+tGjArt2to0seuqUE2V7GUcP5aqKsKOdfMMTXtpcuovJnv+7LKmcKyd1jFkxi64Bq+NvnWHHG8z4VhrGXjeI82ryoo1M3op7/BIbPJkgquAa0Cy6haSmsi/cBAFydyfdlbpMYaFVRLzekzbt8/IDjFu0ue/HMh2foq5le5jS3UyDIatEc1rbCOv2Ontz/DgT2Ow5cTxKHF2KNjENP3okjrtaC1VwNWwwi+7WULAs+rBhyhvfRsq3DSa+9GfR+WARh0+pQO+gqPdZ9PgR6yEQVpNvq+7o5ImHm3zJJE04YOeo5U+I1DHGZtFTRfdqZxfRY0blUXaaGnEcgogX80TcBk8miCq4BjSLbjZYvqdPV3leTD82mPgyJovOUVaGrlxht2qREINdCWbRnULYj1WRJ1eU7WPiRq5XK7fSjpYlloB2gEePdJ13u0lm0QlNUxzWbUrhegbi8cDqO+NWRRmJr3wAJsFXfNIseu2BZtHNA8v3jBmk+1EfOXqU3baF5E5QtR95suLZsfF7xXGXMGY7e9d7wR/y5HJxPNYh7F1802d+V3lMMfuK8Vfz5uLg6tHkKyby6ZnWdlQ7oQquBs2im8G5c2bKtw0mvozPovPBIm5hJF5fs+gQBZcfAPnep/1XQXe0k3dI0qv7y/YwsUM/MtwxUll0LxJZdOxaxs7SDEQ+8w4Ia86iv6ShydZ2GvbElIoDTMSICsNVrQPNotceaBbdVPCw89deM2dZWYn5fi2ElGuXBiZk0flYHok7RyDH+phFz118I3f5HV2/Crij3eMKy/YxcLUgizfqWorUMUYwiy5xQ9JqxXYJTijayA5vC2g+2Np+Czcw+ev+RsjqIb9WCJ7HqIKrQbPoJnH/PisrMhnpftgGNJ2uSerU02V7Gfe4Ams7Snh5G4TeyZMOiz/HWh3GThLRY2aj01YXcYnUo/FpJrKHzeXSqYKrQSyLXlT3suhYvi15aozUlBcEXUsTLXpBmCUiDuF/Pcuip045CUGxW0yunjqC7GgQbrhOiB1mOHMuuGszIJlFd1e8ZlGVyGdnNTpldRGPHja14ggjTyaQSyd4HqMKrgaxLHoO0Sy6s8lLWS7fyFaz6A6WvZzKbBEHDa1PWXTvhh0h7PLKrdJfzfIdDfJdcYiRJZg84NjGs+h8WBGHSLyFFUVcmoMK1zK5S2+Ln0unWfTaA82iGwNNnpOFptOBnEV/5S7929pezJZvihpKEbdmJC7B49JtKZdOFVwNUgruTjCLnmdaFh1PeC6IfNMsutkcO2ayiBPMojtFsB8BiR+5vmSHUbe/LdnRFso3scNbgpzFmlVVDTaLrvtlcNUiPsgarj0Ub5IJf3Ja5TGxc+k0i157sMUseoYJWfRp01jh8PMTxjXNoluCqZE4ySx6KPsRCgd3n9LdTOzwtcZUNntHJynl2/wDRfBjzDkgWpZU5hqVGTt8TfaCP7Lm/ab1k7PkenD7wW4xeXDtIUss0T9OQFjYLHqKvgqRz862kohzW7toI5O75JaYuXSaRTcVp0zklI0cYoVvmWbR9TB0KCsZM20oR1XbwZG4UNOu1hXihq+BAJx7d4ng2EvlDVc9KN9fK5Ln8uRyr+zWOYuuZ875pWgTU3mCfT6rdBeT9Or+lNeOaflMPpo65Qu49oBQlP0cZ8r2sm8qz3r/cuobX7qGp7HxqfUnWtFDlNVEHGPv6tH4cyayx9tWar9WURcVPPgUiryLov5jdVauex591y4oaC8K2lXzCfnKcOM0i64HEItDh4R07UHuFcqkXAuVRef491/0xx9G1awfWXSP1MagX5qzp+qsb/qOTn51f9l+i6Jvs11jnP0ivXKqchb+mT3/Ckhwo5NM5tyLoM4JozY5ePi7BMY6+YbrW94R+TRPdPKJcQ6IcfQKinzmndRpnydPOFiwjml0gr0GyFtxD0pcI9IFD8/1Z9E5onoqRLyZkCLO39qJ4/aU7mREu1AheB6rcwoOogzy6tIc2bmhgE9R1D3klKe9pqwviriO/JYi/w+Vn8AdhtunWXRd4DeO+QiUAcbIyAWPpFwLmEXHlJWx++XDDw3XZLPogUK6Nh4Bs+g5C/9SjDc2FlN3dPLEQ+WWJc/Nds0Kd24VqDbE2o1OgWr/ClGzd8OOIMQmtSORI2dtSzi4eTkHNnCNykid9kXBWvbCAMLzrPcuBbV5wbSO6sbeUBadA4t4oHAizt/aDnI/uFbxK31KqMaNdy0ydUvB7SPZ6FvGG8wYfhn5r9Ze2b03inposguaRdcKvv3t60u6HxRtbN/O7p0Cq09qQh6QM9Ad7/xOVmofAlV2yrVEse9rwnplz/8dCzdIatzIDU7ewo0b0Ia9m7eTT2hEj5npM87CJs3/mAlq9bxVPWoS2XMOrG9g84HWaLzwUyZ7vnHJqbpM3VJw104o8g5y5gXdgbtR6HntleuWgrvnwx+V2E4xrlnIzlFfhZISxbTnbwrvmmD2SV5Jxq80kX35o+Ac3M/uIzc3fXWco5BjsPCujUGoGBwC8JxFpj1BZvwx5pHWtMmXcHnQ1eRu6UBuxIBo77wOuUtvVx5jMuf8Fj9yg6OnAJvJ3os9zIzHLSo3fcY50PGGq5ig1ha9U0ziYlQWnSPi2VmVxxlZggDv3lP7iw5sOaTikEiD0gmex9q36UDMt+m490RRD1RKPIahSB1/0KDg8JNDFHLKQI5J2utIWyBp45qPUxYKu8AqePgV5ByGHBQjrl1TlTcuDRgBqkaK0pCmKAcA6zFc4pFnG0VJsjLPyf6ky0jWZgQZbSSpGz5PsOd2rT85xiHkhW5eZ9UBKdQBzgxYBYw1QnQasHnhusU5RjnXh0uCNiPUdCPesAHr6/NkdUmYqhFnnBFuyIjVYsC6g+1eUN2fWOXdYU3DOZadxlzFaKDTQKEouojdR/v2gFhW/xSpakSyggLndlh3ZUmMXiNKhxFtnBGtYshKlZlGzZ/Y/hhh2Hmh2GGflh1k/NoUKX+KUa6XHoPd2vns0VjzUwPthntcPkTfSROPOgRWV46orsM3YlUM2FkuOgz4C/LvpSgJr/6p2oD+SHyRg5t/wujNjT+HUPHPoMc6O0Yol8K+2MqKJ8LgkDPKiK82YpGdlNUUvBZwpCl/0m3AX6JDEHINz8tdfQ70tOHHjwJbDMNnNugP/GUpK6sZIeoGrJdbDrtl4LSGLyHggNdpBCu9F25gsub9D5dwP2kxklSNIHXD53H2FpWyz4qlIAzP+eCask4yMtYI1Gak6DRgF8Ax5uBdXRJg2ABpcOWMVFXD3wQDeaLyXjruItdK5P3Uw2pZLxT1r/bKsv5sKA31o+6zN8SD9mupA+E8qDb3cQivVvA/kKMXsvdQ1IlC9p5GGF4WGJHKO9FgcCU1hrcwhoPCgDOMmgHHA14LtsRHxQBuMaw0eIEiu2uvo8UI5xm+ghph2gw/cwyJVMdPoTzDX1AjRGnAHzK+umNLdBhwonM0xQDhLlGI+BrFYA840Sl/4hn4iGJLAk03gkw0glQMOB7wIaH5Eyxi0IB9LY33LdnOxI9ei1yqfwo23QjQYkg8kXtMfvlBJuX149gXvshk6xgyYGc56TbwCQR2urLEX7mUa1xA8lufFG9hwKlXbnsosXNX9oetHGq04cczwlQMiUy9xBgDOSC36NyMWecaHWeKdz4KaDYMzh4Sb+UuwBei+g3lZjGmso/SCG73EgTLAU0HwZ7Fl6/sT6YbcApVlngrDWUYnlQhcVdemsLqCGN46TWiLDA8TTCQK2r8NLkn2UzHJAWXtkJek5AEjvYc5PU6iriKAjYZdkGz6HyKC1hRGD7cWq5tMYueYOa7yYxh0yZ2f8nl2n+t01n0uOHri7cyjnKTnyAzeIxJHNzK9jPJEw+b2TPdaD3GvHLaluxgSncycS+sc3C3wg0VnEXXkXU0ErcGeRlv/8Tm1T9k/Cr6GL8gXBiblEXHJE86ApdnJi+mitatXbCWyVlo9bM5ySx62/bEfJuOew/1LLrnSBRxy6hl/VbozLfzITYWPZfcWPRMnWPRr+P8udWQlVmx8drp2iVWmT2wBmFh7P5asUL7r3V6LHrW+5cy5/xqxoIGd3TSuD1lexlHb+EftNN0nTBmR5MvmdSpp+xdPQV3xwGxvzRBgHbcQcdnn688yvhVPmusa6PHovNx8o2Aq4XwJ6aYvCQPrTs6ZtDSkm3WPIXpdi0OdWwkWzt2JJsT7xHGoL0o9JxRy8qMG9hGx6JzTJ9Ox5/XPXAYXs+mrPfO7wxneJ+CLoK37JnZqvFpxqdIsNFrunCLyclddqf8AOOdV5eCJiBmwELY+H6VPa3qJXnS4ZIdwkuto2cQdD6i/s6UXrcUXBLMPk3mMbKmJPwKG1xj7P2Rcy6yr44yHFVnBg4+hsL/Z9gFsSx6IbEsOoT/Em0xOAjBgQPWde3R2Lrt10LXEBw5CDQnrVZCQ9kdt3y5lp9cYshl0S2b0SXtrW+yF1w1b1n9Ozp7/pW8lQ/01bAAznXcC+tAnnKX3PTMbGklX3zYt4uaHgjrIaY/FnHDkTg7o0uGOS4cPAIK11v0/JeuHZ00bi87u4s1IXgeq1sKDgRuQRF/IbeuyD6EnaEl6h5yTFP+5PEcimKQ5yjl18j7yHchO9rcuQT5f4IibyOZEc8d0iw6BgfgIVaewotm0a3B5s3a74az98HrYBbdQeYD8hc/coN5i+vZ0Q2GrCrdxchTrDUkAk+XnTB2Z/k+Jv7FjVbyoolQWXQ+ShGvMCDibBbd3FeTB1W9WH6QkcWb+WSZrh3tIPevPMH4lfcws1sWuBaBOqfgQNA+5ayq4X8g9+415aDg0aDgLym/Bu5k53uJvMeORQ+/xA5mMwaaRceABBw8SLoTFLPQE4bXRWKfX62YBV3giw+JsxuEZvEvbRa2WTUSx+6sOMJ4pJF7eZVwGCnilsA+WTb3ouDNFqxlshdcEbzZ2kBdVHDAIRo5xCD7IJVCiSdyjEcSr5oSqOAQxVa2N/qvn1gWvaAWZdHxADahXkCmBzm57BMp19bOomO0huHOdTCL7ugdAvId97yOiReNQNeOTnxlV9k+xsnHilOfkZJvNouebJWWYwYsUoj4Mzpdm5tFx1SH4aa8NLcaPX/RMQMWW3U8G8HzWB1VcOtBLIueV1uy6HgGthcEmylZHzSLbiXwoHS1MJxkFj2s+oljE/HMaQuS4Rphvipo3dGemS3ZAWzF1hrAJnF0yV32N4gRkejb3tO0OdlMAou4f7l2Ebcki44p/MzMMFzPX7SjZ5BVE+k0i157oFn033+37hNkFHH47DN2P/oL+h408cn+4M/cJTcFbzbrvUt5K+4J3ixHwuitbCyZWB+S55oYjMQtIbjdKEvuhuuCTaTPr4eJdKrgathiFj1HJYsu5i1UeSORHNUe1+Jk0YG8PHZXNmtWU0Iyix6unIDLJJwDG7DvMcmz6ByluaM9s1pCs76l3bVVF4DEMTsqjrDy7SrwqzuNxXpZdI7oAYu1iriFWXRMEYTh75ochuv/i7ZqIp3geYwquBq2mEXPqlHwJ59kT/uh1n0tUg3ycpEc1R7X4mTRMXfvosuXa77WuSx67PC1xVsZZz+L3iyuuaOz3ocA/K4lbeohcSwr3x7pzbS6FgerZtE5orVF4pZn0ZG5Ybj+re3oFWy9RDrB8xhVcDVsPIv+2280hV5/ePxxdm+GmXUDujaQ9d6lzDm/CNumZ3Zr6wXgiWO2VxxSyrctEN1vYeVRxj1G+FxD4adM1ntGzN9hCgXr6mEinSq4GjaeRRf5KSRbzKLHi5RFB2Jj2R26dKnya93KojsHxrIp9IYdLXSttqNTp32eu1j4G+uALLGkyZcqD1uROsYgBrd2Fp2jaCOT8c6PNa6FyKIDDYauKtpsWixhcGtbL5FOs+i1B5JZdHJvNsEK/tRTYodstphFjxMviw5s2VLzWFndyqLHDl+jSKFHWuiav6PtpfLibQxEyha2qYmdg2PpbiZlyildrsVEnCw6JqTz+PIDjFt0ttK1G5Km6V/CKJz8I9n52E159tzg1nb0CoI2A5oPtqRj5rm2HlTB1bDlLPr//kdT6PUNPLvLhx+S7ofpWCOFHjN4WekuxslH+IvUhNFbyg8yzv5Rgrdc+4HAVvhpWOwkjU4xIZ3HCdsqO7t+4WPCtkkWquBq2ODbRd1y2PcCI9FT6Iho9ilSiFyfGbiImEXHXL6MfvmFNRyjlK/bFh9Ts+jKFHp+J8tdc8eYvVRWsoMBqbW8TTWi+y8s2894pDfX5VpkxMyiA3Dd0vhzJrTzeISz6OnCNJs0bk/RJhMiCmO2dsE6Juv9383vkwWurQRVcDWIKXhDkll04KnurIKHm/7IjyXIK0R1x1EClyuwzUm4FjmLDnTrptizIQiFkFNwE7PocSPWCZJCR7xjrMGQVaDgggfgbtE5EIQmTTykx7XIiJlFx6RMOYXVViIVJosOhHR8pWwPXBcZ+0y9MVs7sufs4i3C5xlJ7WhEFVwDm82i21QKvZ1CwX1Id0Mc8Hi2upVIT33ji4y3vxe2zcw5v2S/f9lwPVObfffX/I9t5i9HBy5BcZUnBBh2qIZQeRiO6P4LizbWq51FFVwNG82iK1Loc+eK7VpurbdCGaCtQsG9SbgWP4sOnD+PTh5DyLVuZNEdvUMgAI8f9ZkgrvEx5uQbwT5EVvyEIG3WNJ5cwU4x2ri3Htfiw2bRk8R2WriBSZ/BXnQJlUVn21zPZL57wcjKxmzt2GEfFZs4xF0o11aCKrgaNphFd05n74ODgq82//URZkIq+0RSwUXPogPXrqEf4eQagOwDxHaNMSmL7plbpZgLXZhsLD7GGjy3omQb4xIUK0ibHJlzLjT86D/9rsWHzaKLruBxL6wt/JSRuCCpcK8mj+47v+gzYwXXmK3t5B2sGI7+nEXdMsu1laAKroZtZtF79yJwE5wgBBWcCF27svs3NpJ0P4wjbca3OR9cFbZNa6TQHT0CGp2y1vTgdQ6XkMTKE4xXjpCJ9NihHxWsEzhkrjzG+FX2ErZNglAFV8MGs+goGk2cQuYmOM2ii0NxMbt/2/aDLS62a4zxWXQnvwg2hf7iBqFcy0qQk098oxOMT5HAbyKL6v1e6S5GGqpz2DfBLLr4MTjCE6kt/MHBoklwVQjtMoEdzJbWxJjKRm5txQNlj1vULXNdWwOq4GrYYBYd+bOn921bCXimWXTRuPYH+ukesbn3jc+iSyPSIJSTBiUI5dq9EMkSmlYeZ2QJxUK1Cdg5ubBD0Mfv1VPHprLoQMzAZUXbGXtB5/2DHWfkZOZGbm3FfK0CZ2NoFr32YJtZdFDwJUtId0JEbC2LjhTPGhzcR7oTRiANT6k8xrhFZQvYJjuTqtCvKI0esKh0J+McEC1ss3WahNFbCtYImsuzs4ODQdjkiZUeKCMFVXA1bDAGb9aMVfBxAs9+ZBQ0BheNHTvYvRxMal50o2NwdjrNE0K+LMOnYzCEXYljtgnVICb97R9yFl3XX8fWYnD2Vvgxxq/saaEatJM4sDOzdRxjTGUjtzb7QJkpE8UI6NoaUAVXwwbvg/fqRexJcHofXDTS0ti9HB9PwDUy5T547PA17KDxQMEGjfs/GV9xiHENF2ieEQWOnoHsizUre+qvZmv3wZG9IxsyF3QRsMnEV3YZ+fyXkVvbKgpO74PXGmwwi96jhw3N5YKxwSx6YSG7l+PiSPfDEGkzv0t/84yADTq4sS+GFvYmeHT/D0p2MNIwEacurQvYObk2OsF4ZrYUsM2YgUsL1wt5erKGghOEKrgaNphFJ6jgNIsuGmQV3Pgseuqb32S8/YOArtknwbcz0lAhg9L0t7/PWWgghY5sL4uOJA5FnzHpM78TsMkGg1cYqeA0i05BNplF79SJZtHFg1QWHSt4NKGBV8Zn0QVX8NRpn2fMOidgg05+kRBp+pUbfgzcpuZkw8QMXibsE9wmKLhxWztm0FLjZ4kxEppFtx6urZH/GhSwEfnMM6q+DWbRf/2VZtHrP+Xl7F5u3Jh0PwyROv0rYRU85fUTWXONnZnTGGSJpZXHGXkKufdR1WJih6/OX01GwY0kpOPYsr2MR2YLAdskSP1WcNd2KPIuCj7BinjENRTyteFFbDCLTmRGdAzNoovJ9evo7Fkyro3Pome+eyFr3v8EdJ3y2jFhFZx9Nm3pbWNq2s67yTgIKrjxWxsuwPyb9DO/Txa4Fpz6reDhV1DQXqXt0hRF3kYeww0tYntZdIIPg5N6r64NZtGBX35BJ06QcW1kFt01KoudPE3Q11Glv/OjsHO0Jo7blb3gD2Nq2sj7wfmQVHCjtzb7yFsjISdWpe8HtwbO5SjyDpI2rykJ/gKFnDawlA1m0W1tOhegVKHgtsaFC8QU3EhcQtjpXFxDBXvyyyUomb0kKBTslWT2rr5Fm5ik8QeFarCeETtMYAWPGSRwFh1hBa+oJ1Ojt2/bnnQXrIXb46yCOzSoKfFdhMJ/V6njXIyc85FzQ+UHKoddYBUcgncnX2TvxdZxiUUO3jqMBnoNxfunnY0wnCKQp+IJDOeY6p/4hq+gRrS6wSk4W+InhBHFM/x1GhApuBchxxDkWF1i2IhUGigaPSdBPyH0lemfzxH6WaHgXyP0pVkt/Kq4BsA7zjFQhxGuxYDNAqvglo0cAqp/CjLaCOMZwSYbyAldvIxOHGUN9Tqh2owQpeEkhOGarnzpJHyFQq0GHBgehSkVBxmvkizl9tFd2ZEzgrUZYcoG5XlxbIOlubhB7ifTjKDqBv2QLCeq4hDjVZyjbDBImVvQNKAy/qOu+SlQmxFhgRHAMyKrjXBk58rmdZVbI4A95Ew2/LUZUQYMOzeUPGVr0QYG2StLHPx1G348I1q7gVxR/Msr2Hne/t/eeYDJTZx9fHb3tu/e7l7vzdery/VeXLE5Y2MwnQCmhBZMcaGYYiCUACEQQgKElhCMsQ0YGzAG29imGGMgCakQklBC+xJCMS2gb15Jt7e7t0W7N9Lcnd7fowdea3V6JY2kv+Y/o5Ex4jL+AHZZOvJpQz8FBpMgMKXAgaKnRM7iJdKp7v8pviB1ODDnEXezj15W1tKhnxQENilIiRqUxQiIiwwc10EmKK4TSfE3wXOOIcVfB82xTSe2nuHJXDus4NZsWUPt1bIehQmqYge2KlmzogT0ZPDNF+dUDv0Ub5AZHFSECcwRAr+Cw5ysWEF5nEF2cFA2HNDDC1+dKAzzU7RA0qMKstxI/kXI6/FPfybkHVHBafyXhNbwESFSjzBacJJCBQV5EYISOCz0ruVqlfeCzvH/FCbIjxAUJxLQm+Fb75E9z0Eg3ejgJykoih1YAoPCWEFhaEAfWpzTwv8EQREE9Jh4u2v7dgopvVPlB4mhn8CEHwqsSgJJWbLpI0E5XWHq9Hb5gSRf/inuQBQCWnzJLYV0hWmzev1PONJPIwO6sG9wxE+TIgS5ow7E2z7dKpqLKrhn5pDy5siKECMoixVkxw7gGWxaU87ghUZvuGXKg4Os2IHBTRX8njZaqTfDLUj+KULgmS4fDXpbk38aEUhyT0sw94gl0ulHb4+2yuAgI54gnViKSedGIWPeAkvJ0E9VsYJ0uaNCjKA6QpA29LZgMuk7oZVMUMIo+Amhc0aCLroe0KmLzq8dXCG2HHFQ9IKprFZoTS+nK3RName1QrOvkK7QXdHLaoVIdCrPf1LhmGwKSXJlwSfCZ7H8RPjAXiFzzlKGK1TOgsGFXPJqgGtJuDr4WFVwZzO370bx7MnWyycvz55sZdz6onNsB1fYF33osybMFNyWDQruLmem4JZ0UPDk6l4lC/M6vTn2RTc5ib2O2dqkcdHzDrlEycIKj3bZ0jVsv2ySPXdpzw7B18xHSSdwTzbnYniVzJQ3PCft9tB28JFw64vepsu3yfTXF91eya0v+uuvk5df5pNaYV/0iabgvPqi+7j1RTe5iGMyu9XJ3yZT9DlvhUcbxmR7lOkwrSf9ovMJbpbeBFZwm9QXfdbwnJx9JDfWHUyHLvp77+GILhOfigoo5Qce4L0dUZlgCo6MHvH74Mw+dkZU+Lpo8ZJburaggqtCwYck+0k5tnaTok+IZ1msP9Gfi85xVFV3D5+8OnTROzuhlLOzOaQmenXReZ3eE8ZFz120CsZPmzwr9qKKj3bbemHa7SzHByg6/iZUcJWQjPSc3ST9XlDzvD/G/hMduug8v2zCaZBPHbronL9sMtTzPDpjX8GtGcX9LwrOkkYlC/M6vSeMi1521hrlo6wrPNoDe4XUdkW2vELKz1nXvR0VXC2ci0jmJpL9FEm7W5FE6tBFx6+L6oFx8XXRsa/gSZ7MzseFqlVPsVohEoWSk29n/hUSGM6ln+VwLgP7hPzDr2e4wriY8AoeL9xc9CZuLjpHBUcXXTOam7nWwfPk99yjM/YVnELlu/GXim4Q3Fx0D1g9fFIzddFpBXzqre8oXFjJ0TZ7MuBVstksXyXr2yVMuuwkhiuMC1TwELi56O3cXPRTT4V7e04Oh9QedNG1QurtUMPJXOXmoudU9L8oOAqZDdNKqbnyeYXfXuF1eoOLXsspNTsX3ZJaACPidh6ucHklR7ts6QNsu7FR+nYK5decynadykEFD0GHLjoRXyh78EHeG6EhOnTRP/yQ/O2vvDciFswV3JyS1/mEULlyM6sVUkrP/HXXE4I9j9PDkG4oP2cd29e+iAod0YnUW77rDLbrVA4qeAg6dNGJV3wl/BYOmdFF14zX/0x+z++5lJeLTqlevWvqz99luMIkVyqt1ydXxT53deqiM6r+V17wREc8o7EpOdpt64Rpd3yU+DaNIOuAs7q3CoXLWH5NLy5QwUPQoYtO8siHn/BpCvfM4JCU6M9FLy+H8n30FUKsWqeWUOqiF9ZTcbTnVDBMXXfNy5Nvep3hCq1ZpQN7hbSOI2Iuyev05uyiT2GzqoGXhPzDr1C+fMyjDY3gLwrpvd8bzVaFUHLybR0bBUs1w1XGByp4CPp00Q/l90o4F/Tmok+ZIr4Mzmk0V+VYM4q7tgjl565nuM7SMx7sfFywZbPsw9dw45+b7vqE4QqREHIWng9vgtezfAaadNpdXTB4moHhOguPvq7rKZ63TlTwEPToopeTwYPhDr9ypdaZ3d1aZ5Tg7KKnap1082Yo39JObk8tCl10Su3VL025+U2GqVPmZ/ftFtyVnQzXWXvVXiUKzuv0ngAuetWqpzo2xaeMMY82cwud0rqWrvNTK7vu9/GCCh6CDl10GA5O7Mx2zz1ap9ajiz70SVktefddsnMHfXoY+jar5ih00Sn1N/yx4brXGKbOPLaibxfj7ui0bti/h9YQY/R+Rhc9MSzphQN7hYKjfxTXX8U82jCWS4fSnu0KEceHOc7F8vEwPlDBQ9Cni05EBd+0ifdGaIXeXPSPPiKvsVRFFalYsalzs2BNL2a1QpMlr+sJoXLFo6xWSIYkRvmLTkhclJ61pjPOCnhMsuedA18Qa5zPcJ1Zs0+n60xpPYThOuMFFTwEbi56IzcXXXp/8+qrODSF87IZDxEVnMsnwmylWrvoTU1QsgceADtsztI0tR/lLjoMPP6i4JrUzCq1tYZUXbRr6s8VvcGtnMk3v9F453+iL4MuemLA0OW/iPUVyRFEP9qVKzd3sf6CWMWyh7u3CTFTqwoqeAjcXPQOYuDqos+eBff5ZbG+/MIWXjajnZA1fDJzcNE3boSSzUwnxkJiztU0tR/lLrq9sE7sjs5MgRzNpO6q16bd/gGrFUqUnvkrqgi2qN3m0UVPgOzB83p3Ce7Krnj/MPrRHtgnFBx9XeKbFXadLwmFx/04ZmpVQQUPQbcuOhGN9C1beG8EwpqPPyb79vHeCMVYM0vE7ujrGK7T2zg4APX6JobrTPJk0Bt4alfsd8qQuGh/WJj6M8aGSfa8s3t2CN6pcxmu0+RI7n9eyDqA5QCtCYAKHoI+XXSD+JrwNddobaS7437QHvepNXbRDzsMynTBAoitRcScqV3qQJS76AR6er805afMuqO72unNNos+FVQwbQqnUKFp+c03URbgdY4ZPcTO8pX6eFKPzkU3+7JhJNWEvh0W5WirYaHDu2lPCsRoip5abVDBQ9Cni260QTBnDtztzztPu9SemdrlGiOpNXbRP/+cvP++HNOHh7HvohOpO/oNzDreSQVdc8VzU275J6t1ymtumDWwV0jvPS56au0BF53T+02jdNGrV+8Sv7WdyCvbUY72wD6h8BjGFnrZ2WulRvDoqdUGFTwEPbvoRDTSn3iC90YgjGhpgQKdy9I71IKKlZuhO3oGs+7olPJzN3Q+JtiyyxiukzL5pr813hWjPxuikOzBZX3PCtlzl7Jdbeas03t3MrbQifhUUHT8zWzXmQCo4CHo0UVvkF10ormRji66qmzYIPZhG3LOebrouTApXTijmNZt7flsPh0iFbTZl0PX6SxiNOLnEKVn/obWGS1pBVFSaw9nFz2hcktypbZtEBR+tjUskY42rPbOfye82rBkzTmj5xnB17QgemoNQAUPgZeCuzm66C2yi06ZPRvu+eeeq1FqdNHVY9IkKMr77x+ew9FFpw8PdFK6cGZJ15NC2dkPMEktFbQlNR+awpc9wmSdfowWR88OoWb1riiptYezi57Qd2lqVu/uHt3wpGGPdvaB59AKeHId4++8Fh3/E9Htj5ZaG1DBQ9C5i07QSJ8oHCyOlJunuP/YmKL+htem3fZ+7OXioe7qfTHf4E6A5NqB6S8LaT3HMl+zfpD983mM/XNKxfns+7BRWh8Qmu7dz3y1CYAKHgI3F30aVxfdMvzPG28Msl5VRZ8uukkTFz2wD5uEtXB8uOiUlLZDB14UHEUNo0/tL2hf03x4p6yU2Vgxfqb+/J2W+7+NklpjxpeLbhq1fy4x8mgnuVMH9gpFJzBurTbZk+mJlDFwUpTUmoEKHgI3F71zTLjoElTBt2/XIrVnlhZZxlRqbVz0xYuhEOcHDyFpKxsfLjoB07uAlZHuL2izN6t7q1B+Dss3zSXclV10zVWrnoqUWmN4uujuuF306tXPjtI/lxh5tGmJMFlzCCUn/5wWt8FkjpJaM1DBQ0AXnbJsGdz/zzqL93YgibJ/f2gFfNxRfx17I71s6QP0wcDsY/99l9Izf939tOCu7mG+5olN4fduoPXZ7Hlnq7FyGDON9ThsRLTQm3/9JfPVJgYqeAjookt89JEWndLd/L7pwyu1bZLqLrpUAR95ZVsLSdI4cdEJGOmLmRjpgQVtcnqpzpaf99Ao1xmWab94j97bI6XWkvHiohcee8PAXmbN3yFHO3PWaWq8RAar3SV4pxwQJbWWjFMFN+UQUy4xqvBxKXTRJfr7QQV+8AN1U+vRRa9W3UWnFfAPwo0CPo5cdOI30peO1kgPKeiys9eqVA13V3X17BBqr9wTKbVmmFLGgYtuTk6f8Vuh4MhrWKUOPNpGu7ttndCsQmez8nM3jHTm0UWPi6wnSdEXpPg7UvA+cS6KuJh9Lsn4DUm/d3jKVPD1THTR/WhTDUfYErYFfJyihpEuV8OXqVINd5W39WwTqi4ObRBHQjDZXJ2bheZf7U9s+LWYVK16qudp9jcv6MO2Vyg55Q7ma06YcafgmY+Qwo+J63iSNIlkbyfFX5KkqvBLuk8hhf8m2U+T7GfkKe+PsdePLrofqRp+1VUqpkYXnTmffx6+Ak64uui07p9A9Z9Jj/SRBV12tlqt4UT8ZlnPdsFV2RE2tTaAi17OKbUTXKboyPJ939dsU/uPttHmmq7OgGklJ9/WvTXMoK/ooivEmAG1b8/5w3MKPyDp94Rf2HUiKY722YHw6NBFd7WGcdElbrgBRDxDNdcXXXS2RGoBlxhfLjqRhmEBI31UX4IdWdCqtoZTai9/rnen4CzucLarlCEG4KLXc0rtJo5pURdQR75JQEG3bxRr9yrcT1vWfNd831dRUmvP+FJwx0JStJ9YW4bnZO8gea+HX3h8KfiYRbM3y5DR88UX5IN/8d4IptT9kL2RTik9Q6yGe7OYr1li2i/+JYm4Susfpxitzs5NQtNd7OXbT+as0/p2C+6yAfZrnnlq7y4hufqA2ItqyMJ5kRuSxx6u40lxcOF7V5LCTyIsfCLIvcFGTFnEmBZ+mZTriO8K4rtcnjzLSf4boOD57xDfTNkOSl0sfy8vTHBo5OAQYhc7k6QcIvcqCRMskgPfQST9uKE59SOChuDg4HDB5OBgYUAwRQ6cYuBbEBqkHkGcU4N/OkgOSBdZvRZE/Me0GlQx9NPU4GC+2AQQKWgMDgaHA7qDdCM9s4lL/G6zd3AoODByMI+4mmMFcwOClgjBHJI8fWhOa3BwQEDQFhzMCQjaIwSzIwezILs5Ew64vJ5Z8E26sAF9qpesueFgZkDQFRS4pxPDZLLtSbEFXBwR1zODuLuDguQ+knY0mJzJ/UM/TR8KBsIFPXKQLAbJ/qCfJPcqCPqCArqp0siT9J90ZlDQGzFwtpJJFx/Zu10oPHe+VJ+l2+AR781uf9AdLpguB3R5eo7RAvXPoQcE1tPu7dkmVCx/0tEonwn0SEpbGCboDAhmBQW0sLwjArrxFm9JzaU7e3eDiNvqhn5qh5MhYjAnQtAWEBwQHLQGBHOHArqwiaQfM5S0ZeinkUFzQDAvVtAEl2HEYFAsr2kk9XBiKSDOyXCBE/FzD1Jgq6W3TVf7eqF17df+hX3zIwRT4fYSI5gSECyQD0vaYa6WX9MK/n5HV9BPNKA3QIrDH0wOCA4ODhrCBO6ZpPT09d1PCQ7xGqT3rpRFckC3mf6tvWpoTl1AcEj4gEpDqhTUgmREDBZHCGrkwFxBpq1Q/M2/MUDyKaHVavcSUvy/8AtDO/iHsDwV/cKPSKYyz4zb10Vb+fVFbySGpGgLSF66x8k+tWc2+3UqTc1rXPRqYvKyX+3CwdifIYMvm/D6umg83wcPofGuj6f9PPEvU0Q6x7xT5814VfBOWZjwmmNSe8XztD7o0rwmbkoBQeGCwRLeRTeYXO0bhOaon1MfJc42Un6WKh3YKAZDtD5sHO9jY7kO7phPMh4g6XfDlLkR5iSfHIeCOw8laXcQcz10SqdroCKecX/4JQNBF30kRiP57DPslz52aW2F0tm7l/d2qENqx+H05ulrHGS+5torX+h+WjB7s5mveTjFFc+BqVvOqUV8bGDNLOnYROVbRfMcMDin7xOKWY+hKiH3YTOo0nN+NIzldnD3cST3ZZKzG6a8v8hzQhTc8wNS9JmitWU8Qooi+O2B8FJweImSU190e334vuiB9PWBRvzwh4xTu/m1E/JKbZ0EVSS2fPMN+T8FZ6ylUKPPoo0ksb7ofhrv+k/jLxOshkcpaJPN3btLqLpoa4KbFQupraTmiud7tgulZ96nUpaRGJOJjVdfdAexBfdFLzjqmhm/FZp/9YXaqdsfEZp//QUxqmJlttz/bZQnEI73sbGs4CORerJZAlyarKdI3t8U/a3r2Ii19UC49UXvGot90QNZsYL9UKs8XXROqaEvOtNXujZtgnKZMSP2ktAXndOnyqzFMCWMXA2flkg1PHpBJ9f09ewQKlY8muCWKUsNrydvF2iVX40sIxkjfdGtGSVUVfv3CLkHX6j23S3vsNUDLwqeOvYd2Ch0++leeKdG7MPG8T42vhTcNAneJnN/f3hOwbskc4McGxwwUJthqLk2ZMS27B2k4L3YKdBFj8Knn4JYWK28twMZorERSmQd+491jDnEajj7b4MSGGVrfddWwVnaEnvRUeCqaOvdLdRc/mzgFzEmMAVH/4hWvZvu/sRROFntXJlzTqcKm3PgBWqs3Gh1TH9FKDnlNjVWPnrGl4JTcnZDi7a1BzznjLWk6EtiGWpi8vyAlAjEO/S2eOFn0M/cMpUklZLUW8FCD3yRPBLookfnq69YNoi7+LlPvFJbS5i56JJ8v/yy0uUtBfxc9ByYRkNqZ4LVcCUF3fjL/1PjDeWQ1K7y9p5nhN6dgkvlZnGOLjoludVbd9Xv+l+gVe+LVPK0A6FPRG3rhaY795vU6ZRdsXJTzw6BGKLdnTnex8adghMDyf2dOKrqtzDkmmf58C+eM0gxVfAV8j9zX4W+6FTii7+CIP1uRatHFz06vb2gGldeySa1Hl30GjYuerzyTaF39YQ7hI+SUbroEo13fdx4Z9zVcCUF7WmY2f00ey99ZGpLWkH1ZTv7dgvJtf1scwXC0UXPO/zirh1C+8NCzvxlGqQzWp3tjwhta6FSocbbJen9S/qfF/IXXx59MXTR48NIrG3E2knMtUGzTbnE1k+SCobnmOvEJduJWfHnctBFj8nKlar0akOUM21a3PI9AZCr4Y0HqrHy8nPXUxF3lMT5ceuEqLn8uekvC1N/9pangd+tnzXWjOL2jeAwTL7xn5olrb746e5tKr4k07pWaP51mEHYxg7jUsHVhJuLPoWfi14Xh4suwUrEXfxesuGVevQuesLyPa5ddIkEquHKC7r+2leoADlKoo4Kyih1Wu+xLWu+G9gn1KzeRUxRh2OIH3DRy9iuMhq27PK6a16m+9J453/dNVMMKn8810/+4tViN7mLpH8yv6JzFp4PHdimxP4+Kcf7GCp4CNxc9G5+LnpbHC66HyYiLg02xQVeqUfpoo+m9j3eXXSS0LvhcRV0/TWv9uwQnKXNcW9ZQqnTuo/u2S7Quv+k0+42JDHrIUofEZ2j/ay6UvKPvHr6K0L7Q0LeoZfBYHBmedBFtTHZXDN+KxQef5N/Dtsr2mCxQwe2kxV1YON4H0MFDwFddOVIIs6qTRyJiSTfr7zCezu40nTXJ7Qmrt76oVeb2mOPBGBNL6q84Inup0QdP/VOk8OjWerRkORKzTn4grb18LJY3qJVGmc3GJM6HhFa1gjM7Qs/0IFtu6BBT7xRggoeArrocSGJ+OUxOnpEBF105Yy+7XsCuOgU37TBnm1C2dlrFS4fb0F7G2b17hSqLtke74aNJjXo+IVbup4UBl4SpvzsreS66SZHcsJ5VXXRbdlldVfvG9grNnnf/IazJKjKDSO6qDygK8j3RqH1wdDmb4ZXdHr/CdCB7fArFC6PLvrYAV30eDn/fFCWxN4TRxddIUxq3xPARZeoPP+xri2Co0jRi8YJFLSntr/vOaH64u1x/+XoUttyK9O6jmx9ACq2XVuFqlVPexpmmexxS7lKLnr+4stqr3ieanf7QzBIi7syzFexTW51XfRI8k2YXtGta4WWeN4uRBd97IAuegI0NsLAnlRiLInW5ZEoTJ2K5nkoTfd82nSPsvGUEwJE/FmhetXT6qWIlr1+RtWFT7T85htJyitXbi47636T3a39lph9OdbM4upLd1BR690lNN37ed5hiTpuo8ZgEuV7nbpfaMg5aGX/i4J3auwObGMBVPAQ0EVPGKkmHpeISwNHc4FX6nhd9M5OZvI9MVx0CV/jfPDSl8b20hMuaO80+HhZSvPBCf49i3PMUz+z6sIt9HGl83Ew2Bt+/OfqS5/xNMxMSk6P8lejdNENZhut+OcdegnUuF8EQ4DWeafc8nfnpNi1a/Vc9JjyzeaKNphm/FYoOeX2uP6I430MFTwEdNFHgyTiFyge3dAbcaRh1eGVOi4Xfds2lu99TxgXXUL20gtj+MWjKei6a16BrulFUxL7c4bnmD23MrXrKForb/7V/v4XhIG9oOaVF26hdXOjxU5rykFLJxFbpaLVUrE2e7OSPBlGmytv8WXVl+yoWPFo6wOwfrrjNFfuwRd5p8wxJCl9LlfJRY9invthcrTbHxLa1gvxvhfA8T6GCh4Cuuij5L33QHSuUNoHBInIjh1wJJcs4b0dY5imez6j9VNVU9T96NXenYKzOEERVwNf4/zUjsNa7v+28c7/0Lp5/4uioN/4Z/pIU3Xhk3SqvnRXcs0sk9OblJzhn4xWZ/5hq6sv2S4tQycQ67Xwt/SRgFa0qWQ33vnvpns/n3LLP9yVnaqOGhcfCuSbCW1UvlW26JmDCh4CNxd9Mj8XvZaNi+6H1sEVirgeXfRiRS769u1wDPuZ3kQnkosu4Wua37M9hpc++oKu+9FvExNxDc4xe16Nd8oBkqCLfQM+bbr7k6Z79/c9K+py4EQ1ejvV6P/Ii93zafO9+6f89O/uig5ay/ZOnctEssFFV1b9V7xGpfI9yqOdc9AK+jAT5QNk6qUeDajgIXBz0Xv4uejtzFx0P5KI791LnM5oi+nRRa+N4aL39pK//x2OXl8f49S2ignloktUXvB41xOCvTDiOOBMCloScYW939mmTozsQxd66ueANPsnqtE1rE+pEYCL3sRsbVRVaaVYYde10Rzt3AUrp78iFBye4NgW6KKPHdBFZ8WqVXKz+PLlsRdGJLZulQ/a9Om8N2X8ADXKe9X10in1soiPITt9YpO76OL+PcK02z9UO1FG/wn9Lwr5ico3X1DBQ0AXnS3vvy9LUnZ2mF/RRfdz++3ygTrxRLVSW/L5uejZMKkEeOnbhNIIY7wwLOj6H/2u9xnBVa50jbzOMeiLXsopNRMX3ZTU/jA00OfGM9pbwkcb3v5+YFTN3+iijx3QRWdOSwv54gvQpssuC/1Jhy66Y4SL3tND3nwTjs+OHaRMzQ9SgIuer+L6o2AtgUk9oF/6VsGWVz3yJ7YFXXftKzNeFaou3KJkYV7nGIzoEp/fzy71qF10yTlvWfOdq6w1rj9M4GgbjElt6+FbqHH/5ahTswIVPAR00dXAbIbh06U65sqVvLdmLIG2OSsm3/jnnh1UxFUe05OQjOkn9TwDvcJc5fFJDBKTvEMvBef8jo+0SVe5cjMtSm1yqQQqeAjooqtHWxt55x1QqxdeIAXiZ9yT+DUqujjdfU1iLdhAyK23qm6bhwAuerSBQFREVRfdz+Qb/9K9TbAX1AXOVKOgbVlltVe9CJXxi6JVxnmdY+PRRTdY7LVX7hl4MfHvpMR7tKHz+UtC/pFXJZZuNKkZggoeArroauPv4fbcDlL/fe3yhuDlNGiis57c8ZB8BJ55Rl3bPIQJ7KL7gZekgkdbVa+gM6af3PuM0L1VKD7p52EX4HWOmVLHmYuelJzR9pDQ+ZhA6+AJp47raOfMXzH9VSH/iNF9HTmh1GxBBQ8BXXQNMJnI1VfLKvb883J9fMJjMJCf/YyPduuHlKaD+nYLlSs3aZPOll1ec9nOnu3C5JvfdE5i9xqVbqBV75ornh/YJ7SOri9ZXNDaN8g3i9o3d1DBQ+Dmojfwc9FrNHLRQ6C7+9Md3HRcS+OLarffM9/zFqni5LlNeBddwjttXt/zQuXyR6V/alDQnvqZ7Y8IXVuEoiW3BM5HFz062Qee0/qg0LlZKDj6WuUDt0ZC4dEG+X6FsXyjiz524Oai9/Jz0Ts0ddGDaIYWYX99/LnnSG4uMWryJKON8WWzkVtukfdu505SWkxv9zS3FqnDbIwOXHSJlLZDZ7wqeCfDRx81czhrr9zTsw1Mdb+Oc+uLPuZd9JzBZa1rhb5nhcZfMrvVKinonIOWg3wfwbj2jS762AFddC4E+urffku2bCF5eRpJuRpYrfB+9x/+MKzd5eW8t0lnNPz4Tz1PC/ZctkN8xsCWU1Gzepek48Un3qpl6vGCJSW35vJnqXZP/cV77ooOLVPnzFdFvvmCCh4CuuhaEuI+0RrrrFnk3Xe1kHI1jK8Q4Zb6mdcHj/cJI7r42KdWgk5cdD8NP/lr91NC6gGaijiRG8d3QeP4TW+4Krs0zk4xuoltkvZpxdQOsHoikXfwqumvCh2bhLxDR4wNMWqiX9GyfKvT9o0uutoYkolBmVHMS8GTdemie+eFn2+3g5T/619BUp6TQ1Li+bJ2YqnjJTUVDAQq3K+9NizcS5aECrcfRx1JymKTOl7046L7abjhT11btK6JS3jqZ0gDi03+yevuyk4tU4OLzuk9TVMycTaHzjRaHdlzl7Y+CJ9RKzjqGmJKUiN1lCsa5PtVoeDIq9XIGz212uhEwQv3E5+ytwzRRR9T0Fr57