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<title>Chapter 7: Exercise 2</title>
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<h1>Chapter 7: Exercise 2</h1>
<h2>a.</h2>
<p>\( g(x) = k \) because RSS term is ignored and \( g(x) = k \) would minimize the area
under the curve of \( g^{(0)} \).</p>
<h2>b.</h2>
<p>\( g(x) \alpha x^2 \). \( g(x) \) would be quadratic to minimize the area under the curve
of its first derivative.</p>
<h2>c.</h2>
<p>\( g(x) \alpha x^3 \). \( g(x) \) would be cubic to minimize the area under the curve
of its second derivative. See Eqn 7.11.</p>
<h2>d.</h2>
<p>\( g(x) \alpha x^4 \). \( g(x) \) would be quartic to minimize the area under the curve
of its third derivative.</p>
<h2>e.</h2>
<p>The penalty term no longer matters. This is the formula for linear regression,
to choose g based on minimizing RSS.</p>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch7/2.html
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<div id="chapter-7-exercise-3" class="section level1">
<h1>Chapter 7: Exercise 3</h1>
<pre class="r"><code>x = -2:2
y = 1 + x + -2 * (x-1)^2 * I(x>1)
plot(x, y)</code></pre>
<p><img src="data:image/png;base64,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<h1>Chapter 7: Exercise 4</h1>
<pre><code class="r">x = -2:2
y = c(1 + 0 + 0, # x = -2
1 + 0 + 0, # x = -1
1 + 1 + 0, # x = 0
1 + (1-0) + 0, # x = 1
1 + (1-1) + 0 # x =2
)
plot(x,y)
</code></pre>
<p><img 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<h1>Chapter 7: Exercise 5</h1>
<h2>a</h2>
<p>We'd expect \( \hat{g_2} \) to have the smaller training RSS because it will be a
higher order polynomial due to the order of the derivative penalty function.</p>
<h2>b</h2>
<p>We'd expect \( \hat{g_1} \) to have the smaller test RSS because \( \hat{g_2} \) could
overfit with the extra degree of freedom.</p>
<h2>c</h2>
<p>Trick question. \( \hat{g_1} = \hat{g_2} \) when \( \lambda = 0 \).</p>
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<h1>Chapter 7: Exercise 6</h1>
<h3>a</h3>
<p>Load \( Wage \) dataset. Keep an array of all cross-validation errors. We are performing K-fold cross validation with \( K=10 \).</p>
<pre><code class="r">set.seed(1)
library(ISLR)
library(boot)
all.deltas = rep(NA, 10)
for (i in 1:10) {
glm.fit = glm(wage~poly(age, i), data=Wage)
all.deltas[i] = cv.glm(Wage, glm.fit, K=10)$delta[2]
}
plot(1:10, all.deltas, xlab="Degree", ylab="CV error", type="l", pch=20, lwd=2, ylim=c(1590, 1700))
min.point = min(all.deltas)
sd.points = sd(all.deltas)
abline(h=min.point + 0.2 * sd.points, col="red", lty="dashed")
abline(h=min.point - 0.2 * sd.points, col="red", lty="dashed")
legend("topright", "0.2-standard deviation lines", lty="dashed", col="red")
</code></pre>
<p><img 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" alt="plot of chunk 6a"/> </p>
<p>The cv-plot with standard deviation lines show that \( d=3 \) is the smallest degree giving reasonably small cross-validation error.</p>
<p>We now find best degree using Anova.</p>
<pre><code class="r">fit.1 = lm(wage~poly(age, 1), data=Wage)
fit.2 = lm(wage~poly(age, 2), data=Wage)
fit.3 = lm(wage~poly(age, 3), data=Wage)
fit.4 = lm(wage~poly(age, 4), data=Wage)
fit.5 = lm(wage~poly(age, 5), data=Wage)
fit.6 = lm(wage~poly(age, 6), data=Wage)
fit.7 = lm(wage~poly(age, 7), data=Wage)
fit.8 = lm(wage~poly(age, 8), data=Wage)
fit.9 = lm(wage~poly(age, 9), data=Wage)
fit.10 = lm(wage~poly(age, 10), data=Wage)
anova(fit.1, fit.2, fit.3, fit.4, fit.5, fit.6, fit.7, fit.8, fit.9, fit.10)
</code></pre>
<pre><code>## Analysis of Variance Table
##
## Model 1: wage ~ poly(age, 1)
## Model 2: wage ~ poly(age, 2)
## Model 3: wage ~ poly(age, 3)
## Model 4: wage ~ poly(age, 4)
## Model 5: wage ~ poly(age, 5)
## Model 6: wage ~ poly(age, 6)
## Model 7: wage ~ poly(age, 7)
## Model 8: wage ~ poly(age, 8)
## Model 9: wage ~ poly(age, 9)
## Model 10: wage ~ poly(age, 10)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 2998 5022216
## 2 2997 4793430 1 228786 143.76 <2e-16 ***
## 3 2996 4777674 1 15756 9.90 0.0017 **
## 4 2995 4771604 1 6070 3.81 0.0509 .
## 5 2994 4770322 1 1283 0.81 0.3694
## 6 2993 4766389 1 3932 2.47 0.1161
## 7 2992 4763834 1 2555 1.61 0.2052
## 8 2991 4763707 1 127 0.08 0.7779
## 9 2990 4756703 1 7004 4.40 0.0360 *
## 10 2989 4756701 1 3 0.00 0.9675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p>Anova shows that all polynomials above degree \( 3 \) are insignificant at \( 1% \) significance level.</p>
<p>We now plot the polynomial prediction on the data</p>
<pre><code class="r">plot(wage~age, data=Wage, col="darkgrey")
agelims = range(Wage$age)
age.grid = seq(from=agelims[1], to=agelims[2])
lm.fit = lm(wage~poly(age, 3), data=Wage)
lm.pred = predict(lm.fit, data.frame(age=age.grid))
lines(age.grid, lm.pred, col="blue", lwd=2)
</code></pre>
<p><img 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" alt="plot of chunk 6aa"/> </p>
<h3>b</h3>
<p>We use cut points of up to 10.</p>
<pre><code class="r">all.cvs = rep(NA, 10)
for (i in 2:10) {
Wage$age.cut = cut(Wage$age, i)
lm.fit = glm(wage~age.cut, data=Wage)
all.cvs[i] = cv.glm(Wage, lm.fit, K=10)$delta[2]
}
plot(2:10, all.cvs[-1], xlab="Number of cuts", ylab="CV error", type="l", pch=20, lwd=2)
</code></pre>
<p><img 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" alt="plot of chunk 6b"/> </p>
<p>The cross validation shows that test error is minimum for \( k=8 \) cuts.</p>
<p>We now train the entire data with step function using \( 8 \) cuts and plot it.</p>
<pre><code class="r">lm.fit = glm(wage~cut(age, 8), data=Wage)
agelims = range(Wage$age)
age.grid = seq(from=agelims[1], to=agelims[2])
lm.pred = predict(lm.fit, data.frame(age=age.grid))
plot(wage~age, data=Wage, col="darkgrey")
lines(age.grid, lm.pred, col="red", lwd=2)
</code></pre>
<p><img 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" alt="plot of chunk 6bb"/> </p>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch7/6.html
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<h1>Chapter 7: Exercise 7</h1>
<pre><code class="r">library(ISLR)
set.seed(1)
</code></pre>
<pre><code class="r">summary(Wage$maritl)
</code></pre>
<pre><code>## 1. Never Married 2. Married 3. Widowed 4. Divorced
## 648 2074 19 204
## 5. Separated
## 55
</code></pre>
<pre><code class="r">summary(Wage$jobclass)
</code></pre>
<pre><code>## 1. Industrial 2. Information
## 1544 1456
</code></pre>
<pre><code class="r">par(mfrow = c(1, 2))
plot(Wage$maritl, Wage$wage)
plot(Wage$jobclass, Wage$wage)
</code></pre>
<p><img 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hMwAdQa9UwaujKt1l5e10y0fuPcv2risbvrF6iTu/1BcvukduWsE0A/MflpohAgQIAAAQIECBAgME6B+jx2dVKfweqL+LoE/GuTLyaXJwqBVgVqcqBrpSalbtS1TvW0Px9Ov1+W1LGmVf4qeWjy+XqgNCbw/tR0ePKZ5ANJzfC/OPloohAgQIAAgZUI2P5ZiZLXECBAoF8CZ6W7X00+mHwy+UhSF4y5afKfiUKgVYGuTADVbnAvTc5Oaq+J85PLki8nT06UtQm8Om8r208ndfjXLZI9ksk9gvJQWafAO/P+OtfSF5I679LvJHX416WJQoAAAQIElhKw/bOUjOUECBAYjkBd9WvXpD7jnpdUqYvH1NXBFAKtCnTlELDaDa5OiPWwpE6cW5M/2yZ3SmoSo06yO3kumzxUViBwbV7zZyt4nZesX+A1qaKiECBAgACBlQrY/lmplNcRIECgvwJ1vtDb9rf7ej4kga7sAbRvUJ+WfCmpvSZqNrTOq3JyckjyqEQhQIAAAQIECAxJwPbPkNamsRAgQIAAgY4LdGUCqA71uv8SVnVJ7bp8nkKAAAECBAgQGJKA7Z8hrU1jIUCAAAECHRfoyiFgL4jT0cmhyRnJxcl2ye5J9bFOXKwQIECAAAECBIYkYPtnSGvTWAgQIECAQMcFujIB9IU47ZXUpfF2Tep8QLXXT53351OJE2QFQSFAgAABAgQGJWD7Z1Cr02AIECBAgEC3BboyAVRKVyafSKpP2yQXJAqBvgjU4ZR7J3Xy8i8m30kUAgQIECCwIQHbPxsS8jwBAgT6L7BVhnCvZPPkM8mFiUKgdYGunAPIZVBbX/UabFBgk9T1puSApA5brCvZ1eUeFQIECBAgsJyA7Z/ldDxHgACBYQjskGEcleyT1CRQ7eiwW6IQaF2gK3sANXUZ1DqE7K5LKNbeGT9e4jmLCaxH4NV589eSI+YqeVtu/zU5Pfnm3DI3BAgQIEBgoYDtn4UiHhMgQGBYAvVF8UnJnyXHzw3tk7l9efLE5LK5ZW4ItCLQlT2AmroM6iVR+/4S2TLLd25FVSNjE7hDBvzmiUGfl/vHJPecWOYuAQIECBBYKGD7Z6GIxwQIEBiWwE0znG8k85M/NboTku8mdeSAQqBVga7sATR/GdR3LjL61VwGvuqpLFbuk4U7LvaEZQTWKXBR3n+bZJ+kzl/1+aQefzpRCBAgQIDAUgK2f5aSsbxpgf1S4d2T+rL0n5JLE4UAgekLXJEmtkhqx4trJ5q7fe7b+2cCxN12BLoyAeQyqO2sb61MR+A9qfYjySuTOgF07f1TJ3h7bqIQIECAAIGlBGz/LCVjeZMCf5vK7pzU3sq/ltQk0E7J9xKFAIHpCtQXxR9K6lQRz0+uSup0ET9NvpooBFoV6MoE0GKXQf1RJOqXo35hXAa+1R8Lja1S4FF5/RuTRyf1DVv93N4yuVtycqIQIECAAIHFBGz/LKZiWZMC905lj0lq4uea5IPJ2cnzkj9NFAIEpi9Q5wutL4Y/kNTET50D6BWJQqB1ga5MAG2akR+a3C55XVK7ytXtjZP3JwcmP0kUAl0UuGE69XdJfZM7X56aO7+amACaF3FLgAABAgsFbP8sFPG4aYHaFqk9D2ryZ778W+48bv6BWwIEpi5QOzO8dC5Tb0wDBJYTqGMRu1D+Pp3YJ6kTOL8r+Ztk/6QmhGqSqvawUAh0VeD8dGyvBZ2rn9+6xKNCgAABAgSWErD9s5SM5U0J1DZK7ZE8WfaYfOA+AQIECIxHoCt7AD005HdPLk5q75+bJbVrXJU6VvJvk5oYUgh0UaB24fxSUhOV30xqb7Y6vvf4RCFAgAABAksJ2P5ZSsbypgQ+nIpqr+STkoOT2iOoDv96UqIQIECAwMgEujIB9K243zE5Jfm/SZ0/Zb7USetOn3/glkAHBU5Ln3ZK/iJ5ePL5pH6OFQIECBAgsJyA7Z/ldDzXlMABqehpyZ8llyR/mJyRKAQIECBAYCYC+6bV85LfWdB6HStZVyhoYlfVV6WeoxfU7yEBAgQIECAwO4E6N8ldZ9f8zFu2/TPzVaADBAgQIECgdYGZbf905RxAtXvqHZLaA2iy/Ece3Cb58uRC9wkQIECAAAECAxCw/TOAlWgIBAgQIECgLwJdOQSsvOr8P5XJ4gpKkxruEyBAgAABAkMTsP0ztDVqPAQIECBAoKMCXdkDqKM8ukWAAAECBAgQIECAAAECBAgQ6L+ACaD+r0MjIECAAAECBAgQIECAAAECBAgsK2ACaFkeTxIgQIAAAQIECBAgQIAAAQIE+i/QpXMA9V/TCMYsUJOp+yTbJP+TfDtRCBAgQIAAAQIECBAgsFUI7pVsnnwmuTBRCLQuYA+g1sk1OECBTTKmNyX7J7snZyQPSRQCBAgQIECAAAECBMYtcJMM/6hk7+SeyQXJbolCoHUBewC1Tq7BAQq8OmP6WnLE3NjelttjkpoI+sbcMjcECBAgQIAAAQIECIxLoL4oPik5JDl+buifyu3Lkycml80tc0OgFQF7ALXCrJGBC9wh43tz8pdztzfM7b8md08UAn0V2C4dv22yfV8HoN8ECBAgQIAAgRkL3DTt1xfFNfnzkuT1ySeT85I6ckAh0KqAPYBa5dbYQAUuyrh+kNQxvdclf5jUeYBenCgE+ijwoHT6mcn3k32Tmtx8V6IQIECAAAECBAisXOCKvLS+TLsm2XjubX+U2y8kr5t77IZAawImgFqj1tCABR6RsW2W3Do5O6kZ/V9Ptk4UAn0TuFM6/OHkFkn9LO+QfC45NzkpUQgQIECAAAECBFYmUF8U3y+5Ntk2uSq5Mtkrqe0shUCrAg4Ba5VbYwMVqMmfI5P6cHxWUmf2/0nyskQh0DeBmtB8WDK/UfLj3D8o2S9RCBAgQIAAAQIEVi5Q21BVXpWclnw1md/z5+jcVwi0KmACqFVujQ1Y4NEZ2zbJFkl9gLZ3XRCUXgrUYYz1LdVkqccLl00+7z4BAgQIdFegzun2iuTjyacTE/pBUAi0LHBg2jsz+XbyJ0mV+UPCrn/kXwItCPiQ2gLyjJv41bR/QLJ5UicccwhHEBou9cG4Nq7qg3OVely/W3UiaIVA3wTq8K/3JH+UbJlcnnw0+c1EaV7gd1PlnZOLkzcmda4AhQABAk0JbJqKvpHUSWhvlNThJ89Kaptl/opEuasQIDAlgWNS71uTOrT+5klN+tRnhbo9NFEItCpgAqhV7tYbq3N51AeKOn/H1cmJye8n70iU5gTqpG61N938LH7d1obV7RKFQN8EvpgO1zfE70++kOyR1IeEkxOlWYF/SnV1COnbkppguzCpDcQfJwoBAgSaEKi9kmsS6DeSOrS3JoHqPCS1rWICKAgKgSkL3Cb11+ew+tw9f/RN/f7V54c9k68nCoHWBOZ/CFtrUEOtCtRxpfXH5fzknLmWn5rbW8/dd9OMQG1YzZf5vYDq8YPmF7ol0COBh6avD09ulfxhcruk9gJ6bKI0J/DgVFWTa09Iag+rFyXPTP6/RCFAgEBTAvW3uyZ9Lks+m3w3qb0PfitRCBCYvsDt08T8Thf1OWH+s0J9Dn/c9JvXAoFfFDAB9IseQ3tUH+BenrwkeW1SH+R2Su6SKM0L1Ae6X0vOSGpmf9NEIdA3gdoT5feSmjiub6V+lBye1HKlOYFdUlWdEHKyfCAP6u+2QoAAgaYEdk9F9YGztlHqb3s9/k5iGyUICoEWBP54ro2f5vYeSe31U/erPOD6G/8SaE9gfjayvRa11KbANWnsaxMNnpn79S3QjSeWuduMQFn/R1LfsG2SzM/u565CoFcCtbfPzRb0eOc8rp9tpTmBC1LVfZM6N8B8qXMBzW8Uzi9zS4AAgfUI1LZ+HX5ycvLcpCaf65xjCgEC7QhsPtfM2bl9X1Ln4fqvZO+kPjMoBFoVsAdQq9ytN1Z/aL6V7JPcNTk+qf/0v5wozQrUBlZN+vwwqWPrq/zk+hv/EuiVwNvT24OTOl9E7clWt+9N/iFRmhOojcDaG7Mmjmvi54DksOTZiUKAAIGmBGrPwtrer0mgHZNbJrW38qWJQoDA9AXeOdfEbXNbnxVqAqgmf6rURJBCoFWB+tCqDFfgiAztwKQ+WNSuvjXL/PXkc4nSnEB9k197VtUMfx3nO1/qWzaFQN8EzkmHn5i8OakPDTWpWbsrn5cozQlcm6oemDx9Lpfktv5el7dCgACBpgQOS0X1t6W2Uw5J6m/PlclzEoUAgekLvCFNvCapz923WtBcbQcoBFoVqB9EZbgC78jQfpL8QVIzzsclr0qUZgV2S3V1vpTJclEe/HhygfsEeiRwVvq6X4/629eu1t/l1/e18/pNgEAvBOrw3doLqPbsrL859WVg5chEIUCgHYHfSzPHTjRVe1jX+VkVAq0L1Le7yrAF3pPh1VV9HpbUCaFrt0OlWYH5yZ/6farUh+ftko8mCgECBAgQIEBgVgKPTcNPS/4uqYmf+W3/b+a+QoDA9AXq964mf65I5j8r1P06OfSvJwqBVgXm/xNotVGNERigwOT5UXbN+Opbtn0ShQABAgQIECAwK4E6r9i5yXMnOrB37t984rG7BAhMT+Cv5qreeqKJrebu/9PEMncJtCJgAqgVZo2MQOC2i4yxJoEUAgQIECBAgMCsBGpPgzoP5GS5xeQD9wkQmKrA/Ln9Fjv1iiusTpVe5YsJLPaDuNjrLOuvwK7pep0Euk5QfEJyUqI0K1AnVKzD7OpS8POlJlefMf/ALQECBAgQIEBgBgKHps260tA3kpoIqg+cdcGKryYKAQLTF3hDmqjz/dR5WevL4Tr/T6XKPj/71z8EWhSwB1CL2DNo6k5p863J2cmpyYnJ7ydKswIvnatu/g963dYf+Dc124zaCBAgQIAAAQKrEjglr74gqT2VfzWpbcOaCHpKohAg0I7AOWmmPh/UZ+/5zwu1d159XlAItCpgAqhV7tYbOyot/mlyTHJccsPkd5NbJ0pzAs+fq2qT3Nbv1PxJoS9urgk1ESBAgAABAgRWLfDqvOMmyTuT2kapbZX60PmpRCFAYPoC90wTdfn3q5P6Haz8NNkyqcvDKwRaFagfQGW4AmdnaKdNDK92+/3vZJeJZe42I/AfqWZ+Fr82tOr+5MnemmlFLQQIECBAgACBlQs8Ji+9PHn83Ftq++TAZLO5x24IEJiuwF/MVT/5O1en5qjyhOtv/EugPQETQO1Zz6KlOsfTbSYarm996j/9H08sc7cZgQcuqGb+MLAFiz0kQIAAAQIECLQmcFVaWngS6Pu11rqGCBD44hzBSyYo5i//fuHEMncJtCJgAqgV5pk1ckRaPiN5QHL35MPJ+5L/TZTmBOrbtK2SK5OvJHVS6JoAel6iECBAgAABAgRmJfCkNFxfCNZE0P9JDk+envwgUQgQmL7A/MRPfS74VlKfw74w1+zuc7duCLQmUP8hKMMV+ESGdofkKckWyeuTYxOlWYHas6omfWrXzjvOVf3p3L587r4bAgQIECBAgMAsBD6ZRp+V1JeCdR6g+oKq9gS/eaIQINCOwK3TzJnJrhPNPST368pgCoFWBUwAtco9k8a+kVafM5OWx9Vo7U335uR2Sc3wn5woBAgQIECAAIFZC7wqHagLghyQfCf5t0QhQKA9gfq9q88KtffdjZO6gnAdQaAQaF3ABFDr5BocsMDBAx6boREgQIAAAQL9FTgvXf+H/nZfzwkMQuANgxiFQfRawDmAer36dJ4AAQIECBAgQIAAAQIECBAgsGEBE0AbNvIKAgQIECBAgAABAgQIECBAgECvBUwA9Xr16TwBAgQIECBAgAABAgQIECBAYMMCJoA2bOQVBAgQIECAAAECBAgQIECAAIFeC5gA6vXq03kCBAgQIECAAAECBAgQIECAwIYFTABt2MgrCKxE4Dl50bnJj5MzkhslCgECBAgQIEBg1gLbpAMvSz6SfDy5f6IQINCuwJPS3LHJh5OXJz6HB0FpX6CLP3h1aXofntv/WdDi2gX+NG99VvK4ZNekNq5OS7ZOFAIECBAgsBIB2z8rUfKa1Qpskjd8JbkoeXjylOTZyQMThQCBdgSel2bukzwtOSD5afKiRCEwWoHNMvKXJmcn1ybXJZclX06enDRRXpVKjm6iInUQWCDwwzx+wIJlJ+Xx4QuWeUiAAAECvyhwRB7e9RcXjeqR7Z9Rre6ZDPbRafUNC1q+bR6/Z8EyDwkQmI5A7dhQXwxvuqD6Y/J49wXLPByPwMy2f+rbpi6U16YTN08elnwrqcmfbZM7Ja9OtkgW/ueVRQqBTgjUhOVXF/SkJjO3X7DMQwIECBAgMClg+2dSw/1pCNTeyPWF6mT5Xh7UYWEKAQLTF9g8TXwjuWpBU/UFcv1+KgRaFejKIWD7ZtS1S9yXkkuT+kBdu6qenBySPCpRCHRVoHat/teJzt0m938v+Y+JZe4SIECAAIGFArZ/Fop43LTAqalw/+TGExUflPvfnnjsLgEC0xP4Uaq+PKnPBvPl3rlTh2PWxJBCoFWBruwBVN9M1Anp3rnI6Ot45ZohVQh0VaA2rOoE0N9MTkvul7wy+WCiECBAgACBpQRs/ywlY3lTArWH8huS2uunzv1zy2SnpKlTLKQqhQCBZQSuznN/mZyT1NEtFyf12eFec/dzoxBoT6ArE0AvyJDr/DyHJnUFpfrF2C6p4yKrjw9NFAJdFfhxOla7cD4/qW/YHpd8OFEIECBAgMByArZ/ltPxXFMC705F/5PsldSXVccn9aFUIUCgHYH6oviGyT5JnQuojm75QaIQaF2gKxNAX8jI6z+l2h1u16TOB3RF8ubkE0kdEqYQ6LLANencC7vcQX0jQIAAgc4J2P7p3CoZbIfqUJOKQoDAbATqHLeODpiNvVYnBLpyDqC3p0+7JjXZ81/JfZPDkzqvymuThWdNzyKFAAECBAgQINBrAds/vV59Ok+AAAECBPol0JUJoD3CNn8W9Ofm/teSWyS/meya1DKFAAECBAgQIDAkAds/Q1qbxkKAAAECBDou0JVDwCaZHpwHt08uSc5P6rwqdULdFyUbKgfnBY9b4kW7ZfmZSzxnMQECBAgQIEBglgK2f2apr20CBAgQIDACgS5NANXePt9NPpPskNQEUJU7J3WM/ErKm/OiymLlVVm442JPWEaAAAECBAgQmJGA7Z8ZwWuWAAECBAiMTaArE0DvCPwjkr9OtkuuTA5MDkuemfx2ohAgQIAAAQIEhiRg+2dIa9NYCBAgQIBAxwW6MgF0RJwqVXZOtv3ZvY02+lBuX5FcOvfYDQECBAgQIEBgKAK2f4ayJo2DAAECBAj0QKArE0CTVOfmQaVKHQ62SbJ58pNEIUCAAAECBAgMUcD2zxDXqjERIECAAIEOCXTlKmC7xKQuhVp7+nwkuW0yXw7InaPmH7glQIAAAQIECAxEwPbPQFakYRAgQIAAgT4IdGUC6NBgnZfcPTk5+VRSVwJTCBAgQIAAAQJDFbD9M9Q1a1wECBAgQKCDAl05BOyhsdkruSJ5QfKV5PjkvolCgAABAgQIEBiigO2fIa5VYyJAgAABAh0V6MoeQDXhU3v/zJdjcue1yXFJXRJeIUCAAAECBAgMTcD2z9DWqPEQIECAAIEOC3RlAuiNMXp38pwJq1fm/rHJqyaWuUuAAAECBAgQGIqA7Z+hrEnjIECAAAECPRDoyiFgH47VbsltFpi9MI8/Offcgqc8JECAAAECBAj0WsD2T69Xn84TIECAAIF+CXRlAqjULktOW4TvhCyrKAQIECBAgACBoQnY/hnaGjUeAgQIECDQUYGuHALWUR7dIkCAAAECBAgQIECAAAECBAj0X8AEUP/XoREQIECAAAECBAgQIECAAAECBJYVMAG0LI8nCRAgQIAAAQIECBAgQIAAAQL9FzAB1P91aAQECBAgQIAAAQIECBAgQIAAgWUFTAAty+NJAgQIECBAgAABAgQIECBAgED/BUwA9X8dGgEBAgQIECBAgAABAgQIECBAYFkBE0DL8niSAAECBAgQIECAAAECBAgQINB/ARNA/V+HRkCAAAECBAgQIECAAAECBAgQWFbABNCyPJ4kQIAAAQIECBAgQIAAAQIECPRfwARQ/9ehERAgQIAAAQIECBAgQIAAAQIElhUwAbQsjycJECBAgAABAgQIECBAgAABAv0XuEH/h2AEBDohsG168bhk6+TzyScThQABAgQIECBAgAABAruEYL9ks+TjyVcThUDrAvYAap1cgwMU2C5jek+yVfKduft/kVuFAAECBAgQIECAAIFxC/xahn9kckXy4+Qryf0ThUDrAvYAap1cgwMU+MeM6U3JsXNj+/fcHpPcMzllbpkbAgQIECBAgAABAgTGJbB5hlufDR6WfG1u6Cfltj4/nJb8aG6ZGwKtCNgDqBVmjQxcYPuM70MTY/xp7h+X3HZimbsECBAgQIAAAQIECIxL4EYZ7ueS+cmfGv05yenJr9YDhUCbAiaA2tTW1lAFLszAatfOyVKz/BdPLnCfAAECBAgQIECAAIFRCVya0e6Q1Ckj5ssmufOQ5IL5BW4JtCVgAqgtae0MWeBVGVzlfsmtk9cl9Uf+A4lCgAABAgQIECBAgMA4BWoC6J+TtyZ3SXZPTkzenXwrUQi0KuAcQK1ya2ygAqdmXHUFsOcldSLo2s3z0EQhQIAAAQIECBAgQGDcAu/M8M9NDk7qKmCvTv41UQi0LmACqHVyDQ5UoK7+9bSBjs2wCBAgQIAAAQIECBBYu8Cn8taKQmCmAg4Bmym/xgkQIECAAAECBAgQIECAAAEC0xcwATR9Yy0QIECAAAECBAgQIECAAAECBGYqYAJopvwaJ0CAAAECBAgQIECAAAECBAhMX8AE0PSNtUCAAAECBAgQIECAAAECBAgQmKmACaCZ8mucAAECBAgQIECAAAECBAgQIDB9ARNA0zfWAgECBAgQIECAAAECBAgQIEBgpgImgGbKr3ECBAgQIECAAAECBAgQIECAwPQFTABN31gLBAgQIECAAAECBAgQIECAAIGZCpgAmim/xgkQIECAAAECBAgQIECAAAEC0xe4wfSb0AKBUQjcKqN8arJ1cmryjkQhQIAAAQIECBAgQIDAXUPwyGTz5CPJxxOFQOsC9gBqnVyDAxS4ZcZUEz5fSo5J/jJ5S6IQIECAAAECBAgQIDBugQdl+Icnn0z+M/lY8oREIdC6gAmg1sk1OECBf8iYnpe8OzkluXOySXL/RCFAgAABAgQIECBAYJwCW2XYb0hqwucTyYlJLds/2TlRCLQqYAKoVW6NDVRg04zrCwvGdnIe33zBMg8JECBAgAABAgQIEBiPwLYZ6meT708M+Yrc/0ay48Qydwm0ImACqBVmjQxc4KyMb5+JMW6c+09LzplY5i4BAgQIECBAgAABAuMSuCjDrXOE3mZi2Nvk/mOT8yaWuUugFQEngW6FWSMDF3h5xvft5BnJV+duT8lt7eKpECBAgAABAgQIECAwToHa2+dVyRnJI5Irk1cmf5WYAAqC0q6APYDa9dbaMAW+k2Ftn9wo2Td5V1KTQQoBAgQIECBAgAABAuMWqJM/3y6p84TeN3l6clSiEGhdwB5ArZNrcKACtXvnSwc6NsMiQIAAAQIECBAgQGDtAqfnrS9b+9u9k0AzAl3cA6gmpWpPCoUAAQIECBAgMBYB2z9jWdPGSYAAAQIEZiTQlQmgzTL+2nvi7OSnyfnJZcmXkycnCgECBAgQIEBgaAK2f4a2Ro2HAAECBAh0WKArh4C9NkZ1yeyHJd9KavKnLpl3p+TVyRbJGxKFAAECBAgQIDAUAds/Q1mTxkGAAAECBHog0JU9gPaN1dOSLyWXJtcldU6Vk5NDkkclCgECBAgQIEBgSAK2f4a0No2FAAECBAh0XKArE0B1qNf9l7B6eJb/cInnLCZAgAABAgQI9FXA9k9f15x+EyBAgACBHgp05RCwF8Tu6OTQ5Izk4mS7ZPek+vjQRCFAgAABAgQIDEnA9s+Q1qaxECBAgACBjgt0ZQLoC3HaK7l3smtS5wOqvX7qvD+fSuqQMGVtAuV6RLJlcmTypkRpXuBPUmWdr6pKnci8vBUCBAgQILCcgO2f5XQ816TAi1PZfkl9ybp/cmGiECBAgMDIBLoyAVTsVyafSKpP2yQXJMr6BOo/+n9L/jep//DfmNw9eWqiNCfw7FR1eDI/Ubl57l+TbJIoBAgQIEBgOQHbP8vpeK4Jgf9OJb+WfDLZI6mr7e6Z1Lk3FQIECBAYkUBXzgHkMqjT+aH7QKr9x+SuyT5JnWfpCUmddFJpTmB+8qcmfCp1QvONk0sShQABAgQILCVg+2cpGcubEvjLVFSTPbsmtf13i+SLyYcShQABAgRGJtCVPYCaugzq1ll/Wy2xDuuQnLHtkVF7ofz5hMcJuf+95FHJhxOlOYHDJqp6S+7X3lZL/SxOvNRdAgQIEBixgO2fEa/8lob+gLRTEz7PSWoiqLaHX5/UF4QKgT4L/HY6P/k5pw9jqaME6vPo5X3o7EQf35n7/zLx2N0eC3RlAqi+kajz/9TkxHy5KHdOTg5JXpjU+YA2VH4nL6jJjcXKr2fhuYs9MfBldcjX5yfGuHPunzPx2N1mBP4i1bx4oqraA+jaicfuEiBAgACBhQK2fxaKeNy0QG1P1wfltyWHJjsm9SVgbacoBPoscEI6X58V+1Qemc7WXnj1RXGfylV96qy+Li/QlQmg+cug1uziwrKay8AfnTdXFiuvysL6T29M5YMZ7InJx5OfJvslVyQvTZSfC2yWu3f8+cNV36s/ijdMajb/60lNNlapQ+/u8rN7q//nwrzlrNW/zTsIECBAoEcCtn96tLJ62tX3pN/1ofNpyVeT+yS7J/+bKAT6LFBHOvRtT5r6PFafG/rW7z7/nOj7AoGuTAC5DOqCFdPQw9ot8kHJvnP11fp+xtx9Nz8XqJOOP+XnD1d9r2bx6/11yFdN/tTJoP8jWU+dtbv2WxOFAAECBIYrYPtnuOu2KyPbJB15dlLbhO9P6sPn3yX3TBQCBAgQGJlAVyaAvhD3vRKXgW/2B/BLqe6Vyd/MVfuQ3NakQu0u+bW5ZW422ujHQThknRD1/j2S+oatLgmvECBAgACBDQnY/tmQkOfXK/C5VPDPyW8k581V9qzcfmPuvhsCBNoTOCdN2funPW8tLSLQlQmg6tqVyScW6WN9c1H9/Mkiz1m0vEB9yzM/+VOvPC45I6mJIBNAQVAIECBAgMCMBWz/zHgFDLz5b2Z8dej/2cnzk12SzZP6wkohQKBdgVPabU5rBH5ZoCuXga//jN6eXJp8JLltMl8OyJ2j5h+4XbXATRe84zZ5XCcEVAgQIECAAIHZCtj+ma3/WFqvLwB3S+q8P3VOoIOTaxKFAAECBEYm0JUJoLoqQe2WWlesOjn5VHL7RFmfQP0n/5nkTsmOSe31s1lSuwIrzQucnioPb75aNRIgQIDAQAVs/wx0xXZwWN9Ln2o7sHJtB/unSwQIECDQgkBXDgF7aMZa5wC6IqkTIn4lOT65b6KsXaB27319cmJS3/TUuQbulijTEajd+M+dTtVqJUCAAIEBCtj+GeBK7eCQas/6lyV17pE9kw8kf5UoBAi0K7BzmtsyqS+NFQIzEejKHkA14VN7/8yXY3LntUntsrrD/EK3axJ4xpzhzXL74OSyNdXiTQQIECBAgEDTArZ/mhZV30KB7bOgzgP0xuSgpL5w3Td5cqIQINCuwL3SXJ2LVSEwM4GuTADVf0rvTp4zIVFXrzo2edXEMncJECBAgAABAkMRsP0zlDXZ3XHcP137y+Rjc12sw78eltTeZwoBAgQIjEygK4eAfTjudXK62yzwf2Eef3LuuQVPeUigcwL1M7x/8vLO9UyHCBAgQKCLArZ/urhWhtWn6zKcKxcMqZZ15TPAgq55SIAAAQLTFOjSH/86NOm0RQZ7QpZVFAJdF6hjeuuKLgoBAgQIEFipgO2flUp53VoE/itvel7yP8kJycZJPT4pUQgQaFfgnDRX5+JSCMxMoEsTQDND0DABAgQIECBAgACBAQr8IGN6cnJqUhdbqVJXg61TLSgECLQrcEq7zWmNwC8LdOUcQL/cM0sIECBAgAABAgQIEFivwEdTwVlJXfnrX5I/Tx6RKAQIECAwMgF7AI1shRvuVAXqko6HT7UFlRMgQIAAAQIEVi5Qh3ttmtx+4i1fz/0jkn+fWOYuAQIECIxAwB5AI1jJhtiaQJ1k8dzWWtMQAQIECBAgQGB5gZvn6Y8kdVXdE5KTkx8n2yQKAQLtCuyc5m7bbpNaI/CLAiaAftHDIwIECBAgQIAAAQJDEfjfDOSApL6gekDyu8nfJAoBAu0L3CtNPqT9ZrVI4OcCDgH7ucWQ7+2Twd0o+c/kJ4lCgAABAgQIECAwfIE6CfT3kpcld0t2SGqvoLo6mEKAAAECIxOwB9CwV/jmGd6ZSU381En/6uoPeyTKdAR2S7XPnk7VaiVAgAABAgQIrFpgy7zjQ8m1yWOSByaXJXVeIIUAAQIERiZgD6Bhr/DzMrzzk63mhvnl3H42qeNPL5xb5qY5gdrI2qW56tREgAABAgQIEFiXQG0HPiX5THJqUl8GPiupCSGFAIF2Bc5Jc5e326TWCPyigD2AftFjaI/qBH+HJkcmxyZHJRcl9R+/QoAAAQIECBAgMGyBmvypPX7umVyT3DnZOLlJohAg0K7AKWmu9shTCMxMwATQzOhba/gP09IbkpckN022Tez5FQSFAAECBAgQIDBwgTrfz3XJW5M6+eytklcmtgWDoBAgQGBsAv74D3uN1+69+yU10Vf/+T8gqcPBPp4ozQucnioPb75aNRIgQIAAAQIE1iTwsbzrPsmjkrckv5kckvw0UQgQIEBgZAImgIa9wuscQHW+nwcn9R/9Jsm3EmU6Alem2rrMqkJgCAJPyyDunnw9ecUQBmQMBAgQGKHAFhlzHfJ14+Q5c+Ov7RVXhZ3DcEOgRYH6XFbnDK0vjRUCMxFwCNhM2FtrtP7AfDqpY00/mdRGQJ2k2MRfEBQCBJYU+O88U4cI/Fbyd8k5iUKAAAEC/ROo80HWuX+q1N7gVWp7cP7+zxb4hwCBVgTulVbqUEyFwMwETADNjL6VhjdLK3snNQlUJ4H+3+T8pA4NUwgQILCYQB0isGdS31LdMdk8qfOHvTdRCBAgQKBfAnXoV+0BflVSe/3MbwNul/sKAQIECIxMwATQsFd4XfHrX5LvJvWf/z8ldTLA+f/8c1dpUGC31PXsButTFYFZCOybRmsS6JZJfUt1l+TlyX0ShQABAgT6JVDbfVVq7++jks/Vg5Q6LEwhQIAAgZEJOBRo2Cu8zvdTH+L+T1LnprlR8v7k4kRpXqAOuatD7BQCfRaob4jvm2yf1AeFlya1F1B9e6wQIECAQL8E6lCv+uLvS0n9bb86+XGyQ6IQINCuQB1Sf3m7TWqNwC8K2APoFz2G9uhdGVCdwPWvkn9MvpfcKjklUQgQILCYQP3d2D35YlInf37b3OPam1AhQIAAgX4J1MRP7e1z5+TXklMTkz9BUAjMQKA+g9W5WRUCMxMwATQz+lYaflNaqXV8QlIngd4zqZO6OvFfEBQCBJYUqBNAvyipvYFekvzfpPZwUwgQIECgXwK1LVh7ANX2YN0+KantwB8lCgECBAiMTMAhYMNe4Y/N8PZLPpDUYV/PTI5Inp4ozQucnioPb75aNRJoVaB2Ta4Txm860eojc//eE4/dJUCAwNgF6v/73+gJQp0Tsk4D8NNk/svfM3P/hKQP5cnpZPVXIUCAAAECKxZ4VV559IpfPYwXXpphPGNiKHXsdx17er+JZe4SIEBgUqAOE609Bu8+t3Cv3Na3xbece+yGQJMC9aXEXZusUF2/JDDG7Z9fQrBgo+fH4OzkY8kePAgQmInAzmn1tjNpWaNdE5jZ9o89gLr2o9Bsf+rwjddPVHlS7n87qY3tTyUKAQIEFgqclQV/kLwluTq5IKnJoJo8VggQIECgnwJ/m27vmLw5+XI/h6DXBHovcK+MoCaBXtv7kRhAbwVMAPV21a2o4zUB9OjkfXOvrkuB3jN52dxjNwQIEFhM4IwsfMBiT1hGgAABAgQIECBAgEA/BX6ln93W6xUK1N4/702ekzwpqSs/nJcclyjNC+yWKp/dfLVqJECAAAECBAisW6Cu8njuumtRAQECBAj0VsAeQL1ddSvqeF29Z7PkqUmd+O/DyR8ndRUIpXmBukrSLs1Xq0YCBAgQIECAwLoFTlp3DSogQGA9AufkzZevpwLvJbBeARNA6xXs9vvrxK0vmEu3e6p3BAgQIECAAAECBAgQGK7AKcMdmpH1RcAhYH1ZU/pJgAABAgQIECBAgAABAgQIEFijgAmgNcJ5G4FFBE7PssMXWW4RAQIECBAgQGDWAo9JB3aadSe0T4AAAQKzEzABNDv7tlr+rTT0xuTI5OBEmZ7AlanayRWn56tmAgQIECBAYO0Ce+etN1n7272TAIF1CtQl4G+7zjq8ncC6BEwArYuv829+aHr4vKQmgF6a7JW8KFEIECBAgAABAgQIECBAoD2Be6Wph7TXnJYI/LKACaBfNhnSkldkMAclP0jqKmB1BbDbJXdNFAIECBAgQIAAAQIECBAgQGAkAiaAhr2iv53h1R5AJyYfT76bnJVsmyjNC+yWKp/dfLVqJECAAAECBAisW+BdqcGh6utmVAEBAgT6K2ACqL/rbiU9/7W86GnJ/yQnJDdL/jw5O1GaF9gyVe7SfLVqJECAAAECBAisW+Ck1HD+umtRAQECaxU4J2/85lrf7H0EmhC4QROVqKOzAnWisU2STyR1GNg9kjrxWB1/ekaiECBAgAABAgQIECBAgMD0BU6ZfhNaILC8gAmg5X36/uw1GUDt9bN/smPyhOQDyT2ToxOFAIFhCrwpw6rf+fWWG6WCC9ZbSd5/afL7DdSjCgIECBAgQIAAAQIE1ihgAmiNcD1529Xp59uTh8/1t04IfYvki3OP3TQrcHqqO7zZKtVGYE0C/1/e1cQhvu9NPfV3Y73luvVW4P0ECBAgsG6Bx6SGTyfnrbsmFRAgQIBALwVMAPVyta2407UXwNOTzyd13p8HJXXyvyMTpXmBK1Nl+SoEZi1wSUMdqEnkixqqSzUECBAgMFuBvdP81xMTQLNdD1ofr0CdnqPOGVpfGisEZiLQxQmg6tM2SROHHcwEtcFGb5K66nw9ay0fyxu3Tx6Z3CapPX/+PnlYstZS5w762lrf7H0ECBAgQIDAogK2fxZlsZAAAQKDEajPdTUJ9NrBjMhAeifQlQmgzSJ3WFLnqKlfio2Ty5MzkyOStyZjLFtl0Luvc+Bfyftrz5Q69Ouk5HbJesrFebMJoPUIei+B/gh8uj9d1VMCvRSw/dPL1abTBAgQIECgnwJdmQCqWdCbJ7VnyreSy5Jtkzslr062SN6QjK2clQG/ooFBPyh11BXAmqirge4MtordMrI64fbLBztCAxubwIvGNmDjJdCygO2flsFH3ty7Mn6Hqo/8h8DwCRAYt0ATJwltQnDfVPK05EtJXS2mThha5504OTkkeVSiEOi6QB3Tu0vXO6l/BAgQINAZAds/nVkVo+hI7Ql+/ihGapAEuilwTrr1zW52Ta/GItCVPYC+HPD7J+9cBL6uYPXDRZZbRIAAAQIEZilQhyjfrYEO3Ch11OG11zRQV/2fWV+kKP0QsP3Tj/WklwQIEGhC4JQmKlEHgfUIdGUC6AUZxNHJoUmdZLg2hLdL6vw31ceHJsraBU7IW09a+9u9kwCBkQrcO+OuPTGVxQWetfjiVS+t89y9MPn2qt/pDX0XsP3T9zWo/wQIECBAoEcCXZkA+kLM9krqw8auSZ0PqPb6qfP+fCqpQ8KUtQtclbdWlOkK1CUdD59uE2on0KrAYWntwa22qDEC4xKw/TOu9T3r0T4mHaiT+583645onwABAgRmI9CVCaAafV2p6hMTDDfN/QsSkz8TKO52WqB+hp1csdOrSOcIECDQOQHbP51bJYPt0N4Z2dcTE0CDXcUG1nGButp1nTO0vjRWCMxEoCsngX57Rn/HOYE75PaDydnJ95LXJZsmCgECBAgQGKLA8RlUXfhAGZ+A7Z/xrXMjJkBgvAL3ytAfMt7hG3kXBLqyB9Aewdh6DuS5uf1a8oTkJskrk1r2omRD5Tfygrsu8aK7ZHl9yzbGUt/47Jm8ZoyDN2YCBNYsUIcKKNMXOGb6TWihowK2fzq6YnSLAAECBAgMUaArE0CTtnW+idsnlyR1qcrnJzUJtJIJoMvyuqWuGHZ5nhvr4WSbZezzE2y5q0xJYLfUu3/y8inVr1oCbQus5O9u233SHoGhCtj+Geqa7c643pWuOFS9O+tDTwgQINC6QJcmgH4zo/9u8plkh6QmgKrcOamTJK6knJYXVRYrVf+Oiz1hGYGGBOqY3l0aqks1BAgQIDAOAds/41jPXRjlSV3ohD4QGLHAORl77ZSgEJiZQFcmgN4RgUckf51sl9ShWgcmhyXPTH47UQgQIECAwBAF9s2gTk7mv/gY4hiNaXEB2z+Lu1hKgACBIQqcMsRBGVO/BLoyAXRE2CpV6uzo2/7s3kYbfSi3r0gunXvsZm0CJ+RtJ63trd5FgMCIBe6dsdfEhDJdgfrC4xuJCaDpOnexdts/XVwr+kSAAAECBAYq8CsdHFcdm/zVuX7V4WAmf9a/kq5KFVesvxo1bECgLul4+AZe42kCfRI4rE+d1VcCPRew/dPzFdiD7j8mfdypB/3URQIECBCYkkBX9gB6Vsa36TJj/Fqee/8yz3uKQBcE6tDF2oBXCBAgQIDASgRs/6xEyWuaEqirwn49Oa+pCtVDgMCqBOpIlzpnaH1prBCYiUBXJoB2zej/OHlbUlfyWliWurLXwtd5TIAAAQIE+iZwfDp8Ud86rb+NCOyaWmz/NEKpEgIECHRe4F7pYU0CvbbzPdXBwQp0ZQLoTyJch6NV6qTPSrMC9Y3Pnslrmq1WbQQIDFzg0wMfX1eGd0xXOqIfrQvY/mmdXIMECBAgQGC8Al06B9Bzshrq5M83HO/qmNrIN0vNW0+tdhXPC+yWO8+ef+CWwAAEXjSAMRgCga4L2P7p+hoaTv/elaE4VH0469NICBAgsGqBruwBVB2vkz0/ftUj8AYC3RGoY3p36U53OtmTExrq1Y6p5/sN1FVXXjq4gXpUQYAAgbUK2P5Zq5z3rVbgpNW+wesJEGhU4JzUdnmjNaqMwCoFujQBtMquezkBAj0U2KehPh+bevZvqC7VEJi1wL7pwMnJJbPuiPYJECBAgACBqQmcMrWaVUxghQJdOgRshV32sjUInJD3vGoN7/MWAgTGLXDvcQ+/tdEfmJZ2aK01DREgQIAAAQIECIxSwATQOFb7VRnmFeMY6kxHWZd0PHymPdA4gWYFDmu2OrURIECAwAwFHpO2d5ph+5omQIAAgRkLOARsxitA84MSuDKjcXLFdlbpW9tpRisECBAgQGAwAnVV2K8n5w1mRAbSlEDtFLB7U5WpZ0mBm+WZzZOzl3yFJ5oSOCMV1WczZYGACaAFIB4SINALgQ/0opc6SWBlAsfnZRet7KVeRYAAAQIEGhc4IDU+Nzmp8ZpVOClQF4zZJKmT/yvTE9gqVX87cTXbRYxNAC2CMsBF9Y3PnslrBjg2QyJAYHoCn55e1WqeEDhm4r67BAgQIECgbYGbpsEXJ3WRDYVA3wXunQE8su+DmFb/nQNoWrLdqnezdGfrbnVpkL3ZLaN69iBHZlBjFfDNyVjXvHETIDBEgXdlUA5VH+KaNSYCBAisUMAeQCuE8rJOCtS3FfXN+YUd6d0W6UddyedeHelP7WJau/K+oiP9abIbB6WytzVZoboIECBAgMDABRzeM/AVbHgECBDYkIAJoA0Jeb7LArdO576YvKTLnZxh32qC7K9m2P40m67dOk0ATVNY3W0K7JvGTk4uabNRbREgQIAAAQIECIxLwATQONb3CRnmUL/1uTpjO38cq3HVo9w+77hq1e/yBgI/F6hjqGtiQpmuwIGp/huJCaDpOqudAAECBAgQIDBqAecAGsfqr0mAK8YxVKMkQKBBgcMarEtVBAgQIDBbgcek+Z1m2wWtEyBAgMAsBUwAzVJf2wQIrFXgrWt9o/cRIECAAIGRCtRVYW8y0rEbNgECBAhEwASQHwMCBPoo8IE+dlqfCSwhcHyWX7TEcxYTIECAAAECBAgQaETABFAjjJ2vpL7xOaTzvdRBAgS6JvDprnVooP2pqxleMNCxGRYBAgQIECBAgEBHBEwAdWRFTLkbm6X+rafchuoJEBiewIuGNyQjIuRFQFUAADdySURBVECAwGgF3pWRnzva0Rs4AQIECDgEzM8AAQK9FDiol73WaQIECBAgMDuBuiKsK6fOzl/LBAgQmLmAPYBmvgp0gACBNQg8cg3v8RYCXRXYNx3bpqud0y8CBAgQIECAAIFhCJgAGsZ63NAoTsgLXrWhF3meAAECCwTuveCxh9MRODDV7jCdqtVKgAABAgQIECBA4HoBE0Dj+Em4KsO8YhxDNUoCBBoUOKzBulRFgAABArMVeEya32m2XdA6AQIECMxSwATQLPW1TYDAWgXeutY3eh8BAgQIEBipQF0V9iYjHbthEyBAgEAETAD5MSBAoI8CH+hjp/WZwBICx2f5RUs8ZzEBAgQIECBAgACBRgRMADXC2PlK6hufQzrfSx0kQKBrAp/uWocG2p9jMq4LBjo2wyJAgAABAgQIEOiIgAmgjqyIKXdjs9S/9ZTbUD0BAsMTeNHwhmREBAgQGK3AuzLyc0c7egMnQIAAAYeA+RkgQKCXAgf1stc6TYAAAQIEZidwUpo+f3bNa5kAAQIEZi1gD6BZrwHtEyCwFoFHruVN3kOgowL7pl/bdLRvukWAAAECBAgQIDAQgRsMZByGsbzACXm6vvVRCBDotsB26V6lK+Wu6ch/d6Uz6cclyRDPlXNgxvWNufHlRiEwOoGHZsT3SK4d3cgNeIgCG2dQf59cMcTBGRMBAv0WMAHU7/W30t5flRdWFAJrEdgkbzo46dKlY2+R/vz1WgYzpfd8JfUe20Ddn0gdX26gnqaquFkq+kFTlTVQz51Tx14N1KMKAgS6JfD0dOd1iW2Vbq0XvVmbwJ/lbb+VfHhtb/cuAgQITE/ABND0bNVMYCgCNdnywOQfOzSgLl2dqg6lfU3SxARQnZvhiR1y7lpX3tu1DukPAQKNCPwktXw++XEjtamEwGwF7jXb5rVOgACBpQVMAC1t4xkCBH4ucFnufvznD92bENgi98+aeOwugdUKHJ83XLTaN3k9AQIECBAgQIAAgdUIOAn0arT6+9q90/VD+tt9PSdAgMCgBY7J6IZ4bqNBrzSDI0CAAAECBAj0TcAEUN/W2Nr6u1netvXa3updBAgQIECAAAECBAgQIECAQN8FHALW9zWo/5uH4KYYFhW4cZaa5F2UxkICBAgQIECAAIEJgW1z3zb1BIi7vRXYPj2vq/EpiwiYAFoExaJeCfyf9NbhbUuvso8t/ZRnCBDoiMC+6cfJSV3mXiFAgAABAm0LbJkGX9t2o9ojMEWBI6dYd6+rNgHU69W34s6fkFeetOJXeyEBAgSGLfD3Gd6vJ1d0ZJg7px8/SLpyCewbpi8HJHVVOoUAAQIECBAgQGAgAiaABrIiNzCM+lDRlQ8WG+jqqp/+97zjH1b9rnG84ZYZ5uPHMVSjJLAqgV/Nq5+UXLqqd43nxa/OUG+emAAazzo3UgIExi1wZYb/Z8lHxs1g9AMRqC/59hrIWBofhgmgxklV2LJAXTr5Ky232Zfm6j/zq/vSWf0k0KLAdWmrfjcubrHNPjXVlT2j+mSmrwQIEOizQP2/eE5im7rPa1Hf5wW2y527zD9w+4sCThD7ix4eESBAgAABAgQIECBAgAABAgQGJ2ACaHCrdNEB7Z2lTpS8KI2FBAgQIECAAAECBAgQIEBg+AJdPASs+rRNckGP+W+fvn88+XpHxlBn9q/LpT+yI/2pvvxT8taO9Ec3CBAgQIDArAWGsP0za0PtEyBAgAABAssIdGUCaLP08bDkCcnOycbJ5cmZyRFJ3yYK7pQ+11VmXE4xCIuU+2TZgxdZbhEBAgQIEBiTwNC2f8a07oyVAAECBAj0TqArE0A1UVJXHHlY8q3ksmTbpCZS6mokWyRvSPpWru1bh1vq73UttaOZ5gS2SlV7NFfdoGqqD3CbDmpEBkOAQFsCQ93+actPOwQIECBAgMAqBLoyAbRv+nzv5HsTfa+rO52c1LlrXpj0cQIo3VYIDELgrhnFaYMYyXQG8bXpVKtWAgQGLmD7Z+Ar2PAIECBAgECXBLpyEugvB+X+S8A8PMt/uMRzFhMgQIAAAQIE+ipg+6eva06/CRAgQIBADwW6sgfQC2J3dHJockZycbJdsntSfXxoohAgMDuBz6Tpe8yu+U63XCc179t5yjoNqnMERiRg+2dEK9tQCRAgQIDArAW6MgH0hUDsldRhYLdJbpV8LqnDvj6VOGdMEBQCMxS4Om3/eIbtd7npOkeZ8311eQ3pG4HuCtj+6e660TMCBAgQIDA4ga5MANVJVOtbsKFcBWxwPygGRIAAAQIECDQuYPuncVIVEiBAgAABAksJdGUCyFUwllpDlhMgMCaB+pu825gGvMqx1odlhcCQBGz/XL8262/frsn21z/0L4FeC9RpLBQCBAh0UqArE0D7RqeJq4A9NfUcuIT0bbP8zCWes5gAAQJdEKjDX0/vQkc62odvdbRfukVgrQK2f66X2yU3n18rovcR6JjA+enPxzvWJ90hQIDAzwS6MgE0fxWMdy6yXlZzFbC35P3/d5E6atErkx2XeM5iAgQIECBAgEDbArZ/2hbXHgECBAgQGLFAVyaA6vw/RydNXAXMCaNH/ANt6AR6LvCd9P9+PR/DNLtfFwZQCAxJwPbP9WvzrNwclFw4pJVrLKMVePpoR27gBAh0XqArE0CTV8HYNWo3T36YuApYEBQCBEYjcE1Ges5oRrv6gV61+rd4B4FOC9j+uX711N++8xJXm+z0j6vOrVDg0hW+zssIECDQukBXJoBq4Fcmn2hdQIMECBAgQIAAgdkJ2P6Znb2WCRAgQIDAqAS6NAE0KniDJUCAAIGZCeyalr83s9a73/DZ6eI/dr+bekiAAAECBAgQILAaga5MAD0rnd50mY5/Lc+9f5nnPUWAAAECBFYqsHFeWFEIzFrA9s+s14D2CRAgQIDAiAS6MgG0a8z/OHlbclmysNT5gBQCBAgQINCEQJ1r5HcTJ5xdXPNpiy+2dAoCu6ZO2z9TgFUlAQIECBAg8MsCXZkA+pN07Vfm8sxf7qYlBAgQIECgMYE658qnkx80VuOwKnrMsIbT6dHY/un06tE5AgQIECAwLIGuTACV6nOSNyU3TJw9PwjKigS2zqvuvaJXju9FW2bIyx1aOT4RIyZAgED3BGz/dG+d6BGBsQnUYdHbJrapx7bmhzne+lneZJhDW/+oujQBVJM+j1//kNQwIoEvZ6x1CMcjOjLmzdOPGyd1eEkXymbpxDu70BF9IECAAIElBWz/LEnjCQIEWhKoc60+N7ldS+1phsA0BWoHgX+ZZgN9rrtLE0B9dtT32QhcnmafP5umF211jyytc2c8b9FnLSRAgAABAgQIECDQPYG6+uMzutctPSJAoGmBOu+OQoAAAQIECBAgQIAAAQIECBAgMGABewBNb+U+JlXfbXrV97rmm6b33+j1CHSeAAECBAgQIECAAAECKxfYLy/dMakrXysEZiJgAmh67Hum6vtOr/re11y7mg6tXJABnTK0QRkPAQIECBAgQIAAAQLrFtgqNdQJihUCMxMwATQ9+lNT9RnTq77XNdfM9xW9HsHinT83i49a/KleL70uvf+t5A96PYrpdb5O/n2n6VWvZgIECBAgQIAAAQIECKxfwATQ+g2XquF9eeI1Sz058uW1Z1TtAqn0Q+CcdPMJya370d3We3lVWnxI661qkAABAgQIECBAgAABAqsQMAG0CiwvJTBigZMy9kpXyrHpyP5d6Yx+ECBAgAABAgQIENiAwHF5fpMNvMbTBKYq4CpgU+VV+cgEtst47zGyMRsuAQIECBAgQIAAAQIbFqhTYFy64Zd5BYHpCdgDaHq2ah6fwC4Z8hOTz41v6EbckMCuqefFDdXVRDXbp5ILm6iooTr2aKge1RAgQIAAAQIECBAYnYAJoNGtcgMmMAiBawcxil8exG9nUU0kdqUckY48qyudST+O7FBfdIUAAQIECBAgQIBArwRMAPVqdeksAQJzAgcMVOI7GVelK6X2/unSuZ+actk0Fd0+2bWpCgdWT12mViHQpkD9Tt4xqZPqKwT6LnDDvg9A/6cmUBfBqashv21qLaiYwAYETABtAMjTBFYhcEFee8oqXu+lBAjMRuBf0+yjk6tn03znWz0rPTyz873UwSEJvCuDqd/Ja4Y0KGMZrUBNaH52tKM38OUE6guWbZd7gecITFvABNC0hdU/JoFzM9ijxjRgYx28wBsHOsJjMq6KQoBANwSOTjcqCgECBAgQIDBFAVcBmyKuqgkQINBzgff1vP+6T4AAAQI/F9gzd+uKpQoBAgQIjFTABNBIV7xhE+i5wLE977/uE5gUeFIe7DC5wH0CBAhMQeApqfNWU6hXlQQIrEzguLzsrSt7qVcRmI6ACaDpuKp1nAL1rdo9xjl0oyZAYB0Ce+e926zj/d5KgAABAgQIdF/ginTx0u53Uw+HLGACaMhr19jaFqjLdz+x7Ua1R2CKAgdPsW5VEyBAgAABAgQIECDQooAJoBaxNUWAQGMC1zZWk4qWE9h/uSc9R4AAAQK9Ejg1vb2wVz3WWQIECBBoVMBVwBrlVBkBAi0JHNBSO5oh0IZAnQ/gR200pA0CBEYtcOSoR2/wBGYvsF+6sGPyttl3RQ/GKmACaDpr/rJUe1Cy5XSqX3WttZ43SX6y6ndO5w13T7WnT6fqmdZ6QVo/ZaY90DgBAn0U+FQfO63PBAgQIECAwKoEtsqrt13VO7yYQMMCJoAaBp2r7iO5vSbZbDrVr7rWvfKO3ZJ3rfqd03nDF1PtSdOpeqa1npvWj5ppDzROoFmBNzZbndoIECBAgAABAgQIEJiVgAmg6cl/fHpVr7rmmozaOPnQqt/pDQQIjFngfWMevLETIEBgYAJ7ZjxnJhcNbFyGQ4AAAQIrFHAS6BVC9fxlJ6b/r+v5GHSfwKTAsZMP3CfQc4Enpf879HwMuk+AQPcFnpIu3qr73dRDAoMVOC4jq/P+KQRmJmACaGb0rTZ8ZVq7uNUWx9nYdhn2PcY5dKMmQGAdAnvnvdus4/3eSoAAAQIECHRf4Ip08dLud1MPhyxgAmjIa9fY2hbYJQ0+se1GtUdgigIHT7FuVRMgQIAAAQIECBAg0KKACaAWsTVFgEBjAtc2VpOKlhPYf7knPUeAAAECvRI4Nb29sFc91lkCBAgQaFTABFCjnJ2t7L7p2TM62zsdI7B6gQNW/xbvINBZgTofwI862zsdI0BgKAJHZiBnD2UwxkGghwL7pc8H9bDfujwgAVcBG9DKXGYoW+a57Zd53lPNCFyQak5ppiq1ECAwIoFPjWishkqAAAECBMYqsFUGvu1YB2/c3RCwB1A31oNeDEPg3AzjqGEMxSgI/EzgjRwIECBAgAABAgQIEBiGgAmgYaxHoyBAgMA0BN43jUrVSYAAAQIzEdgzrdYVSxUCBAgQGKmACaBxrPgTM8zXjWOoRjkSgWNHMk7DHIfAkzLMHcYxVKMkQGCGAk9J27eaYfuaJjB2geMCUOf9UwjMTMAE0MzoW234yrR2castjrOx+lbtHuMculETILAOgb3z3m3W8X5vJUCAAAECBLovcEW6eGn3u6mHQxYwATTktWtsbQvskgaf2Haj2iMwRYGDp1i3qgkQIECAAAECBAgQaFHABFCL2JoiQKAxgWsbq0lFywnsv9yTniNAgACBXgmcmt5e2Kse6ywBAgQINCrgMvCNcna2svumZ3dJXt/ZHuoYgdUJHLC6l3s1gakIHJFa79ZAzXX46NuTJiY2H5567F7ewEpRBYEBChw5wDEZEoE+CeyXzu6YvK1PndbXYQmYABrW+lxqNFvmie2XetLyxgQuSE2nNFabiggQ6LrAs7reQf0jQIAAAQIEOiOwVXqybWd6oyOjFHAI2ChXu0FPSeDc1HvUlOpWLYFZCLxxFo1qkwABAgQIECBAgACB5gVMADVvqkYCBAgMReB9QxmIcRAgQIDARnvGoA45VQgQIEBgpAImgMax4k/MMF83jqEa5UgEjh3JOA2TAAECBAg0JfCUVHSrpipTDwECqxY4Lu9466rf5Q0EGhQwAdQgZoerujJ9u7jD/RtK1+pbtXsMZTDGQYAAAQIECBAgQIBAYwJXpCYXamiMU0VrETABtBY17yGwuMAuWfzExZ+ylEAvBQ7uZa91mgABAgQIECBAgACBXxLo4lXAqk/bJHVFJYUAgWEJPLmh4dRkWxN1/Sj1/EdDfRpiNftnUG8e4sCMiUAHBWz/dHClDKxLp2Y8Fw5sTIZDgAABAqsQ6MoeQJulzy9Nzk5+mpyfXJZ8OWniQ16qGXW5b0b/jFELGHxXBK5JR5pIndOqiXqu7QqMfhAgMEoB2z+jXO0zG/SRabm2tRUCBGYjsF+aPWg2TWuVwPUCXdkD6LXpzs2ThyXfSmryZ9vkTsmrky2SNyTK2gS2zNu2X9tbvWsVArXX2imreP0YX/r2MQ7amAkQILCEgO2fJWAsJkCAwAAFtsqY6jOuQmBmAl3ZA2jfCDwt+VJSJ8a6LrkoOTk5JHlUohDousC56eBRXe+k/hFYhcAbV/FaLyVAYPUCtn9Wb+YdBAgQIECAwBoFujIBVId63X+JMTw8y3+4xHMWEyBAgMD0BN43varVTIBABGz/+DFoU2DPNLZdmw1qiwABAgS6JdCVQ8BeEJajk0OTM5K6ZHn9B7V7Un18aLKSUrvV1eFOi5XNs3CTxZ4YwbITM8bPjmCchkiAAAECBPokYPunT2ur/319SoZQJ/Y/rf9DMQICvRQ4Lr0e6+fRXq6wIXa6KxNAXwjuXsm9k12TOh/QFUn9J/WJpA4JW0l5dF5UWazcMQtPX+yJESy7MmOsKNMVqEnL2yefm24zaidAgACBgQjY/hnIijQMAgQIrECgPt8qBAhEoE4MWxM0Ve6QfDCpCYu6RHNd7WfTZL3l91LBH623Eu8nsIzAHnmuTuipEBiKwMFDGYhxdFbgiPTsrp3t3fQ7Zvtn+sZa+LlAbaPc+ecP3SNAgACBGQnMbPunK+cAqg/OW8/hPze3X0tukfxmsmtSyxQCBAgQaFdg/3ab0xqB0QnY/hndKp/pgE9N6xfOtAcaJ0CAAIGZCnRlAmgS4cF5cFhyfvKN5PnJPomydoH75q3PWPvbvZMAAQIECBCYsoDtnykDq36jI2NwNgcCBGYmsF9aPmhmrWuYQAS6NAFUe/vslHwm2SGZL7Wrah0jr6xdoE6Mvf3a3+6dKxS4IK87ZYWv9TICBAgQIFACtn/8HBAgQGAcAnXBom3HMVSj7KpAV04C/Y4APSL566ROpFvn/zkwOSx5ZvLbiUKg6wLnpoNHdb2T+kdgFQJvXMVrvZQAgdUL2P5ZvZl3ECBAgAABAmsU6MoEUJ0EqVJl52R+ZvRDuf+K5NJEIUCAAIF2Bd7XbnNaIzA6Ads/o1vlMx3wnmn9zOSimfZC4wQIECAwM4GuTABNAtReFJUqdTiYsn6BE1PFZ9dfjRoIECBAgACBKQnY/pkSrGr/n8BTcu/NyWn/b4k7BAi0KXBcGtukzQa1RWChQJfOAbSwbx43J1CH1F3cXHVqWkKgDl+8xxLPWUyAAAECBAgQIECAwHgFrsjQHdky3vXfiZGbAOrEatCJgQjsknE8cSBjMQwCJXAwBgIECBAgQIAAAQIEhiFgAmgY69EoCBAgMA2B/adRqToJECBAYCYCp6bVC2fSskYJECBAoBMCJoA6sRqm3on7poVnTL0VDRAgQIAAAQIECHRV4Mh07Oyudk6/CIxAYL+M8aARjNMQOyxgAqjDK6fBrm2ZurZvsD5VLS5wQRafsvhTlhIgQIAAAQIECBAgMGKBrTL2+atdj5jB0Gcp0MWrgM3SQ9sE1iNQV3A5aj0VeC+BhgTemXp2aqCum6SOExqo55LU8YgG6lEFAQIECBAgQIAAAQJrFDABtEY4byNAgECHBQ7scN90jQABAgRmI7Bnmj0zuWg2zWuVAAECBGYtYAJo1mugnfZPTDOfbacprRAgQIAAAQIECHRQ4Cnp05uT0zrYN10iMAaB4zLITcYwUGPsroBzAHV33TTZsytT2cVNVqiuRQW2y9J7LPqMhQQIECBAgAABAgQIjFngigz+0jEDGPvsBUwAzX4d6MFwBHbJUJ44nOEYCQECBAgQIECAAAECBAgMRcAE0FDWpHEQIECAAAECBAgQWFrg1Dx14dJPe4YAAQIEhi5gAmjoa/j68d03N88Yx1CNkgABAgQIECBAYBGBI7Ps7EWWW0SAQDsC+6WZg9ppSisEFhdwEujFXYa2dMsMaPuhDarB8dTlsv+lgfo2Sx03Tj7WQF2fTB0vaqAeVRAgQIAAAQIECBAgMHuBrdKFbWffDT0Ys4AJoDGvfWOfFzgvdx40/6Ajt9d1pB+6QYAAAQIECBAgQIAAAQIDEDABNICVaAiNCFzbSC0qIUCAAAECBAh0U2DPdOvM5KJudk+vCBAgQGDaAiaApi3cjfpPTDc+242uDLYXN83I/jap3To/mvxTohAgQIAAAQIEuiLwlHTkzclpXemQfhAYmcBxGe8mIxuz4XZMwEmgO7ZCptSdK1PvxVOqW7UbbVSTP19Obp2cn7wleVeiECBAgAABAgQIECBAoASuSC5FQWCWAiaAZqmv7aEInJyB1ITPy5MPJ3sleyePSxQCBAgQIECAAAECBAgQIDBzAYeAzXwV6MAABOqwrx2S30nq8qrvTT6V3Cc5OlEIECBAgACBYQm8MMO5R8+GtHP6+2tJ7Rnep/L0dPY7feqwvhIgQKCrAiaAurpmmu3XfVPdXZLXN1ut2uYEtsjtdslPknsmr0z+OPlKohDoo0Ad1nhocqvk8uQvk/MThQABAgSuF3hJbvpyLo/N0tdnJDVhVdsqhyffSPpS+jZh1RdX/WxfYL80uWPytvab1iKB6wVMAI3jJ2HLDHP7cQx1JqP8flp9cPLS5NNJnQB60+QziUKgbwL19+J7yTuSC5Ktk/9IDki+mygECBAgsNFGP+0Jwsbp5yeTHybnJDVpVV8IPiuxnRIEhUCLAlulrW1bbE9TBH5JwDmAfonEAgKrFrhh3vHC5IlJTf78T1Inhd49UQj0TeBP0+Ha2+ebSX1DdV5y56SWKwQIECDQL4GHp7u1V+eFyT8ndYj6bybPTxQCBAgQGJmAPYC6vcJvlO79egNdvEvquHWyTwN1nZU6vtVAPUOqos77c++kvk27Krl7UieB/nqiEOibwP3S4dqT7cVzHf98bjdP6gODQoAAAQL9Eqjtk6uTx891+7O5rb07/3HusRsCfRWoQ6n69GVrHYr5sKQ+352R1CH2fSnfTkcrygAETAB1eyXWYVs1kbDeUuu5dv1toq6TUo8JoF9cI/Vt2kOStyQ1AVTftF2S1F5ACoG+CVyWDtdhX5OlJn8unlzgPgECBAj0QuCi9LI+aG6Z1CWoq9z4+hv/Eui1wM3T+yY+27SBUL9/j0xqMnbj5IPJq5PaM68v5dt96ah+Li9gAmh5n1k/e2Y6UIcWKd0W2Dbdu33ypaT2+nlsUhtbffqjnu4qBH4m8JH8e0jyieSNSZ1Efqfk3YlCgAABAv0S+K9093eTOrdbnQi69givvRBq706FQJ8F6pQLla6XX0kHv5bUl/G1jXVNskdyx2T/pE97AqW7St8F6gdSIUBgfQJ1AugXJf+Q1B5Sdbz9pUnN9CsE+ibwtnS4vqGq3C2pXZXrpKGvSxQCBAgQ6JfAieluney5vpSqv+m7JPUB9OmJQoDA9AVqT6X6Iq2+1D8sqUPs75/cLtkrUQi0KmAPoFa5NTZQgTo/ynuSr06M79Dcv9XEY3cJ9EWgrmxTHxL+ILllckpycFKHNyoECBAg0D+B2quzJoLqHIU/Tg5Lvp8oBAhMX2CbNFF7+Xx0oqkTcr8+h9fEkEKgVQETQK1ya2ygAqdnXHUc7+8ntXvnM5M6ke5bEoVAHwXq22E/v31cc/pMgACBxQXqi6qKQoBAuwJ1+OX5yRHJ85Krk7cmWyfOFxoEpV0BE0DtemttmAIvzbAOS2pmv66sUYdWXpK8P1EIECBAgAABAgQIEBinwEUZ9puS30vqs0KdjP1myclJnRtIIdCqgHMAtcqtsYEK/GfGVecAmr+6xn/nfh3XW4fSKAQIECBAgAABAgQIjFfgNRn6vyU/SWoPoHcm+ycKgdYF7AHUOrkGByrwgYyrohAgQIAAAQIECBAgQGBe4LrcOXwu88vcEpiJgD2AZsKuUQIECBAgQIAAAQIECBAgQIBAewImgNqz1hIBAgQIECBAgAABAgQIECBAYCYCJoBmwq5RAgQIECBAgAABAgQIECBAgEB7AiaA2rPWEgECBAgQIECAAAECBAgQIEBgJgImgGbCrlECBAgQIECAAAECBAgQIECAQHsCJoDas9YSAQIECBAgQIAAAQIECBAgQGAmAiaAZsKuUQIECBAgQIAAAQIECBAgQIBAewImgNqz1hIBAgQIECBAgAABAgQIECBAYCYCJoBmwq5RAgQIECBAgAABAgQIECBAgEB7Ahu319TMW9ozPfhg8oWZ96T9DuyUJu+QXNl+06NqsX6ftkiuGNWoZzPYrdPsZbNpelStcm5ndZfzJ5Or22muU63cJr15UHJup3o1rM6MeftnWGty/aP5jVRR2yrXrb8qNRAgsAaBG+Q95ydfWcN7vWVYArZ/hrU+Ozea30qPXtS5Xg2vQztnSP8yvGF1ckQndLJXw+sU53bWaX05sVU7TWmFAIERC9Q2Sm2rKAQIzEbgkWn20Nk0rVUC1ws4BMxPAgECBAgQIECAAAECBAgQIEBg4AImgAa+gg2PAAECBAgQIECAAAECBAgQIGACyM8AAQIECBAgQIAAAQIECBAgQGDgAiaABr6CDY8AAQIECBAgQIAAAQIECBAgYALIzwABAgQIECBAgAABAgQIECBAYOACJoAGvoINjwABAgQIECBAgAABAgQIECBAYBwCW2SYNxrHUGc6yk3S+k1n2oPxNL7TeIY605Fybof/5mlm43aa0goBAiMWqG2U2lZRCBCYjcBWaXa72TStVQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJrE9h0bW/zLgIECBDosUD97b9xj/uv6wQIzF7gBunCxrPvhh4QILAOgR3zXp8H1wHorQT6JHBgOvv5FXb4mXndJcmtFrz+Y3lc9bRZdk5j1yUnLtLoW+aeu9kiz61m0d/lxX+7mjfktb+V/Pcq37OSl5fvx5P/Sf4l2T3ZUPmvvODa5BYLXrhvHpddrc/1lLvlzV9fQwXfy3t2W8P7pvGWx6bSk5KvJO9MtkuWK3+YJz+14AX1+7PQ4aNZVq9d7meobYdp/Wwu4Njgw/r5O3+Dr7r+BfW35Yrk4qT+9pyVvCzZMqmy1p/B69/d/L+zMP6bDKNc5vPvKxjWzfOaDyYXJt9J6mfx4KQL5cHpxAPW0JG2f5/W0EVvITA4gV0yonOTlf6fXn9v7r9KhfX+XV3ub8py/0fPd/M+ufOl+QduCcxA4E5ps7ZR6zNAbRf9XrKhcs+84EcbelGery+BvpnUdnBtn7VZJn83V/K72GbftEVgkAI3yqhel/wgOXWFI6wJg/ow9p8LXl9/jGYxAXRN2j0nqQ8z8+UGufPVpCY4bja/cI232+d9G5oQWFj1ejdUFtZXj2t89eFmx3qQ8uTk+J/dW/6f/8rT5bNwoqcmyGq9L1yeRasqm+XVO63qHde/uCsf1G6T7pyX3HRuDOVyxNz9pW5qI/fyZP5bkpvk/v8m9TNX9VWpn8HLklsny/0Mte0wjZ/NDHFVpf7u1Ib0BSt8V/1teercazfObU0+1+TaUXPL1vozOPf2xm9mYVxGD0u2mssWKxjVkXnNPyT1s1rl9kn9XN+9Hsy4vDftP2ENfWj792kNXfQWAoMSeEpGc3ry06TLE0DL/U1Z7v/o+ZVlAmhewu2sBD6chp8413h9qfv9ZP4zwdziX7q5Z5asZALoQXldTSzNokz+bq7kd3EWfdRmBwV+pYN96kuXfjsdrQ3+g1bZ4ePy+vqj8/gl3lcfpusX+sL/v51zAdajrO/wSBLAhAgIooDWcheVuykYxAQhFdEKQUzFISRBKCKOY2CoBUEogzrcKpAitLYQWkBKFUKhXAKYG3cGlEBNuCYhBCspSIhYbjZ9ft/Zddbl+77znZOcnJPw/Gees+/uvnt79t339gUgFconIXExjGukuv58nsU/FuujWCZvjsmxGVQnvgWnQiYxLoB6ZJLnBhhb2ZFfjudU1pPMM86D38JDMAIS9fPX19O5yWRLotVzZd8h8AgshOq9sLpKIuU87lLhJ+JqZCPV/Z9pZPlCJVsGfJ+ATA6VkV8WZsAyWASTIbELTIWbYD7sCVOhXN+Z9GWQyOD8FMi7WgLfhmxLZNB+M6QhugAGyne7gHv5KCyFRNwMaqRa/3mKXS/AbkWWNJwZgN8GBxTbdmeZd5XzV8tQOw+jyJuy/yLknWXSL4PyWVDG2STOKVdYzoZMwPVn2azcTkfJKeT6AazoKPcfZ8oxz8CxcDjk2beDy2ADeBg2gjJ+SCLfZspbJjt/Dimbp0FZBmeQ/hvI+8r72xD+HTJBeiPsComB7Djf6b0QFynDr0J3MYwM8flmkfFxlvtAvt9Eq+fdiX35FXIqxNH1sBmUMYHEPOiurh1Knksg7yNlPs6Hw5EwBs6CvOM1sV7htg0NrPUGMvk+Dg6El3r5tGk7J8KTkB9jvgZltOpXnUyGtAFlnELimGJlPMu0EWmjU6dsDPU6pV0/78PkT5uwDBbBZDA00N8G0l9JfyZtb+I5WA7pa3Yaabsvhwsg38cvIH349B8uhfQfHobEKKj3R7O9/u2cyLZwJzwPJ8FfwNNwH3wcEkOhk/a+2l/ekmPOhTzrXPgUJFo9R9de/2pAAx0bGE3OBzvMfRz50qiOgAyYyomaNOKHQeIGuAzeB5PgKUgknX1lXEPiaMhA42VIZz8Duhx7HiSyzKTBwfBnUI1UDm/Cn8PtlR3/RDoDuRWQgUkqtQxGdoONIZNOt0Kifv76egYhZzZytn6urdlf3uNOpFOBPlQc01eLVKRXd3Dyu8nzRci7iufEp+FH8BPI+0zkfk+EYZBOV7y+G/Yq0qez/FyT9T3Y9hgkjoD5EM8j4FHYExKz4ELIO/sHyLvZBgZKfIgbSbn+JXygg5v6F/J8s8iX8jq24D+KbSewjONEtQy18rAV+ZbBRHg/pJFPGX0HpFH9U0hkkP5kI9VVrp8o0q2+uf4om8UtNV2kLOb7zETYi01zvHVj6pbUE9WIl5dhFFTL4M2sj4dEOhxxmjrq65DymDpkJMTjUZBYDNMh5fu9MA3yflN/5fu4CxID1XHK6+uQ58vkTZ75U9BdxMVLkOO+B3tDvJbR6nnzbef7TblOHZHv+XpI9KSuPYn8t0Hq6B3gMcg7WQ9y7a8W6TW5XuERDA28LQykneq0TV9E3n0LKwtZpv5NG5w6+DXIJHy7tut89p8KZUwhcTKsD8thV9gIboLUM/U6pV0/r1VfKPXjXDA0MBAM7MdNvAAp5+0i7fz/FBnSdv8eToPN4WK4EQbBOMh4IefbCpr1R9n8ljHT2WxLXy59/X0g58/3vC3km7waEvkOO2nv6/3l9K+3hL+CfNtbQKvnYJehAQ30xMBoMj/Y4QEZEGWgnLgIrmikuv4FxGGkMyBIBbAjvKtgDsudIRVLKpXh8E74DST/MXAPlPlTccyDRBrqVADNIpVCJiqGwFLYBAbDE5AGP4OUDC5y3o9AIh2Lb0LZkNfPX18vK6N2z5X7nwVl5PwPlSt9sDyKcy6BTBR0F6nQx0Ic5rhEBuAHwE8g7zPxMUgjEH+7QyraHSATQL+FMurre7DjsWLnHSy/BeV7PJ70mRB3eRfvhUTeW9a3ycoAid24j+9CfrUoPbW7tUnsvKbIsJhlnjGkfMfjNPgSJKplqJWHyeS7q5G768+2LJI35fdKmAB53/fBQsjkxLFwIeS6rb651V02uZWWkXt+BFIPrOwEUC7yOBwC1TKYyYK4TxwKtzRSXR2bfJdlpGNSfrN5fwcWO9Zl+QZkMJJ4B3waNoWB6nh77u1HEK8pe9+B26CTSCfwBLgH8nw3Q+rndmUqna84Sr5Eymrq4fWgJ3XtB8mf6yeSvhFOyQpxLYxvpLraljW1XikewYUG1noDKzMBNKZiJ+fZEdq1Xeez/9TKMVNIp05PHfQKHA/pb6Q+L6Nap7Tq5yXvxyD16GDYHZbDDrA3zAVDA/1tIG3+c5AxV3dRnwDKuKuMUSQeLVbSB5pdpNv1R+vfztkcM7U4LotFcHixvgvLBUW60/a+7C+nv7sCtiyOz+LnkDFL+iCtnoNdxtvFwDpvlwcdgM/5be5pf/hM5d7Kj/ZnbMvEQMgAYSS8BDPgs5Bj7oYXIcfsBGX+OaQzmCk//GdJt4s32HkrHAT7QQbSr0MZacD/EnL++fAFqJab+vnr62Rv3GMqo2bPled7MJmKuK9M9MEynaIzYX9odp+tLnkdO/Lcg2FfyHNU4z2sxHs6X+dCOkClo/p16utkbUTe14lQvsekM7GyLeSYX0NiSUFjZYD8ScOS8nw0fB8y8G8XKccfh0ws5teVlOPwNGTyYG+oO27n4YPkvxfKeJLEC7AFpGyPhlGQd5TvJucfAxmwt/vmcs3VVTa5VNu4iL13widgf1gXPgfptPcmPsBBi2sHTmM9njaAQ+FqSMTvPY1U15+k47aM8jxbseF/IfVEYgXEf/Im3d/fP7fwlnicLSm3qV8zifND+CTkm24XG7LzV3AepCxvA5vDN6BdmWJ34/95lXo1kbL6O4jjntS1udcLIPXCf8J2MAjqsSbXK/VncV0DGnirgbJvkD2vwBDoTdv1GseNgwmwBFKv7ADN4tlmG9mWenMONOsLtTjEzRpYbQbSv5wJZ8CPoaeRcl1GvrXB5Uplmba8VX802erfznOVY9N/eqxYz/dYnr/T9r48Ve4h/bJ8x2Xknsp+WyfPUR7nci01UA5S19LHG9CPlQFHfmm5BIYXd5qB0zLYCTKYCDvDpZDIgGwsZDIi6cT9kEFtmT/LPaCsVFJxdBfXkuFgOASSrsYRrGQwmGvm3OdAdYBfP399neyNAWGr53qG/R9OpiIyiOyLSKfmdNgf5kFPYjqZ94TPQ1y/DmW8m8RPIQPBVK77QfyUjuo+6utkbcQD/D0JyveYAd2XIX5y3o0gMQw2a6T6/0/K2TGV2/gl6U2hvNfKrj9KLmTtTUhncwaUMZNEtmVgXW2gWG3rIZNI1TIUh3kvC2A6jIJ9YHZB1veCmdDum1tdZZPb6DZeJ8cucDIcB+sX6ZSHnsYYDsj58r6q8TIrmaQ5CFKOr4NE/H6kker6k/rp6cp6WaZ/w7bhEP9lHEkiHZ7+/v7L+6kvd2XDxMrG9UjHzfLKtnoyeVK/VuuqhaxfCR+CdmWK3Y3jBiVB/AkMhcXQk7r2EvK/CHkXH4X7oaxzSP4hHiC1ptUrf7h5ExrQQLcGVjTJ0a7t+j/ypw4rI5M2iXXgIUg7E9IeXATNoqzzq/u66wtV85rWwOo2sDUXvB2+C2k/exPNvrX6edr1R5O3/u3U1+vny3qn7X157FISW0C1L55+woIiQyfPUWR1sbYaSIVvrHoDozllBsLdxVVkyC/AI4qMGXjcAcdB3s37IIO0DCoSN0D+9cJomAaJVGiZnNgtK8ThcAv05N0m/0jYF6ZDNdKoPwGPQgYYE2EI9CTaPdcsTrQXbA8Z1GYCYFXHVpwwHZkvQQZueaaQ2BgyKdQuXmXnz+Bs+Gkt4wbFet5D8h0GeY6eOrqeYyZB7ieer4DJ8N8wHyZAIs/Q03M3DuyDP3F5FrwfUt6+DnMhEwHdeZ1JnqNgBpSR9Fcg30A92nm4lcz7QCYpch9Hw3/BMshxWR4Ed8JsiMvcZ35t6e+yyS10FClX+UbDWHi5SGcSoDvXw8iT8p5/DfIZuBimwCtQj6vZcAbEVbwl4jfX3xByni/CXVCP59kQr+MhZTjv5HhIecg7bVavzWL7XtCX3z+nbxlL2XMhZCImkzIpw+W33Mrra+S5Bf4ZtobEdpAJnNQT7coUuxv/qdfBLONoIsRlymLcdlrXbkLevKM4z/c3BoZAIu+17PhdT3oS5FlyvStgMrT7nthtaEAD/WhgR66dAVtvo129mn8xlD5j6oPNYTQk0mdNPy/1SdrPm6GMap1SbqsvV1VfqH5e1zWwKgz8Kye5En4MaWvDupAYDZ2M2ZK3u0h/KX2fZv3R7o5ttb/T9r48/ikSC+AoyHe+K+wB94KhgYaBwXroEwMZRB0JN3Vw9mPJ80gl3/dJ/xvk+N/DuTAXEmmE50AqreWQyMDhZMj2JcX6V1nm2E7jd2TMYOLVglQYZVxF4nB4GDLAyABiDAyFnkS75zqFE90NuX7uY1XH1zhhB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alt="plot of chunk 7.1"/> </p>
<p>It appears a married couple makes more money on average than other groups. It
also appears that Informational jobs are higher-wage than Industrial jobs on
average.</p>
<h2>Polynomial and Step functions</h2>
<pre><code class="r">fit = lm(wage ~ maritl, data = Wage)
deviance(fit)
</code></pre>
<pre><code>## [1] 4858941
</code></pre>
<pre><code class="r">fit = lm(wage ~ jobclass, data = Wage)
deviance(fit)
</code></pre>
<pre><code>## [1] 4998547
</code></pre>
<pre><code class="r">fit = lm(wage ~ maritl + jobclass, data = Wage)
deviance(fit)
</code></pre>
<pre><code>## [1] 4654752
</code></pre>
<h2>Splines</h2>
<p>Unable to fit splines on categorical variables.</p>
<h2>GAMs</h2>
<pre><code class="r">library(gam)
</code></pre>
<pre><code>## Loading required package: splines Loaded gam 1.09
</code></pre>
<pre><code class="r">fit = gam(wage ~ maritl + jobclass + s(age, 4), data = Wage)
deviance(fit)
</code></pre>
<pre><code>## [1] 4476501
</code></pre>
<p>Without more advanced techniques, we cannot fit splines to categorical
variables (factors). <code>maritl</code> and <code>jobclass</code> do add statistically significant
improvements to the previously discussed models.</p>
</body>
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统计学习导论_基于R应用习题答案/ch7/7.html
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<h1>Chapter 7: Exercise 8</h1>
<pre><code class="r">library(ISLR)
set.seed(1)
pairs(Auto)
</code></pre>
<p><img 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" alt="plot of chunk 8.1"/> </p>
<p>mpg appears inversely proportional to cylinders, displacement, horsepower,
weight.</p>
<h2>Polynomial</h2>
<pre><code class="r">rss = rep(NA, 10)
fits = list()
for (d in 1:10) {
fits[[d]] = lm(mpg ~ poly(displacement, d), data = Auto)
rss[d] = deviance(fits[[d]])
}
rss
</code></pre>
<pre><code>## [1] 8379 7412 7392 7392 7381 7271 7090 6917 6738 6610
</code></pre>
<pre><code class="r">anova(fits[[1]], fits[[2]], fits[[3]], fits[[4]])
</code></pre>
<pre><code>## Analysis of Variance Table
##
## Model 1: mpg ~ poly(displacement, d)
## Model 2: mpg ~ poly(displacement, d)
## Model 3: mpg ~ poly(displacement, d)
## Model 4: mpg ~ poly(displacement, d)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 390 8379
## 2 389 7412 1 967 50.61 5.5e-12 ***
## 3 388 7392 1 20 1.04 0.31
## 4 387 7392 1 1 0.03 0.86
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<p>Training RSS decreases over time. Quadratic polynomic sufficient from
ANOVA-perspective.</p>
<pre><code class="r">library(glmnet)
</code></pre>
<pre><code>## Loading required package: Matrix Loading required package: lattice Loaded
## glmnet 1.9-5
</code></pre>
<pre><code class="r">library(boot)
</code></pre>
<pre><code>## Attaching package: 'boot'
##
## The following object is masked from 'package:lattice':
##
## melanoma
</code></pre>
<pre><code class="r">cv.errs = rep(NA, 15)
for (d in 1:15) {
fit = glm(mpg ~ poly(displacement, d), data = Auto)
cv.errs[d] = cv.glm(Auto, fit, K = 10)$delta[2]
}
which.min(cv.errs)
</code></pre>
<pre><code>## [1] 11
