diff --git a/README.md b/README.md index 640c73c..0ad5956 100644 --- a/README.md +++ b/README.md @@ -8,6 +8,7 @@ HLS Project for pp4fpgas-cn running on Pynq-Z1/Z2 board ```console sudo pip3 install --upgrade git+https://github.com/xupsh/pp4fpgas-cn-hls.git +//sudo pip3 install --upgrade git+https://github.com/CongHong/pp4fpgas-cn-hls.git ``` > pynq <= v2.2 diff --git a/boards/Pynq-Z1/notebooks/00-Tutorial.ipynb b/boards/Pynq-Z1/notebooks/00-Tutorial.ipynb index 85e9290..2dd9131 100644 --- a/boards/Pynq-Z1/notebooks/00-Tutorial.ipynb +++ b/boards/Pynq-Z1/notebooks/00-Tutorial.ipynb @@ -198,6 +198,13 @@ "The example in the tutorial resource is the driver for stream interface. The driver for other interfaces, such as lite interface, can be learned through other Xilinx University Program's tutorials or workshops." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/boards/Pynq-Z1/notebooks/01-CORDIC.ipynb b/boards/Pynq-Z1/notebooks/01-CORDIC.ipynb index 3a1522b..68de696 100644 --- a/boards/Pynq-Z1/notebooks/01-CORDIC.ipynb +++ b/boards/Pynq-Z1/notebooks/01-CORDIC.ipynb @@ -8,6 +8,15 @@ "导入Overlay" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from pp4fpgas import CordicOverlay\n", + "\n", + "overlay = CordicOverlay()" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -29,19 +38,11 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.6/dist-packages/pp4fpgas/cordic/cordic.bit load ready\n" - ] } ], "source": [ - "from pp4fpgas import CordicOverlay\n", - "\n", - "overlay = CordicOverlay()" + "from pynq import Overlay\n", + "overlay = Overlay('cordic.bit')" ] }, { @@ -52,26 +53,58 @@ "直接读写寄存器来使用overlay中的hls ip" ] }, + { + "attachments": { + "%E5%9C%B0%E5%9D%80%E8%AF%B4%E6%98%8E.JPG": { + "image/jpeg": 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IXYspbdndjdjcS23OM0dv69SehxulePdauoriSK+jvJI7K8nuImthGLQxsREQcDeDgg9a6HTNW1lNR0rT9Q1JbgazYPPFOLdVe3kUKSMDgrhu47VYb4eWH2G1t47u4ja3huYDKu3dJHPksrcYwCQR9Kt6b4RNhIlxNqk93eQWv2W1nkjQfZ04ztUDBJwMk+lSttf63/4AP+vw/wCCYdt4j1mz8IzX11freXk+otp9t5kCokbeeYgxC4zwMke1dnptte2tuU1C/wDtz5yJDCsZHtheKwbbwOieH7nSb7VLi7ilnNzHIY0R4JS5k3KVH9455rQzquj2yqY7vX5XY7nUwQ+WB04JUflTDroQeMNVvrDT7e20WSOPU7+cQ25lUMBgFmJHfCqa5zWfiBe6ToejeII1jnsdSi+zG3OFaO7b7nJP3dwKkdutb0mhweKNUt77xHorxrZRukNreGGaNi+MvhS3IC459TVGL4YaOJpFuZJbixxOLawYKIbUzH5ygA6+memeKX9f16j0HajJ4js7jw5bya2qTX0vkXZjtYypYRu5K5HHKgfStXTNVurnxdrOnTMpgs0gMWFwcupJyfwqpN4Qu5V0cDxDdg6Tgxs0MTNI20puYleflYirLeGrhPEVzq1jrE9s12sazwiGN1bYMDkjIzmq6i6fIqpNq7fECbTzqzfYltFuRD9nTILOy7d2M4GM1Dps2t/8JJq9tda209vpyRsqG1jXfuQnkgZ4I7Vox+HblPFj62dYmbfEITbeTHt8sEkLnGepPOauw6NFDqWo3qyOX1BUV1OMLtUgY/Op+zoPTm+44/w/4l1bUfCEmpWmoSarqhsBMti1l5SLIQOjbRux6Z5re8Ha0ur2MhOrG/mj2+bHLbeRLAxGdrJwR7ZH4mn6N4bvNH0pNOj124lt4YBDADBGGix0OQOenemQ6TcaHd3OrKl1rupXYSGRl8mEiNckcEqvBJ9+ap2uyVexF451u50W00w2t2bNbq/S3lmWDzWVCrE4XB54Hasd/EOtXXw8uNXttRWG4tbqSJZRbqfPQSbAWQ/cOOccVs31jfeKBbLc2l7ocljcLdQTs8Eu5wCMYDN696bdeCRN4fn0uDVbiD7XO1xdziNGaZyQScEYXkDoKS63/rb/AIJXVf13/wCAReJH1zTbDTpbTXHV5bmC2lLWsZ373ALdODg/Sq2qa7qOj+JILHVdUawsBChivnsw0d1IWwyuw4j/AIQBxnPWuiu9E/tDTbS1vruSV7aeOfzgqqXZG3DIAwOnaodd8PS69HLbTanPFYXCbJ7VY0IcdwGIyM+1H+YuhtA5UHIPHUVyPma4vjaewOuMbWKzW7Ef2WPJy7DZnGcYHXrWo2qalaMbe28M3ssMXyJItxbgMo4B5kB/OrK6TG+tPqztIk01ots0RIwoDFu3f5qH5DW1mcTpfxDn1Gy8MJHMGvL8yC8BtmVflidvlJGOqjoa0PBur33iPRYpm8QzNeT2qyMg09UWFjjO0lcN6dTWrB4MtINP0S0W5nKaOWMTHGXyjJ83Hox6VY8O6Bc6BZw2Z1aa8tLeEQwxSQxrtA4B3KATxT01E+hhaZrmqWnhDU9b1XU/tjwSTwQxGBI13JKyJyBkkkD861/B+rXuoWFzbaxJHJqNhcGGdo12hsgMpx/usPyNJL4MsbjRF0q5kea0+3G8kjdVIlJkMmxhjlcn9Kij0OHwtq1xfeHdGllS+REltLMwwxoUzh8MV5IOOPSkvMH5E2r61c6R4u0iGd0Gl6gksLEryk6jepz6FQwx6ipvCWp3etaENTvNoS6mke2ULjbDuITPqSBn8ap61or+ONAm07VbS+0UiRXimWWJpFI7qVLAcZHPrV6S4u9GjgsNM0G6vLaCJUSSKaFFAAxjDuD29KF5g/IwNX1nXU1nxC2n38cUGjW0c6WzwKyzZVmYM3UZxjg1HceN7p9bsJrVo00pUtheqygsGuM7cHtt+U/jV6TwnJrN3e311c3unR6miR3mn/um3KmRjeucZB5waW4+G2gXVpqEc9tG9xevvW7MSGW3wAECNjgLtGKFpuN6ixNrZ8eSac2uObRLVbry/sseTl2XZnGcYHXrUGi+Kry+8ZS29xJEdLu2mj0/ao3b4CA+T3zk4/3TW+mhomqvqAuJTO9mtoTx0BJ3fXJrItPh1odhHpTWECW13psqyi8jiQSznBDB2xzuyc010E/6+7/M6a6NwLWT7EIzPt/d+aSFz745xXOeG7/WNY0HUVnvYUvYL+e2W4WD5VVGxnbnrj1rpJ0kkt3SGUwyMpCyBQdp9cHg1y1v4JurfQtU01fEd6BqMjytMkUavEznLlSF79Pap7h2LfhHV7rVF1OO5uUvYrK8MEN4iBROAoJ4HGQSRkeldFWT4d0R9A01LEXhuIIlCwr5KRiMDt8oGfqa1qoSCiiikMKKKKAM6oJ72a3122t1uDIkzEPE0e0INuchu5yOnPWp6dJprXF3FNc3LyRwv5kcW0AK3bkcmjqBWivZl8RCy+0+fGyOzB49nlkEYAP8XX36VrP0H1FU000tfx3V1cvO0O7ylKhQmeD068etXH6D6ijoHU8rm+LNwPHJ0qK1drYagIMgxHKBDu53f3sH6e/Fao+JJvvFOlRWdpc2+jzC5aW8uIQEnWNM5jOc8EHtWpD8N9Ai13+1DaxPJ50sxQwJjLgD0zxjI9yaq2HwwsLK+tpZNUv7q2tVmjgtJnUxxpKpDKMDPfr1ra8LfL9D0XPCvZdP8/x2DSfitoWryXSwQ3imCA3CARCRpowcZVUJPccEA1l+D/HWr67p2p6vOs9zHEjNFp9vpxQp8xAKysQJDgcjtW14U+HNj4S1J7qyvJJUMRiSJ4IhsBIP31UMemOSa0LLwkNN8H/2Bpup3VsAWK3SBDINzFj1GO+OlJ8ivykylhldQXVb9uvQzLPx3FaeBNJ1rUY7q9e/lEKiC3CuzksB8gY4+7jgmmT/ABU0a20aG9mtrtJ5biS2FkyqsqvH98HLbRjjvU2l/DyLTdLsdPk1i+u4LC8S7txKEGwrn5eFHBLE+tQal8LNK1K2dJLqdZjfS3qTbI3KNJ95drKVI4HUUP2d2JfVeb3u/wDn/wAA6fQdcs/EeiW+qaazNb3C5XcMEEHBB9wRXL+MfFWr+HNftrC28iUa2ot9O3gDyLkHkvzyu05+ox3rp9A0aPQNEg02GUzLCD+8MaIWySeiAAdewrL1nwNYa9f3l5qU80k01usFueP9DAO7dHxw24A5PoKzlbm02OOXLd8u3Qi0q+1jWdYvo4tSSG30qZLWRRbqxuZAis7En7o+bAAxVCLxJrL2cev+fF/Z8mpfZBY+UOIvNMW/f13ZGcdMcVtW/haSy1OS8sdWuYPtOxryMRoRcOoA38j5SQBnFRReC4IroD7fctpy3RvEsCF2LKW3Z3Y3Y3EttzjNHVf16k9DjdK8e61dRXEkV9HeSR2V5PcRNbCMWhjYiIg4G8HBB610OmatrKajpen6hqS3A1mweeKcW6q9vIoUkYHBXDdx2qw3w8sPsNrbx3dxG1vDcwGVdu6SOfJZW4xgEgj6Vb03wibCRLibVJ7u8gtfstrPJGg+zpxnaoGCTgZJ9Klba/1v/wAAH/X4f8Ew7bxHrNn4Rmvrq/W8vLjUW0+28yBUSNvPMQYhcZ4GSPauz022vbW3Kahf/bnzkSGFYyPbC8Vg23gdE8P3Ok32qXF3FLObmOQxojwSlzJuUqP7xzzWhu1XR7ZQY7vX5XY7nUwQmMDpwSo/KmHXQg8YarfWGn29toskcep304htzKoYDALMSO+FU1zmtfEC90nQ9G8QRrHPY6lF9mNvwrR3bfc5J+7uBUjt1rek0ODxRqlvfeI9FkjWyjdIbW88maNi+MvhS3IC459TVGL4YaOJpFuZJbixAnFtYMFENqZj85QAdfTPTPFL+v69R6DtRk8R2dx4ct5NbVJr6XyLvy7WMqWEbuSuRxyoH0rV0zVbq58Xazp0zKYLNIDFhcHLqScn8KqTeELuVdHA8Q3YOk4MbNDEzSNtKbmJHPysast4auE8RXOrWOsT2zXaxrNCIY3VtgwOSMjOarqLp8isk2rt8QJtPOrN9iW0W5EP2dMgs7Lt3YzgYzVfTZtc/wCEl1e2utbae309I2VDaxrv3ITyQM8Y7VpR+HblPFj62dYmbfEITbeTHt8sEkDOM9Sec1dh0aKHUtRvVkcvqCorqcYXapAx+dT9nQenN9xx/h/xLq2o+EJNStNQk1XVDYCZbE2XlIshA6NtG7Hpnmt7wdrS6xYyE6sb+aPb5sctt5EsDEZ2snBHtkfiafo3hu80fSk06PXbiW3hgEMAMEYaLHQ5A56d6ZDpNxod3c6sqXWuajdhIZGXyYSI1yRwSq9SffmqdrslXsWvFWqzaToTyWbKt5PIlvbbxkeY7BRx3xnP4Vy9p4i1rWbvRNNg1FLO5kju476RLdXzNAyrwG6A5z9CK27rTP8AhLbm0HiDRJ7a2sZTOsF08Msc7FSo3BWbpnP1qkvw4trLXDqWhajNpJ3SFYLeGMxx71VW2qV4zsB+tJb6lGVpPibxD4j1eWxt7+Gxls7WUy7bdXSWaOZo888hSFBwD361mT/EjVbpoZrS6S2llsbaW3sfs4ZbiZ3ZWQufuj5eORXWr8P4LW4WTSdUurAG1NtNsVGaUFy7MWYZDEseRUk/w+0mW1u7aMyww3FnFaIiY/ciIko6n+8Cc/hQtlf+t/8AgCe7t/W3/BOpQsY1LjaxAyPQ1xuleI7ybxhc6frN99gmFw8dtp8trtSeIfddJf4mI5IB46YrXOq6tbN5CeHb66WP5BOLi3HmY43YMgIz16VHN4bk1HUbW71PULiaC1nFzDZukY8uQdMuoycZPGaOtw6GUdQ1u11LxL5mrefFpdr5sETWyDLNGzDJAycECsf/AITvWV023Ejwrdx6Tdz3H7oYMsaxsjAehD5x7+1dOnhGc32qz3GtXEyapCYZojDGoA2lRggZ4B/GoLr4eafc332n7XcxsdLfTWVduCrADf0+9hR7UL9P0f8AwCtP6+X/AAS34R1F9StGlk1ebUWKIzCSz8gRkjt8oz+tO8S6xd6JfaNOhX+z57wWt4CvK7xhGz2w+B+NPt4NV0KxC+Zda9jaiRKkMJjAHXJKg9qivrWfxbo15pWq6ZeaTHKg2TmaFmVgchl2s2CCAeRTe+hEdtSbw1qtzrL6ndSFTZJePBaYXqqfKzZ75cN+VZ2rXutXHjOTS9L1FLKOHTRdANAsgd95GGzzjjtir1rBN4V0ex0nSdJu9Tht4QplSaFST3Lb2XJJyTj1qnNoF3rmpvqzzX2hTSW32OWD9zIXj3Fshhu2nk8g1L8v60/zKXn/AFr/AJGHP8QL+503SL+xEMUa2i32pxld2Iy4Tavp/Gc/7NbOoS6z/wAJpp9laa40dnfQTT7BaxttCFMAEjODvPWnD4beHCt0lxYQ3KTW6W8YmiVzbxqu0BCRkdSc+tadn4bitLjSp2upppNMtHtUZ8fvA23LNx1+QdPWq0v/AF5/8AX9f1+JijxVeDx4tsZIjo5n/s/7o3fafL8zOfTquPUiuzrkm+HGiGzx5Si/+1/bP7SESfaPM8zf97HT+H6V1tJbB1MjxTrDaB4ZvdSiRXkhT5A/3dxIAz7ZNcrpXje8tb3UoL3ztZt7W1juo7q3gWMupYrIVGQGVSM5HJ5xmu11fSrXXNHutM1BN9tdRmORfY1hL4JBs7mKfVrqSe5iS2e4CIjCBf8AlmABgA5OT15oW49LCTeKJtZ8P6he+Ftnk2wbZfTrujl2qSxjAPzYIxk4Gc9cVn6n4pv7bwv4cvZboWkeoRq15dxwCRoyYtw2x85BPXg4Fadv4Lj07Rb7StG1GexsrpyyQrGjrbhhh1QEcAkk+xPFMTwVJHY6TEmtXIudJDJbXHkxn5CgQqVxg8Ac9aO/y/4Iv+D/AMA0fCWo3ereFbG91HyzcTISzREbWGSA3BIBIwSOxOK2aztA0S38PaLDpto8jxxbiXkOWZmYsx9OSTwK0ab3EgooopDM6oJ76a3122t1ufMSZiHiePaEG3OQ3c5HTmp6WbSzd3Mcl3cvLFG+9IdoUA4wORyetAFK01e5vPEvlR7f7PaKTyzt5dkKgtn05I/Ctt+g+orMg8O2NtfQXNuJY/IRkSMTOV5IPQn26dK036D6ijoHU8rl+LNwPHJ0qK1drYagIMgxHKBDu53f3sH6e/Fao+JJvvFOlRWVpc2+jzC5aW8uIQEnWNM5jOc8EHtWpD8NtAi13+1DaxPJ50sxQwJjLgD0zxjI9yaq2HwwsLK+tpZNUv7q2tFmjgtJnUxxpKpDKMDPfrnNbXhb5fjY9Fzwr2XT/P8AHYNJ+K2havJdLBFeKYIDcIBEJGmjBxlVQk9xwQDWX4P8davrunanq86z3EcSM0Wn2+nFCnzEArKxAkOByO1bXhT4c2PhLUnurK8klQxGJIngiXYCQfvqoY9Mck1oWXhIab4P/sDTdTurYAsVukCGQbmLHqMd8dKT5FflJlLDK6guq37dehmWfjyK08CaTrWox3V69/KIVEFuFdnJYD5Axx93HBNMn+KmjW2jQ3s1tdpPLcSWwsmVVlV4/vg5baMcd6m0v4eRabpVjp8msX13BYXiXduJQg2Fc/Lwo4JYn1qDUvhXpWpWzpJdTrMb6W9SbZG5RpPvLtZSpHHcUP2d2JfVeb3u/wDn/wAA6fQdcs/EeiW+qaazNb3C5XcMEEHBB9wRXL+MfFWr+HNftrC28iUa2ot9O3gDyLkHkvzyu05+ox3rp9A0aPQNEg02GUzLCD+8MaIWySeiAAdewrK1nwNYa9f3l5qM80k01usFueP9Dwd26PjhtwByfQVnK3NpsccuW75duhHpV9rGs6xfRxalHDb6VMlrIot1Y3MgRWdiT90fNgAVQi8SazJZx6/58X9nyal9kFj5Q4i80xb9/XdkZx0xxW1beFpLLU5Lyx1a5h+07GvIxGhFw6gDfyPlJAGcVFF4LhiugPt9y2nLdG8SwIXYspbdndjdjcS23OM0dV/XqT0ON0vx7rV1FcSRX0d5JHZXk9xE1sIxaGNiIiDxvBwQevSuh0zVtZTUdL0/UNSW4Gs2DzxTi3VXt5FCkjA4K4buO1WG+Hlh9htbeO7uI2t4bmAyrt3SRz5LK3GMAkEfSrem+ETYSJcTapPd3kFr9ltZ5IkH2dOM4UDBJwMk+lSttf63/wCAD/r8P+CYdt4j1mz8IzX11fpeXk+otp9t5kCokbeeYgxC4zwMke1dnptte2tuU1C/+3PnIkMKxke2F4rBtvA6J4fudJvtUuLuKWc3MchjRHglLmTcpUf3jnmtDdquj2ygx3evyux3Opgh8sDpwSo/KmHXQg8YarfWGn29toskcep304htzKoZRgFmJHfCqa5zWviBe6Toeja/Gkc9jqUX2Y2/CtHdt9zkn7u4FSO3Wt6TQ4PFGqW994j0WSNbKN0htbzyZo2L4y+FLcgLjn1NUYvhfo4mkW5kluLACcW1gyqIrUzH5ygA6+memeKX9f16j0HajJ4js7jw5bya2qTX0vkXZjtUKlhG7krkccqB9K1dM1W6ufF2s6dMymCzSAxYXBy6knJ/Cqk3hC7lXRwPEN2DpODGzQxs0jbSm5iRz8rVZbw1cJ4iudWsdYmtmu1jWeEQxurbBgckZGc1XUXT5FZJtXb4gTaedWb7EtotyIfs6ZBZ2XbuxnAxmoNNm1z/AISTV7a61tp7fTkjZUNrGu/chPJAzxjtWinh25TxY+tnWJm3xCE23kx7fLBJAzjPUnmrsOjRQ6lqN6sjl9QVFdTjC7VIGPzqfs6D05vuOP8AD/iXVtR8ISalaahJquqGwEy2JsvKRZCB0baN2PTPNb3g/Wl1ixkJ1b7fNHt82OW28iWBiM7WTgj2yPxp+jeG7zR9KTTo9duJbeGAQwAwRhosdDkDnp3pkOk3Gh3dzqwS61zUbsJDIy+TCRGuSOCVXqT781Ttdkq9iLxzrdzotpphtbs2a3V+lvLMIPNZUKsThcHngdqx38Q61dfDyfV7XUVhuLW6kiWUW6nz0EmwFkP3DjnHFbN9Y33igWy3Npe6HJY3C3UE7PBLucAjGAzevem3XgkTeH59Lg1W4g+1ztcXc4jRmmckEnBGF5A6Ckut/wCtv+CV1X9d/wDgEXiR9c03T9OltNcdXluYLaUtaxnfvcAt04OD06VW1TXtR0jxLBY6rqjWFgIUMV9JZho7qQthldhxH/CAOM5610V3on9oabaWt9dySvbTxz+cFVS7I24ZAGAOO1Q674el12OW2m1OeKwuE2T2qxoQ47gMRlc+1H+YuhtA5UHIPHUVxuleI7ybxhc6frN99gmFw8dtp8trtSeIfddJf4mI5IB46YrYbVNStGNvbeGb2WGL5EkW4twGUcAjMgP51FN4bk1HUbW71PULiaC1nFzDZskY8uQdMuoycZPGaOtw6WMo6hrdrqXiXzNW8+LS7XzYImtkGWaNmGSBk4IFY/8AwnesjTbcSPCt3HpN3PcfuuDLGsbIwHoQ+ce/tXTp4RnN9qs9xrVxMmqQmGaIwxqFG0qMEDPAP41BdfDywub77T9ruY2OlvprKu3BVgBv6fewo9qF+n6P/gFaX/ry/wCCW/COovqVo0smrzaixRGYSWfkCMkdvlGf1p3iXWLvRL7Rp0K/2fPeC1vAV5XeMI2e2HwPxp9vBquhWIXzLrXsbUSJUhhMYA65JUHtUV9az+LdGvNK1TTLzSY5UGyczQsysDkMu1mwQQDyKb30IjtqTeGtVudZfU7qQqbJLx4LTC9VT5WbPfLhvyrO1a91q48ZyaXpeopZRw6aLoBoFkDvvIwxPIHHar1rBN4V0ex0nSdJu9Tht4QhlSaFST3Lb2XJJyTj1qnNoF3rmptqzzX2hTSW32OWD9zIXj3FshgW2nk8g1L8v60/zKXn/Wv+Rhz/ABAv7nTdIv7EQxRraLfanGV3YjLhNq+n8Zz/ALNbOoS6z/wmmn2VprjR2d9BNPsFrGxUIUwASOh3nrTh8NvDhW6S4sIblJoEt4hNErm3jVdoCEjI6k59a07Pw3FaXGlTtdTTSaZaPaoz4/eBtuWbjr8g6etVpf8Arz/4Av6/r8TFHiq8HjxbYyRHRzP/AGf90bvtPl+ZnPp1XHriuzrkm+HGiGzwIlF/9r+2f2kIk+0eZ5m/72On8P0rraS2DqRXRuBayfYhGZ9v7sSkhc++OcVznhu/1jWNB1FZ7yFL2C/ntluFg+VVRsZ25649a6SdJJLd0hlMMjKQsgUHafXB4Nctb+Cbq30LVNNTxHegajI8rTpFGrxM5y5Ugd+ntS7h2LfhHV7rVF1OO5uUvY7K8MEN4iBROAoJ4HGQSRkeldFWT4d0R9A01LAXhuIIlCwr5KRiMDt8o5+prWqhBRRRSGFFFFAGdUE99Nb67bW6XHmLMxDxNHgINuchu5yOnPWp6dJprXF3FNc3LyRwv5kcW0AK3bkcmjqBT0y/uW1DyNSmljmdpPLheAKrKGOCrd+MGtl+g+oqmmmk6gl3dXDzvFuEKlQojz16dTjjmrj9B9RR0A8rm+LNwPHJ0qK1drYagIMgxHKBDu53f3sH6e/Fao+JP27xTpUVla3Nvo8wuWlvLiEBJ1jTOYznPBB7VqQ/DbQItd/tQ2sTyedLMUMCYy4A9M8YyPcmqth8MLGyvraWTVL+6trRZo4LSZ1McaSqQyjAz369a2vC3y/Q9Fzwr2XT/P8AHYNJ+K2havJdLBFeKYIDcIBEJGmjBxlVQk9xwQDWX4P8d6vrunanq86z3EcSM0Wn2+nFCnzEArKxAkOByO1bXhT4c2PhLUnurK8klQxGJIngiXYCQfvqoY9Mck1oWXhIab4P/sDTdTurYAsVukCGQbmLHqMd8dKT5FflJlLDq6guq37dehmWfjyK08CaTrepR3V69/KIVEFuFdnJYD5Axx93HBNMn+KmjW2jQ3s1tdpPLcSWwsmVVlV4/vg5baMcd6m0v4eRabpVjp8msX13BYXiXduJQg2Fc/Lwo4JYn1qDUvhXpWpWzpJdTrMb6W9SbZG+xpPvLtZSpHA6ih+zuxL6rze93/z/AOAdPoOuWfiPRLfVNNZmt7hcruGCCDgg+4Irl/GPirV/Dmv21hbeRKNbUW+m7wB5FyDyX55Xac/UY710+gaNHoGiQabDKZlhB/eGNELZJPRAAOvYVlaz4GsNev7y81GeaSaa3WC3PH+h4O7dHxw24A5PoKzlbm02OOXLd8u3Qj0q+1jWdYvo4tSjht9KmS1kUW6sbmQIrOxJ+6PmwAKoReJNZezj1/z4v7Pk1L7ILExDiLzTFv39d2RnHTHFbVt4WkstTkvLHVrmD7Tsa8jEaEXDqAN/I+UkAZxUUXguGK6A+33Lact0bxLAhdiylt2d2N2NxLbc4zR1X9epPQ43S/HutXUVxJFfR3kkdleT3ETWwjFoY2IiIPG8HBB610WmatrKajpWn6hqS3A1mweeKcW6q9vIoUkYHBXDdx2qdvh5YfYbW3ju7iNreG5gMq7d0kc+SytxjAJBH0q3pvhE2EiXE2qT3d5Ba/ZbWeSJB9nTjOFAwScDJPpUrbX+t/8AgA/6/D/gmJbeI9Zs/CM19dXyXl5PqLafbeZAqJG3nmIMQuM8DJHtXZabbXtrblNQv/tz5yJDCsZHtheKwbbwOieH7nSb7VLi7ilnNzHIY0R4JS5k3KVHZjnmtDdquj2ygx3evyuxy6GCHywOnBKj8qYddCDxhqt9Yafb22iyRx6nfTiG3MqhlGAWYkd8KprnNZ+IF7pOh6Nr8aRz2OpRC2NvwrR3bfc5J+7uBUjt1rek0ODxRqlvfeI9FkjWyjdIbW98maNi+MvhS3IC459TVGL4X6OJpFuZZbiwAnFtYMFEVqZj85QAdfTPTPFL+v69R6DtRk8R2dx4ct5NbVJr6XyLsx2qFSwjdyVyOOVA+laumardXPi7WdOmZTBZpAYsLg5dSTk/hVSbwhdyro4HiG7B0nBjZoI2aRtpTcxI/usast4auU8RXOrWOsTWzXaxrPCIY3VtgwOSMjOarqLp8isk2rt8QJtP/tZvsS2i3Ih+zpkFnZdu7GcDGag02bXP+Ek1e2utbae305I2VDaxrv3ITyQO2O1aMfh25TxY+tnWJm3xCE23kR7fLBJAzjPUnmrsOjRQ6lqN6sjl9QVFdTjC7VIGPzqfs6D05vuOP8P+JdW1HwhJqVpqEmq6obATLYmy8pFkIHAbaN2PTPNb3g/Wl1ixkJ1b7fNHt82OS28iWBiM7WTgj2yPxp+jeG7zR9KTTo9duJbeGAQwAwRhosdDkDnp3pkOk3Gh3dzqwS61zUbsJDIyeTCRGuSOCVXqT781Ttdkq9i14q1WbSdCeSzZVvJ5Et7beMjzHYKOO+M5/CuXtPEWtaxd6JpsGopZ3Mkd3HfSJAr5mgZV4DdAc5+hFbd1pn/CW3NoPEGiT21tYymdYLp4ZY52KlRkKzdM5+tUl+HFtZa4dS0LUZtJO6QrBbwxmOPeqq21SOM7AfrSW+pRl6T4m8Q+I9Ylsbe/hsZbO1lMu23DpLNHM0eeeQpCg4BzzWXP8SNUumhmtLqO2llsbaW3sfs4ZbiZ3ZWQufuj5eORXWr8P4LW4WTSdUurEG1NtNsVGaUFy7MWYZDEseRUk/w+0mW1u7aMyQw3FnFaIiY/ciIko6n+8Cc/hQtlf+t/+AJ9bf1t/wAE6lCxjUuNrEcj0NcbpXiO8m8X3On6xffYJhcPHbafLa7VniH3XSX+JiOSAeOmK1zqurWx8hPDt/dLH8gnFxbjzMcbsGQEZ69Kjm8NyajqNrd6nqFxNBazi5htGSMeXIOmXUZOMnjNHW4dDKOoa3a6l4l8zVvPi0u182CJrZBlmjZhkgZOCBWP/wAJ3rI023Ejwrdx6Tdz3H7rgyxrGyMB6EPnHv7V06eEZzfarPc61cTJqkJhmi8iNQo2lRggZ4B/Gq918PLC5vvtP2u5jY6W+msq7cFWAG/p97Cj2oX6fo/+AVpf+vL/AIJc8I6i+pWjSyavNqLFEZhJZ+QIyR2+UZ/WneJdYu9EvtGnQr/Z894LW8BXld4wjZ7YfA/Gn28Gq6FYhfMutextRIlSGFowB1ySoPaor60n8W6NeaVqmmXmkxyoNk5lhZlYHIZdrNgggHkU3voRHbUm8Narc6y+p3UhU2SXjwWmF6qnys2e+XDflVfU9S1C78WxaBpd2tjstPtdxceUJGILbVVQeByCSealtYJvCuj2Ok6TpN3qcNvCFMqTQqSe5bey5JOScetRSaRcazfQ6wi3egalCjQEkxSmSI84YAsuM8juKXVf1/Wo+n9f1sVtffXbXWNGgtdbMKX8/kSqLWMhcRMxYZBPJXv61h3ni/V7bxpPpUGoLNNFfW9tFYtbACdGQNIxkxwQNzYz26V291oyXlxpc088jSadKZVbj94xRk+b/vonis2+8FWd9NezNcTxzXV3DeLImMwyRgAFeO4HOfU0dQe2nb/P/gFjw9qtzqWoa5DclSllfmCHauML5aNz68sa3KwLPw1cafrV5e2msTJDe3AuJrYwxlS20KQGxkDCit+jog6mR4p1htA8M3upRIryQp8gf7u4kAZ9smuV0rxxeW19qUF752s29rax3SXVvAsZdSxWQqMgMqkZyOTzjNdrq+lWuuaPdaZqCb7a6jMci+xrCXwSDZ3Mc+rXUk9zEls9wERGEC/8swAMAHJyevNC3HoJN4pm1nw/qF74W2eTbBtl9Ou6OXapLGMA/NgjGTgZz1xUtt4oaHw3odxcwS3uo6pChjt7dQDI5Tcx5ICgDJyTTbfwWmnaLfaVo2oz2NldOWSFY0dbdWGHVARwCST7E8Ukfgxo9L0u3Gr3IutJOLS8EaBlXbsKlcbWBHrR3+X/AARa/n/wDo7WZ57VJZYHt3YfNFIQWQ+hwSPyqWorWKSC1jjnna4kUfNKygFj64HAqWgAooooAzqgnvprbXba3W48xJmIeJo9oQbc5DdzkdPep6dJpjXN3FLdXLyRxP5kcW0AK3bkcmgCjpOstf3MbzSvGlxu8iIw4VgP9s9Tjmtt+g+orNtdDjtp4W8+SSK2ZmgiYDEZbrz1PU4zWk/QfUUAeVzfFmceOTpUVq7Ww1AQZBiOUCHdzu/vYP09+K1R8Sft3inSorK0ubfR5hctLeXEICTrGmcxnOeCD2rUh+G2gRa7/ahtYnk86WYoYExlwB6Z4xke5NVbD4YWNlfW0smqX91bWizRwWkzqY40lUhlGBnv161teFvl+h6LnhXsun+f47BpPxW0LV5LpYIrxTBAbhB5QkaaMHGVVCT3HBANZfg/x3q+vadqerzrPcRxIzRafb6cUKfMQCsrECQ4HI7VteFPhzY+EtSe6sruSVDEYkieCJdgJB++qhj0xyTWhZeEhpvg/wDsDTdTurYAsVukCGQbmLHqMd8dKT5FflJlLDK6guq37dehmWfjyK08CaTrepR3V69/KIVEFuFdnJYD5Axx93HBNMn+Kmi22jQ3s1tdpPLcSWwsmVVlV4/vg5baMcd6m0v4eRabpVjp8ms3t3BYXiXduJQg2Fc/Lwo4JYn1qDUvhXpWpWzpJdTrMb6a9SbZG+xpPvLtZSpHA6ij93diX1Xm97v/AJ/8A6fQdcs/EeiW+qaazNb3C5XcMEEHBB9wRXL+MfFWr+HNftrC28iYa0ot9N3gDyLkHkvzyu05+ox3rp/D+jR6BokGmwymZYQf3hjRC2ST0QADr2FZWs+BrDXr+8vNRnmkmmt1gtzx/oeDu3R8cNuAOT6Cs5W5tNjjly3fLt0I9KvtY1nWL6OLUo4bfSpktZFFurG5kCKzsSfuj5sACqEXiTWXs49f8+L+z5NS+yCxMQ4i80xb9/XdkZx0xxW1beFpLLU5Lyx1a5h+07GvIxGhFw6gDfyPlJAGcVFF4LhiugPt9y2nLdG8SwIXYspbdndjdjcS23OM0dV/XqT0ON0vx7rV1FcSRX0d5JHZXk9xE1sIxaGNiIiDxvBwQetdFpmraymo6Vp+oaktwNZsHninFuqvbyKFJGBwVw3cdqnb4eWH2G1gju7iNreG5gMq7d0kc+SytxjAJBH0q3pvhE2EiXE2qT3d5b2v2W1nkiQfZ04zhQMEnAyT6VK21/rf/gA/6/D/AIJiW3iPWbPwjNfXV8l5eT6i2n23mQKiRt55iDELjPAyR7V2Wm217a25TUL/AO3PnIkMKxke2BxWDbeB0Tw/c6TfapcXcUs5uY5DGiPBKXMm5So/vHPNaG7VdHtlBju9fldjl0MEPlgdOCVH5Zph10IPGGq31hp9vbaLJGmp304htjKu5RgFmJHfCqa5zWviBe6Toeja/Gkc9jqUX2Y2/CtHdt9zkn7u4FSO3Wt6TQ4PFGqW994j0WSNbJHSG1vfJmjYvjL4UtyAuOfU1Ri+F+jiaRLmWW4sAJxbWDKoitTMfnKADr6Z6Z4pf1/XqPQdqMniOzuPDlvJrSpNfS+Rd+XaoVLCN3JXI45UD6VraZqt1c+LtZ06ZlMFmkBiwuDl1JOT+FU5vCF5KujgeIboHScGNmgjZpG2lNzEj+6xqy3hq5TxFc6tY6xNbNdrGs0IhjdW2DAwSMjOarqLp8isk2rt8QJtPOrH7EtotyIfs6Zyzsu3djOBjNQabNrn/CSavbXWtNPb6ekbKhtY137kJ5IHbHatFPDt0nix9bOsTNviEJtvIj2+WCSBnGepPNXYdGih1LUb1ZHL6gqK6nGF2qQMfnU/Z0Hpzfccf4f8S6tqPhCTUrTUJNV1Q2AmWxNl5SLIQOA2Bux6Z5re8H60usWMhOrfb5o9vmxyW3kSwMRnaycEe2R+NP0bw3eaPpSadHrtxLbwwCGAGCMNFjocgc9O9Mh0m40O7udWEd1rmo3YSGQp5MJEa5I4JVepPvzVO12Sr2LXirVZtJ0J5LNlF5NIlvbbxkeY7BRx3xnP4Vy9p4i1rWLvRNNg1GOzuZI7uO+kS3V8zQMq8BugOc/Qitu60z/hLbm0HiDRJ7a2sZTOsF00Msc7FSo3BWbpnP1qkvw4trLXDqWhajNpR3SFYLeCMxx71VW2qRxnYD9aS31KMvSfE3iHxHq8tlb38NjLZ2spl224dJZo5mjzzyFIUHAPesuf4kapdNDNaXUdtLLY20tvY/Zwy3Ezuyshc/dHy8ciutX4fw2twsmk6pdWINqbabYqM0oLl2YswyGJY8j1qSf4faTLa3dtGZIYbizitERMfuRESUdT/eBOfwoWyv8A1v8A8AT62/rb/gnUoSY1LjaxHI9DXLaNqupay17q4vUh062uZoY7RYATIsRKszMeckg4xx0q8dV1a2byE8O390sfyCcXFuPMxxuwZARnr0pmn+GjYXk0tnfXFvZXUpuJtPKoyh25bDYyATyQDjrR6B6mDbeI/EC6dpGsSNDcx6zuEdiIwvklo2eIB+p+6Ac+vGK0PBWvzauskeoan5moKga4sJbXyJLZs4IA6sucjPOfWp7HwXHZSWqnUrqa1sN32G3YKBbkgrnIGWwCQM9KeuiXGnai2tzyXOuahHD9nhRVihKoWBI/hB5AOSe1PS4EnjDVrzQtIh1K02+TBdRfbFZc5gLbWI9MZBz7U7RtXuNV8RauiMp0+yaOCMheWl27nOfT5lFDtc+ILO60vVdCu7K2uYGjeWWaFhyMcBHJz+FQaVpcvgvw/b6dplne60d7vLOZYlkZiclmLMoOc449KS8wGeIb3VD4q0jSNLvxYpdwXEsknkrISU2bR83b5jWBdeONXm0G2XT/ALOuqxzXX2rCZVo7YkOVBPG47QPTNbtzpN94h1K11Jhe6BdWKSRRlvIl8xZNu7oWAxtFJbfDvw/DcrJc2kd8qQeSkd3GsgUly7vyPvMW5PsKWtv6+Q9Cvq2oapcX2gzaPrRgs9YlCbBbRvsXymfIJGc/KOtM1bxRfad4rt7eOeN9Ns2gh1BmQbmeYkKR6YO0n/erUsPCFtp9jo1rHczPHo8zSW+7HIKsoQ8dAHwPoKq33w70TUo9Ua9gSW81CUy/bWiQzQHAC7GI427Riq0voLodXRTIkMcKIWLlVALHqfen0gMjxTrDaB4ZvdSjRXkhT5A/3dxIAz7ZNcrpXji8tr7UoL3ztZt7W1jukureBYy6lishUZAZVIzkcnnGa7XV9Ktdc0e60zUE3211GY5F9jWEvgkGzuY59WupJ7mFLZ7hURGEC/8ALMADAzk5PXmjqPSxPa69/wAJPp9zL4fuja2sUm0ai8avHKB97y8nt0yRjrXLp45v7iz0+3a+jtzcy3RGoLAGM8ULhUMcfQs5YdPfFbU/w9g/4R680PTNUu9P065nEoghClYl6tGuRwjHkj3OKszeDfOh09m1J1vdMZjaXUdtEvlIy7SmzG3GKOoiz4L1u58QeF4by/i8q7WSSGdcY+dHKk47dM1vVR0fSbfRNLisbUsyIWZnc5Z2YlmY+5JJq9TYBRRRSAzqgnvZ7fXba2S48xZmIeJo8BBtzkN3OR096np0mmtcXcU1zcvJHDJ5kcW0AK3bkcmjqBVEl8niCG0W9M0ewyzholG1eijI7k/yrXfoPqKr2tglrc3NxvaSS4cMxbsAMBR7CrD9B9RR0A8rm+LM48cnSYrV2thqAgyPKOUCHdzu/vYP09+K1R8Sft3inSobK0ubfR5hctLeXEICTrGmcxnOeCD2rUh+G2gRa7/ahtYnk86WYoYExlwB6Z4xke5NVbD4YWNlfW0smqX91bWizRwWkzqY40lUhlGBnv161teFvl+Nj0XPCvZdP8/x2DSfitoWryXSwRXimCA3CARCRpowcZVUJPccEA1l+D/Her69p2p6vOs9xHEjNFp9vpzIU+YgESMQJDgcjtW14U+HNj4S1J7qyu5JUMRiSJ7eFdgJH8aqGPTHJNaFl4SGm+D/AOwNN1O6tgCxW6QIZBuYseox3x0pPkV+UmUsMrqC6rft16GZZ+PIrTwLpOt6lHdXr38ohUQW4V2clgPkDHH3ccE0yf4qaLbaNDezW12k8txJbCyZVWVXj++Dltoxx3qbS/h5FpulWOnyaze3cFheJd24lVBsK5+XhRwSxPrVfUvhXpWpWzpJdTrMb6a9SbZG+xpPvLtZSpHHcUfu7sS+q83vd/8AP/gHUaDrln4j0S31TTWZre4XK7hggg4IPuCK5fxj4q1fw5r9tYW3kTDWlFvpu8AeRcg8l+eV2nP1GO9dP4f0aPQNEg02GUzLCD+8MaIWySfuoAB17CsrWvAthr9/eXmpTzSTTW6wW54/0PB3bo+OG3AHJ9BWcrc2mxxytd8u3Qj0q+1jWdYvo4tRjht9KmS1kAt1Y3MgRWdiT90fNgAVQi8Say9nHr/nRf2fJqX2QWJiGRF5pi37+u7Izjpjitq28LSWWpyXljq1zD9p2NeRiNCLh1AG/kfKSAM49Kii8GQxXQH2+5bTlujeJYELsWUtuzuxuxuJbbnGaOq/r1J6HG6X491q6iuJIr2O8eOyvJ7iJrYRi0MbEREHjeDgg9a6LTNW1lNR0vT9Q1FbgazYPPFOLdVe3kUKSMDgrhu47VO3w8sPsNrbx3lxG0ENzAZV27pI58llbjGASCPpVvTfCJsJEuJtUnu7y3tfstrPJEg+zpxnCgYJOBkn0qVtr/W//AB/1+H/AATEtvEes2fhGa/ur5Ly8n1FtPtvMgVEjbzzEGIXGeBkj2rstNtr21gKahf/AG585EhhWMj2wOKwbbwOieH7nSb7VLi7ilnNzHIY0R4JS5k3KVHZjnmtDdquj2ygx3evyuxy6GCHywOnBKj8qYdSDxhqt9p+n29toskaanfTiG3Mq7lGAWYkd8KprnNZ+IF7pOh6Nr8aRz2OpRfZjb8K0d233OSfu7gVI7da3pNDg8Uapb33iPRZI1so3SG1vfJmjYvjL4UtyAuOfU1Qi+F+jiaRLmWW4sAJxbWDKoitTMfnKADr6Z6Z4pf1/XqPQfqMniOzuPDlvJrSpNfS+Rd+XaoVLCN3JXPTlQPpWtpmq3Vz4u1nTpmUwWaQGLC4OXUk5P4VTm8IXkq6OB4hugdJwY2aCNmkbaU3MSP7rGrLeGrmPxFc6tY6xNbNdrGs0IhjdW2AgYJGRnNV1F0+RWSbV2+IE2n/ANrH7EtotyIfs6ZBZ2XbuxnAxmoNNm1z/hJNXtrrWmnt9PSNlQ2sa79yE8kDsR2rRTw7cp4sfWzrEzb4hCbbyE2+WCSBnGepPNXYdGih1LUb1ZHL6gqK6nGF2qQMfnU/Z0HpzfccfoHibVtR8ISalaahJquqGwEy2JsvKRZCBwGwN2PTPNb3g/Wl1ixkJ1b7fNHt82OS28iWBiM7WTgj2yPxp+jeG73RtKTTo9duJbeGAQwZgjDRY6HIHPTvTIdJuNDu7nVhHda5qN2EhkKeTCRGuSOCVXqT781Ttdkq9iLxzrdzotpphtbv7Gt1fpbyzCDzSqFWJwuDzwO1Y7+Idauvh5cava6isNxa3UkSyi3U+egk2Ash+4cc44rZvrG98UC2W5tL3Q5LG4W6hnZoJdzAEYwGb170268Eibw/PpcGq3EBu52uLucRIzTOSCTgjC8gdKS63/rb/gj6r+u//AIvEj65pun6dLaa4yvLcwW0pa1jO/e4BbpwcHp0qtqmvajpHiWCx1XVDYWAhQxX0lmGjupC2GV2HEf8IA4znrXRXeif2hptpa313JK9tPHP5wRVLsjbhkAYA47VDrvh6XXo5babU5orC4TZParGhDjvhiMrn2o/zDobQOVByDx1Fcto2q6lrLXuri9SHTra5mhS0WEEusRKszMeckg4xx0q+2qalaMbe28M30sMXyJItxbgMo4B5kB/Oo9P8NNYXk0tnfXFvZXUpuJtPKoyh25bDYyATyQDjrR6B6mDbeI/EC6dpGsSNDcx6zuEdiIwvklo2eIB+p+6Ac+vGK0PBWvzauskeoan5moKga40+W18iS2bOCADyVzkZ5z61PYeC47KS1U6ldTWthu+w27BQLckFc5Ay2ASBnpT10S407UW1ueS51zUI4fs8KKsUJVCwJA+6DyByT2p6XAk8YateaFpEOpWm3yYLqL7YrLnMBbaxHpjIOfanaNq9xqviLV0RlOn2TRwRkLy0u3c5z6fMoodrnxBZ3Wl6roV3ZW1zA0byyzQsORjgI5OfwqDStLl8F6Bb6dplne60d7vLOZYlkZiclmLMoOc449KS8wGeIb3VD4q0jSNLvxYpdwXEsknkrISU2bR83b5jWBdeONXm0G2XT/s66rHNdfasJlWjtiQ5UE8bjtA9M1u3Ok33iHUrXUmF7oF1YpJFGW8iXzFk27uhYDG0Ult8OvD8NyslzaR3ypB5KR3cayBSXLu/I+8xbk+wpa/1+A9Cvq2oapcX2gzaPrRgs9YlCbBbRvsXymfIJGc/KOtM1bxRfad4rt7aOaN9Ns2gh1BmQbmeYkKR6YO0n/erUsPCFtp9jo1rHczPHo8zSW+4DkFWUIeOgD4H0FVb74d6JqUeqNewJLeahKZftrRIZoDgBdjEcbdoxVaX0F0OropkSGOFEZi5VQCx6n3p9ICK6NwLWT7EIzPt/diUkLn3xziuc8N3+saxoOoCe8hS9gv57ZbhYPlVUbGdueuPU10k6SSW7pDKYZGUhZAoO0+uDwa5e38FXVvoeqaaniO8UajI8rTpDGrxM5y5Ugd+ntS7h2LXhHV7rVF1OO5uUvY7K8MEN4iBRMAoJ4HGQSRkeldFWT4d0R9A0xLAXn2iCJQsK+SkYjA7fKOfqa1qoSCiiikMKKKKAM6oJ76e3122tkuPMWZiHiaPaEG3IIbucjp71PTpdMa5u4pbq5eSOJ/MjiCgBW7cjk0AZem6zd3WsR28kqsWaUTQ7ABEFPylW/i/X8K6F+g+orKtPD8drcW7m5lkS1LmCMgDbu65IGT171qv0H1FHQOp5XN8WZx45OkxWrtbDUBBkeUcoEO7nd/ewfp78Vqj4k/bvFOlQ2Vpc2+jzC5aW8uIQEnWNM5jOc8EHtWpD8NtAi13+1DaxPJ50sxQwJjLgD0zxjI9yaq2HwwsbK+tpZNUv7q2tFmjgtJmUxxpKpDKMDPfr1ra8LfL8bHoueFey6f5/jsGk/FbQdXe6WCK8UwQG4QeVvaaMHGVVCT3HBANZfg/wAd6vrunanq86z3EcSMYtPt9OZGT5iARIxAkOByO1bXhT4c2PhLUnurK8klQxGJInt4V2AkH76qGPTHJNX7LwkNN8H/ANgabqd1bAFit0gQyDcxY9RjvjpSfIr8pMpYZXUF1W/br0M2z8eRWngXSda1KO6vXv5RCogtwrs5LAfIGOPu44Jpk/xU0W20aG9ltrtZ5biS2FkyqsqvH98HLbRjjvU2l/DyLTdKsdPk1m9u4LC8S7txKqDYVz8vCjglifWq+pfCvStStnSS6nWY3016k3lxvsaT7y7WUqRwOoofJdiX1Xm97v8A5/8AAOo0HXLPxHolvqmmszW9wuV3DBBBwQfcEVy/jHxVq/hzX7awtvImGtKLfTd4A8i5B5388rtOfqMd66fw/o0egaHBpsMpmWEH94Y0Qtkk/dQADr2FZWteBrDX7+8vNRnmkmmt1gtzx/oeDu3R8cNuAOT6Cs5W5tNjjla75duhHpV9rGs6xfRxajHDb6VMlrIBbqxuZAis7En7o+bAAqhF4k1l7OPX/Oi/s+TUvsgsTEMiLzTFv39d2RnHTHFbVt4WkstTkvLHVriH7Tsa8jEaEXDqAN/I+UkAZxUUXguGK6A/tC5bTlujeJYELsWUtuzuxuxuJbbnGaOq/r1J6HG6X491q6iuJIr2O8eOyvJ7iI2wjFoY2IiIPG8HBB610WmavrKajpen6hqK3A1mwe4inFuqvbyKFJGBwVw3cdqnb4eWH2G1t47y4jaCG5gMq7d0kc+SytxjAJBH0q3pvhE2EiXE2qT3d5b2v2W1nkiQfZ04zhQMEnAyT6VK21/rf/gA/wCvw/4JiW3iPWbPwjNfXV8l5eT6i2n23mQKiRt55iDELjPAyRXZabbXtrAU1G/+3PnIk8lYyPbA4rBtvA6J4fudJvtUuLuKWc3MchjRHglLmTcpUdmOea0N2q6PbKDHd6/K7HLoYIfLA6cEqPyzTDroQeMNVvtP0+3ttFkiTU76cQ2xlXcowCzEjvhVNc5rPxAvdJ0PRtfjSOex1KL7MbfhWju2+5yT93cCpHbrW9JocHijVLe+8R6LJGtlG6Q2t75M0bF8ZfCluQFxz6mqEXwv0cTSJcyy3FgBOLawZVEVqZj85QAdfTPTPFL+v69R6D9Rk8R2dx4ct5NaVJr6XyLvy7VCpYRu5K56crj6VraZqt1c+LtZ06ZlMFmkBiwuDl1JOT+FU5vCF5KujgeIboHScGNmgjZpG2lNzEj+6xqy3hq5j8RXOrWOsTWzXaxrPD5Ebq2wEDBIyM5quounyKyTau3xAm0/+1j9iW0W5EP2dMgs7Lt3YzgYzUGmza5/wkmr211rTT2+npGyobWNd+5CeSB2x2rRTw7dJ4sfWzrEzb4hCbbyE2+WCSBnGepPNXYdGih1LUb1ZHL6gqK6nGF2qQMfnU/Z0Hpzfccf4f8AE2raj4Qk1K01B9V1Q2AmWxNl5SLIQOA2Bux6Z5re8H60usWMhOrfb5o9vmxyW3kSwMRnaycEe2R+NP0bw3e6PpSadHrtxLbwwCGDMEYaLHQ5A56d6ZDpNxod3c6sI7rXNRuwkMhTyYSI1yRwSq9SffmqdrslXsWvFOqzaToTyWbKLyaRLe23jI8x2CjjvjOfwrl7TxHrWsXeiabBqMdncyR3cd9Ilur5mgZV4DdAc5+hFbd1pn/CW3NoPEGiT21tYymcQXTQyxzsVKjIVm6Zz9apL8OLay1w6loWozaSd0hSCCCMxx71VW2qRxnYD9aS31KMvSfE3iHxHq8tlbX8NjLZ2spl224dJZo5mjzzyFIUHAOeazJ/iRql00M1pdR20stjbS29j9n3LcTO7KyFz90fLxyK6xfh/DaXCyaTql1Yg2ptptiozSguXZizDIYljyKkn+H2ky2t3bRmWGG4s4rRETH7kRElHU/3gTn8KFsr/wBb/wDAE+tv62/4J1KEmNS42sRyPQ1y2jarqWste6uL1IdOtrmaFLRYAxkWIlWZmPOSQcY46VeOq6rbHyE8O390sfyCcXFuPMxxuwZARnr0pmn+GmsLyaWzvri3srqU3E2nlUZQ7cthsZAJ5IBx1o9A9TBtvEfiBdO0jWJGguY9Z3COxEYXyS0bPEA/U/dAOfXjFaPgrX5tXWSPUNS8zUFQNcafLa+RJbNnBAB5K5yM859amsPBcdlJaqdSuprWw3fYbdgoFuSCucgZbAJAz0p66JcadqLa3PJc65qEcP2eFFWKEqhYEj+EHkDkntT0uBJ4w1a80LSIdStNvkwXUX2xWXOYC21iPTGQc+1O0bV7jVfEWrojKdPsmjgjIXlpdu5zn0+ZRQ7XPiCzutL1XQruytrmBo3llmhYcjHARyc/hUGlaXL4L8P2+naZZ3utHe7yzmWJZGYnJZizKCTnHHpSXmA/X9UvV1zS9D0uZLae/Esr3DIHMccYGdqngklgOenNU/Ez67ptlYSWut7HkuobWT/RYyH3vtL89Dg/SrNxptx4imtr2a1vNCv7BybecvDISrDDLhWYFT6H0FX7zQ/7Q060tr28lle3uI7gzbVBdkbcMgDAH0oXS/f+vwA4rxL4s1jRPEE2mw6qrzwW1u1vC9qv+mSu7KVZgMJnAxyK63StWu7vxZrGn3G0Q2kcDRqByC6ktk9+RUer+DbLWZ9SluZpVbULaOA7cfujGxZXX/aBOfwpE8MXVvrk+p2WtTwvcpEk8ZgjcP5YxnJGRnNC8wOiooooAyPFOsNoHhm91KNFeSFPkD/d3EgDPtk1yul+OLy2vtSt70Tazb2trHdJdW8CoXUsVkKjIDKpGcjk89a7XV9Ktdc0e60zUE3211GY5F9jWEvgkGzuY59WupJ7mFLZ7hURGEC/8swAMDOTk9eaOo9Ce117/hJ9PuZfD90bW1hl2jUXjV45QPveXk846ZIx161ixeMdRl8GCW2aK51S6uprXT5QmFnVGI88r/dCgse3HvVyf4ewDw9eaHpmqXen6dcziUQQhSsS9WjXI4RjyR7nFXP+EKsbm2gTVZZbqa1jaK3miZrYxIQMqBEVGOBSfWwLfUu+E9QudV8H6Vf37K9zc2kcsrIu0FioJwO3Na9Znh3Qbbw1oNtpVk80kVugUPNIzs2BjOSePoOK06uVm3YlXtqFFFFSM//Z" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![%E5%9C%B0%E5%9D%80%E8%AF%B4%E6%98%8E.JPG](attachment:%E5%9C%B0%E5%9D%80%E8%AF%B4%E6%98%8E.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The data type of theta is ap_fixed<12,2>\n", + "\n", + "theta的数据类型为ap_fixed<12,2>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theta = 0b010000000000 \n", + "overlay.cordic_0.write(0x10, theta)\n", + "overlay.cordic_0.read(0x20)" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "749" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "1028\n", + "4091\n" + ] } ], "source": [ - "theta = 0b010000000000\n", - "overlay.cordic_0.write(0x10, theta)\n", - "overlay.cordic_0.read(0x20)" + "#输出正弦和余弦值\n", + "cos = overlay.cordic_0.read(0x20)\n", + "sin = overlay.cordic_0.read(0x18)\n", + "print(cos)\n", + "print(sin)" ] }, { @@ -112,7 +145,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -160,7 +195,7 @@ { "data": { "text/plain": [ - "'/usr/local/lib/python3.6/dist-packages/pp4fpgas/cordic/cordic.bit'" + "'/home/xilinx/jupyter_notebooks/pp4fpgas/cordic/cordic.bit'" ] }, "execution_count": 6, @@ -203,9 +238,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Help on CordicOverlay in module pp4fpgas.cordic.cordic_overlay object:\n", + "Help on Overlay in module pynq.overlay object:\n", "\n", - "class CordicOverlay(pynq.overlay.Overlay)\n", + "class Overlay(pynq.pl.Bitstream)\n", " | This class keeps track of a single bitstream's state and contents.\n", " | \n", " | The overlay class holds the state of the bitstream and enables run-time\n", @@ -283,15 +318,22 @@ " | {str: {'controller' : str, 'index' : int}}.\n", " | \n", " | Method resolution order:\n", - " | CordicOverlay\n", - " | pynq.overlay.Overlay\n", + " | Overlay\n", " | pynq.pl.Bitstream\n", " | pynq.pl._BitstreamMeta\n", " | builtins.object\n", " | \n", " | Methods defined here:\n", " | \n", - " | __init__(self, bitfile='', **kwargs)\n", + " | __dir__(self)\n", + " | __dir__() -> list\n", + " | default dir() implementation\n", + " | \n", + " | __getattr__(self, key)\n", + " | Overload of __getattr__ to return a driver for an IP or\n", + " | hierarchy. Throws an `RuntimeError` if the overlay is not loaded.\n", + " | \n", + " | __init__(self, bitfile_name, download=True, partial=False, ignore_version=False)\n", " | Return a new Overlay object.\n", " | \n", " | An overlay instantiates a bitstream object as a member initially.\n", @@ -310,22 +352,6 @@ " | This class requires a Vivado TCL file to be next to bitstream file\n", " | with same name (e.g. `base.bit` and `base.tcl`).\n", " | \n", - " | ----------------------------------------------------------------------\n", - " | Data and other attributes defined here:\n", - " | \n", - " | bitfile_name = ''\n", - " | \n", - " | ----------------------------------------------------------------------\n", - " | Methods inherited from pynq.overlay.Overlay:\n", - " | \n", - " | __dir__(self)\n", - " | __dir__() -> list\n", - " | default dir() implementation\n", - " | \n", - " | __getattr__(self, key)\n", - " | Overload of __getattr__ to return a driver for an IP or\n", - " | hierarchy. Throws an `RuntimeError` if the overlay is not loaded.\n", - " | \n", " | download(self)\n", " | The method to download a bitstream onto PL.\n", " | \n", @@ -475,19 +501,54 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/usr/local/lib/python3.6/dist-packages/pp4fpgas/cordic/cordic.bit load ready\n" + "/usr/local/lib/python3.6/dist-packages/pynq_pp4fpgas-1.0-py3.6.egg/pp4fpgas/cordic/cordic.bit load ready\n" ] } ], "source": [ - "cordic = CordicOverlay()" + "from pp4fpgas import CordicOverlay\n", + "\n", + "cordic = CordicOverlay('cordic.bit')" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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h/J+IctT+b8Dzr/heGm/9Ai6/7+LR/wALw03/AKBF1/38WvRf7J07/oH2v/flf8KP7J07/oH2v/flf8KPaUP5PxDlqfzfgedf8Lw03/oEXX/fxaP+F4ab/wBAi6/7+LXov9k6d/0D7X/vyv8AhR/ZOnf9A+1/78r/AIUe0ofyfiHLU/m/A86/4Xhpv/QIuv8Av4tH/C8NN/6BF1/38WvRf7J07/oH2v8A35X/AAo/snTv+gfa/wDflf8ACj2lD+T8Q5an834HnX/C8NN/6BF1/wB/Fo/4Xhpv/QIuv+/i16L/AGTp3/QPtf8Avyv+FH9k6d/0D7X/AL8r/hR7Sh/J+IctT+b8Dzr/AIXhpv8A0CLr/v4tH/C8NN/6BF1/38WvRf7J07/oH2v/AH5X/Cj+ydO/6B9r/wB+V/wo9pQ/k/EOWp/N+B51/wALw03/AKBF1/38Wj/heGm/9Ai6/wC/i16L/ZOnf9A+1/78r/hR/ZOnf9A+1/78r/hR7Sh/J+IctT+b8Dzr/heGm/8AQIuv+/i0f8Lw03/oEXX/AH8WvRf7J07/AKB9r/35X/Cj+ydO/wCgfa/9+V/wo9pQ/k/EOWp/N+B51/wvDTf+gRdf9/Fo/wCF4ab/ANAi6/7+LXov9k6d/wBA+1/78r/hR/ZOnf8AQPtf+/K/4Ue0ofyfiHLU/m/A86/4Xhpv/QIuv+/i0f8AC8NN/wCgRdf9/Fr0X+ydO/6B9r/35X/Cj+ydO/6B9r/35X/Cj2lD+T8Q5an834HnX/C8NN/6BF1/38Wj/heGm/8AQIuv+/i16L/ZOnf9A+1/78r/AIUf2Tp3/QPtf+/K/wCFHtKH8n4hy1P5vwPOv+F4ab/0CLr/AL+LR/wvDTf+gRdf9/Fr0X+ydO/6B9r/AN+V/wAKP7J07/oH2v8A35X/AAo9pQ/k/EOWp/N+B51/wvDTf+gRdf8AfxaP+F4ab/0CLr/v4tei/wBk6d/0D7X/AL8r/hR/ZOnf9A+1/wC/K/4Ue0ofyfiHLU/m/A8Z8bfE2z8U+G30230+eB2lV97uCOD7Vd8N/Fux0Pw3ZabLplxK9tEELq6gNXR/Fmws7fwHLJb2kET/AGiMbkjCnr6itXwHptjL4D0mSWyt3drcEs0Sknk98V0OdH2CfLpfuZKM/ab62PK/FHjGDxj4r0ee2tJLZbd0QiRgSxLg9q+gKqDStPVgVsLYEHIIhXj9Kt1y1qsZqKirJG0IOLbb3Ciiiuc1CiiigAryf4o/HXTPAN5faFDY3s2traiS3kMaG3DMPlLHeGwOpAX/ABr1iqWs6RZa/ot3pWqwiezvImimjJxlT79j3B7GgD5wu9K+I1t8PrLx5qPj+e8g1UQi60ssfKEFwwTCjOzdhxkBRjnB4r6A0DVBrnhvTdWWMxC/tIrkRk52b0DY/DNePaj+z3/Y2l3Fw3jTVbrRtKSW9tNJkX5EkRSy5O7b16kICeema9tggitreOC3jWOKJQiIowFUDAAHpito/D/XkZy3ON8X+JLzTvEEOn22tWenI1r5ojTT5b+7kcsVH7mP7qcdeSTkcY5reF/G+p65J4Q+1RQR/wBsWV7LdqkbD95A8aAqCcqDuY4Oew7Vuaj4PW88Qy6zY6zqWlXNzbpbXQszFtnRCxXO9GKkbz8ykHB/GqMXw3sLPS9HtNK1TU7CbRmn+x3kckbyqkzEujb0ZWXoOVJ+VTnPJNRaGfd+MtZtvD+ua2gtXg0HWZobmArtMtnHt3bST/rAGyOxxjHNdB4K1e/8ReH11u+8mODUGM1lBFyYrc/cDtk5cjk46E47VTtvh1pUFnHaSXmoXNsNSOpzxTzKwupsD/WfLllDAPt4G7rkACpYLSLwW1zFpGmatqFtfXD3X2e28porRmxuCBmUqGbLYyRknGOlGoEnj/W9Q8O+C7nUtGSGS9Sa3jiWcEoxknjjIOPZzXJan4x13SvEJ8OX2tQxz2totzcahb6BcXPmtIzhIxFGzbAAhJZmy3bGDXVXar40sZNI1PR9Y0yAtFcefKIVy0UqSKoId+pUdumeQcVNq/hKPUdaXWLDVb/R9R8j7NLPZGM+dEGLBWWRHU4JODgEZPPagDkJ/H3iCbw/omoTWs2j21wLhdRvV0me58mSNwqfuuHSNxufcwOBgdeabefEi8Fn4etYtSsWm1GCe4udU06wnvYgkThAEiQbgzFhndwhBHJxXUnwLBBa6cmkaxqmm3OnxyRpdRSpK8yyMGfzBKrK+WG7OOD0x0qEfDrT7e0sRpuoahY6hYyTyx6lE6NO7TNul3hkKMGbBIK4GBjGKWo9DjtR8Qa54l0XRs3H2Oe18TwWouX06WFbtcbo5hFIVZRzgrzypwcYr1+uUufANlL4f+wNqeqtcLfDUv7QEyG5NwOjcrsHAxtChQOwqwvii8VAD4V19iBgsUt8n34lpiOb1zxRqGjDxtc6RaWIvLHUdPgiZosef5q26nzCDljiQgHsAPStrRtT1618dTeH9dvLPUEfThfRT29qYDGfM2MhUu2R0IOc+uamm8HafrFrqc1yb63bW57S9uImZA0LwCIoowCB/qlDcnvg9K1v7Etv+EoGvb5ftQs/sezI2bN+/OMZzn3xjtQBpUUUUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHl+k+MPFUfhDw94r1q406W01O4t4LixgtWVo0mcRrIsm85YEqxXGMEjqMmhc/FPVZP7Q1LTyzpZ3ksMGkJol1KbqOOQoxNyo2K7YYjGQvAOea2PAfw7msvCnh6PxBfak39nhLkaTNJGYYbgEkNlV3thvmClyoPQcDGxP8ADu0lmu4oNY1W10u+uDc3WlwSxrDJIWDNhthkVWOSVVwDk9Mmp1K0MDXvEHiK8m8c+TJpq6NoNmzC1ubIyteb7ISlHy4CgE88HIOOMVpLqviPUvFUGiaNdWGm2aaHb3zyvaGVlkd5FCKu9QFwg+mMd8jduPCFhcxeJI3luQPEaeXd7WX92PIEP7v5ePlUHnPPtxWDdeDL+4+Iv2yy1HUdKtYNCgsory0aImQrLKWRldWBO0oc7eD0PUUagYCa3rXinxN4MuLN7Sx1IR6ta3cpiaWONopIkdkTcCdxQYBPG7nOMEk+JWsQ30egXlzDBew3l3Fc6lb6XNcgxQlApWCMsQzeaMknaMH1Aru9O8F6XpVxo0tkbhP7HhuIYVMgbzPPKtI7kjLMWTOcjlj+FOf4eWBvHv7DUdQ0/UjeTXiXsDRl4zMFDx7WQqyHYpwwJyM5osw0OaHjvxLe6Lp8NgkMV/ca+NK+2XWnzQxzwmB5BOsLlWBGBlc9VIzgitTxB4g1bSNQs9Kn8R2FvP8AYhJI9vpUt3c3Eu4rkW8ZJSPjrkknI4xzsW/gawhhsRLe391PaamdUNzPKrPPOUZPn+XAXa+NqhQMDHfL9R8HreeIZdZsdZ1LSrm5t0troWZi2zohYrnejFSN5+ZSDg/jRqLQ5PQNeufE3iLwDq9/GkVzcabqfmrGpVdyvApIB5GducHpnFen1x9r4P0/wnaaRcadFqmoHRI7mG1gR43dluJFZt27bnbtGDkHHXca0rfxLcz3UUTeGNahWRwplkSDamTjccSk4HXgGmgPPbX4geKYfBmg6xqt3YrL4jkSKBLfTJZvsShXZ5CqMWlJVBhQAATycA1pW/jrxHPo2ux6daTapcWDW7W16NHng82KR8SHyHwXeMBjhT82V966b/hAdMXwnpWhQ3N7Cujsj2N7HIouIXUEBs7dpOGIIKkEE5FNHgS3k0++ivtY1W7vb6SKV9RaZUmjaI5j8sIoRAp5wFwcnOaWo9Dlj8S7my8L6vexanYaxPBPbW9qospreeF5m2Hz7cjdgHkbeWwQADyY28X6vqNjr2kreyXkZ0ae5i1M6JcWXkyKMNEVkwG3A5Ug5GDnPFdS3w70+8g1H+3NQv8AVbvUI4opL2do45IliYvH5floqoVYls4yT1z0qzaeDIklvJtW1jUtYmurRrIvdvGojhbqqrEiLk8ZYgk4HPajUNDhlt3/ALR+GR1FYdQli0a9mTdAAu4QwMnyknkcDPqM8VHYaZZ2Pw78C+I7WKL+2rnUNPlnv1QCadrqRVnV36sCJGBB9B6Ct658N3/h7xP4INhDqeuWOlRXdtLcO0JkhWQRLHu+4CoCnoCcL3Na1h8PLCxvbMjUdQm03T7g3NjpUroYLaQ5wRhA5C7jtVmIXt2wWAz/ABhbXl5q/h7SNcmt7nTdR15mMSwlVaGO3eSOGQEkPmRMnscDjjnA8QaZY2dn8UNJtrO3TT4dETUIrZYl8uG4eC4VnVcYUnykOR35713uo+EYNUs5obrUtRMjX4v7a4Eq+ZZSAAARfLgKAD8rBs72znNYXiHwxNpXgHxNBp0WpeINZ162lgmuGMQkd2haNCR8iLGvoo4yeCSaAOw0b/kBWH/XtH/6CK5X4p+Lv+EE8O2HiP7F9v8AsV+v+j+b5W/fFJH97a2Mb89D0rrdMieDSLOKVdskcCKy+hCgEVm+ItNsdXu9EsdVs7e+tJb9vMt7mJZI3xbTEZVgQcEA/UVcf0f5Eniv/DXP/Uk/+Vb/AO016l8Jvij/AMLR0rUL3+x/7L+xTLFs+1edvyuc52Litz/hXHgj/oTfD/8A4K4P/ia1NJ0DR9Aiki0LSbHTI5W3SJZ2yQhz6kKBk1zmxoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/4K/wCQDc/9hbUv/S6eugrn/BX/ACAbn/sLal/6XT0+gup4P+0tYXn/AAm+n6j9kn+w/wBnRwfavLPleZ5kzbN/TdjnHXHNbHgbwr8ZLbwXYxaJrljotivmeVYahbbZov3jE7g0DHk5YZJ4IroP2lv+Sbaf/wBhaP8A9EzV7BWjl7qM+X3mfM2qaR8S0+J9nb3niHTZNdbZ5V0sa+WvBxkeSPf+Gu5/4Rz47f8AQ6aH/wB+V/8AkapPEH/Jf9M/7Zf+gmvXK2rOyh6E01dv1PH/APhHPjt/0Omh/wDflf8A5Go/4Rz47f8AQ6aH/wB+V/8AkavYKK5+byNeXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPH/wDhHPjt/wBDpof/AH5X/wCRqP8AhHPjt/0Omh/9+V/+Rq9goo5vIOXzPH/+Ec+O3/Q6aH/35X/5Go/4Rz47f9Dpof8A35X/AORq9goo5vIOXzPnvx3onxbtvC7yeJvFGk3lj5qAxQRKGLZ4P+oX+dX/AAvoPxmm8L2EmjeLdHt7FogYIpIlLKueh/0c/wAzXefF/wD5J/L/ANfEf861/AH/ACT/AEf/AK9x/M10N/uFLzMkv3lvI4H/AIRz47f9Dpof/flf/kaj/hHPjt/0Omh/9+V/+Rq9gorn5vI15fM8r+DvifxTrWueLdL8YanHqE+jXEVsrRwoihg0yuQVRSQSg6jt2r1SvH/g3/yUn4nf9hYf+jrmvYKJ/EEdgoooqCgooooAyPFv/Il63/2D5/8A0W1W6qeLf+RL1v8A7B8//otqt1tH4TOW4UVz2s+NtJ0PWDpVyt7cX4thd/Z7OzknbyizLv8AlBwAUOc+3qKg0z4h+H9XvrC3sJrl01IH7HdNaSJBcMF3FFkKgFgAePVSOoIoEdRRXAeB/iZZ654e0N9XM8V7qKrF9p+xyJbSXGDmNZCNu7gjGeSCAc8V39ABRXI+KfH2n6Fb6tBD9pmvdPtTLK8NpJLFbMULR+ayjC54PJ4HJwOadB45s7fSNH+3pdXep32nR3r22n2jzuqFRlyqg7V3EgZ6ngZxRcDrKK4bVfiHa22u+GG0+Vr3Statrt1FrbPLLK8flbAqgZBG59wI4wc4wa2rTxpot7a2U8M8g+2Xx09I5IWSSO4CsxjdSMoQEbr7eoouBv0ViXvi7RtOOq/bboxDSFiN2TGx2+YPkC4HzE9ABk5IFR6P4x0vWtYfSoEvLfUI4PtD215aSQOseQA2GAyCT29D6GmBv0UjMEUsxCqBkknpXM6f8QNE1K4t4rYXwF5G8tlLLZSol4qLuPlMVw3yjcB3HIzQB09FYsfi3R5dL0jUIrrfb6yyLZMEOXLIX5H8OFVic9MHNVdK8d6LrF/aWtqbxPt6s9lNPZyRRXYUbiY3YAH5fmHqORxSA6SiiuRsPiX4d1HRptWtmvjYRiPZcNYShZ2dtipEdv7x9x27VyQaYHXUVw3iD4k21h4V1W/0y0umv9OaJJbO6s5EePzD8rMuAdpGcEcEjGc12ltOLm1inVJIxKgcJKhRlyM4KnkH2NICWiiimIKKKyfE2pf2ToT3hvFsts0Cec0BmA3zImNoI67tuc8Zz2oA1qK5bVPiHoWk6jqFlP8AbpptLCte/ZrGWZbdGQOHdlUgDa2fwPoah0fx3BqXibX7ORDDp2l28Vwl60TLGyMhZnZz8uMYK+oBPNK4zr6K53SPG+j6zfwWdv8AbIJbqMy2hu7OSBbpByWjLgbsAg464OcYqhZ/FDw3f6X/AGjbNftaO0aQS/YJsXUj5Aji+X94+QQQucYOehouB2NFcz/wsDQRp8tzLJdRSw3K2bWUlpILnzmG5IxFjcSy8jAIIyc8HCN8QdAi0e/1G7nntI9NljivYbm3eOW3MjBULIRnB3AgjIIzzwaLgdPRXMwePtFmj1Jpftto+m2Zvp4ryykhkNuAT5iqwBYfKRxzng0/SfHWi6zqdvY2pvI5LuJprR7izkijukXG4xswAbAIPHUHIyOaAOjormbP4gaDfX1vBBJciG6mNva3r2ki21xIM/IkpG0k4IHODjgmi48f6DbalLaySXRjhuRaT3i2khtoZiQPLaUDaDkgHnAJAJFAHTUVzHg7xXJ4nl1pZbOW2/s/UHtYy8DoGQYxkt1bOcgdMitrV9XstC0qfUtUm8m1gALvtLHJIAAABJJJAAHJJApgXaK5qHx7ozx6kblb6xk02za+uIbyzkik8gA5kUMPmHykcd+Khj+I2hzWkE8KalIbqQpaRLp83mXYChi8S7csgBB3j5enPIpXA6uiucPjvQzpMN/HJcy+fcmzS1jtZDceeoJaIxY3BlCknIGAM9MUDx3oX9ivqTS3Cql39hNs1rILj7R2hEWNxcgg4A6c9OaAOjoriNf+I9rY+D9U1TSre4kvdPkjils7m1kSSFnI2l0wCFIOQeh6ZrSv/Hek6ffxafJDqMt/LaJerZwWEskoiYsMlVHy4KkEHGDgdSKLgdLRXCWPjKXXfHtvHpGow/8ACP8A9iJqbE25LykyyIRkkFMbRxjOQRVPT/FviM6XoHifUJLQ6Trl3DD/AGetuVkto522wv5u75myU3fLg7jjGOS4WPR6K5HxB4snVrXTdAUx6je6r/Ziy3ds+yLbGZZJQvHmAIpwQcEnrwax9S8S+JNP0XxfaSX1odU8OWY1CK7W0wlzC0UjqrR7vlbdEykg9ADjmi4WPRqzNS/5DWgf9f7/APpLPVrTp3udLtZ5cb5YUdsDjJUE1578XviPD4Dh06W1iS71WB2vI7aQkLs2tDuYjn70wIHfaelUv8/yEer0V84eBv2ornUPEMFj4206xtLS5cRre2e9FgJOAXDs2V9TkY617/pOv6Pr8UkuhatY6nHE22R7O5SYIfQlScGuc2NCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/AAV/yAbn/sLal/6XT10Fc/4K/wCQDc/9hbUv/S6en0F1PP8A9pb/AJJtp/8A2Fo//RM1ewV4/wDtLf8AJNtP/wCwtH/6Jmr2CqfwoS+JnkfiD/kv+mf9sv8A0E165XkfiD/kv+mf9sv/AEE165W+I+GHoZ0t5eoUUUVymwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRXM6144tNI8SLoEOmanqeqNaLeLbWMKtmIuyltzsqjBXnJHUYyTigDpqK44/EvSZrXT20uy1LU7y/MwTT7aFRcRGFgsokV2UIVYgHJ5J4zVz7RdeM/D0F14f1fUfDriZ1l3WcRmVkJVo3SVWAww6j04OKAOlorgPhrc+INVu9V1PUPEdxq+hCU2umme1giacocSTgxIvylgVUc5AJ7iup8U62PDfhHVtaZBJ/Z9pLcCM/xlVJC/iQBQBrUV5xDfeJPDN74WvNb1+TVYdfuVs721kt4kS3mkiZ0aEooYKCm0hi2Qc9a9HoAKK5bWfHdvpWpXllbaLrGrSWCLJePp9ujLbhl3AEu67jt5wu49KRfiP4dDWrT3T21reaYdUtrudNsU0KjLhT13qCCVIzg0AdVRXJL4mi1DxB4UKTajYLq1vdTR2Etug85VVCDKckoQGBAHXdzjFdbQAUVxM3xT0mJtSkTS9YnstJupbXUb6K1Uw2jRttYt825gANxKK2FIJxVzUviBY2epz2On6ZqmtyWkaS3b6ZAsiW6uu5dxZlySvzbV3HBHHIoA6qiuR1T4k6Lp9ro01pDfaudchebTotNg8x5wgUkAEjBw+ecY2tnGKzbH4iadpuhz6hqM2rXYuNem02KB7NfOim+YrAFQ8gbSoPUkjPrQB6BRXmfjT4jtD4G1yWxi1DQdZ0trSSS3vYU81YZLhF3rtLq6kb1ypOCCODXT6L43tdX186NPpeqaVetbm6gTUYFjFxEGClkwx6FlyrYYZ6UAdLRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHDfF//AJJ/L/18R/zrX8Af8k/0f/r3H8zWR8X/APkn8v8A18R/zrX8Af8AJP8AR/8Ar3H8zXU/92Xr+hiv4r9DoqKKK5TY8f8Ag3/yUn4nf9hYf+jrmvYK8f8Ag3/yUn4nf9hYf+jrmvYKufxEw2CiiioKCvNPHHwK8M+P/Ez65rN9q0Fy8SRFLWaJUwo44aNjn8a9LooA8J1L9mrwfoOlXer2epa49xp8D3USyzwlGZFLAMBEDjI5wRXtVVPFv/Il63/2D5//AEW1W62j8JnLc5uLSLtfild6w0A+xSaNBapNuXmRZpWZcZz0ZTnGPyrndH8K6xaeDvh5Yz2Wy50a7jlv081D5Ki2mQnIOG+Z1Hy56/WvRqKLCPJdE0DxPc+B/DPhLUvD8mmtptzaz3d/9qhaIJDIJAE2uWMhKhT8oAJJyRjPrVNZQ6lWGVYYIPcVz/8AwgHhP/oX7D/vyKAOb1LTfEOkt4vsdM0GTVovEDvcWtzHcRIsTvbrEySh2BABTIKhsg44rKm8F6tYapYajLp+tXkcmiWllNDo2rfZJreeEEYb96iuh39dxwQeMHNerW9vDaWsVtbRrFDCgjjjUYCqBgAe2KlosFzyyx8Oa74buPCV9Z+HXuksIdRN/awXyyyQtcPG42vM43vkEnkD73PIy6Twz4hSL/hIBpbPev4mXWH0lLiPzEgFubfaGLbDJg7yN2O2a9RoosFzybWNI1m5bxBr2pWUOkedqGl3dhBfXcYErQSDEUjKWVGY4A5Iyy8nmtfRdS1HU/jJI+paQ2lGHQcCGSeOWT5rgcsYyVAO04GSeCeK7u9srXUbKWz1C3iuraZdskMyB0cehB4NZVv4L8NWmmz2FtodjFa3DK00SQgCQr90t647Z6UWC5sygtC4QKWKkAOOM+/tXmHhbw/r+ma/pS6dpWp6DaQs39qW0uopcac67GwLZC7OuX2kcIFGQR2rtLfwP4YtLqK4ttDsopoXEkbrEAVYHII9wa3aAPMPCGhTj4gaxaAxSaV4blnXTFzlVluwsrqf+uYJXH92Sm+FvD+v6Zr+lLp2lanoNpCzf2pbS6ilxpzrsbAtkLs65faRwgUZBHavRNI0XTNA09bHRbC3sLVWLCG3jCLk9Tgd/erF1awXtpLbXcSzQTKUkjcZDA9QaLBcmrzyw8L6hbfBDTNAvNDhv7y3ghW406S4EZba4ZtkithZByVbONwHNdD/AMIB4T/6F+w/78iuhoA8nk8M+Kb3wn4ksbaDVvsM8EH9nWGt30U1z5qSbpMSBmCoQFADOeQTwK9Ssp5bmxhmubWSzlkQM9vKys0R7qSpKkj2JFT0UAFFFFMQVznj3SrzWvCEtlpkPnXDXVpIE3BflS5jdjkkDhVJ/CujooGcUfD+o/a/H0gtR/xOFQWTb1/fYs1j9ePnBHzY9enNc7b+C9em0vxBoVxYtbx6zoNrbx33nRmOGeK38sxuobd97uARjvXq9FKwXPL/AA54X1CTxNot1faJrVp/ZoeSabVNda6jSQxlAIEEr5B3Hlgvy8YzT9N8Ka5pPgTwO0emibUfDsgludOEsYMgaKSNwrZ2bx5m4ZYA4PNem0UWC555qNt4p1bUtP8AEY0BIRpWo+db6W8sYup4WgeJ2Zg5jDgvlV3AYXk5PFHWfDOveJv7e1Z9Ieze/Om29tp880RlaKC6EskkhVigOGbA3E4X1OK9RoosFzg/GPhvVdV1fXJ7C182O68K3Onwt5iLuuHYlU5IxnPU8e9Wb/w5fXWp+EdkGy3sLW4hunV1Hk77cIuBnnnjjNdnRRYLnjeg+A9VtrbQtGv9H1h3025hM93Nrrtp+yJsrJFEJd247QQhQBSfQc9FpVt4o8OWNz4esNAS7MmpTTwapNLGbXyZZzKWlXeJN6hyNoUgkDnGcehUUWC5yng6x1PS9U8R22oae8MFxqkl7bXYlRknSQDgAHcpGOcge2at+N4dbuPCs0fhrebwyxblidFlaLzF8wRs/wAocpnBbgGr2q+HdI1xom1jTre8aIERmZA20Hrj8qj0vwvoei3TXOk6Xa2czIY2khjCkqSDj6ZA/KgDzaTwjrt5deIri20fVI4dQ8MXNhAdW1Rbid7hjlVIMjLGDu4AOBgk4JrW8T+EdQkvvDWqW9pqNzFp1g9ldWmlX/2S4QMIyGRg6BgDHgruGcgjOK9JoosFzylfBt3H4ZZrjwxdXJuNXN5LbHWGfUIkEXlrMs5k2+dwMqHxt4yTVe58H+KtQ0a0uLv+1rhNN1j7TZ2NxqSpqH2VoTGwNxGwXzAzMwBfp8pbmvXqKLBc8mk8F6pd+GPFMtno+oQXWoRW8drFquqm5urgRPvw7GR0TkkKN3c5xmuu03T9Rn+Ikmv3enPZwT6Hb2+2WSNnjlE0rtGdrHoGXkcH1rq6KLBc8j8BaLdaZ47/ALJ1eHyZx4Z2yxhw2A19cEcqSOjCr2n6H4jm0Pw34RvtIeCDQ7y2kn1UzRGGeG2bdH5aht+5tiAhlG3nk8Z7248P6Rd65b6zc6bbS6lbJshu3jBkjXngN1A+ZvzNaNFgucb4ktda1Gaw1Wz0dhdaDq5lhtzcx5vrcxNG7Ic4UkSnAcj7nOMg1h61Y6jN4T+IviPVbCTTf7R0R4LezmdGlSKG3lO59hZQWaVuATgAd69Oqhq+iaZr9j9i1uwt7+23B/JuIw67h0OD9aLBcdo3/ICsP+vaP/0EV4Z+0f4Qv7u4i8R2Ub3ETWAsHijBZlZZvOBAHUbfMJPbZXv0caRRrHGoVEAVVA4AHQVnal/yGtA/6/3/APSWeqX+f5CPkcx+Lvj/AOPrJ30+3t2igjtJrm0t2WC3iVmO98sfm+Y8ZGcACvZdC/Zf0WxsHt9a8Q396fNLxvaQpb7cgAghvMz90ele5UVgm1satJ7nj/8AwzT4O/6CWuf9/wCH/wCNUf8ADNPg7/oJa5/3/h/+NV7BRVc8u4uSPY8f/wCGafB3/QS1z/v/AA//ABqj/hmnwd/0Etc/7/w//Gq9goo55dw5I9jwf4Y+GrPwf+0V4i0LTJJ5bW10kbHuGDOdxt3OSAB1Y9q94rx/w5/ydj4s/wCwTH/6Da17BRPcUNgoooqCwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/AAV/yAbn/sLal/6XT10Fc/4K/wCQDc/9hbUv/S6en0F1PP8A9pb/AJJtp/8A2Fo//RM1ewV4/wDtLf8AJNtP/wCwtH/6Jmr2CqfwoS+JnkfiD/kv+mf9sv8A0E165XkfiD/kv+mf9sv/AEE165W+I+GHoZ0t5eoUUUVymwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFZd34i0+1uXtUaS7ukOGt7WMysp9Gxwv/AiKai3sJtLc1KKxjr10CceHdUI9Q9tz/5GpP7fu/8AoXNU/wC/lr/8eqvZyFzI2qKxf7fu/wDoXNU/7+Wv/wAeo/t+7/6FzVP+/lr/APHqPZyDmRtUVi/2/d/9C5qn/fy1/wDj1H9v3f8A0Lmqf9/LX/49R7OQcyNqvNNX1TUNL+PEj6dos2ro/huISxW0kSSp/pMuCPMZVIzwRuB6HnFdj/b93/0Lmqf9/LX/AOPVWF6Bqx1MeE9Q+3NALc3G613mMMWCZ87pkk/jR7OQcyODn8FakNPhudf8KnVTeaheajPFpl6Ir3TJJmUoIpN6BxtUB8MMtgjIFb2n6b4xtPhLrdm/2q41qZLkaXDd3aSXEMbAiJZJs7WcZznJHQZOK6f+37v/AKFzVP8Av5a//HqP7fu/+hc1T/v5a/8Ax6j2cg5kWtA0iHQPDmnaRaqFisbaOBQO+1QM/jjNReKdEHiTwjq2is4j/tC0ltw5/gLKQG/AkGsvVng123SDV/B+oXcUbb1WR7bAOMZ/13vWfZ6Lomn3kV3Z+A7uGeFg0civbZU+v+vo9nIOZFKGx8SeJr3wtZ654fk0mDQblb28uZLiJ0uJo4mRFhCMWKkvuJYLgDHWvRqxG126dCr+G9UKsMEeZa8j/v8AVzf/AAjPhz/ontz/AN9W3/x+j2cg5kZniHQvEep+J9ci1LTNX1W0uAq6R9l1YWtjCmwA+eiyK5IfJJ2vkdBxisofDnxH4k8GeGfDOq2qaPBoOnpOLmQwzmW/VSqIAC37terf3sgDoa9FsdRbTbGKzsPCupQW8QwkaSW2FH/f6rH9v3f/AELmqf8Afy1/+PUezkHMjnl0/wARaz4s8Ea5qukfYZLG2vk1KMTxusMkiRqu3DHcrFSRjJAxnBru65+71SS/s5bW88L6lLBMpSSNpLbDA9R/rqwP+EZ8Of8ARPbn/vq2/wDj9Hs5BzI5jRrrxDe6D450DSPDrXf2/XNTtoL/AM+JIIfMcqxmBYP8u7Pyq24YHFB+HVx4c1rUt3h/VvEVvdxwG0n07WWsykiQrGUmXzoxglNwcBiAcdhXoGmXEWi27waV4Qv7SKSVpnSJrUBnbqx/fdTVz+37v/oXNU/7+Wv/AMeo9nIOZHK6V4N1DS9Y8AmHTre3tdHs71LxLactHbvKse1VMjF2G4MM89OccVzOuadrXhqzsJW01Zrq4+IUl5a27TJ+/ikWYqQ2SFJHTdjBxnFeof2/d/8AQuap/wB/LX/49Va9vRqLWxvvCeoTm1nW4gLtany5ACA4/fcEAn86PZyDmRw3izwx4l8aLrWrJokunySWlnY2VhcTw+dKqXiTyyOVcooAGANxJwfUCuy1HRr+f4q6JrEUG6wtdNu4Jpt6ja7vEVGM5OQjcgY4rQ/t+7/6FzVP+/lr/wDHqP7fu/8AoXNU/wC/lr/8eo9nIOZG1RWL/b93/wBC5qn/AH8tf/j1H9v3f/Quap/38tf/AI9R7OQcyNqisX+37v8A6FzVP+/lr/8AHqP7fu/+hc1T/v5a/wDx6j2cg5kbVFYv9v3f/Quap/38tf8A49R/b93/ANC5qn/fy1/+PUezkHMjaorF/t+7/wChc1T/AL+Wv/x6nxeJbDz1gvRNp8zNtVbuPYrHOAA/KEn0DZo9nIOZGvRRRUFBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBw3xf/5J/L/18R/zrX8Af8k/0f8A69x/M1kfF/8A5J/L/wBfEf8AOtfwB/yT/R/+vcfzNdT/AN2Xr+hiv4r9DoqKKK5TY8f+Df8AyUn4nf8AYWH/AKOua9grx/4N/wDJSfid/wBhYf8Ao65r2Crn8RMNgoooqCgoorzTxxofxcv/ABM8/gTxRpOmaQYkC291ErOHx8xyYH4P1oA7Txb/AMiXrf8A2D5//RbVbrxXUvDnx1g0q7l1zxpodzpkcDteQxQqHkhCkuq/6MOSuQOR9RXtVbR+EzluRTXENvt8+aOLe21d7Abj6DPeszQfEdprnhqDWgPslvMX4nYDbtcpyenVa47X28PQfE6/k+IAs/sEukwppbaiqmH78n2hU3ceYcxcD5iMYrlvDJ0mHRvAs3igRJ4aS1vxE1+B5Ed0Zx5Zl38A+X5gUt3z3pXFY9sa7t0jEjzxKjLvDFwAV9c+nI5oF5bM8SC4iLTDdGocZceo9a8ntNN8Ma34x8Jw6XprNoiHV5YIblA0ExDQfPGpJHlbiSowBkAgdKz08P6TY+A9R1C00+3ivLLxh5drcKg326LqixqiN1VQpYbRx8x45NFwse3Vl6drsOpazrGnRxOj6TNHDI7EYcvEkoI/BwPwrUryHxKkkk/jwFJpLIa1ph1FIQSzWghtjMMDkjZnOO2abBHrMFzBdIXtpo5lBwWjcMAfTiqOra7aaVoWqanvW5XTLaS4mihcFsIpYr7E7T1ryzWTaXmqaxJ8JfszL/wjVyl2+j7fJabK/Z1BT5fOA83GPmAP0pdVn8AyeE79PBttE96nhq+V308KBbxeRyt1tI+YsBgMC27ceOTSuFj121vYrq2t5QQhuIhKkbEbsEA9Pxp8VzBO8iQTxyNGcOqOCUPocdK8Zt49Y0zR/EVjeN9p8V6lofn6RqEKlTLAIhut4Rk7WjY5wOW3Kx56YE8Et1p9uvgjVNDfUo9JulFv4d02SK4MZgI2XDea21t20jcN28DHOaLjse43XiWzt/EGl6THieTUWmUPE4IiMSbiGrZryLTZfBM3xI8FnwStiHS2ulmFmoBRPJ+VZcdHznhvm+9XrtNCCiiimIKKKKACiiigAooooAKKKKACmu6xrukYKuQMscdeKdXEfGDyP+FX6h9rEht/tFn5oiB3FftcWcY5zj0oGdlDcQ3CsbeaOUK21ijBsH0OO9EVzBO8iQTxyNGcOqOCUPocdK8e1f7Nd6lrMnwm+ymNfDNxHcvpIAjM+5fIUbOPOC+bj+IA/SovBaWcvjHwy2h6p4eZoYJfOg0PTZI5fJ8vBW6Yytt+baRvG7ePrU3Cx65caqiTW0doi3ZmmWOTy5kHkqVZg5BPI+UDAyeemAas/a7bzlh+0ReaxIVN43HHXA9q8X8KaHpuneBfhpe2dlBHeXmqwNcXIjHmTf6PcEBm6kDOAD0HFUrl/DL+GfGdrCtu3i+bXr37Am0fa2n+0N5Jh/i2g914B3570XHY9is/Etnda1qumuRbyabPFAzyuAJWeJZBt/BwPwrUluYIJI0mnjjeQ4RXcAufQDvXldz4c0zVNV+Jl3rOn2t5dxwxRrJLEH8siwjYlM/d5wcjB4X0FcbG/mpqj+K9Q8PQS3OnWhtjrtlJPPLbm1TH2ZhKuTv35CAtv5PUUXCx9DSXMELhZp442JAAZwCSen54NI13bJcrbvcRLM33Yi4DH6DrXmHh/wAP22pfETTz4kgGqXVl4TsGV76AbvN86X94yNnbJx16glvWuO8Walpt1pGv3UK6Dp97HqryNbSRPcasGjnH70uWBhXC7gcFVTABouKx7lpuuRalrWsabHE6PpM0cMjsRhy8SSgj8HA/CtMkAEk4A6k1yPhX/kffHP8A1/2v/pHDTvibeW9n4CvGvbNbu3lkhilSWVo4kVpVG+Vl5EY6sO4yDwTTA6aK9tZ4nlguYZI4/vukgIX6kdKDfWqxSSG5hEcbbXcyDCH0J7GvCpprU33jIWF1ok0U3gy7JOg2pitmZCRjduYSOocZIxgMBitDxBodnoN14P8As8WiaZo72cjzT6rZ+ZaveFIwrz4ZAZCm/a7k87u5pXHY9na4gS3+0PNGsOA3mFgFwe+elKLiFrfz1ljMON3mBhtx656V4fFaWR8GxyXWuWMOnjxE81jcHSz/AGSjeUQVKPIR5BcvtfcAHPHaq91cPc+Frdba10W00a38SZv7u3SWfSrnMOVl2bhiISlQwDbA4zk80XCx7PrXiPTtC8PTa1dTeZZxAfNCQ28lgoA5weT61ekvbWKPzJbmFE2htzSADB6HPoa8MvLO2uPBfji4hutJ1HT1+ySEaRYNHZRyq37x4yXcFtmN5XgYGec12MGk+HfEfxX842djqOnJ4ZtjZoY1kgCNPMAVXG37vAPYEjvRcVjqpfFGPHUXhq30+WdjYrfS3SyKEijZ2QcE5Jynb1rNsPiHb31xYTHSryDSNTuDbWOqO0ZjmfkLlA29VfadpI546ZFcN8MYWn8QQ2yOR/xShgjJJOxRfXCqPoAAPwqfT9Us9Q8DeCvCdq6f29Y39hHeaep/fWv2WRWmd16quIzhjwdwxnNFx2PS9f8AEltoVlFL5Ul7cXFytnbWtuV3yzNkhMkgDABYkkYAJrGuPH5ttB1q8l0O7S90IeZf2DSx744vLMgkVt21lKg4wc5BGOKyfEs+h2Wq6VrmnCBbPSvEUn9tSRoQsM0ls0ZkkOOxeLLdgRzxWbq13BrWm/FHW9LlS50yXQRZw3cTbo55IoLhn2MOGA81RkcZyO1FwPU7W4F3Zw3Cgqs0auAeoBGa5nx54q0rwbDpOsa3MyW1teM7LGu6RgYJU+Ve/wAzoPQbhmt/Rv8AkBWH/XtH/wCgivnv9pm11Aa9b3bhzpraR5UR/hEwuUL/AIlSn5e1Wv8AP8iT1TwZ8ePBnjbWk0qxkvLC9lOII7+JU84+ilWYZ9iQT2r0mvg7xE+g69440yL4TaPqWnCRIo4reWUmZrrefnVt7Y/g5yMYJ4r2/wAGeEvjza6XOk/iAWExmyRq94t4zrtGNp2y7QDnIyOvesErmr0PoKivH/8AhHPjt/0Omh/9+V/+RqP+Ec+O3/Q6aH/35X/5GquXzFzeR7BRXg99rPxV8H+N/Cmn+KfE1jeWutajHAY7S3jOUEkauCTCpGRJwR79K94pONhp3PH/AA5/ydj4s/7BMf8A6Da17BXj/hz/AJOx8Wf9gmP/ANBta9gpy6Cj1CiiioKCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuf8ABX/IBuf+wtqX/pdPXQVz/gr/AJANz/2FtS/9Lp6fQXU8/wD2lv8Akm2n/wDYWj/9EzV7BXj/AO0t/wAk20//ALC0f/omavYKp/ChL4meR+IP+S/6Z/2y/wDQTXrleR+IP+S/6Z/2y/8AQTXrlb4j4YehnS3l6hRRRXKbBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBQ1y9k0/Q7q4t13ThNsK+sjHag/76Iryn4heL7/AMDLp+geHWWCU24nmunjDs5LEZ+bIySrFiQScivTfFP/ACBY/wDr/sv/AEqirxP44/8AI72f/YOT/wBGSV3YWKk9TnrNrYzYviV49ntZ7mHUJpLe32+dKljEUi3HC7jswMngZ61t+B/il4gu/FlnYa3cre215IIeYERo2PCkFQO+M5zxVjQv+EF/4QHxV9j/AOEi+wf6J9t83yPN/wBafL8vHH3uue3SuP0H+zP+FoaV/YP2v7B9ug8r7bt83qud23j72cY7YrrtCSkuW1vLyMPeVnc+l6KKK806QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKjngiureSC5jWWKRSro4yGB7GpKKAKnhqST+yTaXDM8tjK1szOSSwXlCSepKFD9TWvWLoH/IR17/sIL/6SwVtVnP4jSOwUUUVBQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBw3xf/5J/L/18R/zrX8Af8k/0f8A69x/M1kfF/8A5J/L/wBfEf8AOtfwB/yT/R/+vcfzNdT/AN2Xr+hiv4r9DoqKKK5TY8f+Df8AyUn4nf8AYWH/AKOua9grx/4N/wDJSfid/wBhYf8Ao65r2Crn8RMNgoooqCgooooAyPFv/Il63/2D5/8A0W1W6g8TQvceEtXhiGXksZkUepMZAp8MyXEEc0LBo5FDqw7gjINbR+EzluPKhvvAHBzyKGUMpVgCCMEEdaWimSFNYEqQDtJHBHanUUAc7/YGu/8AQ43/AP4B2v8A8brdt45IbWKOaZp5EQK8rKAZCByxAAAJ68DFS0UAIFCjCgAZzwKAoBJAAJOTgdaWigApAoUkgAFjk4HWlooAQKBnAAycnA60tFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZniHQ4PEWjPpt1JJFG80MpaPG7McqyAc+pQA+1adFACABfugDnPFAUKSQACxycDrS0UAFZeh6Fb6FDdx27vJ9qvZ7xjJjKtK5dlGOwJrUooAKQqGIJAJU5GR0paKACk2qGLADcRgnHJpaKAM3VdOv75ojYa1caYEB3CGGJ9/13qcY9qj0vS9SsrppL/X7nUoyhUQzW8KAHI+bKID2I645rWooARVCKFUBVAwAB0oZQylWAIIwQR1paKAEIBUggEEYINAACgAAADAApaKAEVQqhVACgYAA6UUtFAGBP4XEnjqLxPBqVzbyrZrZS2qJGY5ow7OMkqWB3Pn5SOgrd2qGLBRuIwTjkinUUAJWR4m0D/hI/DdzoqX8+nQXUbQzPbJGWaJlKsnzqQAQeoGR2IrYooAhtbdbSzht0JZYY1QE9SAMVi+KtG07xA2j6XrVpHeWVxfMssEg+VgLaYj8iAQexFdBWbf/PruhIp+ZbuSUj/ZFvKpP5uv5011+f5AUvDHww8G+Db5rzw5oMFpdMCPPZ3ldQeoVnZiv4Yrq6KK5zYKKKKAPH/jJ/yUn4Y/9hY/+jravYK8f+Mn/JSfhj/2Fj/6Otq9gq5fCiVuzx/w5/ydj4s/7BMf/oNrXsFeP+HP+TsfFn/YJj/9Bta9gol0CPUKKKKgoKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/wV/yAbn/ALC2pf8ApdPXQVz/AIK/5ANz/wBhbUv/AEunp9BdTz/9pb/km2n/APYWj/8ARM1ewV4/+0t/yTbT/wDsLR/+iZq9gqn8KEviZ5H4g/5L/pn/AGy/9BNeuV5H4g/5L/pn/bL/ANBNeuVviPhh6GdLeXqFFFFcpsFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFQyXtrDcxW8tzDHPNu8qJpAGk2jJ2jqcDripq5vVlQ+LdFZtLkuXU3G28VmC2nyDO4AYO7oM/hVwjzMiUuVEviG8trvRsWtzDN5Wp2kUnluG2OLqLKnHQj0rhvin8P9T8T31tqmiBJpooPIkgeQIWAYlSpPH8TZyR2rd2oul3/AJelyadnxDCW8xmb7QftMP74bhwG9BxxxXX10xbpWcf62MX7+586xfDXx7Baz20OnzR29xt86JL6IJLtOV3Dfg4PIz0rd8DfCzXrTxRZ6jrkCWVvZyiXb5yu8jDlQNpIxnGc44r1Ox8T2d/4iv8ARoop1uLBkWRmUbDuUsMHOeg9BW1WssRUtZrclU47hRWdr+t2vhvQbvV9QEht7VNzLEu53JIAVR3JJAH1rEsvGOoLrFjYeIfDN5o41FzHazmeOdDIFLbH2H5GKqcdQcHmuQ2Osorjrv4h29rcXcw0q8k0axvPsV3qqtH5cUu4Kx2Ft7IrEKzAcEHrg1NrHjkabqOo29lo19qkekRrLqU1s0YFuGXftAZgXYJ8xA7Edzii4WOroqG0uoL6ygu7SQSwXEayxSL0ZWGQfxBrK8WeJoPCehHUJraa8kaZIILWDHmTSO2Aq546ZP0BpiNuisS48SR/8IYPEelWk2p27Wq3ccNvjzJIyA3yg9SFOcd8Y61APGmm3VzosGj51OTWE8+EQEYjtwMtM5P3VBIXHUsceuEM6KisrRdeh1u41aKGF4zpd+1jIXI+dhGj7h7YkA/CtWmIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAKOgf8hHXv8AsIL/AOksFbVYugf8hHXv+wgv/pLBW1WdT4jSOwUUUVBQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBw3xf/wCSfy/9fEf861/AH/JP9H/69x/M1kfF/wD5J/L/ANfEf861/AH/ACT/AEf/AK9x/M11P/dl6/oYr+K/Q6KiiiuU2PH/AIN/8lJ+J3/YWH/o65r2CvH/AIN/8lJ+J3/YWH/o65r2Crn8RMNgoooqCgooooAK5qGG48NIbZoJrrS1P+jvbxNJJACf9WyKCxUdAVB44I4yeloq4y5SXG5z51+0ViDbapkHHGlXJ/8AadH/AAkFp/z7ap/4Kbr/AON10FFVzx7E8r7nP/8ACQWn/Ptqn/gpuv8A43R/wkFp/wA+2qf+Cm6/+N10FFHPHsHK+5z/APwkFp/z7ap/4Kbr/wCN0f8ACQWn/Ptqn/gpuv8A43XQUUc8ewcr7nP/APCQWn/Ptqn/AIKbr/43R/wkFp/z7ap/4Kbr/wCN10FFHPHsHK+5z/8AwkFp/wA+2qf+Cm6/+N0f8JBaf8+2qf8Agpuv/jddBRRzx7Byvuc//wAJBaf8+2qf+Cm6/wDjdH/CQWn/AD7ap/4Kbr/43Wxf3JstNuboLvMELyBc4ztBOP0rl08Va/HoKa3daDbNYG3W5byL7MixldxO0oASBzjIq4+8rpfiJ6bs0P8AhILT/n21T/wU3X/xuj/hILT/AJ9tU/8ABTdf/G6001jTXaBft1uslxjyo2lUM5IDABSc5wQce9OtdW069uJILK/tbiaP78cUyuyfUA5FLm8v6+4fL5mV/wAJBaf8+2qf+Cm6/wDjdH/CQWn/AD7ap/4Kbr/43WrLqunwXyWU1/ax3Un3IHmUO30XOTRLqunwXyWc9/ax3Un3IHmUO30UnJpc3l/X3By+Zlf8JBaf8+2qf+Cm6/8AjdH/AAkFp/z7ap/4Kbr/AON1q3GradZ3SW13qFrBcSfcilmVWb6AnJpb3VdP00oNRvra0MhwnnzKm76ZPNHN5f19wcvmZP8AwkFp/wA+2qf+Cm6/+N0f8JBaf8+2qf8Agpuv/jda13qlhp8KTX99bWsUn3HmmVFb6EnmifU7C1gWe5vbeGJl3rJJKqqV45BJ6cjn3FHMu39fcHL5mT/wkFp/z7ap/wCCm6/+N0f8JBaf8+2qf+Cm6/8AjdbU13DBYSXjOGgjiMpZOcqBnI9eK5tPEutjTYdXl0OJtMmVJNsFyZLhI2xhtmzB4OcA5qo+9svxE1bqWv8AhILT/n21T/wU3X/xuj/hILT/AJ9tU/8ABTdf/G63EuIZPM8uVG8pism1gdhAyQfQ4IqrJrelRSwxS6nZpJOA0SNcIDID0KjPOfap5k+n9fcPl8zN/wCEgtP+fbVP/BTdf/G6P+EgtP8An21T/wAFN1/8bq4/iCzj8UR6GzqLh7cz5LgfxABMdckEn6CrNtqun3txJb2d/a3E0X+sjimVmT6gHIpt26f19wreZlf8JBaf8+2qf+Cm6/8AjdH/AAkFp/z7ap/4Kbr/AON1qPq+mx3KW8moWqTyMUSJp1DMwOCAM5Jzxin2upWN9JLHZXtvcPCcSLDKrlD6EA8Uuby/r7h8vmZH/CQWn/Ptqn/gpuv/AI3R/wAJBaf8+2qf+Cm6/wDjdGq65qcHiSLR9HsLa5ke0N0z3FwYwAHC44U+op+l67eTa4+j6xp6Wd2Lb7TG0M/mxyJu2nnaCCCRxjvV20vb8SetrjP+EgtP+fbVP/BTdf8Axuj/AISC0/59tU/8FN1/8brVttV0+9uJLezv7W4mi/1kcUysyfUA5FB1bThqH2E6hai8/wCfczL5n/fOc1HN5f19xXL5mV/wkFp/z7ap/wCCm6/+N0f8JBaf8+2qf+Cm6/8Ajdasuq6fBfJZz39rHdSfcgeZQ7fRScmq1n4gs73xBfaRE6+fZrGW+cfOWzkAdflwM/WnfS9v6+4VvMp/8JBaf8+2qf8Agpuv/jdH/CQWn/Ptqn/gpuv/AI3XQUVPPHsPlfc5/wD4SC0/59tU/wDBTdf/ABuj/hILT/n21T/wU3X/AMbroKKOePYOV9zn/wDhILT/AJ9tU/8ABTdf/G6P+EgtP+fbVP8AwU3X/wAbroKKOePYOV9zn/8AhILT/n21T/wU3X/xuj/hILT/AJ9tU/8ABTdf/G66Cijnj2Dlfc5//hILT/n21T/wU3X/AMbo/wCEgtP+fbVP/BTdf/G66Cijnj2Dlfc5/wDt63Kny7TVHbOAv9mXC5PYZZAB9SQB3IqxpenXJ1GTVdUCpcNH5UECncII85IJ7sxAzjj5QBnGTsUUnPSyGo9wooorMsKKKKAPH/jJ/wAlJ+GP/YWP/o62r2CvH/jJ/wAlJ+GP/YWP/o62r2Crl8KJW7PH/Dn/ACdj4s/7BMf/AKDa17BXj/hz/k7HxZ/2CY//AEG1r2CiXQI9QoooqCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/BX/IBuf+wtqX/pdPXQVz/gr/kA3P8A2FtS/wDS6en0F1PP/wBpb/km2n/9haP/ANEzV7BXj/7S3/JNtP8A+wtH/wCiZq9gqn8KEviZ5d438EeJNV8bjWdAaOPy44/Lk87YystVP+Ef+Kv/AEFv/Jlf8K9cordYmSilZO3kZuim73Z5H/wj/wAVf+gt/wCTK/4Uf8I/8Vf+gt/5Mr/hXrlFH1qX8q+4PYruzyP/AIR/4q/9Bb/yZX/Cj/hH/ir/ANBb/wAmV/wr1yij61L+VfcHsV3Z5H/wj/xV/wCgt/5Mr/hR/wAI/wDFX/oLf+TK/wCFeuUUfWpfyr7g9iu7PI/+Ef8Air/0Fv8AyZX/AAo/4R/4q/8AQW/8mV/wr1yij61L+VfcHsV3Z5H/AMI/8Vf+gt/5Mr/hR/wj/wAVf+gt/wCTK/4V65RR9al/KvuD2K7s8j/4R/4q/wDQW/8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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The data type is ap_fixed<12,2>\n", + "\n", + "数据类型为ap_fixed<12,2>" ] }, { @@ -498,7 +559,7 @@ { "data": { "text/plain": [ - "749" + "554" ] }, "execution_count": 11, @@ -507,7 +568,7 @@ } ], "source": [ - "cordic.cordic_0.calc(0b010000000000)" + "cordic.cordic_0.calc(0b010000000000) #同theta" ] }, { diff --git a/boards/Pynq-Z1/notebooks/02-FIR.ipynb b/boards/Pynq-Z1/notebooks/02-FIR.ipynb index 8b91c05..e35909c 100644 --- a/boards/Pynq-Z1/notebooks/02-FIR.ipynb +++ b/boards/Pynq-Z1/notebooks/02-FIR.ipynb @@ -1,13 +1,39 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "from pynq import DefaultIP\n", + "\n", "class FirDriver(DefaultIP):\n", " def __init__(self, description):\n", " super().__init__(description=description)\n", @@ -23,26 +49,97 @@ " self.write(0x10, value)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0 0]\n", - " [0 0]]\n" - ] + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "import pynq.lib.dma\n", "import numpy as np\n", "\n", - "firol = pynq.Overlay(\"./src/fir/fir.bit\")\n", + "firol = pynq.Overlay(\"fir.bit\")" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolution of coefficient h[] of n-order FIR filter with input signal x[] can be expressed by difference equation\n", + "N-阶FIR滤波器的系数h[]与输入信号x[]的卷积可由差分方程表示：\n", "\n", + "$$\n", + "y[i]=\\sum_{j=0}^{N-1}h[j]\\cdot x[i-j]\\quad(2.1)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ContiguousArray([ 0, 2, 2, 0, 0, 4, 10, 14, 14, 12, 12], dtype=uint32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "# dma = overlay.const_multiply.multiply_dma\n", "# multiply = overlay.const_multiply.multiply\n", "\n", @@ -57,40 +154,47 @@ "out_buffer = xlnk.cma_array(shape=(11,), dtype=np.uint32)\n", "\n", "\n", - "# for i in range(11):\n", - "# in_buffer[i] = 1\n", + "for i in range(11):\n", + " in_buffer[i] = 1\n", "\n", - "# filt = [1,0,-1,0,2,3,2,0,-1,0,1]\n", - "# actualfilt = [53,0,-91,0,313,500,313,0,-91,0,53]\n", - "# f.x = 2\n", - "# dma.sendchannel.transfer(in_buffer)\n", - "# dma.recvchannel.transfer(out_buffer)\n", - "# # dma.sendchannel.wait()\n", - "# # dma.recvchannel.wait()\n", - "\n", - "# out_buffer" + "filt = [1,0,-1,0,2,3,2,0,-1,0,1]\n", + "actualfilt = [53,0,-91,0,313,500,313,0,-91,0,53]\n", + "f.x = 2\n", + "dma.sendchannel.transfer(in_buffer)\n", + "dma.recvchannel.transfer(out_buffer)\n", + "dma.sendchannel.wait()\n", + "dma.recvchannel.wait()\n", + "out_buffer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -121,38 +225,45 @@ "\n", "fig2 = plt.figure()\n", "ax2 = fig2.gca()\n", - "\n", - "plt.plot(out_buffer)" + "plt.plot(out_buffer)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FFT transformation, view the waveform\n", + "进行FFT变换，查看波形" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/opt/python3.6/lib/python3.6/site-packages/numpy/core/numeric.py:492: ComplexWarning: Casting complex values to real discards the imaginary part\n", + "/usr/lib/python3/dist-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n", " return array(a, dtype, copy=False, order=order)\n" ] }, { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -163,6 +274,8994 @@ "out = scipy.fftpack.fft(actualfilt)\n", "plt.plot(out)" ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on module matplotlib.pyplot in matplotlib:\n", + "\n", + "NAME\n", + " matplotlib.pyplot\n", + "\n", + "DESCRIPTION\n", + " `matplotlib.pyplot` is a state-based interface to matplotlib. It provides\n", + " a MATLAB-like way of plotting.\n", + " \n", + " pyplot is mainly intended for interactive plots and simple cases of programmatic\n", + " plot generation::\n", + " \n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " \n", + " x = np.arange(0, 5, 0.1)\n", + " y = np.sin(x)\n", + " plt.plot(x, y)\n", + " \n", + " The object-oriented API is recommended for more complex plots.\n", + "\n", + "FUNCTIONS\n", + " acorr(x, hold=None, data=None, **kwargs)\n", + " Plot the autocorrelation of `x`.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " x : sequence of scalar\n", + " \n", + " hold : boolean, optional, *deprecated*, default: True\n", + " \n", + " detrend : callable, optional, default: `mlab.detrend_none`\n", + " x is detrended by the `detrend` callable. Default is no\n", + " normalization.\n", + " \n", + " normed : boolean, optional, default: True\n", + " if True, input vectors are normalised to unit length.\n", + " \n", + " usevlines : boolean, optional, default: True\n", + " if True, Axes.vlines is used to plot the vertical lines from the\n", + " origin to the acorr. Otherwise, Axes.plot is used.\n", + " \n", + " maxlags : integer, optional, default: 10\n", + " number of lags to show. If None, will return all 2 * len(x) - 1\n", + " lags.\n", + " \n", + " Returns\n", + " -------\n", + " (lags, c, line, b) : where:\n", + " \n", + " - `lags` are a length 2`maxlags+1 lag vector.\n", + " - `c` is the 2`maxlags+1 auto correlation vectorI\n", + " - `line` is a `~matplotlib.lines.Line2D` instance returned by\n", + " `plot`.\n", + " - `b` is the x-axis.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None\n", + " Only used if usevlines is False.\n", + " \n", + " marker : string, optional, default: 'o'\n", + " \n", + " Notes\n", + " -----\n", + " The cross correlation is performed with :func:`numpy.correlate` with\n", + " `mode` = 2.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " angle_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, hold=None, data=None, **kwargs)\n", + " Plot the angle spectrum.\n", + " \n", + " Call signature::\n", + " \n", + " angle_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,\n", + " pad_to=None, sides='default', **kwargs)\n", + " \n", + " Compute the angle spectrum (wrapped phase spectrum) of *x*.\n", + " Data is padded to a length of *pad_to* and the windowing function\n", + " *window* is applied to the signal.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. While not increasing the actual resolution of\n", + " the spectrum (the minimum distance between resolvable peaks),\n", + " this can give more points in the plot, allowing for more\n", + " detail. This corresponds to the *n* parameter in the call to fft().\n", + " The default is None, which sets *pad_to* equal to the length of the\n", + " input signal (i.e. no padding).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 1-D array\n", + " The values for the angle spectrum in radians (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *spectrum*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " :func:`magnitude_spectrum`\n", + " :func:`angle_spectrum` plots the magnitudes of the corresponding\n", + " frequencies.\n", + " \n", + " :func:`phase_spectrum`\n", + " :func:`phase_spectrum` plots the unwrapped version of this\n", + " function.\n", + " \n", + " :func:`specgram`\n", + " :func:`specgram` can plot the angle spectrum of segments within the\n", + " signal in a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " annotate(*args, **kwargs)\n", + " Annotate the point ``xy`` with text ``s``.\n", + " \n", + " Additional kwargs are passed to `~matplotlib.text.Text`.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " s : str\n", + " The text of the annotation\n", + " \n", + " xy : iterable\n", + " Length 2 sequence specifying the *(x,y)* point to annotate\n", + " \n", + " xytext : iterable, optional\n", + " Length 2 sequence specifying the *(x,y)* to place the text\n", + " at. If None, defaults to ``xy``.\n", + " \n", + " xycoords : str, Artist, Transform, callable or tuple, optional\n", + " \n", + " The coordinate system that ``xy`` is given in.\n", + " \n", + " For a `str` the allowed values are:\n", + " \n", + " ================= ===============================================\n", + " Property Description\n", + " ================= ===============================================\n", + " 'figure points' points from the lower left of the figure\n", + " 'figure pixels' pixels from the lower left of the figure\n", + " 'figure fraction' fraction of figure from lower left\n", + " 'axes points' points from lower left corner of axes\n", + " 'axes pixels' pixels from lower left corner of axes\n", + " 'axes fraction' fraction of axes from lower left\n", + " 'data' use the coordinate system of the object being\n", + " annotated (default)\n", + " 'polar' *(theta,r)* if not native 'data' coordinates\n", + " ================= ===============================================\n", + " \n", + " If a `~matplotlib.artist.Artist` object is passed in the units are\n", + " fraction if it's bounding box.\n", + " \n", + " If a `~matplotlib.transforms.Transform` object is passed\n", + " in use that to transform ``xy`` to screen coordinates\n", + " \n", + " If a callable it must take a\n", + " `~matplotlib.backend_bases.RendererBase` object as input\n", + " and return a `~matplotlib.transforms.Transform` or\n", + " `~matplotlib.transforms.Bbox` object\n", + " \n", + " If a `tuple` must be length 2 tuple of str, `Artist`,\n", + " `Transform` or callable objects. The first transform is\n", + " used for the *x* coordinate and the second for *y*.\n", + " \n", + " See :ref:`plotting-guide-annotation` for more details.\n", + " \n", + " Defaults to ``'data'``\n", + " \n", + " textcoords : str, `Artist`, `Transform`, callable or tuple, optional\n", + " The coordinate system that ``xytext`` is given, which\n", + " may be different than the coordinate system used for\n", + " ``xy``.\n", + " \n", + " All ``xycoords`` values are valid as well as the following\n", + " strings:\n", + " \n", + " ================= =========================================\n", + " Property Description\n", + " ================= =========================================\n", + " 'offset points' offset (in points) from the *xy* value\n", + " 'offset pixels' offset (in pixels) from the *xy* value\n", + " ================= =========================================\n", + " \n", + " defaults to the input of ``xycoords``\n", + " \n", + " arrowprops : dict, optional\n", + " If not None, properties used to draw a\n", + " `~matplotlib.patches.FancyArrowPatch` arrow between ``xy`` and\n", + " ``xytext``.\n", + " \n", + " If `arrowprops` does not contain the key ``'arrowstyle'`` the\n", + " allowed keys are:\n", + " \n", + " ========== ======================================================\n", + " Key Description\n", + " ========== ======================================================\n", + " width the width of the arrow in points\n", + " headwidth the width of the base of the arrow head in points\n", + " headlength the length of the arrow head in points\n", + " shrink fraction of total length to 'shrink' from both ends\n", + " ? any key to :class:`matplotlib.patches.FancyArrowPatch`\n", + " ========== ======================================================\n", + " \n", + " If the `arrowprops` contains the key ``'arrowstyle'`` the\n", + " above keys are forbidden. The allowed values of\n", + " ``'arrowstyle'`` are:\n", + " \n", + " ============ =============================================\n", + " Name Attrs\n", + " ============ =============================================\n", + " ``'-'`` None\n", + " ``'->'`` head_length=0.4,head_width=0.2\n", + " ``'-['`` widthB=1.0,lengthB=0.2,angleB=None\n", + " ``'|-|'`` widthA=1.0,widthB=1.0\n", + " ``'-|>'`` head_length=0.4,head_width=0.2\n", + " ``'<-'`` head_length=0.4,head_width=0.2\n", + " ``'<->'`` head_length=0.4,head_width=0.2\n", + " ``'<|-'`` head_length=0.4,head_width=0.2\n", + " ``'<|-|>'`` head_length=0.4,head_width=0.2\n", + " ``'fancy'`` head_length=0.4,head_width=0.4,tail_width=0.4\n", + " ``'simple'`` head_length=0.5,head_width=0.5,tail_width=0.2\n", + " ``'wedge'`` tail_width=0.3,shrink_factor=0.5\n", + " ============ =============================================\n", + " \n", + " Valid keys for `~matplotlib.patches.FancyArrowPatch` are:\n", + " \n", + " =============== ==================================================\n", + " Key Description\n", + " =============== ==================================================\n", + " arrowstyle the arrow style\n", + " connectionstyle the connection style\n", + " relpos default is (0.5, 0.5)\n", + " patchA default is bounding box of the text\n", + " patchB default is None\n", + " shrinkA default is 2 points\n", + " shrinkB default is 2 points\n", + " mutation_scale default is text size (in points)\n", + " mutation_aspect default is 1.\n", + " ? any key for :class:`matplotlib.patches.PathPatch`\n", + " =============== ==================================================\n", + " \n", + " Defaults to None\n", + " \n", + " annotation_clip : bool, optional\n", + " Controls the visibility of the annotation when it goes\n", + " outside the axes area.\n", + " \n", + " If `True`, the annotation will only be drawn when the\n", + " ``xy`` is inside the axes. If `False`, the annotation will\n", + " always be drawn regardless of its position.\n", + " \n", + " The default is `None`, which behave as `True` only if\n", + " *xycoords* is \"data\".\n", + " \n", + " Returns\n", + " -------\n", + " Annotation\n", + " \n", + " arrow(x, y, dx, dy, hold=None, **kwargs)\n", + " Add an arrow to the axes.\n", + " \n", + " Draws arrow on specified axis from (`x`, `y`) to (`x` + `dx`,\n", + " `y` + `dy`). Uses FancyArrow patch to construct the arrow.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : float\n", + " X-coordinate of the arrow base\n", + " y : float\n", + " Y-coordinate of the arrow base\n", + " dx : float\n", + " Length of arrow along x-coordinate\n", + " dy : float\n", + " Length of arrow along y-coordinate\n", + " \n", + " Returns\n", + " -------\n", + " a : FancyArrow\n", + " patches.FancyArrow object\n", + " \n", + " Other Parameters\n", + " -----------------\n", + " Optional kwargs (inherited from FancyArrow patch) control the arrow\n", + " construction and properties:\n", + " \n", + " Constructor arguments\n", + " *width*: float (default: 0.001)\n", + " width of full arrow tail\n", + " \n", + " *length_includes_head*: [True | False] (default: False)\n", + " True if head is to be counted in calculating the length.\n", + " \n", + " *head_width*: float or None (default: 3*width)\n", + " total width of the full arrow head\n", + " \n", + " *head_length*: float or None (default: 1.5 * head_width)\n", + " length of arrow head\n", + " \n", + " *shape*: ['full', 'left', 'right'] (default: 'full')\n", + " draw the left-half, right-half, or full arrow\n", + " \n", + " *overhang*: float (default: 0)\n", + " fraction that the arrow is swept back (0 overhang means\n", + " triangular shape). Can be negative or greater than one.\n", + " \n", + " *head_starts_at_zero*: [True | False] (default: False)\n", + " if True, the head starts being drawn at coordinate 0\n", + " instead of ending at coordinate 0.\n", + " \n", + " Other valid kwargs (inherited from :class:`Patch`) are:\n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " The resulting arrow is affected by the axes aspect ratio and limits.\n", + " This may produce an arrow whose head is not square with its stem. To\n", + " create an arrow whose head is square with its stem, use\n", + " :meth:`annotate` for example::\n", + " \n", + " ax.annotate(\"\", xy=(0.5, 0.5), xytext=(0, 0),\n", + " arrowprops=dict(arrowstyle=\"->\"))\n", + " \n", + " autoscale(enable=True, axis='both', tight=None)\n", + " Autoscale the axis view to the data (toggle).\n", + " \n", + " Convenience method for simple axis view autoscaling.\n", + " It turns autoscaling on or off, and then,\n", + " if autoscaling for either axis is on, it performs\n", + " the autoscaling on the specified axis or axes.\n", + " \n", + " *enable*: [True | False | None]\n", + " True (default) turns autoscaling on, False turns it off.\n", + " None leaves the autoscaling state unchanged.\n", + " \n", + " *axis*: ['x' | 'y' | 'both']\n", + " which axis to operate on; default is 'both'\n", + " \n", + " *tight*: [True | False | None]\n", + " If True, set view limits to data limits;\n", + " if False, let the locator and margins expand the view limits;\n", + " if None, use tight scaling if the only artist is an image,\n", + " otherwise treat *tight* as False.\n", + " The *tight* setting is retained for future autoscaling\n", + " until it is explicitly changed.\n", + " \n", + " \n", + " Returns None.\n", + " \n", + " autumn()\n", + " set the default colormap to autumn and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " axes(*args, **kwargs)\n", + " Add an axes to the figure.\n", + " \n", + " The axes is added at position *rect* specified by:\n", + " \n", + " - ``axes()`` by itself creates a default full ``subplot(111)`` window axis.\n", + " \n", + " - ``axes(rect, facecolor='w')`` where *rect* = [left, bottom, width,\n", + " height] in normalized (0, 1) units. *facecolor* is the background\n", + " color for the axis, default white.\n", + " \n", + " - ``axes(h)`` where *h* is an axes instance makes *h* the current\n", + " axis and the parent of *h* the current figure.\n", + " An :class:`~matplotlib.axes.Axes` instance is returned.\n", + " \n", + " ========= ============== ==============================================\n", + " kwarg Accepts Description\n", + " ========= ============== ==============================================\n", + " facecolor color the axes background color\n", + " frameon [True|False] display the frame?\n", + " sharex otherax current axes shares xaxis attribute\n", + " with otherax\n", + " sharey otherax current axes shares yaxis attribute\n", + " with otherax\n", + " polar [True|False] use a polar axes?\n", + " aspect [str | num] ['equal', 'auto'] or a number. If a number\n", + " the ratio of y-unit/x-unit in screen-space.\n", + " Also see\n", + " :meth:`~matplotlib.axes.Axes.set_aspect`.\n", + " ========= ============== ==============================================\n", + " \n", + " Examples:\n", + " \n", + " * :file:`examples/pylab_examples/axes_demo.py` places custom axes.\n", + " * :file:`examples/pylab_examples/shared_axis_demo.py` uses\n", + " *sharex* and *sharey*.\n", + " \n", + " axhline(y=0, xmin=0, xmax=1, hold=None, **kwargs)\n", + " Add a horizontal line across the axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " y : scalar, optional, default: 0\n", + " y position in data coordinates of the horizontal line.\n", + " \n", + " xmin : scalar, optional, default: 0\n", + " Should be between 0 and 1, 0 being the far left of the plot, 1 the\n", + " far right of the plot.\n", + " \n", + " xmax : scalar, optional, default: 1\n", + " Should be between 0 and 1, 0 being the far left of the plot, 1 the\n", + " far right of the plot.\n", + " \n", + " Returns\n", + " -------\n", + " :class:`~matplotlib.lines.Line2D`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,\n", + " with the exception of 'transform':\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " kwargs are passed to :class:`~matplotlib.lines.Line2D` and can be used\n", + " to control the line properties.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " * draw a thick red hline at 'y' = 0 that spans the xrange::\n", + " \n", + " >>> axhline(linewidth=4, color='r')\n", + " \n", + " * draw a default hline at 'y' = 1 that spans the xrange::\n", + " \n", + " >>> axhline(y=1)\n", + " \n", + " * draw a default hline at 'y' = .5 that spans the middle half of\n", + " the xrange::\n", + " \n", + " >>> axhline(y=.5, xmin=0.25, xmax=0.75)\n", + " \n", + " See also\n", + " --------\n", + " hlines : add horizontal lines in data coordinates\n", + " axhspan : add a horizontal span (rectangle) across the axis\n", + " \n", + " axhspan(ymin, ymax, xmin=0, xmax=1, hold=None, **kwargs)\n", + " Add a horizontal span (rectangle) across the axis.\n", + " \n", + " Draw a horizontal span (rectangle) from *ymin* to *ymax*.\n", + " With the default values of *xmin* = 0 and *xmax* = 1, this\n", + " always spans the xrange, regardless of the xlim settings, even\n", + " if you change them, e.g., with the :meth:`set_xlim` command.\n", + " That is, the horizontal extent is in axes coords: 0=left,\n", + " 0.5=middle, 1.0=right but the *y* location is in data\n", + " coordinates.\n", + " \n", + " Parameters\n", + " ----------\n", + " ymin : float\n", + " Lower limit of the horizontal span in data units.\n", + " ymax : float\n", + " Upper limit of the horizontal span in data units.\n", + " xmin : float, optional, default: 0\n", + " Lower limit of the vertical span in axes (relative\n", + " 0-1) units.\n", + " xmax : float, optional, default: 1\n", + " Upper limit of the vertical span in axes (relative\n", + " 0-1) units.\n", + " \n", + " Returns\n", + " -------\n", + " Polygon : `~matplotlib.patches.Polygon`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.patches.Polygon` properties.\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " axvspan : add a vertical span across the axes\n", + " \n", + " axis(*v, **kwargs)\n", + " Convenience method to get or set axis properties.\n", + " \n", + " Calling with no arguments::\n", + " \n", + " >>> axis()\n", + " \n", + " returns the current axes limits ``[xmin, xmax, ymin, ymax]``.::\n", + " \n", + " >>> axis(v)\n", + " \n", + " sets the min and max of the x and y axes, with\n", + " ``v = [xmin, xmax, ymin, ymax]``.::\n", + " \n", + " >>> axis('off')\n", + " \n", + " turns off the axis lines and labels.::\n", + " \n", + " >>> axis('equal')\n", + " \n", + " changes limits of *x* or *y* axis so that equal increments of *x*\n", + " and *y* have the same length; a circle is circular.::\n", + " \n", + " >>> axis('scaled')\n", + " \n", + " achieves the same result by changing the dimensions of the plot box instead\n", + " of the axis data limits.::\n", + " \n", + " >>> axis('tight')\n", + " \n", + " changes *x* and *y* axis limits such that all data is shown. If\n", + " all data is already shown, it will move it to the center of the\n", + " figure without modifying (*xmax* - *xmin*) or (*ymax* -\n", + " *ymin*). Note this is slightly different than in MATLAB.::\n", + " \n", + " >>> axis('image')\n", + " \n", + " is 'scaled' with the axis limits equal to the data limits.::\n", + " \n", + " >>> axis('auto')\n", + " \n", + " and::\n", + " \n", + " >>> axis('normal')\n", + " \n", + " are deprecated. They restore default behavior; axis limits are automatically\n", + " scaled to make the data fit comfortably within the plot box.\n", + " \n", + " if ``len(*v)==0``, you can pass in *xmin*, *xmax*, *ymin*, *ymax*\n", + " as kwargs selectively to alter just those limits without changing\n", + " the others.\n", + " \n", + " >>> axis('square')\n", + " \n", + " changes the limit ranges (*xmax*-*xmin*) and (*ymax*-*ymin*) of\n", + " the *x* and *y* axes to be the same, and have the same scaling,\n", + " resulting in a square plot.\n", + " \n", + " The xmin, xmax, ymin, ymax tuple is returned\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`xlim`, :func:`ylim`\n", + " For setting the x- and y-limits individually.\n", + " \n", + " axvline(x=0, ymin=0, ymax=1, hold=None, **kwargs)\n", + " Add a vertical line across the axes.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : scalar, optional, default: 0\n", + " x position in data coordinates of the vertical line.\n", + " \n", + " ymin : scalar, optional, default: 0\n", + " Should be between 0 and 1, 0 being the bottom of the plot, 1 the\n", + " top of the plot.\n", + " \n", + " ymax : scalar, optional, default: 1\n", + " Should be between 0 and 1, 0 being the bottom of the plot, 1 the\n", + " top of the plot.\n", + " \n", + " Returns\n", + " -------\n", + " :class:`~matplotlib.lines.Line2D`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,\n", + " with the exception of 'transform':\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Examples\n", + " --------\n", + " * draw a thick red vline at *x* = 0 that spans the yrange::\n", + " \n", + " >>> axvline(linewidth=4, color='r')\n", + " \n", + " * draw a default vline at *x* = 1 that spans the yrange::\n", + " \n", + " >>> axvline(x=1)\n", + " \n", + " * draw a default vline at *x* = .5 that spans the middle half of\n", + " the yrange::\n", + " \n", + " >>> axvline(x=.5, ymin=0.25, ymax=0.75)\n", + " \n", + " See also\n", + " --------\n", + " vlines : add vertical lines in data coordinates\n", + " axvspan : add a vertical span (rectangle) across the axis\n", + " \n", + " axvspan(xmin, xmax, ymin=0, ymax=1, hold=None, **kwargs)\n", + " Add a vertical span (rectangle) across the axes.\n", + " \n", + " Draw a vertical span (rectangle) from `xmin` to `xmax`. With\n", + " the default values of `ymin` = 0 and `ymax` = 1. This always\n", + " spans the yrange, regardless of the ylim settings, even if you\n", + " change them, e.g., with the :meth:`set_ylim` command. That is,\n", + " the vertical extent is in axes coords: 0=bottom, 0.5=middle,\n", + " 1.0=top but the y location is in data coordinates.\n", + " \n", + " Parameters\n", + " ----------\n", + " xmin : scalar\n", + " Number indicating the first X-axis coordinate of the vertical\n", + " span rectangle in data units.\n", + " xmax : scalar\n", + " Number indicating the second X-axis coordinate of the vertical\n", + " span rectangle in data units.\n", + " ymin : scalar, optional\n", + " Number indicating the first Y-axis coordinate of the vertical\n", + " span rectangle in relative Y-axis units (0-1). Default to 0.\n", + " ymax : scalar, optional\n", + " Number indicating the second Y-axis coordinate of the vertical\n", + " span rectangle in relative Y-axis units (0-1). Default to 1.\n", + " \n", + " Returns\n", + " -------\n", + " rectangle : matplotlib.patches.Polygon\n", + " Vertical span (rectangle) from (xmin, ymin) to (xmax, ymax).\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs\n", + " Optional parameters are properties of the class\n", + " matplotlib.patches.Polygon.\n", + " \n", + " See Also\n", + " --------\n", + " axhspan : add a horizontal span across the axes\n", + " \n", + " Examples\n", + " --------\n", + " Draw a vertical, green, translucent rectangle from x = 1.25 to\n", + " x = 1.55 that spans the yrange of the axes.\n", + " \n", + " >>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)\n", + " \n", + " bar(*args, **kwargs)\n", + " Make a bar plot.\n", + " \n", + " Call signatures::\n", + " \n", + " bar(x, height, *, align='center', **kwargs)\n", + " bar(x, height, width, *, align='center', **kwargs)\n", + " bar(x, height, width, bottom, *, align='center', **kwargs)\n", + " \n", + " Make a bar plot with rectangles bounded by\n", + " \n", + " .. math::\n", + " \n", + " (x - width/2, x + width/2, bottom, bottom + height)\n", + " \n", + " (left, right, bottom and top edges) by default. *x*,\n", + " *height*, *width*, and *bottom* can be either scalars or\n", + " sequences.\n", + " \n", + " The *align* and *orientation* kwargs control the interpretation of *x*\n", + " and *bottom*\n", + " \n", + " The *align* keyword-only argument controls if *x* is interpreted\n", + " as the center or the left edge of the rectangle.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : sequence of scalars\n", + " the x coordinates of the bars.\n", + " \n", + " *align* controls if *x* is the bar center (default) or\n", + " left edge.\n", + " \n", + " height : scalar or sequence of scalars\n", + " the height(s) of the bars\n", + " \n", + " width : scalar or array-like, optional\n", + " the width(s) of the bars\n", + " default: 0.8\n", + " \n", + " bottom : scalar or array-like, optional\n", + " the y coordinate(s) of the bars\n", + " default: None\n", + " \n", + " align : {'center', 'edge'}, optional, default: 'center'\n", + " If 'center', interpret the *x* argument as the coordinates\n", + " of the centers of the bars. If 'edge', aligns bars by\n", + " their left edges\n", + " \n", + " To align the bars on the right edge pass a negative\n", + " *width* and ``align='edge'``\n", + " \n", + " Returns\n", + " -------\n", + " bars : matplotlib.container.BarContainer\n", + " Container with all of the bars + errorbars\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " color : scalar or array-like, optional\n", + " the colors of the bar faces\n", + " \n", + " edgecolor : scalar or array-like, optional\n", + " the colors of the bar edges\n", + " \n", + " linewidth : scalar or array-like, optional\n", + " width of bar edge(s). If None, use default\n", + " linewidth; If 0, don't draw edges.\n", + " default: None\n", + " \n", + " tick_label : string or array-like, optional\n", + " the tick labels of the bars\n", + " default: None\n", + " \n", + " xerr : scalar or array-like, optional\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " default: None\n", + " \n", + " yerr : scalar or array-like, optional\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " default: None\n", + " \n", + " ecolor : scalar or array-like, optional\n", + " specifies the color of errorbar(s)\n", + " default: None\n", + " \n", + " capsize : scalar, optional\n", + " determines the length in points of the error bar caps\n", + " default: None, which will take the value from the\n", + " ``errorbar.capsize`` :data:`rcParam`.\n", + " \n", + " error_kw : dict, optional\n", + " dictionary of kwargs to be passed to errorbar method. *ecolor* and\n", + " *capsize* may be specified here rather than as independent kwargs.\n", + " \n", + " log : boolean, optional\n", + " If true, sets the axis to be log scale.\n", + " default: False\n", + " \n", + " orientation : {'vertical', 'horizontal'}, optional\n", + " \n", + " This is for internal use, please do not directly use this,\n", + " call `barh` instead.\n", + " \n", + " The orientation of the bars.\n", + " \n", + " See also\n", + " --------\n", + " barh: Plot a horizontal bar plot.\n", + " \n", + " Notes\n", + " -----\n", + " The optional arguments *color*, *edgecolor*, *linewidth*,\n", + " *xerr*, and *yerr* can be either scalars or sequences of\n", + " length equal to the number of bars. This enables you to use\n", + " bar as the basis for stacked bar charts, or candlestick plots.\n", + " Detail: *xerr* and *yerr* are passed directly to\n", + " :meth:`errorbar`, so they can also have shape 2xN for\n", + " independent specification of lower and upper errors.\n", + " \n", + " Other optional kwargs:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'bottom', 'color', 'ecolor', 'edgecolor', 'height', 'left', 'linewidth', 'tick_label', 'width', 'x', 'xerr', 'y', 'yerr'.\n", + " * All positional arguments.\n", + " \n", + " barbs(*args, **kw)\n", + " Plot a 2-D field of barbs.\n", + " \n", + " Call signatures::\n", + " \n", + " barb(U, V, **kw)\n", + " barb(U, V, C, **kw)\n", + " barb(X, Y, U, V, **kw)\n", + " barb(X, Y, U, V, C, **kw)\n", + " \n", + " Arguments:\n", + " \n", + " *X*, *Y*:\n", + " The x and y coordinates of the barb locations\n", + " (default is head of barb; see *pivot* kwarg)\n", + " \n", + " *U*, *V*:\n", + " Give the x and y components of the barb shaft\n", + " \n", + " *C*:\n", + " An optional array used to map colors to the barbs\n", + " \n", + " All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*\n", + " are absent, they will be generated as a uniform grid. If *U* and *V*\n", + " are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``\n", + " match the column and row dimensions of *U*, then *X* and *Y* will be\n", + " expanded with :func:`numpy.meshgrid`.