keil中浮点型计算问题
floata=6000000;a=a-0.2;
a=a/10;
实际运行的结果总是不对，尝试了float换double，先扩大10倍再做减法等方法，都没能得到理想值，网上也没有找到适合的解决办法，特来求助
我的是这样的
data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABKQAAAHYCAYAAABz+zDNAAAgAElEQVR4Aezd368z334X9ul/kHBVFTWpelGJVonIiZGKr3rXi94k/D7KccpFZYnetKEFAW30fGMqcZc4KI9AhUYhBAexW6GWE6Mg2tPd9DSOIEAL54HWEAFRgslTlSNRFaSqrGqNPeM141me8fbe3v7xOpKP7fm51mt99v7a72fN7GKz2YQhj29+85sh/q/r+YsvvuhcXu7g/wgQIECAAAECBAgQIECAAAECBAgkAsWQMCpuE//XFUbF5TGQOra+XOn/CBAgQIAAAQIECBAgQIAAAQIECIQQBgdSuTAqLjdDSi0RIECAAAECBAgQIECAAAECBAgMFRgcSMUD5kIpM6SGctuOAAECBAgQIECAAAECBAgQIEBgcCCVC6PMkFJEBAgQIECAAAECBAgQIECAAAECpwgMDqTiQXOhlBlSp5DblgABAgQIECBAgAABAgQIECDw2AJJILUIk6IIxWTR+Vf3cmGUGVKPXUB6T4AAAQIECBAgQIAAAQIECBA4VWAXSK3CbDQKs8UsjDKBVDxwLpQyQ+pUdtsTIECAAAECBAgQIECAAAECBB5XoAykFpNRmCw2YbPaB1L/5M/9dPgX/+a/GsK3f0sI3/atIXxbfPZgoAYevgbi74Tf8OtD+LEfftzfnHpOgAABAgQIECBAgAABAmcJFKvZaD8rahdIxTAqfLsQ6uGDBwGkEPZoDXxrCH/ki7N+AdmZAAECBAgQIECAAAECBB5ToFhMilDEe0clj3/2r//Lvogf/SJulpCwTg2UNfDtv+4xf3PqNQECBAgQIECAAAECBAicJZDc1Hx/yV4aNvzfP/j7w69942+VJ8ndQ6pv+VkttDMBAtcl8PM/1wysr6t1WkOAAAECBAgQIECAAAECNyDQHUiV943azgD5x+v/o/yre32hU279DRhoIgECpwrU95X71lP3tD0BAgQIECBAgAABAgQIEAh1IJVeupfOkNpsNmUgFa1yoVPfcs4ECNyZQHpJ6511TXcIECBAgAABAgQIECBA4O0F6kCqCp7ic1cg1Rc65da/fRecgQCBiwsIpC5O7oQECBAgQIAAAQIECBC4J4HBgVTsdC506lt+T2D6QoBAaITWPAgQIECAAAECBAgQIECAwKkCgwOpvtApt/7UBtmeAIEbEDBD6gYGSRMJECBAgAABAgQIECBwvQKDA6nYhVzo1Lf8eruvZQQIvEhAIPUiNjsRIECAAAECBAgQIECAwFZgcCDVFzrl1oMmQOAOBQRSdzioukSAAAECBAgQIECAAIHLCQwOpGKTcqFT3/LLdceZCBC4iIBA6iLMTkKAAAECBAgQIECAAIF7FRgcSPWFTrn19wqnXwQeWkAg9dDDr/MECBAgQIAAAQIECBA4V2BwIBVPlAud+paf20j7EyBwZQICqSsbEM0hQIAAAQIECBAgQIDAbQkUm9UsjIoiFPVj0viT7pvNJsRHX+iUW9/mWE5DKIr9Y7rcbbHcLxvP23s9zvv1p+cw/vghFB8+hOLjc1gnXV8/P+3WfQzj58/Jmu3La19/0GALbldAIHW7Y6flBAgQIECAAAECBAgQuAKBbSA1WZShUxU+heTLZr3sNWZIrUMYj0MjZGkbrOchvFUgNR+HUOVf7fNew/sYKE2fP4X1YdYUwqenZgj16SlMPyWtvvb1SVO9vAOB5HfEHfRGFwgQIECAAAECBAgQIEDgwgKDA6ncDKi+5VV/2jOjyllS42rt/vlYIDVPZlfFYKsRLi1DmI73s6ym6SyrGIQls7KqGVr17Kz96V/+ajkP03E102wcpvN969bzcTIDrdpm+zye7+ZAfX4O06c0YWo2Zfn0MczToOrzcxgn21/7+m1v1mFeGk3DMp361eyqd7cgIJC6hVHSRgIECBAgQIAAAQIECFytwOEle6PuS/ZiD/rCp9z6uve7GVL1+44XuUAqzm6qsptyt/U2gEpzjXXyJgZg7cDpbWdIrUPz/MXB+Tu6u18UZzg9PYdpfbneU1gmAVRn4PTxud7/2tdvGyqQqgfs1l8IpG59BLWfAAECBAgQIECAAAEC7ypweFPz1ezt7iH10kAq7jc9dIqhUx1S7QKqavZTnEF10UBqnc6QKsJ4vA+khsyQWj9/LO8btb831Ocwf97fQ+raA6e+9h2OniU3LSCQuunh03gCBAgQIECAAAECBAi8t8BhILVZdAZSsaG5GVB9y+tOvmEgNS1CSK6SC3Gm1eUCqWWYFuMwT65DiyFU+/y1Q8eLMpBKZjzFTdKQZ/n0oXnPqINL9q57fUeXLbplAYHULY+ethMgQIAAAQIECBAgQODdBYrZaBRGs/1NzRezUWcg1Rc65dY3evjSQCqEcPSSvd1xq0vm1rv7SbUDofQY5aWBxfEbrDfafuzNeh7G42l9yd56dz+p9vmPHSKEz2H+MYZKu+v0Pn8K0/Sv7MV7TCV/WS+GVY2bml/7+tj55XR7L63TYI6zWfs+AgKp93F3VgIECBAgQIAAAQIECNyJQLHZrMJsMqpvuj2adF+yF/ubC536lsd9YxhUXU5XPyeX4cUZTvXy5PX+1uAhHLupeQyYqv3j5X3L3fv6kr4QQgyq6pubx5uiJ/ecOnc808vyxtN5WJY3Mh/vLykccoIyhPpQXrpXfGzeQyruHv8KX/Fhu35/ad/+wNe+fjmNN3KfHv0ri/veeHXVAgKpqx4ejSNAgAABAgQIECBAgMC1C3Rcsrd5uxlS166hfW8osN5e1viKIeAbNtah+wQEUn1C1hMgQIAAAQIECBAgQIDAEYHBgVQ8Rt9MqNz6I+e3igCBWxQQSN3iqGkzAQIECBAgQIAAAQIErkZgcCCVC5v6ll9NTzWEAIHXExBIvZ6lIxEgQIAAAQIECBAgQOABBQYHUtGmL3zKrX9AV10mcN8CAqn7Hl+9I0CAAAECBAgQIECAwBsLDA6kcmFT3/I3br/DEyDwHgICqfdQd04CBAgQIECAAAECBAjcjcDgQCr2uC98yq2/Gy0dIUBgKyCQUgkECBAgQIAAAQIECBAgcIbA4EAqFzb1LT+jbXYlQOBaBQRS1zoy2kWAAAECBAgQIECAAIGbEBgcSMXe9IVPufU3IaGRBAgMFxBIDbeyJQECBAgQIECAAAECBAgcCNSB1GoxC6NREYqiCCH5srnZbEJ85MKmvuXtMy6nIYzn7aW38345LcJ4vj67wfPxOLzCYQ7aMdR3WoRQjJPd1/MwHs/D+T1Ljunl/QokvyPut5N6RoAAAQIECBAgQIAAAQJvJVAGUqvZJExmi7BabcOnrkAqNqAvfMqtf63Gz8chLF/rYO95nOU0FNP378k0DaRCCDFsu4JmvefIOPdQAYHUUCnbESBAgAABAgQIECBAgECHQLFZzcJksihnQVWzoboCqVzY1Le8PucyhDj5Kj7aM6TW893yOHtqt02cvVNHNutkebW+CAPDk2WYFkUoxtMwHccZYNOwXE7DeLesPsdyvlsftxmH6bxes+1CDJHiPsXhDKn1fLxdPt0dt33sGmH7Yj5uBj9xVlPDJrFKZzHN0+1Sn3jYZJ+2b3nWdQgxgKrOE4OndiAVzJJqjZS3WQGBVJbGCgIECBAgQIAAAQIECBDoFyg2i0kYTWZhsrtcrxhNOi/Zi4fqC59y69NmxPCpKzApQ6kYsuyuGeu69OzFM6Ri0FJsL5Fbz2OwNC3Ps5yOk1BrHdbJ9Wq52UIxfOq6ZK8Mpcbb48b+xmN3bReTo2k8f4oSQsyCmi7LEMbT/Uax741L/HYBU9LkcuOD4+wOEcOnyjYuKkOw1gypbdve5lLCfU+8ugsBgdRdDKNOECBAgAABAgQIECBA4L0EitVsVM7uGc1Wu1lSq85AKhc29S1vdywXmMTljcvFYiDTutfUWYFUdbDkcrkYItXnXKczpIowbs1iqvpxLJCqjxU3jrOwGgnS7ghlODbtvFdTDI2qgKkRQMUZYkk4VbUlhkrtU3T6dljGE40PAqkQ2rO3qnN5JtAQEEg1OLwhQIAAAQIECBAgQIAAgdMEtoHUaNZ7yV48bF/4lFufNqkzMNnNEGoGOpcMpOKspXGYJ1OIGmFV0oG3DKTqUK41O6oMjwRSySh4+e4CAql3HwINIECAAAECBAgQIECAwC0LFJvNKsxGRZgsdjOkVourniFVzQgqg61iP6Po6CDs7o1UbtM1Q6pcP60v2Vvv7ifVCMh2Jzg7kMpcsle1P86SasyO2q04WOaSvYrM83sICKTeQ905CRAgQIAAAQIECBAgcDcC5V/Z26wWb34PqWlyM/LqxtrxOd5LKYZL1bIyBMrcoHsdZw5Vx2ndEyk/IrubmscbjceD725OHi+ni+FSvEl5XFy9Lm9aPp2HZbmuup9Scozdjc23Nzjf3gsq3Xfb/vwN0GM7j10WV1p0zIYq9ztyU/NjvqXNLsBKnct90sv2dsFdddlg3tSahxcQSD18CQAgQIAAAQIECBAgQIDAOQLbQGqz6b1kL3c5Xt/ycxp3t/sms7SuqY+5G7lfUxu15UoEBFJXMhCaQYAAAQIECBAgQIAAgdsUGBxIxe71hU+59bdJ87atno+r2Vdve57BRzc7ajCVDUPjsl4eBAgQIECAAAECBAgQIEDgVIHBgVQubOpbfmqDbE+AwA0ImCF1A4OkiQQIECBAgAABAgQIELhegcGBVOxCX/iUW3+93dcyAgReJCCQehGbnQgQIECAAAECBAgQIEBgKzA4kMqFTX3LQRMgcIcCAqk7HFRdIkCAAAECBAgQIECAwOUEBgdSsUl94VNu/eW640wECFxEQCB1EWYnIUCAAAECBAgQIECAwL0KDA6kcmFT3/J7hdMvAg8tIJB66OHXeQIECBAgQIAAAQIECJwrMDiQiieK4dMXX3wx+FGFVec20v4ECFyZgEDqygZEcwgQIECAAAECBAgQIHBbAsVsVISiaD5C8mVzs9mE+KjCpRhI/dN/+k97H3G7zv+t52FcFGE8X3eutpAAgRsQSH5H3EBrNZEAAQIECBAgQIAAAQIErkygmI0mYbELnWLwtJqNQlcgFdtdzZAaGkhVIVba5/V8LIxKQbwmcIsCAqlbHDVtJkCAAAECBAgQIECAwNUIHFyyFwOqrkCqCpfOnSElkLqasdcQAi8XEEi93M6eBAgQIECAAAECBAgQIBCagdRqFkaTRWcgFa3MkFIxBAiUAgIphUCAAAECBAgQIECAAAECZwg0Aql4ud5ksekMpNozpI7d3Dxe0td1D6n1fBrGxTgs3T7qjCGzK4ErEBBIXcEgaAIBAgQIECBAgAABAgRuVyAJpFahup9U1yV7sYtmSN3uQGs5gVcVEEi9KqeDESBAgAABAgQIECBA4NEE9oHU7nK9eGPzrkCqPUOq78bmXTOkIq57SD1aienvXQoIpO5yWHWKAAECBAgQIECAAAEClxKoA6nqcr1cIBUbZIbUpYbFeQhcuYBA6soHSPMIECBAgAABAgQIECBw3QJ1IBWDqOrxljOkwnoexkURxnM3krru0tA6AkcEBFJHcKwiQIAAAQIECBAgQIAAgT6BwYFUPFA1Q+rYDc3TddVlfn2NsJ4AgRsTEEjd2IBpLgECBAgQIECAAAECBK5LYHAgVYVLXc8xhOpafl1d1RoCBF5NQCD1apQORIAAAQIECBAgQIAAgUcUGBxIRZxc6FTdwDy3/hFh9ZnAXQsIpO56eHWOAAECBAgQIECAAAECby0wOJDKhU1xuRlSbz1Mjk/gygQEUlc2IJpDgAABAgQIECBAgACB2xIYHEjFbuVCKTOkbmvQtZbA2QICqbMJHYAAAQIECBAgQIAAAQKPLDA4kMqFUWZIPXL56PvDCgikHnbodZwAAQIECBAgQIAAAQKvITA4kIony4VSZki9xlA4BoEbEhBI3dBgaSoBAgQIECBAgAABAgSuT6DYbFZhMRmFoijKx2iyCCH5srnZbEJ85MIoM6Sub1C1iMCbCyS/I978XE5AgAABAgQIECBAgAABAncnUKxmozCZrcrQKQZP8X1XIBV7ngulzJC6u7rQIQLHBQRSx32sJUCAAAECBAgQIECAAIGjAttAarGdBXUskMqFUWZIHfW1ksB9Cgik7nNc9YoAAQIECBAgQIAAAQIXEig2m0WYjEZhNCrCaDQJk8nMDKkL4TsNgZsVEEjd7NBpOAECBAgQIECAAAECBK5BoNisZmHSmCE16QykzJC6huHSBgJXIiCQupKB0AwCBAgQIECAAAECBAjcpkARb2g+W+0v2dssugOp2L1cKOUeUrc5+FpN4MUCAqkX09mRAAECBAgQIECAAAECBELY3UOq/6bmuTDKPaSUEYEHFBBIPeCg6zIBAgQIECBAgAABAgReT6DYbFZhNhmFoijKx2iy6LxkL54yF0qZIfV6A+JIBG5CQCB1E8OkkQQIECBAgAABAgQIELhWgSL+Zb32IyRfNqt1uTDKDKlrHVrtIvCGAsnviDc8i0MTIECAAAECBAgQIECAwJ0KDA6kYv9zoZQZUndaHbpFICcgkMrJWE6AAAECBAgQIECAAAECAwQGB1K5MMoMqQHKNiFwbwICqXsbUf0hQIAAAQIECBAgQIDARQUGB1KxVblQygypi46ZkxF4fwGB1PuPgRYQIECAAAECBAgQIEDghgUGB1K5MMoMqRsefU0n8FIBgdRL5exHgAABAgQIECBAgAABAiGEwYFU1MqFUmZIqSUCDyYgkHqwAdddAgQIECBAgAABAgQIvK7A4EAqF0aZIfW6A+JoBG5CQCB1E8OkkQQIECBAgAABAgQIELhWgcGBVOxALpQyQ+pah1e7CLyRgEDqjWAdlgABAgQIECBAgAABAo8hUAZSi8koFEVRPkaTRQjJl83NZhPiIxdGPdIMqfXzUxh//BCKDx/D+PnziRXyKUzLfbf7T0/e/8TT2ZzAWwokvyPe8jSOTYAAAQIECBAgQIAAAQL3KVBsFpNQjGZhtdoGT5tVdyAVu58LpR5ihtSnp2YI9ekpTD8NL4r5x49h+mkXYn3+HLbvh+9vSwJXJSCQuqrh0BgCBAgQIECAAAECBAjcmkCxmo3CZLELo8rZUN2BVC6MutgMqeU8TMfbWVxFMQ7T+bK2Xs/H9QyvaqZX9Tyer7fbHdm/PtCRF8unj2GeTor6/BzGTyckUq1jr59jQNVaGNZhXvZxGpa7Zre38J7AVQgIpK5iGDSCAAECBAgQIECAAAECtyrQmiG1CovZqPOSvdjBXCh1mRlS67BOQprltAjTfSY1wP+8/TsDqY/PA87bsUkMsz4+h6Q7u40EUh1aFl2jgEDqGkdFmwgQIECAAAECBAgQIHAzAuU9pOIsqWpG0Wgy6wykcmHUxWZIrdMZUkUYj/eB1KAZUkf2D2EZprt7aFUO2+dpqDKvVwukPj+H6cen+rg3UykaSiAVEEilGl4TIECAAAECBAgQIECAwIkCHX9lr/uSvXjcXCj19jOkYmA0DvPkOrYYQg2fIXXu/iEsnz40L7F7wSV75U3Rnz51zIw6cdRsTuC9BQRS7z0Czk+AAAECBAgQIECAAIGbFtjOkNr9Jb3NZhXiX9y7ur+yt56H8XhaX7K33t0PanAgde7+cYjjzKbkL+PFGVOH94D6HKYf4l/Ra8+A+hzmcftk/857SC2n25lqgzt207Wn8bcsIJC65dHTdgIECBAgQIAAAQIECLy7QLFZzMJod7naaDQJi9WmM5CKLX2/GVIhpJfljafzsCxvZD4O1T3L+yTP3T8eP85wKsrA6UPzL+7VJ//UHUjF2VS7/ar943M70Ir3xSqKqRlUtacXVysgkLraodEwAgQIECBAgAABAgQI3IJAxyV73YFULoy62D2kbkHzrDaut5clHt7p/Kyj2pnAmwgIpN6E1UEJECBAgAABAgQIECDwKAKDA6kIkgul3v4eUo8yHPpJ4EYEBFI3MlCaSYAAAQIECBAgQIAAgesUGBxI5cIoM6Suc2C1isCbCgik3pTXwQkQIECAAAECBAgQIHDvAoMDqQiRC6XMkLr3MtE/Ai0BgVQLxFsCBAgQIECAAAECBAgQOEVgcCCVC6OGzJAqiiL80r/4VQ8GauBOaiD9S5x+tv1uUwNqQA2oATWgBtSAGlADakANqIFTa2BwIBVTrlwo1TdDSiClME8tTNtfd80IpK57fPz8GB81oAbUgBpQA2pADagBNaAGrr0GBgdSuTDKDClFfu1Frn2vX6MCqdc3VadM1YAaUANqQA2oATWgBtSAGnikGhgcSD3aDKmv/cgofOlHvu4Sszu5xOyRfqgv0VeBlP9QXqLOnEOdqQE1oAbUgBpQA2pADaiB+62BMpBazSZhNCpCUYzCaLYK6ZfNzWYT4uOxZkj9VPhy8ZXwtSSM+dqvfT38+PNPhi99/BC+9PyNzqDqa7v1xYcfzW7jh+l+f5geaWzT3xGP1G999fOrBtSAGlADakANqAE1oAbUgBp4nRooNotJGUJVwVN8n37ZrJY/0gypo7OjvvGT3WFTe/k3fjJ8+RuvM0iKneO11UD6O+La2qY9fl7UgBpQA2pADagBNaAG1IAaUAPXXwPFYjIKs9V2FlQZPq1mnYHUu8+Q+pkfCl/+zXEW13Ym15d/5KfqWUoxQNour9bvn+vL7o7s3yzUw9lRjfXt4Gk3i+rHn340/OCvJQP+a18NX3pqX/L39fCDZR++En58nWybzMRqnMvyeoy5XFe9CKSuazz8fBgPNaAG1IAaUANqQA2oATWgBm6tBgYHUu8/Q+rr4WtJiPPj0yJ8+WdOKbhh+x+dHRUDolMCqY9fbQUqAqlb+wHR3u6fMYFUt4t64aIG1IAaUANqQA2oATWgBtSAGhhWA4MDqXefIbVOZ0gV4Uu/eR9IDZohdWT/fbFsZ0f9+LGZSWcFUsMGZd8e27O4zhoQSF3nuPh5MS5qQA2oATWgBtSAGlADakAN3EoNFItJESaL/kv23neGVAyKRuEHf2Z/CVwMoYbPkBq2f+/sqKMzpD407xnVecmeH4xb+cHQzuO1KpA67qN++KgBNaAG1IAaUANqQA2oATWgBo7XQLFZzcJktir/kl68h1S8p1T6ZbO6qfm7zpBa/1D40m/+Sn3J3td294MaHEgN2v/r5V/WOzo76kgg9Uu/9tXw5eSv78V7Sh3c1PxnvrK919V0f/8rBXq8QPlcp0/6O8IYXecYGRfjogbUgBpQA2pADagBNaAG1MA110ARA6fVbFLfFHw0W3UGUu87Q+pXQ3pZ3pemPxR+vLyR+Sj8YHJfqWPQffv3zY768acPofjQerTuEfW155+st/lSEk5V7Yr3vSqKr4SvHbsk0LrWfbf8Aqnq55qeBVLq8prqUVvUoxpQA2pADagBNaAG1IAauL0aKAOpahZU9Zx+2ayWvesMqTcPaeLNxoeHWy8r9DgD663PcXsF+DJL/Xxvt/R3xHu3xfn9PKgBNaAG1IAaUANqQA2oATWgBm6vBgYHUu89Q0px3V5xGbP7HTOB1P2OrZ9bY6sG1IAaUANqQA2oATWgBtTAJWpgcCB13zOkFNslis057qfOBFL3M5Z+Lo2lGlADakANqAE1oAbUgBpQA+9RA4MDKTOkFOh7FKhzXmfdCaSuc1z8vBgXNaAG1IAaUANqQA2oATWgBm6lBgYHUufMkIphlv8RIHBHAt/2Lfs/fnBH3dIVAgQIECBAgAABAgQIELiMwOBAKjYnF0p98cUXZWtz6y/TFWchQOBiAgKpi1E7EQECBAgQIECAAAECBO5RYHAglQub4vIYSOXW3yOaPhF4eAGB1MOXAAACBAgQIECAAAECBAicIzA4kIonyYVOZkidMwT2JXCDAgKpGxw0TSZAgAABAgQIECBAgMD1CBSr1SIsZpMwGhVhNFuFzWazvzfMt31L+T4uy4VRZkhdz2BqCYGLCQikLkbtRAQIECBAgAABAgQIELhHgf0MqcXkaCAVO58LpcyQusfS0CcCRwQEUkdwrCJAgAABAgQIECBAgACBPoHBgVQujDJDqo/YegJ3KCCQusNB1SUCBAgQIECAAAECBAhcTmBwIBWblAulzJC63IA5E4GrEBBIXcUwaAQBAgQIECBAgAABAgRuVWBwIJULo8yQutWh124CZwgIpM7AsysBAgQIECBAgAABAgQIDA6kIlUulDJDSiEReDABgdSDDbjuEiBAgAABAgQIECBA4HUFBgdSuTDKDKnXHRBHI3ATAgKpmxgmjSRAgAABAgQIECBAgMC1ChSLSRGKovkIyZfNzWYT4iP+LxdKmSF1rcOrXQTeSCD5HfFGZ3BYAgQIECBAgAABAgQIELhjgf0MqV3wVIZPyZfNKpDKhVFmSN1xdegagZxA8jsit4nlBAgQIECAAAECBAgQIEAgJzA4kIoHyIVSZkjleC0ncKcCAqk7HVjdIkCAAAECBAgQIECAwGUEBgdSuTDKDKnLDJSzELgqAYHUVQ2HxhAgQIAAAQIECBAgQODWBAYHUrFjuVDKDKlbG3btJXCmgEDqTEC7EyBAgAABAgQIECBA4LEFBgdSuTDKDKnHLiC9f1ABgdSDDrxuEyBAgAABAgQIECBA4HUEBgdS8XS5UMoMqdcZDEchcDMCAqmbGSoNJUCAAAECBAgQIECAwDUKDA6kcmGUGVLXOKzaROCNBQRSbwzs8AQIECBAgAABAgQIELhvgcGBVGTIhVJmSN13kegdgQMBgdQBiQUECBAgQIAAAQIECBAgMFyg2GwWYTIqQlHExyhMZqsQki+bm80mxEcujDJDaji2LQncjUDyO+Ju+qQjBAgQIECAAAECBAgQIHAxgWI2GoXJYlWGTpvVKsT3XYFUbFEulDJD6mLj5UQErkNAIHUd46AVBAgQIECAAAECBAgQuFGBg0v2VrPuQCoXRl1shtRyHqbjaibXOEzny5p8PR/vZnhV65FhvjoAACAASURBVPfP4/l6u92R/esDhXWYl+eYhuVut/06rwgQqAUEUjWFFwQIECBAgAABAgQIECBwukAzkFrNwmg0u9IZUuuwTkKi5bQI030mNaDnQ/YXSA2AtAmBxu8IHAQIECBAgAABAgQIECBA4FSBfSC1moXJaBIWm03jy+bV3ENqnc6QKsJ4vA+kBs2QOrL/qWi2J/DwAmZIPXwJACBAgAABAgQIECBAgMA5AmUgtZpNwmiyCKvdDcyv7x5SyzAtxmGeXEcXQ6jhM6TO3f8cYvsSuEMBgdQdDqouESBAgAABAgQIECBA4HICxWyy/ct61Uyoq7yH1HoexuNpfcneenc/qMGB1ND9l9PtvagGH/hyA+VMBK5KQCB1VcOhMQQIECBAgAABAgQIELg1gWJU7G8AXuxeX98MqRDSy/LG03lYljcyH4fqnuV98EP2j/elKoppSG5V1XdY6wk8poBA6jHHXa8JECBAgAABAgQIECDwSgL7e0jtLteLM6W6Aql3/yt7r9Th/GHW28sCpVF5ImsIVAICqUrCMwECBAgQIECAAAECBAi8QGBwIBWPnQulvvjii/LUufUvaJddCBC4ZgGB1DWPjrYRIECAAAECBAgQIEDg6gUGB1K5sCkuj4FUbv3VC2ggAQKnCwikTjezBwECBAgQIECAAAECBAjUAoMDqbhHLnQyQ6r29ILAYwgIpB5jnPWSAAECBAgQIECAAAECbyQwOJDKhVFmSL3RyDgsgWsWEEhd8+hoGwECBAgQIECAAAECBK5eYHAgFXuSC6XMkLr6cdZAAq8rIJB6XU9HI0CAAAECBAgQIECAwIMJDA6kcmGUGVIPVjG6SyAKCKTUAQECBAgQIECAAAECBAicITA4kIrnyIVSZkidMQJ2JXCLAgKpWxw1bSZAgAABAgQIECBAgMDVCBSbzSosZpMwKopQFEUYTWaN2Q+bzSbERy6MMkPqasZSQwhcTkAgdTlrZyJAgAABAgQIECBAgMAdChSr2SiMZosydIrB02I26gykYt9zoZQZUndYGbpE4JiAQOqYjnUECBAgQIAAAQIECBAg0CNwcMleLpDKhVFmSPUIW03gHgUEUvc4qvpEgAABAgQIECBAgACBiwnsAqlVmI2qS/YWZkhdjN+JCNyogEDqRgdOswkQIECAAAECBAgQIHAdAs0ZUqtVmE26L9kzQ+o6BkwrCFyFgEDqKoZBIwgQIECAAAECBAgQIHCrAs1AKt7AfNV9U/PYwVwo5R5Stzr82k3ghQICqRfC2Y0AAQIECBAgQIAAAQIEokAxG43CZLHa3dR8FRaTovOSvVwY5R5SConAAwoIpB5w0HWZAAECBAgQIECAAAECrydQbHaX6RWFe0i9HqsjEbhzAYHUnQ+w7hEgQIAAAQIECBAgQOBtBQ4v2dtszJB6W3NHJ3D7AgKp2x9DPSBAgAABAgQIECBAgMA7CgwOpGIbc5ftuYfUO46gUxN4DwGB1HuoOycBAgQIECBAgAABAgTuRmBwIJULo9xD6m5qQUcIDBcQSA23siUBAgQIECBAgAABAgQIHAgMDqTinrlQygypA1cLCNy3gEDqvsdX7wgQIECAAAECBAgQIPDGAoMDqVwYZYbUG4+QwxO4RgGB1DWOijYRIECAAAECBAgQIEDgZgQGB1KxR7lQygypmxlvDSXwOgICqddxdBQCBAgQIECAAAECBAg8qMDgQCoXRpkh9aCVo9uPLSCQeuzx13sCBAgQIECAAAECBAicKTA4kIrnyYVS7zFDav08DuPn9ZndtzuB9xG4+foVSL1P4TgrAQIECBAgQIAAAQIE7kQgCaQWYVIUoZgsQki+bG42mxAfuTDqfWZILcP0wzR0xVHxi/7002mjs3wqQvFh+4ghVzxG9b74ON8dbB3mT/vl46dpGO/WNbbfHafcv9r303R/vOo8T/PO9udbvg7LxvnnYdno6+H62ufzPIzTdsXXH6dh+Xl7tt725xu1XdPRv9OPv/Udf9yNw9O89J7v2piOUT02T8vy/NW6KqBs9Ge3TV8X1p+XYfkcx7Q4K+iM5+6vv3z99rXzatYnvyOupk0aQoAAAQIECBAgQIAAAQI3I7ALpFZhNhqF2WIWRplAKvYoF0pdeoZU/NJfhQ+V9PrTPMw/bcOkaXzeva/W9z3HsKcKP7bbLsO0CpRCKEOqaTIjKx6/CqTi9gdtiiFQa/99m2N4dFrwsW1fHTGF8Hkd5h+LOvwoj/e0rEOuGFbFMK3eI4ZGaThTtm8atpFOf/v7/NpBzDqeL9v/EELLJ/Yvjlv9v8/rMP3YHpOOdu52iPuPY8hWHaB1/Gpx7/On6UFt9e4Tx/+E+juolSEnuLZtBFLXNiLaQ4AAAQIECBAgQIAAgZsSKAOpxWQUJotN2KzygVQujLr8DKn87JI4y2UaZ9h8nNezf4aOxpBAavy8D3zax92HDM0gq9puv363JAYmaUBUbdj13LdtJnyJIVU9W6cdSLVCtH37utvf1ax0Wdy/PlcZ0Ewb/Tt6/Ez70+NXr/fHqZZsn8vxizOcKtMTjtk40gsDqXiMYfWXr99GO679jUDq2kdI+wgQIECAAAECBAgQIHDVAsVqNipnRZWX5h0JpGIvcqHUJWdI5QKJ2L64LoZR846ZNX2j0BdIhbC9JK669G0cL7nbXU5Wn7u6LC6ZGVSdt9HucnZTM8Cptut87glJGsdOD5Du1xFIhWRZaXek/elhu1439i+P05zd1Fjf9knb2XXwZFmur9X4Vc/tGVjJIY6/PKEt7QOVfeypv1z728e6+vcCqasfIg0kQIAAAQIECBAgQIDANQsUi0kRinjvqORxvfeQ2s4uqS/Laskun+fbS7biTKnn3FatnXZv6yCjXn18plD+krTu/RqBzIfW5Wn1OTMvekKSbMiR7peET/VZkmX7Y3S3v94n8yLun86QCuVstX0odfT4aTtDHOP9Pb0alxm2ZnWlTanHbzczav0OM6T66+94/ab9ufrXAqmrHyINJECAAAECBAgQIECAwDULJDc1P37JXuzEe8+Q2ocar09aBxrVoXsDjWa40Ne2dH0Msxr3O6rOmXvua0tm/csu2cs14vjy2L9GILULj6plaf8PjhTbX11ql6w8GJMhgVR13ufmPbySwx5/2QjHjm966tqjBqce7L23F0i99wg4PwECBAgQIECAAAECBG5aYHAglQujLncPqXX5l/VOm/c0fGxiWJDOxmm+j+HTuLxpen3EVojSFTbMk5tst9c3j18fNfsihjP7m6LHm4LHmUz7EOj0m5rH/fc3AW+3LzYkbX+2YbsVcf8qfCoXxfZ92N/Dqu/45WWW6U3N4/kPbjR//Kbm+5vSx75t7yXW1+6D9W8WSL1t/R70460XCKTeWtjxCRAgQIAAAQIECBAgcNcCdSCVXrrXdcleVMiFUpe4h1RXoPG6I7O9R1Sxu1ysvEdUfYJtIDV9GodqfdEKc+rl6eVmH3aBT7w0rlpe3z9p+1fy0r9EV58u82Idb9pdH2faDMh297iqztNofwzPqv2q59j+3T2wom21X/N5H1hlmrRdnPavOv6H/V8RHHb8dRlA7c8fA679X92Lgdt+3e71blZVuq4O7WKbauujrS9Xpseoz3PC/n1nePv67WvBK68XSL0yqMMRIECAAAECBAgQIEDgsQTqQKq8qflmE+JzVyCVC6MuM0Mqhjf7+xE91hDp7e0L3GH9CqRuvyz1gAABAgQIECBAgAABAu8oMDiQim3MhVKXmCH1jkZOTYBAW0Ag1RbxngABAgQIECBAgAABAgROEMgHUt/+LeVMqWrmVC6MuswMqRN6ZFMCBN5eIAZSu98Rb38yZyBAgAABAgQIECBAgACBexPIB1K7GRCf//ovbi/jM0Pq3sZefwi8TOCf//PGZb0vO4i9CBAgQIAAAQIECBAgQOCRBboDqX/t1yVfOL81eb2dNZXeY8prJmrg0Wqg9TvhkX+D6jsBAgQIECBAgAABAgQIvEigM5D6f/6jqRAqvUeO1+pBDXTXwG/79170i8dOBAgQIECAAAECBAgQIPDYAp2BVLxv1D/7D76SfAFtzYjw5TyxebTZMfprRlisgW8N4Xv/3cf+7an3BAgQIECAAAECBAgQIPBigWwgVd3MvHqOZ8jd2Nxf2Xuxvx0JECBAgAABAgQIECBAgAABAg8nUGxWszAqilDUj0lYbDbljcyrMCo+58Iof2Xv4WpGhwkQIECAAAECBAgQIECAAAECZwlsA6nJ4iCASsOo+Dr+LxdKmSF11hjYmQABAgQIECBAgAABAgQIECDwUAKDA6lcGGWG1EPVi84SIECAAAECBAgQIECAAAECBM4WOLxkbzQJi9XhJXvxTLlQygyps8fBAQgQIECAAAECBAgQIECAAAECDyNweFPzeE+pjkv4cmGUGVIPUys6SoAAAQIECBAgQIAAAQIECBB4FYHDQGqzCJPR7OCeUvFsuVDKDKlXGQsHIUCAAAECBAgQIECAAAECBAg8hEAxG43CaLa/qfliNgqFGVIPMfg6SYAAAQIECBAgQIAAAQIECBB4D4Fis1mF2WQUiqIoH6PJLKw27iH1HoPhnAQIECBAgAABAgQIECBAgACBRxDouGTvMIzabDbZy/XcQ+oRykQfCRAgQIAAAQIECBAgQIAAAQKvJzA4kIqndA+p14N3JAIECBAgQIAAAQIECBAgQIDAowoMDqRyYZQZUo9aOvpNgAABAgQIECBAgAABAgQIEHiZwOBAKh4+F0r5K3svw7cXAQIECBAgQIAAAQIECBAgQOARBQYHUrkwygypRywbfSZAgAABAgQIECBAgAABAgQIvFxgcCAVT5ELpcyQevkA2JMAAQIECBAgQIAAAQIECBAg8GgCgwOpXBhlhtSjlYz+EiBAgAABAgQIECBAgAABAgTOExgcSMXT5EKp95ghtX4eh/Hz+rze25vACwXU3wvh7EaAAAECBAgQIECAAAECBEIIdSC1WszCaFSEoihCMZqF1WYTNskjF0a9zwypZZh+mIauOCoGBdNPp43t8qkIxYftI4Zc8RjV++LjfHewdZg/7ZePn6ZhvFvX2H53nHL/at9P0/3xqvM8zTvbn2/5Oiwb55+HZaOvh+trn8/zME7bFV9/nIbl5+3Zetufb1S5pm//yrcKEBvbPy1D6Glfz+m3bfi8DMvnOCbFWUFlbFt//eTrb0hbbUOAAAECBAgQIECAAAECBB5doAykVrNJmMwWYbVqhlBpIBWhcqHUpWdIxdCgCjeqAVx/mof5p22