</code></pre>
<pre><code class="r">cv.errs
</code></pre>
<pre><code>## [1] 21.50 19.06 19.10 19.41 19.60 19.18 18.80 18.20 17.82 17.75 17.69
## [12] 17.90 18.20 18.02 17.91
</code></pre>
<p>Surprisingly, cross-validation selected a 10th-degree polynomial.</p>
<h2>Step functions</h2>
<pre><code class="r">cv.errs = rep(NA, 10)
for (c in 2:10) {
Auto$dis.cut = cut(Auto$displacement, c)
fit = glm(mpg ~ dis.cut, data = Auto)
cv.errs[c] = cv.glm(Auto, fit, K = 10)$delta[2]
}
which.min(cv.errs)
</code></pre>
<pre><code>## [1] 9
</code></pre>
<pre><code class="r">cv.errs
</code></pre>
<pre><code>## [1] NA 36.41 24.42 24.23 22.46 22.00 21.42 19.80 18.21 19.08
</code></pre>
<h2>Splines</h2>
<pre><code class="r">library(splines)
cv.errs = rep(NA, 10)
for (df in 3:10) {
fit = glm(mpg ~ ns(displacement, df = df), data = Auto)
cv.errs[df] = cv.glm(Auto, fit, K = 10)$delta[2]
}
which.min(cv.errs)
</code></pre>
<pre><code>## [1] 9
</code></pre>
<pre><code class="r">cv.errs
</code></pre>
<pre><code>## [1] NA NA 19.08 19.30 19.28 18.47 18.05 18.33 17.63 17.82
</code></pre>
<h2>GAMs</h2>
<pre><code class="r">library(gam)
</code></pre>
<pre><code>## Loaded gam 1.09
</code></pre>
<pre><code class="r">fit = gam(mpg ~ s(displacement, 4) + s(horsepower, 4), data = Auto)
summary(fit)
</code></pre>
<pre><code>##
## Call: gam(formula = mpg ~ s(displacement, 4) + s(horsepower, 4), data = Auto)
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -11.298 -2.159 -0.439 2.125 17.095
##
## (Dispersion Parameter for gaussian family taken to be 15.35)
##
## Null Deviance: 23819 on 391 degrees of freedom
## Residual Deviance: 5881 on 383 degrees of freedom
## AIC: 2194
##
## Number of Local Scoring Iterations: 2
##
## Anova for Parametric Effects
## Df Sum Sq Mean Sq F value Pr(>F)
## s(displacement, 4) 1 15255 15255 993.5 < 2e-16 ***
## s(horsepower, 4) 1 1038 1038 67.6 3.1e-15 ***
## Residuals 383 5881 15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Anova for Nonparametric Effects
## Npar Df Npar F Pr(F)
## (Intercept)
## s(displacement, 4) 3 13.6 1.9e-08 ***
## s(horsepower, 4) 3 15.6 1.3e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
</body>
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统计学习导论_基于R应用习题答案/ch7/8.html
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<h1>Chapter 6: Exercise 9</h1>
<p>Load the Boston dataset</p>
<pre><code class="r">set.seed(1)
library(MASS)
attach(Boston)
</code></pre>
<h3>a</h3>
<pre><code class="r">lm.fit = lm(nox ~ poly(dis, 3), data = Boston)
summary(lm.fit)
</code></pre>
<pre><code>##
## Call:
## lm(formula = nox ~ poly(dis, 3), data = Boston)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12113 -0.04062 -0.00974 0.02338 0.19490
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55470 0.00276 201.02 < 2e-16 ***
## poly(dis, 3)1 -2.00310 0.06207 -32.27 < 2e-16 ***
## poly(dis, 3)2 0.85633 0.06207 13.80 < 2e-16 ***
## poly(dis, 3)3 -0.31805 0.06207 -5.12 4.3e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0621 on 502 degrees of freedom
## Multiple R-squared: 0.715, Adjusted R-squared: 0.713
## F-statistic: 419 on 3 and 502 DF, p-value: <2e-16
</code></pre>
<pre><code class="r">dislim = range(dis)
dis.grid = seq(from = dislim[1], to = dislim[2], by = 0.1)
lm.pred = predict(lm.fit, list(dis = dis.grid))
plot(nox ~ dis, data = Boston, col = "darkgrey")
lines(dis.grid, lm.pred, col = "red", lwd = 2)
</code></pre>
<p><img 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" alt="plot of chunk 9a"/> </p>
<p>Summary shows that all polynomial terms are significant while predicting nox using dis. Plot shows a smooth curve fitting the data fairly well.</p>
<h3>b</h3>
<p>We plot polynomials of degrees 1 to 10 and save train RSS.</p>
<pre><code class="r">all.rss = rep(NA, 10)
for (i in 1:10) {
lm.fit = lm(nox ~ poly(dis, i), data = Boston)
all.rss[i] = sum(lm.fit$residuals^2)
}
all.rss
</code></pre>
<pre><code>## [1] 2.769 2.035 1.934 1.933 1.915 1.878 1.849 1.836 1.833 1.832
</code></pre>
<p>As expected, train RSS monotonically decreases with degree of polynomial. </p>
<h3>c</h3>
<p>We use a 10-fold cross validation to pick the best polynomial degree.</p>
<pre><code class="r">library(boot)
all.deltas = rep(NA, 10)
for (i in 1:10) {
glm.fit = glm(nox ~ poly(dis, i), data = Boston)
all.deltas[i] = cv.glm(Boston, glm.fit, K = 10)$delta[2]
}
plot(1:10, all.deltas, xlab = "Degree", ylab = "CV error", type = "l", pch = 20,
lwd = 2)
</code></pre>
<p><img 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" alt="plot of chunk 9c"/> </p>
<p>A 10-fold CV shows that the CV error reduces as we increase degree from 1 to 3, stay almost constant till degree 5, and the starts increasing for higher degrees. We pick 4 as the best polynomial degree.</p>
<h3>d</h3>
<p>We see that dis has limits of about 1 and 13 respectively. We split this range in roughly equal 4 intervals and establish knots at \( [4, 7, 11] \). Note: bs function in R expects either df or knots argument. If both are specified, knots are ignored.</p>
<pre><code class="r">library(splines)
sp.fit = lm(nox ~ bs(dis, df = 4, knots = c(4, 7, 11)), data = Boston)
summary(sp.fit)
</code></pre>
<pre><code>##
## Call:
## lm(formula = nox ~ bs(dis, df = 4, knots = c(4, 7, 11)), data = Boston)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.1246 -0.0403 -0.0087 0.0247 0.1929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7393 0.0133 55.54 < 2e-16
## bs(dis, df = 4, knots = c(4, 7, 11))1 -0.0886 0.0250 -3.54 0.00044
## bs(dis, df = 4, knots = c(4, 7, 11))2 -0.3134 0.0168 -18.66 < 2e-16
## bs(dis, df = 4, knots = c(4, 7, 11))3 -0.2662 0.0315 -8.46 3.0e-16
## bs(dis, df = 4, knots = c(4, 7, 11))4 -0.3980 0.0465 -8.56 < 2e-16
## bs(dis, df = 4, knots = c(4, 7, 11))5 -0.2568 0.0900 -2.85 0.00451
## bs(dis, df = 4, knots = c(4, 7, 11))6 -0.3293 0.0633 -5.20 2.9e-07
##
## (Intercept) ***
## bs(dis, df = 4, knots = c(4, 7, 11))1 ***
## bs(dis, df = 4, knots = c(4, 7, 11))2 ***
## bs(dis, df = 4, knots = c(4, 7, 11))3 ***
## bs(dis, df = 4, knots = c(4, 7, 11))4 ***
## bs(dis, df = 4, knots = c(4, 7, 11))5 **
## bs(dis, df = 4, knots = c(4, 7, 11))6 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0619 on 499 degrees of freedom
## Multiple R-squared: 0.718, Adjusted R-squared: 0.715
## F-statistic: 212 on 6 and 499 DF, p-value: <2e-16
</code></pre>
<pre><code class="r">sp.pred = predict(sp.fit, list(dis = dis.grid))
plot(nox ~ dis, data = Boston, col = "darkgrey")
lines(dis.grid, sp.pred, col = "red", lwd = 2)
</code></pre>
<p><img 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" alt="plot of chunk 9d"/> </p>
<p>The summary shows that all terms in spline fit are significant. Plot shows that the spline fits data well except at the extreme values of \( dis \), (especially \( dis > 10 \)). </p>
<h3>e</h3>
<p>We fit regression splines with dfs between 3 and 16. </p>
<pre><code class="r">all.cv = rep(NA, 16)
for (i in 3:16) {
lm.fit = lm(nox ~ bs(dis, df = i), data = Boston)
all.cv[i] = sum(lm.fit$residuals^2)
}
all.cv[-c(1, 2)]
</code></pre>
<pre><code>## [1] 1.934 1.923 1.840 1.834 1.830 1.817 1.826 1.793 1.797 1.789 1.782
## [12] 1.782 1.783 1.784
</code></pre>
<p>Train RSS monotonically decreases till df=14 and then slightly increases for df=15 and df=16.</p>
<h3>f</h3>
<p>Finally, we use a 10-fold cross validation to find best df. We try all integer values of df between 3 and 16.</p>
<pre><code class="r">all.cv = rep(NA, 16)
for (i in 3:16) {
lm.fit = glm(nox ~ bs(dis, df = i), data = Boston)
all.cv[i] = cv.glm(Boston, lm.fit, K = 10)$delta[2]
}
</code></pre>
<pre><code>## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
## Warning: some 'x' values beyond boundary knots may cause ill-conditioned bases
</code></pre>
<pre><code class="r">plot(3:16, all.cv[-c(1, 2)], lwd = 2, type = "l", xlab = "df", ylab = "CV error")
</code></pre>
<p><img 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" alt="plot of chunk 9f"/> </p>
<p>CV error is more jumpy in this case, but attains minimum at df=10. We pick \( 10 \) as the optimal degrees of freedom.</p>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch7/9.html
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# Chapter 7 Lab: Non-linear Modeling
library(ISLR)
attach(Wage)
# Polynomial Regression and Step Functions
fit=lm(wage~poly(age,4),data=Wage)
coef(summary(fit))
fit2=lm(wage~poly(age,4,raw=T),data=Wage)
coef(summary(fit2))
fit2a=lm(wage~age+I(age^2)+I(age^3)+I(age^4),data=Wage)
coef(fit2a)
fit2b=lm(wage~cbind(age,age^2,age^3,age^4),data=Wage)
agelims=range(age)
age.grid=seq(from=agelims[1],to=agelims[2])
preds=predict(fit,newdata=list(age=age.grid),se=TRUE)
se.bands=cbind(preds$fit+2*preds$se.fit,preds$fit-2*preds$se.fit)
par(mfrow=c(1,2),mar=c(4.5,4.5,1,1),oma=c(0,0,4,0))
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Degree-4 Polynomial",outer=T)
lines(age.grid,preds$fit,lwd=2,col="blue")
matlines(age.grid,se.bands,lwd=1,col="blue",lty=3)
preds2=predict(fit2,newdata=list(age=age.grid),se=TRUE)
max(abs(preds$fit-preds2$fit))
fit.1=lm(wage~age,data=Wage)
fit.2=lm(wage~poly(age,2),data=Wage)
fit.3=lm(wage~poly(age,3),data=Wage)
fit.4=lm(wage~poly(age,4),data=Wage)
fit.5=lm(wage~poly(age,5),data=Wage)
anova(fit.1,fit.2,fit.3,fit.4,fit.5)
coef(summary(fit.5))
(-11.983)^2
fit.1=lm(wage~education+age,data=Wage)
fit.2=lm(wage~education+poly(age,2),data=Wage)
fit.3=lm(wage~education+poly(age,3),data=Wage)
anova(fit.1,fit.2,fit.3)
fit=glm(I(wage>250)~poly(age,4),data=Wage,family=binomial)
preds=predict(fit,newdata=list(age=age.grid),se=T)
pfit=exp(preds$fit)/(1+exp(preds$fit))
se.bands.logit = cbind(preds$fit+2*preds$se.fit, preds$fit-2*preds$se.fit)
se.bands = exp(se.bands.logit)/(1+exp(se.bands.logit))
preds=predict(fit,newdata=list(age=age.grid),type="response",se=T)
plot(age,I(wage>250),xlim=agelims,type="n",ylim=c(0,.2))
points(jitter(age), I((wage>250)/5),cex=.5,pch="|",col="darkgrey")
lines(age.grid,pfit,lwd=2, col="blue")
matlines(age.grid,se.bands,lwd=1,col="blue",lty=3)
table(cut(age,4))
fit=lm(wage~cut(age,4),data=Wage)
coef(summary(fit))
# Splines
library(splines)
fit=lm(wage~bs(age,knots=c(25,40,60)),data=Wage)
pred=predict(fit,newdata=list(age=age.grid),se=T)
plot(age,wage,col="gray")
lines(age.grid,pred$fit,lwd=2)
lines(age.grid,pred$fit+2*pred$se,lty="dashed")
lines(age.grid,pred$fit-2*pred$se,lty="dashed")
dim(bs(age,knots=c(25,40,60)))
dim(bs(age,df=6))
attr(bs(age,df=6),"knots")
fit2=lm(wage~ns(age,df=4),data=Wage)
pred2=predict(fit2,newdata=list(age=age.grid),se=T)
lines(age.grid, pred2$fit,col="red",lwd=2)
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Smoothing Spline")
fit=smooth.spline(age,wage,df=16)
fit2=smooth.spline(age,wage,cv=TRUE)
fit2$df
lines(fit,col="red",lwd=2)
lines(fit2,col="blue",lwd=2)
legend("topright",legend=c("16 DF","6.8 DF"),col=c("red","blue"),lty=1,lwd=2,cex=.8)
plot(age,wage,xlim=agelims,cex=.5,col="darkgrey")
title("Local Regression")
fit=loess(wage~age,span=.2,data=Wage)
fit2=loess(wage~age,span=.5,data=Wage)
lines(age.grid,predict(fit,data.frame(age=age.grid)),col="red",lwd=2)
lines(age.grid,predict(fit2,data.frame(age=age.grid)),col="blue",lwd=2)
legend("topright",legend=c("Span=0.2","Span=0.5"),col=c("red","blue"),lty=1,lwd=2,cex=.8)
# GAMs
gam1=lm(wage~ns(year,4)+ns(age,5)+education,data=Wage)
library(gam)
gam.m3=gam(wage~s(year,4)+s(age,5)+education,data=Wage)
par(mfrow=c(1,3))
plot(gam.m3, se=TRUE,col="blue")
plot.gam(gam1, se=TRUE, col="red")
gam.m1=gam(wage~s(age,5)+education,data=Wage)
gam.m2=gam(wage~year+s(age,5)+education,data=Wage)
anova(gam.m1,gam.m2,gam.m3,test="F")
summary(gam.m3)
preds=predict(gam.m2,newdata=Wage)
gam.lo=gam(wage~s(year,df=4)+lo(age,span=0.7)+education,data=Wage)
plot.gam(gam.lo, se=TRUE, col="green")
gam.lo.i=gam(wage~lo(year,age,span=0.5)+education,data=Wage)
library(akima)
plot(gam.lo.i)
gam.lr=gam(I(wage>250)~year+s(age,df=5)+education,family=binomial,data=Wage)
par(mfrow=c(1,3))
plot(gam.lr,se=T,col="green")
table(education,I(wage>250))
gam.lr.s=gam(I(wage>250)~year+s(age,df=5)+education,family=binomial,data=Wage,subset=(education!="1. < HS Grad"))
plot(gam.lr.s,se=T,col="green")
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch7/lab.R
|
R
|
unknown
| 3,959
|
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<body>
<h1>Chapter 8: Exercise 1</h1>
<pre><code class="r">par(xpd = NA)
plot(NA, NA, type = "n", xlim = c(0, 100), ylim = c(0, 100), xlab = "X", ylab = "Y")
# t1: x = 40; (40, 0) (40, 100)
lines(x = c(40, 40), y = c(0, 100))
text(x = 40, y = 108, labels = c("t1"), col = "red")
# t2: y = 75; (0, 75) (40, 75)
lines(x = c(0, 40), y = c(75, 75))
text(x = -8, y = 75, labels = c("t2"), col = "red")
# t3: x = 75; (75,0) (75, 100)
lines(x = c(75, 75), y = c(0, 100))
text(x = 75, y = 108, labels = c("t3"), col = "red")
# t4: x = 20; (20,0) (20, 75)
lines(x = c(20, 20), y = c(0, 75))
text(x = 20, y = 80, labels = c("t4"), col = "red")
# t5: y=25; (75,25) (100,25)
lines(x = c(75, 100), y = c(25, 25))
text(x = 70, y = 25, labels = c("t5"), col = "red")
text(x = (40 + 75)/2, y = 50, labels = c("R1"))
text(x = 20, y = (100 + 75)/2, labels = c("R2"))
text(x = (75 + 100)/2, y = (100 + 25)/2, labels = c("R3"))
text(x = (75 + 100)/2, y = 25/2, labels = c("R4"))
text(x = 30, y = 75/2, labels = c("R5"))
text(x = 10, y = 75/2, labels = c("R6"))
</code></pre>
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<pre><code> [ X<40 ]
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[Y<75] [X<75]
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[X<20] R2 R1 [Y<25]
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R6 R5 R4 R3
</code></pre>
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统计学习导论_基于R应用习题答案/ch8/1.html
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<body>
<h1>Chapter 8: Exercise 10</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
sum(is.na(Hitters$Salary))
</code></pre>
<pre><code>## [1] 59
</code></pre>
<pre><code class="r">Hitters = Hitters[-which(is.na(Hitters$Salary)), ]
sum(is.na(Hitters$Salary))
</code></pre>
<pre><code>## [1] 0
</code></pre>
<pre><code class="r">Hitters$Salary = log(Hitters$Salary)
</code></pre>
<h3>b</h3>
<pre><code class="r">train = 1:200
Hitters.train = Hitters[train, ]
Hitters.test = Hitters[-train, ]
</code></pre>
<h3>c</h3>
<pre><code class="r">library(gbm)
</code></pre>
<pre><code>## Loading required package: survival
## Loading required package: splines
## Loading required package: lattice
## Loading required package: parallel
## Loaded gbm 2.1
</code></pre>
<pre><code class="r">set.seed(103)
pows = seq(-10, -0.2, by = 0.1)
lambdas = 10^pows
length.lambdas = length(lambdas)
train.errors = rep(NA, length.lambdas)
test.errors = rep(NA, length.lambdas)
for (i in 1:length.lambdas) {
boost.hitters = gbm(Salary ~ ., data = Hitters.train, distribution = "gaussian",
n.trees = 1000, shrinkage = lambdas[i])
train.pred = predict(boost.hitters, Hitters.train, n.trees = 1000)
test.pred = predict(boost.hitters, Hitters.test, n.trees = 1000)
train.errors[i] = mean((Hitters.train$Salary - train.pred)^2)
test.errors[i] = mean((Hitters.test$Salary - test.pred)^2)
}
plot(lambdas, train.errors, type = "b", xlab = "Shrinkage", ylab = "Train MSE",
col = "blue", pch = 20)
</code></pre>
<p><img 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" alt="plot of chunk 10c"/> </p>
<h3>d</h3>
<pre><code class="r">plot(lambdas, test.errors, type = "b", xlab = "Shrinkage", ylab = "Test MSE",
col = "red", pch = 20)
</code></pre>
<p><img 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" alt="plot of chunk 10d"/> </p>
<pre><code class="r">min(test.errors)
</code></pre>
<pre><code>## [1] 0.2561
</code></pre>
<pre><code class="r">lambdas[which.min(test.errors)]
</code></pre>
<pre><code>## [1] 0.05012
</code></pre>
<p>Minimum test error is obtained at \( \lambda = 0.05 \).</p>
<h3>e</h3>
<pre><code class="r">lm.fit = lm(Salary ~ ., data = Hitters.train)
lm.pred = predict(lm.fit, Hitters.test)
mean((Hitters.test$Salary - lm.pred)^2)
</code></pre>
<pre><code>## [1] 0.4918
</code></pre>
<pre><code class="r">library(glmnet)
</code></pre>
<pre><code>## Loading required package: Matrix
## Loaded glmnet 1.9-5
</code></pre>
<pre><code class="r">set.seed(134)
x = model.matrix(Salary ~ ., data = Hitters.train)
y = Hitters.train$Salary
x.test = model.matrix(Salary ~ ., data = Hitters.test)
lasso.fit = glmnet(x, y, alpha = 1)
lasso.pred = predict(lasso.fit, s = 0.01, newx = x.test)
mean((Hitters.test$Salary - lasso.pred)^2)
</code></pre>
<pre><code>## [1] 0.4701
</code></pre>
<p>Both linear model and regularization like Lasso have higher test MSE than boosting.</p>
<h3>f</h3>
<pre><code class="r">boost.best = gbm(Salary ~ ., data = Hitters.train, distribution = "gaussian",
n.trees = 1000, shrinkage = lambdas[which.min(test.errors)])
summary(boost.best)
</code></pre>
<p><img src="data:image/png;base64,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alt="plot of chunk 10f"/> </p>
<pre><code>## var rel.inf
## CAtBat CAtBat 22.7563
## CWalks CWalks 10.4280
## CHits CHits 8.6198
## PutOuts PutOuts 6.6159
## Years Years 6.4612
## Walks Walks 6.2331
## CRBI CRBI 6.0927
## CHmRun CHmRun 5.1076
## RBI RBI 4.5322
## CRuns CRuns 4.4728
## Assists Assists 3.8367
## HmRun HmRun 3.1554
## Hits Hits 3.1229
## AtBat AtBat 2.4339
## Errors Errors 2.4324
## Runs Runs 2.1425
## Division Division 0.7042
## NewLeague NewLeague 0.6675
## League League 0.1849
</code></pre>
<p>\( \tt{CAtBat} \), \( \tt{CRBI} \) and \( \tt{CWalks} \) are three most important variables in that order.</p>
<h3>g</h3>
<pre><code class="r">library(randomForest)
</code></pre>
<pre><code>## randomForest 4.6-7
## Type rfNews() to see new features/changes/bug fixes.
</code></pre>
<pre><code class="r">set.seed(21)
rf.hitters = randomForest(Salary ~ ., data = Hitters.train, ntree = 500, mtry = 19)
rf.pred = predict(rf.hitters, Hitters.test)
mean((Hitters.test$Salary - rf.pred)^2)
</code></pre>
<pre><code>## [1] 0.2298
</code></pre>
<p>Test MSE for bagging is about \( 0.23 \), which is slightly lower than the best test MSE for boosting.</p>
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<h1>Chapter 8: Exercise 11</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
train = 1:1000
Caravan$Purchase = ifelse(Caravan$Purchase == "Yes", 1, 0)
Caravan.train = Caravan[train, ]
Caravan.test = Caravan[-train, ]
</code></pre>
<h3>b</h3>
<pre><code class="r">library(gbm)
</code></pre>
<pre><code>## Loading required package: survival
## Loading required package: splines
## Loading required package: lattice
## Loading required package: parallel
## Loaded gbm 2.1
</code></pre>
<pre><code class="r">set.seed(342)
boost.caravan = gbm(Purchase ~ ., data = Caravan.train, n.trees = 1000, shrinkage = 0.01,
distribution = "bernoulli")
</code></pre>
<pre><code>## Warning: variable 50: PVRAAUT has no variation.
## Warning: variable 71: AVRAAUT has no variation.
</code></pre>
<pre><code class="r">summary(boost.caravan)
</code></pre>
<p><img 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" alt="plot of chunk 11b"/> </p>
<pre><code>## var rel.inf
## PPERSAUT PPERSAUT 15.15534
## MKOOPKLA MKOOPKLA 9.23500
## MOPLHOOG MOPLHOOG 8.67017
## MBERMIDD MBERMIDD 5.39404
## MGODGE MGODGE 5.03048
## PBRAND PBRAND 4.83740
## MINK3045 MINK3045 3.94305
## ABRAND ABRAND 3.69693
## MOSTYPE MOSTYPE 3.38769
## PWAPART PWAPART 2.51970
## MGODPR MGODPR 2.43689
## MSKC MSKC 2.34595
## MAUT2 MAUT2 2.30973
## MFWEKIND MFWEKIND 2.27960
## MBERARBG MBERARBG 2.08245
## MSKA MSKA 1.90021
## PBYSTAND PBYSTAND 1.69482
## MGODOV MGODOV 1.61148
## MAUT1 MAUT1 1.59879
## MBERHOOG MBERHOOG 1.56791
## MINK7512 MINK7512 1.36255
## MSKB1 MSKB1 1.35071
## MINKGEM MINKGEM 1.34913
## MRELGE MRELGE 1.28204
## MAUT0 MAUT0 1.19930
## MHHUUR MHHUUR 1.19159
## MFGEKIND MFGEKIND 0.84203
## MRELOV MRELOV 0.78555
## MZPART MZPART 0.72191
## MINK4575 MINK4575 0.70936
## MSKB2 MSKB2 0.66694
## APERSAUT APERSAUT 0.64645
## MGODRK MGODRK 0.62381
## MSKD MSKD 0.58168
## MINKM30 MINKM30 0.54393
## PMOTSCO PMOTSCO 0.52709
## MOPLMIDD MOPLMIDD 0.52092
## MGEMOMV MGEMOMV 0.44231
## MZFONDS MZFONDS 0.43038
## PLEVEN PLEVEN 0.39902
## MHKOOP MHKOOP 0.37672
## MBERARBO MBERARBO 0.36653
## MBERBOER MBERBOER 0.35290
## MINK123M MINK123M 0.33559
## MGEMLEEF MGEMLEEF 0.24938
## MFALLEEN MFALLEEN 0.14899
## MOSHOOFD MOSHOOFD 0.13265
## MOPLLAAG MOPLLAAG 0.05655
## MBERZELF MBERZELF 0.05589
## MAANTHUI MAANTHUI 0.05048
## MRELSA MRELSA 0.00000
## PWABEDR PWABEDR 0.00000
## PWALAND PWALAND 0.00000
## PBESAUT PBESAUT 0.00000
## PVRAAUT PVRAAUT 0.00000
## PAANHANG PAANHANG 0.00000
## PTRACTOR PTRACTOR 0.00000
## PWERKT PWERKT 0.00000
## PBROM PBROM 0.00000
## PPERSONG PPERSONG 0.00000
## PGEZONG PGEZONG 0.00000
## PWAOREG PWAOREG 0.00000
## PZEILPL PZEILPL 0.00000
## PPLEZIER PPLEZIER 0.00000
## PFIETS PFIETS 0.00000
## PINBOED PINBOED 0.00000
## AWAPART AWAPART 0.00000
## AWABEDR AWABEDR 0.00000
## AWALAND AWALAND 0.00000
## ABESAUT ABESAUT 0.00000
## AMOTSCO AMOTSCO 0.00000
## AVRAAUT AVRAAUT 0.00000
## AAANHANG AAANHANG 0.00000
## ATRACTOR ATRACTOR 0.00000
## AWERKT AWERKT 0.00000
## ABROM ABROM 0.00000
## ALEVEN ALEVEN 0.00000
## APERSONG APERSONG 0.00000
## AGEZONG AGEZONG 0.00000
## AWAOREG AWAOREG 0.00000
## AZEILPL AZEILPL 0.00000
## APLEZIER APLEZIER 0.00000
## AFIETS AFIETS 0.00000
## AINBOED AINBOED 0.00000
## ABYSTAND ABYSTAND 0.00000
</code></pre>
<p>\( \tt{PPERSAUT} \), \( \tt{MKOOPKLA} \) and \( \tt{MOPLHOOG} \) are three most important variables in that order.</p>
<h3>c</h3>
<pre><code class="r">boost.prob = predict(boost.caravan, Caravan.test, n.trees = 1000, type = "response")
boost.pred = ifelse(boost.prob > 0.2, 1, 0)
table(Caravan.test$Purchase, boost.pred)
</code></pre>
<pre><code>## boost.pred
## 0 1
## 0 4396 137
## 1 255 34
</code></pre>
<pre><code class="r">34/(137 + 34)
</code></pre>
<pre><code>## [1] 0.1988
</code></pre>
<p>About \( 20 \)% of people predicted to make purchase actually end up making one.</p>
<pre><code class="r">lm.caravan = glm(Purchase ~ ., data = Caravan.train, family = binomial)
</code></pre>
<pre><code>## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
</code></pre>
<pre><code class="r">lm.prob = predict(lm.caravan, Caravan.test, type = "response")
</code></pre>
<pre><code>## Warning: prediction from a rank-deficient fit may be misleading
</code></pre>
<pre><code class="r">lm.pred = ifelse(lm.prob > 0.2, 1, 0)
table(Caravan.test$Purchase, lm.pred)
</code></pre>
<pre><code>## lm.pred
## 0 1
## 0 4183 350
## 1 231 58
</code></pre>
<pre><code class="r">58/(350 + 58)
</code></pre>
<pre><code>## [1] 0.1422
</code></pre>
<p>About \( 14 \)% of people predicted to make purchase using logistic regression actually end up making one. This is lower than boosting.</p>
</body>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch8/11.html
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<body>
<h1>Chapter 8: Exercise 12</h1>
<p>In this exercise I chose to examine the <code>Weekly</code> stock market data from the ISLR
package.</p>
<pre><code class="r">set.seed(1)
library(ISLR)
summary(Weekly)
</code></pre>
<pre><code>## Year Lag1 Lag2 Lag3
## Min. :1990 Min. :-18.195 Min. :-18.195 Min. :-18.195
## 1st Qu.:1995 1st Qu.: -1.154 1st Qu.: -1.154 1st Qu.: -1.158
## Median :2000 Median : 0.241 Median : 0.241 Median : 0.241
## Mean :2000 Mean : 0.151 Mean : 0.151 Mean : 0.147
## 3rd Qu.:2005 3rd Qu.: 1.405 3rd Qu.: 1.409 3rd Qu.: 1.409
## Max. :2010 Max. : 12.026 Max. : 12.026 Max. : 12.026
## Lag4 Lag5 Volume Today
## Min. :-18.195 Min. :-18.195 Min. :0.087 Min. :-18.195
## 1st Qu.: -1.158 1st Qu.: -1.166 1st Qu.:0.332 1st Qu.: -1.154
## Median : 0.238 Median : 0.234 Median :1.003 Median : 0.241
## Mean : 0.146 Mean : 0.140 Mean :1.575 Mean : 0.150
## 3rd Qu.: 1.409 3rd Qu.: 1.405 3rd Qu.:2.054 3rd Qu.: 1.405
## Max. : 12.026 Max. : 12.026 Max. :9.328 Max. : 12.026
## Direction
## Down:484
## Up :605
##
##
##
##
</code></pre>
<pre><code class="r">train = sample(nrow(Weekly), 2/3 * nrow(Weekly))
test = -train
</code></pre>
<h2>Logistic regression</h2>
<pre><code class="r">glm.fit = glm(Direction ~ . - Year - Today, data = Weekly[train, ], family = "binomial")
glm.probs = predict(glm.fit, newdata = Weekly[test, ], type = "response")
glm.pred = rep("Down", length(glm.probs))
glm.pred[glm.probs > 0.5] = "Up"
table(glm.pred, Weekly$Direction[test])
</code></pre>
<pre><code>##
## glm.pred Down Up
## Down 3 2
## Up 176 182
</code></pre>
<pre><code class="r">mean(glm.pred != Weekly$Direction[test])
</code></pre>
<pre><code>## [1] 0.4904
</code></pre>
<h2>Boosting</h2>
<pre><code class="r">library(gbm)
</code></pre>
<pre><code>## Warning: package 'gbm' was built under R version 3.0.2
</code></pre>
<pre><code>## Loading required package: survival Loading required package: splines
## Loading required package: lattice Loading required package: parallel
## Loaded gbm 2.1
</code></pre>
<pre><code class="r">Weekly$BinomialDirection = ifelse(Weekly$Direction == "Up", 1, 0)
boost.weekly = gbm(BinomialDirection ~ . - Year - Today - Direction, data = Weekly[train,
], distribution = "bernoulli", n.trees = 5000)
yhat.boost = predict(boost.weekly, newdata = Weekly[test, ], n.trees = 5000)
yhat.pred = rep(0, length(yhat.boost))
yhat.pred[yhat.boost > 0.5] = 1
table(yhat.pred, Weekly$BinomialDirection[test])
</code></pre>
<pre><code>##
## yhat.pred 0 1
## 0 142 135
## 1 37 49
</code></pre>
<pre><code class="r">mean(yhat.pred != Weekly$BinomialDirection[test])
</code></pre>
<pre><code>## [1] 0.4738
</code></pre>
<h2>Bagging</h2>
<pre><code class="r">Weekly = Weekly[, !(names(Weekly) %in% c("BinomialDirection"))]
library(randomForest)
</code></pre>
<pre><code>## Warning: package 'randomForest' was built under R version 3.0.2
</code></pre>
<pre><code>## randomForest 4.6-7 Type rfNews() to see new features/changes/bug fixes.
</code></pre>
<pre><code class="r">bag.weekly = randomForest(Direction ~ . - Year - Today, data = Weekly, subset = train,
mtry = 6)
yhat.bag = predict(bag.weekly, newdata = Weekly[test, ])
table(yhat.bag, Weekly$Direction[test])
</code></pre>
<pre><code>##
## yhat.bag Down Up
## Down 56 53
## Up 123 131
</code></pre>
<pre><code class="r">mean(yhat.bag != Weekly$Direction[test])
</code></pre>
<pre><code>## [1] 0.4848
</code></pre>
<h2>Random forests</h2>
<pre><code class="r">rf.weekly = randomForest(Direction ~ . - Year - Today, data = Weekly, subset = train,
mtry = 2)
yhat.bag = predict(rf.weekly, newdata = Weekly[test, ])
table(yhat.bag, Weekly$Direction[test])
</code></pre>
<pre><code>##
## yhat.bag Down Up
## Down 43 45
## Up 136 139
</code></pre>
<pre><code class="r">mean(yhat.bag != Weekly$Direction[test])
</code></pre>
<pre><code>## [1] 0.4986
</code></pre>
<h2>Best performance summary</h2>
<p>Boosting resulted in the lowest validation set test error rate.</p>
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<h1>Chapter 8: Exercise 2</h1>
<p>Based on Algorithm 8.2, the first stump will consist of a split on a single
variable. By induction, the residuals of that first fit will result in a second
stump fit to a another distinct, single variable. (* This is my intuition, not
sure if my proof is rigorous enough to support that claim).</p>
<p>\( f(X) = \sum_{j=1}^{p} f_j(X_j) \)</p>
<p>0) \( \hat{f}(x) = 0, r_i = y_i \)</p>
<p>1) a) \( \hat{f}^1(x) = \beta_{1_1} I(X_1 < t_1) + \beta_{0_1} \)</p>
<p>1) b) \( \hat{f}(x) = \lambda\hat{f}^1(x) \)</p>
<p>1) c) \( r_i = y_i - \lambda\hat{f}^1(x_i) \)</p>
<p>To maximize the fit to the residuals, another distinct stump must be fit in the
next and subsequent iterations will each fit \( X_j \)-distinct stumps. The
following is the jth iteration, where \( b=j \):</p>
<p>j) a) \( \hat{f}^j(x) = \beta_{1_j} I(X_j < t_j) + \beta_{0_j} \)</p>
<p>j) b) \( \hat{f}(x) = \lambda\hat{f}^1(X_1) + \dots + \hat{f}^j(X_j) + \dots +
\hat{f}^{p-1}(X_{p-1}) + \hat{f}^p(X_p) \)</p>
<p>Since each iteration's fit is a distinct variable stump, there are only \( p \)
fits based on “j) b)”.</p>
<p>\[ f(X) = \sum_{j=1}^{p} f_j(X_j) \]</p>
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<h1>Chapter 8: Exercise 3</h1>
<pre><code class="r">p = seq(0, 1, 0.01)
gini = p * (1 - p) * 2
entropy = -(p * log(p) + (1 - p) * log(1 - p))
class.err = 1 - pmax(p, 1 - p)
matplot(p, cbind(gini, entropy, class.err), col = c("red", "green", "blue"))
</code></pre>
<p><img src="data:image/png;base64,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统计学习导论_基于R应用习题答案/ch8/3.html
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<h1>Chapter 8: Exercise 4</h1>
<h2>a</h2>
<pre><code> [X1 < 1]
| |
[X2 < 1] 5
| |
[X1 < 0] 15
| |
3 [X2<0]
| |
10 0
</code></pre>
<h2>b</h2>
<pre><code class="r">par(xpd = NA)
plot(NA, NA, type = "n", xlim = c(-2, 2), ylim = c(-3, 3), xlab = "X1", ylab = "X2")
# X2 < 1
lines(x = c(-2, 2), y = c(1, 1))
# X1 < 1 with X2 < 1
lines(x = c(1, 1), y = c(-3, 1))
text(x = (-2 + 1)/2, y = -1, labels = c(-1.8))
text(x = 1.5, y = -1, labels = c(0.63))
# X2 < 2 with X2 >= 1
lines(x = c(-2, 2), y = c(2, 2))
text(x = 0, y = 2.5, labels = c(2.49))
# X1 < 0 with X2<2 and X2>=1
lines(x = c(0, 0), y = c(1, 2))
text(x = -1, y = 1.5, labels = c(-1.06))
text(x = 1, y = 1.5, labels = c(0.21))
</code></pre>
<p><img 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统计学习导论_基于R应用习题答案/ch8/4.html
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<h1>Chapter 8: Exercise 5</h1>
<pre><code class="r">p = c(0.1, 0.15, 0.2, 0.2, 0.55, 0.6, 0.6, 0.65, 0.7, 0.75)
</code></pre>
<h2>Majority approach</h2>
<pre><code class="r">sum(p >= 0.5) > sum(p < 0.5)
</code></pre>
<pre><code>## [1] TRUE
</code></pre>
<p>The number of red predictions is greater than the number of green predictions
based on a 50% threshold, thus RED.</p>
<h2>Average approach</h2>
<pre><code class="r">mean(p)
</code></pre>
<pre><code>## [1] 0.45
</code></pre>
<p>The average of the probabilities is less than the 50% threshold, thus GREEN.</p>
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<h1>Chapter 8: Exercise 6</h1>
<h3>Provide a detailed explanation of the algorithm that is used to fit a regression tree.</h3>
<p>Read section 8.1.1, including Algorithm 8.1.</p>
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<h1>Chapter 8: Exercise 7</h1>
<p>We will try a range of \( \tt{ntree} \) from 1 to 500 and \( \tt{mtry} \) taking typical values of \( p \), \( p/2 \), \( \sqrt{p} \). For Boston data, \( p = 13 \). We use an alternate call to \( \tt{randomForest} \) which takes \( \tt{xtest} \) and \( \tt{ytest} \) as additional arguments and computes test MSE on-the-fly. Test MSE of all tree sizes can be obtained by accessing \( \tt{mse} \) list member of \( \tt{test} \) list member of the model.</p>
<pre><code class="r">library(MASS)
library(randomForest)
</code></pre>
<pre><code>## randomForest 4.6-7
## Type rfNews() to see new features/changes/bug fixes.
</code></pre>
<pre><code class="r">set.seed(1101)
# Construct the train and test matrices
train = sample(dim(Boston)[1], dim(Boston)[1]/2)
X.train = Boston[train, -14]
X.test = Boston[-train, -14]
Y.train = Boston[train, 14]
Y.test = Boston[-train, 14]
p = dim(Boston)[2] - 1
p.2 = p/2
p.sq = sqrt(p)
rf.boston.p = randomForest(X.train, Y.train, xtest = X.test, ytest = Y.test,
mtry = p, ntree = 500)
rf.boston.p.2 = randomForest(X.train, Y.train, xtest = X.test, ytest = Y.test,
mtry = p.2, ntree = 500)
rf.boston.p.sq = randomForest(X.train, Y.train, xtest = X.test, ytest = Y.test,
mtry = p.sq, ntree = 500)
plot(1:500, rf.boston.p$test$mse, col = "green", type = "l", xlab = "Number of Trees",
ylab = "Test MSE", ylim = c(10, 19))
lines(1:500, rf.boston.p.2$test$mse, col = "red", type = "l")
lines(1:500, rf.boston.p.sq$test$mse, col = "blue", type = "l")
legend("topright", c("m=p", "m=p/2", "m=sqrt(p)"), col = c("green", "red", "blue"),
cex = 1, lty = 1)
</code></pre>
<p><img 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fC/InrN/w44HJiJU9UC9NDTmYjhgg6VkyS1tFoB/2ZiOtafAnsDNwF/Bo5mfAdbuaoGP56PgySpDdTqZAfQTQyHO484CXgRMeHN8cAjwJ6jUrrWUt1E71h4SVJLq6eTXQcwhZjVbgqwdERL1LpsopcktY3+An4ScCBwCjFs7gPAP4DtiTHy41FvwFuDlyS1vFpN9L8i5qO/DDgD+CCxktx4V6bcAVGDN+AlSS2tVsAfXvz7huJWy3jsSV9dg7eJXpLU0moF/HgM73rYyU6S1Dacya5+ZcqllGKEQVezCyNJ0kAM+Pr1UO7sbd0Yr7P5SZLahAFfv94mekmSWt5gAX99n58nAPePUFlaXCrT0wnZkyJJUuvrL6wuJJqhd2DFRWa6ialrxzMDXpLU8voLq5cTvenPZOWFZl41OkVrQZlMzOwnSVJLG6w2esiolKK9lIg14Q16SVLLGizgjwUuIK69XwA8CRw10oVqXam3Br+UmM5XkqSWNFjAfwZ4H1GTfwLYHfjCSBeqZeWUiWO2BJjY5NJIktSvwQJ+GRFkRxILz5SJ2vx41kEEvDV4SVLLGizgPwVcSgT7BcD5wH+NdKFa1vIa/FKswUuSWlitueir/aS49dpk5IrSBvKz1+CtwUuSWlqrj+neo9kFqKG3k501eElSyxos4NcDzgEeBzYCvg9MGelCVfn7KO5rcCt2srMGL0lqWYMF/JeBK4n1z+cCWwHfHqGyLGLFWfN6F3Spvt9c2WFykqT2MFjA787yQF8EvAk4eITKsitwDfAGls+aR5/7rcBOdpKkljdYwE8jwqzXQqJH/Ui4GdgHeDPwNVpxOJ6d7CRJbWKwgL8EeHVxfzPgROKa/EiZD7yOuBxwwQjuZyiswUuSWt5gw+Q+CPyIaJ6/BDgD+PQIl6kCfAn4J1Gjb8QLgb37eW5nYMEQymUNXpLUNmoF/DXATsX9R4CDRq84K/hbcWvEw0T5a3khQ72Wv2Iv+qlD2pYkSSOoVsDvMOqlGD4PFrdaXgXMHoZ99PaiX2MYtiVJ0oho9YluWkupUuGp1TpxsRlJUourFfAdrDwevdb49OF2WxP22ZhSpcxTq03EcfCSpBZXK+DLLB973t9tJGwLXEVc8x+tfTamVKmwYMYErMFLklpcKzXRl4FfET32W1OpUmHJpAk4TE6S1OJqdbK7ZdRLsdy3mrjvwZUqZRZN7a3B20QvSWpZtWrw7dyLfmSVKmWWTOrCGrwkqcW1UhN960u5QndXJ9CNAS9JamEGfCNKlTLdXV1ADzHaQJKklmTANyICfgIR8INN8ytJUtMY8I3oKBvwkqS2YMA3IuUK5Q4DXpLU8gz4RpQqPUUnOwNektTSDPhGlCplejqtwUuSWp4B34iUy5Q7OolZ9+xFL0lqWQZ8I0qVHsodvcPkrMFLklqWAd+IlHsod0xIsbpdayyAI0lSDQZ8I0qVnuIavCRJLc2Ab0TKZXIy4CVJLc+Ab0wPPR1ee5cktTwDvhGlSg+VDmvwkqSWZ8A3olTp4V+7bgXZDnaSpJZmwDfiyJ//k5ue91xg7WYXRZKkgRjwjXjdGQ+z7qOP4xh4SVKLM+AbU2bCsgx4HV6S1NIM+Mb0MGFZBQNektTiDPjGWIOXJLUFA74xZbq6DXhJUssz4BvTYw1ektQODPjGVNfgHQsvSWpZBnxjvAYvSWoLBnxjeujqhhgHn5tcFkmS+mXAN6a6Bp+zzfSSpBZlwDemzMSlEAHfgzPaSZJalAHfmN4megNektTSDPjGVPeiN+AlSS3LgG+MTfSSpLZgwDemh4lLExHwZQx4SVKLMuAbU2bCMlheg+9obnEkSarNgG9MbxN9JzbRS5JamAHfmOomegNektSyDPjG2MlOktQWDPjGlK3BS5LagQHfGJvoJUltwYBvTJmu7sQuV22NAS9JamEGfGOiiX7nq/fGcfCSpBZmwDemh67uEjARa/CSpBZmwDcmavA59Qa8E91IklqSAd+IRKarGyqlLqzBS5JamAHfqIlLO6mUOvEavCSphRnwjZq4dAI9naVKyYCXJLUuA75RE5d2sWwC3V1kDHhJUosy4Bs1cekElk1g8WTAgJcktSgDvlFd3V0sm8AzU0gY8JKkFmXAN2rCsk56Oq3BS5JamgHfqAnLMt1d3JS2nYEBL0lqUQZ8o7q6e7h+Rz77yI/3xYluJEktyoBvVEe5zJy1WMykiViDlyS1KAO+cT109rA4TeoEpjS7MJIk1WLAN65MZ09ewqQJwHQyXc0ukCRJfRnwjeuhsyd3565OYBpwLZnU7EJJklTNgG9cmekLy3liz9IHN2BjYFtgdrMLJUlSNQO+cWUO+dOcng54dF02A+4FNmtymSRJWoEB37gy333/reWOnLu7WAf4LbB5swslSVI1A75xPcD8no4SXd1MAa4GtmhymSRJWoEB37gysLDSWWHx0tW6gBuxiV6S1GIM+MaVgflMXFo+4YEfTWOzuycBazW7UJIkVTPgG9cDLGDy4p4Hujfr4J5NJ4Bj4SVJrcWAb1wZWMDUReVHuzcuAZObXSBJkvpqpYDfCrgceAL4HsvneZ8G5GYVqoYI+MmLK/N71kgY8JKkFtRKAf8T4A/ANsQqbSc0tzj96gEWMu3pSiYxmcXTml0gSZL6aqWAfx5wEjAHeB+wH1GrbzVRg5+xAIA1mDur5qsyu5Jb6vhKksaRVgqgh4Adivtl4HjgZFpvzfXoRT9r3qTO0rI8gwWzgFxjPvrPAhuMeukkSaK1Av5TwAXAj4ufLwCuAP7VtBLVdiFwK+s8tvqsiQ/3TGTpTGAxvdfiM1uQeR8R7qs1r5iSpPGsc/CXjJo/EQu3VE8a8wngbGCvOrfRAczo57mJMAyrviVOAuC7CybOmvLw0s7FnTOARcBU4Bmi/C8jAn49MpuSOHPI+5UkqQGtFPAA9xe3XpnoWX95ne/fHziyn+d2BO5b9aL18ZK/37P9+qvPun/ugdOAp4mAnwOsXuxrNeCFwDFkziWxbNj2LUnSIFot4Ifqz8Wtlm8ynMu6bn/jrRuuvcFWD/DK6cDjRMBDBPtmwBJijvrJwD7AecO2b0mSBtFK1+DbzT2lrqULIU0lmuh7h8utDswH/g1sCfwT2Lo5RZQkjVetFPC3EU3y/d1azfWTe3oezhHwvU30ALOAa4BbiGF+dxSPSZI0alop4LcFrgIOIjrD9b21lsQfdzl/xuUVSpN5tgafP02lNAv4PnAGUZu/A3vTS5JGWSsFfBn4FRGWbWEai+YVAd9bg38N82atDVwEXF+87HYi6CVJGjWt1snuW80uQCNmMW9emY5JLL8GP5P5Mxezxtz5QKV4mTV4SdKoa6UafNvZgAfnFgHfW4OfyfyZXaRiOlvoJob9TW9iMSVJ45ABPwRrMWdBJnUC/wF2pFRZjQUzJgGQqAD3FP+2Xh8CSdKYZsAPzdMdlDtI3MrT0zalUupk4fSJVc8f17SSSZLGNQN+aBZ1UI7FcM48+PMAfPRry5e5TZzdlFJJksY9A35onilRiYB/y6kPAT3cvnWtMfvdZLpGtWSSpHHNgB+CtLynPMBM4GFqd6h7ir496TMbk3lTvxvP7EPmEbI98CVJjTPghyiWgc+JCPgHWT5lbbWHgP/p89gewJv72ejE4vW/BnYarrJKksYPA36IVuOpZcRENjOBB6i9XO3HgK36NNNvV9xqeSMx6c9FwM7DV1pJ0nhhwA/R2jzePZnFRwJfIGrqK9fgY6nYe4H1gWh+jyVl7yH3meUusybLA/5qrMFLklaBAT9E3XTdsA6PHUmsHPcg/U9q8wCwAZlZRHhvC1wKbA9EW3/mJcBfgZtJzCXxKLAmmbVH+GNIksYYA36IrmfHCx9jnW2A3xFLxE6HPBPy5D4vfRDYEHgr8Eng7cQ89VsWz7+XaMo/lLTC+PlvA+8ZsBCZCUP9HJKkscWAH6KHWP+OJUzqBI4A/k7U4L8CK/WQf4AI+BcCfyXxf8BdwObF8/sBR5O4q8/7rgKeU3Pnma6iReBe8rPbkSTJgB+qpUx8cG0eXwppCfAMsCmwN7BLn5c+CGwAbEjiweKxu4DNihr4LBKP19jFo8A6QPSuz+xW3N8euAz4OHAucNhwfi5JUnsz4IfuoU25Z3HcTZkY+vY6Vq5130f0mu959pHEHGAt4mTgyppbj7nsO4qf9gN+SaaDaM7vAvYHjgUOLMbsSZLUcsvFtqH0xIVMvWl51/l0Xfybl0Feg1hpLpHSfDKPAfP6bgDYC7h4gJ3kYlKcNxGr010LnFbcdiDxDJm/AweC0+NKksaXbwK/GYkNZ/hbjUf3hHwh5KMgf7h44ebkPuPaM6eRuY3MzAF28Fsyt5P5E5k1yWxc4zVbkvkZmR3InrhJUos4GZrTR8om+hGTLiGa1jekdyx74i4SV/d54THA/5KYP8DGHgKuInEIiSdI3FfjNXE9H76PY+c1XsSJ7drkfoenSuOWNb2R9RSwDfDcfl8Rwf6zQbZzL3DdgK9IVMhUgB2I2e/+VX8xpTYRLV1bERNFHQk8UTyzGpnHgVuAf5A4n8zzif4wlwCXkljajCJLzWLAj6wHievij0GeAumZVdpK4tt1vvIOYAFwMJknSJy+SvvrKzrvzRiklWFVtjsDWESiPKzbVevLvBk4HrgeWAwsI2aB3JEI7SVEC9jZwIuA9YDJxevuJvq2vLTohAqZbYn+LJsAx5H5b6JV61fAS4AvkvkWMf3zi4F/VY1mkcYkA354VDJ0JFYKqoeIXvJ/IGauu2qEy3ER0Ynvh8DxZH5LotbytY06C1ibzCuISXr+SBqghSCzCbAm8aV8DvHFu3oxagAyGxRfrr8mjtmrh6GMWlWZNUjMreN1mxGLJD0PWJeYpKmHaGE6n5jrYevi3/NIlIvfhfuAicBXiQDuAG4lgnZT4nsoE1M5v6PY2yTid+NdxO/zVQPWwBO3FPduJn7nqp1H5ivAJ4gT7huAw8isX2x/PlAm8cigx0BqIwb88FhMfCEt6vP4g8QSslcTzeYjG/Dp2U6Em5D5DvAHMp+u+vJbUSx+sx/wFxI9ZD5FfAF/kPjC6yazKbCQmMTnSuBzwIlk9iSxpKjdTySxpNhmInr3X0vUwnpn5VtC5jzgVOB2Mh8hjtszZDYlcc+wHgsNLrMF0YT9bjLnFo9uT/wuX0YE7v8RfTt2BR4jTiJPBx4ncX+xnZ2Jy1D7EPM27AJ8vLhkNJUI0FlER9dPEb8vc4r93VRVouur7j9d/Ftv69XAEk8X+14uAv4s4m+0o2jiv5Y4Of0NcWL6IuBy4CESC4r3TSdOcA4hTl7OHPDkI7NW8drdiGP7c+By0krfF9KwGk/jpr8JzCYWchlWOaapfW9afj2w95m9gc8A7wY+BukdK797hEST5UHAc0kcUeP5I4CNiUl5/kN8ke9G1MY2AvYnsR2ZdwHLSJxS9d5jiRX0/kV0IvwQsAeJuWQ+DXSR+Eyf/ZWIL8qLiBOf7YBvECcY80j8erg++riXWRd4KfF/uRUxe2IJOImogV9DtJq8DDgR+DPRjN1F4uxi4qUXEyesuxOhdw2JVbvElNkAeJRUNQdEK8o8l2iBWAIcXvx7NXHCsiFxstJNfG/eD/yR6PPyImKxqfuJ47sD8fexA3FSvDPRCnE50VLxHuJv7Sbg4ySeGpXPp2Y5GfgSrDRLqYbRSA6T+0WOL4C+z2wB+ZeQS5Avhrw+5B1HogwDFO7H5Kq57TNrFP9eReZuMtPJnELmPDJTyUwhM4/MHDJHkrmOzHp9ttlJ5jgy3yDzAJmXkbmUzNFkfliEea2yfJXMuSs8n9mJzEl9HptEZjfySrMBVm9rdTI/InMCmYPI/L/ipGZ8yxxB5uLi/+fbZI4hsy+ZV5O5qHj+dDKvKyZM0lBltifzTTK/IHMsmT3IPLf4O9meTN91KXrf93Iyl5N5DZmNybyBWE1SY0vThslZgx8GOc7Ov56ik1vfZyfFNLb5QqI5cHNIxw53GQYp4OVEx6TNiD4BfwFmknhbP95x4KYAACAASURBVK9/OVErORbYpqo5daB97Ar8BNi93854mSkAK9QEo7Z4clGeQ4rHfkI0674EeOVK+495AE4H/puooU4hmkqPJRbxeT6xeM+TwHuKVfl639sJ/BdxeeC7RdNte8nsTdQWdyR6iB9a3D8b+DBwgD3G20TmOUS/gJcSl8EOIZrxbyRaC84BViNaB58gZsjchmhdOQ14YIVOqnHSVhmmvjcaHk2rwXsNfngsIf4oa0hLijs3AW8hQme0/Rl4JRF+dwP/C5zR76sTFxbXT3erK9zjPf8imt0Hes3KTbyJZcCRZD5F5mbi2u9UEkeTeSsx9e+3nn19XOP/IfBuEjcQy+v2Pncl0ew/kfii3J3oL/B+4gvxQ8SX5eNEB8izyOxb9D94EXFSMZfEY3V95tGUeR7RnD6T6Eg2g+jTcQTR9PsP4oToUMO9jSRuA24DvlY88tWiNWtj4vf4KOJvdl1gDaIScTHR/P85YBsy9xPfP/cRlxMWE7NmnrHCSJo4wd6POHmYSdQqVwNOr+pfMIPEAjI7EqMWnkucWNxcYyGs5fKzIximECN5XgjMJfplbAl8h8TlxMiZDuL3d2Zxf13g7215st3irMEPgxyB+cc04NjzfCQRTNdA2n24yzCgaJZfq/gyqfc9JWJo3OhdH4za/H7EanvLii+kC4j+Ab8GXkDMwX8OqU+Hqf63eQQxxe8M4FLiS+hwEpnMe4APEIsEzSVOvjYkOkHdR4yz3gw4icSpVdvcoNjeHsABwHtXaCUYbtHn4Q1FuR8Ysf2oPcWKksuI3+1bizkxZhPfeU8RJ4TrEH0uziVavZ4gThzKwGuJMJ9QvH4CcSIxr/h3XeLkfRvi7+RfRKfJWcRImYlEa8N9xb+zgfOKfV5M9Ln5TLGdBUVZFxP9QrqIuQv2J1qgzn72M9T32XszbBLROfLJujsvRl+V5xEn9reMYKfHptXgDfhhkKOp+JLi9hfg4ETfWlR+HhEcTwCvrqrZ19reRsBGKWqz41tmKyJIP0p8gRxKYuEwbn8C8cV4V/HFOBk4muhQdQ0xDfEZxLCuXYla9LHEF9TVxBfYO0krLQ88HGXrJC417AK8re4vPQl6f3+2I05SHyOCs+9aGL2vXQNYSKJ7kG2uTgz5fRJ4isRDw1TWicSlilcQv++ZOMH4PXFicAfRAXI2MenXzkTLwLLitUuBOcTf8v1F+WYTrav3Fs+tC/ybuByyDXFifxlxsr4X8f19FXFisi7RInYXcbJyK4mbV/HT2cluFIxkJ7uPZTggw8wMi3JcB+37qs7oVZ9/MFhHuwzvzXE9ru/jkzMcnqGUo5fv+BFfAM3a99pFp7T1yXydzOv6PH8mmR3JbEJmrzq2N5XMC8gcRXR2nF313Izi8cPI/JnMJ8n9Xf6RxqBMIpbG3pzM24oOou8gsw+ZXcm8iRji2d/7tyA6725c/E2+hMxryexXdILctcZ7Osm8tOiUegSZA8l0kdm2eM+rhvCJ7GQ3CkayBv9BohnqeuB7wFOp3wDOOxHDlfaCtLjq8S5I3cX2Tiaufb03xaQc5Bg/Pok4uzyH6Ci2VoqapJopsxFwCnGd/0miue+rZH5JNPm/AVi7uH8bMbnKjkRT5wXE2f0y4vrlJOJSThdwPonbR/fDSBpmdrJrc70T3axJXO/qf3gX6RrIvwUOgvwY0XS0FPg00QkOounpKGL43b+JBWk2BW4nxtRfTzQhbwwNXFfXyIgJX/YBevsunEbmb8CfgB8TzfhXEE2Fbwfmk3hl1Rb2fPZeZjLJkzZJQ2fAD4/FRCeVNYlr7IO1jPyaqMV3AxcC9wAbZHjOXNaY+RteMeVwfnPbJ/ji9//OS05Yj4cvPJ3DzkzRmY8cZ4IPEqFvwLeSuE7et/Wm/vH5hrukYeJyscOjb8A/nqMpvR/pYaAnkfftpGejgzj7gPV4eAfgZ+ex/9ffwckAfIlPvO21/OF9t7Dtpt/hA6dWbeBjRM1w05H5OJKkdmfAD4/FxPXTNYiA/w/RzD6Q/zmQc27dhSv334y73zCHtTofZ+2D38Kp9y5i6uS4Jk/3R/jmmbew7Zc+yIl79L4xxfSXNxE9RiVJWokBPzweBjZgeQ3+QegzvetK0vWnc9jiKSze/p/s9vAyJixYh8c+W2xrLtHJ7t/Fi68hJraodg9N6pkpSWp9BvzwuIsI2zWJcF4ATB/oDRkmTGbxhMdYZ+4tbLuM6Di3F7Gk5dPELGy9q2vdREzIAOQE+XmJ/BSwTvb/UJJUg+EwDFIspzqN5TX4BcTkCQPZFrjhZra7/WmmdRKTMyyFVCaW3DyAZwM+zQdWg7w7MfPa2cTJwHXEvOuSJK3AXvTDp0yMc55HfQG/KXAn0bR/AbG8ZO+67Y8S00dWz5w0B/gFcb3/o8Ryk399mmkHwMLrIDnLmSTpWQb88HkA6E5QzlGjHyzgNybGRb8pJrzJ74RnV4B6FPhP78Q3Ib0G8vbE4iIXAR+dwYIdprLoAOD6Yua71VJM5zqInIjx9L8uWgckSWOMTfTD53csX/Wsnhr8RsD9VbPZnUEMfYMI+KtXfku6EdJHIT0JzFvI9G99jv9+YA3m7kbMqfy1ld9T07uA1xPX+yVJY5A1+GGSYvrYXvUE/MbEdffeLVQvI3sRcO0gezwAYC+2euI53NYb1h+ps7ivAN4K/BLyBpAerPN9kqQ2YQ1+ZAzaix5YI0WHvBrSXEh1zVu8BXdet4AZaxHTot6VY+WoAeQErA3pEeAEYhlHSdIYY8CPgLR84puBDMuxL1E54d9sc2MiTyaWqn37yq/K60KeWvxQNb4+XQKsC3mPld+zwvv3HPj5RuTvQ3475AOqHlsTct9x/pKkITDgmyDH+uY3DMe2EszpofMUYt3w3wI751jruHdvOxLrHH+ueGB/4K9Vm/gAcALknfsp7WzgfMjr11eivFE0+6/wWLHUa+5dS/rFwFsh/x3yWcRqSydDvhnyXvXtR5KkMGLrwdeSYwWx/p77TYath3FvnZAvKba9R47lRnufOx3y8yH/LcI6XwB5Zp/3Hwb5mH62/SHIZ0L+FeTvRICv9JopkD8C+SrIf4Z8BeRiRb18HOR7IR8C+WeQj+zz3i0hfwzyGyG/HvLlMU1v7oB8VPGajaMVQpLaTtPWg7eT3SjJsbzrl1KsHb9+YjjX+U49kJdB7kykyzJ8IcOUFLXvTkjXQa4QE+T8tcbQuOuIBWyAPIGYtGcfYhnbg4HXERPvJOKa/TuKAJ9TvPZk4AfAbjFRT94Y+D3k24nRAi8ETgO+D+kPfcr+H+Ary3/OM4EziePVVbQgvAqYAPkNkO4d4sGSpHHBgB9BGVJaPrZ9A2DPvHw62+F2N7AFsXzsn4BDiFD+TvH8ZcA9kH5W4713Fu+FGKq3JTEn/pbAiyE9QwQ0kN9S1KY/BawN9ABvLYK6kO4rat8JWAbpcaLnfj1+CvyN6KjYDXyIGPu/NvBzyCcBF/QZdVDI3yDG9l9V574kacwy4EfOM8QSss8UP68LrE9cg765vzcNwb+Ja++3AWecyAd/D1wG6eJ4Ov13/29NlaJ5/a9AD6TdB9jPz4H3ETX3twGTVgz3Z7d5U+MfASBl4mSl1+eLf++E/G7glcC5kL8AzId0aTydjyFGELwDMOAljXsG/Mh5hGievi3HGPUysZzsCxm5gN8J8gEJrtmTSzZ6P9895bt1vz0dx7NN+gP6E9Ey8H1Id6x6cVdFug24DfKpRD+DuUWw/xU4DHg5cCHkH0N65+iWTZLULKPdye6wDMcW92/KcHGGORkuy4OvFb8qe5wO+Z+QfwP5Jxtx378zvDvDpAxjOOzyHpA/sGLHwfyF4vYdyLs1r2yS1LxOduPJaAf8rAx/y7BehnkZlmQ4N8NjOa5Nj8Ren1P0OE9XsfN2GU7JcHSGJzN8JsfwtJaVo33+qAydGb6cYb1V3FIn5NdA3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alt="plot of chunk 9a"/> </p>
<p>The plot shows that test MSE for single tree is quite high (around 18). It is reduced by adding more trees to the model and stabilizes around a few hundred trees. Test MSE for including all variables at split is slightly higher (around 11) as compared to both using half or square-root number of variables (both slightly less than 10). </p>
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统计学习导论_基于R应用习题答案/ch8/7.html
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<body>
<h1>Chapter 8: Exercise 8</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
attach(Carseats)
set.seed(1)
train = sample(dim(Carseats)[1], dim(Carseats)[1]/2)
Carseats.train = Carseats[train, ]
Carseats.test = Carseats[-train, ]
</code></pre>
<h3>b</h3>
<pre><code class="r">library(tree)
tree.carseats = tree(Sales ~ ., data = Carseats.train)
summary(tree.carseats)
</code></pre>
<pre><code>##
## Regression tree:
## tree(formula = Sales ~ ., data = Carseats.train)
## Variables actually used in tree construction:
## [1] "ShelveLoc" "Price" "Age" "Advertising" "Income"
## [6] "CompPrice"
## Number of terminal nodes: 18
## Residual mean deviance: 2.36 = 429 / 182
## Distribution of residuals:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.260 -1.040 0.102 0.000 0.930 3.910
</code></pre>
<pre><code class="r">plot(tree.carseats)
text(tree.carseats, pretty = 0)
</code></pre>
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" alt="plot of chunk b8"/> </p>
<pre><code class="r">pred.carseats = predict(tree.carseats, Carseats.test)
mean((Carseats.test$Sales - pred.carseats)^2)
</code></pre>
<pre><code>## [1] 4.149
</code></pre>
<p>The test MSE is about \( 4.15 \).</p>
<h3>c</h3>
<pre><code class="r">cv.carseats = cv.tree(tree.carseats, FUN = prune.tree)
par(mfrow = c(1, 2))
plot(cv.carseats$size, cv.carseats$dev, type = "b")
plot(cv.carseats$k, cv.carseats$dev, type = "b")
</code></pre>
<p><img 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" alt="plot of chunk 8c"/> </p>
<pre><code class="r">
# Best size = 9
pruned.carseats = prune.tree(tree.carseats, best = 9)
par(mfrow = c(1, 1))
plot(pruned.carseats)
text(pruned.carseats, pretty = 0)
</code></pre>
<p><img 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alt="plot of chunk 8c"/> </p>
<pre><code class="r">pred.pruned = predict(pruned.carseats, Carseats.test)
mean((Carseats.test$Sales - pred.pruned)^2)
</code></pre>
<pre><code>## [1] 4.993
</code></pre>
<p>Pruning the tree in this case increases the test MSE to \( 4.99 \).</p>
<h3>d</h3>
<pre><code class="r">library(randomForest)