\n", + " \n", + " *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not\n", + " supported at present.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *length*:\n", + " Length of the barb in points; the other parts of the barb\n", + " are scaled against this.\n", + " Default is 7.\n", + " \n", + " *pivot*: [ 'tip' | 'middle' | float ]\n", + " The part of the arrow that is at the grid point; the arrow rotates\n", + " about this point, hence the name *pivot*. Default is 'tip'. Can\n", + " also be a number, which shifts the start of the barb that many\n", + " points from the origin.\n", + " \n", + " *barbcolor*: [ color | color sequence ]\n", + " Specifies the color all parts of the barb except any flags. This\n", + " parameter is analagous to the *edgecolor* parameter for polygons,\n", + " which can be used instead. However this parameter will override\n", + " facecolor.\n", + " \n", + " *flagcolor*: [ color | color sequence ]\n", + " Specifies the color of any flags on the barb. This parameter is\n", + " analagous to the *facecolor* parameter for polygons, which can be\n", + " used instead. However this parameter will override facecolor. If\n", + " this is not set (and *C* has not either) then *flagcolor* will be\n", + " set to match *barbcolor* so that the barb has a uniform color. If\n", + " *C* has been set, *flagcolor* has no effect.\n", + " \n", + " *sizes*:\n", + " A dictionary of coefficients specifying the ratio of a given\n", + " feature to the length of the barb. Only those values one wishes to\n", + " override need to be included. These features include:\n", + " \n", + " - 'spacing' - space between features (flags, full/half barbs)\n", + " \n", + " - 'height' - height (distance from shaft to top) of a flag or\n", + " full barb\n", + " \n", + " - 'width' - width of a flag, twice the width of a full barb\n", + " \n", + " - 'emptybarb' - radius of the circle used for low magnitudes\n", + " \n", + " *fill_empty*:\n", + " A flag on whether the empty barbs (circles) that are drawn should\n", + " be filled with the flag color. If they are not filled, they will\n", + " be drawn such that no color is applied to the center. Default is\n", + " False\n", + " \n", + " *rounding*:\n", + " A flag to indicate whether the vector magnitude should be rounded\n", + " when allocating barb components. If True, the magnitude is\n", + " rounded to the nearest multiple of the half-barb increment. If\n", + " False, the magnitude is simply truncated to the next lowest\n", + " multiple. Default is True\n", + " \n", + " *barb_increments*:\n", + " A dictionary of increments specifying values to associate with\n", + " different parts of the barb. Only those values one wishes to\n", + " override need to be included.\n", + " \n", + " - 'half' - half barbs (Default is 5)\n", + " \n", + " - 'full' - full barbs (Default is 10)\n", + " \n", + " - 'flag' - flags (default is 50)\n", + " \n", + " *flip_barb*:\n", + " Either a single boolean flag or an array of booleans. Single\n", + " boolean indicates whether the lines and flags should point\n", + " opposite to normal for all barbs. An array (which should be the\n", + " same size as the other data arrays) indicates whether to flip for\n", + " each individual barb. Normal behavior is for the barbs and lines\n", + " to point right (comes from wind barbs having these features point\n", + " towards low pressure in the Northern Hemisphere.) Default is\n", + " False\n", + " \n", + " Barbs are traditionally used in meteorology as a way to plot the speed\n", + " and direction of wind observations, but can technically be used to\n", + " plot any two dimensional vector quantity. As opposed to arrows, which\n", + " give vector magnitude by the length of the arrow, the barbs give more\n", + " quantitative information about the vector magnitude by putting slanted\n", + " lines or a triangle for various increments in magnitude, as show\n", + " schematically below::\n", + " \n", + " : /\\ \\\\\n", + " : / \\ \\\\\n", + " : / \\ \\ \\\\\n", + " : / \\ \\ \\\\\n", + " : ------------------------------\n", + " \n", + " .. note the double \\\\ at the end of each line to make the figure\n", + " .. render correctly\n", + " \n", + " The largest increment is given by a triangle (or \"flag\"). After those\n", + " come full lines (barbs). The smallest increment is a half line. There\n", + " is only, of course, ever at most 1 half line. If the magnitude is\n", + " small and only needs a single half-line and no full lines or\n", + " triangles, the half-line is offset from the end of the barb so that it\n", + " can be easily distinguished from barbs with a single full line. The\n", + " magnitude for the barb shown above would nominally be 65, using the\n", + " standard increments of 50, 10, and 5.\n", + " \n", + " linewidths and edgecolors can be used to customize the barb.\n", + " Additional :class:`~matplotlib.collections.PolyCollection` keyword\n", + " arguments:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " barh(*args, **kwargs)\n", + " Make a horizontal bar plot.\n", + " \n", + " Call signatures::\n", + " \n", + " bar(y, width, *, align='center', **kwargs)\n", + " bar(y, width, height, *, align='center', **kwargs)\n", + " bar(y, width, height, left, *, align='center', **kwargs)\n", + " \n", + " Make a horizontal bar plot with rectangles by default bounded by\n", + " \n", + " .. math::\n", + " \n", + " (left, left + width, y - height/2, y + height/2)\n", + " \n", + " (left, right, bottom and top edges) by default. *y*, *width*,\n", + " *height*, and *left* can be either scalars or sequences.\n", + " \n", + " The *align* keyword-only argument controls if *y* is interpreted\n", + " as the center or the bottom edge of the rectangle.\n", + " \n", + " \n", + " Parameters\n", + " ----------\n", + " y : scalar or array-like\n", + " the y coordinate(s) of the bars\n", + " \n", + " *align* controls if *y* is the bar center (default)\n", + " or bottom edge.\n", + " \n", + " width : scalar or array-like\n", + " the width(s) of the bars\n", + " \n", + " height : sequence of scalars, optional, default: 0.8\n", + " the heights of the bars\n", + " \n", + " left : sequence of scalars\n", + " the x coordinates of the left sides of the bars\n", + " \n", + " align : {'center', 'edge'}, optional, default: 'center'\n", + " If 'center', interpret the *y* argument as the coordinates\n", + " of the centers of the bars. If 'edge', aligns bars by\n", + " their bottom edges\n", + " \n", + " To align the bars on the top edge pass a negative\n", + " *height* and ``align='edge'``\n", + " \n", + " Returns\n", + " -------\n", + " `matplotlib.patches.Rectangle` instances.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " color : scalar or array-like, optional\n", + " the colors of the bars\n", + " \n", + " edgecolor : scalar or array-like, optional\n", + " the colors of the bar edges\n", + " \n", + " linewidth : scalar or array-like, optional, default: None\n", + " width of bar edge(s). If None, use default\n", + " linewidth; If 0, don't draw edges.\n", + " \n", + " tick_label : string or array-like, optional, default: None\n", + " the tick labels of the bars\n", + " \n", + " xerr : scalar or array-like, optional, default: None\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " \n", + " yerr : scalar or array-like, optional, default: None\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " \n", + " ecolor : scalar or array-like, optional, default: None\n", + " specifies the color of errorbar(s)\n", + " \n", + " capsize : scalar, optional\n", + " determines the length in points of the error bar caps\n", + " default: None, which will take the value from the\n", + " ``errorbar.capsize`` :data:`rcParam`.\n", + " \n", + " error_kw :\n", + " dictionary of kwargs to be passed to errorbar method. `ecolor` and\n", + " `capsize` may be specified here rather than as independent kwargs.\n", + " \n", + " log : boolean, optional, default: False\n", + " If true, sets the axis to be log scale\n", + " \n", + " See also\n", + " --------\n", + " bar: Plot a vertical bar plot.\n", + " \n", + " Notes\n", + " -----\n", + " The optional arguments *color*, *edgecolor*, *linewidth*,\n", + " *xerr*, and *yerr* can be either scalars or sequences of\n", + " length equal to the number of bars. This enables you to use\n", + " bar as the basis for stacked bar charts, or candlestick plots.\n", + " Detail: *xerr* and *yerr* are passed directly to\n", + " :meth:`errorbar`, so they can also have shape 2xN for\n", + " independent specification of lower and upper errors.\n", + " \n", + " Other optional kwargs:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float\n", + " \n", + " bone()\n", + " set the default colormap to bone and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " box(on=None)\n", + " Turn the axes box on or off. *on* may be a boolean or a string,\n", + " 'on' or 'off'.\n", + " \n", + " If *on* is *None*, toggle state.\n", + " \n", + " boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False, zorder=None, hold=None, data=None)\n", + " Make a box and whisker plot.\n", + " \n", + " Make a box and whisker plot for each column of ``x`` or each\n", + " vector in sequence ``x``. The box extends from the lower to\n", + " upper quartile values of the data, with a line at the median.\n", + " The whiskers extend from the box to show the range of the\n", + " data. Flier points are those past the end of the whiskers.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : Array or a sequence of vectors.\n", + " The input data.\n", + " \n", + " notch : bool, optional (False)\n", + " If `True`, will produce a notched box plot. Otherwise, a\n", + " rectangular boxplot is produced. The notches represent the\n", + " confidence interval (CI) around the median. See the entry\n", + " for the ``bootstrap`` parameter for information regarding\n", + " how the locations of the notches are computed.\n", + " \n", + " .. note::\n", + " \n", + " In cases where the values of the CI are less than the\n", + " lower quartile or greater than the upper quartile, the\n", + " notches will extend beyond the box, giving it a\n", + " distinctive \"flipped\" appearance. This is expected\n", + " behavior and consistent with other statistical\n", + " visualization packages.\n", + " \n", + " sym : str, optional\n", + " The default symbol for flier points. Enter an empty string\n", + " ('') if you don't want to show fliers. If `None`, then the\n", + " fliers default to 'b+' If you want more control use the\n", + " flierprops kwarg.\n", + " \n", + " vert : bool, optional (True)\n", + " If `True` (default), makes the boxes vertical. If `False`,\n", + " everything is drawn horizontally.\n", + " \n", + " whis : float, sequence, or string (default = 1.5)\n", + " As a float, determines the reach of the whiskers to the beyond the\n", + " first and third quartiles. In other words, where IQR is the\n", + " interquartile range (`Q3-Q1`), the upper whisker will extend to\n", + " last datum less than `Q3 + whis*IQR`). Similarly, the lower whisker\n", + " will extend to the first datum greater than `Q1 - whis*IQR`.\n", + " Beyond the whiskers, data\n", + " are considered outliers and are plotted as individual\n", + " points. Set this to an unreasonably high value to force the\n", + " whiskers to show the min and max values. Alternatively, set\n", + " this to an ascending sequence of percentile (e.g., [5, 95])\n", + " to set the whiskers at specific percentiles of the data.\n", + " Finally, ``whis`` can be the string ``'range'`` to force the\n", + " whiskers to the min and max of the data.\n", + " \n", + " bootstrap : int, optional\n", + " Specifies whether to bootstrap the confidence intervals\n", + " around the median for notched boxplots. If ``bootstrap`` is\n", + " None, no bootstrapping is performed, and notches are\n", + " calculated using a Gaussian-based asymptotic approximation\n", + " (see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and\n", + " Kendall and Stuart, 1967). Otherwise, bootstrap specifies\n", + " the number of times to bootstrap the median to determine its\n", + " 95% confidence intervals. Values between 1000 and 10000 are\n", + " recommended.\n", + " \n", + " usermedians : array-like, optional\n", + " An array or sequence whose first dimension (or length) is\n", + " compatible with ``x``. This overrides the medians computed\n", + " by matplotlib for each element of ``usermedians`` that is not\n", + " `None`. When an element of ``usermedians`` is None, the median\n", + " will be computed by matplotlib as normal.\n", + " \n", + " conf_intervals : array-like, optional\n", + " Array or sequence whose first dimension (or length) is\n", + " compatible with ``x`` and whose second dimension is 2. When\n", + " the an element of ``conf_intervals`` is not None, the\n", + " notch locations computed by matplotlib are overridden\n", + " (provided ``notch`` is `True`). When an element of\n", + " ``conf_intervals`` is `None`, the notches are computed by the\n", + " method specified by the other kwargs (e.g., ``bootstrap``).\n", + " \n", + " positions : array-like, optional\n", + " Sets the positions of the boxes. The ticks and limits are\n", + " automatically set to match the positions. Defaults to\n", + " `range(1, N+1)` where N is the number of boxes to be drawn.\n", + " \n", + " widths : scalar or array-like\n", + " Sets the width of each box either with a scalar or a\n", + " sequence. The default is 0.5, or ``0.15*(distance between\n", + " extreme positions)``, if that is smaller.\n", + " \n", + " patch_artist : bool, optional (False)\n", + " If `False` produces boxes with the Line2D artist. Otherwise,\n", + " boxes and drawn with Patch artists.\n", + " \n", + " labels : sequence, optional\n", + " Labels for each dataset. Length must be compatible with\n", + " dimensions of ``x``.\n", + " \n", + " manage_xticks : bool, optional (True)\n", + " If the function should adjust the xlim and xtick locations.\n", + " \n", + " autorange : bool, optional (False)\n", + " When `True` and the data are distributed such that the 25th and\n", + " 75th percentiles are equal, ``whis`` is set to ``'range'`` such\n", + " that the whisker ends are at the minimum and maximum of the\n", + " data.\n", + " \n", + " meanline : bool, optional (False)\n", + " If `True` (and ``showmeans`` is `True`), will try to render\n", + " the mean as a line spanning the full width of the box\n", + " according to ``meanprops`` (see below). Not recommended if\n", + " ``shownotches`` is also True. Otherwise, means will be shown\n", + " as points.\n", + " \n", + " zorder : scalar, optional (None)\n", + " Sets the zorder of the boxplot.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " showcaps : bool, optional (True)\n", + " Show the caps on the ends of whiskers.\n", + " showbox : bool, optional (True)\n", + " Show the central box.\n", + " showfliers : bool, optional (True)\n", + " Show the outliers beyond the caps.\n", + " showmeans : bool, optional (False)\n", + " Show the arithmetic means.\n", + " capprops : dict, optional (None)\n", + " Specifies the style of the caps.\n", + " boxprops : dict, optional (None)\n", + " Specifies the style of the box.\n", + " whiskerprops : dict, optional (None)\n", + " Specifies the style of the whiskers.\n", + " flierprops : dict, optional (None)\n", + " Specifies the style of the fliers.\n", + " medianprops : dict, optional (None)\n", + " Specifies the style of the median.\n", + " meanprops : dict, optional (None)\n", + " Specifies the style of the mean.\n", + " \n", + " Returns\n", + " -------\n", + " result : dict\n", + " A dictionary mapping each component of the boxplot to a list\n", + " of the :class:`matplotlib.lines.Line2D` instances\n", + " created. That dictionary has the following keys (assuming\n", + " vertical boxplots):\n", + " \n", + " - ``boxes``: the main body of the boxplot showing the\n", + " quartiles and the median's confidence intervals if\n", + " enabled.\n", + " \n", + " - ``medians``: horizontal lines at the median of each box.\n", + " \n", + " - ``whiskers``: the vertical lines extending to the most\n", + " extreme, non-outlier data points.\n", + " \n", + " - ``caps``: the horizontal lines at the ends of the\n", + " whiskers.\n", + " \n", + " - ``fliers``: points representing data that extend beyond\n", + " the whiskers (fliers).\n", + " \n", + " - ``means``: points or lines representing the means.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " broken_barh(xranges, yrange, hold=None, data=None, **kwargs)\n", + " Plot horizontal bars.\n", + " \n", + " A collection of horizontal bars spanning *yrange* with a sequence of\n", + " *xranges*.\n", + " \n", + " Required arguments:\n", + " \n", + " ========= ==============================\n", + " Argument Description\n", + " ========= ==============================\n", + " *xranges* sequence of (*xmin*, *xwidth*)\n", + " *yrange* sequence of (*ymin*, *ywidth*)\n", + " ========= ==============================\n", + " \n", + " kwargs are\n", + " :class:`matplotlib.collections.BrokenBarHCollection`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " these can either be a single argument, i.e.,::\n", + " \n", + " facecolors = 'black'\n", + " \n", + " or a sequence of arguments for the various bars, i.e.,::\n", + " \n", + " facecolors = ('black', 'red', 'green')\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " cla()\n", + " Clear the current axes.\n", + " \n", + " clabel(CS, *args, **kwargs)\n", + " Label a contour plot.\n", + " \n", + " Call signature::\n", + " \n", + " clabel(cs, **kwargs)\n", + " \n", + " Adds labels to line contours in *cs*, where *cs* is a\n", + " :class:`~matplotlib.contour.ContourSet` object returned by\n", + " contour.\n", + " \n", + " ::\n", + " \n", + " clabel(cs, v, **kwargs)\n", + " \n", + " only labels contours listed in *v*.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *fontsize*:\n", + " size in points or relative size e.g., 'smaller', 'x-large'\n", + " \n", + " *colors*:\n", + " - if *None*, the color of each label matches the color of\n", + " the corresponding contour\n", + " \n", + " - if one string color, e.g., *colors* = 'r' or *colors* =\n", + " 'red', all labels will be plotted in this color\n", + " \n", + " - if a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different labels will be plotted in different colors in the order\n", + " specified\n", + " \n", + " *inline*:\n", + " controls whether the underlying contour is removed or\n", + " not. Default is *True*.\n", + " \n", + " *inline_spacing*:\n", + " space in pixels to leave on each side of label when\n", + " placing inline. Defaults to 5. This spacing will be\n", + " exact for labels at locations where the contour is\n", + " straight, less so for labels on curved contours.\n", + " \n", + " *fmt*:\n", + " a format string for the label. Default is '%1.3f'\n", + " Alternatively, this can be a dictionary matching contour\n", + " levels with arbitrary strings to use for each contour level\n", + " (i.e., fmt[level]=string), or it can be any callable, such\n", + " as a :class:`~matplotlib.ticker.Formatter` instance, that\n", + " returns a string when called with a numeric contour level.\n", + " \n", + " *manual*:\n", + " if *True*, contour labels will be placed manually using\n", + " mouse clicks. Click the first button near a contour to\n", + " add a label, click the second button (or potentially both\n", + " mouse buttons at once) to finish adding labels. The third\n", + " button can be used to remove the last label added, but\n", + " only if labels are not inline. Alternatively, the keyboard\n", + " can be used to select label locations (enter to end label\n", + " placement, delete or backspace act like the third mouse button,\n", + " and any other key will select a label location).\n", + " \n", + " *manual* can be an iterable object of x,y tuples. Contour labels\n", + " will be created as if mouse is clicked at each x,y positions.\n", + " \n", + " *rightside_up*:\n", + " if *True* (default), label rotations will always be plus\n", + " or minus 90 degrees from level.\n", + " \n", + " *use_clabeltext*:\n", + " if *True* (default is False), ClabelText class (instead of\n", + " matplotlib.Text) is used to create labels. ClabelText\n", + " recalculates rotation angles of texts during the drawing time,\n", + " therefore this can be used if aspect of the axes changes.\n", + " \n", + " clf()\n", + " Clear the current figure.\n", + " \n", + " clim(vmin=None, vmax=None)\n", + " Set the color limits of the current image.\n", + " \n", + " To apply clim to all axes images do::\n", + " \n", + " clim(0, 0.5)\n", + " \n", + " If either *vmin* or *vmax* is None, the image min/max respectively\n", + " will be used for color scaling.\n", + " \n", + " If you want to set the clim of multiple images,\n", + " use, for example::\n", + " \n", + " for im in gca().get_images():\n", + " im.set_clim(0, 0.05)\n", + " \n", + " close(*args)\n", + " Close a figure window.\n", + " \n", + " ``close()`` by itself closes the current figure\n", + " \n", + " ``close(fig)`` closes the `~.Figure` instance *fig*\n", + " \n", + " ``close(num)`` closes the figure number *num*\n", + " \n", + " ``close(name)`` where *name* is a string, closes figure with that label\n", + " \n", + " ``close('all')`` closes all the figure windows\n", + " \n", + " cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=, window=, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, hold=None, data=None, **kwargs)\n", + " Plot the coherence between *x* and *y*.\n", + " \n", + " Plot the coherence between *x* and *y*. Coherence is the\n", + " normalized cross spectral density:\n", + " \n", + " .. math::\n", + " \n", + " C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}}\n", + " \n", + " Parameters\n", + " ----------\n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between blocks. The\n", + " default value is 0 (no overlap).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " The return value is a tuple (*Cxy*, *f*), where *f* are the\n", + " frequencies of the coherence vector.\n", + " \n", + " kwargs are applied to the lines.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " References\n", + " ----------\n", + " Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,\n", + " John Wiley & Sons (1986)\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " colorbar(mappable=None, cax=None, ax=None, **kw)\n", + " Add a colorbar to a plot.\n", + " \n", + " Function signatures for the :mod:`~matplotlib.pyplot` interface; all\n", + " but the first are also method signatures for the\n", + " :meth:`~matplotlib.figure.Figure.colorbar` method::\n", + " \n", + " colorbar(**kwargs)\n", + " colorbar(mappable, **kwargs)\n", + " colorbar(mappable, cax=cax, **kwargs)\n", + " colorbar(mappable, ax=ax, **kwargs)\n", + " \n", + " Parameters\n", + " ----------\n", + " mappable :\n", + " The :class:`~matplotlib.image.Image`,\n", + " :class:`~matplotlib.contour.ContourSet`, etc. to\n", + " which the colorbar applies; this argument is mandatory for the Figure\n", + " :meth:`~matplotlib.figure.Figure.colorbar` method but optional for the\n", + " pyplot :func:`~matplotlib.pyplot.colorbar` function, which sets the\n", + " default to the current image.\n", + " \n", + " cax : :class:`~matplotlib.axes.Axes` object, optional\n", + " Axis into which the colorbar will be drawn\n", + " \n", + " ax : :class:`~matplotlib.axes.Axes`, list of Axes, optional\n", + " Parent axes from which space for a new colorbar axes will be stolen.\n", + " If a list of axes is given they will all be resized to make room for the\n", + " colorbar axes.\n", + " \n", + " use_gridspec : bool, optional\n", + " If *cax* is ``None``, a new *cax* is created as an instance of\n", + " Axes. If *ax* is an instance of Subplot and *use_gridspec* is ``True``,\n", + " *cax* is created as an instance of Subplot using the\n", + " grid_spec module.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " :class:`~matplotlib.colorbar.Colorbar` instance\n", + " See also its base class, :class:`~matplotlib.colorbar.ColorbarBase`.\n", + " Call the :meth:`~matplotlib.colorbar.ColorbarBase.set_label` method\n", + " to label the colorbar.\n", + " \n", + " Notes\n", + " -----\n", + " Additional keyword arguments are of two kinds:\n", + " \n", + " axes properties:\n", + " \n", + " \n", + " ============= ====================================================\n", + " Property Description\n", + " ============= ====================================================\n", + " *orientation* vertical or horizontal\n", + " *fraction* 0.15; fraction of original axes to use for colorbar\n", + " *pad* 0.05 if vertical, 0.15 if horizontal; fraction\n", + " of original axes between colorbar and new image axes\n", + " *shrink* 1.0; fraction by which to multiply the size of the colorbar\n", + " *aspect* 20; ratio of long to short dimensions\n", + " *anchor* (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal;\n", + " the anchor point of the colorbar axes\n", + " *panchor* (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal;\n", + " the anchor point of the colorbar parent axes. If\n", + " False, the parent axes' anchor will be unchanged\n", + " ============= ====================================================\n", + " \n", + " \n", + " colorbar properties:\n", + " \n", + " \n", + " ============ ====================================================\n", + " Property Description\n", + " ============ ====================================================\n", + " *extend* [ 'neither' | 'both' | 'min' | 'max' ]\n", + " If not 'neither', make pointed end(s) for out-of-\n", + " range values. These are set for a given colormap\n", + " using the colormap set_under and set_over methods.\n", + " *extendfrac* [ *None* | 'auto' | length | lengths ]\n", + " If set to *None*, both the minimum and maximum\n", + " triangular colorbar extensions with have a length of\n", + " 5% of the interior colorbar length (this is the\n", + " default setting). If set to 'auto', makes the\n", + " triangular colorbar extensions the same lengths as\n", + " the interior boxes (when *spacing* is set to\n", + " 'uniform') or the same lengths as the respective\n", + " adjacent interior boxes (when *spacing* is set to\n", + " 'proportional'). If a scalar, indicates the length\n", + " of both the minimum and maximum triangular colorbar\n", + " extensions as a fraction of the interior colorbar\n", + " length. A two-element sequence of fractions may also\n", + " be given, indicating the lengths of the minimum and\n", + " maximum colorbar extensions respectively as a\n", + " fraction of the interior colorbar length.\n", + " *extendrect* [ *False* | *True* ]\n", + " If *False* the minimum and maximum colorbar extensions\n", + " will be triangular (the default). If *True* the\n", + " extensions will be rectangular.\n", + " *spacing* [ 'uniform' | 'proportional' ]\n", + " Uniform spacing gives each discrete color the same\n", + " space; proportional makes the space proportional to\n", + " the data interval.\n", + " *ticks* [ None | list of ticks | Locator object ]\n", + " If None, ticks are determined automatically from the\n", + " input.\n", + " *format* [ None | format string | Formatter object ]\n", + " If None, the\n", + " :class:`~matplotlib.ticker.ScalarFormatter` is used.\n", + " If a format string is given, e.g., '%.3f', that is\n", + " used. An alternative\n", + " :class:`~matplotlib.ticker.Formatter` object may be\n", + " given instead.\n", + " *drawedges* [ False | True ] If true, draw lines at color\n", + " boundaries.\n", + " ============ ====================================================\n", + " \n", + " The following will probably be useful only in the context of\n", + " indexed colors (that is, when the mappable has norm=NoNorm()),\n", + " or other unusual circumstances.\n", + " \n", + " ============ ===================================================\n", + " Property Description\n", + " ============ ===================================================\n", + " *boundaries* None or a sequence\n", + " *values* None or a sequence which must be of length 1 less\n", + " than the sequence of *boundaries*. For each region\n", + " delimited by adjacent entries in *boundaries*, the\n", + " color mapped to the corresponding value in values\n", + " will be used.\n", + " ============ ===================================================\n", + " \n", + " \n", + " \n", + " If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend*\n", + " kwarg is included automatically.\n", + " \n", + " Note that the *shrink* kwarg provides a simple way to keep a vertical\n", + " colorbar, for example, from being taller than the axes of the mappable\n", + " to which the colorbar is attached; but it is a manual method requiring\n", + " some trial and error. If the colorbar is too tall (or a horizontal\n", + " colorbar is too wide) use a smaller value of *shrink*.\n", + " \n", + " For more precise control, you can manually specify the positions of\n", + " the axes objects in which the mappable and the colorbar are drawn. In\n", + " this case, do not use any of the axes properties kwargs.\n", + " \n", + " It is known that some vector graphics viewer (svg and pdf) renders white gaps\n", + " between segments of the colorbar. This is due to bugs in the viewers not\n", + " matplotlib. As a workaround the colorbar can be rendered with overlapping\n", + " segments::\n", + " \n", + " cbar = colorbar()\n", + " cbar.solids.set_edgecolor(\"face\")\n", + " draw()\n", + " \n", + " However this has negative consequences in other circumstances. Particularly\n", + " with semi transparent images (alpha < 1) and colorbar extensions and is not\n", + " enabled by default see (issue #1188).\n", + " \n", + " colormaps()\n", + " Matplotlib provides a number of colormaps, and others can be added using\n", + " :func:`~matplotlib.cm.register_cmap`. This function documents the built-in\n", + " colormaps, and will also return a list of all registered colormaps if called.\n", + " \n", + " You can set the colormap for an image, pcolor, scatter, etc,\n", + " using a keyword argument::\n", + " \n", + " imshow(X, cmap=cm.hot)\n", + " \n", + " or using the :func:`set_cmap` function::\n", + " \n", + " imshow(X)\n", + " pyplot.set_cmap('hot')\n", + " pyplot.set_cmap('jet')\n", + " \n", + " In interactive mode, :func:`set_cmap` will update the colormap post-hoc,\n", + " allowing you to see which one works best for your data.\n", + " \n", + " All built-in colormaps can be reversed by appending ``_r``: For instance,\n", + " ``gray_r`` is the reverse of ``gray``.\n", + " \n", + " There are several common color schemes used in visualization:\n", + " \n", + " Sequential schemes\n", + " for unipolar data that progresses from low to high\n", + " Diverging schemes\n", + " for bipolar data that emphasizes positive or negative deviations from a\n", + " central value\n", + " Cyclic schemes\n", + " meant for plotting values that wrap around at the\n", + " endpoints, such as phase angle, wind direction, or time of day\n", + " Qualitative schemes\n", + " for nominal data that has no inherent ordering, where color is used\n", + " only to distinguish categories\n", + " \n", + " Matplotlib ships with 4 perceptually uniform color maps which are\n", + " the recommended color maps for sequential data:\n", + " \n", + " ========= ===================================================\n", + " Colormap Description\n", + " ========= ===================================================\n", + " inferno perceptually uniform shades of black-red-yellow\n", + " magma perceptually uniform shades of black-red-white\n", + " plasma perceptually uniform shades of blue-red-yellow\n", + " viridis perceptually uniform shades of blue-green-yellow\n", + " ========= ===================================================\n", + " \n", + " The following colormaps are based on the `ColorBrewer\n", + " `_ color specifications and designs developed by\n", + " Cynthia Brewer:\n", + " \n", + " ColorBrewer Diverging (luminance is highest at the midpoint, and\n", + " decreases towards differently-colored endpoints):\n", + " \n", + " ======== ===================================\n", + " Colormap Description\n", + " ======== ===================================\n", + " BrBG brown, white, blue-green\n", + " PiYG pink, white, yellow-green\n", + " PRGn purple, white, green\n", + " PuOr orange, white, purple\n", + " RdBu red, white, blue\n", + " RdGy red, white, gray\n", + " RdYlBu red, yellow, blue\n", + " RdYlGn red, yellow, green\n", + " Spectral red, orange, yellow, green, blue\n", + " ======== ===================================\n", + " \n", + " ColorBrewer Sequential (luminance decreases monotonically):\n", + " \n", + " ======== ====================================\n", + " Colormap Description\n", + " ======== ====================================\n", + " Blues white to dark blue\n", + " BuGn white, light blue, dark green\n", + " BuPu white, light blue, dark purple\n", + " GnBu white, light green, dark blue\n", + " Greens white to dark green\n", + " Greys white to black (not linear)\n", + " Oranges white, orange, dark brown\n", + " OrRd white, orange, dark red\n", + " PuBu white, light purple, dark blue\n", + " PuBuGn white, light purple, dark green\n", + " PuRd white, light purple, dark red\n", + " Purples white to dark purple\n", + " RdPu white, pink, dark purple\n", + " Reds white to dark red\n", + " YlGn light yellow, dark green\n", + " YlGnBu light yellow, light green, dark blue\n", + " YlOrBr light yellow, orange, dark brown\n", + " YlOrRd light yellow, orange, dark red\n", + " ======== ====================================\n", + " \n", + " ColorBrewer Qualitative:\n", + " \n", + " (For plotting nominal data, :class:`ListedColormap` is used,\n", + " not :class:`LinearSegmentedColormap`. Different sets of colors are\n", + " recommended for different numbers of categories.)\n", + " \n", + " * Accent\n", + " * Dark2\n", + " * Paired\n", + " * Pastel1\n", + " * Pastel2\n", + " * Set1\n", + " * Set2\n", + " * Set3\n", + " \n", + " A set of colormaps derived from those of the same name provided\n", + " with Matlab are also included:\n", + " \n", + " ========= =======================================================\n", + " Colormap Description\n", + " ========= =======================================================\n", + " autumn sequential linearly-increasing shades of red-orange-yellow\n", + " bone sequential increasing black-white color map with\n", + " a tinge of blue, to emulate X-ray film\n", + " cool linearly-decreasing shades of cyan-magenta\n", + " copper sequential increasing shades of black-copper\n", + " flag repetitive red-white-blue-black pattern (not cyclic at\n", + " endpoints)\n", + " gray sequential linearly-increasing black-to-white\n", + " grayscale\n", + " hot sequential black-red-yellow-white, to emulate blackbody\n", + " radiation from an object at increasing temperatures\n", + " hsv cyclic red-yellow-green-cyan-blue-magenta-red, formed\n", + " by changing the hue component in the HSV color space\n", + " jet a spectral map with dark endpoints, blue-cyan-yellow-red;\n", + " based on a fluid-jet simulation by NCSA [#]_\n", + " pink sequential increasing pastel black-pink-white, meant\n", + " for sepia tone colorization of photographs\n", + " prism repetitive red-yellow-green-blue-purple-...-green pattern\n", + " (not cyclic at endpoints)\n", + " spring linearly-increasing shades of magenta-yellow\n", + " summer sequential linearly-increasing shades of green-yellow\n", + " winter linearly-increasing shades of blue-green\n", + " ========= =======================================================\n", + " \n", + " A set of palettes from the `Yorick scientific visualisation\n", + " package `_, an evolution of\n", + " the GIST package, both by David H. Munro are included:\n", + " \n", + " ============ =======================================================\n", + " Colormap Description\n", + " ============ =======================================================\n", + " gist_earth mapmaker's colors from dark blue deep ocean to green\n", + " lowlands to brown highlands to white mountains\n", + " gist_heat sequential increasing black-red-orange-white, to emulate\n", + " blackbody radiation from an iron bar as it grows hotter\n", + " gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white\n", + " colormap from National Center for Atmospheric\n", + " Research [#]_\n", + " gist_rainbow runs through the colors in spectral order from red to\n", + " violet at full saturation (like *hsv* but not cyclic)\n", + " gist_stern \"Stern special\" color table from Interactive Data\n", + " Language software\n", + " ============ =======================================================\n", + " \n", + " \n", + " Other miscellaneous schemes:\n", + " \n", + " ============= =======================================================\n", + " Colormap Description\n", + " ============= =======================================================\n", + " afmhot sequential black-orange-yellow-white blackbody\n", + " spectrum, commonly used in atomic force microscopy\n", + " brg blue-red-green\n", + " bwr diverging blue-white-red\n", + " coolwarm diverging blue-gray-red, meant to avoid issues with 3D\n", + " shading, color blindness, and ordering of colors [#]_\n", + " CMRmap \"Default colormaps on color images often reproduce to\n", + " confusing grayscale images. The proposed colormap\n", + " maintains an aesthetically pleasing color image that\n", + " automatically reproduces to a monotonic grayscale with\n", + " discrete, quantifiable saturation levels.\" [#]_\n", + " cubehelix Unlike most other color schemes cubehelix was designed\n", + " by D.A. Green to be monotonically increasing in terms\n", + " of perceived brightness. Also, when printed on a black\n", + " and white postscript printer, the scheme results in a\n", + " greyscale with monotonically increasing brightness.\n", + " This color scheme is named cubehelix because the r,g,b\n", + " values produced can be visualised as a squashed helix\n", + " around the diagonal in the r,g,b color cube.\n", + " gnuplot gnuplot's traditional pm3d scheme\n", + " (black-blue-red-yellow)\n", + " gnuplot2 sequential color printable as gray\n", + " (black-blue-violet-yellow-white)\n", + " ocean green-blue-white\n", + " rainbow spectral purple-blue-green-yellow-orange-red colormap\n", + " with diverging luminance\n", + " seismic diverging blue-white-red\n", + " nipy_spectral black-purple-blue-green-yellow-red-white spectrum,\n", + " originally from the Neuroimaging in Python project\n", + " terrain mapmaker's colors, blue-green-yellow-brown-white,\n", + " originally from IGOR Pro\n", + " ============= =======================================================\n", + " \n", + " The following colormaps are redundant and may be removed in future\n", + " versions. It's recommended to use the names in the descriptions\n", + " instead, which produce identical output:\n", + " \n", + " ========= =======================================================\n", + " Colormap Description\n", + " ========= =======================================================\n", + " gist_gray identical to *gray*\n", + " gist_yarg identical to *gray_r*\n", + " binary identical to *gray_r*\n", + " spectral identical to *nipy_spectral* [#]_\n", + " ========= =======================================================\n", + " \n", + " .. rubric:: Footnotes\n", + " \n", + " .. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor\n", + " choice for scientific visualization by many researchers: `Rainbow Color\n", + " Map (Still) Considered Harmful\n", + " `_\n", + " \n", + " .. [#] Resembles \"BkBlAqGrYeOrReViWh200\" from NCAR Command\n", + " Language. See `Color Table Gallery\n", + " `_\n", + " \n", + " .. [#] See `Diverging Color Maps for Scientific Visualization\n", + " `_ by Kenneth Moreland.\n", + " \n", + " .. [#] See `A Color Map for Effective Black-and-White Rendering of\n", + " Color-Scale Images\n", + " `_\n", + " by Carey Rappaport\n", + " \n", + " .. [#] Changed to distinguish from ColorBrewer's *Spectral* map.\n", + " :func:`spectral` still works, but\n", + " ``set_cmap('nipy_spectral')`` is recommended for clarity.\n", + " \n", + " colors()\n", + " .. deprecated:: 2.1\n", + " The colors function was deprecated in version 2.1.\n", + " \n", + " This is a do-nothing function to provide you with help on how\n", + " matplotlib handles colors.\n", + " \n", + " Commands which take color arguments can use several formats to\n", + " specify the colors. For the basic built-in colors, you can use a\n", + " single letter\n", + " \n", + " ===== =======\n", + " Alias Color\n", + " ===== =======\n", + " 'b' blue\n", + " 'g' green\n", + " 'r' red\n", + " 'c' cyan\n", + " 'm' magenta\n", + " 'y' yellow\n", + " 'k' black\n", + " 'w' white\n", + " ===== =======\n", + " \n", + " For a greater range of colors, you have two options. You can\n", + " specify the color using an html hex string, as in::\n", + " \n", + " color = '#eeefff'\n", + " \n", + " or you can pass an R,G,B tuple, where each of R,G,B are in the\n", + " range [0,1].\n", + " \n", + " You can also use any legal html name for a color, for example::\n", + " \n", + " color = 'red'\n", + " color = 'burlywood'\n", + " color = 'chartreuse'\n", + " \n", + " The example below creates a subplot with a dark\n", + " slate gray background::\n", + " \n", + " subplot(111, facecolor=(0.1843, 0.3098, 0.3098))\n", + " \n", + " Here is an example that creates a pale turquoise title::\n", + " \n", + " title('Is this the best color?', color='#afeeee')\n", + " \n", + " connect(s, func)\n", + " Connect event with string *s* to *func*. The signature of *func* is::\n", + " \n", + " def func(event)\n", + " \n", + " where event is a :class:`matplotlib.backend_bases.Event`. The\n", + " following events are recognized\n", + " \n", + " - 'button_press_event'\n", + " - 'button_release_event'\n", + " - 'draw_event'\n", + " - 'key_press_event'\n", + " - 'key_release_event'\n", + " - 'motion_notify_event'\n", + " - 'pick_event'\n", + " - 'resize_event'\n", + " - 'scroll_event'\n", + " - 'figure_enter_event',\n", + " - 'figure_leave_event',\n", + " - 'axes_enter_event',\n", + " - 'axes_leave_event'\n", + " - 'close_event'\n", + " \n", + " For the location events (button and key press/release), if the\n", + " mouse is over the axes, the variable ``event.inaxes`` will be\n", + " set to the :class:`~matplotlib.axes.Axes` the event occurs is\n", + " over, and additionally, the variables ``event.xdata`` and\n", + " ``event.ydata`` will be defined. This is the mouse location\n", + " in data coords. See\n", + " :class:`~matplotlib.backend_bases.KeyEvent` and\n", + " :class:`~matplotlib.backend_bases.MouseEvent` for more info.\n", + " \n", + " Return value is a connection id that can be used with\n", + " :meth:`~matplotlib.backend_bases.Event.mpl_disconnect`.\n", + " \n", + " Examples\n", + " --------\n", + " Usage::\n", + " \n", + " def on_press(event):\n", + " print('you pressed', event.button, event.xdata, event.ydata)\n", + " \n", + " cid = canvas.mpl_connect('button_press_event', on_press)\n", + " \n", + " contour(*args, **kwargs)\n", + " Plot contours.\n", + " \n", + " :func:`~matplotlib.pyplot.contour` and\n", + " :func:`~matplotlib.pyplot.contourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " :func:`~matplotlib.pyplot.contourf` differs from the MATLAB\n", + " version in that it does not draw the polygon edges.\n", + " To draw edges, add line contours with\n", + " calls to :func:`~matplotlib.pyplot.contour`.\n", + " \n", + " \n", + " Call signatures::\n", + " \n", + " contour(Z)\n", + " \n", + " make a contour plot of an array *Z*. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " contour(X,Y,Z)\n", + " \n", + " *X*, *Y* specify the (x, y) coordinates of the surface\n", + " \n", + " ::\n", + " \n", + " contour(Z,N)\n", + " contour(X,Y,Z,N)\n", + " \n", + " contour up to *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " contour(Z,V)\n", + " contour(X,Y,Z,V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contourf(..., V)\n", + " \n", + " fill the ``len(V)-1`` regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " *X* and *Y* must both be 2-D with the same shape as *Z*, or they\n", + " must both be 1-D such that ``len(X)`` is the number of columns in\n", + " *Z* and ``len(Y)`` is the number of rows in *Z*.\n", + " \n", + " ``C = contour(...)`` returns a\n", + " :class:`~matplotlib.contour.QuadContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *corner_mask*: [ *True* | *False* | 'legacy' ]\n", + " Enable/disable corner masking, which only has an effect if *Z* is\n", + " a masked array. If *False*, any quad touching a masked point is\n", + " masked out. If *True*, only the triangular corners of quads\n", + " nearest those points are always masked out, other triangular\n", + " corners comprising three unmasked points are contoured as usual.\n", + " If 'legacy', the old contouring algorithm is used, which is\n", + " equivalent to *False* and is deprecated, only remaining whilst the\n", + " new algorithm is tested fully.\n", + " \n", + " If not specified, the default is taken from\n", + " rcParams['contour.corner_mask'], which is True unless it has\n", + " been modified.\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *vmin*, *vmax*: [ *None* | scalar ]\n", + " If not *None*, either or both of these values will be\n", + " supplied to the :class:`matplotlib.colors.Normalize`\n", + " instance, overriding the default color scaling based on\n", + " *levels*.\n", + " \n", + " *levels*: [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is *None*, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing, overriding the defaults. For\n", + " filled contours, the default is *True*. For line contours,\n", + " it is taken from rcParams['lines.antialiased'].\n", + " \n", + " *nchunk*: [ 0 | integer ]\n", + " If 0, no subdivision of the domain. Specify a positive integer to\n", + " divide the domain into subdomains of *nchunk* by *nchunk* quads.\n", + " Chunking reduces the maximum length of polygons generated by the\n", + " contouring algorithm which reduces the rendering workload passed\n", + " on to the backend and also requires slightly less RAM. It can\n", + " however introduce rendering artifacts at chunk boundaries depending\n", + " on the backend, the *antialiased* flag and value of *alpha*.\n", + " \n", + " contour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified.\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the default is 'solid' unless\n", + " the lines are monochrome. In that case, negative\n", + " contours will take their linestyle from the ``matplotlibrc``\n", + " ``contour.negative_linestyle`` setting.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " contourf-only keyword arguments:\n", + " \n", + " *hatches*:\n", + " A list of cross hatch patterns to use on the filled areas.\n", + " If None, no hatching will be added to the contour.\n", + " Hatching is supported in the PostScript, PDF, SVG and Agg\n", + " backends only.\n", + " \n", + " \n", + " Note: contourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " contourf(*args, **kwargs)\n", + " Plot contours.\n", + " \n", + " :func:`~matplotlib.pyplot.contour` and\n", + " :func:`~matplotlib.pyplot.contourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " :func:`~matplotlib.pyplot.contourf` differs from the MATLAB\n", + " version in that it does not draw the polygon edges.\n", + " To draw edges, add line contours with\n", + " calls to :func:`~matplotlib.pyplot.contour`.\n", + " \n", + " \n", + " Call signatures::\n", + " \n", + " contour(Z)\n", + " \n", + " make a contour plot of an array *Z*. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " contour(X,Y,Z)\n", + " \n", + " *X*, *Y* specify the (x, y) coordinates of the surface\n", + " \n", + " ::\n", + " \n", + " contour(Z,N)\n", + " contour(X,Y,Z,N)\n", + " \n", + " contour up to *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " contour(Z,V)\n", + " contour(X,Y,Z,V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contourf(..., V)\n", + " \n", + " fill the ``len(V)-1`` regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " *X* and *Y* must both be 2-D with the same shape as *Z*, or they\n", + " must both be 1-D such that ``len(X)`` is the number of columns in\n", + " *Z* and ``len(Y)`` is the number of rows in *Z*.\n", + " \n", + " ``C = contour(...)`` returns a\n", + " :class:`~matplotlib.contour.QuadContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *corner_mask*: [ *True* | *False* | 'legacy' ]\n", + " Enable/disable corner masking, which only has an effect if *Z* is\n", + " a masked array. If *False*, any quad touching a masked point is\n", + " masked out. If *True*, only the triangular corners of quads\n", + " nearest those points are always masked out, other triangular\n", + " corners comprising three unmasked points are contoured as usual.\n", + " If 'legacy', the old contouring algorithm is used, which is\n", + " equivalent to *False* and is deprecated, only remaining whilst the\n", + " new algorithm is tested fully.\n", + " \n", + " If not specified, the default is taken from\n", + " rcParams['contour.corner_mask'], which is True unless it has\n", + " been modified.\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *vmin*, *vmax*: [ *None* | scalar ]\n", + " If not *None*, either or both of these values will be\n", + " supplied to the :class:`matplotlib.colors.Normalize`\n", + " instance, overriding the default color scaling based on\n", + " *levels*.\n", + " \n", + " *levels*: [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is *None*, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing, overriding the defaults. For\n", + " filled contours, the default is *True*. For line contours,\n", + " it is taken from rcParams['lines.antialiased'].\n", + " \n", + " *nchunk*: [ 0 | integer ]\n", + " If 0, no subdivision of the domain. Specify a positive integer to\n", + " divide the domain into subdomains of *nchunk* by *nchunk* quads.\n", + " Chunking reduces the maximum length of polygons generated by the\n", + " contouring algorithm which reduces the rendering workload passed\n", + " on to the backend and also requires slightly less RAM. It can\n", + " however introduce rendering artifacts at chunk boundaries depending\n", + " on the backend, the *antialiased* flag and value of *alpha*.\n", + " \n", + " contour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified.\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the default is 'solid' unless\n", + " the lines are monochrome. In that case, negative\n", + " contours will take their linestyle from the ``matplotlibrc``\n", + " ``contour.negative_linestyle`` setting.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " contourf-only keyword arguments:\n", + " \n", + " *hatches*:\n", + " A list of cross hatch patterns to use on the filled areas.\n", + " If None, no hatching will be added to the contour.\n", + " Hatching is supported in the PostScript, PDF, SVG and Agg\n", + " backends only.\n", + " \n", + " \n", + " Note: contourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " cool()\n", + " set the default colormap to cool and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " copper()\n", + " set the default colormap to copper and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " csd(x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, hold=None, data=None, **kwargs)\n", + " Plot the cross-spectral density.\n", + " \n", + " Call signature::\n", + " \n", + " csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,\n", + " window=mlab.window_hanning, noverlap=0, pad_to=None,\n", + " sides='default', scale_by_freq=None, return_line=None, **kwargs)\n", + " \n", + " The cross spectral density :math:`P_{xy}` by Welch's average\n", + " periodogram method. The vectors *x* and *y* are divided into\n", + " *NFFT* length segments. Each segment is detrended by function\n", + " *detrend* and windowed by function *window*. *noverlap* gives\n", + " the length of the overlap between segments. The product of\n", + " the direct FFTs of *x* and *y* are averaged over each segment\n", + " to compute :math:`P_{xy}`, with a scaling to correct for power\n", + " loss due to windowing.\n", + " \n", + " If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero\n", + " padded to *NFFT*.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : 1-D arrays or sequences\n", + " Arrays or sequences containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between segments.\n", + " The default value is 0 (no overlap).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " return_line : bool\n", + " Whether to include the line object plotted in the returned values.\n", + " Default is False.\n", + " \n", + " Returns\n", + " -------\n", + " Pxy : 1-D array\n", + " The values for the cross spectrum `P_{xy}` before scaling\n", + " (complex valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *Pxy*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function.\n", + " Only returned if *return_line* is True.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " For plotting, the power is plotted as\n", + " :math:`10\\log_{10}(P_{xy})` for decibels, though `P_{xy}` itself\n", + " is returned.\n", + " \n", + " References\n", + " ----------\n", + " Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,\n", + " John Wiley & Sons (1986)\n", + " \n", + " See Also\n", + " --------\n", + " :func:`psd`\n", + " :func:`psd` is the equivalent to setting y=x.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " delaxes(*args)\n", + " Remove an axes from the current figure. If *ax*\n", + " doesn't exist, an error will be raised.\n", + " \n", + " ``delaxes()``: delete the current axes\n", + " \n", + " disconnect(cid)\n", + " Disconnect callback id cid\n", + " \n", + " Examples\n", + " --------\n", + " Usage::\n", + " \n", + " cid = canvas.mpl_connect('button_press_event', on_press)\n", + " #...later\n", + " canvas.mpl_disconnect(cid)\n", + " \n", + " draw()\n", + " Redraw the current figure.\n", + " \n", + " This is used to update a figure that has been altered, but not\n", + " automatically re-drawn. If interactive mode is on (:func:`.ion()`), this\n", + " should be only rarely needed, but there may be ways to modify the state of\n", + " a figure without marking it as `stale`. Please report these cases as\n", + " bugs.\n", + " \n", + " A more object-oriented alternative, given any\n", + " :class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that\n", + " was created using a :mod:`~matplotlib.pyplot` function, is::\n", + " \n", + " fig.canvas.draw_idle()\n", + " \n", + " errorbar(x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, hold=None, data=None, **kwargs)\n", + " Plot an errorbar graph.\n", + " \n", + " Plot x versus y with error deltas in yerr and xerr.\n", + " Vertical errorbars are plotted if yerr is not None.\n", + " Horizontal errorbars are plotted if xerr is not None.\n", + " \n", + " x, y, xerr, and yerr can all be scalars, which plots a\n", + " single error bar at x, y.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : scalar or array-like\n", + " y : scalar or array-like\n", + " \n", + " xerr/yerr : scalar or array-like, shape(N,) or shape(2,N), optional\n", + " If a scalar number, len(N) array-like object, or a N-element\n", + " array-like object, errorbars are drawn at +/-value relative\n", + " to the data. Default is None.\n", + " \n", + " If a sequence of shape 2xN, errorbars are drawn at -row1\n", + " and +row2 relative to the data.\n", + " \n", + " fmt : plot format string, optional, default: None\n", + " The plot format symbol. If fmt is 'none' (case-insensitive),\n", + " only the errorbars are plotted. This is used for adding\n", + " errorbars to a bar plot, for example. Default is '',\n", + " an empty plot format string; properties are\n", + " then identical to the defaults for :meth:`plot`.\n", + " \n", + " ecolor : mpl color, optional, default: None\n", + " A matplotlib color arg which gives the color the errorbar lines;\n", + " if None, use the color of the line connecting the markers.\n", + " \n", + " elinewidth : scalar, optional, default: None\n", + " The linewidth of the errorbar lines. If None, use the linewidth.\n", + " \n", + " capsize : scalar, optional, default: None\n", + " The length of the error bar caps in points; if None, it will\n", + " take the value from ``errorbar.capsize``\n", + " :data:`rcParam`.\n", + " \n", + " capthick : scalar, optional, default: None\n", + " An alias kwarg to markeredgewidth (a.k.a. - mew). This\n", + " setting is a more sensible name for the property that\n", + " controls the thickness of the error bar cap in points. For\n", + " backwards compatibility, if mew or markeredgewidth are given,\n", + " then they will over-ride capthick. This may change in future\n", + " releases.\n", + " \n", + " barsabove : bool, optional, default: False\n", + " if True , will plot the errorbars above the plot\n", + " symbols. Default is below.\n", + " \n", + " lolims / uplims / xlolims / xuplims : bool, optional, default:None\n", + " These arguments can be used to indicate that a value gives\n", + " only upper/lower limits. In that case a caret symbol is\n", + " used to indicate this. lims-arguments may be of the same\n", + " type as *xerr* and *yerr*. To use limits with inverted\n", + " axes, :meth:`set_xlim` or :meth:`set_ylim` must be called\n", + " before :meth:`errorbar`.\n", + " \n", + " errorevery : positive integer, optional, default:1\n", + " subsamples the errorbars. e.g., if errorevery=5, errorbars for\n", + " every 5-th datapoint will be plotted. The data plot itself still\n", + " shows all data points.\n", + " \n", + " Returns\n", + " -------\n", + " plotline : :class:`~matplotlib.lines.Line2D` instance\n", + " x, y plot markers and/or line\n", + " caplines : list of :class:`~matplotlib.lines.Line2D` instances\n", + " error bar cap\n", + " barlinecols : list of :class:`~matplotlib.collections.LineCollection`\n", + " horizontal and vertical error ranges.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " All other keyword arguments are passed on to the plot\n", + " command for the markers. For example, this code makes big red\n", + " squares with thick green edges::\n", + " \n", + " x,y,yerr = rand(3,10)\n", + " errorbar(x, y, yerr, marker='s', mfc='red',\n", + " mec='green', ms=20, mew=4)\n", + " \n", + " where mfc, mec, ms and mew are aliases for the longer\n", + " property names, markerfacecolor, markeredgecolor, markersize\n", + " and markeredgewidth.\n", + " \n", + " Valid kwargs for the marker properties are\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'xerr', 'y', 'yerr'.\n", + " \n", + " eventplot(positions, orientation='horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles='solid', hold=None, data=None, **kwargs)\n", + " Plot identical parallel lines at the given positions.\n", + " \n", + " *positions* should be a 1D or 2D array-like object, with each row\n", + " corresponding to a row or column of lines.\n", + " \n", + " This type of plot is commonly used in neuroscience for representing\n", + " neural events, where it is usually called a spike raster, dot raster,\n", + " or raster plot.\n", + " \n", + " However, it is useful in any situation where you wish to show the\n", + " timing or position of multiple sets of discrete events, such as the\n", + " arrival times of people to a business on each day of the month or the\n", + " date of hurricanes each year of the last century.\n", + " \n", + " Parameters\n", + " ----------\n", + " positions : 1D or 2D array-like object\n", + " Each value is an event. If *positions* is a 2D array-like, each\n", + " row corresponds to a row or a column of lines (depending on the\n", + " *orientation* parameter).\n", + " \n", + " orientation : {'horizontal', 'vertical'}, optional\n", + " Controls the direction of the event collections:\n", + " \n", + " - 'horizontal' : the lines are arranged horizontally in rows,\n", + " and are vertical.\n", + " - 'vertical' : the lines are arranged vertically in columns,\n", + " and are horizontal.\n", + " \n", + " lineoffsets : scalar or sequence of scalars, optional, default: 1\n", + " The offset of the center of the lines from the origin, in the\n", + " direction orthogonal to *orientation*.\n", + " \n", + " linelengths : scalar or sequence of scalars, optional, default: 1\n", + " The total height of the lines (i.e. the lines stretches from\n", + " ``lineoffset - linelength/2`` to ``lineoffset + linelength/2``).\n", + " \n", + " linewidths : scalar, scalar sequence or None, optional, default: None\n", + " The line width(s) of the event lines, in points. If it is None,\n", + " defaults to its rcParams setting.\n", + " \n", + " colors : color, sequence of colors or None, optional, default: None\n", + " The color(s) of the event lines. If it is None, defaults to its\n", + " rcParams setting.\n", + " \n", + " linestyles : str or tuple or a sequence of such values, optional\n", + " Default is 'solid'. Valid strings are ['solid', 'dashed',\n", + " 'dashdot', 'dotted', '-', '--', '-.', ':']. Dash tuples\n", + " should be of the form::\n", + " \n", + " (offset, onoffseq),\n", + " \n", + " where *onoffseq* is an even length tuple of on and off ink\n", + " in points.\n", + " \n", + " **kwargs : optional\n", + " Other keyword arguments are line collection properties. See\n", + " :class:`~matplotlib.collections.LineCollection` for a list of\n", + " the valid properties.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " A list of :class:`matplotlib.collections.EventCollection` objects that\n", + " were added.\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " For *linelengths*, *linewidths*, *colors*, and *linestyles*, if only\n", + " a single value is given, that value is applied to all lines. If an\n", + " array-like is given, it must have the same length as *positions*, and\n", + " each value will be applied to the corresponding row of the array.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " .. plot:: mpl_examples/lines_bars_and_markers/eventplot_demo.py\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'linelengths', 'lineoffsets', 'linestyles', 'linewidths', 'positions'.\n", + " \n", + " figimage(*args, **kwargs)\n", + " Adds a non-resampled image to the figure.\n", + " \n", + " call signatures::\n", + " \n", + " figimage(X, **kwargs)\n", + " \n", + " adds a non-resampled array *X* to the figure.\n", + " \n", + " ::\n", + " \n", + " figimage(X, xo, yo)\n", + " \n", + " with pixel offsets *xo*, *yo*,\n", + " \n", + " *X* must be a float array:\n", + " \n", + " * If *X* is MxN, assume luminance (grayscale)\n", + " * If *X* is MxNx3, assume RGB\n", + " * If *X* is MxNx4, assume RGBA\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " ========= =========================================================\n", + " Keyword Description\n", + " ========= =========================================================\n", + " resize a boolean, True or False. If \"True\", then re-size the\n", + " Figure to match the given image size.\n", + " xo or yo An integer, the *x* and *y* image offset in pixels\n", + " cmap a :class:`matplotlib.colors.Colormap` instance, e.g.,\n", + " cm.jet. If *None*, default to the rc ``image.cmap``\n", + " value\n", + " norm a :class:`matplotlib.colors.Normalize` instance. The\n", + " default is normalization(). This scales luminance -> 0-1\n", + " vmin|vmax are used to scale a luminance image to 0-1. If either\n", + " is *None*, the min and max of the luminance values will\n", + " be used. Note if you pass a norm instance, the settings\n", + " for *vmin* and *vmax* will be ignored.\n", + " alpha the alpha blending value, default is *None*\n", + " origin [ 'upper' | 'lower' ] Indicates where the [0,0] index of\n", + " the array is in the upper left or lower left corner of\n", + " the axes. Defaults to the rc image.origin value\n", + " ========= =========================================================\n", + " \n", + " figimage complements the axes image\n", + " (:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled\n", + " to fit the current axes. If you want a resampled image to\n", + " fill the entire figure, you can define an\n", + " :class:`~matplotlib.axes.Axes` with extent [0,0,1,1].\n", + " \n", + " An :class:`matplotlib.image.FigureImage` instance is returned.\n", + " \n", + " Additional kwargs are Artist kwargs passed on to\n", + " :class:`~matplotlib.image.FigureImage`\n", + " \n", + " figlegend(*args, **kwargs)\n", + " Place a legend in the figure.\n", + " \n", + " *labels*\n", + " a sequence of strings\n", + " \n", + " *handles*\n", + " a sequence of :class:`~matplotlib.lines.Line2D` or\n", + " :class:`~matplotlib.patches.Patch` instances\n", + " \n", + " *loc*\n", + " can be a string or an integer specifying the legend\n", + " location\n", + " \n", + " A :class:`matplotlib.legend.Legend` instance is returned.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " To make a legend from existing artists on every axes::\n", + " \n", + " figlegend()\n", + " \n", + " To make a legend for a list of lines and labels::\n", + " \n", + " figlegend( (line1, line2, line3),\n", + " ('label1', 'label2', 'label3'),\n", + " 'upper right' )\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.legend`\n", + " \n", + " fignum_exists(num)\n", + " \n", + " figtext(*args, **kwargs)\n", + " Add text to figure.\n", + " \n", + " Call signature::\n", + " \n", + " text(x, y, s, fontdict=None, **kwargs)\n", + " \n", + " Add text to figure at location *x*, *y* (relative 0-1\n", + " coords). See :func:`~matplotlib.pyplot.text` for the meaning\n", + " of the other arguments.\n", + " \n", + " kwargs control the :class:`~matplotlib.text.Text` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " backgroundcolor: any matplotlib color \n", + " bbox: FancyBboxPatch prop dict \n", + " clip_box: a :class:`matplotlib.transforms.Bbox` instance \n", + " clip_on: [True | False] \n", + " clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] \n", + " color: any matplotlib color \n", + " contains: a callable function \n", + " family or fontfamily or fontname or name: [FONTNAME | 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ] \n", + " figure: a `~.Figure` instance \n", + " fontproperties or font_properties: a :class:`matplotlib.font_manager.FontProperties` instance \n", + " gid: an id string \n", + " horizontalalignment or ha: [ 'center' | 'right' | 'left' ] \n", + " label: object \n", + " linespacing: float (multiple of font size) \n", + " multialignment or ma: ['left' | 'right' | 'center' ] \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " position: (x,y) \n", + " rasterized: bool or None \n", + " rotation: [ angle in degrees | 'vertical' | 'horizontal' ] \n", + " rotation_mode: [ None | \"default\" | \"anchor\" ]\n", + " size or fontsize: [size in points | 'xx-small' | 'x-small' | 'small' | 'medium' | 'large' | 'x-large' | 'xx-large' ] \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " stretch or fontstretch: [a numeric value in range 0-1000 | 'ultra-condensed' | 'extra-condensed' | 'condensed' | 'semi-condensed' | 'normal' | 'semi-expanded' | 'expanded' | 'extra-expanded' | 'ultra-expanded' ] \n", + " style or fontstyle: [ 'normal' | 'italic' | 'oblique'] \n", + " text: string or anything printable with '%s' conversion. \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " usetex: bool or None \n", + " variant or fontvariant: [ 'normal' | 'small-caps' ] \n", + " verticalalignment or va: [ 'center' | 'top' | 'bottom' | 'baseline' ] \n", + " visible: bool \n", + " weight or fontweight: [a numeric value in range 0-1000 | 'ultralight' | 'light' | 'normal' | 'regular' | 'book' | 'medium' | 'roman' | 'semibold' | 'demibold' | 'demi' | 'bold' | 'heavy' | 'extra bold' | 'black' ] \n", + " wrap: bool\n", + " x: float \n", + " y: float \n", + " zorder: float\n", + " \n", + " figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=, clear=False, **kwargs)\n", + " Creates a new figure.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " num : integer or string, optional, default: none\n", + " If not provided, a new figure will be created, and the figure number\n", + " will be incremented. The figure objects holds this number in a `number`\n", + " attribute.\n", + " If num is provided, and a figure with this id already exists, make\n", + " it active, and returns a reference to it. If this figure does not\n", + " exists, create it and returns it.\n", + " If num is a string, the window title will be set to this figure's\n", + " `num`.\n", + " \n", + " figsize : tuple of integers, optional, default: None\n", + " width, height in inches. If not provided, defaults to rc\n", + " figure.figsize.\n", + " \n", + " dpi : integer, optional, default: None\n", + " resolution of the figure. If not provided, defaults to rc figure.dpi.\n", + " \n", + " facecolor :\n", + " the background color. If not provided, defaults to rc figure.facecolor.\n", + " \n", + " edgecolor :\n", + " the border color. If not provided, defaults to rc figure.edgecolor.\n", + " \n", + " frameon : bool, optional, default: True\n", + " If False, suppress drawing the figure frame.\n", + " \n", + " FigureClass : class derived from matplotlib.figure.Figure\n", + " Optionally use a custom Figure instance.\n", + " \n", + " clear : bool, optional, default: False\n", + " If True and the figure already exists, then it is cleared.\n", + " \n", + " Returns\n", + " -------\n", + " figure : Figure\n", + " The Figure instance returned will also be passed to new_figure_manager\n", + " in the backends, which allows to hook custom Figure classes into the\n", + " pylab interface. Additional kwargs will be passed to the figure init\n", + " function.\n", + " \n", + " Notes\n", + " -----\n", + " If you are creating many figures, make sure you explicitly call \"close\"\n", + " on the figures you are not using, because this will enable pylab\n", + " to properly clean up the memory.\n", + " \n", + " rcParams defines the default values, which can be modified in the\n", + " matplotlibrc file\n", + " \n", + " fill(*args, **kwargs)\n", + " Plot filled polygons.\n", + " \n", + " Parameters\n", + " ----------\n", + " args : a variable length argument\n", + " It allowing for multiple\n", + " *x*, *y* pairs with an optional color format string; see\n", + " :func:`~matplotlib.pyplot.plot` for details on the argument\n", + " parsing. For example, each of the following is legal::\n", + " \n", + " ax.fill(x, y)\n", + " ax.fill(x, y, \"b\")\n", + " ax.fill(x, y, \"b\", x, y, \"r\")\n", + " \n", + " An arbitrary number of *x*, *y*, *color* groups can be specified::\n", + " ax.fill(x1, y1, 'g', x2, y2, 'r')\n", + " \n", + " Returns\n", + " -------\n", + " a list of :class:`~matplotlib.patches.Patch`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : :class:`~matplotlib.patches.Polygon` properties\n", + " \n", + " Notes\n", + " -----\n", + " The same color strings that :func:`~matplotlib.pyplot.plot`\n", + " supports are supported by the fill format string.\n", + " \n", + " If you would like to fill below a curve, e.g., shade a region\n", + " between 0 and *y* along *x*, use :meth:`fill_between`\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " fill_between(x, y1, y2=0, where=None, interpolate=False, step=None, hold=None, data=None, **kwargs)\n", + " Make filled polygons between two curves.\n", + " \n", + " \n", + " Create a :class:`~matplotlib.collections.PolyCollection`\n", + " filling the regions between *y1* and *y2* where\n", + " ``where==True``\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array\n", + " An N-length array of the x data\n", + " \n", + " y1 : array\n", + " An N-length array (or scalar) of the y data\n", + " \n", + " y2 : array\n", + " An N-length array (or scalar) of the y data\n", + " \n", + " where : array, optional\n", + " If `None`, default to fill between everywhere. If not `None`,\n", + " it is an N-length numpy boolean array and the fill will\n", + " only happen over the regions where ``where==True``.\n", + " \n", + " interpolate : bool, optional\n", + " If `True`, interpolate between the two lines to find the\n", + " precise point of intersection. Otherwise, the start and\n", + " end points of the filled region will only occur on explicit\n", + " values in the *x* array.\n", + " \n", + " step : {'pre', 'post', 'mid'}, optional\n", + " If not None, fill with step logic.\n", + " \n", + " \n", + " Notes\n", + " -----\n", + " \n", + " Additional Keyword args passed on to the\n", + " :class:`~matplotlib.collections.PolyCollection`.\n", + " \n", + " kwargs control the :class:`~matplotlib.patches.Polygon` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " \n", + " :meth:`fill_betweenx`\n", + " for filling between two sets of x-values\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'where', 'x', 'y1', 'y2'.\n", + " \n", + " fill_betweenx(y, x1, x2=0, where=None, step=None, interpolate=False, hold=None, data=None, **kwargs)\n", + " Make filled polygons between two horizontal curves.\n", + " \n", + " \n", + " Create a :class:`~matplotlib.collections.PolyCollection`\n", + " filling the regions between *x1* and *x2* where\n", + " ``where==True``\n", + " \n", + " Parameters\n", + " ----------\n", + " y : array\n", + " An N-length array of the y data\n", + " \n", + " x1 : array\n", + " An N-length array (or scalar) of the x data\n", + " \n", + " x2 : array, optional\n", + " An N-length array (or scalar) of the x data\n", + " \n", + " where : array, optional\n", + " If *None*, default to fill between everywhere. If not *None*,\n", + " it is a N length numpy boolean array and the fill will\n", + " only happen over the regions where ``where==True``\n", + " \n", + " step : {'pre', 'post', 'mid'}, optional\n", + " If not None, fill with step logic.\n", + " \n", + " interpolate : bool, optional\n", + " If `True`, interpolate between the two lines to find the\n", + " precise point of intersection. Otherwise, the start and\n", + " end points of the filled region will only occur on explicit\n", + " values in the *x* array.\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " keyword args passed on to the\n", + " :class:`~matplotlib.collections.PolyCollection`\n", + " \n", + " kwargs control the :class:`~matplotlib.patches.Polygon` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " \n", + " :meth:`fill_between`\n", + " for filling between two sets of y-values\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'where', 'x1', 'x2', 'y'.\n", + " \n", + " findobj(o=None, match=None, include_self=True)\n", + " Find artist objects.\n", + " \n", + " Recursively find all :class:`~matplotlib.artist.Artist` instances\n", + " contained in self.\n", + " \n", + " *match* can be\n", + " \n", + " - None: return all objects contained in artist.\n", + " \n", + " - function with signature ``boolean = match(artist)``\n", + " used to filter matches\n", + " \n", + " - class instance: e.g., Line2D. Only return artists of class type.\n", + " \n", + " If *include_self* is True (default), include self in the list to be\n", + " checked for a match.\n", + " \n", + " flag()\n", + " set the default colormap to flag and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " gca(**kwargs)\n", + " Get the current :class:`~matplotlib.axes.Axes` instance on the\n", + " current figure matching the given keyword args, or create one.\n", + " \n", + " Examples\n", + " --------\n", + " To get the current polar axes on the current figure::\n", + " \n", + " plt.gca(projection='polar')\n", + " \n", + " If the current axes doesn't exist, or isn't a polar one, the appropriate\n", + " axes will be created and then returned.\n", + " \n", + " See Also\n", + " --------\n", + " matplotlib.figure.Figure.gca : The figure's gca method.\n", + " \n", + " gcf()\n", + " Get a reference to the current figure.\n", + " \n", + " gci()\n", + " Get the current colorable artist. Specifically, returns the\n", + " current :class:`~matplotlib.cm.ScalarMappable` instance (image or\n", + " patch collection), or *None* if no images or patch collections\n", + " have been defined. The commands :func:`~matplotlib.pyplot.imshow`\n", + " and :func:`~matplotlib.pyplot.figimage` create\n", + " :class:`~matplotlib.image.Image` instances, and the commands\n", + " :func:`~matplotlib.pyplot.pcolor` and\n", + " :func:`~matplotlib.pyplot.scatter` create\n", + " :class:`~matplotlib.collections.Collection` instances. The\n", + " current image is an attribute of the current axes, or the nearest\n", + " earlier axes in the current figure that contains an image.\n", + " \n", + " get_current_fig_manager()\n", + " \n", + " get_figlabels()\n", + " Return a list of existing figure labels.\n", + " \n", + " get_fignums()\n", + " Return a list of existing figure numbers.\n", + " \n", + " get_plot_commands()\n", + " Get a sorted list of all of the plotting commands.\n", + " \n", + " ginput(*args, **kwargs)\n", + " Blocking call to interact with a figure.\n", + " \n", + " Wait until the user clicks *n* times on the figure, and return the\n", + " coordinates of each click in a list.\n", + " \n", + " The buttons used for the various actions (adding points, removing\n", + " points, terminating the inputs) can be overridden via the\n", + " arguments *mouse_add*, *mouse_pop* and *mouse_stop*, that give\n", + " the associated mouse button: 1 for left, 2 for middle, 3 for\n", + " right.\n", + " \n", + " Parameters\n", + " ----------\n", + " n : int, optional, default: 1\n", + " Number of mouse clicks to accumulate. If negative, accumulate\n", + " clicks until the input is terminated manually.\n", + " timeout : scalar, optional, default: 30\n", + " Number of seconds to wait before timing out. If zero or negative\n", + " will never timeout.\n", + " show_clicks : bool, optional, default: False\n", + " If True, show a red cross at the location of each click.\n", + " mouse_add : int, one of (1, 2, 3), optional, default: 1 (left click)\n", + " Mouse button used to add points.\n", + " mouse_pop : int, one of (1, 2, 3), optional, default: 3 (right click)\n", + " Mouse button used to remove the most recently added point.\n", + " mouse_stop : int, one of (1, 2, 3), optional, default: 2 (middle click)\n", + " Mouse button used to stop input.\n", + " \n", + " Returns\n", + " -------\n", + " points : list of tuples\n", + " A list of the clicked (x, y) coordinates.\n", + " \n", + " Notes\n", + " -----\n", + " The keyboard can also be used to select points in case your mouse\n", + " does not have one or more of the buttons. The delete and backspace\n", + " keys act like right clicking (i.e., remove last point), the enter key\n", + " terminates input and any other key (not already used by the window\n", + " manager) selects a point.\n", + " \n", + " gray()\n", + " set the default colormap to gray and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " grid(b=None, which='major', axis='both', **kwargs)\n", + " Turn the axes grids on or off.\n", + " \n", + " Set the axes grids on or off; *b* is a boolean. (For MATLAB\n", + " compatibility, *b* may also be a string, 'on' or 'off'.)\n", + " \n", + " If *b* is *None* and ``len(kwargs)==0``, toggle the grid state. If\n", + " *kwargs* are supplied, it is assumed that you want a grid and *b*\n", + " is thus set to *True*.\n", + " \n", + " *which* can be 'major' (default), 'minor', or 'both' to control\n", + " whether major tick grids, minor tick grids, or both are affected.\n", + " \n", + " *axis* can be 'both' (default), 'x', or 'y' to control which\n", + " set of gridlines are drawn.\n", + " \n", + " *kwargs* are used to set the grid line properties, e.g.,::\n", + " \n", + " ax.grid(color='r', linestyle='-', linewidth=2)\n", + " \n", + " Valid :class:`~matplotlib.lines.Line2D` kwargs are\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float\n", + " \n", + " hexbin(x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='face', reduce_C_function=, mincnt=None, marginals=False, hold=None, data=None, **kwargs)\n", + " Make a hexagonal binning plot.\n", + " \n", + " Make a hexagonal binning plot of *x* versus *y*, where *x*,\n", + " *y* are 1-D sequences of the same length, *N*. If *C* is *None*\n", + " (the default), this is a histogram of the number of occurrences\n", + " of the observations at (x[i],y[i]).\n", + " \n", + " If *C* is specified, it specifies values at the coordinate\n", + " (x[i],y[i]). These values are accumulated for each hexagonal\n", + " bin and then reduced according to *reduce_C_function*, which\n", + " defaults to numpy's mean function (np.mean). (If *C* is\n", + " specified, it must also be a 1-D sequence of the same length\n", + " as *x* and *y*.)\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : array or masked array\n", + " \n", + " C : array or masked array, optional, default is *None*\n", + " \n", + " gridsize : int or (int, int), optional, default is 100\n", + " The number of hexagons in the *x*-direction, default is\n", + " 100. The corresponding number of hexagons in the\n", + " *y*-direction is chosen such that the hexagons are\n", + " approximately regular. Alternatively, gridsize can be a\n", + " tuple with two elements specifying the number of hexagons\n", + " in the *x*-direction and the *y*-direction.\n", + " \n", + " bins : {'log'} or int or sequence, optional, default is *None*\n", + " If *None*, no binning is applied; the color of each hexagon\n", + " directly corresponds to its count value.\n", + " \n", + " If 'log', use a logarithmic scale for the color\n", + " map. Internally, :math:`log_{10}(i+1)` is used to\n", + " determine the hexagon color.\n", + " \n", + " If an integer, divide the counts in the specified number\n", + " of bins, and color the hexagons accordingly.\n", + " \n", + " If a sequence of values, the values of the lower bound of\n", + " the bins to be used.\n", + " \n", + " xscale : {'linear', 'log'}, optional, default is 'linear'\n", + " Use a linear or log10 scale on the horizontal axis.\n", + " \n", + " yscale : {'linear', 'log'}, optional, default is 'linear'\n", + " Use a linear or log10 scale on the vertical axis.\n", + " \n", + " mincnt : int > 0, optional, default is *None*\n", + " If not *None*, only display cells with more than *mincnt*\n", + " number of points in the cell\n", + " \n", + " marginals : bool, optional, default is *False*\n", + " if marginals is *True*, plot the marginal density as\n", + " colormapped rectagles along the bottom of the x-axis and\n", + " left of the y-axis\n", + " \n", + " extent : scalar, optional, default is *None*\n", + " The limits of the bins. The default assigns the limits\n", + " based on *gridsize*, *x*, *y*, *xscale* and *yscale*.\n", + " \n", + " If *xscale* or *yscale* is set to 'log', the limits are\n", + " expected to be the exponent for a power of 10. E.g. for\n", + " x-limits of 1 and 50 in 'linear' scale and y-limits\n", + " of 10 and 1000 in 'log' scale, enter (1, 50, 1, 3).\n", + " \n", + " Order of scalars is (left, right, bottom, top).