YNI3Pu/fV+r7nGPbMdwHNdttlmFaBUghlSDVNZmTF41eBVNz+oE0xZGntv29zDI9OC0627asjphA+r8P8Y1GHJ+XxnpZ1yBXDqhim1XvEUCyGP9X/yvZNQ7Wkr/3Vbrnnvv1j+8cxBKsO0PIJPe2rdut9/jQ9qI3efeL4nVA/B30dcgLbECBAgAABAgQIECBAgAABArVAsVnNwmSyaMyGSoOo6nUujLr8DKn87JT15xgixdk/83r2T93TnhdDAqnx8z7waR9uH1I0g6xqu/363ZIYyKQBUbVh13Pftu1wZ3eMGFLVs33agU8rRNu3r7v9Xc1Kl/XtX/rGGUxVn9tt7mlfeq6jr18YSMVjDquffP0dbZeVBAgQIECAAAECBAgQIECAQC1QbBaTMJrMwqS+XG8SFh0zpeIeuVDqkjOk9sFH3Yf6RVwXw6j5x/Zsp3qT7Iu+QCqE7SVx1aVv43jJXTKjqjx3dVlcMjOqOmGj3eXspiGXhu327glZGseuThif0/06Ap90VlJf+9PDdr3u27/yrZ7Ly/RSp572dZ2zc1na584N8gvLPvTUT9Y6f1hrCBAgQIAAAQIECBAgQIAAgZZAsZqNyvtGjWar3SypVZjNrvUeUtvZKfVlX63OLJ/n20vC4kyp59xWrZ12b+ugpF59fKbQOgYoSaCyDyq692sENh9iGFVfTFefMfuiJ2TZn7t1hHS/nsBnf4zu9reOfPC2b//adzczaj1ghlQamB2cMLcg7XNum8zy/vo5Xn+Zw1pMgAABAgQIECBAgAABAgQItAS2gdRo1rhkbzEZhVlrllTc771nSO1Dj1YvXuFtHZhUx2oHJtXy+rkZTvS1LV0fw6zG/ZTqY2Ze9LUls/5ll+xl2tCzOO1f16apb9x2+ty8x1ZX+NR3zK7zNGaFdW7w8oUvas/LT2dPAgQIECBAgAABAgQIECBwtwLFZrMKs1ERJovdDKnVIkyu8q/srcu/rHfavKfh4xbDhvSm3833MXwalzdNr48YQ6Dqfkit+zFV28yTm3i3w4zm8as98s8x0NnfFD2EUN4va3/Z3+k3NY8zofY3GW+3L7YkbX++Zds1ffungVQI8dzbe33Vx23P4Cr7t29fvV3fizNmSB0/9NvW3/FzW0uAAAECBAgQIECAAAECBO5LoPwre5syhCrKS/eK0XXeQ6or8HjdodjeI6rY3QeqvEdUfYJtIDV9GodqfdEKc+rl1X2kyuddoBLDlmp5fZnf9q/kpZf91afLvFjHm4LXx5k2A7LdPa6q8zTaH8Ozar/qObZ/dw+sMhyrljeehwVCffvHsKxuV/WXCtNLHnval+FoLE7PUZ3rFNvGwTrevH39dZzUIgIECBAgQIAAAQIECBAgcKcC20Bqs2lcslf9Zb30OXe53mX+yl4Mb06/UfmdjpluXVxA/V2c3AkJECBAgAABAgQIECBA4K4FBgdSUSEXSl3yr+zd9WjoHAECBAgQIECAAAECBAgQIEDgAQQGB1K5MOoyM6QeYCR0kQABAgQIECBAgAABAgQIECDwIAKDA6nokQulzJB6kGrRTQIECBAgQIAAAQIECBAgQIDAKwgMDqRyYZQZUq8wCg5BgAABAgQIECBAgAABAgQIEHgggcGBVDTJhVJmSD1QxegqAQIECBAgQIAAAQIECBAgQOBMgcGBVC6MMkPqzBGwOwECBAgQIECAAAECBAgQIEDgwQQGB1LRJRdKmSH1YFWjuwQIECBAgAABAgQIECBAgACBMwSK2agIRdF8jGarsNlsGo9cGGWG1Bn6diVAgAABAgQIECBAgAABAgQIPKBAMRtNwiIJn1azUegKpKJNLpQyQ+oBK0eXCRAgQIAAAQIECBAgQIAAAQIvFDi4ZK8dUFUzpXJhlBlSL5S3GwECBAgQIECAAAECBAgQIEDgQQWagdRqFkaTReNSvSqQij65UMoMqQetHt0mQIAAAQIECBAgQIAAAQIECLxAoBFIxcv1JovmvaOqQCoXRpkh9QJ1uxAgQIAAAQIECBAgQIAAAQIEHlggCaRWIXe5Xgyl4v9yoZQZUg9cQbpOgAABAgQIECBAgAABAgQIEDhRYB9IHblcLwZSuTDKDKkTxW1OgAABAgQIECBAgAABAgQIEHhwgTqQOna5nhlSD14luk+AAAECBAgQIECAAAECBAgQeEWBOpCq7hWVezZD6hXVHYoAAQIECBAgQIAAAQIECBAg8MACgwOpaJQLpdxD6oErSNcJECBAgAABAgQIECBAgAABAicKDA6kcmGUe0idKG5zAgQIECBAgAABAgQIECBAgMCDCwwOpKJTLpQyQ+rBq0j3CRAgQIAAAQIECBAgQIAAAQInCAwOpHJhlBlSJ2jblAABAgQIECBAgAABAgQIECBAIAwOpKJVLpQyQ0olESBAgAABAgQIECBAgAABAgQIDBUYHEjlwigzpIZS244AAQIECBAgQIAAAQIECBAgQCAKDA6k4sa5UOo9Zkitn8dh/Lw2igQIECBAgAABAlci4PPZlQyEZhAgQIAAgRsQKDabVVhMRqEoivIxmizCZrM5eOTCqPeZIbUM0w/T0BVHxQ9C00+nyK/D/GMRig/jMP+c7PdpGooPRSg+zncL12H5NN4u+1CE8dM8LHfnWj7F/bePGJLFNlTv9/snx+58mT9+5+YvXJj2Ifa5bRX7cstB3623/4XDajcCBAgQIPCmAsM+X+U/n71p4xycAAECBAgQuEmBYjUbhclsVQdQ5fvFYSAVe5cLpS49Q6rrX9/Wn+Zh/mkbBk3j8+79oFH5PA/jMmRa1pvH4Gb8cRqqJfH9/HMSgX3eBllVoLNdX+8eQliGaR1mpcu7X/cdv3uvE5fGfn7sDvJOPNK7bD5PxuNdGuCkBAgQIEDgwQSqz1PbQOr456uuz2cPxqW7BAgQIECAwAkC20AqCaBiIDVKAqpqtlQujLr8DKn8v76tP8cQaDuraZnOduoDiUHN0zzUgUf5fnnw/thhzgqkduc7dvwQ1mGeztD6OK/DsvgBMM7IGj9Ny2BtO7NrH6bF46azuDpnb1UzwuJxOi+F3NmWs8amYfm8nUGWzgjbh3P72WJVn6rzx+3jvjEALNKA6dN8O3blTLNxmD5XUWAIIfrsZqDVbf9QNGd39bb/PL+qH54JECBAgMCjCQz7fJX/fPZoXvpLgAABAgQIDBMoNptFmIxGYTQqwmg0CZPJLKw6LtmLh8uFUpecIXXsX9/KYOZjDJZal9/1WVSB0KdpGcbEcCmGK3VAtVt+7DBnBVIDjn/QpzJ8m9eXLW77Pg1VEBfbcxAsxX72zNrK+Zbn/7SbIbabHZYeP+5XBVKlU1efytBoHMZPy7COgeHn3XO5w3q7bIccA6zG8UIyHkcG4mj705DyJX5HzmsVAQIECBC4V4HtZ4zjn69y//29VxP9IkCAAAECBM4XKDarWZg0ZkhNwmx1eMleLoy67Ayp7b++JXNnGgLL592soRg2pDNsGlt1vKkCqbAuZ+lUoc3VBFJ1+5ptT0OwQYHQSwOprv1agdOg87f2afTmczpDqgjjj68YSJ3tF+tuf5+w/Sytahaa9XzUx/7norLw87H9b5XfD34/VD8T6fOt/Hxs/0vd//nq+Oezxn/vvSFAgAABAgQI7ASKeEPzRgC1mHReshe3z4VSl5oh9Wb/+pYJLOpAqiuQaZVQGg6VqwbsUx+ib9tM+9JzDgqE+s4TQnlD9nTmU7YvrXBp0Plb+9T9j/fbijeVr2Zg7drxajOkXstv32CvCBAgQIAAgZ3Am30+I0yAAAECBAjctcDuHlKtm5pf5T2k1uVf1svNjjprlDKBRR1Ilfdgal0CV17ytb9MLX4YK572rWu/72vfwSV2reMPuWSvEeB0hT8vDaTKy+WSyyAzl+xVQVa8AWp5k/j2vai62hRhynZN60v24v7xXmCN/rTasC7vQ3V4g/bch+JX8esbROsJECBAgMDDCbzh57OHs9RhAgQIECDwWALFZrMKs8koFEVRPkaTRf0X96obmsfn+L/3nCGVCxrOHq4YhtSXY+1Cl8ayalp9nD3UvGl4OqMn3nQ8hkrVZSvxJunJ3+Qb1My+4/fd1DyeuwxxOm7wPY83e6/7uXtdB2h9l5TE5h/e1LwKoMrOlQHa7rjxZuXJTc/Lfdvnrtq6kykDvN020W5Z3qg9CcHirKkYaFXHieeo7wk1pP39NzU/5jdoAG1EgAABAgQeTODNPp89mKPuEiBAgACBRxQo0tDp2OtcGHWZe0itT79R+SOO5gX7HAOjRiB1wXM7FQECBAgQIHANAj6fXcMoaAMBAgQIELhVgcGBVOxgLpS61D2kbhX5XtqdzrJ6yQywe3HQDwIECBAgQIAAAQIECBAgQOA8gcGBVC6MuswMqfM6aW8CBAgQIECAAAECBAgQIECAAIHrERgcSMUm50IpM6SuZ0C1hAABAgQIECBAgAABAgQIECBw7QKDA6lcGGWG1LUPsfYRIECAAAECBAgQIECAAAECBK5LYHAgFZudC6XMkLquQdUaAgQIECBAgAABAgQIECBAgMA1CwwOpHJhlBlS1zy82kaAAAECBAgQIECAAAECBAgQuD6BwYFUbHoulDJD6voGVosIECBAgAABAgQIECBAgAABAtcqUAZSi8koFEVRPkaTRVhtNmHTeuTCKDOkrnVotYsAAQIECBAgQIAAAQIECBAgcJ0CxWYxCcVoFlarXQi1WoTF4jCQis3PhVJmSF3n4GoVAQIECBAgQIAAAQIECBAgQOAaBYrVbBQmjQBqESajmRlS1zha2kSAAAECBAgQIECAAAECBAgQuAOB1gypVVjM4uV7k7BoXbIX+2qG1B2MuC4QIECAAAECBAgQIECAAAECBN5ZoLyHVJwltb+H1CxMRoeBVC6Mcg+pdx5BpydAgAABAgQIECBAgAABAgQI3JhAx1/Z675kL/YrF0q5h9SNjbrmEiBAgAABAgQIECBAgAABAgTeUWA7Q6q+PG8V4l/cmzXuKbW9wXkujDJD6h1Hz6kJECBAgAABAgQIECBAgAABAjcoUGwWszAqivKSvVG8VK/6a3t1SLUNpGLfcqGUGVI3OPKaTIAAAQIECBAgQIAAAQIECBB4J4GOS/a2AdSmFUjlwigzpN5p5JyWAAECBAgQIECAAAECBAgQIHCjAoMDqdi/XChlhtSNjr5mEyBAgAABAgQIECBAgAABAgTeQWBwIJULo8yQeodRc0oCBAgQIECAAAECBAgQIECAwA0LDA6kYh9zoZQZUjdcAZpOgAABAgQIECBAgAABAgQIELiwwOBAKhdGmSF14RFzOgIECBAgQIAAAQIECBAgQIDAjQsMDqRiP3OhlBlSN14Fmk+AAAECBAgQIECAAAECBAgQuKDA4EAqF0aZIXXB0XIqAgQIECBAgAABAgQIECBAgMAdCAwOpGJfc6HUe8yQWj+Pw/h5fQdDoAsECBAgQIAAAQJRwOc7dUCAAAECBB5HoAykVrNJGI2KUBSjMJqtwmazOXjkwqj3mSG1DNMP09AVR8UPMtNPJw7gp2koPhSNx/hp3nn8E498dZsvn8ZJP19gletRYtgZFPatzx33wsuXT8XJQWesuVg/J9fda/btnX2jW/tnqHha1RCrmgAAIABJREFUntzDl/inJ3nx/gP9Xnz8tJEXfn2J+px/7Bj/+Du1qoGBvi+hec3+Xfv4vtnv75fAv9k+8b/vRSg+zl/lDK9ZH/kGrcM8+W/r+Gkaxq/U/vw5D9e8tH7jfsWHcZh/Pjzmay6JY9H/38n857vXbItjESBAgAABAtchUGwWk2YItZiEyeIwkIrNzYVSl54hFT/UtEOP9ad5mH9al/+yNo3Pu/dDmZvHXIeXfrAber6u7eYfp+H0r/BdR8os+zwP44/dQV5mj5MXNx0Pd+9bf7jHbSyJ/er/oP32fXlv37c8/5v/fNz4v8wf87lEfR6cP/6+qQKpXem/VX1con99P70H/e/b4dT1F/j9fWqT3m77ZZieGOgc83/r+iiPn8zYjp8/XjuQOta/1xiH+ce3C6Sqz2PbcTj++Sxu0/589xr9cwwCBAgQIEDgOgWKxWQUZqskgFrNwmiyuOIZUvl/PVt/jh9it/+yujzxX/oOPgS1v0xVx97NpDqcQdX+F9Jx8wPpsf3juVoztE6fcdM6/8d5I9za/gtoaxbDKR/4P823tmU7x2H63B2dHTi26r5vfWvz8u129sU4zJ+ne6eP0+a/5h7zDaEMGKNp/KC7rI6TBoBHZnAMOX/s1/Q5Gcf02LEXZ7fvXP9WfTy16rML/gXLusY3Livt46yBqs7bPkf8w5Cfj2P7x36cW789x6/62Jgllv589Yx/CGeMzwCfc+tzSClUX5i7aqDaP7uud3yO+/T2r2pA7vnI+MZjH63fAf7n/vz3/v4e4NeYXfUx/j5JAoje+szB7ZYfOX+vXzxE4/wx3D8hkBrg31sfjfMX4fC/78f7H48/fl52z6qu27f3rkz2s8CO1He9f/O/341/ADlSv9uWx39kS2ZHt8c/hFAFUnXb4u/qj683U3zY57P857vjI2AtAQIECBAgcKsC3YHUaHYQSMUOXsMMqe0Hv66L9bb3HYgfoKoPVqcMSuO4n9flMdIPfPOn1syi+AH2aX9JwXb//QfSdfyAmHwh7ds/trX6QndKu6ttD/pcfsBufZiMH2yTNlX7Dnteh3US8sUvSKlPdYyGY7Uwee5bn2zaeBlDofJLQt2GOEb7GWVDfEP5oX1cztoo+/J52ehTPGGufX3nj/sVH6ahCkLjh//0X3nPb995/tt+5euzgX3Gm5xf6RPHazd+bZ/qlLn94/p0vKvt28/5/c/zq86TO35cXv88fF6GcfplP7b9zN8f1fmPPR/zie07uz6Pnbwcn/0X5rT2091yfjGQO/b7Zbtfvn77+pe24djrXPvK4/fU7zH/vvEv29T3++no7+/jfvHnLc4crv8X//uQXKI1qH31zl0vjp//uF8Mn/a/G2ItlOHJif+tOubfVx+v0f/yd9oucG/+tyoG4tPGfw/KADr579e27vL1HcWP9a8ake1xknHeregb/+3xt4FZ+Q82jfGojn7e87YGjn8+y7X/vDPbmwABAgQIELhmgcGBVC6Muuw9pLb/etY9NyeE5fNuVlD8sJ2ZwZMbjPLDUjV740P7w3sy86XeJn752gciIexmZ1UfSNMPdJl/4WzuP+wDZ2f74/Fbl8bE7eKH0MY9IY5+oek88n7h53SGThHGHy8dSLX6sguPyhBgoO/hl4J996pXuQ/EB4Ffev7W6/JY6ReQ12jf2f5H6rPq/Cs85/zi8jqwiedJfZLz5vaPm5zzhSzOsipnT9Y/n29Uv+VYt2p10PifPz7HfI76D2pfMkiZl9X5j41hdl3v+Bz3Odq/THu7FufaN+T4Vf8PjjvUN/MzUR8vHicX0hz1i3b7fzypj1e9GNq+avuu56Pn3wb92Z//rn4f62vX+Xt+Pxwdv9fof6tN7X+QagdQh7//jtd3PHy2vpJzd9dvz/jv9i//0SXOMH/aB2PJoc9+2f/57Pjnu7Mb4AAECBAgQIDAVQoUi0nRvGdU5pK92PpcKHWpe0h1f9h6Hdf02PHDZLzX0j74GvaBrm7J53gvq3SG1LD9h3zgrM+RvogfqN80kNr9a3ryL+wHH/B37Ukd0yZWr/vWV9u1n48HQsN8D78EtM9ybIZUK2RohVAHHo0vWee27/X8yx4f1Oehw0uX5Mb3uM/+bLn94xZDfj669389v+7j79pffrE9rJNtWH0kENh3f/vqheNzzOe4/8D6bLez9f7Y+atNu/2Gj095nA6f4/2rzt7/3N2+nkBld9h8/wf6Nn5ndLQ1G9L0+W3PfzhvpjrHwPZVmx88952/x6+r39m+Hpy8XpD37zl/+Y9JJ/x81mc89iKapJ8h0hC+Obv34Cgd9R23Oda/6hjd9ds3/tu9YyA1LW/G3mp3dfA3fu5u+xuf1OEJECBAgACBdxcoNqtZmCR/WS/eU6rrpua5MOpyM6TWhx/wXpGv/WEovq//QtRutlG8R0TufzEwSS+JKEOtJCQqp/Mf2T8eNw1dYqA1zvwlwa42pPuW61/zkr3yy8G0vqQm3qA0zjZp/Iv3rlFtx3Zb+9a3t6/e910yN8T3vEDq+CWDsV8Nj9aXrLPa9wr+ffVZOlf3IUnqtvIf+pwb3z6f6vi5/eP6tMZzPx+d+7+CX2/74jmSS6DaIVTf+A8an6oRmedjPn3+fe3LnLKx+MVfmAeMT59PX/8aDT3yprN+WuFzuXvr5zsuO+Y/yLfjmI2mlk4dwckAv/Yf6aju51P9zhrUvkZjkjcDzn98fGJg8hqX7O3D4Pbvh+Pn384mPvbf96S3HS8PA7nyvncHv0e3QVR5L8TkBujxgH31XW1TzXhu969qVK5++8a/cfxYh41/kKuPvv3rh+2grVp91vPbfr47q2l2JkCAAAECBN5UoNhsNmE1m4SiKMrHKAmn4rrqEVuRC6UuMUMq90HrVXSqL+Lxcp760ob44bH5Pn5or29a/HF3L6JdA+KXsWljffoBO27Uvqloc/9yi/hBsLoksPEBfUgvWzdFbd3UvOxLdezq+eADc/480b/qe7w/xrJ8X30BiB/I9/ePqbbbX5LYtz5/3mpN+WWvvNHt7jwHPsd8u89ffRkrw4Oj7d992cycP7Upj5nU0/5eOue0b/sv/JXr6f6x/X31Wd34vRrTSn7Yc+dNl3f11e/TPT77+tm2oQx5q3FqjH///mkbTvfrP35n/+vfJbH9x8Z/2Pj0jUTOJ+37y+qz78yx/enPf7uG+v3SNh6Oz3GfdN98/4714Xj7hh4/578987Hx7z7//vdT23dnnfz+TtvY5VdeMtb479O4/Ku0e5Vj7dtvlXt17Pzpuuz4lP+AUtVQDPd3Jo2fodzZt8tz/oPO3/PzefzMsa3jw//+d+1U/rfhcAbSkN/Puf4N+e9X3/hvf39tf27T32X7/37FzlR1etj+rq6esiyOUfNcp+xtWwIECBAgQOCWBcpAqgqdjj3nwqjLzJCK4VD7S84ts2v7qQLvPf7vff5TvU7ffhvA+lJwupw9CBAgMEigbxbcoIPc20Y+393biOoPAQIECBA4RWBwIBUPmgulLjFD6pRO2fa+BBqzL074F/PXUnjv879WP44eJ1528yaXYhw9q5UECBC4e4F01lHRuLT37ruugwQIECBAgACBowKDA6lcGHWZGVJH+2AlAQIECBAgQIAAAQIECBAgQIDADQkMDqRin3KhlBlSNzTimkqAAAECBAgQIECAAAECBAgQeGeBwYFULowyQ+qdR9DpCRAgQIAAAQIECBAgQIAAAQI3JjA4kIr9yoVSZkjd2KhrLgECBAgQIECAAAECBAgQIEDgHQUGB1K5MMoMqXccPacmQIAAAQIECBAgQIAAAQIECNygwOBAKvYtF0qZIXWDI6/JBAgQIECAAAECBAgQIECAAIF3EihWq0VYzCZhNCrCaLYKm82m85ELoy4yQ2o9D+OiCEVRhOkyhLCclq/r92EZprv1cVkxnjc5k+2r9evmFr3vltPt+cv9q3OVjendtbFBPM54furZG4co38zH49A4TDQaz8P5Rz48lyUECBAgQIAAAQIECBAgQIAAgdcU2M+QWkyOBlLxpLlQ6hIzpNbz8TaMqnofQ6Z2ILQLZZbTVliz2ye3vDrkkOfYjtcIlLrONR9PQ8zbev/X1fcyp9sFdr0HsAEBAgQIECBAgAABAgQIECBA4P0EBgdSuTDqIjOkQghDAql6m+W0MzR6q0AqnjfOnBpPp/VMrqIdLiWztA4CrWQGWDoDq523VWUyH2eCp95ZUusQ9y2KaViaSlVxeiZAgAABAgQIECBAgAABAgQuLDA4kIrtyoVSl5ohlYY1ZQDUvGYtxBlG25xlHaYdl6+9VSAVbcpQKoZQu6AnnusgeNpt17U8HmPYDKl4eWJuJlVc1z07bFtXAqmtg/8nQIAAAQIECBAgQIAAAQIE3lNgcCCVC6Pec4ZUI9iJs6KSKUUxEErelsZvHUg1zpeZpRWDq0a7k9EfFEiVs6mq4C3ZefcyO3vqcFNLCBAgQIAAAQIECBAgQIAAAQLvIjA4kIqty4VSl5ohdSzwGXLT8XYgVV/idwJ9LlA6OJZA6gRVmxIgQIAAAQIECBAgQIAAAQKPJDA4kMqFUdcxQ2rZcYlevDyteWnb9QdS+8vt1vN4P6qumVBnXLJX3ceqkew9UrnrKwECBAgQIECAAAECBAgQIHANAsViEm9y3XqMZmGz2TQesbG5UOqtZ0jF2UdVG8sspQpW2jcSr++fFEObqk/jMJ9P6/2r41TPQ7OZYzOwjravvM9V2p6qXfG5GZit46yqqt3J/ajahZK9LK/npubbPnSFXO0zeE+AAAECBAgQIECAAAECBAgQeDuB/QypVgDVDqRyYdSlZki9HcENHjkGch1JWgycOhbvOrjuueH5DTpoMgECBAgQIECAAAECBAgQIHCTAoMDqdi7XCj11jOkblL2jRs9H+8v7ytP1TM76o2b4/AECBAgQIAAAQIECBAgQIAAgcECgwOpXBhlhtRgaxsSIECAAAECBAgQIECAAAECBAiEEAYHUlErF0rd9AypOLOoum9T53PzPk+qhgABAgQIECBAgAABAgQIECBA4DyBwYFULowyQ+q8AbA3AQIECBAgQIAAAQIECBAgQODRBAYHUhEmF0rd9AypRxtx/SVAgAABAgQIECBAgAABAgQIvLPA4EAqF0aZIfXOI+j0BAgQIECAAAECBAgQIECAAIEbExgcSMV+5UIpM6RubNQ1lwABAgQIECBAgAABAgQIECDwjgLFZrMIk1ERivKG3qMwma3CZrM5eOTCKDOk3nH0nJoAAQIECBAgQIAAAQIECBAgcIMCxWw0CpPFLoRarcL2/WEgFfuWC6XMkLrBkddkAgQIECBAgAABAgQIECBAgMA7CRxcsreaxYDqMJDKhVFmSL3TyDktAQIECBAgQIAAAQIECBAgQOBGBZqB1GoWRqNZWHVcshf7lwulzJC60dHXbAIECBAgQIAAAQIECBAgQIDAOwjsA6nVLExGk7DoCKPiPaVyYZQZUu8wak5JgAABAgQIECBAgAABAgQIELhhgTKQWs0mYTRZdM6Mqm5wHvuYC6XMkLrhCtB0AgQIECBAgAABAgQIECBAgMCFBYrZpPmX9dxD6sIj4HQECBAgQIAAAQIECBAgQIAAgQcTKEZFEYrWo+um5tHFDKkHqw7dJUCAAAECBAgQIECAAAECBAi8gcD+HlKZe0dVl+zlwij3kHqDUXFIAgQIECBAgAABAgQIECBAgMAdCwwOpKJBLpRyD6k7rhBdI0CAAAECBAgQIECAAAECBAi8ssDgQCoXRpkh9coj4nAECBAgQIAAAQIECBAgQIAAgTsXGBxIRYdcKGWG1J1Xie4RIECAAAECBAgQIECAAAECBF5RYHAglQujzJB6xdFwKAIECBAgQIAAAQIECBAgQIDAAwgMDqSiRS6UMkPqASpFFwkQIECAAAECBAgQIECAAAECryQwOJDKhVFmSL3SSDgMAQIECBAgQIAAAQIECBAgQOBBBAYHUtEjF0q9xwyp9fM4jJ/XDzJMukngNAE/H6d52ZoAAQIECBAgQIAAAQIELitQbDarsJhNwqgoQlEUYTSZhdVmEzatRy6Mep8ZUssw/TANXXFU/CI+/XQa4vKpCMWH7SOGXPEY1fvi43x3sHWYP+2Xj5+mYbxb19h+d5xy/2rfT9P98arzPM07259v+TosG+efh2Wjr4fra5/P8zBO2xVff5yG5eft2Xrbn2/Udk1H/04//tZ3/HE3Dk/z0nu+a2M6RvXYPC3L81frqoCy0Z/dNn1dqI5RHzsaDdw3PXY8TtWOdPlrvY5966/v/M/Ha7XDcQgQIECAAAECBAgQIECAwDkCxWo2CqPZog6gFuX7Vf2+CqbiSXKh1KVnSMUv5e0v/etP8zD/tA2TpvF5934oTgx7qvBju88yTKtAKYQypJomM7Li8atAKm5/0KYYArX237c5hkenBRfb9tURUwif12H+sajDifJ4T8s65IphVQzT6j1iaJQGLGX7pmEb6fS3v8+xHZSs4/my/Q8htHxi/+K41f/7vA7Tj+0x6Wjnboe4/ziGbNUBWsevFvc9H4xj3w4nrJ+n7Tthv7hpVc9b5+P1/ZZ9OLHZNidAgAABAgQIECBAgAABAp0CB5fs5QKpXBh1+RlS+dkf688xRIqzf+b17J/OXncsHBJIjZ/3gU/7EPsQoBlkVdvt1++WxMAkDYiqDbue+7bNhC8xpKpn07QDqVaItm9fd/u7mpUui/vX5yoDlGmjf0ePn2l/evzq9f441ZLtczl+z8k5TzhmeqSu48dlceZUOSuummnWDpeSWWL74HF35NiWar/kOfVK25B7Pay+8z8fueNaToAAAQIECBAgQIAAAQIELi2wC6RWYTaqLtnbz5aqZkfF5/i/XCh1yRlSXYFBhVYGBx/nYd4xs6baJvfcF0iFsL0krgoWxvGSu93lZPGYVWjRuFQvOVmj3eXspmaAk2x6+PLT9GBGWLpR49jpinS/jkAqJMv62p8etut1Y/8ydGnObmqsT2ZOlcdK29l18GRZrq/V+FXP7RlYySGOvswdv2x/cpljPM9B8NQK+donOmeGVDzWtg3H6zvX/nZbvCdAgAABAgQIECBAgAABAu8p0JwhtVqF2SRewnd4yV4ujLrsDKnt7I/6sqyW3PJ5vr1kK86Ues5t1dpp97YOMurVx2cK5S9J696vDBPq2TGty9Pqc2Ze9AQ22RAi3S8Jn+qzJMv2x+huf71P5kXcvzHjp5yttg+ljh4/bWeIY7y/p1fjMsMjgU89fruZUetXnCEVu3zQv0ab9yj7fu6XVa/ODaT66/v4z0fVDs8ECBAgQIAAAQIECBAgQOC9BZqBVLyR+WoWRqPZVd5D6tiX/XMh60CjOlBvoNH88t/XtnR9DLMa9zuqzpl77mtLZv3LLtnLNeL48ti/RiDVCnHS/h8cKba/4/LFgzEZEkhV531u3sPr4JyZBbl2HvTvHQKpTJPrxbm21xt4QYAAAQIECBAgQIAAAQIErkSgmI1GYbKoZkStwmJShGJyeNne+8+QWpd/We+0eU/DleOX+XQ2TvN9DJ/G5U3T6yO2QpSuMCCdEdNe3zx+fdTsixjONC4R281AqkKg029qHmdC7W8C3m5fbEja/mzDdivi/lVbykWxfR/297DqO355mWV6U/N4/oMbzR+/qfn+pvSxb9t7ifW1u72+q51xm4P+vSiQSmeMTcM485ci220a9v5tfz6GtcFWBAgQIECAAAECBAgQIEBgmECx2V2mVxT7e0it4kyp1iMeLhdKXeIeUrmgYFg3h2y1vUdUeQ+o8gbWyV+oKy8jG4fp0/bm1tv7RDXDnGq/5vNum3hpXHUZWn3/pO1fyUv/El1fK9fxpt31cabNgGx3j6vqPOU9rqoDxvCs2q96Tu6HFG2r/ZrP+z5Wh+p8TvtXHT8a7v4q4bDjr8sAan/+5mWNMXDbr9u93s2qStdV5yzvj1Vbd7a6sTA9Rn2e3fHT9pehW9Lf7fm24Vu9X23Q9CtnxlXrEv9GQ1745u1/Pl7YMLsRIECAAAECBAgQIECAAIEOgcNL9lpBVBVM5cKoy9xDKoY3+9klHf2wiMADC/j5eODB13UCBAgQIECAAAECBAjcpMDgQCr2LhdKXWKG1E3qajQBAgQIECBAgAABAgQIECBAgMCBwOBAKhdGXWaG1EG7LSBAgAABAgQIECBAgAABAgQIELhRgcGBVOxfLpQyQ+pGR1+zCRAgQIAAAQIECBAgQIAAAQLvIDA4kMqFUWZIvcOoOSUBAgQIECBAgAABAgQIECBA4IYFBgdSsY+5UMoMqRuuAE0nQIAAAQIECBAgQIAAAQIECFxYYHAglQujzJC68Ig5HQECBAgQIECAAAECBAgQIEDgxgUGB1Kxn7lQygypG68CzSdAgAABAgQIECBAgAABAgQIXFAgCaQWYVIUoZgswmazOXjkwigzpC44Wk5FgAABAgQIECBAgAABAgQIELgDgV0gtQqz0SjMFrMwygRSsa+5UMoMqTuoBF0gQIAAAQIECBAgQIAAAQIECFxIoAykFpNRmCw2YbPKB1K5MMoMqQuNlNMQIECAAAECBAgQIECAAAECBO5EoFjNRvtZUUcCqdjfXChlhtSdVINuECBAgAABAgQIECBAgAABAgQuIFAsJkUo4r2jksdotnIPqQvgOwUBAgQIECBAgAABAgQIECBA4BEFkpuaH79kL+KYIfWIJaLPBAgQIECAAAECBAgQIECAAIHXFRgcSOXCKPeQet0BcTQCBAgQIECAAAECBAgQIECAwL0L1IFUeule1yV7ESIXSrmH1L2Xif4RIECAAAECBAgQIECAAAECBF5PoA6kNpvNwX2j0mW5MMoMqdcbDEciQIAAAQIECBAgQIAAAQIECDyCwOBAKmLkQikzpB6hVPSRAAECBAgQIECAAAECBAgQIPA6AoMDqVwYZYbU6wyEoxAgQIAAAQIECBAgQIAAAQIEHkVgcCAVQXKhlBlSj1Iu+kmAAAECBAgQIECAAAECBAgQOF9gcCCVC6PMkDp/EByBAAECBAgQIECAAAECBAgQIPBIAoMDqYiSC6XMkHqkktFXAgQIECBAgAABAgQIECBAgMB5AoMDqVwYZYbUeQNgbwIECBAgQIAAAQIECBAgQIDAowkMDqQiTC6Ueo8ZUuvncRg/rx9tvPT3QQTU94MMtG4SIECAAAECBAgQIEDgQQWKzWoWRkURivoxCYvNJmxaj1wY9T4zpJZh+mEauuKo+EV++um00Vw+FaH4sH3EkCseo3pffJzvDrYO86f98vHTNIx36xrb745T7l/t+2m6P151nqd5Z/vzLV+HZeP887Bs9PVwfe3zeR7Gabvi64/TsPy8PVtv+/ONKtf07V/5VgFiY/unZQg97es5fbm6Okc9brGP8dgn/i8ep2rnibsO2jz2vb8+8/U96CQ2IkCAAAECBAgQIECAAAECVy6wDaQmi4MAqh1IxX7kQqlLz5CKX+rbocH60zzMP23DpGl83r0f6h/DnvkuoNnuswzTKlAKoQyppsmMrHj8KpCK2x+0KYYsrf33bY7h0WnBx7Z9dcQUwud1mH8s6nCjPN7Tsg65YlgVw7R6jxiKpQFN2b5pqCKbvvb3OfbtH9s/jiFYdaCWT+hpX7Vb3/NBO/p2OGH9PG3/CfvFTat6jO3rq8+37MOJzbY5AQIECBAgQIAAAQIECBB4E4HBgVQujLr8DKn87JH15xgixdk/83r2z1C1IYHU+Hkf+LSPuw8RmkFWtd1+/W5JDGTSgKjasOu5b9t2uLM7Rgyp6tk47cCnFaLt29fd/q5mpcv69i99n6f7Prfb3NO+9FzHXu/bsd8qLoszp8pZbdVMsXa4FM+/W7cPDnfHiG2t9kuea9v9qY6+Glaf+fo+enArCRAgQIAAAQIECBAgQIDADQkcXrI3moTF6vCSvdinXCh1yRlSXYFD5V0GDx/nYf6xPdup2iL/3BdIhbC9JK4KJsbxkrtkRlUVejQu1UtO12h3ObtpyKVbuwN8mh7MCEsOfTg7q1qZ7tcR+KSzkvraXx0y99y3f+VbPZeX6SUzyNK21OfoanO9svtFwznZpGxfcplibMdB8NQK6ZLdy5fnzJCKB9i24Xh95trfbov3BAgQIECAAAECBAgQIEDglgUOb2oe7ynVcQlfLoy67Ayp7eyR+rKvlvzyeb69JCzOlHrObdXaafe2Dkrq1cdnCq1jWJIEKvsgoXu/MoyoZ9dsL9uqT9X3Ig2WOrbdn7u1Mt2vK9xJlu2P0d3+1pEP3vbtX/vuZkatB8yQ6gypDs7cXLBvx+Hyxoym1CbZNLd/3OTcQKq/Po/Xd9JMLwkQIECAAAECBAgQIECAwE0LFDfdeo0nQIAAAQIECBAgQIAAAQIECBC4OYHiF3/xF4MHAzWgBtSAGlADakANqAE1oAbUgBpQA2pADaiBS9WAQEogJ5BUA2pADagBNaAG1IAaUANqQA2oATWgBtTARWtAIKXgLlpwl0panUeqrwbUgBpQA2pADagBNaAG1IAaUANq4HprQCAlkBJIqQE1oAbUgBpQA2pADagBNaAG1IAaUANq4KI1IJBScBctOOn09abTxsbYqAE1oAbUgBpQA2pADagBNaAG1MClakAgJZASSKkBNaAG1IAaUANqQA2oATWgBtSAGlADauCiNSCQUnAXLbhLJa3OI9VXA2pADagBNaAG1IAaUANqQA2oATVwvTUgkBJICaTUgBpQA2pADagBNaAG1IAaUANqQA2