</code></pre>
<pre><code>## randomForest 4.6-7
## Type rfNews() to see new features/changes/bug fixes.
</code></pre>
<pre><code class="r">bag.carseats = randomForest(Sales ~ ., data = Carseats.train, mtry = 10, ntree = 500,
importance = T)
bag.pred = predict(bag.carseats, Carseats.test)
mean((Carseats.test$Sales - bag.pred)^2)
</code></pre>
<pre><code>## [1] 2.586
</code></pre>
<pre><code class="r">importance(bag.carseats)
</code></pre>
<pre><code>## %IncMSE IncNodePurity
## CompPrice 13.8790 131.095
## Income 5.6042 77.033
## Advertising 14.1720 129.218
## Population 0.6071 65.196
## Price 53.6119 506.604
## ShelveLoc 44.0311 323.189
## Age 20.1751 189.269
## Education 1.4244 41.811
## Urban -1.9640 8.124
## US 5.6989 14.307
</code></pre>
<p>Bagging improves the test MSE to \( 2.58 \). We also see that \( \tt{Price} \), \( \tt{ShelveLoc} \) and \( \tt{Age} \) are three most important predictors of \( \tt{Sale} \).</p>
<h3>e</h3>
<pre><code class="r">rf.carseats = randomForest(Sales ~ ., data = Carseats.train, mtry = 5, ntree = 500,
importance = T)
rf.pred = predict(rf.carseats, Carseats.test)
mean((Carseats.test$Sales - rf.pred)^2)
</code></pre>
<pre><code>## [1] 2.87
</code></pre>
<pre><code class="r">importance(rf.carseats)
</code></pre>
<pre><code>## %IncMSE IncNodePurity
## CompPrice 11.2746 126.64
## Income 4.4397 101.63
## Advertising 12.9346 137.96
## Population 0.2725 78.78
## Price 49.2418 449.52
## ShelveLoc 38.8406 283.46
## Age 19.1329 195.14
## Education 1.9818 54.26
## Urban -2.2083 11.35
## US 6.6487 26.71
</code></pre>
<p>In this case, random forest worsens the MSE on test set to \( 2.87 \). Changing \( m \) varies test MSE between \( 2.6 \) to \( 3 \). We again see that \( \tt{Price} \), \( \tt{ShelveLoc} \) and \( \tt{Age} \) are three most important predictors of \( \tt{Sale} \).</p>
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统计学习导论_基于R应用习题答案/ch8/8.html
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<h1>Chapter 8: Exercise 9</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
attach(OJ)
set.seed(1013)
train = sample(dim(OJ)[1], 800)
OJ.train = OJ[train, ]
OJ.test = OJ[-train, ]
</code></pre>
<h3>b</h3>
<pre><code class="r">library(tree)
oj.tree = tree(Purchase ~ ., data = OJ.train)
summary(oj.tree)
</code></pre>
<pre><code>##
## Classification tree:
## tree(formula = Purchase ~ ., data = OJ.train)
## Variables actually used in tree construction:
## [1] "LoyalCH" "PriceDiff"
## Number of terminal nodes: 7
## Residual mean deviance: 0.752 = 596 / 793
## Misclassification error rate: 0.155 = 124 / 800
</code></pre>
<p>The tree only uses two variables: \( \tt{LoyalCH} \) and \( \tt{PriceDiff} \). It has \( 7 \) terminal nodes. Training error rate (misclassification error) for the tree is \( 0.155 \).</p>
<h3>c</h3>
<pre><code class="r">oj.tree
</code></pre>
<pre><code>## node), split, n, deviance, yval, (yprob)
## * denotes terminal node
##
## 1) root 800 1000 CH ( 0.60 0.40 )
## 2) LoyalCH < 0.5036 359 400 MM ( 0.28 0.72 )
## 4) LoyalCH < 0.276142 170 100 MM ( 0.11 0.89 ) *
## 5) LoyalCH > 0.276142 189 300 MM ( 0.42 0.58 )
## 10) PriceDiff < 0.05 79 80 MM ( 0.19 0.81 ) *
## 11) PriceDiff > 0.05 110 100 CH ( 0.59 0.41 ) *
## 3) LoyalCH > 0.5036 441 300 CH ( 0.87 0.13 )
## 6) LoyalCH < 0.764572 186 200 CH ( 0.75 0.25 )
## 12) PriceDiff < -0.165 29 30 MM ( 0.28 0.72 ) *
## 13) PriceDiff > -0.165 157 100 CH ( 0.83 0.17 )
## 26) PriceDiff < 0.265 82 100 CH ( 0.73 0.27 ) *
## 27) PriceDiff > 0.265 75 30 CH ( 0.95 0.05 ) *
## 7) LoyalCH > 0.764572 255 90 CH ( 0.96 0.04 ) *
</code></pre>
<p>Let's pick terminal node labeled “10)”. The splitting variable at this node is \( \tt{PriceDiff} \). The splitting value of this node is \( 0.05 \). There are \( 79 \) points in the subtree below this node. The deviance for all points contained in region below this node is \( 80 \). A * in the line denotes that this is in fact a terminal node. The prediction at this node is \( \tt{Sales} \) = \( \tt{MM} \). About \( 19 \)% points in this node have \( \tt{CH} \) as value of \( \tt{Sales} \). Remaining \( 81 \)% points have \( \tt{MM} \) as value of \( \tt{Sales} \).</p>
<h3>d</h3>
<pre><code class="r">plot(oj.tree)
text(oj.tree, pretty = 0)
</code></pre>
<p><img 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" alt="plot of chunk 9d"/> </p>
<p>\( \tt{LoyalCH} \) is the most important variable of the tree, in fact top 3 nodes contain \( \tt{LoyalCH} \). If \( \tt{LoyalCH} < 0.27 \), the tree predicts \( \tt{MM} \). If \( \tt{LoyalCH} > 0.76 \), the tree predicts \( \tt{CH} \). For intermediate values of \( \tt{LoyalCH} \), the decision also depends on the value of \( \tt{PriceDiff} \).</p>
<h3>e</h3>
<pre><code class="r">oj.pred = predict(oj.tree, OJ.test, type = "class")
table(OJ.test$Purchase, oj.pred)
</code></pre>
<pre><code>## oj.pred
## CH MM
## CH 152 19
## MM 32 67
</code></pre>
<h3>f</h3>
<pre><code class="r">cv.oj = cv.tree(oj.tree, FUN = prune.tree)
</code></pre>
<h3>g</h3>
<pre><code class="r">plot(cv.oj$size, cv.oj$dev, type = "b", xlab = "Tree Size", ylab = "Deviance")
</code></pre>
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" alt="plot of chunk 9g"/> </p>
<h3>h</h3>
<p>Size of 6 gives lowest cross-validation error.</p>
<h3>i</h3>
<pre><code class="r">oj.pruned = prune.tree(oj.tree, best = 6)
</code></pre>
<h3>j</h3>
<pre><code class="r">summary(oj.pruned)
</code></pre>
<pre><code>##
## Classification tree:
## snip.tree(tree = oj.tree, nodes = 13L)
## Variables actually used in tree construction:
## [1] "LoyalCH" "PriceDiff"
## Number of terminal nodes: 6
## Residual mean deviance: 0.769 = 610 / 794
## Misclassification error rate: 0.155 = 124 / 800
</code></pre>
<p>Misclassification error of pruned tree is exactly same as that of original tree — \( 0.155 \).</p>
<h3>k</h3>
<pre><code class="r">pred.unpruned = predict(oj.tree, OJ.test, type = "class")
misclass.unpruned = sum(OJ.test$Purchase != pred.unpruned)
misclass.unpruned/length(pred.unpruned)
</code></pre>
<pre><code>## [1] 0.1889
</code></pre>
<pre><code class="r">pred.pruned = predict(oj.pruned, OJ.test, type = "class")
misclass.pruned = sum(OJ.test$Purchase != pred.pruned)
misclass.pruned/length(pred.pruned)
</code></pre>
<pre><code>## [1] 0.1889
</code></pre>
<p>Pruned and unpruned trees have same test error rate of \( 0.189 \).</p>
</body>
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch8/9.html
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HTML
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# Chapter 8 Lab: Decision Trees
# Fitting Classification Trees
library(tree)
library(ISLR)
attach(Carseats)
High=ifelse(Sales<=8,"No","Yes")
Carseats=data.frame(Carseats,High)
tree.carseats=tree(High~.-Sales,Carseats)
summary(tree.carseats)
plot(tree.carseats)
text(tree.carseats,pretty=0)
tree.carseats
set.seed(2)
train=sample(1:nrow(Carseats), 200)
Carseats.test=Carseats[-train,]
High.test=High[-train]
tree.carseats=tree(High~.-Sales,Carseats,subset=train)
tree.pred=predict(tree.carseats,Carseats.test,type="class")
table(tree.pred,High.test)
(86+57)/200
set.seed(3)
cv.carseats=cv.tree(tree.carseats,FUN=prune.misclass)
names(cv.carseats)
cv.carseats
par(mfrow=c(1,2))
plot(cv.carseats$size,cv.carseats$dev,type="b")
plot(cv.carseats$k,cv.carseats$dev,type="b")
prune.carseats=prune.misclass(tree.carseats,best=9)
plot(prune.carseats)
text(prune.carseats,pretty=0)
tree.pred=predict(prune.carseats,Carseats.test,type="class")
table(tree.pred,High.test)
(94+60)/200
prune.carseats=prune.misclass(tree.carseats,best=15)
plot(prune.carseats)
text(prune.carseats,pretty=0)
tree.pred=predict(prune.carseats,Carseats.test,type="class")
table(tree.pred,High.test)
(86+62)/200
# Fitting Regression Trees
library(MASS)
set.seed(1)
train = sample(1:nrow(Boston), nrow(Boston)/2)
tree.boston=tree(medv~.,Boston,subset=train)
summary(tree.boston)
plot(tree.boston)
text(tree.boston,pretty=0)
cv.boston=cv.tree(tree.boston)
plot(cv.boston$size,cv.boston$dev,type='b')
prune.boston=prune.tree(tree.boston,best=5)
plot(prune.boston)
text(prune.boston,pretty=0)
yhat=predict(tree.boston,newdata=Boston[-train,])
boston.test=Boston[-train,"medv"]
plot(yhat,boston.test)
abline(0,1)
mean((yhat-boston.test)^2)
# Bagging and Random Forests
library(randomForest)
set.seed(1)
bag.boston=randomForest(medv~.,data=Boston,subset=train,mtry=13,importance=TRUE)
bag.boston
yhat.bag = predict(bag.boston,newdata=Boston[-train,])
plot(yhat.bag, boston.test)
abline(0,1)
mean((yhat.bag-boston.test)^2)
bag.boston=randomForest(medv~.,data=Boston,subset=train,mtry=13,ntree=25)
yhat.bag = predict(bag.boston,newdata=Boston[-train,])
mean((yhat.bag-boston.test)^2)
set.seed(1)
rf.boston=randomForest(medv~.,data=Boston,subset=train,mtry=6,importance=TRUE)
yhat.rf = predict(rf.boston,newdata=Boston[-train,])
mean((yhat.rf-boston.test)^2)
importance(rf.boston)
varImpPlot(rf.boston)
# Boosting
library(gbm)
set.seed(1)
boost.boston=gbm(medv~.,data=Boston[train,],distribution="gaussian",n.trees=5000,interaction.depth=4)
summary(boost.boston)
par(mfrow=c(1,2))
plot(boost.boston,i="rm")
plot(boost.boston,i="lstat")
yhat.boost=predict(boost.boston,newdata=Boston[-train,],n.trees=5000)
mean((yhat.boost-boston.test)^2)
boost.boston=gbm(medv~.,data=Boston[train,],distribution="gaussian",n.trees=5000,interaction.depth=4,shrinkage=0.2,verbose=F)
yhat.boost=predict(boost.boston,newdata=Boston[-train,],n.trees=5000)
mean((yhat.boost-boston.test)^2)
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2301_76574743/tjjm-R
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统计学习导论_基于R应用习题答案/ch8/lab.R
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R
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<h1>Chapter 9: Exercise 1</h1>
<pre><code class="r">x1 = -10:10
x2 = 1 + 3 * x1
plot(x1, x2, type = "l", col = "red")
text(c(0), c(-20), "greater than 0", col = "red")
text(c(0), c(20), "less than 0", col = "red")
lines(x1, 1 - x1/2)
text(c(0), c(-15), "less than 0")
text(c(0), c(15), "greater than 0")
</code></pre>
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" alt="plot of chunk 1"/> </p>
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统计学习导论_基于R应用习题答案/ch9/1.html
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<h1>Chapter 9: Exercise 2</h1>
<p>\( (1+X_1)^2 + (2-X_2)^2 = 4 \) is a circle with radius 2 and center (-1, 2).</p>
<h2>a</h2>
<pre><code class="r">radius = 2
plot(NA, NA, type = "n", xlim = c(-4, 2), ylim = c(-1, 5), asp = 1, xlab = "X1",
ylab = "X2")
symbols(c(-1), c(2), circles = c(radius), add = TRUE, inches = FALSE)
</code></pre>
<p><img 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" alt="plot of chunk 2a"/> </p>
<h2>b</h2>
<pre><code class="r">radius = 2
plot(NA, NA, type = "n", xlim = c(-4, 2), ylim = c(-1, 5), asp = 1, xlab = "X1",
ylab = "X2")
symbols(c(-1), c(2), circles = c(radius), add = TRUE, inches = FALSE)
text(c(-1), c(2), "< 4")
text(c(-4), c(2), "> 4")
</code></pre>
<p><img 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" alt="plot of chunk 2b"/> </p>
<h2>c</h2>
<p>To restate the boundary, outside the circle is blue, inside and on is red.</p>
<pre><code class="r">radius = 2
plot(c(0, -1, 2, 3), c(0, 1, 2, 8), col = c("blue", "red", "blue", "blue"),
type = "p", asp = 1, xlab = "X1", ylab = "X2")
symbols(c(-1), c(2), circles = c(radius), add = TRUE, inches = FALSE)
</code></pre>
<p><img 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alt="plot of chunk 2c"/> </p>
<h2>d</h2>
<p>The decision boundary is a sum of quadratic terms when expanded.</p>
<p>\[
(1+X_1)^2 + (2-X_2)^2 > 4 \\
1 + 2 X_1 + X_1^2 + 4 - 4 X_2 + X_2^2 > 4 \\
5 + 2 X_1 - 4 X_2 + X_1^2 + X_2^2 > 4
\]</p>
</body>
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统计学习导论_基于R应用习题答案/ch9/2.html
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<h1>Chapter 9: Exercise 3</h1>
<h2>a</h2>
<pre><code class="r">x1 = c(3, 2, 4, 1, 2, 4, 4)
x2 = c(4, 2, 4, 4, 1, 3, 1)
colors = c("red", "red", "red", "red", "blue", "blue", "blue")
plot(x1, x2, col = colors, xlim = c(0, 5), ylim = c(0, 5))
</code></pre>
<p><img 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" alt="plot of chunk 3a"/> </p>
<h2>b</h2>
<p>The maximal margin classifier has to be in between observations #2, #3 and #5, #6.</p>
<p>\[
(2,2), (4,4) \\
(2,1), (4,3) \\
=> (2,1.5), (4,3.5) \\
b = (3.5 - 1.5) / (4 - 2) = 1 \\
a = X_2 - X_1 = 1.5 - 2 = -0.5
\]</p>
<pre><code class="r">plot(x1, x2, col = colors, xlim = c(0, 5), ylim = c(0, 5))
abline(-0.5, 1)
</code></pre>
<p><img 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" alt="plot of chunk 3b"/> </p>
<h2>c</h2>
<p>\( 0.5 - X_1 + X_2 > 0 \)</p>
<h2>d</h2>
<pre><code class="r">plot(x1, x2, col = colors, xlim = c(0, 5), ylim = c(0, 5))
abline(-0.5, 1)
abline(-1, 1, lty = 2)
abline(0, 1, lty = 2)
</code></pre>
<p><img 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" alt="plot of chunk 3d"/> </p>
<h2>e</h2>
<pre><code class="r">plot(x1, x2, col = colors, xlim = c(0, 5), ylim = c(0, 5))
abline(-0.5, 1)
arrows(2, 1, 2, 1.5)
arrows(2, 2, 2, 1.5)
arrows(4, 4, 4, 3.5)
arrows(4, 3, 4, 3.5)
</code></pre>
<p><img 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" alt="plot of chunk 3e"/> </p>
<h2>f</h2>
<p>A slight movement of observation #7 (4,1) blue would not have an effect on the
maximal margin hyperplane since its movement would be outside of the margin.</p>
<h2>g</h2>
<pre><code class="r">plot(x1, x2, col = colors, xlim = c(0, 5), ylim = c(0, 5))
abline(-0.8, 1)
</code></pre>
<p><img 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" alt="plot of chunk 3g"/> </p>
<p>\( -0.8 - X_1 + X_2 > 0 \) </p>
<h2>h</h2>
<pre><code class="r">plot(x1, x2, col = colors, xlim = c(0, 5), ylim = c(0, 5))
points(c(4), c(2), col = c("red"))
</code></pre>
<p><img 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统计学习导论_基于R应用习题答案/ch9/3.html
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<h1>Chapter 9: Exercise 4</h1>
<p>We create a random initial dataset which lies along the parabola \( y = 3*x^2 + 4 \). We then separate the two classes by translating them along Y-axis.</p>
<pre><code class="r">set.seed(131)
x = rnorm(100)
y = 3 * x^2 + 4 + rnorm(100)
train = sample(100, 50)
y[train] = y[train] + 3
y[-train] = y[-train] - 3
# Plot using different colors
plot(x[train], y[train], pch="+", lwd=4, col="red", ylim=c(-4, 20), xlab="X", ylab="Y")
points(x[-train], y[-train], pch="o", lwd=4, col="blue")
</code></pre>
<p><img 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" alt="plot of chunk 4a"/> </p>
<p>The plot clearly shows non-linear separation. We now create both train and test dataframes by taking half of positive and negative classes and creating a new <code>z</code> vector of 0 and 1 for classes. </p>
<pre><code class="r">set.seed(315)
z = rep(0, 100)
z[train] = 1
# Take 25 observations each from train and -train
final.train = c(sample(train, 25), sample(setdiff(1:100, train), 25))
data.train = data.frame(x=x[final.train], y=y[final.train], z=as.factor(z[final.train]))
data.test = data.frame(x=x[-final.train], y=y[-final.train], z=as.factor(z[-final.train]))
library(e1071)
</code></pre>
<pre><code>## Loading required package: class
</code></pre>
<pre><code class="r">svm.linear = svm(z~., data=data.train, kernel="linear", cost=10)
plot(svm.linear, data.train)
</code></pre>
<p><img 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" alt="plot of chunk 4b"/> </p>
<pre><code class="r">table(z[final.train], predict(svm.linear, data.train))
</code></pre>
<pre><code>##
## 0 1
## 0 20 5
## 1 5 20
</code></pre>
<p>The plot shows the linear boundary. The classifier makes \( 10 \) classification errors on train data.</p>
<p>Next, we train an SVM with polynomial kernel</p>
<pre><code class="r">set.seed(32545)
svm.poly = svm(z~., data=data.train, kernel="polynomial", cost=10)
plot(svm.poly, data.train)
</code></pre>
<p><img 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" alt="plot of chunk 4c"/> </p>
<pre><code class="r">table(z[final.train], predict(svm.poly, data.train))
</code></pre>
<pre><code>##
## 0 1
## 0 13 12
## 1 3 22
</code></pre>
<p>This is a default polynomial kernel with degree 3. It makes \( 15 \) errors on train data.</p>
<p>Finally, we train an SVM with radial basis kernel with gamma of 1.</p>
<pre><code class="r">set.seed(996)
svm.radial = svm(z~., data=data.train, kernel="radial", gamma=1, cost=10)
plot(svm.radial, data.train)
</code></pre>
<p><img 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" alt="plot of chunk 4d"/> </p>
<pre><code class="r">table(z[final.train], predict(svm.radial, data.train))
</code></pre>
<pre><code>##
## 0 1
## 0 25 0
## 1 0 25
</code></pre>
<p>This classifier perfectly classifies train data!.</p>
<p>Here are how the test errors look like.</p>
<pre><code class="r">plot(svm.linear, data.test)
</code></pre>
<p><img 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" alt="plot of chunk 4e"/> </p>
<pre><code class="r">plot(svm.poly, data.test)
</code></pre>
<p><img 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" alt="plot of chunk 4e"/> </p>
<pre><code class="r">plot(svm.radial, data.test)
</code></pre>
<p><img 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alt="plot of chunk 4e"/> </p>
<pre><code class="r">table(z[-final.train], predict(svm.linear, data.test))
</code></pre>
<pre><code>##
## 0 1
## 0 21 4
## 1 2 23
</code></pre>
<pre><code class="r">table(z[-final.train], predict(svm.poly, data.test))
</code></pre>
<pre><code>##
## 0 1
## 0 16 9
## 1 5 20
</code></pre>
<pre><code class="r">table(z[-final.train], predict(svm.radial, data.test))
</code></pre>
<pre><code>##
## 0 1
## 0 25 0
## 1 0 25
</code></pre>
<p>The tables show that linear, polynomial and radial basis kernels classify 6, 14, and 0 test points incorrectly respectively. Radial basis kernel is the best and has a zero test misclassification error. </p>
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统计学习导论_基于R应用习题答案/ch9/4.html
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<h1>Chapter 9: Exercise 5</h1>
<h3>a</h3>
<pre><code class="r">set.seed(421)
x1 = runif(500) - 0.5
x2 = runif(500) - 0.5
y = 1 * (x1^2 - x2^2 > 0)
</code></pre>
<h3>b</h3>
<pre><code class="r">plot(x1[y == 0], x2[y == 0], col = "red", xlab = "X1", ylab = "X2", pch = "+")
points(x1[y == 1], x2[y == 1], col = "blue", pch = 4)
</code></pre>
<p><img 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" alt="plot of chunk 5b"/> </p>
<p>The plot clearly shows non-linear decision boundary.</p>
<h3>c</h3>
<pre><code class="r">lm.fit = glm(y ~ x1 + x2, family = binomial)
summary(lm.fit)
</code></pre>
<pre><code>##
## Call:
## glm(formula = y ~ x1 + x2, family = binomial)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.28 -1.23 1.09 1.13 1.17
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1200 0.0897 1.34 0.18
## x1 -0.1688 0.3085 -0.55 0.58
## x2 -0.0820 0.3148 -0.26 0.79
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 691.35 on 499 degrees of freedom
## Residual deviance: 690.99 on 497 degrees of freedom
## AIC: 697
##
## Number of Fisher Scoring iterations: 3
</code></pre>
<p>Both variables are insignificant for predicting \( y \).</p>
<h3>d</h3>
<pre><code class="r">data = data.frame(x1 = x1, x2 = x2, y = y)
lm.prob = predict(lm.fit, data, type = "response")
lm.pred = ifelse(lm.prob > 0.52, 1, 0)
data.pos = data[lm.pred == 1, ]
data.neg = data[lm.pred == 0, ]
plot(data.pos$x1, data.pos$x2, col = "blue", xlab = "X1", ylab = "X2", pch = "+")
points(data.neg$x1, data.neg$x2, col = "red", pch = 4)
</code></pre>
<p><img 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" alt="plot of chunk 5d"/> </p>
<p>With the given model and a probability threshold of 0.5, all points are classified to single class and no decision boundary can be shown. Hence we shift the probability threshold to 0.52 to show a meaningful decision boundary. This boundary is linear as seen in the figure.</p>
<h3>e</h3>
<p>We use squares, product interaction terms to fit the model.</p>
<pre><code class="r">lm.fit = glm(y ~ poly(x1, 2) + poly(x2, 2) + I(x1 * x2), data = data, family = binomial)
</code></pre>
<pre><code>## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
</code></pre>
<h3>f</h3>
<pre><code class="r">lm.prob = predict(lm.fit, data, type = "response")
lm.pred = ifelse(lm.prob > 0.5, 1, 0)
data.pos = data[lm.pred == 1, ]
data.neg = data[lm.pred == 0, ]
plot(data.pos$x1, data.pos$x2, col = "blue", xlab = "X1", ylab = "X2", pch = "+")
points(data.neg$x1, data.neg$x2, col = "red", pch = 4)
</code></pre>
<p><img 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" alt="plot of chunk 5f"/> </p>
<p>This non-linear decision boundary closely resembles the true decision boundary.</p>
<h3>g</h3>
<pre><code class="r">library(e1071)
</code></pre>
<pre><code>## Loading required package: class
</code></pre>
<pre><code class="r">svm.fit = svm(as.factor(y) ~ x1 + x2, data, kernel = "linear", cost = 0.1)
svm.pred = predict(svm.fit, data)
data.pos = data[svm.pred == 1, ]
data.neg = data[svm.pred == 0, ]
plot(data.pos$x1, data.pos$x2, col = "blue", xlab = "X1", ylab = "X2", pch = "+")
points(data.neg$x1, data.neg$x2, col = "red", pch = 4)
</code></pre>
<p><img 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" alt="plot of chunk 5g"/> </p>
<p>A linear kernel, even with low cost fails to find non-linear decision boundary and classifies all points to a single class.</p>
<h3>h</h3>
<pre><code class="r">svm.fit = svm(as.factor(y) ~ x1 + x2, data, gamma = 1)
svm.pred = predict(svm.fit, data)
data.pos = data[svm.pred == 1, ]
data.neg = data[svm.pred == 0, ]
plot(data.pos$x1, data.pos$x2, col = "blue", xlab = "X1", ylab = "X2", pch = "+")
points(data.neg$x1, data.neg$x2, col = "red", pch = 4)
</code></pre>
<p><img 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alt="plot of chunk 5h"/> </p>
<p>Again, the non-linear decision boundary on predicted labels closely resembles the true decision boundary.</p>
<h3>i</h3>
<p>This experiment enforces the idea that SVMs with non-linear kernel are extremely powerful in finding non-linear boundary. Both, logistic regression with non-interactions and SVMs with linear kernels fail to find the decision boundary. Adding interaction terms to logistic regression seems to give them same power as radial-basis kernels. However, there is some manual efforts and tuning involved in picking right interaction terms. This effort can become prohibitive with large number of features. Radial basis kernels, on the other hand, only require tuning of one parameter - gamma - which can be easily done using cross-validation.</p>
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统计学习导论_基于R应用习题答案/ch9/5.html
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<h1>Chapter 9: Exercise 6</h1>
<h3>a</h3>
<p>We randomly generate 1000 points and scatter them across line \( x = y \) with wide margin. We also create noisy points along the line \( 5x -4y - 50 = 0 \). These points make the classes barely separable and also shift the maximum margin classifier.</p>
<pre><code class="r">set.seed(3154)
# Class one
x.one = runif(500, 0, 90)
y.one = runif(500, x.one + 10, 100)
x.one.noise = runif(50, 20, 80)
y.one.noise = 5/4 * (x.one.noise - 10) + 0.1
# Class zero
x.zero = runif(500, 10, 100)
y.zero = runif(500, 0, x.zero - 10)
x.zero.noise = runif(50, 20, 80)
y.zero.noise = 5/4 * (x.zero.noise - 10) - 0.1
# Combine all
class.one = seq(1, 550)
x = c(x.one, x.one.noise, x.zero, x.zero.noise)
y = c(y.one, y.one.noise, y.zero, y.zero.noise)
plot(x[class.one], y[class.one], col = "blue", pch = "+", ylim = c(0, 100))
points(x[-class.one], y[-class.one], col = "red", pch = 4)
</code></pre>
<p><img 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" alt="plot of chunk 6a"/> </p>
<p>The plot shows that classes are barely separable. The noisy points create a fictitious boundary \( 5x - 4y - 50 = 0 \).</p>
<h3>b</h3>
<p>We create a z variable according to classes.</p>
<pre><code class="r">library(e1071)
</code></pre>
<pre><code>## Loading required package: class
</code></pre>
<pre><code class="r">set.seed(555)
z = rep(0, 1100)
z[class.one] = 1
data = data.frame(x = x, y = y, z = z)
tune.out = tune(svm, as.factor(z) ~ ., data = data, kernel = "linear", ranges = list(cost = c(0.01,
0.1, 1, 5, 10, 100, 1000, 10000)))
summary(tune.out)
</code></pre>
<pre><code>##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost
## 10000
##
## - best performance: 0
##
## - Detailed performance results:
## cost error dispersion
## 1 1e-02 0.05636 0.02260
## 2 1e-01 0.04636 0.01841
## 3 1e+00 0.04545 0.01868
## 4 5e+00 0.05000 0.02153
## 5 1e+01 0.04818 0.02102
## 6 1e+02 0.04727 0.02134
## 7 1e+03 0.02364 0.02019
## 8 1e+04 0.00000 0.00000
</code></pre>
<pre><code class="r">data.frame(cost = tune.out$performances$cost, misclass = tune.out$performances$error *
1100)
</code></pre>
<pre><code>## cost misclass
## 1 1e-02 62
## 2 1e-01 51
## 3 1e+00 50
## 4 5e+00 55
## 5 1e+01 53
## 6 1e+02 52
## 7 1e+03 26
## 8 1e+04 0
</code></pre>
<p>The table above shows train-misclassification error for all costs. A cost of 10000 seems to classify all points correctly. This also corresponds to a cross-validation error of 0.</p>
<h3>c</h3>
<p>We now generate a random test-set of same size. This test-set satisfies the true decision boundary \( x = y \). </p>
<pre><code class="r">set.seed(1111)
x.test = runif(1000, 0, 100)
class.one = sample(1000, 500)
y.test = rep(NA, 1000)
# Set y > x for class.one
for (i in class.one) {
y.test[i] = runif(1, x.test[i], 100)
}
# set y < x for class.zero
for (i in setdiff(1:1000, class.one)) {
y.test[i] = runif(1, 0, x.test[i])
}
plot(x.test[class.one], y.test[class.one], col = "blue", pch = "+")
points(x.test[-class.one], y.test[-class.one], col = "red", pch = 4)
</code></pre>
<p><img 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alt="plot of chunk 6c"/> </p>
<p>We now make same predictions using all linear svms with all costs used in previous part. </p>
<pre><code class="r">set.seed(30012)
z.test = rep(0, 1000)
z.test[class.one] = 1
all.costs = c(0.01, 0.1, 1, 5, 10, 100, 1000, 10000)
test.errors = rep(NA, 8)
data.test = data.frame(x = x.test, y = y.test, z = z.test)
for (i in 1:length(all.costs)) {
svm.fit = svm(as.factor(z) ~ ., data = data, kernel = "linear", cost = all.costs[i])
svm.predict = predict(svm.fit, data.test)
test.errors[i] = sum(svm.predict != data.test$z)
}
data.frame(cost = all.costs, `test misclass` = test.errors)
</code></pre>
<pre><code>## cost test.misclass
## 1 1e-02 57
## 2 1e-01 17
## 3 1e+00 5
## 4 5e+00 1
## 5 1e+01 0
## 6 1e+02 204
## 7 1e+03 227
## 8 1e+04 227
</code></pre>
<p>\( \tt{cost} = 10 \) seems to be performing better on test data, making the least number of classification errors. This is much smaller than optimal value of 10000 for training data.</p>
<h3>d</h3>
<p>We again see an overfitting phenomenon for linear kernel. A large cost tries to fit correctly classify noisy-points and hence overfits the train data. A small cost, however, makes a few errors on the noisy test points and performs better on test data.</p>
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统计学习导论_基于R应用习题答案/ch9/6.html
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<body>
<h1>Chapter 9: Exercise 8</h1>
<h3>a</h3>
<pre><code class="r">library(ISLR)
set.seed(9004)
train = sample(dim(OJ)[1], 800)
OJ.train = OJ[train, ]
OJ.test = OJ[-train, ]
</code></pre>
<h3>b</h3>
<pre><code class="r">library(e1071)
</code></pre>
<pre><code>## Warning: package 'e1071' was built under R version 3.0.2
</code></pre>
<pre><code>## Loading required package: class
</code></pre>
<pre><code class="r">svm.linear = svm(Purchase ~ ., kernel = "linear", data = OJ.train, cost = 0.01)
summary(svm.linear)
</code></pre>
<pre><code>##
## Call:
## svm(formula = Purchase ~ ., data = OJ.train, kernel = "linear",
## cost = 0.01)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 0.01
## gamma: 0.05556
##
## Number of Support Vectors: 432
##
## ( 217 215 )
##
##
## Number of Classes: 2
##
## Levels:
## CH MM
</code></pre>
<p>Support vector classifier creates 432 support vectors out of 800 training points. Out of these, 217 belong to level \( \tt{CH} \) and remaining 215 belong to level \( \tt{MM} \).</p>
<h3>c</h3>
<pre><code class="r">train.pred = predict(svm.linear, OJ.train)
table(OJ.train$Purchase, train.pred)
</code></pre>
<pre><code>## train.pred
## CH MM
## CH 439 53
## MM 82 226
</code></pre>
<pre><code class="r">(82 + 53)/(439 + 53 + 82 + 226)
</code></pre>
<pre><code>## [1] 0.1688
</code></pre>
<pre><code class="r">test.pred = predict(svm.linear, OJ.test)
table(OJ.test$Purchase, test.pred)
</code></pre>
<pre><code>## test.pred
## CH MM
## CH 142 19
## MM 29 80
</code></pre>
<pre><code class="r">(19 + 29)/(142 + 19 + 29 + 80)
</code></pre>
<pre><code>## [1] 0.1778
</code></pre>
<p>The training error rate is \( 16.9 \)% and test error rate is about \( 17.8 \)%.</p>
<h3>d</h3>
<pre><code class="r">set.seed(1554)
tune.out = tune(svm, Purchase ~ ., data = OJ.train, kernel = "linear", ranges = list(cost = 10^seq(-2,
1, by = 0.25)))
summary(tune.out)
</code></pre>
<pre><code>##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost
## 0.3162
##
## - best performance: 0.1687
##
## - Detailed performance results:
## cost error dispersion
## 1 0.01000 0.1688 0.03692
## 2 0.01778 0.1688 0.03398
## 3 0.03162 0.1713 0.03230
## 4 0.05623 0.1725 0.03162
## 5 0.10000 0.1700 0.03291
## 6 0.17783 0.1712 0.03336
## 7 0.31623 0.1687 0.03499
## 8 0.56234 0.1700 0.03129
## 9 1.00000 0.1687 0.03398
## 10 1.77828 0.1688 0.03241
## 11 3.16228 0.1688 0.03294
## 12 5.62341 0.1713 0.03121
## 13 10.00000 0.1713 0.03283
</code></pre>
<p>Tuning shows that optimal cost is 0.3162</p>
<h3>e</h3>
<pre><code class="r">svm.linear = svm(Purchase ~ ., kernel = "linear", data = OJ.train, cost = tune.out$best.parameters$cost)
train.pred = predict(svm.linear, OJ.train)
table(OJ.train$Purchase, train.pred)
</code></pre>
<pre><code>## train.pred
## CH MM
## CH 435 57
## MM 71 237
</code></pre>
<pre><code class="r">(57 + 71)/(435 + 57 + 71 + 237)
</code></pre>
<pre><code>## [1] 0.16
</code></pre>
<pre><code class="r">test.pred = predict(svm.linear, OJ.test)
table(OJ.test$Purchase, test.pred)
</code></pre>
<pre><code>## test.pred
## CH MM
## CH 141 20
## MM 29 80
</code></pre>
<pre><code class="r">(29 + 20)/(141 + 20 + 29 + 80)
</code></pre>
<pre><code>## [1] 0.1815
</code></pre>
<p>The training error decreases to \( 16 \)% but test error slightly increases to \( 18.1 \)% by using best cost.</p>
<h3>f</h3>
<pre><code class="r">set.seed(410)
svm.radial = svm(Purchase ~ ., data = OJ.train, kernel = "radial")
summary(svm.radial)
</code></pre>
<pre><code>##
## Call:
## svm(formula = Purchase ~ ., data = OJ.train, kernel = "radial")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
## gamma: 0.05556
##
## Number of Support Vectors: 367
##
## ( 184 183 )
##
##
## Number of Classes: 2
##
## Levels:
## CH MM
</code></pre>
<pre><code class="r">train.pred = predict(svm.radial, OJ.train)
table(OJ.train$Purchase, train.pred)
</code></pre>
<pre><code>## train.pred
## CH MM
## CH 452 40
## MM 78 230
</code></pre>
<pre><code class="r">(40 + 78)/(452 + 40 + 78 + 230)
</code></pre>
<pre><code>## [1] 0.1475
</code></pre>
<pre><code class="r">test.pred = predict(svm.radial, OJ.test)
table(OJ.test$Purchase, test.pred)
</code></pre>
<pre><code>## test.pred
## CH MM
## CH 146 15
## MM 27 82
</code></pre>
<pre><code class="r">(27 + 15)/(146 + 15 + 27 + 82)
</code></pre>
<pre><code>## [1] 0.1556
</code></pre>
<p>The radial basis kernel with default gamma creates 367 support vectors, out of which, 184 belong to level \( \tt{CH} \) and remaining 183 belong to level \( \tt{MM} \). The classifier has a training error of \( 14.7 \)% and a test error of \( 15.6 \)% which is a slight improvement over linear kernel. We now use cross validation to find optimal gamma.</p>
<pre><code class="r">set.seed(755)
tune.out = tune(svm, Purchase ~ ., data = OJ.train, kernel = "radial", ranges = list(cost = 10^seq(-2,
1, by = 0.25)))
summary(tune.out)
</code></pre>
<pre><code>##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost
## 0.5623
##
## - best performance: 0.165
##
## - Detailed performance results:
## cost error dispersion
## 1 0.01000 0.3850 0.06258
## 2 0.01778 0.3850 0.06258
## 3 0.03162 0.3762 0.06908
## 4 0.05623 0.2100 0.03855
## 5 0.10000 0.1862 0.03143
## 6 0.17783 0.1837 0.03230
## 7 0.31623 0.1712 0.03438
## 8 0.56234 0.1650 0.03764
## 9 1.00000 0.1750 0.03584
## 10 1.77828 0.1738 0.04059
## 11 3.16228 0.1763 0.03748
## 12 5.62341 0.1763 0.03839
## 13 10.00000 0.1738 0.03459
</code></pre>
<pre><code class="r">svm.radial = svm(Purchase ~ ., data = OJ.train, kernel = "radial", cost = tune.out$best.parameters$cost)
train.pred = predict(svm.radial, OJ.train)
table(OJ.train$Purchase, train.pred)
</code></pre>
<pre><code>## train.pred
## CH MM
## CH 452 40
## MM 77 231
</code></pre>
<pre><code class="r">(77 + 40)/(452 + 40 + 77 + 231)
</code></pre>
<pre><code>## [1] 0.1462
</code></pre>
<pre><code class="r">test.pred = predict(svm.radial, OJ.test)
table(OJ.test$Purchase, test.pred)
</code></pre>
<pre><code>## test.pred
## CH MM
## CH 146 15
## MM 28 81
</code></pre>
<pre><code class="r">(28 + 15)/(146 + 15 + 28 + 81)
</code></pre>
<pre><code>## [1] 0.1593
</code></pre>
<p>Tuning slightly decreases training error to \( 14.6 \)% and slightly increases test error to \( 16 \)% which is still better than linear kernel.</p>
<h3>g</h3>
<pre><code class="r">set.seed(8112)
svm.poly = svm(Purchase ~ ., data = OJ.train, kernel = "poly", degree = 2)
summary(svm.poly)
</code></pre>
<pre><code>##
## Call:
## svm(formula = Purchase ~ ., data = OJ.train, kernel = "poly",
## degree = 2)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: polynomial
## cost: 1
## degree: 2
## gamma: 0.05556
## coef.0: 0
##
## Number of Support Vectors: 452
##
## ( 232 220 )
##
##
## Number of Classes: 2
##
## Levels:
## CH MM
</code></pre>
<pre><code class="r">train.pred = predict(svm.poly, OJ.train)
table(OJ.train$Purchase, train.pred)
</code></pre>
<pre><code>## train.pred
## CH MM
## CH 460 32
## MM 105 203
</code></pre>
<pre><code class="r">(32 + 105)/(460 + 32 + 105 + 203)
</code></pre>
<pre><code>## [1] 0.1713
</code></pre>
<pre><code class="r">test.pred = predict(svm.poly, OJ.test)
table(OJ.test$Purchase, test.pred)
</code></pre>
<pre><code>## test.pred
## CH MM
## CH 149 12
## MM 37 72
</code></pre>
<pre><code class="r">(12 + 37)/(149 + 12 + 37 + 72)
</code></pre>
<pre><code>## [1] 0.1815
</code></pre>
<p>Summary shows that polynomial kernel produces 452 support vectors, out of which, 232 belong to level \( \tt{CH} \) and remaining 220 belong to level \( \tt{MM} \). This kernel produces a train error of \( 17.1 \)% and a test error of \( 18.1 \)% which are slightly higher than the errors produces by radial kernel but lower than the errors produced by linear kernel.</p>
<pre><code class="r">set.seed(322)
tune.out = tune(svm, Purchase ~ ., data = OJ.train, kernel = "poly", degree = 2,
ranges = list(cost = 10^seq(-2, 1, by = 0.25)))
summary(tune.out)
</code></pre>
<pre><code>##
## Parameter tuning of 'svm':
##
## - sampling method: 10-fold cross validation
##
## - best parameters:
## cost
## 5.623
##
## - best performance: 0.1837
##
## - Detailed performance results:
## cost error dispersion
## 1 0.01000 0.3850 0.05426
## 2 0.01778 0.3675 0.05076
## 3 0.03162 0.3575 0.05177
## 4 0.05623 0.3425 0.04937
## 5 0.10000 0.3150 0.05231
## 6 0.17783 0.2487 0.03929
## 7 0.31623 0.2088 0.05684
## 8 0.56234 0.2088 0.05653
## 9 1.00000 0.2000 0.06095
## 10 1.77828 0.1938 0.04497
## 11 3.16228 0.1862 0.04185
## 12 5.62341 0.1837 0.03336
## 13 10.00000 0.1837 0.04042
</code></pre>
<pre><code class="r">svm.poly = svm(Purchase ~ ., data = OJ.train, kernel = "poly", degree = 2, cost = tune.out$best.parameters$cost)
train.pred = predict(svm.poly, OJ.train)
table(OJ.train$Purchase, train.pred)
</code></pre>
<pre><code>## train.pred
## CH MM
## CH 455 37
## MM 84 224
</code></pre>
<pre><code class="r">(37 + 84)/(455 + 37 + 84 + 224)
</code></pre>
<pre><code>## [1] 0.1512
</code></pre>
<pre><code class="r">test.pred = predict(svm.poly, OJ.test)
table(OJ.test$Purchase, test.pred)
</code></pre>
<pre><code>## test.pred
## CH MM
## CH 148 13
## MM 34 75
</code></pre>
<pre><code class="r">(13 + 34)/(148 + 13 + 34 + 75)
</code></pre>
<pre><code>## [1] 0.1741
</code></pre>
<p>Tuning reduces the training error to \( 15.12 \)% and test error to \( 17.4 \)% which is worse than radial kernel but slightly better than linear kernel.</p>
<h3>h</h3>
<p>Overall, radial basis kernel seems to be producing minimum misclassification error on both train and test data.</p>
</body>
</html>
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch9/8.html
|
HTML
|
unknown
| 23,683
|
## Online course quiz question: 9.R
## Explanation: Logistic regression is similar to SVM with a linear kernel.
library(MASS)
svm_error <- function() {
# 1) generate a random training sample to train on + fit
# build training set
x0 = mvrnorm(50,rep(0,10),diag(10))
x1 = mvrnorm(50,rep(c(1,0),c(5,5)),diag(10))
train = rbind(x0,x1)
classes = rep(c(0,1),c(50,50))
dat=data.frame(train,classes=as.factor(classes))
# fit
# svmfit=svm(classes~.,data=dat,kernel="linear")
svmfit = glm(classes~., data=dat, family="binomial")
# 2) evaluate the number of mistakes we make on a large test set = 1000 samples
test_x0 = mvrnorm(500,rep(0,10),diag(10))
test_x1 = mvrnorm(500,rep(c(1,0),c(5,5)),diag(10))
test = rbind(test_x0,test_x1)
test_classes = rep(c(0,1),c(500,500))
test_dat = data.frame(test,test_classes=as.factor(test_classes))
fit = predict(svmfit,test_dat)
fit = ifelse(fit < 0.5, 0, 1)
error = sum(fit != test_dat$test_classes)/1000
return(error)
}
# 3) repeat (1-2) many times and averaging the error rate for each trial
errors = replicate(1000, svm_error())
print(mean(errors))
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch9/9.R
|
R
|
unknown
| 1,128
|
set.seed(1)
x = matrix(rnorm(20*2), ncol=2)
y=c(rep(-1, 10), rep(1, 10))
x[y == 1, ]=x[y == 1, ] + 1
plot(x, col=(3-y))
# Train svm
dat=data.frame(x=x, y=as.factor(y))
library(e1071)
svmfit = svm(y~., data=dat, kernel="linear", cost=10, scale=F)
plot(svmfit, dat)
svmfit$index
summary(svmfit)
# Tune svm
set.seed(1)
tune.out = tune(svm, y~., data=dat, kernel="linear",
ranges=list(cost=c(0.001 , 0.01, 0.1, 1,5,10,100)))
summary(tune.out)
bestmod = tune.out$best.model
summary(bestmod)
# Predict
xtest = matrix(rnorm(20*2), ncol=2)
ytest = sample(c(-1, 1), 20, replace=T)
xtest[ytest == 1, ] = xtest[ytest == 1, ] + 1
testdat = data.frame(x=xtest, y=as.factor(ytest))
ypred = predict(bestmod, testdat)
table(predict=ypred, truth=testdat$y)
# Create linearly separable classes and plot them
x[y==1 ,]= x[y==1 ,]+0.5
plot(x, col =(y+5) /2, pch =19)
# Train svm with very large value of cost to create separating hyperplane
dat=data.frame(x=x, y=as.factor(y))
svmfit = svm(y~., data=dat, kernel="linear", cost=1e5)
summary(svmfit)
plot(svmfit, dat)
# Full support vector machines with radial basis kernels
# Generate Data
set.seed (1)
x = matrix(rnorm(200*2), ncol=2)
x[1:100, ] = x[1:100, ] + 2
x[101:150, ] = x[101:150, ] - 2
y = c(rep(1, 150), rep(2, 50))
dat = data.frame(x=x, y=as.factor(y))
plot(x, col=y)
train = sample(200, 100)
svmfit = svm(y~., data=dat[train, ], kernel="radial", gamma=1, cost=1)
plot(svmfit, dat[train, ])
summary(svmfit)
# Cross validation using SVM
set.seed(1)
tune.out = tune(svm, y~., data=dat[train, ], kernel="radial",
range=data.frame(cost=c(0.1, 1, 10, 100, 100), gamma=c(0.5, 1, 2, 3, 4)))
summary(tune.out)
tune.pred = predict(tune.out$best.model, dat[-train, ])
table(dat[-train, "y"], tune.pred)
# Generate ROC curve
library(ROCR)
rocplot = function(pred, truth, ...) {
predob = prediction(pred, truth)
perf = performance(predob, "tpr", "fpr")
plot(perf, ...)
}
svmfit.opt = svm(y~., data=dat[train ,], kernel ="radial", gamma=2, cost=1, decision.values=T)
fitted = attributes(predict(svmfit.opt, dat[train, ], decision.values=T))$decision.values
par(mfrow=c(1, 2))
rocplot(fitted, dat[train, "y"], main="Training data")
# Plot by increasing lamdba
svmfit.flex = svm(y~., data=dat[train, ], kernel="radial", gamma=50, cost=1, decision.values=T)
fitted = attributes(predict(svmfit.flex, dat[train, ], decision.values=T))$decision.values
rocplot(fitted, dat[train, "y"], col="red", add=T)
# Make same plots on test data
fitted = attributes(predict(svmfit.opt, dat[-train, ], decision.values=T))$decision.values
rocplot(fitted, dat[-train, "y"], main="Test Data")
fitted = attributes(predict(svmfit.flex, dat[-train, ], decision.values=T))$decision.values
rocplot(fitted, dat[-train, "y"], col="red", add=T)
# SVM with multiple classes
set.seed(1)
x = rbind(x, matrix(rnorm(50*2), ncol=2))
y = c(y, rep(0, 50))
x[y==0, 2] = x[y==0, 2] + 2
dat=data.frame(x=x, y=as.factor(y))
par(mfrow=c(1, 1))
plot(x, col=(y+1))
svmfit = svm(y~., data=dat, kernel="radial", cost=10, gamma=1)
plot(svmfit, dat)
# SVM on gene expression data
library(ISLR)
names(Khan)
dim(Khan$xtrain)
dim(Khan$xtest)
length(Khan$ytrain)
length(Khan$ytest)
table(Khan$ytrain)
table(Khan$ytest)
dat = data.frame(x=Khan$xtrain, y=as.factor(Khan$ytrain))
out = svm(y~., data=dat, kernel="linear", cost=10)
summary(out)
table(out$fitted, dat$y)
dat.test = data.frame(x=Khan$xtest, y=as.factor(Khan$ytest))
pred = predict(out, dat.test)
table(pred, dat.test$y)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch9/Lab_chapter9.R
|
R
|
unknown
| 3,507
|
n.samples = 300
y = sample(c(0, 1), n.samples, replace=T)
x = matrix(rep(0, n.samples * 10), ncol=10)
for (i in 1:n.samples) {
if (y[i] == 0)
x[i, ] = rnorm(10)
else
x[i, ] = rnorm(10, mean=c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0))
}
total.0 = seq(1, n.samples)[y == 0]
total.1 = seq(1, n.samples)[y == 1]
train.0 = sample(total.0, 50)
train.1 = sample(total.1, 50)
train = c(train.0, train.1)
library(e1071)
dat = data.frame(x=x, y=as.factor(y))
svm.fit = svm(y~., data=dat[train, ], kernel="linear")
svm.pred = predict(svm.fit, dat[-train, ])
mean(dat[-train, "y"] != svm.pred)
glm.fit = glm(y~., dat[train, ], family=binomial)
glm.prob = predict(glm.fit, dat[-train, ], type="response")
glm.pred = ifelse(glm.prob > 0.5, 1, 0)
sum(dat[-train, "y"] != glm.pred)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch9/R_exercise_video.R
|
R
|
unknown
| 772
|
# Chapter 9 Lab: Support Vector Machines
# Support Vector Classifier
set.seed(1)
x=matrix(rnorm(20*2), ncol=2)
y=c(rep(-1,10), rep(1,10))
x[y==1,]=x[y==1,] + 1
plot(x, col=(3-y))
dat=data.frame(x=x, y=as.factor(y))
library(e1071)
svmfit=svm(y~., data=dat, kernel="linear", cost=10,scale=FALSE)
plot(svmfit, dat)
svmfit$index
summary(svmfit)
svmfit=svm(y~., data=dat, kernel="linear", cost=0.1,scale=FALSE)
plot(svmfit, dat)
svmfit$index
set.seed(1)
tune.out=tune(svm,y~.,data=dat,kernel="linear",ranges=list(cost=c(0.001, 0.01, 0.1, 1,5,10,100)))
summary(tune.out)
bestmod=tune.out$best.model
summary(bestmod)
xtest=matrix(rnorm(20*2), ncol=2)
ytest=sample(c(-1,1), 20, rep=TRUE)
xtest[ytest==1,]=xtest[ytest==1,] + 1
testdat=data.frame(x=xtest, y=as.factor(ytest))
ypred=predict(bestmod,testdat)
table(predict=ypred, truth=testdat$y)
svmfit=svm(y~., data=dat, kernel="linear", cost=.01,scale=FALSE)
ypred=predict(svmfit,testdat)
table(predict=ypred, truth=testdat$y)
x[y==1,]=x[y==1,]+0.5
plot(x, col=(y+5)/2, pch=19)
dat=data.frame(x=x,y=as.factor(y))
svmfit=svm(y~., data=dat, kernel="linear", cost=1e5)
summary(svmfit)
plot(svmfit, dat)
svmfit=svm(y~., data=dat, kernel="linear", cost=1)
summary(svmfit)
plot(svmfit,dat)
# Support Vector Machine
set.seed(1)
x=matrix(rnorm(200*2), ncol=2)
x[1:100,]=x[1:100,]+2
x[101:150,]=x[101:150,]-2
y=c(rep(1,150),rep(2,50))
dat=data.frame(x=x,y=as.factor(y))
plot(x, col=y)
train=sample(200,100)
svmfit=svm(y~., data=dat[train,], kernel="radial", gamma=1, cost=1)
plot(svmfit, dat[train,])
summary(svmfit)
svmfit=svm(y~., data=dat[train,], kernel="radial",gamma=1,cost=1e5)
plot(svmfit,dat[train,])
set.seed(1)
tune.out=tune(svm, y~., data=dat[train,], kernel="radial", ranges=list(cost=c(0.1,1,10,100,1000),gamma=c(0.5,1,2,3,4)))
summary(tune.out)
table(true=dat[-train,"y"], pred=predict(tune.out$best.model,newx=dat[-train,]))
# ROC Curves
library(ROCR)
rocplot=function(pred, truth, ...){
predob = prediction(pred, truth)
perf = performance(predob, "tpr", "fpr")
plot(perf,...)}
svmfit.opt=svm(y~., data=dat[train,], kernel="radial",gamma=2, cost=1,decision.values=T)
fitted=attributes(predict(svmfit.opt,dat[train,],decision.values=TRUE))$decision.values
par(mfrow=c(1,2))
rocplot(fitted,dat[train,"y"],main="Training Data")
svmfit.flex=svm(y~., data=dat[train,], kernel="radial",gamma=50, cost=1, decision.values=T)
fitted=attributes(predict(svmfit.flex,dat[train,],decision.values=T))$decision.values
rocplot(fitted,dat[train,"y"],add=T,col="red")
fitted=attributes(predict(svmfit.opt,dat[-train,],decision.values=T))$decision.values
rocplot(fitted,dat[-train,"y"],main="Test Data")
fitted=attributes(predict(svmfit.flex,dat[-train,],decision.values=T))$decision.values
rocplot(fitted,dat[-train,"y"],add=T,col="red")
# SVM with Multiple Classes
set.seed(1)
x=rbind(x, matrix(rnorm(50*2), ncol=2))
y=c(y, rep(0,50))
x[y==0,2]=x[y==0,2]+2
dat=data.frame(x=x, y=as.factor(y))
par(mfrow=c(1,1))
plot(x,col=(y+1))
svmfit=svm(y~., data=dat, kernel="radial", cost=10, gamma=1)
plot(svmfit, dat)
# Application to Gene Expression Data
library(ISLR)
names(Khan)
dim(Khan$xtrain)
dim(Khan$xtest)
length(Khan$ytrain)
length(Khan$ytest)
table(Khan$ytrain)
table(Khan$ytest)
dat=data.frame(x=Khan$xtrain, y=as.factor(Khan$ytrain))
out=svm(y~., data=dat, kernel="linear",cost=10)
summary(out)
table(out$fitted, dat$y)
dat.te=data.frame(x=Khan$xtest, y=as.factor(Khan$ytest))
pred.te=predict(out, newdata=dat.te)
table(pred.te, dat.te$y)
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/ch9/lab.R
|
R
|
unknown
| 3,494
|
<html>
<body>
<h3><i>An Introduction to Statistical Learning</i> Unofficial Solutions</h3>
<p>
<a href="https://github.com/asadoughi/stat-learning">Fork the solutions!</a><br />
<a href="https://twitter.com/princehonest">Twitter me @princehonest</a><br />
<a href="http://www.statlearning.com">Official book website</a><br />
</p>
<p>
Check out Github <a href="https://github.com/asadoughi/stat-learning/issues?state=open">issues</a> and <a href="https://github.com/asadoughi/stat-learning/">repo</a> for the latest updates.
</p>
<script>
exercise_count = [10, 15, 13, 9, 11, 12, 12, 8, 11];
for (var chapter = 2; chapter <= 10; chapter++) {
for (var exercise = 1; exercise <= exercise_count[chapter-2]; exercise++) {
link = "ch" + chapter + "/" + exercise + ".html";
if (chapter == 2) {
if (exercise < 8)
link = "https://raw.githubusercontent.com/asadoughi/stat-learning/master/ch2/answers";
else
link = "https://raw.githubusercontent.com/asadoughi/stat-learning/master/ch2/applied.R";
} else if (chapter == 3) {
if (exercise < 7)
link = "https://raw.githubusercontent.com/asadoughi/stat-learning/master/ch3/answers";
else
link = "ch3/applied.html";
}
document.write("<a href='" + link + "'>Chapter " + chapter + " Exercise " + exercise + "</a>");
document.write("<br />");
}
document.write("<br />");
}
</script>
</body>
</html>
|
2301_76574743/tjjm-R
|
统计学习导论_基于R应用习题答案/index.html
|
HTML
|
unknown
| 1,516
|
#include "Com_Util.h"
#include <INTRINS.H>
void Com_Util_Delay1ms(u16 count)
{
u8 i,j;
while (count>0)
{
count--;
_nop_();
i=2;
j=199;
do
{
while(--j);
} while (--i);
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/10/1019-数码管-模块化编程/src/Com/Com_Util.c
|
C
|
unknown
| 198
|
#ifndef _INT_DIGITALTUBE_H__
#define _INT_DIGITALTUBE_H__
#include "Com_Util.h"
#define SGM_ED P36
#define LED_ED P34
void Int_DigitalTube_Init();
void Int_DigitalTube_DisplayNum(u32 num);
void Int_DeigitalTUbe_Refresh();
#endif // 1
|
2301_77966824/51-microcontroller-learning
|
2024/10/1019-数码管-模块化编程/src/Int/Int_DigitalTube.h
|
C
|
unknown
| 237
|
#include <Int_digitalTube.h>
void main()
{
Int_DigitalTube_Init();
Int_DigitalTube_DisplayNum(1314520);
while(1)
{
Int_DeigitalTUbe_Refresh();
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/10/1019-数码管-模块化编程/src/main.c
|
C
|
unknown
| 156
|
#!/usr/bin/env python
#coding=utf-8
# stcflash Copyright (C) 2013 laborer (laborer@126.com)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import time
import logging
import sys
import serial
import os.path
import binascii
import struct
import argparse
PROTOCOL_89 = "89"
PROTOCOL_12C5A = "12c5a"
PROTOCOL_12C52 = "12c52"
PROTOCOL_12Cx052 = "12cx052"
PROTOCOL_8 = "8"
PROTOCOL_15 = '15'
PROTOSET_89 = [PROTOCOL_89]
PROTOSET_12 = [PROTOCOL_12C5A, PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_12B = [PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_8 = [PROTOCOL_8]
PROTOSET_15 = [PROTOCOL_15]
PROTOSET_PARITY = [PROTOCOL_12C5A, PROTOCOL_12C52]
class Programmer:
def __init__(self, conn, protocol=None):
self.conn = conn
self.protocol = protocol
self.conn.timeout = 0.05
if self.protocol in PROTOSET_PARITY:
self.conn.parity = serial.PARITY_EVEN
else:
self.conn.parity = serial.PARITY_NONE
self.chkmode = 0
def __conn_read(self, size):
buf = bytearray()
while len(buf) < size:
s = bytearray(self.conn.read(size - len(buf)))
buf += s
logging.debug("recv: " + " ".join(["%02X" % i for i in s]))
if len(s) == 0:
raise IOError()
return list(buf)
def __conn_write(self, s):
logging.debug("send: " + " ".join(["%02X" % i for i in s]))
self.conn.write(bytearray(s))
def __conn_baudrate(self, baud, flush=True):
logging.debug("baud: %d" % baud)
if flush:
if self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
self.conn.flush()
time.sleep(0.2)
self.conn.baudrate = baud
def __model_database(self, model):
modelmap = {0xE0: ("12", 1, {(0x00, 0x1F): ("C54", ""),
(0x60, 0x7F): ("C54", "AD"),
(0x80, 0x9F): ("LE54", ""),
(0xE0, 0xFF): ("LE54", "AD"),
}),
0xE1: ("12", 1, {(0x00, 0x1F): ("C52", ""),
(0x20, 0x3F): ("C52", "PWM"),
(0x60, 0x7F): ("C52", "AD"),
(0x80, 0x9F): ("LE52", ""),
(0xA0, 0xBF): ("LE52", "PWM"),
(0xE0, 0xFF): ("LE52", "AD"),
}),
0xE2: ("11", 1, {(0x00, 0x1F): ("F", ""),
(0x20, 0x3F): ("F", "E"),
(0x70, 0x7F): ("F", ""),
(0x80, 0x9F): ("L", ""),
(0xA0, 0xBF): ("L", "E"),
(0xF0, 0xFF): ("L", ""),
}),
0xE6: ("12", 1, {(0x00, 0x1F): ("C56", ""),
(0x60, 0x7F): ("C56", "AD"),
(0x80, 0x9F): ("LE56", ""),
(0xE0, 0xFF): ("LE56", "AD"),
}),
0xD1: ("12", 2, {(0x20, 0x3F): ("C5A", "CCP"),
(0x40, 0x5F): ("C5A", "AD"),
(0x60, 0x7F): ("C5A", "S2"),
(0xA0, 0xBF): ("LE5A", "CCP"),
(0xC0, 0xDF): ("LE5A", "AD"),
(0xE0, 0xFF): ("LE5A", "S2"),
}),
0xD2: ("10", 1, {(0x00, 0x0F): ("F", ""),
(0x60, 0x6F): ("F", "XE"),
(0x70, 0x7F): ("F", "X"),
(0xA0, 0xAF): ("L", ""),
(0xE0, 0xEF): ("L", "XE"),
(0xF0, 0xFF): ("L", "X"),
}),
0xD3: ("11", 2, {(0x00, 0x1F): ("F", ""),
(0x40, 0x5F): ("F", "X"),
(0x60, 0x7F): ("F", "XE"),
(0xA0, 0xBF): ("L", ""),
(0xC0, 0xDF): ("L", "X"),
(0xE0, 0xFF): ("L", "XE"),
}),
0xF0: ("89", 4, {(0x00, 0x10): ("C5", "RC"),
(0x20, 0x30): ("C5", "RC"), #STC90C5xRC
}),
0xF1: ("89", 4, {(0x00, 0x10): ("C5", "RD+"),
(0x20, 0x30): ("C5", "RD+"), #STC90C5xRD+
}),
0xF2: ("12", 1, {(0x00, 0x0F): ("C", "052"),
(0x10, 0x1F): ("C", "052AD"),
(0x20, 0x2F): ("LE", "052"),
(0x30, 0x3F): ("LE", "052AD"),
}),
0xF2A0: ("15W", 1, {(0xA0, 0xA5): ("1", ""), #STC15W1系列
}),
0xF400: ("15F", 8, {(0x00, 0x07): ("2K", "S2"), #STC15F2K系列
}),
0xF407: ("15F", 60, {(0x07, 0x08): ("2K", "S2"), #STC15F2K系列
}),
0xF408: ("15F", 61, {(0x08, 0x09): ("2K", "S2"), #STC15F2K系列
}),
0xF400: ("15F", 4, {(0x09, 0x0C): ("4", "AD"), #STC15FAD系列
}),
0xF410: ("15F", 8, {(0x10, 0x17): ("1K", "AS"), #STC15F1KAS系列
}),
0xF417: ("15F", 60, {(0x17, 0x18): ("1K", "AS"), #STC15F1KAS系列
}),
0xF418: ("15F", 61, {(0x18, 0x19): ("1K", "AS"), #STC15F1KAS系列
}),
0xF420: ("15F", 8, {(0x20, 0x27): ("1K", "S"), #STC15F1KS系列
}),
0xF427: ("15F", 60, {(0x27, 0x28): ("1K", "S"), #STC15F1KS系列
}),
0xF440: ("15F", 8, {(0x40, 0x47): ("1K", "S2"), #STC15F1KS2系列
}),
0xF447: ("15F", 60, {(0x47, 0x48): ("1K", "S2"), #STC15F1KS2系列
}),
0xF448: ("15F", 61, {(0x48, 0x49): ("1K", "S2"), #STC15F1KS2系列
}),
0xF44C: ("15F", 13, {(0x4C, 0x4D): ("4", "AD"),
}),
0xF450: ("15F", 8, {(0x50, 0x57): ("1K", "AS"), #STC15F1KAS系列
}),
0xF457: ("15F", 60, {(0x57, 0x58): ("1K", "AS"), #STC15F1KAS系列
}),
0xF458: ("15F", 61, {(0x58, 0x59): ("1K", "AS"), #STC15F1KAS系列
}),
0xF460: ("15F", 8, {(0x60, 0x67): ("1K", "S"), #STC15F1KS系列
}),
0xF467: ("15F", 60, {(0x67, 0x68): ("1K", "S"), #STC15F1KS系列
}),
0xF468: ("15F", 61, {(0x68, 0x69): ("1K", "S"), #STC15F1KS系列
}),
0xF480: ("15L", 8, {(0x80, 0x87): ("2K", "S2"), #STC15L2KS2系列
}),
0xF487: ("15L", 60, {(0x87, 0x88): ("2K", "S2"), #STC15L2KS2系列
}),
0xF488: ("15L", 61, {(0x88, 0x89): ("2K", "S2"), #STC15L2KS2系列
}),
0xF489: ("15L", 5, {(0x89, 0x8C): ("4", "AD"), #STC15L4AD系列
}),
0xF490: ("15L", 8, {(0x90, 0x97): ("2K", "AS"), #STC15L2KAS系列
}),
0xF497: ("15L", 60, {(0x97, 0x98): ("2K", "AS"), #STC15L2KAS系列
}),
0xF498: ("15L", 61, {(0x98, 0x99): ("2K", "AS"), #STC15L2KAS系列
}),
0xF4A0: ("15L", 8, {(0xA0, 0xA7): ("2K", "S"), #STC15L2KS系列
}),
0xF4A7: ("15L", 60, {(0xA7, 0xA8): ("2K", "S"), #STC15L2KS系列
}),
0xF4A8: ("15L", 61, {(0xA8, 0xA9): ("2K", "S"), #STC15L2KS系列
}),
0xF4C0: ("15L", 8, {(0xC0, 0xC7): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C7: ("15L", 60, {(0xC7, 0xC8): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C8: ("15L", 61, {(0xC8, 0xC9): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4CC: ("15L", 13, {(0xCC, 0xCD): ("4", "AD"),
}),
0xF4D0: ("15L", 8, {(0xD0, 0xD7): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D7: ("15L", 60, {(0xD7, 0xD8): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D8: ("15L", 61, {(0xD8, 0xD9): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4E0: ("15L", 8, {(0xE0, 0xE7): ("1K", "S"), #STC15L1KS系列
}),
0xF4E7: ("15L", 60, {(0xE7, 0xE8): ("1K", "S"), #STC15L1KS系列
}),
0xF4E8: ("15L", 61, {(0xE8, 0xE9): ("1K", "S"), #STC15L1KS系列
}),
0xF500: ("15W", 1, {(0x00, 0x04): ("1", "SW"), #STC15W1SW系列
}),
0xF507: ("15W", 1, {(0x07, 0x0B): ("1", "S"), #STC15W1S系列
}),
0xF510: ("15W", 1, {(0x10, 0x14): ("2", "S"), #STC15W2S系列
}),
0xF514: ("15W", 8, {(0x14, 0x17): ("1K", "S"), #STC15W1KS系列
}),
0xF518: ("15W", 4, {(0x18, 0x1A): ("4", "S"), #STC15W4S系列
}),
0xF51A: ("15W", 4, {(0x1A, 0x1C): ("4", "S"), #STC15W4S系列
}),
0xF51C: ("15W", 4, {(0x1C, 0x1F): ("4", "AS"), #STC15W4AS系列
}),
0xF51F: ("15W", 10, {(0x19, 0x20): ("4", "AS"), #STC15W4AS系列
}),
0xF520: ("15W", 12, {(0x20, 0x21): ("4", "AS"), #STC15W4AS系列
}),
0xF522: ("15W", 16, {(0x22, 0x23): ("4K", "S4"), #STC15W4KS4系列
}),
0xF523: ("15W",24, {(0x23, 0x24): ("4K", "S4"), #STC15W4KS4系列
}),
0xF524: ("15W", 32, {(0x24, 0x25): ("4K", "S4"), #STC15W4KS4系列
}),
0xF525: ("15W", 40, {(0x25, 0x26): ("4K", "S4"), #STC15W4KS4系列
}),
0xF526: ("15W", 48, {(0x26, 0x27): ("4K", "S4"), #STC15W4KS4系列
}),
0xF527: ("15W", 56, {(0x27, 0x28): ("4K", "S4"), #STC15W4KS4系列
}),
0xF529: ("15W", 1, {(0x29, 0x2B): ("4", "A4"), #STC15W4AS系列
}),
0xF52C: ("15W", 8, {(0x2C, 0x2E): ("1K", "PWM"), #STC15W1KPWM系列
}),
0xF52E: ("15W", 20, {(0x2E, 0x2F): ("1K", "S"), #STC15W1KS系列
}),
0xF52F: ("15W", 32, {(0x2F, 0x30): ("2K", "S2"), #STC15W2KS2系列
}),
0xF530: ("15W", 48, {(0x30, 0x31): ("2K", "S2"), #STC15W2KS2系列
}),
0xF531: ("15W", 32, {(0x31, 0x32): ("2K", "S2"), #STC15W2KS2系列
}),
0xF533: ("15W", 20, {(0x33, 0x34): ("1K", "S2"), #STC15W1KS2系列
}),
0xF534: ("15W", 32, {(0x34, 0x35): ("1K", "S2"), #STC15W1KS2系列
}),
0xF535: ("15W", 48, {(0x35, 0x36): ("1K", "S2"), #STC15W1KS2系列
}),
0xF544: ("15W", 5, {(0x44, 0x45): ("", "SW"), #STC15SW系列
}),
0xF554: ("15W", 5, {(0x54, 0x55): ("2", "S"), #STC15W2S系列
}),
0xF557: ("15W", 29, {(0x57, 0x58): ("1K", "S"), #STC15W1KS系列
}),
0xF55C: ("15W", 13, {(0x5C, 0x5D): ("4", "S"), #STC15W4S系列
}),
0xF568: ("15W", 58, {(0x68, 0x69): ("4K", "S4"), #STC15W4KS4系列
}),
0xF569: ("15W", 61, {(0x69, 0x6A): ("4K", "S4"), #STC15W4KS4系列
}),
0xF56C: ("15W", 58, {(0x6C, 0x6D): ("4K", "S4-Student"), #STC15W4KS4系列
}),
0xF57E: ("15U", 8, {(0x7E, 0x85): ("4K", "S4"), #STC15U4KS4系列
}),
0xF600: ("15H", 8, {(0x00, 0x08): ("4K", "S4"), #STC154K系列
}),
0xF620: ("8A", 8, {(0x20, 0x28): ("8K", "S4A12"), #STC8A8K系列
}),
0xF628: ("8A", 60, {(0x28, 0x29): ("8K", "S4A12"), #STC8A8K系列
}),
0xF630: ("8F", 8, {(0x30, 0x38): ("2K", "S4"), #STC8F2K系列
}),
0xF638: ("8F", 60, {(0x38, 0x39): ("2K", "S4"), #STC8F2K系列
}),
0xF640: ("8F", 8, {(0x40, 0x48): ("2K", "S2"), #STC8F2K系列
}),
0xF648: ("8F", 60, {(0x48, 0x49): ("2K", "S2"), #STC8F2K系列
}),
0xF650: ("8A", 8, {(0x50, 0x58): ("4K", "S2A12"), #STC8A4K系列
}),
0xF658: ("8A", 60, {(0x58, 0x59): ("4K", "S2A12"), #STC8A4K系列
}),
0xF660: ("8F", 2, {(0x60, 0x66): ("1K", "S2"), #STC8F1K系列
}),
0xF666: ("8F", 17, {(0x66, 0x67): ("1K", "S2"), #STC8F1K系列
}),
0xF670: ("8F", 2, {(0x70, 0x76): ("1K", ""), #STC8F1K系列
}),
0xF676: ("8F", 17, {(0x76, 0x77): ("1K", ""), #STC8F1K系列
}),
0xF700: ("8C", 2, {(0x00, 0x06): ("1K", ""), #STC8C系列
}),
0xF730: ("8H", 2, {(0x30, 0x36): ("1K", ""), #STC8H1K系列
}),
0xF736: ("8H", 17, {(0x36, 0x37): ("1K", ""), #STC8H1K系列
}),
0xF740: ("8H", 8, {(0x40, 0x42): ("3K", "S4"), #STC8H3K系列
}),
0xF742: ("8H", 60, {(0x42, 0x43): ("3K", "S4"), #STC8H3K系列
}),
0xF743: ("8H", 64, {(0x43, 0x44): ("3K", "S4"), #STC8H3K系列
}),
0xF748: ("8H", 16, {(0x48, 0x4A): ("3K", "S2"), #STC8H3K系列
}),
0xF74A: ("8H", 60, {(0x4A, 0x4B): ("3K", "S2"), #STC8H3K系列
}),
0xF74B: ("8H", 64, {(0x4B, 0x4C): ("3K", "S2"), #STC8H3K系列
}),
0xF750: ("8G", 2, {(0x50, 0x56): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF756: ("8G", 17, {(0x56, 0x57): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF760: ("8G", 16, {(0x60, 0x62): ("2K", "S4"), #STC8G2K系列
}),
0xF762: ("8G", 60, {(0x62, 0x63): ("2K", "S4"), #STC8G2K系列
}),
0xF763: ("8G", 64, {(0x63, 0x64): ("2K", "S4"), #STC8G2K系列
}),
0xF768: ("8G", 16, {(0x68, 0x6A): ("2K", "S2"), #STC8G2K系列
}),
0xF76A: ("8G", 60, {(0x6A, 0x6B): ("2K", "S2"), #STC8G2K系列
}),
0xF76B: ("8G", 64, {(0x6B, 0x6C): ("2K", "S2"), #STC8G2K系列
}),
0xF770: ("8G", 2, {(0x70, 0x76): ("1K", "T"), #STC8G2K系列
}),
0xF776: ("8G", 17, {(0x76, 0x77): ("1K", "T"), #STC8G2K系列
}),
0xF780: ("8H", 16, {(0x80, 0x82): ("8K", "U"), #STC8H8K系列
}),
0xF782: ("8H", 60, {(0x82, 0x83): ("8K", "U"), #STC8H8K系列
}),
0xF783: ("8H", 64, {(0x83, 0x84): ("8K", "U"), #STC8H8K系列
}),
0xF790: ("8G", 2, {(0x90, 0x96): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF796: ("8G", 17, {(0x96, 0x97): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF7A0: ("8G", 2, {(0xA0, 0xA6): ("1K", "-8PIN"), #STC8G1K系列
}),
0xF7A6: ("8G", 17, {(0xA6, 0xA7): ("1K", "-8PIN"), #STC8G1K系列
}),
}
iapmcu = ((0xD1, 0x3F), (0xD1, 0x5F), (0xD1, 0x7F), (0xF4, 0x4D), (0xF4, 0x99), (0xF4, 0xD9), (0xF5, 0x58),
(0xD2, 0x7E), (0xD2, 0xFE), (0xF4, 0x09), (0xF4, 0x59), (0xF4, 0xA9), (0xF4, 0xE9), (0xF5, 0x5D),
(0xD3, 0x5F), (0xD3, 0xDF), (0xF4, 0x19), (0xF4, 0x69), (0xF4, 0xC9), (0xF5, 0x45), (0xF5, 0x62),
(0xE2, 0x76), (0xE2, 0xF6), (0xF4, 0x49), (0xF4, 0x89), (0xF4, 0xCD), (0xF5, 0x55), (0xF5, 0x69),
(0xF5, 0x6A), (0xF5, 0x6D),
)
try:
model = tuple(model)
if self.model[0] in [0xF4, 0xF5, 0xF6, 0xF7]:
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
elif self.model[0] == 0xF2 and self.model[1] in range(0xA0, 0xA6):
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
self.protocol = PROTOCOL_15
else:
prefix, romratio, fixmap = modelmap[model[0]]
if model[0] in (0xF0, 0xF1) and 0x20 <= model[1] <= 0x30:
prefix = "90"
for key, value in fixmap.items():
if key[0] <= model[1] <= key[1]:
break
else:
raise KeyError()
infix, postfix = value
romsize = romratio * (model[1] - key[0])
try:
romsize = {(0xF0, 0x03): 13}[model]
except KeyError:
pass
if model[0] in (0xF0, 0xF1):
romfix = str(model[1] - key[0])
elif model[0] in (0xF2,):
romfix = str(romsize)
else:
romfix = "%02d" % romsize
name = "IAP" if model in iapmcu else "STC"
name += prefix + infix + romfix + postfix
return (name, romsize)
except KeyError:
return ("Unknown %02X %02X" % model, None)
def recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def first_recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
time.sleep(0.02) #加上20ms延时，增大接收成功率
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def send(self, cmd, dat):
buf = [0x46, 0xB9, 0x6A]
n = 1 + 2 + 1 + len(dat) + self.chkmode + 1
buf += [n >> 8, n & 0xFF, cmd]
buf += dat
chksum = sum(buf[2:])
if self.chkmode > 1:
buf += [(chksum >> 8) & 0xFF]
buf += [chksum & 0xFF, 0x16]
self.__conn_write(buf)
def detect(self):
for i in range(500):
try:
if self.protocol in [PROTOCOL_89,PROTOCOL_12C52,PROTOCOL_12Cx052,PROTOCOL_12C5A]:
self.__conn_write([0x7F,0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
else:
self.__conn_write([0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
break
except IOError:
pass
else:
raise IOError()
self.info = dat[16:]
self.version = "%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[1])
self.model = self.info[3:5]
self.name, self.romsize = self.__model_database(self.model)
logging.info("Model ID: %02X %02X" % tuple(self.model))
logging.info("Model name: %s" % self.name)
logging.info("ROM size: %s" % self.romsize)
if self.protocol is None:
try:
self.protocol = {0xF0: PROTOCOL_89, #STC89/90C5xRC
0xF1: PROTOCOL_89, #STC89/90C5xRD+
0xF2: PROTOCOL_12Cx052, #STC12Cx052
0xD1: PROTOCOL_12C5A, #STC12C5Ax
0xD2: PROTOCOL_12C5A, #STC10Fx
0xE1: PROTOCOL_12C52, #STC12C52x
0xE2: PROTOCOL_12C5A, #STC11Fx
0xE6: PROTOCOL_12C52, #STC12C56x
0xF4: PROTOCOL_15, #STC15系列
0xF5: PROTOCOL_15, #STC15系列
0xF6: PROTOCOL_8, #STC8系列
0xF7: PROTOCOL_8, #STC8系列
}[self.model[0]]
except KeyError:
pass
if self.protocol in PROTOSET_8:
self.fosc = (dat[0]*0x1000000 +dat[1]*0x10000+dat[2]*0x100) /1000000
self.internal_vol = (dat[34]*256+dat[35])
self.wakeup_fosc = (dat[22]*256+dat[23]) /1000
self.test_year = str(hex(dat[36])).replace("0x",'')
self.test_month = str(hex(dat[37])).replace("0x",'')
self.test_day = str(hex(dat[38])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
if dat[10] == 191:
self.det_low_vol = 2.2
else:
self.det_low_vol = (191 - dat[10])*0.3 + 2.1
elif self.protocol in PROTOSET_15:
self.fosc = (dat[7]*0x1000000 +dat[8]*0x10000+dat[9]*0x100) /1000000
self.wakeup_fosc = (dat[0]*256+dat[1]) /1000
self.internal_vol = (dat[34]*256+dat[35])
self.test_year = str(hex(dat[41])).replace("0x",'')
self.test_month = str(hex(dat[42])).replace("0x",'')
self.test_day = str(hex(dat[43])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
else:
self.fosc = (float(sum(dat[0:16:2]) * 256 + sum(dat[1:16:2])) / 8
* self.conn.baudrate / 580974)
if self.protocol in PROTOSET_PARITY or self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.chkmode = 2
self.conn.parity = serial.PARITY_EVEN
else:
self.chkmode = 1
self.conn.parity = serial.PARITY_NONE
if self.protocol is not None:
del self.info[-self.chkmode:]
logging.info("Protocol ID: %s" % self.protocol)
logging.info("Checksum mode: %d" % self.chkmode)
logging.info("UART Parity: %s"
% {serial.PARITY_NONE: "NONE",
serial.PARITY_EVEN: "EVEN",
}[self.conn.parity])
for i in range(0, len(self.info), 16):
logging.info("Info string [%d]: %s"
% (i // 16,
" ".join(["%02X" % j for j in self.info[i:i+16]])))
def print_info(self):
print("系统时钟频率: %.3fMHz" % self.fosc)
if self.protocol in PROTOSET_8:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("低压检测电压: %.1f V" %self.det_low_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
if self.protocol in PROTOSET_15:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
print("单片机型号: %s" % self.name)
print("固件版本号: %s" % self.version)
if self.romsize is not None:
print("程序空间: %dKB" % self.romsize)
if self.protocol == PROTOCOL_89:
switches = [( 2, 0x80, "Reset stops "),
( 2, 0x40, "Internal XRAM"),
( 2, 0x20, "Normal ALE pin"),
( 2, 0x10, "Full gain oscillator"),
( 2, 0x08, "Not erase data EEPROM"),
( 2, 0x04, "Download regardless of P1"),
( 2, 0x01, "12T mode")]
elif self.protocol == PROTOCOL_12C5A:
switches = [( 6, 0x40, "Disable reset2 low level detect"),
( 6, 0x01, "Reset pin not use as I/O port"),
( 7, 0x80, "Disable long power-on-reset latency"),
( 7, 0x40, "Oscillator high gain"),
( 7, 0x02, "External system clock source"),
( 8, 0x20, "WDT disable after power-on-reset"),
( 8, 0x04, "WDT count in idle mode"),
(10, 0x02, "Not erase data EEPROM"),
(10, 0x01, "Download regardless of P1")]
print(" WDT prescal: %d" % 2**((self.info[8] & 0x07) + 1))
elif self.protocol in PROTOSET_12B:
switches = [(8, 0x02, "Not erase data EEPROM")]
else:
switches = []
for pos, bit, desc in switches:
print(" [%c] %s" % ("X" if self.info[pos] & bit else " ", desc))
def handshake(self):
baud0 = self.conn.baudrate
if self.protocol in PROTOSET_8:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(24.0 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(24.0 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value = 0x7B
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value = 0xB0
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value = 0x5A
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value = 0x35
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value = 0x7B
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value = 0x5A
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value = 0x1A
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value = 0x12
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value = 0x5A
else:
foc_value = 0x6B
baudstr = [0x00, 0x00, Timer1_value >> 8, Timer1_value & 0xff, 0x01, foc_value, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
elif self.protocol in PROTOSET_15:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(22.1184 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(22.1184 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value_1 = 0x40
foc_value_2 = 0x9F
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value_1 = 0x40
foc_value_2 = 0xDC
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value_1 = 0x40
foc_value_2 = 0x79
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value_1 = 0x40
foc_value_2 = 0x4F
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value_1 = 0x80
foc_value_2 = 0xA2
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value_1 = 0x80
foc_value_2 = 0x7D
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value_1 = 0x40
foc_value_2 = 0x31
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value_1 = 0xC0
foc_value_2 = 0x9f
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value_1 = 0xC0
foc_value_2 = 0x7B
baudstr = [0x6d, 0x40, Timer1_value >> 8, Timer1_value & 0xff, foc_value_1,foc_value_2, 0x81]
#baudstr = [0x6b, 0x40, 0xff,0xf4, 0x40,0x92, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
else:
for baud in [115200, 57600, 38400, 28800, 19200,
14400, 9600, 4800, 2400, 1200]:
t = self.fosc * 1000000 / baud / 32
if self.protocol not in PROTOSET_89:
t *= 2
if abs(round(t) - t) / t > 0.03:
continue
if self.protocol in PROTOSET_89:
tcfg = 0x10000 - int(t + 0.5)
else:
if t > 0xFF:
continue
tcfg = 0xC000 + 0x100 - int(t + 0.5)
baudstr = [tcfg >> 8,
tcfg & 0xFF,
0xFF - (tcfg >> 8),
min((256 - (tcfg & 0xFF)) * 2, 0xFE),
int(baud0 / 60)]
logging.info("Test baudrate %d (accuracy %0.4f) using config %s"
% (baud,
abs(round(t) - t) / t,
" ".join(["%02X" % i for i in baudstr])))
if self.protocol in PROTOSET_89:
freqlist = (40, 20, 10, 5)
else:
freqlist = (30, 24, 20, 12, 6, 3, 2, 1)
for twait in range(0, len(freqlist)):
if self.fosc > freqlist[twait]:
break
logging.info("Waiting time config %02X" % (0x80 + twait))
self.send(0x8F, baudstr + [0x80 + twait])
try:
self.__conn_baudrate(baud)
cmd, dat = self.recv()
break
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
else:
raise IOError()
logging.info("Change baudrate to %d" % baud)
self.send(0x8E, baudstr)
self.__conn_baudrate(baud)
self.baudrate = baud
cmd, dat = self.recv()
def erase(self):
if self.protocol in PROTOSET_89:
self.send(0x84, [0x01, 0x33, 0x33, 0x33, 0x33, 0x33, 0x33])
cmd, dat = self.recv(10)
assert cmd == 0x80
elif self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0x05, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
self.send(0x03, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
for i in range(7):
dat[i] = hex(dat[i])
dat[i] = str(dat[i])
dat[i] = dat[i].replace("0x",'')
if len(dat[i]) == 1:
dat_value = list(dat[i])
dat_value.insert(0, '0')
dat[i] = ''.join(dat_value)
serial_number = ""
for i in dat:
serial_number = serial_number +str(i)
self.serial_number = str(serial_number)
print("\r")
sys.stdout.write("芯片出厂序列号: ")
sys.stdout.write(self.serial_number.upper())
sys.stdout.flush()
print("\r")
else:
self.send(0x84, ([0x00, 0x00, self.romsize * 4,
0x00, 0x00, self.romsize * 4]
+ [0x00] * 12
+ [i for i in range(0x80, 0x0D, -1)]))
cmd, dat = self.recv(10)
if dat:
logging.info("Serial number: "
+ " ".join(["%02X" % j for j in dat]))
def flash(self, code):
code = list(code) + [0xff] * (511 - (len(code) - 1) % 512)
for i in range(0, len(code), 128):
logging.info("Flash code region (%04X, %04X)" % (i, i + 127))
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
flag_test = 1
addr = [i >> 8, i & 0xFF, 0x5A, 0xA5]
if flag_test == 1:
self.send(0x22, addr + code[i:i+128])
flag_test = 10
else:
self.send(0x02, addr + code[i:i+128])
else:
addr = [0, 0, i >> 8, i & 0xFF, 0, 128]
self.send(0x00, addr + code[i:i+128])
cmd, dat = self.recv()
#assert dat[0] == sum(code[i:i+128]) % 256
yield (i + 128.0) / len(code)
def options(self, **kwargs):
erase_eeprom = kwargs.get("erase_eeprom", None)
dat = []
fosc = list(bytearray(struct.pack(">I", int(self.fosc * 1000000))))
if self.protocol == PROTOCOL_89:
if erase_eeprom is not None:
self.info[2] &= 0xF7
self.info[2] |= 0x00 if erase_eeprom else 0x08
dat = self.info[2:3] + [0xFF]*3
elif self.protocol == PROTOCOL_12C5A:
if erase_eeprom is not None:
self.info[10] &= 0xFD
self.info[10] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:9] + [0xFF]*5 + self.info[10:11]
+ [0xFF]*6 + fosc)
elif self.protocol in PROTOSET_12B:
if erase_eeprom is not None:
self.info[8] &= 0xFD
self.info[8] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:11] + fosc + self.info[12:16] + [0xFF]*4
+ self.info[8:9] + [0xFF]*7 + fosc + [0xFF]*3)
elif erase_eeprom is not None:
logging.info("Modifying options is not supported for this target")
return False
if dat:
self.send(0x8D, dat)
cmd, dat = self.recv()
return True
def terminate(self):
logging.info("Send termination command")
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0xFF, [])
else:
self.send(0x82, [])
self.conn.flush()
time.sleep(0.2)
def unknown_packet_1(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (50 00 00 36 01 ...)")