\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " cmap : object, optional, default is *None*\n", + " a :class:`matplotlib.colors.Colormap` instance. If *None*,\n", + " defaults to rc ``image.cmap``.\n", + " \n", + " norm : object, optional, default is *None*\n", + " :class:`matplotlib.colors.Normalize` instance is used to\n", + " scale luminance data to 0,1.\n", + " \n", + " vmin, vmax : scalar, optional, default is *None*\n", + " *vmin* and *vmax* are used in conjunction with *norm* to\n", + " normalize luminance data. If *None*, the min and max of the\n", + " color array *C* are used. Note if you pass a norm instance\n", + " your settings for *vmin* and *vmax* will be ignored.\n", + " \n", + " alpha : scalar between 0 and 1, optional, default is *None*\n", + " the alpha value for the patches\n", + " \n", + " linewidths : scalar, optional, default is *None*\n", + " If *None*, defaults to 1.0.\n", + " \n", + " edgecolors : {'face', 'none', *None*} or mpl color, optional, default is 'face'\n", + " \n", + " If 'face', draws the edges in the same color as the fill color.\n", + " \n", + " If 'none', no edge is drawn; this can sometimes lead to unsightly\n", + " unpainted pixels between the hexagons.\n", + " \n", + " If *None*, draws outlines in the default color.\n", + " \n", + " If a matplotlib color arg, draws outlines in the specified color.\n", + " \n", + " Returns\n", + " -------\n", + " object\n", + " a :class:`~matplotlib.collections.PolyCollection` instance; use\n", + " :meth:`~matplotlib.collections.PolyCollection.get_array` on\n", + " this :class:`~matplotlib.collections.PolyCollection` to get\n", + " the counts in each hexagon.\n", + " \n", + " If *marginals* is *True*, horizontal\n", + " bar and vertical bar (both PolyCollections) will be attached\n", + " to the return collection as attributes *hbar* and *vbar*.\n", + " \n", + " Notes\n", + " --------\n", + " The standard descriptions of all the\n", + " :class:`~matplotlib.collections.Collection` parameters:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " hist(x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, normed=None, hold=None, data=None, **kwargs)\n", + " Plot a histogram.\n", + " \n", + " Compute and draw the histogram of *x*. The return value is a\n", + " tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,\n", + " [*patches0*, *patches1*,...]) if the input contains multiple\n", + " data.\n", + " \n", + " Multiple data can be provided via *x* as a list of datasets\n", + " of potentially different length ([*x0*, *x1*, ...]), or as\n", + " a 2-D ndarray in which each column is a dataset. Note that\n", + " the ndarray form is transposed relative to the list form.\n", + " \n", + " Masked arrays are not supported at present.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : (n,) array or sequence of (n,) arrays\n", + " Input values, this takes either a single array or a sequency of\n", + " arrays which are not required to be of the same length\n", + " \n", + " bins : integer or sequence or 'auto', optional\n", + " If an integer is given, ``bins + 1`` bin edges are calculated and\n", + " returned, consistent with :func:`numpy.histogram`.\n", + " \n", + " If `bins` is a sequence, gives bin edges, including left edge of\n", + " first bin and right edge of last bin. In this case, `bins` is\n", + " returned unmodified.\n", + " \n", + " All but the last (righthand-most) bin is half-open. In other\n", + " words, if `bins` is::\n", + " \n", + " [1, 2, 3, 4]\n", + " \n", + " then the first bin is ``[1, 2)`` (including 1, but excluding 2) and\n", + " the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which\n", + " *includes* 4.\n", + " \n", + " Unequally spaced bins are supported if *bins* is a sequence.\n", + " \n", + " If Numpy 1.11 is installed, may also be ``'auto'``.\n", + " \n", + " Default is taken from the rcParam ``hist.bins``.\n", + " \n", + " range : tuple or None, optional\n", + " The lower and upper range of the bins. Lower and upper outliers\n", + " are ignored. If not provided, *range* is ``(x.min(), x.max())``.\n", + " Range has no effect if *bins* is a sequence.\n", + " \n", + " If *bins* is a sequence or *range* is specified, autoscaling\n", + " is based on the specified bin range instead of the\n", + " range of x.\n", + " \n", + " Default is ``None``\n", + " \n", + " density : boolean, optional\n", + " If ``True``, the first element of the return tuple will\n", + " be the counts normalized to form a probability density, i.e.,\n", + " the area (or integral) under the histogram will sum to 1.\n", + " This is achieved by dividing the count by the number of\n", + " observations times the bin width and not dividing by the total\n", + " number of observations. If *stacked* is also ``True``, the sum of\n", + " the histograms is normalized to 1.\n", + " \n", + " Default is ``None`` for both *normed* and *density*. If either is\n", + " set, then that value will be used. If neither are set, then the\n", + " args will be treated as ``False``.\n", + " \n", + " If both *density* and *normed* are set an error is raised.\n", + " \n", + " weights : (n, ) array_like or None, optional\n", + " An array of weights, of the same shape as *x*. Each value in *x*\n", + " only contributes its associated weight towards the bin count\n", + " (instead of 1). If *normed* or *density* is ``True``,\n", + " the weights are normalized, so that the integral of the density\n", + " over the range remains 1.\n", + " \n", + " Default is ``None``\n", + " \n", + " cumulative : boolean, optional\n", + " If ``True``, then a histogram is computed where each bin gives the\n", + " counts in that bin plus all bins for smaller values. The last bin\n", + " gives the total number of datapoints. If *normed* or *density*\n", + " is also ``True`` then the histogram is normalized such that the\n", + " last bin equals 1. If *cumulative* evaluates to less than 0\n", + " (e.g., -1), the direction of accumulation is reversed.\n", + " In this case, if *normed* and/or *density* is also ``True``, then\n", + " the histogram is normalized such that the first bin equals 1.\n", + " \n", + " Default is ``False``\n", + " \n", + " bottom : array_like, scalar, or None\n", + " Location of the bottom baseline of each bin. If a scalar,\n", + " the base line for each bin is shifted by the same amount.\n", + " If an array, each bin is shifted independently and the length\n", + " of bottom must match the number of bins. If None, defaults to 0.\n", + " \n", + " Default is ``None``\n", + " \n", + " histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional\n", + " The type of histogram to draw.\n", + " \n", + " - 'bar' is a traditional bar-type histogram. If multiple data\n", + " are given the bars are aranged side by side.\n", + " \n", + " - 'barstacked' is a bar-type histogram where multiple\n", + " data are stacked on top of each other.\n", + " \n", + " - 'step' generates a lineplot that is by default\n", + " unfilled.\n", + " \n", + " - 'stepfilled' generates a lineplot that is by default\n", + " filled.\n", + " \n", + " Default is 'bar'\n", + " \n", + " align : {'left', 'mid', 'right'}, optional\n", + " Controls how the histogram is plotted.\n", + " \n", + " - 'left': bars are centered on the left bin edges.\n", + " \n", + " - 'mid': bars are centered between the bin edges.\n", + " \n", + " - 'right': bars are centered on the right bin edges.\n", + " \n", + " Default is 'mid'\n", + " \n", + " orientation : {'horizontal', 'vertical'}, optional\n", + " If 'horizontal', `~matplotlib.pyplot.barh` will be used for\n", + " bar-type histograms and the *bottom* kwarg will be the left edges.\n", + " \n", + " rwidth : scalar or None, optional\n", + " The relative width of the bars as a fraction of the bin width. If\n", + " ``None``, automatically compute the width.\n", + " \n", + " Ignored if *histtype* is 'step' or 'stepfilled'.\n", + " \n", + " Default is ``None``\n", + " \n", + " log : boolean, optional\n", + " If ``True``, the histogram axis will be set to a log scale. If\n", + " *log* is ``True`` and *x* is a 1D array, empty bins will be\n", + " filtered out and only the non-empty ``(n, bins, patches)``\n", + " will be returned.\n", + " \n", + " Default is ``False``\n", + " \n", + " color : color or array_like of colors or None, optional\n", + " Color spec or sequence of color specs, one per dataset. Default\n", + " (``None``) uses the standard line color sequence.\n", + " \n", + " Default is ``None``\n", + " \n", + " label : string or None, optional\n", + " String, or sequence of strings to match multiple datasets. Bar\n", + " charts yield multiple patches per dataset, but only the first gets\n", + " the label, so that the legend command will work as expected.\n", + " \n", + " default is ``None``\n", + " \n", + " stacked : boolean, optional\n", + " If ``True``, multiple data are stacked on top of each other If\n", + " ``False`` multiple data are aranged side by side if histtype is\n", + " 'bar' or on top of each other if histtype is 'step'\n", + " \n", + " Default is ``False``\n", + " \n", + " Returns\n", + " -------\n", + " n : array or list of arrays\n", + " The values of the histogram bins. See *normed* or *density*\n", + " and *weights* for a description of the possible semantics.\n", + " If input *x* is an array, then this is an array of length\n", + " *nbins*. If input is a sequence arrays\n", + " ``[data1, data2,..]``, then this is a list of arrays with\n", + " the values of the histograms for each of the arrays in the\n", + " same order.\n", + " \n", + " bins : array\n", + " The edges of the bins. Length nbins + 1 (nbins left edges and right\n", + " edge of last bin). Always a single array even when multiple data\n", + " sets are passed in.\n", + " \n", + " patches : list or list of lists\n", + " Silent list of individual patches used to create the histogram\n", + " or list of such list if multiple input datasets.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.patches.Patch` properties\n", + " \n", + " See also\n", + " --------\n", + " hist2d : 2D histograms\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'weights', 'x'.\n", + " \n", + " hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, hold=None, data=None, **kwargs)\n", + " Make a 2D histogram plot.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y: array_like, shape (n, )\n", + " Input values\n", + " \n", + " bins: [None | int | [int, int] | array_like | [array, array]]\n", + " \n", + " The bin specification:\n", + " \n", + " - If int, the number of bins for the two dimensions\n", + " (nx=ny=bins).\n", + " \n", + " - If [int, int], the number of bins in each dimension\n", + " (nx, ny = bins).\n", + " \n", + " - If array_like, the bin edges for the two dimensions\n", + " (x_edges=y_edges=bins).\n", + " \n", + " - If [array, array], the bin edges in each dimension\n", + " (x_edges, y_edges = bins).\n", + " \n", + " The default value is 10.\n", + " \n", + " range : array_like shape(2, 2), optional, default: None\n", + " The leftmost and rightmost edges of the bins along each dimension\n", + " (if not specified explicitly in the bins parameters): [[xmin,\n", + " xmax], [ymin, ymax]]. All values outside of this range will be\n", + " considered outliers and not tallied in the histogram.\n", + " \n", + " normed : boolean, optional, default: False\n", + " Normalize histogram.\n", + " \n", + " weights : array_like, shape (n, ), optional, default: None\n", + " An array of values w_i weighing each sample (x_i, y_i).\n", + " \n", + " cmin : scalar, optional, default: None\n", + " All bins that has count less than cmin will not be displayed and\n", + " these count values in the return value count histogram will also\n", + " be set to nan upon return\n", + " \n", + " cmax : scalar, optional, default: None\n", + " All bins that has count more than cmax will not be displayed (set\n", + " to none before passing to imshow) and these count values in the\n", + " return value count histogram will also be set to nan upon return\n", + " \n", + " Returns\n", + " -------\n", + " The return value is ``(counts, xedges, yedges, Image)``.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " cmap : {Colormap, string}, optional\n", + " A :class:`matplotlib.colors.Colormap` instance. If not set, use rc\n", + " settings.\n", + " \n", + " norm : Normalize, optional\n", + " A :class:`matplotlib.colors.Normalize` instance is used to\n", + " scale luminance data to ``[0, 1]``. If not set, defaults to\n", + " ``Normalize()``.\n", + " \n", + " vmin/vmax : {None, scalar}, optional\n", + " Arguments passed to the `Normalize` instance.\n", + " \n", + " alpha : ``0 <= scalar <= 1`` or ``None``, optional\n", + " The alpha blending value.\n", + " \n", + " See also\n", + " --------\n", + " hist : 1D histogram\n", + " \n", + " Notes\n", + " -----\n", + " Rendering the histogram with a logarithmic color scale is\n", + " accomplished by passing a :class:`colors.LogNorm` instance to\n", + " the *norm* keyword argument. Likewise, power-law normalization\n", + " (similar in effect to gamma correction) can be accomplished with\n", + " :class:`colors.PowerNorm`.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'weights', 'x', 'y'.\n", + " \n", + " hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)\n", + " Plot horizontal lines at each `y` from `xmin` to `xmax`.\n", + " \n", + " Parameters\n", + " ----------\n", + " y : scalar or sequence of scalar\n", + " y-indexes where to plot the lines.\n", + " \n", + " xmin, xmax : scalar or 1D array_like\n", + " Respective beginning and end of each line. If scalars are\n", + " provided, all lines will have same length.\n", + " \n", + " colors : array_like of colors, optional, default: 'k'\n", + " \n", + " linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional\n", + " \n", + " label : string, optional, default: ''\n", + " \n", + " Returns\n", + " -------\n", + " lines : `~matplotlib.collections.LineCollection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.collections.LineCollection` properties.\n", + " \n", + " See also\n", + " --------\n", + " vlines : vertical lines\n", + " axhline: horizontal line across the axes\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'xmax', 'xmin', 'y'.\n", + " \n", + " hold(b=None)\n", + " .. deprecated:: 2.0\n", + " pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " \n", + " Set the hold state. If *b* is None (default), toggle the\n", + " hold state, else set the hold state to boolean value *b*::\n", + " \n", + " hold() # toggle hold\n", + " hold(True) # hold is on\n", + " hold(False) # hold is off\n", + " \n", + " When *hold* is *True*, subsequent plot commands will add elements to\n", + " the current axes. When *hold* is *False*, the current axes and\n", + " figure will be cleared on the next plot command.\n", + " \n", + " hot()\n", + " set the default colormap to hot and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " hsv()\n", + " set the default colormap to hsv and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " imread(*args, **kwargs)\n", + " Read an image from a file into an array.\n", + " \n", + " *fname* may be a string path, a valid URL, or a Python\n", + " file-like object. If using a file object, it must be opened in binary\n", + " mode.\n", + " \n", + " If *format* is provided, will try to read file of that type,\n", + " otherwise the format is deduced from the filename. If nothing can\n", + " be deduced, PNG is tried.\n", + " \n", + " Return value is a :class:`numpy.array`. For grayscale images, the\n", + " return array is MxN. For RGB images, the return value is MxNx3.\n", + " For RGBA images the return value is MxNx4.\n", + " \n", + " matplotlib can only read PNGs natively, but if `PIL\n", + " `_ is installed, it will\n", + " use it to load the image and return an array (if possible) which\n", + " can be used with :func:`~matplotlib.pyplot.imshow`. Note, URL strings\n", + " may not be compatible with PIL. Check the PIL documentation for more\n", + " information.\n", + " \n", + " imsave(*args, **kwargs)\n", + " Save an array as in image file.\n", + " \n", + " The output formats available depend on the backend being used.\n", + " \n", + " Parameters\n", + " ----------\n", + " fname : str or file-like\n", + " Path string to a filename, or a Python file-like object.\n", + " If *format* is *None* and *fname* is a string, the output\n", + " format is deduced from the extension of the filename.\n", + " arr : array-like\n", + " An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array.\n", + " vmin, vmax: [ None | scalar ]\n", + " *vmin* and *vmax* set the color scaling for the image by fixing the\n", + " values that map to the colormap color limits. If either *vmin*\n", + " or *vmax* is None, that limit is determined from the *arr*\n", + " min/max value.\n", + " cmap : matplotlib.colors.Colormap, optional\n", + " For example, ``cm.viridis``. If ``None``, defaults to the\n", + " ``image.cmap`` rcParam.\n", + " format : str\n", + " One of the file extensions supported by the active backend. Most\n", + " backends support png, pdf, ps, eps and svg.\n", + " origin : [ 'upper' | 'lower' ]\n", + " Indicates whether the ``(0, 0)`` index of the array is in the\n", + " upper left or lower left corner of the axes. Defaults to the\n", + " ``image.origin`` rcParam.\n", + " dpi : int\n", + " The DPI to store in the metadata of the file. This does not affect the\n", + " resolution of the output image.\n", + " \n", + " imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, hold=None, data=None, **kwargs)\n", + " Display an image on the axes.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4)\n", + " Display the image in `X` to current axes. `X` may be an\n", + " array or a PIL image. If `X` is an array, it\n", + " can have the following shapes and types:\n", + " \n", + " - MxN -- values to be mapped (float or int)\n", + " - MxNx3 -- RGB (float or uint8)\n", + " - MxNx4 -- RGBA (float or uint8)\n", + " \n", + " The value for each component of MxNx3 and MxNx4 float arrays\n", + " should be in the range 0.0 to 1.0. MxN arrays are mapped\n", + " to colors based on the `norm` (mapping scalar to scalar)\n", + " and the `cmap` (mapping the normed scalar to a color).\n", + " \n", + " cmap : `~matplotlib.colors.Colormap`, optional, default: None\n", + " If None, default to rc `image.cmap` value. `cmap` is ignored\n", + " if `X` is 3-D, directly specifying RGB(A) values.\n", + " \n", + " aspect : ['auto' | 'equal' | scalar], optional, default: None\n", + " If 'auto', changes the image aspect ratio to match that of the\n", + " axes.\n", + " \n", + " If 'equal', and `extent` is None, changes the axes aspect ratio to\n", + " match that of the image. If `extent` is not `None`, the axes\n", + " aspect ratio is changed to match that of the extent.\n", + " \n", + " If None, default to rc ``image.aspect`` value.\n", + " \n", + " interpolation : string, optional, default: None\n", + " Acceptable values are 'none', 'nearest', 'bilinear', 'bicubic',\n", + " 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser',\n", + " 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',\n", + " 'lanczos'\n", + " \n", + " If `interpolation` is None, default to rc `image.interpolation`.\n", + " See also the `filternorm` and `filterrad` parameters.\n", + " If `interpolation` is 'none', then no interpolation is performed\n", + " on the Agg, ps and pdf backends. Other backends will fall back to\n", + " 'nearest'.\n", + " \n", + " norm : `~matplotlib.colors.Normalize`, optional, default: None\n", + " A `~matplotlib.colors.Normalize` instance is used to scale\n", + " a 2-D float `X` input to the (0, 1) range for input to the\n", + " `cmap`. If `norm` is None, use the default func:`normalize`.\n", + " If `norm` is an instance of `~matplotlib.colors.NoNorm`,\n", + " `X` must be an array of integers that index directly into\n", + " the lookup table of the `cmap`.\n", + " \n", + " vmin, vmax : scalar, optional, default: None\n", + " `vmin` and `vmax` are used in conjunction with norm to normalize\n", + " luminance data. Note if you pass a `norm` instance, your\n", + " settings for `vmin` and `vmax` will be ignored.\n", + " \n", + " alpha : scalar, optional, default: None\n", + " The alpha blending value, between 0 (transparent) and 1 (opaque)\n", + " \n", + " origin : ['upper' | 'lower'], optional, default: None\n", + " Place the [0,0] index of the array in the upper left or lower left\n", + " corner of the axes. If None, default to rc `image.origin`.\n", + " \n", + " extent : scalars (left, right, bottom, top), optional, default: None\n", + " The location, in data-coordinates, of the lower-left and\n", + " upper-right corners. If `None`, the image is positioned such that\n", + " the pixel centers fall on zero-based (row, column) indices.\n", + " \n", + " shape : scalars (columns, rows), optional, default: None\n", + " For raw buffer images\n", + " \n", + " filternorm : scalar, optional, default: 1\n", + " A parameter for the antigrain image resize filter. From the\n", + " antigrain documentation, if `filternorm` = 1, the filter\n", + " normalizes integer values and corrects the rounding errors. It\n", + " doesn't do anything with the source floating point values, it\n", + " corrects only integers according to the rule of 1.0 which means\n", + " that any sum of pixel weights must be equal to 1.0. So, the\n", + " filter function must produce a graph of the proper shape.\n", + " \n", + " filterrad : scalar, optional, default: 4.0\n", + " The filter radius for filters that have a radius parameter, i.e.\n", + " when interpolation is one of: 'sinc', 'lanczos' or 'blackman'\n", + " \n", + " Returns\n", + " -------\n", + " image : `~matplotlib.image.AxesImage`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.artist.Artist` properties.\n", + " \n", + " See also\n", + " --------\n", + " matshow : Plot a matrix or an array as an image.\n", + " \n", + " Notes\n", + " -----\n", + " Unless *extent* is used, pixel centers will be located at integer\n", + " coordinates. In other words: the origin will coincide with the center\n", + " of pixel (0, 0).\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " inferno()\n", + " set the default colormap to inferno and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " install_repl_displayhook()\n", + " Install a repl display hook so that any stale figure are automatically\n", + " redrawn when control is returned to the repl.\n", + " \n", + " This works with IPython terminals and kernels,\n", + " as well as vanilla python shells.\n", + " \n", + " ioff()\n", + " Turn interactive mode off.\n", + " \n", + " ion()\n", + " Turn interactive mode on.\n", + " \n", + " ishold()\n", + " .. deprecated:: 2.0\n", + " pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " \n", + " Return the hold status of the current axes.\n", + " \n", + " isinteractive()\n", + " Return status of interactive mode.\n", + " \n", + " jet()\n", + " set the default colormap to jet and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " legend(*args, **kwargs)\n", + " Places a legend on the axes.\n", + " \n", + " To make a legend for lines which already exist on the axes\n", + " (via plot for instance), simply call this function with an iterable\n", + " of strings, one for each legend item. For example::\n", + " \n", + " ax.plot([1, 2, 3])\n", + " ax.legend(['A simple line'])\n", + " \n", + " However, in order to keep the \"label\" and the legend element\n", + " instance together, it is preferable to specify the label either at\n", + " artist creation, or by calling the\n", + " :meth:`~matplotlib.artist.Artist.set_label` method on the artist::\n", + " \n", + " line, = ax.plot([1, 2, 3], label='Inline label')\n", + " # Overwrite the label by calling the method.\n", + " line.set_label('Label via method')\n", + " ax.legend()\n", + " \n", + " Specific lines can be excluded from the automatic legend element\n", + " selection by defining a label starting with an underscore.\n", + " This is default for all artists, so calling :meth:`legend` without\n", + " any arguments and without setting the labels manually will result in\n", + " no legend being drawn.\n", + " \n", + " For full control of which artists have a legend entry, it is possible\n", + " to pass an iterable of legend artists followed by an iterable of\n", + " legend labels respectively::\n", + " \n", + " legend((line1, line2, line3), ('label1', 'label2', 'label3'))\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " loc : int or string or pair of floats, default: 'upper right'\n", + " The location of the legend. Possible codes are:\n", + " \n", + " =============== =============\n", + " Location String Location Code\n", + " =============== =============\n", + " 'best' 0\n", + " 'upper right' 1\n", + " 'upper left' 2\n", + " 'lower left' 3\n", + " 'lower right' 4\n", + " 'right' 5\n", + " 'center left' 6\n", + " 'center right' 7\n", + " 'lower center' 8\n", + " 'upper center' 9\n", + " 'center' 10\n", + " =============== =============\n", + " \n", + " \n", + " Alternatively can be a 2-tuple giving ``x, y`` of the lower-left\n", + " corner of the legend in axes coordinates (in which case\n", + " ``bbox_to_anchor`` will be ignored).\n", + " \n", + " bbox_to_anchor : `~.BboxBase` or pair of floats\n", + " Specify any arbitrary location for the legend in `bbox_transform`\n", + " coordinates (default Axes coordinates).\n", + " \n", + " For example, to put the legend's upper right hand corner in the\n", + " center of the axes the following keywords can be used::\n", + " \n", + " loc='upper right', bbox_to_anchor=(0.5, 0.5)\n", + " \n", + " ncol : integer\n", + " The number of columns that the legend has. Default is 1.\n", + " \n", + " prop : None or :class:`matplotlib.font_manager.FontProperties` or dict\n", + " The font properties of the legend. If None (default), the current\n", + " :data:`matplotlib.rcParams` will be used.\n", + " \n", + " fontsize : int or float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}\n", + " Controls the font size of the legend. If the value is numeric the\n", + " size will be the absolute font size in points. String values are\n", + " relative to the current default font size. This argument is only\n", + " used if `prop` is not specified.\n", + " \n", + " numpoints : None or int\n", + " The number of marker points in the legend when creating a legend\n", + " entry for a line/:class:`matplotlib.lines.Line2D`.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.numpoints`` :data:`rcParam`.\n", + " \n", + " scatterpoints : None or int\n", + " The number of marker points in the legend when creating a legend\n", + " entry for a scatter plot/\n", + " :class:`matplotlib.collections.PathCollection`.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.scatterpoints`` :data:`rcParam`.\n", + " \n", + " scatteryoffsets : iterable of floats\n", + " The vertical offset (relative to the font size) for the markers\n", + " created for a scatter plot legend entry. 0.0 is at the base the\n", + " legend text, and 1.0 is at the top. To draw all markers at the\n", + " same height, set to ``[0.5]``. Default ``[0.375, 0.5, 0.3125]``.\n", + " \n", + " markerscale : None or int or float\n", + " The relative size of legend markers compared with the originally\n", + " drawn ones. Default is ``None`` which will take the value from\n", + " the ``legend.markerscale`` :data:`rcParam `.\n", + " \n", + " markerfirst : bool\n", + " If *True*, legend marker is placed to the left of the legend label.\n", + " If *False*, legend marker is placed to the right of the legend\n", + " label.\n", + " Default is *True*.\n", + " \n", + " frameon : None or bool\n", + " Control whether the legend should be drawn on a patch (frame).\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.frameon`` :data:`rcParam`.\n", + " \n", + " fancybox : None or bool\n", + " Control whether round edges should be enabled around\n", + " the :class:`~matplotlib.patches.FancyBboxPatch` which\n", + " makes up the legend's background.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.fancybox`` :data:`rcParam`.\n", + " \n", + " shadow : None or bool\n", + " Control whether to draw a shadow behind the legend.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.shadow`` :data:`rcParam`.\n", + " \n", + " framealpha : None or float\n", + " Control the alpha transparency of the legend's background.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.framealpha`` :data:`rcParam`.\n", + " If shadow is activated and framealpha is ``None`` the\n", + " default value is being ignored.\n", + " \n", + " facecolor : None or \"inherit\" or a color spec\n", + " Control the legend's background color.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.facecolor`` :data:`rcParam`.\n", + " If ``\"inherit\"``, it will take the ``axes.facecolor``\n", + " :data:`rcParam`.\n", + " \n", + " edgecolor : None or \"inherit\" or a color spec\n", + " Control the legend's background patch edge color.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.edgecolor`` :data:`rcParam`.\n", + " If ``\"inherit\"``, it will take the ``axes.edgecolor``\n", + " :data:`rcParam`.\n", + " \n", + " mode : {\"expand\", None}\n", + " If `mode` is set to ``\"expand\"`` the legend will be horizontally\n", + " expanded to fill the axes area (or `bbox_to_anchor` if defines\n", + " the legend's size).\n", + " \n", + " bbox_transform : None or :class:`matplotlib.transforms.Transform`\n", + " The transform for the bounding box (`bbox_to_anchor`). For a value\n", + " of ``None`` (default) the Axes'\n", + " :data:`~matplotlib.axes.Axes.transAxes` transform will be used.\n", + " \n", + " title : str or None\n", + " The legend's title. Default is no title (``None``).\n", + " \n", + " borderpad : float or None\n", + " The fractional whitespace inside the legend border.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.borderpad`` :data:`rcParam`.\n", + " \n", + " labelspacing : float or None\n", + " The vertical space between the legend entries.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.labelspacing`` :data:`rcParam`.\n", + " \n", + " handlelength : float or None\n", + " The length of the legend handles.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.handlelength`` :data:`rcParam`.\n", + " \n", + " handletextpad : float or None\n", + " The pad between the legend handle and text.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.handletextpad`` :data:`rcParam`.\n", + " \n", + " borderaxespad : float or None\n", + " The pad between the axes and legend border.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.borderaxespad`` :data:`rcParam`.\n", + " \n", + " columnspacing : float or None\n", + " The spacing between columns.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.columnspacing`` :data:`rcParam`.\n", + " \n", + " handler_map : dict or None\n", + " The custom dictionary mapping instances or types to a legend\n", + " handler. This `handler_map` updates the default handler map\n", + " found at :func:`matplotlib.legend.Legend.get_legend_handler_map`.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " :class:`matplotlib.legend.Legend` instance\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " Not all kinds of artist are supported by the legend command. See\n", + " :ref:`sphx_glr_tutorials_intermediate_legend_guide.py` for details.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " .. plot:: gallery/api/legend.py\n", + " \n", + " locator_params(axis='both', tight=None, **kwargs)\n", + " Control behavior of tick locators.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *axis*\n", + " ['x' | 'y' | 'both'] Axis on which to operate;\n", + " default is 'both'.\n", + " \n", + " *tight*\n", + " [True | False | None] Parameter passed to :meth:`autoscale_view`.\n", + " Default is None, for no change.\n", + " \n", + " Remaining keyword arguments are passed to directly to the\n", + " :meth:`~matplotlib.ticker.MaxNLocator.set_params` method.\n", + " \n", + " Typically one might want to reduce the maximum number\n", + " of ticks and use tight bounds when plotting small\n", + " subplots, for example::\n", + " \n", + " ax.locator_params(tight=True, nbins=4)\n", + " \n", + " Because the locator is involved in autoscaling,\n", + " :meth:`autoscale_view` is called automatically after\n", + " the parameters are changed.\n", + " \n", + " This presently works only for the\n", + " :class:`~matplotlib.ticker.MaxNLocator` used\n", + " by default on linear axes, but it may be generalized.\n", + " \n", + " loglog(*args, **kwargs)\n", + " Make a plot with log scaling on both the *x* and *y* axis.\n", + " \n", + " :func:`~matplotlib.pyplot.loglog` supports all the keyword\n", + " arguments of :func:`~matplotlib.pyplot.plot` and\n", + " :meth:`matplotlib.axes.Axes.set_xscale` /\n", + " :meth:`matplotlib.axes.Axes.set_yscale`.\n", + " \n", + " Notable keyword arguments:\n", + " \n", + " *basex*/*basey*: scalar > 1\n", + " Base of the *x*/*y* logarithm\n", + " \n", + " *subsx*/*subsy*: [ *None* | sequence ]\n", + " The location of the minor *x*/*y* ticks; *None* defaults\n", + " to autosubs, which depend on the number of decades in the\n", + " plot; see :meth:`matplotlib.axes.Axes.set_xscale` /\n", + " :meth:`matplotlib.axes.Axes.set_yscale` for details\n", + " \n", + " *nonposx*/*nonposy*: ['mask' | 'clip' ]\n", + " Non-positive values in *x* or *y* can be masked as\n", + " invalid, or clipped to a very small positive number\n", + " \n", + " The remaining valid kwargs are\n", + " :class:`~matplotlib.lines.Line2D` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float\n", + " \n", + " magma()\n", + " set the default colormap to magma and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, scale=None, hold=None, data=None, **kwargs)\n", + " Plot the magnitude spectrum.\n", + " \n", + " Call signature::\n", + " \n", + " magnitude_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,\n", + " pad_to=None, sides='default', **kwargs)\n", + " \n", + " Compute the magnitude spectrum of *x*. Data is padded to a\n", + " length of *pad_to* and the windowing function *window* is applied to\n", + " the signal.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. While not increasing the actual resolution of\n", + " the spectrum (the minimum distance between resolvable peaks),\n", + " this can give more points in the plot, allowing for more\n", + " detail. This corresponds to the *n* parameter in the call to fft().\n", + " The default is None, which sets *pad_to* equal to the length of the\n", + " input signal (i.e. no padding).\n", + " \n", + " scale : [ 'default' | 'linear' | 'dB' ]\n", + " The scaling of the values in the *spec*. 'linear' is no scaling.\n", + " 'dB' returns the values in dB scale, i.e., the dB amplitude\n", + " (20 * log10). 'default' is 'linear'.\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 1-D array\n", + " The values for the magnitude spectrum before scaling (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *spectrum*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " :func:`psd`\n", + " :func:`psd` plots the power spectral density.`.\n", + " \n", + " :func:`angle_spectrum`\n", + " :func:`angle_spectrum` plots the angles of the corresponding\n", + " frequencies.\n", + " \n", + " :func:`phase_spectrum`\n", + " :func:`phase_spectrum` plots the phase (unwrapped angle) of the\n", + " corresponding frequencies.\n", + " \n", + " :func:`specgram`\n", + " :func:`specgram` can plot the magnitude spectrum of segments within\n", + " the signal in a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " margins(*args, **kw)\n", + " Set or retrieve autoscaling margins.\n", + " \n", + " signatures::\n", + " \n", + " margins()\n", + " \n", + " returns xmargin, ymargin\n", + " \n", + " ::\n", + " \n", + " margins(margin)\n", + " \n", + " margins(xmargin, ymargin)\n", + " \n", + " margins(x=xmargin, y=ymargin)\n", + " \n", + " margins(..., tight=False)\n", + " \n", + " All three forms above set the xmargin and ymargin parameters.\n", + " All keyword parameters are optional. A single argument\n", + " specifies both xmargin and ymargin. The *tight* parameter\n", + " is passed to :meth:`autoscale_view`, which is executed after\n", + " a margin is changed; the default here is *True*, on the\n", + " assumption that when margins are specified, no additional\n", + " padding to match tick marks is usually desired. Setting\n", + " *tight* to *None* will preserve the previous setting.\n", + " \n", + " Specifying any margin changes only the autoscaling; for example,\n", + " if *xmargin* is not None, then *xmargin* times the X data\n", + " interval will be added to each end of that interval before\n", + " it is used in autoscaling.\n", + " \n", + " matshow(A, fignum=None, **kw)\n", + " Display an array as a matrix in a new figure window.\n", + " \n", + " The origin is set at the upper left hand corner and rows (first\n", + " dimension of the array) are displayed horizontally. The aspect\n", + " ratio of the figure window is that of the array, unless this would\n", + " make an excessively short or narrow figure.\n", + " \n", + " Tick labels for the xaxis are placed on top.\n", + " \n", + " With the exception of *fignum*, keyword arguments are passed to\n", + " :func:`~matplotlib.pyplot.imshow`. You may set the *origin*\n", + " kwarg to \"lower\" if you want the first row in the array to be\n", + " at the bottom instead of the top.\n", + " \n", + " \n", + " *fignum*: [ None | integer | False ]\n", + " By default, :func:`matshow` creates a new figure window with\n", + " automatic numbering. If *fignum* is given as an integer, the\n", + " created figure will use this figure number. Because of how\n", + " :func:`matshow` tries to set the figure aspect ratio to be the\n", + " one of the array, if you provide the number of an already\n", + " existing figure, strange things may happen.\n", + " \n", + " If *fignum* is *False* or 0, a new figure window will **NOT** be created.\n", + " \n", + " minorticks_off()\n", + " Remove minor ticks from the current plot.\n", + " \n", + " minorticks_on()\n", + " Display minor ticks on the current plot.\n", + " \n", + " Displaying minor ticks reduces performance; turn them off using\n", + " minorticks_off() if drawing speed is a problem.\n", + " \n", + " nipy_spectral()\n", + " set the default colormap to nipy_spectral and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " over(func, *args, **kwargs)\n", + " .. deprecated:: 2.0\n", + " pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " \n", + " Call a function with hold(True).\n", + " \n", + " Calls::\n", + " \n", + " func(*args, **kwargs)\n", + " \n", + " with ``hold(True)`` and then restores the hold state.\n", + " \n", + " pause(interval)\n", + " Pause for *interval* seconds.\n", + " \n", + " If there is an active figure, it will be updated and displayed before the\n", + " pause, and the GUI event loop (if any) will run during the pause.\n", + " \n", + " This can be used for crude animation. For more complex animation, see\n", + " :mod:`matplotlib.animation`.\n", + " \n", + " Note\n", + " ----\n", + " This function is experimental; its behavior may be changed or extended in a\n", + " future release.\n", + " \n", + " pcolor(*args, **kwargs)\n", + " Create a pseudocolor plot of a 2-D array.\n", + " \n", + " Call signatures::\n", + " \n", + " pcolor(C, **kwargs)\n", + " pcolor(X, Y, C, **kwargs)\n", + " \n", + " pcolor can be very slow for large arrays; consider\n", + " using the similar but much faster\n", + " :func:`~matplotlib.pyplot.pcolormesh` instead.\n", + " \n", + " Parameters\n", + " ----------\n", + " C : array_like\n", + " An array of color values.\n", + " \n", + " X, Y : array_like, optional\n", + " If given, specify the (x, y) coordinates of the colored\n", + " quadrilaterals; the quadrilateral for ``C[i,j]`` has corners at::\n", + " \n", + " (X[i, j], Y[i, j]),\n", + " (X[i, j+1], Y[i, j+1]),\n", + " (X[i+1, j], Y[i+1, j]),\n", + " (X[i+1, j+1], Y[i+1, j+1])\n", + " \n", + " Ideally the dimensions of ``X`` and ``Y`` should be one greater\n", + " than those of ``C``; if the dimensions are the same, then the last\n", + " row and column of ``C`` will be ignored.\n", + " \n", + " Note that the column index corresponds to the\n", + " x-coordinate, and the row index corresponds to y; for\n", + " details, see the :ref:`Grid Orientation\n", + " ` section below.\n", + " \n", + " If either or both of ``X`` and ``Y`` are 1-D arrays or column\n", + " vectors, they will be expanded as needed into the appropriate 2-D\n", + " arrays, making a rectangular grid.\n", + " \n", + " cmap : `~matplotlib.colors.Colormap`, optional, default: None\n", + " If `None`, default to rc settings.\n", + " \n", + " norm : `matplotlib.colors.Normalize`, optional, default: None\n", + " An instance is used to scale luminance data to (0, 1).\n", + " If `None`, defaults to :func:`normalize`.\n", + " \n", + " vmin, vmax : scalar, optional, default: None\n", + " ``vmin`` and ``vmax`` are used in conjunction with ``norm`` to\n", + " normalize luminance data. If either is `None`, it is autoscaled to\n", + " the respective min or max of the color array ``C``. If not `None`,\n", + " ``vmin`` or ``vmax`` passed in here override any pre-existing\n", + " values supplied in the ``norm`` instance.\n", + " \n", + " edgecolors : {None, 'none', color, color sequence}\n", + " If None, the rc setting is used by default.\n", + " If 'none', edges will not be visible.\n", + " An mpl color or sequence of colors will set the edge color.\n", + " \n", + " alpha : scalar, optional, default: None\n", + " The alpha blending value, between 0 (transparent) and 1 (opaque).\n", + " \n", + " snap : bool, optional, default: False\n", + " Whether to snap the mesh to pixel boundaries.\n", + " \n", + " Returns\n", + " -------\n", + " collection : `matplotlib.collections.Collection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " antialiaseds : bool, optional, default: False\n", + " The default ``antialiaseds`` is False if the default\n", + " ``edgecolors=\"none\"`` is used. This eliminates artificial lines\n", + " at patch boundaries, and works regardless of the value of alpha.\n", + " If ``edgecolors`` is not \"none\", then the default ``antialiaseds``\n", + " is taken from ``rcParams['patch.antialiased']``, which defaults to\n", + " True. Stroking the edges may be preferred if ``alpha`` is 1, but\n", + " will cause artifacts otherwise.\n", + " \n", + " **kwargs :\n", + " \n", + " Any unused keyword arguments are passed along to the\n", + " `~matplotlib.collections.PolyCollection` constructor:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " pcolormesh : for an explanation of the differences between\n", + " pcolor and pcolormesh.\n", + " \n", + " Notes\n", + " -----\n", + " .. _axes-pcolor-grid-orientation:\n", + " \n", + " ``X``, ``Y`` and ``C`` may be masked arrays. If either C[i, j], or one\n", + " of the vertices surrounding C[i,j] (``X`` or ``Y`` at [i, j], [i+1, j],\n", + " [i, j+1], [i+1, j+1]) is masked, nothing is plotted.\n", + " \n", + " The grid orientation follows the MATLAB convention: an array ``C`` with\n", + " shape (nrows, ncolumns) is plotted with the column number as ``X`` and\n", + " the row number as ``Y``, increasing up; hence it is plotted the way the\n", + " array would be printed, except that the ``Y`` axis is reversed. That\n", + " is, ``C`` is taken as ``C`` (y, x).\n", + " \n", + " Similarly for :func:`meshgrid`::\n", + " \n", + " x = np.arange(5)\n", + " y = np.arange(3)\n", + " X, Y = np.meshgrid(x, y)\n", + " \n", + " is equivalent to::\n", + " \n", + " X = array([[0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4]])\n", + " \n", + " Y = array([[0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 1],\n", + " [2, 2, 2, 2, 2]])\n", + " \n", + " so if you have::\n", + " \n", + " C = rand(len(x), len(y))\n", + " \n", + " then you need to transpose C::\n", + " \n", + " pcolor(X, Y, C.T)\n", + " \n", + " or::\n", + " \n", + " pcolor(C.T)\n", + " \n", + " MATLAB :func:`pcolor` always discards the last row and column of ``C``,\n", + " but Matplotlib displays the last row and column if ``X`` and ``Y`` are\n", + " not specified, or if ``X`` and ``Y`` have one more row and column than\n", + " ``C``.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " pcolormesh(*args, **kwargs)\n", + " Plot a quadrilateral mesh.\n", + " \n", + " Call signatures::\n", + " \n", + " pcolormesh(C)\n", + " pcolormesh(X, Y, C)\n", + " pcolormesh(C, **kwargs)\n", + " \n", + " Create a pseudocolor plot of a 2-D array.\n", + " \n", + " pcolormesh is similar to :func:`~matplotlib.pyplot.pcolor`,\n", + " but uses a different mechanism and returns a different\n", + " object; pcolor returns a\n", + " :class:`~matplotlib.collections.PolyCollection` but pcolormesh\n", + " returns a\n", + " :class:`~matplotlib.collections.QuadMesh`. It is much faster,\n", + " so it is almost always preferred for large arrays.\n", + " \n", + " *C* may be a masked array, but *X* and *Y* may not. Masked\n", + " array support is implemented via *cmap* and *norm*; in\n", + " contrast, :func:`~matplotlib.pyplot.pcolor` simply does not\n", + " draw quadrilaterals with masked colors or vertices.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A :class:`matplotlib.colors.Colormap` instance. If *None*, use\n", + " rc settings.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance is used to\n", + " scale luminance data to 0,1. If *None*, defaults to\n", + " :func:`normalize`.\n", + " \n", + " *vmin*/*vmax*: [ *None* | scalar ]\n", + " *vmin* and *vmax* are used in conjunction with *norm* to\n", + " normalize luminance data. If either is *None*, it\n", + " is autoscaled to the respective min or max\n", + " of the color array *C*. If not *None*, *vmin* or\n", + " *vmax* passed in here override any pre-existing values\n", + " supplied in the *norm* instance.\n", + " \n", + " *shading*: [ 'flat' | 'gouraud' ]\n", + " 'flat' indicates a solid color for each quad. When\n", + " 'gouraud', each quad will be Gouraud shaded. When gouraud\n", + " shading, edgecolors is ignored.\n", + " \n", + " *edgecolors*: [*None* | ``'None'`` | ``'face'`` | color |\n", + " color sequence]\n", + " \n", + " If *None*, the rc setting is used by default.\n", + " \n", + " If ``'None'``, edges will not be visible.\n", + " \n", + " If ``'face'``, edges will have the same color as the faces.\n", + " \n", + " An mpl color or sequence of colors will set the edge color\n", + " \n", + " *alpha*: ``0 <= scalar <= 1`` or *None*\n", + " the alpha blending value\n", + " \n", + " Return value is a :class:`matplotlib.collections.QuadMesh`\n", + " object.\n", + " \n", + " kwargs can be used to control the\n", + " :class:`matplotlib.collections.QuadMesh` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.pcolor`\n", + " For an explanation of the grid orientation\n", + " (:ref:`Grid Orientation `)\n", + " and the expansion of 1-D *X* and/or *Y* to 2-D arrays.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " phase_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, hold=None, data=None, **kwargs)\n", + " Plot the phase spectrum.\n", + " \n", + " Call signature::\n", + " \n", + " phase_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,\n", + " pad_to=None, sides='default', **kwargs)\n", + " \n", + " Compute the phase spectrum (unwrapped angle spectrum) of *x*.\n", + " Data is padded to a length of *pad_to* and the windowing function\n", + " *window* is applied to the signal.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. While not increasing the actual resolution of\n", + " the spectrum (the minimum distance between resolvable peaks),\n", + " this can give more points in the plot, allowing for more\n", + " detail. This corresponds to the *n* parameter in the call to fft().\n", + " The default is None, which sets *pad_to* equal to the length of the\n", + " input signal (i.e. no padding).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 1-D array\n", + " The values for the phase spectrum in radians (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *spectrum*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " :func:`magnitude_spectrum`\n", + " :func:`magnitude_spectrum` plots the magnitudes of the\n", + " corresponding frequencies.\n", + " \n", + " :func:`angle_spectrum`\n", + " :func:`angle_spectrum` plots the wrapped version of this function.\n", + " \n", + " :func:`specgram`\n", + " :func:`specgram` can plot the phase spectrum of segments within the\n", + " signal in a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, hold=None, data=None)\n", + " Plot a pie chart.\n", + " \n", + " Make a pie chart of array *x*. The fractional area of each\n", + " wedge is given by ``x/sum(x)``. If ``sum(x) <= 1``, then the\n", + " values of x give the fractional area directly and the array\n", + " will not be normalized. The wedges are plotted\n", + " counterclockwise, by default starting from the x-axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array-like\n", + " The input array used to make the pie chart.\n", + " \n", + " explode : array-like, optional, default: None\n", + " If not *None*, is a ``len(x)`` array which specifies the\n", + " fraction of the radius with which to offset each wedge.\n", + " \n", + " labels : list, optional, default: None\n", + " A sequence of strings providing the labels for each wedge\n", + " \n", + " colors : array-like, optional, default: None\n", + " A sequence of matplotlib color args through which the pie chart\n", + " will cycle. If `None`, will use the colors in the currently\n", + " active cycle.\n", + " \n", + " autopct : None (default), string, or function, optional\n", + " If not *None*, is a string or function used to label the wedges\n", + " with their numeric value. The label will be placed inside the\n", + " wedge. If it is a format string, the label will be ``fmt%pct``.\n", + " If it is a function, it will be called.\n", + " \n", + " pctdistance : float, optional, default: 0.6\n", + " The ratio between the center of each pie slice and the\n", + " start of the text generated by *autopct*. Ignored if\n", + " *autopct* is *None*.\n", + " \n", + " shadow : bool, optional, default: False\n", + " Draw a shadow beneath the pie.\n", + " \n", + " labeldistance : float, optional, default: 1.1\n", + " The radial distance at which the pie labels are drawn\n", + " \n", + " startangle : float, optional, default: None\n", + " If not *None*, rotates the start of the pie chart by *angle*\n", + " degrees counterclockwise from the x-axis.\n", + " \n", + " radius : float, optional, default: None\n", + " The radius of the pie, if *radius* is *None* it will be set to 1.\n", + " \n", + " counterclock : bool, optional, default: True\n", + " Specify fractions direction, clockwise or counterclockwise.\n", + " \n", + " wedgeprops : dict, optional, default: None\n", + " Dict of arguments passed to the wedge objects making the pie.\n", + " For example, you can pass in``wedgeprops = {'linewidth': 3}``\n", + " to set the width of the wedge border lines equal to 3.\n", + " For more details, look at the doc/arguments of the wedge object.\n", + " By default ``clip_on=False``.\n", + " \n", + " textprops : dict, optional, default: None\n", + " Dict of arguments to pass to the text objects.\n", + " \n", + " center : list of float, optional, default: (0, 0)\n", + " Center position of the chart. Takes value (0, 0) or is a\n", + " sequence of 2 scalars.\n", + " \n", + " frame : bool, optional, default: False\n", + " Plot axes frame with the chart if true.\n", + " \n", + " rotatelabels : bool, optional, default: False\n", + " Rotate each label to the angle of the corresponding slice if true.\n", + " \n", + " Returns\n", + " -------\n", + " patches : list\n", + " A sequence of :class:`matplotlib.patches.Wedge` instances\n", + " \n", + " texts : list\n", + " A is a list of the label :class:`matplotlib.text.Text` instances.\n", + " \n", + " autotexts : list\n", + " A is a list of :class:`~matplotlib.text.Text` instances for the\n", + " numeric labels. Is returned only if parameter *autopct* is\n", + " not *None*.\n", + " \n", + " Notes\n", + " -----\n", + " The pie chart will probably look best if the figure and axes are\n", + " square, or the Axes aspect is equal.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'explode', 'labels', 'x'.\n", + " \n", + " pink()\n", + " set the default colormap to pink and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " plasma()\n", + " set the default colormap to plasma and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " plot(*args, **kwargs)\n", + " Plot lines and/or markers to the\n", + " :class:`~matplotlib.axes.Axes`. *args* is a variable length\n", + " argument, allowing for multiple *x*, *y* pairs with an\n", + " optional format string. For example, each of the following is\n", + " legal::\n", + " \n", + " plot(x, y) # plot x and y using default line style and color\n", + " plot(x, y, 'bo') # plot x and y using blue circle markers\n", + " plot(y) # plot y using x as index array 0..N-1\n", + " plot(y, 'r+') # ditto, but with red plusses\n", + " \n", + " If *x* and/or *y* is 2-dimensional, then the corresponding columns\n", + " will be plotted.\n", + " \n", + " If used with labeled data, make sure that the color spec is not\n", + " included as an element in data, as otherwise the last case\n", + " ``plot(\"v\",\"r\", data={\"v\":..., \"r\":...)``\n", + " can be interpreted as the first case which would do ``plot(v, r)``\n", + " using the default line style and color.\n", + " \n", + " If not used with labeled data (i.e., without a data argument),\n", + " an arbitrary number of *x*, *y*, *fmt* groups can be specified, as in::\n", + " \n", + " a.plot(x1, y1, 'g^', x2, y2, 'g-')\n", + " \n", + " Return value is a list of lines that were added.\n", + " \n", + " By default, each line is assigned a different style specified by a\n", + " 'style cycle'. To change this behavior, you can edit the\n", + " axes.prop_cycle rcParam.\n", + " \n", + " The following format string characters are accepted to control\n", + " the line style or marker:\n", + " \n", + " ================ ===============================\n", + " character description\n", + " ================ ===============================\n", + " ``'-'`` solid line style\n", + " ``'--'`` dashed line style\n", + " ``'-.'`` dash-dot line style\n", + " ``':'`` dotted line style\n", + " ``'.'`` point marker\n", + " ``','`` pixel marker\n", + " ``'o'`` circle marker\n", + " ``'v'`` triangle_down marker\n", + " ``'^'`` triangle_up marker\n", + " ``'<'`` triangle_left marker\n", + " ``'>'`` triangle_right marker\n", + " ``'1'`` tri_down marker\n", + " ``'2'`` tri_up marker\n", + " ``'3'`` tri_left marker\n", + " ``'4'`` tri_right marker\n", + " ``'s'`` square marker\n", + " ``'p'`` pentagon marker\n", + " ``'*'`` star marker\n", + " ``'h'`` hexagon1 marker\n", + " ``'H'`` hexagon2 marker\n", + " ``'+'`` plus marker\n", + " ``'x'`` x marker\n", + " ``'D'`` diamond marker\n", + " ``'d'`` thin_diamond marker\n", + " ``'|'`` vline marker\n", + " ``'_'`` hline marker\n", + " ================ ===============================\n", + " \n", + " \n", + " The following color abbreviations are supported:\n", + " \n", + " ========== ========\n", + " character color\n", + " ========== ========\n", + " 'b' blue\n", + " 'g' green\n", + " 'r' red\n", + " 'c' cyan\n", + " 'm' magenta\n", + " 'y' yellow\n", + " 'k' black\n", + " 'w' white\n", + " ========== ========\n", + " \n", + " In addition, you can specify colors in many weird and\n", + " wonderful ways, including full names (``'green'``), hex\n", + " strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\n", + " grayscale intensities as a string (``'0.8'``). Of these, the\n", + " string specifications can be used in place of a ``fmt`` group,\n", + " but the tuple forms can be used only as ``kwargs``.\n", + " \n", + " Line styles and colors are combined in a single format string, as in\n", + " ``'bo'`` for blue circles.\n", + " \n", + " The *kwargs* can be used to set line properties (any property that has\n", + " a ``set_*`` method). You can use this to set a line label (for auto\n", + " legends), linewidth, anitialising, marker face color, etc. Here is an\n", + " example::\n", + " \n", + " plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n", + " plot([1,2,3], [1,4,9], 'rs', label='line 2')\n", + " axis([0, 4, 0, 10])\n", + " legend()\n", + " \n", + " If you make multiple lines with one plot command, the kwargs\n", + " apply to all those lines, e.g.::\n", + " \n", + " plot(x1, y1, x2, y2, antialiased=False)\n", + " \n", + " Neither line will be antialiased.\n", + " \n", + " You do not need to use format strings, which are just\n", + " abbreviations. All of the line properties can be controlled\n", + " by keyword arguments. For example, you can set the color,\n", + " marker, linestyle, and markercolor with::\n", + " \n", + " plot(x, y, color='green', linestyle='dashed', marker='o',\n", + " markerfacecolor='blue', markersize=12).\n", + " \n", + " See :class:`~matplotlib.lines.Line2D` for details.\n", + " \n", + " The kwargs are :class:`~matplotlib.lines.Line2D` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " kwargs *scalex* and *scaley*, if defined, are passed on to\n", + " :meth:`~matplotlib.axes.Axes.autoscale_view` to determine\n", + " whether the *x* and *y* axes are autoscaled; the default is\n", + " *True*.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, hold=None, data=None, **kwargs)\n", + " A plot with data that contains dates.\n", + " \n", + " Similar to the :func:`~matplotlib.pyplot.plot` command, except\n", + " the *x* or *y* (or both) data is considered to be dates, and the\n", + " axis is labeled accordingly.\n", + " \n", + " *x* and/or *y* can be a sequence of dates represented as float\n", + " days since 0001-01-01 UTC.\n", + " \n", + " Note if you are using custom date tickers and formatters, it\n", + " may be necessary to set the formatters/locators after the call\n", + " to :meth:`plot_date` since :meth:`plot_date` will set the\n", + " default tick locator to\n", + " :class:`matplotlib.dates.AutoDateLocator` (if the tick\n", + " locator is not already set to a\n", + " :class:`matplotlib.dates.DateLocator` instance) and the\n", + " default tick formatter to\n", + " :class:`matplotlib.dates.AutoDateFormatter` (if the tick\n", + " formatter is not already set to a\n", + " :class:`matplotlib.dates.DateFormatter` instance).\n", + " \n", + " \n", + " Parameters\n", + " ----------\n", + " fmt : string\n", + " The plot format string.\n", + " \n", + " tz : [ *None* | timezone string | :class:`tzinfo` instance]\n", + " The time zone to use in labeling dates. If *None*, defaults to rc\n", + " value.\n", + " \n", + " xdate : boolean\n", + " If *True*, the *x*-axis will be labeled with dates.\n", + " \n", + " ydate : boolean\n", + " If *True*, the *y*-axis will be labeled with dates.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " lines\n", + " \n", + " \n", + " See Also\n", + " --------\n", + " matplotlib.dates : helper functions on dates\n", + " matplotlib.dates.date2num : how to convert dates to num\n", + " matplotlib.dates.num2date : how to convert num to dates\n", + " matplotlib.dates.drange : how floating point dates\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " plotfile(fname, cols=(0,), plotfuncs=None, comments='#', skiprows=0, checkrows=5, delimiter=',', names=None, subplots=True, newfig=True, **kwargs)\n", + " Plot the data in a file.\n", + " \n", + " *cols* is a sequence of column identifiers to plot. An identifier\n", + " is either an int or a string. If it is an int, it indicates the\n", + " column number. If it is a string, it indicates the column header.\n", + " matplotlib will make column headers lower case, replace spaces with\n", + " underscores, and remove all illegal characters; so ``'Adj Close*'``\n", + " will have name ``'adj_close'``.\n", + " \n", + " - If len(*cols*) == 1, only that column will be plotted on the *y* axis.\n", + " \n", + " - If len(*cols*) > 1, the first element will be an identifier for\n", + " data for the *x* axis and the remaining elements will be the\n", + " column indexes for multiple subplots if *subplots* is *True*\n", + " (the default), or for lines in a single subplot if *subplots*\n", + " is *False*.\n", + " \n", + " *plotfuncs*, if not *None*, is a dictionary mapping identifier to\n", + " an :class:`~matplotlib.axes.Axes` plotting function as a string.\n", + " Default is 'plot', other choices are 'semilogy', 'fill', 'bar',\n", + " etc. You must use the same type of identifier in the *cols*\n", + " vector as you use in the *plotfuncs* dictionary, e.g., integer\n", + " column numbers in both or column names in both. If *subplots*\n", + " is *False*, then including any function such as 'semilogy'\n", + " that changes the axis scaling will set the scaling for all\n", + " columns.\n", + " \n", + " *comments*, *skiprows*, *checkrows*, *delimiter*, and *names*\n", + " are all passed on to :func:`matplotlib.pylab.csv2rec` to\n", + " load the data into a record array.\n", + " \n", + " If *newfig* is *True*, the plot always will be made in a new figure;\n", + " if *False*, it will be made in the current figure if one exists,\n", + " else in a new figure.\n", + " \n", + " kwargs are passed on to plotting functions.\n", + " \n", + " Example usage::\n", + " \n", + " # plot the 2nd and 4th column against the 1st in two subplots\n", + " plotfile(fname, (0,1,3))\n", + " \n", + " # plot using column names; specify an alternate plot type for volume\n", + " plotfile(fname, ('date', 'volume', 'adj_close'),\n", + " plotfuncs={'volume': 'semilogy'})\n", + " \n", + " Note: plotfile is intended as a convenience for quickly plotting\n", + " data from flat files; it is not intended as an alternative\n", + " interface to general plotting with pyplot or matplotlib.\n", + " \n", + " plotting()\n", + " ============================ ======================================================================================================================================================================================\n", + " Function Description \n", + " ============================ ======================================================================================================================================================================================\n", + " `acorr` Plot the autocorrelation of `x`. \n", + " `angle_spectrum` Plot the angle spectrum. \n", + " `annotate` Annotate the point ``xy`` with text ``s``. \n", + " `arrow` Add an arrow to the axes. \n", + " `autoscale` Autoscale the axis view to the data (toggle). \n", + " `axes` Add an axes to the figure. \n", + " `axhline` Add a horizontal line across the axis. \n", + " `axhspan` Add a horizontal span (rectangle) across the axis. \n", + " `axis` Convenience method to get or set axis properties. \n", + " `axvline` Add a vertical line across the axes. \n", + " `axvspan` Add a vertical span (rectangle) across the axes. \n", + " `bar` Make a bar plot. \n", + " `barbs` Plot a 2-D field of barbs. \n", + " `barh` Make a horizontal bar plot. \n", + " `box` Turn the axes box on or off. \n", + " `boxplot` Make a box and whisker plot. \n", + " `broken_barh` Plot horizontal bars. \n", + " `cla` Clear the current axes. \n", + " `clabel` Label a contour plot. \n", + " `clf` Clear the current figure. \n", + " `clim` Set the color limits of the current image. \n", + " `close` Close a figure window. \n", + " `cohere` Plot the coherence between *x* and *y*. \n", + " `colorbar` Add a colorbar to a plot. \n", + " `contour` Plot contours. \n", + " `contourf` Plot contours. \n", + " `csd` Plot the cross-spectral density. \n", + " `delaxes` Remove an axes from the current figure. \n", + " `draw` Redraw the current figure. \n", + " `errorbar` Plot an errorbar graph. \n", + " `eventplot` Plot identical parallel lines at the given positions. \n", + " `figimage` Adds a non-resampled image to the figure. \n", + " `figlegend` Place a legend in the figure. \n", + " `fignum_exists` \n", + " `figtext` Add text to figure. \n", + " `figure` Creates a new figure. \n", + " `fill` Plot filled polygons. \n", + " `fill_between` Make filled polygons between two curves. \n", + " `fill_betweenx` Make filled polygons between two horizontal curves. \n", + " `findobj` Find artist objects. \n", + " `gca` Get the current :class:`~matplotlib.axes.Axes` instance on the current figure matching the given keyword args, or create one. \n", + " `gcf` Get a reference to the current figure. \n", + " `gci` Get the current colorable artist. \n", + " `get_figlabels` Return a list of existing figure labels. \n", + " `get_fignums` Return a list of existing figure numbers. \n", + " `grid` Turn the axes grids on or off. \n", + " `hexbin` Make a hexagonal binning plot. \n", + " `hist` Plot a histogram. \n", + " `hist2d` Make a 2D histogram plot. \n", + " `hlines` Plot horizontal lines at each `y` from `xmin` to `xmax`. \n", + " `hold` .. \n", + " `imread` Read an image from a file into an array. \n", + " `imsave` Save an array as in image file. \n", + " `imshow` Display an image on the axes. \n", + " `install_repl_displayhook` Install a repl display hook so that any stale figure are automatically redrawn when control is returned to the repl. \n", + " `ioff` Turn interactive mode off. \n", + " `ion` Turn interactive mode on. \n", + " `ishold` .. \n", + " `isinteractive` Return status of interactive mode. \n", + " `legend` Places a legend on the axes. \n", + " `locator_params` Control behavior of tick locators. \n", + " `loglog` Make a plot with log scaling on both the *x* and *y* axis. \n", + " `magnitude_spectrum` Plot the magnitude spectrum. \n", + " `margins` Set or retrieve autoscaling margins. \n", + " `matshow` Display an array as a matrix in a new figure window. \n", + " `minorticks_off` Remove minor ticks from the current plot. \n", + " `minorticks_on` Display minor ticks on the current plot. \n", + " `over` .. \n", + " `pause` Pause for *interval* seconds. \n", + " `pcolor` Create a pseudocolor plot of a 2-D array. \n", + " `pcolormesh` Plot a quadrilateral mesh. \n", + " `phase_spectrum` Plot the phase spectrum. \n", + " `pie` Plot a pie chart. \n", + " `plot` Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`. \n", + " `plot_date` A plot with data that contains dates. \n", + " `plotfile` Plot the data in a file. \n", + " `polar` Make a polar plot. \n", + " `psd` Plot the power spectral density. \n", + " `quiver` Plot a 2-D field of arrows. \n", + " `quiverkey` Add a key to a quiver plot. \n", + " `rc` Set the current rc params. \n", + " `rc_context` Return a context manager for managing rc settings. \n", + " `rcdefaults` Restore the rc params from Matplotlib's internal defaults. \n", + " `rgrids` Get or set the radial gridlines on a polar plot. \n", + " `savefig` Save the current figure. \n", + " `sca` Set the current Axes instance to *ax*. \n", + " `scatter` Make a scatter plot of `x` vs `y`. \n", + " `sci` Set the current image. \n", + " `semilogx` Make a plot with log scaling on the *x* axis. \n", + " `semilogy` Make a plot with log scaling on the *y* axis. \n", + " `set_cmap` Set the default colormap. \n", + " `setp` Set a property on an artist object. \n", + " `show` Display a figure. \n", + " `specgram` Plot a spectrogram. \n", + " `spy` Plot the sparsity pattern on a 2-D array. \n", + " `stackplot` Draws a stacked area plot. \n", + " `stem` Create a stem plot. \n", + " `step` Make a step plot. \n", + " `streamplot` Draws streamlines of a vector flow. \n", + " `subplot` Return a subplot axes at the given grid position. \n", + " `subplot2grid` Create an axis at specific location inside a regular grid. \n", + " `subplot_tool` Launch a subplot tool window for a figure. \n", + " `subplots` Create a figure and a set of subplots This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.\n", + " `subplots_adjust` Tune the subplot layout. \n", + " `suptitle` Add a centered title to the figure. \n", + " `switch_backend` Switch the default backend. \n", + " `table` Add a table to the current axes. \n", + " `text` Add text to the axes. \n", + " `thetagrids` Get or set the theta locations of the gridlines in a polar plot. \n", + " `tick_params` Change the appearance of ticks and tick labels. \n", + " `ticklabel_format` Change the `~matplotlib.ticker.ScalarFormatter` used by default for linear axes. \n", + " `tight_layout` Automatically adjust subplot parameters to give specified padding. \n", + " `title` Set a title of the current axes. \n", + " `tricontour` Draw contours on an unstructured triangular grid. \n", + " `tricontourf` Draw contours on an unstructured triangular grid. \n", + " `tripcolor` Create a pseudocolor plot of an unstructured triangular grid. \n", + " `triplot` Draw a unstructured triangular grid as lines and/or markers. \n", + " `twinx` Make a second axes that shares the *x*-axis. \n", + " `twiny` Make a second axes that shares the *y*-axis. \n", + " `uninstall_repl_displayhook` Uninstalls the matplotlib display hook. \n", + " `violinplot` Make a violin plot. \n", + " `vlines` Plot vertical lines. \n", + " `xcorr` Plot the cross correlation between *x* and *y*. \n", + " `xkcd` Turns on `xkcd `_ sketch-style drawing mode. \n", + " `xlabel` Set the *x* axis label of the current axis. \n", + " `xlim` Get or set the *x* limits of the current axes. \n", + " `xscale` Set the scaling of the *x*-axis. \n", + " `xticks` Get or set the *x*-limits of the current tick locations and labels. \n", + " `ylabel` Set the *y* axis label of the current axis. \n", + " `ylim` Get or set the *y*-limits of the current axes. \n", + " `yscale` Set the scaling of the *y*-axis. \n", + " `yticks` Get or set the *y*-limits of the current tick locations and labels. \n", + " ============================ ======================================================================================================================================================================================\n", + " \n", + " polar(*args, **kwargs)\n", + " Make a polar plot.\n", + " \n", + " call signature::\n", + " \n", + " polar(theta, r, **kwargs)\n", + " \n", + " Multiple *theta*, *r* arguments are supported, with format\n", + " strings, as in :func:`~matplotlib.pyplot.plot`.\n", + " \n", + " prism()\n", + " set the default colormap to prism and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " psd(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, hold=None, data=None, **kwargs)\n", + " Plot the power spectral density.\n", + " \n", + " Call signature::\n", + " \n", + " psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,\n", + " window=mlab.window_hanning, noverlap=0, pad_to=None,\n", + " sides='default', scale_by_freq=None, return_line=None, **kwargs)\n", + " \n", + " The power spectral density :math:`P_{xx}` by Welch's average\n", + " periodogram method. The vector *x* is divided into *NFFT* length\n", + " segments. Each segment is detrended by function *detrend* and\n", + " windowed by function *window*. *noverlap* gives the length of\n", + " the overlap between segments. The :math:`|\\mathrm{fft}(i)|^2`\n", + " of each segment :math:`i` are averaged to compute :math:`P_{xx}`,\n", + " with a scaling to correct for power loss due to windowing.\n", + " \n", + " If len(*x*) < *NFFT*, it will be zero padded to *NFFT*.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between segments.\n", + " The default value is 0 (no overlap).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " return_line : bool\n", + " Whether to include the line object plotted in the returned values.\n", + " Default is False.\n", + " \n", + " Returns\n", + " -------\n", + " Pxx : 1-D array\n", + " The values for the power spectrum `P_{xx}` before scaling\n", + " (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *Pxx*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function.\n", + " Only returned if *return_line* is True.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " For plotting, the power is plotted as\n", + " :math:`10\\log_{10}(P_{xx})` for decibels, though *Pxx* itself\n", + " is returned.\n", + " \n", + " References\n", + " ----------\n", + " Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,\n", + " John Wiley & Sons (1986)\n", + " \n", + " See Also\n", + " --------\n", + " :func:`specgram`\n", + " :func:`specgram` differs in the default overlap; in not returning\n", + " the mean of the segment periodograms; in returning the times of the\n", + " segments; and in plotting a colormap instead of a line.\n", + " \n", + " :func:`magnitude_spectrum`\n", + " :func:`magnitude_spectrum` plots the magnitude spectrum.\n", + " \n", + " :func:`csd`\n", + " :func:`csd` plots the spectral density between two signals.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " quiver(*args, **kw)\n", + " Plot a 2-D field of arrows.\n", + " \n", + " Call signatures::\n", + " \n", + " quiver(U, V, **kw)\n", + " quiver(U, V, C, **kw)\n", + " quiver(X, Y, U, V, **kw)\n", + " quiver(X, Y, U, V, C, **kw)\n", + " \n", + " *U* and *V* are the arrow data, *X* and *Y* set the location of the\n", + " arrows, and *C* sets the color of the arrows. These arguments may be 1-D or\n", + " 2-D arrays or sequences.\n", + " \n", + " If *X* and *Y* are absent, they will be generated as a uniform grid.\n", + " If *U* and *V* are 2-D arrays and *X* and *Y* are 1-D, and if ``len(X)`` and\n", + " ``len(Y)`` match the column and row dimensions of *U*, then *X* and *Y* will be\n", + " expanded with :func:`numpy.meshgrid`.\n", + " \n", + " The default settings auto-scales the length of the arrows to a reasonable size.\n", + " To change this behavior see the *scale* and *scale_units* kwargs.\n", + " \n", + " The defaults give a slightly swept-back arrow; to make the head a\n", + " triangle, make *headaxislength* the same as *headlength*. To make the\n", + " arrow more pointed, reduce *headwidth* or increase *headlength* and\n", + " *headaxislength*. To make the head smaller relative to the shaft,\n", + " scale down all the head parameters. You will probably do best to leave\n", + " minshaft alone.\n", + " \n", + " *linewidths* and *edgecolors* can be used to customize the arrow\n", + " outlines.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : 1D or 2D array, sequence, optional\n", + " The x coordinates of the arrow locations\n", + " Y : 1D or 2D array, sequence, optional\n", + " The y coordinates of the arrow locations\n", + " U : 1D or 2D array or masked array, sequence\n", + " The x components of the arrow vectors\n", + " V : 1D or 2D array or masked array, sequence\n", + " The y components of the arrow vectors\n", + " C : 1D or 2D array, sequence, optional\n", + " The arrow colors\n", + " units : [ 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y' | 'xy' ]\n", + " The arrow dimensions (except for *length*) are measured in multiples of\n", + " this unit.\n", + " \n", + " 'width' or 'height': the width or height of the axis\n", + " \n", + " 'dots' or 'inches': pixels or inches, based on the figure dpi\n", + " \n", + " 'x', 'y', or 'xy': respectively *X*, *Y*, or :math:`\\sqrt{X^2 + Y^2}`\n", + " in data units\n", + " \n", + " The arrows scale differently depending on the units. For\n", + " 'x' or 'y', the arrows get larger as one zooms in; for other\n", + " units, the arrow size is independent of the zoom state. For\n", + " 'width or 'height', the arrow size increases with the width and\n", + " height of the axes, respectively, when the window is resized;\n", + " for 'dots' or 'inches', resizing does not change the arrows.\n", + " angles : [ 'uv' | 'xy' ], array, optional\n", + " Method for determining the angle of the arrows. Default is 'uv'.\n", + " \n", + " 'uv': the arrow axis aspect ratio is 1 so that\n", + " if *U*==*V* the orientation of the arrow on the plot is 45 degrees\n", + " counter-clockwise from the horizontal axis (positive to the right).\n", + " \n", + " 'xy': arrows point from (x,y) to (x+u, y+v).\n", + " Use this for plotting a gradient field, for example.\n", + " \n", + " Alternatively, arbitrary angles may be specified as an array\n", + " of values in degrees, counter-clockwise from the horizontal axis.\n", + " \n", + " Note: inverting a data axis will correspondingly invert the\n", + " arrows only with ``angles='xy'``.\n", + " scale : None, float, optional\n", + " Number of data units per arrow length unit, e.g., m/s per plot width; a\n", + " smaller scale parameter makes the arrow longer. Default is *None*.\n", + " \n", + " If *None*, a simple autoscaling algorithm is used, based on the average\n", + " vector length and the number of vectors. The arrow length unit is given by\n", + " the *scale_units* parameter\n", + " scale_units : [ 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y' | 'xy' ], None, optional\n", + " If the *scale* kwarg is *None*, the arrow length unit. Default is *None*.\n", + " \n", + " e.g. *scale_units* is 'inches', *scale* is 2.0, and\n", + " ``(u,v) = (1,0)``, then the vector will be 0.5 inches long.\n", + " \n", + " If *scale_units* is 'width'/'height', then the vector will be half the\n", + " width/height of the axes.\n", + " \n", + " If *scale_units* is 'x' then the vector will be 0.5 x-axis\n", + " units. To plot vectors in the x-y plane, with u and v having\n", + " the same units as x and y, use\n", + " ``angles='xy', scale_units='xy', scale=1``.\n", + " width : scalar, optional\n", + " Shaft width in arrow units; default depends on choice of units,\n", + " above, and number of vectors; a typical starting value is about\n", + " 0.005 times the width of the plot.\n", + " headwidth : scalar, optional\n", + " Head width as multiple of shaft width, default is 3\n", + " headlength : scalar, optional\n", + " Head length as multiple of shaft width, default is 5\n", + " headaxislength : scalar, optional\n", + " Head length at shaft intersection, default is 4.5\n", + " minshaft : scalar, optional\n", + " Length below which arrow scales, in units of head length. Do not\n", + " set this to less than 1, or small arrows will look terrible!\n", + " Default is 1\n", + " minlength : scalar, optional\n", + " Minimum length as a multiple of shaft width; if an arrow length\n", + " is less than this, plot a dot (hexagon) of this diameter instead.\n", + " Default is 1.\n", + " pivot : [ 'tail' | 'mid' | 'middle' | 'tip' ], optional\n", + " The part of the arrow that is at the grid point; the arrow rotates\n", + " about this point, hence the name *pivot*.\n", + " color : [ color | color sequence ], optional\n", + " This is a synonym for the\n", + " :class:`~matplotlib.collections.PolyCollection` facecolor kwarg.\n", + " If *C* has been set, *color* has no effect.\n", + " \n", + " Notes\n", + " -----\n", + " Additional :class:`~matplotlib.collections.PolyCollection`\n", + " keyword arguments:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " quiverkey : Add a key to a quiver plot\n", + " \n", + " quiverkey(*args, **kw)\n", + " Add a key to a quiver plot.\n", + " \n", + " Call signature::\n", + " \n", + " quiverkey(Q, X, Y, U, label, **kw)\n", + " \n", + " Arguments:\n", + " \n", + " *Q*:\n", + " The Quiver instance returned by a call to quiver.\n", + " \n", + " *X*, *Y*:\n", + " The location of the key; additional explanation follows.\n", + " \n", + " *U*:\n", + " The length of the key\n", + " \n", + " *label*:\n", + " A string with the length and units of the key\n", + " \n", + " Keyword arguments:\n", + " \n", + " *angle* = 0\n", + " The angle of the key arrow. Measured in degrees anti-clockwise from the\n", + " x-axis.\n", + " \n", + " *coordinates* = [ 'axes' | 'figure' | 'data' | 'inches' ]\n", + " Coordinate system and units for *X*, *Y*: 'axes' and 'figure' are\n", + " normalized coordinate systems with 0,0 in the lower left and 1,1\n", + " in the upper right; 'data' are the axes data coordinates (used for\n", + " the locations of the vectors in the quiver plot itself); 'inches'\n", + " is position in the figure in inches, with 0,0 at the lower left\n", + " corner.\n", + " \n", + " *color*:\n", + " overrides face and edge colors from *Q*.\n", + " \n", + " *labelpos* = [ 'N' | 'S' | 'E' | 'W' ]\n", + " Position the label above, below, to the right, to the left of the\n", + " arrow, respectively.\n", + " \n", + " *labelsep*:\n", + " Distance in inches between the arrow and the label. Default is\n", + " 0.1\n", + " \n", + " *labelcolor*:\n", + " defaults to default :class:`~matplotlib.text.Text` color.\n", + " \n", + " *fontproperties*:\n", + " A dictionary with keyword arguments accepted by the\n", + " :class:`~matplotlib.font_manager.FontProperties` initializer:\n", + " *family*, *style*, *variant*, *size*, *weight*\n", + " \n", + " Any additional keyword arguments are used to override vector\n", + " properties taken from *Q*.\n", + " \n", + " The positioning of the key depends on *X*, *Y*, *coordinates*, and\n", + " *labelpos*. If *labelpos* is 'N' or 'S', *X*, *Y* give the position\n", + " of the middle of the key arrow. If *labelpos* is 'E', *X*, *Y*\n", + " positions the head, and if *labelpos* is 'W', *X*, *Y* positions the\n", + " tail; in either of these two cases, *X*, *Y* is somewhere in the\n", + " middle of the arrow+label key object.\n", + " \n", + " rc(*args, **kwargs)\n", + " Set the current rc params. Group is the grouping for the rc, e.g.,\n", + " for ``lines.linewidth`` the group is ``lines``, for\n", + " ``axes.facecolor``, the group is ``axes``, and so on. Group may\n", + " also be a list or tuple of group names, e.g., (*xtick*, *ytick*).\n", + " *kwargs* is a dictionary attribute name/value pairs, e.g.,::\n", + " \n", + " rc('lines', linewidth=2, color='r')\n", + " \n", + " sets the current rc params and is equivalent to::\n", + " \n", + " rcParams['lines.linewidth'] = 2\n", + " rcParams['lines.color'] = 'r'\n", + " \n", + " The following aliases are available to save typing for interactive\n", + " users:\n", + " \n", + " ===== =================\n", + " Alias Property\n", + " ===== =================\n", + " 'lw' 'linewidth'\n", + " 'ls' 'linestyle'\n", + " 'c' 'color'\n", + " 'fc' 'facecolor'\n", + " 'ec' 'edgecolor'\n", + " 'mew' 'markeredgewidth'\n", + " 'aa' 'antialiased'\n", + " ===== =================\n", + " \n", + " Thus you could abbreviate the above rc command as::\n", + " \n", + " rc('lines', lw=2, c='r')\n", + " \n", + " \n", + " Note you can use python's kwargs dictionary facility to store\n", + " dictionaries of default parameters. e.g., you can customize the\n", + " font rc as follows::\n", + " \n", + " font = {'family' : 'monospace',\n", + " 'weight' : 'bold',\n", + " 'size' : 'larger'}\n", + " \n", + " rc('font', **font) # pass in the font dict as kwargs\n", + " \n", + " This enables you to easily switch between several configurations. Use\n", + " ``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to\n", + " restore the default rc params after changes.\n", + " \n", + " rc_context(rc=None, fname=None)\n", + " Return a context manager for managing rc settings.