oATVw0RoQSCm4ixacdPp602ljY2zUgBpQA2pADagBNaAG1IAaUANq4FI1IJASSAmk1IAaUANqQA2oATWgBtSAGlADakANqAE1cNEaEEgpuIsW3KWSVueR6qsBNaAG1IAaUANqQA2oATWgBtSAGriBStozAAAdrUlEQVTeGhBICaQEUmpADagBNaAG1IAaUANqQA2oATWgBtSAGrhoDQikFNxFC046fb3ptLExNmpADagBNaAG1IAaUANqQA2oATVwqRoQSAmkBFJqQA2oATWgBtSAGlADakANqAE1oAbUgBq4aA0IpBTcRQvuUkmr80j11YAaUANqQA2oATWgBtSAGlADakANXG8NFN/85jeDBwM1oAbUgBpQA2pADagBNaAG1IAaUANqQA2ogUvVgEBKICeQVANqQA2oATWgBtSAGlADakANqAE1oAbUwEVroAj+R4AAAQIECBAgQIAAAQIECBAgQOCCAsU3f+JPBQ8GakANqAE1oAbUgBpQA2pADagBNaAG1IAaUAOXqgGBlEBOIKkG1IAaUANqQA2oATWgBtSAGlADakANqIGL1kDx1a9+NXgwUANqQA2oATWgBtSAGlADakANqAE1oAbUgBq4VA24h9QFr490KgIECBAgQIAAAQIECBAgQIAAgRAEUqqAAAECBAgQIECAAAECBAgQIEDgogICqYtyOxkBAgQIECBAgAABAgQIECBAgIBASg0QIECAAAECBAgQIECAAAECBAhcVKD42i+ugwcDNaAG1IAaUANqQA2ogVNroP2p9dT9ba/m1IAaUANqQA08bg0U/+Nf+7vBg4EaUANqQA2oATWgBtTAqTXQDqRO3d/2ak4NqAE1oAbUwOPWQPE//fW/FzwYqAE1oAbUgBpQA2pADZxaA+1A6tT9ba/m1IAaUANqQA08bg0UP/c3fil4MFADakANqAE1oAbUgBo4tQbagdSp+9tezakBNaAG1IAaeNwaKP7n/+3vBw8GakANqAE1oAbUgBp4vBr4+e/5nnDKo10j7UCqvd77x6spY27M1YAaUANqYGgNFF//m/8geDBQA2pADagBNaAG1MDj1cApYVTctl0j7UCqvd77x6spY27M1YAaUANqYGgNFD//t/5h8GCgBtSAGlADakANqIHbrIGiKEL7MXgsT5wh1T5uO5Bqr//h316E7/ljqeufCt9TfHf4j5fJsj/2faH4jV+EP5f9TNqxz8G2Q7ZJzrn8InxH8X3hhw+Ok2xjne8IakANqAE1oAbetAaK1Td+OXgwUANqQA2oATWgBtTAbdZADKOqsauCqep933M6Q6oKl+Ky6n/p+vi6fbxqu+q5vX71x78vFL/9J/f7/fHvC9/xG787fMcf+Ll62X/9B5rvD47xjZ8M31t8d/iBv3hsfIZsE/f/ufADv7EIRWxD8X3hR3wOrsfh0P2Yt3W81IAaUANq4PwaKH7hb/9K8GCgBtSAGlADakANqIHbrIEYQlVjVwVS1fu+53bgVAVL8bm9Lr5vHy/dPr5ur/+Fn/0ifMd3fRH+m93nzfnvKML3/sF02dfDD3zXd4cf+NloH1/vZ3t9xx/8eviFv/2nw/cmM8C2y+K2yfLf8ad37787/MAf/Eo9W2y/bde4xv2/EuY+Bx+OGRMmakANqAE1cKEaKP7K3/nV4MFADagBNaAG1IAaUAO3WQMxhKrGrgqkqvd9z12hUy6Mitu2j9cOpNrr/8rf+Xr4vd81Cr/3Z6NtfP2V8KP186+Gv/KzPxS+47t+KPz5g8+jPxV+SxG3jfvF19UxtmP0o7+zCMXv/KmkPXGbZNl/GYOpav+ucU2P37XessOxZMJEDagBNaAGXrcGir/6v/+j4MFADagBNaAG1IAaUAO3WQMxhKrGrgqkqvd9z+1AKg2Y2uvi+/bx0u3j6/b6+P7P/6FR+M4/9L+Ev/qXfih85+/8M+U2MVD6LX/iH4W/+ie+sl1XfR79E/sZTkUxCv/JX4pj8mfKQGr7uut917L2Pu2xjeu/En60Oq/nzrHrGk/L2rXkvZpQA2pADby0Boq/tv7HwYOBGlADakANqAE1oAZurwaqAKrrech4pqFTFS7FZdX/0vXxdfuY1XbVc3t9+f4v/+Hwnd/1h8Mf/c9G4bf+yZ3xn5yE4nf9dPijv6toLvuuPxz+2/Kz6U+H31qMwn/6l+P26euu913L2vvszlt/7o3rJ+GP1u/b673vHEteBz8DnPysqAE1oAZeXgPFX/+7vxY8GKgBNaAG1IAaUANq4LZrIIZSp45hO3Dqe98+fhVEVc/t9dv3vxB+35dG4Tu/9P3hx+rPnT8dfmtcViTL/qvvL0Oqcp/4uhiF3/ffxzHZhkvb1/F9PF4RvvM//4Wkv+1t2u/bYxvXJ+eu29XezvvuMeXCRQ2oATWgBs6vgeJv/L3PwYOBGlADakANqAE1oAZuuwZiIHXqGPYFUO317eNXQVT13F5fvf8LP/ibQvGl/yL8heRz5499uQjFl/9s0uY/G35bdQPzL39/+G3Fbwq/73/Yjkm5bVGE7/zBX9htn24bjxHf77c/fN8e27j994cfS9pTtdVz28p7NaEG1IAaUANvUwPF//pL/2fwYKAG1IAaUANqQA2ogcergXbg1Pe+XSNVEFU9t9d7/3g1ZcyNuRpQA2pADQytgeJv/v3/K3gwUANqQA2oATWgBtTA49VAXwDVXt+ukSqIqp7b671/vJoy5sZcDagBNaAGhtZA8bf+wT8JHgzUgBpQA2pADagBNaAGTq2BKoiqnk/d3/ZqTg2oATWgBtTA49ZA8Y1/+M3gwUANqAE1oAbUgBpQA2rg1Bqogqjq+dT9ba/m1IAaUANqQA08bg0U1QcIzwQIECBAgAABAgQIECBAgAABAgQuISCQuoSycxAgQIAAAQIECBAgQIAAAQIECNQCxU/8xE8EDwZqQA2oATWgBtSAGlADp9RA/WkyefHVr341eDBQA2pADagBNaAGhtRA8Xt+z+8JHgzUgBpQA2pADagBNaAGhtbAL//yLycx1P5l/PD5zW9+04OBGlADakANqAE10FsDLtnbf4byigABAgQIECDwEALnhkYCKaHbuTVkfzWkBtSAGlADAqmH+NipkwQIECBAgACBvcC5XwKGBFL7s3lFgEAq8Cu/8ivpW68JBDWhCNoCj1ITAqn2yHtPgAABAgQIELhzAYHUnQ+w7l21wKN80bzqQbiyxqmJKxuQK2jOo9SEQOoKik0TCBAgQIAAAQKXFBBIXVLbuQg0BR7li2az194dE1ATx3Qec92j1IRA6jHrW68JECBAgACBBxYQSD3w4Ov6uws8yhfNd4e+oQaoiRsarAs19VFqQiB1oYJyGgIECBAgQIDAtQgIpK5lJLTjEQUe5YvmI47tS/usJl4qd7/7PUpNCKTut4b1jAABAgQIECDQKXAskPrdv/t39/6ZZjc172S1kMAggUf5ojkIw0algJpQCG2BR6kJgVR75L0nQIAAAQIECNy5QC6QimFU9chtE5cLpO68QHTvTQUe5YvmmyLe2cHVxJ0N6Ct051FqQiD1CsXiEAQIECBAgACBWxLoCpuqICp97truvEBqGaZFEYrpssUVl4/DfN1a7C2BOxTIfdFcToswbv8QLKcdPy8Vip+bSuLWn4/VRBF/Z7YeB3Vy6wDafyCQq4mDDW98gUDqxgdQ8wkQIECAAAECpwq0g6Z2CNV+397+5TOktl+gx+MiNDMpX6xPHUPb365A9otmR/gUQ6rmz0rabz83qcYtv87WRN0pY11TPMiL/pq4DwiB1H2Mo14QIECAAAECBAYLtAOmKoBKl3ctq9afG0jNl/Pw+7/rPwz/37/17SF8+7eE8G3fGsK3xWcPBmpADagBNaAGTq6B+N/S3/DrQ/ixHx78WeAaNhRIXcMoaAMBAgQIECBA4IICVbBUPcfwqXqdPueWnxtI/Xd/6i8Kn4RvakANqAE1oAZevQa+NYQ/8sUFP1GcdyqB1Hl+9iZAgAABAgQI3JxAGjq95PW5gdT/+2/8K76EvPqXEDMKTp5RYAz8HKoBNXCPNfDtv+5mPpcIpG5mqDSUAAECBAgQIPA6Ai8JodJ9zg2k0uDgj/w7/34Iv/bTzZuax3vp1DfxrW523r6HypH363kY1/vvbgicvxHP66A6CoGBAkfvDRNrdzwP67AO83FV+yGE3p8JPw8D+a9ys6M1UbY4Hd/4ehq2fxpiGaZlvcSN0m223azvQeZ34lWO+7FG9ddEa++f/7lmuNhafa1vBVLXOjLaRYAAAQIECBB4I4E0XHrJ67MDqfK+UdsZPX/s3/6XwnSZfJEqvzhVX7bSL+XJNqXLkfeNY7wRosMSeKHA8S+au5pfVsFUCKFRz7mfCT8PLxyOq9jteE3EJjbHdz0fl3+RsXredqK5TShDzd1N8Rs1dBVd1ogegf6a6DhAfT/Gb/3/27ubV0muMg7A5X+Q4D5iIKBrdRZ3kVXAhSAIQkBjGFCuG8EsMxg3IhlQkMkyxCFEV2EWCXEcVwFRNy4c40YjszMG0YWMH0hAyZGqvtX3VHV1n+766Fun6wnc3O469XHqOW9XV/2mum9H4zwnCaTmOS56RYAAAQIECBCYTKBPCBUvMziQij8iUV0olXcxXdwNUt4JUv+Lf9VW3yXSvtja9bxsK6oLtskQrZhAT4HUhWYZMpR3CJ7derDawl6viTKoiv4iX3VHVfzaidbXs98Wm04gVRPtQKoKKc/Ow3l8F10VWkXj3AihHBOnG71p1pyuiY7txu+tHc1znCSQmuOo6BMBAgQIECBAYEKBOFzq83jUQKq6AaS8AK8vnlcX1tVH9hoXXLsCqBKr1b4Ounxkb8JSsuoeAskLzUYQW25gz9dE/LG+8/Pmx2C9HnqM1PEWSdZE+/hWfYqzCEXjo8irY+D5+SrQLI+hjWY1cLwBHWFL6Zro2IhAqgPFJAIECBAgQIAAgVkJ9Amh4mX6B1IXDBmeNM9qAHUma4FeF5pZ77HOpwT61MT6+6HWK2+F8uvpHuQo0Kcm4u9nzGWf3SGVy0jpJwECBAgQIEBgJIE4XOrzWCA10kBYzSIFel1oLlJqOTt9cE2UdzvVH21eMwmk1hQn8ODgmij3OcN/7BFInUCx2gUCBAgQIECAwCECfUKoeBmB1CHa5iXQFOh1odlchWcnJrB/TdQf36w/4hxDCKRijdwf718T0Z4KpCIMDwkQIECAAAECBGYpEIdLfR4LpGY5rDqViUCvC81M9k03+wmoiX5up7xUr5oQSJ1ySdg3AgQIECBAgMBpCPQJoeJlBFKnUQf24moEel1oXk1XbfVIAmriSNAZbaZXTQikMhphXSVAgAABAgQILFQgDpf6PBZILbRw7PYoAr0uNEfZspXMVUBNzHVkrq5fvWpCIHV1A2bLBAgQIECAAAEC+wn0CaHiZQRS+zmbi0CXQK8Lza4VmXYyAmriZIZytB3pVRMCqdH8rYgAAQIECBAgQGAigThc6vN4n0Dq7t27YdtP/JeAts1j+nY/NmzUgBpQA2qgXQM5vrf6K3sTnehZLQECBAgQIEBgrgJ9Qqh4mX0CqZ37PoN/xY33x+OHgcHxDMqLyOL1234YrGugrAmvweO9BnOwLmvi4P9m8N56aJ8FUoeKmZ8AAQIECBAgkLnA0JPxuQdS337r/eQIDTWwvIvHvjUgkBLGtQNJgZTjSft4IpBKvo2bgQABAgQIECBAIEeB9onvoc/nHEg9/8Z74Xtv/z3cePO9nUNz6D6b3wXjWDUgkBJICaQcT1LHE4HUzrdwjQQIECBAgAABArkKpE6EU+1zDaRuvPnncOtX/wkv/yaEH/ziXztDqdQ+anfBOFUNCKQEUgIpx5fU8UUglesZln4TIECAAAECBAjsFEidCKfa5xhIfeut98P3f/7vKowqA6nX3vkw3P71P8Mnv/Rmp0VqH7W7YJyqBrYGUk8VoSjKn8dD8dJFaHPjyYtpRSiefXH9nUPF1NN9v9Ol9REsyprYqLc711djf+1muP/wTrheXAs373tdbjg9PE0TgVTnW7eJBAgQIECAAAECuQsMPaGfWyD1wk/+El765T/C7d9+WAVSr77zYXjr3f+Gxz7/eig+9XLncA01sPxpXgQeY1w7A6kyjIoDpyoEeS4UxZMXwUj5uA6qpp7uDqb2HUxTP98MpMoAqgjX79Svs6GB1NDl6374fYxjRLkNgVTnW7eJBAgQIECAAAECuQsMPaGeWyD1zCvvhu/87G9VCPXj363CqE88/UYVRgmkXEAOrfexl98MpJ4LxRNPb96RU94F9dRzl9OffXwVWk09/Qh3BE0d8OS2/u5AKr4jamigNHR5x5GxjwOp9Qmkcj/T0n8CBAgQIECAAIFOgdSJcKp9boHU13/0IJSh1At3/xre+P3/wme++tN1GLVvIHXnev1xqSIU1+9sfnzmRD8Wkhpr7eNfiG8EUlXAFH00rw6n6gCqDojqIGrq6fX2/L4MAye2aAdS5d1Rq49vFuHazfvhYfsje/dvhmvRPJd3Uj0Mm8eyMoy6XJ/j2/iv6SmOkwKpztMXEwkQIECAAAECBHIXGHryPLdA6rt3/xSevf3H8OVX3m0EUdvCqHL8thu4k2C7TR4XcnPvf2cgtf443u1Q1B/fmzp42rb+icOX3O5eOkZ/24HURgDVCKRWAdM6hKrCqfhuqvp1Gh/L4sd1u99zPlYIpHI/09J/AgQIECBAgACBToGhJ+FjBlL1Sfehv9s79pUfvrsRSLXniZ9vGNRfIFzdSdB1cefibcPMXWM7gs3t9VLWeiPkqO98qoOg+vm2wGjq6XU//G6O04QeZU00X1/tACl6XgVQ18Od6PVX3hW1Dqg6j2XR8tFyzW1ur1nzHd+mfk+M37eSjx97JIT6JznzPGYo5tENvSBAgAABAgQIEDiWwNCLizEDqTH3+ZlX/rC+Qyq13oZB4wLvfrh5TSDV8HEB2woLhl2c7h1I1cFUHYTUQdTU0+vt+Z1fILX1WCaQyu2YJpBKvYtrJ0CAAAECBAgQyFJg6In5mIFUr5PuHeof+XT3X9VrL9IwKO8oqP60+sPwcOvHX4aFEI3tCXhGDXhys90IpF6P/4Lei6F4ogjFjduheOnp7r+yN/V0QdTRgqj6TrmyJpp13A6Q4ufl4+iOqDiE2nosi5d3LGtaz9Oj13tjfXdU+TuT/9whlclA6SYBAgQIECBAYCyBoSfjYwZSY+3ToetpGpR3RV186e+16+G6O6RaF8fzvGBrjmE+fdwMpOrw6aIG47+sV94NVX8hdRlS1WHR1NPr7fh9aT6hxWGBVB2c119UHt/Ruf1Ytv6yc3+0IYvjm0Dq0Hd18xMgQIAAAQIECGQhMPRCfsxAqtdJ9wjKQw0sn08ANLex6gykJgw71iGWbRwlXOrjvRlIeX3N7XV77P70em90h9QIZwdWQYAAAQIECBAgMKnA0BPrMQOpSXd0x8qHGljeBXPfGhBIRXd6CcmqkEwg5XjSPp4IpHa8gWsiQIAAAQIECBDIV6B94nvo8zEDqV4n3SPQH7rP5nfBOFYNCKQEUu27qARSji/t40uv90Z3SI1wdmAVBAgQIECAAAECkwq0T3wPfT5mIDXpju5Y+aH7bH4XjGPVgEBKICWQcjxJHU8EUjvewDURIECAAAECBAjkK5A6EU61jxlI9TrpHoE+tY/aXTBOVQMCKYGUQMrxJXV86fXe6A6pEc4OrIIAAQIECBAgQGBSgdSJcKp9zEBq0h3dsfLUPmp3wThVDQikBFICKceX1PFFILXjDVwTAQIECBAgQIBAvgKpE+FU+5iBVK+T7nzp9ZxAKGveDwM1oAZSNXDw4dIdUgeTWYAAAQIECBAgQODIAqnAKdU+ZiB15F23OQJXLlBehPqPQCygJmINj0uBXjUhkFI8BAgQIECAAAECcxdIBU6p9jEDqV4n3XMH1j8COwTU/A6chTapiYUO/I7d7lUTAqkdopoIECBAgAABAgRmIZAKnFLtYwZSswDRCQJHFOh1oXnE/tnU8QXUxPHN577FXjUhkJr7sOofAQIECBAgQIBAKnBKtY8ZSPU66TaEBDIWUPMZD95EXVcTE8FmvNpeNSGQynjEdZ0AAQIECBAgsBCBVOCUah8lkPrYIyGUJ8+T/HcvnBdFKKKf83uTbCiflT64Fc6Ks3DrQT5dPtWe9rrQPFUM+1UJqAmF0BboVRPle+qk763tXg5/XgxfhTUQIECAAAECBAjkJJAKnFLtowRSF/+S+/Zrr05AVwZSh4YvfZaZoOtWefICvS40T15l2TuoJpY9/l17f3BNfPDB6h956rukulY6w2kCqRkOii4RIECAAAECBKYUSAVOqfbBgdTHPxqdOD8aPb64a6o+ofabjRpQA2pADaiBPWqg9V465UnEiOsWSI2IaVUECBAgQIAAgRwEUoFTqn1wIPX8N/c4uRZOVR9pdCGmVtSAGlADauCQGvji53I4Fan6KJDKZqh0lAABAgQIECAwjkAqcEq1Dw6kyt34xteiC4zWv+wecuJt3shRiCfEUwNqQA0stwYeDeELnx3nROFIaxFIHQnaZggQIECAAAECcxFIBU6p9lECqQuM+nsyDv1977z80vJt3xO17fug2tPj5/HjEEL1JeDNL0Yvqm9Gb80XD+rWZUII986rL1lvfrn65rrK/VrN02rbtu6L6Wfxt5V3TQvR+qr28xB/z/vW7cbLxfsaHoRbZ6VPcz2NWTzpFKhrvbPRxEUKqIlFDvvOnV5KTQikdpaBRgIECBAgQIDA6QmkAqdU+5iB1DDdbaFIFL40NtCeHj+PH9eBVFfY0povXn9H0LNu3iuQWu3P9kCqqz+rLTy4dbYReDWnRf3u6Of+gVRtvi0MXO+xB1sElnKhuWX3Te4QUBMdKAuftJSaEEgtvNDtPgECBAgQILA8gVTglGqfTyBVj10UtlST2s/3ma+9TPm8CI07j3auu2zctUwZJpXtcajUmr8RFJVtcejTmrfepeh3GUC1+3s5LV7fal3ru7WS2637UYZR9eNowx4eJLCUC82DUBY+s5pYeAF07P5SakIg1TH4JhEgQIAAAQIETlkgFTil2ucXSLVHaxW4FMXlR+5WQU0cypTLNJ+vPgZYhNVH8+q7pC7XkfzIXrnKKtyJl7lVfbStDoqqu5bOboUHVZdX2z8/X93dVPZ3HRK1+lbNvrHue+uPAq729SLsurgbqzGtvb7GuuKQqWnSNqr64X+DBJZyoTkIaWELq4mFDfgeu7uUmhBI7VEMZiFAgAABAgQInJJAKnBKtc8/kMpltNrhTy791s8hAku50BxitLRl1cTSRjy9v0upCYFUuhbMQYAAAQIECBA4KYFU4JRqF0iNVQ4CqbEkc1rPUi40cxqTq+6rmrjqEZjf9pdSEwKp+dWeHhEgQIAAAQIEJhVIBU6pdoHUWMMjkBpLMqf1LOVCM6cxueq+qomrHoH5bX8pNSGQml/t6REBAgQIECBAYFKBVOCUahdITTo8Vn7iAku50DzxYRx199TEqJwnsbKl1IRA6iTK1U4QIECAAAECBPYXSAVOqXaB1P7W5iTQFljKhWZ7vz3fLqAmttsstWUpNSGQWmqF228CBAgQIEBgsQKpwCnVLpBabOnY8REElnKhOQLVYlahJhYz1Hvv6FJqQiC1d0mYkQABAgQIECBwGgKpwCnVLpA6jTqwF1cjUF5o+mGgBtRAqgau5gh13K0KpI7rbWsECBAgQIAAgSsXSAVOqXaB1JUPoQ4QIECAAIHsBQRS2Q+hHSBAgAABAgQIHCaQCpxS7QKpw7zNTYAAAQIECGwKCKQ2TUwhQIAAAQIECJy0QCpwSrULpE66POwcAQIECBA4ioBA6ijMNkKAAAECBAgQmI9AKnBKtQuk5jOWekKAAAECBHIVEEjlOnL6TYAAAQIECBDoKZAKnFLtAqme8BYjQIAAAQIE1gICqTWFBwQIECBAgACBZQikAqdUu0BqGXViLwkQIECAwJQCAqkpda2bAAECBAgQIDBDgVTglGoXSM1wUHWJAAECBAhkJiCQymzAdJcAAQIECBAgMFQgFTil2gVSQ0fA8gQIECBAgIBASg0QIECAAAECBBYmkAqcUu0CqYUVjN0lQIAAAQITCAikJkC1SgIECBAgQIDAnAVSgVOqXSA159HVNwIECBAgkIeAQCqPcdJLAgQIECBAgMBoAqnAKdUukBptKKyIAAECBAgsVkAgtdiht+MECBAgQIDAUgVSgVOqXSC11Mqx3wQIECBAYDyB/wOUqSOOXel86wAAAABJRU5ErkJggg==
呃，现在换成double好像可以了，应该是float精度不够导致的 硬汉兄，这个 floata=6000000 在MDK中一个浮点变量与一个带小数的浮点常数 是否需要在常数后加 f 如：1.23f 很疑惑 是不是不同的开发环境，不同的编译器这个小f 不同 dghwjh 发表于 2021-9-7 16:51
硬汉兄，这个 floata=6000000 在MDK中一个浮点变量与一个带小数的浮点常数 是否需要在常数后加 f ...
对于不支持硬件浮点和支持硬件单精度浮点的芯片，我使用IDE定义的float型都会加上f，保证全称是单精度计算。
不加的时候，很容易出现双精度运算，导致执行时间较长。 data:image/png;base64,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浮点运算 MDK 这个设置有关吗？ 如果我默认设置成双精度 两个变量为float做运算为 单精度 如果一个float 乘以一个不带f的浮点数 是不是结果 肯定是双精度
dghwjh 发表于 2021-9-8 17:18
浮点运算 MDK 这个设置有关吗？ 如果我默认设置成双精度 两个变量为float做运算为 单精度 如果一 ...
对，理论上是的，以H7的硬件双精度为例，也会有这种问题。单精度计算还是要比双精度快些。
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