self.send(0x50, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8F and not dat
def unknown_packet_2(self):
if self.protocol not in PROTOSET_PARITY and self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
for i in range(5):
logging.info("Send unknown packet (80 00 00 36 01 ...)")
self.send(0x80, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x80 and not dat
def unknown_packet_3(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (69 00 00 36 01 ...)")
self.send(0x69, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8D and not dat
def autoisp(conn, baud, magic):
if not magic:
return
bak = conn.baudrate
conn.baudrate = baud
conn.write(bytearray(ord(i) for i in magic))
conn.flush()
time.sleep(0.5)
conn.baudrate = bak
def program(prog, code, erase_eeprom=None):
sys.stdout.write("检测目标...")
sys.stdout.flush()
prog.detect()
print("完成")
prog.print_info()
if prog.protocol is None:
raise IOError("未知目标")
if code is None:
return
prog.unknown_packet_1()
sys.stdout.write("切换至最高波特率: ")
sys.stdout.flush()
prog.handshake()
print("%d bps"% prog.baudrate)
prog.unknown_packet_2()
sys.stdout.write("开始擦除芯片...")
sys.stdout.flush()
time_start = time.time()
prog.erase()
print("擦除完成")
print("代码长度: %d bytes" % len(code))
# print("Programming: ", end="", flush=True)
sys.stdout.write("正在下载用户代码...")
sys.stdout.flush()
oldbar = 0
for progress in prog.flash(code):
bar = int(progress * 25)
sys.stdout.write("#" * (bar - oldbar))
sys.stdout.flush()
oldbar = bar
print(" 完成")
prog.unknown_packet_3()
sys.stdout.write("设置选项...")
sys.stdout.flush()
if prog.options(erase_eeprom=erase_eeprom):
print("设置完成")
else:
print("设置失败")
prog.terminate()
time_end = time.time()
print("耗时: %.3fs"% (time_end-time_start))
# Convert Intel HEX code to binary format
def hex2bin(code):
buf = bytearray()
base = 0
line = 0
for rec in code.splitlines():
# Calculate the line number of the current record
line += 1
try:
# bytes(...) is to support python<=2.6
# bytearray(...) is to support python<=2.7
n = bytearray(binascii.a2b_hex(bytes(rec[1:3])))[0]
dat = bytearray(binascii.a2b_hex(bytes(rec[1:n*2+11])))
except:
raise Exception("Line %d: Invalid format" % line)
if rec[0] != ord(":"):
raise Exception("Line %d: Missing start code \":\"" % line)
if sum(dat) & 0xFF != 0:
raise Exception("Line %d: Incorrect checksum" % line)
if dat[3] == 0: # Data record
addr = base + (dat[1] << 8) + dat[2]
# Allocate memory space and fill it with 0xFF
buf[len(buf):] = [0xFF] * (addr + n - len(buf))
# Copy data to the buffer
buf[addr:addr+n] = dat[4:-1]
elif dat[3] == 1: # EOF record
if n != 0:
raise Exception("Line %d: Incorrect data length" % line)
elif dat[3] == 2: # Extended segment address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 4
elif dat[3] == 4: # Extended linear address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 16
else:
raise Exception("Line %d: Unsupported record type" % line)
return buf
def stc_type_map(type, value):
if type == 0xF6:
if value in range(0x01,0x09):
return 0xF600
elif value in range(0x21,0x29):
return 0xF620
elif value == 0x29:
return 0xF628
elif value in range(0x31,0x39):
return 0xF630
elif value == 0x39:
return 0xF638
elif value in range(0x41,0x49):
return 0xF640
elif value == 0x49:
return 0xF648
elif value in range(0x51,0x59):
return 0xF650
elif value == 0x59:
return 0xF658
elif value in range(0x61,0x67):
return 0xF660
elif value == 0x67:
return 0xF666
elif value in range(0x71,0x77):
return 0xF670
elif value == 0x77:
return 0xF676
if type == 0xF7:
if value in range(0x01,0x07):
return 0xF700
elif value in range(0x31,0x37):
return 0xF730
elif value == 0x37:
return 0xF736
elif value in range(0x41,0x43):
return 0xF740
elif value == 0x43:
return 0xF742
elif value == 0x44:
return 0xF743
elif value in range(0x49,0x4B):
return 0xF748
elif value == 0x4B:
return 0xF74A
elif value == 0x4C:
return 0xF74B
elif value in range(0x51,0x57):
return 0xF750
elif value == 0x57:
return 0xF756
elif value in range(0x61,0x63):
return 0xF760
elif value == 0x63:
return 0xF762
elif value == 0x64:
return 0xF763
elif value in range(0x69,0x6B):
return 0xF768
elif value == 0x6B:
return 0xF76A
elif value == 0x6C:
return 0xF76B
elif value in range(0x71,0x77):
return 0xF770
elif value == 0x77:
return 0xF776
elif value in range(0x81,0x83):
return 0xF780
elif value == 0x83:
return 0xF782
elif value == 0x84:
return 0xF783
elif value in range(0x91,0x97):
return 0xF790
elif value == 0x97:
return 0xF796
elif value in range(0xA1,0xA7):
return 0xF7A0
elif value == 0xA7:
return 0xF7A6
if type == 0xF4:
if value in range(0x01,0x08):
return 0xF400
elif value == 0x08:
return 0xF407
elif value == 0x09:
return 0xF408
elif value in range(0x0A,0x0D):
return 0xF409
elif value in range(0x11,0x18):
return 0xF410
elif value == 0x18:
return 0xF417
elif value == 0x19:
return 0xF418
elif value in range(0x21,0x28):
return 0xF420
elif value == 0x28:
return 0xF427
elif value == 0x29:
return 0xF428
elif value in range(0x41,0x48):
return 0xF440
elif value == 0x48:
return 0xF447
elif value == 0x49:
return 0xF448
elif value == 0x4D:
return 0xF44C
elif value in range(0x51,0x57):
return 0xF450
elif value == 0x58:
return 0xF457
elif value == 0x59:
return 0xF458
elif value in range(0x61,0x68):
return 0xF460
elif value == 0x68:
return 0xF467
elif value == 0x69:
return 0xF468
elif value in range(0x81,0x88):
return 0xF480
elif value == 0x88:
return 0xF487
elif value == 0x89:
return 0xF488
elif value in range(0x8A,0x8D):
return 0xF489
elif value in range(0x91,0x98):
return 0xF490
elif value == 0x98:
return 0xF497
elif value == 0x99:
return 0xF498
elif value in range(0xA1,0xA8):
return 0xF4A0
elif value == 0xA8:
return 0xF4A7
elif value == 0xA9:
return 0xF4A8
elif value in range(0xC1,0xC8):
return 0xF4C0
elif value == 0xC8:
return 0xF4C7
elif value == 0xC9:
return 0xF4C8
elif value == 0xCD:
return 0xF4CC
elif value in range(0xD1,0xD8):
return 0xF4D0
elif value == 0xD8:
return 0xF4D7
elif value == 0xD9:
return 0xF4D8
elif value in range(0xE1,0xE8):
return 0xF4E0
elif value == 0xE8:
return 0xF4E7
elif value == 0xE9:
return 0xF4E8
if type == 0xF5:
if value in range(0x01,0x05):
return 0xF500
elif value in range(0x08,0x0C):
return 0xF507
elif value in range(0x11,15):
return 0xF510
elif value in range(0x15,0x18):
return 0xF514
elif value in range(0x19,0x1B):
return 0xF518
elif value in range(0x1B,0x1D):
return 0xF51A
elif value in range(0x1D,0x20):
return 0xF51C
elif value == 0x20:
return 0xF51F
elif value == 0x21:
return 0xF520
elif value == 0x23:
return 0xF522
elif value == 0x24:
return 0xF523
elif value == 0x25:
return 0xF524
elif value == 0x26:
return 0xF525
elif value == 0x27:
return 0xF526
elif value == 0x28:
return 0xF527
elif value in range(0x2A,0x2C):
return 0xF529
elif value in range(0x2D,0x2F):
return 0xF52C
elif value == 0x2F:
return 0xF52E
elif value == 0x30:
return 0xF52F
elif value == 0x31:
return 0xF530
elif value == 0x32:
return 0xF531
elif value == 0x34:
return 0xF533
elif value == 0x35:
return 0xF534
elif value == 0x36:
return 0xF535
elif value == 0x45:
return 0xF544
elif value == 0x55:
return 0xF554
elif value == 0x58:
return 0xF557
elif value == 0x5D:
return 0xF55C
elif value == 0x69:
return 0xF568
elif value == 0x6A:
return 0xF569
elif value == 0x6D:
return 0xF56C
elif value in range(0x7F,0x86):
return 0xF57E
if type == 0xF2:
if value in range(0xA0,0xA6):
return 0xF2A0
def main():
if sys.platform == "win32":
port = "COM3"
elif sys.platform == "darwin":
port = "/dev/tty.usbserial"
else:
port = "/dev/ttyUSB0"
parser = argparse.ArgumentParser(
description=("Stcflash, a command line programmer for "
+ "STC 8051 microcontroller.\n"
+ "https://github.com/laborer/stcflash"))
parser.add_argument("image",
help="code image (bin/hex)",
type=argparse.FileType("rb"), nargs='?')
parser.add_argument("-p", "--port",
help="serial port device (default: %s)" % port,
default=port)
parser.add_argument("-l", "--lowbaud",
help="initial baud rate (default: 2400)",
type=int,
default=2400)
parser.add_argument("-hb", "--highbaud",
help="initial baud rate (default: 115200)",
type=int,
default=115200)
parser.add_argument("-r", "--protocol",
help="protocol to use for programming",
choices=["89", "12c5a", "12c52", "12cx052", "8", "15", "auto"],
default="auto")
parser.add_argument("-a", "--aispbaud",
help="baud rate for AutoISP (default: 4800)",
type=int,
default=4800)
parser.add_argument("-m", "--aispmagic",
help="magic word for AutoISP")
parser.add_argument("-v", "--verbose",
help="be verbose",
default=0,
action="count")
parser.add_argument("-e", "--erase_eeprom",
help=("erase data eeprom during next download"
+"(experimental)"),
action="store_true")
parser.add_argument("-ne", "--not_erase_eeprom",
help=("do not erase data eeprom next download"
+"(experimental)"),
action="store_true")
opts = parser.parse_args()
opts.loglevel = (logging.CRITICAL,
logging.INFO,
logging.DEBUG)[min(2, opts.verbose)]
opts.protocol = {'89': PROTOCOL_89,
'12c5a': PROTOCOL_12C5A,
'12c52': PROTOCOL_12C52,
'12cx052': PROTOCOL_12Cx052,
'8': PROTOCOL_8,
'15': PROTOCOL_15,
'auto': None}[opts.protocol]
if not opts.erase_eeprom and not opts.not_erase_eeprom:
opts.erase_eeprom = None
logging.basicConfig(format=("%(levelname)s: "
+ "[%(relativeCreated)d] "
+ "%(message)s"),
level=opts.loglevel)
if opts.image:
code = bytearray(opts.image.read())
opts.image.close()
if os.path.splitext(opts.image.name)[1] in (".hex", ".ihx"):
code = hex2bin(code)
else:
code = None
print("通信端口：%s 最低波特率：%d bps" % (opts.port, opts.lowbaud))
global highbaud_pre
highbaud_pre = opts.highbaud
with serial.Serial(port=opts.port,
baudrate=opts.lowbaud,
parity=serial.PARITY_NONE) as conn:
if opts.aispmagic:
autoisp(conn, opts.aispbaud, opts.aispmagic)
program(Programmer(conn, opts.protocol), code, opts.erase_eeprom)
if __name__ == "__main__":
main()
|
2301_77966824/51-microcontroller-learning
|
2024/10/1019-数码管-模块化编程/tools/stcflash.py
|
Python
|
unknown
| 57,810
|
#!/usr/bin/env python
#coding=utf-8
# stcflash Copyright (C) 2013 laborer (laborer@126.com)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import time
import logging
import sys
import serial
import os.path
import binascii
import struct
import argparse
PROTOCOL_89 = "89"
PROTOCOL_12C5A = "12c5a"
PROTOCOL_12C52 = "12c52"
PROTOCOL_12Cx052 = "12cx052"
PROTOCOL_8 = "8"
PROTOCOL_15 = '15'
PROTOSET_89 = [PROTOCOL_89]
PROTOSET_12 = [PROTOCOL_12C5A, PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_12B = [PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_8 = [PROTOCOL_8]
PROTOSET_15 = [PROTOCOL_15]
PROTOSET_PARITY = [PROTOCOL_12C5A, PROTOCOL_12C52]
class Programmer:
def __init__(self, conn, protocol=None):
self.conn = conn
self.protocol = protocol
self.conn.timeout = 0.05
if self.protocol in PROTOSET_PARITY:
self.conn.parity = serial.PARITY_EVEN
else:
self.conn.parity = serial.PARITY_NONE
self.chkmode = 0
def __conn_read(self, size):
buf = bytearray()
while len(buf) < size:
s = bytearray(self.conn.read(size - len(buf)))
buf += s
logging.debug("recv: " + " ".join(["%02X" % i for i in s]))
if len(s) == 0:
raise IOError()
return list(buf)
def __conn_write(self, s):
logging.debug("send: " + " ".join(["%02X" % i for i in s]))
self.conn.write(bytearray(s))
def __conn_baudrate(self, baud, flush=True):
logging.debug("baud: %d" % baud)
if flush:
if self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
self.conn.flush()
time.sleep(0.2)
self.conn.baudrate = baud
def __model_database(self, model):
modelmap = {0xE0: ("12", 1, {(0x00, 0x1F): ("C54", ""),
(0x60, 0x7F): ("C54", "AD"),
(0x80, 0x9F): ("LE54", ""),
(0xE0, 0xFF): ("LE54", "AD"),
}),
0xE1: ("12", 1, {(0x00, 0x1F): ("C52", ""),
(0x20, 0x3F): ("C52", "PWM"),
(0x60, 0x7F): ("C52", "AD"),
(0x80, 0x9F): ("LE52", ""),
(0xA0, 0xBF): ("LE52", "PWM"),
(0xE0, 0xFF): ("LE52", "AD"),
}),
0xE2: ("11", 1, {(0x00, 0x1F): ("F", ""),
(0x20, 0x3F): ("F", "E"),
(0x70, 0x7F): ("F", ""),
(0x80, 0x9F): ("L", ""),
(0xA0, 0xBF): ("L", "E"),
(0xF0, 0xFF): ("L", ""),
}),
0xE6: ("12", 1, {(0x00, 0x1F): ("C56", ""),
(0x60, 0x7F): ("C56", "AD"),
(0x80, 0x9F): ("LE56", ""),
(0xE0, 0xFF): ("LE56", "AD"),
}),
0xD1: ("12", 2, {(0x20, 0x3F): ("C5A", "CCP"),
(0x40, 0x5F): ("C5A", "AD"),
(0x60, 0x7F): ("C5A", "S2"),
(0xA0, 0xBF): ("LE5A", "CCP"),
(0xC0, 0xDF): ("LE5A", "AD"),
(0xE0, 0xFF): ("LE5A", "S2"),
}),
0xD2: ("10", 1, {(0x00, 0x0F): ("F", ""),
(0x60, 0x6F): ("F", "XE"),
(0x70, 0x7F): ("F", "X"),
(0xA0, 0xAF): ("L", ""),
(0xE0, 0xEF): ("L", "XE"),
(0xF0, 0xFF): ("L", "X"),
}),
0xD3: ("11", 2, {(0x00, 0x1F): ("F", ""),
(0x40, 0x5F): ("F", "X"),
(0x60, 0x7F): ("F", "XE"),
(0xA0, 0xBF): ("L", ""),
(0xC0, 0xDF): ("L", "X"),
(0xE0, 0xFF): ("L", "XE"),
}),
0xF0: ("89", 4, {(0x00, 0x10): ("C5", "RC"),
(0x20, 0x30): ("C5", "RC"), #STC90C5xRC
}),
0xF1: ("89", 4, {(0x00, 0x10): ("C5", "RD+"),
(0x20, 0x30): ("C5", "RD+"), #STC90C5xRD+
}),
0xF2: ("12", 1, {(0x00, 0x0F): ("C", "052"),
(0x10, 0x1F): ("C", "052AD"),
(0x20, 0x2F): ("LE", "052"),
(0x30, 0x3F): ("LE", "052AD"),
}),
0xF2A0: ("15W", 1, {(0xA0, 0xA5): ("1", ""), #STC15W1系列
}),
0xF400: ("15F", 8, {(0x00, 0x07): ("2K", "S2"), #STC15F2K系列
}),
0xF407: ("15F", 60, {(0x07, 0x08): ("2K", "S2"), #STC15F2K系列
}),
0xF408: ("15F", 61, {(0x08, 0x09): ("2K", "S2"), #STC15F2K系列
}),
0xF400: ("15F", 4, {(0x09, 0x0C): ("4", "AD"), #STC15FAD系列
}),
0xF410: ("15F", 8, {(0x10, 0x17): ("1K", "AS"), #STC15F1KAS系列
}),
0xF417: ("15F", 60, {(0x17, 0x18): ("1K", "AS"), #STC15F1KAS系列
}),
0xF418: ("15F", 61, {(0x18, 0x19): ("1K", "AS"), #STC15F1KAS系列
}),
0xF420: ("15F", 8, {(0x20, 0x27): ("1K", "S"), #STC15F1KS系列
}),
0xF427: ("15F", 60, {(0x27, 0x28): ("1K", "S"), #STC15F1KS系列
}),
0xF440: ("15F", 8, {(0x40, 0x47): ("1K", "S2"), #STC15F1KS2系列
}),
0xF447: ("15F", 60, {(0x47, 0x48): ("1K", "S2"), #STC15F1KS2系列
}),
0xF448: ("15F", 61, {(0x48, 0x49): ("1K", "S2"), #STC15F1KS2系列
}),
0xF44C: ("15F", 13, {(0x4C, 0x4D): ("4", "AD"),
}),
0xF450: ("15F", 8, {(0x50, 0x57): ("1K", "AS"), #STC15F1KAS系列
}),
0xF457: ("15F", 60, {(0x57, 0x58): ("1K", "AS"), #STC15F1KAS系列
}),
0xF458: ("15F", 61, {(0x58, 0x59): ("1K", "AS"), #STC15F1KAS系列
}),
0xF460: ("15F", 8, {(0x60, 0x67): ("1K", "S"), #STC15F1KS系列
}),
0xF467: ("15F", 60, {(0x67, 0x68): ("1K", "S"), #STC15F1KS系列
}),
0xF468: ("15F", 61, {(0x68, 0x69): ("1K", "S"), #STC15F1KS系列
}),
0xF480: ("15L", 8, {(0x80, 0x87): ("2K", "S2"), #STC15L2KS2系列
}),
0xF487: ("15L", 60, {(0x87, 0x88): ("2K", "S2"), #STC15L2KS2系列
}),
0xF488: ("15L", 61, {(0x88, 0x89): ("2K", "S2"), #STC15L2KS2系列
}),
0xF489: ("15L", 5, {(0x89, 0x8C): ("4", "AD"), #STC15L4AD系列
}),
0xF490: ("15L", 8, {(0x90, 0x97): ("2K", "AS"), #STC15L2KAS系列
}),
0xF497: ("15L", 60, {(0x97, 0x98): ("2K", "AS"), #STC15L2KAS系列
}),
0xF498: ("15L", 61, {(0x98, 0x99): ("2K", "AS"), #STC15L2KAS系列
}),
0xF4A0: ("15L", 8, {(0xA0, 0xA7): ("2K", "S"), #STC15L2KS系列
}),
0xF4A7: ("15L", 60, {(0xA7, 0xA8): ("2K", "S"), #STC15L2KS系列
}),
0xF4A8: ("15L", 61, {(0xA8, 0xA9): ("2K", "S"), #STC15L2KS系列
}),
0xF4C0: ("15L", 8, {(0xC0, 0xC7): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C7: ("15L", 60, {(0xC7, 0xC8): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C8: ("15L", 61, {(0xC8, 0xC9): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4CC: ("15L", 13, {(0xCC, 0xCD): ("4", "AD"),
}),
0xF4D0: ("15L", 8, {(0xD0, 0xD7): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D7: ("15L", 60, {(0xD7, 0xD8): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D8: ("15L", 61, {(0xD8, 0xD9): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4E0: ("15L", 8, {(0xE0, 0xE7): ("1K", "S"), #STC15L1KS系列
}),
0xF4E7: ("15L", 60, {(0xE7, 0xE8): ("1K", "S"), #STC15L1KS系列
}),
0xF4E8: ("15L", 61, {(0xE8, 0xE9): ("1K", "S"), #STC15L1KS系列
}),
0xF500: ("15W", 1, {(0x00, 0x04): ("1", "SW"), #STC15W1SW系列
}),
0xF507: ("15W", 1, {(0x07, 0x0B): ("1", "S"), #STC15W1S系列
}),
0xF510: ("15W", 1, {(0x10, 0x14): ("2", "S"), #STC15W2S系列
}),
0xF514: ("15W", 8, {(0x14, 0x17): ("1K", "S"), #STC15W1KS系列
}),
0xF518: ("15W", 4, {(0x18, 0x1A): ("4", "S"), #STC15W4S系列
}),
0xF51A: ("15W", 4, {(0x1A, 0x1C): ("4", "S"), #STC15W4S系列
}),
0xF51C: ("15W", 4, {(0x1C, 0x1F): ("4", "AS"), #STC15W4AS系列
}),
0xF51F: ("15W", 10, {(0x19, 0x20): ("4", "AS"), #STC15W4AS系列
}),
0xF520: ("15W", 12, {(0x20, 0x21): ("4", "AS"), #STC15W4AS系列
}),
0xF522: ("15W", 16, {(0x22, 0x23): ("4K", "S4"), #STC15W4KS4系列
}),
0xF523: ("15W",24, {(0x23, 0x24): ("4K", "S4"), #STC15W4KS4系列
}),
0xF524: ("15W", 32, {(0x24, 0x25): ("4K", "S4"), #STC15W4KS4系列
}),
0xF525: ("15W", 40, {(0x25, 0x26): ("4K", "S4"), #STC15W4KS4系列
}),
0xF526: ("15W", 48, {(0x26, 0x27): ("4K", "S4"), #STC15W4KS4系列
}),
0xF527: ("15W", 56, {(0x27, 0x28): ("4K", "S4"), #STC15W4KS4系列
}),
0xF529: ("15W", 1, {(0x29, 0x2B): ("4", "A4"), #STC15W4AS系列
}),
0xF52C: ("15W", 8, {(0x2C, 0x2E): ("1K", "PWM"), #STC15W1KPWM系列
}),
0xF52E: ("15W", 20, {(0x2E, 0x2F): ("1K", "S"), #STC15W1KS系列
}),
0xF52F: ("15W", 32, {(0x2F, 0x30): ("2K", "S2"), #STC15W2KS2系列
}),
0xF530: ("15W", 48, {(0x30, 0x31): ("2K", "S2"), #STC15W2KS2系列
}),
0xF531: ("15W", 32, {(0x31, 0x32): ("2K", "S2"), #STC15W2KS2系列
}),
0xF533: ("15W", 20, {(0x33, 0x34): ("1K", "S2"), #STC15W1KS2系列
}),
0xF534: ("15W", 32, {(0x34, 0x35): ("1K", "S2"), #STC15W1KS2系列
}),
0xF535: ("15W", 48, {(0x35, 0x36): ("1K", "S2"), #STC15W1KS2系列
}),
0xF544: ("15W", 5, {(0x44, 0x45): ("", "SW"), #STC15SW系列
}),
0xF554: ("15W", 5, {(0x54, 0x55): ("2", "S"), #STC15W2S系列
}),
0xF557: ("15W", 29, {(0x57, 0x58): ("1K", "S"), #STC15W1KS系列
}),
0xF55C: ("15W", 13, {(0x5C, 0x5D): ("4", "S"), #STC15W4S系列
}),
0xF568: ("15W", 58, {(0x68, 0x69): ("4K", "S4"), #STC15W4KS4系列
}),
0xF569: ("15W", 61, {(0x69, 0x6A): ("4K", "S4"), #STC15W4KS4系列
}),
0xF56C: ("15W", 58, {(0x6C, 0x6D): ("4K", "S4-Student"), #STC15W4KS4系列
}),
0xF57E: ("15U", 8, {(0x7E, 0x85): ("4K", "S4"), #STC15U4KS4系列
}),
0xF600: ("15H", 8, {(0x00, 0x08): ("4K", "S4"), #STC154K系列
}),
0xF620: ("8A", 8, {(0x20, 0x28): ("8K", "S4A12"), #STC8A8K系列
}),
0xF628: ("8A", 60, {(0x28, 0x29): ("8K", "S4A12"), #STC8A8K系列
}),
0xF630: ("8F", 8, {(0x30, 0x38): ("2K", "S4"), #STC8F2K系列
}),
0xF638: ("8F", 60, {(0x38, 0x39): ("2K", "S4"), #STC8F2K系列
}),
0xF640: ("8F", 8, {(0x40, 0x48): ("2K", "S2"), #STC8F2K系列
}),
0xF648: ("8F", 60, {(0x48, 0x49): ("2K", "S2"), #STC8F2K系列
}),
0xF650: ("8A", 8, {(0x50, 0x58): ("4K", "S2A12"), #STC8A4K系列
}),
0xF658: ("8A", 60, {(0x58, 0x59): ("4K", "S2A12"), #STC8A4K系列
}),
0xF660: ("8F", 2, {(0x60, 0x66): ("1K", "S2"), #STC8F1K系列
}),
0xF666: ("8F", 17, {(0x66, 0x67): ("1K", "S2"), #STC8F1K系列
}),
0xF670: ("8F", 2, {(0x70, 0x76): ("1K", ""), #STC8F1K系列
}),
0xF676: ("8F", 17, {(0x76, 0x77): ("1K", ""), #STC8F1K系列
}),
0xF700: ("8C", 2, {(0x00, 0x06): ("1K", ""), #STC8C系列
}),
0xF730: ("8H", 2, {(0x30, 0x36): ("1K", ""), #STC8H1K系列
}),
0xF736: ("8H", 17, {(0x36, 0x37): ("1K", ""), #STC8H1K系列
}),
0xF740: ("8H", 8, {(0x40, 0x42): ("3K", "S4"), #STC8H3K系列
}),
0xF742: ("8H", 60, {(0x42, 0x43): ("3K", "S4"), #STC8H3K系列
}),
0xF743: ("8H", 64, {(0x43, 0x44): ("3K", "S4"), #STC8H3K系列
}),
0xF748: ("8H", 16, {(0x48, 0x4A): ("3K", "S2"), #STC8H3K系列
}),
0xF74A: ("8H", 60, {(0x4A, 0x4B): ("3K", "S2"), #STC8H3K系列
}),
0xF74B: ("8H", 64, {(0x4B, 0x4C): ("3K", "S2"), #STC8H3K系列
}),
0xF750: ("8G", 2, {(0x50, 0x56): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF756: ("8G", 17, {(0x56, 0x57): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF760: ("8G", 16, {(0x60, 0x62): ("2K", "S4"), #STC8G2K系列
}),
0xF762: ("8G", 60, {(0x62, 0x63): ("2K", "S4"), #STC8G2K系列
}),
0xF763: ("8G", 64, {(0x63, 0x64): ("2K", "S4"), #STC8G2K系列
}),
0xF768: ("8G", 16, {(0x68, 0x6A): ("2K", "S2"), #STC8G2K系列
}),
0xF76A: ("8G", 60, {(0x6A, 0x6B): ("2K", "S2"), #STC8G2K系列
}),
0xF76B: ("8G", 64, {(0x6B, 0x6C): ("2K", "S2"), #STC8G2K系列
}),
0xF770: ("8G", 2, {(0x70, 0x76): ("1K", "T"), #STC8G2K系列
}),
0xF776: ("8G", 17, {(0x76, 0x77): ("1K", "T"), #STC8G2K系列
}),
0xF780: ("8H", 16, {(0x80, 0x82): ("8K", "U"), #STC8H8K系列
}),
0xF782: ("8H", 60, {(0x82, 0x83): ("8K", "U"), #STC8H8K系列
}),
0xF783: ("8H", 64, {(0x83, 0x84): ("8K", "U"), #STC8H8K系列
}),
0xF790: ("8G", 2, {(0x90, 0x96): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF796: ("8G", 17, {(0x96, 0x97): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF7A0: ("8G", 2, {(0xA0, 0xA6): ("1K", "-8PIN"), #STC8G1K系列
}),
0xF7A6: ("8G", 17, {(0xA6, 0xA7): ("1K", "-8PIN"), #STC8G1K系列
}),
}
iapmcu = ((0xD1, 0x3F), (0xD1, 0x5F), (0xD1, 0x7F), (0xF4, 0x4D), (0xF4, 0x99), (0xF4, 0xD9), (0xF5, 0x58),
(0xD2, 0x7E), (0xD2, 0xFE), (0xF4, 0x09), (0xF4, 0x59), (0xF4, 0xA9), (0xF4, 0xE9), (0xF5, 0x5D),
(0xD3, 0x5F), (0xD3, 0xDF), (0xF4, 0x19), (0xF4, 0x69), (0xF4, 0xC9), (0xF5, 0x45), (0xF5, 0x62),
(0xE2, 0x76), (0xE2, 0xF6), (0xF4, 0x49), (0xF4, 0x89), (0xF4, 0xCD), (0xF5, 0x55), (0xF5, 0x69),
(0xF5, 0x6A), (0xF5, 0x6D),
)
try:
model = tuple(model)
if self.model[0] in [0xF4, 0xF5, 0xF6, 0xF7]:
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
elif self.model[0] == 0xF2 and self.model[1] in range(0xA0, 0xA6):
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
self.protocol = PROTOCOL_15
else:
prefix, romratio, fixmap = modelmap[model[0]]
if model[0] in (0xF0, 0xF1) and 0x20 <= model[1] <= 0x30:
prefix = "90"
for key, value in fixmap.items():
if key[0] <= model[1] <= key[1]:
break
else:
raise KeyError()
infix, postfix = value
romsize = romratio * (model[1] - key[0])
try:
romsize = {(0xF0, 0x03): 13}[model]
except KeyError:
pass
if model[0] in (0xF0, 0xF1):
romfix = str(model[1] - key[0])
elif model[0] in (0xF2,):
romfix = str(romsize)
else:
romfix = "%02d" % romsize
name = "IAP" if model in iapmcu else "STC"
name += prefix + infix + romfix + postfix
return (name, romsize)
except KeyError:
return ("Unknown %02X %02X" % model, None)
def recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def first_recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
time.sleep(0.02) #加上20ms延时，增大接收成功率
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def send(self, cmd, dat):
buf = [0x46, 0xB9, 0x6A]
n = 1 + 2 + 1 + len(dat) + self.chkmode + 1
buf += [n >> 8, n & 0xFF, cmd]
buf += dat
chksum = sum(buf[2:])
if self.chkmode > 1:
buf += [(chksum >> 8) & 0xFF]
buf += [chksum & 0xFF, 0x16]
self.__conn_write(buf)
def detect(self):
for i in range(500):
try:
if self.protocol in [PROTOCOL_89,PROTOCOL_12C52,PROTOCOL_12Cx052,PROTOCOL_12C5A]:
self.__conn_write([0x7F,0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
else:
self.__conn_write([0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
break
except IOError:
pass
else:
raise IOError()
self.info = dat[16:]
self.version = "%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[1])
self.model = self.info[3:5]
self.name, self.romsize = self.__model_database(self.model)
logging.info("Model ID: %02X %02X" % tuple(self.model))
logging.info("Model name: %s" % self.name)
logging.info("ROM size: %s" % self.romsize)
if self.protocol is None:
try:
self.protocol = {0xF0: PROTOCOL_89, #STC89/90C5xRC
0xF1: PROTOCOL_89, #STC89/90C5xRD+
0xF2: PROTOCOL_12Cx052, #STC12Cx052
0xD1: PROTOCOL_12C5A, #STC12C5Ax
0xD2: PROTOCOL_12C5A, #STC10Fx
0xE1: PROTOCOL_12C52, #STC12C52x
0xE2: PROTOCOL_12C5A, #STC11Fx
0xE6: PROTOCOL_12C52, #STC12C56x
0xF4: PROTOCOL_15, #STC15系列
0xF5: PROTOCOL_15, #STC15系列
0xF6: PROTOCOL_8, #STC8系列
0xF7: PROTOCOL_8, #STC8系列
}[self.model[0]]
except KeyError:
pass
if self.protocol in PROTOSET_8:
self.fosc = (dat[0]*0x1000000 +dat[1]*0x10000+dat[2]*0x100) /1000000
self.internal_vol = (dat[34]*256+dat[35])
self.wakeup_fosc = (dat[22]*256+dat[23]) /1000
self.test_year = str(hex(dat[36])).replace("0x",'')
self.test_month = str(hex(dat[37])).replace("0x",'')
self.test_day = str(hex(dat[38])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
if dat[10] == 191:
self.det_low_vol = 2.2
else:
self.det_low_vol = (191 - dat[10])*0.3 + 2.1
elif self.protocol in PROTOSET_15:
self.fosc = (dat[7]*0x1000000 +dat[8]*0x10000+dat[9]*0x100) /1000000
self.wakeup_fosc = (dat[0]*256+dat[1]) /1000
self.internal_vol = (dat[34]*256+dat[35])
self.test_year = str(hex(dat[41])).replace("0x",'')
self.test_month = str(hex(dat[42])).replace("0x",'')
self.test_day = str(hex(dat[43])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
else:
self.fosc = (float(sum(dat[0:16:2]) * 256 + sum(dat[1:16:2])) / 8
* self.conn.baudrate / 580974)
if self.protocol in PROTOSET_PARITY or self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.chkmode = 2
self.conn.parity = serial.PARITY_EVEN
else:
self.chkmode = 1
self.conn.parity = serial.PARITY_NONE
if self.protocol is not None:
del self.info[-self.chkmode:]
logging.info("Protocol ID: %s" % self.protocol)
logging.info("Checksum mode: %d" % self.chkmode)
logging.info("UART Parity: %s"
% {serial.PARITY_NONE: "NONE",
serial.PARITY_EVEN: "EVEN",
}[self.conn.parity])
for i in range(0, len(self.info), 16):
logging.info("Info string [%d]: %s"
% (i // 16,
" ".join(["%02X" % j for j in self.info[i:i+16]])))
def print_info(self):
print("系统时钟频率: %.3fMHz" % self.fosc)
if self.protocol in PROTOSET_8:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("低压检测电压: %.1f V" %self.det_low_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
if self.protocol in PROTOSET_15:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
print("单片机型号: %s" % self.name)
print("固件版本号: %s" % self.version)
if self.romsize is not None:
print("程序空间: %dKB" % self.romsize)
if self.protocol == PROTOCOL_89:
switches = [( 2, 0x80, "Reset stops "),
( 2, 0x40, "Internal XRAM"),
( 2, 0x20, "Normal ALE pin"),
( 2, 0x10, "Full gain oscillator"),
( 2, 0x08, "Not erase data EEPROM"),
( 2, 0x04, "Download regardless of P1"),
( 2, 0x01, "12T mode")]
elif self.protocol == PROTOCOL_12C5A:
switches = [( 6, 0x40, "Disable reset2 low level detect"),
( 6, 0x01, "Reset pin not use as I/O port"),
( 7, 0x80, "Disable long power-on-reset latency"),
( 7, 0x40, "Oscillator high gain"),
( 7, 0x02, "External system clock source"),
( 8, 0x20, "WDT disable after power-on-reset"),
( 8, 0x04, "WDT count in idle mode"),
(10, 0x02, "Not erase data EEPROM"),
(10, 0x01, "Download regardless of P1")]
print(" WDT prescal: %d" % 2**((self.info[8] & 0x07) + 1))
elif self.protocol in PROTOSET_12B:
switches = [(8, 0x02, "Not erase data EEPROM")]
else:
switches = []
for pos, bit, desc in switches:
print(" [%c] %s" % ("X" if self.info[pos] & bit else " ", desc))
def handshake(self):
baud0 = self.conn.baudrate
if self.protocol in PROTOSET_8:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(24.0 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(24.0 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value = 0x7B
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value = 0xB0
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value = 0x5A
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value = 0x35
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value = 0x7B
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value = 0x5A
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value = 0x1A
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value = 0x12
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value = 0x5A
else:
foc_value = 0x6B
baudstr = [0x00, 0x00, Timer1_value >> 8, Timer1_value & 0xff, 0x01, foc_value, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
elif self.protocol in PROTOSET_15:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(22.1184 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(22.1184 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value_1 = 0x40
foc_value_2 = 0x9F
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value_1 = 0x40
foc_value_2 = 0xDC
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value_1 = 0x40
foc_value_2 = 0x79
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value_1 = 0x40
foc_value_2 = 0x4F
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value_1 = 0x80
foc_value_2 = 0xA2
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value_1 = 0x80
foc_value_2 = 0x7D
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value_1 = 0x40
foc_value_2 = 0x31
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value_1 = 0xC0
foc_value_2 = 0x9f
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value_1 = 0xC0
foc_value_2 = 0x7B
baudstr = [0x6d, 0x40, Timer1_value >> 8, Timer1_value & 0xff, foc_value_1,foc_value_2, 0x81]
#baudstr = [0x6b, 0x40, 0xff,0xf4, 0x40,0x92, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
else:
for baud in [115200, 57600, 38400, 28800, 19200,
14400, 9600, 4800, 2400, 1200]:
t = self.fosc * 1000000 / baud / 32
if self.protocol not in PROTOSET_89:
t *= 2
if abs(round(t) - t) / t > 0.03:
continue
if self.protocol in PROTOSET_89:
tcfg = 0x10000 - int(t + 0.5)
else:
if t > 0xFF:
continue
tcfg = 0xC000 + 0x100 - int(t + 0.5)
baudstr = [tcfg >> 8,
tcfg & 0xFF,
0xFF - (tcfg >> 8),
min((256 - (tcfg & 0xFF)) * 2, 0xFE),
int(baud0 / 60)]
logging.info("Test baudrate %d (accuracy %0.4f) using config %s"
% (baud,
abs(round(t) - t) / t,
" ".join(["%02X" % i for i in baudstr])))
if self.protocol in PROTOSET_89:
freqlist = (40, 20, 10, 5)
else:
freqlist = (30, 24, 20, 12, 6, 3, 2, 1)
for twait in range(0, len(freqlist)):
if self.fosc > freqlist[twait]:
break
logging.info("Waiting time config %02X" % (0x80 + twait))
self.send(0x8F, baudstr + [0x80 + twait])
try:
self.__conn_baudrate(baud)
cmd, dat = self.recv()
break
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
else:
raise IOError()
logging.info("Change baudrate to %d" % baud)
self.send(0x8E, baudstr)
self.__conn_baudrate(baud)
self.baudrate = baud
cmd, dat = self.recv()
def erase(self):
if self.protocol in PROTOSET_89:
self.send(0x84, [0x01, 0x33, 0x33, 0x33, 0x33, 0x33, 0x33])
cmd, dat = self.recv(10)
assert cmd == 0x80
elif self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0x05, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
self.send(0x03, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
for i in range(7):
dat[i] = hex(dat[i])
dat[i] = str(dat[i])
dat[i] = dat[i].replace("0x",'')
if len(dat[i]) == 1:
dat_value = list(dat[i])
dat_value.insert(0, '0')
dat[i] = ''.join(dat_value)
serial_number = ""
for i in dat:
serial_number = serial_number +str(i)
self.serial_number = str(serial_number)
print("\r")
sys.stdout.write("芯片出厂序列号: ")
sys.stdout.write(self.serial_number.upper())
sys.stdout.flush()
print("\r")
else:
self.send(0x84, ([0x00, 0x00, self.romsize * 4,
0x00, 0x00, self.romsize * 4]
+ [0x00] * 12
+ [i for i in range(0x80, 0x0D, -1)]))
cmd, dat = self.recv(10)
if dat:
logging.info("Serial number: "
+ " ".join(["%02X" % j for j in dat]))
def flash(self, code):
code = list(code) + [0xff] * (511 - (len(code) - 1) % 512)
for i in range(0, len(code), 128):
logging.info("Flash code region (%04X, %04X)" % (i, i + 127))
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
flag_test = 1
addr = [i >> 8, i & 0xFF, 0x5A, 0xA5]
if flag_test == 1:
self.send(0x22, addr + code[i:i+128])
flag_test = 10
else:
self.send(0x02, addr + code[i:i+128])
else:
addr = [0, 0, i >> 8, i & 0xFF, 0, 128]
self.send(0x00, addr + code[i:i+128])
cmd, dat = self.recv()
#assert dat[0] == sum(code[i:i+128]) % 256
yield (i + 128.0) / len(code)
def options(self, **kwargs):
erase_eeprom = kwargs.get("erase_eeprom", None)
dat = []
fosc = list(bytearray(struct.pack(">I", int(self.fosc * 1000000))))
if self.protocol == PROTOCOL_89:
if erase_eeprom is not None:
self.info[2] &= 0xF7
self.info[2] |= 0x00 if erase_eeprom else 0x08
dat = self.info[2:3] + [0xFF]*3
elif self.protocol == PROTOCOL_12C5A:
if erase_eeprom is not None:
self.info[10] &= 0xFD
self.info[10] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:9] + [0xFF]*5 + self.info[10:11]
+ [0xFF]*6 + fosc)
elif self.protocol in PROTOSET_12B:
if erase_eeprom is not None:
self.info[8] &= 0xFD
self.info[8] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:11] + fosc + self.info[12:16] + [0xFF]*4
+ self.info[8:9] + [0xFF]*7 + fosc + [0xFF]*3)
elif erase_eeprom is not None:
logging.info("Modifying options is not supported for this target")
return False
if dat:
self.send(0x8D, dat)
cmd, dat = self.recv()
return True
def terminate(self):
logging.info("Send termination command")
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0xFF, [])
else:
self.send(0x82, [])
self.conn.flush()
time.sleep(0.2)
def unknown_packet_1(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (50 00 00 36 01 ...)")
self.send(0x50, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8F and not dat
def unknown_packet_2(self):
if self.protocol not in PROTOSET_PARITY and self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
for i in range(5):
logging.info("Send unknown packet (80 00 00 36 01 ...)")
self.send(0x80, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x80 and not dat
def unknown_packet_3(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (69 00 00 36 01 ...)")
self.send(0x69, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8D and not dat
def autoisp(conn, baud, magic):
if not magic:
return
bak = conn.baudrate
conn.baudrate = baud
conn.write(bytearray(ord(i) for i in magic))
conn.flush()
time.sleep(0.5)
conn.baudrate = bak
def program(prog, code, erase_eeprom=None):
sys.stdout.write("检测目标...")
sys.stdout.flush()
prog.detect()
print("完成")
prog.print_info()
if prog.protocol is None:
raise IOError("未知目标")
if code is None:
return
prog.unknown_packet_1()
sys.stdout.write("切换至最高波特率: ")
sys.stdout.flush()
prog.handshake()
print("%d bps"% prog.baudrate)
prog.unknown_packet_2()
sys.stdout.write("开始擦除芯片...")
sys.stdout.flush()
time_start = time.time()
prog.erase()
print("擦除完成")
print("代码长度: %d bytes" % len(code))
# print("Programming: ", end="", flush=True)
sys.stdout.write("正在下载用户代码...")
sys.stdout.flush()
oldbar = 0
for progress in prog.flash(code):
bar = int(progress * 25)
sys.stdout.write("#" * (bar - oldbar))
sys.stdout.flush()
oldbar = bar
print(" 完成")
prog.unknown_packet_3()
sys.stdout.write("设置选项...")
sys.stdout.flush()
if prog.options(erase_eeprom=erase_eeprom):
print("设置完成")
else:
print("设置失败")
prog.terminate()
time_end = time.time()
print("耗时: %.3fs"% (time_end-time_start))
# Convert Intel HEX code to binary format
def hex2bin(code):
buf = bytearray()
base = 0
line = 0
for rec in code.splitlines():
# Calculate the line number of the current record
line += 1
try:
# bytes(...) is to support python<=2.6
# bytearray(...) is to support python<=2.7
n = bytearray(binascii.a2b_hex(bytes(rec[1:3])))[0]
dat = bytearray(binascii.a2b_hex(bytes(rec[1:n*2+11])))
except:
raise Exception("Line %d: Invalid format" % line)
if rec[0] != ord(":"):
raise Exception("Line %d: Missing start code \":\"" % line)
if sum(dat) & 0xFF != 0:
raise Exception("Line %d: Incorrect checksum" % line)
if dat[3] == 0: # Data record
addr = base + (dat[1] << 8) + dat[2]
# Allocate memory space and fill it with 0xFF
buf[len(buf):] = [0xFF] * (addr + n - len(buf))
# Copy data to the buffer
buf[addr:addr+n] = dat[4:-1]
elif dat[3] == 1: # EOF record
if n != 0:
raise Exception("Line %d: Incorrect data length" % line)
elif dat[3] == 2: # Extended segment address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 4
elif dat[3] == 4: # Extended linear address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 16
else:
raise Exception("Line %d: Unsupported record type" % line)
return buf
def stc_type_map(type, value):
if type == 0xF6:
if value in range(0x01,0x09):
return 0xF600
elif value in range(0x21,0x29):
return 0xF620
elif value == 0x29:
return 0xF628
elif value in range(0x31,0x39):
return 0xF630
elif value == 0x39:
return 0xF638
elif value in range(0x41,0x49):
return 0xF640
elif value == 0x49:
return 0xF648
elif value in range(0x51,0x59):
return 0xF650
elif value == 0x59:
return 0xF658
elif value in range(0x61,0x67):
return 0xF660
elif value == 0x67:
return 0xF666
elif value in range(0x71,0x77):
return 0xF670
elif value == 0x77:
return 0xF676
if type == 0xF7:
if value in range(0x01,0x07):
return 0xF700
elif value in range(0x31,0x37):
return 0xF730
elif value == 0x37:
return 0xF736
elif value in range(0x41,0x43):
return 0xF740
elif value == 0x43:
return 0xF742
elif value == 0x44:
return 0xF743
elif value in range(0x49,0x4B):
return 0xF748
elif value == 0x4B:
return 0xF74A
elif value == 0x4C:
return 0xF74B
elif value in range(0x51,0x57):
return 0xF750
elif value == 0x57:
return 0xF756
elif value in range(0x61,0x63):
return 0xF760
elif value == 0x63:
return 0xF762
elif value == 0x64:
return 0xF763
elif value in range(0x69,0x6B):
return 0xF768
elif value == 0x6B:
return 0xF76A
elif value == 0x6C:
return 0xF76B
elif value in range(0x71,0x77):
return 0xF770
elif value == 0x77:
return 0xF776
elif value in range(0x81,0x83):
return 0xF780
elif value == 0x83:
return 0xF782
elif value == 0x84:
return 0xF783
elif value in range(0x91,0x97):
return 0xF790
elif value == 0x97:
return 0xF796
elif value in range(0xA1,0xA7):
return 0xF7A0
elif value == 0xA7:
return 0xF7A6
if type == 0xF4:
if value in range(0x01,0x08):
return 0xF400
elif value == 0x08:
return 0xF407
elif value == 0x09:
return 0xF408
elif value in range(0x0A,0x0D):
return 0xF409
elif value in range(0x11,0x18):
return 0xF410
elif value == 0x18:
return 0xF417
elif value == 0x19:
return 0xF418
elif value in range(0x21,0x28):
return 0xF420
elif value == 0x28:
return 0xF427
elif value == 0x29:
return 0xF428
elif value in range(0x41,0x48):
return 0xF440
elif value == 0x48:
return 0xF447
elif value == 0x49:
return 0xF448
elif value == 0x4D:
return 0xF44C
elif value in range(0x51,0x57):
return 0xF450
elif value == 0x58:
return 0xF457
elif value == 0x59:
return 0xF458
elif value in range(0x61,0x68):
return 0xF460
elif value == 0x68:
return 0xF467
elif value == 0x69:
return 0xF468
elif value in range(0x81,0x88):
return 0xF480
elif value == 0x88:
return 0xF487
elif value == 0x89:
return 0xF488
elif value in range(0x8A,0x8D):
return 0xF489
elif value in range(0x91,0x98):
return 0xF490
elif value == 0x98:
return 0xF497
elif value == 0x99:
return 0xF498
elif value in range(0xA1,0xA8):
return 0xF4A0
elif value == 0xA8:
return 0xF4A7
elif value == 0xA9:
return 0xF4A8
elif value in range(0xC1,0xC8):
return 0xF4C0
elif value == 0xC8:
return 0xF4C7
elif value == 0xC9:
return 0xF4C8
elif value == 0xCD:
return 0xF4CC
elif value in range(0xD1,0xD8):
return 0xF4D0
elif value == 0xD8:
return 0xF4D7
elif value == 0xD9:
return 0xF4D8
elif value in range(0xE1,0xE8):
return 0xF4E0
elif value == 0xE8:
return 0xF4E7
elif value == 0xE9:
return 0xF4E8
if type == 0xF5:
if value in range(0x01,0x05):
return 0xF500
elif value in range(0x08,0x0C):
return 0xF507
elif value in range(0x11,15):
return 0xF510
elif value in range(0x15,0x18):
return 0xF514
elif value in range(0x19,0x1B):
return 0xF518
elif value in range(0x1B,0x1D):
return 0xF51A
elif value in range(0x1D,0x20):
return 0xF51C
elif value == 0x20:
return 0xF51F
elif value == 0x21:
return 0xF520
elif value == 0x23:
return 0xF522
elif value == 0x24:
return 0xF523
elif value == 0x25:
return 0xF524
elif value == 0x26:
return 0xF525
elif value == 0x27:
return 0xF526
elif value == 0x28:
return 0xF527
elif value in range(0x2A,0x2C):
return 0xF529
elif value in range(0x2D,0x2F):
return 0xF52C
elif value == 0x2F:
return 0xF52E
elif value == 0x30:
return 0xF52F
elif value == 0x31:
return 0xF530
elif value == 0x32:
return 0xF531
elif value == 0x34:
return 0xF533
elif value == 0x35:
return 0xF534
elif value == 0x36:
return 0xF535
elif value == 0x45:
return 0xF544
elif value == 0x55:
return 0xF554
elif value == 0x58:
return 0xF557
elif value == 0x5D:
return 0xF55C
elif value == 0x69:
return 0xF568
elif value == 0x6A:
return 0xF569
elif value == 0x6D:
return 0xF56C
elif value in range(0x7F,0x86):
return 0xF57E
if type == 0xF2:
if value in range(0xA0,0xA6):
return 0xF2A0
def main():
if sys.platform == "win32":
port = "COM3"
elif sys.platform == "darwin":
port = "/dev/tty.usbserial"
else:
port = "/dev/ttyUSB0"
parser = argparse.ArgumentParser(
description=("Stcflash, a command line programmer for "
+ "STC 8051 microcontroller.\n"
+ "https://github.com/laborer/stcflash"))
parser.add_argument("image",
help="code image (bin/hex)",
type=argparse.FileType("rb"), nargs='?')