\n", + " \n", + " This allows one to do::\n", + " \n", + " with mpl.rc_context(fname='screen.rc'):\n", + " plt.plot(x, a)\n", + " with mpl.rc_context(fname='print.rc'):\n", + " plt.plot(x, b)\n", + " plt.plot(x, c)\n", + " \n", + " The 'a' vs 'x' and 'c' vs 'x' plots would have settings from\n", + " 'screen.rc', while the 'b' vs 'x' plot would have settings from\n", + " 'print.rc'.\n", + " \n", + " A dictionary can also be passed to the context manager::\n", + " \n", + " with mpl.rc_context(rc={'text.usetex': True}, fname='screen.rc'):\n", + " plt.plot(x, a)\n", + " \n", + " The 'rc' dictionary takes precedence over the settings loaded from\n", + " 'fname'. Passing a dictionary only is also valid. For example a\n", + " common usage is::\n", + " \n", + " with mpl.rc_context(rc={'interactive': False}):\n", + " fig, ax = plt.subplots()\n", + " ax.plot(range(3), range(3))\n", + " fig.savefig('A.png', format='png')\n", + " plt.close(fig)\n", + " \n", + " rcdefaults()\n", + " Restore the rc params from Matplotlib's internal defaults.\n", + " \n", + " See Also\n", + " --------\n", + " rc_file_defaults :\n", + " Restore the rc params from the rc file originally loaded by Matplotlib.\n", + " matplotlib.style.use :\n", + " Use a specific style file. Call ``style.use('default')`` to restore\n", + " the default style.\n", + " \n", + " rgrids(*args, **kwargs)\n", + " Get or set the radial gridlines on a polar plot.\n", + " \n", + " call signatures::\n", + " \n", + " lines, labels = rgrids()\n", + " lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs)\n", + " \n", + " When called with no arguments, :func:`rgrid` simply returns the\n", + " tuple (*lines*, *labels*), where *lines* is an array of radial\n", + " gridlines (:class:`~matplotlib.lines.Line2D` instances) and\n", + " *labels* is an array of tick labels\n", + " (:class:`~matplotlib.text.Text` instances). When called with\n", + " arguments, the labels will appear at the specified radial\n", + " distances and angles.\n", + " \n", + " *labels*, if not *None*, is a len(*radii*) list of strings of the\n", + " labels to use at each angle.\n", + " \n", + " If *labels* is None, the rformatter will be used\n", + " \n", + " Examples::\n", + " \n", + " # set the locations of the radial gridlines and labels\n", + " lines, labels = rgrids( (0.25, 0.5, 1.0) )\n", + " \n", + " # set the locations and labels of the radial gridlines and labels\n", + " lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )\n", + " \n", + " savefig(*args, **kwargs)\n", + " Save the current figure.\n", + " \n", + " Call signature::\n", + " \n", + " savefig(fname, dpi=None, facecolor='w', edgecolor='w',\n", + " orientation='portrait', papertype=None, format=None,\n", + " transparent=False, bbox_inches=None, pad_inches=0.1,\n", + " frameon=None)\n", + " \n", + " The output formats available depend on the backend being used.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " fname : str or file-like object\n", + " A string containing a path to a filename, or a Python\n", + " file-like object, or possibly some backend-dependent object\n", + " such as :class:`~matplotlib.backends.backend_pdf.PdfPages`.\n", + " \n", + " If *format* is *None* and *fname* is a string, the output\n", + " format is deduced from the extension of the filename. If\n", + " the filename has no extension, the value of the rc parameter\n", + " ``savefig.format`` is used.\n", + " \n", + " If *fname* is not a string, remember to specify *format* to\n", + " ensure that the correct backend is used.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " \n", + " dpi : [ *None* | scalar > 0 | 'figure']\n", + " The resolution in dots per inch. If *None* it will default to\n", + " the value ``savefig.dpi`` in the matplotlibrc file. If 'figure'\n", + " it will set the dpi to be the value of the figure.\n", + " \n", + " facecolor : color spec or None, optional\n", + " the facecolor of the figure; if None, defaults to savefig.facecolor\n", + " \n", + " edgecolor : color spec or None, optional\n", + " the edgecolor of the figure; if None, defaults to savefig.edgecolor\n", + " \n", + " orientation : {'landscape', 'portrait'}\n", + " not supported on all backends; currently only on postscript output\n", + " \n", + " papertype : str\n", + " One of 'letter', 'legal', 'executive', 'ledger', 'a0' through\n", + " 'a10', 'b0' through 'b10'. Only supported for postscript\n", + " output.\n", + " \n", + " format : str\n", + " One of the file extensions supported by the active\n", + " backend. Most backends support png, pdf, ps, eps and svg.\n", + " \n", + " transparent : bool\n", + " If *True*, the axes patches will all be transparent; the\n", + " figure patch will also be transparent unless facecolor\n", + " and/or edgecolor are specified via kwargs.\n", + " This is useful, for example, for displaying\n", + " a plot on top of a colored background on a web page. The\n", + " transparency of these patches will be restored to their\n", + " original values upon exit of this function.\n", + " \n", + " frameon : bool\n", + " If *True*, the figure patch will be colored, if *False*, the\n", + " figure background will be transparent. If not provided, the\n", + " rcParam 'savefig.frameon' will be used.\n", + " \n", + " bbox_inches : str or `~matplotlib.transforms.Bbox`, optional\n", + " Bbox in inches. Only the given portion of the figure is\n", + " saved. If 'tight', try to figure out the tight bbox of\n", + " the figure. If None, use savefig.bbox\n", + " \n", + " pad_inches : scalar, optional\n", + " Amount of padding around the figure when bbox_inches is\n", + " 'tight'. If None, use savefig.pad_inches\n", + " \n", + " bbox_extra_artists : list of `~matplotlib.artist.Artist`, optional\n", + " A list of extra artists that will be considered when the\n", + " tight bbox is calculated.\n", + " \n", + " sca(ax)\n", + " Set the current Axes instance to *ax*.\n", + " \n", + " The current Figure is updated to the parent of *ax*.\n", + " \n", + " scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)\n", + " Make a scatter plot of `x` vs `y`.\n", + " \n", + " Marker size is scaled by `s` and marker color is mapped to `c`.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : array_like, shape (n, )\n", + " Input data\n", + " \n", + " s : scalar or array_like, shape (n, ), optional\n", + " size in points^2. Default is `rcParams['lines.markersize'] ** 2`.\n", + " \n", + " c : color, sequence, or sequence of color, optional, default: 'b'\n", + " `c` can be a single color format string, or a sequence of color\n", + " specifications of length `N`, or a sequence of `N` numbers to be\n", + " mapped to colors using the `cmap` and `norm` specified via kwargs\n", + " (see below). Note that `c` should not be a single numeric RGB or\n", + " RGBA sequence because that is indistinguishable from an array of\n", + " values to be colormapped. `c` can be a 2-D array in which the\n", + " rows are RGB or RGBA, however, including the case of a single\n", + " row to specify the same color for all points.\n", + " \n", + " marker : `~matplotlib.markers.MarkerStyle`, optional, default: 'o'\n", + " See `~matplotlib.markers` for more information on the different\n", + " styles of markers scatter supports. `marker` can be either\n", + " an instance of the class or the text shorthand for a particular\n", + " marker.\n", + " \n", + " cmap : `~matplotlib.colors.Colormap`, optional, default: None\n", + " A `~matplotlib.colors.Colormap` instance or registered name.\n", + " `cmap` is only used if `c` is an array of floats. If None,\n", + " defaults to rc `image.cmap`.\n", + " \n", + " norm : `~matplotlib.colors.Normalize`, optional, default: None\n", + " A `~matplotlib.colors.Normalize` instance is used to scale\n", + " luminance data to 0, 1. `norm` is only used if `c` is an array of\n", + " floats. If `None`, use the default :func:`normalize`.\n", + " \n", + " vmin, vmax : scalar, optional, default: None\n", + " `vmin` and `vmax` are used in conjunction with `norm` to normalize\n", + " luminance data. If either are `None`, the min and max of the\n", + " color array is used. Note if you pass a `norm` instance, your\n", + " settings for `vmin` and `vmax` will be ignored.\n", + " \n", + " alpha : scalar, optional, default: None\n", + " The alpha blending value, between 0 (transparent) and 1 (opaque)\n", + " \n", + " linewidths : scalar or array_like, optional, default: None\n", + " If None, defaults to (lines.linewidth,).\n", + " \n", + " verts : sequence of (x, y), optional\n", + " If `marker` is None, these vertices will be used to\n", + " construct the marker. The center of the marker is located\n", + " at (0,0) in normalized units. The overall marker is rescaled\n", + " by ``s``.\n", + " \n", + " edgecolors : color or sequence of color, optional, default: None\n", + " If None, defaults to 'face'\n", + " \n", + " If 'face', the edge color will always be the same as\n", + " the face color.\n", + " \n", + " If it is 'none', the patch boundary will not\n", + " be drawn.\n", + " \n", + " For non-filled markers, the `edgecolors` kwarg\n", + " is ignored and forced to 'face' internally.\n", + " \n", + " Returns\n", + " -------\n", + " paths : `~matplotlib.collections.PathCollection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.collections.Collection` properties\n", + " \n", + " See Also\n", + " --------\n", + " plot : to plot scatter plots when markers are identical in size and\n", + " color\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " * The `plot` function will be faster for scatterplots where markers\n", + " don't vary in size or color.\n", + " \n", + " * Any or all of `x`, `y`, `s`, and `c` may be masked arrays, in which\n", + " case all masks will be combined and only unmasked points will be\n", + " plotted.\n", + " \n", + " Fundamentally, scatter works with 1-D arrays; `x`, `y`, `s`, and `c`\n", + " may be input as 2-D arrays, but within scatter they will be\n", + " flattened. The exception is `c`, which will be flattened only if its\n", + " size matches the size of `x` and `y`.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'c', 'color', 'edgecolors', 'facecolor', 'facecolors', 'linewidths', 's', 'x', 'y'.\n", + " \n", + " sci(im)\n", + " Set the current image. This image will be the target of colormap\n", + " commands like :func:`~matplotlib.pyplot.jet`,\n", + " :func:`~matplotlib.pyplot.hot` or\n", + " :func:`~matplotlib.pyplot.clim`). The current image is an\n", + " attribute of the current axes.\n", + " \n", + " semilogx(*args, **kwargs)\n", + " Make a plot with log scaling on the *x* axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " basex : float, optional\n", + " Base of the *x* logarithm. The scalar should be larger\n", + " than 1.\n", + " \n", + " subsx : array_like, optional\n", + " The location of the minor xticks; *None* defaults to\n", + " autosubs, which depend on the number of decades in the\n", + " plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for\n", + " details.\n", + " \n", + " nonposx : string, optional, {'mask', 'clip'}\n", + " Non-positive values in *x* can be masked as\n", + " invalid, or clipped to a very small positive number.\n", + " \n", + " Returns\n", + " -------\n", + " `~matplotlib.pyplot.plot`\n", + " Log-scaled plot on the *x* axis.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " This function supports all the keyword arguments of\n", + " :func:`~matplotlib.pyplot.plot` and\n", + " :meth:`matplotlib.axes.Axes.set_xscale`.\n", + " \n", + " semilogy(*args, **kwargs)\n", + " Make a plot with log scaling on the *y* axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " basey : float, optional\n", + " Base of the *y* logarithm. The scalar should be larger\n", + " than 1.\n", + " \n", + " subsy : array_like, optional\n", + " The location of the minor yticks; *None* defaults to\n", + " autosubs, which depend on the number of decades in the\n", + " plot; see :meth:`~matplotlib.axes.Axes.set_yscale` for\n", + " details.\n", + " \n", + " nonposy : string, optional, {'mask', 'clip'}\n", + " Non-positive values in *y* can be masked as\n", + " invalid, or clipped to a very small positive number.\n", + " \n", + " Returns\n", + " -------\n", + " `~matplotlib.pyplot.plot`\n", + " Log-scaled plot on the *y* axis.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " This function supports all the keyword arguments of\n", + " :func:`~matplotlib.pyplot.plot` and\n", + " :meth:`matplotlib.axes.Axes.set_yscale`.\n", + " \n", + " set_cmap(cmap)\n", + " Set the default colormap. Applies to the current image if any.\n", + " See help(colormaps) for more information.\n", + " \n", + " *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or\n", + " the name of a registered colormap.\n", + " \n", + " See :func:`matplotlib.cm.register_cmap` and\n", + " :func:`matplotlib.cm.get_cmap`.\n", + " \n", + " setp(*args, **kwargs)\n", + " Set a property on an artist object.\n", + " \n", + " matplotlib supports the use of :func:`setp` (\"set property\") and\n", + " :func:`getp` to set and get object properties, as well as to do\n", + " introspection on the object. For example, to set the linestyle of a\n", + " line to be dashed, you can do::\n", + " \n", + " >>> line, = plot([1,2,3])\n", + " >>> setp(line, linestyle='--')\n", + " \n", + " If you want to know the valid types of arguments, you can provide\n", + " the name of the property you want to set without a value::\n", + " \n", + " >>> setp(line, 'linestyle')\n", + " linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ]\n", + " \n", + " If you want to see all the properties that can be set, and their\n", + " possible values, you can do::\n", + " \n", + " >>> setp(line)\n", + " ... long output listing omitted\n", + " \n", + " You may specify another output file to `setp` if `sys.stdout` is not\n", + " acceptable for some reason using the `file` keyword-only argument::\n", + " \n", + " >>> with fopen('output.log') as f:\n", + " >>> setp(line, file=f)\n", + " \n", + " :func:`setp` operates on a single instance or a iterable of\n", + " instances. If you are in query mode introspecting the possible\n", + " values, only the first instance in the sequence is used. When\n", + " actually setting values, all the instances will be set. e.g.,\n", + " suppose you have a list of two lines, the following will make both\n", + " lines thicker and red::\n", + " \n", + " >>> x = arange(0,1.0,0.01)\n", + " >>> y1 = sin(2*pi*x)\n", + " >>> y2 = sin(4*pi*x)\n", + " >>> lines = plot(x, y1, x, y2)\n", + " >>> setp(lines, linewidth=2, color='r')\n", + " \n", + " :func:`setp` works with the MATLAB style string/value pairs or\n", + " with python kwargs. For example, the following are equivalent::\n", + " \n", + " >>> setp(lines, 'linewidth', 2, 'color', 'r') # MATLAB style\n", + " >>> setp(lines, linewidth=2, color='r') # python style\n", + " \n", + " show(*args, **kw)\n", + " Display a figure.\n", + " When running in ipython with its pylab mode, display all\n", + " figures and return to the ipython prompt.\n", + " \n", + " In non-interactive mode, display all figures and block until\n", + " the figures have been closed; in interactive mode it has no\n", + " effect unless figures were created prior to a change from\n", + " non-interactive to interactive mode (not recommended). In\n", + " that case it displays the figures but does not block.\n", + " \n", + " A single experimental keyword argument, *block*, may be\n", + " set to True or False to override the blocking behavior\n", + " described above.\n", + " \n", + " specgram(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, hold=None, data=None, **kwargs)\n", + " Plot a spectrogram.\n", + " \n", + " Call signature::\n", + " \n", + " specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,\n", + " window=mlab.window_hanning, noverlap=128,\n", + " cmap=None, xextent=None, pad_to=None, sides='default',\n", + " scale_by_freq=None, mode='default', scale='default',\n", + " **kwargs)\n", + " \n", + " Compute and plot a spectrogram of data in *x*. Data are split into\n", + " *NFFT* length segments and the spectrum of each section is\n", + " computed. The windowing function *window* is applied to each\n", + " segment, and the amount of overlap of each segment is\n", + " specified with *noverlap*. The spectrogram is plotted as a colormap\n", + " (using imshow).\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data.\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " mode : [ 'default' | 'psd' | 'magnitude' | 'angle' | 'phase' ]\n", + " What sort of spectrum to use. Default is 'psd', which takes\n", + " the power spectral density. 'complex' returns the complex-valued\n", + " frequency spectrum. 'magnitude' returns the magnitude spectrum.\n", + " 'angle' returns the phase spectrum without unwrapping. 'phase'\n", + " returns the phase spectrum with unwrapping.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between blocks. The\n", + " default value is 128.\n", + " \n", + " scale : [ 'default' | 'linear' | 'dB' ]\n", + " The scaling of the values in the *spec*. 'linear' is no scaling.\n", + " 'dB' returns the values in dB scale. When *mode* is 'psd',\n", + " this is dB power (10 * log10). Otherwise this is dB amplitude\n", + " (20 * log10). 'default' is 'dB' if *mode* is 'psd' or\n", + " 'magnitude' and 'linear' otherwise. This must be 'linear'\n", + " if *mode* is 'angle' or 'phase'.\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " cmap :\n", + " A :class:`matplotlib.colors.Colormap` instance; if *None*, use\n", + " default determined by rc\n", + " \n", + " xextent : [None | (xmin, xmax)]\n", + " The image extent along the x-axis. The default sets *xmin* to the\n", + " left border of the first bin (*spectrum* column) and *xmax* to the\n", + " right border of the last bin. Note that for *noverlap>0* the width\n", + " of the bins is smaller than those of the segments.\n", + " \n", + " **kwargs :\n", + " Additional kwargs are passed on to imshow which makes the\n", + " specgram image\n", + " \n", + " Notes\n", + " -----\n", + " *detrend* and *scale_by_freq* only apply when *mode* is set to\n", + " 'psd'\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 2-D array\n", + " Columns are the periodograms of successive segments.\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the rows in *spectrum*.\n", + " \n", + " t : 1-D array\n", + " The times corresponding to midpoints of segments (i.e., the columns\n", + " in *spectrum*).\n", + " \n", + " im : instance of class :class:`~matplotlib.image.AxesImage`\n", + " The image created by imshow containing the spectrogram\n", + " \n", + " See Also\n", + " --------\n", + " :func:`psd`\n", + " :func:`psd` differs in the default overlap; in returning the mean\n", + " of the segment periodograms; in not returning times; and in\n", + " generating a line plot instead of colormap.\n", + " \n", + " :func:`magnitude_spectrum`\n", + " A single spectrum, similar to having a single segment when *mode*\n", + " is 'magnitude'. Plots a line instead of a colormap.\n", + " \n", + " :func:`angle_spectrum`\n", + " A single spectrum, similar to having a single segment when *mode*\n", + " is 'angle'. Plots a line instead of a colormap.\n", + " \n", + " :func:`phase_spectrum`\n", + " A single spectrum, similar to having a single segment when *mode*\n", + " is 'phase'. Plots a line instead of a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " spectral()\n", + " set the default colormap to spectral and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " spring()\n", + " set the default colormap to spring and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " spy(Z, precision=0, marker=None, markersize=None, aspect='equal', **kwargs)\n", + " Plot the sparsity pattern on a 2-D array.\n", + " \n", + " ``spy(Z)`` plots the sparsity pattern of the 2-D array *Z*.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " Z : sparse array (n, m)\n", + " The array to be plotted.\n", + " \n", + " precision : float, optional, default: 0\n", + " If *precision* is 0, any non-zero value will be plotted; else,\n", + " values of :math:`|Z| > precision` will be plotted.\n", + " \n", + " For :class:`scipy.sparse.spmatrix` instances, there is a special\n", + " case: if *precision* is 'present', any value present in the array\n", + " will be plotted, even if it is identically zero.\n", + " \n", + " origin : [\"upper\", \"lower\"], optional, default: \"upper\"\n", + " Place the [0,0] index of the array in the upper left or lower left\n", + " corner of the axes.\n", + " \n", + " aspect : ['auto' | 'equal' | scalar], optional, default: \"equal\"\n", + " \n", + " If 'equal', and `extent` is None, changes the axes aspect ratio to\n", + " match that of the image. If `extent` is not `None`, the axes\n", + " aspect ratio is changed to match that of the extent.\n", + " \n", + " \n", + " If 'auto', changes the image aspect ratio to match that of the\n", + " axes.\n", + " \n", + " If None, default to rc ``image.aspect`` value.\n", + " \n", + " Two plotting styles are available: image or marker. Both\n", + " are available for full arrays, but only the marker style\n", + " works for :class:`scipy.sparse.spmatrix` instances.\n", + " \n", + " If *marker* and *markersize* are *None*, an image will be\n", + " returned and any remaining kwargs are passed to\n", + " :func:`~matplotlib.pyplot.imshow`; else, a\n", + " :class:`~matplotlib.lines.Line2D` object will be returned with\n", + " the value of marker determining the marker type, and any\n", + " remaining kwargs passed to the\n", + " :meth:`~matplotlib.axes.Axes.plot` method.\n", + " \n", + " If *marker* and *markersize* are *None*, useful kwargs include:\n", + " \n", + " * *cmap*\n", + " * *alpha*\n", + " \n", + " See also\n", + " --------\n", + " imshow : for image options.\n", + " plot : for plotting options\n", + " \n", + " stackplot(x, *args, **kwargs)\n", + " Draws a stacked area plot.\n", + " \n", + " *x* : 1d array of dimension N\n", + " \n", + " *y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension\n", + " 1xN. The data is assumed to be unstacked. Each of the following\n", + " calls is legal::\n", + " \n", + " stackplot(x, y) # where y is MxN\n", + " stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm\n", + " \n", + " Keyword arguments:\n", + " \n", + " *baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle']\n", + " Method used to calculate the baseline. 'zero' is just a\n", + " simple stacked plot. 'sym' is symmetric around zero and\n", + " is sometimes called `ThemeRiver`. 'wiggle' minimizes the\n", + " sum of the squared slopes. 'weighted_wiggle' does the\n", + " same but weights to account for size of each layer.\n", + " It is also called `Streamgraph`-layout. More details\n", + " can be found at http://leebyron.com/streamgraph/.\n", + " \n", + " \n", + " *labels* : A list or tuple of labels to assign to each data series.\n", + " \n", + " \n", + " *colors* : A list or tuple of colors. These will be cycled through and\n", + " used to colour the stacked areas.\n", + " All other keyword arguments are passed to\n", + " :func:`~matplotlib.Axes.fill_between`\n", + " \n", + " Returns *r* : A list of\n", + " :class:`~matplotlib.collections.PolyCollection`, one for each\n", + " element in the stacked area plot.\n", + " \n", + " stem(*args, **kwargs)\n", + " Create a stem plot.\n", + " \n", + " Call signatures::\n", + " \n", + " stem(y, linefmt='b-', markerfmt='bo', basefmt='r-')\n", + " stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')\n", + " \n", + " A stem plot plots vertical lines (using *linefmt*) at each *x*\n", + " location from the baseline to *y*, and places a marker there\n", + " using *markerfmt*. A horizontal line at 0 is plotted using\n", + " *basefmt*.\n", + " \n", + " If no *x* values are provided, the default is (0, 1, ..., len(y) - 1)\n", + " \n", + " Return value is a tuple (*markerline*, *stemlines*,\n", + " *baseline*). See :class:`~matplotlib.container.StemContainer`\n", + " \n", + " .. seealso::\n", + " This\n", + " `document `_\n", + " for details.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " step(x, y, *args, **kwargs)\n", + " Make a step plot.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array_like\n", + " 1-D sequence, and it is assumed, but not checked,\n", + " that it is uniformly increasing.\n", + " \n", + " y : array_like\n", + " 1-D sequence\n", + " \n", + " Returns\n", + " -------\n", + " list\n", + " List of lines that were added.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " where : [ 'pre' | 'post' | 'mid' ]\n", + " If 'pre' (the default), the interval from\n", + " ``x[i]`` to ``x[i+1]`` has level ``y[i+1]``.\n", + " \n", + " If 'post', that interval has level ``y[i]``.\n", + " \n", + " If 'mid', the jumps in *y* occur half-way between the\n", + " *x*-values.\n", + " \n", + " Notes\n", + " -----\n", + " Additional parameters are the same as those for\n", + " :func:`~matplotlib.pyplot.plot`.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " streamplot(x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, transform=None, zorder=None, start_points=None, maxlength=4.0, integration_direction='both', hold=None, data=None)\n", + " Draws streamlines of a vector flow.\n", + " \n", + " *x*, *y* : 1d arrays\n", + " an *evenly spaced* grid.\n", + " *u*, *v* : 2d arrays\n", + " x and y-velocities. Number of rows should match length of y, and\n", + " the number of columns should match x.\n", + " *density* : float or 2-tuple\n", + " Controls the closeness of streamlines. When `density = 1`, the domain\n", + " is divided into a 30x30 grid---*density* linearly scales this grid.\n", + " Each cell in the grid can have, at most, one traversing streamline.\n", + " For different densities in each direction, use [density_x, density_y].\n", + " *linewidth* : numeric or 2d array\n", + " vary linewidth when given a 2d array with the same shape as velocities.\n", + " *color* : matplotlib color code, or 2d array\n", + " Streamline color. When given an array with the same shape as\n", + " velocities, *color* values are converted to colors using *cmap*.\n", + " *cmap* : :class:`~matplotlib.colors.Colormap`\n", + " Colormap used to plot streamlines and arrows. Only necessary when using\n", + " an array input for *color*.\n", + " *norm* : :class:`~matplotlib.colors.Normalize`\n", + " Normalize object used to scale luminance data to 0, 1. If None, stretch\n", + " (min, max) to (0, 1). Only necessary when *color* is an array.\n", + " *arrowsize* : float\n", + " Factor scale arrow size.\n", + " *arrowstyle* : str\n", + " Arrow style specification.\n", + " See :class:`~matplotlib.patches.FancyArrowPatch`.\n", + " *minlength* : float\n", + " Minimum length of streamline in axes coordinates.\n", + " *start_points*: Nx2 array\n", + " Coordinates of starting points for the streamlines.\n", + " In data coordinates, the same as the ``x`` and ``y`` arrays.\n", + " *zorder* : int\n", + " any number\n", + " *maxlength* : float\n", + " Maximum length of streamline in axes coordinates.\n", + " *integration_direction* : ['forward', 'backward', 'both']\n", + " Integrate the streamline in forward, backward or both directions.\n", + " \n", + " Returns:\n", + " \n", + " *stream_container* : StreamplotSet\n", + " Container object with attributes\n", + " \n", + " - lines: `matplotlib.collections.LineCollection` of streamlines\n", + " \n", + " - arrows: collection of `matplotlib.patches.FancyArrowPatch`\n", + " objects representing arrows half-way along stream\n", + " lines.\n", + " \n", + " This container will probably change in the future to allow changes\n", + " to the colormap, alpha, etc. for both lines and arrows, but these\n", + " changes should be backward compatible.\n", + " \n", + " subplot(*args, **kwargs)\n", + " Return a subplot axes at the given grid position.\n", + " \n", + " Call signature::\n", + " \n", + " subplot(nrows, ncols, index, **kwargs)\n", + " \n", + " In the current figure, create and return an `~.Axes`, at position *index*\n", + " of a (virtual) grid of *nrows* by *ncols* axes. Indexes go from 1 to\n", + " ``nrows * ncols``, incrementing in row-major order.\n", + " \n", + " If *nrows*, *ncols* and *index* are all less than 10, they can also be\n", + " given as a single, concatenated, three-digit number.\n", + " \n", + " For example, ``subplot(2, 3, 3)`` and ``subplot(233)`` both create an\n", + " `~.Axes` at the top right corner of the current figure, occupying half of\n", + " the figure height and a third of the figure width.\n", + " \n", + " .. note::\n", + " \n", + " Creating a subplot will delete any pre-existing subplot that overlaps\n", + " with it beyond sharing a boundary::\n", + " \n", + " import matplotlib.pyplot as plt\n", + " # plot a line, implicitly creating a subplot(111)\n", + " plt.plot([1,2,3])\n", + " # now create a subplot which represents the top plot of a grid\n", + " # with 2 rows and 1 column. Since this subplot will overlap the\n", + " # first, the plot (and its axes) previously created, will be removed\n", + " plt.subplot(211)\n", + " plt.plot(range(12))\n", + " plt.subplot(212, facecolor='y') # creates 2nd subplot with yellow background\n", + " \n", + " If you do not want this behavior, use the\n", + " :meth:`~matplotlib.figure.Figure.add_subplot` method or the\n", + " :func:`~matplotlib.pyplot.axes` function instead.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *facecolor*:\n", + " The background color of the subplot, which can be any valid\n", + " color specifier. See :mod:`matplotlib.colors` for more\n", + " information.\n", + " \n", + " *polar*:\n", + " A boolean flag indicating whether the subplot plot should be\n", + " a polar projection. Defaults to *False*.\n", + " \n", + " *projection*:\n", + " A string giving the name of a custom projection to be used\n", + " for the subplot. This projection must have been previously\n", + " registered. See :mod:`matplotlib.projections`.\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.axes`\n", + " For additional information on :func:`axes` and\n", + " :func:`subplot` keyword arguments.\n", + " \n", + " :file:`gallery/pie_and_polar_charts/polar_scatter.py`\n", + " For an example\n", + " \n", + " **Example:**\n", + " \n", + " .. plot:: gallery/subplots_axes_and_figures/subplot.py\n", + " \n", + " subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs)\n", + " Create an axis at specific location inside a regular grid.\n", + " \n", + " Parameters\n", + " ----------\n", + " shape : sequence of 2 ints\n", + " Shape of grid in which to place axis.\n", + " First entry is number of rows, second entry is number of columns.\n", + " \n", + " loc : sequence of 2 ints\n", + " Location to place axis within grid.\n", + " First entry is row number, second entry is column number.\n", + " \n", + " rowspan : int\n", + " Number of rows for the axis to span to the right.\n", + " \n", + " colspan : int\n", + " Number of columns for the axis to span downwards.\n", + " \n", + " fig : `Figure`, optional\n", + " Figure to place axis in. Defaults to current figure.\n", + " \n", + " **kwargs\n", + " Additional keyword arguments are handed to `add_subplot`.\n", + " \n", + " \n", + " Notes\n", + " -----\n", + " The following call ::\n", + " \n", + " subplot2grid(shape, loc, rowspan=1, colspan=1)\n", + " \n", + " is identical to ::\n", + " \n", + " gridspec=GridSpec(shape[0], shape[1])\n", + " subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)\n", + " subplot(subplotspec)\n", + " \n", + " subplot_tool(targetfig=None)\n", + " Launch a subplot tool window for a figure.\n", + " \n", + " A :class:`matplotlib.widgets.SubplotTool` instance is returned.\n", + " \n", + " subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)\n", + " Create a figure and a set of subplots\n", + " \n", + " This utility wrapper makes it convenient to create common layouts of\n", + " subplots, including the enclosing figure object, in a single call.\n", + " \n", + " Parameters\n", + " ----------\n", + " nrows, ncols : int, optional, default: 1\n", + " Number of rows/columns of the subplot grid.\n", + " \n", + " sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False\n", + " Controls sharing of properties among x (`sharex`) or y (`sharey`)\n", + " axes:\n", + " \n", + " - True or 'all': x- or y-axis will be shared among all\n", + " subplots.\n", + " - False or 'none': each subplot x- or y-axis will be\n", + " independent.\n", + " - 'row': each subplot row will share an x- or y-axis.\n", + " - 'col': each subplot column will share an x- or y-axis.\n", + " \n", + " When subplots have a shared x-axis along a column, only the x tick\n", + " labels of the bottom subplot are visible. Similarly, when subplots\n", + " have a shared y-axis along a row, only the y tick labels of the first\n", + " column subplot are visible.\n", + " \n", + " squeeze : bool, optional, default: True\n", + " - If True, extra dimensions are squeezed out from the returned Axes\n", + " object:\n", + " \n", + " - if only one subplot is constructed (nrows=ncols=1), the\n", + " resulting single Axes object is returned as a scalar.\n", + " - for Nx1 or 1xN subplots, the returned object is a 1D numpy\n", + " object array of Axes objects are returned as numpy 1D arrays.\n", + " - for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.\n", + " \n", + " - If False, no squeezing at all is done: the returned Axes object is\n", + " always a 2D array containing Axes instances, even if it ends up\n", + " being 1x1.\n", + " \n", + " subplot_kw : dict, optional\n", + " Dict with keywords passed to the\n", + " :meth:`~matplotlib.figure.Figure.add_subplot` call used to create each\n", + " subplot.\n", + " \n", + " gridspec_kw : dict, optional\n", + " Dict with keywords passed to the\n", + " :class:`~matplotlib.gridspec.GridSpec` constructor used to create the\n", + " grid the subplots are placed on.\n", + " \n", + " **fig_kw :\n", + " All additional keyword arguments are passed to the :func:`figure` call.\n", + " \n", + " Returns\n", + " -------\n", + " fig : :class:`matplotlib.figure.Figure` object\n", + " \n", + " ax : Axes object or array of Axes objects.\n", + " \n", + " ax can be either a single :class:`matplotlib.axes.Axes` object or an\n", + " array of Axes objects if more than one subplot was created. The\n", + " dimensions of the resulting array can be controlled with the squeeze\n", + " keyword, see above.\n", + " \n", + " Examples\n", + " --------\n", + " First create some toy data:\n", + " \n", + " >>> x = np.linspace(0, 2*np.pi, 400)\n", + " >>> y = np.sin(x**2)\n", + " \n", + " Creates just a figure and only one subplot\n", + " \n", + " >>> fig, ax = plt.subplots()\n", + " >>> ax.plot(x, y)\n", + " >>> ax.set_title('Simple plot')\n", + " \n", + " Creates two subplots and unpacks the output array immediately\n", + " \n", + " >>> f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n", + " >>> ax1.plot(x, y)\n", + " >>> ax1.set_title('Sharing Y axis')\n", + " >>> ax2.scatter(x, y)\n", + " \n", + " Creates four polar axes, and accesses them through the returned array\n", + " \n", + " >>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n", + " >>> axes[0, 0].plot(x, y)\n", + " >>> axes[1, 1].scatter(x, y)\n", + " \n", + " Share a X axis with each column of subplots\n", + " \n", + " >>> plt.subplots(2, 2, sharex='col')\n", + " \n", + " Share a Y axis with each row of subplots\n", + " \n", + " >>> plt.subplots(2, 2, sharey='row')\n", + " \n", + " Share both X and Y axes with all subplots\n", + " \n", + " >>> plt.subplots(2, 2, sharex='all', sharey='all')\n", + " \n", + " Note that this is the same as\n", + " \n", + " >>> plt.subplots(2, 2, sharex=True, sharey=True)\n", + " \n", + " See Also\n", + " --------\n", + " figure\n", + " subplot\n", + " \n", + " subplots_adjust(*args, **kwargs)\n", + " Tune the subplot layout.\n", + " \n", + " call signature::\n", + " \n", + " subplots_adjust(left=None, bottom=None, right=None, top=None,\n", + " wspace=None, hspace=None)\n", + " \n", + " The parameter meanings (and suggested defaults) are::\n", + " \n", + " left = 0.125 # the left side of the subplots of the figure\n", + " right = 0.9 # the right side of the subplots of the figure\n", + " bottom = 0.1 # the bottom of the subplots of the figure\n", + " top = 0.9 # the top of the subplots of the figure\n", + " wspace = 0.2 # the amount of width reserved for blank space between subplots,\n", + " # expressed as a fraction of the average axis width\n", + " hspace = 0.2 # the amount of height reserved for white space between subplots,\n", + " # expressed as a fraction of the average axis height\n", + " \n", + " The actual defaults are controlled by the rc file\n", + " \n", + " summer()\n", + " set the default colormap to summer and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " suptitle(*args, **kwargs)\n", + " Add a centered title to the figure.\n", + " \n", + " kwargs are :class:`matplotlib.text.Text` properties. Using figure\n", + " coordinates, the defaults are:\n", + " \n", + " x : 0.5\n", + " The x location of the text in figure coords\n", + " \n", + " y : 0.98\n", + " The y location of the text in figure coords\n", + " \n", + " horizontalalignment : 'center'\n", + " The horizontal alignment of the text\n", + " \n", + " verticalalignment : 'top'\n", + " The vertical alignment of the text\n", + " \n", + " If the `fontproperties` keyword argument is given then the\n", + " rcParams defaults for `fontsize` (`figure.titlesize`) and\n", + " `fontweight` (`figure.titleweight`) will be ignored in favour\n", + " of the `FontProperties` defaults.\n", + " \n", + " A :class:`matplotlib.text.Text` instance is returned.\n", + " \n", + " Example::\n", + " \n", + " fig.suptitle('this is the figure title', fontsize=12)\n", + " \n", + " switch_backend(newbackend)\n", + " Switch the default backend. This feature is **experimental**, and\n", + " is only expected to work switching to an image backend. e.g., if\n", + " you have a bunch of PostScript scripts that you want to run from\n", + " an interactive ipython session, you may want to switch to the PS\n", + " backend before running them to avoid having a bunch of GUI windows\n", + " popup. If you try to interactively switch from one GUI backend to\n", + " another, you will explode.\n", + " \n", + " Calling this command will close all open windows.\n", + " \n", + " table(**kwargs)\n", + " Add a table to the current axes.\n", + " \n", + " Call signature::\n", + " \n", + " table(cellText=None, cellColours=None,\n", + " cellLoc='right', colWidths=None,\n", + " rowLabels=None, rowColours=None, rowLoc='left',\n", + " colLabels=None, colColours=None, colLoc='center',\n", + " loc='bottom', bbox=None):\n", + " \n", + " Returns a :class:`matplotlib.table.Table` instance. Either `cellText`\n", + " or `cellColours` must be provided. For finer grained control over\n", + " tables, use the :class:`~matplotlib.table.Table` class and add it to\n", + " the axes with :meth:`~matplotlib.axes.Axes.add_table`.\n", + " \n", + " Thanks to John Gill for providing the class and table.\n", + " \n", + " kwargs control the :class:`~matplotlib.table.Table`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " contains: a callable function \n", + " figure: a `~.Figure` instance \n", + " fontsize: a float in points \n", + " gid: an id string \n", + " label: object \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float\n", + " \n", + " text(x, y, s, fontdict=None, withdash=False, **kwargs)\n", + " Add text to the axes.\n", + " \n", + " Add text in string `s` to axis at location `x`, `y`, data\n", + " coordinates.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : scalars\n", + " data coordinates\n", + " \n", + " s : string\n", + " text\n", + " \n", + " fontdict : dictionary, optional, default: None\n", + " A dictionary to override the default text properties. If fontdict\n", + " is None, the defaults are determined by your rc parameters.\n", + " \n", + " withdash : boolean, optional, default: False\n", + " Creates a `~matplotlib.text.TextWithDash` instance instead of a\n", + " `~matplotlib.text.Text` instance.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.text.Text` properties.\n", + " Other miscellaneous text parameters.\n", + " \n", + " Examples\n", + " --------\n", + " Individual keyword arguments can be used to override any given\n", + " parameter::\n", + " \n", + " >>> text(x, y, s, fontsize=12)\n", + " \n", + " The default transform specifies that text is in data coords,\n", + " alternatively, you can specify text in axis coords (0,0 is\n", + " lower-left and 1,1 is upper-right). The example below places\n", + " text in the center of the axes::\n", + " \n", + " >>> text(0.5, 0.5,'matplotlib', horizontalalignment='center',\n", + " ... verticalalignment='center',\n", + " ... transform=ax.transAxes)\n", + " \n", + " You can put a rectangular box around the text instance (e.g., to\n", + " set a background color) by using the keyword `bbox`. `bbox` is\n", + " a dictionary of `~matplotlib.patches.Rectangle`\n", + " properties. For example::\n", + " \n", + " >>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))\n", + " \n", + " thetagrids(*args, **kwargs)\n", + " Get or set the theta locations of the gridlines in a polar plot.\n", + " \n", + " If no arguments are passed, return a tuple (*lines*, *labels*)\n", + " where *lines* is an array of radial gridlines\n", + " (:class:`~matplotlib.lines.Line2D` instances) and *labels* is an\n", + " array of tick labels (:class:`~matplotlib.text.Text` instances)::\n", + " \n", + " lines, labels = thetagrids()\n", + " \n", + " Otherwise the syntax is::\n", + " \n", + " lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1)\n", + " \n", + " set the angles at which to place the theta grids (these gridlines\n", + " are equal along the theta dimension).\n", + " \n", + " *angles* is in degrees.\n", + " \n", + " *labels*, if not *None*, is a len(angles) list of strings of the\n", + " labels to use at each angle.\n", + " \n", + " If *labels* is *None*, the labels will be ``fmt%angle``.\n", + " \n", + " *frac* is the fraction of the polar axes radius at which to place\n", + " the label (1 is the edge). e.g., 1.05 is outside the axes and 0.95\n", + " is inside the axes.\n", + " \n", + " Return value is a list of tuples (*lines*, *labels*):\n", + " \n", + " - *lines* are :class:`~matplotlib.lines.Line2D` instances\n", + " \n", + " - *labels* are :class:`~matplotlib.text.Text` instances.\n", + " \n", + " Note that on input, the *labels* argument is a list of strings,\n", + " and on output it is a list of :class:`~matplotlib.text.Text`\n", + " instances.\n", + " \n", + " Examples::\n", + " \n", + " # set the locations of the radial gridlines and labels\n", + " lines, labels = thetagrids( range(45,360,90) )\n", + " \n", + " # set the locations and labels of the radial gridlines and labels\n", + " lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )\n", + " \n", + " tick_params(axis='both', **kwargs)\n", + " Change the appearance of ticks and tick labels.\n", + " \n", + " Parameters\n", + " ----------\n", + " axis : {'x', 'y', 'both'}, optional\n", + " Which axis to apply the parameters to.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " \n", + " axis : {'x', 'y', 'both'}\n", + " Axis on which to operate; default is 'both'.\n", + " \n", + " reset : bool\n", + " If *True*, set all parameters to defaults\n", + " before processing other keyword arguments. Default is\n", + " *False*.\n", + " \n", + " which : {'major', 'minor', 'both'}\n", + " Default is 'major'; apply arguments to *which* ticks.\n", + " \n", + " direction : {'in', 'out', 'inout'}\n", + " Puts ticks inside the axes, outside the axes, or both.\n", + " \n", + " length : float\n", + " Tick length in points.\n", + " \n", + " width : float\n", + " Tick width in points.\n", + " \n", + " color : color\n", + " Tick color; accepts any mpl color spec.\n", + " \n", + " pad : float\n", + " Distance in points between tick and label.\n", + " \n", + " labelsize : float or str\n", + " Tick label font size in points or as a string (e.g., 'large').\n", + " \n", + " labelcolor : color\n", + " Tick label color; mpl color spec.\n", + " \n", + " colors : color\n", + " Changes the tick color and the label color to the same value:\n", + " mpl color spec.\n", + " \n", + " zorder : float\n", + " Tick and label zorder.\n", + " \n", + " bottom, top, left, right : bool or {'on', 'off'}\n", + " controls whether to draw the respective ticks.\n", + " \n", + " labelbottom, labeltop, labelleft, labelright : bool or {'on', 'off'}\n", + " controls whether to draw the\n", + " respective tick labels.\n", + " \n", + " labelrotation : float\n", + " Tick label rotation\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " Usage ::\n", + " \n", + " ax.tick_params(direction='out', length=6, width=2, colors='r')\n", + " \n", + " This will make all major ticks be red, pointing out of the box,\n", + " and with dimensions 6 points by 2 points. Tick labels will\n", + " also be red.\n", + " \n", + " ticklabel_format(**kwargs)\n", + " Change the `~matplotlib.ticker.ScalarFormatter` used by\n", + " default for linear axes.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " ============== =========================================\n", + " Keyword Description\n", + " ============== =========================================\n", + " *style* [ 'sci' (or 'scientific') | 'plain' ]\n", + " plain turns off scientific notation\n", + " *scilimits* (m, n), pair of integers; if *style*\n", + " is 'sci', scientific notation will\n", + " be used for numbers outside the range\n", + " 10`m`:sup: to 10`n`:sup:.\n", + " Use (0,0) to include all numbers.\n", + " *useOffset* [True | False | offset]; if True,\n", + " the offset will be calculated as needed;\n", + " if False, no offset will be used; if a\n", + " numeric offset is specified, it will be\n", + " used.\n", + " *axis* [ 'x' | 'y' | 'both' ]\n", + " *useLocale* If True, format the number according to\n", + " the current locale. This affects things\n", + " such as the character used for the\n", + " decimal separator. If False, use\n", + " C-style (English) formatting. The\n", + " default setting is controlled by the\n", + " axes.formatter.use_locale rcparam.\n", + " *useMathText* If True, render the offset and scientific\n", + " notation in mathtext\n", + " ============== =========================================\n", + " \n", + " Only the major ticks are affected.\n", + " If the method is called when the\n", + " :class:`~matplotlib.ticker.ScalarFormatter` is not the\n", + " :class:`~matplotlib.ticker.Formatter` being used, an\n", + " :exc:`AttributeError` will be raised.\n", + " \n", + " tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None)\n", + " Automatically adjust subplot parameters to give specified padding.\n", + " \n", + " Parameters\n", + " ----------\n", + " pad : float\n", + " padding between the figure edge and the edges of subplots, as a fraction of the font-size.\n", + " h_pad, w_pad : float\n", + " padding (height/width) between edges of adjacent subplots.\n", + " Defaults to `pad_inches`.\n", + " rect : if rect is given, it is interpreted as a rectangle\n", + " (left, bottom, right, top) in the normalized figure\n", + " coordinate that the whole subplots area (including\n", + " labels) will fit into. Default is (0, 0, 1, 1).\n", + " \n", + " title(s, *args, **kwargs)\n", + " Set a title of the current axes.\n", + " \n", + " Set one of the three available axes titles. The available titles are\n", + " positioned above the axes in the center, flush with the left edge,\n", + " and flush with the right edge.\n", + " \n", + " .. seealso::\n", + " See :func:`~matplotlib.pyplot.text` for adding text\n", + " to the current axes\n", + " \n", + " Parameters\n", + " ----------\n", + " label : str\n", + " Text to use for the title\n", + " \n", + " fontdict : dict\n", + " A dictionary controlling the appearance of the title text,\n", + " the default `fontdict` is:\n", + " \n", + " {'fontsize': rcParams['axes.titlesize'],\n", + " 'fontweight' : rcParams['axes.titleweight'],\n", + " 'verticalalignment': 'baseline',\n", + " 'horizontalalignment': loc}\n", + " \n", + " loc : {'center', 'left', 'right'}, str, optional\n", + " Which title to set, defaults to 'center'\n", + " \n", + " Returns\n", + " -------\n", + " text : :class:`~matplotlib.text.Text`\n", + " The matplotlib text instance representing the title\n", + " \n", + " Other parameters\n", + " ----------------\n", + " kwargs : text properties\n", + " Other keyword arguments are text properties, see\n", + " :class:`~matplotlib.text.Text` for a list of valid text\n", + " properties.\n", + " \n", + " tricontour(*args, **kwargs)\n", + " Draw contours on an unstructured triangular grid.\n", + " :func:`~matplotlib.pyplot.tricontour` and\n", + " :func:`~matplotlib.pyplot.tricontourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " The triangulation can be specified in one of two ways; either::\n", + " \n", + " tricontour(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " tricontour(x, y, ...)\n", + " tricontour(x, y, triangles, ...)\n", + " tricontour(x, y, triangles=triangles, ...)\n", + " tricontour(x, y, mask=mask, ...)\n", + " tricontour(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of\n", + " these possibilities.\n", + " \n", + " The remaining arguments may be::\n", + " \n", + " tricontour(..., Z)\n", + " \n", + " where *Z* is the array of values to contour, one per point\n", + " in the triangulation. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, N)\n", + " \n", + " contour *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontourf(..., Z, V)\n", + " \n", + " fill the (len(*V*)-1) regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " ``C = tricontour(...)`` returns a\n", + " :class:`~matplotlib.contour.TriContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *levels* [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is None, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " \n", + " tricontour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the 'solid' is used.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " If contour is using a monochrome colormap and the contour\n", + " level is less than 0, then the linestyle specified\n", + " in ``contour.negative_linestyle`` in ``matplotlibrc``\n", + " will be used.\n", + " \n", + " tricontourf-only keyword arguments:\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing\n", + " \n", + " Note: tricontourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " tricontourf(*args, **kwargs)\n", + " Draw contours on an unstructured triangular grid.\n", + " :func:`~matplotlib.pyplot.tricontour` and\n", + " :func:`~matplotlib.pyplot.tricontourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " The triangulation can be specified in one of two ways; either::\n", + " \n", + " tricontour(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " tricontour(x, y, ...)\n", + " tricontour(x, y, triangles, ...)\n", + " tricontour(x, y, triangles=triangles, ...)\n", + " tricontour(x, y, mask=mask, ...)\n", + " tricontour(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of\n", + " these possibilities.\n", + " \n", + " The remaining arguments may be::\n", + " \n", + " tricontour(..., Z)\n", + " \n", + " where *Z* is the array of values to contour, one per point\n", + " in the triangulation. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, N)\n", + " \n", + " contour *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontourf(..., Z, V)\n", + " \n", + " fill the (len(*V*)-1) regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " ``C = tricontour(...)`` returns a\n", + " :class:`~matplotlib.contour.TriContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *levels* [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is None, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " \n", + " tricontour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the 'solid' is used.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " If contour is using a monochrome colormap and the contour\n", + " level is less than 0, then the linestyle specified\n", + " in ``contour.negative_linestyle`` in ``matplotlibrc``\n", + " will be used.\n", + " \n", + " tricontourf-only keyword arguments:\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing\n", + " \n", + " Note: tricontourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " tripcolor(*args, **kwargs)\n", + " Create a pseudocolor plot of an unstructured triangular grid.\n", + " \n", + " The triangulation can be specified in one of two ways; either::\n", + " \n", + " tripcolor(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " tripcolor(x, y, ...)\n", + " tripcolor(x, y, triangles, ...)\n", + " tripcolor(x, y, triangles=triangles, ...)\n", + " tripcolor(x, y, mask=mask, ...)\n", + " tripcolor(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of these\n", + " possibilities.\n", + " \n", + " The next argument must be *C*, the array of color values, either\n", + " one per point in the triangulation if color values are defined at\n", + " points, or one per triangle in the triangulation if color values\n", + " are defined at triangles. If there are the same number of points\n", + " and triangles in the triangulation it is assumed that color\n", + " values are defined at points; to force the use of color values at\n", + " triangles use the kwarg ``facecolors=C`` instead of just ``C``.\n", + " \n", + " *shading* may be 'flat' (the default) or 'gouraud'. If *shading*\n", + " is 'flat' and C values are defined at points, the color values\n", + " used for each triangle are from the mean C of the triangle's\n", + " three points. If *shading* is 'gouraud' then color values must be\n", + " defined at points.\n", + " \n", + " The remaining kwargs are the same as for\n", + " :meth:`~matplotlib.axes.Axes.pcolor`.\n", + " \n", + " triplot(*args, **kwargs)\n", + " Draw a unstructured triangular grid as lines and/or markers.\n", + " \n", + " The triangulation to plot can be specified in one of two ways;\n", + " either::\n", + " \n", + " triplot(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " triplot(x, y, ...)\n", + " triplot(x, y, triangles, ...)\n", + " triplot(x, y, triangles=triangles, ...)\n", + " triplot(x, y, mask=mask, ...)\n", + " triplot(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of these\n", + " possibilities.\n", + " \n", + " The remaining args and kwargs are the same as for\n", + " :meth:`~matplotlib.axes.Axes.plot`.\n", + " \n", + " Return a list of 2 :class:`~matplotlib.lines.Line2D` containing\n", + " respectively:\n", + " \n", + " - the lines plotted for triangles edges\n", + " - the markers plotted for triangles nodes\n", + " \n", + " twinx(ax=None)\n", + " Make a second axes that shares the *x*-axis. The new axes will\n", + " overlay *ax* (or the current axes if *ax* is *None*). The ticks\n", + " for *ax2* will be placed on the right, and the *ax2* instance is\n", + " returned.\n", + " \n", + " .. seealso::\n", + " \n", + " :file:`examples/api_examples/two_scales.py`\n", + " For an example\n", + " \n", + " twiny(ax=None)\n", + " Make a second axes that shares the *y*-axis. The new axis will\n", + " overlay *ax* (or the current axes if *ax* is *None*). The ticks\n", + " for *ax2* will be placed on the top, and the *ax2* instance is\n", + " returned.\n", + " \n", + " uninstall_repl_displayhook()\n", + " Uninstalls the matplotlib display hook.\n", + " \n", + " .. warning\n", + " \n", + " Need IPython >= 2 for this to work. For IPython < 2 will raise a\n", + " ``NotImplementedError``\n", + " \n", + " .. warning\n", + " \n", + " If you are using vanilla python and have installed another\n", + " display hook this will reset ``sys.displayhook`` to what ever\n", + " function was there when matplotlib installed it's displayhook,\n", + " possibly discarding your changes.\n", + " \n", + " violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None, hold=None, data=None)\n", + " Make a violin plot.\n", + " \n", + " Make a violin plot for each column of *dataset* or each vector in\n", + " sequence *dataset*. Each filled area extends to represent the\n", + " entire data range, with optional lines at the mean, the median,\n", + " the minimum, and the maximum.\n", + " \n", + " Parameters\n", + " ----------\n", + " dataset : Array or a sequence of vectors.\n", + " The input data.\n", + " \n", + " positions : array-like, default = [1, 2, ..., n]\n", + " Sets the positions of the violins. The ticks and limits are\n", + " automatically set to match the positions.\n", + " \n", + " vert : bool, default = True.\n", + " If true, creates a vertical violin plot.\n", + " Otherwise, creates a horizontal violin plot.\n", + " \n", + " widths : array-like, default = 0.5\n", + " Either a scalar or a vector that sets the maximal width of\n", + " each violin. The default is 0.5, which uses about half of the\n", + " available horizontal space.\n", + " \n", + " showmeans : bool, default = False\n", + " If `True`, will toggle rendering of the means.\n", + " \n", + " showextrema : bool, default = True\n", + " If `True`, will toggle rendering of the extrema.\n", + " \n", + " showmedians : bool, default = False\n", + " If `True`, will toggle rendering of the medians.\n", + " \n", + " points : scalar, default = 100\n", + " Defines the number of points to evaluate each of the\n", + " gaussian kernel density estimations at.\n", + " \n", + " bw_method : str, scalar or callable, optional\n", + " The method used to calculate the estimator bandwidth. This can be\n", + " 'scott', 'silverman', a scalar constant or a callable. If a\n", + " scalar, this will be used directly as `kde.factor`. If a\n", + " callable, it should take a `GaussianKDE` instance as its only\n", + " parameter and return a scalar. If None (default), 'scott' is used.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " result : dict\n", + " A dictionary mapping each component of the violinplot to a\n", + " list of the corresponding collection instances created. The\n", + " dictionary has the following keys:\n", + " \n", + " - ``bodies``: A list of the\n", + " :class:`matplotlib.collections.PolyCollection` instances\n", + " containing the filled area of each violin.\n", + " \n", + " - ``cmeans``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the mean values of each of the\n", + " violin's distribution.\n", + " \n", + " - ``cmins``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the bottom of each violin's\n", + " distribution.\n", + " \n", + " - ``cmaxes``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the top of each violin's\n", + " distribution.\n", + " \n", + " - ``cbars``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the centers of each violin's\n", + " distribution.\n", + " \n", + " - ``cmedians``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the median values of each of the\n", + " violin's distribution.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'dataset'.\n", + " \n", + " viridis()\n", + " set the default colormap to viridis and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " vlines(x, ymin, ymax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)\n", + " Plot vertical lines.\n", + " \n", + " Plot vertical lines at each `x` from `ymin` to `ymax`.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : scalar or 1D array_like\n", + " x-indexes where to plot the lines.\n", + " \n", + " ymin, ymax : scalar or 1D array_like\n", + " Respective beginning and end of each line. If scalars are\n", + " provided, all lines will have same length.\n", + " \n", + " colors : array_like of colors, optional, default: 'k'\n", + " \n", + " linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional\n", + " \n", + " label : string, optional, default: ''\n", + " \n", + " Returns\n", + " -------\n", + " lines : `~matplotlib.collections.LineCollection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.collections.LineCollection` properties.\n", + " \n", + " See also\n", + " --------\n", + " hlines : horizontal lines\n", + " axvline: vertical line across the axes\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'x', 'ymax', 'ymin'.\n", + " \n", + " waitforbuttonpress(*args, **kwargs)\n", + " Blocking call to interact with the figure.\n", + " \n", + " This will return True is a key was pressed, False if a mouse\n", + " button was pressed and None if *timeout* was reached without\n", + " either being pressed.\n", + " \n", + " If *timeout* is negative, does not timeout.\n", + " \n", + " winter()\n", + " set the default colormap to winter and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " xcorr(x, y, normed=True, detrend=, usevlines=True, maxlags=10, hold=None, data=None, **kwargs)\n", + " Plot the cross correlation between *x* and *y*.\n", + " \n", + " The correlation with lag k is defined as sum_n x[n+k] * conj(y[n]).\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " x : sequence of scalars of length n\n", + " \n", + " y : sequence of scalars of length n\n", + " \n", + " hold : boolean, optional, *deprecated*, default: True\n", + " \n", + " detrend : callable, optional, default: `mlab.detrend_none`\n", + " x is detrended by the `detrend` callable. Default is no\n", + " normalization.\n", + " \n", + " normed : boolean, optional, default: True\n", + " if True, input vectors are normalised to unit length.\n", + " \n", + " usevlines : boolean, optional, default: True\n", + " if True, Axes.vlines is used to plot the vertical lines from the\n", + " origin to the acorr. Otherwise, Axes.plot is used.\n", + " \n", + " maxlags : integer, optional, default: 10\n", + " number of lags to show. If None, will return all 2 * len(x) - 1\n", + " lags.\n", + " \n", + " Returns\n", + " -------\n", + " (lags, c, line, b) : where:\n", + " \n", + " - `lags` are a length 2`maxlags+1 lag vector.\n", + " - `c` is the 2`maxlags+1 auto correlation vectorI\n", + " - `line` is a `~matplotlib.lines.Line2D` instance returned by\n", + " `plot`.\n", + " - `b` is the x-axis (none, if plot is used).\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None\n", + " Only used if usevlines is False.\n", + " \n", + " marker : string, optional, default: 'o'\n", + " \n", + " Notes\n", + " -----\n", + " The cross correlation is performed with :func:`numpy.correlate` with\n", + " `mode` = 2.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " xkcd(scale=1, length=100, randomness=2)\n", + " Turns on `xkcd `_ sketch-style drawing mode.\n", + " This will only have effect on things drawn after this function is\n", + " called.\n", + " \n", + " For best results, the \"Humor Sans\" font should be installed: it is\n", + " not included with matplotlib.\n", + " \n", + " Parameters\n", + " ----------\n", + " scale : float, optional\n", + " The amplitude of the wiggle perpendicular to the source line.\n", + " length : float, optional\n", + " The length of the wiggle along the line.\n", + " randomness : float, optional\n", + " The scale factor by which the length is shrunken or expanded.\n", + " \n", + " Notes\n", + " -----\n", + " This function works by a number of rcParams, so it will probably\n", + " override others you have set before.\n", + " \n", + " If you want the effects of this function to be temporary, it can\n", + " be used as a context manager, for example::\n", + " \n", + " with plt.xkcd():\n", + " # This figure will be in XKCD-style\n", + " fig1 = plt.figure()\n", + " # ...\n", + " \n", + " # This figure will be in regular style\n", + " fig2 = plt.figure()\n", + " \n", + " xlabel(s, *args, **kwargs)\n", + " Set the *x* axis label of the current axis.\n", + " \n", + " Default override is::\n", + " \n", + " override = {\n", + " 'fontsize' : 'small',\n", + " 'verticalalignment' : 'top',\n", + " 'horizontalalignment' : 'center'\n", + " }\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.text`\n", + " For information on how override and the optional args work\n", + " \n", + " xlim(*args, **kwargs)\n", + " Get or set the *x* limits of the current axes.\n", + " \n", + " ::\n", + " \n", + " xmin, xmax = xlim() # return the current xlim\n", + " xlim( (xmin, xmax) ) # set the xlim to xmin, xmax\n", + " xlim( xmin, xmax ) # set the xlim to xmin, xmax\n", + " \n", + " If you do not specify args, you can pass the xmin and xmax as\n", + " kwargs, e.g.::\n", + " \n", + " xlim(xmax=3) # adjust the max leaving min unchanged\n", + " xlim(xmin=1) # adjust the min leaving max unchanged\n", + " \n", + " Setting limits turns autoscaling off for the x-axis.\n", + " \n", + " The new axis limits are returned as a length 2 tuple.\n", + " \n", + " xscale(*args, **kwargs)\n", + " Set the scaling of the *x*-axis.\n", + " \n", + " call signature::\n", + " \n", + " xscale(scale, **kwargs)\n", + " \n", + " The available scales are: 'linear' | 'log' | 'logit' | 'symlog'\n", + " \n", + " Different keywords may be accepted, depending on the scale:\n", + " \n", + " 'linear'\n", + " \n", + " \n", + " \n", + " \n", + " 'log'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *nonposx*/*nonposy*: ['mask' | 'clip' ]\n", + " non-positive values in *x* or *y* can be masked as\n", + " invalid, or clipped to a very small positive number\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " \n", + " 'logit'\n", + " \n", + " *nonpos*: ['mask' | 'clip' ]\n", + " values beyond ]0, 1[ can be masked as invalid, or clipped to a number\n", + " very close to 0 or 1\n", + " \n", + " \n", + " 'symlog'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *linthreshx*/*linthreshy*:\n", + " A single float which defines the range (-*x*, *x*), within\n", + " which the plot is linear. This avoids having the plot go to\n", + " infinity around zero.\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " *linscalex*/*linscaley*:\n", + " This allows the linear range (-*linthresh* to *linthresh*)\n", + " to be stretched relative to the logarithmic range. Its\n", + " value is the number of decades to use for each half of the\n", + " linear range. For example, when *linscale* == 1.0 (the\n", + " default), the space used for the positive and negative\n", + " halves of the linear range will be equal to one decade in\n", + " the logarithmic range.\n", + " \n", + " xticks(*args, **kwargs)\n", + " Get or set the *x*-limits of the current tick locations and labels.\n", + " \n", + " ::\n", + " \n", + " # return locs, labels where locs is an array of tick locations and\n", + " # labels is an array of tick labels.\n", + " locs, labels = xticks()\n", + " \n", + " # set the locations of the xticks\n", + " xticks( arange(6) )\n", + " \n", + " # set the locations and labels of the xticks\n", + " xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )\n", + " \n", + " The keyword args, if any, are :class:`~matplotlib.text.Text`\n", + " properties. For example, to rotate long labels::\n", + " \n", + " xticks( arange(12), calendar.month_name[1:13], rotation=17 )\n", + " \n", + " ylabel(s, *args, **kwargs)\n", + " Set the *y* axis label of the current axis.\n", + " \n", + " Defaults override is::\n", + " \n", + " override = {\n", + " 'fontsize' : 'small',\n", + " 'verticalalignment' : 'center',\n", + " 'horizontalalignment' : 'right',\n", + " 'rotation'='vertical' : }\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.text`\n", + " For information on how override and the optional args\n", + " work.\n", + " \n", + " ylim(*args, **kwargs)\n", + " Get or set the *y*-limits of the current axes.\n", + " \n", + " ::\n", + " \n", + " ymin, ymax = ylim() # return the current ylim\n", + " ylim( (ymin, ymax) ) # set the ylim to ymin, ymax\n", + " ylim( ymin, ymax ) # set the ylim to ymin, ymax\n", + " \n", + " If you do not specify args, you can pass the *ymin* and *ymax* as\n", + " kwargs, e.g.::\n", + " \n", + " ylim(ymax=3) # adjust the max leaving min unchanged\n", + " ylim(ymin=1) # adjust the min leaving max unchanged\n", + " \n", + " Setting limits turns autoscaling off for the y-axis.\n", + " \n", + " The new axis limits are returned as a length 2 tuple.\n", + " \n", + " yscale(*args, **kwargs)\n", + " Set the scaling of the *y*-axis.\n", + " \n", + " call signature::\n", + " \n", + " yscale(scale, **kwargs)\n", + " \n", + " The available scales are: 'linear' | 'log' | 'logit' | 'symlog'\n", + " \n", + " Different keywords may be accepted, depending on the scale:\n", + " \n", + " 'linear'\n", + " \n", + " \n", + " \n", + " \n", + " 'log'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *nonposx*/*nonposy*: ['mask' | 'clip' ]\n", + " non-positive values in *x* or *y* can be masked as\n", + " invalid, or clipped to a very small positive number\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " \n", + " 'logit'\n", + " \n", + " *nonpos*: ['mask' | 'clip' ]\n", + " values beyond ]0, 1[ can be masked as invalid, or clipped to a number\n", + " very close to 0 or 1\n", + " \n", + " \n", + " 'symlog'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *linthreshx*/*linthreshy*:\n", + " A single float which defines the range (-*x*, *x*), within\n", + " which the plot is linear. This avoids having the plot go to\n", + " infinity around zero.\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " *linscalex*/*linscaley*:\n", + " This allows the linear range (-*linthresh* to *linthresh*)\n", + " to be stretched relative to the logarithmic range. Its\n", + " value is the number of decades to use for each half of the\n", + " linear range. For example, when *linscale* == 1.0 (the\n", + " default), the space used for the positive and negative\n", + " halves of the linear range will be equal to one decade in\n", + " the logarithmic range.\n", + " \n", + " yticks(*args, **kwargs)\n", + " Get or set the *y*-limits of the current tick locations and labels.\n", + " \n", + " ::\n", + " \n", + " # return locs, labels where locs is an array of tick locations and\n", + " # labels is an array of tick labels.\n", + " locs, labels = yticks()\n", + " \n", + " # set the locations of the yticks\n", + " yticks( arange(6) )\n", + " \n", + " # set the locations and labels of the yticks\n", + " yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )\n", + " \n", + " The keyword args, if any, are :class:`~matplotlib.text.Text`\n", + " properties. For example, to rotate long labels::\n", + " \n", + " yticks( arange(12), calendar.month_name[1:13], rotation=45 )\n", + "\n", + "DATA\n", + " absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n", + " division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...\n", + " print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n", + " rcParams = RcParams({'_internal.classic_mode': False,\n", + " ...nor.widt...\n", + " rcParamsDefault = RcParams({'_internal.classic_mode': False,\n", + " ...n...\n", + " unicode_literals = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', ...\n", + "\n", + "FILE\n", + " /usr/lib/python3/dist-packages/matplotlib/pyplot.py\n", + "\n", + "\n" + ] + } + ], + "source": [ + "help(plt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -181,7 +9280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/03-FFT.ipynb b/boards/Pynq-Z1/notebooks/03-FFT.ipynb index fb0baad..ef7f313 100644 --- a/boards/Pynq-Z1/notebooks/03-FFT.ipynb +++ b/boards/Pynq-Z1/notebooks/03-FFT.ipynb @@ -1,36 +1,93 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-6.35797575e+14 -2.02136454e+11 -1.22271478e+10 ... 6.08481460e-56\n", - " 3.53695122e-53 -5.53954517e-79]\n", - "[1.60955566e+02 5.98436356e+01 6.94320222e+01 ... 6.88452640e-63\n", - " 1.43055084e-61 2.01207879e-67]\n" - ] + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "import pynq.lib.dma\n", "import numpy as np\n", - "dftol = pynq.Overlay(\"./src/fft/fft.bit\")\n", + "dftol = pynq.Overlay(\"fft.bit\")\n", "\n", "dma0 = dftol.axi_dma_0\n", - "dma1 = dftol.axi_dma_1\n", - "\n", - "\n", + "dma1 = dftol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5.23776000e+05 -2.62032922e+05 -1.30894375e+05 ..., 2.12414865e-03\n", + " 2.92085903e-03 -1.20488164e-06]\n", + "[ 0.00000000e+00 7.23699658e+03 6.43042285e+03 ..., 1.76354559e-04\n", + " 1.79434821e-04 3.56945311e-05]\n" + ] + } + ], + "source": [ + "#生成输入数据并输出\n", "from pynq import Xlnk\n", "xlnk = Xlnk()\n", - "samplereal = xlnk.cma_array(shape=(1024,), dtype=np.float)\n", - "sampleimag = xlnk.cma_array(shape=(1024,), dtype=np.float)\n", - "outreal = xlnk.cma_array(shape=(1024,), dtype=np.float)\n", - "outimag = xlnk.cma_array(shape=(1024,), dtype=np.float)\n", + "samplereal = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "sampleimag = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "outreal = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "outimag = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", "\n", "for i in range(1024):\n", " samplereal[i] = i\n", @@ -48,24 +105,24 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -83,6 +140,10 @@ } ], "source": [ + "#画图\n", + "from pynq import Overlay\n", + "import pynq.lib.dma\n", + "\n", "import pylab as py\n", "import scipy as scipy\n", "import matplotlib.pyplot as plt\n", @@ -92,12 +153,12 @@ "actualreal = samplereal[0:128]\n", "fig1 = plt.figure()\n", "ax1 = fig1.gca()\n", - "plt.plot(actualreal)\n", + "plt.plot(outreal)\n", "\n", "fig2 = plt.figure()\n", "ax2 = fig2.gca()\n", "\n", - "plt.plot(outreal)" + "plt.plot(outimag)" ] }, { @@ -124,7 +185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/04-DFT.ipynb b/boards/Pynq-Z1/notebooks/04-DFT.ipynb index 443de5c..6d1764a 100644 --- a/boards/Pynq-Z1/notebooks/04-DFT.ipynb +++ b/boards/Pynq-Z1/notebooks/04-DFT.ipynb @@ -1,120 +1,156 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "from pynq import DefaultIP\n", + "\n", "class DftDriver(DefaultIP):\n", " def __init__(self, description):\n", " super().__init__(description=description)\n", " \n", - " bindto = ['xilinx.com:hls:dft:1.0']\n", - " \n", - " @property\n", - " def x(self):\n", - " return self.read(0x10)\n", - " \n", - " @x.setter\n", - " def x(self, value):\n", - " self.write(0x10, value)" + " bindto = ['xilinx.com:hls:dft:1.0']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-4.66436218e-35 -3.79919940e-35 -1.17361422e-34 -4.02489431e-35\n", - " -1.22778087e-33 -6.69561914e-35 -6.53010887e-34 -4.73959488e-35\n", - " -1.64005038e-34 -3.58102732e-34 -2.93403523e-34 -2.10648720e-34\n", - " -7.80002052e-33 -1.21423918e-33 -5.77026933e-34 -2.39958919e-32\n", - " -2.68802750e-33 -2.28403361e-33 -9.35280155e-33 -2.72519257e-32\n", - " -3.18741576e-32 -1.65822636e-31 -1.48681860e-31 -1.35277374e-30\n", - " -2.15704263e-30 -4.95996469e-29 -3.38717326e-29 -1.86565674e-28\n", - " -1.09493950e-27 -2.76164605e-26 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[ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "import pynq.lib.dma\n", - "import numpy as np", - "\n", - "dftol = pynq.Overlay(\"./src/dft/dft.bit\")\n", + "import numpy as np\n", + "dftol = pynq.Overlay(\"dft.bit\")\n", "\n", "dma0 = dftol.axi_dma_0\n", - "dma1 = dftol.axi_dma_1\n", + "dma1 = dftol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Fourier series is expressed as follows:\n", + "傅里叶级数的表现形式如下：\n", "\n", + "$$\n", + "\\begin{aligned}\n", + "f(t)&\\sim\\frac{a_{0}}{2}+a_{1}cos(t)+a_{2}cos(2t)+a_{3}cos(3t)+\\cdots \\\\ \n", + "&b_{1}sin(t)+b_{2}sin(2t)+b_{3}sin(3t)+\\cdots \\\\\n", + "&\\sim\\frac{a_{0}}{2}+{\\sum_{n=1}^{\\infty}}(a_{n}cos(nt)+b_{n}sin(nt)\n", + "\\end{aligned}\n", + "\\quad\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The parameter a_ {0}, a_ {1}... and b_ {0}, b_ {1}... computation formula is as follows:\n", + "其中参数a_{0},a_{1}...和b_{0},b_{1}...的计算公式如下：\n", "\n", + "$$\n", + "\\begin{aligned}\n", + "a_{0}&=\\frac{1}{\\pi}\\int_{-\\pi}^{\\pi}f(t)dt \\\\\n", + "a_{n}&=\\frac{1}{\\pi}\\int_{-\\pi}^{\\pi}f(t)cos(nt)dt \\\\\n", + "b_{n}&=\\frac{1}{\\pi}\\int_{-\\pi}^{\\pi}f(t)sin(nt)dt\n", + "\\end{aligned}\n", + "\\quad\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ "from pynq import Xlnk\n", "xlnk = Xlnk()\n", - "samplereal = xlnk.cma_array(shape=(256,), dtype=np.float)\n", - "sampleimag = xlnk.cma_array(shape=(256,), dtype=np.float)\n", - "outreal = xlnk.cma_array(shape=(128,), dtype=np.float)\n", - "outimag = xlnk.cma_array(shape=(128,), dtype=np.float)\n", + "samplereal = xlnk.cma_array(shape=(256,), dtype=np.float32)\n", + "sampleimag = xlnk.cma_array(shape=(256,), dtype=np.float32)\n", + "outreal = xlnk.cma_array(shape=(128,), dtype=np.float32)\n", + "outimag = xlnk.cma_array(shape=(128,), dtype=np.float32)\n", "\n", "for i in range(128):\n", " samplereal[i] = 1\n", @@ -125,35 +161,123 @@ "\n", "for j in range(128):\n", " sampleimag[j] = 0\n", - " \n", "dma0.sendchannel.transfer(samplereal)\n", "dma1.sendchannel.transfer(sampleimag)\n", "dma0.recvchannel.transfer(outreal)\n", - "dma1.recvchannel.transfer(outimag)\n", + "dma1.recvchannel.transfer(outimag)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.28000000e+02 -1.05679035e-04 -1.06751919e-04 -1.02400780e-04\n", + " -1.03890896e-04 -1.13725662e-04 -9.99569893e-05 -9.96589661e-05\n", + " -8.95261765e-05 -1.11162663e-04 -1.10268593e-04 -9.33408737e-05\n", + " -8.80956650e-05 -9.39965248e-05 -9.47713852e-05 -8.60691071e-05\n", + " -8.57710838e-05 -1.03712082e-04 -1.15334988e-04 -9.72151756e-05\n", + " -1.01268291e-04 -1.04188919e-04 -7.32243061e-05 -1.01685524e-04\n", + " -7.39991665e-05 -1.04993582e-04 -5.56111336e-05 -8.18669796e-05\n", + " -8.52942467e-05 -8.57859850e-05 -6.06626272e-05 -9.56729054e-05\n", + " -6.26959663e-05 -7.34664500e-05 -1.10194087e-04 -6.93947077e-05\n", + " 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2.39983201e-04 2.82794237e-04 3.08800489e-04\n", + " 1.39212061e-04 1.77212059e-04 2.82160938e-04 2.96190381e-04\n", + " 2.67401338e-04 3.67835164e-04 9.25660133e-05 4.14997339e-04\n", + " 4.20778990e-04 5.84661961e-04 4.18454409e-04 5.87582588e-04\n", + " 5.99503517e-04 2.78353691e-04 7.01725483e-04 7.28487968e-04\n", + " 7.06911087e-04 8.83519650e-04 7.99238682e-04 9.42885876e-04\n", + " 1.09153986e-03 1.22094154e-03 1.29455328e-03 1.37537718e-03\n", + " 1.56307220e-03 1.94227695e-03 2.22218037e-03 2.72601843e-03\n", + " 3.37558985e-03 4.37468290e-03 6.94578886e-03 1.35775805e-02]\n", + "[ 0.00000000e+00 1.02669001e-05 -9.96142626e-06 -8.22544098e-06\n", + " -9.49203968e-06 -4.03821468e-06 -1.25467777e-05 -1.49905682e-05\n", + " -1.92224979e-05 -2.83718109e-05 -2.15172768e-05 -2.99811363e-05\n", + " -3.56435776e-05 -1.93119049e-05 -4.14848328e-05 -3.36170197e-05\n", + " -3.99351120e-05 -3.85642052e-05 -3.78489494e-05 -5.29289246e-05\n", + " -4.93526459e-05 -5.54919243e-05 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-2.21490860e-04 -2.63750553e-04 -2.34246254e-04\n", + " -1.63912773e-04 -2.17080116e-04 -1.94132328e-04 -2.36749649e-04\n", + " -2.48491764e-04 -2.19106674e-04 -2.34305859e-04 -2.20000744e-04\n", + " -2.37584114e-04 -2.51948833e-04 -2.00867653e-04 -2.57432461e-04\n", + " -2.94566154e-04 -2.62618065e-04 -2.23934650e-04 -2.07483768e-04\n", + " -1.16825104e-04 -2.82347202e-04 -1.15275383e-04 -2.69472599e-04\n", + " -2.08199024e-04 -2.27272511e-04 -1.06632710e-04 -2.30967999e-04\n", + " -3.68654728e-04 -2.84314156e-04 -5.05626202e-04 -2.30193138e-04\n", + " -2.58386135e-04 -2.36988068e-04 -3.60608101e-04 -3.11732292e-04\n", + " -2.18391418e-04 -2.89916992e-04 -2.95162201e-04 -3.28958035e-04\n", + " -2.81572342e-04 5.18560410e-06 -3.53723764e-04 -3.40133905e-04\n", + " -3.83913517e-04 -2.85893679e-04 -2.80231237e-04 -3.38971615e-04\n", + " -2.73302197e-04 -3.10257077e-04 -3.85843217e-04 -3.24144959e-04]\n" + ] + } + ], + "source": [ "print(outreal)\n", "print(outimag)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 19, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -180,13 +304,19 @@ "actualreal = samplereal[0:128]\n", "fig1 = plt.figure()\n", "ax1 = fig1.gca()\n", - "plt.plot(actualreal)\n", + "plt.plot(outreal)\n", "\n", "fig2 = plt.figure()\n", "ax2 = fig2.gca()\n", - "\n", - "plt.plot(outreal)" + "plt.plot(outimag)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -205,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/06-SPMV.ipynb b/boards/Pynq-Z1/notebooks/06-SPMV.ipynb new file mode 100644 index 0000000..15e6be9 --- /dev/null +++ b/boards/Pynq-Z1/notebooks/06-SPMV.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "spmvol = pynq.Overlay(\"spmv.bit\")\n", + "\n", + "dma0 = spmvol.axi_dma_0\n", + "dma1 = spmvol.axi_dma_1\n", + "dma2 = spmvol.axi_dma_2\n", + "dma3 = spmvol.axi_dma_3" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# As a data structure, **CRS** is composed of three arrays. The values (**values**) array holds the values of non-zero elements of the matrix. The columnIndex (**columnIndex**) array and row pointer (** rowPtr**) array encode the position information of non-zero elements. A column index stores the elements of each column, and a row pointer contains the value of the first element of each row.\n", + "**CRS** 作为一种数据结构，由3个数组组成。值(**values**)数组保存矩阵中非零元素的值。列索引(**columnIndex**)数组和行指针（**rowPtr**）数组对非零元素的位置信息进行编码。列索引存储每一列的元素，行指针包含每一行第一个元素的值。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/crs.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "rowPtr = xlnk.cma_array(shape=(5,), dtype=np.int)\n", + "columnIndex = xlnk.cma_array(shape=(9,), dtype=np.int)\n", + "values = xlnk.cma_array(shape=(9,), dtype=np.float32)\n", + "x = xlnk.cma_array(shape=(4,), dtype=np.float32)\n", + "y = xlnk.cma_array(shape=(4,), dtype=np.float32)\n", + "\n", + "rowPtr[0] = 0\n", + "rowPtr[1] = 2\n", + "rowPtr[2] = 4\n", + "rowPtr[3] = 7\n", + "rowPtr[4] = 9\n", + "\n", + "columnIndex[0] = 0\n", + "columnIndex[1] = 1\n", + "columnIndex[2] = 1\n", + "columnIndex[3] = 2\n", + "columnIndex[4] = 0\n", + "columnIndex[5] = 2\n", + "columnIndex[6] = 3\n", + "columnIndex[7] = 1\n", + "columnIndex[8] = 3\n", + "\n", + "values[0] = 3\n", + "values[1] = 4\n", + "values[2] = 5\n", + "values[3] = 9\n", + "values[4] = 2\n", + "values[5] = 3\n", + "values[6] = 1\n", + "values[7] = 4\n", + "values[8] = 6\n", + "\n", + "x[0] = 1\n", + "x[1] = 2\n", + "x[2] = 3\n", + "x[3] = 4\n", + "\n", + "dma0.sendchannel.transfer(rowPtr)\n", + "dma1.sendchannel.transfer(columnIndex)\n", + "dma2.sendchannel.transfer(values)\n", + "dma3.sendchannel.transfer(x)\n", + "dma0.recvchannel.transfer(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 11. 37. 15. 32.]