NnDtXI6Pfkkefxx+HKKxULS0jhskiTZd91FNm4kv/vd8IZFF26ECyZXavt6oftpwZ7DQcQpmbNOa9sg6fhfXeVt8X54eryTc9CK6ou30d3v3SlMve09Nw9HImdQXfnmy4RX8LTbSN5fSMHbxHOWouXRRdcSV4vSJamU01P18stBKPfvDyPoZjPx+UiS4qd75ampZBuNYSRbGir2uONgw6YorvJYi9BF1xRrJam/7q8wXJvmIu6vjUo63r9HbB8/4adq5+XoolOS8og5Oa/ygidafvNN724YH5Wqp6duhtp5w17R2fOXaSDfym8mzJnwCp7/D5Lz7DhQcJ4jurQRI6eaQcLu0+yZZO4BQ4L++bCqfvcdWfsA2fgI2bYNurW73ZFlPdxFl5JCPB5Zrzc9SjasD5bsz4cke5BMbkhwy8FF5/R5EVs5Pxddta+LxsQDHdJJw42vdz0pFH3vRi1Th/RF904drLp4R/c2oesJoWLZo8nVPSrlBRc90fNzNBjM9txFy+pverr/BaFtvTDtjg/dFdpVukd2CAf53icUHKV67Zuniz64gFtuDSn8LLyCJxXBKJf+yWAl+W+Bghd8SIwmuXU4KY0Y7QoCR3CQGl8gb0+cf6UocAYHKaEB3QWDPfxPGgQmGriCA1/sgNjEkUmHmD2DHHICmTGf/OttENn/CUHmNp3WroUadOD0cPCcDRtCLfFv6POA5I0vJYuOIZMDh+o0ixPdHi/Ud+IN6C4oWdgUJUiOEHiiBQZr7GVUCug5Jl0p9J90psYBXAtJpO6aV3t3CRUrHpXmhFnYGyFwRwyMUQNDUtAc6ZyxZlTU/+S5zqegSi5b60awwAyO2CuU3JvhwBUxoPsbc5lRBtCzSDyjjBZ79vzlVRc/PbAXhotvvlcoOe16c3qStMtJYt8VerGrHRAiB7BhRpI9b9n0V4SSH1wtL5NKggLHmAnSggN7HAHdzTmH6+KDCpEU3PU94jyCOA+XJ/sckv+mqODvEUc1seTBMu4OueYSJmgPCArCB652Yi2MELTJgbOJ+AaD5gQFRcFBa5xBcXDQEhqkH0/slcE/NQcEJfEHTQHBpIgBPT6OehgQXhp9gh4EOWiMM5AGN6O7IPm0WYRYiMVEDjsM/O2BAWhGp3Xzb74JmL4m3wrw38CZVKyPPZYMDpJFi0hDjfjitviMQWiiXPi/YwpUY+WgIv5gMrGWEnMWSe4fWs9kefiLsIG9KjhoiDOoDgrsNcQzC26/9tqhn+qjBjVBgb0uclAbLqgNCjwzYAr7E/2TMEFdxMBRHxxURwvo0aAxPUOkP/dOO5BWD8vPfsRaTqxiocACDfEEk4MCWliRAnqeZ54izqkY+qkCTgaKKZNYykjGwGntD4GOd24WJt+5MesIWl210J+s4rlBzxBp4aBgauRgmhxAHzYz8c6Rt5nuu3OagqAxOCiNGNA7g006SapsZauXVZ4Pwt37DAyqln/E1b7WGfSqMaUR51T5ILiaIgQlAUFz/EFxQCD6aamHy0fDnEvKrjhnQOw1Zy4dWqY1OCiKM2gLDgqHA3pY6DlmzgnzE73Vhw3orc8dKcgPCDpiBIYs0nkaD7dFDczEQB96nTAZPaE/RlLwkXBz0bu5uejOZmIw80nN0X0inIbABBedRwc8It6Eebno9AZl4TR2bsg5RkW871mh+0mB1oXVTu2ZHXsZX9P8mst2ta4R+nbDqG6lZ64tXnIHMY6qt7YpFU4ztTDYsg9YRmvc9Fmo5ymhdS20Mnvq5WZugzVMX3RtkJpLKPRZov95ulXXaJaa431swYELueVminclKRFI8TcwlY4YYXfsKziCINpgz6tuuOGPYt821UVcOb6mg2pW725Z8x3V8YF9Qt21r1ae/7i7qstg4vR4HYDJ6SNGU878ZVWrhmrcwcI9RsicfUbLA9AEn1zdy3tbNGLC9GRLKgIP3D4dJseBob+OfQUH349XX/Rqbn3ReT2uc0zNsy96Hr++6FkwcSFSQU++6Y2e7YKjUMXX/xI7xxyFDRkzTqIyBHXJPULXk0LpWfdXrHh00vd/SX812twmR4xheQ2OxN++p2JNU4BeH7Si8vzHys/bUHnB41S16Zb0iMKdH1W4jXa5hUhrDMbqS3b0PSc0/2o/jTVOzvE+NmEUPDpjX8GT+/j1Re+Uexlpj3fEs9aET+2o46Zl9kp+fdEnyf0ftCdKQU++8a8zXhXKlz2sfWqF+JoX1Kze1fqg0Hzf17RuTmW0/wVh4CWh/kevlp+zrmL5xhJZ1l3Dk8VlLfBI/RnCYDCa7MnyklaH2ZtdteopegRgOncDrfWDWL8g63XzfV+13P8tfZbIP+KHvsZB79TYw3/DiC6av1pFHzla10EbROExP9I6twjH+5h+FDz5FEVLoouOIPoha+7S3l3ikC8FY30sHkfx1NS2Q31NB6X3Hd+2QWhZ823Lb4ZlPXAa2CPUXvnbsnMeLD/vocCp7Kw1LWu+kzX6BVmp2x8SWu7/H6xtjSzW3mkHprQuUqLXY4GcwfNm/Bb2gveG8GHCK3janSTlanhHLOsxkvpT4hiMsTy66FqCLrqWgIvOqRPdGHTR/djzaxquf02NyrgG55ijeEpq++KUloP9E0h8/3HtG6kuf0f1OnBqXStMu+19T/0M/8KpnUcYzCyHVNbURTcYqi/ZPvCiUHTcTQaDw87pc2wEXXQ1yX8bFDlnD8ndR/L/RjxLYyyvQxfdzc9F9/Fzn3il5uyic+oQztFFV1jQ2fPEyvg2wZbNTIG4nd5OYuExCj3R0EU3Wl1t66CTQMHRsnOuw5sJ0YGCxwu66AiiT+z5tfAllK1CwbHX894WJDIGI4y1vk9o26BT5zwQVPAQeCk4jDiBLroOUlsL0UXXlHgLuv663/c+IzTc8EdHfq3GqVkB46JzqoOr7aJnzvx+y/3fdm0RipbcYrQ4An/S4c2EoIKPgJuL3q9LFz1Wt4SJlxqGb0IXXUMSKGhf4/zOx4Sux4XCY67TODUTYFz0qZxSq+aiG63OtnUCvC9235dhF9DhzYSggo8AXXQEQSj117/Wu1NouP4PzmJOcoiImJzeqlVPDeyDt9GTawd4b87YAhU8BB266LYqfi46p6FNOaYGFz3GgBxqYcnVpYs+ioL2Ns3v2Axf9264/jV7fo2WqUfDRHLRs+edPf1VofNxoXjJz0Js8xB0eDMhqOAj0KOL3oUuunaAi87pS9n2KnTREyH7wPM6NsEnQQuOulbj1IkxMVz0zJmnttz/LX18KjrhZiWvvOnwZkJQwUeALjqCICOpv/61nh0w8FfBUdp9MkOfiLb51r7nhKZ7Ph1rQ6+PNVDBQ9Cpi87p4wk6NL7QRdcYhgXtKKivu+blIR2PXR/XqYteNpo/d1ddtHXgJXA84u1GqMObCUEFHwG66FqiQ+MLXXSNYV7QoOPXvkJ1vGe7EL1xXKcuemsif5gx45SW+/9HtVtq8k5gpDgd3kwIKvgI0EVHECQm9oK6huv/MP1lof663zuKpvDenPGLIWP6SS33fwue+d2f+JoOMtmTeW/SeAIVPARuLnqtLl30Rj55OabWqYueCRMXVC3onMFlHZugs3rtVXsLj71By9RRMLqJtZhTajuxKnPRTQ5P5UVP0kp37y6h6d7PPPUzR5lahzcTggo+Am4u+oAuXfT5fPJyTK1TF70UJi5oUNA585e3Pyx0bxUG9olV8sIGab5njuqpw2JKI85pnFIrcNFNdnfVhaDdnU8IRcf9xNfIxoPW4c2EoIKPAF10BEESwFXanDX3LFol798DjbllSx+w5+BQMEFkDIBhLnVUKz7xVrafRdMnqOAh6NFFr0QXXTtoLRhddC3RvqBT2w+ru/rl9keEvmfFXutHXqXxBnB20UeYLZmzT69Y8ahsmN/zaUrLQqMKjd06vJkQVPAR6NFF70YXXTscDeiiawrHcyz/tMNqL9/bsx3c9bpwDeUqYUojLk4uOjETK3TPt5hs7vJz1zX/6gsQ7meE9oeFouNv9jWqWBg6vJkQVPARoIuOIAhDnOCuL6VVcqmhvPbKF8rPXQ9jrRs4mW5qkjnz9PKzHx3YC00JHZuEpns/9007MKV1Ee/tmrCggoeALrqW6ND4QhddY8bOOSZJecejQvtDQv/zAq2cDvdgZ6rmRpd2LrohyWK0u8vPWdf86y+ocPfuBOEuPO7GtK6jjDa3RhshMnYKWktQwUNAF11LfPzOPl6pObvohXxS83TRx+Q5lt53fOacMzo2ihXzvdCza9KpdxUdfxP8Nmo1V9VFN5it9L+Zc84sO3tt+Tnr6XNI/wtSdfsz79TBjNmHJHEaDm5sFrTaoIKHgC46giCa4SpvS2ldVHfNy/L7aKKal5xyR9nSNbKgG5NoPZfzVhpNJntyxbJHys8VJXsPNG23rQPhLqLV7e6jjTYX5y3UK6jgIXBz0Wt06aLz6m/DLzVHF92cQ5JSOaXm6KKPn3PMr+a0bt62HgSdaiWt4YLffuWeSaffU3bWmqITboZFjaZosm4glvwEt9lgSqIrh/9TyV6+seT7v2y+7yto16YV7UdhKjzuxtT2xZGatsP2RdeGcVTQDEEFD4Gbiz4dXXRdpObpoldzc9FtpTBxYfyeY66ytvTe76V2HJ41+/SOzULbQyNk/Ycg66Vn3hc0nXFf5UUPeVvEplmqxSMncTy0iuWPhPxhoFhLk9RkD73RGgfT+5coqWibkomrbVR7nTDjt6BHAyp4COiiIwgyZnGVtUqynjn79M7NoLAhU9sGEPreZ4aFOOxE1ZkuGfS3D0NbNhXrtK6j6JRBJdvq5L27SAxQwUPQo4tegS66dqCLrjE6PMcoBSeenNp+lKTFI6eMgRONVocaedFF15jxq+C+K4l9VrQFbL0k5XqScvXwlHZr7NXq0UXvQRddO9BF1xgdnmOmVLFGwCU1uujaMk4V3HUMKfqcJJ8TbRn3qaTw3yTvLyT/dZL/BkxFn8VeM7roCIIgyLhgPCp4qUAKPyH5/yDJZ0RbzHUiKf4m7pWji64lTn5ffuCV2pKPLrqm6PAcgxFdijil5uii66+gyfhUcOdhxNYN9euJpODJvdxcdFcrMSTxSe1bwCcvxTuPT17HZDA5uWAr5eai07y8UvM8xw7gkxe+LtrAJ7XRxs1F53VFE67n2MJ543XE2uIvSPKZ0RagCl70VYyVpFxHfFcQ3+Xy5FkOZjtV8Px3iG8mtBtSUheLQ56GDQ6NHBxC7HViikOII1KwaDiQWglhTv2IoCE4ODhcMDk4WBgQTJEDpxjQky0k8MyW1zP800EBwdRYwXy5I0f4oDE4GAwNvIPE1RQcHBg5mEdczbGCuQFBS8RAKiaY0xocHBAQtAUHcwKC9gjB7MjBLDnwDBBXx9CcCIFnFnF3BgczA4KuCMGMgKA7OJgu7zsN/HPkYCBc0CMHyWKQ7A/64WkzdtAXFLg7YII5fTAzKOhVEPTABkiBRwzc/qA7XDA9cjAjOOgKCGZGCDoDgllBAS0sb+RA2mVa6PKcdjgZIgZzIgRtAcEBwUFrQDA3KKCXvzynJXLQHBDMixU0wWUYMRgcCsQV0kvbNzgimB8cTIscTJVblqMFUwICUUBtZXLgnxMY0BsgHBZ/MDkgODg4aAgXLAoO6iMHdQHBIeEDKg2pUlALkhExWBwhqJEDcwWZtoLTk/Goia3gJ5GC90nx1zDRIO2XilbLqw5uK+fmoluK+OQlXN0n6flKeyz5xMCp32BSGjcX3eTl1nbA8xyr5ZPX6IIx8DnlJtZJfDLzuqIJ13NsLNfBbf0k5Qboc06n1FtCf42t4MeTrM3EPpO4TiBZW8F1T7stdlJeCk4rArysbJ590fm5T7xS00d9cw6f1Dz7opfBxAUdnmMwLjqnb22YPLLRpD06LGgyttvBqUAXfkjy34SpaH/orzEVPISs7aTo49iLYV90BEEQZFwwlhU8OvEquPMwUvxt7MW49UWv1mNfdAc/94lXauiL7uGTmmNf9KQMmLigw3OMc190Ti66DguajHMFd58+Yq4h4vJZW6BGHxM9uui9/Fz0hXzyckzN00Wv0aWLrr9zTKcuuv4KmoxnBS/6lLiD3ybznEVKBOK9QP5n4X+I+zRiyiEGF/QzL/yY+K6OvVp00REEQZBxwXhU8FIBXvQueBs0GiT7Mnm+53RS/B3xLpf/WfAuLFD0JSn+CuQ7a6uilevRRS/n56JP4ZOXY2qeLnq2Ll10/Z1j4KJzMluMNn4uuv4KmoxPBXcdSRwL5Ml5KDFXy/PN5cR11PA/iTg0OiyzkNi6lK4cXXQtSeHnPvFK7UQXXVt0eI4lpcnjJ3BIzc9F12FBk/Gp4KqCLjqCIAgyLkAFD4Gbi16FLrouUnN20VP4pEYXXUvQRddPalTwELi56DPRRddFaucUri56EZ/U6KJrCWcXvYNPah0WNEEFHwG66AiCIMi4ABU8BHTRtUT6/IquUlvyeLroJh266Po7xzi76CV8UuuwoAkq+AjQRdcS6dM/ukqNLrrG6PAcS0rXpYuuv4ImqOAjQBcdQRAEGReggofA7euiHF30MnTRtUOnLno6TFzQ4TmGLrp+UqOCh6BDFz25D1107dCpi14OExd0eI6hi66f1KjgIaCLjiAIgowLUMFD4OaiV6KLrovU6KJrjA7PMaOTWAs4pUYXXVtQwUPg5qLP0qWLvohPXo6pebrotbp00fV3joGL3swpNUcXXX8FTVDBR4AuOoIgCDIuQAUPQYcuupWji97AJy/H1Dxd9Cxduuj6O8c4u+jFfFLrsKAJKvgI0EXXEh0aX86pxJzLJzW66DpJzdlF7+STWocFTVDBR4AuOoIgCDIuQAUPgaeLbtA6qYS1FF107bDkElMyn9ToouskNbro+kmNCh6CHl30fnTRtYOvi24t4pMaXXQtQRddP6lRwUNAFx1BEAQZF6CCh8DNRa/QpYtezycvx9ScXXQfn9Q8XXT9nWM6ddH1V9AEFXwE3Fz02VxddAef1CmH8MnLMTVnF53T3ZWni66/c0ynLrr+Cpqggo8AXXQEQRBkXDCxFdxcTXJ2koIPSfG3pPA/JO3u2H/CzUUv0zqjH2sJMZj4pKa1Ql7wSm3OJkY3p9QZxOTlkzopFSYu6PAcM9hh4CA+qS3EwunDpjosaMpBgxNZwZPPBuFO/xWxH0CytoAup98T409kBf9Qk+0LILlb64x+3K28WuCJby6nxPxSOypJEqfGaFsJiDgXrHkwcUGH55jRSZycNMVohnYiLuiwoCkL5izkllt9aB3c4Bz+Z9bjpPDjGH8iKbgk4oUfaTflv6t1xsDUBbxSv8MnL8fUBe+Rgg/4pX6fU+r3uaXW4zn2ASn4F6fUH3JLrcOCpnXS7P2qSujYwrkY7PToFLxL8v5A8l7TfPo9j6SYGqcJP+nzHNNhah3u8h9AsPRD1pbQ/U25jviuIL7L5cmzguS/RfL/RvLf1HaiGf/KI68/9Rt8Uufx22s+qWnG17kdbc6pX+dzwHV3jr0pljKXo80xtT4L+k0wPXSCMY0UfUp8P4yxGBVxLrhO4JOX4jyCW2peR5tjalsnMXJ6M9oyjZg4vSZsroSJCzo8x4iL2GdySm0k9vl8MuuxoLmmZoutnfguJd7zYfJdNeJno/xkGLPPFq2JcyF5KXTj5IL7+8TIaYwRXkebY2rHgcRcxSe1fQaxcOplZO2AiQs6PMdMxcS1mFPqTOI6lk9qHRY039Rs8ZxNir6S2/dLhNBfM9aRwv8So4JXaVDBtUSHZz4quMbo8BxDBcfUE4nMh0jRl8ToUbQwKriW6PDMRwXXGB2eY6jgmHrCkLmBFH4Wh0L5rlBzayKjUwXndLQ5pkYF1xgdnmM8FTyLn4Lrr6D5ptaAvL/Am3rEGMefpFyt2tZExbOMGKx8UrvPUGpQMIfX0eaY2rmAmDmNtmGfTSyNfFLbumHigg7PsaRJxMWpe2pSNrdOuTosaL6pNaDoG1LwNinaT4q/kacSgaRcyXuzEARBEASJivtk4jo6aHKfwM1IRBAEQRAEQRAEQRAEQRAEQZDxju+H0P+t6AtS/BXJe52Ya9RKZMomKdfEWMa7kniWQyc3afJeqO5QS64TiWOeWiv3XkYKPyHFAnz4wNYfcTFzBfGuGt5lOvlW07nMNsPoJbkvkeL/QflmbmK22hCkj+LBzr5PHAsiLpZUSLwXB+/sZcSg2nsBjoOI63tqrVwmCXbBFPULaK5jxBPbf1afT1zHqbAldpJ+H8n/Jyn6Gt5GydrG/l0Pg5Wk30vy/wEpij4nWTvoIY6wpAvGmwosaO8lJKmI8fZQ7AeQnBeHvqf8GcnZTSyT2WdJuwNGx5L6F9HL2bs6wnImOBmC9vpi1V/ESD5D9WHKkiYR35URD6x9AG7Uw3u9HP6pBvTGGHRsVxFrsyqJxhf0rpuzj3jOgyMP1+bnqgxEaXCQ1DvCjDwTAr06Ct4jhf8GOQBF+BoGclcJWx980kil9XsvIMVfkLQ74V2qnD3wer5lWvgl7XNh7B0Ymec/8kSPErOX3ZJI3hvw+TkqGfQypwc2awujNQfgPhWeADMegJ3N3g47a58efklbB4z0C9+kC9hZlQY+tUwleX+EsRFUBQr6uxh36cxHxV0eOqvpJZa9k/2WJFXAtZz1JJR12i/gOsrdB5rCMkWRmOIp6H2degtcrbm/I4ZwT5umLNA7+CbdUEHTP7QNsNwYifTfwJ7S/zoWktSfwGcg8t+A5weG0IuRnqU5e4nnXOhZlP0cKfoEHkjCYIWTH75JF7DXzkNYbkwIvmtJ0Wck/+8qpjCkiPfk/xLXURG24VL4ZJj/9C78GA6CGlA5CFKHb0nymaokGl/YAyqh9EZES8rzA8Yp3KfBJVDwNuhUdOg9wXMO4+xhKfoKbqT0iUWV8QEMcE3Ru4qf/L+Q9DXhl7XPggd7lXCfCLvpf5mL3tup1DLv2Uhv45kbh/+Z8xLJ3Bx+SWuruLNJjDdgJHm/h+udlm/6r9RKYWkkk0TPgd5VzOXRlsx8mGSsV2sz/Bi8xNww/M/ks0BoLPVMU7iDHkTdp8DzmKUpzJLGDChocwXL7GEJeemenurFX0V8gEwQC7F1Bc2gD6u5fwq/JN1rWxvT7JFxf5/kvkJSroWndPUo+D/iPgl00xnhjXv6EEsvAQ2gx9ZxsBaJxjX02YZWxtliroRBD1J+DI+L0QGHahXj7GFxfY84DwZnTI3xAahO0cPoPGx4Dtibb4Z/T19ScJVejc94iOT+dvifpmK41jzns0xhriEFH8HN3A+tneW/Fd5GkBTcmMlyA8LiWECch4L7EfgcxRZjCnEvATePCmVMBafVcI0x+qDpxDJFzRxOSGFrDZddVHBruJ9UxVwNDn+I4DInYw1IZxhEBXfMVje7hK0falsU3yoV6+D0aVxqMqD7FU3B31drAwKh26B6o9g4x1wHtyP3yaqs3LEwdmWz4F32zw9RoM+uarwsb5tOiveTpLLhOcnfFy+3cHVPUPCv2W+DRPYuGCE/kLw/EB/ThgNrE/il1oCBU5yHQwcAY2q4hVthZ9manFHIegKqS6qSVChWNmMq+AZ1N2MknrPAz0xSsxGW1gFpCktDmJ8kBWfrAMTEMUhy90I7jnqYCsRnti8jdPaQ6uA9Km6ABL2OwKkWawQpV6il4FnboWFCIoaC/0uVDQgBtuGw2IvpmdSfiiOyulVZOb2xx1RwWiku/AAe7Is+Jbl/VL0TCE2nhoI75op7GlDjts+EnQpb0aYKTp9goSfhN9BumHYXyy3JfRnKNGjOiyT15yxTWDtg12iNb3hOkzgnXOcucCc+BGOf7iwNMiK0LLAie6vqCk5P0dgKvg6KmC5GH3Xy/kaci9TdJImc3aq0tgeSvYPkvBD+J1r60Db6X/Gs/j+x9wW7zpkjyVgP7XQ0o/tEtVLQu2KpACWY/Wz4cxuwDDXRfgP/pcdfjYdV8Bn2Q5u7hEoKnvkQSf3F8D9BPQ8Nv6Sk4DCA2Fck/23ovKcS9HGx8CO4t9DzKvcVbt8s5gj0eT4XJnrMRz4ouk+Fg+M5b1QpPBeIneLOhQ639uAO2EoUPO8v0F/deQhJuxucWFpSSaPr5mSuE/tJinvtuzT0VyYKTu/h3ouCUjjmiHsacMuS5oRX8Hkk91Vwh5LPINm74RRNv3+0m+Qndx9J/VnwnL2sFbxdNMbTAua0RVRwWyfJfQ1us7T6RutKdGfV6x5PxoyCZ6yD5nh6VqdcL7738V+oLapK+j2QRdWB2dPugMdsW1/4X005cHF5lkLXi8zN8AAD3epifek4YWz90I6T9QQp+LdqHWlM0ByccjXJ+zPJ/UN4aTY44F0AWkl3HQ3GS8F7UNzMe33QZwNr5/A/favYt4PTXQh5/Cv+GvqphsV3Mcl6Gk5vKiu5v4PNU6kllJ5FdM20Gk4v6oJ34e5hsKmSaMxSLIBJDg/G38JbD4HQK67oawb39iKx+iylCBnAVomCB2JpEE+G0TXaOo+BvYbO3v8Ve8IHt0QzUXDn4uAU4gc1YE8D7ld0TtFX8Igek/Q18KjJ6syEGvfNoXNSfhph6YSQjPHA79haG2FnlTwhp94KO2vKYrk9gYwRBQ/CCv3rQpo22OI6BkpEVb+RrpymUP5FD/pwS+881hYVN0nCfSYp/Dziex+syH8TNCsmruOhOck+i2lug1wJ9Y+bXfA2vHMxSWD5iqLvKigvehVLKYq+hK6hUP+N9TIRJe81sc+PythmQ68q+qSEEFG+qfSk/VLdLPEqOKXgrdAnDbao5KLTJ2SoZwU8sqbdBg+NSl7tsTbDw48hwmu28ZL1GMl5fvifpgK43r1MD6mlHh60Ai8l37XwtGzwRf6bIcwVsLPGbJbbE8hYVHCxH1TmY2ptD1VVeIPpcLXWT6QLWYizT5FF7PPWGXvB0UMT2VV4bS2QlCvEyrWyjWE+4kTy6SDW0gQV0jWwMa4jgzrejBJrGxgOcpbvwdVNr+jUGxU9s3lOh3YTDaDXnZvTN2XGFKZskO/AJg+ViFfBkyZBw5ZXzcEKVFLwpBJo0U4+d3hO9nZ4n1QJ0BWBXR085aagbqLmGni0ZtufE/zSfwZ16c/YAGaaEsPBd7lYB1eta3qWBgpeKSq48punmeT9iWSsVWVjZPlW8zubUPsW4q770NoovNqm/vgbziOgambrVTdLzh6S83LsxRwLxIaGGepujO8Skqd+nRf62B+kaMmsZ+C+qjaWFuhOQ59bdA50hP4f8WnyvTbH/DCdrunl5h/mhT71pd8vN9DT2ln2TrjqVRruQyLvdWjEUQNa+yv8gJjFUYySz4YL2X2W/BO9rukup98m/zP3FWjCk9qR6RGA4SnYiY65GiQy40H5n/TOA71G4/n4rBKoShZ+RKxiwblOgIz+aj4tTbqzftOYlmnyUmLKFZc8GgyBSG+OMyHrMRXfB5eg+0LPalOwjZC9DfZa6oltmUJyniP2ORAb02F7Cv9L7PPZb0nK9arrhe+HcMGGbQ815cEu570q/zP9PnDPksT3we1zSf5fYdgo5hgzoVXaP2SK4xAYzgWeHpme4Wm3wbG1iE0AxizoWAKX8+nwT4MD9jr/LXlJ+lPKj+Ryt/XBo5oa7eAhqNcXPZDAvugp18Be+7+pmv+O+Jq2AQ47fZIv+q8qndl8q0narXIpWBqh7xCMLmJnn2h8kXyq2MHvg+FWFTqVKmjsiAv3adBMQxPRyhq93QWuv0DseS4hVdLpLULqtUtvdH7VYw408XwnDzIDLQi3s05ghctK6mFOnxUDdcp+IOxy2lDzdN5rsMvSqLb0zpD3R8btwvDyy2dw2GEQG5WqJ0bo2yONOQmddbcP/2LrhJ1N/7X8z+zdcDRgZ7+Gjcn7KzGXqrA90ogu4ni2Be9D+WbvYJ9CGtEFxgX6J/yXxv5hJ7M2w15LAxSbq8QxSD8Vd/kLeChNvZX9xlAyHxHHQPs44FoW4DiwTLFWHmYtMAVVZyJaMXSX6bOKRPrd4s5+Lhd0/ttBna9YYfSKWT4VL2fxIFMxjdS5LmHg0ejLofvSF6BQ/ncDDXbY67yh0V1SbhCX/Fy+j9Gncbv674anXgcDMqjNpADjhYop3WvXMfI/YWfFRnPY909UudaIeB+TziXpjgojzKjZVDReMNdBr2D38UET86Hq6E0sMEvg+mmtM/Cf5nrwwdxLoAeaqi8L0AdI9wlDm3SyOkNAGMWvu54U+upoUjHscqCS0gqa6zjYHpW6KFta4ZDSPVVjYGo/UHB0Z4NflaKVU7qzgfcx20yw8WFnIw+fzmBjFok93qXyPTFo4EFWGFNJ8mkBZ/Vpw6/UOQ6CvTamDC2ZC2/i0F2mO+4fH485dB+Hd3lox9m+uUZPVFrEQSmWyFUzWhuluxxo4NMznN7k6QKOCG8hMYHmdcwbOqNUG7/UMhWaD+i+uI6Fly+GMcFeB7YO0wcVaa+hJ6Fqfe8DsXUHjaekEp7zoMFIwtoGez38qq+V2BfAyeA6PtoHIEaPtQVGdoVSOApr3wiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIAiCIBOc/wdYF//r)
]]
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)
]]}}&]
[s0; &]
[s0; [2 Versatile 2D scatter graph control. It does not require GUI 
so it works in command line programs.]&]
[s0;2 &]
[s0; [2 In case of GUI it can be used ][^topic`:`/`/ScatterCtrl`/src`/ScatterCtrl`$en`-us`#ScatterCtrl`:`:class^2 S
catterCtrl][2  that is based on ScatterDraw.]&]
[s1; &]
[s0; [2 ScatterDraw data is included in ][^topic`:`/`/ScatterDraw`/src`/DataSource`$en`-us^2 D
ataSource][2 .]&]
[s0;2 &]
[s0; [2 In ][^topic`:`/`/ScatterDraw`/srcdoc`/Overview`$en`-us^2 ScatterCtrl`_demo][2  
there is an overview of control possibilities.]&]
[s0;2 &]
[s0; [2 Special characteristics are explained apart:]&]
[s0;i150;O0; [^topic`:`/`/ScatterDraw`/srcdoc`/Overview`_en`-us^2 Overview]&]
[s0;i150;O0; [^topic`:`/`/ScatterDraw`/srcdoc`/LegendTable`_en`-us^2 LegendTable]&]
[s0;i150;O0; [^topic`:`/`/ScatterDraw`/srcdoc`/Units`_en`-us^2 Units]&]
[s0;i150;O0; [^topic`:`/`/ScatterDraw`/srcdoc`/Responsiveness`_en`-us^2 Responsiveness]&]
[s0;i150;O0; [^topic`:`/`/ScatterDraw`/srcdoc`/2DSurfaces`_en`-us^2 2D 
surfaces]&]
[s0; &]
[s1;%- &]
[ {{10000F(128)G(128)@1 [s0; [*2 Constructor Detail]]}}&]
[s6;%- &]
[s5;:ScatterDraw`:`:ScatterDraw`(`):%- [* ScatterDraw]()&]
[s3; Initializes the class.&]
[s1;%- &]
[s0;%- &]
[ {{10000F(128)G(128)@1 [s0; [*2 Public Member List]]}}&]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatX:%- [_^Callback3^ Callback3]<String[@(0.0.255) `&], 
[@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatX]&]
[s3; If set this callback will give the String to be painted in the 
pop window and table data to refer X axis data.&]
[s3; The input values are the X axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatXGridUnits:%- [_^Upp`:`:Callback3^ Callback3]<String[@(0.0.255) `&
], [@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatXGridUnits]&]
[s3; If set this callback will give the String to be painted beside 
every X axis grid line.&]
[s3; The input values are the X axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatDeltaX:%- [_^Callback3^ Callback3]<String[@(0.0.255) `&],
 [@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatDeltaX]&]
[s3; If set this callback will give the String to be painted in the 
pop window representing the delta between two X axis points.&]
[s3; The input values are the X axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatY:%- [_^Callback3^ Callback3]<String[@(0.0.255) `&], 
[@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatY]&]
[s3; If set this callback will give the String to be painted in the 
pop window and table data to refer Y axis data.&]
[s3; The input values are the Y axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatYGridUnits:%- [_^Upp`:`:Callback3^ Callback3]<String[@(0.0.255) `&
], [@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatYGridUnits]&]
[s3; If set this callback will give the String to be painted beside 
every Y axis grid line.&]
[s3; The input values are the Y axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatDeltaY:%- [_^Callback3^ Callback3]<String[@(0.0.255) `&],
 [@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatDeltaY]&]
[s3; If set this callback will give the String to be painted in the 
pop window representing the delta between two Y axis points. 
The input values are the Y axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatY2:%- [_^Callback3^ Callback3]<String[@(0.0.255) `&], 
[@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatY2]&]
[s3; If set this callback will give the String to be painted in the 
pop window and table data to refer secondary Y axis data.&]
[s3; The input values are the Y axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatY2GridUnits:%- [_^Upp`:`:Callback3^ Callback3]<String[@(0.0.255) `&
], [@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatY2GridUnits]&]
[s3; If set this callback will give the String to be painted beside 
every secondary Y axis grid line.&]
[s3; The input values are the secondary Y axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:cbModifFormatDeltaY2:%- [_^Callback3^ Callback3]<String[@(0.0.255) `&
], [@(0.0.255) int], [@(0.0.255) double]>_[* cbModifFormatDeltaY2]&]
[s3; If set this callback will give the String to be painted in the 
pop window representing the secondary delta between two Y axis 
points. The input values are the Y axis value index and value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetGridLinesX:%- [_^Upp`:`:Callback1^ Callback1]<[_^Upp`:`:Vector^ Ve
ctor]<[@(0.0.255) double]>`&>_[* SetGridLinesX]&]
[s3; If set this callback will return the locations to set the X 
axis grid lines, and these values will be used instead of the 
set by SetMinUnits.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetGridLinesY:%- [_^Upp`:`:Callback1^ Callback1]<[_^Upp`:`:Vector^ Ve
ctor]<[@(0.0.255) double]>`&>_[* SetGridLinesY]&]
[s3; If set this callback will return the locations to set the Y 
axis grid lines, and these values will be used instead of the 
set by SetMinUnits.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:WhenZoomScroll:%- [_^Callback^ Callback]_[* WhenZoomScroll]&]
[s3; Callback called when the user does a zoom or a scroll.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:WhenSetRange:%- [_^Callback^ Callback]_[* WhenSetRange]&]
[s3; Callback called when some of the control ranges is changed.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:WhenSetXYMin:%- [_^Callback^ Callback]_[* WhenSetXYMin]&]
[s3; Callback called when scatter plot origin is changed.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:WhenPainter:%- [_^Upp`:`:Callback1^ Callback1]<Painter[@(0.0.255) `&]>
_[* WhenPainter]&]
[s3; Callback called before control Paint with Painter to draw additional 
graphics.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:WhenDraw:%- [_^Upp`:`:Callback1^ Callback1]<Draw[@(0.0.255) `&]>_[* Whe
nDraw]&]
[s3; Callback called before control Paint with Draw to draw additional 
graphics&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:WhenZoomToFit:%- [_^Upp`:`:Callback^ Callback]_[* WhenZoomToFit]&]
[s3; Callback called after ZoomToFit() function.&]
[s1;%- &]
[s6;%- &]
[s5;:Upp`:`:ScatterDraw`:`:WhenRemoveSeries:%- [_^Upp`:`:Function^ Function]_<[@(0.0.255) b
ool]([@(0.0.255) int])>_[* WhenRemoveSeries]&]
[s3; Callback called just before a data series is removed. The argument 
is the data series to remove. If returns false, the series is 
not removed.&]
[s1;%- &]
[s6;%- &]
[s5;:Upp`:`:ScatterDraw`:`:WhenSwapSeries:%- [_^Upp`:`:Function^ Function]_<[@(0.0.255) b
ool]([@(0.0.255) int], [@(0.0.255) int])>_[* WhenSwapSeries]&]
[s3; Callback called just before two data series are swapped. The 
arguments are the data series to be swapped. If returns false, 
the series are not swapped.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetSize`(const Upp`:`:Size`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetSize]([_^Size^ Size]_[*@3 sz])&]
[s3; Sets the control size with [%-*@3 sz]. Functions like GetImage() 
will return a bitmap of [%-*@3 sz] size.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetSize`(`)const:%- [@(0.0.255) virtual] [_^Size^ Size]_[* GetSize]()_[@(0.0.255) c
onst]&]
[s3; Returns the control size. Functions like GetImage() will return 