parser.add_argument("-p", "--port",
help="serial port device (default: %s)" % port,
default=port)
parser.add_argument("-l", "--lowbaud",
help="initial baud rate (default: 2400)",
type=int,
default=2400)
parser.add_argument("-hb", "--highbaud",
help="initial baud rate (default: 115200)",
type=int,
default=115200)
parser.add_argument("-r", "--protocol",
help="protocol to use for programming",
choices=["89", "12c5a", "12c52", "12cx052", "8", "15", "auto"],
default="auto")
parser.add_argument("-a", "--aispbaud",
help="baud rate for AutoISP (default: 4800)",
type=int,
default=4800)
parser.add_argument("-m", "--aispmagic",
help="magic word for AutoISP")
parser.add_argument("-v", "--verbose",
help="be verbose",
default=0,
action="count")
parser.add_argument("-e", "--erase_eeprom",
help=("erase data eeprom during next download"
+"(experimental)"),
action="store_true")
parser.add_argument("-ne", "--not_erase_eeprom",
help=("do not erase data eeprom next download"
+"(experimental)"),
action="store_true")
opts = parser.parse_args()
opts.loglevel = (logging.CRITICAL,
logging.INFO,
logging.DEBUG)[min(2, opts.verbose)]
opts.protocol = {'89': PROTOCOL_89,
'12c5a': PROTOCOL_12C5A,
'12c52': PROTOCOL_12C52,
'12cx052': PROTOCOL_12Cx052,
'8': PROTOCOL_8,
'15': PROTOCOL_15,
'auto': None}[opts.protocol]
if not opts.erase_eeprom and not opts.not_erase_eeprom:
opts.erase_eeprom = None
logging.basicConfig(format=("%(levelname)s: "
+ "[%(relativeCreated)d] "
+ "%(message)s"),
level=opts.loglevel)
if opts.image:
code = bytearray(opts.image.read())
opts.image.close()
if os.path.splitext(opts.image.name)[1] in (".hex", ".ihx"):
code = hex2bin(code)
else:
code = None
print("通信端口：%s 最低波特率：%d bps" % (opts.port, opts.lowbaud))
global highbaud_pre
highbaud_pre = opts.highbaud
with serial.Serial(port=opts.port,
baudrate=opts.lowbaud,
parity=serial.PARITY_NONE) as conn:
if opts.aispmagic:
autoisp(conn, opts.aispbaud, opts.aispmagic)
program(Programmer(conn, opts.protocol), code, opts.erase_eeprom)
if __name__ == "__main__":
main()
|
2301_77966824/51-microcontroller-learning
|
2024/10/20241015数码管/tools/stcflash.py
|
Python
|
unknown
| 57,810
|
#include<STC89C5xRC.H>
#include<INTRINS.H>
void Delay100ms(void) //@11.0592MHz
{
unsigned char data i, j;
i = 180;
j = 73;
do
{
while (--j);
} while (--i);
}
void main()
{
unsigned char tmp=0x01;
while(1)
{
P0=~tmp;
Delay100ms();
tmp <<=1;
if(tmp==0x00)
{
tmp=0x01;
}
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/10/24241016_01_HelloWorld-eide/HelloWorld-eide/src/main.c
|
C
|
unknown
| 305
|
#!/usr/bin/env python
#coding=utf-8
# stcflash Copyright (C) 2013 laborer (laborer@126.com)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import time
import logging
import sys
import serial
import os.path
import binascii
import struct
import argparse
PROTOCOL_89 = "89"
PROTOCOL_12C5A = "12c5a"
PROTOCOL_12C52 = "12c52"
PROTOCOL_12Cx052 = "12cx052"
PROTOCOL_8 = "8"
PROTOCOL_15 = '15'
PROTOSET_89 = [PROTOCOL_89]
PROTOSET_12 = [PROTOCOL_12C5A, PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_12B = [PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_8 = [PROTOCOL_8]
PROTOSET_15 = [PROTOCOL_15]
PROTOSET_PARITY = [PROTOCOL_12C5A, PROTOCOL_12C52]
class Programmer:
def __init__(self, conn, protocol=None):
self.conn = conn
self.protocol = protocol
self.conn.timeout = 0.05
if self.protocol in PROTOSET_PARITY:
self.conn.parity = serial.PARITY_EVEN
else:
self.conn.parity = serial.PARITY_NONE
self.chkmode = 0
def __conn_read(self, size):
buf = bytearray()
while len(buf) < size:
s = bytearray(self.conn.read(size - len(buf)))
buf += s
logging.debug("recv: " + " ".join(["%02X" % i for i in s]))
if len(s) == 0:
raise IOError()
return list(buf)
def __conn_write(self, s):
logging.debug("send: " + " ".join(["%02X" % i for i in s]))
self.conn.write(bytearray(s))
def __conn_baudrate(self, baud, flush=True):
logging.debug("baud: %d" % baud)
if flush:
if self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
self.conn.flush()
time.sleep(0.2)
self.conn.baudrate = baud
def __model_database(self, model):
modelmap = {0xE0: ("12", 1, {(0x00, 0x1F): ("C54", ""),
(0x60, 0x7F): ("C54", "AD"),
(0x80, 0x9F): ("LE54", ""),
(0xE0, 0xFF): ("LE54", "AD"),
}),
0xE1: ("12", 1, {(0x00, 0x1F): ("C52", ""),
(0x20, 0x3F): ("C52", "PWM"),
(0x60, 0x7F): ("C52", "AD"),
(0x80, 0x9F): ("LE52", ""),
(0xA0, 0xBF): ("LE52", "PWM"),
(0xE0, 0xFF): ("LE52", "AD"),
}),
0xE2: ("11", 1, {(0x00, 0x1F): ("F", ""),
(0x20, 0x3F): ("F", "E"),
(0x70, 0x7F): ("F", ""),
(0x80, 0x9F): ("L", ""),
(0xA0, 0xBF): ("L", "E"),
(0xF0, 0xFF): ("L", ""),
}),
0xE6: ("12", 1, {(0x00, 0x1F): ("C56", ""),
(0x60, 0x7F): ("C56", "AD"),
(0x80, 0x9F): ("LE56", ""),
(0xE0, 0xFF): ("LE56", "AD"),
}),
0xD1: ("12", 2, {(0x20, 0x3F): ("C5A", "CCP"),
(0x40, 0x5F): ("C5A", "AD"),
(0x60, 0x7F): ("C5A", "S2"),
(0xA0, 0xBF): ("LE5A", "CCP"),
(0xC0, 0xDF): ("LE5A", "AD"),
(0xE0, 0xFF): ("LE5A", "S2"),
}),
0xD2: ("10", 1, {(0x00, 0x0F): ("F", ""),
(0x60, 0x6F): ("F", "XE"),
(0x70, 0x7F): ("F", "X"),
(0xA0, 0xAF): ("L", ""),
(0xE0, 0xEF): ("L", "XE"),
(0xF0, 0xFF): ("L", "X"),
}),
0xD3: ("11", 2, {(0x00, 0x1F): ("F", ""),
(0x40, 0x5F): ("F", "X"),
(0x60, 0x7F): ("F", "XE"),
(0xA0, 0xBF): ("L", ""),
(0xC0, 0xDF): ("L", "X"),
(0xE0, 0xFF): ("L", "XE"),
}),
0xF0: ("89", 4, {(0x00, 0x10): ("C5", "RC"),
(0x20, 0x30): ("C5", "RC"), #STC90C5xRC
}),
0xF1: ("89", 4, {(0x00, 0x10): ("C5", "RD+"),
(0x20, 0x30): ("C5", "RD+"), #STC90C5xRD+
}),
0xF2: ("12", 1, {(0x00, 0x0F): ("C", "052"),
(0x10, 0x1F): ("C", "052AD"),
(0x20, 0x2F): ("LE", "052"),
(0x30, 0x3F): ("LE", "052AD"),
}),
0xF2A0: ("15W", 1, {(0xA0, 0xA5): ("1", ""), #STC15W1系列
}),
0xF400: ("15F", 8, {(0x00, 0x07): ("2K", "S2"), #STC15F2K系列
}),
0xF407: ("15F", 60, {(0x07, 0x08): ("2K", "S2"), #STC15F2K系列
}),
0xF408: ("15F", 61, {(0x08, 0x09): ("2K", "S2"), #STC15F2K系列
}),
0xF400: ("15F", 4, {(0x09, 0x0C): ("4", "AD"), #STC15FAD系列
}),
0xF410: ("15F", 8, {(0x10, 0x17): ("1K", "AS"), #STC15F1KAS系列
}),
0xF417: ("15F", 60, {(0x17, 0x18): ("1K", "AS"), #STC15F1KAS系列
}),
0xF418: ("15F", 61, {(0x18, 0x19): ("1K", "AS"), #STC15F1KAS系列
}),
0xF420: ("15F", 8, {(0x20, 0x27): ("1K", "S"), #STC15F1KS系列
}),
0xF427: ("15F", 60, {(0x27, 0x28): ("1K", "S"), #STC15F1KS系列
}),
0xF440: ("15F", 8, {(0x40, 0x47): ("1K", "S2"), #STC15F1KS2系列
}),
0xF447: ("15F", 60, {(0x47, 0x48): ("1K", "S2"), #STC15F1KS2系列
}),
0xF448: ("15F", 61, {(0x48, 0x49): ("1K", "S2"), #STC15F1KS2系列
}),
0xF44C: ("15F", 13, {(0x4C, 0x4D): ("4", "AD"),
}),
0xF450: ("15F", 8, {(0x50, 0x57): ("1K", "AS"), #STC15F1KAS系列
}),
0xF457: ("15F", 60, {(0x57, 0x58): ("1K", "AS"), #STC15F1KAS系列
}),
0xF458: ("15F", 61, {(0x58, 0x59): ("1K", "AS"), #STC15F1KAS系列
}),
0xF460: ("15F", 8, {(0x60, 0x67): ("1K", "S"), #STC15F1KS系列
}),
0xF467: ("15F", 60, {(0x67, 0x68): ("1K", "S"), #STC15F1KS系列
}),
0xF468: ("15F", 61, {(0x68, 0x69): ("1K", "S"), #STC15F1KS系列
}),
0xF480: ("15L", 8, {(0x80, 0x87): ("2K", "S2"), #STC15L2KS2系列
}),
0xF487: ("15L", 60, {(0x87, 0x88): ("2K", "S2"), #STC15L2KS2系列
}),
0xF488: ("15L", 61, {(0x88, 0x89): ("2K", "S2"), #STC15L2KS2系列
}),
0xF489: ("15L", 5, {(0x89, 0x8C): ("4", "AD"), #STC15L4AD系列
}),
0xF490: ("15L", 8, {(0x90, 0x97): ("2K", "AS"), #STC15L2KAS系列
}),
0xF497: ("15L", 60, {(0x97, 0x98): ("2K", "AS"), #STC15L2KAS系列
}),
0xF498: ("15L", 61, {(0x98, 0x99): ("2K", "AS"), #STC15L2KAS系列
}),
0xF4A0: ("15L", 8, {(0xA0, 0xA7): ("2K", "S"), #STC15L2KS系列
}),
0xF4A7: ("15L", 60, {(0xA7, 0xA8): ("2K", "S"), #STC15L2KS系列
}),
0xF4A8: ("15L", 61, {(0xA8, 0xA9): ("2K", "S"), #STC15L2KS系列
}),
0xF4C0: ("15L", 8, {(0xC0, 0xC7): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C7: ("15L", 60, {(0xC7, 0xC8): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C8: ("15L", 61, {(0xC8, 0xC9): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4CC: ("15L", 13, {(0xCC, 0xCD): ("4", "AD"),
}),
0xF4D0: ("15L", 8, {(0xD0, 0xD7): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D7: ("15L", 60, {(0xD7, 0xD8): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D8: ("15L", 61, {(0xD8, 0xD9): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4E0: ("15L", 8, {(0xE0, 0xE7): ("1K", "S"), #STC15L1KS系列
}),
0xF4E7: ("15L", 60, {(0xE7, 0xE8): ("1K", "S"), #STC15L1KS系列
}),
0xF4E8: ("15L", 61, {(0xE8, 0xE9): ("1K", "S"), #STC15L1KS系列
}),
0xF500: ("15W", 1, {(0x00, 0x04): ("1", "SW"), #STC15W1SW系列
}),
0xF507: ("15W", 1, {(0x07, 0x0B): ("1", "S"), #STC15W1S系列
}),
0xF510: ("15W", 1, {(0x10, 0x14): ("2", "S"), #STC15W2S系列
}),
0xF514: ("15W", 8, {(0x14, 0x17): ("1K", "S"), #STC15W1KS系列
}),
0xF518: ("15W", 4, {(0x18, 0x1A): ("4", "S"), #STC15W4S系列
}),
0xF51A: ("15W", 4, {(0x1A, 0x1C): ("4", "S"), #STC15W4S系列
}),
0xF51C: ("15W", 4, {(0x1C, 0x1F): ("4", "AS"), #STC15W4AS系列
}),
0xF51F: ("15W", 10, {(0x19, 0x20): ("4", "AS"), #STC15W4AS系列
}),
0xF520: ("15W", 12, {(0x20, 0x21): ("4", "AS"), #STC15W4AS系列
}),
0xF522: ("15W", 16, {(0x22, 0x23): ("4K", "S4"), #STC15W4KS4系列
}),
0xF523: ("15W",24, {(0x23, 0x24): ("4K", "S4"), #STC15W4KS4系列
}),
0xF524: ("15W", 32, {(0x24, 0x25): ("4K", "S4"), #STC15W4KS4系列
}),
0xF525: ("15W", 40, {(0x25, 0x26): ("4K", "S4"), #STC15W4KS4系列
}),
0xF526: ("15W", 48, {(0x26, 0x27): ("4K", "S4"), #STC15W4KS4系列
}),
0xF527: ("15W", 56, {(0x27, 0x28): ("4K", "S4"), #STC15W4KS4系列
}),
0xF529: ("15W", 1, {(0x29, 0x2B): ("4", "A4"), #STC15W4AS系列
}),
0xF52C: ("15W", 8, {(0x2C, 0x2E): ("1K", "PWM"), #STC15W1KPWM系列
}),
0xF52E: ("15W", 20, {(0x2E, 0x2F): ("1K", "S"), #STC15W1KS系列
}),
0xF52F: ("15W", 32, {(0x2F, 0x30): ("2K", "S2"), #STC15W2KS2系列
}),
0xF530: ("15W", 48, {(0x30, 0x31): ("2K", "S2"), #STC15W2KS2系列
}),
0xF531: ("15W", 32, {(0x31, 0x32): ("2K", "S2"), #STC15W2KS2系列
}),
0xF533: ("15W", 20, {(0x33, 0x34): ("1K", "S2"), #STC15W1KS2系列
}),
0xF534: ("15W", 32, {(0x34, 0x35): ("1K", "S2"), #STC15W1KS2系列
}),
0xF535: ("15W", 48, {(0x35, 0x36): ("1K", "S2"), #STC15W1KS2系列
}),
0xF544: ("15W", 5, {(0x44, 0x45): ("", "SW"), #STC15SW系列
}),
0xF554: ("15W", 5, {(0x54, 0x55): ("2", "S"), #STC15W2S系列
}),
0xF557: ("15W", 29, {(0x57, 0x58): ("1K", "S"), #STC15W1KS系列
}),
0xF55C: ("15W", 13, {(0x5C, 0x5D): ("4", "S"), #STC15W4S系列
}),
0xF568: ("15W", 58, {(0x68, 0x69): ("4K", "S4"), #STC15W4KS4系列
}),
0xF569: ("15W", 61, {(0x69, 0x6A): ("4K", "S4"), #STC15W4KS4系列
}),
0xF56C: ("15W", 58, {(0x6C, 0x6D): ("4K", "S4-Student"), #STC15W4KS4系列
}),
0xF57E: ("15U", 8, {(0x7E, 0x85): ("4K", "S4"), #STC15U4KS4系列
}),
0xF600: ("15H", 8, {(0x00, 0x08): ("4K", "S4"), #STC154K系列
}),
0xF620: ("8A", 8, {(0x20, 0x28): ("8K", "S4A12"), #STC8A8K系列
}),
0xF628: ("8A", 60, {(0x28, 0x29): ("8K", "S4A12"), #STC8A8K系列
}),
0xF630: ("8F", 8, {(0x30, 0x38): ("2K", "S4"), #STC8F2K系列
}),
0xF638: ("8F", 60, {(0x38, 0x39): ("2K", "S4"), #STC8F2K系列
}),
0xF640: ("8F", 8, {(0x40, 0x48): ("2K", "S2"), #STC8F2K系列
}),
0xF648: ("8F", 60, {(0x48, 0x49): ("2K", "S2"), #STC8F2K系列
}),
0xF650: ("8A", 8, {(0x50, 0x58): ("4K", "S2A12"), #STC8A4K系列
}),
0xF658: ("8A", 60, {(0x58, 0x59): ("4K", "S2A12"), #STC8A4K系列
}),
0xF660: ("8F", 2, {(0x60, 0x66): ("1K", "S2"), #STC8F1K系列
}),
0xF666: ("8F", 17, {(0x66, 0x67): ("1K", "S2"), #STC8F1K系列
}),
0xF670: ("8F", 2, {(0x70, 0x76): ("1K", ""), #STC8F1K系列
}),
0xF676: ("8F", 17, {(0x76, 0x77): ("1K", ""), #STC8F1K系列
}),
0xF700: ("8C", 2, {(0x00, 0x06): ("1K", ""), #STC8C系列
}),
0xF730: ("8H", 2, {(0x30, 0x36): ("1K", ""), #STC8H1K系列
}),
0xF736: ("8H", 17, {(0x36, 0x37): ("1K", ""), #STC8H1K系列
}),
0xF740: ("8H", 8, {(0x40, 0x42): ("3K", "S4"), #STC8H3K系列
}),
0xF742: ("8H", 60, {(0x42, 0x43): ("3K", "S4"), #STC8H3K系列
}),
0xF743: ("8H", 64, {(0x43, 0x44): ("3K", "S4"), #STC8H3K系列
}),
0xF748: ("8H", 16, {(0x48, 0x4A): ("3K", "S2"), #STC8H3K系列
}),
0xF74A: ("8H", 60, {(0x4A, 0x4B): ("3K", "S2"), #STC8H3K系列
}),
0xF74B: ("8H", 64, {(0x4B, 0x4C): ("3K", "S2"), #STC8H3K系列
}),
0xF750: ("8G", 2, {(0x50, 0x56): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF756: ("8G", 17, {(0x56, 0x57): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF760: ("8G", 16, {(0x60, 0x62): ("2K", "S4"), #STC8G2K系列
}),
0xF762: ("8G", 60, {(0x62, 0x63): ("2K", "S4"), #STC8G2K系列
}),
0xF763: ("8G", 64, {(0x63, 0x64): ("2K", "S4"), #STC8G2K系列
}),
0xF768: ("8G", 16, {(0x68, 0x6A): ("2K", "S2"), #STC8G2K系列
}),
0xF76A: ("8G", 60, {(0x6A, 0x6B): ("2K", "S2"), #STC8G2K系列
}),
0xF76B: ("8G", 64, {(0x6B, 0x6C): ("2K", "S2"), #STC8G2K系列
}),
0xF770: ("8G", 2, {(0x70, 0x76): ("1K", "T"), #STC8G2K系列
}),
0xF776: ("8G", 17, {(0x76, 0x77): ("1K", "T"), #STC8G2K系列
}),
0xF780: ("8H", 16, {(0x80, 0x82): ("8K", "U"), #STC8H8K系列
}),
0xF782: ("8H", 60, {(0x82, 0x83): ("8K", "U"), #STC8H8K系列
}),
0xF783: ("8H", 64, {(0x83, 0x84): ("8K", "U"), #STC8H8K系列
}),
0xF790: ("8G", 2, {(0x90, 0x96): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF796: ("8G", 17, {(0x96, 0x97): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF7A0: ("8G", 2, {(0xA0, 0xA6): ("1K", "-8PIN"), #STC8G1K系列
}),
0xF7A6: ("8G", 17, {(0xA6, 0xA7): ("1K", "-8PIN"), #STC8G1K系列
}),
}
iapmcu = ((0xD1, 0x3F), (0xD1, 0x5F), (0xD1, 0x7F), (0xF4, 0x4D), (0xF4, 0x99), (0xF4, 0xD9), (0xF5, 0x58),
(0xD2, 0x7E), (0xD2, 0xFE), (0xF4, 0x09), (0xF4, 0x59), (0xF4, 0xA9), (0xF4, 0xE9), (0xF5, 0x5D),
(0xD3, 0x5F), (0xD3, 0xDF), (0xF4, 0x19), (0xF4, 0x69), (0xF4, 0xC9), (0xF5, 0x45), (0xF5, 0x62),
(0xE2, 0x76), (0xE2, 0xF6), (0xF4, 0x49), (0xF4, 0x89), (0xF4, 0xCD), (0xF5, 0x55), (0xF5, 0x69),
(0xF5, 0x6A), (0xF5, 0x6D),
)
try:
model = tuple(model)
if self.model[0] in [0xF4, 0xF5, 0xF6, 0xF7]:
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
elif self.model[0] == 0xF2 and self.model[1] in range(0xA0, 0xA6):
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
self.protocol = PROTOCOL_15
else:
prefix, romratio, fixmap = modelmap[model[0]]
if model[0] in (0xF0, 0xF1) and 0x20 <= model[1] <= 0x30:
prefix = "90"
for key, value in fixmap.items():
if key[0] <= model[1] <= key[1]:
break
else:
raise KeyError()
infix, postfix = value
romsize = romratio * (model[1] - key[0])
try:
romsize = {(0xF0, 0x03): 13}[model]
except KeyError:
pass
if model[0] in (0xF0, 0xF1):
romfix = str(model[1] - key[0])
elif model[0] in (0xF2,):
romfix = str(romsize)
else:
romfix = "%02d" % romsize
name = "IAP" if model in iapmcu else "STC"
name += prefix + infix + romfix + postfix
return (name, romsize)
except KeyError:
return ("Unknown %02X %02X" % model, None)
def recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def first_recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
time.sleep(0.02) #加上20ms延时，增大接收成功率
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def send(self, cmd, dat):
buf = [0x46, 0xB9, 0x6A]
n = 1 + 2 + 1 + len(dat) + self.chkmode + 1
buf += [n >> 8, n & 0xFF, cmd]
buf += dat
chksum = sum(buf[2:])
if self.chkmode > 1:
buf += [(chksum >> 8) & 0xFF]
buf += [chksum & 0xFF, 0x16]
self.__conn_write(buf)
def detect(self):
for i in range(500):
try:
if self.protocol in [PROTOCOL_89,PROTOCOL_12C52,PROTOCOL_12Cx052,PROTOCOL_12C5A]:
self.__conn_write([0x7F,0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
else:
self.__conn_write([0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
break
except IOError:
pass
else:
raise IOError()
self.info = dat[16:]
self.version = "%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[1])
self.model = self.info[3:5]
self.name, self.romsize = self.__model_database(self.model)
logging.info("Model ID: %02X %02X" % tuple(self.model))
logging.info("Model name: %s" % self.name)
logging.info("ROM size: %s" % self.romsize)
if self.protocol is None:
try:
self.protocol = {0xF0: PROTOCOL_89, #STC89/90C5xRC
0xF1: PROTOCOL_89, #STC89/90C5xRD+
0xF2: PROTOCOL_12Cx052, #STC12Cx052
0xD1: PROTOCOL_12C5A, #STC12C5Ax
0xD2: PROTOCOL_12C5A, #STC10Fx
0xE1: PROTOCOL_12C52, #STC12C52x
0xE2: PROTOCOL_12C5A, #STC11Fx
0xE6: PROTOCOL_12C52, #STC12C56x
0xF4: PROTOCOL_15, #STC15系列
0xF5: PROTOCOL_15, #STC15系列
0xF6: PROTOCOL_8, #STC8系列
0xF7: PROTOCOL_8, #STC8系列
}[self.model[0]]
except KeyError:
pass
if self.protocol in PROTOSET_8:
self.fosc = (dat[0]*0x1000000 +dat[1]*0x10000+dat[2]*0x100) /1000000
self.internal_vol = (dat[34]*256+dat[35])
self.wakeup_fosc = (dat[22]*256+dat[23]) /1000
self.test_year = str(hex(dat[36])).replace("0x",'')
self.test_month = str(hex(dat[37])).replace("0x",'')
self.test_day = str(hex(dat[38])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
if dat[10] == 191:
self.det_low_vol = 2.2
else:
self.det_low_vol = (191 - dat[10])*0.3 + 2.1
elif self.protocol in PROTOSET_15:
self.fosc = (dat[7]*0x1000000 +dat[8]*0x10000+dat[9]*0x100) /1000000
self.wakeup_fosc = (dat[0]*256+dat[1]) /1000
self.internal_vol = (dat[34]*256+dat[35])
self.test_year = str(hex(dat[41])).replace("0x",'')
self.test_month = str(hex(dat[42])).replace("0x",'')
self.test_day = str(hex(dat[43])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
else:
self.fosc = (float(sum(dat[0:16:2]) * 256 + sum(dat[1:16:2])) / 8
* self.conn.baudrate / 580974)
if self.protocol in PROTOSET_PARITY or self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.chkmode = 2
self.conn.parity = serial.PARITY_EVEN
else:
self.chkmode = 1
self.conn.parity = serial.PARITY_NONE
if self.protocol is not None:
del self.info[-self.chkmode:]
logging.info("Protocol ID: %s" % self.protocol)
logging.info("Checksum mode: %d" % self.chkmode)
logging.info("UART Parity: %s"
% {serial.PARITY_NONE: "NONE",
serial.PARITY_EVEN: "EVEN",
}[self.conn.parity])
for i in range(0, len(self.info), 16):
logging.info("Info string [%d]: %s"
% (i // 16,
" ".join(["%02X" % j for j in self.info[i:i+16]])))
def print_info(self):
print("系统时钟频率: %.3fMHz" % self.fosc)
if self.protocol in PROTOSET_8:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("低压检测电压: %.1f V" %self.det_low_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
if self.protocol in PROTOSET_15:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
print("单片机型号: %s" % self.name)
print("固件版本号: %s" % self.version)
if self.romsize is not None:
print("程序空间: %dKB" % self.romsize)
if self.protocol == PROTOCOL_89:
switches = [( 2, 0x80, "Reset stops "),
( 2, 0x40, "Internal XRAM"),
( 2, 0x20, "Normal ALE pin"),
( 2, 0x10, "Full gain oscillator"),
( 2, 0x08, "Not erase data EEPROM"),
( 2, 0x04, "Download regardless of P1"),
( 2, 0x01, "12T mode")]
elif self.protocol == PROTOCOL_12C5A:
switches = [( 6, 0x40, "Disable reset2 low level detect"),
( 6, 0x01, "Reset pin not use as I/O port"),
( 7, 0x80, "Disable long power-on-reset latency"),
( 7, 0x40, "Oscillator high gain"),
( 7, 0x02, "External system clock source"),
( 8, 0x20, "WDT disable after power-on-reset"),
( 8, 0x04, "WDT count in idle mode"),
(10, 0x02, "Not erase data EEPROM"),
(10, 0x01, "Download regardless of P1")]
print(" WDT prescal: %d" % 2**((self.info[8] & 0x07) + 1))
elif self.protocol in PROTOSET_12B:
switches = [(8, 0x02, "Not erase data EEPROM")]
else:
switches = []
for pos, bit, desc in switches:
print(" [%c] %s" % ("X" if self.info[pos] & bit else " ", desc))
def handshake(self):
baud0 = self.conn.baudrate
if self.protocol in PROTOSET_8:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(24.0 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(24.0 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value = 0x7B
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value = 0xB0
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value = 0x5A
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value = 0x35
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value = 0x7B
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value = 0x5A
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value = 0x1A
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value = 0x12
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value = 0x5A
else:
foc_value = 0x6B
baudstr = [0x00, 0x00, Timer1_value >> 8, Timer1_value & 0xff, 0x01, foc_value, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
elif self.protocol in PROTOSET_15:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(22.1184 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(22.1184 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value_1 = 0x40
foc_value_2 = 0x9F
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value_1 = 0x40
foc_value_2 = 0xDC
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value_1 = 0x40
foc_value_2 = 0x79
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value_1 = 0x40
foc_value_2 = 0x4F
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value_1 = 0x80
foc_value_2 = 0xA2
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value_1 = 0x80
foc_value_2 = 0x7D
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value_1 = 0x40
foc_value_2 = 0x31
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value_1 = 0xC0
foc_value_2 = 0x9f
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value_1 = 0xC0
foc_value_2 = 0x7B
baudstr = [0x6d, 0x40, Timer1_value >> 8, Timer1_value & 0xff, foc_value_1,foc_value_2, 0x81]
#baudstr = [0x6b, 0x40, 0xff,0xf4, 0x40,0x92, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
else:
for baud in [115200, 57600, 38400, 28800, 19200,
14400, 9600, 4800, 2400, 1200]:
t = self.fosc * 1000000 / baud / 32
if self.protocol not in PROTOSET_89:
t *= 2
if abs(round(t) - t) / t > 0.03:
continue
if self.protocol in PROTOSET_89:
tcfg = 0x10000 - int(t + 0.5)
else:
if t > 0xFF:
continue
tcfg = 0xC000 + 0x100 - int(t + 0.5)
baudstr = [tcfg >> 8,
tcfg & 0xFF,
0xFF - (tcfg >> 8),
min((256 - (tcfg & 0xFF)) * 2, 0xFE),
int(baud0 / 60)]
logging.info("Test baudrate %d (accuracy %0.4f) using config %s"
% (baud,
abs(round(t) - t) / t,
" ".join(["%02X" % i for i in baudstr])))
if self.protocol in PROTOSET_89:
freqlist = (40, 20, 10, 5)
else:
freqlist = (30, 24, 20, 12, 6, 3, 2, 1)
for twait in range(0, len(freqlist)):
if self.fosc > freqlist[twait]:
break
logging.info("Waiting time config %02X" % (0x80 + twait))
self.send(0x8F, baudstr + [0x80 + twait])
try:
self.__conn_baudrate(baud)
cmd, dat = self.recv()
break
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
else:
raise IOError()
logging.info("Change baudrate to %d" % baud)
self.send(0x8E, baudstr)
self.__conn_baudrate(baud)
self.baudrate = baud
cmd, dat = self.recv()
def erase(self):
if self.protocol in PROTOSET_89:
self.send(0x84, [0x01, 0x33, 0x33, 0x33, 0x33, 0x33, 0x33])
cmd, dat = self.recv(10)
assert cmd == 0x80
elif self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0x05, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
self.send(0x03, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
for i in range(7):
dat[i] = hex(dat[i])
dat[i] = str(dat[i])
dat[i] = dat[i].replace("0x",'')
if len(dat[i]) == 1:
dat_value = list(dat[i])
dat_value.insert(0, '0')
dat[i] = ''.join(dat_value)
serial_number = ""
for i in dat:
serial_number = serial_number +str(i)
self.serial_number = str(serial_number)
print("\r")
sys.stdout.write("芯片出厂序列号: ")
sys.stdout.write(self.serial_number.upper())
sys.stdout.flush()
print("\r")
else:
self.send(0x84, ([0x00, 0x00, self.romsize * 4,
0x00, 0x00, self.romsize * 4]
+ [0x00] * 12
+ [i for i in range(0x80, 0x0D, -1)]))
cmd, dat = self.recv(10)
if dat:
logging.info("Serial number: "
+ " ".join(["%02X" % j for j in dat]))
def flash(self, code):
code = list(code) + [0xff] * (511 - (len(code) - 1) % 512)
for i in range(0, len(code), 128):
logging.info("Flash code region (%04X, %04X)" % (i, i + 127))
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
flag_test = 1
addr = [i >> 8, i & 0xFF, 0x5A, 0xA5]
if flag_test == 1:
self.send(0x22, addr + code[i:i+128])
flag_test = 10
else:
self.send(0x02, addr + code[i:i+128])
else:
addr = [0, 0, i >> 8, i & 0xFF, 0, 128]
self.send(0x00, addr + code[i:i+128])
cmd, dat = self.recv()
#assert dat[0] == sum(code[i:i+128]) % 256
yield (i + 128.0) / len(code)
def options(self, **kwargs):
erase_eeprom = kwargs.get("erase_eeprom", None)
dat = []
fosc = list(bytearray(struct.pack(">I", int(self.fosc * 1000000))))
if self.protocol == PROTOCOL_89:
if erase_eeprom is not None:
self.info[2] &= 0xF7
self.info[2] |= 0x00 if erase_eeprom else 0x08
dat = self.info[2:3] + [0xFF]*3
elif self.protocol == PROTOCOL_12C5A:
if erase_eeprom is not None:
self.info[10] &= 0xFD
self.info[10] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:9] + [0xFF]*5 + self.info[10:11]
+ [0xFF]*6 + fosc)
elif self.protocol in PROTOSET_12B:
if erase_eeprom is not None:
self.info[8] &= 0xFD
self.info[8] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:11] + fosc + self.info[12:16] + [0xFF]*4
+ self.info[8:9] + [0xFF]*7 + fosc + [0xFF]*3)
elif erase_eeprom is not None:
logging.info("Modifying options is not supported for this target")
return False
if dat:
self.send(0x8D, dat)
cmd, dat = self.recv()
return True
def terminate(self):
logging.info("Send termination command")
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0xFF, [])
else:
self.send(0x82, [])
self.conn.flush()
time.sleep(0.2)
def unknown_packet_1(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (50 00 00 36 01 ...)")
self.send(0x50, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8F and not dat
def unknown_packet_2(self):
if self.protocol not in PROTOSET_PARITY and self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
for i in range(5):
logging.info("Send unknown packet (80 00 00 36 01 ...)")
self.send(0x80, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x80 and not dat
def unknown_packet_3(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (69 00 00 36 01 ...)")
self.send(0x69, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8D and not dat
def autoisp(conn, baud, magic):
if not magic:
return
bak = conn.baudrate
conn.baudrate = baud
conn.write(bytearray(ord(i) for i in magic))
conn.flush()
time.sleep(0.5)
conn.baudrate = bak
def program(prog, code, erase_eeprom=None):
sys.stdout.write("检测目标...")
sys.stdout.flush()
prog.detect()
print("完成")
prog.print_info()
if prog.protocol is None:
raise IOError("未知目标")
if code is None:
return
prog.unknown_packet_1()
sys.stdout.write("切换至最高波特率: ")
sys.stdout.flush()
prog.handshake()
print("%d bps"% prog.baudrate)
prog.unknown_packet_2()
sys.stdout.write("开始擦除芯片...")
sys.stdout.flush()
time_start = time.time()
prog.erase()
print("擦除完成")
print("代码长度: %d bytes" % len(code))
# print("Programming: ", end="", flush=True)
sys.stdout.write("正在下载用户代码...")
sys.stdout.flush()
oldbar = 0
for progress in prog.flash(code):
bar = int(progress * 25)
sys.stdout.write("#" * (bar - oldbar))
sys.stdout.flush()
oldbar = bar
print(" 完成")
prog.unknown_packet_3()
sys.stdout.write("设置选项...")
sys.stdout.flush()
if prog.options(erase_eeprom=erase_eeprom):
print("设置完成")
else:
print("设置失败")
prog.terminate()
time_end = time.time()
print("耗时: %.3fs"% (time_end-time_start))
# Convert Intel HEX code to binary format
def hex2bin(code):
buf = bytearray()
base = 0
line = 0
for rec in code.splitlines():
# Calculate the line number of the current record
line += 1
try:
# bytes(...) is to support python<=2.6
# bytearray(...) is to support python<=2.7
n = bytearray(binascii.a2b_hex(bytes(rec[1:3])))[0]
dat = bytearray(binascii.a2b_hex(bytes(rec[1:n*2+11])))
except:
raise Exception("Line %d: Invalid format" % line)
if rec[0] != ord(":"):
raise Exception("Line %d: Missing start code \":\"" % line)
if sum(dat) & 0xFF != 0:
raise Exception("Line %d: Incorrect checksum" % line)
if dat[3] == 0: # Data record
addr = base + (dat[1] << 8) + dat[2]
# Allocate memory space and fill it with 0xFF
buf[len(buf):] = [0xFF] * (addr + n - len(buf))
# Copy data to the buffer
buf[addr:addr+n] = dat[4:-1]
elif dat[3] == 1: # EOF record
if n != 0:
raise Exception("Line %d: Incorrect data length" % line)
elif dat[3] == 2: # Extended segment address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 4
elif dat[3] == 4: # Extended linear address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 16
else:
raise Exception("Line %d: Unsupported record type" % line)
return buf
def stc_type_map(type, value):
if type == 0xF6:
if value in range(0x01,0x09):
return 0xF600
elif value in range(0x21,0x29):
return 0xF620
elif value == 0x29:
return 0xF628
elif value in range(0x31,0x39):
return 0xF630
elif value == 0x39:
return 0xF638
elif value in range(0x41,0x49):
return 0xF640
elif value == 0x49:
return 0xF648
elif value in range(0x51,0x59):
return 0xF650
elif value == 0x59:
return 0xF658
elif value in range(0x61,0x67):
return 0xF660
elif value == 0x67:
return 0xF666
elif value in range(0x71,0x77):
return 0xF670
elif value == 0x77:
return 0xF676
if type == 0xF7:
if value in range(0x01,0x07):
return 0xF700
elif value in range(0x31,0x37):
return 0xF730
elif value == 0x37:
return 0xF736
elif value in range(0x41,0x43):
return 0xF740
elif value == 0x43:
return 0xF742
elif value == 0x44:
return 0xF743
elif value in range(0x49,0x4B):
return 0xF748
elif value == 0x4B:
return 0xF74A
elif value == 0x4C:
return 0xF74B
elif value in range(0x51,0x57):
return 0xF750
elif value == 0x57:
return 0xF756
elif value in range(0x61,0x63):
return 0xF760
elif value == 0x63:
return 0xF762
elif value == 0x64:
return 0xF763
elif value in range(0x69,0x6B):
return 0xF768
elif value == 0x6B:
return 0xF76A
elif value == 0x6C:
return 0xF76B
elif value in range(0x71,0x77):
return 0xF770
elif value == 0x77:
return 0xF776
elif value in range(0x81,0x83):
return 0xF780
elif value == 0x83:
return 0xF782
elif value == 0x84:
return 0xF783
elif value in range(0x91,0x97):
return 0xF790
elif value == 0x97:
return 0xF796
elif value in range(0xA1,0xA7):
return 0xF7A0
elif value == 0xA7:
return 0xF7A6
if type == 0xF4:
if value in range(0x01,0x08):
return 0xF400
elif value == 0x08:
return 0xF407
elif value == 0x09:
return 0xF408
elif value in range(0x0A,0x0D):
return 0xF409
elif value in range(0x11,0x18):
return 0xF410
elif value == 0x18:
return 0xF417
elif value == 0x19:
return 0xF418
elif value in range(0x21,0x28):
return 0xF420
elif value == 0x28:
return 0xF427
elif value == 0x29:
return 0xF428
elif value in range(0x41,0x48):
return 0xF440
elif value == 0x48:
return 0xF447
elif value == 0x49:
return 0xF448
elif value == 0x4D:
return 0xF44C
elif value in range(0x51,0x57):
return 0xF450
elif value == 0x58:
return 0xF457
elif value == 0x59:
return 0xF458
elif value in range(0x61,0x68):
return 0xF460
elif value == 0x68:
return 0xF467
elif value == 0x69:
return 0xF468
elif value in range(0x81,0x88):
return 0xF480
elif value == 0x88:
return 0xF487
elif value == 0x89:
return 0xF488
elif value in range(0x8A,0x8D):
return 0xF489
elif value in range(0x91,0x98):
return 0xF490
elif value == 0x98:
return 0xF497
elif value == 0x99:
return 0xF498
elif value in range(0xA1,0xA8):
return 0xF4A0
elif value == 0xA8:
return 0xF4A7
elif value == 0xA9:
return 0xF4A8
elif value in range(0xC1,0xC8):
return 0xF4C0
elif value == 0xC8:
return 0xF4C7
elif value == 0xC9:
return 0xF4C8
elif value == 0xCD:
return 0xF4CC
elif value in range(0xD1,0xD8):
return 0xF4D0
elif value == 0xD8:
return 0xF4D7
elif value == 0xD9:
return 0xF4D8
elif value in range(0xE1,0xE8):
return 0xF4E0
elif value == 0xE8:
return 0xF4E7
elif value == 0xE9:
return 0xF4E8
if type == 0xF5:
if value in range(0x01,0x05):
return 0xF500
elif value in range(0x08,0x0C):
return 0xF507
elif value in range(0x11,15):
return 0xF510
elif value in range(0x15,0x18):
return 0xF514
elif value in range(0x19,0x1B):
return 0xF518
elif value in range(0x1B,0x1D):
return 0xF51A
elif value in range(0x1D,0x20):
return 0xF51C
elif value == 0x20:
return 0xF51F
elif value == 0x21:
return 0xF520
elif value == 0x23:
return 0xF522
elif value == 0x24:
return 0xF523
elif value == 0x25:
return 0xF524
elif value == 0x26:
return 0xF525
elif value == 0x27:
return 0xF526
elif value == 0x28:
return 0xF527
elif value in range(0x2A,0x2C):
return 0xF529
elif value in range(0x2D,0x2F):
return 0xF52C
elif value == 0x2F:
return 0xF52E
elif value == 0x30:
return 0xF52F
elif value == 0x31:
return 0xF530
elif value == 0x32:
return 0xF531
elif value == 0x34:
return 0xF533
elif value == 0x35:
return 0xF534
elif value == 0x36:
return 0xF535
elif value == 0x45:
return 0xF544
elif value == 0x55:
return 0xF554
elif value == 0x58:
return 0xF557
elif value == 0x5D:
return 0xF55C
elif value == 0x69:
return 0xF568
elif value == 0x6A:
return 0xF569
elif value == 0x6D:
return 0xF56C
elif value in range(0x7F,0x86):
return 0xF57E
if type == 0xF2:
if value in range(0xA0,0xA6):
return 0xF2A0
def main():
if sys.platform == "win32":
port = "COM3"
elif sys.platform == "darwin":
port = "/dev/tty.usbserial"
else:
port = "/dev/ttyUSB0"
parser = argparse.ArgumentParser(
description=("Stcflash, a command line programmer for "
+ "STC 8051 microcontroller.\n"
+ "https://github.com/laborer/stcflash"))
parser.add_argument("image",
help="code image (bin/hex)",
type=argparse.FileType("rb"), nargs='?')
parser.add_argument("-p", "--port",
help="serial port device (default: %s)" % port,
default=port)
parser.add_argument("-l", "--lowbaud",
help="initial baud rate (default: 2400)",
type=int,
default=2400)
parser.add_argument("-hb", "--highbaud",
help="initial baud rate (default: 115200)",
type=int,
default=115200)
parser.add_argument("-r", "--protocol",
help="protocol to use for programming",
choices=["89", "12c5a", "12c52", "12cx052", "8", "15", "auto"],
default="auto")
parser.add_argument("-a", "--aispbaud",
help="baud rate for AutoISP (default: 4800)",
type=int,
default=4800)
parser.add_argument("-m", "--aispmagic",
help="magic word for AutoISP")
parser.add_argument("-v", "--verbose",
help="be verbose",
default=0,
action="count")
parser.add_argument("-e", "--erase_eeprom",
help=("erase data eeprom during next download"
+"(experimental)"),
action="store_true")
parser.add_argument("-ne", "--not_erase_eeprom",
help=("do not erase data eeprom next download"
+"(experimental)"),
action="store_true")
opts = parser.parse_args()
opts.loglevel = (logging.CRITICAL,
logging.INFO,
logging.DEBUG)[min(2, opts.verbose)]
opts.protocol = {'89': PROTOCOL_89,
'12c5a': PROTOCOL_12C5A,
'12c52': PROTOCOL_12C52,
'12cx052': PROTOCOL_12Cx052,
'8': PROTOCOL_8,
'15': PROTOCOL_15,
'auto': None}[opts.protocol]
if not opts.erase_eeprom and not opts.not_erase_eeprom:
opts.erase_eeprom = None
logging.basicConfig(format=("%(levelname)s: "
+ "[%(relativeCreated)d] "
+ "%(message)s"),
level=opts.loglevel)
if opts.image:
code = bytearray(opts.image.read())
opts.image.close()
if os.path.splitext(opts.image.name)[1] in (".hex", ".ihx"):
code = hex2bin(code)
else:
code = None
print("通信端口：%s 最低波特率：%d bps" % (opts.port, opts.lowbaud))
global highbaud_pre
highbaud_pre = opts.highbaud
with serial.Serial(port=opts.port,
baudrate=opts.lowbaud,
parity=serial.PARITY_NONE) as conn:
if opts.aispmagic:
autoisp(conn, opts.aispbaud, opts.aispmagic)
program(Programmer(conn, opts.protocol), code, opts.erase_eeprom)
if __name__ == "__main__":
main()
|
2301_77966824/51-microcontroller-learning
|
2024/10/24241016_01_HelloWorld-eide/HelloWorld-eide/tools/stcflash.py
|
Python
|
unknown
| 57,810
|
#include "Com_Util.h"
#include <INTRINS.H>
void Com_Util_Delay1ms(u16 count)
{
u8 i,j;
while (count>0)
{
count--;
_nop_();
i=2;
j=199;
do
{
while(--j);
} while (--i);
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/10/Hello-template/src/Com/Com_Util.c
|
C
|
unknown
| 198
|
#include <Int_digitalTube.h>
void main()
{
while(1)
{
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/10/Hello-template/src/main.c
|
C
|
unknown
| 60
|
#!/usr/bin/env python
#coding=utf-8
# stcflash Copyright (C) 2013 laborer (laborer@126.com)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import time
import logging
import sys
import serial
import os.path
import binascii
import struct
import argparse
PROTOCOL_89 = "89"
PROTOCOL_12C5A = "12c5a"
PROTOCOL_12C52 = "12c52"
PROTOCOL_12Cx052 = "12cx052"
PROTOCOL_8 = "8"
PROTOCOL_15 = '15'
PROTOSET_89 = [PROTOCOL_89]
PROTOSET_12 = [PROTOCOL_12C5A, PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_12B = [PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_8 = [PROTOCOL_8]
PROTOSET_15 = [PROTOCOL_15]
PROTOSET_PARITY = [PROTOCOL_12C5A, PROTOCOL_12C52]
class Programmer:
def __init__(self, conn, protocol=None):
self.conn = conn
self.protocol = protocol
self.conn.timeout = 0.05
if self.protocol in PROTOSET_PARITY:
self.conn.parity = serial.PARITY_EVEN
else:
self.conn.parity = serial.PARITY_NONE
self.chkmode = 0
def __conn_read(self, size):
buf = bytearray()
while len(buf) < size:
s = bytearray(self.conn.read(size - len(buf)))
buf += s
logging.debug("recv: " + " ".join(["%02X" % i for i in s]))
if len(s) == 0:
raise IOError()
return list(buf)
def __conn_write(self, s):
logging.debug("send: " + " ".join(["%02X" % i for i in s]))
self.conn.write(bytearray(s))
def __conn_baudrate(self, baud, flush=True):
logging.debug("baud: %d" % baud)
if flush:
if self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
self.conn.flush()
time.sleep(0.2)
self.conn.baudrate = baud
def __model_database(self, model):
modelmap = {0xE0: ("12", 1, {(0x00, 0x1F): ("C54", ""),
(0x60, 0x7F): ("C54", "AD"),
(0x80, 0x9F): ("LE54", ""),
(0xE0, 0xFF): ("LE54", "AD"),
}),
0xE1: ("12", 1, {(0x00, 0x1F): ("C52", ""),
(0x20, 0x3F): ("C52", "PWM"),
(0x60, 0x7F): ("C52", "AD"),
(0x80, 0x9F): ("LE52", ""),
(0xA0, 0xBF): ("LE52", "PWM"),
(0xE0, 0xFF): ("LE52", "AD"),
}),
0xE2: ("11", 1, {(0x00, 0x1F): ("F", ""),
(0x20, 0x3F): ("F", "E"),
(0x70, 0x7F): ("F", ""),
(0x80, 0x9F): ("L", ""),
(0xA0, 0xBF): ("L", "E"),
(0xF0, 0xFF): ("L", ""),
}),
0xE6: ("12", 1, {(0x00, 0x1F): ("C56", ""),
(0x60, 0x7F): ("C56", "AD"),
(0x80, 0x9F): ("LE56", ""),
(0xE0, 0xFF): ("LE56", "AD"),
}),
0xD1: ("12", 2, {(0x20, 0x3F): ("C5A", "CCP"),
(0x40, 0x5F): ("C5A", "AD"),
(0x60, 0x7F): ("C5A", "S2"),
(0xA0, 0xBF): ("LE5A", "CCP"),
(0xC0, 0xDF): ("LE5A", "AD"),
(0xE0, 0xFF): ("LE5A", "S2"),
}),
0xD2: ("10", 1, {(0x00, 0x0F): ("F", ""),
(0x60, 0x6F): ("F", "XE"),
(0x70, 0x7F): ("F", "X"),
(0xA0, 0xAF): ("L", ""),
(0xE0, 0xEF): ("L", "XE"),
(0xF0, 0xFF): ("L", "X"),
}),
0xD3: ("11", 2, {(0x00, 0x1F): ("F", ""),
(0x40, 0x5F): ("F", "X"),
(0x60, 0x7F): ("F", "XE"),
(0xA0, 0xBF): ("L", ""),
(0xC0, 0xDF): ("L", "X"),
(0xE0, 0xFF): ("L", "XE"),
}),
0xF0: ("89", 4, {(0x00, 0x10): ("C5", "RC"),
(0x20, 0x30): ("C5", "RC"), #STC90C5xRC
}),
0xF1: ("89", 4, {(0x00, 0x10): ("C5", "RD+"),
(0x20, 0x30): ("C5", "RD+"), #STC90C5xRD+
}),
0xF2: ("12", 1, {(0x00, 0x0F): ("C", "052"),
(0x10, 0x1F): ("C", "052AD"),
(0x20, 0x2F): ("LE", "052"),
(0x30, 0x3F): ("LE", "052AD"),
}),
0xF2A0: ("15W", 1, {(0xA0, 0xA5): ("1", ""), #STC15W1系列
}),
0xF400: ("15F", 8, {(0x00, 0x07): ("2K", "S2"), #STC15F2K系列
}),
0xF407: ("15F", 60, {(0x07, 0x08): ("2K", "S2"), #STC15F2K系列
}),
0xF408: ("15F", 61, {(0x08, 0x09): ("2K", "S2"), #STC15F2K系列
}),
0xF400: ("15F", 4, {(0x09, 0x0C): ("4", "AD"), #STC15FAD系列
}),
0xF410: ("15F", 8, {(0x10, 0x17): ("1K", "AS"), #STC15F1KAS系列
}),
0xF417: ("15F", 60, {(0x17, 0x18): ("1K", "AS"), #STC15F1KAS系列
}),
0xF418: ("15F", 61, {(0x18, 0x19): ("1K", "AS"), #STC15F1KAS系列
}),
0xF420: ("15F", 8, {(0x20, 0x27): ("1K", "S"), #STC15F1KS系列
}),
0xF427: ("15F", 60, {(0x27, 0x28): ("1K", "S"), #STC15F1KS系列
}),
0xF440: ("15F", 8, {(0x40, 0x47): ("1K", "S2"), #STC15F1KS2系列
}),
0xF447: ("15F", 60, {(0x47, 0x48): ("1K", "S2"), #STC15F1KS2系列
}),
0xF448: ("15F", 61, {(0x48, 0x49): ("1K", "S2"), #STC15F1KS2系列
}),
0xF44C: ("15F", 13, {(0x4C, 0x4D): ("4", "AD"),
}),
0xF450: ("15F", 8, {(0x50, 0x57): ("1K", "AS"), #STC15F1KAS系列
}),
0xF457: ("15F", 60, {(0x57, 0x58): ("1K", "AS"), #STC15F1KAS系列
}),
0xF458: ("15F", 61, {(0x58, 0x59): ("1K", "AS"), #STC15F1KAS系列
}),
0xF460: ("15F", 8, {(0x60, 0x67): ("1K", "S"), #STC15F1KS系列
}),
0xF467: ("15F", 60, {(0x67, 0x68): ("1K", "S"), #STC15F1KS系列
}),
0xF468: ("15F", 61, {(0x68, 0x69): ("1K", "S"), #STC15F1KS系列
}),
0xF480: ("15L", 8, {(0x80, 0x87): ("2K", "S2"), #STC15L2KS2系列
}),
0xF487: ("15L", 60, {(0x87, 0x88): ("2K", "S2"), #STC15L2KS2系列
}),
0xF488: ("15L", 61, {(0x88, 0x89): ("2K", "S2"), #STC15L2KS2系列
}),
0xF489: ("15L", 5, {(0x89, 0x8C): ("4", "AD"), #STC15L4AD系列
}),
0xF490: ("15L", 8, {(0x90, 0x97): ("2K", "AS"), #STC15L2KAS系列
}),
0xF497: ("15L", 60, {(0x97, 0x98): ("2K", "AS"), #STC15L2KAS系列
}),
0xF498: ("15L", 61, {(0x98, 0x99): ("2K", "AS"), #STC15L2KAS系列
}),
0xF4A0: ("15L", 8, {(0xA0, 0xA7): ("2K", "S"), #STC15L2KS系列
}),
0xF4A7: ("15L", 60, {(0xA7, 0xA8): ("2K", "S"), #STC15L2KS系列
}),
0xF4A8: ("15L", 61, {(0xA8, 0xA9): ("2K", "S"), #STC15L2KS系列
}),
0xF4C0: ("15L", 8, {(0xC0, 0xC7): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C7: ("15L", 60, {(0xC7, 0xC8): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C8: ("15L", 61, {(0xC8, 0xC9): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4CC: ("15L", 13, {(0xCC, 0xCD): ("4", "AD"),
}),
0xF4D0: ("15L", 8, {(0xD0, 0xD7): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D7: ("15L", 60, {(0xD7, 0xD8): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D8: ("15L", 61, {(0xD8, 0xD9): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4E0: ("15L", 8, {(0xE0, 0xE7): ("1K", "S"), #STC15L1KS系列
}),
0xF4E7: ("15L", 60, {(0xE7, 0xE8): ("1K", "S"), #STC15L1KS系列
}),
0xF4E8: ("15L", 61, {(0xE8, 0xE9): ("1K", "S"), #STC15L1KS系列
}),
0xF500: ("15W", 1, {(0x00, 0x04): ("1", "SW"), #STC15W1SW系列
}),
0xF507: ("15W", 1, {(0x07, 0x0B): ("1", "S"), #STC15W1S系列
}),
0xF510: ("15W", 1, {(0x10, 0x14): ("2", "S"), #STC15W2S系列
}),
0xF514: ("15W", 8, {(0x14, 0x17): ("1K", "S"), #STC15W1KS系列
}),
0xF518: ("15W", 4, {(0x18, 0x1A): ("4", "S"), #STC15W4S系列
}),
0xF51A: ("15W", 4, {(0x1A, 0x1C): ("4", "S"), #STC15W4S系列
}),
0xF51C: ("15W", 4, {(0x1C, 0x1F): ("4", "AS"), #STC15W4AS系列
}),
0xF51F: ("15W", 10, {(0x19, 0x20): ("4", "AS"), #STC15W4AS系列
}),
0xF520: ("15W", 12, {(0x20, 0x21): ("4", "AS"), #STC15W4AS系列
}),
0xF522: ("15W", 16, {(0x22, 0x23): ("4K", "S4"), #STC15W4KS4系列
}),
0xF523: ("15W",24, {(0x23, 0x24): ("4K", "S4"), #STC15W4KS4系列
}),
0xF524: ("15W", 32, {(0x24, 0x25): ("4K", "S4"), #STC15W4KS4系列
}),
0xF525: ("15W", 40, {(0x25, 0x26): ("4K", "S4"), #STC15W4KS4系列
}),
0xF526: ("15W", 48, {(0x26, 0x27): ("4K", "S4"), #STC15W4KS4系列
}),
0xF527: ("15W", 56, {(0x27, 0x28): ("4K", "S4"), #STC15W4KS4系列
}),
0xF529: ("15W", 1, {(0x29, 0x2B): ("4", "A4"), #STC15W4AS系列
}),
0xF52C: ("15W", 8, {(0x2C, 0x2E): ("1K", "PWM"), #STC15W1KPWM系列
}),
0xF52E: ("15W", 20, {(0x2E, 0x2F): ("1K", "S"), #STC15W1KS系列
}),
0xF52F: ("15W", 32, {(0x2F, 0x30): ("2K", "S2"), #STC15W2KS2系列
}),
0xF530: ("15W", 48, {(0x30, 0x31): ("2K", "S2"), #STC15W2KS2系列
}),
0xF531: ("15W", 32, {(0x31, 0x32): ("2K", "S2"), #STC15W2KS2系列
}),
0xF533: ("15W", 20, {(0x33, 0x34): ("1K", "S2"), #STC15W1KS2系列
}),
0xF534: ("15W", 32, {(0x34, 0x35): ("1K", "S2"), #STC15W1KS2系列
}),
0xF535: ("15W", 48, {(0x35, 0x36): ("1K", "S2"), #STC15W1KS2系列
}),
0xF544: ("15W", 5, {(0x44, 0x45): ("", "SW"), #STC15SW系列
}),
0xF554: ("15W", 5, {(0x54, 0x55): ("2", "S"), #STC15W2S系列
}),
0xF557: ("15W", 29, {(0x57, 0x58): ("1K", "S"), #STC15W1KS系列
}),
0xF55C: ("15W", 13, {(0x5C, 0x5D): ("4", "S"), #STC15W4S系列
}),
0xF568: ("15W", 58, {(0x68, 0x69): ("4K", "S4"), #STC15W4KS4系列
}),
0xF569: ("15W", 61, {(0x69, 0x6A): ("4K", "S4"), #STC15W4KS4系列
}),
0xF56C: ("15W", 58, {(0x6C, 0x6D): ("4K", "S4-Student"), #STC15W4KS4系列
}),
0xF57E: ("15U", 8, {(0x7E, 0x85): ("4K", "S4"), #STC15U4KS4系列
}),
0xF600: ("15H", 8, {(0x00, 0x08): ("4K", "S4"), #STC154K系列
}),
0xF620: ("8A", 8, {(0x20, 0x28): ("8K", "S4A12"), #STC8A8K系列
}),
0xF628: ("8A", 60, {(0x28, 0x29): ("8K", "S4A12"), #STC8A8K系列
}),
0xF630: ("8F", 8, {(0x30, 0x38): ("2K", "S4"), #STC8F2K系列
}),
0xF638: ("8F", 60, {(0x38, 0x39): ("2K", "S4"), #STC8F2K系列
}),
0xF640: ("8F", 8, {(0x40, 0x48): ("2K", "S2"), #STC8F2K系列
}),
0xF648: ("8F", 60, {(0x48, 0x49): ("2K", "S2"), #STC8F2K系列
}),
0xF650: ("8A", 8, {(0x50, 0x58): ("4K", "S2A12"), #STC8A4K系列
}),
0xF658: ("8A", 60, {(0x58, 0x59): ("4K", "S2A12"), #STC8A4K系列
}),
0xF660: ("8F", 2, {(0x60, 0x66): ("1K", "S2"), #STC8F1K系列
}),
0xF666: ("8F", 17, {(0x66, 0x67): ("1K", "S2"), #STC8F1K系列
}),
0xF670: ("8F", 2, {(0x70, 0x76): ("1K", ""), #STC8F1K系列
}),
0xF676: ("8F", 17, {(0x76, 0x77): ("1K", ""), #STC8F1K系列
}),
0xF700: ("8C", 2, {(0x00, 0x06): ("1K", ""), #STC8C系列
}),
0xF730: ("8H", 2, {(0x30, 0x36): ("1K", ""), #STC8H1K系列
}),
0xF736: ("8H", 17, {(0x36, 0x37): ("1K", ""), #STC8H1K系列
}),
0xF740: ("8H", 8, {(0x40, 0x42): ("3K", "S4"), #STC8H3K系列
}),
0xF742: ("8H", 60, {(0x42, 0x43): ("3K", "S4"), #STC8H3K系列
}),
0xF743: ("8H", 64, {(0x43, 0x44): ("3K", "S4"), #STC8H3K系列
}),
0xF748: ("8H", 16, {(0x48, 0x4A): ("3K", "S2"), #STC8H3K系列
}),
0xF74A: ("8H", 60, {(0x4A, 0x4B): ("3K", "S2"), #STC8H3K系列
}),
0xF74B: ("8H", 64, {(0x4B, 0x4C): ("3K", "S2"), #STC8H3K系列
}),
0xF750: ("8G", 2, {(0x50, 0x56): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF756: ("8G", 17, {(0x56, 0x57): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF760: ("8G", 16, {(0x60, 0x62): ("2K", "S4"), #STC8G2K系列
}),
0xF762: ("8G", 60, {(0x62, 0x63): ("2K", "S4"), #STC8G2K系列
}),
0xF763: ("8G", 64, {(0x63, 0x64): ("2K", "S4"), #STC8G2K系列
}),
0xF768: ("8G", 16, {(0x68, 0x6A): ("2K", "S2"), #STC8G2K系列
}),
0xF76A: ("8G", 60, {(0x6A, 0x6B): ("2K", "S2"), #STC8G2K系列
}),
0xF76B: ("8G", 64, {(0x6B, 0x6C): ("2K", "S2"), #STC8G2K系列
}),
0xF770: ("8G", 2, {(0x70, 0x76): ("1K", "T"), #STC8G2K系列
}),
0xF776: ("8G", 17, {(0x76, 0x77): ("1K", "T"), #STC8G2K系列
}),
0xF780: ("8H", 16, {(0x80, 0x82): ("8K", "U"), #STC8H8K系列
}),
0xF782: ("8H", 60, {(0x82, 0x83): ("8K", "U"), #STC8H8K系列
}),
0xF783: ("8H", 64, {(0x83, 0x84): ("8K", "U"), #STC8H8K系列
}),
0xF790: ("8G", 2, {(0x90, 0x96): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF796: ("8G", 17, {(0x96, 0x97): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF7A0: ("8G", 2, {(0xA0, 0xA6): ("1K", "-8PIN"), #STC8G1K系列
}),
0xF7A6: ("8G", 17, {(0xA6, 0xA7): ("1K", "-8PIN"), #STC8G1K系列
}),
}
iapmcu = ((0xD1, 0x3F), (0xD1, 0x5F), (0xD1, 0x7F), (0xF4, 0x4D), (0xF4, 0x99), (0xF4, 0xD9), (0xF5, 0x58),
(0xD2, 0x7E), (0xD2, 0xFE), (0xF4, 0x09), (0xF4, 0x59), (0xF4, 0xA9), (0xF4, 0xE9), (0xF5, 0x5D),
(0xD3, 0x5F), (0xD3, 0xDF), (0xF4, 0x19), (0xF4, 0x69), (0xF4, 0xC9), (0xF5, 0x45), (0xF5, 0x62),
(0xE2, 0x76), (0xE2, 0xF6), (0xF4, 0x49), (0xF4, 0x89), (0xF4, 0xCD), (0xF5, 0x55), (0xF5, 0x69),
(0xF5, 0x6A), (0xF5, 0x6D),
)
try:
model = tuple(model)
if self.model[0] in [0xF4, 0xF5, 0xF6, 0xF7]:
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
elif self.model[0] == 0xF2 and self.model[1] in range(0xA0, 0xA6):
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
self.protocol = PROTOCOL_15
else:
prefix, romratio, fixmap = modelmap[model[0]]
if model[0] in (0xF0, 0xF1) and 0x20 <= model[1] <= 0x30:
prefix = "90"
for key, value in fixmap.items():
if key[0] <= model[1] <= key[1]:
break
else:
raise KeyError()
infix, postfix = value
romsize = romratio * (model[1] - key[0])
try:
romsize = {(0xF0, 0x03): 13}[model]
except KeyError:
pass
if model[0] in (0xF0, 0xF1):
romfix = str(model[1] - key[0])
elif model[0] in (0xF2,):
romfix = str(romsize)
else:
romfix = "%02d" % romsize
name = "IAP" if model in iapmcu else "STC"
name += prefix + infix + romfix + postfix
return (name, romsize)
except KeyError:
return ("Unknown %02X %02X" % model, None)
def recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def first_recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
time.sleep(0.02) #加上20ms延时，增大接收成功率
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def send(self, cmd, dat):
buf = [0x46, 0xB9, 0x6A]
n = 1 + 2 + 1 + len(dat) + self.chkmode + 1
buf += [n >> 8, n & 0xFF, cmd]
buf += dat
chksum = sum(buf[2:])
if self.chkmode > 1:
buf += [(chksum >> 8) & 0xFF]
buf += [chksum & 0xFF, 0x16]
self.__conn_write(buf)
def detect(self):
for i in range(500):
try:
if self.protocol in [PROTOCOL_89,PROTOCOL_12C52,PROTOCOL_12Cx052,PROTOCOL_12C5A]:
self.__conn_write([0x7F,0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
else:
self.__conn_write([0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
break
except IOError:
pass
else:
raise IOError()
self.info = dat[16:]
self.version = "%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[1])
self.model = self.info[3:5]
self.name, self.romsize = self.__model_database(self.model)
logging.info("Model ID: %02X %02X" % tuple(self.model))
logging.info("Model name: %s" % self.name)
logging.info("ROM size: %s" % self.romsize)
if self.protocol is None:
try:
self.protocol = {0xF0: PROTOCOL_89, #STC89/90C5xRC
0xF1: PROTOCOL_89, #STC89/90C5xRD+
0xF2: PROTOCOL_12Cx052, #STC12Cx052
0xD1: PROTOCOL_12C5A, #STC12C5Ax
0xD2: PROTOCOL_12C5A, #STC10Fx
0xE1: PROTOCOL_12C52, #STC12C52x
0xE2: PROTOCOL_12C5A, #STC11Fx
0xE6: PROTOCOL_12C52, #STC12C56x
0xF4: PROTOCOL_15, #STC15系列
0xF5: PROTOCOL_15, #STC15系列
0xF6: PROTOCOL_8, #STC8系列
0xF7: PROTOCOL_8, #STC8系列
}[self.model[0]]
except KeyError:
pass
if self.protocol in PROTOSET_8:
self.fosc = (dat[0]*0x1000000 +dat[1]*0x10000+dat[2]*0x100) /1000000
self.internal_vol = (dat[34]*256+dat[35])
self.wakeup_fosc = (dat[22]*256+dat[23]) /1000
self.test_year = str(hex(dat[36])).replace("0x",'')
self.test_month = str(hex(dat[37])).replace("0x",'')
self.test_day = str(hex(dat[38])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
if dat[10] == 191:
self.det_low_vol = 2.2
else:
self.det_low_vol = (191 - dat[10])*0.3 + 2.1
elif self.protocol in PROTOSET_15:
self.fosc = (dat[7]*0x1000000 +dat[8]*0x10000+dat[9]*0x100) /1000000
self.wakeup_fosc = (dat[0]*256+dat[1]) /1000
self.internal_vol = (dat[34]*256+dat[35])
self.test_year = str(hex(dat[41])).replace("0x",'')
self.test_month = str(hex(dat[42])).replace("0x",'')
self.test_day = str(hex(dat[43])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
else:
self.fosc = (float(sum(dat[0:16:2]) * 256 + sum(dat[1:16:2])) / 8
* self.conn.baudrate / 580974)
if self.protocol in PROTOSET_PARITY or self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.chkmode = 2
self.conn.parity = serial.PARITY_EVEN
else:
self.chkmode = 1
self.conn.parity = serial.PARITY_NONE
if self.protocol is not None:
del self.info[-self.chkmode:]
logging.info("Protocol ID: %s" % self.protocol)
logging.info("Checksum mode: %d" % self.chkmode)
logging.info("UART Parity: %s"
% {serial.PARITY_NONE: "NONE",
serial.PARITY_EVEN: "EVEN",
}[self.conn.parity])
for i in range(0, len(self.info), 16):
logging.info("Info string [%d]: %s"
% (i // 16,
" ".join(["%02X" % j for j in self.info[i:i+16]])))
def print_info(self):
print("系统时钟频率: %.3fMHz" % self.fosc)
if self.protocol in PROTOSET_8:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("低压检测电压: %.1f V" %self.det_low_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
if self.protocol in PROTOSET_15:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
print("单片机型号: %s" % self.name)
print("固件版本号: %s" % self.version)
if self.romsize is not None:
print("程序空间: %dKB" % self.romsize)
if self.protocol == PROTOCOL_89:
switches = [( 2, 0x80, "Reset stops "),
( 2, 0x40, "Internal XRAM"),
( 2, 0x20, "Normal ALE pin"),
( 2, 0x10, "Full gain oscillator"),
( 2, 0x08, "Not erase data EEPROM"),
( 2, 0x04, "Download regardless of P1"),
( 2, 0x01, "12T mode")]
elif self.protocol == PROTOCOL_12C5A:
switches = [( 6, 0x40, "Disable reset2 low level detect"),
( 6, 0x01, "Reset pin not use as I/O port"),
( 7, 0x80, "Disable long power-on-reset latency"),
( 7, 0x40, "Oscillator high gain"),
( 7, 0x02, "External system clock source"),
( 8, 0x20, "WDT disable after power-on-reset"),
( 8, 0x04, "WDT count in idle mode"),
(10, 0x02, "Not erase data EEPROM"),
(10, 0x01, "Download regardless of P1")]
print(" WDT prescal: %d" % 2**((self.info[8] & 0x07) + 1))
elif self.protocol in PROTOSET_12B:
switches = [(8, 0x02, "Not erase data EEPROM")]
else:
switches = []
for pos, bit, desc in switches:
print(" [%c] %s" % ("X" if self.info[pos] & bit else " ", desc))
def handshake(self):
baud0 = self.conn.baudrate
if self.protocol in PROTOSET_8:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(24.0 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(24.0 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value = 0x7B
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value = 0xB0
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value = 0x5A
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value = 0x35
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value = 0x7B
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value = 0x5A
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value = 0x1A
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value = 0x12
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value = 0x5A
else:
foc_value = 0x6B
baudstr = [0x00, 0x00, Timer1_value >> 8, Timer1_value & 0xff, 0x01, foc_value, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
elif self.protocol in PROTOSET_15:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(22.1184 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(22.1184 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value_1 = 0x40
foc_value_2 = 0x9F
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value_1 = 0x40
foc_value_2 = 0xDC
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value_1 = 0x40
foc_value_2 = 0x79
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value_1 = 0x40
foc_value_2 = 0x4F
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value_1 = 0x80
foc_value_2 = 0xA2
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value_1 = 0x80
foc_value_2 = 0x7D
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value_1 = 0x40
foc_value_2 = 0x31
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value_1 = 0xC0
foc_value_2 = 0x9f
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value_1 = 0xC0
foc_value_2 = 0x7B
baudstr = [0x6d, 0x40, Timer1_value >> 8, Timer1_value & 0xff, foc_value_1,foc_value_2, 0x81]
#baudstr = [0x6b, 0x40, 0xff,0xf4, 0x40,0x92, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
else:
for baud in [115200, 57600, 38400, 28800, 19200,
14400, 9600, 4800, 2400, 1200]:
t = self.fosc * 1000000 / baud / 32
if self.protocol not in PROTOSET_89:
t *= 2
if abs(round(t) - t) / t > 0.03:
continue
if self.protocol in PROTOSET_89:
tcfg = 0x10000 - int(t + 0.5)
else:
if t > 0xFF:
continue
tcfg = 0xC000 + 0x100 - int(t + 0.5)
baudstr = [tcfg >> 8,
tcfg & 0xFF,
0xFF - (tcfg >> 8),
min((256 - (tcfg & 0xFF)) * 2, 0xFE),
int(baud0 / 60)]
logging.info("Test baudrate %d (accuracy %0.4f) using config %s"
% (baud,
abs(round(t) - t) / t,
" ".join(["%02X" % i for i in baudstr])))
if self.protocol in PROTOSET_89:
freqlist = (40, 20, 10, 5)
else:
freqlist = (30, 24, 20, 12, 6, 3, 2, 1)
for twait in range(0, len(freqlist)):
if self.fosc > freqlist[twait]:
break
logging.info("Waiting time config %02X" % (0x80 + twait))
self.send(0x8F, baudstr + [0x80 + twait])
try:
self.__conn_baudrate(baud)
cmd, dat = self.recv()
break
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
else:
raise IOError()
logging.info("Change baudrate to %d" % baud)
self.send(0x8E, baudstr)
self.__conn_baudrate(baud)
self.baudrate = baud
cmd, dat = self.recv()
def erase(self):
if self.protocol in PROTOSET_89:
self.send(0x84, [0x01, 0x33, 0x33, 0x33, 0x33, 0x33, 0x33])
cmd, dat = self.recv(10)
assert cmd == 0x80
elif self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0x05, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
self.send(0x03, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
for i in range(7):
dat[i] = hex(dat[i])
dat[i] = str(dat[i])
dat[i] = dat[i].replace("0x",'')
if len(dat[i]) == 1:
dat_value = list(dat[i])
dat_value.insert(0, '0')
dat[i] = ''.join(dat_value)
serial_number = ""
for i in dat:
serial_number = serial_number +str(i)
self.serial_number = str(serial_number)
print("\r")
sys.stdout.write("芯片出厂序列号: ")
sys.stdout.write(self.serial_number.upper())
sys.stdout.flush()
print("\r")
else:
self.send(0x84, ([0x00, 0x00, self.romsize * 4,
0x00, 0x00, self.romsize * 4]
+ [0x00] * 12
+ [i for i in range(0x80, 0x0D, -1)]))
cmd, dat = self.recv(10)
if dat:
logging.info("Serial number: "
+ " ".join(["%02X" % j for j in dat]))
def flash(self, code):
code = list(code) + [0xff] * (511 - (len(code) - 1) % 512)
for i in range(0, len(code), 128):
logging.info("Flash code region (%04X, %04X)" % (i, i + 127))
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
flag_test = 1
addr = [i >> 8, i & 0xFF, 0x5A, 0xA5]
if flag_test == 1:
self.send(0x22, addr + code[i:i+128])
flag_test = 10
else:
self.send(0x02, addr + code[i:i+128])
else:
addr = [0, 0, i >> 8, i & 0xFF, 0, 128]
self.send(0x00, addr + code[i:i+128])
cmd, dat = self.recv()
#assert dat[0] == sum(code[i:i+128]) % 256
yield (i + 128.0) / len(code)
def options(self, **kwargs):
erase_eeprom = kwargs.get("erase_eeprom", None)
dat = []
fosc = list(bytearray(struct.pack(">I", int(self.fosc * 1000000))))
if self.protocol == PROTOCOL_89:
if erase_eeprom is not None:
self.info[2] &= 0xF7
self.info[2] |= 0x00 if erase_eeprom else 0x08
dat = self.info[2:3] + [0xFF]*3
elif self.protocol == PROTOCOL_12C5A:
if erase_eeprom is not None:
self.info[10] &= 0xFD
self.info[10] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:9] + [0xFF]*5 + self.info[10:11]
+ [0xFF]*6 + fosc)
elif self.protocol in PROTOSET_12B:
if erase_eeprom is not None:
self.info[8] &= 0xFD
self.info[8] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:11] + fosc + self.info[12:16] + [0xFF]*4
+ self.info[8:9] + [0xFF]*7 + fosc + [0xFF]*3)
elif erase_eeprom is not None:
logging.info("Modifying options is not supported for this target")
return False
if dat:
self.send(0x8D, dat)
cmd, dat = self.recv()
return True
def terminate(self):
logging.info("Send termination command")
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0xFF, [])
else:
self.send(0x82, [])
self.conn.flush()
time.sleep(0.2)
def unknown_packet_1(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (50 00 00 36 01 ...)")
self.send(0x50, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8F and not dat
def unknown_packet_2(self):
if self.protocol not in PROTOSET_PARITY and self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
for i in range(5):
logging.info("Send unknown packet (80 00 00 36 01 ...)")
self.send(0x80, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x80 and not dat
def unknown_packet_3(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (69 00 00 36 01 ...)")
self.send(0x69, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8D and not dat
def autoisp(conn, baud, magic):
if not magic:
return
bak = conn.baudrate
conn.baudrate = baud
conn.write(bytearray(ord(i) for i in magic))
conn.flush()
time.sleep(0.5)
conn.baudrate = bak
def program(prog, code, erase_eeprom=None):
sys.stdout.write("检测目标...")
sys.stdout.flush()
prog.detect()
print("完成")
prog.print_info()
if prog.protocol is None:
raise IOError("未知目标")
if code is None:
return
prog.unknown_packet_1()
sys.stdout.write("切换至最高波特率: ")
sys.stdout.flush()
prog.handshake()
print("%d bps"% prog.baudrate)
prog.unknown_packet_2()
sys.stdout.write("开始擦除芯片...")
sys.stdout.flush()
time_start = time.time()
prog.erase()
print("擦除完成")
print("代码长度: %d bytes" % len(code))
# print("Programming: ", end="", flush=True)
sys.stdout.write("正在下载用户代码...")
sys.stdout.flush()
oldbar = 0
for progress in prog.flash(code):
bar = int(progress * 25)
sys.stdout.write("#" * (bar - oldbar))
sys.stdout.flush()
oldbar = bar
print(" 完成")
prog.unknown_packet_3()
sys.stdout.write("设置选项...")
sys.stdout.flush()
if prog.options(erase_eeprom=erase_eeprom):
print("设置完成")
else:
print("设置失败")
prog.terminate()
time_end = time.time()
print("耗时: %.3fs"% (time_end-time_start))
# Convert Intel HEX code to binary format
def hex2bin(code):
buf = bytearray()
base = 0
line = 0
for rec in code.splitlines():
# Calculate the line number of the current record
line += 1
try:
# bytes(...) is to support python<=2.6
# bytearray(...) is to support python<=2.7
n = bytearray(binascii.a2b_hex(bytes(rec[1:3])))[0]
dat = bytearray(binascii.a2b_hex(bytes(rec[1:n*2+11])))
except:
raise Exception("Line %d: Invalid format" % line)
if rec[0] != ord(":"):
raise Exception("Line %d: Missing start code \":\"" % line)
if sum(dat) & 0xFF != 0:
raise Exception("Line %d: Incorrect checksum" % line)
if dat[3] == 0: # Data record
addr = base + (dat[1] << 8) + dat[2]
# Allocate memory space and fill it with 0xFF
buf[len(buf):] = [0xFF] * (addr + n - len(buf))
# Copy data to the buffer
buf[addr:addr+n] = dat[4:-1]
elif dat[3] == 1: # EOF record
if n != 0:
raise Exception("Line %d: Incorrect data length" % line)
elif dat[3] == 2: # Extended segment address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 4
elif dat[3] == 4: # Extended linear address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 16
else:
raise Exception("Line %d: Unsupported record type" % line)
return buf
def stc_type_map(type, value):
if type == 0xF6:
if value in range(0x01,0x09):
return 0xF600
elif value in range(0x21,0x29):
return 0xF620
elif value == 0x29:
return 0xF628
elif value in range(0x31,0x39):
return 0xF630
elif value == 0x39:
return 0xF638
elif value in range(0x41,0x49):
return 0xF640
elif value == 0x49:
return 0xF648
elif value in range(0x51,0x59):
return 0xF650
elif value == 0x59:
return 0xF658
elif value in range(0x61,0x67):
return 0xF660
elif value == 0x67:
return 0xF666
elif value in range(0x71,0x77):
return 0xF670
elif value == 0x77:
return 0xF676
if type == 0xF7:
if value in range(0x01,0x07):
return 0xF700
elif value in range(0x31,0x37):
return 0xF730
elif value == 0x37:
return 0xF736
elif value in range(0x41,0x43):
return 0xF740
elif value == 0x43:
return 0xF742
elif value == 0x44:
return 0xF743
elif value in range(0x49,0x4B):
return 0xF748
elif value == 0x4B:
return 0xF74A
elif value == 0x4C:
return 0xF74B
elif value in range(0x51,0x57):
return 0xF750
elif value == 0x57:
return 0xF756
elif value in range(0x61,0x63):
return 0xF760
elif value == 0x63:
return 0xF762
elif value == 0x64:
return 0xF763
elif value in range(0x69,0x6B):
return 0xF768
elif value == 0x6B:
return 0xF76A
elif value == 0x6C:
return 0xF76B
elif value in range(0x71,0x77):
return 0xF770
elif value == 0x77:
return 0xF776
elif value in range(0x81,0x83):
return 0xF780
elif value == 0x83:
return 0xF782
elif value == 0x84:
return 0xF783
elif value in range(0x91,0x97):
return 0xF790
elif value == 0x97:
return 0xF796
elif value in range(0xA1,0xA7):
return 0xF7A0
elif value == 0xA7:
return 0xF7A6
if type == 0xF4:
if value in range(0x01,0x08):
return 0xF400
elif value == 0x08:
return 0xF407
elif value == 0x09:
return 0xF408
elif value in range(0x0A,0x0D):
return 0xF409
elif value in range(0x11,0x18):
return 0xF410
elif value == 0x18:
return 0xF417
elif value == 0x19:
return 0xF418
elif value in range(0x21,0x28):
return 0xF420
elif value == 0x28:
return 0xF427
elif value == 0x29:
return 0xF428
elif value in range(0x41,0x48):
return 0xF440
elif value == 0x48:
return 0xF447
elif value == 0x49:
return 0xF448
elif value == 0x4D:
return 0xF44C
elif value in range(0x51,0x57):
return 0xF450
elif value == 0x58:
return 0xF457
elif value == 0x59:
return 0xF458
elif value in range(0x61,0x68):
return 0xF460
elif value == 0x68:
return 0xF467
elif value == 0x69:
return 0xF468
elif value in range(0x81,0x88):
return 0xF480
elif value == 0x88:
return 0xF487
elif value == 0x89:
return 0xF488
elif value in range(0x8A,0x8D):
return 0xF489
elif value in range(0x91,0x98):
return 0xF490
elif value == 0x98:
return 0xF497
elif value == 0x99:
return 0xF498
elif value in range(0xA1,0xA8):
return 0xF4A0
elif value == 0xA8:
return 0xF4A7
elif value == 0xA9:
return 0xF4A8
elif value in range(0xC1,0xC8):
return 0xF4C0
elif value == 0xC8:
return 0xF4C7
elif value == 0xC9:
return 0xF4C8
elif value == 0xCD:
return 0xF4CC
elif value in range(0xD1,0xD8):
return 0xF4D0
elif value == 0xD8:
return 0xF4D7
elif value == 0xD9:
return 0xF4D8
elif value in range(0xE1,0xE8):
return 0xF4E0
elif value == 0xE8:
return 0xF4E7
elif value == 0xE9:
return 0xF4E8
if type == 0xF5:
if value in range(0x01,0x05):
return 0xF500
elif value in range(0x08,0x0C):
return 0xF507
elif value in range(0x11,15):
return 0xF510
elif value in range(0x15,0x18):
return 0xF514
elif value in range(0x19,0x1B):
return 0xF518
elif value in range(0x1B,0x1D):
return 0xF51A
elif value in range(0x1D,0x20):
return 0xF51C
elif value == 0x20:
return 0xF51F
elif value == 0x21:
return 0xF520
elif value == 0x23:
return 0xF522
elif value == 0x24:
return 0xF523
elif value == 0x25:
return 0xF524
elif value == 0x26:
return 0xF525
elif value == 0x27:
return 0xF526
elif value == 0x28:
return 0xF527
elif value in range(0x2A,0x2C):
return 0xF529
elif value in range(0x2D,0x2F):
return 0xF52C
elif value == 0x2F:
return 0xF52E
elif value == 0x30:
return 0xF52F
elif value == 0x31:
return 0xF530
elif value == 0x32:
return 0xF531
elif value == 0x34:
return 0xF533
elif value == 0x35:
return 0xF534
elif value == 0x36:
return 0xF535
elif value == 0x45:
return 0xF544
elif value == 0x55:
return 0xF554
elif value == 0x58:
return 0xF557
elif value == 0x5D:
return 0xF55C
elif value == 0x69:
return 0xF568
elif value == 0x6A:
return 0xF569
elif value == 0x6D:
return 0xF56C
elif value in range(0x7F,0x86):
return 0xF57E
if type == 0xF2:
if value in range(0xA0,0xA6):
return 0xF2A0
def main():
if sys.platform == "win32":
port = "COM3"
elif sys.platform == "darwin":
port = "/dev/tty.usbserial"
else:
port = "/dev/ttyUSB0"
parser = argparse.ArgumentParser(
description=("Stcflash, a command line programmer for "
+ "STC 8051 microcontroller.\n"
+ "https://github.com/laborer/stcflash"))
parser.add_argument("image",
help="code image (bin/hex)",
type=argparse.FileType("rb"), nargs='?')