\n" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write test cases\n", + "写测试用例" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11 37 15 32]\n" + ] + } + ], + "source": [ + "testx = ([1,2,3,4])\n", + "testm = ([3,4,0,0],[0,5,9,0],[2,0,3,1],[0,4,0,6])\n", + "testy = xlnk.cma_array(shape=(4,), dtype=np.int)\n", + "for i in range(4):\n", + " y0 = 0\n", + " for j in range(4):\n", + " y0 += testm[i][j] * testx[j]\n", + " \n", + " testy[i] = y0\n", + " \n", + "print(testy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare the data, correct 1, incorrect -1\n", + "对比数据,正确1,不正确为-1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "for i in range(4):\n", + " sigma = 1 if testy[i] == y[i] else -1\n", + " \n", + "print(sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z1/notebooks/06-SPVM.ipynb b/boards/Pynq-Z1/notebooks/06-SPVM.ipynb deleted file mode 100644 index f98b172..0000000 --- a/boards/Pynq-Z1/notebooks/06-SPVM.ipynb +++ /dev/null @@ -1,133 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ContiguousArray([11, 37, 15, 32])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pynq.lib.dma\n", - "import numpy as np", - "\n", - "spmvol = pynq.Overlay(\"./src/spmv/spmv.bit\")\n", - "\n", - "dma0 = spmvol.axi_dma_0\n", - "dma1 = spmvol.axi_dma_1\n", - "dma2 = spmvol.axi_dma_2\n", - "dma3 = spmvol.axi_dma_3\n", - "\n", - "\n", - "from pynq import Xlnk\n", - "xlnk = Xlnk()\n", - "rowPtr = xlnk.cma_array(shape=(5,), dtype=np.int)\n", - "columnIndex = xlnk.cma_array(shape=(9,), dtype=np.int)\n", - "values = xlnk.cma_array(shape=(9,), dtype=np.int)\n", - "x = xlnk.cma_array(shape=(4,), dtype=np.int)\n", - "y = xlnk.cma_array(shape=(4,), dtype=np.int)\n", - "t1 = xlnk.cma_array(shape=(4,), dtype=np.int)\n", - "t2 = xlnk.cma_array(shape=(4,), dtype=np.int)\n", - "t3 = xlnk.cma_array(shape=(4,), dtype=np.int)\n", - "\n", - "rowPtr[0] = 0\n", - "rowPtr[1] = 2\n", - "rowPtr[2] = 4\n", - "rowPtr[3] = 7\n", - "rowPtr[4] = 9\n", - "\n", - "columnIndex[0] = 0\n", - "columnIndex[1] = 1\n", - "columnIndex[2] = 1\n", - "columnIndex[3] = 2\n", - "columnIndex[4] = 0\n", - "columnIndex[5] = 2\n", - "columnIndex[6] = 3\n", - "columnIndex[7] = 1\n", - "columnIndex[8] = 3\n", - "\n", - "values[0] = 3\n", - "values[1] = 4\n", - "values[2] = 5\n", - "values[3] = 9\n", - "values[4] = 2\n", - "values[5] = 3\n", - "values[6] = 1\n", - "values[7] = 4\n", - "values[8] = 6\n", - "\n", - "x[0] = 1\n", - "x[1] = 2\n", - "x[2] = 3\n", - "x[3] = 4\n", - "\n", - "dma0.sendchannel.transfer(rowPtr)\n", - "dma1.sendchannel.transfer(columnIndex)\n", - "dma2.sendchannel.transfer(values)\n", - "dma3.sendchannel.transfer(x)\n", - "dma0.recvchannel.transfer(y)\n", - "dma1.recvchannel.transfer(t1)\n", - "dma2.recvchannel.transfer(t2)\n", - "dma3.recvchannel.transfer(t3)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[11 37 15 32]\n" - ] - } - ], - "source": [ - "\n", - "testx = ([1,2,3,4])\n", - "testm = ([3,4,0,0],[0,5,9,0],[2,0,3,1],[0,4,0,6])\n", - "testy = xlnk.cma_array(shape=(4,), dtype=np.int)\n", - "for i in range(4):\n", - " y0 = 0\n", - " for j in range(4):\n", - " y0 += testm[i][j] * testx[j]\n", - " \n", - " testy[i] = y0\n", - " \n", - "print(testy)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/boards/Pynq-Z1/notebooks/07-MATRIXM.ipynb b/boards/Pynq-Z1/notebooks/07-MATRIXM.ipynb index eef0e10..8c92023 100644 --- a/boards/Pynq-Z1/notebooks/07-MATRIXM.ipynb +++ b/boards/Pynq-Z1/notebooks/07-MATRIXM.ipynb @@ -1,54 +1,110 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]\n", - " ...\n", - " [2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]]\n", - "[[2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]\n", - " ...\n", - " [2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]\n", - " [2 2 2 ... 2 2 2]]\n", - "[[128 128 128 ... 128 128 128]\n", - " [128 128 128 ... 128 128 128]\n", - " [128 128 128 ... 128 128 128]\n", - " ...\n", - " [128 128 128 ... 128 128 128]\n", - " [128 128 128 ... 128 128 128]\n", - " [128 128 128 ... 128 128 128]]\n" - ] + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "import pynq.lib.dma\n", "import numpy as np\n", "\n", - "mmol = pynq.Overlay(\"./src/matrixm/matrixm.bit\")\n", + "mmol = pynq.Overlay(\"matrixm.bit\")\n", "\n", "dma0 = mmol.axi_dma_0\n", - "dma1 = mmol.axi_dma_1\n", - "\n", - "\n", + "dma1 = mmol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " ..., \n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]]\n", + "[[2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " ..., \n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]]\n", + "[[128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]\n", + " ..., \n", + " [128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]]\n" + ] + } + ], + "source": [ + "#生成输入数据,并输出结果\n", "from pynq import Xlnk\n", "xlnk = Xlnk()\n", "A = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", "B = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", "AB = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", - "test = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", "\n", "for i in range(32):\n", " for j in range(32):\n", @@ -58,12 +114,18 @@ "dma0.sendchannel.transfer(A)\n", "dma1.sendchannel.transfer(B)\n", "dma0.recvchannel.transfer(AB)\n", - "dma1.recvchannel.transfer(test)\n", "\n", "print(A)\n", "print(B)\n", "print(AB)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -82,7 +144,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/08-HISTOGRAM.ipynb b/boards/Pynq-Z1/notebooks/08-HISTOGRAM.ipynb index 84e584e..f1d9651 100644 --- a/boards/Pynq-Z1/notebooks/08-HISTOGRAM.ipynb +++ b/boards/Pynq-Z1/notebooks/08-HISTOGRAM.ipynb @@ -1,31 +1,132 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "from pynq import DefaultIP\n", + "\n", "class SumDriver(DefaultIP):\n", " def __init__(self, description):\n", " super().__init__(description=description)\n", " \n", - " bindto = ['xilinx.com:histogram::1.0']\n", - " \n", - " @property\n", - " def x(self):\n", - " return self.read(0x10)\n", - " \n", - " @x.setter\n", - " def x(self, value):\n", - " self.write(0x10, value)" + " bindto = ['xilinx.com:histogram::1.0']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "hisol = pynq.Overlay(\"histogram.bit\")\n", + "\n", + "# dma = overlay.const_multiply.multiply_dma\n", + "# multiply = overlay.const_multiply.multiply\n", + "\n", + "dma = hisol.axi_dma_0\n", + "# s = sumol.sum_0" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The following is an example of a histogram\n", + "以下为直方图实例" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/histogram_introd.jpg)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -58,18 +159,6 @@ } ], "source": [ - "import pynq.lib.dma\n", - "import numpy as np\n", - "\n", - "hisol = pynq.Overlay(\"./src/histogram/histogram.bit\")\n", - "\n", - "# dma = overlay.const_multiply.multiply_dma\n", - "# multiply = overlay.const_multiply.multiply\n", - "\n", - "dma = hisol.axi_dma_0\n", - "# s = sumol.sum_0\n", - "\n", - "\n", "from pynq import Xlnk\n", "\n", "xlnk = Xlnk()\n", @@ -105,9 +194,17 @@ "print(result)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -116,15 +213,15 @@ "" ] }, - "execution_count": 45, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -158,6 +255,13 @@ "\n", "plt.stem(result)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -176,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/08-SUM.ipynb b/boards/Pynq-Z1/notebooks/08-SUM.ipynb index d6d1409..9988946 100644 --- a/boards/Pynq-Z1/notebooks/08-SUM.ipynb +++ b/boards/Pynq-Z1/notebooks/08-SUM.ipynb @@ -1,8 +1,76 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "sumol = pynq.Overlay(\"sum.bit\")\n", + "\n", + "# dma = overlay.const_multiply.multiply_dma\n", + "# multiply = overlay.const_multiply.multiply\n", + "\n", + "dma = sumol.axi_dma_0\n", + "# s = sumol.sum_0" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -25,18 +93,6 @@ } ], "source": [ - "import pynq.lib.dma\n", - "import numpy as np\n", - "\n", - "sumol = pynq.Overlay(\"./src/sum/sumtest.bit\")\n", - "\n", - "# dma = overlay.const_multiply.multiply_dma\n", - "# multiply = overlay.const_multiply.multiply\n", - "\n", - "dma = sumol.axi_dma_0\n", - "# s = sumol.sum_0\n", - "\n", - "\n", "from pynq import Xlnk\n", "\n", "xlnk = Xlnk()\n", @@ -55,26 +111,34 @@ "print(out_buffer)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 9, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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Mvv8LqqrXp1+XZPCfjCQzVTU96T4Wy/4ny/4nZzn3Dsurf4d6JKkxBr8kNeZ0DP4tk27gJNn/ZNn/5Czn3mEZ9X/ajfFLkk7sdDzjlySdwGkT/Ek2JXkkyYEk75t0Pws53k3sk7wiybYkX+y+rpl0ryeSZEWSLyS5q5u/MMmO7vvwqSQvnnSPx5NkdZLbk+xP8nCSf7mcjn+S/9T923koyZ8kOXMpH/8kf5jkiSQPzVk27/HOwO90+7E7yQ9PrvN/7nW+/v9r9+9nd5I7k6yes+79Xf+PJHnLZLqe32kR/ElWADcBbwU2AD+XZMNku1rQsZvYbwAuA97d9fw+4HNVdRHwuW5+KbuewX2Yj/kw8FtV9WrgG8B1E+mqn48Cf1lVrwXewGA/lsXxT3Ie8CvAdFVdDKwArmFpH/+PA5uGlh3veL8VuKh7bAZ+/xT1eCIf5/n9bwMurqofAv438H6A7mf5GuD13XN+r8upJeG0CH5gI3Cgqg5W1XeATwJXTbinEzrBTeyvAm7tym4Flux9ipOsBf4d8AfdfBjcfvP2rmTJ9p/k5cAbGdxkiKr6TlU9xTI6/gz+W/WzkqwEvgd4nCV8/Kvq88CTQ4uPd7yvAv57d1e/e4HVSc49NZ3Ob77+q+qvq+pIN3svsLabvgr4ZFU9W1WPAgcY5NSScLoE/3nAY3PmD3XLloWhm9ifU1WPd6v+EThnQm318dvArwPPdfOvBJ6a84OwlL8PFwKzwB91Q1V/kOQlLJPj393S9L8xuPvd48A3gZ0sn+N/zPGO93L8mf6PwGe76SXd/+kS/MvWiW5iX4NLrpbkZVdJfgJ4oqp2TrqXRVoJ/DDw+1V1KfB/GRrWWeLHfw2Ds8oLge8DXsLzhyGWlaV8vBeS5IMMhm9vm3QvfZwuwX8YOH/O/Npu2ZJ2nJvY/59jv9J2X5+YVH8LuBx4W5IvMxhau4LBmPnqbugBlvb34RBwqKp2dPO3M3gjWC7H/8eAR6tqtqq+C9zB4HuyXI7/Mcc73svmZzrJO4GfAH6+/v/18Uu6/9Ml+O8HLuquaHgxgz+qbJ1wTyd0gpvYbwWu7aavBf7Hqe6tj6p6f1Wtrar1DI733VX188B24OqubCn3/4/AY0l+oFt0JbCPZXL8GQzxXJbke7p/S8f6XxbHf47jHe+twDu6q3suA745Z0hoyUiyicFw59uq6v/NWbUVuCbJGUkuZPBH6vsm0eO8quq0eAA/zuCv6l8CPjjpfnr0+6MMfq3dDezqHj/OYJz8c8AXgb8BXjHpXnvsy78B7uqmX8XgH/gB4E+BMybd3wn6vgSY6b4HfwasWU7HH7gB2A88BHwCOGMpH3/gTxj8PeK7DH7juu54x5vBfb5v6n6e9zC4emkp9n+AwVj+sZ/hm+fUf7Dr/xHgrZPuf+7DT+5KUmNOl6EeSVJPBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY35J9hML8tbvWNaAAAAAElFTkSuQmCC\n", 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -108,6 +172,13 @@ "\n", "plt.plot(out_buffer)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -126,7 +197,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/09-VideoSystem.ipynb b/boards/Pynq-Z1/notebooks/09-VideoSystem.ipynb index f015b18..0c68d9b 100644 --- a/boards/Pynq-Z1/notebooks/09-VideoSystem.ipynb +++ b/boards/Pynq-Z1/notebooks/09-VideoSystem.ipynb @@ -1,23 +1,59 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "import pynq.lib.dma\n", "import numpy as np\n", "\n", - "vsol = pynq.Overlay(\"./src/vs/vs.bit\")\n", + "vsol = pynq.Overlay(\"vs.bit\")\n", "\n", "dma0 = vsol.axi_dma_0\n", "dma1 = vsol.axi_dma_1\n", - "dma2 = vsol.axi_dma_2\n", - "\n", - "\n", + "dma2 = vsol.axi_dma_2" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ "from pynq import Xlnk\n", "xlnk = Xlnk()\n", "\n", @@ -39,38 +75,37 @@ "dma2.sendchannel.transfer(Bin)\n", "dma0.recvchannel.transfer(Rout)\n", "dma1.recvchannel.transfer(Gout)\n", - "dma2.recvchannel.transfer(Bout)\n", - "\n", - "\n" + "dma2.recvchannel.transfer(Bout)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "现在做一个滤镜测试" + "# Do a filter test\n", + "做一个滤镜测试" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ - "" + "" ] }, - "execution_count": 57, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from PIL import Image\n", - "vstest = Image.open('./src/vs/vstest_32x20.png')\n", + "vstest = Image.open('./data/vstest_32x20.png')\n", "\n", "testarray = np.array(vstest)\n", "\n", @@ -82,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -147,11 +182,10 @@ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n", - " dtype=uint32)" + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint32)" ] }, - "execution_count": 60, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +193,7 @@ "source": [ "import pynq.lib.dma\n", "\n", - "vsol = pynq.Overlay(\"./src/vs/vs.bit\")\n", + "vsol = pynq.Overlay(\"vs.bit\")\n", "\n", "dma0 = vsol.axi_dma_0\n", "dma1 = vsol.axi_dma_1\n", @@ -193,17 +227,17 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 66, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -214,9 +248,16 @@ "newimage[:,:,1] = Gout;\n", "newimage[:,:,2] = Bout;\n", "img = Image.fromarray(newimage,'RGB')\n", - "img.save('./src/vs/vstest_after.png')\n", - "img" + "img.save('./data/vstest_after.png')\n", + "img\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -235,7 +276,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/10-SORT.ipynb b/boards/Pynq-Z1/notebooks/10-SORT.ipynb index 5e09f08..23e3c03 100644 --- a/boards/Pynq-Z1/notebooks/10-SORT.ipynb +++ b/boards/Pynq-Z1/notebooks/10-SORT.ipynb @@ -1,34 +1,105 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "input is:\n", - "[49 26 76 71 35 72 76 73 40 46 64 12 72 74 75 59]\n", - "sorted output:\n", - "[12 26 35 40 46 49 59 64 71 72 72 73 74 75 76 76]\n" - ] + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "import pynq.lib.dma\n", "import numpy as np\n", "import random\n", - "sortol = pynq.Overlay(\"./src/sort/sort.bit\")\n", + "sortol = pynq.Overlay(\"sort.bit\")\n", "actualin = np.array\n", "# dma = overlay.const_multiply.multiply_dma\n", "# multiply = overlay.const_multiply.multiply\n", "\n", "dma = sortol.axi_dma_0\n", - "# s = sumol.sum_0\n", - "\n", - "\n", + "# s = sumol.sum_0" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Insertion sort is a basic sorting algorithm whose core idea is to insert a new element into an ordered array and keep it in order. In each step, insert a record to be sorted into the appropriate place in the previously sorted file according to the size of its key value until all the records are inserted.\n", + "插入排序是一种基本的排序算法，其核心思想是将一个新的元素插入到一个有序数组中，并继续保持有序。每步将一个待排序的记录，按其关键码值的大小插入前面已经排序的文件中适当位置上，直到全部插入完为止。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/10.1.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input is:\n", + "[ 53 27 41 57 5 59 100 17 8 34 74 33 90 25 68 78]\n", + "sorted output:\n", + "[ 5 8 17 25 27 33 34 41 53 57 59 68 74 78 90 100]\n" + ] + } + ], + "source": [ "from pynq import Xlnk\n", "\n", "xlnk = Xlnk()\n", @@ -54,6 +125,13 @@ "print(\"sorted output:\")\n", "print(out_buffer)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -72,7 +150,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/11-HUFFMAN.ipynb b/boards/Pynq-Z1/notebooks/11-HUFFMAN.ipynb index 28769aa..5ee3d4d 100644 --- a/boards/Pynq-Z1/notebooks/11-HUFFMAN.ipynb +++ b/boards/Pynq-Z1/notebooks/11-HUFFMAN.ipynb @@ -1,42 +1,69 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "import pynq.lib.dma\n", "import numpy as np\n", - "\n", - "hmol = pynq.Overlay(\"./src/huffman/huffman.bit\")\n", + "hmol = pynq.Overlay(\"huffman.bit\")\n", "\n", "dma0 = hmol.axi_dma_0\n", - "dma1 = hmol.axi_dma_1\n", - "\n", - "from pynq import Xlnk\n", - "xlnk = Xlnk()\n", - "inputvalue = xlnk.cma_array(shape=(256,), dtype=np.uint16)\n", - "inputfrequency = xlnk.cma_array(shape=(256,), dtype=np.uint32)\n", - "encoding_v = xlnk.cma_array(shape=(256,), dtype=np.uint32)\n", - "num_nonzero_symbol = xlnk.cma_array(shape=(1,), dtype=np.int)\n", - "\n", - "for i in range(256):\n", - " inputvalue[i] = i;\n", - " inputfrequency[i] = ;\n", - "\n", - "\n", - "dma0.sendchannel.transfer(inputvalue)\n", - "dma1.sendchannel.transfer(inputfrequency)\n", - "dma0.recvchannel.transfer(encoding_v)\n", - "dma1.recvchannel.transfer(num_nonzero_symbol)\n" + "dma1 = hmol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Canonical hoffman codes as follows:\n", + "Canonical霍夫曼编码如下：" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/canonical_huffman_flow.jpg)" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -87,21 +114,16 @@ " 967, 8040, 232, 4328], dtype=uint32)" ] }, - "execution_count": 60, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "hmol = pynq.Overlay(\"./src/huffman/huffman.bit\")\n", - "\n", - "dma0 = hmol.axi_dma_0\n", - "dma1 = hmol.axi_dma_1\n", - "\n", - "\n", + "#生成输入数据,并输出结果\n", "data = []\n", "frequency = np.zeros(256, dtype = np.uint32)\n", - "with open(\"./src/huffman/huffman256.txt\") as f:\n", + "with open(\"./data/huffman256.txt\") as f:\n", " for line in f:\n", " data.append(line.split(\"\\n\"))\n", "\n", @@ -128,6 +150,13 @@ "\n", "encoding_v" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -135,6 +164,18 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/boards/Pynq-Z1/notebooks/data/10.1.png b/boards/Pynq-Z1/notebooks/data/10.1.png new file mode 100644 index 0000000..e57ecfd Binary files /dev/null and b/boards/Pynq-Z1/notebooks/data/10.1.png differ diff --git a/boards/Pynq-Z1/notebooks/data/8ptFFT.jpg b/boards/Pynq-Z1/notebooks/data/8ptFFT.jpg new file mode 100644 index 0000000..c4ebf43 Binary files /dev/null and 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at end of file diff --git a/boards/Pynq-Z2/notebooks/00-Tutorial.ipynb b/boards/Pynq-Z2/notebooks/00-Tutorial.ipynb new file mode 100644 index 0000000..2dd9131 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/00-Tutorial.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [中文教程](#在pynq中实现pp4fpga算法教程)\n", + "- [English tutorial](#Tutorial-for-algorithms-in-pp4fpga-with-pynq-board)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 在pynq中实现pp4fpga算法教程 \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "*（以下教程提供一种可行的方案，读者可以通过pynq官方文档和xilinx大学计划提供的其他教程探索其他实现方式）*\n", + "\n", + "\n", + "*（教程和所有工程都在2017.4版本下运行）*\n", + "\n", + "\n", + "基于在pp4fpga-cn的github目录中提供的代码示例我们需要以下几个步骤：\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "1. 在Vivado HLS中修改C/C++代码以生成可以接入block design的HLS算法IP核\n", + "2. 在Vivado中设计block design\n", + "3. Vivado将生成的.bit和.tcl文件拷入sd卡\n", + "4. 在pynq的jupyter notebook中写这个算法的python驱动\n", + "\n", + "以下我们以哈夫曼编码作为示范:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.在Vivado HLS中修改C/C++代码以生成可以接入block design的HLS算法IP核\n", + "\n", + "\n", + "\n", + "首先我们这里暂时不需要创造握手的控制信号，所以将顶层函数的接口类型改为ap_ctrl_none。\n", + "\n", + "输入输出的接口这里使用的是axis接口（axi stream），注意axis接口要求读/写顺序进行，如果某函数的读写不是顺序进行，可以建立一个temp数组顺序读入后操作或可以使用其他接口。\n", + "\n", + "有时候片上资源会不够用，这时候需要对函数做一些优化以节省资源，例如for循环pipeline，具体每个算法的优化方式可以在pp4fpga的书中找到。\n", + "\n", + "修改后的directive面板如下图所示。\n", + "\n", + "\n", + "![](./data/directive.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.在Vivado中设计block design\n", + "\n", + "我们将生成的HLS IP添加到Vivado工程的IP目录下。\n", + "\n", + "可以看到哈夫曼编码的IP有两个输入，两个输出，全部都是stream接口。stream接口需要DMA（AXI Direct Memory Access）与PS相连，所以我们再添加两个DMA。\n", + "\n", + "通过AXI Interconnect与PS相连，然后分配读写的路线，剩余内容可以auto-connect。\n", + "\n", + "最终的block design如下图所示。\n", + "\n", + "\n", + "![](./data/block.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.Vivado将生成的.bit和.tcl文件拷入sd卡\n", + "\n", + "完成block design后我们点击generate bitstream，生成这个设计的比特流文件。\n", + "\n", + "打开Vivado里的Tcl Console，使用命令 “write_bd_tcl <路径>” 生成tcl文件。\n", + "\n", + "将.bit和.tcl文件改成一样的名字，将他们放入sd卡。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.在pynq的jupyter notebook中写这个算法的python驱动\n", + "\n", + "打开pynq的jupyter notebook，用python编写这个程序的python驱动。\n", + "\n", + "教程的示例中提供的是stream接口的驱动的编写方式，其他例如lite接口驱动的示例可以从其他Xilinx大学计划的教程中获取。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial for algorithms in pp4fpga with pynq board" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*(This Tutorial provides one of the methods to to achieve the algorithms in the book and readers can explore other possible ways through offcial pynq documents and other Xilinx University Program tutorials.)*\n", + "\n", + "*(All the tutorial source files are under Vivado/Vivado HLS 2017.4 version.)*\n", + "\n", + "\n", + "With the code examples in pp4fpga-cn github repository(pp4fpga), we need following steps to build a project:\n", + "\n", + "\n", + "1. Revise the C/C++ code to create interfaces that can connect our HLS algorithm IP cores to the block design.\n", + "2. Finish the block design in Vivado.\n", + "3. Copy the .bit and .tcl file generated by Vivado to our sd card.\n", + "4. Write the project driver with python in pynq's jupyter notebook.\n", + "\n", + "\n", + "A example of achieving Huffman Encoding is provided as followed:\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Revise the C/C++ code to create interfaces that can connect our HLS algorithm IP cores to the block design\n", + "\n", + "We don't need to create handshake signals for now so first top function's interface type should be changed to ap_ctrl_none.\n", + "\n", + "We would use axis(axi stream) as input/output's interface type. Notice that axis interface requires that reading and writing have to be in sequences. If certain algorithms' r/w is not sequential, one easy solution is to create a temp array to read the input and do the manipulation on the temp array.\n", + "\n", + "Sometimes limited on-chip resources ask us to do some optimization first to pass the compilation, for example forloop's pipeline. Readers can find these optimizations illustrated in *pp4fpga*.\n", + "\n", + "The directive board after the revision is shown in the following picture.\n", + "\n", + "\n", + "\n", + "![](./data/directive.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Finish the block design in Vivado.\n", + "\n", + "\n", + "Then we need to add the HLS IP to the IP repository of our Vivado project.\n", + "\n", + "We can see Huffman Encoding's IP core has two inputs and two outputs, all under stream interfaces now. Stream interfaces need DMA(AXI Direct Memory Access) to be connected with PS. So we will add two DMA blocks.\n", + "\n", + "These interfaces are connected to PS through AXI Interconnect, and we need to assign the path for read/write. All the other ports can be handled by auto-connect button.\n", + "\n", + "The final block design is shown in the following picture.\n", + "\n", + "![](./data/block.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Copy the .bit and .tcl file generated by Vivado to our sd card.\n", + "\n", + "Having finished the block design, we can get the .bit file by clickong on generate bitstream button.\n", + "\n", + "Open the Tcl Console in Vivado, type the command \"write_bd_tcl \" to output the tcl file.\n", + " \n", + "Make sure that .bit and .tcl have the same file name, and put them into our sd card." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Write the project driver with python in pynq's jupyter notebook.\n", + "\n", + "Open the jupyter notebook in pynq, write the program's driver with python.\n", + "\n", + "The example in the tutorial resource is the driver for stream interface. The driver for other interfaces, such as lite interface, can be learned through other Xilinx University Program's tutorials or workshops." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/01-CORDIC.ipynb b/boards/Pynq-Z2/notebooks/01-CORDIC.ipynb deleted file mode 120000 index 4603118..0000000 --- a/boards/Pynq-Z2/notebooks/01-CORDIC.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/01-CORDIC.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/01-CORDIC.ipynb b/boards/Pynq-Z2/notebooks/01-CORDIC.ipynb new file mode 100644 index 0000000..68de696 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/01-CORDIC.ipynb @@ -0,0 +1,603 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from pp4fpgas import CordicOverlay\n", + "\n", + "overlay = CordicOverlay()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pynq import Overlay\n", + "overlay = Overlay('cordic.bit')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Register level driver\n", + "直接读写寄存器来使用overlay中的hls ip" + ] + }, + { + "attachments": { + "%E5%9C%B0%E5%9D%80%E8%AF%B4%E6%98%8E.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![%E5%9C%B0%E5%9D%80%E8%AF%B4%E6%98%8E.JPG](attachment:%E5%9C%B0%E5%9D%80%E8%AF%B4%E6%98%8E.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The data type of theta is ap_fixed<12,2>\n", + "\n", + "theta的数据类型为ap_fixed<12,2>" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "theta = 0b010000000000 \n", + "overlay.cordic_0.write(0x10, theta)\n", + "overlay.cordic_0.read(0x20)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1028\n", + "4091\n" + ] + } + ], + "source": [ + "#输出正弦和余弦值\n", + "cos = overlay.cordic_0.read(0x20)\n", + "sin = overlay.cordic_0.read(0x18)\n", + "print(cos)\n", + "print(sin)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learn something about this overlay\n", + "了解overlay相关的api，从中获取该overlay包含的信息" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'cordic_0': {'addr_range': 65536,\n", + " 'driver': pynq.overlay.DefaultIP,\n", + " 'fullpath': 'cordic_0',\n", + " 'gpio': {},\n", + " 'interrupts': {},\n", + " 'phys_addr': 1136656384,\n", + " 'state': None,\n", + " 'type': 'xilinx.com:hls:cordic:1.0'}}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlay.ip_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlay.gpio_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: {'divisor0': 5, 'divisor1': 2, 'enable': 1},\n", + " 1: {'divisor0': 1, 'divisor1': 1, 'enable': 0},\n", + " 2: {'divisor0': 1, 'divisor1': 1, 'enable': 0},\n", + " 3: {'divisor0': 1, 'divisor1': 1, 'enable': 0}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlay.clock_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/xilinx/jupyter_notebooks/pp4fpgas/cordic/cordic.bit'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlay.bitfile_name" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overlay.hierarchy_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on Overlay in module pynq.overlay object:\n", + "\n", + "class Overlay(pynq.pl.Bitstream)\n", + " | This class keeps track of a single bitstream's state and contents.\n", + " | \n", + " | The overlay class holds the state of the bitstream and enables run-time\n", + " | protection of bindlings.\n", + " | \n", + " | Our definition of overlay is: \"post-bitstream configurable design\".\n", + " | Hence, this class must expose configurability through content discovery\n", + " | and runtime protection.\n", + " | \n", + " | The overlay class exposes the IP and hierarchies as attributes in the\n", + " | overlay. If no other drivers are available the `DefaultIP` is constructed\n", + " | for IP cores at top level and `DefaultHierarchy` for any hierarchies that\n", + " | contain addressable IP. Custom drivers can be bound to IP and hierarchies\n", + " | by subclassing `DefaultIP` and `DefaultHierarchy`. See the help entries\n", + " | for those class for more details.\n", + " | \n", + " | This class stores four dictionaries: IP, GPIO, interrupt controller\n", + " | and interrupt pin dictionaries.\n", + " | \n", + " | Each entry of the IP dictionary is a mapping:\n", + " | 'name' -> {phys_addr, addr_range, type, config, state}, where\n", + " | name (str) is the key of the entry.\n", + " | phys_addr (int) is the physical address of the IP.\n", + " | addr_range (int) is the address range of the IP.\n", + " | type (str) is the type of the IP.\n", + " | config (dict) is a dictionary of the configuration parameters.\n", + " | state (str) is the state information about the IP.\n", + " | \n", + " | Each entry of the GPIO dictionary is a mapping:\n", + " | 'name' -> {pin, state}, where\n", + " | name (str) is the key of the entry.\n", + " | pin (int) is the user index of the GPIO, starting from 0.\n", + " | state (str) is the state information about the GPIO.\n", + " | \n", + " | Each entry in the interrupt controller dictionary is a mapping:\n", + " | 'name' -> {parent, index}, where\n", + " | name (str) is the name of the interrupt controller.\n", + " | parent (str) is the name of the parent controller or '' if attached\n", + " | directly to the PS7.\n", + " | index (int) is the index of the interrupt attached to.\n", + " | \n", + " | Each entry in the interrupt pin dictionary is a mapping:\n", + " | 'name' -> {controller, index}, where\n", + " | name (str) is the name of the pin.\n", + " | controller (str) is the name of the interrupt controller.\n", + " | index (int) is the line index.\n", + " | \n", + " | Attributes\n", + " | ----------\n", + " | bitfile_name : str\n", + " | The absolute path of the bitstream.\n", + " | bitstream : Bitstream\n", + " | The corresponding bitstream object.\n", + " | ip_dict : dict\n", + " | All the addressable IPs from PS7. Key is the name of the IP; value is\n", + " | a dictionary mapping the physical address, address range, IP type,\n", + " | configuration dictionary, and the state associated with that IP:\n", + " | {str: {'phys_addr' : int, 'addr_range' : int, 'type' : str, 'config' : dict, 'state' : str}}.\n", + " | gpio_dict : dict\n", + " | All the GPIO pins controlled by PS7. Key is the name of the GPIO pin;\n", + " | value is a dictionary mapping user index (starting from 0),\n", + " | and the state associated with that GPIO pin:\n", + " | {str: {'index' : int, 'state' : str}}.\n", + " | interrupt_controllers : dict\n", + " | All AXI interrupt controllers in the system attached to\n", + " | a PS7 interrupt line. Key is the name of the controller;\n", + " | value is a dictionary mapping parent interrupt controller and the\n", + " | line index of this interrupt:\n", + " | {str: {'parent': str, 'index' : int}}.\n", + " | The PS7 is the root of the hierarchy and is unnamed.\n", + " | interrupt_pins : dict\n", + " | All pins in the design attached to an interrupt controller.\n", + " | Key is the name of the pin; value is a dictionary\n", + " | mapping the interrupt controller and the line index used:\n", + " | {str: {'controller' : str, 'index' : int}}.\n", + " | \n", + " | Method resolution order:\n", + " | Overlay\n", + " | pynq.pl.Bitstream\n", + " | pynq.pl._BitstreamMeta\n", + " | builtins.object\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __dir__(self)\n", + " | __dir__() -> list\n", + " | default dir() implementation\n", + " | \n", + " | __getattr__(self, key)\n", + " | Overload of __getattr__ to return a driver for an IP or\n", + " | hierarchy. Throws an `RuntimeError` if the overlay is not loaded.\n", + " | \n", + " | __init__(self, bitfile_name, download=True, partial=False, ignore_version=False)\n", + " | Return a new Overlay object.\n", + " | \n", + " | An overlay instantiates a bitstream object as a member initially.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | bitfile_name : str\n", + " | The bitstream name or absolute path as a string.\n", + " | download : bool\n", + " | Whether the overlay should be downloaded.\n", + " | partial :\n", + " | Flag to indicate whether or not the bitstream is partial.\n", + " | \n", + " | Note\n", + " | ----\n", + " | This class requires a Vivado TCL file to be next to bitstream file\n", + " | with same name (e.g. `base.bit` and `base.tcl`).\n", + " | \n", + " | download(self)\n", + " | The method to download a bitstream onto PL.\n", + " | \n", + " | Note\n", + " | ----\n", + " | After the bitstream has been downloaded, the \"timestamp\" in PL will be\n", + " | updated. In addition, all the dictionaries on PL will\n", + " | be reset automatically.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | None\n", + " | \n", + " | is_loaded(self)\n", + " | This method checks whether a bitstream is loaded.\n", + " | \n", + " | This method returns true if the loaded PL bitstream is same\n", + " | as this Overlay's member bitstream.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | bool\n", + " | True if bitstream is loaded.\n", + " | \n", + " | load_ip_data(self, ip_name, data)\n", + " | This method loads the data to the addressable IP.\n", + " | \n", + " | Calls the method in the super class to load the data. This method can\n", + " | be used to program the IP. For example, users can use this method to\n", + " | load the program to the Microblaze processors on PL.\n", + " | \n", + " | Note\n", + " | ----\n", + " | The data is assumed to be in binary format (.bin). The data name will\n", + " | be stored as a state information in the IP dictionary.\n", + " | \n", + " | Parameters\n", + " | ----------\n", + " | ip_name : str\n", + " | The name of the addressable IP.\n", + " | data : str\n", + " | The absolute path of the data to be loaded.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | None\n", + " | \n", + " | reset(self)\n", + " | This function resets all the dictionaries kept in the overlay.\n", + " | \n", + " | This function should be used with caution. In most cases, only those\n", + " | dictionaries keeping track of states need to be updated.\n", + " | \n", + " | Returns\n", + " | -------\n", + " | None\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Methods inherited from pynq.pl.Bitstream:\n", + " | \n", + " | convert_bit_to_bin(self)\n", + " | The method to convert a .bit file to .bin file.\n", + " | \n", + " | A .bit file is generated by Vivado, but .bin files are needed\n", + " | by the Zynq Ultrascale FPGA manager driver. Users must specify\n", + " | the absolute path to the source .bit file, and the destination\n", + " | .bin file and have read/write access to both paths. \n", + " | \n", + " | Note\n", + " | ----\n", + " | Imlemented based on: https://blog.aeste.my/?p=2892\n", + " | \n", + " | Returns\n", + " | -------\n", + " | None\n", + " | \n", + " | parse_bit_header(self)\n", + " | The method to parse the header of a bitstream.\n", + " | \n", + " | The returned dictionary has the following keys:\n", + " | \"design\": str, the Vivado project name that generated the bitstream;\n", + " | \"version\": str, the Vivado tool version that generated the bitstream;\n", + " | \"part\": str, the Xilinx part name that the bitstream targets;\n", + " | \"date\": str, the date the bitstream was compiled on;\n", + " | \"time\": str, the time the bitstream finished compilation;\n", + " | \"length\": int, total length of the bitstream (in bytes);\n", + " | \"data\": binary, binary data in .bit file format\n", + " | \n", + " | Returns\n", + " | -------\n", + " | Dict\n", + " | A dictionary containing the header information.\n", + " | \n", + " | Note\n", + " | ----\n", + " | Implemented based on: https://blog.aeste.my/?p=2892\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data and other attributes inherited from pynq.pl.Bitstream:\n", + " | \n", + " | BS_FPGA_MAN = '/sys/class/fpga_manager/fpga0/firmware'\n", + " | \n", + " | BS_FPGA_MAN_FLAGS = '/sys/class/fpga_manager/fpga0/flags'\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors inherited from pynq.pl._BitstreamMeta:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + "\n" + ] + } + ], + "source": [ + "help(overlay)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from pynq import DefaultIP\n", + "\n", + "class cordicDriver(DefaultIP):\n", + " def __init__(self, description):\n", + " super().__init__(description=description)\n", + "\n", + " bindto = ['xilinx.com:hls:cordic:1.0']\n", + "\n", + " def calc(self, theta):\n", + " self.write(0x10, theta)\n", + " return self.read(0x20)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/pynq_pp4fpgas-1.0-py3.6.egg/pp4fpgas/cordic/cordic.bit load ready\n" + ] + } + ], + "source": [ + "from pp4fpgas import CordicOverlay\n", + "\n", + "cordic = CordicOverlay('cordic.bit')" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The data type is ap_fixed<12,2>\n", + "\n", + "数据类型为ap_fixed<12,2>" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "554" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cordic.cordic_0.calc(0b010000000000) #同theta" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/02-FIR.ipynb b/boards/Pynq-Z2/notebooks/02-FIR.ipynb deleted file mode 120000 index 8272b1b..0000000 --- a/boards/Pynq-Z2/notebooks/02-FIR.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/02-FIR.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/02-FIR.ipynb b/boards/Pynq-Z2/notebooks/02-FIR.ipynb new file mode 100644 index 0000000..e35909c --- /dev/null +++ b/boards/Pynq-Z2/notebooks/02-FIR.ipynb @@ -0,0 +1,9288 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pynq import DefaultIP\n", + "\n", + "class FirDriver(DefaultIP):\n", + " def __init__(self, description):\n", + " super().__init__(description=description)\n", + " \n", + " bindto = ['xilinx.com:hls:fir:1.0']\n", + " \n", + " @property\n", + " def x(self):\n", + " return self.read(0x10)\n", + " \n", + " @x.setter\n", + " def x(self, value):\n", + " self.write(0x10, value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "firol = pynq.Overlay(\"fir.bit\")" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolution of coefficient h[] of n-order FIR filter with input signal x[] can be expressed by difference equation\n", + "N-阶FIR滤波器的系数h[]与输入信号x[]的卷积可由差分方程表示：\n", + "\n", + "$$\n", + "y[i]=\\sum_{j=0}^{N-1}h[j]\\cdot x[i-j]\\quad(2.1)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ContiguousArray([ 0, 2, 2, 0, 0, 4, 10, 14, 14, 12, 12], dtype=uint32)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dma = overlay.const_multiply.multiply_dma\n", + "# multiply = overlay.const_multiply.multiply\n", + "\n", + "dma = firol.axi_dma_0\n", + "f = firol.fir_0\n", + "\n", + "\n", + "from pynq import Xlnk\n", + "\n", + "xlnk = Xlnk()\n", + "in_buffer = xlnk.cma_array(shape=(11,), dtype=np.uint32)\n", + "out_buffer = xlnk.cma_array(shape=(11,), dtype=np.uint32)\n", + "\n", + "\n", + "for i in range(11):\n", + " in_buffer[i] = 1\n", + "\n", + "filt = [1,0,-1,0,2,3,2,0,-1,0,1]\n", + "actualfilt = [53,0,-91,0,313,500,313,0,-91,0,53]\n", + "f.x = 2\n", + "dma.sendchannel.transfer(in_buffer)\n", + "dma.recvchannel.transfer(out_buffer)\n", + "dma.sendchannel.wait()\n", + "dma.recvchannel.wait()\n", + "out_buffer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pylab as py\n", + "import scipy as scipy\n", + "import matplotlib.pyplot as plt\n", + "import scipy.fftpack\n", + "import numpy.fft\n", + "\n", + "\n", + "fig1 = plt.figure()\n", + "ax1 = fig1.gca()\n", + "plt.plot(filt)\n", + "\n", + "fig2 = plt.figure()\n", + "ax2 = fig2.gca()\n", + "plt.plot(out_buffer)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FFT transformation, view the waveform\n", + "进行FFT变换，查看波形" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python3/dist-packages/numpy/core/numeric.py:531: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "out = scipy.fftpack.fft(actualfilt)\n", + "plt.plot(out)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on module matplotlib.pyplot in matplotlib:\n", + "\n", + "NAME\n", + " matplotlib.pyplot\n", + "\n", + "DESCRIPTION\n", + " `matplotlib.pyplot` is a state-based interface to matplotlib. It provides\n", + " a MATLAB-like way of plotting.\n", + " \n", + " pyplot is mainly intended for interactive plots and simple cases of programmatic\n", + " plot generation::\n", + " \n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " \n", + " x = np.arange(0, 5, 0.1)\n", + " y = np.sin(x)\n", + " plt.plot(x, y)\n", + " \n", + " The object-oriented API is recommended for more complex plots.\n", + "\n", + "FUNCTIONS\n", + " acorr(x, hold=None, data=None, **kwargs)\n", + " Plot the autocorrelation of `x`.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " x : sequence of scalar\n", + " \n", + " hold : boolean, optional, *deprecated*, default: True\n", + " \n", + " detrend : callable, optional, default: `mlab.detrend_none`\n", + " x is detrended by the `detrend` callable. Default is no\n", + " normalization.\n", + " \n", + " normed : boolean, optional, default: True\n", + " if True, input vectors are normalised to unit length.\n", + " \n", + " usevlines : boolean, optional, default: True\n", + " if True, Axes.vlines is used to plot the vertical lines from the\n", + " origin to the acorr. Otherwise, Axes.plot is used.\n", + " \n", + " maxlags : integer, optional, default: 10\n", + " number of lags to show. If None, will return all 2 * len(x) - 1\n", + " lags.\n", + " \n", + " Returns\n", + " -------\n", + " (lags, c, line, b) : where:\n", + " \n", + " - `lags` are a length 2`maxlags+1 lag vector.\n", + " - `c` is the 2`maxlags+1 auto correlation vectorI\n", + " - `line` is a `~matplotlib.lines.Line2D` instance returned by\n", + " `plot`.\n", + " - `b` is the x-axis.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None\n", + " Only used if usevlines is False.\n", + " \n", + " marker : string, optional, default: 'o'\n", + " \n", + " Notes\n", + " -----\n", + " The cross correlation is performed with :func:`numpy.correlate` with\n", + " `mode` = 2.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " angle_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, hold=None, data=None, **kwargs)\n", + " Plot the angle spectrum.\n", + " \n", + " Call signature::\n", + " \n", + " angle_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,\n", + " pad_to=None, sides='default', **kwargs)\n", + " \n", + " Compute the angle spectrum (wrapped phase spectrum) of *x*.\n", + " Data is padded to a length of *pad_to* and the windowing function\n", + " *window* is applied to the signal.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. While not increasing the actual resolution of\n", + " the spectrum (the minimum distance between resolvable peaks),\n", + " this can give more points in the plot, allowing for more\n", + " detail. This corresponds to the *n* parameter in the call to fft().\n", + " The default is None, which sets *pad_to* equal to the length of the\n", + " input signal (i.e. no padding).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 1-D array\n", + " The values for the angle spectrum in radians (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *spectrum*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " :func:`magnitude_spectrum`\n", + " :func:`angle_spectrum` plots the magnitudes of the corresponding\n", + " frequencies.\n", + " \n", + " :func:`phase_spectrum`\n", + " :func:`phase_spectrum` plots the unwrapped version of this\n", + " function.\n", + " \n", + " :func:`specgram`\n", + " :func:`specgram` can plot the angle spectrum of segments within the\n", + " signal in a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " annotate(*args, **kwargs)\n", + " Annotate the point ``xy`` with text ``s``.\n", + " \n", + " Additional kwargs are passed to `~matplotlib.text.Text`.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " s : str\n", + " The text of the annotation\n", + " \n", + " xy : iterable\n", + " Length 2 sequence specifying the *(x,y)* point to annotate\n", + " \n", + " xytext : iterable, optional\n", + " Length 2 sequence specifying the *(x,y)* to place the text\n", + " at. If None, defaults to ``xy``.\n", + " \n", + " xycoords : str, Artist, Transform, callable or tuple, optional\n", + " \n", + " The coordinate system that ``xy`` is given in.\n", + " \n", + " For a `str` the allowed values are:\n", + " \n", + " ================= ===============================================\n", + " Property Description\n", + " ================= ===============================================\n", + " 'figure points' points from the lower left of the figure\n", + " 'figure pixels' pixels from the lower left of the figure\n", + " 'figure fraction' fraction of figure from lower left\n", + " 'axes points' points from lower left corner of axes\n", + " 'axes pixels' pixels from lower left corner of axes\n", + " 'axes fraction' fraction of axes from lower left\n", + " 'data' use the coordinate system of the object being\n", + " annotated (default)\n", + " 'polar' *(theta,r)* if not native 'data' coordinates\n", + " ================= ===============================================\n", + " \n", + " If a `~matplotlib.artist.Artist` object is passed in the units are\n", + " fraction if it's bounding box.\n", + " \n", + " If a `~matplotlib.transforms.Transform` object is passed\n", + " in use that to transform ``xy`` to screen coordinates\n", + " \n", + " If a callable it must take a\n", + " `~matplotlib.backend_bases.RendererBase` object as input\n", + " and return a `~matplotlib.transforms.Transform` or\n", + " `~matplotlib.transforms.Bbox` object\n", + " \n", + " If a `tuple` must be length 2 tuple of str, `Artist`,\n", + " `Transform` or callable objects. The first transform is\n", + " used for the *x* coordinate and the second for *y*.\n", + " \n", + " See :ref:`plotting-guide-annotation` for more details.\n", + " \n", + " Defaults to ``'data'``\n", + " \n", + " textcoords : str, `Artist`, `Transform`, callable or tuple, optional\n", + " The coordinate system that ``xytext`` is given, which\n", + " may be different than the coordinate system used for\n", + " ``xy``.\n", + " \n", + " All ``xycoords`` values are valid as well as the following\n", + " strings:\n", + " \n", + " ================= =========================================\n", + " Property Description\n", + " ================= =========================================\n", + " 'offset points' offset (in points) from the *xy* value\n", + " 'offset pixels' offset (in pixels) from the *xy* value\n", + " ================= =========================================\n", + " \n", + " defaults to the input of ``xycoords``\n", + " \n", + " arrowprops : dict, optional\n", + " If not None, properties used to draw a\n", + " `~matplotlib.patches.FancyArrowPatch` arrow between ``xy`` and\n", + " ``xytext``.\n", + " \n", + " If `arrowprops` does not contain the key ``'arrowstyle'`` the\n", + " allowed keys are:\n", + " \n", + " ========== ======================================================\n", + " Key Description\n", + " ========== ======================================================\n", + " width the width of the arrow in points\n", + " headwidth the width of the base of the arrow head in points\n", + " headlength the length of the arrow head in points\n", + " shrink fraction of total length to 'shrink' from both ends\n", + " ? any key to :class:`matplotlib.patches.FancyArrowPatch`\n", + " ========== ======================================================\n", + " \n", + " If the `arrowprops` contains the key ``'arrowstyle'`` the\n", + " above keys are forbidden. The allowed values of\n", + " ``'arrowstyle'`` are:\n", + " \n", + " ============ =============================================\n", + " Name Attrs\n", + " ============ =============================================\n", + " ``'-'`` None\n", + " ``'->'`` head_length=0.4,head_width=0.2\n", + " ``'-['`` widthB=1.0,lengthB=0.2,angleB=None\n", + " ``'|-|'`` widthA=1.0,widthB=1.0\n", + " ``'-|>'`` head_length=0.4,head_width=0.2\n", + " ``'<-'`` head_length=0.4,head_width=0.2\n", + " ``'<->'`` head_length=0.4,head_width=0.2\n", + " ``'<|-'`` head_length=0.4,head_width=0.2\n", + " ``'<|-|>'`` head_length=0.4,head_width=0.2\n", + " ``'fancy'`` head_length=0.4,head_width=0.4,tail_width=0.4\n", + " ``'simple'`` head_length=0.5,head_width=0.5,tail_width=0.2\n", + " ``'wedge'`` tail_width=0.3,shrink_factor=0.5\n", + " ============ =============================================\n", + " \n", + " Valid keys for `~matplotlib.patches.FancyArrowPatch` are:\n", + " \n", + " =============== ==================================================\n", + " Key Description\n", + " =============== ==================================================\n", + " arrowstyle the arrow style\n", + " connectionstyle the connection style\n", + " relpos default is (0.5, 0.5)\n", + " patchA default is bounding box of the text\n", + " patchB default is None\n", + " shrinkA default is 2 points\n", + " shrinkB default is 2 points\n", + " mutation_scale default is text size (in points)\n", + " mutation_aspect default is 1.\n", + " ? any key for :class:`matplotlib.patches.PathPatch`\n", + " =============== ==================================================\n", + " \n", + " Defaults to None\n", + " \n", + " annotation_clip : bool, optional\n", + " Controls the visibility of the annotation when it goes\n", + " outside the axes area.\n", + " \n", + " If `True`, the annotation will only be drawn when the\n", + " ``xy`` is inside the axes. If `False`, the annotation will\n", + " always be drawn regardless of its position.\n", + " \n", + " The default is `None`, which behave as `True` only if\n", + " *xycoords* is \"data\".\n", + " \n", + " Returns\n", + " -------\n", + " Annotation\n", + " \n", + " arrow(x, y, dx, dy, hold=None, **kwargs)\n", + " Add an arrow to the axes.\n", + " \n", + " Draws arrow on specified axis from (`x`, `y`) to (`x` + `dx`,\n", + " `y` + `dy`). Uses FancyArrow patch to construct the arrow.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : float\n", + " X-coordinate of the arrow base\n", + " y : float\n", + " Y-coordinate of the arrow base\n", + " dx : float\n", + " Length of arrow along x-coordinate\n", + " dy : float\n", + " Length of arrow along y-coordinate\n", + " \n", + " Returns\n", + " -------\n", + " a : FancyArrow\n", + " patches.FancyArrow object\n", + " \n", + " Other Parameters\n", + " -----------------\n", + " Optional kwargs (inherited from FancyArrow patch) control the arrow\n", + " construction and properties:\n", + " \n", + " Constructor arguments\n", + " *width*: float (default: 0.001)\n", + " width of full arrow tail\n", + " \n", + " *length_includes_head*: [True | False] (default: False)\n", + " True if head is to be counted in calculating the length.\n", + " \n", + " *head_width*: float or None (default: 3*width)\n", + " total width of the full arrow head\n", + " \n", + " *head_length*: float or None (default: 1.5 * head_width)\n", + " length of arrow head\n", + " \n", + " *shape*: ['full', 'left', 'right'] (default: 'full')\n", + " draw the left-half, right-half, or full arrow\n", + " \n", + " *overhang*: float (default: 0)\n", + " fraction that the arrow is swept back (0 overhang means\n", + " triangular shape). Can be negative or greater than one.\n", + " \n", + " *head_starts_at_zero*: [True | False] (default: False)\n", + " if True, the head starts being drawn at coordinate 0\n", + " instead of ending at coordinate 0.\n", + " \n", + " Other valid kwargs (inherited from :class:`Patch`) are:\n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " The resulting arrow is affected by the axes aspect ratio and limits.\n", + " This may produce an arrow whose head is not square with its stem. To\n", + " create an arrow whose head is square with its stem, use\n", + " :meth:`annotate` for example::\n", + " \n", + " ax.annotate(\"\", xy=(0.5, 0.5), xytext=(0, 0),\n", + " arrowprops=dict(arrowstyle=\"->\"))\n", + " \n", + " autoscale(enable=True, axis='both', tight=None)\n", + " Autoscale the axis view to the data (toggle).\n", + " \n", + " Convenience method for simple axis view autoscaling.\n", + " It turns autoscaling on or off, and then,\n", + " if autoscaling for either axis is on, it performs\n", + " the autoscaling on the specified axis or axes.\n", + " \n", + " *enable*: [True | False | None]\n", + " True (default) turns autoscaling on, False turns it off.\n", + " None leaves the autoscaling state unchanged.\n", + " \n", + " *axis*: ['x' | 'y' | 'both']\n", + " which axis to operate on; default is 'both'\n", + " \n", + " *tight*: [True | False | None]\n", + " If True, set view limits to data limits;\n", + " if False, let the locator and margins expand the view limits;\n", + " if None, use tight scaling if the only artist is an image,\n", + " otherwise treat *tight* as False.\n", + " The *tight* setting is retained for future autoscaling\n", + " until it is explicitly changed.\n", + " \n", + " \n", + " Returns None.\n", + " \n", + " autumn()\n", + " set the default colormap to autumn and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " axes(*args, **kwargs)\n", + " Add an axes to the figure.\n", + " \n", + " The axes is added at position *rect* specified by:\n", + " \n", + " - ``axes()`` by itself creates a default full ``subplot(111)`` window axis.\n", + " \n", + " - ``axes(rect, facecolor='w')`` where *rect* = [left, bottom, width,\n", + " height] in normalized (0, 1) units. *facecolor* is the background\n", + " color for the axis, default white.\n", + " \n", + " - ``axes(h)`` where *h* is an axes instance makes *h* the current\n", + " axis and the parent of *h* the current figure.\n", + " An :class:`~matplotlib.axes.Axes` instance is returned.\n", + " \n", + " ========= ============== ==============================================\n", + " kwarg Accepts Description\n", + " ========= ============== ==============================================\n", + " facecolor color the axes background color\n", + " frameon [True|False] display the frame?\n", + " sharex otherax current axes shares xaxis attribute\n", + " with otherax\n", + " sharey otherax current axes shares yaxis attribute\n", + " with otherax\n", + " polar [True|False] use a polar axes?\n", + " aspect [str | num] ['equal', 'auto'] or a number. If a number\n", + " the ratio of y-unit/x-unit in screen-space.\n", + " Also see\n", + " :meth:`~matplotlib.axes.Axes.set_aspect`.\n", + " ========= ============== ==============================================\n", + " \n", + " Examples:\n", + " \n", + " * :file:`examples/pylab_examples/axes_demo.py` places custom axes.\n", + " * :file:`examples/pylab_examples/shared_axis_demo.py` uses\n", + " *sharex* and *sharey*.\n", + " \n", + " axhline(y=0, xmin=0, xmax=1, hold=None, **kwargs)\n", + " Add a horizontal line across the axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " y : scalar, optional, default: 0\n", + " y position in data coordinates of the horizontal line.\n", + " \n", + " xmin : scalar, optional, default: 0\n", + " Should be between 0 and 1, 0 being the far left of the plot, 1 the\n", + " far right of the plot.\n", + " \n", + " xmax : scalar, optional, default: 1\n", + " Should be between 0 and 1, 0 being the far left of the plot, 1 the\n", + " far right of the plot.\n", + " \n", + " Returns\n", + " -------\n", + " :class:`~matplotlib.lines.Line2D`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,\n", + " with the exception of 'transform':\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " kwargs are passed to :class:`~matplotlib.lines.Line2D` and can be used\n", + " to control the line properties.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " * draw a thick red hline at 'y' = 0 that spans the xrange::\n", + " \n", + " >>> axhline(linewidth=4, color='r')\n", + " \n", + " * draw a default hline at 'y' = 1 that spans the xrange::\n", + " \n", + " >>> axhline(y=1)\n", + " \n", + " * draw a default hline at 'y' = .5 that spans the middle half of\n", + " the xrange::\n", + " \n", + " >>> axhline(y=.5, xmin=0.25, xmax=0.75)\n", + " \n", + " See also\n", + " --------\n", + " hlines : add horizontal lines in data coordinates\n", + " axhspan : add a horizontal span (rectangle) across the axis\n", + " \n", + " axhspan(ymin, ymax, xmin=0, xmax=1, hold=None, **kwargs)\n", + " Add a horizontal span (rectangle) across the axis.\n", + " \n", + " Draw a horizontal span (rectangle) from *ymin* to *ymax*.\n", + " With the default values of *xmin* = 0 and *xmax* = 1, this\n", + " always spans the xrange, regardless of the xlim settings, even\n", + " if you change them, e.g., with the :meth:`set_xlim` command.\n", + " That is, the horizontal extent is in axes coords: 0=left,\n", + " 0.5=middle, 1.0=right but the *y* location is in data\n", + " coordinates.\n", + " \n", + " Parameters\n", + " ----------\n", + " ymin : float\n", + " Lower limit of the horizontal span in data units.\n", + " ymax : float\n", + " Upper limit of the horizontal span in data units.\n", + " xmin : float, optional, default: 0\n", + " Lower limit of the vertical span in axes (relative\n", + " 0-1) units.\n", + " xmax : float, optional, default: 1\n", + " Upper limit of the vertical span in axes (relative\n", + " 0-1) units.\n", + " \n", + " Returns\n", + " -------\n", + " Polygon : `~matplotlib.patches.Polygon`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.patches.Polygon` properties.\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " axvspan : add a vertical span across the axes\n", + " \n", + " axis(*v, **kwargs)\n", + " Convenience method to get or set axis properties.\n", + " \n", + " Calling with no arguments::\n", + " \n", + " >>> axis()\n", + " \n", + " returns the current axes limits ``[xmin, xmax, ymin, ymax]``.::\n", + " \n", + " >>> axis(v)\n", + " \n", + " sets the min and max of the x and y axes, with\n", + " ``v = [xmin, xmax, ymin, ymax]``.::\n", + " \n", + " >>> axis('off')\n", + " \n", + " turns off the axis lines and labels.::\n", + " \n", + " >>> axis('equal')\n", + " \n", + " changes limits of *x* or *y* axis so that equal increments of *x*\n", + " and *y* have the same length; a circle is circular.::\n", + " \n", + " >>> axis('scaled')\n", + " \n", + " achieves the same result by changing the dimensions of the plot box instead\n", + " of the axis data limits.::\n", + " \n", + " >>> axis('tight')\n", + " \n", + " changes *x* and *y* axis limits such that all data is shown. If\n", + " all data is already shown, it will move it to the center of the\n", + " figure without modifying (*xmax* - *xmin*) or (*ymax* -\n", + " *ymin*). Note this is slightly different than in MATLAB.::\n", + " \n", + " >>> axis('image')\n", + " \n", + " is 'scaled' with the axis limits equal to the data limits.::\n", + " \n", + " >>> axis('auto')\n", + " \n", + " and::\n", + " \n", + " >>> axis('normal')\n", + " \n", + " are deprecated. They restore default behavior; axis limits are automatically\n", + " scaled to make the data fit comfortably within the plot box.\n", + " \n", + " if ``len(*v)==0``, you can pass in *xmin*, *xmax*, *ymin*, *ymax*\n", + " as kwargs selectively to alter just those limits without changing\n", + " the others.\n", + " \n", + " >>> axis('square')\n", + " \n", + " changes the limit ranges (*xmax*-*xmin*) and (*ymax*-*ymin*) of\n", + " the *x* and *y* axes to be the same, and have the same scaling,\n", + " resulting in a square plot.\n", + " \n", + " The xmin, xmax, ymin, ymax tuple is returned\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`xlim`, :func:`ylim`\n", + " For setting the x- and y-limits individually.\n", + " \n", + " axvline(x=0, ymin=0, ymax=1, hold=None, **kwargs)\n", + " Add a vertical line across the axes.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : scalar, optional, default: 0\n", + " x position in data coordinates of the vertical line.\n", + " \n", + " ymin : scalar, optional, default: 0\n", + " Should be between 0 and 1, 0 being the bottom of the plot, 1 the\n", + " top of the plot.\n", + " \n", + " ymax : scalar, optional, default: 1\n", + " Should be between 0 and 1, 0 being the bottom of the plot, 1 the\n", + " top of the plot.\n", + " \n", + " Returns\n", + " -------\n", + " :class:`~matplotlib.lines.Line2D`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Valid kwargs are :class:`~matplotlib.lines.Line2D` properties,\n", + " with the exception of 'transform':\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Examples\n", + " --------\n", + " * draw a thick red vline at *x* = 0 that spans the yrange::\n", + " \n", + " >>> axvline(linewidth=4, color='r')\n", + " \n", + " * draw a default vline at *x* = 1 that spans the yrange::\n", + " \n", + " >>> axvline(x=1)\n", + " \n", + " * draw a default vline at *x* = .5 that spans the middle half of\n", + " the yrange::\n", + " \n", + " >>> axvline(x=.5, ymin=0.25, ymax=0.75)\n", + " \n", + " See also\n", + " --------\n", + " vlines : add vertical lines in data coordinates\n", + " axvspan : add a vertical span (rectangle) across the axis\n", + " \n", + " axvspan(xmin, xmax, ymin=0, ymax=1, hold=None, **kwargs)\n", + " Add a vertical span (rectangle) across the axes.\n", + " \n", + " Draw a vertical span (rectangle) from `xmin` to `xmax`. With\n", + " the default values of `ymin` = 0 and `ymax` = 1. This always\n", + " spans the yrange, regardless of the ylim settings, even if you\n", + " change them, e.g., with the :meth:`set_ylim` command. That is,\n", + " the vertical extent is in axes coords: 0=bottom, 0.5=middle,\n", + " 1.0=top but the y location is in data coordinates.\n", + " \n", + " Parameters\n", + " ----------\n", + " xmin : scalar\n", + " Number indicating the first X-axis coordinate of the vertical\n", + " span rectangle in data units.\n", + " xmax : scalar\n", + " Number indicating the second X-axis coordinate of the vertical\n", + " span rectangle in data units.\n", + " ymin : scalar, optional\n", + " Number indicating the first Y-axis coordinate of the vertical\n", + " span rectangle in relative Y-axis units (0-1). Default to 0.\n", + " ymax : scalar, optional\n", + " Number indicating the second Y-axis coordinate of the vertical\n", + " span rectangle in relative Y-axis units (0-1). Default to 1.\n", + " \n", + " Returns\n", + " -------\n", + " rectangle : matplotlib.patches.Polygon\n", + " Vertical span (rectangle) from (xmin, ymin) to (xmax, ymax).\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs\n", + " Optional parameters are properties of the class\n", + " matplotlib.patches.Polygon.\n", + " \n", + " See Also\n", + " --------\n", + " axhspan : add a horizontal span across the axes\n", + " \n", + " Examples\n", + " --------\n", + " Draw a vertical, green, translucent rectangle from x = 1.25 to\n", + " x = 1.55 that spans the yrange of the axes.\n", + " \n", + " >>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)\n", + " \n", + " bar(*args, **kwargs)\n", + " Make a bar plot.\n", + " \n", + " Call signatures::\n", + " \n", + " bar(x, height, *, align='center', **kwargs)\n", + " bar(x, height, width, *, align='center', **kwargs)\n", + " bar(x, height, width, bottom, *, align='center', **kwargs)\n", + " \n", + " Make a bar plot with rectangles bounded by\n", + " \n", + " .. math::\n", + " \n", + " (x - width/2, x + width/2, bottom, bottom + height)\n", + " \n", + " (left, right, bottom and top edges) by default. *x*,\n", + " *height*, *width*, and *bottom* can be either scalars or\n", + " sequences.\n", + " \n", + " The *align* and *orientation* kwargs control the interpretation of *x*\n", + " and *bottom*\n", + " \n", + " The *align* keyword-only argument controls if *x* is interpreted\n", + " as the center or the left edge of the rectangle.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : sequence of scalars\n", + " the x coordinates of the bars.\n", + " \n", + " *align* controls if *x* is the bar center (default) or\n", + " left edge.\n", + " \n", + " height : scalar or sequence of scalars\n", + " the height(s) of the bars\n", + " \n", + " width : scalar or array-like, optional\n", + " the width(s) of the bars\n", + " default: 0.8\n", + " \n", + " bottom : scalar or array-like, optional\n", + " the y coordinate(s) of the bars\n", + " default: None\n", + " \n", + " align : {'center', 'edge'}, optional, default: 'center'\n", + " If 'center', interpret the *x* argument as the coordinates\n", + " of the centers of the bars. If 'edge', aligns bars by\n", + " their left edges\n", + " \n", + " To align the bars on the right edge pass a negative\n", + " *width* and ``align='edge'``\n", + " \n", + " Returns\n", + " -------\n", + " bars : matplotlib.container.BarContainer\n", + " Container with all of the bars + errorbars\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " color : scalar or array-like, optional\n", + " the colors of the bar faces\n", + " \n", + " edgecolor : scalar or array-like, optional\n", + " the colors of the bar edges\n", + " \n", + " linewidth : scalar or array-like, optional\n", + " width of bar edge(s). If None, use default\n", + " linewidth; If 0, don't draw edges.\n", + " default: None\n", + " \n", + " tick_label : string or array-like, optional\n", + " the tick labels of the bars\n", + " default: None\n", + " \n", + " xerr : scalar or array-like, optional\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " default: None\n", + " \n", + " yerr : scalar or array-like, optional\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " default: None\n", + " \n", + " ecolor : scalar or array-like, optional\n", + " specifies the color of errorbar(s)\n", + " default: None\n", + " \n", + " capsize : scalar, optional\n", + " determines the length in points of the error bar caps\n", + " default: None, which will take the value from the\n", + " ``errorbar.capsize`` :data:`rcParam`.\n", + " \n", + " error_kw : dict, optional\n", + " dictionary of kwargs to be passed to errorbar method. *ecolor* and\n", + " *capsize* may be specified here rather than as independent kwargs.\n", + " \n", + " log : boolean, optional\n", + " If true, sets the axis to be log scale.\n", + " default: False\n", + " \n", + " orientation : {'vertical', 'horizontal'}, optional\n", + " \n", + " This is for internal use, please do not directly use this,\n", + " call `barh` instead.\n", + " \n", + " The orientation of the bars.\n", + " \n", + " See also\n", + " --------\n", + " barh: Plot a horizontal bar plot.\n", + " \n", + " Notes\n", + " -----\n", + " The optional arguments *color*, *edgecolor*, *linewidth*,\n", + " *xerr*, and *yerr* can be either scalars or sequences of\n", + " length equal to the number of bars. This enables you to use\n", + " bar as the basis for stacked bar charts, or candlestick plots.\n", + " Detail: *xerr* and *yerr* are passed directly to\n", + " :meth:`errorbar`, so they can also have shape 2xN for\n", + " independent specification of lower and upper errors.\n", + " \n", + " Other optional kwargs:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'bottom', 'color', 'ecolor', 'edgecolor', 'height', 'left', 'linewidth', 'tick_label', 'width', 'x', 'xerr', 'y', 'yerr'.\n", + " * All positional arguments.\n", + " \n", + " barbs(*args, **kw)\n", + " Plot a 2-D field of barbs.\n", + " \n", + " Call signatures::\n", + " \n", + " barb(U, V, **kw)\n", + " barb(U, V, C, **kw)\n", + " barb(X, Y, U, V, **kw)\n", + " barb(X, Y, U, V, C, **kw)\n", + " \n", + " Arguments:\n", + " \n", + " *X*, *Y*:\n", + " The x and y coordinates of the barb locations\n", + " (default is head of barb; see *pivot* kwarg)\n", + " \n", + " *U*, *V*:\n", + " Give the x and y components of the barb shaft\n", + " \n", + " *C*:\n", + " An optional array used to map colors to the barbs\n", + " \n", + " All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*\n", + " are absent, they will be generated as a uniform grid. If *U* and *V*\n", + " are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``\n", + " match the column and row dimensions of *U*, then *X* and *Y* will be\n", + " expanded with :func:`numpy.meshgrid`.\n", + " \n", + " *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not\n", + " supported at present.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *length*:\n", + " Length of the barb in points; the other parts of the barb\n", + " are scaled against this.\n", + " Default is 7.\n", + " \n", + " *pivot*: [ 'tip' | 'middle' | float ]\n", + " The part of the arrow that is at the grid point; the arrow rotates\n", + " about this point, hence the name *pivot*. Default is 'tip'. Can\n", + " also be a number, which shifts the start of the barb that many\n", + " points from the origin.\n", + " \n", + " *barbcolor*: [ color | color sequence ]\n", + " Specifies the color all parts of the barb except any flags. This\n", + " parameter is analagous to the *edgecolor* parameter for polygons,\n", + " which can be used instead. However this parameter will override\n", + " facecolor.\n", + " \n", + " *flagcolor*: [ color | color sequence ]\n", + " Specifies the color of any flags on the barb. This parameter is\n", + " analagous to the *facecolor* parameter for polygons, which can be\n", + " used instead. However this parameter will override facecolor. If\n", + " this is not set (and *C* has not either) then *flagcolor* will be\n", + " set to match *barbcolor* so that the barb has a uniform color. If\n", + " *C* has been set, *flagcolor* has no effect.\n", + " \n", + " *sizes*:\n", + " A dictionary of coefficients specifying the ratio of a given\n", + " feature to the length of the barb. Only those values one wishes to\n", + " override need to be included. These features include:\n", + " \n", + " - 'spacing' - space between features (flags, full/half barbs)\n", + " \n", + " - 'height' - height (distance from shaft to top) of a flag or\n", + " full barb\n", + " \n", + " - 'width' - width of a flag, twice the width of a full barb\n", + " \n", + " - 'emptybarb' - radius of the circle used for low magnitudes\n", + " \n", + " *fill_empty*:\n", + " A flag on whether the empty barbs (circles) that are drawn should\n", + " be filled with the flag color. If they are not filled, they will\n", + " be drawn such that no color is applied to the center. Default is\n", + " False\n", + " \n", + " *rounding*:\n", + " A flag to indicate whether the vector magnitude should be rounded\n", + " when allocating barb components. If True, the magnitude is\n", + " rounded to the nearest multiple of the half-barb increment. If\n", + " False, the magnitude is simply truncated to the next lowest\n", + " multiple. Default is True\n", + " \n", + " *barb_increments*:\n", + " A dictionary of increments specifying values to associate with\n", + " different parts of the barb. Only those values one wishes to\n", + " override need to be included.\n", + " \n", + " - 'half' - half barbs (Default is 5)\n", + " \n", + " - 'full' - full barbs (Default is 10)\n", + " \n", + " - 'flag' - flags (default is 50)\n", + " \n", + " *flip_barb*:\n", + " Either a single boolean flag or an array of booleans. Single\n", + " boolean indicates whether the lines and flags should point\n", + " opposite to normal for all barbs. An array (which should be the\n", + " same size as the other data arrays) indicates whether to flip for\n", + " each individual barb. Normal behavior is for the barbs and lines\n", + " to point right (comes from wind barbs having these features point\n", + " towards low pressure in the Northern Hemisphere.) Default is\n", + " False\n", + " \n", + " Barbs are traditionally used in meteorology as a way to plot the speed\n", + " and direction of wind observations, but can technically be used to\n", + " plot any two dimensional vector quantity. As opposed to arrows, which\n", + " give vector magnitude by the length of the arrow, the barbs give more\n", + " quantitative information about the vector magnitude by putting slanted\n", + " lines or a triangle for various increments in magnitude, as show\n", + " schematically below::\n", + " \n", + " : /\\ \\\\\n", + " : / \\ \\\\\n", + " : / \\ \\ \\\\\n", + " : / \\ \\ \\\\\n", + " : ------------------------------\n", + " \n", + " .. note the double \\\\ at the end of each line to make the figure\n", + " .. render correctly\n", + " \n", + " The largest increment is given by a triangle (or \"flag\"). After those\n", + " come full lines (barbs). The smallest increment is a half line. There\n", + " is only, of course, ever at most 1 half line. If the magnitude is\n", + " small and only needs a single half-line and no full lines or\n", + " triangles, the half-line is offset from the end of the barb so that it\n", + " can be easily distinguished from barbs with a single full line. The\n", + " magnitude for the barb shown above would nominally be 65, using the\n", + " standard increments of 50, 10, and 5.\n", + " \n", + " linewidths and edgecolors can be used to customize the barb.\n", + " Additional :class:`~matplotlib.collections.PolyCollection` keyword\n", + " arguments:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " barh(*args, **kwargs)\n", + " Make a horizontal bar plot.\n", + " \n", + " Call signatures::\n", + " \n", + " bar(y, width, *, align='center', **kwargs)\n", + " bar(y, width, height, *, align='center', **kwargs)\n", + " bar(y, width, height, left, *, align='center', **kwargs)\n", + " \n", + " Make a horizontal bar plot with rectangles by default bounded by\n", + " \n", + " .. math::\n", + " \n", + " (left, left + width, y - height/2, y + height/2)\n", + " \n", + " (left, right, bottom and top edges) by default. *y*, *width*,\n", + " *height*, and *left* can be either scalars or sequences.\n", + " \n", + " The *align* keyword-only argument controls if *y* is interpreted\n", + " as the center or the bottom edge of the rectangle.\n", + " \n", + " \n", + " Parameters\n", + " ----------\n", + " y : scalar or array-like\n", + " the y coordinate(s) of the bars\n", + " \n", + " *align* controls if *y* is the bar center (default)\n", + " or bottom edge.\n", + " \n", + " width : scalar or array-like\n", + " the width(s) of the bars\n", + " \n", + " height : sequence of scalars, optional, default: 0.8\n", + " the heights of the bars\n", + " \n", + " left : sequence of scalars\n", + " the x coordinates of the left sides of the bars\n", + " \n", + " align : {'center', 'edge'}, optional, default: 'center'\n", + " If 'center', interpret the *y* argument as the coordinates\n", + " of the centers of the bars. If 'edge', aligns bars by\n", + " their bottom edges\n", + " \n", + " To align the bars on the top edge pass a negative\n", + " *height* and ``align='edge'``\n", + " \n", + " Returns\n", + " -------\n", + " `matplotlib.patches.Rectangle` instances.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " color : scalar or array-like, optional\n", + " the colors of the bars\n", + " \n", + " edgecolor : scalar or array-like, optional\n", + " the colors of the bar edges\n", + " \n", + " linewidth : scalar or array-like, optional, default: None\n", + " width of bar edge(s). If None, use default\n", + " linewidth; If 0, don't draw edges.\n", + " \n", + " tick_label : string or array-like, optional, default: None\n", + " the tick labels of the bars\n", + " \n", + " xerr : scalar or array-like, optional, default: None\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " \n", + " yerr : scalar or array-like, optional, default: None\n", + " if not None, will be used to generate errorbar(s) on the bar chart\n", + " \n", + " ecolor : scalar or array-like, optional, default: None\n", + " specifies the color of errorbar(s)\n", + " \n", + " capsize : scalar, optional\n", + " determines the length in points of the error bar caps\n", + " default: None, which will take the value from the\n", + " ``errorbar.capsize`` :data:`rcParam`.\n", + " \n", + " error_kw :\n", + " dictionary of kwargs to be passed to errorbar method. `ecolor` and\n", + " `capsize` may be specified here rather than as independent kwargs.\n", + " \n", + " log : boolean, optional, default: False\n", + " If true, sets the axis to be log scale\n", + " \n", + " See also\n", + " --------\n", + " bar: Plot a vertical bar plot.\n", + " \n", + " Notes\n", + " -----\n", + " The optional arguments *color*, *edgecolor*, *linewidth*,\n", + " *xerr*, and *yerr* can be either scalars or sequences of\n", + " length equal to the number of bars. This enables you to use\n", + " bar as the basis for stacked bar charts, or candlestick plots.\n", + " Detail: *xerr* and *yerr* are passed directly to\n", + " :meth:`errorbar`, so they can also have shape 2xN for\n", + " independent specification of lower and upper errors.\n", + " \n", + " Other optional kwargs:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or aa: [True | False] or None for default \n", + " capstyle: ['butt' | 'round' | 'projecting'] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color: matplotlib color spec\n", + " contains: a callable function \n", + " edgecolor or ec: mpl color spec, None, 'none', or 'auto' \n", + " facecolor or fc: mpl color spec, or None for default, or 'none' for no color \n", + " figure: a `~.Figure` instance \n", + " fill: [True | False] \n", + " gid: an id string \n", + " hatch: ['/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] \n", + " joinstyle: ['miter' | 'round' | 'bevel'] \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float or None for default \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float\n", + " \n", + " bone()\n", + " set the default colormap to bone and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " box(on=None)\n", + " Turn the axes box on or off. *on* may be a boolean or a string,\n", + " 'on' or 'off'.\n", + " \n", + " If *on* is *None*, toggle state.\n", + " \n", + " boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False, zorder=None, hold=None, data=None)\n", + " Make a box and whisker plot.\n", + " \n", + " Make a box and whisker plot for each column of ``x`` or each\n", + " vector in sequence ``x``. The box extends from the lower to\n", + " upper quartile values of the data, with a line at the median.\n", + " The whiskers extend from the box to show the range of the\n", + " data. Flier points are those past the end of the whiskers.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : Array or a sequence of vectors.\n", + " The input data.\n", + " \n", + " notch : bool, optional (False)\n", + " If `True`, will produce a notched box plot. Otherwise, a\n", + " rectangular boxplot is produced. The notches represent the\n", + " confidence interval (CI) around the median. See the entry\n", + " for the ``bootstrap`` parameter for information regarding\n", + " how the locations of the notches are computed.\n", + " \n", + " .. note::\n", + " \n", + " In cases where the values of the CI are less than the\n", + " lower quartile or greater than the upper quartile, the\n", + " notches will extend beyond the box, giving it a\n", + " distinctive \"flipped\" appearance. This is expected\n", + " behavior and consistent with other statistical\n", + " visualization packages.\n", + " \n", + " sym : str, optional\n", + " The default symbol for flier points. Enter an empty string\n", + " ('') if you don't want to show fliers. If `None`, then the\n", + " fliers default to 'b+' If you want more control use the\n", + " flierprops kwarg.\n", + " \n", + " vert : bool, optional (True)\n", + " If `True` (default), makes the boxes vertical. If `False`,\n", + " everything is drawn horizontally.\n", + " \n", + " whis : float, sequence, or string (default = 1.5)\n", + " As a float, determines the reach of the whiskers to the beyond the\n", + " first and third quartiles. In other words, where IQR is the\n", + " interquartile range (`Q3-Q1`), the upper whisker will extend to\n", + " last datum less than `Q3 + whis*IQR`). Similarly, the lower whisker\n", + " will extend to the first datum greater than `Q1 - whis*IQR`.\n", + " Beyond the whiskers, data\n", + " are considered outliers and are plotted as individual\n", + " points. Set this to an unreasonably high value to force the\n", + " whiskers to show the min and max values. Alternatively, set\n", + " this to an ascending sequence of percentile (e.g., [5, 95])\n", + " to set the whiskers at specific percentiles of the data.\n", + " Finally, ``whis`` can be the string ``'range'`` to force the\n", + " whiskers to the min and max of the data.\n", + " \n", + " bootstrap : int, optional\n", + " Specifies whether to bootstrap the confidence intervals\n", + " around the median for notched boxplots. If ``bootstrap`` is\n", + " None, no bootstrapping is performed, and notches are\n", + " calculated using a Gaussian-based asymptotic approximation\n", + " (see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and\n", + " Kendall and Stuart, 1967). Otherwise, bootstrap specifies\n", + " the number of times to bootstrap the median to determine its\n", + " 95% confidence intervals. Values between 1000 and 10000 are\n", + " recommended.\n", + " \n", + " usermedians : array-like, optional\n", + " An array or sequence whose first dimension (or length) is\n", + " compatible with ``x``. This overrides the medians computed\n", + " by matplotlib for each element of ``usermedians`` that is not\n", + " `None`. When an element of ``usermedians`` is None, the median\n", + " will be computed by matplotlib as normal.\n", + " \n", + " conf_intervals : array-like, optional\n", + " Array or sequence whose first dimension (or length) is\n", + " compatible with ``x`` and whose second dimension is 2. When\n", + " the an element of ``conf_intervals`` is not None, the\n", + " notch locations computed by matplotlib are overridden\n", + " (provided ``notch`` is `True`). When an element of\n", + " ``conf_intervals`` is `None`, the notches are computed by the\n", + " method specified by the other kwargs (e.g., ``bootstrap``).\n", + " \n", + " positions : array-like, optional\n", + " Sets the positions of the boxes. The ticks and limits are\n", + " automatically set to match the positions. Defaults to\n", + " `range(1, N+1)` where N is the number of boxes to be drawn.\n", + " \n", + " widths : scalar or array-like\n", + " Sets the width of each box either with a scalar or a\n", + " sequence. The default is 0.5, or ``0.15*(distance between\n", + " extreme positions)``, if that is smaller.\n", + " \n", + " patch_artist : bool, optional (False)\n", + " If `False` produces boxes with the Line2D artist. Otherwise,\n", + " boxes and drawn with Patch artists.\n", + " \n", + " labels : sequence, optional\n", + " Labels for each dataset. Length must be compatible with\n", + " dimensions of ``x``.\n", + " \n", + " manage_xticks : bool, optional (True)\n", + " If the function should adjust the xlim and xtick locations.\n", + " \n", + " autorange : bool, optional (False)\n", + " When `True` and the data are distributed such that the 25th and\n", + " 75th percentiles are equal, ``whis`` is set to ``'range'`` such\n", + " that the whisker ends are at the minimum and maximum of the\n", + " data.\n", + " \n", + " meanline : bool, optional (False)\n", + " If `True` (and ``showmeans`` is `True`), will try to render\n", + " the mean as a line spanning the full width of the box\n", + " according to ``meanprops`` (see below). Not recommended if\n", + " ``shownotches`` is also True. Otherwise, means will be shown\n", + " as points.\n", + " \n", + " zorder : scalar, optional (None)\n", + " Sets the zorder of the boxplot.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " showcaps : bool, optional (True)\n", + " Show the caps on the ends of whiskers.\n", + " showbox : bool, optional (True)\n", + " Show the central box.\n", + " showfliers : bool, optional (True)\n", + " Show the outliers beyond the caps.\n", + " showmeans : bool, optional (False)\n", + " Show the arithmetic means.\n", + " capprops : dict, optional (None)\n", + " Specifies the style of the caps.\n", + " boxprops : dict, optional (None)\n", + " Specifies the style of the box.\n", + " whiskerprops : dict, optional (None)\n", + " Specifies the style of the whiskers.\n", + " flierprops : dict, optional (None)\n", + " Specifies the style of the fliers.\n", + " medianprops : dict, optional (None)\n", + " Specifies the style of the median.\n", + " meanprops : dict, optional (None)\n", + " Specifies the style of the mean.\n", + " \n", + " Returns\n", + " -------\n", + " result : dict\n", + " A dictionary mapping each component of the boxplot to a list\n", + " of the :class:`matplotlib.lines.Line2D` instances\n", + " created. That dictionary has the following keys (assuming\n", + " vertical boxplots):\n", + " \n", + " - ``boxes``: the main body of the boxplot showing the\n", + " quartiles and the median's confidence intervals if\n", + " enabled.\n", + " \n", + " - ``medians``: horizontal lines at the median of each box.\n", + " \n", + " - ``whiskers``: the vertical lines extending to the most\n", + " extreme, non-outlier data points.\n", + " \n", + " - ``caps``: the horizontal lines at the ends of the\n", + " whiskers.\n", + " \n", + " - ``fliers``: points representing data that extend beyond\n", + " the whiskers (fliers).\n", + " \n", + " - ``means``: points or lines representing the means.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " broken_barh(xranges, yrange, hold=None, data=None, **kwargs)\n", + " Plot horizontal bars.\n", + " \n", + " A collection of horizontal bars spanning *yrange* with a sequence of\n", + " *xranges*.\n", + " \n", + " Required arguments:\n", + " \n", + " ========= ==============================\n", + " Argument Description\n", + " ========= ==============================\n", + " *xranges* sequence of (*xmin*, *xwidth*)\n", + " *yrange* sequence of (*ymin*, *ywidth*)\n", + " ========= ==============================\n", + " \n", + " kwargs are\n", + " :class:`matplotlib.collections.BrokenBarHCollection`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " these can either be a single argument, i.e.,::\n", + " \n", + " facecolors = 'black'\n", + " \n", + " or a sequence of arguments for the various bars, i.e.,::\n", + " \n", + " facecolors = ('black', 'red', 'green')\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " cla()\n", + " Clear the current axes.\n", + " \n", + " clabel(CS, *args, **kwargs)\n", + " Label a contour plot.