a bitmap of [%-*@3 sz] size.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:Responsive`(bool`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* Responsive]([@(0.0.255) bool]_[*@3 responsive]_`=_[@(0.0.255) true], 
[@(0.0.255) double]_[*@3 factor]_`=_[@3 1])&]
[s3; If [%-*@3 responsive] is true, ScatterDraw size will not affect 
significatively to control look.&]
[s3; Normal [%-*@3 factor] is 1. If higher, fonts, margins and lines 
scale will be higher with control scale..&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:IsResponsive`(`):%- [@(0.0.255) bool]_[* IsResponsive]()&]
[s3; Returns true if responsiveness is set.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetResponsivenessFactor`(`):%- [@(0.0.255) double]_[* GetResponsivene
ssFactor]()&]
[s3; Returns responsiveness factor.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotScaleX`(`):%- [@(0.0.255) double]_[* GetPlotScaleX]()&]
[s3; Returns responsiveness scale in X axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotScaleY`(`):%- [@(0.0.255) double]_[* GetPlotScaleY]()&]
[s3; Returns responsiveness scale in Y axis&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotScaleAvg`(`):%- [@(0.0.255) double]_[* GetPlotScaleAvg]()&]
[s3; Returns average responsiveness scale&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetColor]([@(0.0.255) const]_[_^Color^ Color][@(0.0.255) `&]_[*@3 color])&]
[s3; Sets [%-*@3 color] .as graph background color.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetTitle`(const Upp`:`:String`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetTitle]([@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[*@3 title])&]
[s3; Sets [%-*@3 title] as graph title.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetTitle`(`):%- [@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[* G
etTitle]()&]
[s3; Returns graph title.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetTitleFont`(const Upp`:`:Font`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetTitleFont]([@(0.0.255) const]_[_^Font^ Font][@(0.0.255) `&]_[*@3 fontTitle])&]
[s3; Sets [%-*@3 fontTitle] as title font.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetTitleColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ ScatterDra
w][@(0.0.255) `&]_[* SetTitleColor]([@(0.0.255) const]_[_^Color^ Color][@(0.0.255) `&]_[*@3 c
olorTitle])&]
[s3; Sets [%-*@3 colorTitle] as title text color.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetTitleColor`(`):%- [_^Upp`:`:Color^ Upp`::Color][@(0.0.255) `&]_[* Get
TitleColor]()&]
[s3; Returns the title color.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetTitleFont`(`):%- [_^Font^ Font][@(0.0.255) `&]_[* GetTitleFont]()&]
[s3; Returns the title font.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLabels`(const Upp`:`:String`&`,const Upp`:`:String`&`,const Upp`:`:String`&`):%- [@(0.0.255) v
oid]_[* SetLabels]([@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[*@3 xLabel], 
[@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[*@3 yLabel], [@(0.0.255) const]_[_^String^ S
tring][@(0.0.255) `&]_[*@3 yLabel2]_`=_`"`")&]
[s3; Sets the labels of the horizontal axis ([%-*@3 xLabel]), vertical 
axis ([%-*@3 yLabel]) and secondary vertical axis ([%-*@3 yLabel2]).&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLabelX`(const Upp`:`:String`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetLabelX]([@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[*@3 xLabel])&]
[s3; Sets [%-*@3 xLabel] as the label of the horizontal axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLabelX`(`):%- [@(0.0.255) const]_[_^String^ String]_`&[* GetLabelX](
)&]
[s3; Returns the label of the horizontal axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLabelY`(const Upp`:`:String`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetLabelY]([@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[*@3 yLabel])&]
[s3; Sets [%-*@3 yLabel] as the label of the vertical axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLabelY`(`):%- [@(0.0.255) const]_[_^String^ String]_`&[* GetLabelY](
)&]
[s3; Returns the label of the vertical axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLabelY2`(const Upp`:`:String`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetLabelY2]([@(0.0.255) const]_[_^String^ String][@(0.0.255) `&]_[*@3 yLabel])&]
[s3; Sets [%-*@3 yLabel] as the label of the secondary vertical axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLabelY2`(`):%- [@(0.0.255) const]_[_^String^ String]_`&[* GetLabelY2
]()&]
[s3; Returns the label of the secondary vertical axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLabelsFont`(const Upp`:`:Font`&`):%- [_^ScatterDraw^ ScatterDraw
][@(0.0.255) `&]_[* SetLabelsFont]([@(0.0.255) const]_[_^Font^ Font][@(0.0.255) `&]_[*@3 font
Labels])&]
[s3; Sets [%-*@3 fontLabels] as the labels font.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLabelsFont`(`):%- [_^Font^ Font]_[* GetLabelsFont]()&]
[s3; Returns labels font.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLabelsColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ ScatterDr
aw][@(0.0.255) `&]_[* SetLabelsColor]([@(0.0.255) const]_[_^Color^ Color][@(0.0.255) `&]_[*@3 c
olorLabels])&]
[s3; Sets [%-*@3 colorLabels] as the color of the labels.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLabelsColor`(`):%- [_^Upp`:`:Color^ Upp`::Color]_[* GetLabelsColor](
)&]
[s3; Returns labels color.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetPlotAreaMargin`(int`,int`,int`,int`):%- [_^ScatterDraw^ ScatterD
raw][@(0.0.255) `&]_[* SetPlotAreaMargin]([@(0.0.255) int]_[*@3 hLeft], 
[@(0.0.255) int]_[*@3 hRight], [@(0.0.255) int]_[*@3 vTop], [@(0.0.255) int]_[*@3 vBottom])&]
[s3; Sets the left([%-*@3 hLeft]), right ([%-*@3 hRight]), top([%-*@3 vTop]) 
and bottom ([%-*@3 vBottom]) margins of the scatter plots inside 
ScatterDraw control.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetPlotAreaLeftMargin`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetPlotAreaLeftMargin]([@(0.0.255) int]_[*@3 margin])&]
[s3; Sets the left [%-*@3 margin] of scatter plot inside ScatterDraw 
control.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotAreaLeftMargin`(`):%- [@(0.0.255) int]_[* GetPlotAreaLeftMargi
n]()&]
[s3; Returns the plot area left margin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetPlotAreaRightMargin`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetPlotAreaRightMargin]([@(0.0.255) int]_[*@3 margin])&]
[s3; Sets the right [%-*@3 margin] of scatter plot inside ScatterDraw 
control.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotAreaRightMargin`(`):%- [@(0.0.255) int]_[* GetPlotAreaRightMar
gin]()&]
[s3; Returns the plot area right margin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetPlotAreaTopMargin`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetPlotAreaTopMargin]([@(0.0.255) int]_[*@3 margin])&]
[s3; Sets the top [%-*@3 margin] of scatter plot inside ScatterDraw 
control.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotAreaTopMargin`(`):%- [@(0.0.255) int]_[* GetPlotAreaTopMargin](
)&]
[s3; Returns the plot area top margin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetPlotAreaBottomMargin`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetPlotAreaBottomMargin]([@(0.0.255) int]_[*@3 margin])&]
[s3; Sets the bottom [%-*@3 margin] of scatter plot inside ScatterDraw 
control.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotAreaBottomMargin`(`):%- [@(0.0.255) int]_[* GetPlotAreaBottomM
argin]()&]
[s3; Returns the plot area bottom margin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetPlotAreaColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ Scatter
Draw][@(0.0.255) `&]_[* SetPlotAreaColor]([@(0.0.255) const]_[_^Color^ Color][@(0.0.255) `&
]_[*@3 p`_a`_color])&]
[s3; Sets [%-*@3 p`_a`_color] as the plot area background color.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetPlotAreaColor`(`):%- [_^Color^ Color][@(0.0.255) `&]_[* GetPlotAreaC
olor]()&]
[s3; Returns the plot area background color.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetAxisColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ ScatterDraw
][@(0.0.255) `&]_[* SetAxisColor]([@(0.0.255) const]_[_^Color^ Color][@(0.0.255) `&]_[*@3 axi
s`_color])&]
[s3; Sets [%-*@3 axis`_color] as the color of the axis.&]
[s0; -|
@@image:756&406
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&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetAxisWidth`(double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_
[* SetAxisWidth]([@(0.0.255) int]_[*@3 axis`_width])&]
[s3; Sets [%-*@3 axis`_width] as the width of the axis in pixels.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetGridColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ ScatterDraw
][@(0.0.255) `&]_[* SetGridColor]([@(0.0.255) const]_[_^Color^ Color][@(0.0.255) `&]_[*@3 gri
d`_color])&]
[s3; Sets [%-*@3 grid`_color] as the color of the grid.&]
[s0; -|
@@image:737&400
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&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetGridColor`(`):%- [_^Upp`:`:Color^ Color]_`&[* GetGridColor]()&]
[s3; Returns grid color.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetGridWidth`(double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_
[* SetGridWidth]([@(0.0.255) double]_[*@3 grid`_width])&]
[s3; Sets [%-*@3 grid`_width] as the width of the grid.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetGridWidth`(`):%- [@(0.0.255) double]_[* GetGridWidth]()&]
[s3; Returns the grid width.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetGridDash`(const char`*`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetGridDash]([@(0.0.255) const]_[@(0.0.255) char]_`*[*@3 dash])&]
[s3; Sets [%-*@3 dash] as the grid dash style.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetGridDash`(`):%- [@(0.0.255) const]_[@(0.0.255) char]_`*[* GetGridDas
h]()&]
[s3; Gets grid dash style.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetGridWidth`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* S