parser.add_argument("-p", "--port",
help="serial port device (default: %s)" % port,
default=port)
parser.add_argument("-l", "--lowbaud",
help="initial baud rate (default: 2400)",
type=int,
default=2400)
parser.add_argument("-hb", "--highbaud",
help="initial baud rate (default: 115200)",
type=int,
default=115200)
parser.add_argument("-r", "--protocol",
help="protocol to use for programming",
choices=["89", "12c5a", "12c52", "12cx052", "8", "15", "auto"],
default="auto")
parser.add_argument("-a", "--aispbaud",
help="baud rate for AutoISP (default: 4800)",
type=int,
default=4800)
parser.add_argument("-m", "--aispmagic",
help="magic word for AutoISP")
parser.add_argument("-v", "--verbose",
help="be verbose",
default=0,
action="count")
parser.add_argument("-e", "--erase_eeprom",
help=("erase data eeprom during next download"
+"(experimental)"),
action="store_true")
parser.add_argument("-ne", "--not_erase_eeprom",
help=("do not erase data eeprom next download"
+"(experimental)"),
action="store_true")
opts = parser.parse_args()
opts.loglevel = (logging.CRITICAL,
logging.INFO,
logging.DEBUG)[min(2, opts.verbose)]
opts.protocol = {'89': PROTOCOL_89,
'12c5a': PROTOCOL_12C5A,
'12c52': PROTOCOL_12C52,
'12cx052': PROTOCOL_12Cx052,
'8': PROTOCOL_8,
'15': PROTOCOL_15,
'auto': None}[opts.protocol]
if not opts.erase_eeprom and not opts.not_erase_eeprom:
opts.erase_eeprom = None
logging.basicConfig(format=("%(levelname)s: "
+ "[%(relativeCreated)d] "
+ "%(message)s"),
level=opts.loglevel)
if opts.image:
code = bytearray(opts.image.read())
opts.image.close()
if os.path.splitext(opts.image.name)[1] in (".hex", ".ihx"):
code = hex2bin(code)
else:
code = None
print("通信端口：%s 最低波特率：%d bps" % (opts.port, opts.lowbaud))
global highbaud_pre
highbaud_pre = opts.highbaud
with serial.Serial(port=opts.port,
baudrate=opts.lowbaud,
parity=serial.PARITY_NONE) as conn:
if opts.aispmagic:
autoisp(conn, opts.aispbaud, opts.aispmagic)
program(Programmer(conn, opts.protocol), code, opts.erase_eeprom)
if __name__ == "__main__":
main()
|
2301_77966824/51-microcontroller-learning
|
2024/10/Hello-template/tools/stcflash.py
|
Python
|
unknown
| 57,810
|
#include "Com_Util.h"
#include <INTRINS.H>
void Com_Util_Delay1ms(u16 count)
{
u8 i,j;
while (count>0)
{
count--;
_nop_();
i=2;
j=199;
do
{
while(--j);
} while (--i);
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/template/src/Com/Com_Util.c
|
C
|
unknown
| 198
|
#include <Int_digitalTube.h>
void main()
{
while(1)
{
}
}
|
2301_77966824/51-microcontroller-learning
|
2024/template/src/main.c
|
C
|
unknown
| 60
|
#!/usr/bin/env python
#coding=utf-8
# stcflash Copyright (C) 2013 laborer (laborer@126.com)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import time
import logging
import sys
import serial
import os.path
import binascii
import struct
import argparse
PROTOCOL_89 = "89"
PROTOCOL_12C5A = "12c5a"
PROTOCOL_12C52 = "12c52"
PROTOCOL_12Cx052 = "12cx052"
PROTOCOL_8 = "8"
PROTOCOL_15 = '15'
PROTOSET_89 = [PROTOCOL_89]
PROTOSET_12 = [PROTOCOL_12C5A, PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_12B = [PROTOCOL_12C52, PROTOCOL_12Cx052]
PROTOSET_8 = [PROTOCOL_8]
PROTOSET_15 = [PROTOCOL_15]
PROTOSET_PARITY = [PROTOCOL_12C5A, PROTOCOL_12C52]
class Programmer:
def __init__(self, conn, protocol=None):
self.conn = conn
self.protocol = protocol
self.conn.timeout = 0.05
if self.protocol in PROTOSET_PARITY:
self.conn.parity = serial.PARITY_EVEN
else:
self.conn.parity = serial.PARITY_NONE
self.chkmode = 0
def __conn_read(self, size):
buf = bytearray()
while len(buf) < size:
s = bytearray(self.conn.read(size - len(buf)))
buf += s
logging.debug("recv: " + " ".join(["%02X" % i for i in s]))
if len(s) == 0:
raise IOError()
return list(buf)
def __conn_write(self, s):
logging.debug("send: " + " ".join(["%02X" % i for i in s]))
self.conn.write(bytearray(s))
def __conn_baudrate(self, baud, flush=True):
logging.debug("baud: %d" % baud)
if flush:
if self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
self.conn.flush()
time.sleep(0.2)
self.conn.baudrate = baud
def __model_database(self, model):
modelmap = {0xE0: ("12", 1, {(0x00, 0x1F): ("C54", ""),
(0x60, 0x7F): ("C54", "AD"),
(0x80, 0x9F): ("LE54", ""),
(0xE0, 0xFF): ("LE54", "AD"),
}),
0xE1: ("12", 1, {(0x00, 0x1F): ("C52", ""),
(0x20, 0x3F): ("C52", "PWM"),
(0x60, 0x7F): ("C52", "AD"),
(0x80, 0x9F): ("LE52", ""),
(0xA0, 0xBF): ("LE52", "PWM"),
(0xE0, 0xFF): ("LE52", "AD"),
}),
0xE2: ("11", 1, {(0x00, 0x1F): ("F", ""),
(0x20, 0x3F): ("F", "E"),
(0x70, 0x7F): ("F", ""),
(0x80, 0x9F): ("L", ""),
(0xA0, 0xBF): ("L", "E"),
(0xF0, 0xFF): ("L", ""),
}),
0xE6: ("12", 1, {(0x00, 0x1F): ("C56", ""),
(0x60, 0x7F): ("C56", "AD"),
(0x80, 0x9F): ("LE56", ""),
(0xE0, 0xFF): ("LE56", "AD"),
}),
0xD1: ("12", 2, {(0x20, 0x3F): ("C5A", "CCP"),
(0x40, 0x5F): ("C5A", "AD"),
(0x60, 0x7F): ("C5A", "S2"),
(0xA0, 0xBF): ("LE5A", "CCP"),
(0xC0, 0xDF): ("LE5A", "AD"),
(0xE0, 0xFF): ("LE5A", "S2"),
}),
0xD2: ("10", 1, {(0x00, 0x0F): ("F", ""),
(0x60, 0x6F): ("F", "XE"),
(0x70, 0x7F): ("F", "X"),
(0xA0, 0xAF): ("L", ""),
(0xE0, 0xEF): ("L", "XE"),
(0xF0, 0xFF): ("L", "X"),
}),
0xD3: ("11", 2, {(0x00, 0x1F): ("F", ""),
(0x40, 0x5F): ("F", "X"),
(0x60, 0x7F): ("F", "XE"),
(0xA0, 0xBF): ("L", ""),
(0xC0, 0xDF): ("L", "X"),
(0xE0, 0xFF): ("L", "XE"),
}),
0xF0: ("89", 4, {(0x00, 0x10): ("C5", "RC"),
(0x20, 0x30): ("C5", "RC"), #STC90C5xRC
}),
0xF1: ("89", 4, {(0x00, 0x10): ("C5", "RD+"),
(0x20, 0x30): ("C5", "RD+"), #STC90C5xRD+
}),
0xF2: ("12", 1, {(0x00, 0x0F): ("C", "052"),
(0x10, 0x1F): ("C", "052AD"),
(0x20, 0x2F): ("LE", "052"),
(0x30, 0x3F): ("LE", "052AD"),
}),
0xF2A0: ("15W", 1, {(0xA0, 0xA5): ("1", ""), #STC15W1系列
}),
0xF400: ("15F", 8, {(0x00, 0x07): ("2K", "S2"), #STC15F2K系列
}),
0xF407: ("15F", 60, {(0x07, 0x08): ("2K", "S2"), #STC15F2K系列
}),
0xF408: ("15F", 61, {(0x08, 0x09): ("2K", "S2"), #STC15F2K系列
}),
0xF400: ("15F", 4, {(0x09, 0x0C): ("4", "AD"), #STC15FAD系列
}),
0xF410: ("15F", 8, {(0x10, 0x17): ("1K", "AS"), #STC15F1KAS系列
}),
0xF417: ("15F", 60, {(0x17, 0x18): ("1K", "AS"), #STC15F1KAS系列
}),
0xF418: ("15F", 61, {(0x18, 0x19): ("1K", "AS"), #STC15F1KAS系列
}),
0xF420: ("15F", 8, {(0x20, 0x27): ("1K", "S"), #STC15F1KS系列
}),
0xF427: ("15F", 60, {(0x27, 0x28): ("1K", "S"), #STC15F1KS系列
}),
0xF440: ("15F", 8, {(0x40, 0x47): ("1K", "S2"), #STC15F1KS2系列
}),
0xF447: ("15F", 60, {(0x47, 0x48): ("1K", "S2"), #STC15F1KS2系列
}),
0xF448: ("15F", 61, {(0x48, 0x49): ("1K", "S2"), #STC15F1KS2系列
}),
0xF44C: ("15F", 13, {(0x4C, 0x4D): ("4", "AD"),
}),
0xF450: ("15F", 8, {(0x50, 0x57): ("1K", "AS"), #STC15F1KAS系列
}),
0xF457: ("15F", 60, {(0x57, 0x58): ("1K", "AS"), #STC15F1KAS系列
}),
0xF458: ("15F", 61, {(0x58, 0x59): ("1K", "AS"), #STC15F1KAS系列
}),
0xF460: ("15F", 8, {(0x60, 0x67): ("1K", "S"), #STC15F1KS系列
}),
0xF467: ("15F", 60, {(0x67, 0x68): ("1K", "S"), #STC15F1KS系列
}),
0xF468: ("15F", 61, {(0x68, 0x69): ("1K", "S"), #STC15F1KS系列
}),
0xF480: ("15L", 8, {(0x80, 0x87): ("2K", "S2"), #STC15L2KS2系列
}),
0xF487: ("15L", 60, {(0x87, 0x88): ("2K", "S2"), #STC15L2KS2系列
}),
0xF488: ("15L", 61, {(0x88, 0x89): ("2K", "S2"), #STC15L2KS2系列
}),
0xF489: ("15L", 5, {(0x89, 0x8C): ("4", "AD"), #STC15L4AD系列
}),
0xF490: ("15L", 8, {(0x90, 0x97): ("2K", "AS"), #STC15L2KAS系列
}),
0xF497: ("15L", 60, {(0x97, 0x98): ("2K", "AS"), #STC15L2KAS系列
}),
0xF498: ("15L", 61, {(0x98, 0x99): ("2K", "AS"), #STC15L2KAS系列
}),
0xF4A0: ("15L", 8, {(0xA0, 0xA7): ("2K", "S"), #STC15L2KS系列
}),
0xF4A7: ("15L", 60, {(0xA7, 0xA8): ("2K", "S"), #STC15L2KS系列
}),
0xF4A8: ("15L", 61, {(0xA8, 0xA9): ("2K", "S"), #STC15L2KS系列
}),
0xF4C0: ("15L", 8, {(0xC0, 0xC7): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C7: ("15L", 60, {(0xC7, 0xC8): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4C8: ("15L", 61, {(0xC8, 0xC9): ("1K", "S2"), #STC15L1KS2系列
}),
0xF4CC: ("15L", 13, {(0xCC, 0xCD): ("4", "AD"),
}),
0xF4D0: ("15L", 8, {(0xD0, 0xD7): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D7: ("15L", 60, {(0xD7, 0xD8): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4D8: ("15L", 61, {(0xD8, 0xD9): ("1K", "AS"), #STC15L1KS2系列
}),
0xF4E0: ("15L", 8, {(0xE0, 0xE7): ("1K", "S"), #STC15L1KS系列
}),
0xF4E7: ("15L", 60, {(0xE7, 0xE8): ("1K", "S"), #STC15L1KS系列
}),
0xF4E8: ("15L", 61, {(0xE8, 0xE9): ("1K", "S"), #STC15L1KS系列
}),
0xF500: ("15W", 1, {(0x00, 0x04): ("1", "SW"), #STC15W1SW系列
}),
0xF507: ("15W", 1, {(0x07, 0x0B): ("1", "S"), #STC15W1S系列
}),
0xF510: ("15W", 1, {(0x10, 0x14): ("2", "S"), #STC15W2S系列
}),
0xF514: ("15W", 8, {(0x14, 0x17): ("1K", "S"), #STC15W1KS系列
}),
0xF518: ("15W", 4, {(0x18, 0x1A): ("4", "S"), #STC15W4S系列
}),
0xF51A: ("15W", 4, {(0x1A, 0x1C): ("4", "S"), #STC15W4S系列
}),
0xF51C: ("15W", 4, {(0x1C, 0x1F): ("4", "AS"), #STC15W4AS系列
}),
0xF51F: ("15W", 10, {(0x19, 0x20): ("4", "AS"), #STC15W4AS系列
}),
0xF520: ("15W", 12, {(0x20, 0x21): ("4", "AS"), #STC15W4AS系列
}),
0xF522: ("15W", 16, {(0x22, 0x23): ("4K", "S4"), #STC15W4KS4系列
}),
0xF523: ("15W",24, {(0x23, 0x24): ("4K", "S4"), #STC15W4KS4系列
}),
0xF524: ("15W", 32, {(0x24, 0x25): ("4K", "S4"), #STC15W4KS4系列
}),
0xF525: ("15W", 40, {(0x25, 0x26): ("4K", "S4"), #STC15W4KS4系列
}),
0xF526: ("15W", 48, {(0x26, 0x27): ("4K", "S4"), #STC15W4KS4系列
}),
0xF527: ("15W", 56, {(0x27, 0x28): ("4K", "S4"), #STC15W4KS4系列
}),
0xF529: ("15W", 1, {(0x29, 0x2B): ("4", "A4"), #STC15W4AS系列
}),
0xF52C: ("15W", 8, {(0x2C, 0x2E): ("1K", "PWM"), #STC15W1KPWM系列
}),
0xF52E: ("15W", 20, {(0x2E, 0x2F): ("1K", "S"), #STC15W1KS系列
}),
0xF52F: ("15W", 32, {(0x2F, 0x30): ("2K", "S2"), #STC15W2KS2系列
}),
0xF530: ("15W", 48, {(0x30, 0x31): ("2K", "S2"), #STC15W2KS2系列
}),
0xF531: ("15W", 32, {(0x31, 0x32): ("2K", "S2"), #STC15W2KS2系列
}),
0xF533: ("15W", 20, {(0x33, 0x34): ("1K", "S2"), #STC15W1KS2系列
}),
0xF534: ("15W", 32, {(0x34, 0x35): ("1K", "S2"), #STC15W1KS2系列
}),
0xF535: ("15W", 48, {(0x35, 0x36): ("1K", "S2"), #STC15W1KS2系列
}),
0xF544: ("15W", 5, {(0x44, 0x45): ("", "SW"), #STC15SW系列
}),
0xF554: ("15W", 5, {(0x54, 0x55): ("2", "S"), #STC15W2S系列
}),
0xF557: ("15W", 29, {(0x57, 0x58): ("1K", "S"), #STC15W1KS系列
}),
0xF55C: ("15W", 13, {(0x5C, 0x5D): ("4", "S"), #STC15W4S系列
}),
0xF568: ("15W", 58, {(0x68, 0x69): ("4K", "S4"), #STC15W4KS4系列
}),
0xF569: ("15W", 61, {(0x69, 0x6A): ("4K", "S4"), #STC15W4KS4系列
}),
0xF56C: ("15W", 58, {(0x6C, 0x6D): ("4K", "S4-Student"), #STC15W4KS4系列
}),
0xF57E: ("15U", 8, {(0x7E, 0x85): ("4K", "S4"), #STC15U4KS4系列
}),
0xF600: ("15H", 8, {(0x00, 0x08): ("4K", "S4"), #STC154K系列
}),
0xF620: ("8A", 8, {(0x20, 0x28): ("8K", "S4A12"), #STC8A8K系列
}),
0xF628: ("8A", 60, {(0x28, 0x29): ("8K", "S4A12"), #STC8A8K系列
}),
0xF630: ("8F", 8, {(0x30, 0x38): ("2K", "S4"), #STC8F2K系列
}),
0xF638: ("8F", 60, {(0x38, 0x39): ("2K", "S4"), #STC8F2K系列
}),
0xF640: ("8F", 8, {(0x40, 0x48): ("2K", "S2"), #STC8F2K系列
}),
0xF648: ("8F", 60, {(0x48, 0x49): ("2K", "S2"), #STC8F2K系列
}),
0xF650: ("8A", 8, {(0x50, 0x58): ("4K", "S2A12"), #STC8A4K系列
}),
0xF658: ("8A", 60, {(0x58, 0x59): ("4K", "S2A12"), #STC8A4K系列
}),
0xF660: ("8F", 2, {(0x60, 0x66): ("1K", "S2"), #STC8F1K系列
}),
0xF666: ("8F", 17, {(0x66, 0x67): ("1K", "S2"), #STC8F1K系列
}),
0xF670: ("8F", 2, {(0x70, 0x76): ("1K", ""), #STC8F1K系列
}),
0xF676: ("8F", 17, {(0x76, 0x77): ("1K", ""), #STC8F1K系列
}),
0xF700: ("8C", 2, {(0x00, 0x06): ("1K", ""), #STC8C系列
}),
0xF730: ("8H", 2, {(0x30, 0x36): ("1K", ""), #STC8H1K系列
}),
0xF736: ("8H", 17, {(0x36, 0x37): ("1K", ""), #STC8H1K系列
}),
0xF740: ("8H", 8, {(0x40, 0x42): ("3K", "S4"), #STC8H3K系列
}),
0xF742: ("8H", 60, {(0x42, 0x43): ("3K", "S4"), #STC8H3K系列
}),
0xF743: ("8H", 64, {(0x43, 0x44): ("3K", "S4"), #STC8H3K系列
}),
0xF748: ("8H", 16, {(0x48, 0x4A): ("3K", "S2"), #STC8H3K系列
}),
0xF74A: ("8H", 60, {(0x4A, 0x4B): ("3K", "S2"), #STC8H3K系列
}),
0xF74B: ("8H", 64, {(0x4B, 0x4C): ("3K", "S2"), #STC8H3K系列
}),
0xF750: ("8G", 2, {(0x50, 0x56): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF756: ("8G", 17, {(0x56, 0x57): ("1K", "-20/16pin"), #STC8G1K系列
}),
0xF760: ("8G", 16, {(0x60, 0x62): ("2K", "S4"), #STC8G2K系列
}),
0xF762: ("8G", 60, {(0x62, 0x63): ("2K", "S4"), #STC8G2K系列
}),
0xF763: ("8G", 64, {(0x63, 0x64): ("2K", "S4"), #STC8G2K系列
}),
0xF768: ("8G", 16, {(0x68, 0x6A): ("2K", "S2"), #STC8G2K系列
}),
0xF76A: ("8G", 60, {(0x6A, 0x6B): ("2K", "S2"), #STC8G2K系列
}),
0xF76B: ("8G", 64, {(0x6B, 0x6C): ("2K", "S2"), #STC8G2K系列
}),
0xF770: ("8G", 2, {(0x70, 0x76): ("1K", "T"), #STC8G2K系列
}),
0xF776: ("8G", 17, {(0x76, 0x77): ("1K", "T"), #STC8G2K系列
}),
0xF780: ("8H", 16, {(0x80, 0x82): ("8K", "U"), #STC8H8K系列
}),
0xF782: ("8H", 60, {(0x82, 0x83): ("8K", "U"), #STC8H8K系列
}),
0xF783: ("8H", 64, {(0x83, 0x84): ("8K", "U"), #STC8H8K系列
}),
0xF790: ("8G", 2, {(0x90, 0x96): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF796: ("8G", 17, {(0x96, 0x97): ("1K", "A-8PIN"), #STC8G1K系列
}),
0xF7A0: ("8G", 2, {(0xA0, 0xA6): ("1K", "-8PIN"), #STC8G1K系列
}),
0xF7A6: ("8G", 17, {(0xA6, 0xA7): ("1K", "-8PIN"), #STC8G1K系列
}),
}
iapmcu = ((0xD1, 0x3F), (0xD1, 0x5F), (0xD1, 0x7F), (0xF4, 0x4D), (0xF4, 0x99), (0xF4, 0xD9), (0xF5, 0x58),
(0xD2, 0x7E), (0xD2, 0xFE), (0xF4, 0x09), (0xF4, 0x59), (0xF4, 0xA9), (0xF4, 0xE9), (0xF5, 0x5D),
(0xD3, 0x5F), (0xD3, 0xDF), (0xF4, 0x19), (0xF4, 0x69), (0xF4, 0xC9), (0xF5, 0x45), (0xF5, 0x62),
(0xE2, 0x76), (0xE2, 0xF6), (0xF4, 0x49), (0xF4, 0x89), (0xF4, 0xCD), (0xF5, 0x55), (0xF5, 0x69),
(0xF5, 0x6A), (0xF5, 0x6D),
)
try:
model = tuple(model)
if self.model[0] in [0xF4, 0xF5, 0xF6, 0xF7]:
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
elif self.model[0] == 0xF2 and self.model[1] in range(0xA0, 0xA6):
prefix, romratio, fixmap = modelmap[stc_type_map(model[0],model[1])]
self.protocol = PROTOCOL_15
else:
prefix, romratio, fixmap = modelmap[model[0]]
if model[0] in (0xF0, 0xF1) and 0x20 <= model[1] <= 0x30:
prefix = "90"
for key, value in fixmap.items():
if key[0] <= model[1] <= key[1]:
break
else:
raise KeyError()
infix, postfix = value
romsize = romratio * (model[1] - key[0])
try:
romsize = {(0xF0, 0x03): 13}[model]
except KeyError:
pass
if model[0] in (0xF0, 0xF1):
romfix = str(model[1] - key[0])
elif model[0] in (0xF2,):
romfix = str(romsize)
else:
romfix = "%02d" % romsize
name = "IAP" if model in iapmcu else "STC"
name += prefix + infix + romfix + postfix
return (name, romsize)
except KeyError:
return ("Unknown %02X %02X" % model, None)
def recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def first_recv(self, timeout = 1, start = [0x46, 0xB9, 0x68]):
timeout += time.time()
while time.time() < timeout:
try:
if self.__conn_read(len(start)) == start:
time.sleep(0.02) #加上20ms延时，增大接收成功率
break
except IOError:
continue
else:
logging.debug("recv(..): Timeout")
raise IOError()
chksum = start[-1]
s = self.__conn_read(2)
n = s[0] * 256 + s[1]
if n > 64:
logging.debug("recv(..): Incorrect packet size")
raise IOError()
chksum += sum(s)
s = self.__conn_read(n - 3)
if s[n - 4] != 0x16:
logging.debug("recv(..): Missing terminal symbol")
raise IOError()
chksum += sum(s[:-(1+self.chkmode)])
if self.chkmode > 0 and chksum & 0xFF != s[-2]:
logging.debug("recv(..): Incorrect checksum[0]")
raise IOError()
elif self.chkmode > 1 and (chksum >> 8) & 0xFF != s[-3]:
logging.debug("recv(..): Incorrect checksum[1]")
raise IOError()
return (s[0], s[1:-(1+self.chkmode)])
def send(self, cmd, dat):
buf = [0x46, 0xB9, 0x6A]
n = 1 + 2 + 1 + len(dat) + self.chkmode + 1
buf += [n >> 8, n & 0xFF, cmd]
buf += dat
chksum = sum(buf[2:])
if self.chkmode > 1:
buf += [(chksum >> 8) & 0xFF]
buf += [chksum & 0xFF, 0x16]
self.__conn_write(buf)
def detect(self):
for i in range(500):
try:
if self.protocol in [PROTOCOL_89,PROTOCOL_12C52,PROTOCOL_12Cx052,PROTOCOL_12C5A]:
self.__conn_write([0x7F,0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
else:
self.__conn_write([0x7F])
cmd, dat = self.first_recv(0.03, [0x68])
break
except IOError:
pass
else:
raise IOError()
self.info = dat[16:]
self.version = "%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[1])
self.model = self.info[3:5]
self.name, self.romsize = self.__model_database(self.model)
logging.info("Model ID: %02X %02X" % tuple(self.model))
logging.info("Model name: %s" % self.name)
logging.info("ROM size: %s" % self.romsize)
if self.protocol is None:
try:
self.protocol = {0xF0: PROTOCOL_89, #STC89/90C5xRC
0xF1: PROTOCOL_89, #STC89/90C5xRD+
0xF2: PROTOCOL_12Cx052, #STC12Cx052
0xD1: PROTOCOL_12C5A, #STC12C5Ax
0xD2: PROTOCOL_12C5A, #STC10Fx
0xE1: PROTOCOL_12C52, #STC12C52x
0xE2: PROTOCOL_12C5A, #STC11Fx
0xE6: PROTOCOL_12C52, #STC12C56x
0xF4: PROTOCOL_15, #STC15系列
0xF5: PROTOCOL_15, #STC15系列
0xF6: PROTOCOL_8, #STC8系列
0xF7: PROTOCOL_8, #STC8系列
}[self.model[0]]
except KeyError:
pass
if self.protocol in PROTOSET_8:
self.fosc = (dat[0]*0x1000000 +dat[1]*0x10000+dat[2]*0x100) /1000000
self.internal_vol = (dat[34]*256+dat[35])
self.wakeup_fosc = (dat[22]*256+dat[23]) /1000
self.test_year = str(hex(dat[36])).replace("0x",'')
self.test_month = str(hex(dat[37])).replace("0x",'')
self.test_day = str(hex(dat[38])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
if dat[10] == 191:
self.det_low_vol = 2.2
else:
self.det_low_vol = (191 - dat[10])*0.3 + 2.1
elif self.protocol in PROTOSET_15:
self.fosc = (dat[7]*0x1000000 +dat[8]*0x10000+dat[9]*0x100) /1000000
self.wakeup_fosc = (dat[0]*256+dat[1]) /1000
self.internal_vol = (dat[34]*256+dat[35])
self.test_year = str(hex(dat[41])).replace("0x",'')
self.test_month = str(hex(dat[42])).replace("0x",'')
self.test_day = str(hex(dat[43])).replace("0x",'')
self.version = "%d.%d.%d%c" % (self.info[0] >> 4,
self.info[0] & 0x0F,
self.info[5],
self.info[1])
else:
self.fosc = (float(sum(dat[0:16:2]) * 256 + sum(dat[1:16:2])) / 8
* self.conn.baudrate / 580974)
if self.protocol in PROTOSET_PARITY or self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.chkmode = 2
self.conn.parity = serial.PARITY_EVEN
else:
self.chkmode = 1
self.conn.parity = serial.PARITY_NONE
if self.protocol is not None:
del self.info[-self.chkmode:]
logging.info("Protocol ID: %s" % self.protocol)
logging.info("Checksum mode: %d" % self.chkmode)
logging.info("UART Parity: %s"
% {serial.PARITY_NONE: "NONE",
serial.PARITY_EVEN: "EVEN",
}[self.conn.parity])
for i in range(0, len(self.info), 16):
logging.info("Info string [%d]: %s"
% (i // 16,
" ".join(["%02X" % j for j in self.info[i:i+16]])))
def print_info(self):
print("系统时钟频率: %.3fMHz" % self.fosc)
if self.protocol in PROTOSET_8:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("低压检测电压: %.1f V" %self.det_low_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
if self.protocol in PROTOSET_15:
print("掉电唤醒定时器频率: %.3fKHz" % self.wakeup_fosc)
print("内部参考电压: %d mV" %self.internal_vol)
print("内部安排测试时间: 20%s年%s月%s日" %(self.test_year,self.test_month,self.test_day))
print("单片机型号: %s" % self.name)
print("固件版本号: %s" % self.version)
if self.romsize is not None:
print("程序空间: %dKB" % self.romsize)
if self.protocol == PROTOCOL_89:
switches = [( 2, 0x80, "Reset stops "),
( 2, 0x40, "Internal XRAM"),
( 2, 0x20, "Normal ALE pin"),
( 2, 0x10, "Full gain oscillator"),
( 2, 0x08, "Not erase data EEPROM"),
( 2, 0x04, "Download regardless of P1"),
( 2, 0x01, "12T mode")]
elif self.protocol == PROTOCOL_12C5A:
switches = [( 6, 0x40, "Disable reset2 low level detect"),
( 6, 0x01, "Reset pin not use as I/O port"),
( 7, 0x80, "Disable long power-on-reset latency"),
( 7, 0x40, "Oscillator high gain"),
( 7, 0x02, "External system clock source"),
( 8, 0x20, "WDT disable after power-on-reset"),
( 8, 0x04, "WDT count in idle mode"),
(10, 0x02, "Not erase data EEPROM"),
(10, 0x01, "Download regardless of P1")]
print(" WDT prescal: %d" % 2**((self.info[8] & 0x07) + 1))
elif self.protocol in PROTOSET_12B:
switches = [(8, 0x02, "Not erase data EEPROM")]
else:
switches = []
for pos, bit, desc in switches:
print(" [%c] %s" % ("X" if self.info[pos] & bit else " ", desc))
def handshake(self):
baud0 = self.conn.baudrate
if self.protocol in PROTOSET_8:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(24.0 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(24.0 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value = 0x7B
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value = 0xB0
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value = 0x5A
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value = 0x35
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value = 0x7B
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value = 0x5A
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value = 0x1A
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value = 0x12
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value = 0x5A
else:
foc_value = 0x6B
baudstr = [0x00, 0x00, Timer1_value >> 8, Timer1_value & 0xff, 0x01, foc_value, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
elif self.protocol in PROTOSET_15:
baud = 115200 #若没指定波特率，默认为115200
if highbaud_pre != 115200:
baud = highbaud_pre
#支持460800以内的任意波特率
#典型波特率：460800、230400、115200、57600、38400、28800、19200、14400、9600、4800
if baud in range(460801):
#定时器1重载值计算微调，可能由于目标芯片的差异性需要微调
if baud in [300000,350000]:
Timer1_value = int(65536.2 - float(22.1184 * 1000000 / 4 / baud))
else:
Timer1_value = int(65536.5 - float(22.1184 * 1000000 / 4 / baud))
if self.fosc < 24.5 and self.fosc > 23.5: #24M
foc_value_1 = 0x40
foc_value_2 = 0x9F
elif self.fosc < 27.5 and self.fosc > 26.5: #27M
foc_value_1 = 0x40
foc_value_2 = 0xDC
elif self.fosc < 22.7 and self.fosc > 21.7: #22.1184M
foc_value_1 = 0x40
foc_value_2 = 0x79
elif self.fosc < 20.5 and self.fosc > 19.5: #20M
foc_value_1 = 0x40
foc_value_2 = 0x4F
elif self.fosc < 12.3 and self.fosc > 11.7: #12M
foc_value_1 = 0x80
foc_value_2 = 0xA2
elif self.fosc < 11.4 and self.fosc > 10.8: #11.0592M
foc_value_1 = 0x80
foc_value_2 = 0x7D
elif self.fosc < 18.8 and self.fosc > 18.0: #18.432M
foc_value_1 = 0x40
foc_value_2 = 0x31
elif self.fosc < 6.3 and self.fosc > 5.7:#6M
foc_value_1 = 0xC0
foc_value_2 = 0x9f
elif self.fosc < 5.9 and self.fosc > 5.0: #5.5296M
foc_value_1 = 0xC0
foc_value_2 = 0x7B
baudstr = [0x6d, 0x40, Timer1_value >> 8, Timer1_value & 0xff, foc_value_1,foc_value_2, 0x81]
#baudstr = [0x6b, 0x40, 0xff,0xf4, 0x40,0x92, 0x81]
self.send(0x01, baudstr )
try:
cmd, dat = self.recv()
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
logging.info("Change baudrate to %d" % baud)
self.__conn_baudrate(baud)
self.baudrate = baud
else:
for baud in [115200, 57600, 38400, 28800, 19200,
14400, 9600, 4800, 2400, 1200]:
t = self.fosc * 1000000 / baud / 32
if self.protocol not in PROTOSET_89:
t *= 2
if abs(round(t) - t) / t > 0.03:
continue
if self.protocol in PROTOSET_89:
tcfg = 0x10000 - int(t + 0.5)
else:
if t > 0xFF:
continue
tcfg = 0xC000 + 0x100 - int(t + 0.5)
baudstr = [tcfg >> 8,
tcfg & 0xFF,
0xFF - (tcfg >> 8),
min((256 - (tcfg & 0xFF)) * 2, 0xFE),
int(baud0 / 60)]
logging.info("Test baudrate %d (accuracy %0.4f) using config %s"
% (baud,
abs(round(t) - t) / t,
" ".join(["%02X" % i for i in baudstr])))
if self.protocol in PROTOSET_89:
freqlist = (40, 20, 10, 5)
else:
freqlist = (30, 24, 20, 12, 6, 3, 2, 1)
for twait in range(0, len(freqlist)):
if self.fosc > freqlist[twait]:
break
logging.info("Waiting time config %02X" % (0x80 + twait))
self.send(0x8F, baudstr + [0x80 + twait])
try:
self.__conn_baudrate(baud)
cmd, dat = self.recv()
break
except Exception:
logging.info("Cannot use baudrate %d" % baud)
time.sleep(0.2)
self.conn.flushInput()
finally:
self.__conn_baudrate(baud0, False)
else:
raise IOError()
logging.info("Change baudrate to %d" % baud)
self.send(0x8E, baudstr)
self.__conn_baudrate(baud)
self.baudrate = baud
cmd, dat = self.recv()
def erase(self):
if self.protocol in PROTOSET_89:
self.send(0x84, [0x01, 0x33, 0x33, 0x33, 0x33, 0x33, 0x33])
cmd, dat = self.recv(10)
assert cmd == 0x80
elif self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0x05, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
self.send(0x03, [0x00, 0x00, 0x5A, 0xA5])
cmd, dat = self.recv(10)
for i in range(7):
dat[i] = hex(dat[i])
dat[i] = str(dat[i])
dat[i] = dat[i].replace("0x",'')
if len(dat[i]) == 1:
dat_value = list(dat[i])
dat_value.insert(0, '0')
dat[i] = ''.join(dat_value)
serial_number = ""
for i in dat:
serial_number = serial_number +str(i)
self.serial_number = str(serial_number)
print("\r")
sys.stdout.write("芯片出厂序列号: ")
sys.stdout.write(self.serial_number.upper())
sys.stdout.flush()
print("\r")
else:
self.send(0x84, ([0x00, 0x00, self.romsize * 4,
0x00, 0x00, self.romsize * 4]
+ [0x00] * 12
+ [i for i in range(0x80, 0x0D, -1)]))
cmd, dat = self.recv(10)
if dat:
logging.info("Serial number: "
+ " ".join(["%02X" % j for j in dat]))
def flash(self, code):
code = list(code) + [0xff] * (511 - (len(code) - 1) % 512)
for i in range(0, len(code), 128):
logging.info("Flash code region (%04X, %04X)" % (i, i + 127))
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
flag_test = 1
addr = [i >> 8, i & 0xFF, 0x5A, 0xA5]
if flag_test == 1:
self.send(0x22, addr + code[i:i+128])
flag_test = 10
else:
self.send(0x02, addr + code[i:i+128])
else:
addr = [0, 0, i >> 8, i & 0xFF, 0, 128]
self.send(0x00, addr + code[i:i+128])
cmd, dat = self.recv()
#assert dat[0] == sum(code[i:i+128]) % 256
yield (i + 128.0) / len(code)
def options(self, **kwargs):
erase_eeprom = kwargs.get("erase_eeprom", None)
dat = []
fosc = list(bytearray(struct.pack(">I", int(self.fosc * 1000000))))
if self.protocol == PROTOCOL_89:
if erase_eeprom is not None:
self.info[2] &= 0xF7
self.info[2] |= 0x00 if erase_eeprom else 0x08
dat = self.info[2:3] + [0xFF]*3
elif self.protocol == PROTOCOL_12C5A:
if erase_eeprom is not None:
self.info[10] &= 0xFD
self.info[10] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:9] + [0xFF]*5 + self.info[10:11]
+ [0xFF]*6 + fosc)
elif self.protocol in PROTOSET_12B:
if erase_eeprom is not None:
self.info[8] &= 0xFD
self.info[8] |= 0x00 if erase_eeprom else 0x02
dat = (self.info[6:11] + fosc + self.info[12:16] + [0xFF]*4
+ self.info[8:9] + [0xFF]*7 + fosc + [0xFF]*3)
elif erase_eeprom is not None:
logging.info("Modifying options is not supported for this target")
return False
if dat:
self.send(0x8D, dat)
cmd, dat = self.recv()
return True
def terminate(self):
logging.info("Send termination command")
if self.protocol in PROTOSET_8 or self.protocol in PROTOSET_15:
self.send(0xFF, [])
else:
self.send(0x82, [])
self.conn.flush()
time.sleep(0.2)
def unknown_packet_1(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (50 00 00 36 01 ...)")
self.send(0x50, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8F and not dat
def unknown_packet_2(self):
if self.protocol not in PROTOSET_PARITY and self.protocol not in PROTOSET_8 and self.protocol not in PROTOSET_15:
for i in range(5):
logging.info("Send unknown packet (80 00 00 36 01 ...)")
self.send(0x80, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x80 and not dat
def unknown_packet_3(self):
if self.protocol in PROTOSET_PARITY:
logging.info("Send unknown packet (69 00 00 36 01 ...)")
self.send(0x69, [0x00, 0x00, 0x36, 0x01] + self.model)
cmd, dat = self.recv()
assert cmd == 0x8D and not dat
def autoisp(conn, baud, magic):
if not magic:
return
bak = conn.baudrate
conn.baudrate = baud
conn.write(bytearray(ord(i) for i in magic))
conn.flush()
time.sleep(0.5)
conn.baudrate = bak
def program(prog, code, erase_eeprom=None):
sys.stdout.write("检测目标...")
sys.stdout.flush()
prog.detect()
print("完成")
prog.print_info()
if prog.protocol is None:
raise IOError("未知目标")
if code is None:
return
prog.unknown_packet_1()
sys.stdout.write("切换至最高波特率: ")
sys.stdout.flush()
prog.handshake()
print("%d bps"% prog.baudrate)
prog.unknown_packet_2()
sys.stdout.write("开始擦除芯片...")
sys.stdout.flush()
time_start = time.time()
prog.erase()
print("擦除完成")
print("代码长度: %d bytes" % len(code))
# print("Programming: ", end="", flush=True)
sys.stdout.write("正在下载用户代码...")
sys.stdout.flush()
oldbar = 0
for progress in prog.flash(code):
bar = int(progress * 25)
sys.stdout.write("#" * (bar - oldbar))
sys.stdout.flush()
oldbar = bar
print(" 完成")
prog.unknown_packet_3()
sys.stdout.write("设置选项...")
sys.stdout.flush()
if prog.options(erase_eeprom=erase_eeprom):
print("设置完成")
else:
print("设置失败")
prog.terminate()
time_end = time.time()
print("耗时: %.3fs"% (time_end-time_start))
# Convert Intel HEX code to binary format
def hex2bin(code):
buf = bytearray()
base = 0
line = 0
for rec in code.splitlines():
# Calculate the line number of the current record
line += 1
try:
# bytes(...) is to support python<=2.6
# bytearray(...) is to support python<=2.7
n = bytearray(binascii.a2b_hex(bytes(rec[1:3])))[0]
dat = bytearray(binascii.a2b_hex(bytes(rec[1:n*2+11])))
except:
raise Exception("Line %d: Invalid format" % line)
if rec[0] != ord(":"):
raise Exception("Line %d: Missing start code \":\"" % line)
if sum(dat) & 0xFF != 0:
raise Exception("Line %d: Incorrect checksum" % line)
if dat[3] == 0: # Data record
addr = base + (dat[1] << 8) + dat[2]
# Allocate memory space and fill it with 0xFF
buf[len(buf):] = [0xFF] * (addr + n - len(buf))
# Copy data to the buffer
buf[addr:addr+n] = dat[4:-1]
elif dat[3] == 1: # EOF record
if n != 0:
raise Exception("Line %d: Incorrect data length" % line)
elif dat[3] == 2: # Extended segment address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 4
elif dat[3] == 4: # Extended linear address record
if n != 2:
raise Exception("Line %d: Incorrect data length" % line)
base = ((dat[4] << 8) + dat[5]) << 16
else:
raise Exception("Line %d: Unsupported record type" % line)
return buf
def stc_type_map(type, value):
if type == 0xF6:
if value in range(0x01,0x09):
return 0xF600
elif value in range(0x21,0x29):
return 0xF620
elif value == 0x29:
return 0xF628
elif value in range(0x31,0x39):
return 0xF630
elif value == 0x39:
return 0xF638
elif value in range(0x41,0x49):
return 0xF640
elif value == 0x49:
return 0xF648
elif value in range(0x51,0x59):
return 0xF650
elif value == 0x59:
return 0xF658
elif value in range(0x61,0x67):
return 0xF660
elif value == 0x67:
return 0xF666
elif value in range(0x71,0x77):
return 0xF670
elif value == 0x77:
return 0xF676
if type == 0xF7:
if value in range(0x01,0x07):
return 0xF700
elif value in range(0x31,0x37):
return 0xF730
elif value == 0x37:
return 0xF736
elif value in range(0x41,0x43):
return 0xF740
elif value == 0x43:
return 0xF742
elif value == 0x44:
return 0xF743
elif value in range(0x49,0x4B):
return 0xF748
elif value == 0x4B:
return 0xF74A
elif value == 0x4C:
return 0xF74B
elif value in range(0x51,0x57):
return 0xF750
elif value == 0x57:
return 0xF756
elif value in range(0x61,0x63):
return 0xF760
elif value == 0x63:
return 0xF762
elif value == 0x64:
return 0xF763
elif value in range(0x69,0x6B):
return 0xF768
elif value == 0x6B:
return 0xF76A
elif value == 0x6C:
return 0xF76B
elif value in range(0x71,0x77):
return 0xF770
elif value == 0x77:
return 0xF776
elif value in range(0x81,0x83):
return 0xF780
elif value == 0x83:
return 0xF782
elif value == 0x84:
return 0xF783
elif value in range(0x91,0x97):
return 0xF790
elif value == 0x97:
return 0xF796
elif value in range(0xA1,0xA7):
return 0xF7A0
elif value == 0xA7:
return 0xF7A6
if type == 0xF4:
if value in range(0x01,0x08):
return 0xF400
elif value == 0x08:
return 0xF407
elif value == 0x09:
return 0xF408
elif value in range(0x0A,0x0D):
return 0xF409
elif value in range(0x11,0x18):
return 0xF410
elif value == 0x18:
return 0xF417
elif value == 0x19:
return 0xF418
elif value in range(0x21,0x28):
return 0xF420
elif value == 0x28:
return 0xF427
elif value == 0x29:
return 0xF428
elif value in range(0x41,0x48):
return 0xF440
elif value == 0x48:
return 0xF447
elif value == 0x49:
return 0xF448
elif value == 0x4D:
return 0xF44C
elif value in range(0x51,0x57):
return 0xF450
elif value == 0x58:
return 0xF457
elif value == 0x59:
return 0xF458
elif value in range(0x61,0x68):
return 0xF460
elif value == 0x68:
return 0xF467
elif value == 0x69:
return 0xF468
elif value in range(0x81,0x88):
return 0xF480
elif value == 0x88:
return 0xF487
elif value == 0x89:
return 0xF488
elif value in range(0x8A,0x8D):
return 0xF489
elif value in range(0x91,0x98):
return 0xF490
elif value == 0x98:
return 0xF497
elif value == 0x99:
return 0xF498
elif value in range(0xA1,0xA8):
return 0xF4A0
elif value == 0xA8:
return 0xF4A7
elif value == 0xA9:
return 0xF4A8
elif value in range(0xC1,0xC8):
return 0xF4C0
elif value == 0xC8:
return 0xF4C7
elif value == 0xC9:
return 0xF4C8
elif value == 0xCD:
return 0xF4CC
elif value in range(0xD1,0xD8):
return 0xF4D0
elif value == 0xD8:
return 0xF4D7
elif value == 0xD9:
return 0xF4D8
elif value in range(0xE1,0xE8):
return 0xF4E0
elif value == 0xE8:
return 0xF4E7
elif value == 0xE9:
return 0xF4E8
if type == 0xF5:
if value in range(0x01,0x05):
return 0xF500
elif value in range(0x08,0x0C):
return 0xF507
elif value in range(0x11,15):
return 0xF510
elif value in range(0x15,0x18):
return 0xF514
elif value in range(0x19,0x1B):
return 0xF518
elif value in range(0x1B,0x1D):
return 0xF51A
elif value in range(0x1D,0x20):
return 0xF51C
elif value == 0x20:
return 0xF51F
elif value == 0x21:
return 0xF520
elif value == 0x23:
return 0xF522
elif value == 0x24:
return 0xF523
elif value == 0x25:
return 0xF524
elif value == 0x26:
return 0xF525
elif value == 0x27:
return 0xF526
elif value == 0x28:
return 0xF527
elif value in range(0x2A,0x2C):
return 0xF529
elif value in range(0x2D,0x2F):
return 0xF52C
elif value == 0x2F:
return 0xF52E
elif value == 0x30:
return 0xF52F
elif value == 0x31:
return 0xF530
elif value == 0x32:
return 0xF531
elif value == 0x34:
return 0xF533
elif value == 0x35:
return 0xF534
elif value == 0x36:
return 0xF535
elif value == 0x45:
return 0xF544
elif value == 0x55:
return 0xF554
elif value == 0x58:
return 0xF557
elif value == 0x5D:
return 0xF55C
elif value == 0x69:
return 0xF568
elif value == 0x6A:
return 0xF569
elif value == 0x6D:
return 0xF56C
elif value in range(0x7F,0x86):
return 0xF57E
if type == 0xF2:
if value in range(0xA0,0xA6):
return 0xF2A0
def main():
if sys.platform == "win32":
port = "COM3"
elif sys.platform == "darwin":
port = "/dev/tty.usbserial"
else:
port = "/dev/ttyUSB0"
parser = argparse.ArgumentParser(
description=("Stcflash, a command line programmer for "
+ "STC 8051 microcontroller.\n"
+ "https://github.com/laborer/stcflash"))
parser.add_argument("image",
help="code image (bin/hex)",
type=argparse.FileType("rb"), nargs='?')
parser.add_argument("-p", "--port",
help="serial port device (default: %s)" % port,
default=port)
parser.add_argument("-l", "--lowbaud",
help="initial baud rate (default: 2400)",
type=int,
default=2400)
parser.add_argument("-hb", "--highbaud",
help="initial baud rate (default: 115200)",
type=int,
default=115200)
parser.add_argument("-r", "--protocol",
help="protocol to use for programming",
choices=["89", "12c5a", "12c52", "12cx052", "8", "15", "auto"],
default="auto")
parser.add_argument("-a", "--aispbaud",
help="baud rate for AutoISP (default: 4800)",
type=int,
default=4800)
parser.add_argument("-m", "--aispmagic",
help="magic word for AutoISP")
parser.add_argument("-v", "--verbose",
help="be verbose",
default=0,
action="count")
parser.add_argument("-e", "--erase_eeprom",
help=("erase data eeprom during next download"
+"(experimental)"),
action="store_true")
parser.add_argument("-ne", "--not_erase_eeprom",
help=("do not erase data eeprom next download"
+"(experimental)"),
action="store_true")
opts = parser.parse_args()
opts.loglevel = (logging.CRITICAL,
logging.INFO,
logging.DEBUG)[min(2, opts.verbose)]
opts.protocol = {'89': PROTOCOL_89,
'12c5a': PROTOCOL_12C5A,
'12c52': PROTOCOL_12C52,
'12cx052': PROTOCOL_12Cx052,
'8': PROTOCOL_8,
'15': PROTOCOL_15,
'auto': None}[opts.protocol]
if not opts.erase_eeprom and not opts.not_erase_eeprom:
opts.erase_eeprom = None
logging.basicConfig(format=("%(levelname)s: "
+ "[%(relativeCreated)d] "
+ "%(message)s"),
level=opts.loglevel)
if opts.image:
code = bytearray(opts.image.read())
opts.image.close()
if os.path.splitext(opts.image.name)[1] in (".hex", ".ihx"):
code = hex2bin(code)
else:
code = None
print("通信端口：%s 最低波特率：%d bps" % (opts.port, opts.lowbaud))
global highbaud_pre
highbaud_pre = opts.highbaud
with serial.Serial(port=opts.port,
baudrate=opts.lowbaud,
parity=serial.PARITY_NONE) as conn:
if opts.aispmagic:
autoisp(conn, opts.aispbaud, opts.aispmagic)
program(Programmer(conn, opts.protocol), code, opts.erase_eeprom)
if __name__ == "__main__":
main()
|
2301_77966824/51-microcontroller-learning
|
2024/template/tools/stcflash.py
|
Python
|
unknown
| 57,810
|
$NOMOD51
;------------------------------------------------------------------------------
; This file is part of the C51 Compiler package
; Copyright (c) 1988-2005 Keil Elektronik GmbH and Keil Software, Inc.
; Version 8.01
;
; *** <<< Use Configuration Wizard in Context Menu >>> ***
;------------------------------------------------------------------------------
; STARTUP.A51: This code is executed after processor reset.
;
; To translate this file use A51 with the following invocation:
;
; A51 STARTUP.A51
;
; To link the modified STARTUP.OBJ file to your application use the following
; Lx51 invocation:
;
; Lx51 your object file list, STARTUP.OBJ controls
;
;------------------------------------------------------------------------------
;
; User-defined <h> Power-On Initialization of Memory
;
; With the following EQU statements the initialization of memory
; at processor reset can be defined:
;
; <o> IDATALEN: IDATA memory size <0x0-0x100>
; <i> Note: The absolute start-address of IDATA memory is always 0
; <i> The IDATA space overlaps physically the DATA and BIT areas.
IDATALEN EQU 80H
;
; <o> XDATASTART: XDATA memory start address <0x0-0xFFFF>
; <i> The absolute start address of XDATA memory
XDATASTART EQU 0
;
; <o> XDATALEN: XDATA memory size <0x0-0xFFFF>
; <i> The length of XDATA memory in bytes.
XDATALEN EQU 0
;
; <o> PDATASTART: PDATA memory start address <0x0-0xFFFF>
; <i> The absolute start address of PDATA memory
PDATASTART EQU 0H
;
; <o> PDATALEN: PDATA memory size <0x0-0xFF>
; <i> The length of PDATA memory in bytes.
PDATALEN EQU 0H
;
;</h>
;------------------------------------------------------------------------------
;
;<h> Reentrant Stack Initialization
;
; The following EQU statements define the stack pointer for reentrant
; functions and initialized it:
;
; <h> Stack Space for reentrant functions in the SMALL model.
; <q> IBPSTACK: Enable SMALL model reentrant stack
; <i> Stack space for reentrant functions in the SMALL model.
IBPSTACK EQU 0 ; set to 1 if small reentrant is used.
; <o> IBPSTACKTOP: End address of SMALL model stack <0x0-0xFF>
; <i> Set the top of the stack to the highest location.
IBPSTACKTOP EQU 0xFF +1 ; default 0FFH+1
; </h>
;
; <h> Stack Space for reentrant functions in the LARGE model.
; <q> XBPSTACK: Enable LARGE model reentrant stack
; <i> Stack space for reentrant functions in the LARGE model.
XBPSTACK EQU 0 ; set to 1 if large reentrant is used.
; <o> XBPSTACKTOP: End address of LARGE model stack <0x0-0xFFFF>
; <i> Set the top of the stack to the highest location.
XBPSTACKTOP EQU 0xFFFF +1 ; default 0FFFFH+1
; </h>
;
; <h> Stack Space for reentrant functions in the COMPACT model.
; <q> PBPSTACK: Enable COMPACT model reentrant stack
; <i> Stack space for reentrant functions in the COMPACT model.
PBPSTACK EQU 0 ; set to 1 if compact reentrant is used.
;
; <o> PBPSTACKTOP: End address of COMPACT model stack <0x0-0xFFFF>
; <i> Set the top of the stack to the highest location.
PBPSTACKTOP EQU 0xFF +1 ; default 0FFH+1
; </h>
;</h>
;------------------------------------------------------------------------------
;
; Memory Page for Using the Compact Model with 64 KByte xdata RAM
; <e>Compact Model Page Definition
;
; <i>Define the XDATA page used for PDATA variables.
; <i>PPAGE must conform with the PPAGE set in the linker invocation.
;
; Enable pdata memory page initalization
PPAGEENABLE EQU 0 ; set to 1 if pdata object are used.
;
; <o> PPAGE number <0x0-0xFF>
; <i> uppermost 256-byte address of the page used for PDATA variables.
PPAGE EQU 0
;
; <o> SFR address which supplies uppermost address byte <0x0-0xFF>
; <i> most 8051 variants use P2 as uppermost address byte
PPAGE_SFR DATA 0A0H
;
; </e>
;------------------------------------------------------------------------------
; Standard SFR Symbols
ACC DATA 0E0H
B DATA 0F0H
SP DATA 81H
DPL DATA 82H
DPH DATA 83H
NAME ?C_STARTUP
?C_C51STARTUP SEGMENT CODE
?STACK SEGMENT IDATA
RSEG ?STACK
DS 1
EXTRN CODE (?C_START)
PUBLIC ?C_STARTUP
CSEG AT 0
?C_STARTUP: LJMP STARTUP1
RSEG ?C_C51STARTUP
STARTUP1:
IF IDATALEN <> 0
MOV R0,#IDATALEN - 1
CLR A
IDATALOOP: MOV @R0,A
DJNZ R0,IDATALOOP
ENDIF
IF XDATALEN <> 0
MOV DPTR,#XDATASTART
MOV R7,#LOW (XDATALEN)
IF (LOW (XDATALEN)) <> 0
MOV R6,#(HIGH (XDATALEN)) +1
ELSE
MOV R6,#HIGH (XDATALEN)
ENDIF
CLR A
XDATALOOP: MOVX @DPTR,A
INC DPTR
DJNZ R7,XDATALOOP
DJNZ R6,XDATALOOP
ENDIF
IF PPAGEENABLE <> 0
MOV PPAGE_SFR,#PPAGE
ENDIF
IF PDATALEN <> 0
MOV R0,#LOW (PDATASTART)
MOV R7,#LOW (PDATALEN)
CLR A
PDATALOOP: MOVX @R0,A
INC R0
DJNZ R7,PDATALOOP
ENDIF
IF IBPSTACK <> 0
EXTRN DATA (?C_IBP)
MOV ?C_IBP,#LOW IBPSTACKTOP
ENDIF
IF XBPSTACK <> 0
EXTRN DATA (?C_XBP)
MOV ?C_XBP,#HIGH XBPSTACKTOP
MOV ?C_XBP+1,#LOW XBPSTACKTOP
ENDIF
IF PBPSTACK <> 0
EXTRN DATA (?C_PBP)
MOV ?C_PBP,#LOW PBPSTACKTOP
ENDIF
MOV SP,#?STACK-1
; This code is required if you use L51_BANK.A51 with Banking Mode 4
;<h> Code Banking
; <q> Select Bank 0 for L51_BANK.A51 Mode 4
#if 0
; <i> Initialize bank mechanism to code bank 0 when using L51_BANK.A51 with Banking Mode 4.
EXTRN CODE (?B_SWITCH0)
CALL ?B_SWITCH0 ; init bank mechanism to code bank 0
#endif
;</h>
LJMP ?C_START
END
|
2301_77966824/51-microcontroller-learning
|
HolleWorld/STARTUP.A51
|
Assembly
|
unknown
| 6,178
|
$NOMOD51
;------------------------------------------------------------------------------
; This file is part of the C51 Compiler package
; Copyright (c) 1988-2005 Keil Elektronik GmbH and Keil Software, Inc.
; Version 8.01
;
; *** <<< Use Configuration Wizard in Context Menu >>> ***
;------------------------------------------------------------------------------
; STARTUP.A51: This code is executed after processor reset.
;
; To translate this file use A51 with the following invocation:
;
; A51 STARTUP.A51
;
; To link the modified STARTUP.OBJ file to your application use the following
; Lx51 invocation:
;
; Lx51 your object file list, STARTUP.OBJ controls
;
;------------------------------------------------------------------------------
;
; User-defined <h> Power-On Initialization of Memory
;
; With the following EQU statements the initialization of memory
; at processor reset can be defined:
;
; <o> IDATALEN: IDATA memory size <0x0-0x100>
; <i> Note: The absolute start-address of IDATA memory is always 0
; <i> The IDATA space overlaps physically the DATA and BIT areas.
IDATALEN EQU 80H
;
; <o> XDATASTART: XDATA memory start address <0x0-0xFFFF>
; <i> The absolute start address of XDATA memory
XDATASTART EQU 0
;
; <o> XDATALEN: XDATA memory size <0x0-0xFFFF>
; <i> The length of XDATA memory in bytes.
XDATALEN EQU 0
;
; <o> PDATASTART: PDATA memory start address <0x0-0xFFFF>
; <i> The absolute start address of PDATA memory
PDATASTART EQU 0H
;
; <o> PDATALEN: PDATA memory size <0x0-0xFF>
; <i> The length of PDATA memory in bytes.
PDATALEN EQU 0H
;
;</h>
;------------------------------------------------------------------------------
;
;<h> Reentrant Stack Initialization
;
; The following EQU statements define the stack pointer for reentrant
; functions and initialized it:
;
; <h> Stack Space for reentrant functions in the SMALL model.
; <q> IBPSTACK: Enable SMALL model reentrant stack
; <i> Stack space for reentrant functions in the SMALL model.
IBPSTACK EQU 0 ; set to 1 if small reentrant is used.
; <o> IBPSTACKTOP: End address of SMALL model stack <0x0-0xFF>
; <i> Set the top of the stack to the highest location.
IBPSTACKTOP EQU 0xFF +1 ; default 0FFH+1
; </h>
;
; <h> Stack Space for reentrant functions in the LARGE model.
; <q> XBPSTACK: Enable LARGE model reentrant stack
; <i> Stack space for reentrant functions in the LARGE model.
XBPSTACK EQU 0 ; set to 1 if large reentrant is used.
; <o> XBPSTACKTOP: End address of LARGE model stack <0x0-0xFFFF>
; <i> Set the top of the stack to the highest location.
XBPSTACKTOP EQU 0xFFFF +1 ; default 0FFFFH+1
; </h>
;
; <h> Stack Space for reentrant functions in the COMPACT model.
; <q> PBPSTACK: Enable COMPACT model reentrant stack
; <i> Stack space for reentrant functions in the COMPACT model.
PBPSTACK EQU 0 ; set to 1 if compact reentrant is used.
;
; <o> PBPSTACKTOP: End address of COMPACT model stack <0x0-0xFFFF>
; <i> Set the top of the stack to the highest location.
PBPSTACKTOP EQU 0xFF +1 ; default 0FFH+1
; </h>
;</h>
;------------------------------------------------------------------------------
;
; Memory Page for Using the Compact Model with 64 KByte xdata RAM
; <e>Compact Model Page Definition
;
; <i>Define the XDATA page used for PDATA variables.
; <i>PPAGE must conform with the PPAGE set in the linker invocation.
;
; Enable pdata memory page initalization
PPAGEENABLE EQU 0 ; set to 1 if pdata object are used.
;
; <o> PPAGE number <0x0-0xFF>
; <i> uppermost 256-byte address of the page used for PDATA variables.
PPAGE EQU 0
;
; <o> SFR address which supplies uppermost address byte <0x0-0xFF>
; <i> most 8051 variants use P2 as uppermost address byte
PPAGE_SFR DATA 0A0H
;
; </e>
;------------------------------------------------------------------------------
; Standard SFR Symbols
ACC DATA 0E0H
B DATA 0F0H
SP DATA 81H
DPL DATA 82H
DPH DATA 83H
NAME ?C_STARTUP
?C_C51STARTUP SEGMENT CODE
?STACK SEGMENT IDATA
RSEG ?STACK
DS 1
EXTRN CODE (?C_START)
PUBLIC ?C_STARTUP
CSEG AT 0
?C_STARTUP: LJMP STARTUP1
RSEG ?C_C51STARTUP
STARTUP1:
IF IDATALEN <> 0
MOV R0,#IDATALEN - 1
CLR A
IDATALOOP: MOV @R0,A
DJNZ R0,IDATALOOP
ENDIF
IF XDATALEN <> 0
MOV DPTR,#XDATASTART
MOV R7,#LOW (XDATALEN)
IF (LOW (XDATALEN)) <> 0
MOV R6,#(HIGH (XDATALEN)) +1
ELSE
MOV R6,#HIGH (XDATALEN)
ENDIF
CLR A
XDATALOOP: MOVX @DPTR,A
INC DPTR
DJNZ R7,XDATALOOP
DJNZ R6,XDATALOOP
ENDIF
IF PPAGEENABLE <> 0
MOV PPAGE_SFR,#PPAGE
ENDIF
IF PDATALEN <> 0
MOV R0,#LOW (PDATASTART)
MOV R7,#LOW (PDATALEN)
CLR A
PDATALOOP: MOVX @R0,A
INC R0
DJNZ R7,PDATALOOP
ENDIF
IF IBPSTACK <> 0
EXTRN DATA (?C_IBP)
MOV ?C_IBP,#LOW IBPSTACKTOP
ENDIF
IF XBPSTACK <> 0
EXTRN DATA (?C_XBP)
MOV ?C_XBP,#HIGH XBPSTACKTOP
MOV ?C_XBP+1,#LOW XBPSTACKTOP
ENDIF
IF PBPSTACK <> 0
EXTRN DATA (?C_PBP)
MOV ?C_PBP,#LOW PBPSTACKTOP
ENDIF
MOV SP,#?STACK-1
; This code is required if you use L51_BANK.A51 with Banking Mode 4
;<h> Code Banking
; <q> Select Bank 0 for L51_BANK.A51 Mode 4
#if 0
; <i> Initialize bank mechanism to code bank 0 when using L51_BANK.A51 with Banking Mode 4.
EXTRN CODE (?B_SWITCH0)
CALL ?B_SWITCH0 ; init bank mechanism to code bank 0
#endif
;</h>
LJMP ?C_START
END
|
2301_77966824/51-microcontroller-learning
|
流水灯/STARTUP.A51
|
Assembly
|
unknown
| 6,178
|
#include<STC89C5xRC.H>
#include<INTRINS.H>
void Delay100ms(void) //@11.0592MHz
{
unsigned char data i, j;
i = 180;
j = 73;
do
{
while (--j);
} while (--i);
}
void main()
{
unsigned char tmp=0x01;
while(1)
{
P0=~tmp;
Delay100ms();
tmp <<=1;
if(tmp==0x00)
{
tmp=0x01;
}
}
}
|
2301_77966824/51-microcontroller-learning
|
流水灯/main.c
|
C
|
unknown
| 305
|
$NOMOD51
;------------------------------------------------------------------------------
; This file is part of the C51 Compiler package
; Copyright (c) 1988-2005 Keil Elektronik GmbH and Keil Software, Inc.
; Version 8.01
;
; *** <<< Use Configuration Wizard in Context Menu >>> ***
;------------------------------------------------------------------------------
; STARTUP.A51: This code is executed after processor reset.
;
; To translate this file use A51 with the following invocation:
;
; A51 STARTUP.A51
;
; To link the modified STARTUP.OBJ file to your application use the following
; Lx51 invocation:
;
; Lx51 your object file list, STARTUP.OBJ controls
;
;------------------------------------------------------------------------------
;
; User-defined <h> Power-On Initialization of Memory
;
; With the following EQU statements the initialization of memory
; at processor reset can be defined:
;
; <o> IDATALEN: IDATA memory size <0x0-0x100>
; <i> Note: The absolute start-address of IDATA memory is always 0
; <i> The IDATA space overlaps physically the DATA and BIT areas.
IDATALEN EQU 80H
;
; <o> XDATASTART: XDATA memory start address <0x0-0xFFFF>
; <i> The absolute start address of XDATA memory
XDATASTART EQU 0
;
; <o> XDATALEN: XDATA memory size <0x0-0xFFFF>
; <i> The length of XDATA memory in bytes.
XDATALEN EQU 0
;
; <o> PDATASTART: PDATA memory start address <0x0-0xFFFF>
; <i> The absolute start address of PDATA memory
PDATASTART EQU 0H
;
; <o> PDATALEN: PDATA memory size <0x0-0xFF>
; <i> The length of PDATA memory in bytes.
PDATALEN EQU 0H
;
;</h>
;------------------------------------------------------------------------------
;
;<h> Reentrant Stack Initialization
;
; The following EQU statements define the stack pointer for reentrant
; functions and initialized it:
;
; <h> Stack Space for reentrant functions in the SMALL model.
; <q> IBPSTACK: Enable SMALL model reentrant stack
; <i> Stack space for reentrant functions in the SMALL model.
IBPSTACK EQU 0 ; set to 1 if small reentrant is used.
; <o> IBPSTACKTOP: End address of SMALL model stack <0x0-0xFF>
; <i> Set the top of the stack to the highest location.
IBPSTACKTOP EQU 0xFF +1 ; default 0FFH+1
; </h>
;
; <h> Stack Space for reentrant functions in the LARGE model.
; <q> XBPSTACK: Enable LARGE model reentrant stack
; <i> Stack space for reentrant functions in the LARGE model.
XBPSTACK EQU 0 ; set to 1 if large reentrant is used.
; <o> XBPSTACKTOP: End address of LARGE model stack <0x0-0xFFFF>
; <i> Set the top of the stack to the highest location.
XBPSTACKTOP EQU 0xFFFF +1 ; default 0FFFFH+1
; </h>
;
; <h> Stack Space for reentrant functions in the COMPACT model.
; <q> PBPSTACK: Enable COMPACT model reentrant stack
; <i> Stack space for reentrant functions in the COMPACT model.
PBPSTACK EQU 0 ; set to 1 if compact reentrant is used.
;
; <o> PBPSTACKTOP: End address of COMPACT model stack <0x0-0xFFFF>
; <i> Set the top of the stack to the highest location.
PBPSTACKTOP EQU 0xFF +1 ; default 0FFH+1
; </h>
;</h>
;------------------------------------------------------------------------------
;
; Memory Page for Using the Compact Model with 64 KByte xdata RAM
; <e>Compact Model Page Definition
;
; <i>Define the XDATA page used for PDATA variables.
; <i>PPAGE must conform with the PPAGE set in the linker invocation.
;
; Enable pdata memory page initalization
PPAGEENABLE EQU 0 ; set to 1 if pdata object are used.
;
; <o> PPAGE number <0x0-0xFF>
; <i> uppermost 256-byte address of the page used for PDATA variables.
PPAGE EQU 0
;
; <o> SFR address which supplies uppermost address byte <0x0-0xFF>
; <i> most 8051 variants use P2 as uppermost address byte
PPAGE_SFR DATA 0A0H
;
; </e>
;------------------------------------------------------------------------------
; Standard SFR Symbols
ACC DATA 0E0H
B DATA 0F0H
SP DATA 81H
DPL DATA 82H
DPH DATA 83H
NAME ?C_STARTUP
?C_C51STARTUP SEGMENT CODE
?STACK SEGMENT IDATA
RSEG ?STACK
DS 1
EXTRN CODE (?C_START)
PUBLIC ?C_STARTUP
CSEG AT 0
?C_STARTUP: LJMP STARTUP1
RSEG ?C_C51STARTUP
STARTUP1:
IF IDATALEN <> 0
MOV R0,#IDATALEN - 1
CLR A
IDATALOOP: MOV @R0,A
DJNZ R0,IDATALOOP
ENDIF
IF XDATALEN <> 0
MOV DPTR,#XDATASTART
MOV R7,#LOW (XDATALEN)
IF (LOW (XDATALEN)) <> 0
MOV R6,#(HIGH (XDATALEN)) +1
ELSE
MOV R6,#HIGH (XDATALEN)
ENDIF
CLR A
XDATALOOP: MOVX @DPTR,A
INC DPTR
DJNZ R7,XDATALOOP
DJNZ R6,XDATALOOP
ENDIF
IF PPAGEENABLE <> 0
MOV PPAGE_SFR,#PPAGE
ENDIF
IF PDATALEN <> 0
MOV R0,#LOW (PDATASTART)
MOV R7,#LOW (PDATALEN)
CLR A
PDATALOOP: MOVX @R0,A
INC R0
DJNZ R7,PDATALOOP
ENDIF
IF IBPSTACK <> 0
EXTRN DATA (?C_IBP)
MOV ?C_IBP,#LOW IBPSTACKTOP
ENDIF
IF XBPSTACK <> 0
EXTRN DATA (?C_XBP)
MOV ?C_XBP,#HIGH XBPSTACKTOP
MOV ?C_XBP+1,#LOW XBPSTACKTOP
ENDIF
IF PBPSTACK <> 0
EXTRN DATA (?C_PBP)
MOV ?C_PBP,#LOW PBPSTACKTOP
ENDIF
MOV SP,#?STACK-1
; This code is required if you use L51_BANK.A51 with Banking Mode 4
;<h> Code Banking
; <q> Select Bank 0 for L51_BANK.A51 Mode 4
#if 0
; <i> Initialize bank mechanism to code bank 0 when using L51_BANK.A51 with Banking Mode 4.