\n", + " \n", + " Call signature::\n", + " \n", + " clabel(cs, **kwargs)\n", + " \n", + " Adds labels to line contours in *cs*, where *cs* is a\n", + " :class:`~matplotlib.contour.ContourSet` object returned by\n", + " contour.\n", + " \n", + " ::\n", + " \n", + " clabel(cs, v, **kwargs)\n", + " \n", + " only labels contours listed in *v*.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *fontsize*:\n", + " size in points or relative size e.g., 'smaller', 'x-large'\n", + " \n", + " *colors*:\n", + " - if *None*, the color of each label matches the color of\n", + " the corresponding contour\n", + " \n", + " - if one string color, e.g., *colors* = 'r' or *colors* =\n", + " 'red', all labels will be plotted in this color\n", + " \n", + " - if a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different labels will be plotted in different colors in the order\n", + " specified\n", + " \n", + " *inline*:\n", + " controls whether the underlying contour is removed or\n", + " not. Default is *True*.\n", + " \n", + " *inline_spacing*:\n", + " space in pixels to leave on each side of label when\n", + " placing inline. Defaults to 5. This spacing will be\n", + " exact for labels at locations where the contour is\n", + " straight, less so for labels on curved contours.\n", + " \n", + " *fmt*:\n", + " a format string for the label. Default is '%1.3f'\n", + " Alternatively, this can be a dictionary matching contour\n", + " levels with arbitrary strings to use for each contour level\n", + " (i.e., fmt[level]=string), or it can be any callable, such\n", + " as a :class:`~matplotlib.ticker.Formatter` instance, that\n", + " returns a string when called with a numeric contour level.\n", + " \n", + " *manual*:\n", + " if *True*, contour labels will be placed manually using\n", + " mouse clicks. Click the first button near a contour to\n", + " add a label, click the second button (or potentially both\n", + " mouse buttons at once) to finish adding labels. The third\n", + " button can be used to remove the last label added, but\n", + " only if labels are not inline. Alternatively, the keyboard\n", + " can be used to select label locations (enter to end label\n", + " placement, delete or backspace act like the third mouse button,\n", + " and any other key will select a label location).\n", + " \n", + " *manual* can be an iterable object of x,y tuples. Contour labels\n", + " will be created as if mouse is clicked at each x,y positions.\n", + " \n", + " *rightside_up*:\n", + " if *True* (default), label rotations will always be plus\n", + " or minus 90 degrees from level.\n", + " \n", + " *use_clabeltext*:\n", + " if *True* (default is False), ClabelText class (instead of\n", + " matplotlib.Text) is used to create labels. ClabelText\n", + " recalculates rotation angles of texts during the drawing time,\n", + " therefore this can be used if aspect of the axes changes.\n", + " \n", + " clf()\n", + " Clear the current figure.\n", + " \n", + " clim(vmin=None, vmax=None)\n", + " Set the color limits of the current image.\n", + " \n", + " To apply clim to all axes images do::\n", + " \n", + " clim(0, 0.5)\n", + " \n", + " If either *vmin* or *vmax* is None, the image min/max respectively\n", + " will be used for color scaling.\n", + " \n", + " If you want to set the clim of multiple images,\n", + " use, for example::\n", + " \n", + " for im in gca().get_images():\n", + " im.set_clim(0, 0.05)\n", + " \n", + " close(*args)\n", + " Close a figure window.\n", + " \n", + " ``close()`` by itself closes the current figure\n", + " \n", + " ``close(fig)`` closes the `~.Figure` instance *fig*\n", + " \n", + " ``close(num)`` closes the figure number *num*\n", + " \n", + " ``close(name)`` where *name* is a string, closes figure with that label\n", + " \n", + " ``close('all')`` closes all the figure windows\n", + " \n", + " cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend=, window=, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, hold=None, data=None, **kwargs)\n", + " Plot the coherence between *x* and *y*.\n", + " \n", + " Plot the coherence between *x* and *y*. Coherence is the\n", + " normalized cross spectral density:\n", + " \n", + " .. math::\n", + " \n", + " C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}}\n", + " \n", + " Parameters\n", + " ----------\n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between blocks. The\n", + " default value is 0 (no overlap).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " The return value is a tuple (*Cxy*, *f*), where *f* are the\n", + " frequencies of the coherence vector.\n", + " \n", + " kwargs are applied to the lines.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " References\n", + " ----------\n", + " Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,\n", + " John Wiley & Sons (1986)\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " colorbar(mappable=None, cax=None, ax=None, **kw)\n", + " Add a colorbar to a plot.\n", + " \n", + " Function signatures for the :mod:`~matplotlib.pyplot` interface; all\n", + " but the first are also method signatures for the\n", + " :meth:`~matplotlib.figure.Figure.colorbar` method::\n", + " \n", + " colorbar(**kwargs)\n", + " colorbar(mappable, **kwargs)\n", + " colorbar(mappable, cax=cax, **kwargs)\n", + " colorbar(mappable, ax=ax, **kwargs)\n", + " \n", + " Parameters\n", + " ----------\n", + " mappable :\n", + " The :class:`~matplotlib.image.Image`,\n", + " :class:`~matplotlib.contour.ContourSet`, etc. to\n", + " which the colorbar applies; this argument is mandatory for the Figure\n", + " :meth:`~matplotlib.figure.Figure.colorbar` method but optional for the\n", + " pyplot :func:`~matplotlib.pyplot.colorbar` function, which sets the\n", + " default to the current image.\n", + " \n", + " cax : :class:`~matplotlib.axes.Axes` object, optional\n", + " Axis into which the colorbar will be drawn\n", + " \n", + " ax : :class:`~matplotlib.axes.Axes`, list of Axes, optional\n", + " Parent axes from which space for a new colorbar axes will be stolen.\n", + " If a list of axes is given they will all be resized to make room for the\n", + " colorbar axes.\n", + " \n", + " use_gridspec : bool, optional\n", + " If *cax* is ``None``, a new *cax* is created as an instance of\n", + " Axes. If *ax* is an instance of Subplot and *use_gridspec* is ``True``,\n", + " *cax* is created as an instance of Subplot using the\n", + " grid_spec module.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " :class:`~matplotlib.colorbar.Colorbar` instance\n", + " See also its base class, :class:`~matplotlib.colorbar.ColorbarBase`.\n", + " Call the :meth:`~matplotlib.colorbar.ColorbarBase.set_label` method\n", + " to label the colorbar.\n", + " \n", + " Notes\n", + " -----\n", + " Additional keyword arguments are of two kinds:\n", + " \n", + " axes properties:\n", + " \n", + " \n", + " ============= ====================================================\n", + " Property Description\n", + " ============= ====================================================\n", + " *orientation* vertical or horizontal\n", + " *fraction* 0.15; fraction of original axes to use for colorbar\n", + " *pad* 0.05 if vertical, 0.15 if horizontal; fraction\n", + " of original axes between colorbar and new image axes\n", + " *shrink* 1.0; fraction by which to multiply the size of the colorbar\n", + " *aspect* 20; ratio of long to short dimensions\n", + " *anchor* (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal;\n", + " the anchor point of the colorbar axes\n", + " *panchor* (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal;\n", + " the anchor point of the colorbar parent axes. If\n", + " False, the parent axes' anchor will be unchanged\n", + " ============= ====================================================\n", + " \n", + " \n", + " colorbar properties:\n", + " \n", + " \n", + " ============ ====================================================\n", + " Property Description\n", + " ============ ====================================================\n", + " *extend* [ 'neither' | 'both' | 'min' | 'max' ]\n", + " If not 'neither', make pointed end(s) for out-of-\n", + " range values. These are set for a given colormap\n", + " using the colormap set_under and set_over methods.\n", + " *extendfrac* [ *None* | 'auto' | length | lengths ]\n", + " If set to *None*, both the minimum and maximum\n", + " triangular colorbar extensions with have a length of\n", + " 5% of the interior colorbar length (this is the\n", + " default setting). If set to 'auto', makes the\n", + " triangular colorbar extensions the same lengths as\n", + " the interior boxes (when *spacing* is set to\n", + " 'uniform') or the same lengths as the respective\n", + " adjacent interior boxes (when *spacing* is set to\n", + " 'proportional'). If a scalar, indicates the length\n", + " of both the minimum and maximum triangular colorbar\n", + " extensions as a fraction of the interior colorbar\n", + " length. A two-element sequence of fractions may also\n", + " be given, indicating the lengths of the minimum and\n", + " maximum colorbar extensions respectively as a\n", + " fraction of the interior colorbar length.\n", + " *extendrect* [ *False* | *True* ]\n", + " If *False* the minimum and maximum colorbar extensions\n", + " will be triangular (the default). If *True* the\n", + " extensions will be rectangular.\n", + " *spacing* [ 'uniform' | 'proportional' ]\n", + " Uniform spacing gives each discrete color the same\n", + " space; proportional makes the space proportional to\n", + " the data interval.\n", + " *ticks* [ None | list of ticks | Locator object ]\n", + " If None, ticks are determined automatically from the\n", + " input.\n", + " *format* [ None | format string | Formatter object ]\n", + " If None, the\n", + " :class:`~matplotlib.ticker.ScalarFormatter` is used.\n", + " If a format string is given, e.g., '%.3f', that is\n", + " used. An alternative\n", + " :class:`~matplotlib.ticker.Formatter` object may be\n", + " given instead.\n", + " *drawedges* [ False | True ] If true, draw lines at color\n", + " boundaries.\n", + " ============ ====================================================\n", + " \n", + " The following will probably be useful only in the context of\n", + " indexed colors (that is, when the mappable has norm=NoNorm()),\n", + " or other unusual circumstances.\n", + " \n", + " ============ ===================================================\n", + " Property Description\n", + " ============ ===================================================\n", + " *boundaries* None or a sequence\n", + " *values* None or a sequence which must be of length 1 less\n", + " than the sequence of *boundaries*. For each region\n", + " delimited by adjacent entries in *boundaries*, the\n", + " color mapped to the corresponding value in values\n", + " will be used.\n", + " ============ ===================================================\n", + " \n", + " \n", + " \n", + " If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend*\n", + " kwarg is included automatically.\n", + " \n", + " Note that the *shrink* kwarg provides a simple way to keep a vertical\n", + " colorbar, for example, from being taller than the axes of the mappable\n", + " to which the colorbar is attached; but it is a manual method requiring\n", + " some trial and error. If the colorbar is too tall (or a horizontal\n", + " colorbar is too wide) use a smaller value of *shrink*.\n", + " \n", + " For more precise control, you can manually specify the positions of\n", + " the axes objects in which the mappable and the colorbar are drawn. In\n", + " this case, do not use any of the axes properties kwargs.\n", + " \n", + " It is known that some vector graphics viewer (svg and pdf) renders white gaps\n", + " between segments of the colorbar. This is due to bugs in the viewers not\n", + " matplotlib. As a workaround the colorbar can be rendered with overlapping\n", + " segments::\n", + " \n", + " cbar = colorbar()\n", + " cbar.solids.set_edgecolor(\"face\")\n", + " draw()\n", + " \n", + " However this has negative consequences in other circumstances. Particularly\n", + " with semi transparent images (alpha < 1) and colorbar extensions and is not\n", + " enabled by default see (issue #1188).\n", + " \n", + " colormaps()\n", + " Matplotlib provides a number of colormaps, and others can be added using\n", + " :func:`~matplotlib.cm.register_cmap`. This function documents the built-in\n", + " colormaps, and will also return a list of all registered colormaps if called.\n", + " \n", + " You can set the colormap for an image, pcolor, scatter, etc,\n", + " using a keyword argument::\n", + " \n", + " imshow(X, cmap=cm.hot)\n", + " \n", + " or using the :func:`set_cmap` function::\n", + " \n", + " imshow(X)\n", + " pyplot.set_cmap('hot')\n", + " pyplot.set_cmap('jet')\n", + " \n", + " In interactive mode, :func:`set_cmap` will update the colormap post-hoc,\n", + " allowing you to see which one works best for your data.\n", + " \n", + " All built-in colormaps can be reversed by appending ``_r``: For instance,\n", + " ``gray_r`` is the reverse of ``gray``.\n", + " \n", + " There are several common color schemes used in visualization:\n", + " \n", + " Sequential schemes\n", + " for unipolar data that progresses from low to high\n", + " Diverging schemes\n", + " for bipolar data that emphasizes positive or negative deviations from a\n", + " central value\n", + " Cyclic schemes\n", + " meant for plotting values that wrap around at the\n", + " endpoints, such as phase angle, wind direction, or time of day\n", + " Qualitative schemes\n", + " for nominal data that has no inherent ordering, where color is used\n", + " only to distinguish categories\n", + " \n", + " Matplotlib ships with 4 perceptually uniform color maps which are\n", + " the recommended color maps for sequential data:\n", + " \n", + " ========= ===================================================\n", + " Colormap Description\n", + " ========= ===================================================\n", + " inferno perceptually uniform shades of black-red-yellow\n", + " magma perceptually uniform shades of black-red-white\n", + " plasma perceptually uniform shades of blue-red-yellow\n", + " viridis perceptually uniform shades of blue-green-yellow\n", + " ========= ===================================================\n", + " \n", + " The following colormaps are based on the `ColorBrewer\n", + " `_ color specifications and designs developed by\n", + " Cynthia Brewer:\n", + " \n", + " ColorBrewer Diverging (luminance is highest at the midpoint, and\n", + " decreases towards differently-colored endpoints):\n", + " \n", + " ======== ===================================\n", + " Colormap Description\n", + " ======== ===================================\n", + " BrBG brown, white, blue-green\n", + " PiYG pink, white, yellow-green\n", + " PRGn purple, white, green\n", + " PuOr orange, white, purple\n", + " RdBu red, white, blue\n", + " RdGy red, white, gray\n", + " RdYlBu red, yellow, blue\n", + " RdYlGn red, yellow, green\n", + " Spectral red, orange, yellow, green, blue\n", + " ======== ===================================\n", + " \n", + " ColorBrewer Sequential (luminance decreases monotonically):\n", + " \n", + " ======== ====================================\n", + " Colormap Description\n", + " ======== ====================================\n", + " Blues white to dark blue\n", + " BuGn white, light blue, dark green\n", + " BuPu white, light blue, dark purple\n", + " GnBu white, light green, dark blue\n", + " Greens white to dark green\n", + " Greys white to black (not linear)\n", + " Oranges white, orange, dark brown\n", + " OrRd white, orange, dark red\n", + " PuBu white, light purple, dark blue\n", + " PuBuGn white, light purple, dark green\n", + " PuRd white, light purple, dark red\n", + " Purples white to dark purple\n", + " RdPu white, pink, dark purple\n", + " Reds white to dark red\n", + " YlGn light yellow, dark green\n", + " YlGnBu light yellow, light green, dark blue\n", + " YlOrBr light yellow, orange, dark brown\n", + " YlOrRd light yellow, orange, dark red\n", + " ======== ====================================\n", + " \n", + " ColorBrewer Qualitative:\n", + " \n", + " (For plotting nominal data, :class:`ListedColormap` is used,\n", + " not :class:`LinearSegmentedColormap`. Different sets of colors are\n", + " recommended for different numbers of categories.)\n", + " \n", + " * Accent\n", + " * Dark2\n", + " * Paired\n", + " * Pastel1\n", + " * Pastel2\n", + " * Set1\n", + " * Set2\n", + " * Set3\n", + " \n", + " A set of colormaps derived from those of the same name provided\n", + " with Matlab are also included:\n", + " \n", + " ========= =======================================================\n", + " Colormap Description\n", + " ========= =======================================================\n", + " autumn sequential linearly-increasing shades of red-orange-yellow\n", + " bone sequential increasing black-white color map with\n", + " a tinge of blue, to emulate X-ray film\n", + " cool linearly-decreasing shades of cyan-magenta\n", + " copper sequential increasing shades of black-copper\n", + " flag repetitive red-white-blue-black pattern (not cyclic at\n", + " endpoints)\n", + " gray sequential linearly-increasing black-to-white\n", + " grayscale\n", + " hot sequential black-red-yellow-white, to emulate blackbody\n", + " radiation from an object at increasing temperatures\n", + " hsv cyclic red-yellow-green-cyan-blue-magenta-red, formed\n", + " by changing the hue component in the HSV color space\n", + " jet a spectral map with dark endpoints, blue-cyan-yellow-red;\n", + " based on a fluid-jet simulation by NCSA [#]_\n", + " pink sequential increasing pastel black-pink-white, meant\n", + " for sepia tone colorization of photographs\n", + " prism repetitive red-yellow-green-blue-purple-...-green pattern\n", + " (not cyclic at endpoints)\n", + " spring linearly-increasing shades of magenta-yellow\n", + " summer sequential linearly-increasing shades of green-yellow\n", + " winter linearly-increasing shades of blue-green\n", + " ========= =======================================================\n", + " \n", + " A set of palettes from the `Yorick scientific visualisation\n", + " package `_, an evolution of\n", + " the GIST package, both by David H. Munro are included:\n", + " \n", + " ============ =======================================================\n", + " Colormap Description\n", + " ============ =======================================================\n", + " gist_earth mapmaker's colors from dark blue deep ocean to green\n", + " lowlands to brown highlands to white mountains\n", + " gist_heat sequential increasing black-red-orange-white, to emulate\n", + " blackbody radiation from an iron bar as it grows hotter\n", + " gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white\n", + " colormap from National Center for Atmospheric\n", + " Research [#]_\n", + " gist_rainbow runs through the colors in spectral order from red to\n", + " violet at full saturation (like *hsv* but not cyclic)\n", + " gist_stern \"Stern special\" color table from Interactive Data\n", + " Language software\n", + " ============ =======================================================\n", + " \n", + " \n", + " Other miscellaneous schemes:\n", + " \n", + " ============= =======================================================\n", + " Colormap Description\n", + " ============= =======================================================\n", + " afmhot sequential black-orange-yellow-white blackbody\n", + " spectrum, commonly used in atomic force microscopy\n", + " brg blue-red-green\n", + " bwr diverging blue-white-red\n", + " coolwarm diverging blue-gray-red, meant to avoid issues with 3D\n", + " shading, color blindness, and ordering of colors [#]_\n", + " CMRmap \"Default colormaps on color images often reproduce to\n", + " confusing grayscale images. The proposed colormap\n", + " maintains an aesthetically pleasing color image that\n", + " automatically reproduces to a monotonic grayscale with\n", + " discrete, quantifiable saturation levels.\" [#]_\n", + " cubehelix Unlike most other color schemes cubehelix was designed\n", + " by D.A. Green to be monotonically increasing in terms\n", + " of perceived brightness. Also, when printed on a black\n", + " and white postscript printer, the scheme results in a\n", + " greyscale with monotonically increasing brightness.\n", + " This color scheme is named cubehelix because the r,g,b\n", + " values produced can be visualised as a squashed helix\n", + " around the diagonal in the r,g,b color cube.\n", + " gnuplot gnuplot's traditional pm3d scheme\n", + " (black-blue-red-yellow)\n", + " gnuplot2 sequential color printable as gray\n", + " (black-blue-violet-yellow-white)\n", + " ocean green-blue-white\n", + " rainbow spectral purple-blue-green-yellow-orange-red colormap\n", + " with diverging luminance\n", + " seismic diverging blue-white-red\n", + " nipy_spectral black-purple-blue-green-yellow-red-white spectrum,\n", + " originally from the Neuroimaging in Python project\n", + " terrain mapmaker's colors, blue-green-yellow-brown-white,\n", + " originally from IGOR Pro\n", + " ============= =======================================================\n", + " \n", + " The following colormaps are redundant and may be removed in future\n", + " versions. It's recommended to use the names in the descriptions\n", + " instead, which produce identical output:\n", + " \n", + " ========= =======================================================\n", + " Colormap Description\n", + " ========= =======================================================\n", + " gist_gray identical to *gray*\n", + " gist_yarg identical to *gray_r*\n", + " binary identical to *gray_r*\n", + " spectral identical to *nipy_spectral* [#]_\n", + " ========= =======================================================\n", + " \n", + " .. rubric:: Footnotes\n", + " \n", + " .. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor\n", + " choice for scientific visualization by many researchers: `Rainbow Color\n", + " Map (Still) Considered Harmful\n", + " `_\n", + " \n", + " .. [#] Resembles \"BkBlAqGrYeOrReViWh200\" from NCAR Command\n", + " Language. See `Color Table Gallery\n", + " `_\n", + " \n", + " .. [#] See `Diverging Color Maps for Scientific Visualization\n", + " `_ by Kenneth Moreland.\n", + " \n", + " .. [#] See `A Color Map for Effective Black-and-White Rendering of\n", + " Color-Scale Images\n", + " `_\n", + " by Carey Rappaport\n", + " \n", + " .. [#] Changed to distinguish from ColorBrewer's *Spectral* map.\n", + " :func:`spectral` still works, but\n", + " ``set_cmap('nipy_spectral')`` is recommended for clarity.\n", + " \n", + " colors()\n", + " .. deprecated:: 2.1\n", + " The colors function was deprecated in version 2.1.\n", + " \n", + " This is a do-nothing function to provide you with help on how\n", + " matplotlib handles colors.\n", + " \n", + " Commands which take color arguments can use several formats to\n", + " specify the colors. For the basic built-in colors, you can use a\n", + " single letter\n", + " \n", + " ===== =======\n", + " Alias Color\n", + " ===== =======\n", + " 'b' blue\n", + " 'g' green\n", + " 'r' red\n", + " 'c' cyan\n", + " 'm' magenta\n", + " 'y' yellow\n", + " 'k' black\n", + " 'w' white\n", + " ===== =======\n", + " \n", + " For a greater range of colors, you have two options. You can\n", + " specify the color using an html hex string, as in::\n", + " \n", + " color = '#eeefff'\n", + " \n", + " or you can pass an R,G,B tuple, where each of R,G,B are in the\n", + " range [0,1].\n", + " \n", + " You can also use any legal html name for a color, for example::\n", + " \n", + " color = 'red'\n", + " color = 'burlywood'\n", + " color = 'chartreuse'\n", + " \n", + " The example below creates a subplot with a dark\n", + " slate gray background::\n", + " \n", + " subplot(111, facecolor=(0.1843, 0.3098, 0.3098))\n", + " \n", + " Here is an example that creates a pale turquoise title::\n", + " \n", + " title('Is this the best color?', color='#afeeee')\n", + " \n", + " connect(s, func)\n", + " Connect event with string *s* to *func*. The signature of *func* is::\n", + " \n", + " def func(event)\n", + " \n", + " where event is a :class:`matplotlib.backend_bases.Event`. The\n", + " following events are recognized\n", + " \n", + " - 'button_press_event'\n", + " - 'button_release_event'\n", + " - 'draw_event'\n", + " - 'key_press_event'\n", + " - 'key_release_event'\n", + " - 'motion_notify_event'\n", + " - 'pick_event'\n", + " - 'resize_event'\n", + " - 'scroll_event'\n", + " - 'figure_enter_event',\n", + " - 'figure_leave_event',\n", + " - 'axes_enter_event',\n", + " - 'axes_leave_event'\n", + " - 'close_event'\n", + " \n", + " For the location events (button and key press/release), if the\n", + " mouse is over the axes, the variable ``event.inaxes`` will be\n", + " set to the :class:`~matplotlib.axes.Axes` the event occurs is\n", + " over, and additionally, the variables ``event.xdata`` and\n", + " ``event.ydata`` will be defined. This is the mouse location\n", + " in data coords. See\n", + " :class:`~matplotlib.backend_bases.KeyEvent` and\n", + " :class:`~matplotlib.backend_bases.MouseEvent` for more info.\n", + " \n", + " Return value is a connection id that can be used with\n", + " :meth:`~matplotlib.backend_bases.Event.mpl_disconnect`.\n", + " \n", + " Examples\n", + " --------\n", + " Usage::\n", + " \n", + " def on_press(event):\n", + " print('you pressed', event.button, event.xdata, event.ydata)\n", + " \n", + " cid = canvas.mpl_connect('button_press_event', on_press)\n", + " \n", + " contour(*args, **kwargs)\n", + " Plot contours.\n", + " \n", + " :func:`~matplotlib.pyplot.contour` and\n", + " :func:`~matplotlib.pyplot.contourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " :func:`~matplotlib.pyplot.contourf` differs from the MATLAB\n", + " version in that it does not draw the polygon edges.\n", + " To draw edges, add line contours with\n", + " calls to :func:`~matplotlib.pyplot.contour`.\n", + " \n", + " \n", + " Call signatures::\n", + " \n", + " contour(Z)\n", + " \n", + " make a contour plot of an array *Z*. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " contour(X,Y,Z)\n", + " \n", + " *X*, *Y* specify the (x, y) coordinates of the surface\n", + " \n", + " ::\n", + " \n", + " contour(Z,N)\n", + " contour(X,Y,Z,N)\n", + " \n", + " contour up to *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " contour(Z,V)\n", + " contour(X,Y,Z,V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contourf(..., V)\n", + " \n", + " fill the ``len(V)-1`` regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " *X* and *Y* must both be 2-D with the same shape as *Z*, or they\n", + " must both be 1-D such that ``len(X)`` is the number of columns in\n", + " *Z* and ``len(Y)`` is the number of rows in *Z*.\n", + " \n", + " ``C = contour(...)`` returns a\n", + " :class:`~matplotlib.contour.QuadContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *corner_mask*: [ *True* | *False* | 'legacy' ]\n", + " Enable/disable corner masking, which only has an effect if *Z* is\n", + " a masked array. If *False*, any quad touching a masked point is\n", + " masked out. If *True*, only the triangular corners of quads\n", + " nearest those points are always masked out, other triangular\n", + " corners comprising three unmasked points are contoured as usual.\n", + " If 'legacy', the old contouring algorithm is used, which is\n", + " equivalent to *False* and is deprecated, only remaining whilst the\n", + " new algorithm is tested fully.\n", + " \n", + " If not specified, the default is taken from\n", + " rcParams['contour.corner_mask'], which is True unless it has\n", + " been modified.\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *vmin*, *vmax*: [ *None* | scalar ]\n", + " If not *None*, either or both of these values will be\n", + " supplied to the :class:`matplotlib.colors.Normalize`\n", + " instance, overriding the default color scaling based on\n", + " *levels*.\n", + " \n", + " *levels*: [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is *None*, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing, overriding the defaults. For\n", + " filled contours, the default is *True*. For line contours,\n", + " it is taken from rcParams['lines.antialiased'].\n", + " \n", + " *nchunk*: [ 0 | integer ]\n", + " If 0, no subdivision of the domain. Specify a positive integer to\n", + " divide the domain into subdomains of *nchunk* by *nchunk* quads.\n", + " Chunking reduces the maximum length of polygons generated by the\n", + " contouring algorithm which reduces the rendering workload passed\n", + " on to the backend and also requires slightly less RAM. It can\n", + " however introduce rendering artifacts at chunk boundaries depending\n", + " on the backend, the *antialiased* flag and value of *alpha*.\n", + " \n", + " contour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified.\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the default is 'solid' unless\n", + " the lines are monochrome. In that case, negative\n", + " contours will take their linestyle from the ``matplotlibrc``\n", + " ``contour.negative_linestyle`` setting.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " contourf-only keyword arguments:\n", + " \n", + " *hatches*:\n", + " A list of cross hatch patterns to use on the filled areas.\n", + " If None, no hatching will be added to the contour.\n", + " Hatching is supported in the PostScript, PDF, SVG and Agg\n", + " backends only.\n", + " \n", + " \n", + " Note: contourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " contourf(*args, **kwargs)\n", + " Plot contours.\n", + " \n", + " :func:`~matplotlib.pyplot.contour` and\n", + " :func:`~matplotlib.pyplot.contourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " :func:`~matplotlib.pyplot.contourf` differs from the MATLAB\n", + " version in that it does not draw the polygon edges.\n", + " To draw edges, add line contours with\n", + " calls to :func:`~matplotlib.pyplot.contour`.\n", + " \n", + " \n", + " Call signatures::\n", + " \n", + " contour(Z)\n", + " \n", + " make a contour plot of an array *Z*. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " contour(X,Y,Z)\n", + " \n", + " *X*, *Y* specify the (x, y) coordinates of the surface\n", + " \n", + " ::\n", + " \n", + " contour(Z,N)\n", + " contour(X,Y,Z,N)\n", + " \n", + " contour up to *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " contour(Z,V)\n", + " contour(X,Y,Z,V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contourf(..., V)\n", + " \n", + " fill the ``len(V)-1`` regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " contour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " *X* and *Y* must both be 2-D with the same shape as *Z*, or they\n", + " must both be 1-D such that ``len(X)`` is the number of columns in\n", + " *Z* and ``len(Y)`` is the number of rows in *Z*.\n", + " \n", + " ``C = contour(...)`` returns a\n", + " :class:`~matplotlib.contour.QuadContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *corner_mask*: [ *True* | *False* | 'legacy' ]\n", + " Enable/disable corner masking, which only has an effect if *Z* is\n", + " a masked array. If *False*, any quad touching a masked point is\n", + " masked out. If *True*, only the triangular corners of quads\n", + " nearest those points are always masked out, other triangular\n", + " corners comprising three unmasked points are contoured as usual.\n", + " If 'legacy', the old contouring algorithm is used, which is\n", + " equivalent to *False* and is deprecated, only remaining whilst the\n", + " new algorithm is tested fully.\n", + " \n", + " If not specified, the default is taken from\n", + " rcParams['contour.corner_mask'], which is True unless it has\n", + " been modified.\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *vmin*, *vmax*: [ *None* | scalar ]\n", + " If not *None*, either or both of these values will be\n", + " supplied to the :class:`matplotlib.colors.Normalize`\n", + " instance, overriding the default color scaling based on\n", + " *levels*.\n", + " \n", + " *levels*: [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is *None*, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing, overriding the defaults. For\n", + " filled contours, the default is *True*. For line contours,\n", + " it is taken from rcParams['lines.antialiased'].\n", + " \n", + " *nchunk*: [ 0 | integer ]\n", + " If 0, no subdivision of the domain. Specify a positive integer to\n", + " divide the domain into subdomains of *nchunk* by *nchunk* quads.\n", + " Chunking reduces the maximum length of polygons generated by the\n", + " contouring algorithm which reduces the rendering workload passed\n", + " on to the backend and also requires slightly less RAM. It can\n", + " however introduce rendering artifacts at chunk boundaries depending\n", + " on the backend, the *antialiased* flag and value of *alpha*.\n", + " \n", + " contour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified.\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the default is 'solid' unless\n", + " the lines are monochrome. In that case, negative\n", + " contours will take their linestyle from the ``matplotlibrc``\n", + " ``contour.negative_linestyle`` setting.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " contourf-only keyword arguments:\n", + " \n", + " *hatches*:\n", + " A list of cross hatch patterns to use on the filled areas.\n", + " If None, no hatching will be added to the contour.\n", + " Hatching is supported in the PostScript, PDF, SVG and Agg\n", + " backends only.\n", + " \n", + " \n", + " Note: contourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " cool()\n", + " set the default colormap to cool and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " copper()\n", + " set the default colormap to copper and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " csd(x, y, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, hold=None, data=None, **kwargs)\n", + " Plot the cross-spectral density.\n", + " \n", + " Call signature::\n", + " \n", + " csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,\n", + " window=mlab.window_hanning, noverlap=0, pad_to=None,\n", + " sides='default', scale_by_freq=None, return_line=None, **kwargs)\n", + " \n", + " The cross spectral density :math:`P_{xy}` by Welch's average\n", + " periodogram method. The vectors *x* and *y* are divided into\n", + " *NFFT* length segments. Each segment is detrended by function\n", + " *detrend* and windowed by function *window*. *noverlap* gives\n", + " the length of the overlap between segments. The product of\n", + " the direct FFTs of *x* and *y* are averaged over each segment\n", + " to compute :math:`P_{xy}`, with a scaling to correct for power\n", + " loss due to windowing.\n", + " \n", + " If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero\n", + " padded to *NFFT*.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : 1-D arrays or sequences\n", + " Arrays or sequences containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between segments.\n", + " The default value is 0 (no overlap).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " return_line : bool\n", + " Whether to include the line object plotted in the returned values.\n", + " Default is False.\n", + " \n", + " Returns\n", + " -------\n", + " Pxy : 1-D array\n", + " The values for the cross spectrum `P_{xy}` before scaling\n", + " (complex valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *Pxy*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function.\n", + " Only returned if *return_line* is True.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " For plotting, the power is plotted as\n", + " :math:`10\\log_{10}(P_{xy})` for decibels, though `P_{xy}` itself\n", + " is returned.\n", + " \n", + " References\n", + " ----------\n", + " Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,\n", + " John Wiley & Sons (1986)\n", + " \n", + " See Also\n", + " --------\n", + " :func:`psd`\n", + " :func:`psd` is the equivalent to setting y=x.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " delaxes(*args)\n", + " Remove an axes from the current figure. If *ax*\n", + " doesn't exist, an error will be raised.\n", + " \n", + " ``delaxes()``: delete the current axes\n", + " \n", + " disconnect(cid)\n", + " Disconnect callback id cid\n", + " \n", + " Examples\n", + " --------\n", + " Usage::\n", + " \n", + " cid = canvas.mpl_connect('button_press_event', on_press)\n", + " #...later\n", + " canvas.mpl_disconnect(cid)\n", + " \n", + " draw()\n", + " Redraw the current figure.\n", + " \n", + " This is used to update a figure that has been altered, but not\n", + " automatically re-drawn. If interactive mode is on (:func:`.ion()`), this\n", + " should be only rarely needed, but there may be ways to modify the state of\n", + " a figure without marking it as `stale`. Please report these cases as\n", + " bugs.\n", + " \n", + " A more object-oriented alternative, given any\n", + " :class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that\n", + " was created using a :mod:`~matplotlib.pyplot` function, is::\n", + " \n", + " fig.canvas.draw_idle()\n", + " \n", + " errorbar(x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=None, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None, hold=None, data=None, **kwargs)\n", + " Plot an errorbar graph.\n", + " \n", + " Plot x versus y with error deltas in yerr and xerr.\n", + " Vertical errorbars are plotted if yerr is not None.\n", + " Horizontal errorbars are plotted if xerr is not None.\n", + " \n", + " x, y, xerr, and yerr can all be scalars, which plots a\n", + " single error bar at x, y.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : scalar or array-like\n", + " y : scalar or array-like\n", + " \n", + " xerr/yerr : scalar or array-like, shape(N,) or shape(2,N), optional\n", + " If a scalar number, len(N) array-like object, or a N-element\n", + " array-like object, errorbars are drawn at +/-value relative\n", + " to the data. Default is None.\n", + " \n", + " If a sequence of shape 2xN, errorbars are drawn at -row1\n", + " and +row2 relative to the data.\n", + " \n", + " fmt : plot format string, optional, default: None\n", + " The plot format symbol. If fmt is 'none' (case-insensitive),\n", + " only the errorbars are plotted. This is used for adding\n", + " errorbars to a bar plot, for example. Default is '',\n", + " an empty plot format string; properties are\n", + " then identical to the defaults for :meth:`plot`.\n", + " \n", + " ecolor : mpl color, optional, default: None\n", + " A matplotlib color arg which gives the color the errorbar lines;\n", + " if None, use the color of the line connecting the markers.\n", + " \n", + " elinewidth : scalar, optional, default: None\n", + " The linewidth of the errorbar lines. If None, use the linewidth.\n", + " \n", + " capsize : scalar, optional, default: None\n", + " The length of the error bar caps in points; if None, it will\n", + " take the value from ``errorbar.capsize``\n", + " :data:`rcParam`.\n", + " \n", + " capthick : scalar, optional, default: None\n", + " An alias kwarg to markeredgewidth (a.k.a. - mew). This\n", + " setting is a more sensible name for the property that\n", + " controls the thickness of the error bar cap in points. For\n", + " backwards compatibility, if mew or markeredgewidth are given,\n", + " then they will over-ride capthick. This may change in future\n", + " releases.\n", + " \n", + " barsabove : bool, optional, default: False\n", + " if True , will plot the errorbars above the plot\n", + " symbols. Default is below.\n", + " \n", + " lolims / uplims / xlolims / xuplims : bool, optional, default:None\n", + " These arguments can be used to indicate that a value gives\n", + " only upper/lower limits. In that case a caret symbol is\n", + " used to indicate this. lims-arguments may be of the same\n", + " type as *xerr* and *yerr*. To use limits with inverted\n", + " axes, :meth:`set_xlim` or :meth:`set_ylim` must be called\n", + " before :meth:`errorbar`.\n", + " \n", + " errorevery : positive integer, optional, default:1\n", + " subsamples the errorbars. e.g., if errorevery=5, errorbars for\n", + " every 5-th datapoint will be plotted. The data plot itself still\n", + " shows all data points.\n", + " \n", + " Returns\n", + " -------\n", + " plotline : :class:`~matplotlib.lines.Line2D` instance\n", + " x, y plot markers and/or line\n", + " caplines : list of :class:`~matplotlib.lines.Line2D` instances\n", + " error bar cap\n", + " barlinecols : list of :class:`~matplotlib.collections.LineCollection`\n", + " horizontal and vertical error ranges.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " All other keyword arguments are passed on to the plot\n", + " command for the markers. For example, this code makes big red\n", + " squares with thick green edges::\n", + " \n", + " x,y,yerr = rand(3,10)\n", + " errorbar(x, y, yerr, marker='s', mfc='red',\n", + " mec='green', ms=20, mew=4)\n", + " \n", + " where mfc, mec, ms and mew are aliases for the longer\n", + " property names, markerfacecolor, markeredgecolor, markersize\n", + " and markeredgewidth.\n", + " \n", + " Valid kwargs for the marker properties are\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'xerr', 'y', 'yerr'.\n", + " \n", + " eventplot(positions, orientation='horizontal', lineoffsets=1, linelengths=1, linewidths=None, colors=None, linestyles='solid', hold=None, data=None, **kwargs)\n", + " Plot identical parallel lines at the given positions.\n", + " \n", + " *positions* should be a 1D or 2D array-like object, with each row\n", + " corresponding to a row or column of lines.\n", + " \n", + " This type of plot is commonly used in neuroscience for representing\n", + " neural events, where it is usually called a spike raster, dot raster,\n", + " or raster plot.\n", + " \n", + " However, it is useful in any situation where you wish to show the\n", + " timing or position of multiple sets of discrete events, such as the\n", + " arrival times of people to a business on each day of the month or the\n", + " date of hurricanes each year of the last century.\n", + " \n", + " Parameters\n", + " ----------\n", + " positions : 1D or 2D array-like object\n", + " Each value is an event. If *positions* is a 2D array-like, each\n", + " row corresponds to a row or a column of lines (depending on the\n", + " *orientation* parameter).\n", + " \n", + " orientation : {'horizontal', 'vertical'}, optional\n", + " Controls the direction of the event collections:\n", + " \n", + " - 'horizontal' : the lines are arranged horizontally in rows,\n", + " and are vertical.\n", + " - 'vertical' : the lines are arranged vertically in columns,\n", + " and are horizontal.\n", + " \n", + " lineoffsets : scalar or sequence of scalars, optional, default: 1\n", + " The offset of the center of the lines from the origin, in the\n", + " direction orthogonal to *orientation*.\n", + " \n", + " linelengths : scalar or sequence of scalars, optional, default: 1\n", + " The total height of the lines (i.e. the lines stretches from\n", + " ``lineoffset - linelength/2`` to ``lineoffset + linelength/2``).\n", + " \n", + " linewidths : scalar, scalar sequence or None, optional, default: None\n", + " The line width(s) of the event lines, in points. If it is None,\n", + " defaults to its rcParams setting.\n", + " \n", + " colors : color, sequence of colors or None, optional, default: None\n", + " The color(s) of the event lines. If it is None, defaults to its\n", + " rcParams setting.\n", + " \n", + " linestyles : str or tuple or a sequence of such values, optional\n", + " Default is 'solid'. Valid strings are ['solid', 'dashed',\n", + " 'dashdot', 'dotted', '-', '--', '-.', ':']. Dash tuples\n", + " should be of the form::\n", + " \n", + " (offset, onoffseq),\n", + " \n", + " where *onoffseq* is an even length tuple of on and off ink\n", + " in points.\n", + " \n", + " **kwargs : optional\n", + " Other keyword arguments are line collection properties. See\n", + " :class:`~matplotlib.collections.LineCollection` for a list of\n", + " the valid properties.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " A list of :class:`matplotlib.collections.EventCollection` objects that\n", + " were added.\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " For *linelengths*, *linewidths*, *colors*, and *linestyles*, if only\n", + " a single value is given, that value is applied to all lines. If an\n", + " array-like is given, it must have the same length as *positions*, and\n", + " each value will be applied to the corresponding row of the array.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " .. plot:: mpl_examples/lines_bars_and_markers/eventplot_demo.py\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'linelengths', 'lineoffsets', 'linestyles', 'linewidths', 'positions'.\n", + " \n", + " figimage(*args, **kwargs)\n", + " Adds a non-resampled image to the figure.\n", + " \n", + " call signatures::\n", + " \n", + " figimage(X, **kwargs)\n", + " \n", + " adds a non-resampled array *X* to the figure.\n", + " \n", + " ::\n", + " \n", + " figimage(X, xo, yo)\n", + " \n", + " with pixel offsets *xo*, *yo*,\n", + " \n", + " *X* must be a float array:\n", + " \n", + " * If *X* is MxN, assume luminance (grayscale)\n", + " * If *X* is MxNx3, assume RGB\n", + " * If *X* is MxNx4, assume RGBA\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " ========= =========================================================\n", + " Keyword Description\n", + " ========= =========================================================\n", + " resize a boolean, True or False. If \"True\", then re-size the\n", + " Figure to match the given image size.\n", + " xo or yo An integer, the *x* and *y* image offset in pixels\n", + " cmap a :class:`matplotlib.colors.Colormap` instance, e.g.,\n", + " cm.jet. If *None*, default to the rc ``image.cmap``\n", + " value\n", + " norm a :class:`matplotlib.colors.Normalize` instance. The\n", + " default is normalization(). This scales luminance -> 0-1\n", + " vmin|vmax are used to scale a luminance image to 0-1. If either\n", + " is *None*, the min and max of the luminance values will\n", + " be used. Note if you pass a norm instance, the settings\n", + " for *vmin* and *vmax* will be ignored.\n", + " alpha the alpha blending value, default is *None*\n", + " origin [ 'upper' | 'lower' ] Indicates where the [0,0] index of\n", + " the array is in the upper left or lower left corner of\n", + " the axes. Defaults to the rc image.origin value\n", + " ========= =========================================================\n", + " \n", + " figimage complements the axes image\n", + " (:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled\n", + " to fit the current axes. If you want a resampled image to\n", + " fill the entire figure, you can define an\n", + " :class:`~matplotlib.axes.Axes` with extent [0,0,1,1].\n", + " \n", + " An :class:`matplotlib.image.FigureImage` instance is returned.\n", + " \n", + " Additional kwargs are Artist kwargs passed on to\n", + " :class:`~matplotlib.image.FigureImage`\n", + " \n", + " figlegend(*args, **kwargs)\n", + " Place a legend in the figure.\n", + " \n", + " *labels*\n", + " a sequence of strings\n", + " \n", + " *handles*\n", + " a sequence of :class:`~matplotlib.lines.Line2D` or\n", + " :class:`~matplotlib.patches.Patch` instances\n", + " \n", + " *loc*\n", + " can be a string or an integer specifying the legend\n", + " location\n", + " \n", + " A :class:`matplotlib.legend.Legend` instance is returned.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " To make a legend from existing artists on every axes::\n", + " \n", + " figlegend()\n", + " \n", + " To make a legend for a list of lines and labels::\n", + " \n", + " figlegend( (line1, line2, line3),\n", + " ('label1', 'label2', 'label3'),\n", + " 'upper right' )\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.legend`\n", + " \n", + " fignum_exists(num)\n", + " \n", + " figtext(*args, **kwargs)\n", + " Add text to figure.\n", + " \n", + " Call signature::\n", + " \n", + " text(x, y, s, fontdict=None, **kwargs)\n", + " \n", + " Add text to figure at location *x*, *y* (relative 0-1\n", + " coords). See :func:`~matplotlib.pyplot.text` for the meaning\n", + " of the other arguments.\n", + " \n", + " kwargs control the :class:`~matplotlib.text.Text` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " backgroundcolor: any matplotlib color \n", + " bbox: FancyBboxPatch prop dict \n", + " clip_box: a :class:`matplotlib.transforms.Bbox` instance \n", + " clip_on: [True | False] \n", + " clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] \n", + " color: any matplotlib color \n", + " contains: a callable function \n", + " family or fontfamily or fontname or name: [FONTNAME | 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ] \n", + " figure: a `~.Figure` instance \n", + " fontproperties or font_properties: a :class:`matplotlib.font_manager.FontProperties` instance \n", + " gid: an id string \n", + " horizontalalignment or ha: [ 'center' | 'right' | 'left' ] \n", + " label: object \n", + " linespacing: float (multiple of font size) \n", + " multialignment or ma: ['left' | 'right' | 'center' ] \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " position: (x,y) \n", + " rasterized: bool or None \n", + " rotation: [ angle in degrees | 'vertical' | 'horizontal' ] \n", + " rotation_mode: [ None | \"default\" | \"anchor\" ]\n", + " size or fontsize: [size in points | 'xx-small' | 'x-small' | 'small' | 'medium' | 'large' | 'x-large' | 'xx-large' ] \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " stretch or fontstretch: [a numeric value in range 0-1000 | 'ultra-condensed' | 'extra-condensed' | 'condensed' | 'semi-condensed' | 'normal' | 'semi-expanded' | 'expanded' | 'extra-expanded' | 'ultra-expanded' ] \n", + " style or fontstyle: [ 'normal' | 'italic' | 'oblique'] \n", + " text: string or anything printable with '%s' conversion. \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " usetex: bool or None \n", + " variant or fontvariant: [ 'normal' | 'small-caps' ] \n", + " verticalalignment or va: [ 'center' | 'top' | 'bottom' | 'baseline' ] \n", + " visible: bool \n", + " weight or fontweight: [a numeric value in range 0-1000 | 'ultralight' | 'light' | 'normal' | 'regular' | 'book' | 'medium' | 'roman' | 'semibold' | 'demibold' | 'demi' | 'bold' | 'heavy' | 'extra bold' | 'black' ] \n", + " wrap: bool\n", + " x: float \n", + " y: float \n", + " zorder: float\n", + " \n", + " figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=, clear=False, **kwargs)\n", + " Creates a new figure.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " num : integer or string, optional, default: none\n", + " If not provided, a new figure will be created, and the figure number\n", + " will be incremented. The figure objects holds this number in a `number`\n", + " attribute.\n", + " If num is provided, and a figure with this id already exists, make\n", + " it active, and returns a reference to it. If this figure does not\n", + " exists, create it and returns it.\n", + " If num is a string, the window title will be set to this figure's\n", + " `num`.\n", + " \n", + " figsize : tuple of integers, optional, default: None\n", + " width, height in inches. If not provided, defaults to rc\n", + " figure.figsize.\n", + " \n", + " dpi : integer, optional, default: None\n", + " resolution of the figure. If not provided, defaults to rc figure.dpi.\n", + " \n", + " facecolor :\n", + " the background color. If not provided, defaults to rc figure.facecolor.\n", + " \n", + " edgecolor :\n", + " the border color. If not provided, defaults to rc figure.edgecolor.\n", + " \n", + " frameon : bool, optional, default: True\n", + " If False, suppress drawing the figure frame.\n", + " \n", + " FigureClass : class derived from matplotlib.figure.Figure\n", + " Optionally use a custom Figure instance.\n", + " \n", + " clear : bool, optional, default: False\n", + " If True and the figure already exists, then it is cleared.\n", + " \n", + " Returns\n", + " -------\n", + " figure : Figure\n", + " The Figure instance returned will also be passed to new_figure_manager\n", + " in the backends, which allows to hook custom Figure classes into the\n", + " pylab interface. Additional kwargs will be passed to the figure init\n", + " function.\n", + " \n", + " Notes\n", + " -----\n", + " If you are creating many figures, make sure you explicitly call \"close\"\n", + " on the figures you are not using, because this will enable pylab\n", + " to properly clean up the memory.\n", + " \n", + " rcParams defines the default values, which can be modified in the\n", + " matplotlibrc file\n", + " \n", + " fill(*args, **kwargs)\n", + " Plot filled polygons.\n", + " \n", + " Parameters\n", + " ----------\n", + " args : a variable length argument\n", + " It allowing for multiple\n", + " *x*, *y* pairs with an optional color format string; see\n", + " :func:`~matplotlib.pyplot.plot` for details on the argument\n", + " parsing. For example, each of the following is legal::\n", + " \n", + " ax.fill(x, y)\n", + " ax.fill(x, y, \"b\")\n", + " ax.fill(x, y, \"b\", x, y, \"r\")\n", + " \n", + " An arbitrary number of *x*, *y*, *color* groups can be specified::\n", + " ax.fill(x1, y1, 'g', x2, y2, 'r')\n", + " \n", + " Returns\n", + " -------\n", + " a list of :class:`~matplotlib.patches.Patch`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : :class:`~matplotlib.patches.Polygon` properties\n", + " \n", + " Notes\n", + " -----\n", + " The same color strings that :func:`~matplotlib.pyplot.plot`\n", + " supports are supported by the fill format string.\n", + " \n", + " If you would like to fill below a curve, e.g., shade a region\n", + " between 0 and *y* along *x*, use :meth:`fill_between`\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " fill_between(x, y1, y2=0, where=None, interpolate=False, step=None, hold=None, data=None, **kwargs)\n", + " Make filled polygons between two curves.\n", + " \n", + " \n", + " Create a :class:`~matplotlib.collections.PolyCollection`\n", + " filling the regions between *y1* and *y2* where\n", + " ``where==True``\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array\n", + " An N-length array of the x data\n", + " \n", + " y1 : array\n", + " An N-length array (or scalar) of the y data\n", + " \n", + " y2 : array\n", + " An N-length array (or scalar) of the y data\n", + " \n", + " where : array, optional\n", + " If `None`, default to fill between everywhere. If not `None`,\n", + " it is an N-length numpy boolean array and the fill will\n", + " only happen over the regions where ``where==True``.\n", + " \n", + " interpolate : bool, optional\n", + " If `True`, interpolate between the two lines to find the\n", + " precise point of intersection. Otherwise, the start and\n", + " end points of the filled region will only occur on explicit\n", + " values in the *x* array.\n", + " \n", + " step : {'pre', 'post', 'mid'}, optional\n", + " If not None, fill with step logic.\n", + " \n", + " \n", + " Notes\n", + " -----\n", + " \n", + " Additional Keyword args passed on to the\n", + " :class:`~matplotlib.collections.PolyCollection`.\n", + " \n", + " kwargs control the :class:`~matplotlib.patches.Polygon` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " \n", + " :meth:`fill_betweenx`\n", + " for filling between two sets of x-values\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'where', 'x', 'y1', 'y2'.\n", + " \n", + " fill_betweenx(y, x1, x2=0, where=None, step=None, interpolate=False, hold=None, data=None, **kwargs)\n", + " Make filled polygons between two horizontal curves.\n", + " \n", + " \n", + " Create a :class:`~matplotlib.collections.PolyCollection`\n", + " filling the regions between *x1* and *x2* where\n", + " ``where==True``\n", + " \n", + " Parameters\n", + " ----------\n", + " y : array\n", + " An N-length array of the y data\n", + " \n", + " x1 : array\n", + " An N-length array (or scalar) of the x data\n", + " \n", + " x2 : array, optional\n", + " An N-length array (or scalar) of the x data\n", + " \n", + " where : array, optional\n", + " If *None*, default to fill between everywhere. If not *None*,\n", + " it is a N length numpy boolean array and the fill will\n", + " only happen over the regions where ``where==True``\n", + " \n", + " step : {'pre', 'post', 'mid'}, optional\n", + " If not None, fill with step logic.\n", + " \n", + " interpolate : bool, optional\n", + " If `True`, interpolate between the two lines to find the\n", + " precise point of intersection. Otherwise, the start and\n", + " end points of the filled region will only occur on explicit\n", + " values in the *x* array.\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " keyword args passed on to the\n", + " :class:`~matplotlib.collections.PolyCollection`\n", + " \n", + " kwargs control the :class:`~matplotlib.patches.Polygon` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " \n", + " :meth:`fill_between`\n", + " for filling between two sets of y-values\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'where', 'x1', 'x2', 'y'.\n", + " \n", + " findobj(o=None, match=None, include_self=True)\n", + " Find artist objects.\n", + " \n", + " Recursively find all :class:`~matplotlib.artist.Artist` instances\n", + " contained in self.\n", + " \n", + " *match* can be\n", + " \n", + " - None: return all objects contained in artist.\n", + " \n", + " - function with signature ``boolean = match(artist)``\n", + " used to filter matches\n", + " \n", + " - class instance: e.g., Line2D. Only return artists of class type.\n", + " \n", + " If *include_self* is True (default), include self in the list to be\n", + " checked for a match.\n", + " \n", + " flag()\n", + " set the default colormap to flag and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " gca(**kwargs)\n", + " Get the current :class:`~matplotlib.axes.Axes` instance on the\n", + " current figure matching the given keyword args, or create one.\n", + " \n", + " Examples\n", + " --------\n", + " To get the current polar axes on the current figure::\n", + " \n", + " plt.gca(projection='polar')\n", + " \n", + " If the current axes doesn't exist, or isn't a polar one, the appropriate\n", + " axes will be created and then returned.\n", + " \n", + " See Also\n", + " --------\n", + " matplotlib.figure.Figure.gca : The figure's gca method.\n", + " \n", + " gcf()\n", + " Get a reference to the current figure.\n", + " \n", + " gci()\n", + " Get the current colorable artist. Specifically, returns the\n", + " current :class:`~matplotlib.cm.ScalarMappable` instance (image or\n", + " patch collection), or *None* if no images or patch collections\n", + " have been defined. The commands :func:`~matplotlib.pyplot.imshow`\n", + " and :func:`~matplotlib.pyplot.figimage` create\n", + " :class:`~matplotlib.image.Image` instances, and the commands\n", + " :func:`~matplotlib.pyplot.pcolor` and\n", + " :func:`~matplotlib.pyplot.scatter` create\n", + " :class:`~matplotlib.collections.Collection` instances. The\n", + " current image is an attribute of the current axes, or the nearest\n", + " earlier axes in the current figure that contains an image.\n", + " \n", + " get_current_fig_manager()\n", + " \n", + " get_figlabels()\n", + " Return a list of existing figure labels.\n", + " \n", + " get_fignums()\n", + " Return a list of existing figure numbers.\n", + " \n", + " get_plot_commands()\n", + " Get a sorted list of all of the plotting commands.\n", + " \n", + " ginput(*args, **kwargs)\n", + " Blocking call to interact with a figure.\n", + " \n", + " Wait until the user clicks *n* times on the figure, and return the\n", + " coordinates of each click in a list.\n", + " \n", + " The buttons used for the various actions (adding points, removing\n", + " points, terminating the inputs) can be overridden via the\n", + " arguments *mouse_add*, *mouse_pop* and *mouse_stop*, that give\n", + " the associated mouse button: 1 for left, 2 for middle, 3 for\n", + " right.\n", + " \n", + " Parameters\n", + " ----------\n", + " n : int, optional, default: 1\n", + " Number of mouse clicks to accumulate. If negative, accumulate\n", + " clicks until the input is terminated manually.\n", + " timeout : scalar, optional, default: 30\n", + " Number of seconds to wait before timing out. If zero or negative\n", + " will never timeout.\n", + " show_clicks : bool, optional, default: False\n", + " If True, show a red cross at the location of each click.\n", + " mouse_add : int, one of (1, 2, 3), optional, default: 1 (left click)\n", + " Mouse button used to add points.\n", + " mouse_pop : int, one of (1, 2, 3), optional, default: 3 (right click)\n", + " Mouse button used to remove the most recently added point.\n", + " mouse_stop : int, one of (1, 2, 3), optional, default: 2 (middle click)\n", + " Mouse button used to stop input.\n", + " \n", + " Returns\n", + " -------\n", + " points : list of tuples\n", + " A list of the clicked (x, y) coordinates.\n", + " \n", + " Notes\n", + " -----\n", + " The keyboard can also be used to select points in case your mouse\n", + " does not have one or more of the buttons. The delete and backspace\n", + " keys act like right clicking (i.e., remove last point), the enter key\n", + " terminates input and any other key (not already used by the window\n", + " manager) selects a point.\n", + " \n", + " gray()\n", + " set the default colormap to gray and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " grid(b=None, which='major', axis='both', **kwargs)\n", + " Turn the axes grids on or off.\n", + " \n", + " Set the axes grids on or off; *b* is a boolean. (For MATLAB\n", + " compatibility, *b* may also be a string, 'on' or 'off'.)\n", + " \n", + " If *b* is *None* and ``len(kwargs)==0``, toggle the grid state. If\n", + " *kwargs* are supplied, it is assumed that you want a grid and *b*\n", + " is thus set to *True*.\n", + " \n", + " *which* can be 'major' (default), 'minor', or 'both' to control\n", + " whether major tick grids, minor tick grids, or both are affected.\n", + " \n", + " *axis* can be 'both' (default), 'x', or 'y' to control which\n", + " set of gridlines are drawn.\n", + " \n", + " *kwargs* are used to set the grid line properties, e.g.,::\n", + " \n", + " ax.grid(color='r', linestyle='-', linewidth=2)\n", + " \n", + " Valid :class:`~matplotlib.lines.Line2D` kwargs are\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float\n", + " \n", + " hexbin(x, y, C=None, gridsize=100, bins=None, xscale='linear', yscale='linear', extent=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors='face', reduce_C_function=, mincnt=None, marginals=False, hold=None, data=None, **kwargs)\n", + " Make a hexagonal binning plot.\n", + " \n", + " Make a hexagonal binning plot of *x* versus *y*, where *x*,\n", + " *y* are 1-D sequences of the same length, *N*. If *C* is *None*\n", + " (the default), this is a histogram of the number of occurrences\n", + " of the observations at (x[i],y[i]).\n", + " \n", + " If *C* is specified, it specifies values at the coordinate\n", + " (x[i],y[i]). These values are accumulated for each hexagonal\n", + " bin and then reduced according to *reduce_C_function*, which\n", + " defaults to numpy's mean function (np.mean). (If *C* is\n", + " specified, it must also be a 1-D sequence of the same length\n", + " as *x* and *y*.)\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : array or masked array\n", + " \n", + " C : array or masked array, optional, default is *None*\n", + " \n", + " gridsize : int or (int, int), optional, default is 100\n", + " The number of hexagons in the *x*-direction, default is\n", + " 100. The corresponding number of hexagons in the\n", + " *y*-direction is chosen such that the hexagons are\n", + " approximately regular. Alternatively, gridsize can be a\n", + " tuple with two elements specifying the number of hexagons\n", + " in the *x*-direction and the *y*-direction.\n", + " \n", + " bins : {'log'} or int or sequence, optional, default is *None*\n", + " If *None*, no binning is applied; the color of each hexagon\n", + " directly corresponds to its count value.\n", + " \n", + " If 'log', use a logarithmic scale for the color\n", + " map. Internally, :math:`log_{10}(i+1)` is used to\n", + " determine the hexagon color.\n", + " \n", + " If an integer, divide the counts in the specified number\n", + " of bins, and color the hexagons accordingly.\n", + " \n", + " If a sequence of values, the values of the lower bound of\n", + " the bins to be used.\n", + " \n", + " xscale : {'linear', 'log'}, optional, default is 'linear'\n", + " Use a linear or log10 scale on the horizontal axis.\n", + " \n", + " yscale : {'linear', 'log'}, optional, default is 'linear'\n", + " Use a linear or log10 scale on the vertical axis.\n", + " \n", + " mincnt : int > 0, optional, default is *None*\n", + " If not *None*, only display cells with more than *mincnt*\n", + " number of points in the cell\n", + " \n", + " marginals : bool, optional, default is *False*\n", + " if marginals is *True*, plot the marginal density as\n", + " colormapped rectagles along the bottom of the x-axis and\n", + " left of the y-axis\n", + " \n", + " extent : scalar, optional, default is *None*\n", + " The limits of the bins. The default assigns the limits\n", + " based on *gridsize*, *x*, *y*, *xscale* and *yscale*.\n", + " \n", + " If *xscale* or *yscale* is set to 'log', the limits are\n", + " expected to be the exponent for a power of 10. E.g. for\n", + " x-limits of 1 and 50 in 'linear' scale and y-limits\n", + " of 10 and 1000 in 'log' scale, enter (1, 50, 1, 3).\n", + " \n", + " Order of scalars is (left, right, bottom, top).\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " cmap : object, optional, default is *None*\n", + " a :class:`matplotlib.colors.Colormap` instance. If *None*,\n", + " defaults to rc ``image.cmap``.\n", + " \n", + " norm : object, optional, default is *None*\n", + " :class:`matplotlib.colors.Normalize` instance is used to\n", + " scale luminance data to 0,1.\n", + " \n", + " vmin, vmax : scalar, optional, default is *None*\n", + " *vmin* and *vmax* are used in conjunction with *norm* to\n", + " normalize luminance data. If *None*, the min and max of the\n", + " color array *C* are used. Note if you pass a norm instance\n", + " your settings for *vmin* and *vmax* will be ignored.\n", + " \n", + " alpha : scalar between 0 and 1, optional, default is *None*\n", + " the alpha value for the patches\n", + " \n", + " linewidths : scalar, optional, default is *None*\n", + " If *None*, defaults to 1.0.\n", + " \n", + " edgecolors : {'face', 'none', *None*} or mpl color, optional, default is 'face'\n", + " \n", + " If 'face', draws the edges in the same color as the fill color.\n", + " \n", + " If 'none', no edge is drawn; this can sometimes lead to unsightly\n", + " unpainted pixels between the hexagons.\n", + " \n", + " If *None*, draws outlines in the default color.\n", + " \n", + " If a matplotlib color arg, draws outlines in the specified color.\n", + " \n", + " Returns\n", + " -------\n", + " object\n", + " a :class:`~matplotlib.collections.PolyCollection` instance; use\n", + " :meth:`~matplotlib.collections.PolyCollection.get_array` on\n", + " this :class:`~matplotlib.collections.PolyCollection` to get\n", + " the counts in each hexagon.\n", + " \n", + " If *marginals* is *True*, horizontal\n", + " bar and vertical bar (both PolyCollections) will be attached\n", + " to the return collection as attributes *hbar* and *vbar*.\n", + " \n", + " Notes\n", + " --------\n", + " The standard descriptions of all the\n", + " :class:`~matplotlib.collections.Collection` parameters:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " hist(x, bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, normed=None, hold=None, data=None, **kwargs)\n", + " Plot a histogram.\n", + " \n", + " Compute and draw the histogram of *x*. The return value is a\n", + " tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,\n", + " [*patches0*, *patches1*,...]) if the input contains multiple\n", + " data.\n", + " \n", + " Multiple data can be provided via *x* as a list of datasets\n", + " of potentially different length ([*x0*, *x1*, ...]), or as\n", + " a 2-D ndarray in which each column is a dataset. Note that\n", + " the ndarray form is transposed relative to the list form.\n", + " \n", + " Masked arrays are not supported at present.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : (n,) array or sequence of (n,) arrays\n", + " Input values, this takes either a single array or a sequency of\n", + " arrays which are not required to be of the same length\n", + " \n", + " bins : integer or sequence or 'auto', optional\n", + " If an integer is given, ``bins + 1`` bin edges are calculated and\n", + " returned, consistent with :func:`numpy.histogram`.\n", + " \n", + " If `bins` is a sequence, gives bin edges, including left edge of\n", + " first bin and right edge of last bin. In this case, `bins` is\n", + " returned unmodified.\n", + " \n", + " All but the last (righthand-most) bin is half-open. In other\n", + " words, if `bins` is::\n", + " \n", + " [1, 2, 3, 4]\n", + " \n", + " then the first bin is ``[1, 2)`` (including 1, but excluding 2) and\n", + " the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which\n", + " *includes* 4.\n", + " \n", + " Unequally spaced bins are supported if *bins* is a sequence.\n", + " \n", + " If Numpy 1.11 is installed, may also be ``'auto'``.\n", + " \n", + " Default is taken from the rcParam ``hist.bins``.\n", + " \n", + " range : tuple or None, optional\n", + " The lower and upper range of the bins. Lower and upper outliers\n", + " are ignored. If not provided, *range* is ``(x.min(), x.max())``.\n", + " Range has no effect if *bins* is a sequence.\n", + " \n", + " If *bins* is a sequence or *range* is specified, autoscaling\n", + " is based on the specified bin range instead of the\n", + " range of x.\n", + " \n", + " Default is ``None``\n", + " \n", + " density : boolean, optional\n", + " If ``True``, the first element of the return tuple will\n", + " be the counts normalized to form a probability density, i.e.,\n", + " the area (or integral) under the histogram will sum to 1.\n", + " This is achieved by dividing the count by the number of\n", + " observations times the bin width and not dividing by the total\n", + " number of observations. If *stacked* is also ``True``, the sum of\n", + " the histograms is normalized to 1.\n", + " \n", + " Default is ``None`` for both *normed* and *density*. If either is\n", + " set, then that value will be used. If neither are set, then the\n", + " args will be treated as ``False``.\n", + " \n", + " If both *density* and *normed* are set an error is raised.\n", + " \n", + " weights : (n, ) array_like or None, optional\n", + " An array of weights, of the same shape as *x*. Each value in *x*\n", + " only contributes its associated weight towards the bin count\n", + " (instead of 1). If *normed* or *density* is ``True``,\n", + " the weights are normalized, so that the integral of the density\n", + " over the range remains 1.\n", + " \n", + " Default is ``None``\n", + " \n", + " cumulative : boolean, optional\n", + " If ``True``, then a histogram is computed where each bin gives the\n", + " counts in that bin plus all bins for smaller values. The last bin\n", + " gives the total number of datapoints. If *normed* or *density*\n", + " is also ``True`` then the histogram is normalized such that the\n", + " last bin equals 1. If *cumulative* evaluates to less than 0\n", + " (e.g., -1), the direction of accumulation is reversed.\n", + " In this case, if *normed* and/or *density* is also ``True``, then\n", + " the histogram is normalized such that the first bin equals 1.\n", + " \n", + " Default is ``False``\n", + " \n", + " bottom : array_like, scalar, or None\n", + " Location of the bottom baseline of each bin. If a scalar,\n", + " the base line for each bin is shifted by the same amount.\n", + " If an array, each bin is shifted independently and the length\n", + " of bottom must match the number of bins. If None, defaults to 0.\n", + " \n", + " Default is ``None``\n", + " \n", + " histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional\n", + " The type of histogram to draw.\n", + " \n", + " - 'bar' is a traditional bar-type histogram. If multiple data\n", + " are given the bars are aranged side by side.\n", + " \n", + " - 'barstacked' is a bar-type histogram where multiple\n", + " data are stacked on top of each other.\n", + " \n", + " - 'step' generates a lineplot that is by default\n", + " unfilled.\n", + " \n", + " - 'stepfilled' generates a lineplot that is by default\n", + " filled.\n", + " \n", + " Default is 'bar'\n", + " \n", + " align : {'left', 'mid', 'right'}, optional\n", + " Controls how the histogram is plotted.\n", + " \n", + " - 'left': bars are centered on the left bin edges.\n", + " \n", + " - 'mid': bars are centered between the bin edges.\n", + " \n", + " - 'right': bars are centered on the right bin edges.\n", + " \n", + " Default is 'mid'\n", + " \n", + " orientation : {'horizontal', 'vertical'}, optional\n", + " If 'horizontal', `~matplotlib.pyplot.barh` will be used for\n", + " bar-type histograms and the *bottom* kwarg will be the left edges.\n", + " \n", + " rwidth : scalar or None, optional\n", + " The relative width of the bars as a fraction of the bin width. If\n", + " ``None``, automatically compute the width.\n", + " \n", + " Ignored if *histtype* is 'step' or 'stepfilled'.\n", + " \n", + " Default is ``None``\n", + " \n", + " log : boolean, optional\n", + " If ``True``, the histogram axis will be set to a log scale. If\n", + " *log* is ``True`` and *x* is a 1D array, empty bins will be\n", + " filtered out and only the non-empty ``(n, bins, patches)``\n", + " will be returned.\n", + " \n", + " Default is ``False``\n", + " \n", + " color : color or array_like of colors or None, optional\n", + " Color spec or sequence of color specs, one per dataset. Default\n", + " (``None``) uses the standard line color sequence.\n", + " \n", + " Default is ``None``\n", + " \n", + " label : string or None, optional\n", + " String, or sequence of strings to match multiple datasets. Bar\n", + " charts yield multiple patches per dataset, but only the first gets\n", + " the label, so that the legend command will work as expected.\n", + " \n", + " default is ``None``\n", + " \n", + " stacked : boolean, optional\n", + " If ``True``, multiple data are stacked on top of each other If\n", + " ``False`` multiple data are aranged side by side if histtype is\n", + " 'bar' or on top of each other if histtype is 'step'\n", + " \n", + " Default is ``False``\n", + " \n", + " Returns\n", + " -------\n", + " n : array or list of arrays\n", + " The values of the histogram bins. See *normed* or *density*\n", + " and *weights* for a description of the possible semantics.\n", + " If input *x* is an array, then this is an array of length\n", + " *nbins*. If input is a sequence arrays\n", + " ``[data1, data2,..]``, then this is a list of arrays with\n", + " the values of the histograms for each of the arrays in the\n", + " same order.\n", + " \n", + " bins : array\n", + " The edges of the bins. Length nbins + 1 (nbins left edges and right\n", + " edge of last bin). Always a single array even when multiple data\n", + " sets are passed in.\n", + " \n", + " patches : list or list of lists\n", + " Silent list of individual patches used to create the histogram\n", + " or list of such list if multiple input datasets.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.patches.Patch` properties\n", + " \n", + " See also\n", + " --------\n", + " hist2d : 2D histograms\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'weights', 'x'.\n", + " \n", + " hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, hold=None, data=None, **kwargs)\n", + " Make a 2D histogram plot.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y: array_like, shape (n, )\n", + " Input values\n", + " \n", + " bins: [None | int | [int, int] | array_like | [array, array]]\n", + " \n", + " The bin specification:\n", + " \n", + " - If int, the number of bins for the two dimensions\n", + " (nx=ny=bins).\n", + " \n", + " - If [int, int], the number of bins in each dimension\n", + " (nx, ny = bins).\n", + " \n", + " - If array_like, the bin edges for the two dimensions\n", + " (x_edges=y_edges=bins).\n", + " \n", + " - If [array, array], the bin edges in each dimension\n", + " (x_edges, y_edges = bins).\n", + " \n", + " The default value is 10.\n", + " \n", + " range : array_like shape(2, 2), optional, default: None\n", + " The leftmost and rightmost edges of the bins along each dimension\n", + " (if not specified explicitly in the bins parameters): [[xmin,\n", + " xmax], [ymin, ymax]]. All values outside of this range will be\n", + " considered outliers and not tallied in the histogram.\n", + " \n", + " normed : boolean, optional, default: False\n", + " Normalize histogram.\n", + " \n", + " weights : array_like, shape (n, ), optional, default: None\n", + " An array of values w_i weighing each sample (x_i, y_i).\n", + " \n", + " cmin : scalar, optional, default: None\n", + " All bins that has count less than cmin will not be displayed and\n", + " these count values in the return value count histogram will also\n", + " be set to nan upon return\n", + " \n", + " cmax : scalar, optional, default: None\n", + " All bins that has count more than cmax will not be displayed (set\n", + " to none before passing to imshow) and these count values in the\n", + " return value count histogram will also be set to nan upon return\n", + " \n", + " Returns\n", + " -------\n", + " The return value is ``(counts, xedges, yedges, Image)``.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " cmap : {Colormap, string}, optional\n", + " A :class:`matplotlib.colors.Colormap` instance. If not set, use rc\n", + " settings.\n", + " \n", + " norm : Normalize, optional\n", + " A :class:`matplotlib.colors.Normalize` instance is used to\n", + " scale luminance data to ``[0, 1]``. If not set, defaults to\n", + " ``Normalize()``.\n", + " \n", + " vmin/vmax : {None, scalar}, optional\n", + " Arguments passed to the `Normalize` instance.\n", + " \n", + " alpha : ``0 <= scalar <= 1`` or ``None``, optional\n", + " The alpha blending value.\n", + " \n", + " See also\n", + " --------\n", + " hist : 1D histogram\n", + " \n", + " Notes\n", + " -----\n", + " Rendering the histogram with a logarithmic color scale is\n", + " accomplished by passing a :class:`colors.LogNorm` instance to\n", + " the *norm* keyword argument. Likewise, power-law normalization\n", + " (similar in effect to gamma correction) can be accomplished with\n", + " :class:`colors.PowerNorm`.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'weights', 'x', 'y'.\n", + " \n", + " hlines(y, xmin, xmax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)\n", + " Plot horizontal lines at each `y` from `xmin` to `xmax`.\n", + " \n", + " Parameters\n", + " ----------\n", + " y : scalar or sequence of scalar\n", + " y-indexes where to plot the lines.\n", + " \n", + " xmin, xmax : scalar or 1D array_like\n", + " Respective beginning and end of each line. If scalars are\n", + " provided, all lines will have same length.\n", + " \n", + " colors : array_like of colors, optional, default: 'k'\n", + " \n", + " linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional\n", + " \n", + " label : string, optional, default: ''\n", + " \n", + " Returns\n", + " -------\n", + " lines : `~matplotlib.collections.LineCollection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.collections.LineCollection` properties.\n", + " \n", + " See also\n", + " --------\n", + " vlines : vertical lines\n", + " axhline: horizontal line across the axes\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'xmax', 'xmin', 'y'.\n", + " \n", + " hold(b=None)\n", + " .. deprecated:: 2.0\n", + " pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " \n", + " Set the hold state. If *b* is None (default), toggle the\n", + " hold state, else set the hold state to boolean value *b*::\n", + " \n", + " hold() # toggle hold\n", + " hold(True) # hold is on\n", + " hold(False) # hold is off\n", + " \n", + " When *hold* is *True*, subsequent plot commands will add elements to\n", + " the current axes. When *hold* is *False*, the current axes and\n", + " figure will be cleared on the next plot command.\n", + " \n", + " hot()\n", + " set the default colormap to hot and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " hsv()\n", + " set the default colormap to hsv and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " imread(*args, **kwargs)\n", + " Read an image from a file into an array.\n", + " \n", + " *fname* may be a string path, a valid URL, or a Python\n", + " file-like object. If using a file object, it must be opened in binary\n", + " mode.\n", + " \n", + " If *format* is provided, will try to read file of that type,\n", + " otherwise the format is deduced from the filename. If nothing can\n", + " be deduced, PNG is tried.\n", + " \n", + " Return value is a :class:`numpy.array`. For grayscale images, the\n", + " return array is MxN. For RGB images, the return value is MxNx3.\n", + " For RGBA images the return value is MxNx4.\n", + " \n", + " matplotlib can only read PNGs natively, but if `PIL\n", + " `_ is installed, it will\n", + " use it to load the image and return an array (if possible) which\n", + " can be used with :func:`~matplotlib.pyplot.imshow`. Note, URL strings\n", + " may not be compatible with PIL. Check the PIL documentation for more\n", + " information.\n", + " \n", + " imsave(*args, **kwargs)\n", + " Save an array as in image file.\n", + " \n", + " The output formats available depend on the backend being used.\n", + " \n", + " Parameters\n", + " ----------\n", + " fname : str or file-like\n", + " Path string to a filename, or a Python file-like object.\n", + " If *format* is *None* and *fname* is a string, the output\n", + " format is deduced from the extension of the filename.\n", + " arr : array-like\n", + " An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array.\n", + " vmin, vmax: [ None | scalar ]\n", + " *vmin* and *vmax* set the color scaling for the image by fixing the\n", + " values that map to the colormap color limits. If either *vmin*\n", + " or *vmax* is None, that limit is determined from the *arr*\n", + " min/max value.\n", + " cmap : matplotlib.colors.Colormap, optional\n", + " For example, ``cm.viridis``. If ``None``, defaults to the\n", + " ``image.cmap`` rcParam.\n", + " format : str\n", + " One of the file extensions supported by the active backend. Most\n", + " backends support png, pdf, ps, eps and svg.\n", + " origin : [ 'upper' | 'lower' ]\n", + " Indicates whether the ``(0, 0)`` index of the array is in the\n", + " upper left or lower left corner of the axes. Defaults to the\n", + " ``image.origin`` rcParam.\n", + " dpi : int\n", + " The DPI to store in the metadata of the file. This does not affect the\n", + " resolution of the output image.