etGridWidth]([@(0.0.255) int]_[*@3 grid`_width])&]
[s3; Sets [%-*@3 grid`_width] as the width of the grid in pixels.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:ShowVGrid`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* Sho
wVGrid]([@(0.0.255) bool]_[*@3 show])&]
[s3; If [%-*@3 show] is true the vertical grid is shown&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:ShowHGrid`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* Sho
wHGrid]([@(0.0.255) bool]_[*@3 show])&]
[s3; If [%-*@3 show] is true the horizontal grid is shown&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:ShowLegend`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* Sh
owLegend]([@(0.0.255) bool]_[*@3 show]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 show] is true graphs name, color and style will be shown.&]
[s3; [^topic`:`/`/ScatterDraw`/srcdoc`/LegendTable`$en`-us^ Here ]there 
are described legend details.&]
[s0; -|
@@image:1375&500
(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)
&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetShowLegend`(`):%- [@(0.0.255) bool]_[* GetShowLegend]()&]
[s3; Returns true if legend is shown.&]
[s3; [^topic`:`/`/ScatterDraw`/srcdoc`/LegendTable`$en`-us^ Here ]there 
are described legend details.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendPos`(const Upp`:`:Point`&`):%- [_^ScatterDraw^ ScatterDraw
][@(0.0.255) `&]_[* SetLegendPos]([@(0.0.255) const]_[_^Point^ Point]_`&[*@3 pos])&]
[s3; Sets the coordinates ([%-*@3 pos]) of the legend table corner 
relative to the plot corner.&]
[s3; [^topic`:`/`/ScatterDraw`/srcdoc`/LegendTable`$en`-us^ Here ]there 
are described legend details.&]
[s0; -|
@@image:1331&693
(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)
&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendPosX`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* S
etLegendPosX]([@(0.0.255) int]_[*@3 x])&]
[s3; Sets the [%-*@3 x] coordinate.of the legend table corner relative 
to the plot corner.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendPosY`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* S
etLegendPosY]([@(0.0.255) int]_[*@3 y])&]
[s3; Sets the [%-*@3 y] coordinate.of the legend table corner relative 
to the plot corner.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendPos`(`):%- [_^Point^ Point][@(0.0.255) `&]_[* GetLegendPos]()&]
[s3; Returns the pos of the legend corner relative to the plot corner.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendFont`(const Upp`:`:Font`&`):%- [_^ScatterDraw^ ScatterDraw
][@(0.0.255) `&]_[* SetLegendFont]([@(0.0.255) const]_[_^Upp`:`:Font^ Font]_`&[*@3 fnt])&]
[s3; Sets [%-*@3 fnt] as the legend font.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendFont`(`):%- [_^Upp`:`:Font^ Font]_`&[* GetLegendFont]()&]
[s3; Gets legend font.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendNumCols`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetLegendNumCols]([@(0.0.255) int]_[*@3 num])&]
[s3; Sets the number [%-*@3 num] of legend columns.&]
[s3;i150;O0; 3 columns&]
[s3; -|-|
@@image:1875&468
(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)
&]
[s3;i150;O0; 2 columns&]
[s3; -|-|
@@image:1318&543
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&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendNumCols`(`):%- [@(0.0.255) int]_[* GetLegendNumCols]()&]
[s3; Returns the number of legend columns.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendRowSpacing`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetLegendRowSpacing]([@(0.0.255) int]_[*@3 num])&]
[s3; Sets the height ([%-*@3 num]) between legend rows.&]
[s3;i150;O0; num `= 0&]
[s3; -|-|
@@image:775&612
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&]
[s3;i150;O0; num `= 5&]
[s3; -|-|
@@image:731&737
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&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendRowSpacing`(`):%- [@(0.0.255) int]_[* GetLegendRowSpacing]()
&]
[s3; Returns the height between legend rows.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:LEGEND`_TOP:%- [@(0.0.255) enum]_LEGEND`_POS_[* LEGEND`_TOP]&]
[s3; 
@@image:1800&325
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&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:LEGEND`_ANCHOR`_LEFT`_TOP:%- [@(0.0.255) enum]_LEGEND`_POS_[* LEGEND`_
ANCHOR`_LEFT`_TOP]&]
[s3; 
@@image:987&843
(A54AhwAAAAAAAAAAAHic7Z09kqM6FEZZFwtiHbME9jB7IO64c0dOPGnnXS/k6QeJi6QrG1vC+Oo7RU1hLGOGgz4EQu55BgAAAAAAAAAAAAAAAABAw1zHvnP043WzaJjevXXgebTGjUG/IHoHfBShP/Iaaj+aNY1tFk+DC+XtPPhUjGFVQ5VNX09hVgbWI1tnO3BKHjar6q6zyZ9n1Qr/A6X5a9j7qUe82gr6SNsYZs8D45Ncza656xby51iYPQ936+wuYLYGldIYZt8OzAIKzEoFZs8P0lgqMAsoMCsVmD0/SGOpwCygwKxUYPb8II2lArOAArNSOY/ZQx4O+gyCPfPpadw99FCWfGBWKq/sQ5g9MzArlWbTuOv+qansZp+KBs1ap3Qqu/HzpL5ime6PR7vOfTfvHgJjviL/qQbTuK5ZY2py82OFoYZjP3fDPMDsskI/hVrdND8xJVC1qZ8PGIX2hFl5abzVUdJsUq7a5/24XWQqMs1nVWYYlpero6iYrp7mZTxKBmbdCvNyi2m1WCNex2phWqRT+/7dsNikU5cDaZxaeeUWlMVUQC2X1EQ9GVnUyzIfFzNLwuo/bz/F06BZ9xXVr3qu4yooSFPObNzg0itBGu8xW4tp3eEqk+PstSTMJk/QBiU3Xg6zxzNsg1dDk5ZL47gYc13sm1V64tvhzaaxeGBWKkhjqcCsVJDGUoFZqSCNpdKa2T9dx01l/wtvp7U0PsLsNES3Knj03cO93X5Rn1AKmC1sdrlZ7OZr/MjvNC5Hgj6E2IOnlTR29+NyZot046m9zXXPlCfXzd+EWeKisNmE3NQjSrbDZs1nY2To3d1hZycsdi91k10FJfbhx5idD6yzGmdk0UHcqRqtpVP7/t2omPo395PtTLdfiX34SWYdx7WNbQWcaE30D77QFHXzcTG7hOurG1K9toQm0phw6FXP2DtBQTuHM5tqDtlOuylaeO8vMMBsYbO+4bo+SRyfeVNmM88QK490+WONtNbSuD7TNngNNGm5NI6Lpa+Lg+el2FMtzEqltTRuB5iVCtJYKjArFaSxVFo22/2TfAw0m8Z2TE/ZzT4VjZutI3eIblVk8GNEnlh/rmu/zTSmQ/HKbvn2lu5Yo6f2uvbI5noFGjEbdr+VGT6bPCruDqUqSK6fr4U0vqP1BbMpufpub/SgQzB4Xd/TH5bxtfT+flAs7IkPyHfQtmB2DuSW+zEDZvMXI04HdTcY6dS+fzcuNvDPUmQ7A5h9KDKN1zUzP1NRdvsN9jdg5mBYpNFBJfr5uJhekn2uEedZsmbuB0gqyHVpGXeTc2YTmmynHdMGzvXUNpLGB+Lbw/5J4vjMmzSbPEGbFc09U3NzrTWYLc3aX07OgzRpuTSOiyWvi8NefI7W0rgdYFYqSGOpwKxUkMZSgVmpII2lArNSQRqXp/qYd/8tuQ/CbGEOGPNuVm2G3+4z+xxlt7u82Zf6YPkp5pgx73qA7Z3K3oTZSlrTcg8Y837V/fb3/mJBK2l8WJ3VVB7zviyH2TdRa8y779PbbfY5yu4VCWbnSmPep21u7PhtGZh9iQPGvCdWkgBpXJraY97pF8FsiyCNpQKzUjkojaeh63rXCriOfWeoMnIBZi2HmL2OQ987s9qrUepnHtkqmN3LEWk8Df04DotZ4pNXC7OvU9+s8mda8ItZPxPMF9sqmLVUT2Nlz1zBEbO+nubM+m2g2/PIPMxa4n1IeXx/MqtXFTaoqmyd7bZ8sNmfy9fC5edu4d/b99f37XfP+vVHHPxHX9mHD5jVbWKC8uhdCz3P6t3uhP7ebvfVvvYVPEddz67V8y1t4686JDZdVdidVXA3z5qtc3eRBq+7nk2fY9Nb9YrZSloZuTqLQ7drflojWv/l8m1ekkMhLBa+jtaWO4YauW98lFbLsuudDuLu52LmqH3/blRMLchWTfM1bAncXayF3++0wbP4ppHt5uNiZkk22nPRD7P1WM6HidMiZzZRAa1wpmbuMysyjY/j5+Z2tb+eic+8KbPJE7RbUXI5+wEDzJZmvZpdaxpNWi6N42Lp62JSKJvUSGOpwKxUkMZSgVmpiExjYBFmFnB8ehoDDpgFFJiVCsyeH6SxVGAWUGBWKjB7fp5L4/8BKV+UlQ==)
&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:LEGEND`_ANCHOR`_RIGHT`_TOP:%- [@(0.0.255) enum]_LEGEND`_POS_[* LEGEND
`_ANCHOR`_RIGHT`_TOP]&]
[s3; 
@@image:806&775
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&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:LEGEND`_ANCHOR`_LEFT`_BOTTOM:%- [@(0.0.255) enum]_LEGEND`_POS_[* LEGE
ND`_ANCHOR`_LEFT`_BOTTOM]&]
[s3; 
@@image:1018&1025
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&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:LEGEND`_ANCHOR`_RIGHT`_BOTTOM:%- [@(0.0.255) enum]_LEGEND`_POS_[* LEG
END`_ANCHOR`_RIGHT`_BOTTOM]&]
[s3; 
@@image:875&950
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&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendAnchor`(LEGEND`_POS`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetLegendAnchor]([@(0.0.255) LEGEND`_POS]_[*@3 anchor])&]
[s3; Sets with [%-*@3 anchor ]the legend table position:&]
[s3;i150;O0; LEGEND`_TOP,&]
[s3;i150;O0; LEGEND`_ANCHOR`_LEFT`_TOP, &]
[s3;i150;O0; LEGEND`_ANCHOR`_RIGHT`_TOP, &]
[s3;i150;O0; LEGEND`_ANCHOR`_LEFT`_BOTTOM, &]
[s3;i150;O0; LEGEND`_ANCHOR`_RIGHT`_BOTTOM&]
[s0; &]
[s0;= 
@@image:2505&1695
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)
&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendAnchor`(`):%- [@(0.0.255) LEGEND`_POS]_[* GetLegendAnchor]()
&]
[s3; Returns the legend table position.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendFillColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ Scatt
erDraw][@(0.0.255) `&]_[* SetLegendFillColor]([@(0.0.255) const]_[_^Color^ Color]_`&[*@3 co
lor])&]
[s3; Sets with [%-*@3 color] the legend background color.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetLegendBorderColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ Sca
tterDraw][@(0.0.255) `&]_[* SetLegendBorderColor]([@(0.0.255) const]_[_^Color^ Color]_`&[*@3 c
olor])&]
[s3; Sets with [%-*@3 color] the legend border color.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendFillColor`(`):%- [_^Color^ Color][@(0.0.255) `&]_[* GetLegendF
illColor]()&]
[s3; Returns the legend background color&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegendBorderColor`(`):%- [_^Color^ Color][@(0.0.255) `&]_[* GetLegen
dBorderColor]()&]
[s3; Returns the legend border color.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetMode`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* SetMod
e]([@(0.0.255) int]_[*@3 `_mode]_`=_MD`_ANTIALIASED)&]
[s3; Sets the drawing [%-*@3 mode]. Values are:&]
[s0;l288;i150;O0; [2 Drawn with Draw]&]
[s0;l448;i150;O0;%- [2 MD`_DRAW]&]
[s0;l288;i150;O0;%- [2 Drawn with PAINTER]&]
[s0;l448;i150;O0;%- [2 MD`_ANTIALIASED-|`= MODE`_ANTIALIASED,]&]
[s0;l448;i150;O0;%- [2 MD`_NOAA        -|`= MODE`_NOAA,]&]
[s0;l448;i150;O0;%- [2 MD`_SUBPIXEL-|`= MODE`_SUBPIXEL]&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetMode`(`):%- [@(0.0.255) int]_[* GetMode]()&]
[s3; Returns the drawing mode as documented in SetMode().&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:ZoomToFit`(bool`,bool`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* ZoomToFit]([@(0.0.255) bool]_[*@3 horizontal], [@(0.0.255) bool]_[*@3 vertical]_`=_[@(0.0.255) f
alse], [@(0.0.255) double]_[*@3 factor]_`=_[@3 0])&]
[s3; Rescales the x axis if [%-*@3 horizontal ]is true and y axis if 
[%-*@3 vertical] is true to show all graphs data.on the control. 