EXTRN CODE (?B_SWITCH0)
CALL ?B_SWITCH0 ; init bank mechanism to code bank 0
#endif
;</h>
LJMP ?C_START
END
|
2301_77966824/51-microcontroller-learning
|
闪烁LED/STARTUP.A51
|
Assembly
|
unknown
| 6,178
|
#include<STC89C5xRC.H>
#include<INTRINS.H>
void Delay500ms(void) //@11.0592MHz
{
unsigned char data i, j, k;
_nop_();
i = 4;
j = 129;
k = 119;
do
{
do
{
while (--k);
} while (--j);
} while (--i);
}
void main()
{
while(1)
{
P00 = ~P00;
Delay500ms();
}
}
|
2301_77966824/51-microcontroller-learning
|
闪烁LED/main.c
|
C
|
unknown
| 280
|
#include <stdio.h>
#include <time.h>
int main() {
// Get the rime information
time_t rawtime;
struct tm *timeinfo;
time(&rawtime);
timeinfo = localtime(&rawtime);
// Translate time information to strings
char buffer[80];
strftime(buffer, 80, "%Y-%m-%d %H:%M:%S", timeinfo);
// Print the strings
printf("Now Time is: %s\n", buffer);
return 0;
}
|
2301_78305256/3121006433
|
time.c
|
C
|
unknown
| 404
|
t=0:pi/20:2*pi; %定义参数t[0,2pi]，每隔pi/100标注一个。
[x,y,z]= cylinder(2+sin(t),30); %将x,y,z定义为花瓶型并绘图(cylinder本身为圆柱，这里将表面调整了)
subplot(2,2,1); %创建一个可以画四个图的子图，这里画其中第一个
mesh(x,y,z); %mesh为绘制网格曲面图的函数
subplot(2,2,2); %画第二个图
[x,y,z]=sphere; %将x,y,z定义为球形并绘图
mesh(x,y,z);
subplot(2,1,2); %画第三个图
[x,y,z]=peaks(30); %将x,y,z定义为多峰型并绘图
mesh(x,y,z);
|
2301_77966824/some-learning-about-matlab
|
2023/2023_01_18/2023_01_18_01.m
|
MATLAB
|
unknown
| 673
|
x = 0:0.5:4;
y = x*2;
plot(x,y)
|
2301_77966824/some-learning-about-matlab
|
2024/2024_03_04.m
|
MATLAB
|
unknown
| 31
|
pi
format longE
pi
format shortE
|
2301_77966824/some-learning-about-matlab
|
2024/2024_05/2024_05_21/2024_05_21_01/m2024_05_21_01.m
|
Objective-C
|
unknown
| 32
|
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>重庆大学文渊阁读书协会 - 赛博文学自留地</title>
<style>
:root {
--neon-cyan: #00ffff;
--neon-pink: #ff006e;
--neon-purple: #8b00ff;
--deep-black: #0a0a0a;
--glass-white: rgba(255, 255, 255, 0.05);
--glass-border: rgba(255, 255, 255, 0.1);
}
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
html {
scroll-behavior: smooth;
}
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'PingFang SC', sans-serif;
background: var(--deep-black);
color: #fff;
overflow-x: hidden;
cursor: none;
}
/* 自定义光标样式 */
.cursor {
position: fixed;
width: 20px;
height: 20px;
border: 2px solid var(--neon-cyan);
border-radius: 50%;
pointer-events: none;
z-index: 9999;
mix-blend-mode: difference;
transition: transform 0.2s ease;
}
.cursor-follower {
position: fixed;
width: 40px;
height: 40px;
background: radial-gradient(circle, rgba(0, 255, 255, 0.2) 0%, transparent 70%);
border-radius: 50%;
pointer-events: none;
z-index: 9998;
transition: transform 0.3s ease;
}
.cursor.hover {
transform: scale(1.5);
background-color: var(--neon-pink);
border-color: var(--neon-pink);
}
.cursor-follower.hover {
transform: scale(1.2);
background: radial-gradient(circle, rgba(255, 0, 110, 0.3) 0%, transparent 70%);
}
.cursor.click {
transform: scale(0.8);
background-color: var(--neon-purple);
border-color: var(--neon-purple);
}
#matrix-rain {
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
z-index: -1;
opacity: 0.3;
}
/* 导航栏样式 */
.navbar {
position: fixed;
top: 0;
left: 0;
width: 100%;
background: rgba(10, 10, 10, 0.9);
backdrop-filter: blur(10px);
border-bottom: 1px solid var(--glass-border);
z-index: 1000;
padding: 15px 0;
transition: all 0.3s ease;
}
.navbar-container {
max-width: 1200px;
margin: 0 auto;
display: flex;
justify-content: space-between;
align-items: center;
padding: 0 20px;
}
.navbar-brand {
font-size: 1.5rem;
font-weight: 700;
background: linear-gradient(90deg, var(--neon-cyan), var(--neon-pink));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
text-decoration: none;
}
.navbar-nav {
display: flex;
list-style: none;
gap: 30px;
}
.navbar-nav a {
color: rgba(255, 255, 255, 0.8);
text-decoration: none;
font-weight: 500;
transition: all 0.3s ease;
position: relative;
padding: 5px 0;
}
.navbar-nav a:hover {
color: var(--neon-cyan);
}
.navbar-nav a::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 0;
height: 2px;
background: var(--neon-cyan);
transition: width 0.3s ease;
}
.navbar-nav a:hover::after {
width: 100%;
}
/* 主内容区域 */
.hero {
height: 100vh;
display: flex;
align-items: center;
justify-content: center;
position: relative;
overflow: hidden;
}
.hero::before {
content: '';
position: absolute;
inset: 0;
background: radial-gradient(circle at center, transparent 0%, rgba(0, 0, 0, 0.8) 100%);
z-index: 1;
}
.hero-content {
text-align: center;
z-index: 2;
position: relative;
}
.main-title {
font-size: 12vw;
font-weight: 900;
background: linear-gradient(45deg, var(--neon-cyan), var(--neon-pink), var(--neon-purple));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
animation: title-pulse 3s ease-in-out infinite;
}
@keyframes title-pulse {
0%, 100% {
transform: scale(1);
}
50% {
transform: scale(1.02);
}
}
.subtitle {
font-size: 1.2rem;
color: rgba(255, 255, 255, 0.8);
font-weight: 300;
letter-spacing: 0.2em;
text-transform: uppercase;
opacity: 0;
animation: fade-in-up 1s ease-out 0.5s forwards;
}
@keyframes fade-in-up {
from {
opacity: 0;
transform: translateY(30px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
.philosophy-section {
padding: 100px 20px;
background: linear-gradient(180deg, rgba(0, 0, 0, 0.9) 0%, rgba(0, 0, 0, 0.5) 100%);
}
.philosophy-text {
max-width: 800px;
margin: 0 auto;
font-size: 1.2rem;
line-height: 2;
text-align: center;
color: rgba(255, 255, 255, 0.9);
}
.activities-showcase {
padding: 80px 20px;
}
.section-title {
font-size: 2.5rem;
font-weight: 700;
text-align: center;
margin-bottom: 50px;
background: linear-gradient(90deg, var(--neon-cyan), var(--neon-pink));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.activity-grid {
display: grid;
gap: 30px;
max-width: 1200px;
margin: 0 auto;
}
.activity-card {
background: var(--glass-white);
border: 1px solid var(--glass-border);
border-radius: 20px;
padding: 30px;
position: relative;
overflow: hidden;
transition: all 0.3s ease;
}
.activity-card:hover {
transform: translateY(-8px);
box-shadow: 0 20px 40px rgba(0, 255, 255, 0.2);
border-color: var(--neon-cyan);
}
.card-number {
position: absolute;
top: 15px;
right: 20px;
font-size: 3rem;
font-weight: 900;
color: rgba(255, 255, 255, 0.1);
}
.card-title {
font-size: 1.5rem;
margin-bottom: 15px;
color: var(--neon-cyan);
}
.card-description {
font-size: 1rem;
line-height: 1.8;
color: rgba(255, 255, 255, 0.8);
margin-bottom: 20px;
}
.card-link {
display: inline-block;
color: var(--neon-pink);
text-decoration: none;
font-weight: 500;
transition: all 0.3s ease;
}
.card-link:hover {
text-shadow: 0 0 8px var(--neon-pink);
}
.departments-section {
padding: 80px 20px;
background: linear-gradient(135deg, var(--deep-black) 0%, #2a2a2a 100%);
}
.dept-container {
display: flex;
flex-wrap: wrap;
justify-content: center;
gap: 40px;
}
.dept-item {
width: 280px;
height: 280px;
position: relative;
transform-style: preserve-3d;
transition: transform 0.6s ease;
cursor: pointer;
}
.dept-item:hover {
transform: rotateY(180deg);
}
.dept-front, .dept-back {
position: absolute;
width: 100%;
height: 100%;
backface-visibility: hidden;
border-radius: 20px;
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
padding: 25px;
background: var(--glass-white);
border: 1px solid var(--glass-border);
}
.dept-back {
transform: rotateY(180deg);
background: linear-gradient(135deg, var(--neon-cyan), var(--neon-purple));
border: none;
}
.dept-icon {
font-size: 2.5rem;
margin-bottom: 15px;
}
.dept-name {
font-size: 1.4rem;
font-weight: 600;
color: var(--neon-cyan);
}
.dept-back .dept-name {
color: #fff;
margin-bottom: 15px;
}
.dept-desc {
color: rgba(255, 255, 255, 0.9);
text-align: center;
line-height: 1.6;
}
.learn-more-btn {
margin-top: 15px;
background: none;
border: none;
color: white;
cursor: pointer;
font-size: 1rem;
font-weight: 500;
transition: all 0.3s ease;
padding: 8px 16px;
border-radius: 20px;
border: 1px solid rgba(255, 255, 255, 0.3);
}
.learn-more-btn:hover {
background: rgba(255, 255, 255, 0.1);
text-shadow: 0 0 8px white;
}
/* 部门详情页样式 */
.dept-detail-page {
display: none;
position: fixed;
top: 0;
left: 0;
width: 100%;
height: 100%;
background: rgba(0, 0, 0, 0.9);
z-index: 2000;
overflow-y: auto;
padding: 20px;
}
.dept-detail-content {
max-width: 1000px;
margin: 80px auto;
background: var(--deep-black);
border-radius: 20px;
overflow: hidden;
box-shadow: 0 0 50px rgba(0, 255, 255, 0.3);
border: 1px solid var(--neon-cyan);
}
.dept-detail-header {
height: 300px;
position: relative;
overflow: hidden;
}
.dept-detail-image {
width: 100%;
height: 100%;
object-fit: cover;
filter: brightness(0.7);
}
.dept-detail-overlay {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
background: linear-gradient(to bottom, transparent 0%, rgba(0, 0, 0, 0.8) 100%);
display: flex;
align-items: flex-end;
padding: 30px;
}
.dept-detail-title {
font-size: 3rem;
font-weight: 900;
background: linear-gradient(90deg, var(--neon-cyan), var(--neon-pink));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
margin-bottom: 10px;
}
.dept-detail-body {
padding: 40px;
}
.dept-detail-description {
font-size: 1.2rem;
line-height: 1.8;
margin-bottom: 30px;
color: rgba(255, 255, 255, 0.9);
}
.dept-detail-features {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 20px;
margin-bottom: 40px;
}
.feature-item {
background: var(--glass-white);
border: 1px solid var(--glass-border);
border-radius: 10px;
padding: 20px;
transition: all 0.3s ease;
}
.feature-item:hover {
border-color: var(--neon-cyan);
transform: translateY(-5px);
}
.feature-title {
font-size: 1.2rem;
color: var(--neon-cyan);
margin-bottom: 10px;
}
.feature-desc {
color: rgba(255, 255, 255, 0.8);
line-height: 1.6;
}
.close-detail {
position: absolute;
top: 20px;
right: 20px;
width: 40px;
height: 40px;
background: rgba(0, 0, 0, 0.5);
border: 1px solid var(--neon-cyan);
border-radius: 50%;
display: flex;
align-items: center;
justify-content: center;
color: var(--neon-cyan);
font-size: 1.5rem;
cursor: pointer;
transition: all 0.3s ease;
z-index: 10;
}
.close-detail:hover {
background: var(--neon-cyan);
color: var(--deep-black);
}
.join-cta {
padding: 100px 20px;
text-align: center;
background: radial-gradient(circle at center, rgba(0, 255, 255, 0.1) 0%, transparent 70%);
}
.cta-title {
font-size: 2.8rem;
font-weight: 900;
margin-bottom: 20px;
background: linear-gradient(45deg, var(--neon-cyan), var(--neon-pink));
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
animation: glitch-text 2s infinite;
}
@keyframes glitch-text {
0%, 100% {
text-shadow: none;
}
25% {
text-shadow: -2px 0 var(--neon-pink), 2px 0 var(--neon-cyan);
}
75% {
text-shadow: 2px 0 var(--neon-pink), -2px 0 var(--neon-cyan);
}
}
.cta-subtitle {
font-size: 1.2rem;
color: rgba(255, 255, 255, 0.8);
margin-bottom: 40px;
letter-spacing: 0.1em;
line-height: 1.6;
}
.join-button {
display: inline-block;
padding: 16px 50px;
background: linear-gradient(45deg, var(--neon-cyan), var(--neon-purple));
color: #fff;
text-decoration: none;
border-radius: 50px;
font-size: 1.1rem;
font-weight: 600;
transition: all 0.3s ease;
box-shadow: 0 10px 30px rgba(0, 255, 255, 0.3);
}
.join-button:hover {
transform: translateY(-3px);
box-shadow: 0 15px 40px rgba(0, 255, 255, 0.5);
}
.wechat-info {
margin-top: 30px;
font-size: 1rem;
color: rgba(255, 255, 255, 0.6);
}
.wechat-id {
display: inline-block;
margin-top: 10px;
padding: 8px 16px;
border: 1px solid var(--neon-cyan);
border-radius: 30px;
color: var(--neon-cyan);
transition: all 0.3s ease;
}
.wechat-id:hover {
background: var(--neon-cyan);
color: var(--deep-black);
}
/* 滚动动画 */
.fade-in {
opacity: 0;
transform: translateY(30px);
transition: all 0.8s ease;
}
.fade-in.visible {
opacity: 1;
transform: translateY(0);
}
/* 小屏优化 */
@media (min-width: 600px) {
.activity-grid {
grid-template-columns: repeat(2, 1fr);
}
.main-title {
font-size: 5rem;
}
}
/* 移动设备优化 */
@media (max-width: 768px) {
.cursor, .cursor-follower {
display: none; /* 在移动设备上隐藏光标 */
}
body {
cursor: auto;
}
.main-title {
font-size: 3.5rem;
}
.section-title {
font-size: 2rem;
}
.dept-item {
width: 250px;
height: 250px;
}
.cta-title {
font-size: 2rem;
}
.join-button {
padding: 14px 40px;
font-size: 1rem;
}
.activity-grid {
grid-template-columns: 1fr;
}
.navbar-nav {
gap: 15px;
}
.navbar-nav a {
font-size: 0.9rem;
}
.dept-detail-title {
font-size: 2rem;
}
.dept-detail-body {
padding: 20px;
}
.dept-detail-features {
grid-template-columns: 1fr;
}
}
</style>
</head>
<body>
<!-- 自定义光标 -->
<div class="cursor"></div>
<div class="cursor-follower"></div>
<!-- 导航栏 -->
<nav class="navbar">
<div class="navbar-container">
<a href="#" class="navbar-brand">文渊阁</a>
<ul class="navbar-nav">
<li><a href="#home">首页</a></li>
<li><a href="#philosophy">理念</a></li>
<li><a href="#activities">活动</a></li>
<li><a href="#departments">部门</a></li>
<li><a href="#join">加入我们</a></li>
</ul>
</div>
</nav>
<!-- 矩阵雨背景 -->
<canvas id="matrix-rain"></canvas>
<!-- 主内容区域 -->
<section class="hero" id="home">
<div class="hero-content">
<h1 class="main-title">文渊阁</h1>
<p class="subtitle">赛博空间的哲学栖息地</p>
</div>
</section>
<section class="philosophy-section" id="philosophy">
<div class="philosophy-text fade-in">
当纸质书成为生活的不必要品，碎片化的信息构成了人与人交往的互联世界，
线下小规模的读书协会存在是否还有意义？当经费不足以支撑我们精神梦想的时候，
我们的社团成为了这样的一个赛博社团——一群"小众"的学生，通过大众的科技聚集在一起，
重建精神家园的收容所。
</div>
</section>
<section class="activities-showcase" id="activities">
<h2 class="section-title fade-in">RECENT ACTIVITIES</h2>
<div class="activity-grid">
<div class="activity-card fade-in">
<div class="card-number">02</div>
<h3 class="card-title">博雅·读书·生活系列活动（二）</h3>
<p class="card-description">在赛博朋克的世界里，我们寻找着人文与科技的平衡点。让阅读成为生活的防火墙，抵御信息的洪流。</p>
<a href="https://mp.weixin.qq.com/s/ZMoOvZ2y837wuklNwUXABQ" class="card-link" target="_blank">点此查看活动详情 →</a>
</div>
<div class="activity-card fade-in">
<div class="card-number">01</div>
<h3 class="card-title">博雅·读书·生活系列活动（一）</h3>
<p class="card-description">当二进制遇见诗经，当算法碰撞哲学。在这里，我们用代码编译诗意，用数据流传承文明。</p>
<a href="https://mp.weixin.qq.com/s/o3Ty5l7M5iFOn6yUCw2sPQ" class="card-link" target="_blank">点此查看活动详情 →</a>
</div>
</div>
</section>
<section class="departments-section" id="departments">
<h2 class="section-title fade-in">DEPARTMENTS</h2>
<div class="dept-container">
<div class="dept-item fade-in" data-dept="propaganda">
<div class="dept-front">
<div class="dept-icon">📡</div>
<h3 class="dept-name">宣传部</h3>
</div>
<div class="dept-back">
<h3 class="dept-name">宣传部</h3>
<p class="dept-desc">在信息的海洋中，我们是灯塔的守护者。用创意的代码，编译思想的火花。</p>
<button class="learn-more-btn">了解更多 →</button>
</div>
</div>
<div class="dept-item fade-in" data-dept="organization">
<div class="dept-front">
<div class="dept-icon">⚙️</div>
<h3 class="dept-name">组织部</h3>
</div>
<div class="dept-back">
<h3 class="dept-name">组织部</h3>
<p class="dept-desc">协调虚拟与现实的接口，管理人文与科技的融合。让每一次活动都成为一次思想的升级。</p>
<button class="learn-more-btn">了解更多 →</button>
</div>
</div>
<div class="dept-item fade-in" data-dept="learning">
<div class="dept-front">
<div class="dept-icon">🔮</div>
<h3 class="dept-name">学习部</h3>
</div>
<div class="dept-back">
<h3 class="dept-name">学习部</h3>
<p class="dept-desc">在知识的矩阵中探索，让每一本书都成为通往新世界的端口。构建思维的神经网络。</p>
<button class="learn-more-btn">了解更多 →</button>
</div>
</div>
</div>
</section>
<!-- 部门详情页 -->
<div id="propaganda-detail" class="dept-detail-page">
<div class="dept-detail-content">
<div class="dept-detail-header">
<img src="" alt="宣传部" class="dept-detail-image" id="propaganda-image">
<div class="dept-detail-overlay">
<div>
<h2 class="dept-detail-title">宣传部</h2>
<p class="subtitle">信息海洋中的灯塔守护者</p>
</div>
</div>
<div class="close-detail">×</div>
</div>
<div class="dept-detail-body">
<p class="dept-detail-description">
我们热衷于传播知识、扩散梦想，让热爱在校园发光。在信息的洪流中，我们搭建起思想的桥梁，用创意的代码编译思想的火花，让每一个声音都能被听见，每一个梦想都能被看见。
</p>
<div class="dept-detail-features">
<div class="feature-item">
<h3 class="feature-title">创意设计</h3>
<p class="feature-desc">运用赛博美学设计海报、视频和数字内容，让每一次宣传都成为视觉盛宴。</p>
</div>
<div class="feature-item">
<h3 class="feature-title">数字传播</h3>
<p class="feature-desc">通过社交媒体、公众号等数字渠道，将知识与思想传播到校园的每一个角落。</p>
</div>
<div class="feature-item">
<h3 class="feature-title">活动记录</h3>
<p class="feature-desc">用镜头和文字记录每一次活动的精彩瞬间，构建文渊阁的数字记忆库。</p>
</div>
</div>
</div>
</div>
</div>
<div id="organization-detail" class="dept-detail-page">
<div class="dept-detail-content">
<div class="dept-detail-header">
<img src="" alt="组织部" class="dept-detail-image" id="organization-image">
<div class="dept-detail-overlay">
<div>
<h2 class="dept-detail-title">组织部</h2>
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<div class="dept-detail-features">
<div class="feature-item">
<h3 class="feature-title">读书会组织</h3>
<p class="feature-desc">策划并主持各类主题读书会，促进思想交流与知识共享。</p>
</div>
<div class="feature-item">
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if (!e.target.classList.contains('learn-more-btn')) {
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item.style.transform = 'rotateY(0deg)';
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item.style.transform = 'rotateY(180deg)';
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button.addEventListener('click', () => {
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page.style.display = 'none';
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document.body.style.overflow = 'auto';
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// 点击详情页背景关闭
detailPages.forEach(page => {
page.addEventListener('click', (e) => {
if (e.target === page) {
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document.body.style.overflow = 'auto';
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</body>
</html>
|
2301_78337374/wenyuange
|
文渊阁25纳新-1014.html
|
HTML
|
unknown
| 36,746
|
from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import os
import logging
from config import config
from utils.session_manager import SessionManager
from utils.image_processor import ImageProcessor
from utils.gemini_client import GeminiClient
from utils.error_handler import setup_error_handlers
from utils.rate_limiter import RateLimiter
def create_app(config_name=None):
"""创建Flask应用实例"""
app = Flask(__name__)
# 加载配置
config_name = config_name or os.environ.get('FLASK_ENV', 'default')
app.config.from_object(config[config_name])
config[config_name].init_app(app)
# 初始化CORS
CORS(app, origins=app.config['CORS_ORIGINS'])
# 设置日志
if not app.debug:
logging.basicConfig(level=logging.INFO)
# 初始化组件
session_manager = SessionManager(app.config['REDIS_URL'], app.config['SESSION_TIMEOUT'])
image_processor = ImageProcessor(app.config['UPLOAD_FOLDER'], app.config['ALLOWED_EXTENSIONS'])
gemini_client = GeminiClient(app.config['GEMINI_API_KEY'], app.config['GEMINI_MODEL'])
rate_limiter = RateLimiter(app.config['API_RATE_LIMIT'])
# 注册错误处理器
setup_error_handlers(app)
# 注册蓝图
from routes.api import create_api_blueprint
api_bp = create_api_blueprint(session_manager, image_processor, gemini_client, rate_limiter)
app.register_blueprint(api_bp, url_prefix='/api/v1')
# 健康检查端点
@app.route('/health')
def health_check():
return jsonify({
'status': 'healthy',
'service': 'image-generation-proxy',
'version': '1.0.0'
})
# 根路径
@app.route('/')
def index():
return jsonify({
'message': '图生图转接API服务',
'version': '1.0.0',
'endpoints': {
'health': '/health',
'api_docs': '/api/v1/docs',
'generate': '/api/v1/generate',
'session': '/api/v1/session'
}
})
return app
if __name__ == '__main__':
app = create_app()
app.run(host='0.0.0.0', port=5000, debug=True)
|
2301_78526554/tosto
|
app.py
|
Python
|
unknown
| 2,221
|
#!/bin/bash
# 图生图API自动部署脚本
# 适用于Ubuntu/Debian系统
set -e
echo "🚀 开始自动部署图生图API服务..."
# 检测系统
if [[ -f /etc/os-release ]]; then
. /etc/os-release
OS=$NAME
VER=$VERSION_ID
else
echo "❌ 无法检测系统版本"
exit 1
fi
echo "检测到系统: $OS $VER"
# 更新系统
echo "📦 更新系统包..."
if [[ $OS == *"Ubuntu"* ]] || [[ $OS == *"Debian"* ]]; then
sudo apt update && sudo apt upgrade -y
sudo apt install -y python3 python3-pip python3-venv nginx git curl
elif [[ $OS == *"CentOS"* ]] || [[ $OS == *"Red Hat"* ]]; then
sudo dnf update -y
sudo dnf install -y python3 python3-pip nginx git curl
else
echo "❌ 不支持的系统: $OS"
exit 1
fi
# 创建用户和目录
echo "👤 创建服务用户..."
sudo useradd -r -s /bin/false imageapi || true
sudo mkdir -p /opt/image-gen-api
sudo chown imageapi:imageapi /opt/image-gen-api
# 切换到项目目录
cd /opt/image-gen-api
# 如果是从git克隆，取消注释下面的行
# git clone https://github.com/your-repo/image-gen-api.git .
echo "📁 请确保项目文件已上传到 /opt/image-gen-api"
echo "按任意键继续..."
read -n 1
# 创建虚拟环境
echo "🐍 创建Python虚拟环境..."
sudo -u imageapi python3 -m venv venv
sudo -u imageapi ./venv/bin/pip install --upgrade pip
# 安装依赖
echo "📦 安装Python依赖..."
sudo -u imageapi ./venv/bin/pip install -r requirements.txt
# 配置环境变量
echo "⚙️ 配置环境变量..."
if [[ ! -f .env ]]; then
sudo -u imageapi cp .env.production .env
echo "请编辑 /opt/image-gen-api/.env 文件，填入您的Gemini API密钥"
echo "GEMINI_API_KEY=your_api_key_here"
echo "按任意键继续..."
read -n 1
fi
# 创建日志目录
sudo -u imageapi mkdir -p logs uploads
# 生成systemd服务文件
echo "🔧 配置systemd服务..."
cat > /tmp/image-gen-api.service << EOF
[Unit]
Description=图生图转接API服务
After=network.target
[Service]
Type=simple
User=imageapi
Group=imageapi
WorkingDirectory=/opt/image-gen-api
Environment=PATH=/opt/image-gen-api/venv/bin
ExecStart=/opt/image-gen-api/venv/bin/python -m gunicorn -w 4 -b 127.0.0.1:5000 --timeout 120 app:create_app()
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target
EOF
sudo mv /tmp/image-gen-api.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable image-gen-api
# 配置Nginx
echo "🌐 配置Nginx..."
cat > /tmp/image-gen-api-nginx << EOF
server {
listen 80;
server_name _;
client_max_body_size 20M;
location / {
proxy_pass http://127.0.0.1:5000;
proxy_set_header Host \$host;
proxy_set_header X-Real-IP \$remote_addr;
proxy_set_header X-Forwarded-For \$proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto \$scheme;
proxy_connect_timeout 60s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;
}
access_log /var/log/nginx/image-gen-api.access.log;
error_log /var/log/nginx/image-gen-api.error.log;
}
EOF
sudo mv /tmp/image-gen-api-nginx /etc/nginx/sites-available/image-gen-api
sudo ln -sf /etc/nginx/sites-available/image-gen-api /etc/nginx/sites-enabled/
sudo rm -f /etc/nginx/sites-enabled/default
# 测试Nginx配置
sudo nginx -t
# 启动服务
echo "🚀 启动服务..."
sudo systemctl start image-gen-api
sudo systemctl reload nginx
# 检查服务状态
echo "📊 检查服务状态..."
sleep 3
sudo systemctl status image-gen-api --no-pager
# 测试API
echo "🔍 测试API..."
sleep 2
if curl -f http://localhost/health > /dev/null 2>&1; then
echo "✅ API服务正常运行"
else
echo "❌ API服务可能有问题，请检查日志"
sudo journalctl -u image-gen-api --no-pager -n 20
fi
# 配置防火墙
echo "🔒 配置防火墙..."
if command -v ufw > /dev/null; then
sudo ufw allow 80
sudo ufw allow 443
sudo ufw --force enable
elif command -v firewall-cmd > /dev/null; then
sudo firewall-cmd --permanent --add-service=http
sudo firewall-cmd --permanent --add-service=https
sudo firewall-cmd --reload
fi
echo ""
echo "🎉 部署完成！"
echo "================================"
echo "服务状态: sudo systemctl status image-gen-api"
echo "查看日志: sudo journalctl -u image-gen-api -f"
echo "重启服务: sudo systemctl restart image-gen-api"
echo "API地址: http://your-server-ip/"
echo "API文档: http://your-server-ip/api/v1/docs"
echo ""
echo "⚠️ 重要提醒:"
echo "1. 请确保已在 /opt/image-gen-api/.env 中配置正确的API密钥"
echo "2. 如需HTTPS，请配置SSL证书"
echo "3. 建议设置域名解析"
echo "================================"
|
2301_78526554/tosto
|
auto_deploy.sh
|
Shell
|
unknown
| 4,739
|
import os
from dotenv import load_dotenv
load_dotenv()
class Config:
"""应用配置类"""
# Flask基础配置
SECRET_KEY = os.environ.get('SECRET_KEY') or 'dev-secret-key-change-in-production'
DEBUG = os.environ.get('FLASK_DEBUG', 'False').lower() == 'true'
# Gemini API配置
GEMINI_API_KEY = os.environ.get('GEMINI_API_KEY')
GEMINI_MODEL = "gemini-2.0-flash-preview-image-generation"
# Redis配置
REDIS_URL = os.environ.get('REDIS_URL', 'redis://localhost:6379/0')
# 文件上传配置
MAX_CONTENT_LENGTH = int(os.environ.get('MAX_CONTENT_LENGTH', 16777216)) # 16MB
UPLOAD_FOLDER = os.environ.get('UPLOAD_FOLDER', 'uploads')
ALLOWED_EXTENSIONS = set(os.environ.get('ALLOWED_EXTENSIONS', 'png,jpg,jpeg,gif,webp').split(','))
# API配置
API_RATE_LIMIT = int(os.environ.get('API_RATE_LIMIT', 100))
SESSION_TIMEOUT = int(os.environ.get('SESSION_TIMEOUT', 3600))
# CORS配置
CORS_ORIGINS = ["*"] # 生产环境中应该限制具体域名
@staticmethod
def init_app(app):
"""初始化应用配置"""
# 创建上传目录
upload_path = os.path.join(os.getcwd(), Config.UPLOAD_FOLDER)
if not os.path.exists(upload_path):
os.makedirs(upload_path)
class DevelopmentConfig(Config):
"""开发环境配置"""
DEBUG = True
class ProductionConfig(Config):
"""生产环境配置"""
DEBUG = False
config = {
'development': DevelopmentConfig,
'production': ProductionConfig,
'default': DevelopmentConfig
}
|
2301_78526554/tosto
|
config.py
|
Python
|
unknown
| 1,588
|
#!/usr/bin/env python3
"""
持久化部署脚本
支持Windows服务、Linux systemd服务等方式
"""
import os
import sys
import platform
import subprocess
import shutil
from pathlib import Path
def create_windows_service():
"""创建Windows服务配置"""
service_script = """
import win32serviceutil
import win32service
import win32event
import servicemanager
import socket
import sys
import os
from pathlib import Path
# 添加项目路径到Python路径
project_path = Path(__file__).parent
sys.path.insert(0, str(project_path))
from app import create_app
class ImageGenAPIService(win32serviceutil.ServiceFramework):
_svc_name_ = "ImageGenAPI"
_svc_display_name_ = "图生图转接API服务"
_svc_description_ = "基于Gemini 2.0 Flash的图片生成和编辑API服务"
def __init__(self, args):
win32serviceutil.ServiceFramework.__init__(self, args)
self.hWaitStop = win32event.CreateEvent(None, 0, 0, None)
socket.setdefaulttimeout(60)
def SvcStop(self):
self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING)
win32event.SetEvent(self.hWaitStop)
def SvcDoRun(self):
servicemanager.LogMsg(servicemanager.EVENTLOG_INFORMATION_TYPE,
servicemanager.PYS_SERVICE_STARTED,
(self._svc_name_, ''))
self.main()
def main(self):
try:
# 切换到项目目录
os.chdir(str(project_path))
# 创建Flask应用
app = create_app()
# 启动服务
app.run(host='0.0.0.0', port=5000, debug=False)
except Exception as e:
servicemanager.LogErrorMsg(f"服务启动失败: {e}")
if __name__ == '__main__':
win32serviceutil.HandleCommandLine(ImageGenAPIService)
"""
with open('windows_service.py', 'w', encoding='utf-8') as f:
f.write(service_script)
print("✅ Windows服务脚本已创建: windows_service.py")
print("安装服务命令: python windows_service.py install")
print("启动服务命令: python windows_service.py start")
print("停止服务命令: python windows_service.py stop")
print("卸载服务命令: python windows_service.py remove")
def create_systemd_service():
"""创建Linux systemd服务配置"""
current_dir = os.getcwd()
python_path = sys.executable
service_content = f"""[Unit]
Description=图生图转接API服务
After=network.target
[Service]
Type=simple
User=www-data
Group=www-data
WorkingDirectory={current_dir}
Environment=PATH={os.path.dirname(python_path)}
ExecStart={python_path} -m gunicorn -w 4 -b 0.0.0.0:5000 --timeout 120 app:create_app()
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target
"""
service_file = 'image-gen-api.service'
with open(service_file, 'w', encoding='utf-8') as f:
f.write(service_content)
print(f"✅ Systemd服务文件已创建: {service_file}")
print("安装步骤:")
print(f"1. sudo cp {service_file} /etc/systemd/system/")
print("2. sudo systemctl daemon-reload")
print("3. sudo systemctl enable image-gen-api")
print("4. sudo systemctl start image-gen-api")
print("5. sudo systemctl status image-gen-api")
def create_nginx_config():
"""创建Nginx反向代理配置"""
nginx_config = """server {
listen 80;
server_name your-domain.com; # 替换为您的域名
# 限制请求大小
client_max_body_size 20M;
# API代理
location / {
proxy_pass http://127.0.0.1:5000;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
# 超时设置
proxy_connect_timeout 60s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;
}
# 静态文件
location /static/ {
alias /path/to/your/static/files/;
expires 1y;
add_header Cache-Control "public, immutable";
}
# 日志
access_log /var/log/nginx/image-gen-api.access.log;
error_log /var/log/nginx/image-gen-api.error.log;
}
# HTTPS配置 (可选)
server {
listen 443 ssl;
server_name your-domain.com;
ssl_certificate /path/to/your/certificate.crt;
ssl_certificate_key /path/to/your/private.key;
client_max_body_size 20M;
location / {
proxy_pass http://127.0.0.1:5000;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
proxy_connect_timeout 60s;
proxy_send_timeout 60s;
proxy_read_timeout 60s;
}
access_log /var/log/nginx/image-gen-api-ssl.access.log;
error_log /var/log/nginx/image-gen-api-ssl.error.log;
}
"""
with open('nginx.conf', 'w', encoding='utf-8') as f:
f.write(nginx_config)
print("✅ Nginx配置文件已创建: nginx.conf")
print("配置步骤:")
print("1. 编辑nginx.conf，替换域名和路径")
print("2. sudo cp nginx.conf /etc/nginx/sites-available/image-gen-api")
print("3. sudo ln -s /etc/nginx/sites-available/image-gen-api /etc/nginx/sites-enabled/")
print("4. sudo nginx -t")
print("5. sudo systemctl reload nginx")
def create_supervisor_config():
"""创建Supervisor配置"""
current_dir = os.getcwd()
python_path = sys.executable
supervisor_config = f"""[program:image-gen-api]
command={python_path} -m gunicorn -w 4 -b 127.0.0.1:5000 --timeout 120 app:create_app()
directory={current_dir}
user=www-data
autostart=true
autorestart=true
redirect_stderr=true
stdout_logfile=/var/log/supervisor/image-gen-api.log
stdout_logfile_maxbytes=10MB
stdout_logfile_backups=5
environment=PATH="{os.path.dirname(python_path)}"
"""
with open('supervisor.conf', 'w', encoding='utf-8') as f:
f.write(supervisor_config)
print("✅ Supervisor配置文件已创建: supervisor.conf")
print("配置步骤:")
print("1. sudo cp supervisor.conf /etc/supervisor/conf.d/image-gen-api.conf")
print("2. sudo supervisorctl reread")
print("3. sudo supervisorctl update")
print("4. sudo supervisorctl start image-gen-api")
print("5. sudo supervisorctl status image-gen-api")
def create_pm2_config():
"""创建PM2配置"""
pm2_config = {
"name": "image-gen-api",
"script": "run.py",
"interpreter": "python3",
"instances": 1,
"exec_mode": "fork",
"watch": False,
"max_memory_restart": "500M",
"env": {
"FLASK_ENV": "production"
},
"log_file": "./logs/app.log",
"out_file": "./logs/out.log",
"error_file": "./logs/error.log",
"log_date_format": "YYYY-MM-DD HH:mm:ss Z"
}
import json
with open('ecosystem.config.json', 'w', encoding='utf-8') as f:
json.dump(pm2_config, f, indent=2, ensure_ascii=False)
# 创建日志目录
os.makedirs('logs', exist_ok=True)
print("✅ PM2配置文件已创建: ecosystem.config.json")
print("使用步骤:")
print("1. npm install -g pm2")
print("2. pm2 start ecosystem.config.json")
print("3. pm2 save")
print("4. pm2 startup")
def create_production_run_script():
"""创建生产环境运行脚本"""
if platform.system() == "Windows":
script_content = """@echo off
echo 启动生产环境服务...
REM 检查.env文件
if not exist .env (
echo 错误: .env文件不存在，请先配置环境变量
pause
exit /b 1
)
REM 创建日志目录
if not exist logs mkdir logs
REM 启动服务
echo 启动图生图转接API服务 (生产模式)...
python -m gunicorn -w 4 -b 0.0.0.0:5000 --timeout 120 --access-logfile logs/access.log --error-logfile logs/error.log app:create_app()
pause
"""
with open('start_production.bat', 'w', encoding='utf-8') as f:
f.write(script_content)
print("✅ Windows生产环境脚本已创建: start_production.bat")
else:
script_content = """#!/bin/bash
echo "启动生产环境服务..."
# 检查.env文件
if [ ! -f .env ]; then
echo "错误: .env文件不存在，请先配置环境变量"
exit 1
fi
# 创建日志目录
mkdir -p logs
# 启动服务
echo "启动图生图转接API服务 (生产模式)..."
python3 -m gunicorn -w 4 -b 0.0.0.0:5000 --timeout 120 \\
--access-logfile logs/access.log \\
--error-logfile logs/error.log \\
--pid logs/gunicorn.pid \\
--daemon \\
app:create_app()
echo "服务已在后台启动"
echo "PID文件: logs/gunicorn.pid"
echo "访问日志: logs/access.log"
echo "错误日志: logs/error.log"
echo ""
echo "停止服务: kill \$(cat logs/gunicorn.pid)"
"""
with open('start_production.sh', 'w', encoding='utf-8') as f:
f.write(script_content)
os.chmod('start_production.sh', 0o755)
print("✅ Linux生产环境脚本已创建: start_production.sh")
def main():
"""主函数"""
print("🚀 图生图API持久化部署配置生成器")
print("=" * 50)
system = platform.system()
print(f"检测到系统: {system}")
print()
# 创建生产环境运行脚本
create_production_run_script()
print()
if system == "Windows":
print("Windows部署选项:")
print("1. Windows服务 (推荐)")
create_windows_service()
print()
print("2. PM2 (需要Node.js)")
create_pm2_config()
print()
elif system == "Linux":
print("Linux部署选项:")
print("1. Systemd服务 (推荐)")
create_systemd_service()
print()
print("2. Supervisor")
create_supervisor_config()
print()
print("3. PM2 (需要Node.js)")
create_pm2_config()
print()
print("4. Nginx反向代理配置")
create_nginx_config()
print()
print("📋 部署建议:")
print("1. 生产环境建议使用Gunicorn + Nginx")
print("2. 确保.env文件中的配置适合生产环境")
print("3. 定期备份日志文件")
print("4. 监控服务状态和资源使用")
print("5. 设置防火墙规则")
if __name__ == "__main__":
main()
|
2301_78526554/tosto
|
deploy.py
|
Python
|
unknown
| 10,485
|
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>图生图API客户端示例</title>
<style>
body {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
max-width: 1200px;
margin: 0 auto;
padding: 20px;
background-color: #f5f5f5;
}
.container {
background: white;
border-radius: 10px;
padding: 30px;
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
}
h1 {
color: #333;
text-align: center;
margin-bottom: 30px;
}
.form-group {
margin-bottom: 20px;
}
label {
display: block;
margin-bottom: 5px;
font-weight: bold;
color: #555;
}
input[type="text"], textarea, input[type="file"] {
width: 100%;
padding: 10px;
border: 1px solid #ddd;
border-radius: 5px;
font-size: 16px;
}
textarea {
height: 100px;
resize: vertical;
}
button {
background-color: #007bff;
color: white;
padding: 12px 24px;
border: none;
border-radius: 5px;
cursor: pointer;
font-size: 16px;
margin-right: 10px;
}
button:hover {
background-color: #0056b3;
}
button:disabled {
background-color: #ccc;
cursor: not-allowed;
}
.result {
margin-top: 30px;
padding: 20px;
border-radius: 5px;
background-color: #f8f9fa;
}
.error {
background-color: #f8d7da;
color: #721c24;
border: 1px solid #f5c6cb;
}
.success {
background-color: #d4edda;
color: #155724;
border: 1px solid #c3e6cb;
}
.image-container {
text-align: center;
margin: 20px 0;
}
.generated-image {
max-width: 100%;
max-height: 500px;
border-radius: 10px;
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
}
.session-info {
background-color: #e7f3ff;
padding: 15px;
border-radius: 5px;
margin-bottom: 20px;
}
.loading {
text-align: center;
padding: 20px;
}
.spinner {
border: 4px solid #f3f3f3;
border-top: 4px solid #3498db;
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 2s linear infinite;
margin: 0 auto;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
</style>
</head>
<body>
<div class="container">
<h1>🎨 图生图API客户端示例</h1>
<div class="session-info" id="sessionInfo">
<strong>会话状态:</strong> <span id="sessionStatus">未创建</span>
<button onclick="createSession()">创建新会话</button>
</div>
<div class="form-group">
<label for="prompt">文本提示:</label>
<textarea id="prompt" placeholder="请输入您想要生成或编辑图片的描述...">生成一只可爱的小猫咪，坐在花园里</textarea>
</div>
<div class="form-group">
<label for="imageFile">上传图片 (可选，用于图片编辑):</label>
<input type="file" id="imageFile" accept="image/*" onchange="previewImage()">
</div>
<div class="image-container" id="previewContainer" style="display: none;">
<h3>预览图片:</h3>
<img id="previewImage" class="generated-image">
</div>
<div class="form-group">
<button onclick="generateImage()" id="generateBtn">🎨 生成/编辑图片</button>
<button onclick="clearResults()">🗑️ 清除结果</button>
</div>
<div id="loading" class="loading" style="display: none;">
<div class="spinner"></div>
<p>正在生成图片，请稍候...</p>
</div>
<div id="result" class="result" style="display: none;"></div>
</div>
<script>
let currentSessionId = null;
const API_BASE = 'http://localhost:5000/api/v1';
// 创建会话
async function createSession() {
try {
const response = await fetch(`${API_BASE}/session`, {
method: 'POST'
});
const data = await response.json();
if (data.success) {
currentSessionId = data.data.session_id;
document.getElementById('sessionStatus').textContent = `已创建 (${currentSessionId.substring(0, 8)}...)`;
showResult('会话创建成功！', 'success');
} else {
showResult('会话创建失败: ' + data.message, 'error');
}
} catch (error) {
showResult('会话创建失败: ' + error.message, 'error');
}
}
// 预览上传的图片
function previewImage() {
const file = document.getElementById('imageFile').files[0];
if (file) {
const reader = new FileReader();
reader.onload = function(e) {
document.getElementById('previewImage').src = e.target.result;
document.getElementById('previewContainer').style.display = 'block';
};
reader.readAsDataURL(file);
} else {
document.getElementById('previewContainer').style.display = 'none';
}
}
// 生成图片
async function generateImage() {
const prompt = document.getElementById('prompt').value.trim();
if (!prompt) {
showResult('请输入文本提示', 'error');
return;
}
const generateBtn = document.getElementById('generateBtn');
generateBtn.disabled = true;
document.getElementById('loading').style.display = 'block';
document.getElementById('result').style.display = 'none';
try {
const formData = new FormData();
formData.append('prompt', prompt);
if (currentSessionId) {
formData.append('session_id', currentSessionId);
}
const imageFile = document.getElementById('imageFile').files[0];
if (imageFile) {
formData.append('image', imageFile);
}
const response = await fetch(`${API_BASE}/generate`, {
method: 'POST',
body: formData
});
const data = await response.json();
if (data.success) {
currentSessionId = data.data.session_id;
document.getElementById('sessionStatus').textContent = `已创建 (${currentSessionId.substring(0, 8)}...)`;
let resultHtml = '<h3>✅ 生成成功！</h3>';
if (data.data.text) {
resultHtml += `<p><strong>AI响应:</strong> ${data.data.text}</p>`;
}
if (data.data.image) {
resultHtml += `
<div class="image-container">
<h4>生成的图片:</h4>
<img src="${data.data.image}" class="generated-image" alt="生成的图片">
<br><br>
<button onclick="downloadImage('${data.data.image}')">📥 下载图片</button>
</div>
`;
}
showResult(resultHtml, 'success');
} else {
showResult('生成失败: ' + data.message, 'error');
}
} catch (error) {
showResult('生成失败: ' + error.message, 'error');
} finally {
generateBtn.disabled = false;
document.getElementById('loading').style.display = 'none';
}
}
// 下载图片
function downloadImage(base64Data) {
const link = document.createElement('a');
link.href = base64Data;
link.download = `generated_image_${Date.now()}.png`;
document.body.appendChild(link);
link.click();
document.body.removeChild(link);
}
// 显示结果
function showResult(message, type) {
const resultDiv = document.getElementById('result');
resultDiv.innerHTML = message;
resultDiv.className = `result ${type}`;
resultDiv.style.display = 'block';
}
// 清除结果
function clearResults() {
document.getElementById('result').style.display = 'none';
document.getElementById('previewContainer').style.display = 'none';
document.getElementById('imageFile').value = '';
}
// 页面加载时自动创建会话
window.onload = function() {
createSession();
};
</script>
</body>
</html>
|
2301_78526554/tosto
|
example_client.html
|
HTML
|
unknown
| 9,873
|
#!/usr/bin/env python3
"""
服务管理脚本
用于启动、停止、重启和监控服务
"""
import os
import sys
import signal
import time
import psutil
import argparse
import subprocess
from pathlib import Path
class ServiceManager:
def __init__(self):
self.project_dir = Path(__file__).parent
self.pid_file = self.project_dir / "logs" / "gunicorn.pid"
self.log_dir = self.project_dir / "logs"
# 确保日志目录存在
self.log_dir.mkdir(exist_ok=True)
def is_running(self):
"""检查服务是否运行"""
if not self.pid_file.exists():
return False
try:
with open(self.pid_file, 'r') as f:
pid = int(f.read().strip())
# 检查进程是否存在
return psutil.pid_exists(pid)
except (ValueError, FileNotFoundError):
return False
def get_pid(self):
"""获取服务PID"""
if not self.pid_file.exists():
return None
try:
with open(self.pid_file, 'r') as f:
return int(f.read().strip())
except (ValueError, FileNotFoundError):
return None
def start(self, workers=4, port=5000, daemon=True):
"""启动服务"""
if self.is_running():
print("❌ 服务已在运行中")
return False
print("🚀 启动服务...")
# 检查环境配置
if not (self.project_dir / ".env").exists():
print("❌ .env文件不存在，请先配置环境变量")
return False
# 构建启动命令
cmd = [
sys.executable, "-m", "gunicorn",
"-w", str(workers),
"-b", f"0.0.0.0:{port}",
"--timeout", "120",
"--access-logfile", str(self.log_dir / "access.log"),
"--error-logfile", str(self.log_dir / "error.log"),
"--pid", str(self.pid_file),
"app:create_app()"
]
if daemon:
cmd.append("--daemon")
try:
# 切换到项目目录
os.chdir(self.project_dir)
# 启动服务
subprocess.run(cmd, check=True)
if daemon:
# 等待一下确保服务启动
time.sleep(2)
if self.is_running():
print("✅ 服务启动成功")
print(f"PID: {self.get_pid()}")
print(f"访问地址: http://localhost:{port}")
return True
else:
print("❌ 服务启动失败")
return False
else:
print("✅ 服务已启动（前台模式）")
return True
except subprocess.CalledProcessError as e:
print(f"❌ 启动失败: {e}")
return False
def stop(self):
"""停止服务"""
if not self.is_running():
print("❌ 服务未运行")
return False
pid = self.get_pid()
if not pid:
print("❌ 无法获取服务PID")
return False
print(f"🛑 停止服务 (PID: {pid})...")
try:
# 发送SIGTERM信号
os.kill(pid, signal.SIGTERM)
# 等待进程结束
for _ in range(10):
if not psutil.pid_exists(pid):
break
time.sleep(1)
# 如果还没结束，强制杀死
if psutil.pid_exists(pid):
print("强制停止服务...")
os.kill(pid, signal.SIGKILL)
time.sleep(1)
# 清理PID文件
if self.pid_file.exists():
self.pid_file.unlink()
print("✅ 服务已停止")
return True
except ProcessLookupError:
print("✅ 服务已停止")
if self.pid_file.exists():
self.pid_file.unlink()
return True
except Exception as e:
print(f"❌ 停止失败: {e}")
return False
def restart(self, workers=4, port=5000):
"""重启服务"""
print("🔄 重启服务...")
self.stop()
time.sleep(2)
return self.start(workers, port)
def status(self):
"""查看服务状态"""
print("📊 服务状态:")
if self.is_running():
pid = self.get_pid()
try:
process = psutil.Process(pid)
print(f"✅ 服务运行中")
print(f"PID: {pid}")
print(f"启动时间: {time.ctime(process.create_time())}")
print(f"CPU使用率: {process.cpu_percent():.1f}%")
print(f"内存使用: {process.memory_info().rss / 1024 / 1024:.1f} MB")
print(f"线程数: {process.num_threads()}")
# 检查端口
connections = process.connections()
for conn in connections:
if conn.status == 'LISTEN':
print(f"监听端口: {conn.laddr.port}")
except psutil.NoSuchProcess:
print("❌ 进程不存在")
else:
print("❌ 服务未运行")
# 检查日志文件
print("\n📝 日志文件:")
for log_file in ["access.log", "error.log", "app.log"]:
log_path = self.log_dir / log_file
if log_path.exists():
size = log_path.stat().st_size / 1024 / 1024
print(f" {log_file}: {size:.1f} MB")
else:
print(f" {log_file}: 不存在")
def logs(self, log_type="error", lines=50):
"""查看日志"""
log_file = self.log_dir / f"{log_type}.log"
if not log_file.exists():
print(f"❌ 日志文件不存在: {log_file}")
return
print(f"📝 {log_type}日志 (最后{lines}行):")
print("-" * 50)
try:
# 使用tail命令或Python实现
if os.name == 'posix': # Linux/macOS
subprocess.run(['tail', '-n', str(lines), str(log_file)])
else: # Windows
with open(log_file, 'r', encoding='utf-8') as f:
lines_list = f.readlines()
for line in lines_list[-lines:]:
print(line.rstrip())
except Exception as e:
print(f"❌ 读取日志失败: {e}")
def health_check(self):
"""健康检查"""
import requests
print("🔍 健康检查...")
try:
response = requests.get("http://localhost:5000/health", timeout=10)
if response.status_code == 200:
data = response.json()
print("✅ 服务健康")
print(f"状态: {data.get('status')}")
print(f"版本: {data.get('version')}")
else:
print(f"❌ 健康检查失败: HTTP {response.status_code}")
except requests.exceptions.RequestException as e:
print(f"❌ 健康检查失败: {e}")
def main():
parser = argparse.ArgumentParser(description="图生图API服务管理")
parser.add_argument('action', choices=['start', 'stop', 'restart', 'status', 'logs', 'health'],
help='操作类型')
parser.add_argument('-w', '--workers', type=int, default=4, help='工作进程数')
parser.add_argument('-p', '--port', type=int, default=5000, help='端口号')
parser.add_argument('--foreground', action='store_true', help='前台运行')
parser.add_argument('--log-type', choices=['access', 'error', 'app'], default='error',
help='日志类型')
parser.add_argument('--lines', type=int, default=50, help='显示日志行数')
args = parser.parse_args()
manager = ServiceManager()
if args.action == 'start':
manager.start(args.workers, args.port, not args.foreground)
elif args.action == 'stop':
manager.stop()
elif args.action == 'restart':
manager.restart(args.workers, args.port)
elif args.action == 'status':
manager.status()
elif args.action == 'logs':
manager.logs(args.log_type, args.lines)
elif args.action == 'health':
manager.health_check()
if __name__ == "__main__":
main()
|
2301_78526554/tosto
|
manage.py
|
Python
|
unknown
| 8,782
|
# 路由包
|
2301_78526554/tosto
|
routes/__init__.py
|
Python
|
unknown
| 12
|
from flask import Blueprint, request, jsonify, send_file
from werkzeug.utils import secure_filename
import os
import logging
from utils.error_handler import APIError
from typing import Optional
def create_api_blueprint(session_manager, image_processor, gemini_client, rate_limiter):
"""创建API蓝图"""
api = Blueprint('api', __name__)
def check_rate_limit():
"""检查速率限制"""
allowed, remaining = rate_limiter.is_allowed()
if not allowed:
reset_time = rate_limiter.get_reset_time()
raise APIError(
message=f"请求频率过高，请在 {reset_time} 后重试",
status_code=429,
error_code="RATE_LIMIT_EXCEEDED"
)
return remaining
@api.route('/docs')
def api_docs():
"""API文档"""
return jsonify({
'title': '图生图转接API文档',
'version': '1.0.0',
'description': '基于Gemini 2.0 Flash的图片生成和编辑API',
'endpoints': {
'POST /generate': {
'description': '生成或编辑图片',
'parameters': {
'prompt': '文本提示（必需）',
'image': '输入图片（可选，base64或文件上传）',
'session_id': '会话ID（可选，用于多轮对话）'
},
'response': {
'success': '是否成功',
'data': {
'image': '生成的图片（base64）',
'text': 'AI响应文本',
'session_id': '会话ID'
},
'message': '响应消息'
}
},
'POST /session': {
'description': '创建新会话',
'response': {
'success': True,
'data': {'session_id': '会话ID'}
}
},
'GET /session/{session_id}': {
'description': '获取会话信息',
'response': {
'success': True,
'data': '会话数据'
}
},
'DELETE /session/{session_id}': {
'description': '删除会话',
'response': {
'success': True,
'message': '会话已删除'
}
}
},
'examples': {
'text_to_image': {
'request': {
'prompt': '生成一只可爱的小猫咪'
}
},
'image_editing': {
'request': {
'prompt': '将这只猫咪的背景改为花园',
'image': 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA...'
}
}
}
})
@api.route('/generate', methods=['POST'])
def generate_image():
"""生成或编辑图片"""
try:
# 检查速率限制
remaining_requests = check_rate_limit()
# 获取请求数据
data = request.get_json() if request.is_json else {}
# 获取参数
prompt = data.get('prompt') or request.form.get('prompt')
session_id = data.get('session_id') or request.form.get('session_id')
if not prompt:
raise APIError("缺少必需参数: prompt", 400, "MISSING_PROMPT")
# 处理会话
if session_id:
session_data = session_manager.get_session(session_id)
if not session_data:
raise APIError("会话不存在或已过期", 404, "SESSION_NOT_FOUND")
conversation_history = session_data.get('conversation_history', [])
else:
session_id = session_manager.create_session()
conversation_history = []
# 处理图片输入
input_image_path = None
input_image_base64 = None
# 检查base64图片
if 'image' in data and data['image']:
input_image_base64 = data['image']
# 验证并保存图片
temp_path, error = image_processor.process_base64_image(input_image_base64)
if error:
raise APIError(f"图片处理失败: {error}", 400, "IMAGE_PROCESSING_ERROR")
input_image_path = temp_path
# 检查文件上传
elif 'image' in request.files:
file = request.files['image']
temp_path, error = image_processor.process_uploaded_file(file)
if error:
raise APIError(f"文件上传失败: {error}", 400, "FILE_UPLOAD_ERROR")
input_image_path = temp_path
# 转换为base64用于存储
input_image_base64 = image_processor.image_to_base64(temp_path)
# 调用Gemini API
if input_image_path:
# 图片编辑
output_image_base64, ai_response, error = gemini_client.edit_image_with_base64(
input_image_base64, prompt, conversation_history
)
else:
# 文本生成图片
output_image_base64, ai_response, error = gemini_client.generate_image_from_text(
prompt, conversation_history
)
# 清理临时文件
if input_image_path:
image_processor.cleanup_file(input_image_path)
if error:
raise APIError(f"图片生成失败: {error}", 502, "GENERATION_ERROR")
# 更新会话
session_manager.add_to_conversation(
session_id, prompt, ai_response or "图片已生成",
input_image_base64, output_image_base64
)
# 构建响应
response_data = {
'success': True,
'data': {
'image': output_image_base64,
'text': ai_response,
'session_id': session_id
},
'message': '图片生成成功'
}
# 添加速率限制头
response = jsonify(response_data)
response.headers['X-RateLimit-Remaining'] = str(remaining_requests - 1)
response.headers['X-RateLimit-Reset'] = str(rate_limiter.get_reset_time())
return response
except APIError as e:
return jsonify(e.to_dict()), e.status_code
except Exception as e:
logging.error(f"生成图片时发生错误: {e}")
return jsonify({
'success': False,
'error': 'INTERNAL_ERROR',
'message': '服务器内部错误',
'status_code': 500
}), 500
@api.route('/session', methods=['POST'])
def create_session():
"""创建新会话"""
try:
session_id = session_manager.create_session()
return jsonify({
'success': True,
'data': {
'session_id': session_id
},
'message': '会话创建成功'
})
except Exception as e:
logging.error(f"创建会话时发生错误: {e}")
return jsonify({
'success': False,
'error': 'INTERNAL_ERROR',
'message': '创建会话失败',
'status_code': 500
}), 500
@api.route('/session/<session_id>', methods=['GET'])
def get_session(session_id):
"""获取会话信息"""
try:
session_data = session_manager.get_session(session_id)
if not session_data:
raise APIError("会话不存在或已过期", 404, "SESSION_NOT_FOUND")
# 移除敏感信息
safe_session_data = {
'session_id': session_data['session_id'],
'created_at': session_data['created_at'],
'last_activity': session_data['last_activity'],
'conversation_count': len(session_data.get('conversation_history', []))
}
return jsonify({
'success': True,
'data': safe_session_data,
'message': '获取会话信息成功'
})
except APIError as e:
return jsonify(e.to_dict()), e.status_code
except Exception as e:
logging.error(f"获取会话时发生错误: {e}")
return jsonify({
'success': False,
'error': 'INTERNAL_ERROR',
'message': '获取会话失败',
'status_code': 500
}), 500
@api.route('/session/<session_id>/history', methods=['GET'])
def get_conversation_history(session_id):
"""获取对话历史"""
try:
limit = request.args.get('limit', 10, type=int)
history = session_manager.get_conversation_history(session_id, limit)
if history is None:
raise APIError("会话不存在或已过期", 404, "SESSION_NOT_FOUND")
# 处理历史记录，移除base64图片数据以减少响应大小
safe_history = []
for entry in history:
safe_entry = {
'timestamp': entry['timestamp'],
'user_input': entry['user_input'],
'ai_response': entry['ai_response'],
'has_input_image': bool(entry.get('input_image')),
'has_output_image': bool(entry.get('output_image'))
}
safe_history.append(safe_entry)
return jsonify({
'success': True,
'data': {
'history': safe_history,
'total_count': len(safe_history)
},
'message': '获取对话历史成功'
})
except APIError as e:
return jsonify(e.to_dict()), e.status_code
except Exception as e:
logging.error(f"获取对话历史时发生错误: {e}")
return jsonify({
'success': False,
'error': 'INTERNAL_ERROR',
'message': '获取对话历史失败',
'status_code': 500
}), 500
@api.route('/session/<session_id>', methods=['DELETE'])
def delete_session(session_id):
"""删除会话"""
try:
success = session_manager.delete_session(session_id)
if not success:
raise APIError("会话不存在", 404, "SESSION_NOT_FOUND")
return jsonify({
'success': True,
'message': '会话删除成功'
})
except APIError as e:
return jsonify(e.to_dict()), e.status_code
except Exception as e:
logging.error(f"删除会话时发生错误: {e}")
return jsonify({
'success': False,
'error': 'INTERNAL_ERROR',
'message': '删除会话失败',
'status_code': 500
}), 500
return api
|
2301_78526554/tosto
|
routes/api.py
|
Python
|
unknown
| 11,833
|
#!/usr/bin/env python3
"""
图生图转接API服务启动脚本
"""
import os
import sys
from app import create_app
def main():
"""主函数"""
# 检查环境变量
if not os.environ.get('GEMINI_API_KEY'):
print("错误: 请设置GEMINI_API_KEY环境变量")
print("请复制.env.example为.env并填入您的API密钥")
sys.exit(1)
# 创建应用
app = create_app()
# 获取配置
host = os.environ.get('HOST', '0.0.0.0')
port = int(os.environ.get('PORT', 5000))
debug = os.environ.get('FLASK_DEBUG', 'False').lower() == 'true'
print(f"启动图生图转接API服务...")
print(f"服务地址: http://{host}:{port}")
print(f"API文档: http://{host}:{port}/api/v1/docs")
print(f"健康检查: http://{host}:{port}/health")
# 启动服务
app.run(host=host, port=port, debug=debug)
if __name__ == '__main__':
main()
|
2301_78526554/tosto
|
run.py
|
Python
|
unknown
| 927
|
@echo off
echo 启动图生图转接API服务...
REM 检查Python是否安装
python --version >nul 2>&1
if errorlevel 1 (
echo 错误: 未找到Python，请先安装Python 3.8+
pause
exit /b 1
)
REM 检查.env文件是否存在
if not exist .env (
echo 警告: .env文件不存在，请复制.env.example为.env并配置API密钥
echo 正在复制.env.example为.env...
copy .env.example .env
echo 请编辑.env文件，填入您的Gemini API密钥后重新运行此脚本
pause
exit /b 1
)
REM 检查是否已安装依赖
if not exist venv (
echo 创建虚拟环境...
python -m venv venv
)
REM 激活虚拟环境
call venv\Scripts\activate.bat
REM 安装依赖
echo 安装依赖包...
pip install -r requirements.txt
REM 创建上传目录
if not exist uploads mkdir uploads
REM 启动服务
echo.
echo ========================================
echo 图生图转接API服务启动中...
echo 服务地址: http://localhost:5000
echo API文档: http://localhost:5000/api/v1/docs
echo 健康检查: http://localhost:5000/health
echo 前端示例: example_client.html
echo ========================================
echo.
echo 按 Ctrl+C 停止服务
echo.
python run.py
pause
|
2301_78526554/tosto
|
start.bat
|
Batchfile
|
unknown
| 1,227
|
#!/bin/bash
echo "启动图生图转接API服务..."
# 检查Python是否安装
if ! command -v python3 &> /dev/null; then
echo "错误: 未找到Python3，请先安装Python 3.8+"
exit 1
fi
# 检查.env文件是否存在
if [ ! -f .env ]; then
echo "警告: .env文件不存在，请复制.env.example为.env并配置API密钥"
echo "正在复制.env.example为.env..."
cp .env.example .env
echo "请编辑.env文件，填入您的Gemini API密钥后重新运行此脚本"
exit 1
fi
# 检查是否已创建虚拟环境
if [ ! -d "venv" ]; then
echo "创建虚拟环境..."
python3 -m venv venv
fi
# 激活虚拟环境
source venv/bin/activate
# 安装依赖
echo "安装依赖包..."
pip install -r requirements.txt
# 创建上传目录
mkdir -p uploads
# 启动服务
echo ""
echo "========================================"
echo "图生图转接API服务启动中..."
echo "服务地址: http://localhost:5000"
echo "API文档: http://localhost:5000/api/v1/docs"
echo "健康检查: http://localhost:5000/health"
echo "前端示例: example_client.html"
echo "========================================"
echo ""
echo "按 Ctrl+C 停止服务"
echo ""
python3 run.py
|
2301_78526554/tosto
|
start.sh
|
Shell
|
unknown
| 1,211
|
@echo off
echo 启动生产环境服务...
REM 检查.env文件
if not exist .env (
echo 错误: .env文件不存在，请先配置环境变量
pause
exit /b 1
)
REM 创建日志目录
if not exist logs mkdir logs
REM 启动服务
echo 启动图生图转接API服务 (生产模式)...
python -m gunicorn -w 4 -b 0.0.0.0:5000 --timeout 120 --access-logfile logs/access.log --error-logfile logs/error.log app:create_app()
pause
|
2301_78526554/tosto
|
start_production.bat
|
Batchfile
|
unknown
| 460
|
# 工具类包
|
2301_78526554/tosto
|
utils/__init__.py
|
Python
|
unknown
| 15
|
from flask import jsonify, request
import logging
import traceback
def setup_error_handlers(app):
"""设置错误处理器"""
@app.errorhandler(400)
def bad_request(error):
return jsonify({
'error': 'Bad Request',
'message': '请求参数错误',
'status_code': 400
}), 400
@app.errorhandler(401)
def unauthorized(error):
return jsonify({
'error': 'Unauthorized',
'message': '未授权访问',
'status_code': 401
}), 401
@app.errorhandler(403)
def forbidden(error):
return jsonify({
'error': 'Forbidden',
'message': '禁止访问',
'status_code': 403
}), 403
@app.errorhandler(404)
def not_found(error):
return jsonify({
'error': 'Not Found',
'message': '请求的资源不存在',
'status_code': 404
}), 404
@app.errorhandler(413)
def request_entity_too_large(error):
return jsonify({
'error': 'Request Entity Too Large',
'message': '上传文件过大',
'status_code': 413
}), 413
@app.errorhandler(429)
def rate_limit_exceeded(error):
return jsonify({
'error': 'Rate Limit Exceeded',
'message': '请求频率过高，请稍后再试',
'status_code': 429
}), 429
@app.errorhandler(500)
def internal_server_error(error):
logging.error(f"Internal Server Error: {error}")
logging.error(f"Request: {request.method} {request.url}")
logging.error(f"Traceback: {traceback.format_exc()}")
return jsonify({
'error': 'Internal Server Error',
'message': '服务器内部错误',
'status_code': 500
}), 500
@app.errorhandler(502)
def bad_gateway(error):
return jsonify({
'error': 'Bad Gateway',
'message': '上游服务错误',
'status_code': 502
}), 502
@app.errorhandler(503)
def service_unavailable(error):
return jsonify({
'error': 'Service Unavailable',
'message': '服务暂时不可用',
'status_code': 503
}), 503
@app.errorhandler(Exception)
def handle_exception(error):
"""处理未捕获的异常"""
logging.error(f"Unhandled Exception: {error}")
logging.error(f"Request: {request.method} {request.url}")
logging.error(f"Traceback: {traceback.format_exc()}")
return jsonify({
'error': 'Internal Server Error',
'message': '服务器内部错误',
'status_code': 500
}), 500
class APIError(Exception):
"""自定义API错误"""
def __init__(self, message: str, status_code: int = 400, error_code: str = None):
super().__init__(message)
self.message = message
self.status_code = status_code
self.error_code = error_code
def to_dict(self):
return {
'error': self.error_code or 'API Error',
'message': self.message,
'status_code': self.status_code
}
|
2301_78526554/tosto
|
utils/error_handler.py
|
Python
|
unknown
| 3,281
|
import google.generativeai as genai
from google.generativeai import types
from PIL import Image
from io import BytesIO
import base64
from typing import Dict, List, Optional, Tuple, Union
import logging
class GeminiClient:
"""Gemini API客户端"""
def __init__(self, api_key: str, model_name: str = "gemini-2.0-flash-preview-image-generation"):
"""
初始化Gemini客户端
Args:
api_key: Gemini API密钥
model_name: 模型名称
"""
if not api_key:
raise ValueError("Gemini API密钥不能为空")
self.api_key = api_key
self.model_name = model_name
# 配置API
genai.configure(api_key=api_key)
try:
self.client = genai.Client()
# 测试连接
self._test_connection()
except Exception as e:
logging.error(f"初始化Gemini客户端失败: {e}")
raise
def _test_connection(self):
"""测试API连接"""
try:
# 简单的测试请求
response = self.client.models.generate_content(
model=self.model_name,
contents=["测试连接"],
config=types.GenerateContentConfig(
response_modalities=['TEXT']
)
)
logging.info("Gemini API连接测试成功")
except Exception as e:
logging.error(f"Gemini API连接测试失败: {e}")
raise
def generate_image_from_text(self, prompt: str, conversation_history: List[Dict] = None) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""
根据文本提示生成图片
Args:
prompt: 文本提示
conversation_history: 对话历史
Returns:
(生成的图片base64, 响应文本, 错误信息)
"""
try:
# 构建内容
contents = []
# 添加对话历史上下文
if conversation_history:
for entry in conversation_history[-3:]: # 只取最近3轮对话
if entry.get('user_input'):
contents.append(entry['user_input'])
if entry.get('ai_response'):
contents.append(entry['ai_response'])
# 添加当前提示
contents.append(prompt)
# 调用API
response = self.client.models.generate_content(
model=self.model_name,
contents=contents,
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE']
)
)
return self._process_response(response)
except Exception as e:
error_msg = f"生成图片失败: {str(e)}"
logging.error(error_msg)
return None, None, error_msg
def edit_image_with_text(self, image_path: str, prompt: str, conversation_history: List[Dict] = None) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""
根据文本提示编辑图片
Args:
image_path: 输入图片路径
prompt: 编辑提示
conversation_history: 对话历史
Returns:
(生成的图片base64, 响应文本, 错误信息)
"""
try:
# 加载图片
image = Image.open(image_path)
# 构建内容
contents = []
# 添加对话历史上下文
if conversation_history:
for entry in conversation_history[-3:]: # 只取最近3轮对话
if entry.get('user_input'):
contents.append(entry['user_input'])
if entry.get('ai_response'):
contents.append(entry['ai_response'])
# 添加当前提示和图片
contents.extend([prompt, image])
# 调用API
response = self.client.models.generate_content(
model=self.model_name,
contents=contents,
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE']
)
)
return self._process_response(response)
except Exception as e:
error_msg = f"编辑图片失败: {str(e)}"
logging.error(error_msg)
return None, None, error_msg
def edit_image_with_base64(self, base64_image: str, prompt: str, conversation_history: List[Dict] = None) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""
根据base64图片和文本提示编辑图片
Args:
base64_image: base64编码的图片
prompt: 编辑提示
conversation_history: 对话历史
Returns:
(生成的图片base64, 响应文本, 错误信息)
"""
try:
# 处理base64图片
if base64_image.startswith('data:image/'):
base64_image = base64_image.split(',', 1)[1]
# 解码图片
image_bytes = base64.b64decode(base64_image)
image = Image.open(BytesIO(image_bytes))
# 构建内容
contents = []
# 添加对话历史上下文
if conversation_history:
for entry in conversation_history[-3:]: # 只取最近3轮对话
if entry.get('user_input'):
contents.append(entry['user_input'])
if entry.get('ai_response'):
contents.append(entry['ai_response'])
# 添加当前提示和图片
contents.extend([prompt, image])
# 调用API
response = self.client.models.generate_content(
model=self.model_name,
contents=contents,
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE']
)
)
return self._process_response(response)
except Exception as e:
error_msg = f"编辑图片失败: {str(e)}"
logging.error(error_msg)
return None, None, error_msg
def _process_response(self, response) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""
处理API响应
Args:
response: Gemini API响应
Returns:
(生成的图片base64, 响应文本, 错误信息)
"""
try:
if not response.candidates:
return None, None, "API返回空响应"
candidate = response.candidates[0]
if not candidate.content or not candidate.content.parts:
return None, None, "响应中没有内容"
text_response = None
image_base64 = None
# 处理响应部分
for part in candidate.content.parts:
if part.text is not None:
text_response = part.text
elif part.inline_data is not None:
# 将图片数据转换为base64
image_data = part.inline_data.data
mime_type = part.inline_data.mime_type
image_base64 = f"data:{mime_type};base64,{base64.b64encode(image_data).decode('utf-8')}"
if not text_response and not image_base64:
return None, None, "响应中没有有效内容"
return image_base64, text_response, None
except Exception as e:
error_msg = f"处理响应失败: {str(e)}"
logging.error(error_msg)
return None, None, error_msg
|
2301_78526554/tosto
|
utils/gemini_client.py
|
Python
|
unknown
| 8,216
|
import os
import base64
import uuid
from io import BytesIO
from PIL import Image
from typing import Optional, Tuple, Union
import mimetypes
class ImageProcessor:
"""图片处理器"""
def __init__(self, upload_folder: str, allowed_extensions: set):
"""
初始化图片处理器
Args:
upload_folder: 上传文件夹路径
allowed_extensions: 允许的文件扩展名
"""
self.upload_folder = upload_folder
self.allowed_extensions = allowed_extensions
# 确保上传目录存在
if not os.path.exists(upload_folder):
os.makedirs(upload_folder)
def is_allowed_file(self, filename: str) -> bool:
"""检查文件扩展名是否允许"""
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in self.allowed_extensions
def validate_image(self, image_data: Union[str, bytes]) -> Tuple[bool, str]:
"""
验证图片数据
Args:
image_data: 图片数据（base64字符串或字节）
Returns:
(是否有效, 错误信息)
"""
try:
if isinstance(image_data, str):
# 处理base64数据
if image_data.startswith('data:image/'):
# 移除data URL前缀
image_data = image_data.split(',', 1)[1]
# 解码base64
image_bytes = base64.b64decode(image_data)
else:
image_bytes = image_data
# 尝试打开图片
image = Image.open(BytesIO(image_bytes))
image.verify() # 验证图片完整性
# 检查图片尺寸
if image.size[0] > 4096 or image.size[1] > 4096:
return False, "图片尺寸过大，最大支持4096x4096像素"
# 检查文件大小（16MB限制）
if len(image_bytes) > 16 * 1024 * 1024:
return False, "图片文件过大，最大支持16MB"
return True, ""
except Exception as e:
return False, f"图片格式无效: {str(e)}"
def process_base64_image(self, base64_data: str) -> Tuple[Optional[str], Optional[str]]:
"""
处理base64图片数据
Args:
base64_data: base64编码的图片数据
Returns:
(保存的文件路径, 错误信息)
"""
try:
# 验证图片
is_valid, error_msg = self.validate_image(base64_data)
if not is_valid:
return None, error_msg
# 处理data URL
if base64_data.startswith('data:image/'):
header, base64_data = base64_data.split(',', 1)
# 从header中提取文件类型
mime_type = header.split(';')[0].split(':')[1]
extension = mimetypes.guess_extension(mime_type)
if not extension:
extension = '.png'
else:
extension = '.png' # 默认扩展名
# 解码图片
image_bytes = base64.b64decode(base64_data)
# 生成唯一文件名
filename = f"{uuid.uuid4()}{extension}"
filepath = os.path.join(self.upload_folder, filename)
# 保存文件
with open(filepath, 'wb') as f:
f.write(image_bytes)
return filepath, None
except Exception as e:
return None, f"处理图片失败: {str(e)}"
def process_uploaded_file(self, file) -> Tuple[Optional[str], Optional[str]]:
"""
处理上传的文件
Args:
file: Flask上传的文件对象
Returns:
(保存的文件路径, 错误信息)
"""
try:
if not file or file.filename == '':
return None, "未选择文件"
if not self.is_allowed_file(file.filename):
return None, f"不支持的文件类型，支持的格式: {', '.join(self.allowed_extensions)}"
# 读取文件数据
file_data = file.read()
# 验证图片
is_valid, error_msg = self.validate_image(file_data)
if not is_valid:
return None, error_msg
# 生成唯一文件名
extension = '.' + file.filename.rsplit('.', 1)[1].lower()
filename = f"{uuid.uuid4()}{extension}"
filepath = os.path.join(self.upload_folder, filename)
# 保存文件
with open(filepath, 'wb') as f:
f.write(file_data)
return filepath, None
except Exception as e:
return None, f"处理上传文件失败: {str(e)}"
def image_to_base64(self, image_path: str) -> Optional[str]:
"""
将图片文件转换为base64字符串
Args:
image_path: 图片文件路径
Returns:
base64编码的图片数据
"""
try:
with open(image_path, 'rb') as f:
image_data = f.read()
# 获取MIME类型
mime_type, _ = mimetypes.guess_type(image_path)
if not mime_type:
mime_type = 'image/png'
# 编码为base64
base64_data = base64.b64encode(image_data).decode('utf-8')
# 返回data URL格式
return f"data:{mime_type};base64,{base64_data}"
except Exception as e:
print(f"转换图片为base64失败: {e}")
return None
def cleanup_file(self, filepath: str) -> bool:
"""
清理临时文件
Args:
filepath: 文件路径
Returns:
是否成功删除
"""
try:
if os.path.exists(filepath):
os.remove(filepath)
return True
return False
except Exception as e:
print(f"删除文件失败: {e}")
return False
|
2301_78526554/tosto
|
utils/image_processor.py
|
Python
|
unknown
| 6,489
|
import time
from collections import defaultdict
from typing import Dict
from flask import request
import threading
class RateLimiter:
"""API速率限制器"""
def __init__(self, max_requests_per_hour: int = 100):
"""
初始化速率限制器
Args:
max_requests_per_hour: 每小时最大请求数
"""
self.max_requests_per_hour = max_requests_per_hour
self.requests: Dict[str, list] = defaultdict(list)
self.lock = threading.Lock()
def is_allowed(self, client_id: str = None) -> tuple[bool, int]:
"""
检查是否允许请求
Args:
client_id: 客户端ID，如果为None则使用IP地址
Returns:
(是否允许, 剩余请求数)
"""
if client_id is None:
client_id = self._get_client_id()
current_time = time.time()
hour_ago = current_time - 3600 # 一小时前
with self.lock:
# 清理过期的请求记录
self.requests[client_id] = [
req_time for req_time in self.requests[client_id]
if req_time > hour_ago
]
# 检查是否超过限制
if len(self.requests[client_id]) >= self.max_requests_per_hour:
remaining = 0
return False, remaining
# 记录当前请求
self.requests[client_id].append(current_time)
remaining = self.max_requests_per_hour - len(self.requests[client_id])
return True, remaining
def get_reset_time(self, client_id: str = None) -> int:
"""
获取速率限制重置时间
Args:
client_id: 客户端ID
Returns:
重置时间戳
"""
if client_id is None:
client_id = self._get_client_id()
with self.lock:
if not self.requests[client_id]:
return int(time.time())
# 返回最早请求的一小时后
earliest_request = min(self.requests[client_id])
return int(earliest_request + 3600)
def _get_client_id(self) -> str:
"""获取客户端ID（使用IP地址）"""
return request.environ.get('HTTP_X_FORWARDED_FOR', request.remote_addr)
def cleanup_expired_records(self):
"""清理过期的请求记录"""
current_time = time.time()
hour_ago = current_time - 3600
with self.lock:
for client_id in list(self.requests.keys()):
self.requests[client_id] = [
req_time for req_time in self.requests[client_id]
if req_time > hour_ago
]
# 如果没有记录，删除该客户端
if not self.requests[client_id]:
del self.requests[client_id]
|
2301_78526554/tosto
|
utils/rate_limiter.py
|
Python
|
unknown
| 3,044
|
import redis
import json
import uuid
from datetime import datetime, timedelta
from typing import Dict, List, Optional
class SessionManager:
"""会话管理器，用于支持多轮对话"""
def __init__(self, redis_url: str, session_timeout: int = 3600):
"""
初始化会话管理器
Args:
redis_url: Redis连接URL
session_timeout: 会话超时时间（秒）
"""
try:
self.redis_client = redis.from_url(redis_url)
self.redis_client.ping() # 测试连接
except Exception as e:
print(f"Redis连接失败，使用内存存储: {e}")
self.redis_client = None
self._memory_store = {}
self.session_timeout = session_timeout
def create_session(self) -> str:
"""创建新会话"""
session_id = str(uuid.uuid4())
session_data = {
'session_id': session_id,
'created_at': datetime.now().isoformat(),
'last_activity': datetime.now().isoformat(),
'conversation_history': [],
'context': {}
}
self._store_session(session_id, session_data)
return session_id
def get_session(self, session_id: str) -> Optional[Dict]:
"""获取会话数据"""
if not session_id:
return None
session_data = self._get_session(session_id)
if not session_data:
return None
# 检查会话是否过期
last_activity = datetime.fromisoformat(session_data['last_activity'])
if datetime.now() - last_activity > timedelta(seconds=self.session_timeout):
self.delete_session(session_id)
return None
return session_data
def update_session(self, session_id: str, data: Dict) -> bool:
"""更新会话数据"""
session_data = self.get_session(session_id)
if not session_data:
return False
session_data.update(data)
session_data['last_activity'] = datetime.now().isoformat()
self._store_session(session_id, session_data)
return True
def add_to_conversation(self, session_id: str, user_input: str, ai_response: str,
input_image: Optional[str] = None, output_image: Optional[str] = None) -> bool:
"""添加对话记录到会话"""
session_data = self.get_session(session_id)
if not session_data:
return False
conversation_entry = {
'timestamp': datetime.now().isoformat(),
'user_input': user_input,
'ai_response': ai_response,
'input_image': input_image,
'output_image': output_image
}
session_data['conversation_history'].append(conversation_entry)
session_data['last_activity'] = datetime.now().isoformat()
self._store_session(session_id, session_data)
return True
def get_conversation_history(self, session_id: str, limit: int = 10) -> List[Dict]:
"""获取对话历史"""
session_data = self.get_session(session_id)
if not session_data:
return []
history = session_data.get('conversation_history', [])
return history[-limit:] if limit > 0 else history
def delete_session(self, session_id: str) -> bool:
"""删除会话"""
try:
if self.redis_client:
self.redis_client.delete(f"session:{session_id}")
else:
self._memory_store.pop(session_id, None)
return True
except Exception as e:
print(f"删除会话失败: {e}")
return False
def _store_session(self, session_id: str, data: Dict):
"""存储会话数据"""
try:
if self.redis_client:
self.redis_client.setex(
f"session:{session_id}",
self.session_timeout,
json.dumps(data, ensure_ascii=False)
)
else:
self._memory_store[session_id] = data
except Exception as e:
print(f"存储会话失败: {e}")
def _get_session(self, session_id: str) -> Optional[Dict]:
"""获取会话数据"""
try:
if self.redis_client:
data = self.redis_client.get(f"session:{session_id}")
return json.loads(data) if data else None
else:
return self._memory_store.get(session_id)
except Exception as e:
print(f"获取会话失败: {e}")
return None
|
2301_78526554/tosto
|
utils/session_manager.py
|
Python
|
unknown
| 4,773
|
import win32serviceutil
import win32service
import win32event
import servicemanager
import socket
import sys
import os
from pathlib import Path
# 添加项目路径到Python路径
project_path = Path(__file__).parent
sys.path.insert(0, str(project_path))
from app import create_app
class ImageGenAPIService(win32serviceutil.ServiceFramework):
_svc_name_ = "ImageGenAPI"
_svc_display_name_ = "图生图转接API服务"
_svc_description_ = "基于Gemini 2.0 Flash的图片生成和编辑API服务"
def __init__(self, args):
win32serviceutil.ServiceFramework.__init__(self, args)
self.hWaitStop = win32event.CreateEvent(None, 0, 0, None)
socket.setdefaulttimeout(60)
def SvcStop(self):
self.ReportServiceStatus(win32service.SERVICE_STOP_PENDING)
win32event.SetEvent(self.hWaitStop)
def SvcDoRun(self):
servicemanager.LogMsg(servicemanager.EVENTLOG_INFORMATION_TYPE,
servicemanager.PYS_SERVICE_STARTED,
(self._svc_name_, ''))
self.main()
def main(self):
try:
# 切换到项目目录
os.chdir(str(project_path))
# 创建Flask应用
app = create_app()
# 启动服务
app.run(host='0.0.0.0', port=5000, debug=False)
except Exception as e:
servicemanager.LogErrorMsg(f"服务启动失败: {e}")
if __name__ == '__main__':
win32serviceutil.HandleCommandLine(ImageGenAPIService)
|
2301_78526554/tosto
|
windows_service.py
|
Python
|
unknown
| 1,613
|
import difflib
import sys
import os
import re
import cProfile
import io
import pstats
def calculate_similarity(original_text, plagiarized_text):
differ = difflib.SequenceMatcher(None, original_text, plagiarized_text)
return differ.ratio() * 100
def check_file_paths(original_file_path, plagiarized_file_path):
try:
# 检查文件路径格式是否符合要求
orig_pattern = re.compile(r'^\.\/test\/orig\.txt$')
plagiarized_pattern = re.compile(r'^\.\/test\/orig_0\.8_.+\.txt$')
if not orig_pattern.match(original_file_path):
raise ValueError("Invalid original file path.")