\n", + " \n", + " imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, hold=None, data=None, **kwargs)\n", + " Display an image on the axes.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4)\n", + " Display the image in `X` to current axes. `X` may be an\n", + " array or a PIL image. If `X` is an array, it\n", + " can have the following shapes and types:\n", + " \n", + " - MxN -- values to be mapped (float or int)\n", + " - MxNx3 -- RGB (float or uint8)\n", + " - MxNx4 -- RGBA (float or uint8)\n", + " \n", + " The value for each component of MxNx3 and MxNx4 float arrays\n", + " should be in the range 0.0 to 1.0. MxN arrays are mapped\n", + " to colors based on the `norm` (mapping scalar to scalar)\n", + " and the `cmap` (mapping the normed scalar to a color).\n", + " \n", + " cmap : `~matplotlib.colors.Colormap`, optional, default: None\n", + " If None, default to rc `image.cmap` value. `cmap` is ignored\n", + " if `X` is 3-D, directly specifying RGB(A) values.\n", + " \n", + " aspect : ['auto' | 'equal' | scalar], optional, default: None\n", + " If 'auto', changes the image aspect ratio to match that of the\n", + " axes.\n", + " \n", + " If 'equal', and `extent` is None, changes the axes aspect ratio to\n", + " match that of the image. If `extent` is not `None`, the axes\n", + " aspect ratio is changed to match that of the extent.\n", + " \n", + " If None, default to rc ``image.aspect`` value.\n", + " \n", + " interpolation : string, optional, default: None\n", + " Acceptable values are 'none', 'nearest', 'bilinear', 'bicubic',\n", + " 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser',\n", + " 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc',\n", + " 'lanczos'\n", + " \n", + " If `interpolation` is None, default to rc `image.interpolation`.\n", + " See also the `filternorm` and `filterrad` parameters.\n", + " If `interpolation` is 'none', then no interpolation is performed\n", + " on the Agg, ps and pdf backends. Other backends will fall back to\n", + " 'nearest'.\n", + " \n", + " norm : `~matplotlib.colors.Normalize`, optional, default: None\n", + " A `~matplotlib.colors.Normalize` instance is used to scale\n", + " a 2-D float `X` input to the (0, 1) range for input to the\n", + " `cmap`. If `norm` is None, use the default func:`normalize`.\n", + " If `norm` is an instance of `~matplotlib.colors.NoNorm`,\n", + " `X` must be an array of integers that index directly into\n", + " the lookup table of the `cmap`.\n", + " \n", + " vmin, vmax : scalar, optional, default: None\n", + " `vmin` and `vmax` are used in conjunction with norm to normalize\n", + " luminance data. Note if you pass a `norm` instance, your\n", + " settings for `vmin` and `vmax` will be ignored.\n", + " \n", + " alpha : scalar, optional, default: None\n", + " The alpha blending value, between 0 (transparent) and 1 (opaque)\n", + " \n", + " origin : ['upper' | 'lower'], optional, default: None\n", + " Place the [0,0] index of the array in the upper left or lower left\n", + " corner of the axes. If None, default to rc `image.origin`.\n", + " \n", + " extent : scalars (left, right, bottom, top), optional, default: None\n", + " The location, in data-coordinates, of the lower-left and\n", + " upper-right corners. If `None`, the image is positioned such that\n", + " the pixel centers fall on zero-based (row, column) indices.\n", + " \n", + " shape : scalars (columns, rows), optional, default: None\n", + " For raw buffer images\n", + " \n", + " filternorm : scalar, optional, default: 1\n", + " A parameter for the antigrain image resize filter. From the\n", + " antigrain documentation, if `filternorm` = 1, the filter\n", + " normalizes integer values and corrects the rounding errors. It\n", + " doesn't do anything with the source floating point values, it\n", + " corrects only integers according to the rule of 1.0 which means\n", + " that any sum of pixel weights must be equal to 1.0. So, the\n", + " filter function must produce a graph of the proper shape.\n", + " \n", + " filterrad : scalar, optional, default: 4.0\n", + " The filter radius for filters that have a radius parameter, i.e.\n", + " when interpolation is one of: 'sinc', 'lanczos' or 'blackman'\n", + " \n", + " Returns\n", + " -------\n", + " image : `~matplotlib.image.AxesImage`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.artist.Artist` properties.\n", + " \n", + " See also\n", + " --------\n", + " matshow : Plot a matrix or an array as an image.\n", + " \n", + " Notes\n", + " -----\n", + " Unless *extent* is used, pixel centers will be located at integer\n", + " coordinates. In other words: the origin will coincide with the center\n", + " of pixel (0, 0).\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " inferno()\n", + " set the default colormap to inferno and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " install_repl_displayhook()\n", + " Install a repl display hook so that any stale figure are automatically\n", + " redrawn when control is returned to the repl.\n", + " \n", + " This works with IPython terminals and kernels,\n", + " as well as vanilla python shells.\n", + " \n", + " ioff()\n", + " Turn interactive mode off.\n", + " \n", + " ion()\n", + " Turn interactive mode on.\n", + " \n", + " ishold()\n", + " .. deprecated:: 2.0\n", + " pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " \n", + " Return the hold status of the current axes.\n", + " \n", + " isinteractive()\n", + " Return status of interactive mode.\n", + " \n", + " jet()\n", + " set the default colormap to jet and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " legend(*args, **kwargs)\n", + " Places a legend on the axes.\n", + " \n", + " To make a legend for lines which already exist on the axes\n", + " (via plot for instance), simply call this function with an iterable\n", + " of strings, one for each legend item. For example::\n", + " \n", + " ax.plot([1, 2, 3])\n", + " ax.legend(['A simple line'])\n", + " \n", + " However, in order to keep the \"label\" and the legend element\n", + " instance together, it is preferable to specify the label either at\n", + " artist creation, or by calling the\n", + " :meth:`~matplotlib.artist.Artist.set_label` method on the artist::\n", + " \n", + " line, = ax.plot([1, 2, 3], label='Inline label')\n", + " # Overwrite the label by calling the method.\n", + " line.set_label('Label via method')\n", + " ax.legend()\n", + " \n", + " Specific lines can be excluded from the automatic legend element\n", + " selection by defining a label starting with an underscore.\n", + " This is default for all artists, so calling :meth:`legend` without\n", + " any arguments and without setting the labels manually will result in\n", + " no legend being drawn.\n", + " \n", + " For full control of which artists have a legend entry, it is possible\n", + " to pass an iterable of legend artists followed by an iterable of\n", + " legend labels respectively::\n", + " \n", + " legend((line1, line2, line3), ('label1', 'label2', 'label3'))\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " loc : int or string or pair of floats, default: 'upper right'\n", + " The location of the legend. Possible codes are:\n", + " \n", + " =============== =============\n", + " Location String Location Code\n", + " =============== =============\n", + " 'best' 0\n", + " 'upper right' 1\n", + " 'upper left' 2\n", + " 'lower left' 3\n", + " 'lower right' 4\n", + " 'right' 5\n", + " 'center left' 6\n", + " 'center right' 7\n", + " 'lower center' 8\n", + " 'upper center' 9\n", + " 'center' 10\n", + " =============== =============\n", + " \n", + " \n", + " Alternatively can be a 2-tuple giving ``x, y`` of the lower-left\n", + " corner of the legend in axes coordinates (in which case\n", + " ``bbox_to_anchor`` will be ignored).\n", + " \n", + " bbox_to_anchor : `~.BboxBase` or pair of floats\n", + " Specify any arbitrary location for the legend in `bbox_transform`\n", + " coordinates (default Axes coordinates).\n", + " \n", + " For example, to put the legend's upper right hand corner in the\n", + " center of the axes the following keywords can be used::\n", + " \n", + " loc='upper right', bbox_to_anchor=(0.5, 0.5)\n", + " \n", + " ncol : integer\n", + " The number of columns that the legend has. Default is 1.\n", + " \n", + " prop : None or :class:`matplotlib.font_manager.FontProperties` or dict\n", + " The font properties of the legend. If None (default), the current\n", + " :data:`matplotlib.rcParams` will be used.\n", + " \n", + " fontsize : int or float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'}\n", + " Controls the font size of the legend. If the value is numeric the\n", + " size will be the absolute font size in points. String values are\n", + " relative to the current default font size. This argument is only\n", + " used if `prop` is not specified.\n", + " \n", + " numpoints : None or int\n", + " The number of marker points in the legend when creating a legend\n", + " entry for a line/:class:`matplotlib.lines.Line2D`.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.numpoints`` :data:`rcParam`.\n", + " \n", + " scatterpoints : None or int\n", + " The number of marker points in the legend when creating a legend\n", + " entry for a scatter plot/\n", + " :class:`matplotlib.collections.PathCollection`.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.scatterpoints`` :data:`rcParam`.\n", + " \n", + " scatteryoffsets : iterable of floats\n", + " The vertical offset (relative to the font size) for the markers\n", + " created for a scatter plot legend entry. 0.0 is at the base the\n", + " legend text, and 1.0 is at the top. To draw all markers at the\n", + " same height, set to ``[0.5]``. Default ``[0.375, 0.5, 0.3125]``.\n", + " \n", + " markerscale : None or int or float\n", + " The relative size of legend markers compared with the originally\n", + " drawn ones. Default is ``None`` which will take the value from\n", + " the ``legend.markerscale`` :data:`rcParam `.\n", + " \n", + " markerfirst : bool\n", + " If *True*, legend marker is placed to the left of the legend label.\n", + " If *False*, legend marker is placed to the right of the legend\n", + " label.\n", + " Default is *True*.\n", + " \n", + " frameon : None or bool\n", + " Control whether the legend should be drawn on a patch (frame).\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.frameon`` :data:`rcParam`.\n", + " \n", + " fancybox : None or bool\n", + " Control whether round edges should be enabled around\n", + " the :class:`~matplotlib.patches.FancyBboxPatch` which\n", + " makes up the legend's background.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.fancybox`` :data:`rcParam`.\n", + " \n", + " shadow : None or bool\n", + " Control whether to draw a shadow behind the legend.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.shadow`` :data:`rcParam`.\n", + " \n", + " framealpha : None or float\n", + " Control the alpha transparency of the legend's background.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.framealpha`` :data:`rcParam`.\n", + " If shadow is activated and framealpha is ``None`` the\n", + " default value is being ignored.\n", + " \n", + " facecolor : None or \"inherit\" or a color spec\n", + " Control the legend's background color.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.facecolor`` :data:`rcParam`.\n", + " If ``\"inherit\"``, it will take the ``axes.facecolor``\n", + " :data:`rcParam`.\n", + " \n", + " edgecolor : None or \"inherit\" or a color spec\n", + " Control the legend's background patch edge color.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.edgecolor`` :data:`rcParam`.\n", + " If ``\"inherit\"``, it will take the ``axes.edgecolor``\n", + " :data:`rcParam`.\n", + " \n", + " mode : {\"expand\", None}\n", + " If `mode` is set to ``\"expand\"`` the legend will be horizontally\n", + " expanded to fill the axes area (or `bbox_to_anchor` if defines\n", + " the legend's size).\n", + " \n", + " bbox_transform : None or :class:`matplotlib.transforms.Transform`\n", + " The transform for the bounding box (`bbox_to_anchor`). For a value\n", + " of ``None`` (default) the Axes'\n", + " :data:`~matplotlib.axes.Axes.transAxes` transform will be used.\n", + " \n", + " title : str or None\n", + " The legend's title. Default is no title (``None``).\n", + " \n", + " borderpad : float or None\n", + " The fractional whitespace inside the legend border.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.borderpad`` :data:`rcParam`.\n", + " \n", + " labelspacing : float or None\n", + " The vertical space between the legend entries.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.labelspacing`` :data:`rcParam`.\n", + " \n", + " handlelength : float or None\n", + " The length of the legend handles.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.handlelength`` :data:`rcParam`.\n", + " \n", + " handletextpad : float or None\n", + " The pad between the legend handle and text.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.handletextpad`` :data:`rcParam`.\n", + " \n", + " borderaxespad : float or None\n", + " The pad between the axes and legend border.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.borderaxespad`` :data:`rcParam`.\n", + " \n", + " columnspacing : float or None\n", + " The spacing between columns.\n", + " Measured in font-size units.\n", + " Default is ``None`` which will take the value from the\n", + " ``legend.columnspacing`` :data:`rcParam`.\n", + " \n", + " handler_map : dict or None\n", + " The custom dictionary mapping instances or types to a legend\n", + " handler. This `handler_map` updates the default handler map\n", + " found at :func:`matplotlib.legend.Legend.get_legend_handler_map`.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " :class:`matplotlib.legend.Legend` instance\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " Not all kinds of artist are supported by the legend command. See\n", + " :ref:`sphx_glr_tutorials_intermediate_legend_guide.py` for details.\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " .. plot:: gallery/api/legend.py\n", + " \n", + " locator_params(axis='both', tight=None, **kwargs)\n", + " Control behavior of tick locators.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *axis*\n", + " ['x' | 'y' | 'both'] Axis on which to operate;\n", + " default is 'both'.\n", + " \n", + " *tight*\n", + " [True | False | None] Parameter passed to :meth:`autoscale_view`.\n", + " Default is None, for no change.\n", + " \n", + " Remaining keyword arguments are passed to directly to the\n", + " :meth:`~matplotlib.ticker.MaxNLocator.set_params` method.\n", + " \n", + " Typically one might want to reduce the maximum number\n", + " of ticks and use tight bounds when plotting small\n", + " subplots, for example::\n", + " \n", + " ax.locator_params(tight=True, nbins=4)\n", + " \n", + " Because the locator is involved in autoscaling,\n", + " :meth:`autoscale_view` is called automatically after\n", + " the parameters are changed.\n", + " \n", + " This presently works only for the\n", + " :class:`~matplotlib.ticker.MaxNLocator` used\n", + " by default on linear axes, but it may be generalized.\n", + " \n", + " loglog(*args, **kwargs)\n", + " Make a plot with log scaling on both the *x* and *y* axis.\n", + " \n", + " :func:`~matplotlib.pyplot.loglog` supports all the keyword\n", + " arguments of :func:`~matplotlib.pyplot.plot` and\n", + " :meth:`matplotlib.axes.Axes.set_xscale` /\n", + " :meth:`matplotlib.axes.Axes.set_yscale`.\n", + " \n", + " Notable keyword arguments:\n", + " \n", + " *basex*/*basey*: scalar > 1\n", + " Base of the *x*/*y* logarithm\n", + " \n", + " *subsx*/*subsy*: [ *None* | sequence ]\n", + " The location of the minor *x*/*y* ticks; *None* defaults\n", + " to autosubs, which depend on the number of decades in the\n", + " plot; see :meth:`matplotlib.axes.Axes.set_xscale` /\n", + " :meth:`matplotlib.axes.Axes.set_yscale` for details\n", + " \n", + " *nonposx*/*nonposy*: ['mask' | 'clip' ]\n", + " Non-positive values in *x* or *y* can be masked as\n", + " invalid, or clipped to a very small positive number\n", + " \n", + " The remaining valid kwargs are\n", + " :class:`~matplotlib.lines.Line2D` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float\n", + " \n", + " magma()\n", + " set the default colormap to magma and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " magnitude_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, scale=None, hold=None, data=None, **kwargs)\n", + " Plot the magnitude spectrum.\n", + " \n", + " Call signature::\n", + " \n", + " magnitude_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,\n", + " pad_to=None, sides='default', **kwargs)\n", + " \n", + " Compute the magnitude spectrum of *x*. Data is padded to a\n", + " length of *pad_to* and the windowing function *window* is applied to\n", + " the signal.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. While not increasing the actual resolution of\n", + " the spectrum (the minimum distance between resolvable peaks),\n", + " this can give more points in the plot, allowing for more\n", + " detail. This corresponds to the *n* parameter in the call to fft().\n", + " The default is None, which sets *pad_to* equal to the length of the\n", + " input signal (i.e. no padding).\n", + " \n", + " scale : [ 'default' | 'linear' | 'dB' ]\n", + " The scaling of the values in the *spec*. 'linear' is no scaling.\n", + " 'dB' returns the values in dB scale, i.e., the dB amplitude\n", + " (20 * log10). 'default' is 'linear'.\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 1-D array\n", + " The values for the magnitude spectrum before scaling (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *spectrum*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " :func:`psd`\n", + " :func:`psd` plots the power spectral density.`.\n", + " \n", + " :func:`angle_spectrum`\n", + " :func:`angle_spectrum` plots the angles of the corresponding\n", + " frequencies.\n", + " \n", + " :func:`phase_spectrum`\n", + " :func:`phase_spectrum` plots the phase (unwrapped angle) of the\n", + " corresponding frequencies.\n", + " \n", + " :func:`specgram`\n", + " :func:`specgram` can plot the magnitude spectrum of segments within\n", + " the signal in a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " margins(*args, **kw)\n", + " Set or retrieve autoscaling margins.\n", + " \n", + " signatures::\n", + " \n", + " margins()\n", + " \n", + " returns xmargin, ymargin\n", + " \n", + " ::\n", + " \n", + " margins(margin)\n", + " \n", + " margins(xmargin, ymargin)\n", + " \n", + " margins(x=xmargin, y=ymargin)\n", + " \n", + " margins(..., tight=False)\n", + " \n", + " All three forms above set the xmargin and ymargin parameters.\n", + " All keyword parameters are optional. A single argument\n", + " specifies both xmargin and ymargin. The *tight* parameter\n", + " is passed to :meth:`autoscale_view`, which is executed after\n", + " a margin is changed; the default here is *True*, on the\n", + " assumption that when margins are specified, no additional\n", + " padding to match tick marks is usually desired. Setting\n", + " *tight* to *None* will preserve the previous setting.\n", + " \n", + " Specifying any margin changes only the autoscaling; for example,\n", + " if *xmargin* is not None, then *xmargin* times the X data\n", + " interval will be added to each end of that interval before\n", + " it is used in autoscaling.\n", + " \n", + " matshow(A, fignum=None, **kw)\n", + " Display an array as a matrix in a new figure window.\n", + " \n", + " The origin is set at the upper left hand corner and rows (first\n", + " dimension of the array) are displayed horizontally. The aspect\n", + " ratio of the figure window is that of the array, unless this would\n", + " make an excessively short or narrow figure.\n", + " \n", + " Tick labels for the xaxis are placed on top.\n", + " \n", + " With the exception of *fignum*, keyword arguments are passed to\n", + " :func:`~matplotlib.pyplot.imshow`. You may set the *origin*\n", + " kwarg to \"lower\" if you want the first row in the array to be\n", + " at the bottom instead of the top.\n", + " \n", + " \n", + " *fignum*: [ None | integer | False ]\n", + " By default, :func:`matshow` creates a new figure window with\n", + " automatic numbering. If *fignum* is given as an integer, the\n", + " created figure will use this figure number. Because of how\n", + " :func:`matshow` tries to set the figure aspect ratio to be the\n", + " one of the array, if you provide the number of an already\n", + " existing figure, strange things may happen.\n", + " \n", + " If *fignum* is *False* or 0, a new figure window will **NOT** be created.\n", + " \n", + " minorticks_off()\n", + " Remove minor ticks from the current plot.\n", + " \n", + " minorticks_on()\n", + " Display minor ticks on the current plot.\n", + " \n", + " Displaying minor ticks reduces performance; turn them off using\n", + " minorticks_off() if drawing speed is a problem.\n", + " \n", + " nipy_spectral()\n", + " set the default colormap to nipy_spectral and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " over(func, *args, **kwargs)\n", + " .. deprecated:: 2.0\n", + " pyplot.hold is deprecated.\n", + " Future behavior will be consistent with the long-time default:\n", + " plot commands add elements without first clearing the\n", + " Axes and/or Figure.\n", + " \n", + " Call a function with hold(True).\n", + " \n", + " Calls::\n", + " \n", + " func(*args, **kwargs)\n", + " \n", + " with ``hold(True)`` and then restores the hold state.\n", + " \n", + " pause(interval)\n", + " Pause for *interval* seconds.\n", + " \n", + " If there is an active figure, it will be updated and displayed before the\n", + " pause, and the GUI event loop (if any) will run during the pause.\n", + " \n", + " This can be used for crude animation. For more complex animation, see\n", + " :mod:`matplotlib.animation`.\n", + " \n", + " Note\n", + " ----\n", + " This function is experimental; its behavior may be changed or extended in a\n", + " future release.\n", + " \n", + " pcolor(*args, **kwargs)\n", + " Create a pseudocolor plot of a 2-D array.\n", + " \n", + " Call signatures::\n", + " \n", + " pcolor(C, **kwargs)\n", + " pcolor(X, Y, C, **kwargs)\n", + " \n", + " pcolor can be very slow for large arrays; consider\n", + " using the similar but much faster\n", + " :func:`~matplotlib.pyplot.pcolormesh` instead.\n", + " \n", + " Parameters\n", + " ----------\n", + " C : array_like\n", + " An array of color values.\n", + " \n", + " X, Y : array_like, optional\n", + " If given, specify the (x, y) coordinates of the colored\n", + " quadrilaterals; the quadrilateral for ``C[i,j]`` has corners at::\n", + " \n", + " (X[i, j], Y[i, j]),\n", + " (X[i, j+1], Y[i, j+1]),\n", + " (X[i+1, j], Y[i+1, j]),\n", + " (X[i+1, j+1], Y[i+1, j+1])\n", + " \n", + " Ideally the dimensions of ``X`` and ``Y`` should be one greater\n", + " than those of ``C``; if the dimensions are the same, then the last\n", + " row and column of ``C`` will be ignored.\n", + " \n", + " Note that the column index corresponds to the\n", + " x-coordinate, and the row index corresponds to y; for\n", + " details, see the :ref:`Grid Orientation\n", + " ` section below.\n", + " \n", + " If either or both of ``X`` and ``Y`` are 1-D arrays or column\n", + " vectors, they will be expanded as needed into the appropriate 2-D\n", + " arrays, making a rectangular grid.\n", + " \n", + " cmap : `~matplotlib.colors.Colormap`, optional, default: None\n", + " If `None`, default to rc settings.\n", + " \n", + " norm : `matplotlib.colors.Normalize`, optional, default: None\n", + " An instance is used to scale luminance data to (0, 1).\n", + " If `None`, defaults to :func:`normalize`.\n", + " \n", + " vmin, vmax : scalar, optional, default: None\n", + " ``vmin`` and ``vmax`` are used in conjunction with ``norm`` to\n", + " normalize luminance data. If either is `None`, it is autoscaled to\n", + " the respective min or max of the color array ``C``. If not `None`,\n", + " ``vmin`` or ``vmax`` passed in here override any pre-existing\n", + " values supplied in the ``norm`` instance.\n", + " \n", + " edgecolors : {None, 'none', color, color sequence}\n", + " If None, the rc setting is used by default.\n", + " If 'none', edges will not be visible.\n", + " An mpl color or sequence of colors will set the edge color.\n", + " \n", + " alpha : scalar, optional, default: None\n", + " The alpha blending value, between 0 (transparent) and 1 (opaque).\n", + " \n", + " snap : bool, optional, default: False\n", + " Whether to snap the mesh to pixel boundaries.\n", + " \n", + " Returns\n", + " -------\n", + " collection : `matplotlib.collections.Collection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " antialiaseds : bool, optional, default: False\n", + " The default ``antialiaseds`` is False if the default\n", + " ``edgecolors=\"none\"`` is used. This eliminates artificial lines\n", + " at patch boundaries, and works regardless of the value of alpha.\n", + " If ``edgecolors`` is not \"none\", then the default ``antialiaseds``\n", + " is taken from ``rcParams['patch.antialiased']``, which defaults to\n", + " True. Stroking the edges may be preferred if ``alpha`` is 1, but\n", + " will cause artifacts otherwise.\n", + " \n", + " **kwargs :\n", + " \n", + " Any unused keyword arguments are passed along to the\n", + " `~matplotlib.collections.PolyCollection` constructor:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " pcolormesh : for an explanation of the differences between\n", + " pcolor and pcolormesh.\n", + " \n", + " Notes\n", + " -----\n", + " .. _axes-pcolor-grid-orientation:\n", + " \n", + " ``X``, ``Y`` and ``C`` may be masked arrays. If either C[i, j], or one\n", + " of the vertices surrounding C[i,j] (``X`` or ``Y`` at [i, j], [i+1, j],\n", + " [i, j+1], [i+1, j+1]) is masked, nothing is plotted.\n", + " \n", + " The grid orientation follows the MATLAB convention: an array ``C`` with\n", + " shape (nrows, ncolumns) is plotted with the column number as ``X`` and\n", + " the row number as ``Y``, increasing up; hence it is plotted the way the\n", + " array would be printed, except that the ``Y`` axis is reversed. That\n", + " is, ``C`` is taken as ``C`` (y, x).\n", + " \n", + " Similarly for :func:`meshgrid`::\n", + " \n", + " x = np.arange(5)\n", + " y = np.arange(3)\n", + " X, Y = np.meshgrid(x, y)\n", + " \n", + " is equivalent to::\n", + " \n", + " X = array([[0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4]])\n", + " \n", + " Y = array([[0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 1],\n", + " [2, 2, 2, 2, 2]])\n", + " \n", + " so if you have::\n", + " \n", + " C = rand(len(x), len(y))\n", + " \n", + " then you need to transpose C::\n", + " \n", + " pcolor(X, Y, C.T)\n", + " \n", + " or::\n", + " \n", + " pcolor(C.T)\n", + " \n", + " MATLAB :func:`pcolor` always discards the last row and column of ``C``,\n", + " but Matplotlib displays the last row and column if ``X`` and ``Y`` are\n", + " not specified, or if ``X`` and ``Y`` have one more row and column than\n", + " ``C``.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " pcolormesh(*args, **kwargs)\n", + " Plot a quadrilateral mesh.\n", + " \n", + " Call signatures::\n", + " \n", + " pcolormesh(C)\n", + " pcolormesh(X, Y, C)\n", + " pcolormesh(C, **kwargs)\n", + " \n", + " Create a pseudocolor plot of a 2-D array.\n", + " \n", + " pcolormesh is similar to :func:`~matplotlib.pyplot.pcolor`,\n", + " but uses a different mechanism and returns a different\n", + " object; pcolor returns a\n", + " :class:`~matplotlib.collections.PolyCollection` but pcolormesh\n", + " returns a\n", + " :class:`~matplotlib.collections.QuadMesh`. It is much faster,\n", + " so it is almost always preferred for large arrays.\n", + " \n", + " *C* may be a masked array, but *X* and *Y* may not. Masked\n", + " array support is implemented via *cmap* and *norm*; in\n", + " contrast, :func:`~matplotlib.pyplot.pcolor` simply does not\n", + " draw quadrilaterals with masked colors or vertices.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A :class:`matplotlib.colors.Colormap` instance. If *None*, use\n", + " rc settings.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance is used to\n", + " scale luminance data to 0,1. If *None*, defaults to\n", + " :func:`normalize`.\n", + " \n", + " *vmin*/*vmax*: [ *None* | scalar ]\n", + " *vmin* and *vmax* are used in conjunction with *norm* to\n", + " normalize luminance data. If either is *None*, it\n", + " is autoscaled to the respective min or max\n", + " of the color array *C*. If not *None*, *vmin* or\n", + " *vmax* passed in here override any pre-existing values\n", + " supplied in the *norm* instance.\n", + " \n", + " *shading*: [ 'flat' | 'gouraud' ]\n", + " 'flat' indicates a solid color for each quad. When\n", + " 'gouraud', each quad will be Gouraud shaded. When gouraud\n", + " shading, edgecolors is ignored.\n", + " \n", + " *edgecolors*: [*None* | ``'None'`` | ``'face'`` | color |\n", + " color sequence]\n", + " \n", + " If *None*, the rc setting is used by default.\n", + " \n", + " If ``'None'``, edges will not be visible.\n", + " \n", + " If ``'face'``, edges will have the same color as the faces.\n", + " \n", + " An mpl color or sequence of colors will set the edge color\n", + " \n", + " *alpha*: ``0 <= scalar <= 1`` or *None*\n", + " the alpha blending value\n", + " \n", + " Return value is a :class:`matplotlib.collections.QuadMesh`\n", + " object.\n", + " \n", + " kwargs can be used to control the\n", + " :class:`matplotlib.collections.QuadMesh` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.pcolor`\n", + " For an explanation of the grid orientation\n", + " (:ref:`Grid Orientation `)\n", + " and the expansion of 1-D *X* and/or *Y* to 2-D arrays.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " phase_spectrum(x, Fs=None, Fc=None, window=None, pad_to=None, sides=None, hold=None, data=None, **kwargs)\n", + " Plot the phase spectrum.\n", + " \n", + " Call signature::\n", + " \n", + " phase_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning,\n", + " pad_to=None, sides='default', **kwargs)\n", + " \n", + " Compute the phase spectrum (unwrapped angle spectrum) of *x*.\n", + " Data is padded to a length of *pad_to* and the windowing function\n", + " *window* is applied to the signal.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. While not increasing the actual resolution of\n", + " the spectrum (the minimum distance between resolvable peaks),\n", + " this can give more points in the plot, allowing for more\n", + " detail. This corresponds to the *n* parameter in the call to fft().\n", + " The default is None, which sets *pad_to* equal to the length of the\n", + " input signal (i.e. no padding).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 1-D array\n", + " The values for the phase spectrum in radians (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *spectrum*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " :func:`magnitude_spectrum`\n", + " :func:`magnitude_spectrum` plots the magnitudes of the\n", + " corresponding frequencies.\n", + " \n", + " :func:`angle_spectrum`\n", + " :func:`angle_spectrum` plots the wrapped version of this function.\n", + " \n", + " :func:`specgram`\n", + " :func:`specgram` can plot the phase spectrum of segments within the\n", + " signal in a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None, center=(0, 0), frame=False, rotatelabels=False, hold=None, data=None)\n", + " Plot a pie chart.\n", + " \n", + " Make a pie chart of array *x*. The fractional area of each\n", + " wedge is given by ``x/sum(x)``. If ``sum(x) <= 1``, then the\n", + " values of x give the fractional area directly and the array\n", + " will not be normalized. The wedges are plotted\n", + " counterclockwise, by default starting from the x-axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array-like\n", + " The input array used to make the pie chart.\n", + " \n", + " explode : array-like, optional, default: None\n", + " If not *None*, is a ``len(x)`` array which specifies the\n", + " fraction of the radius with which to offset each wedge.\n", + " \n", + " labels : list, optional, default: None\n", + " A sequence of strings providing the labels for each wedge\n", + " \n", + " colors : array-like, optional, default: None\n", + " A sequence of matplotlib color args through which the pie chart\n", + " will cycle. If `None`, will use the colors in the currently\n", + " active cycle.\n", + " \n", + " autopct : None (default), string, or function, optional\n", + " If not *None*, is a string or function used to label the wedges\n", + " with their numeric value. The label will be placed inside the\n", + " wedge. If it is a format string, the label will be ``fmt%pct``.\n", + " If it is a function, it will be called.\n", + " \n", + " pctdistance : float, optional, default: 0.6\n", + " The ratio between the center of each pie slice and the\n", + " start of the text generated by *autopct*. Ignored if\n", + " *autopct* is *None*.\n", + " \n", + " shadow : bool, optional, default: False\n", + " Draw a shadow beneath the pie.\n", + " \n", + " labeldistance : float, optional, default: 1.1\n", + " The radial distance at which the pie labels are drawn\n", + " \n", + " startangle : float, optional, default: None\n", + " If not *None*, rotates the start of the pie chart by *angle*\n", + " degrees counterclockwise from the x-axis.\n", + " \n", + " radius : float, optional, default: None\n", + " The radius of the pie, if *radius* is *None* it will be set to 1.\n", + " \n", + " counterclock : bool, optional, default: True\n", + " Specify fractions direction, clockwise or counterclockwise.\n", + " \n", + " wedgeprops : dict, optional, default: None\n", + " Dict of arguments passed to the wedge objects making the pie.\n", + " For example, you can pass in``wedgeprops = {'linewidth': 3}``\n", + " to set the width of the wedge border lines equal to 3.\n", + " For more details, look at the doc/arguments of the wedge object.\n", + " By default ``clip_on=False``.\n", + " \n", + " textprops : dict, optional, default: None\n", + " Dict of arguments to pass to the text objects.\n", + " \n", + " center : list of float, optional, default: (0, 0)\n", + " Center position of the chart. Takes value (0, 0) or is a\n", + " sequence of 2 scalars.\n", + " \n", + " frame : bool, optional, default: False\n", + " Plot axes frame with the chart if true.\n", + " \n", + " rotatelabels : bool, optional, default: False\n", + " Rotate each label to the angle of the corresponding slice if true.\n", + " \n", + " Returns\n", + " -------\n", + " patches : list\n", + " A sequence of :class:`matplotlib.patches.Wedge` instances\n", + " \n", + " texts : list\n", + " A is a list of the label :class:`matplotlib.text.Text` instances.\n", + " \n", + " autotexts : list\n", + " A is a list of :class:`~matplotlib.text.Text` instances for the\n", + " numeric labels. Is returned only if parameter *autopct* is\n", + " not *None*.\n", + " \n", + " Notes\n", + " -----\n", + " The pie chart will probably look best if the figure and axes are\n", + " square, or the Axes aspect is equal.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'explode', 'labels', 'x'.\n", + " \n", + " pink()\n", + " set the default colormap to pink and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " plasma()\n", + " set the default colormap to plasma and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " plot(*args, **kwargs)\n", + " Plot lines and/or markers to the\n", + " :class:`~matplotlib.axes.Axes`. *args* is a variable length\n", + " argument, allowing for multiple *x*, *y* pairs with an\n", + " optional format string. For example, each of the following is\n", + " legal::\n", + " \n", + " plot(x, y) # plot x and y using default line style and color\n", + " plot(x, y, 'bo') # plot x and y using blue circle markers\n", + " plot(y) # plot y using x as index array 0..N-1\n", + " plot(y, 'r+') # ditto, but with red plusses\n", + " \n", + " If *x* and/or *y* is 2-dimensional, then the corresponding columns\n", + " will be plotted.\n", + " \n", + " If used with labeled data, make sure that the color spec is not\n", + " included as an element in data, as otherwise the last case\n", + " ``plot(\"v\",\"r\", data={\"v\":..., \"r\":...)``\n", + " can be interpreted as the first case which would do ``plot(v, r)``\n", + " using the default line style and color.\n", + " \n", + " If not used with labeled data (i.e., without a data argument),\n", + " an arbitrary number of *x*, *y*, *fmt* groups can be specified, as in::\n", + " \n", + " a.plot(x1, y1, 'g^', x2, y2, 'g-')\n", + " \n", + " Return value is a list of lines that were added.\n", + " \n", + " By default, each line is assigned a different style specified by a\n", + " 'style cycle'. To change this behavior, you can edit the\n", + " axes.prop_cycle rcParam.\n", + " \n", + " The following format string characters are accepted to control\n", + " the line style or marker:\n", + " \n", + " ================ ===============================\n", + " character description\n", + " ================ ===============================\n", + " ``'-'`` solid line style\n", + " ``'--'`` dashed line style\n", + " ``'-.'`` dash-dot line style\n", + " ``':'`` dotted line style\n", + " ``'.'`` point marker\n", + " ``','`` pixel marker\n", + " ``'o'`` circle marker\n", + " ``'v'`` triangle_down marker\n", + " ``'^'`` triangle_up marker\n", + " ``'<'`` triangle_left marker\n", + " ``'>'`` triangle_right marker\n", + " ``'1'`` tri_down marker\n", + " ``'2'`` tri_up marker\n", + " ``'3'`` tri_left marker\n", + " ``'4'`` tri_right marker\n", + " ``'s'`` square marker\n", + " ``'p'`` pentagon marker\n", + " ``'*'`` star marker\n", + " ``'h'`` hexagon1 marker\n", + " ``'H'`` hexagon2 marker\n", + " ``'+'`` plus marker\n", + " ``'x'`` x marker\n", + " ``'D'`` diamond marker\n", + " ``'d'`` thin_diamond marker\n", + " ``'|'`` vline marker\n", + " ``'_'`` hline marker\n", + " ================ ===============================\n", + " \n", + " \n", + " The following color abbreviations are supported:\n", + " \n", + " ========== ========\n", + " character color\n", + " ========== ========\n", + " 'b' blue\n", + " 'g' green\n", + " 'r' red\n", + " 'c' cyan\n", + " 'm' magenta\n", + " 'y' yellow\n", + " 'k' black\n", + " 'w' white\n", + " ========== ========\n", + " \n", + " In addition, you can specify colors in many weird and\n", + " wonderful ways, including full names (``'green'``), hex\n", + " strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\n", + " grayscale intensities as a string (``'0.8'``). Of these, the\n", + " string specifications can be used in place of a ``fmt`` group,\n", + " but the tuple forms can be used only as ``kwargs``.\n", + " \n", + " Line styles and colors are combined in a single format string, as in\n", + " ``'bo'`` for blue circles.\n", + " \n", + " The *kwargs* can be used to set line properties (any property that has\n", + " a ``set_*`` method). You can use this to set a line label (for auto\n", + " legends), linewidth, anitialising, marker face color, etc. Here is an\n", + " example::\n", + " \n", + " plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n", + " plot([1,2,3], [1,4,9], 'rs', label='line 2')\n", + " axis([0, 4, 0, 10])\n", + " legend()\n", + " \n", + " If you make multiple lines with one plot command, the kwargs\n", + " apply to all those lines, e.g.::\n", + " \n", + " plot(x1, y1, x2, y2, antialiased=False)\n", + " \n", + " Neither line will be antialiased.\n", + " \n", + " You do not need to use format strings, which are just\n", + " abbreviations. All of the line properties can be controlled\n", + " by keyword arguments. For example, you can set the color,\n", + " marker, linestyle, and markercolor with::\n", + " \n", + " plot(x, y, color='green', linestyle='dashed', marker='o',\n", + " markerfacecolor='blue', markersize=12).\n", + " \n", + " See :class:`~matplotlib.lines.Line2D` for details.\n", + " \n", + " The kwargs are :class:`~matplotlib.lines.Line2D` properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " kwargs *scalex* and *scaley*, if defined, are passed on to\n", + " :meth:`~matplotlib.axes.Axes.autoscale_view` to determine\n", + " whether the *x* and *y* axes are autoscaled; the default is\n", + " *True*.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " plot_date(x, y, fmt='o', tz=None, xdate=True, ydate=False, hold=None, data=None, **kwargs)\n", + " A plot with data that contains dates.\n", + " \n", + " Similar to the :func:`~matplotlib.pyplot.plot` command, except\n", + " the *x* or *y* (or both) data is considered to be dates, and the\n", + " axis is labeled accordingly.\n", + " \n", + " *x* and/or *y* can be a sequence of dates represented as float\n", + " days since 0001-01-01 UTC.\n", + " \n", + " Note if you are using custom date tickers and formatters, it\n", + " may be necessary to set the formatters/locators after the call\n", + " to :meth:`plot_date` since :meth:`plot_date` will set the\n", + " default tick locator to\n", + " :class:`matplotlib.dates.AutoDateLocator` (if the tick\n", + " locator is not already set to a\n", + " :class:`matplotlib.dates.DateLocator` instance) and the\n", + " default tick formatter to\n", + " :class:`matplotlib.dates.AutoDateFormatter` (if the tick\n", + " formatter is not already set to a\n", + " :class:`matplotlib.dates.DateFormatter` instance).\n", + " \n", + " \n", + " Parameters\n", + " ----------\n", + " fmt : string\n", + " The plot format string.\n", + " \n", + " tz : [ *None* | timezone string | :class:`tzinfo` instance]\n", + " The time zone to use in labeling dates. If *None*, defaults to rc\n", + " value.\n", + " \n", + " xdate : boolean\n", + " If *True*, the *x*-axis will be labeled with dates.\n", + " \n", + " ydate : boolean\n", + " If *True*, the *y*-axis will be labeled with dates.\n", + " \n", + " \n", + " Returns\n", + " -------\n", + " lines\n", + " \n", + " \n", + " See Also\n", + " --------\n", + " matplotlib.dates : helper functions on dates\n", + " matplotlib.dates.date2num : how to convert dates to num\n", + " matplotlib.dates.num2date : how to convert num to dates\n", + " matplotlib.dates.drange : how floating point dates\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " plotfile(fname, cols=(0,), plotfuncs=None, comments='#', skiprows=0, checkrows=5, delimiter=',', names=None, subplots=True, newfig=True, **kwargs)\n", + " Plot the data in a file.\n", + " \n", + " *cols* is a sequence of column identifiers to plot. An identifier\n", + " is either an int or a string. If it is an int, it indicates the\n", + " column number. If it is a string, it indicates the column header.\n", + " matplotlib will make column headers lower case, replace spaces with\n", + " underscores, and remove all illegal characters; so ``'Adj Close*'``\n", + " will have name ``'adj_close'``.\n", + " \n", + " - If len(*cols*) == 1, only that column will be plotted on the *y* axis.\n", + " \n", + " - If len(*cols*) > 1, the first element will be an identifier for\n", + " data for the *x* axis and the remaining elements will be the\n", + " column indexes for multiple subplots if *subplots* is *True*\n", + " (the default), or for lines in a single subplot if *subplots*\n", + " is *False*.\n", + " \n", + " *plotfuncs*, if not *None*, is a dictionary mapping identifier to\n", + " an :class:`~matplotlib.axes.Axes` plotting function as a string.\n", + " Default is 'plot', other choices are 'semilogy', 'fill', 'bar',\n", + " etc. You must use the same type of identifier in the *cols*\n", + " vector as you use in the *plotfuncs* dictionary, e.g., integer\n", + " column numbers in both or column names in both. If *subplots*\n", + " is *False*, then including any function such as 'semilogy'\n", + " that changes the axis scaling will set the scaling for all\n", + " columns.\n", + " \n", + " *comments*, *skiprows*, *checkrows*, *delimiter*, and *names*\n", + " are all passed on to :func:`matplotlib.pylab.csv2rec` to\n", + " load the data into a record array.\n", + " \n", + " If *newfig* is *True*, the plot always will be made in a new figure;\n", + " if *False*, it will be made in the current figure if one exists,\n", + " else in a new figure.\n", + " \n", + " kwargs are passed on to plotting functions.\n", + " \n", + " Example usage::\n", + " \n", + " # plot the 2nd and 4th column against the 1st in two subplots\n", + " plotfile(fname, (0,1,3))\n", + " \n", + " # plot using column names; specify an alternate plot type for volume\n", + " plotfile(fname, ('date', 'volume', 'adj_close'),\n", + " plotfuncs={'volume': 'semilogy'})\n", + " \n", + " Note: plotfile is intended as a convenience for quickly plotting\n", + " data from flat files; it is not intended as an alternative\n", + " interface to general plotting with pyplot or matplotlib.\n", + " \n", + " plotting()\n", + " ============================ ======================================================================================================================================================================================\n", + " Function Description \n", + " ============================ ======================================================================================================================================================================================\n", + " `acorr` Plot the autocorrelation of `x`. \n", + " `angle_spectrum` Plot the angle spectrum. \n", + " `annotate` Annotate the point ``xy`` with text ``s``. \n", + " `arrow` Add an arrow to the axes. \n", + " `autoscale` Autoscale the axis view to the data (toggle). \n", + " `axes` Add an axes to the figure. \n", + " `axhline` Add a horizontal line across the axis. \n", + " `axhspan` Add a horizontal span (rectangle) across the axis. \n", + " `axis` Convenience method to get or set axis properties. \n", + " `axvline` Add a vertical line across the axes. \n", + " `axvspan` Add a vertical span (rectangle) across the axes. \n", + " `bar` Make a bar plot. \n", + " `barbs` Plot a 2-D field of barbs. \n", + " `barh` Make a horizontal bar plot. \n", + " `box` Turn the axes box on or off. \n", + " `boxplot` Make a box and whisker plot. \n", + " `broken_barh` Plot horizontal bars. \n", + " `cla` Clear the current axes. \n", + " `clabel` Label a contour plot. \n", + " `clf` Clear the current figure. \n", + " `clim` Set the color limits of the current image. \n", + " `close` Close a figure window. \n", + " `cohere` Plot the coherence between *x* and *y*. \n", + " `colorbar` Add a colorbar to a plot. \n", + " `contour` Plot contours. \n", + " `contourf` Plot contours. \n", + " `csd` Plot the cross-spectral density. \n", + " `delaxes` Remove an axes from the current figure. \n", + " `draw` Redraw the current figure. \n", + " `errorbar` Plot an errorbar graph. \n", + " `eventplot` Plot identical parallel lines at the given positions. \n", + " `figimage` Adds a non-resampled image to the figure. \n", + " `figlegend` Place a legend in the figure. \n", + " `fignum_exists` \n", + " `figtext` Add text to figure. \n", + " `figure` Creates a new figure. \n", + " `fill` Plot filled polygons. \n", + " `fill_between` Make filled polygons between two curves. \n", + " `fill_betweenx` Make filled polygons between two horizontal curves. \n", + " `findobj` Find artist objects. \n", + " `gca` Get the current :class:`~matplotlib.axes.Axes` instance on the current figure matching the given keyword args, or create one. \n", + " `gcf` Get a reference to the current figure. \n", + " `gci` Get the current colorable artist. \n", + " `get_figlabels` Return a list of existing figure labels. \n", + " `get_fignums` Return a list of existing figure numbers. \n", + " `grid` Turn the axes grids on or off. \n", + " `hexbin` Make a hexagonal binning plot. \n", + " `hist` Plot a histogram. \n", + " `hist2d` Make a 2D histogram plot. \n", + " `hlines` Plot horizontal lines at each `y` from `xmin` to `xmax`. \n", + " `hold` .. \n", + " `imread` Read an image from a file into an array. \n", + " `imsave` Save an array as in image file. \n", + " `imshow` Display an image on the axes. \n", + " `install_repl_displayhook` Install a repl display hook so that any stale figure are automatically redrawn when control is returned to the repl. \n", + " `ioff` Turn interactive mode off. \n", + " `ion` Turn interactive mode on. \n", + " `ishold` .. \n", + " `isinteractive` Return status of interactive mode. \n", + " `legend` Places a legend on the axes. \n", + " `locator_params` Control behavior of tick locators. \n", + " `loglog` Make a plot with log scaling on both the *x* and *y* axis. \n", + " `magnitude_spectrum` Plot the magnitude spectrum. \n", + " `margins` Set or retrieve autoscaling margins. \n", + " `matshow` Display an array as a matrix in a new figure window. \n", + " `minorticks_off` Remove minor ticks from the current plot. \n", + " `minorticks_on` Display minor ticks on the current plot. \n", + " `over` .. \n", + " `pause` Pause for *interval* seconds. \n", + " `pcolor` Create a pseudocolor plot of a 2-D array. \n", + " `pcolormesh` Plot a quadrilateral mesh. \n", + " `phase_spectrum` Plot the phase spectrum. \n", + " `pie` Plot a pie chart. \n", + " `plot` Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`. \n", + " `plot_date` A plot with data that contains dates. \n", + " `plotfile` Plot the data in a file. \n", + " `polar` Make a polar plot. \n", + " `psd` Plot the power spectral density. \n", + " `quiver` Plot a 2-D field of arrows. \n", + " `quiverkey` Add a key to a quiver plot. \n", + " `rc` Set the current rc params. \n", + " `rc_context` Return a context manager for managing rc settings. \n", + " `rcdefaults` Restore the rc params from Matplotlib's internal defaults. \n", + " `rgrids` Get or set the radial gridlines on a polar plot. \n", + " `savefig` Save the current figure. \n", + " `sca` Set the current Axes instance to *ax*. \n", + " `scatter` Make a scatter plot of `x` vs `y`. \n", + " `sci` Set the current image. \n", + " `semilogx` Make a plot with log scaling on the *x* axis. \n", + " `semilogy` Make a plot with log scaling on the *y* axis. \n", + " `set_cmap` Set the default colormap. \n", + " `setp` Set a property on an artist object. \n", + " `show` Display a figure. \n", + " `specgram` Plot a spectrogram. \n", + " `spy` Plot the sparsity pattern on a 2-D array. \n", + " `stackplot` Draws a stacked area plot. \n", + " `stem` Create a stem plot. \n", + " `step` Make a step plot. \n", + " `streamplot` Draws streamlines of a vector flow. \n", + " `subplot` Return a subplot axes at the given grid position. \n", + " `subplot2grid` Create an axis at specific location inside a regular grid. \n", + " `subplot_tool` Launch a subplot tool window for a figure. \n", + " `subplots` Create a figure and a set of subplots This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.\n", + " `subplots_adjust` Tune the subplot layout. \n", + " `suptitle` Add a centered title to the figure. \n", + " `switch_backend` Switch the default backend. \n", + " `table` Add a table to the current axes. \n", + " `text` Add text to the axes. \n", + " `thetagrids` Get or set the theta locations of the gridlines in a polar plot. \n", + " `tick_params` Change the appearance of ticks and tick labels. \n", + " `ticklabel_format` Change the `~matplotlib.ticker.ScalarFormatter` used by default for linear axes. \n", + " `tight_layout` Automatically adjust subplot parameters to give specified padding. \n", + " `title` Set a title of the current axes. \n", + " `tricontour` Draw contours on an unstructured triangular grid. \n", + " `tricontourf` Draw contours on an unstructured triangular grid. \n", + " `tripcolor` Create a pseudocolor plot of an unstructured triangular grid. \n", + " `triplot` Draw a unstructured triangular grid as lines and/or markers. \n", + " `twinx` Make a second axes that shares the *x*-axis. \n", + " `twiny` Make a second axes that shares the *y*-axis. \n", + " `uninstall_repl_displayhook` Uninstalls the matplotlib display hook. \n", + " `violinplot` Make a violin plot. \n", + " `vlines` Plot vertical lines. \n", + " `xcorr` Plot the cross correlation between *x* and *y*. \n", + " `xkcd` Turns on `xkcd `_ sketch-style drawing mode. \n", + " `xlabel` Set the *x* axis label of the current axis. \n", + " `xlim` Get or set the *x* limits of the current axes. \n", + " `xscale` Set the scaling of the *x*-axis. \n", + " `xticks` Get or set the *x*-limits of the current tick locations and labels. \n", + " `ylabel` Set the *y* axis label of the current axis. \n", + " `ylim` Get or set the *y*-limits of the current axes. \n", + " `yscale` Set the scaling of the *y*-axis. \n", + " `yticks` Get or set the *y*-limits of the current tick locations and labels. \n", + " ============================ ======================================================================================================================================================================================\n", + " \n", + " polar(*args, **kwargs)\n", + " Make a polar plot.\n", + " \n", + " call signature::\n", + " \n", + " polar(theta, r, **kwargs)\n", + " \n", + " Multiple *theta*, *r* arguments are supported, with format\n", + " strings, as in :func:`~matplotlib.pyplot.plot`.\n", + " \n", + " prism()\n", + " set the default colormap to prism and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " psd(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, pad_to=None, sides=None, scale_by_freq=None, return_line=None, hold=None, data=None, **kwargs)\n", + " Plot the power spectral density.\n", + " \n", + " Call signature::\n", + " \n", + " psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,\n", + " window=mlab.window_hanning, noverlap=0, pad_to=None,\n", + " sides='default', scale_by_freq=None, return_line=None, **kwargs)\n", + " \n", + " The power spectral density :math:`P_{xx}` by Welch's average\n", + " periodogram method. The vector *x* is divided into *NFFT* length\n", + " segments. Each segment is detrended by function *detrend* and\n", + " windowed by function *window*. *noverlap* gives the length of\n", + " the overlap between segments. The :math:`|\\mathrm{fft}(i)|^2`\n", + " of each segment :math:`i` are averaged to compute :math:`P_{xx}`,\n", + " with a scaling to correct for power loss due to windowing.\n", + " \n", + " If len(*x*) < *NFFT*, it will be zero padded to *NFFT*.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between segments.\n", + " The default value is 0 (no overlap).\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " return_line : bool\n", + " Whether to include the line object plotted in the returned values.\n", + " Default is False.\n", + " \n", + " Returns\n", + " -------\n", + " Pxx : 1-D array\n", + " The values for the power spectrum `P_{xx}` before scaling\n", + " (real valued)\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the elements in *Pxx*\n", + " \n", + " line : a :class:`~matplotlib.lines.Line2D` instance\n", + " The line created by this function.\n", + " Only returned if *return_line* is True.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " For plotting, the power is plotted as\n", + " :math:`10\\log_{10}(P_{xx})` for decibels, though *Pxx* itself\n", + " is returned.\n", + " \n", + " References\n", + " ----------\n", + " Bendat & Piersol -- Random Data: Analysis and Measurement Procedures,\n", + " John Wiley & Sons (1986)\n", + " \n", + " See Also\n", + " --------\n", + " :func:`specgram`\n", + " :func:`specgram` differs in the default overlap; in not returning\n", + " the mean of the segment periodograms; in returning the times of the\n", + " segments; and in plotting a colormap instead of a line.\n", + " \n", + " :func:`magnitude_spectrum`\n", + " :func:`magnitude_spectrum` plots the magnitude spectrum.\n", + " \n", + " :func:`csd`\n", + " :func:`csd` plots the spectral density between two signals.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " quiver(*args, **kw)\n", + " Plot a 2-D field of arrows.\n", + " \n", + " Call signatures::\n", + " \n", + " quiver(U, V, **kw)\n", + " quiver(U, V, C, **kw)\n", + " quiver(X, Y, U, V, **kw)\n", + " quiver(X, Y, U, V, C, **kw)\n", + " \n", + " *U* and *V* are the arrow data, *X* and *Y* set the location of the\n", + " arrows, and *C* sets the color of the arrows. These arguments may be 1-D or\n", + " 2-D arrays or sequences.\n", + " \n", + " If *X* and *Y* are absent, they will be generated as a uniform grid.\n", + " If *U* and *V* are 2-D arrays and *X* and *Y* are 1-D, and if ``len(X)`` and\n", + " ``len(Y)`` match the column and row dimensions of *U*, then *X* and *Y* will be\n", + " expanded with :func:`numpy.meshgrid`.\n", + " \n", + " The default settings auto-scales the length of the arrows to a reasonable size.\n", + " To change this behavior see the *scale* and *scale_units* kwargs.\n", + " \n", + " The defaults give a slightly swept-back arrow; to make the head a\n", + " triangle, make *headaxislength* the same as *headlength*. To make the\n", + " arrow more pointed, reduce *headwidth* or increase *headlength* and\n", + " *headaxislength*. To make the head smaller relative to the shaft,\n", + " scale down all the head parameters. You will probably do best to leave\n", + " minshaft alone.\n", + " \n", + " *linewidths* and *edgecolors* can be used to customize the arrow\n", + " outlines.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : 1D or 2D array, sequence, optional\n", + " The x coordinates of the arrow locations\n", + " Y : 1D or 2D array, sequence, optional\n", + " The y coordinates of the arrow locations\n", + " U : 1D or 2D array or masked array, sequence\n", + " The x components of the arrow vectors\n", + " V : 1D or 2D array or masked array, sequence\n", + " The y components of the arrow vectors\n", + " C : 1D or 2D array, sequence, optional\n", + " The arrow colors\n", + " units : [ 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y' | 'xy' ]\n", + " The arrow dimensions (except for *length*) are measured in multiples of\n", + " this unit.\n", + " \n", + " 'width' or 'height': the width or height of the axis\n", + " \n", + " 'dots' or 'inches': pixels or inches, based on the figure dpi\n", + " \n", + " 'x', 'y', or 'xy': respectively *X*, *Y*, or :math:`\\sqrt{X^2 + Y^2}`\n", + " in data units\n", + " \n", + " The arrows scale differently depending on the units. For\n", + " 'x' or 'y', the arrows get larger as one zooms in; for other\n", + " units, the arrow size is independent of the zoom state. For\n", + " 'width or 'height', the arrow size increases with the width and\n", + " height of the axes, respectively, when the window is resized;\n", + " for 'dots' or 'inches', resizing does not change the arrows.\n", + " angles : [ 'uv' | 'xy' ], array, optional\n", + " Method for determining the angle of the arrows. Default is 'uv'.\n", + " \n", + " 'uv': the arrow axis aspect ratio is 1 so that\n", + " if *U*==*V* the orientation of the arrow on the plot is 45 degrees\n", + " counter-clockwise from the horizontal axis (positive to the right).\n", + " \n", + " 'xy': arrows point from (x,y) to (x+u, y+v).\n", + " Use this for plotting a gradient field, for example.\n", + " \n", + " Alternatively, arbitrary angles may be specified as an array\n", + " of values in degrees, counter-clockwise from the horizontal axis.\n", + " \n", + " Note: inverting a data axis will correspondingly invert the\n", + " arrows only with ``angles='xy'``.\n", + " scale : None, float, optional\n", + " Number of data units per arrow length unit, e.g., m/s per plot width; a\n", + " smaller scale parameter makes the arrow longer. Default is *None*.\n", + " \n", + " If *None*, a simple autoscaling algorithm is used, based on the average\n", + " vector length and the number of vectors. The arrow length unit is given by\n", + " the *scale_units* parameter\n", + " scale_units : [ 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y' | 'xy' ], None, optional\n", + " If the *scale* kwarg is *None*, the arrow length unit. Default is *None*.\n", + " \n", + " e.g. *scale_units* is 'inches', *scale* is 2.0, and\n", + " ``(u,v) = (1,0)``, then the vector will be 0.5 inches long.\n", + " \n", + " If *scale_units* is 'width'/'height', then the vector will be half the\n", + " width/height of the axes.\n", + " \n", + " If *scale_units* is 'x' then the vector will be 0.5 x-axis\n", + " units. To plot vectors in the x-y plane, with u and v having\n", + " the same units as x and y, use\n", + " ``angles='xy', scale_units='xy', scale=1``.\n", + " width : scalar, optional\n", + " Shaft width in arrow units; default depends on choice of units,\n", + " above, and number of vectors; a typical starting value is about\n", + " 0.005 times the width of the plot.\n", + " headwidth : scalar, optional\n", + " Head width as multiple of shaft width, default is 3\n", + " headlength : scalar, optional\n", + " Head length as multiple of shaft width, default is 5\n", + " headaxislength : scalar, optional\n", + " Head length at shaft intersection, default is 4.5\n", + " minshaft : scalar, optional\n", + " Length below which arrow scales, in units of head length. Do not\n", + " set this to less than 1, or small arrows will look terrible!\n", + " Default is 1\n", + " minlength : scalar, optional\n", + " Minimum length as a multiple of shaft width; if an arrow length\n", + " is less than this, plot a dot (hexagon) of this diameter instead.\n", + " Default is 1.\n", + " pivot : [ 'tail' | 'mid' | 'middle' | 'tip' ], optional\n", + " The part of the arrow that is at the grid point; the arrow rotates\n", + " about this point, hence the name *pivot*.\n", + " color : [ color | color sequence ], optional\n", + " This is a synonym for the\n", + " :class:`~matplotlib.collections.PolyCollection` facecolor kwarg.\n", + " If *C* has been set, *color* has no effect.\n", + " \n", + " Notes\n", + " -----\n", + " Additional :class:`~matplotlib.collections.PolyCollection`\n", + " keyword arguments:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float or None \n", + " animated: bool \n", + " antialiased or antialiaseds: Boolean or sequence of booleans \n", + " array: ndarray\n", + " clim: a length 2 sequence of floats \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " cmap: a colormap or registered colormap name \n", + " color: matplotlib color arg or sequence of rgba tuples\n", + " contains: a callable function \n", + " edgecolor or edgecolors: matplotlib color spec or sequence of specs \n", + " facecolor or facecolors: matplotlib color spec or sequence of specs \n", + " figure: a `~.Figure` instance \n", + " gid: an id string \n", + " hatch: [ '/' | '\\\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] \n", + " label: object \n", + " linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or linewidths or lw: float or sequence of floats \n", + " norm: `~.Normalize`\n", + " offset_position: [ 'screen' | 'data' ] \n", + " offsets: float or sequence of floats \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " urls: List[str] or None \n", + " visible: bool \n", + " zorder: float \n", + " \n", + " See Also\n", + " --------\n", + " quiverkey : Add a key to a quiver plot\n", + " \n", + " quiverkey(*args, **kw)\n", + " Add a key to a quiver plot.\n", + " \n", + " Call signature::\n", + " \n", + " quiverkey(Q, X, Y, U, label, **kw)\n", + " \n", + " Arguments:\n", + " \n", + " *Q*:\n", + " The Quiver instance returned by a call to quiver.\n", + " \n", + " *X*, *Y*:\n", + " The location of the key; additional explanation follows.\n", + " \n", + " *U*:\n", + " The length of the key\n", + " \n", + " *label*:\n", + " A string with the length and units of the key\n", + " \n", + " Keyword arguments:\n", + " \n", + " *angle* = 0\n", + " The angle of the key arrow. Measured in degrees anti-clockwise from the\n", + " x-axis.\n", + " \n", + " *coordinates* = [ 'axes' | 'figure' | 'data' | 'inches' ]\n", + " Coordinate system and units for *X*, *Y*: 'axes' and 'figure' are\n", + " normalized coordinate systems with 0,0 in the lower left and 1,1\n", + " in the upper right; 'data' are the axes data coordinates (used for\n", + " the locations of the vectors in the quiver plot itself); 'inches'\n", + " is position in the figure in inches, with 0,0 at the lower left\n", + " corner.\n", + " \n", + " *color*:\n", + " overrides face and edge colors from *Q*.\n", + " \n", + " *labelpos* = [ 'N' | 'S' | 'E' | 'W' ]\n", + " Position the label above, below, to the right, to the left of the\n", + " arrow, respectively.\n", + " \n", + " *labelsep*:\n", + " Distance in inches between the arrow and the label. Default is\n", + " 0.1\n", + " \n", + " *labelcolor*:\n", + " defaults to default :class:`~matplotlib.text.Text` color.\n", + " \n", + " *fontproperties*:\n", + " A dictionary with keyword arguments accepted by the\n", + " :class:`~matplotlib.font_manager.FontProperties` initializer:\n", + " *family*, *style*, *variant*, *size*, *weight*\n", + " \n", + " Any additional keyword arguments are used to override vector\n", + " properties taken from *Q*.\n", + " \n", + " The positioning of the key depends on *X*, *Y*, *coordinates*, and\n", + " *labelpos*. If *labelpos* is 'N' or 'S', *X*, *Y* give the position\n", + " of the middle of the key arrow. If *labelpos* is 'E', *X*, *Y*\n", + " positions the head, and if *labelpos* is 'W', *X*, *Y* positions the\n", + " tail; in either of these two cases, *X*, *Y* is somewhere in the\n", + " middle of the arrow+label key object.\n", + " \n", + " rc(*args, **kwargs)\n", + " Set the current rc params. Group is the grouping for the rc, e.g.,\n", + " for ``lines.linewidth`` the group is ``lines``, for\n", + " ``axes.facecolor``, the group is ``axes``, and so on. Group may\n", + " also be a list or tuple of group names, e.g., (*xtick*, *ytick*).\n", + " *kwargs* is a dictionary attribute name/value pairs, e.g.,::\n", + " \n", + " rc('lines', linewidth=2, color='r')\n", + " \n", + " sets the current rc params and is equivalent to::\n", + " \n", + " rcParams['lines.linewidth'] = 2\n", + " rcParams['lines.color'] = 'r'\n", + " \n", + " The following aliases are available to save typing for interactive\n", + " users:\n", + " \n", + " ===== =================\n", + " Alias Property\n", + " ===== =================\n", + " 'lw' 'linewidth'\n", + " 'ls' 'linestyle'\n", + " 'c' 'color'\n", + " 'fc' 'facecolor'\n", + " 'ec' 'edgecolor'\n", + " 'mew' 'markeredgewidth'\n", + " 'aa' 'antialiased'\n", + " ===== =================\n", + " \n", + " Thus you could abbreviate the above rc command as::\n", + " \n", + " rc('lines', lw=2, c='r')\n", + " \n", + " \n", + " Note you can use python's kwargs dictionary facility to store\n", + " dictionaries of default parameters. e.g., you can customize the\n", + " font rc as follows::\n", + " \n", + " font = {'family' : 'monospace',\n", + " 'weight' : 'bold',\n", + " 'size' : 'larger'}\n", + " \n", + " rc('font', **font) # pass in the font dict as kwargs\n", + " \n", + " This enables you to easily switch between several configurations. Use\n", + " ``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to\n", + " restore the default rc params after changes.\n", + " \n", + " rc_context(rc=None, fname=None)\n", + " Return a context manager for managing rc settings.\n", + " \n", + " This allows one to do::\n", + " \n", + " with mpl.rc_context(fname='screen.rc'):\n", + " plt.plot(x, a)\n", + " with mpl.rc_context(fname='print.rc'):\n", + " plt.plot(x, b)\n", + " plt.plot(x, c)\n", + " \n", + " The 'a' vs 'x' and 'c' vs 'x' plots would have settings from\n", + " 'screen.rc', while the 'b' vs 'x' plot would have settings from\n", + " 'print.rc'.\n", + " \n", + " A dictionary can also be passed to the context manager::\n", + " \n", + " with mpl.rc_context(rc={'text.usetex': True}, fname='screen.rc'):\n", + " plt.plot(x, a)\n", + " \n", + " The 'rc' dictionary takes precedence over the settings loaded from\n", + " 'fname'. Passing a dictionary only is also valid. For example a\n", + " common usage is::\n", + " \n", + " with mpl.rc_context(rc={'interactive': False}):\n", + " fig, ax = plt.subplots()\n", + " ax.plot(range(3), range(3))\n", + " fig.savefig('A.png', format='png')\n", + " plt.close(fig)\n", + " \n", + " rcdefaults()\n", + " Restore the rc params from Matplotlib's internal defaults.\n", + " \n", + " See Also\n", + " --------\n", + " rc_file_defaults :\n", + " Restore the rc params from the rc file originally loaded by Matplotlib.\n", + " matplotlib.style.use :\n", + " Use a specific style file. Call ``style.use('default')`` to restore\n", + " the default style.\n", + " \n", + " rgrids(*args, **kwargs)\n", + " Get or set the radial gridlines on a polar plot.\n", + " \n", + " call signatures::\n", + " \n", + " lines, labels = rgrids()\n", + " lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs)\n", + " \n", + " When called with no arguments, :func:`rgrid` simply returns the\n", + " tuple (*lines*, *labels*), where *lines* is an array of radial\n", + " gridlines (:class:`~matplotlib.lines.Line2D` instances) and\n", + " *labels* is an array of tick labels\n", + " (:class:`~matplotlib.text.Text` instances). When called with\n", + " arguments, the labels will appear at the specified radial\n", + " distances and angles.\n", + " \n", + " *labels*, if not *None*, is a len(*radii*) list of strings of the\n", + " labels to use at each angle.\n", + " \n", + " If *labels* is None, the rformatter will be used\n", + " \n", + " Examples::\n", + " \n", + " # set the locations of the radial gridlines and labels\n", + " lines, labels = rgrids( (0.25, 0.5, 1.0) )\n", + " \n", + " # set the locations and labels of the radial gridlines and labels\n", + " lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )\n", + " \n", + " savefig(*args, **kwargs)\n", + " Save the current figure.\n", + " \n", + " Call signature::\n", + " \n", + " savefig(fname, dpi=None, facecolor='w', edgecolor='w',\n", + " orientation='portrait', papertype=None, format=None,\n", + " transparent=False, bbox_inches=None, pad_inches=0.1,\n", + " frameon=None)\n", + " \n", + " The output formats available depend on the backend being used.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " fname : str or file-like object\n", + " A string containing a path to a filename, or a Python\n", + " file-like object, or possibly some backend-dependent object\n", + " such as :class:`~matplotlib.backends.backend_pdf.PdfPages`.\n", + " \n", + " If *format* is *None* and *fname* is a string, the output\n", + " format is deduced from the extension of the filename. If\n", + " the filename has no extension, the value of the rc parameter\n", + " ``savefig.format`` is used.\n", + " \n", + " If *fname* is not a string, remember to specify *format* to\n", + " ensure that the correct backend is used.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " \n", + " dpi : [ *None* | scalar > 0 | 'figure']\n", + " The resolution in dots per inch. If *None* it will default to\n", + " the value ``savefig.dpi`` in the matplotlibrc file. If 'figure'\n", + " it will set the dpi to be the value of the figure.\n", + " \n", + " facecolor : color spec or None, optional\n", + " the facecolor of the figure; if None, defaults to savefig.facecolor\n", + " \n", + " edgecolor : color spec or None, optional\n", + " the edgecolor of the figure; if None, defaults to savefig.edgecolor\n", + " \n", + " orientation : {'landscape', 'portrait'}\n", + " not supported on all backends; currently only on postscript output\n", + " \n", + " papertype : str\n", + " One of 'letter', 'legal', 'executive', 'ledger', 'a0' through\n", + " 'a10', 'b0' through 'b10'. Only supported for postscript\n", + " output.\n", + " \n", + " format : str\n", + " One of the file extensions supported by the active\n", + " backend. Most backends support png, pdf, ps, eps and svg.\n", + " \n", + " transparent : bool\n", + " If *True*, the axes patches will all be transparent; the\n", + " figure patch will also be transparent unless facecolor\n", + " and/or edgecolor are specified via kwargs.\n", + " This is useful, for example, for displaying\n", + " a plot on top of a colored background on a web page. The\n", + " transparency of these patches will be restored to their\n", + " original values upon exit of this function.\n", + " \n", + " frameon : bool\n", + " If *True*, the figure patch will be colored, if *False*, the\n", + " figure background will be transparent. If not provided, the\n", + " rcParam 'savefig.frameon' will be used.\n", + " \n", + " bbox_inches : str or `~matplotlib.transforms.Bbox`, optional\n", + " Bbox in inches. Only the given portion of the figure is\n", + " saved. If 'tight', try to figure out the tight bbox of\n", + " the figure. If None, use savefig.bbox\n", + " \n", + " pad_inches : scalar, optional\n", + " Amount of padding around the figure when bbox_inches is\n", + " 'tight'. If None, use savefig.pad_inches\n", + " \n", + " bbox_extra_artists : list of `~matplotlib.artist.Artist`, optional\n", + " A list of extra artists that will be considered when the\n", + " tight bbox is calculated.\n", + " \n", + " sca(ax)\n", + " Set the current Axes instance to *ax*.\n", + " \n", + " The current Figure is updated to the parent of *ax*.\n", + " \n", + " scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, hold=None, data=None, **kwargs)\n", + " Make a scatter plot of `x` vs `y`.\n", + " \n", + " Marker size is scaled by `s` and marker color is mapped to `c`.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : array_like, shape (n, )\n", + " Input data\n", + " \n", + " s : scalar or array_like, shape (n, ), optional\n", + " size in points^2. Default is `rcParams['lines.markersize'] ** 2`.\n", + " \n", + " c : color, sequence, or sequence of color, optional, default: 'b'\n", + " `c` can be a single color format string, or a sequence of color\n", + " specifications of length `N`, or a sequence of `N` numbers to be\n", + " mapped to colors using the `cmap` and `norm` specified via kwargs\n", + " (see below). Note that `c` should not be a single numeric RGB or\n", + " RGBA sequence because that is indistinguishable from an array of\n", + " values to be colormapped. `c` can be a 2-D array in which the\n", + " rows are RGB or RGBA, however, including the case of a single\n", + " row to specify the same color for all points.\n", + " \n", + " marker : `~matplotlib.markers.MarkerStyle`, optional, default: 'o'\n", + " See `~matplotlib.markers` for more information on the different\n", + " styles of markers scatter supports. `marker` can be either\n", + " an instance of the class or the text shorthand for a particular\n", + " marker.\n", + " \n", + " cmap : `~matplotlib.colors.Colormap`, optional, default: None\n", + " A `~matplotlib.colors.Colormap` instance or registered name.\n", + " `cmap` is only used if `c` is an array of floats. If None,\n", + " defaults to rc `image.cmap`.\n", + " \n", + " norm : `~matplotlib.colors.Normalize`, optional, default: None\n", + " A `~matplotlib.colors.Normalize` instance is used to scale\n", + " luminance data to 0, 1. `norm` is only used if `c` is an array of\n", + " floats. If `None`, use the default :func:`normalize`.\n", + " \n", + " vmin, vmax : scalar, optional, default: None\n", + " `vmin` and `vmax` are used in conjunction with `norm` to normalize\n", + " luminance data. If either are `None`, the min and max of the\n", + " color array is used. Note if you pass a `norm` instance, your\n", + " settings for `vmin` and `vmax` will be ignored.\n", + " \n", + " alpha : scalar, optional, default: None\n", + " The alpha blending value, between 0 (transparent) and 1 (opaque)\n", + " \n", + " linewidths : scalar or array_like, optional, default: None\n", + " If None, defaults to (lines.linewidth,).\n", + " \n", + " verts : sequence of (x, y), optional\n", + " If `marker` is None, these vertices will be used to\n", + " construct the marker. The center of the marker is located\n", + " at (0,0) in normalized units. The overall marker is rescaled\n", + " by ``s``.\n", + " \n", + " edgecolors : color or sequence of color, optional, default: None\n", + " If None, defaults to 'face'\n", + " \n", + " If 'face', the edge color will always be the same as\n", + " the face color.\n", + " \n", + " If it is 'none', the patch boundary will not\n", + " be drawn.\n", + " \n", + " For non-filled markers, the `edgecolors` kwarg\n", + " is ignored and forced to 'face' internally.\n", + " \n", + " Returns\n", + " -------\n", + " paths : `~matplotlib.collections.PathCollection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.collections.Collection` properties\n", + " \n", + " See Also\n", + " --------\n", + " plot : to plot scatter plots when markers are identical in size and\n", + " color\n", + " \n", + " Notes\n", + " -----\n", + " \n", + " * The `plot` function will be faster for scatterplots where markers\n", + " don't vary in size or color.\n", + " \n", + " * Any or all of `x`, `y`, `s`, and `c` may be masked arrays, in which\n", + " case all masks will be combined and only unmasked points will be\n", + " plotted.\n", + " \n", + " Fundamentally, scatter works with 1-D arrays; `x`, `y`, `s`, and `c`\n", + " may be input as 2-D arrays, but within scatter they will be\n", + " flattened. The exception is `c`, which will be flattened only if its\n", + " size matches the size of `x` and `y`.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'c', 'color', 'edgecolors', 'facecolor', 'facecolors', 'linewidths', 's', 'x', 'y'.\n", + " \n", + " sci(im)\n", + " Set the current image. This image will be the target of colormap\n", + " commands like :func:`~matplotlib.pyplot.jet`,\n", + " :func:`~matplotlib.pyplot.hot` or\n", + " :func:`~matplotlib.pyplot.clim`). The current image is an\n", + " attribute of the current axes.\n", + " \n", + " semilogx(*args, **kwargs)\n", + " Make a plot with log scaling on the *x* axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " basex : float, optional\n", + " Base of the *x* logarithm. The scalar should be larger\n", + " than 1.\n", + " \n", + " subsx : array_like, optional\n", + " The location of the minor xticks; *None* defaults to\n", + " autosubs, which depend on the number of decades in the\n", + " plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for\n", + " details.\n", + " \n", + " nonposx : string, optional, {'mask', 'clip'}\n", + " Non-positive values in *x* can be masked as\n", + " invalid, or clipped to a very small positive number.\n", + " \n", + " Returns\n", + " -------\n", + " `~matplotlib.pyplot.plot`\n", + " Log-scaled plot on the *x* axis.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " This function supports all the keyword arguments of\n", + " :func:`~matplotlib.pyplot.plot` and\n", + " :meth:`matplotlib.axes.Axes.set_xscale`.\n", + " \n", + " semilogy(*args, **kwargs)\n", + " Make a plot with log scaling on the *y* axis.\n", + " \n", + " Parameters\n", + " ----------\n", + " basey : float, optional\n", + " Base of the *y* logarithm. The scalar should be larger\n", + " than 1.\n", + " \n", + " subsy : array_like, optional\n", + " The location of the minor yticks; *None* defaults to\n", + " autosubs, which depend on the number of decades in the\n", + " plot; see :meth:`~matplotlib.axes.Axes.set_yscale` for\n", + " details.\n", + " \n", + " nonposy : string, optional, {'mask', 'clip'}\n", + " Non-positive values in *y* can be masked as\n", + " invalid, or clipped to a very small positive number.\n", + " \n", + " Returns\n", + " -------\n", + " `~matplotlib.pyplot.plot`\n", + " Log-scaled plot on the *y* axis.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs :\n", + " Keyword arguments control the :class:`~matplotlib.lines.Line2D`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " antialiased or aa: [True | False] \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " color or c: any matplotlib color \n", + " contains: a callable function \n", + " dash_capstyle: ['butt' | 'round' | 'projecting'] \n", + " dash_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " dashes: sequence of on/off ink in points \n", + " drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n", + " figure: a `~.Figure` instance \n", + " fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n", + " gid: an id string \n", + " label: object \n", + " linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n", + " linewidth or lw: float value in points \n", + " marker: :mod:`A valid marker style `\n", + " markeredgecolor or mec: any matplotlib color \n", + " markeredgewidth or mew: float value in points \n", + " markerfacecolor or mfc: any matplotlib color \n", + " markerfacecoloralt or mfcalt: any matplotlib color \n", + " markersize or ms: float \n", + " markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: float distance in points or callable pick function ``fn(artist, event)`` \n", + " pickradius: float distance in points\n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " solid_capstyle: ['butt' | 'round' | 'projecting'] \n", + " solid_joinstyle: ['miter' | 'round' | 'bevel'] \n", + " transform: a :class:`matplotlib.transforms.Transform` instance \n", + " url: a url string \n", + " visible: bool \n", + " xdata: 1D array \n", + " ydata: 1D array \n", + " zorder: float \n", + " \n", + " Notes\n", + " -----\n", + " This function supports all the keyword arguments of\n", + " :func:`~matplotlib.pyplot.plot` and\n", + " :meth:`matplotlib.axes.Axes.set_yscale`.\n", + " \n", + " set_cmap(cmap)\n", + " Set the default colormap. Applies to the current image if any.\n", + " See help(colormaps) for more information.\n", + " \n", + " *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or\n", + " the name of a registered colormap.\n", + " \n", + " See :func:`matplotlib.cm.register_cmap` and\n", + " :func:`matplotlib.cm.get_cmap`.\n", + " \n", + " setp(*args, **kwargs)\n", + " Set a property on an artist object.\n", + " \n", + " matplotlib supports the use of :func:`setp` (\"set property\") and\n", + " :func:`getp` to set and get object properties, as well as to do\n", + " introspection on the object. For example, to set the linestyle of a\n", + " line to be dashed, you can do::\n", + " \n", + " >>> line, = plot([1,2,3])\n", + " >>> setp(line, linestyle='--')\n", + " \n", + " If you want to know the valid types of arguments, you can provide\n", + " the name of the property you want to set without a value::\n", + " \n", + " >>> setp(line, 'linestyle')\n", + " linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ]\n", + " \n", + " If you want to see all the properties that can be set, and their\n", + " possible values, you can do::\n", + " \n", + " >>> setp(line)\n", + " ... long output listing omitted\n", + " \n", + " You may specify another output file to `setp` if `sys.stdout` is not\n", + " acceptable for some reason using the `file` keyword-only argument::\n", + " \n", + " >>> with fopen('output.log') as f:\n", + " >>> setp(line, file=f)\n", + " \n", + " :func:`setp` operates on a single instance or a iterable of\n", + " instances. If you are in query mode introspecting the possible\n", + " values, only the first instance in the sequence is used. When\n", + " actually setting values, all the instances will be set. e.g.,\n", + " suppose you have a list of two lines, the following will make both\n", + " lines thicker and red::\n", + " \n", + " >>> x = arange(0,1.0,0.01)\n", + " >>> y1 = sin(2*pi*x)\n", + " >>> y2 = sin(4*pi*x)\n", + " >>> lines = plot(x, y1, x, y2)\n", + " >>> setp(lines, linewidth=2, color='r')\n", + " \n", + " :func:`setp` works with the MATLAB style string/value pairs or\n", + " with python kwargs. For example, the following are equivalent::\n", + " \n", + " >>> setp(lines, 'linewidth', 2, 'color', 'r') # MATLAB style\n", + " >>> setp(lines, linewidth=2, color='r') # python style\n", + " \n", + " show(*args, **kw)\n", + " Display a figure.