[%-*@3 factor] indicates the fit factor (0 fills the control) .&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Zoom`(double`,bool`,bool`):%- [@(0.0.255) void]_[* Zoom]([@(0.0.255) do
uble]_[*@3 scale], [@(0.0.255) bool]_[*@3 hor]_`=_[@(0.0.255) true], 
[@(0.0.255) bool]_[*@3 ver]_`=_[@(0.0.255) true])&]
[s3; Zooms plots by [%-*@3 scale] factor horizontally if [%-*@3 hor] 
is true and vertically if [%-*@3 ver] is true.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Scroll`(double`,double`):%- [@(0.0.255) void]_[* Scroll]([@(0.0.255) do
uble]_[*@3 factorX], [@(0.0.255) double]_[*@3 factorY])&]
[s3; Scrolls plots horizontally by [%-*@3 factorX] and vertically by 
factor [%-*@3 factorY].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:LinkedWith`(ScatterDraw`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* LinkedWith]([_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[*@3 ctrl])&]
[s3; Links zoom and scroll events with [%-*@3 ctrl].&]
[s3; All zoom and scroll in either of both controls will affect the 
other control.&]
[s1; &]
[s3; 
@@image:1708&1310
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)
&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Unlinked`(`):%- [@(0.0.255) void]_[* Unlinked]()&]
[s3; Unlinks series.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:AddSurf`(Upp`:`:DataSourceSurf`&`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* AddSurf]([_^Upp`:`:DataSourceSurf^ DataSourceSurf]_`&[*@3 surf])&]
[s3; Adds the 2D surface [%-*@3 surf].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetSurfMinZ`(double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_
[* SetSurfMinZ]([@(0.0.255) double]_[*@3 val])&]
[s3; Sets [%-*@3 val] as the minimum 2D surface Z value.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetSurfMinZ`(`)const:%- [@(0.0.255) double]_[* GetSurfMinZ]()_[@(0.0.255) c
onst]&]
[s3; Returns the minimum 2D surface Z value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetSurfMaxZ`(double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_
[* SetSurfMaxZ]([@(0.0.255) double]_[*@3 val])&]
[s3; Sets [%-*@3 val] as the maximum 2D surface Z value.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetSurfMaxZ`(`)const:%- [@(0.0.255) double]_[* GetSurfMaxZ]()_[@(0.0.255) c
onst]&]
[s3; Returns the maximum 2D surface Z value.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:ZoomToFitZ`(`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* ZoomTo
FitZ]()&]
[s3; Calculates the minimum and maximum 2D surface Z values and sets 
them as minimum ans maximum values to be plotted.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:ShowRainbowPalette`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* ShowRainbowPalette]([@(0.0.255) bool]_[*@3 show]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 show] is true 2D surface rainbow color palette will 
be shown.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetShowRainbowPalette`(`):%- [@(0.0.255) bool]_[* GetShowRainbowPalet
te]()&]
[s3; Returns true if 2D surface rainbow color palette is shown.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetRainbowPalettePos`(const Upp`:`:Point`&`):%- [_^ScatterDraw^ Sca
tterDraw][@(0.0.255) `&]_[* SetRainbowPalettePos]([@(0.0.255) const]_[_^Upp`:`:Point^ Poi
nt]_`&[*@3 p])&]
[s3; Sets [%-*@3 p] as the position of 2D surface rainbow color palette.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetRainbowPalettePos`(`):%- [_^Upp`:`:Point^ Point][@(0.0.255) `&]_[* G
etRainbowPalettePos]()&]
[s3; Returns the 2D surface rainbow color palette position.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetRainbowPaletteSize`(const Upp`:`:Size`&`):%- [_^ScatterDraw^ Sca
tterDraw][@(0.0.255) `&]_[* SetRainbowPaletteSize]([@(0.0.255) const]_[_^Upp`:`:Size^ Siz
e]_`&[*@3 sz])&]
[s3; Sets  [%-*@3 sz] as the Size of 2D surface rainbow color palette.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetRainbowPaletteSize`(`):%- [_^Upp`:`:Size^ Size][@(0.0.255) `&]_[* Ge
tRainbowPaletteSize]()&]
[s3; Returns the 2D surface rainbow color palette size.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetZoomStyleX`(ZoomStyle`):%- [_^ScatterDraw^ ScatterDraw]_`&[* SetZo
omStyleX](ZoomStyle_[*@3 style]_`=_TO`_CENTER)&]
[s3; Sets the X zoom [%-*@3 style]. Valid values are:&]
[s3;i150;O0; TO`_CENTER: Zoom is centered to the control center.&]
[s3;i150;O0; FROM`_BASE: Zoom is centered to the X origin.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetZoomStyleY`(ZoomStyle`):%- [_^ScatterDraw^ ScatterDraw]_`&[* SetZo
omStyleY](ZoomStyle_[*@3 style]_`=_TO`_CENTER)&]
[s3; Sets the Y zoom [%-*@3 style]. Valid values are:&]
[s3;i150;O0; TO`_CENTER: Zoom is centered to the control center.&]
[s3;i150;O0; FROM`_BASE: Zoom is centered to the Y origin.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetRange`(double`,double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetRange]([@(0.0.255) double]_[*@3 rx], [@(0.0.255) double]_[*@3 ry][*  
`= Null], [@(0.0.255) double]_[*@3 ry2][*  `= Null])&]
[s3; Sets the horizontal ([%-*@3 rx]), vertical ([%-*@3 ry]) and secondary 
vertical ([%-*@3 ry2]) axis ranges.&]
[s3; Range is the visible with of the plot in series units (not in 
pixels).&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetXRange`(`)const:%- [@(0.0.255) double]_[* GetXRange]()[@(0.0.255) co
nst]&]
[s3; Returns the x axis range.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYRange`(`)const:%- [@(0.0.255) double]_[* GetYRange]()[@(0.0.255) co
nst]&]
[s3; Returns the y axis range.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetY2Range`(`)const:%- [@(0.0.255) double]_[* GetY2Range]()[@(0.0.255) c
onst]&]
[s3; Returns the secondary y axis range.&]
[s1;%- &]
[s6;%- &]
[s5;:Upp`:`:ScatterDraw`:`:SetMajorUnits`(double`,double`,double`):%- [_^Upp`:`:ScatterDraw^ S
catterDraw]_`&[* SetMajorUnits]([@(0.0.255) double]_[*@3 ux], [@(0.0.255) double]_[*@3 uy],
 [@(0.0.255) double]_[*@3 uy2])&]
[s3; Sets the horizontal ([%-*@3 ux]), vertical ([%-*@3 uy]) or secondary 
vertical ([%-*@3 uy2]) distance between grid lines.&]
[s1; &]
[s6; &]
[s5;:ScatterDraw`:`:SetMajorUnitsNum`(int`,int`):%- [_^ScatterDraw^ ScatterDraw]_`&[* Set
MajorUnitsNum]([@(0.0.255) int]_[*@3 nx], [@(0.0.255) int]_[*@3 ny])&]
[s3; Sets the horizontal ([%-*@3 nx]) and vertical ([%-*@3 ny]) number 
of grid lines.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetMajorUnitsX`(`):%- [@(0.0.255) double]_[* GetMajorUnitsX]()&]
[s3; Returns the distance between grid lines in X axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetMajorUnitsY`(`):%- [@(0.0.255) double]_[* GetMajorUnitsY]()&]
[s3; Returns the distance between grid lines in Y axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetMajorUnitsY2`(`):%- [@(0.0.255) double]_[* GetMajorUnitsY2]()&]
[s3; Returns the distance between grid lines in secondary Y axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetMinUnits`(double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetMinUnits]([@(0.0.255) double]_[*@3 ux], [@(0.0.255) double]_[*@3 uy])&]
[s3; Sets with [%-*@3 ux] and [%-*@3 uy] the first X and Y axis grid 
lines location in real units from the lower left corner. If either 
[%-*@3 ux] or [%-*@3 uy] is not visible, it ensures that values obtained 
adding or substracting SetMajorUnits() values will be visible.&]
[s3; If [^topic`:`/`/ScatterDraw`/src`/ScatterDraw`_en`-us`#ScatterDraw`:`:SetGridLinesX^ S
etGridLinesX ]or [^topic`:`/`/ScatterDraw`/src`/ScatterDraw`_en`-us`#ScatterDraw`:`:SetGridLinesY^ S
etGridLinesY ]callback is set, the values of X or Y axis respectively 
will not be used.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetXMinUnit`(`)const:%- [@(0.0.255) double]_[* GetXMinUnit]_()_[@(0.0.255) c
onst]&]
[s3; Returns the first X axis grid line location.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYMinUnit`(`)const:%- [@(0.0.255) double]_[* GetYMinUnit]_()_[@(0.0.255) c
onst]&]
[s3; Returns the first Y axis grid line location.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYMinUnit2`(`)const:%- [@(0.0.255) double]_[* GetYMinUnit2]_()_[@(0.0.255) c
onst]&]
[s3; Returns the first secondary Y axis grid line location`-&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetXYMin`(double`,double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetXYMin]([@(0.0.255) double]_[*@3 xmin],[@(0.0.255) double]_[*@3 ymin],[@(0.0.255) doub
le]_[*@3 ymin2]_`=_[@3 0])&]
[s3; Sets with [%-*@3 xmin], [%-*@3 ymin] and [%-*@3 ymin2] the X, Y and 
secondary Y axis origin.&]
[s1; &]
[s6; &]
[s5;:ScatterDraw`:`:GetXMin`(`)const:%- [@(0.0.255) double]_[* GetXMin]_()_[@(0.0.255) cons
t]&]
[s3; Returns the X axis origin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYMin`(`)const:%- [@(0.0.255) double]_[* GetYMin]_()_[@(0.0.255) cons
t]&]
[s3; Returns the Y axis origin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYMin2`(`)const:%- [@(0.0.255) double]_[* GetYMin2]_()_[@(0.0.255) co
nst]&]
[s3; Returns the secondary Y axis origin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetY2Min`(`)const:%- [@(0.0.255) double]_[* GetY2Min]_()_[@(0.0.255) co
nst]&]
[s3; Returns the secondary Y axis origin.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetXMax`(`)const:%- [@(0.0.255) double]_[* GetXMax]()_[@(0.0.255) const
]&]
[s3; Returns the X axis end.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYMax`(`)const:%- [@(0.0.255) double]_[* GetYMax]()_[@(0.0.255) const
]&]
[s3; Returns the Y axis end.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetY2Max`(`)const:%- [@(0.0.255) double]_[* GetY2Max]()_[@(0.0.255) con
st]&]
[s3; Returns the secondary Y axis end.&]
[s1;%- &]
[s6; &]
[s5;:ScatterDraw`:`:AddSeries`(double`*`,int`,double`,double`):%- [_^ScatterDraw^ Scatt
erDraw]_`&[* AddSeries]([@(0.0.255) double]_`*[*@3 yData], [@(0.0.255) int]_[*@3 numData], 
[@(0.0.255) double]_[*@3 x0]_`=_[@3 0], [@(0.0.255) double]_[*@3 deltaX]_`=_[@3 1])&]
[s3; Adds a new series stored in [%-*@3 yData ]C array with [%-*@3 numData] 
where the [%-*@3 yData`[0`] ]X value is [%-*@3 x0] and the horizontal 
distance between [%-*@3 yData] values is [%-*@3 deltaX].&]
[s3; [%-*@3 yData] has to be stored in a permanent location during 
ScatterDraw life to avoid memory problems.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:AddSeries`(double`*`,double`*`,int`):%- [_^ScatterDraw^ ScatterDraw
]_`&[* AddSeries]([@(0.0.255) double]_`*[*@3 xData], [@(0.0.255) double]_`*[*@3 yData], 
[@(0.0.255) int]_[*@3 numData])&]
[s3; Adds a new series stored in [%-*@3 xData] and [%-*@3 yData] C arrays 
with [%-*@3 numData].&]
[s3; [%-*@3 xData] and [%-*@3 yData] has to be stored in a permanent 
location during ScatterDraw life to avoid memory problems.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:AddSeries`(Upp`:`:Vector`<double`>`&`,Upp`:`:Vector`<double`>`&`):%- [_^ScatterDraw^ S
catterDraw]_`&[* AddSeries]([_^Vector^ Vector]<[@(0.0.255) double]>_`&[*@3 xData], 
[_^Vector^ Vector]<[@(0.0.255) double]>_`&[*@3 yData])&]
[s3; Adds a new series stored in [%-*@3 xData] and [%-*@3 yData] [%-_^Vector^ Vector][%- <][%-@(0.0.255) d
ouble][%- >].&]
[s3; [%-*@3 xData] and [%-*@3 yData] has to be stored in a permanent 
location during ScatterDraw life to avoid memory problems.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:AddSeries`(Upp`:`:Array`<double`>`&`,Upp`:`:Array`<double`>`&`):%- [_^ScatterDraw^ S
catterDraw]_`&[* AddSeries]([_^Array^ Array]<[@(0.0.255) double]>_`&[*@3 xData], 
[_^Array^ Array]<[@(0.0.255) double]>_`&[*@3 yData])&]
[s3; Adds a new series stored in [%-*@3 xData] and [%-*@3 yData] [%-_^Vector^ Array][%- <][%-@(0.0.255) d
ouble][%- >].&]
[s3; [%-*@3 xData] and [%-*@3 yData] has to be stored in a permanent 
location during ScatterDraw life to avoid memory problems.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:AddSeries`(Upp`:`:Vector`<Upp`:`:Pointf`>`&`):%- [_^ScatterDraw^ Sc
atterDraw]_`&[* AddSeries]([_^Vector^ Vector]<[_^Pointf^ Pointf]>_`&[*@3 points])&]
[s3; Adds a new series stored in [%-*@3 points] [%-_^Vector^ Vector][%- <][%-_^Pointf^ Pointf
][%- >].&]
[s3; [%-*@3 points] has to be stored in a permanent location during 
ScatterDraw life to avoid memory problems.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:AddSeries`(Upp`:`:Array`<Upp`:`:Pointf`>`&`):%- [_^ScatterDraw^ Sca
tterDraw]_`&[* AddSeries]([_^Array^ Array]<[_^Pointf^ Pointf]>_`&[*@3 points])&]
[s3; Adds a new series stored in [%-*@3 points] [%-_^Vector^ Array][%- <][%-_^Pointf^ Pointf][%- >
].&]
[s3; [%-*@3 points] has to be stored in a permanent location during 
ScatterDraw life to avoid memory problems.&]
[s1; &]
[s6;%- &]
[s5;:Upp`:`:ScatterDraw`:`:AddSeries`(Upp`:`:Vector`<Upp`:`:Vector`<double`>`>`&`):%- [_^Upp`:`:ScatterDraw^ S
catterDraw]_`&[* AddSeries]([_^Upp`:`:Vector^ Vector]<[_^Upp`:`:Vector^ Vector]_<[@(0.0.255) d
ouble]>_>_`&[*@3 data])&]
[s3; Adds a new series stored in [%-*@3 data] .&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Opacity`(double`):%- [_^ScatterDraw^ ScatterDraw]_`&[* Opacity]([@(0.0.255) d
ouble]_[*@3 opacity]_`=_[@3 1])&]
[s3; Sets the series [%-*@3 opacity] .from 1 (opaque) to 0 (transparent/invisible).&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Legend`(const Upp`:`:String`):%- [_^ScatterDraw^ ScatterDraw]_`&[* Le
gend]([@(0.0.255) const]_[_^String^ String]_[*@3 legend])&]
[s3; Sets the series [%-*@3 legend].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Legend`(int`,const Upp`:`:String`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* Legend]([@(0.0.255) int]_[*@3 index], [@(0.0.255) const]_[_^String^ String]_[*@3 legend])
&]
[s3; Sets the [%-*@3 legend] for [%-*@3 index] series.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetLegend`(int`):%- [@(0.0.255) const]_[_^String^ String][@(0.0.255) `&
]_[* GetLegend]([@(0.0.255) int]_[*@3 index])&]
[s3; Returns the legend for [%-*@3 index] series.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Units`(const Upp`:`:String`,const Upp`:`:String`):%- [_^ScatterDraw^ S
catterDraw]_`&[* Units]([@(0.0.255) const]_[_^String^ String]_[*@3 unitsY], 
[@(0.0.255) const]_[_^String^ String]_[*@3 unitsX]_`=_`"`")&]
[s3; Sets the series units for Y axis ([%-*@3 unitsY]) and X axis ([%-*@3 unitsX]).&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Units`(int`,const Upp`:`:String`,const Upp`:`:String`):%- [_^ScatterDraw^ S