if not plagiarized_pattern.match(plagiarized_file_path):
raise ValueError("Invalid plagiarized file path.")
except ValueError as ve:
print("Invalid file path:", str(ve))
return False
return True
def main():
# 检查命令行参数，一共四个，多了少了都不行。
if len(sys.argv) != 4:
print("Usage: python main.py <original_file> <plagiarized_file> <answer_file>")
return
original_file_path = sys.argv[1]
plagiarized_file_path = sys.argv[2]
answer_file_path = sys.argv[3]
# 检查文件路径是否有效
if not check_file_paths(original_file_path, plagiarized_file_path):
return
# 处理文件
with open(original_file_path, 'r', encoding='utf-8') as original_file:
original_text = original_file.read()
with open(plagiarized_file_path, 'r', encoding='utf-8') as plagiarized_file:
plagiarized_text = plagiarized_file.read()
# 开始性能分析
pr = cProfile.Profile()
pr.enable()
# 计算相似度
similarity_percentage = calculate_similarity(original_text, plagiarized_text)
# 结束性能分析
pr.disable()
# 将性能分析结果写入文件
with open('cprofile_output.profile', 'w', encoding='utf-8') as profile_file:
s = io.StringIO()
sortby = 'cumulative'
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
profile_file.write(s.getvalue())
# 写入答案文件
with open(answer_file_path, 'w', encoding='utf-8') as answer_file:
answer_file.write("重复率：{:.2f}%".format(similarity_percentage))
if __name__ == "__main__":
main()
|
2301_78305256/PaperContentSimilarityDetection
|
main.py
|
Python
|
unknown
| 2,337
|
import platform
import sys
import cpuinfo
import psutil
def get_system_environment():
# 获取操作系统信息
os_name = platform.system()
os_version = platform.release()
# 获取Python版本信息
python_version = sys.version
# 获取CPU信息
cpu_info = cpuinfo.get_cpu_info()
cpu_architecture = cpu_info.get('arch')
# 获取内存信息
total_memory = psutil.virtual_memory().total
# 打印系统环境信息
print("操作系统：", os_name)
print("操作系统版本：", os_version)
print("Python版本：", python_version)
print("CPU架构：", cpu_architecture)
print("内存总量：", total_memory, "bytes")
if __name__ == "__main__":
get_system_environment()
|
2301_78305256/PaperContentSimilarityDetection
|
system.py
|
Python
|
unknown
| 747
|
import math
import numpy as np
import matplotlib.pyplot as plt
from itertools import permutations, combinations
from collections import Counter
import scipy.stats as stats
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def trigonometry():
print("=== 三角函数 ===")
# 基本三角函数
x = np.linspace(-2*np.pi, 2*np.pi, 400)
y_sin = np.sin(x)
y_cos = np.cos(x)
y_tan = np.tan(x)
# 三角恒等式验证
angle = np.pi/4 # 45度
identity1 = np.sin(angle)**2 + np.cos(angle)**2
identity2 = np.sin(2*angle) - 2*np.sin(angle)*np.cos(angle)
print(f"恒等式验证:")
print(f" sin²({angle:.2f}) + cos²({angle:.2f}) = {identity1:.6f} (应为1)")
print(f" sin(2×{angle:.2f}) - 2sin({angle:.2f})cos({angle:.2f}) = {identity2:.6f} (应为0)")
# 可视化
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
# 基本三角函数
axes[0, 0].plot(x, y_sin, 'r-', label='y = sin(x)')
axes[0, 0].set_title('正弦函数')
axes[0, 0].grid(True)
axes[0, 0].legend()
axes[0, 1].plot(x, y_cos, 'b-', label='y = cos(x)')
axes[0, 1].set_title('余弦函数')
axes[0, 1].grid(True)
axes[0, 1].legend()
# 正切函数（排除奇点）
x_tan = x[np.abs(np.cos(x)) > 0.01] # 排除cos(x)接近0的点
y_tan_filtered = np.tan(x_tan)
axes[1, 0].plot(x_tan, y_tan_filtered, 'g-', label='y = tan(x)')
axes[1, 0].set_ylim(-5, 5)
axes[1, 0].set_title('正切函数')
axes[1, 0].grid(True)
axes[1, 0].legend()
# 单位圆
theta = np.linspace(0, 2*np.pi, 100)
x_circle = np.cos(theta)
y_circle = np.sin(theta)
angle_show = np.pi/3 # 60度
x_point = np.cos(angle_show)
y_point = np.sin(angle_show)
axes[1, 1].plot(x_circle, y_circle, 'k-', linewidth=2)
axes[1, 1].plot([0, x_point], [0, y_point], 'r-', linewidth=2)
axes[1, 1].plot(x_point, y_point, 'ro', markersize=8)
axes[1, 1].text(x_point+0.1, y_point+0.1, f'({x_point:.2f}, {y_point:.2f})')
axes[1, 1].axhline(0, color='black', linewidth=0.5)
axes[1, 1].axvline(0, color='black', linewidth=0.5)
axes[1, 1].set_xlim(-1.5, 1.5)
axes[1, 1].set_ylim(-1.5, 1.5)
axes[1, 1].set_aspect('equal')
axes[1, 1].set_title('单位圆')
axes[1, 1].grid(True)
plt.tight_layout()
plt.show()
trigonometry()
|
2301_78992106/juniorAndHighSchoolMath
|
三角函数.py
|
Python
|
unknown
| 2,656
|
import matplotlib.pyplot as plt
import numpy as np
import sympy as sp
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def inequalities_and_complex():
print("=== 不等式与复数 ===")
# 不等式求解
x = sp.Symbol('x')
inequality1 = 2*x + 3 > 7
inequality2 = x**2 - 4 <= 0
print(f"不等式 {inequality1}")
solution1 = sp.solve(inequality1, x)
print(f"解集: {solution1}")
print(f"\n不等式 {inequality2}")
solution2 = sp.solve(inequality2, x)
print(f"解集: {solution2}")
# 复数运算
z1 = 2 + 3j
z2 = 1 - 2j
print(f"\n复数 z1 = {z1}")
print(f"复数 z2 = {z2}")
print(f"加法: z1 + z2 = {z1 + z2}")
print(f"乘法: z1 × z2 = {z1 * z2}")
print(f"除法: z1 ÷ z2 = {z1 / z2}")
print(f"模: |z1| = {abs(z1):.2f}")
print(f"辐角: arg(z1) = {np.angle(z1):.2f} rad")
# 复平面可视化
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
# 不等式解集可视化
x_vals = np.linspace(-3, 3, 400)
y_vals = x_vals**2 - 4
ax1.plot(x_vals, y_vals, 'b-', label='y = x² - 4')
ax1.fill_between(x_vals, y_vals, -5, where=(y_vals <= 0), alpha=0.3, color='red')
ax1.axhline(0, color='black', linewidth=0.5)
ax1.axvline(0, color='black', linewidth=0.5)
ax1.set_title('不等式 x² - 4 ≤ 0 的解集')
ax1.grid(True)
ax1.legend()
# 复平面
complex_points = [z1, z2, z1 + z2, z1 * z2]
colors = ['red', 'blue', 'green', 'purple']
labels = ['z1', 'z2', 'z1+z2', 'z1×z2']
for point, color, label in zip(complex_points, colors, labels):
ax2.plot(point.real, point.imag, 'o', color=color, label=label, markersize=8)
ax2.axhline(0, color='black', linewidth=0.5)
ax2.axvline(0, color='black', linewidth=0.5)
ax2.set_xlim(-3, 6)
ax2.set_ylim(-4, 4)
ax2.set_aspect('equal')
ax2.set_title('复平面')
ax2.grid(True)
ax2.legend()
plt.tight_layout()
plt.show()
inequalities_and_complex()
|
2301_78992106/juniorAndHighSchoolMath
|
不等式与复数.py
|
Python
|
unknown
| 2,306
|
import matplotlib.pyplot as plt
import numpy as np
import sympy as sp
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def sets_and_functions():
print("=== 集合与函数 ===")
# 集合运算
A = {1, 2, 3, 4, 5}
B = {3, 4, 5, 6, 7}
print(f"集合 A: {A}")
print(f"集合 B: {B}")
print(f"并集 A∪B: {A | B}")
print(f"交集 A∩B: {A & B}")
print(f"差集 A-B: {A - B}")
print(f"对称差集 AΔB: {A ^ B}")
# 函数定义与运算
x = sp.Symbol('x')
f = 2*x + 3
g = x**2 - 1
print(f"\n函数 f(x) = {f}")
print(f"函数 g(x) = {g}")
print(f"复合函数 f(g(x)) = {f.subs(x, g)}")
print(f"复合函数 g(f(x)) = {g.subs(x, f)}")
def exponential_log_functions():
print("\n=== 指数函数与对数函数 ===")
# 定义x范围
x = np.linspace(-2, 2, 400)
# 指数函数
y_exp = 2**x
y_exp_e = np.exp(x)
# 对数函数
x_log = np.linspace(0.1, 4, 400)
y_log2 = np.log2(x_log)
y_ln = np.log(x_log)
# 绘制图形
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
# 指数函数
axes[0, 0].plot(x, y_exp, 'r-', label='y = 2^x')
axes[0, 0].set_title('指数函数 y = 2^x')
axes[0, 0].grid(True)
axes[0, 0].legend()
axes[0, 1].plot(x, y_exp_e, 'b-', label='y = e^x')
axes[0, 1].set_title('自然指数函数 y = e^x')
axes[0, 1].grid(True)
axes[0, 1].legend()
# 对数函数
axes[1, 0].plot(x_log, y_log2, 'g-', label='y = log₂x')
axes[1, 0].set_title('对数函数 y = log₂x')
axes[1, 0].grid(True)
axes[1, 0].legend()
axes[1, 1].plot(x_log, y_ln, 'm-', label='y = ln x')
axes[1, 1].set_title('自然对数函数 y = ln x')
axes[1, 1].grid(True)
axes[1, 1].legend()
plt.tight_layout()
plt.show()
sets_and_functions()
exponential_log_functions()
|
2301_78992106/juniorAndHighSchoolMath
|
代数部分-集合与函数.py
|
Python
|
unknown
| 2,179
|
import math
import numpy as np
import matplotlib.pyplot as plt
from itertools import permutations, combinations
from collections import Counter
import scipy.stats as stats
from mpl_toolkits.mplot3d import Axes3D
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def solid_geometry_and_vectors():
print("=== 立体几何与向量 ===")
# 向量运算
v1 = np.array([1, 2, 3])
v2 = np.array([4, 5, 6])
print(f"向量 v1 = {v1}")
print(f"向量 v2 = {v2}")
print(f"向量加法: v1 + v2 = {v1 + v2}")
print(f"向量点积: v1 · v2 = {np.dot(v1, v2)}")
print(f"向量叉积: v1 × v2 = {np.cross(v1, v2)}")
print(f"向量模长: |v1| = {np.linalg.norm(v1):.2f}")
# 空间几何可视化
fig = plt.figure(figsize=(15, 5))
# 1. 向量
ax1 = fig.add_subplot(131, projection='3d')
# 绘制向量
ax1.quiver(0, 0, 0, v1[0], v1[1], v1[2], color='r', arrow_length_ratio=0.1, label='v1')
ax1.quiver(0, 0, 0, v2[0], v2[1], v2[2], color='b', arrow_length_ratio=0.1, label='v2')
ax1.quiver(0, 0, 0, v1[0]+v2[0], v1[1]+v2[1], v1[2]+v2[2], color='g', arrow_length_ratio=0.1, label='v1+v2')
ax1.set_xlim([0, 6])
ax1.set_ylim([0, 8])
ax1.set_zlim([0, 10])
ax1.set_xlabel('X')
ax1.set_ylabel('Y')
ax1.set_zlabel('Z')
ax1.legend()
ax1.set_title('向量运算')
# 2. 平面
ax2 = fig.add_subplot(132, projection='3d')
# 平面方程: 2x + 3y + 4z = 12
xx, yy = np.meshgrid(range(-2, 3), range(-2, 3))
zz = (12 - 2*xx - 3*yy) / 4
ax2.plot_surface(xx, yy, zz, alpha=0.7, color='cyan')
ax2.set_xlabel('X')
ax2.set_ylabel('Y')
ax2.set_zlabel('Z')
ax2.set_title('平面: 2x + 3y + 4z = 12')
# 3. 球体
ax3 = fig.add_subplot(133, projection='3d')
u = np.linspace(0, 2 * np.pi, 30)
v = np.linspace(0, np.pi, 30)
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
ax3.plot_surface(x, y, z, color='lightcoral', alpha=0.7)
ax3.set_xlabel('X')
ax3.set_ylabel('Y')
ax3.set_zlabel('Z')
ax3.set_title('球体')
plt.tight_layout()
plt.show()
solid_geometry_and_vectors()
|
2301_78992106/juniorAndHighSchoolMath
|
几何部分-立体几何与向量.py
|
Python
|
unknown
| 2,532
|
import math
import numpy as np
import matplotlib.pyplot as plt
from itertools import permutations, combinations
from collections import Counter
import scipy.stats as stats
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def analytic_geometry():
print("=== 解析几何 ===")
# 直线方程
def line_equation(point1, point2):
"""计算两点确定的直线方程"""
x1, y1 = point1
x2, y2 = point2
if x2 == x1: # 垂直线
return f"x = {x1}"
else:
k = (y2 - y1) / (x2 - x1)
b = y1 - k * x1
return f"y = {k:.2f}x + {b:.2f}"
# 圆方程
def circle_equation(center, radius):
"""圆的方程"""
h, k = center
return f"(x - {h})² + (y - {k})² = {radius}²"
# 示例
point_A = (1, 2)
point_B = (3, 4)
circle_center = (0, 0)
circle_r = 3
line_eq = line_equation(point_A, point_B)
circle_eq = circle_equation(circle_center, circle_r)
print(f"点A{point_A}和点B{point_B}确定的直线: {line_eq}")
print(f"圆心{circle_center}半径{circle_r}的圆: {circle_eq}")
# 可视化
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
# 直线
x_line = np.linspace(0, 4, 100)
y_line = (x_line - 1) + 2 # y = x + 1
ax1.plot(x_line, y_line, 'b-', linewidth=2, label=line_eq)
ax1.plot([point_A[0], point_B[0]], [point_A[1], point_B[1]], 'ro', markersize=8)
ax1.text(point_A[0], point_A[1], ' A', fontsize=12)
ax1.text(point_B[0], point_B[1], ' B', fontsize=12)
ax1.set_xlim(0, 4)
ax1.set_ylim(0, 5)
ax1.grid(True)
ax1.set_aspect('equal')
ax1.legend()
ax1.set_title('直线')
# 圆
theta = np.linspace(0, 2*np.pi, 100)
x_circle = circle_center[0] + circle_r * np.cos(theta)
y_circle = circle_center[1] + circle_r * np.sin(theta)
ax2.plot(x_circle, y_circle, 'r-', linewidth=2, label=circle_eq)
ax2.plot(circle_center[0], circle_center[1], 'go', markersize=8)
ax2.text(circle_center[0], circle_center[1], ' 圆心', fontsize=12)
ax2.set_xlim(-4, 4)
ax2.set_ylim(-4, 4)
ax2.grid(True)
ax2.set_aspect('equal')
ax2.legend()
ax2.set_title('圆')
plt.tight_layout()
plt.show()
analytic_geometry()
|
2301_78992106/juniorAndHighSchoolMath
|
几何部分-解析几何.py
|
Python
|
unknown
| 2,591
|
import matplotlib.pyplot as plt
import numpy as np
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def function_visualization():
# 定义x范围
x = np.linspace(-5, 5 ,400)
# 定义不同函数
y_linear = 2 * x + 1
y_quadratic = x**2 - 4 # 二次函数
y_exponential = 2**x # 指数函数
y_trigonometric = np.sin(x) # 正弦型函数
# 绘制图像
plt.figure(figsize=(12,8))
plt.subplot(2,2,1)
plt.plot(x, y_linear, 'r-', label = 'y = 2x + 1')
plt.title('一次函数')
plt.grid(True)
plt.legend()
plt.subplot(2,2,2)
plt.plot(x, y_quadratic, 'g-', label = 'y = x² - 4')
plt.title('二次函数')
plt.grid(True)
plt.legend()
plt.subplot(2,2,3)
plt.plot(x, y_exponential, 'b-', label='y = 2^x')
plt.title('指数函数')
plt.grid('指数函数')
plt.grid(True)
plt.legend()
plt.subplot(2,2,4)
plt.plot(x, y_trigonometric, 'h-', label='y = sin(x)')
plt.title('正弦型函数')
plt.grid('正弦型函数')
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()
function_visualization()
|
2301_78992106/juniorAndHighSchoolMath
|
函数可视化.py
|
Python
|
unknown
| 1,384
|
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def plane_geometry():
# 创建图形和坐标轴
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
# 圆形
circle = patches.Circle((0, 0), radius=1, fill=False, edgecolor='blue', linewidth=2)
axes[0, 0].add_patch(circle)
axes[0, 0].set_xlim(-1.5, 1.5)
axes[0, 0].set_ylim(-1.5, 1.5)
axes[0, 0].set_aspect('equal')
axes[0, 0].set_title(f'圆形 (半径=1)\n面积={np.pi:.2f}, 周长={2*np.pi:.2f}')
axes[0, 0].grid(True)
# 矩形
rectangle = patches.Rectangle((0, 0), 3, 2, fill=False, edgecolor='red', linewidth=2)
axes[0, 1].add_patch(rectangle)
axes[0, 1].set_xlim(-0.5, 3.5)
axes[0, 1].set_ylim(-0.5, 2.5)
axes[0, 1].set_aspect('equal')
axes[0, 1].set_title(f'矩形 (3×2)\n面积=6, 周长=10')
axes[0, 1].grid(True)
# 三角形
triangle = patches.Polygon([[0, 0], [3, 0], [1.5, 2]], fill=False, edgecolor='green', linewidth=2)
axes[0, 2].add_patch(triangle)
axes[0, 2].set_xlim(-0.5, 3.5)
axes[0, 2].set_ylim(-0.5, 2.5)
axes[0, 2].set_aspect('equal')
axes[0, 2].set_title('三角形')
axes[0, 2].grid(True)
# 多边形
hexagon = patches.RegularPolygon((0, 0), 6, radius=1, orientation=0,
fill=False, edgecolor='purple', linewidth=2)
axes[1, 0].add_patch(hexagon)
axes[1, 0].set_xlim(-1.5, 1.5)
axes[1, 0].set_ylim(-1.5, 1.5)
axes[1, 0].set_aspect('equal')
axes[1, 0].set_title('正六边形')
axes[1, 0].grid(True)
# 椭圆
ellipse = patches.Ellipse((0, 0), 3, 2, fill=False, edgecolor='orange', linewidth=2)
axes[1, 1].add_patch(ellipse)
axes[1, 1].set_xlim(-2, 2)
axes[1, 1].set_ylim(-1.5, 1.5)
axes[1, 1].set_aspect('equal')
axes[1, 1].set_title('椭圆')
axes[1, 1].grid(True)
# 隐藏最后一个子图
axes[1, 2].axis('off')
plt.tight_layout()
plt.show()
plane_geometry()
|
2301_78992106/juniorAndHighSchoolMath
|
图像与几何1-平面图形计算与可视化.py
|
Python
|
unknown
| 2,329
|
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def solid_geometry_fixed():
fig = plt.figure(figsize=(15, 5))
# 方法1: 使用plot_surface绘制立方体
ax1 = fig.add_subplot(131, projection='3d')
# 定义立方体的六个面
# 底面 (z=0)
x_bottom = np.array([[0, 1], [0, 1]])
y_bottom = np.array([[0, 0], [1, 1]])
z_bottom = np.array([[0, 0], [0, 0]])
# 顶面 (z=1)
x_top = np.array([[0, 1], [0, 1]])
y_top = np.array([[0, 0], [1, 1]])
z_top = np.array([[1, 1], [1, 1]])
# 前面 (y=0)
x_front = np.array([[0, 1], [0, 1]])
y_front = np.array([[0, 0], [0, 0]])
z_front = np.array([[0, 0], [1, 1]])
# 后面 (y=1)
x_back = np.array([[0, 1], [0, 1]])
y_back = np.array([[1, 1], [1, 1]])
z_back = np.array([[0, 0], [1, 1]])
# 左面 (x=0)
x_left = np.array([[0, 0], [0, 0]])
y_left = np.array([[0, 1], [0, 1]])
z_left = np.array([[0, 0], [1, 1]])
# 右面 (x=1)
x_right = np.array([[1, 1], [1, 1]])
y_right = np.array([[0, 1], [0, 1]])
z_right = np.array([[0, 0], [1, 1]])
# 绘制所有面
ax1.plot_surface(x_bottom, y_bottom, z_bottom, alpha=0.7, color='blue')
ax1.plot_surface(x_top, y_top, z_top, alpha=0.7, color='blue')
ax1.plot_surface(x_front, y_front, z_front, alpha=0.7, color='red')
ax1.plot_surface(x_back, y_back, z_back, alpha=0.7, color='red')
ax1.plot_surface(x_left, y_left, z_left, alpha=0.7, color='green')
ax1.plot_surface(x_right, y_right, z_right, alpha=0.7, color='green')
ax1.set_title('立方体')
ax1.set_xlabel('X')
ax1.set_ylabel('Y')
ax1.set_zlabel('Z')
ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
ax1.set_zlim(0, 1)
# 方法2: 使用voxels绘制立方体（更简单的方法）
ax2 = fig.add_subplot(132, projection='3d')
# 创建一个3D数组表示立方体
cube = np.ones((2, 2, 2), dtype=bool)
# 使用voxels绘制
ax2.voxels(cube, alpha=0.7, edgecolor='k')
ax2.set_title('立方体 (voxels方法)')
ax2.set_xlabel('X')
ax2.set_ylabel('Y')
ax2.set_zlabel('Z')
# 球体
ax3 = fig.add_subplot(133, projection='3d')
u = np.linspace(0, 2 * np.pi, 30)
v = np.linspace(0, np.pi, 30)
x = np.outer(np.cos(u), np.sin(v))
y = np.outer(np.sin(u), np.sin(v))
z = np.outer(np.ones(np.size(u)), np.cos(v))
ax3.plot_surface(x, y, z, color='lightblue', alpha=0.7)
ax3.set_title('球体')
ax3.set_xlabel('X')
ax3.set_ylabel('Y')
ax3.set_zlabel('Z')
plt.tight_layout()
plt.show()
solid_geometry_fixed()
|
2301_78992106/juniorAndHighSchoolMath
|
图像与几何2-立体图形可视化.py
|
Python
|
unknown
| 3,026
|
import matplotlib.pyplot as plt
import numpy as np
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def coordinate_geometry():
# 创建图形
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
# 笛卡尔坐标系
x = np.linspace(-5, 5, 100)
y1 = 2*x + 1
y2 = -0.5*x + 3
axes[0].plot(x, y1, 'b-', label='y = 2x + 1')
axes[0].plot(x, y2, 'r-', label='y = -0.5x + 3')
# 找到交点
A = np.array([[2, -1], [0.5, 1]])
B = np.array([-1, 3])
intersection = np.linalg.solve(A, B)
axes[0].plot(intersection[0], intersection[1], 'ro', markersize=8)
axes[0].text(intersection[0]+0.2, intersection[1], f'({intersection[0]:.1f}, {intersection[1]:.1f})')
axes[0].set_xlim(-5, 5)
axes[0].set_ylim(-5, 5)
axes[0].axhline(0, color='black', linewidth=0.5)
axes[0].axvline(0, color='black', linewidth=0.5)
axes[0].grid(True)
axes[0].set_title('笛卡尔坐标系与直线交点')
axes[0].legend()
# 极坐标系
ax_polar = fig.add_subplot(122, projection='polar')
theta = np.linspace(0, 2*np.pi, 100)
r = 2 * np.sin(3*theta) # 三叶玫瑰线
ax_polar.plot(theta, r, 'g-')
ax_polar.set_title('极坐标系: 三叶玫瑰线 r = 2sin(3θ)')
ax_polar.grid(True)
plt.tight_layout()
plt.show()
coordinate_geometry()
|
2301_78992106/juniorAndHighSchoolMath
|
图像与几何3-坐标系与几何证明.py
|
Python
|
unknown
| 1,586
|
import numpy as np
import matplotlib.pyplot as plt
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def word_problems():
# 问题1: 行程问题
def distance_problem():
# 两车从A、B两地同时出发，相向而行
speed_a = 60 # 车A速度 km/h
speed_b = 80 # 车B速度 km/h
distance_ab = 300 # A、B两地距离 km
# 计算相遇时间
meeting_time = distance_ab / (speed_a + speed_b)
# 计算相遇地点
meeting_point_a = speed_a * meeting_time
meeting_point_b = speed_b * meeting_time
print(f"行程问题:")
print(f" 两车在 {meeting_time:.2f} 小时后相遇")
print(f" 相遇点距离A地 {meeting_point_a:.2f} km")
print(f" 相遇点距离B地 {meeting_point_b:.2f} km")
# 可视化
time_points = np.linspace(0, meeting_time * 1.2, 100)
position_a = speed_a * time_points
position_b = distance_ab - speed_b * time_points
plt.figure(figsize=(10, 4))
plt.plot(time_points, position_a, 'b-', label='车A位置')
plt.plot(time_points, position_b, 'r-', label='车B位置')
plt.axhline(y=meeting_point_a, color='gray', linestyle='--', alpha=0.7)
plt.axvline(x=meeting_time, color='gray', linestyle='--', alpha=0.7)
plt.plot(meeting_time, meeting_point_a, 'go', markersize=8, label='相遇点')
plt.xlabel('时间 (小时)')
plt.ylabel('距离A地 (km)')
plt.title('两车相遇问题')
plt.legend()
plt.grid(True)
plt.show()
# 问题2: 混合问题
def mixture_problem():
# 有两种浓度的盐水，需要混合得到特定浓度的盐水
concentration1 = 0.15 # 15%的盐水
concentration2 = 0.40 # 40%的盐水
target_concentration = 0.25 # 目标浓度25%
total_amount = 100 # 总重量100kg
# 设需要第一种盐水x kg，第二种盐水y kg
# x + y = 100
# 0.15x + 0.40y = 0.25 * 100
# 构建方程组
A = np.array([[1, 1], [0.15, 0.40]])
B = np.array([total_amount, target_concentration * total_amount])
solution = np.linalg.solve(A, B)
print(f"\n混合问题:")
print(f" 需要 {solution[0]:.2f} kg 的 {concentration1*100}% 盐水")
print(f" 需要 {solution[1]:.2f} kg 的 {concentration2*100}% 盐水")
print(f" 混合后得到 {total_amount} kg 的 {target_concentration*100}% 盐水")
distance_problem()
mixture_problem()
word_problems()
|
2301_78992106/juniorAndHighSchoolMath
|
实际应用与数学建模-应用题求解1.py
|
Python
|
unknown
| 2,911
|
import math
import numpy as np
import matplotlib.pyplot as plt
from itertools import permutations, combinations
from collections import Counter
import scipy.stats as stats
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def calculus_basics():
print("=== 微积分基础 ===")
# 导数
x = sp.Symbol('x')
f = x**3 - 3*x**2 + 2*x + 1
f_prime = sp.diff(f, x)
f_double_prime = sp.diff(f, x, 2)
print(f"函数 f(x) = {f}")
print(f"一阶导数 f'(x) = {f_prime}")
print(f"二阶导数 f''(x) = {f_double_prime}")
# 在特定点的导数值
x0 = 2
f_prime_at_x0 = f_prime.subs(x, x0)
print(f"在 x={x0} 处的导数值: {f_prime_at_x0}")
# 积分
indefinite_integral = sp.integrate(f, x)
definite_integral = sp.integrate(f, (x, 0, 2))
print(f"\n不定积分 ∫f(x)dx = {indefinite_integral} + C")
print(f"定积分 ∫₀²f(x)dx = {definite_integral}")
# 可视化
x_vals = np.linspace(-1, 3, 400)
# 将符号表达式转换为数值函数
f_lambdified = sp.lambdify(x, f, 'numpy')
f_prime_lambdified = sp.lambdify(x, f_prime, 'numpy')
y_vals = f_lambdified(x_vals)
y_prime_vals = f_prime_lambdified(x_vals)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
# 函数和导数
ax1.plot(x_vals, y_vals, 'b-', label=f'f(x) = {sp.latex(f)}', linewidth=2)
ax1.plot(x_vals, y_prime_vals, 'r-', label=f"f'(x) = {sp.latex(f_prime)}", linewidth=2)
ax1.axhline(0, color='black', linewidth=0.5)
ax1.axvline(0, color='black', linewidth=0.5)
ax1.grid(True)
ax1.legend()
ax1.set_title('函数及其导数')
# 积分面积
x_integral = np.linspace(0, 2, 100)
y_integral = f_lambdified(x_integral)
ax2.plot(x_vals, y_vals, 'b-', label=f'f(x)', linewidth=2)
ax2.fill_between(x_integral, y_integral, alpha=0.3, color='green', label='积分面积')
ax2.axhline(0, color='black', linewidth=0.5)
ax2.axvline(0, color='black', linewidth=0.5)
ax2.grid(True)
ax2.legend()
ax2.set_title(f'定积分 ∫₀²f(x)dx = {definite_integral:.2f}')
plt.tight_layout()
plt.show()
calculus_basics()
|
2301_78992106/juniorAndHighSchoolMath
|
微积分基础.py
|
Python
|
unknown
| 2,470
|
import math
import numpy as np
import matplotlib.pyplot as plt
from itertools import permutations, combinations
from collections import Counter
import scipy.stats as stats
# 设置中文字体
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def combinatorics_and_probability():
print("=== 排列组合与概率统计 ===")
# 1. 排列组合基础
print("\n1. 排列组合基础")
items = ['A', 'B', 'C', 'D']
# 排列
print("排列 P(4, 2):")
perms = list(permutations(items, 2))
for i, perm in enumerate(perms[:8], 1):
print(f" {i}. {perm}")
print(f" 总排列数: {len(perms)}")
print(f" 公式验证: P(4,2) = 4!/(4-2)! = {math.perm(4, 2)}")
# 组合
print("\n组合 C(4, 2):")
combs = list(combinations(items, 2))
for i, comb in enumerate(combs):
print(f" {i+1}. {comb}")
print(f" 总组合数: {len(combs)}")
print(f" 公式验证: C(4,2) = 4!/(2!×(4-2)!) = {math.comb(4, 2)}")
# 2. 概率计算
print("\n2. 概率计算")
def binomial_probability(n, k, p):
"""二项分布概率"""
comb = math.comb(n, k)
return comb * (p ** k) * ((1 - p) ** (n - k))
# 掷硬币概率
n_coin = 10
p_head = 0.5
print(f"掷硬币{n_coin}次概率分布:")
probabilities = []
for k in range(n_coin + 1):
prob = binomial_probability(n_coin, k, p_head)
probabilities.append(prob)
print(f" 正面出现{k}次: {prob:.4f} ({prob*100:.2f}%)")
# 3. 生日悖论
print("\n3. 生日悖论")
def birthday_probability(n):
"""计算n个人中至少两人生日相同的概率"""
if n > 365:
return 1.0
prob_no_match = 1.0
for i in range(n):
prob_no_match *= (365 - i) / 365
return 1 - prob_no_match
for n in [23, 30, 40, 50]:
prob = birthday_probability(n)
print(f" {n}人中至少两人生日相同的概率: {prob:.4f} ({prob*100:.2f}%)")
# 4. 统计描述
print("\n4. 统计描述分析")
# 生成模拟数据（身高数据）
np.random.seed(42) # 设置随机种子保证结果可重现
data = np.random.normal(170, 10, 1000) # 均值170，标准差10
print(f"数据统计描述 (1000个样本):")
print(f" 均值: {np.mean(data):.2f}")
print(f" 中位数: {np.median(data):.2f}")
# 修复众数计算
try:
# 方法1: 使用scipy.stats.mode
mode_result = stats.mode(data)
# 不同版本的scipy返回格式不同，需要兼容处理
if hasattr(mode_result, 'mode'):
mode_value = mode_result.mode[0] if hasattr(mode_result.mode, '__getitem__') else mode_result.mode
else:
mode_value = mode_result[0]
print(f" 众数: {mode_value:.2f}")
except:
# 方法2: 手动计算众数
hist, bin_edges = np.histogram(data, bins=30)
max_bin_index = np.argmax(hist)
mode_value = (bin_edges[max_bin_index] + bin_edges[max_bin_index + 1]) / 2
print(f" 众数(近似): {mode_value:.2f}")
print(f" 标准差: {np.std(data):.2f}")
print(f" 方差: {np.var(data):.2f}")
print(f" 极差: {np.ptp(data):.2f}")
print(f" 四分位距: {np.percentile(data, 75) - np.percentile(data, 25):.2f}")
# 5. 条件概率和贝叶斯定理
print("\n5. 条件概率与贝叶斯定理")
# 疾病检测例子
prevalence = 0.01 # 疾病患病率 1%
sensitivity = 0.95 # 检测灵敏度 95%
specificity = 0.90 # 检测特异度 90%
# 计算检测阳性的情况下真正患病的概率
p_disease = prevalence
p_positive_given_disease = sensitivity
p_positive_given_no_disease = 1 - specificity
p_positive = (p_disease * p_positive_given_disease +
(1 - p_disease) * p_positive_given_no_disease)
p_disease_given_positive = (p_disease * p_positive_given_disease) / p_positive
print(f"疾病检测贝叶斯分析:")
print(f" 患病率: {p_disease:.3f}")
print(f" 检测灵敏度: {sensitivity:.3f}")
print(f" 检测特异度: {specificity:.3f}")
print(f" 检测阳性时真正患病的概率: {p_disease_given_positive:.3f}")
# 可视化
visualize_probability_stats(probabilities, data, n_coin)
def visualize_probability_stats(probabilities, data, n_coin):
"""可视化概率统计结果"""
fig, axes = plt.subplots(2, 3, figsize=(18, 12))
# 1. 二项分布
k_values = range(n_coin + 1)
axes[0, 0].bar(k_values, probabilities, alpha=0.7, color='skyblue', edgecolor='black')
axes[0, 0].set_xlabel('成功次数 (k)')
axes[0, 0].set_ylabel('概率')
axes[0, 0].set_title('二项分布 B(10, 0.5)')
axes[0, 0].grid(True, alpha=0.3)
# 添加概率值标签
for k, prob in zip(k_values, probabilities):
if prob > 0.05: # 只显示概率较大的标签
axes[0, 0].text(k, prob + 0.01, f'{prob:.3f}',
ha='center', va='bottom', fontsize=8)
# 2. 生日悖论
n_people = range(1, 61)
birthday_probs = [birthday_probability(n) for n in n_people]
axes[0, 1].plot(n_people, birthday_probs, 'r-', linewidth=2)
axes[0, 1].axhline(y=0.5, color='gray', linestyle='--', alpha=0.7)
axes[0, 1].axvline(x=23, color='gray', linestyle='--', alpha=0.7)
axes[0, 1].set_xlabel('人数')
axes[0, 1].set_ylabel('概率')
axes[0, 1].set_title('生日悖论')
axes[0, 1].grid(True, alpha=0.3)
axes[0, 1].text(23, 0.5, '23人 → 50%', ha='right', va='bottom')
# 3. 数据直方图
axes[0, 2].hist(data, bins=30, density=True, alpha=0.7, color='lightgreen', edgecolor='black')
axes[0, 2].set_xlabel('身高 (cm)')
axes[0, 2].set_ylabel('密度')
axes[0, 2].set_title('身高数据分布')
axes[0, 2].grid(True, alpha=0.3)
# 添加正态分布曲线
x = np.linspace(data.min(), data.max(), 100)
pdf = stats.norm.pdf(x, data.mean(), data.std())
axes[0, 2].plot(x, pdf, 'r-', linewidth=2, label='正态分布拟合')
axes[0, 2].legend()
# 4. 箱线图
axes[1, 0].boxplot(data, vert=True, patch_artist=True)
axes[1, 0].set_title('数据箱线图')
axes[1, 0].set_ylabel('身高 (cm)')
axes[1, 0].grid(True, alpha=0.3)
# 5. 累积分布函数
data_sorted = np.sort(data)
cdf = np.arange(1, len(data) + 1) / len(data)
axes[1, 1].plot(data_sorted, cdf, 'b-', linewidth=2)
axes[1, 1].set_xlabel('身高 (cm)')
axes[1, 1].set_ylabel('累积概率')
axes[1, 1].set_title('累积分布函数 (CDF)')
axes[1, 1].grid(True, alpha=0.3)
# 6. QQ图（正态性检验）
stats.probplot(data, dist="norm", plot=axes[1, 2])
axes[1, 2].set_title('QQ图 - 正态性检验')
plt.tight_layout()
plt.show()
def advanced_probability_examples():
"""高级概率例子"""
print("\n=== 高级概率例子 ===")
# 1. 蒙提霍尔问题
print("\n1. 蒙提霍尔问题 (三门问题)")
def monty_hall_simulation(n_trials=10000):
stay_wins = 0
switch_wins = 0
for _ in range(n_trials):
# 随机放置汽车
doors = [0, 0, 0] # 0代表山羊，1代表汽车
car_position = np.random.randint(0, 3)
doors[car_position] = 1
# 玩家随机选择一扇门
player_choice = np.random.randint(0, 3)
# 主持人打开一扇有山羊的门（不是玩家选择的，也不是有汽车的）
host_choices = [i for i in range(3) if i != player_choice and doors[i] == 0]
host_choice = np.random.choice(host_choices)
# 计算坚持和换门的获胜次数
if doors[player_choice] == 1:
stay_wins += 1
# 换到另一扇未打开的门
switch_choice = [i for i in range(3) if i != player_choice and i != host_choice][0]
if doors[switch_choice] == 1:
switch_wins += 1
return stay_wins / n_trials, switch_wins / n_trials
stay_prob, switch_prob = monty_hall_simulation(10000)
print(f" 坚持原选择的获胜概率: {stay_prob:.4f}")
print(f" 换门的获胜概率: {switch_prob:.4f}")
print(f" 理论值: 坚持=1/3≈0.333, 换门=2/3≈0.667")
# 2. 泊松分布
print("\n2. 泊松分布例子")
def poisson_probability(lambd, k):
"""泊松分布概率"""
return (lambd ** k * math.exp(-lambd)) / math.factorial(k)
# 模拟商店每小时顾客数
lambd = 5 # 平均每小时5个顾客
print(f"平均每小时{lambd}个顾客的概率分布:")
for k in range(10):
prob = poisson_probability(lambd, k)
print(f" {k}个顾客: {prob:.4f}")
# 3. 几何分布
print("\n3. 几何分布例子")
def geometric_probability(p, k):
"""几何分布概率"""
return (1 - p) ** (k - 1) * p
# 抛硬币直到出现正面
p_head = 0.5
print(f"抛硬币直到出现正面的概率分布:")
for k in range(1, 6):
prob = geometric_probability(p_head, k)
print(f" 第{k}次出现正面: {prob:.4f}")
def birthday_probability(n):
"""计算n个人中至少两人生日相同的概率"""
if n > 365:
return 1.0
prob_no_match = 1.0
for i in range(n):
prob_no_match *= (365 - i) / 365
return 1 - prob_no_match
def practical_applications():
"""实际应用案例"""
print("\n=== 实际应用案例 ===")
# 1. 彩票中奖概率
print("\n1. 双色球中奖概率计算")
def lottery_probability():
# 双色球规则: 33选6 + 16选1
total_red = math.comb(33, 6)
total_blue = 16
total_combinations = total_red * total_blue
print(f"总组合数: {total_combinations:,}")
print("各奖项中奖概率:")
# 一等奖: 6+1
prob_1st = 1 / total_combinations
print(f" 一等奖: 1/{total_combinations:,} ≈ {prob_1st:.10f}")
# 二等奖: 6+0
prob_2nd = (math.comb(6, 6) * math.comb(27, 0) * 15) / total_combinations
print(f" 二等奖: {prob_2nd:.8f}")
# 三等奖: 5+1
prob_3rd = (math.comb(6, 5) * math.comb(27, 1) * 1) / total_combinations
print(f" 三等奖: {prob_3rd:.8f}")
lottery_probability()
# 2. 产品质量检验
print("\n2. 产品质量检验抽样方案")
def quality_control():
n_samples = 50 # 抽样数量
defect_rate = 0.02 # 缺陷率
acceptance_number = 1 # 可接受缺陷数
# 计算接受概率
prob_accept = 0
for k in range(acceptance_number + 1):
prob = binomial_probability(n_samples, k, defect_rate)
prob_accept += prob
print(f"抽样方案: n={n_samples}, c={acceptance_number}")
print(f"缺陷率: {defect_rate*100}%")
print(f"接受概率: {prob_accept:.4f}")
# 绘制OC曲线
defect_rates = np.linspace(0, 0.1, 100)
accept_probs = []
for p in defect_rates:
prob_acc = 0
for k in range(acceptance_number + 1):
prob_acc += binomial_probability(n_samples, k, p)
accept_probs.append(prob_acc)
plt.figure(figsize=(10, 6))
plt.plot(defect_rates, accept_probs, 'b-', linewidth=2)
plt.axvline(x=defect_rate, color='r', linestyle='--', label=f'当前缺陷率 ({defect_rate*100}%)')
plt.xlabel('缺陷率')
plt.ylabel('接受概率')
plt.title('操作特性曲线 (OC曲线)')
plt.grid(True, alpha=0.3)
plt.legend()
plt.show()
quality_control()
def binomial_probability(n, k, p):
"""二项分布概率"""
comb = math.comb(n, k)
return comb * (p ** k) * ((1 - p) ** (n - k))
# 运行所有函数
if __name__ == "__main__":
combinatorics_and_probability()
advanced_probability_examples()
practical_applications()
|
2301_78992106/juniorAndHighSchoolMath
|
排列组合和概率统计.py
|
Python
|
unknown
| 12,864
|
import matplotlib.pyplot as plt
import numpy as np
import sympy as sp
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def sequences_and_series():
print("=== 数列与级数 ===")
# 等差数列
def arithmetic_sequence(a1, d, n):
return [a1 + i * d for i in range(n)]
# 等比数列
def geometric_sequence(a1, r, n):
return [a1 * (r ** i) for i in range(n)]
# 斐波那契数列
def fibonacci_sequence(n):
fib = [0, 1]
for i in range(2, n):
fib.append(fib[i-1] + fib[i-2])
return fib[:n]
# 示例
arith_seq = arithmetic_sequence(2, 3, 10)
geo_seq = geometric_sequence(2, 3, 10)
fib_seq = fibonacci_sequence(15)
print(f"等差数列 (首项2, 公差3): {arith_seq}")
print(f"等比数列 (首项2, 公比3): {geo_seq}")
print(f"斐波那契数列: {fib_seq}")
# 数列求和
arith_sum = sum(arith_seq)
geo_sum = sum(geo_seq)
print(f"\n等差数列和: {arith_sum}")
print(f"等比数列和: {geo_sum}")
# 数列极限
def sequence_limit(sequence_func, n_terms=1000):
terms = [sequence_func(n) for n in range(1, n_terms + 1)]
return terms[-1]
# 示例: 1/n 的极限
limit_seq = sequence_limit(lambda n: 1/n)
print(f"数列 1/n 的极限近似值: {limit_seq}")
# 可视化数列
plt.figure(figsize=(12, 4))
plt.subplot(1, 3, 1)
plt.plot(range(1, 11), arith_seq, 'bo-')
plt.title('等差数列')
plt.grid(True)
plt.subplot(1, 3, 2)
plt.plot(range(1, 11), geo_seq, 'ro-')
plt.title('等比数列')
plt.grid(True)
plt.subplot(1, 3, 3)
plt.plot(range(1, 16), fib_seq, 'go-')
plt.title('斐波那契数列')
plt.grid(True)
plt.tight_layout()
plt.show()
sequences_and_series()
|
2301_78992106/juniorAndHighSchoolMath
|
数列.py
|
Python
|
unknown
| 2,129
|
import matplotlib.pyplot as plt
import numpy as np
import sympy as sp
import math
from itertools import permutations, combinations
from scipy import stats
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def sequences_and_induction():
print("=== 数列与数学归纳法 ===")
# 数学归纳法示例：证明 1 + 2 + 3 + ... + n = n(n+1)/2
def sum_formula(n):
"""直接计算和"""
return n * (n + 1) // 2
def sum_sequential(n):
"""顺序计算和"""
return sum(range(1, n + 1))
# 验证基础情况
print("数学归纳法验证:")
print("基础情况 (n=1):")
print(f" 左边: 1 = 1")
print(f" 右边: 1×(1+1)/2 = {sum_formula(1)}")
print(f" 验证: {1 == sum_formula(1)}")
# 验证归纳步骤
print("\n归纳步骤 (假设n=k成立，证明n=k+1成立):")
k = 5 # 示例
assumed_sum = sum_formula(k) # 归纳假设
next_sum_sequential = sum_sequential(k + 1)
next_sum_formula = sum_formula(k + 1)
print(f" 假设 1+2+...+{k} = {k}({k}+1)/2 = {assumed_sum}")
print(f" 那么 1+2+...+{k}+{k+1} = {assumed_sum} + {k+1} = {assumed_sum + k + 1}")
print(f" 公式计算: ({k+1})({k+1}+1)/2 = {next_sum_formula}")
print(f" 验证: {assumed_sum + k + 1 == next_sum_formula}")
# 数列极限
def sequence_convergence(sequence_func, n_terms=20):
"""研究数列的收敛性"""
terms = [sequence_func(n) for n in range(1, n_terms + 1)]
return terms
# 收敛数列示例: a_n = 1 + 1/n
convergent_seq = sequence_convergence(lambda n: 1 + 1/n)
# 发散数列示例: a_n = n
divergent_seq = sequence_convergence(lambda n: n)
# 可视化
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
plt.plot(range(1, 21), convergent_seq, 'bo-', label='aₙ = 1 + 1/n')
plt.axhline(y=1, color='r', linestyle='--', label='极限值 = 1')
plt.title('收敛数列')
plt.xlabel('n')
plt.ylabel('aₙ')
plt.grid(True)
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(range(1, 21), divergent_seq, 'ro-', label='aₙ = n')
plt.title('发散数列')
plt.xlabel('n')
plt.ylabel('aₙ')
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()
# 验证多个n值
print(f"\n公式验证 (前10个自然数):")
for n in range(1, 11):
formula_result = sum_formula(n)
sequential_result = sum_sequential(n)
print(f" n={n}: 公式={formula_result}, 计算={sequential_result}, 正确={formula_result == sequential_result}")
sequences_and_induction()
|
2301_78992106/juniorAndHighSchoolMath
|
数列与数学归纳法.py
|
Python
|
unknown
| 2,936
|
import sympy as sp
import numpy as np
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def equation_solving():
x = sp.Symbol('x')
y = sp.Symbol('y')
equation = 2 * x + 5 - 13
solution = sp.solve(equation, x)
print(f"方程2x + 5 -13的解: x = {solution[0]}")
# 一元二次方程
equation2 = x**2 - 5*x +6
solution2 = sp.solve(equation2, x)
print(f"方程x² - 5x + 6 = 0的解: {solution2}")
#方程组
eq1 = 2 * x + 3 *y - 7
eq2 = 4 * x - y -3
solution3 = sp.solve((eq1, eq2), (x, y))
print(f"方程组解:{solution3}" )
equation_solving()
|
2301_78992106/juniorAndHighSchoolMath
|
方程求解.py
|
Python
|
unknown
| 823
|
import math
import fractions
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def rational_number_demo():
f1 = fractions.Fraction(3,4)
f2 = fractions.Fraction(2,5)
print(f"分数1:{f1}")
print(f"分数2:{f2}")
print(f"加法: {f1} + {f2} = {f1 + f2}")
print(f"减法：{f1} - {f2} = {f1 - f2}")
print(f"乘法: {f1} * {f2} = {f1 * f2}")
print(f"除法: {f1} / {f2} = {f1 / f2}")
# 实数运算
print(f"\nΠ的近似值: {math.pi}")
print(f"e的近似值: {math.e}" )
print(f"根号2的近似值:{math.sqrt(2)}" )
rational_number_demo()
|
2301_78992106/juniorAndHighSchoolMath
|
有理数与实数运算.py
|
Python
|
unknown
| 804
|
import random
import matplotlib.pyplot as plt
import numpy as np
from collections import Counter
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def probability_demo():
# 模拟抛硬币
def coin_toss_simulation(n_tosses):
results = [random.choice(['正面', '反面']) for _ in range(n_tosses)]
return results
# 模拟掷骰子
def dice_roll_simulation(n_rolls):
results = [random.randint(1, 6) for _ in range(n_rolls)]
return results
# 创建图形
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
# 抛硬币实验
coin_results = coin_toss_simulation(1000)
coin_counts = Counter(coin_results)
axes[0].bar(coin_counts.keys(), coin_counts.values(), color=['skyblue', 'lightcoral'])
axes[0].set_title('抛硬币实验 (1000次)')
axes[0].set_ylabel('频数')
# 计算理论概率和实际频率
theoretical_prob = 0.5
actual_freq_heads = coin_counts['正面'] / 1000
actual_freq_tails = coin_counts['反面'] / 1000
print(f"抛硬币实验:")
print(f" 正面理论概率: {theoretical_prob:.3f}, 实际频率: {actual_freq_heads:.3f}")
print(f" 反面理论概率: {theoretical_prob:.3f}, 实际频率: {actual_freq_tails:.3f}")
# 掷骰子实验
dice_results = dice_roll_simulation(1000)
dice_counts = Counter(dice_results)
# 按骰子点数排序
dice_points = sorted(dice_counts.keys())
dice_frequencies = [dice_counts[point] for point in dice_points]
axes[1].bar(dice_points, dice_frequencies, color='lightgreen', edgecolor='black')
axes[1].set_title('掷骰子实验 (1000次)')
axes[1].set_xlabel('骰子点数')
axes[1].set_ylabel('频数')
# 计算理论概率和实际频率
theoretical_prob_dice = 1/6
print(f"\n掷骰子实验:")
for point in dice_points:
actual_freq = dice_counts[point] / 1000
print(f" 点数{point}: 理论概率 {theoretical_prob_dice:.3f}, 实际频率 {actual_freq:.3f}")
plt.tight_layout()
plt.show()
probability_demo()
|
2301_78992106/juniorAndHighSchoolMath
|
概率基础.py
|
Python
|
unknown
| 2,353
|
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def mathematical_modeling():
# 示例1: 人口增长模型
def population_growth_model():
# 生成模拟数据
years = np.arange(2000, 2021)
# 使用指数增长模型: P(t) = P0 * e^(rt)
base_population = 1000
growth_rate = 0.02
population = base_population * np.exp(growth_rate * (years - 2000))
# 添加一些随机噪声
np.random.seed(42)
population_noisy = population + np.random.normal(0, 50, len(years))
# 定义指数模型函数
def exp_model(t, P0, r):
return P0 * np.exp(r * (t - 2000))
# 拟合模型
popt, pcov = curve_fit(exp_model, years, population_noisy, p0=[1000, 0.02])
# 预测未来人口
future_years = np.arange(2000, 2031)
predicted_population = exp_model(future_years, *popt)
# 可视化
plt.figure(figsize=(10, 6))
plt.scatter(years, population_noisy, color='blue', label='实际数据')
plt.plot(future_years, predicted_population, 'r-', label=f'拟合模型: P(t) = {popt[0]:.1f} * e^({popt[1]:.3f}t)')
plt.axvline(x=2020, color='gray', linestyle='--', alpha=0.7, label='当前年份')
plt.xlabel('年份')
plt.ylabel('人口')
plt.title('人口增长模型')
plt.legend()
plt.grid(True)
plt.show()
print(f"人口增长模型参数:")
print(f" 初始人口 P0 = {popt[0]:.2f}")
print(f" 增长率 r = {popt[1]:.4f}")
print(f" 2030年预测人口: {exp_model(2030, *popt):.0f}")
# 示例2: 抛物线运动模型
def projectile_motion():
# 初始条件
v0 = 50 # 初始速度 m/s
angle = 45 # 发射角度 度
g = 9.8 # 重力加速度 m/s²
# 转换为弧度
theta = np.radians(angle)
# 计算飞行时间
t_total = 2 * v0 * np.sin(theta) / g
# 生成时间点
t = np.linspace(0, t_total, 100)
# 计算位置
x = v0 * np.cos(theta) * t
y = v0 * np.sin(theta) * t - 0.5 * g * t**2
# 可视化
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2)
plt.xlabel('水平距离 (m)')
plt.ylabel('高度 (m)')
plt.title('抛物线运动模型')
plt.grid(True)
# 标记关键点
max_height = (v0 * np.sin(theta))**2 / (2 * g)
max_range = v0**2 * np.sin(2*theta) / g
plt.axhline(y=max_height, color='r', linestyle='--', alpha=0.7, label=f'最大高度: {max_height:.1f}m')
plt.axvline(x=max_range, color='g', linestyle='--', alpha=0.7, label=f'最大射程: {max_range:.1f}m')
plt.legend()
plt.show()
print(f"\n抛物线运动模型:")
print(f" 最大高度: {max_height:.2f} m")
print(f" 最大射程: {max_range:.2f} m")
print(f" 飞行时间: {t_total:.2f} s")
population_growth_model()
projectile_motion()
mathematical_modeling()
|
2301_78992106/juniorAndHighSchoolMath
|
简单数学建模.py
|
Python
|
unknown
| 3,556
|
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def set_chinese_font():
"""设置中文字体"""
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
set_chinese_font()
def statistics_demo():
# 生成模拟数据
np.random.seed(42)
data = {
'数学': np.random.normal(75, 10, 100),
'语文': np.random.normal(80, 8, 100),
'英语': np.random.normal(78, 12, 100)
}
df = pd.DataFrame(data)
# 创建统计图表
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
# 直方图
axes[0, 0].hist(df['数学'], bins=15, alpha=0.7, color='skyblue', edgecolor='black')
axes[0, 0].set_title('数学成绩分布')
axes[0, 0].set_xlabel('分数')
axes[0, 0].set_ylabel('频数')
# 箱线图
box_data = [df['数学'], df['语文'], df['英语']]
axes[0, 1].boxplot(box_data, labels=['数学', '语文', '英语'])
axes[0, 1].set_title('各科成绩箱线图')
axes[0, 1].set_ylabel('分数')
# 散点图
axes[1, 0].scatter(df['数学'], df['英语'], alpha=0.7, color='green')
axes[1, 0].set_title('数学与英语成绩关系')
axes[1, 0].set_xlabel('数学成绩')
axes[1, 0].set_ylabel('英语成绩')
# 饼图 - 成绩等级分布
math_grades = pd.cut(df['数学'], bins=[0, 60, 70, 80, 90, 100],
labels=['不及格', '及格', '中等', '良好', '优秀'])
grade_counts = math_grades.value_counts()
axes[1, 1].pie(grade_counts.values, labels=grade_counts.index, autopct='%1.1f%%')
axes[1, 1].set_title('数学成绩等级分布')
plt.tight_layout()
plt.show()
# 输出基本统计量
print("各科成绩统计描述:")
print(df.describe())
statistics_demo()
|
2301_78992106/juniorAndHighSchoolMath
|
统计与概率-数据收集与统计图表.py
|
Python
|
unknown
| 1,959
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
农产品市场价格管理平台
前后端一体化Web应用
"""
from fastapi import FastAPI, HTTPException, BackgroundTasks
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse, JSONResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
import os
import json
import pandas as pd
from datetime import datetime, timedelta
import asyncio
import threading
import time
import logging
from pathlib import Path
# 配置日志
logger = logging.getLogger(__name__)
# 导入自定义模块
from core.data_manager import get_data_manager
from core.crawler import get_crawler
from core.report_crawler import get_report_crawler
from core.report_analyzer import ReportAnalyzer
from core.scheduler import get_scheduler
from core.config import config
# 创建FastAPI应用
app = FastAPI(
title="农产品市场价格管理平台",
description="前后端一体化的市场价格数据管理系统",
version="2.0.0"
)
# 添加CORS中间件
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# 全局变量
data_manager = get_data_manager()
crawler = get_crawler()
report_crawler = get_report_crawler()
scheduler = get_scheduler()
report_analyzer = ReportAnalyzer(data_manager)
# 数据模型
class CrawlConfig(BaseModel):
enabled: bool = True
interval_minutes: int = 30
provinces: List[str] = []
class SearchQuery(BaseModel):
province: Optional[str] = None
variety: Optional[str] = None
market: Optional[str] = None
date_from: Optional[str] = None
date_to: Optional[str] = None
limit: int = 100
# 静态文件服务
app.mount("/static", StaticFiles(directory="static"), name="static")
@app.get("/", response_class=HTMLResponse)
async def root():
"""主页"""
return FileResponse("static/index.html")
@app.get("/api/health")
async def health_check():
"""健康检查"""
return {
"status": "healthy",
"timestamp": datetime.now().isoformat(),
"version": "2.0.0"
}
@app.get("/api/stats")
async def get_stats():
"""获取系统统计信息"""
try:
stats = data_manager.get_statistics()
crawler_status = crawler.get_status()
scheduler_status = scheduler.get_status()
return {
"success": True,
"data": {
"data_stats": stats,
"crawler_status": crawler_status,
"scheduler_status": scheduler_status
}
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/search")
async def search_data(query: SearchQuery):
"""搜索数据"""
try:
results = data_manager.search_data(
province=query.province,
variety=query.variety,
market=query.market,
date_from=query.date_from,
date_to=query.date_to,
limit=query.limit
)
return {
"success": True,
"count": len(results),
"data": results
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/data/latest")
async def get_latest_data(limit: int = 50):
"""获取最新数据"""
try:
results = data_manager.get_latest_data(limit)
return {
"success": True,
"count": len(results),
"data": results
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/data/provinces")
async def get_provinces():
"""获取省份列表"""
try:
provinces = data_manager.get_provinces()
return {
"success": True,
"data": provinces
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/data/varieties")
async def get_varieties():
"""获取品种列表"""
try:
varieties = data_manager.get_varieties()
return {
"success": True,
"data": varieties
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/data/markets")
async def get_markets():
"""获取市场列表"""
try:
markets = data_manager.get_markets()
return {
"success": True,
"data": markets
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/crawler/start")
async def start_crawler(background_tasks: BackgroundTasks):
"""启动爬虫"""
try:
background_tasks.add_task(crawler.start_crawling)
return {"success": True, "message": "爬虫已启动"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/crawler/stop")
async def stop_crawler():
"""停止爬虫"""
try:
crawler.stop_crawling()
return {"success": True, "message": "爬虫已停止"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/crawler/status")
async def get_crawler_status():
"""获取爬虫状态"""
try:
status = crawler.get_status()
return {"success": True, "data": status}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/crawler/config")
async def update_crawler_config(config: CrawlConfig):
"""更新爬虫配置"""
try:
crawler.update_config(config.model_dump())
return {"success": True, "message": "配置已更新"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# 报告爬虫API接口
@app.post("/api/report-crawler/start")
async def start_report_crawler(background_tasks: BackgroundTasks):
"""启动报告爬虫"""
try:
background_tasks.add_task(report_crawler.start_crawling)
return {"success": True, "message": "报告爬虫已启动"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/report-crawler/stop")
async def stop_report_crawler():
"""停止报告爬虫"""
try:
report_crawler.stop_crawling()
return {"success": True, "message": "报告爬虫已停止"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/report-crawler/status")
async def get_report_crawler_status():
"""获取报告爬虫状态"""
try:
status = report_crawler.get_status()
return {"success": True, "data": status}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.post("/api/report-crawler/crawl-once")
async def crawl_reports_once(background_tasks: BackgroundTasks, full_crawl: bool = True):
"""执行一次报告爬取
Args:
full_crawl: 是否进行完整爬取（所有页面）
"""
try:
# 临时设置爬取模式
original_config = report_crawler.config.get('full_crawl', True)
report_crawler.config['full_crawl'] = full_crawl
background_tasks.add_task(report_crawler.crawl_once)
# 恢复原始配置
report_crawler.config['full_crawl'] = original_config
crawl_mode = "完整爬取" if full_crawl else "快速爬取"
return {"success": True, "message": f"开始执行单次报告爬取（{crawl_mode}）"}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/reports/latest")
async def get_latest_reports(limit: int = 20, page: int = 1, report_type: str = "all"):
"""获取最新报告
Args:
limit: 每页数量
page: 页码
report_type: 报告类型 (all, daily, monthly, yearly)
"""
try:
reports_file = data_manager.data_dir / "analysis_reports.csv"
if not reports_file.exists():
return {
"success": True,
"count": 0,
"data": [],
"total": 0,
"page": page,
"total_pages": 0,
"report_type": report_type
}
import pandas as pd
df = pd.read_csv(reports_file, encoding='utf-8-sig')
if df.empty:
return {
"success": True,
"count": 0,
"data": [],
"total": 0,
"page": page,
"total_pages": 0,
"report_type": report_type
}
# 清理数据
df = df.fillna('')
# 根据报告类型过滤
if report_type != "all":
if report_type == "daily":
df = df[df['报告类型'] == '农产品批发市场价格日报']
elif report_type == "monthly":
df = df[df['报告类型'] == '农业分析报告']
elif report_type == "yearly":
# 目前没有年报，预留接口
df = df[df['报告类型'].str.contains('年报', na=False)]
# 按爬取时间排序
if '爬取时间' in df.columns:
df = df.sort_values('爬取时间', ascending=False)
# 计算分页
total_records = len(df)
total_pages = (total_records + limit - 1) // limit
start_idx = (page - 1) * limit
end_idx = start_idx + limit
# 获取当前页数据
page_df = df.iloc[start_idx:end_idx]
# 转换为字典并清理数值
records = page_df.to_dict('records')
return {
"success": True,
"count": len(records),
"data": records,
"total": total_records,
"page": page,
"total_pages": total_pages,
"limit": limit,
"report_type": report_type
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/reports/stats")
async def get_report_stats():
"""获取报告统计信息"""
try:
reports_file = data_manager.data_dir / "analysis_reports.csv"
if not reports_file.exists():
return {
"total": 0,
"daily_count": 0,
"monthly_count": 0,
"yearly_count": 0,
"types": []
}
import pandas as pd
df = pd.read_csv(reports_file, encoding='utf-8-sig')
if df.empty:
return {
"total": 0,
"daily_count": 0,
"monthly_count": 0,
"yearly_count": 0,
"types": []
}
# 统计各类型报告数量
daily_count = len(df[df['报告类型'] == '农产品批发市场价格日报'])
monthly_count = len(df[df['报告类型'] == '农业分析报告'])
yearly_count = len(df[df['报告类型'].str.contains('年报', na=False)])
# 获取所有报告类型
types = df['报告类型'].value_counts().to_dict()
return {
"total": len(df),
"daily_count": daily_count,
"monthly_count": monthly_count,
"yearly_count": yearly_count,
"types": types
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/reports/export")
async def export_reports():
"""导出报告数据"""
try:
reports_file = data_manager.data_dir / "analysis_reports.csv"
if not reports_file.exists():
raise HTTPException(status_code=404, detail="报告文件不存在")
return FileResponse(
reports_file,
media_type='application/octet-stream',
filename=f"analysis_reports_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/export/csv")
async def export_csv(
province: Optional[str] = None,
variety: Optional[str] = None,
market: Optional[str] = None,
date_from: Optional[str] = None,
date_to: Optional[str] = None
):
"""导出CSV文件"""
try:
file_path = data_manager.export_csv(
province=province,
variety=variety,
market=market,
date_from=date_from,
date_to=date_to
)
return FileResponse(
file_path,
media_type='application/octet-stream',
filename=f"market_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
)
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/dashboard/trends")
async def get_dashboard_trends(days: int = 30):
"""获取仪表板趋势数据"""
try:
# 获取价格数据趋势
price_trends = await get_price_trends(days)
# 获取报告数据趋势
report_trends = await get_report_trends()
# 获取关键指标
key_metrics = await get_key_metrics()
return {
"success": True,
"data": {
"price_trends": price_trends,
"report_trends": report_trends,
"key_metrics": key_metrics,
"last_update": datetime.now().isoformat(),
"time_range": f"{days}天"
}
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
async def get_price_trends(days: int = 30):
"""获取价格趋势数据"""
try:
# 从CSV文件获取指定天数的价格数据
csv_file = data_manager.data_dir / "market_prices.csv"
import pandas as pd
real_data = []
if csv_file.exists():
df = pd.read_csv(csv_file, encoding='utf-8-sig')
if not df.empty:
# 清理数据
df = df.fillna(0)
# 按日期分组计算平均价格
if '交易日期' in df.columns and '平均价' in df.columns:
df['交易日期'] = pd.to_datetime(df['交易日期'], errors='coerce')
df = df.dropna(subset=['交易日期'])
# 获取指定天数的数据
recent_date = df['交易日期'].max()
start_date = recent_date - pd.Timedelta(days=days)
recent_df = df[df['交易日期'] >= start_date]
# 按日期分组计算平均价格
daily_avg = recent_df.groupby('交易日期')['平均价'].mean().reset_index()
daily_avg = daily_avg.sort_values('交易日期')
for _, row in daily_avg.iterrows():
real_data.append({
'交易日期': row['交易日期'].strftime('%Y-%m-%d'),
'平均价': float(row['平均价'])
})
# 只返回真实数据，不生成模拟数据
return real_data
except Exception as e:
logger.error(f"获取价格趋势失败: {e}")
return []
async def get_report_trends():
"""获取报告趋势数据"""
try:
# 从CSV文件获取报告数据
reports_file = data_manager.data_dir / "analysis_reports.csv"
if not reports_file.exists():
return []
import pandas as pd
df = pd.read_csv(reports_file, encoding='utf-8-sig')
if df.empty:
return []
# 按报告类型统计
type_counts = df['报告类型'].value_counts().to_dict()
# 按日期统计报告数量
if '爬取时间' in df.columns:
df['爬取时间'] = pd.to_datetime(df['爬取时间'], errors='coerce')
df = df.dropna(subset=['爬取时间'])
df['日期'] = df['爬取时间'].dt.date
daily_counts = df.groupby('日期').size().reset_index(name='报告数量')
daily_counts['日期'] = daily_counts['日期'].astype(str)
return {
"type_distribution": type_counts,
"daily_counts": daily_counts.to_dict('records')
}
return {"type_distribution": type_counts, "daily_counts": []}
except Exception as e:
logger.error(f"获取报告趋势失败: {e}")
return {}
async def get_key_metrics():
"""获取关键指标"""
try:
metrics = {}
# 价格指标
csv_file = data_manager.data_dir / "market_prices.csv"
if csv_file.exists():
import pandas as pd
df = pd.read_csv(csv_file, encoding='utf-8-sig')
if not df.empty:
df = df.fillna(0)
# 计算价格指标
if '平均价' in df.columns:
metrics['avg_price'] = float(df['平均价'].mean())
metrics['max_price'] = float(df['平均价'].max())
metrics['min_price'] = float(df['平均价'].min())
# 计算价格变化趋势
if '交易日期' in df.columns and '平均价' in df.columns:
df['交易日期'] = pd.to_datetime(df['交易日期'], errors='coerce')
df = df.dropna(subset=['交易日期'])
df = df.sort_values('交易日期')
if len(df) >= 2:
recent_price = df.iloc[-1]['平均价']
previous_price = df.iloc[-2]['平均价']
if previous_price > 0:
price_change = ((recent_price - previous_price) / previous_price) * 100
metrics['price_change_percent'] = round(price_change, 2)
# 报告指标
reports_file = data_manager.data_dir / "analysis_reports.csv"
if reports_file.exists():
import pandas as pd
df = pd.read_csv(reports_file, encoding='utf-8-sig')
if not df.empty:
metrics['total_reports'] = len(df)
metrics['report_types'] = df['报告类型'].nunique() if '报告类型' in df.columns else 0
return metrics
except Exception as e:
logger.error(f"获取关键指标失败: {e}")
return {}
@app.get("/api/dashboard/data")
async def get_dashboard_data():
"""获取仪表盘数据"""
try:
dashboard_data = report_analyzer.get_dashboard_data()
return dashboard_data
except Exception as e:
logger.error(f"获取仪表盘数据失败: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/api/dashboard/trends")
async def get_trends_data(category: str = "all"):
"""获取趋势数据
Args:
category: 数据类别 (all, price_index, vegetables, fruits, meat, aquatic)
"""
try:
dashboard_data = report_analyzer.get_dashboard_data()
if not dashboard_data.get("success"):
return dashboard_data
trends_data = dashboard_data["data"]
if category == "all":
return {"success": True, "data": trends_data}
elif category == "price_index":
return {"success": True, "data": {"price_index_trend": trends_data.get("price_index_trend", [])}}
elif category in ["vegetables", "fruits", "meat", "aquatic"]:
category_trends = trends_data.get("category_price_trends", {})
return {"success": True, "data": {category: category_trends.get(category, [])}}
else:
return {"success": False, "error": "无效的数据类别"}
except Exception as e:
logger.error(f"获取趋势数据失败: {e}")
raise HTTPException(status_code=500, detail=str(e))
@app.on_event("startup")
async def startup_event():
"""应用启动事件"""
print("🚀 农产品市场价格管理平台启动中...")
# 初始化数据管理器
data_manager.initialize()
# 启动调度器
scheduler.start()
print("✅ 平台启动完成!")
print(f"📊 管理界面: http://localhost:8000")
print(f"📖 API文档: http://localhost:8000/docs")
@app.on_event("shutdown")
async def shutdown_event():
"""应用关闭事件"""
print("🛑 平台正在关闭...")
# 停止爬虫
crawler.stop_crawling()
# 停止调度器
scheduler.stop()
print("✅ 平台已安全关闭")
if __name__ == "__main__":
import uvicorn
uvicorn.run(
"app:app",
host="0.0.0.0",
port=8000,
reload=True,
log_level="info"
)
|
2301_78526554/agricultural-platform
|
app.py
|
Python
|
unknown
| 20,850
|
# 核心模块
|
2301_78526554/agricultural-platform
|
core/__init__.py
|
Python
|
unknown
| 15
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
配置管理模块
"""
import os
import json
from pathlib import Path
from typing import Dict, Any, List
class Config:
"""配置管理类"""
def __init__(self, config_file: str = "config.json"):
self.config_file = config_file
self.config = self.load_config()
def load_config(self) -> Dict[str, Any]:
"""加载配置文件"""
default_config = {
"app": {
"name": "农产品市场价格管理平台",
"version": "2.0.0",
"debug": False
},
"server": {
"host": "0.0.0.0",
"port": 8000,
"workers": 1
},
"data": {
"csv_dir": "data",
"backup_dir": "backups",
"max_records": 100000,
"cleanup_days": 30
},
"crawler": {
"enabled": True,
"interval_minutes": 30,
"timeout_seconds": 30,
"retry_times": 3,
"concurrent_requests": 5,
"provinces": [
{"name": "北京", "code": "110000"},
{"name": "天津", "code": "120000"},
{"name": "河北", "code": "130000"},
{"name": "山西", "code": "140000"},
{"name": "内蒙古", "code": "150000"},
{"name": "辽宁", "code": "210000"},
{"name": "吉林", "code": "220000"},
{"name": "黑龙江", "code": "230000"},
{"name": "上海", "code": "310000"},
{"name": "江苏", "code": "320000"},
{"name": "浙江", "code": "330000"},
{"name": "安徽", "code": "340000"},
{"name": "福建", "code": "350000"},
{"name": "江西", "code": "360000"},
{"name": "山东", "code": "370000"},
{"name": "河南", "code": "410000"},
{"name": "湖北", "code": "420000"},
{"name": "湖南", "code": "430000"},
{"name": "广东", "code": "440000"},
{"name": "广西", "code": "450000"},
{"name": "海南", "code": "460000"},
{"name": "重庆", "code": "500000"},
{"name": "四川", "code": "510000"},
{"name": "贵州", "code": "520000"},
{"name": "云南", "code": "530000"},
{"name": "西藏", "code": "540000"},
{"name": "陕西", "code": "610000"},
{"name": "甘肃", "code": "620000"},
{"name": "青海", "code": "630000"},
{"name": "宁夏", "code": "640000"},
{"name": "新疆", "code": "650000"}
]
},
"report_crawler": {
"enabled": True,
"full_crawl": True, # 是否进行完整爬取（所有页面）
"max_reports_per_type": 1000, # 每种类型最大爬取数量
"interval_hours": 6, # 爬取间隔（小时）
"timeout_seconds": 30,
"retry_times": 3,
"page_delay_seconds": 2 # 页面间延迟
},
"api": {
"base_url": "https://pfsc.agri.cn/pfsc/api",
"headers": {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
"Accept": "application/json, text/plain, */*",
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8"
}
}
}
if os.path.exists(self.config_file):
try:
with open(self.config_file, 'r', encoding='utf-8') as f:
user_config = json.load(f)
# 合并配置
self._merge_config(default_config, user_config)
except Exception as e:
print(f"加载配置文件失败: {e}")
return default_config
def _merge_config(self, default: Dict, user: Dict):
"""递归合并配置"""
for key, value in user.items():
if key in default:
if isinstance(default[key], dict) and isinstance(value, dict):
self._merge_config(default[key], value)
else:
default[key] = value
else:
default[key] = value
def save_config(self):
"""保存配置到文件"""
try:
with open(self.config_file, 'w', encoding='utf-8') as f:
json.dump(self.config, f, ensure_ascii=False, indent=2)
except Exception as e:
print(f"保存配置文件失败: {e}")
def get(self, key: str, default=None):
"""获取配置值"""
keys = key.split('.')
value = self.config
for k in keys:
if isinstance(value, dict) and k in value:
value = value[k]
else:
return default
return value
def set(self, key: str, value: Any):
"""设置配置值"""
keys = key.split('.')
config = self.config
for k in keys[:-1]:
if k not in config:
config[k] = {}
config = config[k]
config[keys[-1]] = value
def get_provinces(self) -> List[Dict[str, str]]:
"""获取省份列表"""
return self.get('crawler.provinces', [])
def get_api_config(self) -> Dict[str, Any]:
"""获取API配置"""
return self.get('api', {})
def get_crawler_config(self) -> Dict[str, Any]:
"""获取爬虫配置"""
return self.get('crawler', {})
def get_data_config(self) -> Dict[str, Any]:
"""获取数据配置"""
return self.get('data', {})
def get_report_crawler_config(self) -> Dict[str, Any]:
"""获取报告爬虫配置"""
return self.get('report_crawler', {})
# 全局配置实例
config = Config()
|
2301_78526554/agricultural-platform
|
core/config.py
|
Python
|
unknown
| 6,326
|