catterDraw]_`&[* Units]([@(0.0.255) int]_[*@3 index], [@(0.0.255) const]_[_^String^ String]_
[*@3 unitsY], [@(0.0.255) const]_[_^String^ String]_[*@3 unitsX]_`=_`"`")&]
[s3; Sets the [%-*@3 index] series units for Y axis ([%-*@3 unitsY]) 
and X axis ([%-*@3 unitsX]).&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetUnitsX`(int`):%- [@(0.0.255) const]_[_^String^ String]_[* GetUnitsX](
[@(0.0.255) int]_[*@3 index])&]
[s3; Returns the X axis units for [%-*@3 index] series.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetUnitsY`(int`):%- [@(0.0.255) const]_[_^String^ String]_[* GetUnitsY](
[@(0.0.255) int]_[*@3 index])&]
[s3; Returns the Y axis units for [%-*@3 index] series.&]
[s1; &]
[s6;%- &]
[s5;:Upp`:`:ScatterDraw`:`:LegendLine`(bool`):%- [_^Upp`:`:ScatterDraw^ ScatterDraw]_`&
[* LegendLine]([@(0.0.255) bool]_[*@3 b]_`=_[@(0.0.255) true])&]
[s3; Draw line in series legend even if the series does not plot 
lines (NoPlot).&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:IsValid`(int`)const:%- [@(0.0.255) bool]_[* IsValid]([@(0.0.255) int]_[*@3 i
ndex])_[@(0.0.255) const]&]
[s3; Returns true if [%-*@3 index] is between 0 and GetCount()`-1..&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDrawXReticle`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetDrawXReticle]([@(0.0.255) bool]_[*@3 set]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 set] is true the small lines and texts beside every 
X grid line are shown.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDrawYReticle`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetDrawYReticle]([@(0.0.255) bool]_[*@3 set]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 set] is true the small lines and texts to the left of 
every Y grid line are shown.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDrawY2Reticle`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetDrawY2Reticle]([@(0.0.255) bool]_[*@3 set]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 set] is true the small lines and texts to the right 
of every Y grid line are shown.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetDrawXReticle`(`):%- [@(0.0.255) bool]_[* GetDrawXReticle]()&]
[s3; Returns true if the the small lines and texts beside every X 
grid line are shown.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetDrawYReticle`(`):%- [@(0.0.255) bool]_[* GetDrawYReticle]()&]
[s3; Returns true if the the small lines and texts to the left of 
every Y grid line are shown.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetDrawY2Reticle`(`):%- [@(0.0.255) bool]_[* GetDrawY2Reticle]()&]
[s3; Returns true if the the small lines and texts to the right of 
every Y grid line are shown.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetReticleFont`(const Upp`:`:Font`&`):%- [_^ScatterDraw^ ScatterDra
w]_`&[* SetReticleFont]([@(0.0.255) const]_[_^Upp`:`:Font^ Font]_`&[*@3 fnt])&]
[s3; Sets [%-*@3 fnt] as reticle font..&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetReticleFont`(`):%- [_^Upp`:`:Font^ Font]_`&[* GetReticleFont]()&]
[s3; Gets reticle font.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetReticleColor`(const Upp`:`:Color`&`):%- [_^ScatterDraw^ ScatterD
raw]_`&[* SetReticleColor]([@(0.0.255) const]_[_^Upp`:`:Color^ Color]_`&[*@3 col])&]
[s3; Sets [%-*@3 col] as reticle color.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetReticleColor`(`):%- [_^Upp`:`:Color^ Color]_`&[* GetReticleColor](
)&]
[s3; Gets reticle color&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDataPrimaryY`(int`,bool`):%- [@(0.0.255) void]_[* SetDataPrimaryY](
[@(0.0.255) int]_[*@3 index], [@(0.0.255) bool]_[*@3 primary]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 primary] is true, [%-*@3 index] series is considered to 
be a primary series so it uses the left vertical axis. If false 
it uses the right vertical axis. In this case right vertical 
grid is activated.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDataPrimaryY`(bool`):%- [_^ScatterDraw^ ScatterDraw]_`&[* SetDataP
rimaryY]([@(0.0.255) bool]_[*@3 primary])&]
[s3; If [%-*@3 primary] is true, last added series is considered to 
be a primary series so it uses the left vertical axis. &]
[s3; If false it uses the right vertical axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDataSecondaryY`(int`,bool`):%- [@(0.0.255) void]_[* SetDataSeconda
ryY]([@(0.0.255) int]_[*@3 index], [@(0.0.255) bool]_[*@3 secondary])&]
[s3; Opposed to SetDataPrimaryY() if [%-*@3 secondary] is true, [%-*@3 index] 
series is considered to be a secondary series so it uses the 
right vertical axis. In this case right vertical grid is activated.&]
[s3; If false it uses the left vertical axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetDataSecondaryY`(bool`):%- [_^ScatterDraw^ ScatterDraw]_`&[* SetDat
aSecondaryY]([@(0.0.255) bool]_[*@3 secondary])&]
[s3; Opposed to SetDataPrimaryY() if [%-*@3 secondary] is true, last 
added series is considered to be a secondary series so it uses 
the right vertical axis. In this case right vertical grid is 
activated.&]
[s3; If false it uses the left vertical axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:IsDataPrimaryY`(int`):%- [@(0.0.255) bool]_[* IsDataPrimaryY]([@(0.0.255) i
nt]_[*@3 index])&]
[s3; Returns true if [%-*@3 index] series is primary..&]
[s1; &]
[s6; &]
[s5;:ScatterDraw`:`:SetSequentialX`(int`,bool`):%- [@(0.0.255) void]_[* SetSequentialX]([@(0.0.255) i
nt]_[*@3 index], [@(0.0.255) bool]_[*@3 sequential]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 sequential] is true, [%-*@3 index] series is considered 
to be sequential so all data points are ordered following the 
X axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetSequentialX`(bool`):%- [_^ScatterDraw^ ScatterDraw]_`&[* SetSequen
tialX]([@(0.0.255) bool]_[*@3 sequential]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 sequential] is true the last added series is considered 
to be sequential so all data points are ordered following the 
X axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetSequentialXAll`(bool`):%- [_^ScatterDraw^ ScatterDraw]_`&[* SetSeq
uentialXAll]([@(0.0.255) bool]_[*@3 sequential]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 sequential] is true all series are considered to be 
sequential so all data points are ordered following the X axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetSequentialX`(int`):%- [@(0.0.255) bool]_[* GetSequentialX]([@(0.0.255) i
nt]_[*@3 index])&]
[s3; Returns true if [%-*@3 index] data series is considered to be 
sequential so all data points are ordered following the X axis.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetSequentialX`(`):%- [@(0.0.255) bool]_[* GetSequentialX]()&]
[s3; Returns true if  the last added series is considered to be sequential 
so all data points are ordered following the X axis.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:Show`(int`,bool`):%- [@(0.0.255) void]_[* Show]([@(0.0.255) int]_[*@3 ind
ex], [@(0.0.255) bool]_[*@3 show]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 show] is true it sets the opacity of [%-*@3 index] data 
series to 1. If false, the opacity will be set to 0.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:IsVisible`(int`):%- [@(0.0.255) bool]_[* IsVisible]([@(0.0.255) int]_[*@3 i
ndex])&]
[s3; Returns true if [%-*@3 index] data series opacity is greater than 
0.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:ShowAll`(bool`):%- [_^ScatterDraw^ ScatterDraw]_`&[* ShowAll]([@(0.0.255) b
ool]_[*@3 show]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 show] is true it sets the opacity of all series to 1. 
If false, the opacity will be set to 0.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:RemoveSeries`(int`):%- [@(0.0.255) bool]_[* RemoveSeries]([@(0.0.255) i
nt]_[*@3 index])&]
[s3; Remove [%-*@3 index] data series from control. It does not delete 
the series data.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:RemoveAllSeries`(`):%- [_^ScatterDraw^ ScatterDraw]_`&[* RemoveAllSer
ies]()&]
[s3; Remove all data series from control. It does not delete the 
series data.&]
[s1;%- &]
[s6;%- &]
[s5;:Upp`:`:ScatterDraw`:`:SwapSeries`(int`,int`):%- [@(0.0.255) bool]_[* SwapSeries]([@(0.0.255) i
nt]_[*@3 i1], [@(0.0.255) int]_[*@3 i2])&]
[s3; Swaps the series [%-*@3 i1] with [%-*@3 i2].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetImage`(`):%- [_^Upp`:`:Image^ Image]_[* GetImage]()&]
[s3; Returns the scatter plot as an Image.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:Id`(int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* Id]([@(0.0.255) i
nt]_[*@3 id])&]
[s3; Sets the [%-*@3 id] of the last added data series.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Id`(int`,int`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* Id]([@(0.0.255) i
nt]_[*@3 index], [@(0.0.255) int]_[*@3 id])&]
[s3; Sets the [%-*@3 id] if [%-*@3 index] data series.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetId`(int`):%- [@(0.0.255) int]_[* GetId]([@(0.0.255) int]_[*@3 index])&]
[s3; Returns the id of [%-*@3 index] data series.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetDrawing`(`):%- [_^Drawing^ Drawing]_[* GetDrawing]()&]
[s3; Returns the control Drawing.&]
[s1;%- &]
[s6; &]
[s5;:ScatterDraw`:`:GetXByPoint`(double`):%- [@(0.0.255) double]_[* GetXByPoint]([@(0.0.255) d
ouble]_[*@3 x])&]
[s3; Returns the X axis value of pixel [%-*@3 x] in data set units.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYByPoint`(double`):%- [@(0.0.255) double]_[* GetYByPoint]([@(0.0.255) d
ouble]_[*@3 y])&]
[s3; Returns theY axis value of pixel [%-*@3 y] in data set units.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetY2ByPoint`(double`):%- [@(0.0.255) double]_[* GetY2ByPoint]([@(0.0.255) d
ouble]_[*@3 y])&]
[s3; Returns the secondary Y axis value of pixel [%-*@3 y] in data 
set units.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetXPointByValue`(double`):%- [@(0.0.255) double]_[* GetXPointByValue
]([@(0.0.255) double]_[*@3 x])&]
[s3; Returns the X axis pixel value of [%-*@3 x] in data set units.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetYPointByValue`(double`):%- [@(0.0.255) double]_[* GetYPointByValue
]([@(0.0.255) double]_[*@3 y])&]
[s3; Returns the Y axis pixel value of [%-*@3 y] in data set units.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SaveAsMetafile`(const char`*`)const:%- [@(0.0.255) void]_[* SaveAsMet
afile]([@(0.0.255) const]_[@(0.0.255) char`*]_[*@3 file])_[@(0.0.255) const]&]
[s0;%- [*@(28.0.200)1 Windows]&]
[s3; Saves the control as a windows metafile in [%-*@3 file].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetMinZoom`(double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetMinZoom]([@(0.0.255) double]_[*@3 x], [@(0.0.255) double]_[*@3 y]_`=_`-[@3 1])&]
[s3; Sets [%-*@3 x] and [%-*@3 y] as the minimum visible range. If [%-*@3 y] 
is `-1, only [%-*@3 x] zoom is considered.&]
[s3; Equal to SetMinRange().&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetMaxZoom`(double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetMaxZoom]([@(0.0.255) double]_[*@3 x], [@(0.0.255) double]_[*@3 y]_`=_`-[@3 1])&]
[s3; Sets [%-*@3 x] and [%-*@3 y] as the maximum visible range. If [%-*@3 y] 
is `-1, only [%-*@3 x] zoom is considered.&]
[s3; Equal to SetMaxRange().&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetMinRange`(double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetMinRange]([@(0.0.255) double]_[*@3 x], [@(0.0.255) double]_[*@3 y]_`=_`-[@3 1])&]
[s3; Sets [%-*@3 x] and [%-*@3 y] as the minimum visible range. If [%-*@3 y] 
is `-1, only [%-*@3 x] zoom is considered.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetMaxRange`(double`,double`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&
]_[* SetMaxRange]([@(0.0.255) double]_[*@3 x], [@(0.0.255) double]_[*@3 y]_`=_`-[@3 1])&]
[s3; Sets [%-*@3 x] and [%-*@3 y] as the maximum visible range. If [%-*@3 y] 
is `-1, only [%-*@3 x] zoom is considered.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:SetFastViewX`(bool`):%- [_^ScatterDraw^ ScatterDraw][@(0.0.255) `&]_[* S
etFastViewX]([@(0.0.255) bool]_[*@3 set]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 set] is true, only one point per screen pixel will be 
drawn. The Y value of this point will be the average of all data 
points between two pixels.&]
[s3; In case of big datasets and zoom in with big detail, this option 
can accelerate strongly the control refresh.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetFastViewX`(`):%- [@(0.0.255) bool]_[* GetFastViewX]()&]
[s3; Returns true if FastViewX has been set.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetCount`(`):%- [@(0.0.255) int]_[* GetCount]()&]
[s3; Returns the number of series loaded.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:IsEmpty`(`):%- [@(0.0.255) bool]_[* IsEmpty]()&]
[s3; Returns true if there are no series.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:NoPlot`(`):%- [_^ScatterDraw^ ScatterDraw]_`&[* NoPlot]()&]
[s3; Set to avoid showing the series. However the mar can be shown.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Stacked`(bool`):%- [_^ScatterDraw^ ScatterDraw]_`&[* Stacked]([@(0.0.255) b
ool]_[*@3 stacked]_`=_[@(0.0.255) true])&]
[s3; If [%-*@3 stacked] is true it is considered that all series are 
stacked.&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:NoMark`(`):%- [_^ScatterDraw^ ScatterDraw]_`&[* NoMark]()&]
[s3; Set to avoid showing mark in series. However the series can 
be shown.&]
[s1;%- &]
[s6;%- &]
[s5;:ScatterDraw`:`:Stroke`(int`,double`,Upp`:`:Color`):%- [_^ScatterDraw^ ScatterDraw]_
`&[* Stroke]([@(0.0.255) int]_[*@3 index], [@(0.0.255) double]_[*@3 thickness], 
[_^Upp`:`:Color^ Color]_[*@3 color])&]
[s3; Sets [%-*@3 index] series [%-*@3 thickness] and [%-*@3 color].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:Stroke`(double`,Upp`:`:Color`):%- [_^ScatterDraw^ ScatterDraw]_`&[* S
troke]([@(0.0.255) double]_[*@3 thickness], [_^Upp`:`:Color^ Color]_[*@3 color]_`=_Null)&]
[s3; Sets the series line [%-*@3 thickness] and [%-*@3 color].&]
[s1; &]
[s6;%- &]
[s5;:ScatterDraw`:`:GetStroke`(int`,double`&`,Upp`:`:Color`&`):%- [@(0.0.255) void]_[* Ge
tStroke]([@(0.0.255) int]_[*@3 index], [@(0.0.255) double]_`&[*@3 thickness], 
[_^Upp`:`:Color^ Color]_`&[*@3 color])&]
[s3; Gets [%-*@3 index] series [%-*@3 thickness] and [%-*@3 color].&]
[s1; &]
[s1;%- ]]