\n", + " When running in ipython with its pylab mode, display all\n", + " figures and return to the ipython prompt.\n", + " \n", + " In non-interactive mode, display all figures and block until\n", + " the figures have been closed; in interactive mode it has no\n", + " effect unless figures were created prior to a change from\n", + " non-interactive to interactive mode (not recommended). In\n", + " that case it displays the figures but does not block.\n", + " \n", + " A single experimental keyword argument, *block*, may be\n", + " set to True or False to override the blocking behavior\n", + " described above.\n", + " \n", + " specgram(x, NFFT=None, Fs=None, Fc=None, detrend=None, window=None, noverlap=None, cmap=None, xextent=None, pad_to=None, sides=None, scale_by_freq=None, mode=None, scale=None, vmin=None, vmax=None, hold=None, data=None, **kwargs)\n", + " Plot a spectrogram.\n", + " \n", + " Call signature::\n", + " \n", + " specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,\n", + " window=mlab.window_hanning, noverlap=128,\n", + " cmap=None, xextent=None, pad_to=None, sides='default',\n", + " scale_by_freq=None, mode='default', scale='default',\n", + " **kwargs)\n", + " \n", + " Compute and plot a spectrogram of data in *x*. Data are split into\n", + " *NFFT* length segments and the spectrum of each section is\n", + " computed. The windowing function *window* is applied to each\n", + " segment, and the amount of overlap of each segment is\n", + " specified with *noverlap*. The spectrogram is plotted as a colormap\n", + " (using imshow).\n", + " \n", + " Parameters\n", + " ----------\n", + " x : 1-D array or sequence\n", + " Array or sequence containing the data.\n", + " \n", + " Fs : scalar\n", + " The sampling frequency (samples per time unit). It is used\n", + " to calculate the Fourier frequencies, freqs, in cycles per time\n", + " unit. The default value is 2.\n", + " \n", + " window : callable or ndarray\n", + " A function or a vector of length *NFFT*. To create window\n", + " vectors see :func:`window_hanning`, :func:`window_none`,\n", + " :func:`numpy.blackman`, :func:`numpy.hamming`,\n", + " :func:`numpy.bartlett`, :func:`scipy.signal`,\n", + " :func:`scipy.signal.get_window`, etc. The default is\n", + " :func:`window_hanning`. If a function is passed as the\n", + " argument, it must take a data segment as an argument and\n", + " return the windowed version of the segment.\n", + " \n", + " sides : [ 'default' | 'onesided' | 'twosided' ]\n", + " Specifies which sides of the spectrum to return. Default gives the\n", + " default behavior, which returns one-sided for real data and both\n", + " for complex data. 'onesided' forces the return of a one-sided\n", + " spectrum, while 'twosided' forces two-sided.\n", + " \n", + " pad_to : integer\n", + " The number of points to which the data segment is padded when\n", + " performing the FFT. This can be different from *NFFT*, which\n", + " specifies the number of data points used. While not increasing\n", + " the actual resolution of the spectrum (the minimum distance between\n", + " resolvable peaks), this can give more points in the plot,\n", + " allowing for more detail. This corresponds to the *n* parameter\n", + " in the call to fft(). The default is None, which sets *pad_to*\n", + " equal to *NFFT*\n", + " \n", + " NFFT : integer\n", + " The number of data points used in each block for the FFT.\n", + " A power 2 is most efficient. The default value is 256.\n", + " This should *NOT* be used to get zero padding, or the scaling of the\n", + " result will be incorrect. Use *pad_to* for this instead.\n", + " \n", + " detrend : {'default', 'constant', 'mean', 'linear', 'none'} or callable\n", + " The function applied to each segment before fft-ing,\n", + " designed to remove the mean or linear trend. Unlike in\n", + " MATLAB, where the *detrend* parameter is a vector, in\n", + " matplotlib is it a function. The :mod:`~matplotlib.pylab`\n", + " module defines :func:`~matplotlib.pylab.detrend_none`,\n", + " :func:`~matplotlib.pylab.detrend_mean`, and\n", + " :func:`~matplotlib.pylab.detrend_linear`, but you can use\n", + " a custom function as well. You can also use a string to choose\n", + " one of the functions. 'default', 'constant', and 'mean' call\n", + " :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls\n", + " :func:`~matplotlib.pylab.detrend_linear`. 'none' calls\n", + " :func:`~matplotlib.pylab.detrend_none`.\n", + " \n", + " scale_by_freq : boolean, optional\n", + " Specifies whether the resulting density values should be scaled\n", + " by the scaling frequency, which gives density in units of Hz^-1.\n", + " This allows for integration over the returned frequency values.\n", + " The default is True for MATLAB compatibility.\n", + " \n", + " mode : [ 'default' | 'psd' | 'magnitude' | 'angle' | 'phase' ]\n", + " What sort of spectrum to use. Default is 'psd', which takes\n", + " the power spectral density. 'complex' returns the complex-valued\n", + " frequency spectrum. 'magnitude' returns the magnitude spectrum.\n", + " 'angle' returns the phase spectrum without unwrapping. 'phase'\n", + " returns the phase spectrum with unwrapping.\n", + " \n", + " noverlap : integer\n", + " The number of points of overlap between blocks. The\n", + " default value is 128.\n", + " \n", + " scale : [ 'default' | 'linear' | 'dB' ]\n", + " The scaling of the values in the *spec*. 'linear' is no scaling.\n", + " 'dB' returns the values in dB scale. When *mode* is 'psd',\n", + " this is dB power (10 * log10). Otherwise this is dB amplitude\n", + " (20 * log10). 'default' is 'dB' if *mode* is 'psd' or\n", + " 'magnitude' and 'linear' otherwise. This must be 'linear'\n", + " if *mode* is 'angle' or 'phase'.\n", + " \n", + " Fc : integer\n", + " The center frequency of *x* (defaults to 0), which offsets\n", + " the x extents of the plot to reflect the frequency range used\n", + " when a signal is acquired and then filtered and downsampled to\n", + " baseband.\n", + " \n", + " cmap :\n", + " A :class:`matplotlib.colors.Colormap` instance; if *None*, use\n", + " default determined by rc\n", + " \n", + " xextent : [None | (xmin, xmax)]\n", + " The image extent along the x-axis. The default sets *xmin* to the\n", + " left border of the first bin (*spectrum* column) and *xmax* to the\n", + " right border of the last bin. Note that for *noverlap>0* the width\n", + " of the bins is smaller than those of the segments.\n", + " \n", + " **kwargs :\n", + " Additional kwargs are passed on to imshow which makes the\n", + " specgram image\n", + " \n", + " Notes\n", + " -----\n", + " *detrend* and *scale_by_freq* only apply when *mode* is set to\n", + " 'psd'\n", + " \n", + " Returns\n", + " -------\n", + " spectrum : 2-D array\n", + " Columns are the periodograms of successive segments.\n", + " \n", + " freqs : 1-D array\n", + " The frequencies corresponding to the rows in *spectrum*.\n", + " \n", + " t : 1-D array\n", + " The times corresponding to midpoints of segments (i.e., the columns\n", + " in *spectrum*).\n", + " \n", + " im : instance of class :class:`~matplotlib.image.AxesImage`\n", + " The image created by imshow containing the spectrogram\n", + " \n", + " See Also\n", + " --------\n", + " :func:`psd`\n", + " :func:`psd` differs in the default overlap; in returning the mean\n", + " of the segment periodograms; in not returning times; and in\n", + " generating a line plot instead of colormap.\n", + " \n", + " :func:`magnitude_spectrum`\n", + " A single spectrum, similar to having a single segment when *mode*\n", + " is 'magnitude'. Plots a line instead of a colormap.\n", + " \n", + " :func:`angle_spectrum`\n", + " A single spectrum, similar to having a single segment when *mode*\n", + " is 'angle'. Plots a line instead of a colormap.\n", + " \n", + " :func:`phase_spectrum`\n", + " A single spectrum, similar to having a single segment when *mode*\n", + " is 'phase'. Plots a line instead of a colormap.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x'.\n", + " \n", + " spectral()\n", + " set the default colormap to spectral and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " spring()\n", + " set the default colormap to spring and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " spy(Z, precision=0, marker=None, markersize=None, aspect='equal', **kwargs)\n", + " Plot the sparsity pattern on a 2-D array.\n", + " \n", + " ``spy(Z)`` plots the sparsity pattern of the 2-D array *Z*.\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " Z : sparse array (n, m)\n", + " The array to be plotted.\n", + " \n", + " precision : float, optional, default: 0\n", + " If *precision* is 0, any non-zero value will be plotted; else,\n", + " values of :math:`|Z| > precision` will be plotted.\n", + " \n", + " For :class:`scipy.sparse.spmatrix` instances, there is a special\n", + " case: if *precision* is 'present', any value present in the array\n", + " will be plotted, even if it is identically zero.\n", + " \n", + " origin : [\"upper\", \"lower\"], optional, default: \"upper\"\n", + " Place the [0,0] index of the array in the upper left or lower left\n", + " corner of the axes.\n", + " \n", + " aspect : ['auto' | 'equal' | scalar], optional, default: \"equal\"\n", + " \n", + " If 'equal', and `extent` is None, changes the axes aspect ratio to\n", + " match that of the image. If `extent` is not `None`, the axes\n", + " aspect ratio is changed to match that of the extent.\n", + " \n", + " \n", + " If 'auto', changes the image aspect ratio to match that of the\n", + " axes.\n", + " \n", + " If None, default to rc ``image.aspect`` value.\n", + " \n", + " Two plotting styles are available: image or marker. Both\n", + " are available for full arrays, but only the marker style\n", + " works for :class:`scipy.sparse.spmatrix` instances.\n", + " \n", + " If *marker* and *markersize* are *None*, an image will be\n", + " returned and any remaining kwargs are passed to\n", + " :func:`~matplotlib.pyplot.imshow`; else, a\n", + " :class:`~matplotlib.lines.Line2D` object will be returned with\n", + " the value of marker determining the marker type, and any\n", + " remaining kwargs passed to the\n", + " :meth:`~matplotlib.axes.Axes.plot` method.\n", + " \n", + " If *marker* and *markersize* are *None*, useful kwargs include:\n", + " \n", + " * *cmap*\n", + " * *alpha*\n", + " \n", + " See also\n", + " --------\n", + " imshow : for image options.\n", + " plot : for plotting options\n", + " \n", + " stackplot(x, *args, **kwargs)\n", + " Draws a stacked area plot.\n", + " \n", + " *x* : 1d array of dimension N\n", + " \n", + " *y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension\n", + " 1xN. The data is assumed to be unstacked. Each of the following\n", + " calls is legal::\n", + " \n", + " stackplot(x, y) # where y is MxN\n", + " stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm\n", + " \n", + " Keyword arguments:\n", + " \n", + " *baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle']\n", + " Method used to calculate the baseline. 'zero' is just a\n", + " simple stacked plot. 'sym' is symmetric around zero and\n", + " is sometimes called `ThemeRiver`. 'wiggle' minimizes the\n", + " sum of the squared slopes. 'weighted_wiggle' does the\n", + " same but weights to account for size of each layer.\n", + " It is also called `Streamgraph`-layout. More details\n", + " can be found at http://leebyron.com/streamgraph/.\n", + " \n", + " \n", + " *labels* : A list or tuple of labels to assign to each data series.\n", + " \n", + " \n", + " *colors* : A list or tuple of colors. These will be cycled through and\n", + " used to colour the stacked areas.\n", + " All other keyword arguments are passed to\n", + " :func:`~matplotlib.Axes.fill_between`\n", + " \n", + " Returns *r* : A list of\n", + " :class:`~matplotlib.collections.PolyCollection`, one for each\n", + " element in the stacked area plot.\n", + " \n", + " stem(*args, **kwargs)\n", + " Create a stem plot.\n", + " \n", + " Call signatures::\n", + " \n", + " stem(y, linefmt='b-', markerfmt='bo', basefmt='r-')\n", + " stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-')\n", + " \n", + " A stem plot plots vertical lines (using *linefmt*) at each *x*\n", + " location from the baseline to *y*, and places a marker there\n", + " using *markerfmt*. A horizontal line at 0 is plotted using\n", + " *basefmt*.\n", + " \n", + " If no *x* values are provided, the default is (0, 1, ..., len(y) - 1)\n", + " \n", + " Return value is a tuple (*markerline*, *stemlines*,\n", + " *baseline*). See :class:`~matplotlib.container.StemContainer`\n", + " \n", + " .. seealso::\n", + " This\n", + " `document `_\n", + " for details.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All positional and all keyword arguments.\n", + " \n", + " step(x, y, *args, **kwargs)\n", + " Make a step plot.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array_like\n", + " 1-D sequence, and it is assumed, but not checked,\n", + " that it is uniformly increasing.\n", + " \n", + " y : array_like\n", + " 1-D sequence\n", + " \n", + " Returns\n", + " -------\n", + " list\n", + " List of lines that were added.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " where : [ 'pre' | 'post' | 'mid' ]\n", + " If 'pre' (the default), the interval from\n", + " ``x[i]`` to ``x[i+1]`` has level ``y[i+1]``.\n", + " \n", + " If 'post', that interval has level ``y[i]``.\n", + " \n", + " If 'mid', the jumps in *y* occur half-way between the\n", + " *x*-values.\n", + " \n", + " Notes\n", + " -----\n", + " Additional parameters are the same as those for\n", + " :func:`~matplotlib.pyplot.plot`.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " streamplot(x, y, u, v, density=1, linewidth=None, color=None, cmap=None, norm=None, arrowsize=1, arrowstyle='-|>', minlength=0.1, transform=None, zorder=None, start_points=None, maxlength=4.0, integration_direction='both', hold=None, data=None)\n", + " Draws streamlines of a vector flow.\n", + " \n", + " *x*, *y* : 1d arrays\n", + " an *evenly spaced* grid.\n", + " *u*, *v* : 2d arrays\n", + " x and y-velocities. Number of rows should match length of y, and\n", + " the number of columns should match x.\n", + " *density* : float or 2-tuple\n", + " Controls the closeness of streamlines. When `density = 1`, the domain\n", + " is divided into a 30x30 grid---*density* linearly scales this grid.\n", + " Each cell in the grid can have, at most, one traversing streamline.\n", + " For different densities in each direction, use [density_x, density_y].\n", + " *linewidth* : numeric or 2d array\n", + " vary linewidth when given a 2d array with the same shape as velocities.\n", + " *color* : matplotlib color code, or 2d array\n", + " Streamline color. When given an array with the same shape as\n", + " velocities, *color* values are converted to colors using *cmap*.\n", + " *cmap* : :class:`~matplotlib.colors.Colormap`\n", + " Colormap used to plot streamlines and arrows. Only necessary when using\n", + " an array input for *color*.\n", + " *norm* : :class:`~matplotlib.colors.Normalize`\n", + " Normalize object used to scale luminance data to 0, 1. If None, stretch\n", + " (min, max) to (0, 1). Only necessary when *color* is an array.\n", + " *arrowsize* : float\n", + " Factor scale arrow size.\n", + " *arrowstyle* : str\n", + " Arrow style specification.\n", + " See :class:`~matplotlib.patches.FancyArrowPatch`.\n", + " *minlength* : float\n", + " Minimum length of streamline in axes coordinates.\n", + " *start_points*: Nx2 array\n", + " Coordinates of starting points for the streamlines.\n", + " In data coordinates, the same as the ``x`` and ``y`` arrays.\n", + " *zorder* : int\n", + " any number\n", + " *maxlength* : float\n", + " Maximum length of streamline in axes coordinates.\n", + " *integration_direction* : ['forward', 'backward', 'both']\n", + " Integrate the streamline in forward, backward or both directions.\n", + " \n", + " Returns:\n", + " \n", + " *stream_container* : StreamplotSet\n", + " Container object with attributes\n", + " \n", + " - lines: `matplotlib.collections.LineCollection` of streamlines\n", + " \n", + " - arrows: collection of `matplotlib.patches.FancyArrowPatch`\n", + " objects representing arrows half-way along stream\n", + " lines.\n", + " \n", + " This container will probably change in the future to allow changes\n", + " to the colormap, alpha, etc. for both lines and arrows, but these\n", + " changes should be backward compatible.\n", + " \n", + " subplot(*args, **kwargs)\n", + " Return a subplot axes at the given grid position.\n", + " \n", + " Call signature::\n", + " \n", + " subplot(nrows, ncols, index, **kwargs)\n", + " \n", + " In the current figure, create and return an `~.Axes`, at position *index*\n", + " of a (virtual) grid of *nrows* by *ncols* axes. Indexes go from 1 to\n", + " ``nrows * ncols``, incrementing in row-major order.\n", + " \n", + " If *nrows*, *ncols* and *index* are all less than 10, they can also be\n", + " given as a single, concatenated, three-digit number.\n", + " \n", + " For example, ``subplot(2, 3, 3)`` and ``subplot(233)`` both create an\n", + " `~.Axes` at the top right corner of the current figure, occupying half of\n", + " the figure height and a third of the figure width.\n", + " \n", + " .. note::\n", + " \n", + " Creating a subplot will delete any pre-existing subplot that overlaps\n", + " with it beyond sharing a boundary::\n", + " \n", + " import matplotlib.pyplot as plt\n", + " # plot a line, implicitly creating a subplot(111)\n", + " plt.plot([1,2,3])\n", + " # now create a subplot which represents the top plot of a grid\n", + " # with 2 rows and 1 column. Since this subplot will overlap the\n", + " # first, the plot (and its axes) previously created, will be removed\n", + " plt.subplot(211)\n", + " plt.plot(range(12))\n", + " plt.subplot(212, facecolor='y') # creates 2nd subplot with yellow background\n", + " \n", + " If you do not want this behavior, use the\n", + " :meth:`~matplotlib.figure.Figure.add_subplot` method or the\n", + " :func:`~matplotlib.pyplot.axes` function instead.\n", + " \n", + " Keyword arguments:\n", + " \n", + " *facecolor*:\n", + " The background color of the subplot, which can be any valid\n", + " color specifier. See :mod:`matplotlib.colors` for more\n", + " information.\n", + " \n", + " *polar*:\n", + " A boolean flag indicating whether the subplot plot should be\n", + " a polar projection. Defaults to *False*.\n", + " \n", + " *projection*:\n", + " A string giving the name of a custom projection to be used\n", + " for the subplot. This projection must have been previously\n", + " registered. See :mod:`matplotlib.projections`.\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.axes`\n", + " For additional information on :func:`axes` and\n", + " :func:`subplot` keyword arguments.\n", + " \n", + " :file:`gallery/pie_and_polar_charts/polar_scatter.py`\n", + " For an example\n", + " \n", + " **Example:**\n", + " \n", + " .. plot:: gallery/subplots_axes_and_figures/subplot.py\n", + " \n", + " subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs)\n", + " Create an axis at specific location inside a regular grid.\n", + " \n", + " Parameters\n", + " ----------\n", + " shape : sequence of 2 ints\n", + " Shape of grid in which to place axis.\n", + " First entry is number of rows, second entry is number of columns.\n", + " \n", + " loc : sequence of 2 ints\n", + " Location to place axis within grid.\n", + " First entry is row number, second entry is column number.\n", + " \n", + " rowspan : int\n", + " Number of rows for the axis to span to the right.\n", + " \n", + " colspan : int\n", + " Number of columns for the axis to span downwards.\n", + " \n", + " fig : `Figure`, optional\n", + " Figure to place axis in. Defaults to current figure.\n", + " \n", + " **kwargs\n", + " Additional keyword arguments are handed to `add_subplot`.\n", + " \n", + " \n", + " Notes\n", + " -----\n", + " The following call ::\n", + " \n", + " subplot2grid(shape, loc, rowspan=1, colspan=1)\n", + " \n", + " is identical to ::\n", + " \n", + " gridspec=GridSpec(shape[0], shape[1])\n", + " subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)\n", + " subplot(subplotspec)\n", + " \n", + " subplot_tool(targetfig=None)\n", + " Launch a subplot tool window for a figure.\n", + " \n", + " A :class:`matplotlib.widgets.SubplotTool` instance is returned.\n", + " \n", + " subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw)\n", + " Create a figure and a set of subplots\n", + " \n", + " This utility wrapper makes it convenient to create common layouts of\n", + " subplots, including the enclosing figure object, in a single call.\n", + " \n", + " Parameters\n", + " ----------\n", + " nrows, ncols : int, optional, default: 1\n", + " Number of rows/columns of the subplot grid.\n", + " \n", + " sharex, sharey : bool or {'none', 'all', 'row', 'col'}, default: False\n", + " Controls sharing of properties among x (`sharex`) or y (`sharey`)\n", + " axes:\n", + " \n", + " - True or 'all': x- or y-axis will be shared among all\n", + " subplots.\n", + " - False or 'none': each subplot x- or y-axis will be\n", + " independent.\n", + " - 'row': each subplot row will share an x- or y-axis.\n", + " - 'col': each subplot column will share an x- or y-axis.\n", + " \n", + " When subplots have a shared x-axis along a column, only the x tick\n", + " labels of the bottom subplot are visible. Similarly, when subplots\n", + " have a shared y-axis along a row, only the y tick labels of the first\n", + " column subplot are visible.\n", + " \n", + " squeeze : bool, optional, default: True\n", + " - If True, extra dimensions are squeezed out from the returned Axes\n", + " object:\n", + " \n", + " - if only one subplot is constructed (nrows=ncols=1), the\n", + " resulting single Axes object is returned as a scalar.\n", + " - for Nx1 or 1xN subplots, the returned object is a 1D numpy\n", + " object array of Axes objects are returned as numpy 1D arrays.\n", + " - for NxM, subplots with N>1 and M>1 are returned as a 2D arrays.\n", + " \n", + " - If False, no squeezing at all is done: the returned Axes object is\n", + " always a 2D array containing Axes instances, even if it ends up\n", + " being 1x1.\n", + " \n", + " subplot_kw : dict, optional\n", + " Dict with keywords passed to the\n", + " :meth:`~matplotlib.figure.Figure.add_subplot` call used to create each\n", + " subplot.\n", + " \n", + " gridspec_kw : dict, optional\n", + " Dict with keywords passed to the\n", + " :class:`~matplotlib.gridspec.GridSpec` constructor used to create the\n", + " grid the subplots are placed on.\n", + " \n", + " **fig_kw :\n", + " All additional keyword arguments are passed to the :func:`figure` call.\n", + " \n", + " Returns\n", + " -------\n", + " fig : :class:`matplotlib.figure.Figure` object\n", + " \n", + " ax : Axes object or array of Axes objects.\n", + " \n", + " ax can be either a single :class:`matplotlib.axes.Axes` object or an\n", + " array of Axes objects if more than one subplot was created. The\n", + " dimensions of the resulting array can be controlled with the squeeze\n", + " keyword, see above.\n", + " \n", + " Examples\n", + " --------\n", + " First create some toy data:\n", + " \n", + " >>> x = np.linspace(0, 2*np.pi, 400)\n", + " >>> y = np.sin(x**2)\n", + " \n", + " Creates just a figure and only one subplot\n", + " \n", + " >>> fig, ax = plt.subplots()\n", + " >>> ax.plot(x, y)\n", + " >>> ax.set_title('Simple plot')\n", + " \n", + " Creates two subplots and unpacks the output array immediately\n", + " \n", + " >>> f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n", + " >>> ax1.plot(x, y)\n", + " >>> ax1.set_title('Sharing Y axis')\n", + " >>> ax2.scatter(x, y)\n", + " \n", + " Creates four polar axes, and accesses them through the returned array\n", + " \n", + " >>> fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))\n", + " >>> axes[0, 0].plot(x, y)\n", + " >>> axes[1, 1].scatter(x, y)\n", + " \n", + " Share a X axis with each column of subplots\n", + " \n", + " >>> plt.subplots(2, 2, sharex='col')\n", + " \n", + " Share a Y axis with each row of subplots\n", + " \n", + " >>> plt.subplots(2, 2, sharey='row')\n", + " \n", + " Share both X and Y axes with all subplots\n", + " \n", + " >>> plt.subplots(2, 2, sharex='all', sharey='all')\n", + " \n", + " Note that this is the same as\n", + " \n", + " >>> plt.subplots(2, 2, sharex=True, sharey=True)\n", + " \n", + " See Also\n", + " --------\n", + " figure\n", + " subplot\n", + " \n", + " subplots_adjust(*args, **kwargs)\n", + " Tune the subplot layout.\n", + " \n", + " call signature::\n", + " \n", + " subplots_adjust(left=None, bottom=None, right=None, top=None,\n", + " wspace=None, hspace=None)\n", + " \n", + " The parameter meanings (and suggested defaults) are::\n", + " \n", + " left = 0.125 # the left side of the subplots of the figure\n", + " right = 0.9 # the right side of the subplots of the figure\n", + " bottom = 0.1 # the bottom of the subplots of the figure\n", + " top = 0.9 # the top of the subplots of the figure\n", + " wspace = 0.2 # the amount of width reserved for blank space between subplots,\n", + " # expressed as a fraction of the average axis width\n", + " hspace = 0.2 # the amount of height reserved for white space between subplots,\n", + " # expressed as a fraction of the average axis height\n", + " \n", + " The actual defaults are controlled by the rc file\n", + " \n", + " summer()\n", + " set the default colormap to summer and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " suptitle(*args, **kwargs)\n", + " Add a centered title to the figure.\n", + " \n", + " kwargs are :class:`matplotlib.text.Text` properties. Using figure\n", + " coordinates, the defaults are:\n", + " \n", + " x : 0.5\n", + " The x location of the text in figure coords\n", + " \n", + " y : 0.98\n", + " The y location of the text in figure coords\n", + " \n", + " horizontalalignment : 'center'\n", + " The horizontal alignment of the text\n", + " \n", + " verticalalignment : 'top'\n", + " The vertical alignment of the text\n", + " \n", + " If the `fontproperties` keyword argument is given then the\n", + " rcParams defaults for `fontsize` (`figure.titlesize`) and\n", + " `fontweight` (`figure.titleweight`) will be ignored in favour\n", + " of the `FontProperties` defaults.\n", + " \n", + " A :class:`matplotlib.text.Text` instance is returned.\n", + " \n", + " Example::\n", + " \n", + " fig.suptitle('this is the figure title', fontsize=12)\n", + " \n", + " switch_backend(newbackend)\n", + " Switch the default backend. This feature is **experimental**, and\n", + " is only expected to work switching to an image backend. e.g., if\n", + " you have a bunch of PostScript scripts that you want to run from\n", + " an interactive ipython session, you may want to switch to the PS\n", + " backend before running them to avoid having a bunch of GUI windows\n", + " popup. If you try to interactively switch from one GUI backend to\n", + " another, you will explode.\n", + " \n", + " Calling this command will close all open windows.\n", + " \n", + " table(**kwargs)\n", + " Add a table to the current axes.\n", + " \n", + " Call signature::\n", + " \n", + " table(cellText=None, cellColours=None,\n", + " cellLoc='right', colWidths=None,\n", + " rowLabels=None, rowColours=None, rowLoc='left',\n", + " colLabels=None, colColours=None, colLoc='center',\n", + " loc='bottom', bbox=None):\n", + " \n", + " Returns a :class:`matplotlib.table.Table` instance. Either `cellText`\n", + " or `cellColours` must be provided. For finer grained control over\n", + " tables, use the :class:`~matplotlib.table.Table` class and add it to\n", + " the axes with :meth:`~matplotlib.axes.Axes.add_table`.\n", + " \n", + " Thanks to John Gill for providing the class and table.\n", + " \n", + " kwargs control the :class:`~matplotlib.table.Table`\n", + " properties:\n", + " \n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n", + " alpha: float (0.0 transparent through 1.0 opaque) \n", + " animated: bool \n", + " clip_box: a `~.Bbox` instance \n", + " clip_on: bool \n", + " clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] \n", + " contains: a callable function \n", + " figure: a `~.Figure` instance \n", + " fontsize: a float in points \n", + " gid: an id string \n", + " label: object \n", + " path_effects: `~.AbstractPathEffect` \n", + " picker: [None | bool | float | callable] \n", + " rasterized: bool or None \n", + " sketch_params: (scale: float, length: float, randomness: float) \n", + " snap: bool or None \n", + " transform: `~.Transform` \n", + " url: a url string \n", + " visible: bool \n", + " zorder: float\n", + " \n", + " text(x, y, s, fontdict=None, withdash=False, **kwargs)\n", + " Add text to the axes.\n", + " \n", + " Add text in string `s` to axis at location `x`, `y`, data\n", + " coordinates.\n", + " \n", + " Parameters\n", + " ----------\n", + " x, y : scalars\n", + " data coordinates\n", + " \n", + " s : string\n", + " text\n", + " \n", + " fontdict : dictionary, optional, default: None\n", + " A dictionary to override the default text properties. If fontdict\n", + " is None, the defaults are determined by your rc parameters.\n", + " \n", + " withdash : boolean, optional, default: False\n", + " Creates a `~matplotlib.text.TextWithDash` instance instead of a\n", + " `~matplotlib.text.Text` instance.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.text.Text` properties.\n", + " Other miscellaneous text parameters.\n", + " \n", + " Examples\n", + " --------\n", + " Individual keyword arguments can be used to override any given\n", + " parameter::\n", + " \n", + " >>> text(x, y, s, fontsize=12)\n", + " \n", + " The default transform specifies that text is in data coords,\n", + " alternatively, you can specify text in axis coords (0,0 is\n", + " lower-left and 1,1 is upper-right). The example below places\n", + " text in the center of the axes::\n", + " \n", + " >>> text(0.5, 0.5,'matplotlib', horizontalalignment='center',\n", + " ... verticalalignment='center',\n", + " ... transform=ax.transAxes)\n", + " \n", + " You can put a rectangular box around the text instance (e.g., to\n", + " set a background color) by using the keyword `bbox`. `bbox` is\n", + " a dictionary of `~matplotlib.patches.Rectangle`\n", + " properties. For example::\n", + " \n", + " >>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))\n", + " \n", + " thetagrids(*args, **kwargs)\n", + " Get or set the theta locations of the gridlines in a polar plot.\n", + " \n", + " If no arguments are passed, return a tuple (*lines*, *labels*)\n", + " where *lines* is an array of radial gridlines\n", + " (:class:`~matplotlib.lines.Line2D` instances) and *labels* is an\n", + " array of tick labels (:class:`~matplotlib.text.Text` instances)::\n", + " \n", + " lines, labels = thetagrids()\n", + " \n", + " Otherwise the syntax is::\n", + " \n", + " lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1)\n", + " \n", + " set the angles at which to place the theta grids (these gridlines\n", + " are equal along the theta dimension).\n", + " \n", + " *angles* is in degrees.\n", + " \n", + " *labels*, if not *None*, is a len(angles) list of strings of the\n", + " labels to use at each angle.\n", + " \n", + " If *labels* is *None*, the labels will be ``fmt%angle``.\n", + " \n", + " *frac* is the fraction of the polar axes radius at which to place\n", + " the label (1 is the edge). e.g., 1.05 is outside the axes and 0.95\n", + " is inside the axes.\n", + " \n", + " Return value is a list of tuples (*lines*, *labels*):\n", + " \n", + " - *lines* are :class:`~matplotlib.lines.Line2D` instances\n", + " \n", + " - *labels* are :class:`~matplotlib.text.Text` instances.\n", + " \n", + " Note that on input, the *labels* argument is a list of strings,\n", + " and on output it is a list of :class:`~matplotlib.text.Text`\n", + " instances.\n", + " \n", + " Examples::\n", + " \n", + " # set the locations of the radial gridlines and labels\n", + " lines, labels = thetagrids( range(45,360,90) )\n", + " \n", + " # set the locations and labels of the radial gridlines and labels\n", + " lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )\n", + " \n", + " tick_params(axis='both', **kwargs)\n", + " Change the appearance of ticks and tick labels.\n", + " \n", + " Parameters\n", + " ----------\n", + " axis : {'x', 'y', 'both'}, optional\n", + " Which axis to apply the parameters to.\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " \n", + " axis : {'x', 'y', 'both'}\n", + " Axis on which to operate; default is 'both'.\n", + " \n", + " reset : bool\n", + " If *True*, set all parameters to defaults\n", + " before processing other keyword arguments. Default is\n", + " *False*.\n", + " \n", + " which : {'major', 'minor', 'both'}\n", + " Default is 'major'; apply arguments to *which* ticks.\n", + " \n", + " direction : {'in', 'out', 'inout'}\n", + " Puts ticks inside the axes, outside the axes, or both.\n", + " \n", + " length : float\n", + " Tick length in points.\n", + " \n", + " width : float\n", + " Tick width in points.\n", + " \n", + " color : color\n", + " Tick color; accepts any mpl color spec.\n", + " \n", + " pad : float\n", + " Distance in points between tick and label.\n", + " \n", + " labelsize : float or str\n", + " Tick label font size in points or as a string (e.g., 'large').\n", + " \n", + " labelcolor : color\n", + " Tick label color; mpl color spec.\n", + " \n", + " colors : color\n", + " Changes the tick color and the label color to the same value:\n", + " mpl color spec.\n", + " \n", + " zorder : float\n", + " Tick and label zorder.\n", + " \n", + " bottom, top, left, right : bool or {'on', 'off'}\n", + " controls whether to draw the respective ticks.\n", + " \n", + " labelbottom, labeltop, labelleft, labelright : bool or {'on', 'off'}\n", + " controls whether to draw the\n", + " respective tick labels.\n", + " \n", + " labelrotation : float\n", + " Tick label rotation\n", + " \n", + " Examples\n", + " --------\n", + " \n", + " Usage ::\n", + " \n", + " ax.tick_params(direction='out', length=6, width=2, colors='r')\n", + " \n", + " This will make all major ticks be red, pointing out of the box,\n", + " and with dimensions 6 points by 2 points. Tick labels will\n", + " also be red.\n", + " \n", + " ticklabel_format(**kwargs)\n", + " Change the `~matplotlib.ticker.ScalarFormatter` used by\n", + " default for linear axes.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " ============== =========================================\n", + " Keyword Description\n", + " ============== =========================================\n", + " *style* [ 'sci' (or 'scientific') | 'plain' ]\n", + " plain turns off scientific notation\n", + " *scilimits* (m, n), pair of integers; if *style*\n", + " is 'sci', scientific notation will\n", + " be used for numbers outside the range\n", + " 10`m`:sup: to 10`n`:sup:.\n", + " Use (0,0) to include all numbers.\n", + " *useOffset* [True | False | offset]; if True,\n", + " the offset will be calculated as needed;\n", + " if False, no offset will be used; if a\n", + " numeric offset is specified, it will be\n", + " used.\n", + " *axis* [ 'x' | 'y' | 'both' ]\n", + " *useLocale* If True, format the number according to\n", + " the current locale. This affects things\n", + " such as the character used for the\n", + " decimal separator. If False, use\n", + " C-style (English) formatting. The\n", + " default setting is controlled by the\n", + " axes.formatter.use_locale rcparam.\n", + " *useMathText* If True, render the offset and scientific\n", + " notation in mathtext\n", + " ============== =========================================\n", + " \n", + " Only the major ticks are affected.\n", + " If the method is called when the\n", + " :class:`~matplotlib.ticker.ScalarFormatter` is not the\n", + " :class:`~matplotlib.ticker.Formatter` being used, an\n", + " :exc:`AttributeError` will be raised.\n", + " \n", + " tight_layout(pad=1.08, h_pad=None, w_pad=None, rect=None)\n", + " Automatically adjust subplot parameters to give specified padding.\n", + " \n", + " Parameters\n", + " ----------\n", + " pad : float\n", + " padding between the figure edge and the edges of subplots, as a fraction of the font-size.\n", + " h_pad, w_pad : float\n", + " padding (height/width) between edges of adjacent subplots.\n", + " Defaults to `pad_inches`.\n", + " rect : if rect is given, it is interpreted as a rectangle\n", + " (left, bottom, right, top) in the normalized figure\n", + " coordinate that the whole subplots area (including\n", + " labels) will fit into. Default is (0, 0, 1, 1).\n", + " \n", + " title(s, *args, **kwargs)\n", + " Set a title of the current axes.\n", + " \n", + " Set one of the three available axes titles. The available titles are\n", + " positioned above the axes in the center, flush with the left edge,\n", + " and flush with the right edge.\n", + " \n", + " .. seealso::\n", + " See :func:`~matplotlib.pyplot.text` for adding text\n", + " to the current axes\n", + " \n", + " Parameters\n", + " ----------\n", + " label : str\n", + " Text to use for the title\n", + " \n", + " fontdict : dict\n", + " A dictionary controlling the appearance of the title text,\n", + " the default `fontdict` is:\n", + " \n", + " {'fontsize': rcParams['axes.titlesize'],\n", + " 'fontweight' : rcParams['axes.titleweight'],\n", + " 'verticalalignment': 'baseline',\n", + " 'horizontalalignment': loc}\n", + " \n", + " loc : {'center', 'left', 'right'}, str, optional\n", + " Which title to set, defaults to 'center'\n", + " \n", + " Returns\n", + " -------\n", + " text : :class:`~matplotlib.text.Text`\n", + " The matplotlib text instance representing the title\n", + " \n", + " Other parameters\n", + " ----------------\n", + " kwargs : text properties\n", + " Other keyword arguments are text properties, see\n", + " :class:`~matplotlib.text.Text` for a list of valid text\n", + " properties.\n", + " \n", + " tricontour(*args, **kwargs)\n", + " Draw contours on an unstructured triangular grid.\n", + " :func:`~matplotlib.pyplot.tricontour` and\n", + " :func:`~matplotlib.pyplot.tricontourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " The triangulation can be specified in one of two ways; either::\n", + " \n", + " tricontour(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " tricontour(x, y, ...)\n", + " tricontour(x, y, triangles, ...)\n", + " tricontour(x, y, triangles=triangles, ...)\n", + " tricontour(x, y, mask=mask, ...)\n", + " tricontour(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of\n", + " these possibilities.\n", + " \n", + " The remaining arguments may be::\n", + " \n", + " tricontour(..., Z)\n", + " \n", + " where *Z* is the array of values to contour, one per point\n", + " in the triangulation. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, N)\n", + " \n", + " contour *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontourf(..., Z, V)\n", + " \n", + " fill the (len(*V*)-1) regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " ``C = tricontour(...)`` returns a\n", + " :class:`~matplotlib.contour.TriContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *levels* [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is None, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " \n", + " tricontour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the 'solid' is used.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " If contour is using a monochrome colormap and the contour\n", + " level is less than 0, then the linestyle specified\n", + " in ``contour.negative_linestyle`` in ``matplotlibrc``\n", + " will be used.\n", + " \n", + " tricontourf-only keyword arguments:\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing\n", + " \n", + " Note: tricontourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " tricontourf(*args, **kwargs)\n", + " Draw contours on an unstructured triangular grid.\n", + " :func:`~matplotlib.pyplot.tricontour` and\n", + " :func:`~matplotlib.pyplot.tricontourf` draw contour lines and\n", + " filled contours, respectively. Except as noted, function\n", + " signatures and return values are the same for both versions.\n", + " \n", + " The triangulation can be specified in one of two ways; either::\n", + " \n", + " tricontour(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " tricontour(x, y, ...)\n", + " tricontour(x, y, triangles, ...)\n", + " tricontour(x, y, triangles=triangles, ...)\n", + " tricontour(x, y, mask=mask, ...)\n", + " tricontour(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of\n", + " these possibilities.\n", + " \n", + " The remaining arguments may be::\n", + " \n", + " tricontour(..., Z)\n", + " \n", + " where *Z* is the array of values to contour, one per point\n", + " in the triangulation. The level values are chosen\n", + " automatically.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, N)\n", + " \n", + " contour *N* automatically-chosen levels.\n", + " \n", + " ::\n", + " \n", + " tricontour(..., Z, V)\n", + " \n", + " draw contour lines at the values specified in sequence *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontourf(..., Z, V)\n", + " \n", + " fill the (len(*V*)-1) regions between the values in *V*,\n", + " which must be in increasing order.\n", + " \n", + " ::\n", + " \n", + " tricontour(Z, **kwargs)\n", + " \n", + " Use keyword args to control colors, linewidth, origin, cmap ... see\n", + " below for more details.\n", + " \n", + " ``C = tricontour(...)`` returns a\n", + " :class:`~matplotlib.contour.TriContourSet` object.\n", + " \n", + " Optional keyword arguments:\n", + " \n", + " *colors*: [ *None* | string | (mpl_colors) ]\n", + " If *None*, the colormap specified by cmap will be used.\n", + " \n", + " If a string, like 'r' or 'red', all levels will be plotted in this\n", + " color.\n", + " \n", + " If a tuple of matplotlib color args (string, float, rgb, etc),\n", + " different levels will be plotted in different colors in the order\n", + " specified.\n", + " \n", + " *alpha*: float\n", + " The alpha blending value\n", + " \n", + " *cmap*: [ *None* | Colormap ]\n", + " A cm :class:`~matplotlib.colors.Colormap` instance or\n", + " *None*. If *cmap* is *None* and *colors* is *None*, a\n", + " default Colormap is used.\n", + " \n", + " *norm*: [ *None* | Normalize ]\n", + " A :class:`matplotlib.colors.Normalize` instance for\n", + " scaling data values to colors. If *norm* is *None* and\n", + " *colors* is *None*, the default linear scaling is used.\n", + " \n", + " *levels* [level0, level1, ..., leveln]\n", + " A list of floating point numbers indicating the level\n", + " curves to draw, in increasing order; e.g., to draw just\n", + " the zero contour pass ``levels=[0]``\n", + " \n", + " *origin*: [ *None* | 'upper' | 'lower' | 'image' ]\n", + " If *None*, the first value of *Z* will correspond to the\n", + " lower left corner, location (0,0). If 'image', the rc\n", + " value for ``image.origin`` will be used.\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *extent*: [ *None* | (x0,x1,y0,y1) ]\n", + " \n", + " If *origin* is not *None*, then *extent* is interpreted as\n", + " in :func:`matplotlib.pyplot.imshow`: it gives the outer\n", + " pixel boundaries. In this case, the position of Z[0,0]\n", + " is the center of the pixel, not a corner. If *origin* is\n", + " *None*, then (*x0*, *y0*) is the position of Z[0,0], and\n", + " (*x1*, *y1*) is the position of Z[-1,-1].\n", + " \n", + " This keyword is not active if *X* and *Y* are specified in\n", + " the call to contour.\n", + " \n", + " *locator*: [ *None* | ticker.Locator subclass ]\n", + " If *locator* is None, the default\n", + " :class:`~matplotlib.ticker.MaxNLocator` is used. The\n", + " locator is used to determine the contour levels if they\n", + " are not given explicitly via the *V* argument.\n", + " \n", + " *extend*: [ 'neither' | 'both' | 'min' | 'max' ]\n", + " Unless this is 'neither', contour levels are automatically\n", + " added to one or both ends of the range so that all data\n", + " are included. These added ranges are then mapped to the\n", + " special colormap values which default to the ends of the\n", + " colormap range, but can be set via\n", + " :meth:`matplotlib.colors.Colormap.set_under` and\n", + " :meth:`matplotlib.colors.Colormap.set_over` methods.\n", + " \n", + " *xunits*, *yunits*: [ *None* | registered units ]\n", + " Override axis units by specifying an instance of a\n", + " :class:`matplotlib.units.ConversionInterface`.\n", + " \n", + " \n", + " tricontour-only keyword arguments:\n", + " \n", + " *linewidths*: [ *None* | number | tuple of numbers ]\n", + " If *linewidths* is *None*, the default width in\n", + " ``lines.linewidth`` in ``matplotlibrc`` is used.\n", + " \n", + " If a number, all levels will be plotted with this linewidth.\n", + " \n", + " If a tuple, different levels will be plotted with different\n", + " linewidths in the order specified\n", + " \n", + " *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ]\n", + " If *linestyles* is *None*, the 'solid' is used.\n", + " \n", + " *linestyles* can also be an iterable of the above strings\n", + " specifying a set of linestyles to be used. If this\n", + " iterable is shorter than the number of contour levels\n", + " it will be repeated as necessary.\n", + " \n", + " If contour is using a monochrome colormap and the contour\n", + " level is less than 0, then the linestyle specified\n", + " in ``contour.negative_linestyle`` in ``matplotlibrc``\n", + " will be used.\n", + " \n", + " tricontourf-only keyword arguments:\n", + " \n", + " *antialiased*: [ *True* | *False* ]\n", + " enable antialiasing\n", + " \n", + " Note: tricontourf fills intervals that are closed at the top; that\n", + " is, for boundaries *z1* and *z2*, the filled region is::\n", + " \n", + " z1 < z <= z2\n", + " \n", + " There is one exception: if the lowest boundary coincides with\n", + " the minimum value of the *z* array, then that minimum value\n", + " will be included in the lowest interval.\n", + " \n", + " tripcolor(*args, **kwargs)\n", + " Create a pseudocolor plot of an unstructured triangular grid.\n", + " \n", + " The triangulation can be specified in one of two ways; either::\n", + " \n", + " tripcolor(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " tripcolor(x, y, ...)\n", + " tripcolor(x, y, triangles, ...)\n", + " tripcolor(x, y, triangles=triangles, ...)\n", + " tripcolor(x, y, mask=mask, ...)\n", + " tripcolor(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of these\n", + " possibilities.\n", + " \n", + " The next argument must be *C*, the array of color values, either\n", + " one per point in the triangulation if color values are defined at\n", + " points, or one per triangle in the triangulation if color values\n", + " are defined at triangles. If there are the same number of points\n", + " and triangles in the triangulation it is assumed that color\n", + " values are defined at points; to force the use of color values at\n", + " triangles use the kwarg ``facecolors=C`` instead of just ``C``.\n", + " \n", + " *shading* may be 'flat' (the default) or 'gouraud'. If *shading*\n", + " is 'flat' and C values are defined at points, the color values\n", + " used for each triangle are from the mean C of the triangle's\n", + " three points. If *shading* is 'gouraud' then color values must be\n", + " defined at points.\n", + " \n", + " The remaining kwargs are the same as for\n", + " :meth:`~matplotlib.axes.Axes.pcolor`.\n", + " \n", + " triplot(*args, **kwargs)\n", + " Draw a unstructured triangular grid as lines and/or markers.\n", + " \n", + " The triangulation to plot can be specified in one of two ways;\n", + " either::\n", + " \n", + " triplot(triangulation, ...)\n", + " \n", + " where triangulation is a :class:`matplotlib.tri.Triangulation`\n", + " object, or\n", + " \n", + " ::\n", + " \n", + " triplot(x, y, ...)\n", + " triplot(x, y, triangles, ...)\n", + " triplot(x, y, triangles=triangles, ...)\n", + " triplot(x, y, mask=mask, ...)\n", + " triplot(x, y, triangles, mask=mask, ...)\n", + " \n", + " in which case a Triangulation object will be created. See\n", + " :class:`~matplotlib.tri.Triangulation` for a explanation of these\n", + " possibilities.\n", + " \n", + " The remaining args and kwargs are the same as for\n", + " :meth:`~matplotlib.axes.Axes.plot`.\n", + " \n", + " Return a list of 2 :class:`~matplotlib.lines.Line2D` containing\n", + " respectively:\n", + " \n", + " - the lines plotted for triangles edges\n", + " - the markers plotted for triangles nodes\n", + " \n", + " twinx(ax=None)\n", + " Make a second axes that shares the *x*-axis. The new axes will\n", + " overlay *ax* (or the current axes if *ax* is *None*). The ticks\n", + " for *ax2* will be placed on the right, and the *ax2* instance is\n", + " returned.\n", + " \n", + " .. seealso::\n", + " \n", + " :file:`examples/api_examples/two_scales.py`\n", + " For an example\n", + " \n", + " twiny(ax=None)\n", + " Make a second axes that shares the *y*-axis. The new axis will\n", + " overlay *ax* (or the current axes if *ax* is *None*). The ticks\n", + " for *ax2* will be placed on the top, and the *ax2* instance is\n", + " returned.\n", + " \n", + " uninstall_repl_displayhook()\n", + " Uninstalls the matplotlib display hook.\n", + " \n", + " .. warning\n", + " \n", + " Need IPython >= 2 for this to work. For IPython < 2 will raise a\n", + " ``NotImplementedError``\n", + " \n", + " .. warning\n", + " \n", + " If you are using vanilla python and have installed another\n", + " display hook this will reset ``sys.displayhook`` to what ever\n", + " function was there when matplotlib installed it's displayhook,\n", + " possibly discarding your changes.\n", + " \n", + " violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None, hold=None, data=None)\n", + " Make a violin plot.\n", + " \n", + " Make a violin plot for each column of *dataset* or each vector in\n", + " sequence *dataset*. Each filled area extends to represent the\n", + " entire data range, with optional lines at the mean, the median,\n", + " the minimum, and the maximum.\n", + " \n", + " Parameters\n", + " ----------\n", + " dataset : Array or a sequence of vectors.\n", + " The input data.\n", + " \n", + " positions : array-like, default = [1, 2, ..., n]\n", + " Sets the positions of the violins. The ticks and limits are\n", + " automatically set to match the positions.\n", + " \n", + " vert : bool, default = True.\n", + " If true, creates a vertical violin plot.\n", + " Otherwise, creates a horizontal violin plot.\n", + " \n", + " widths : array-like, default = 0.5\n", + " Either a scalar or a vector that sets the maximal width of\n", + " each violin. The default is 0.5, which uses about half of the\n", + " available horizontal space.\n", + " \n", + " showmeans : bool, default = False\n", + " If `True`, will toggle rendering of the means.\n", + " \n", + " showextrema : bool, default = True\n", + " If `True`, will toggle rendering of the extrema.\n", + " \n", + " showmedians : bool, default = False\n", + " If `True`, will toggle rendering of the medians.\n", + " \n", + " points : scalar, default = 100\n", + " Defines the number of points to evaluate each of the\n", + " gaussian kernel density estimations at.\n", + " \n", + " bw_method : str, scalar or callable, optional\n", + " The method used to calculate the estimator bandwidth. This can be\n", + " 'scott', 'silverman', a scalar constant or a callable. If a\n", + " scalar, this will be used directly as `kde.factor`. If a\n", + " callable, it should take a `GaussianKDE` instance as its only\n", + " parameter and return a scalar. If None (default), 'scott' is used.\n", + " \n", + " Returns\n", + " -------\n", + " \n", + " result : dict\n", + " A dictionary mapping each component of the violinplot to a\n", + " list of the corresponding collection instances created. The\n", + " dictionary has the following keys:\n", + " \n", + " - ``bodies``: A list of the\n", + " :class:`matplotlib.collections.PolyCollection` instances\n", + " containing the filled area of each violin.\n", + " \n", + " - ``cmeans``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the mean values of each of the\n", + " violin's distribution.\n", + " \n", + " - ``cmins``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the bottom of each violin's\n", + " distribution.\n", + " \n", + " - ``cmaxes``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the top of each violin's\n", + " distribution.\n", + " \n", + " - ``cbars``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the centers of each violin's\n", + " distribution.\n", + " \n", + " - ``cmedians``: A\n", + " :class:`matplotlib.collections.LineCollection` instance\n", + " created to identify the median values of each of the\n", + " violin's distribution.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'dataset'.\n", + " \n", + " viridis()\n", + " set the default colormap to viridis and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " vlines(x, ymin, ymax, colors='k', linestyles='solid', label='', hold=None, data=None, **kwargs)\n", + " Plot vertical lines.\n", + " \n", + " Plot vertical lines at each `x` from `ymin` to `ymax`.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : scalar or 1D array_like\n", + " x-indexes where to plot the lines.\n", + " \n", + " ymin, ymax : scalar or 1D array_like\n", + " Respective beginning and end of each line. If scalars are\n", + " provided, all lines will have same length.\n", + " \n", + " colors : array_like of colors, optional, default: 'k'\n", + " \n", + " linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional\n", + " \n", + " label : string, optional, default: ''\n", + " \n", + " Returns\n", + " -------\n", + " lines : `~matplotlib.collections.LineCollection`\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " **kwargs : `~matplotlib.collections.LineCollection` properties.\n", + " \n", + " See also\n", + " --------\n", + " hlines : horizontal lines\n", + " axvline: vertical line across the axes\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'colors', 'x', 'ymax', 'ymin'.\n", + " \n", + " waitforbuttonpress(*args, **kwargs)\n", + " Blocking call to interact with the figure.\n", + " \n", + " This will return True is a key was pressed, False if a mouse\n", + " button was pressed and None if *timeout* was reached without\n", + " either being pressed.\n", + " \n", + " If *timeout* is negative, does not timeout.\n", + " \n", + " winter()\n", + " set the default colormap to winter and apply to current image if any.\n", + " See help(colormaps) for more information\n", + " \n", + " xcorr(x, y, normed=True, detrend=, usevlines=True, maxlags=10, hold=None, data=None, **kwargs)\n", + " Plot the cross correlation between *x* and *y*.\n", + " \n", + " The correlation with lag k is defined as sum_n x[n+k] * conj(y[n]).\n", + " \n", + " Parameters\n", + " ----------\n", + " \n", + " x : sequence of scalars of length n\n", + " \n", + " y : sequence of scalars of length n\n", + " \n", + " hold : boolean, optional, *deprecated*, default: True\n", + " \n", + " detrend : callable, optional, default: `mlab.detrend_none`\n", + " x is detrended by the `detrend` callable. Default is no\n", + " normalization.\n", + " \n", + " normed : boolean, optional, default: True\n", + " if True, input vectors are normalised to unit length.\n", + " \n", + " usevlines : boolean, optional, default: True\n", + " if True, Axes.vlines is used to plot the vertical lines from the\n", + " origin to the acorr. Otherwise, Axes.plot is used.\n", + " \n", + " maxlags : integer, optional, default: 10\n", + " number of lags to show. If None, will return all 2 * len(x) - 1\n", + " lags.\n", + " \n", + " Returns\n", + " -------\n", + " (lags, c, line, b) : where:\n", + " \n", + " - `lags` are a length 2`maxlags+1 lag vector.\n", + " - `c` is the 2`maxlags+1 auto correlation vectorI\n", + " - `line` is a `~matplotlib.lines.Line2D` instance returned by\n", + " `plot`.\n", + " - `b` is the x-axis (none, if plot is used).\n", + " \n", + " Other Parameters\n", + " ----------------\n", + " linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None\n", + " Only used if usevlines is False.\n", + " \n", + " marker : string, optional, default: 'o'\n", + " \n", + " Notes\n", + " -----\n", + " The cross correlation is performed with :func:`numpy.correlate` with\n", + " `mode` = 2.\n", + " \n", + " .. note::\n", + " In addition to the above described arguments, this function can take a\n", + " **data** keyword argument. If such a **data** argument is given, the\n", + " following arguments are replaced by **data[]**:\n", + " \n", + " * All arguments with the following names: 'x', 'y'.\n", + " \n", + " xkcd(scale=1, length=100, randomness=2)\n", + " Turns on `xkcd `_ sketch-style drawing mode.\n", + " This will only have effect on things drawn after this function is\n", + " called.\n", + " \n", + " For best results, the \"Humor Sans\" font should be installed: it is\n", + " not included with matplotlib.\n", + " \n", + " Parameters\n", + " ----------\n", + " scale : float, optional\n", + " The amplitude of the wiggle perpendicular to the source line.\n", + " length : float, optional\n", + " The length of the wiggle along the line.\n", + " randomness : float, optional\n", + " The scale factor by which the length is shrunken or expanded.\n", + " \n", + " Notes\n", + " -----\n", + " This function works by a number of rcParams, so it will probably\n", + " override others you have set before.\n", + " \n", + " If you want the effects of this function to be temporary, it can\n", + " be used as a context manager, for example::\n", + " \n", + " with plt.xkcd():\n", + " # This figure will be in XKCD-style\n", + " fig1 = plt.figure()\n", + " # ...\n", + " \n", + " # This figure will be in regular style\n", + " fig2 = plt.figure()\n", + " \n", + " xlabel(s, *args, **kwargs)\n", + " Set the *x* axis label of the current axis.\n", + " \n", + " Default override is::\n", + " \n", + " override = {\n", + " 'fontsize' : 'small',\n", + " 'verticalalignment' : 'top',\n", + " 'horizontalalignment' : 'center'\n", + " }\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.text`\n", + " For information on how override and the optional args work\n", + " \n", + " xlim(*args, **kwargs)\n", + " Get or set the *x* limits of the current axes.\n", + " \n", + " ::\n", + " \n", + " xmin, xmax = xlim() # return the current xlim\n", + " xlim( (xmin, xmax) ) # set the xlim to xmin, xmax\n", + " xlim( xmin, xmax ) # set the xlim to xmin, xmax\n", + " \n", + " If you do not specify args, you can pass the xmin and xmax as\n", + " kwargs, e.g.::\n", + " \n", + " xlim(xmax=3) # adjust the max leaving min unchanged\n", + " xlim(xmin=1) # adjust the min leaving max unchanged\n", + " \n", + " Setting limits turns autoscaling off for the x-axis.\n", + " \n", + " The new axis limits are returned as a length 2 tuple.\n", + " \n", + " xscale(*args, **kwargs)\n", + " Set the scaling of the *x*-axis.\n", + " \n", + " call signature::\n", + " \n", + " xscale(scale, **kwargs)\n", + " \n", + " The available scales are: 'linear' | 'log' | 'logit' | 'symlog'\n", + " \n", + " Different keywords may be accepted, depending on the scale:\n", + " \n", + " 'linear'\n", + " \n", + " \n", + " \n", + " \n", + " 'log'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *nonposx*/*nonposy*: ['mask' | 'clip' ]\n", + " non-positive values in *x* or *y* can be masked as\n", + " invalid, or clipped to a very small positive number\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " \n", + " 'logit'\n", + " \n", + " *nonpos*: ['mask' | 'clip' ]\n", + " values beyond ]0, 1[ can be masked as invalid, or clipped to a number\n", + " very close to 0 or 1\n", + " \n", + " \n", + " 'symlog'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *linthreshx*/*linthreshy*:\n", + " A single float which defines the range (-*x*, *x*), within\n", + " which the plot is linear. This avoids having the plot go to\n", + " infinity around zero.\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " *linscalex*/*linscaley*:\n", + " This allows the linear range (-*linthresh* to *linthresh*)\n", + " to be stretched relative to the logarithmic range. Its\n", + " value is the number of decades to use for each half of the\n", + " linear range. For example, when *linscale* == 1.0 (the\n", + " default), the space used for the positive and negative\n", + " halves of the linear range will be equal to one decade in\n", + " the logarithmic range.\n", + " \n", + " xticks(*args, **kwargs)\n", + " Get or set the *x*-limits of the current tick locations and labels.\n", + " \n", + " ::\n", + " \n", + " # return locs, labels where locs is an array of tick locations and\n", + " # labels is an array of tick labels.\n", + " locs, labels = xticks()\n", + " \n", + " # set the locations of the xticks\n", + " xticks( arange(6) )\n", + " \n", + " # set the locations and labels of the xticks\n", + " xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )\n", + " \n", + " The keyword args, if any, are :class:`~matplotlib.text.Text`\n", + " properties. For example, to rotate long labels::\n", + " \n", + " xticks( arange(12), calendar.month_name[1:13], rotation=17 )\n", + " \n", + " ylabel(s, *args, **kwargs)\n", + " Set the *y* axis label of the current axis.\n", + " \n", + " Defaults override is::\n", + " \n", + " override = {\n", + " 'fontsize' : 'small',\n", + " 'verticalalignment' : 'center',\n", + " 'horizontalalignment' : 'right',\n", + " 'rotation'='vertical' : }\n", + " \n", + " .. seealso::\n", + " \n", + " :func:`~matplotlib.pyplot.text`\n", + " For information on how override and the optional args\n", + " work.\n", + " \n", + " ylim(*args, **kwargs)\n", + " Get or set the *y*-limits of the current axes.\n", + " \n", + " ::\n", + " \n", + " ymin, ymax = ylim() # return the current ylim\n", + " ylim( (ymin, ymax) ) # set the ylim to ymin, ymax\n", + " ylim( ymin, ymax ) # set the ylim to ymin, ymax\n", + " \n", + " If you do not specify args, you can pass the *ymin* and *ymax* as\n", + " kwargs, e.g.::\n", + " \n", + " ylim(ymax=3) # adjust the max leaving min unchanged\n", + " ylim(ymin=1) # adjust the min leaving max unchanged\n", + " \n", + " Setting limits turns autoscaling off for the y-axis.\n", + " \n", + " The new axis limits are returned as a length 2 tuple.\n", + " \n", + " yscale(*args, **kwargs)\n", + " Set the scaling of the *y*-axis.\n", + " \n", + " call signature::\n", + " \n", + " yscale(scale, **kwargs)\n", + " \n", + " The available scales are: 'linear' | 'log' | 'logit' | 'symlog'\n", + " \n", + " Different keywords may be accepted, depending on the scale:\n", + " \n", + " 'linear'\n", + " \n", + " \n", + " \n", + " \n", + " 'log'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *nonposx*/*nonposy*: ['mask' | 'clip' ]\n", + " non-positive values in *x* or *y* can be masked as\n", + " invalid, or clipped to a very small positive number\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " \n", + " 'logit'\n", + " \n", + " *nonpos*: ['mask' | 'clip' ]\n", + " values beyond ]0, 1[ can be masked as invalid, or clipped to a number\n", + " very close to 0 or 1\n", + " \n", + " \n", + " 'symlog'\n", + " \n", + " *basex*/*basey*:\n", + " The base of the logarithm\n", + " \n", + " *linthreshx*/*linthreshy*:\n", + " A single float which defines the range (-*x*, *x*), within\n", + " which the plot is linear. This avoids having the plot go to\n", + " infinity around zero.\n", + " \n", + " *subsx*/*subsy*:\n", + " Where to place the subticks between each major tick.\n", + " Should be a sequence of integers. For example, in a log10\n", + " scale: ``[2, 3, 4, 5, 6, 7, 8, 9]``\n", + " \n", + " will place 8 logarithmically spaced minor ticks between\n", + " each major tick.\n", + " \n", + " *linscalex*/*linscaley*:\n", + " This allows the linear range (-*linthresh* to *linthresh*)\n", + " to be stretched relative to the logarithmic range. Its\n", + " value is the number of decades to use for each half of the\n", + " linear range. For example, when *linscale* == 1.0 (the\n", + " default), the space used for the positive and negative\n", + " halves of the linear range will be equal to one decade in\n", + " the logarithmic range.\n", + " \n", + " yticks(*args, **kwargs)\n", + " Get or set the *y*-limits of the current tick locations and labels.\n", + " \n", + " ::\n", + " \n", + " # return locs, labels where locs is an array of tick locations and\n", + " # labels is an array of tick labels.\n", + " locs, labels = yticks()\n", + " \n", + " # set the locations of the yticks\n", + " yticks( arange(6) )\n", + " \n", + " # set the locations and labels of the yticks\n", + " yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )\n", + " \n", + " The keyword args, if any, are :class:`~matplotlib.text.Text`\n", + " properties. For example, to rotate long labels::\n", + " \n", + " yticks( arange(12), calendar.month_name[1:13], rotation=45 )\n", + "\n", + "DATA\n", + " absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n", + " division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192...\n", + " print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n", + " rcParams = RcParams({'_internal.classic_mode': False,\n", + " ...nor.widt...\n", + " rcParamsDefault = RcParams({'_internal.classic_mode': False,\n", + " ...n...\n", + " unicode_literals = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', ...\n", + "\n", + "FILE\n", + " /usr/lib/python3/dist-packages/matplotlib/pyplot.py\n", + "\n", + "\n" + ] + } + ], + "source": [ + "help(plt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/03-FFT.ipynb b/boards/Pynq-Z2/notebooks/03-FFT.ipynb deleted file mode 120000 index e0bb3ab..0000000 --- a/boards/Pynq-Z2/notebooks/03-FFT.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/03-FFT.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/03-FFT.ipynb b/boards/Pynq-Z2/notebooks/03-FFT.ipynb new file mode 100644 index 0000000..ef7f313 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/03-FFT.ipynb @@ -0,0 +1,193 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "dftol = pynq.Overlay(\"fft.bit\")\n", + "\n", + "dma0 = dftol.axi_dma_0\n", + "dma1 = dftol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5.23776000e+05 -2.62032922e+05 -1.30894375e+05 ..., 2.12414865e-03\n", + " 2.92085903e-03 -1.20488164e-06]\n", + "[ 0.00000000e+00 7.23699658e+03 6.43042285e+03 ..., 1.76354559e-04\n", + " 1.79434821e-04 3.56945311e-05]\n" + ] + } + ], + "source": [ + "#生成输入数据并输出\n", + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "samplereal = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "sampleimag = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "outreal = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "outimag = xlnk.cma_array(shape=(1024,), dtype=np.float32)\n", + "\n", + "for i in range(1024):\n", + " samplereal[i] = i\n", + "\n", + "for j in range(1024):\n", + " sampleimag[j] = 0\n", + " \n", + "dma0.sendchannel.transfer(samplereal)\n", + "dma1.sendchannel.transfer(sampleimag)\n", + "dma0.recvchannel.transfer(outreal)\n", + "dma1.recvchannel.transfer(outimag)\n", + "print(outreal)\n", + "print(outimag)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#画图\n", + "from pynq import Overlay\n", + "import pynq.lib.dma\n", + "\n", + "import pylab as py\n", + "import scipy as scipy\n", + "import matplotlib.pyplot as plt\n", + "import scipy.fftpack\n", + "import numpy.fft\n", + "\n", + "actualreal = samplereal[0:128]\n", + "fig1 = plt.figure()\n", + "ax1 = fig1.gca()\n", + "plt.plot(outreal)\n", + "\n", + "fig2 = plt.figure()\n", + "ax2 = fig2.gca()\n", + "\n", + "plt.plot(outimag)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/04-DFT.ipynb b/boards/Pynq-Z2/notebooks/04-DFT.ipynb deleted file mode 120000 index ee45adc..0000000 --- a/boards/Pynq-Z2/notebooks/04-DFT.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/04-DFT.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/04-DFT.ipynb b/boards/Pynq-Z2/notebooks/04-DFT.ipynb new file mode 100644 index 0000000..6d1764a --- /dev/null +++ b/boards/Pynq-Z2/notebooks/04-DFT.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pynq import DefaultIP\n", + "\n", + "class DftDriver(DefaultIP):\n", + " def __init__(self, description):\n", + " super().__init__(description=description)\n", + " \n", + " bindto = ['xilinx.com:hls:dft:1.0']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "dftol = pynq.Overlay(\"dft.bit\")\n", + "\n", + "dma0 = dftol.axi_dma_0\n", + "dma1 = dftol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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CxPEf/RN9c/7+R/416BRRddgs+55//wALE8R/9E31z/v5H/jR/wALE8R/9E31z/v5H/jXoFFF12Cz7nn/APwsTxH/ANE31z/v5H/jR/wsTxH/ANE31z/v5H/jXoFFF12Cz7nn/wDwsTxH/wBE31z/AL+R/wCNH/CxPEf/AETfXP8Av5H/AI16BRRddgs+55//AMLE8R4Y/wDCuNc4UkfvI+cDp+NGg+J9U8R+MYP7V8Lah4f+z2Fx5f21lPn7pIM7dvptGf8AeFegVh6h/wAjjpn/AF4Xf/oy3q4fF9/5Ey+E5fxT4mv9H1uVbS7WQxxoIdPjCF5XbP3o2AdlPADxsduDlflOa114qvYdPuWt9Yjnhg85ra+2RkXciQRSJDwNp3NJIMLhsR4Bzk16BRTJMjxNqU+naRix3m9upVt7cRoHYM33mCnhiqB3weDsrkYfFmt3txDchpbaGKa1t7tSI/JgkL7Jg4KmT7wZQVIGSpOBk13VnpkFi7PBJdMWGD593LMPwDsQPwq5QB5vb+KrybTp5Dr8Mdx5u0rJc2wESiXBxiMmM4+UGTIyQDyQam0PxZPqOuww3GsMkWY1jjeW1iM2WZclcMXzgfNGwDdVxnFehUUWA4m5vNetZtQWx1OS7mh1SK1hhu44gjIYY5SpKICCS7Lu5wCDgkc7vhbV31zQ/t0gdS1zcIEkTYyBJnQKw7EBQD7itmigDMbxHpCX15ZzahDDNZNGtwJj5YQyKWQbmwCSATwT0qfTtWstV+1fYJvN+yXL2s/yMuyVcbl5AzjI5HFcd4xlUG/F/wCJ7GG3W4hEVtJapK1pmGTcGG5T8wy+CfmVSm1w5Rup0R5n/tDz9Wg1PbfSKnkqo+zLxiFtv8S988881q4pRuQm72NSiuBupfEy6xPawi/NtLq0FwswRsJAsyJJEDjhSNjY7qZewNUZZ/EX73jWR/xLy52Ry8an8+AOP9V06fuuF9TnK5dj0yisfxNJff2R9m0qKZ7m7lWANC20xIeXbf8AwEIGwx/iKjvXO2N74in1HTWvINQit4zHBqoEZAadWZVeLjJiYgM5HG0p28ygDuqKw9FbVW8JuX/5CebjyvtoYDd5j+Xu77cbenbpXGeGR4u/4WT4g8z+xfM8yz/tLHm7dmzjyffbnO7jOO1aRjzJu+xLdmd5otzDDqmtpLIqNJqSqgY4Ln7JCcD1OAT9Aa3IpVmhSVAwV1DAOhVgD6qcEH2PNctZ+X/b2oeZ97+1h5f3PvfYY/73PTP3fm/4DurCt9Y+KC20S2/hzT3iCAI0sh3FccE75t2cf3ufXmlKlzO6dtt2OM7aHpNFedf2z8Vf+ha0v/v4v/x6j+2fir/0LWl/9/F/+PUvYP8AmX3or2i7P7j0WivOv7Z+Kv8A0LWl/wDfxf8A49R/bPxV/wCha0v/AL+L/wDHqPYP+Zfeg9ouz+49Forzr+2fir/0LWl/9/F/+PUf2z8Vf+ha0v8A7+L/APHqPYP+Zfeg9ouz+49Forzr+2fir/0LWl/9/F/+PUf2z8Vf+ha0v/v4v/x6j2D/AJl96D2i7P7j0WuR+Kf/ACTXVf8Atj/6OSsj+2fir/0LWl/9/F/+PVheNNT8f3HhG9i8Q6HYWunN5fnTQuCy/vF24/et/FgdD1rSlRaqRd1uupE6icXo/uPS/CX/ACJeif8AYPg/9FrXI/Ej/kdPA/8A2ED/AOjIawB418b+GPCOl3E+kaammGGKG2nfLNIvl5TIWTOSq56D8K3/AIkf8jp4H/7CB/8ARkNXGm4Vrvrf8iXJShb0DRv+S/8AiD/sHp/6DBXotedaN/yX/wAQf9g9P/QYK9FrCvvH0X5GlPZ+rCvOvjZ/yJdp/wBhBP8A0XJXotedfGz/AJEu0/7CCf8AouSjDfxohV+Bh8SP+R08D/8AYQP/AKMho0b/AJL/AOIP+wen/oMFHxI/5HTwP/2ED/6Mho0b/kv/AIg/7B6f+gwVvH+F/wBuv8zN/H81+R6LRRRXAdIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHP+Af8Akm3hn/sE2v8A6JWugrn/AAD/AMk28M/9gm1/9ErXQU3uJbBRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAZfieC/ufCOsQaOZF1CWxmS1McnlsJTGQmGyNp3Y5yMV5P8GtF+Ilpaaw+o6lHbQPcFI/7TVr4NKksyz7Ak67D5mdxOd5II6ZPtlc/wCCv+QDc/8AYW1L/wBLp6pOysS1dnK/E618UJ8Mdda+1jSJrcWp8yOHSpY3YZHAY3DAf98mtzRLPxcfD+nGLW9FVDaxbVbRpWIGwYBP2oZ+uBXP/HTxfYaB4Il0a8huXuNbgljt2iVSiFCmd5LAj7w6A966DwR400TWfBGlXkd7FagwCMxXUqJIpQlDkbj1Kkj2IqteW4tOaxyOrW3iQfG/Q0k1XS2vDYsY5l0yQRqu2bgp9oJJ687h1HHHPefY/GP/AEHdD/8ABLN/8lVx2savprfHfQZ11C1MK2DBpBOu0HbPwTnHcfnXoP8Ab+j/APQWsf8AwJT/ABrSttH0/wAyYbv1M/7H4x/6Duh/+CWb/wCSqPsfjH/oO6H/AOCWb/5KrQ/t/R/+gtY/+BKf40f2/o//AEFrH/wJT/GsNTTQz/sfjH/oO6H/AOCWb/5Ko+x+Mf8AoO6H/wCCWb/5KrQ/t/R/+gtY/wDgSn+NH9v6P/0FrH/wJT/GjUNDP+x+Mf8AoO6H/wCCWb/5Ko+x+Mf+g7of/glm/wDkqtD+39H/AOgtY/8AgSn+NH9v6P8A9Bax/wDAlP8AGjUNDP8AsfjH/oO6H/4JZv8A5Ko+x+Mf+g7of/glm/8AkqtD+39H/wCgtY/+BKf40f2/o/8A0FrH/wACU/xo1DQz/sfjH/oO6H/4JZv/AJKo+x+Mf+g7of8A4JZv/kqtD+39H/6C1j/4Ep/jR/b+j/8AQWsf/AlP8aNQ0M/7H4x/6Duh/wDglm/+SqPsfjH/AKDuh/8Aglm/+Sq0P7f0f/oLWP8A4Ep/jR/b+j/9Bax/8CU/xo1DQybyw8cPZTLaa/oaTlCI2/seVcN2OTcMP/HTWLoNj4vs/GMH/Caavp+pb7C4+zfYrYw+XiSDduz1zlcfQ12H/CQaN/0FrH/wJT/GsqXUrHUPGOn/AGC8t7ry7C63+TKr7cyW+M4PHQ/lV0/i+/8AImXwmB4p0PVr3XJbnR7NvtLxpHDeSPHshHOWVtyyxsMngB1bgEctieHQrttIdIbGbT0XV7S6t7MXCr5cSGDzM7GKkZWVtuTknOMmte/8U2OnapLZTxznyIlmnlQKVhQ5wxXO8j5TllUgdyMHAPE9mtqJbiC7gf7ZFZNC8OXSSTZs3bSQBiRTnOBnHXiqJKutXcfiLS203w/e2V49xIqXJS4VhHBnL7gpzhgNnHPz57E1kadpGrx3CHX9HbVLqMwxW14LhFEKxttL7t29d2BIdoJO7aemK7HUb+LTNOmvJ1d0jHCRjLuxOFVR3YkgAepFY7+NNNW5hSOOeWGYW5W4Qx7f3xAj+UsHOdw6KcZoAxZtK8R3+n2NnbwXGmyWgmEs8ssZWcHOEDI5ZQ2AC2Ayg8YPI7SxGNOtx9lNniJR9mJX9zx9z5SRx04OOKw/+E2svskl0bO5WFZBGjtJAPMYvsA/1ny8932j8cCp7bxVDdakllHYXiyEL5hkMSeUWzwQzhm6dVDA9QTQBu0VzkvjO1tPtLajYX1lDa3KW000ojZUdgrDOx2O0K4YtjAGc9DW3ZXsV/A8sG7ak0kJ3DHzI5Rv1U0wJgiqzMFAZvvEDk0KipnYoXccnAxk+tOooEFFFFAFOHSNNt71ryDT7WK5YktOkChyT1JYDPNXKKKACiiigDM0W3hm1TW3ljV2j1JWQsMlD9khGR6HBI+hNbkUawwpEhYqihQXcsxA9WOST7nmsjQP+Qjr3/YQX/0lgraqKjfMXHYKKKKzLCiiigAooooAKKKKACuR+Kf/ACTXVf8Atj/6OSuurkfin/yTXVf+2P8A6OStaP8AFj6oip8DOR8f/wDJFvDP/bp/6TtWv8SP+R08D/8AYQP/AKMhrI8f/wDJFvDP/bp/6TtWv8SP+R08D/8AYQP/AKMhrtjvH/t78jnez+QaN/yX/wAQf9g9P/QYK9FrzrRv+S/+IP8AsHp/6DBXotctfePovyNqez9WFedfGz/kS7T/ALCCf+i5K9Frzr42f8iXaf8AYQT/ANFyUYb+NEKvwMPiR/yOngf/ALCB/wDRkNGjf8l/8Qf9g9P/AEGCj4kf8jp4H/7CB/8ARkNGjf8AJf8AxB/2D0/9BgreP8L/ALdf5mb+P5r8j0WiiiuA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf8AAP8AyTbwz/2CbX/0StdBXP8AgH/km3hn/sE2v/ola6Cm9xLYKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/BX/ACAbn/sLal/6XT1c8T6nNovhHWNUtVjeexsZrmNZASpZIywBAIOMj1FeT/Br4la5r1prFvP4ekv0t7g3KnTPKQo1xLNK4fzplyNxIXbnABz2JpRbVyW0nY3vjp4QsNf8ES6zeTXKXGiQSyW6xMoRy5TO8FST90dCO9WPC3wh8FQeFdOFzosV9K8CyPcXXzSOW+bkjA4zgcdAPrUfxO13ULn4Y67DN4V1e0R7Uhp5pbQonI5ISdm/IGtzRPEOpp4f05V8H61IFtYgHWayw3yDkZuAfzFVd8otOY4XVfh94Uh+M+i6VFodqtjPZNJJAFO1mxNyef8AZX8q7j/hU/gT/oWLH/vk/wCNcnq2tX7fG/Q7hvDOqJKliwW1aW18xxtm5BE2zHJ6sDweOme8/wCEj1T/AKEvXP8Av9Y//JNa1b2jbt/mRC136mf/AMKn8Cf9CxY/98n/ABo/4VP4E/6Fix/75P8AjWh/wkeqf9CXrn/f6x/+SaP+Ej1T/oS9c/7/AFj/APJNYXl3NLLsZ/8AwqfwJ/0LFj/3yf8AGj/hU/gT/oWLH/vk/wCNaH/CR6p/0Jeuf9/rH/5Jo/4SPVP+hL1z/v8AWP8A8k0Xl3Cy7Gf/AMKn8Cf9CxY/98n/ABo/4VP4E/6Fix/75P8AjWh/wkeqf9CXrn/f6x/+SaP+Ej1T/oS9c/7/AFj/APJNF5dwsuxn/wDCp/An/QsWP/fJ/wAaP+FT+BP+hYsf++T/AI1of8JHqn/Ql65/3+sf/kmj/hI9U/6EvXP+/wBY/wDyTReXcLLsZ/8AwqfwJ/0LFj/3yf8AGj/hU/gT/oWLH/vk/wCNaH/CR6p/0Jeuf9/rH/5Jo/4SPVP+hL1z/v8AWP8A8k0Xl3Cy7Gf/AMKn8Cf9CxY/98n/ABo/4VP4E/6Fix/75P8AjWh/wkeqf9CXrn/f6x/+SaP+Ej1T/oS9c/7/AFj/APJNF5dwsuxn/wDCp/AuCP8AhGbH5gVPynkEYI61Bpvg3w94R8Y2v/CN6Tb6d9qsLnzvJBHmbZINufpuP51o3nirV7aymmi8Ea5I8aFlTzrP5j6fLOx/IGsXQfE+qeI/GMH9q+FtQ8P/AGewuPL+2sp8/dJBnbt9Noz/ALwq4X5tfP8AIUrcuhra14RstfuN2o3Fy9uSrG1yhTI7hipdM8ZCsAcc9TmaLw7CllLBNd3Vw813DdyTSlN7vEYyudqgY/dIDgevc5rn/FPia/0fW5VtLtZDHGgh0+MIXlds/ejYB2U8APGx24OV+U5r3fiqSDSpfL8SW7SG6MFhcOsSrdt5Eb5ZjhBGrO24gA4AAO77z0JOmuNNuNcSODXrSCK2ilWYR2920glZc7Q4Ma8A4YYPVV7dalt4E0iz5t/OWVJ1mgmype22sSEQ7eEwxXBz8pxV3xNqU+naRix3m9upVt7cRoHYM33mCnglUDvg9dlc5p3iybUp0OoaoujzxGGMWJiVzcS7tkw2n52w4ZBsIxjccg4oA17fwbDawvFb6rfRoxJAVLcYy2SD+6+cckYfcOc9QCG6Z4Is9Iu0nsr26TawZkMUBViGLH/lllQSx4QqAOgFZM3jO9k0+x/seS31C+YTfa4YirtAFz8xQHJIAOEGCxGOOo7SxmjuNOt5oLkXcckSslwMfvQRw3GBz144o0AqSaDaSzTySNI3n3i3bqSCpZY1jxjH3dqDj1zzTtD0W28P6UmnWLStAkkjp5r7mXe7Ptz6DdgZ5wBknrWjRTEFFFFABRRRQAUUUUAFFFFAFHQP+Qjr3/YQX/0lgrarF0D/AJCOvf8AYQX/ANJYK2qzqfEaR2CiiioKCiiigAooooAKKKKACuR+Kf8AyTXVf+2P/o5K66uR+Kf/ACTXVf8Atj/6OStaP8WPqiKnwM5Hx/8A8kW8M/8Abp/6TtWv8SP+R08D/wDYQP8A6MhrI8f/APJFvDP/AG6f+k7Vr/Ej/kdPA/8A2ED/AOjIa7Y7x/7e/I53s/kGjf8AJf8AxB/2D0/9Bgr0WvOtG/5L/wCIP+wen/oMFei1y194+i/I2p7P1YV518bP+RLtP+wgn/ouSvRa86+Nn/Il2n/YQT/0XJRhv40Qq/Aw+JH/ACOngf8A7CB/9GQ0aN/yX/xB/wBg9P8A0GCj4kf8jp4H/wCwgf8A0ZDRo3/Jf/EH/YPT/wBBgreP8L/t1/mZv4/mvyPRaKKK4DpCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5/wD/yTbwz/wBgm1/9ErXQVz/gH/km3hn/ALBNr/6JWugpvcS2CiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/wAFf8gG5/7C2pf+l09dBXP+Cv8AkA3P/YW1L/0unp9BdTP+LP8AySfxD/16H+YroNA/5FrTP+vSL/0AV5H+0h4b1XWNJ0nU9OtfOs9KS5e8k8xF8pW8racEgnO09AelWPh94Y+JemeAtMtbLWNK023CO8drcWvnOiu7OCXU4Od2fbOO1XZcu5N3zbG/rP8AyX/w/wD9g9//AEGevRa8L1PTfHq/F/R4Z9e0ttUazYw3C2ZEaJiXgrnk8N+YrtP7H+Kf/Q06H/4L2/xrWqrqPp/mTB6v1PQKK8//ALH+Kf8A0NOh/wDgvb/Gj+x/in/0NOh/+C9v8awt5ml/I9Aorz/+x/in/wBDTof/AIL2/wAaP7H+Kf8A0NOh/wDgvb/Gi3mF/I9Aorz/APsf4p/9DTof/gvb/Gj+x/in/wBDTof/AIL2/wAaLeYX8j0CivP/AOx/in/0NOh/+C9v8aP7H+Kf/Q06H/4L2/xot5hfyPQKK8//ALH+Kf8A0NOh/wDgvb/Gj+x/in/0NOh/+C9v8aLeYX8j0CivP/7H+Kf/AENOh/8Agvb/ABo/sf4p/wDQ06H/AOC9v8aLeYX8j0CsPUP+Rx0z/rwu/wD0Zb1zf9j/ABTw3/FU6Hnacf8AEvbg447+tGg2Pi+z8Ywf8Jpq+n6lvsLj7N9itjD5eJIN27PXOVx9DVwVpff+RMvhOzoriPFOh6te65Lc6PZt9peNI4byR49kI5yytuWWNhk8AOrcAjlsV7vRL19KltYfDtwkNxdExRpPDusIjBGrOqmQJ5hdXKkE7SxY8khmSdpZ6ZBYuzwSXTFhg+fdyzD8A7ED8KuVzetXcfiLS203w/e2V49xIqXJS4VhHBnL7gpzhgNnHPz57E1zsPhbX5biG81KGKS4tJrWMuBGZriOJ8GRJScpuADFTjILLwTkAHo1Feb2/hq+XTriC40KbzZJd0kkcVmDKBLuGSXPmg8Eq+3gHkMADNoeg6nb67Dc6joShVMYjdIbdxAoZsYLyl48Ag7U3AdFyAKLgehUVxNz4N3TagNPtI9PafVIpYbm0CI6RLDH8wx2Eqsdp4JzkEHnd8LQalb6Ht1uJIr1rm4eRY33LhpnZSp9CCCAeQDg80AbNFFFMQUUUUAFFFFABRRRQBR0D/kI69/2EF/9JYK2qxdA/wCQjr3/AGEF/wDSWCtqs6nxGkdgoooqCgooooAKKKKACiiigArkfin/AMk11X/tj/6OSuurkfin/wAk11X/ALY/+jkrWj/Fj6oip8DOR8f/APJFvDP/AG6f+k7Vr/Ej/kdPA/8A2ED/AOjIayPH/wDyRbwz/wBun/pO1a/xI/5HTwP/ANhA/wDoyGu2O8f+3vyOd7P5Bo3/ACX/AMQf9g9P/QYK9FrzrRv+S/8AiD/sHp/6DBXotctfePovyNqez9WFedfGz/kS7T/sIJ/6Lkr0WvOvjZ/yJdp/2EE/9FyUYb+NEKvwMPiR/wAjp4H/AOwgf/RkNGjf8l/8Qf8AYPT/ANBgo+JH/I6eB/8AsIH/ANGQ0aN/yX/xB/2D0/8AQYK3j/C/7df5mb+P5r8j0WiiiuA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf8A/8k28M/8AYJtf/RK10Fc/4B/5Jt4Z/wCwTa/+iVroKb3EtgooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAK+oX1tpem3OoX0nlWtrC880m0tsRQWY4GScAHgVx/wv8AFei+I9I1GHRr37TJbajdzyr5TptSe6nkiPzAZ3LzgdO+DXWavpkOtaHfaXdNIkF9byW0jRkBgrqVJBIIzg+hrzf4a/CjwppumahcXNhHqzzX1zbAanDFOIlguZolKZTgsoBb1IHTpVK1tSXe50nxZ/5JP4h/69D/ADFdBoH/ACLWmf8AXpF/6AK4X4neDfDFh8MddurHw3pFtcRWpaOaGxiR0ORyGC5FbmieBfCMvh/TpJfC2iu72sTMzadESxKDJJ209OUNbmRrP/Jf/D//AGD3/wDQZ69FryPVvCXhuP436HYR+H9LSzlsWeS3WyjEbttm5K4wTwPyFd5/wgPg7/oU9D/8FsP/AMTWtbaHp+rIhe79ToKK5/8A4QHwd/0Keh/+C2H/AOJo/wCEB8Hf9Cnof/gth/8Aiaw0NNToKK5//hAfB3/Qp6H/AOC2H/4mj/hAfB3/AEKeh/8Agth/+Jo0DU6Ciuf/AOEB8Hf9Cnof/gth/wDiaP8AhAfB3/Qp6H/4LYf/AImjQNToKK5//hAfB3/Qp6H/AOC2H/4mj/hAfB3/AEKeh/8Agth/+Jo0DU6Ciuf/AOEB8Hf9Cnof/gth/wDiaP8AhAfB3/Qp6H/4LYf/AImjQNToKK5//hAfB3/Qp6H/AOC2H/4mj/hAfB3/AEKeh/8Agth/+Jo0DU3Zp4raFpriVIokGWeRgqqPcmudl1Kx1Dxjp/2C8t7ry7C63+TKr7cyW+M4PHQ/lTpvh54NngeJ/CujKsilWMdjGhwevKgEVl6b4N8PeEfGNr/wjek2+nfarC587yQR5m2SDbn6bj+dXT+L7/yJlsaN/wCKbHTtUlsp45z5ESzTyoFKwoc4YrneR8pyyqQO5GDhkniu1hsJLqezvIhBL5dxGyLug+RXy3zYPyupABLHOAMggJrXhGy1+43ajcXL25KsbXKFMjuGKl0zxkKwBxz1OWXHhCC5025tJ9Sv5BdsxuZGMW6YGNY8EbNo+VFwQAQRnPJzWpJr6jfxaZp015OrukY4SMZd2JwqqO7EkAD1IqhH4mtbmWMWFvdXsTJE7z28YZYllwULDIY5BBO0HAOTgUlxptxriRwa9aQRW0UqzCO3u2kErLnaHBjXgHDDB6qvbrXsvBtrpjMNN1C/tIHkDyQRSIFcBywXO3cAAQnykfKoHagCzP4p0y2srW6uJHjiut3lZTJYg42gDksSQABkkkAVrROZYUdo2iLKCUfG5fY4JGfoTXPDwTYSRxQ391dX1vCJBBDOIsQ7wQSpVFYHB4Ocj1rftoWt7WKF5pLho0CmWXG9yB944AGT7AUAS0UUUxBRRRQAUUUUAFFFFABRRRQBR0D/AJCOvf8AYQX/ANJYK2qxdA/5COvf9hBf/SWCtqs6nxGkdgoooqCgooooAKKKKACiiigArkfin/yTXVf+2P8A6OSuurkfin/yTXVf+2P/AKOStaP8WPqiKnwM5Hx//wAkW8M/9un/AKTtWv8AEj/kdPA//YQP/oyGsjx//wAkW8M/9un/AKTtWv8AEj/kdPA//YQP/oyGu2O8f+3vyOd7P5Bo3/Jf/EH/AGD0/wDQYK9FrzrRv+S/+IP+wen/AKDBXotctfePovyNqez9WFedfGz/AJEu0/7CCf8AouSvRa86+Nn/ACJdp/2EE/8ARclGG/jRCr8DD4kf8jp4H/7CB/8ARkNGjf8AJf8AxB/2D0/9Bgo+JH/I6eB/+wgf/RkNGjf8l/8AEH/YPT/0GCt4/wAL/t1/mZv4/mvyPRaKKK4DpCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5/wD/wAk28M/9gm1/wDRK10Fc/4B/wCSbeGf+wTa/wDola6Cm9xLYKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/BX/IBuf+wtqX/pdPXQVz/gr/kA3P8A2FtS/wDS6en0F1PM/wBpDxJquj6TpOmaddeTZ6qlyl5H5aN5qr5W0ZIJGNx6EdasfD74keJ5fAWmGbwhqutMqOgvrfykSVVdlAA4+6AF6fw12nxZ/wCST+If+vQ/zFdBoH/ItaZ/16Rf+gCruuXYmz5tzxvU/GOsy/F/R9QfwXqkdxFZsiWLOnmSjEvzDtj5j/3ya7T/AIWJ4j/6Jvrn/fyP/GotZ/5L/wCH/wDsHv8A+gz16LWtXaPp/mTBO79Tz/8A4WJ4j/6Jvrn/AH8j/wAaP+FieI/+ib65/wB/I/8AGvQKKwuuxpZ9zz//AIWJ4j/6Jvrn/fyP/Gj/AIWJ4j/6Jvrn/fyP/GvQKKLrsFn3PP8A/hYniP8A6Jvrn/fyP/Gj/hYniP8A6Jvrn/fyP/GvQKKLrsFn3PP/APhYniP/AKJvrn/fyP8Axo/4WJ4j/wCib65/38j/AMa9AoouuwWfc8//AOFieI/+ib65/wB/I/8AGj/hYniP/om+uf8AfyP/ABr0Cii67BZ9zz//AIWJ4j/6Jvrn/fyP/Gj/AIWJ4j/6Jvrn/fyP/GvQKKLrsFn3PP8A/hYniPDH/hXGucKSP3kfOB0/GjQfE+qeI/GMH9q+FtQ8P/Z7C48v7aynz90kGdu302jP+8K9ArD1D/kcdM/68Lv/ANGW9XD4vv8AyJl8Jy/inxNf6Prcq2l2shjjQQ6fGELyu2fvRsA7KeAHjY7cHK/Kcw3fii5t/D97c2+uRXBtZmFrPsjH20CCOQrnG04Z3G1cMQuAQQTXe0YpkmR4m1KfTtIxY7ze3Uq29uI0DsGbqwU8Eqgd8HrsrkYfFmt3txDchpbaGKa1t7tSI/JgkL7Jg4KmT7wZQVIGSpOBk13VnpkFi7PBJdMWGD593LMPwDsQPwq5QB5vb+KrybTp5Dr8Mdx5u0rJc2wESiXBxiMmM4+UGTIyQDyQam0PxZPqOuww3GsMkWY1jjeW1iM2WZclcMXzgfNGwDdVxnFehUUWA4m5vNetZtQWx1OS7mh1SK1hhu44gjIYY5SpKICCS7Lu5wCDgkc7vhbV31zQ/t0gdS1zcIEkTYyBJnQKw7EBQD7itmigAooopiCiiigAooooAKKKKAKOgf8AIR17/sIL/wCksFbVYugf8hHXv+wgv/pLBW1WdT4jSOwUUUVBQUUUUAFFFFABRRRQAVyPxT/5Jrqv/bH/ANHJXXVyPxT/AOSa6r/2x/8ARyVrR/ix9URU+BnI+P8A/ki3hn/t0/8ASdq1/iR/yOngf/sIH/0ZDWR4/wD+SLeGf+3T/wBJ2rX+JH/I6eB/+wgf/RkNdsd4/wDb35HO9n8g0b/kv/iD/sHp/wCgwV6LXnWjf8l/8Qf9g9P/AEGCvRa5a+8fRfkbU9n6sK86+Nn/ACJdp/2EE/8ARclei1518bP+RLtP+wgn/ouSjDfxohV+Bh8SP+R08D/9hA/+jIaNG/5L/wCIP+wen/oMFHxI/wCR08D/APYQP/oyGjRv+S/+IP8AsHp/6DBW8f4X/br/ADM38fzX5HotFFFcB0hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/4B/5Jt4Z/7BNr/wCiVroK5/wD/wAk28M/9gm1/wDRK10FN7iWwUUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGX4ngv7nwjrEGjmRdQlsZktTHJ5bCUxkJhsjad2OcjFeT/AAa0X4iWlprD6jqUdtA9wUj/ALTVr4NKksyz7Ak67D5mdxOd5II6ZPtlc/4K/wCQDc/9hbUv/S6eqTsrEtXZyvxOtfFCfDHXWvtY0ia3FqfMjh0qWN2GRwGNwwH/AHya3NEs/Fx8P6cYtb0VUNrFtVtGlYgbBgE/ahn64Fc/8dPF9hoHgiXRryG5e41uCWO3aJVKIUKZ3ksCPvDoD3roPBHjTRNZ8EaVeR3sVqDAIzFdSokilCUORuPUqSPYiq15bi05rHI6tbeJB8b9DSTVdLa8NixjmXTJBGq7ZuCn2gknrzuHUccc959j8Y/9B3Q//BLN/wDJVcdrGr6a3x30GddQtTCtgwaQTrtB2z8E5x3H516D/b+j/wDQWsf/AAJT/GtK20fT/MmG79TP+x+Mf+g7of8A4JZv/kqj7H4x/wCg7of/AIJZv/kqtD+39H/6C1j/AOBKf40f2/o//QWsf/AlP8aw1NNDP+x+Mf8AoO6H/wCCWb/5Ko+x+Mf+g7of/glm/wDkqtD+39H/AOgtY/8AgSn+NH9v6P8A9Bax/wDAlP8AGjUNDP8AsfjH/oO6H/4JZv8A5Ko+x+Mf+g7of/glm/8AkqtD+39H/wCgtY/+BKf40f2/o/8A0FrH/wACU/xo1DQz/sfjH/oO6H/4JZv/AJKo+x+Mf+g7of8A4JZv/kqtD+39H/6C1j/4Ep/jR/b+j/8AQWsf/AlP8aNQ0M/7H4x/6Duh/wDglm/+SqPsfjH/AKDuh/8Aglm/+Sq0P7f0f/oLWP8A4Ep/jR/b+j/9Bax/8CU/xo1DQz/sfjH/AKDuh/8Aglm/+SqPsfjH/oO6H/4JZv8A5KrQ/t/R/wDoLWP/AIEp/jR/b+j/APQWsf8AwJT/ABo1DQybyw8cPZTLaa/oaTlCI2/seVcN2OTcMP8Ax01i6DY+L7PxjB/wmmr6fqW+wuPs32K2MPl4kg3bs9c5XH0Ndh/wkGjf9Bax/wDAlP8AGsqXUrHUPGOn/YLy3uvLsLrf5MqvtzJb4zg8dD+VXT+L7/yJl8JzXivw9r19fa3/AGVG5g1SzFs5E6rjYm5SASMZO+M9P9YD0GaLvQNTfxBFcS6W93apPcPfASx4v42ZTAuGYZ8sY4fAG04znnoNT8YaXpL6ql4Zg2l26XEoVM+YrZACc/Mc4GPVl9aJvFtlFfizS3upppXeO2WNV/0loyBIEJYfcJ53bRwcZwarQkptfTWnhK30WHUbX/hI/scVt5b3SGRJmRVLkE5bbkv6kDjJIrIg8P6pabrTVdF/tixtYpIbGOGdAI8kOrAu4K4DeWGGWURZH3q7aLU7SbR01RZQtm8AuBK424jK7txz045rDn8d6dBZR3RtrtozbvcSgiNGgVGKuGDOCWVgwIXJ4NAFea11qTSdW06KwninmnaW3umljeLBZSP4w579QM4961/DdndWGny22oIWuFnYvdlgTeZwRKeeCRgFeACuFG0LVabxjZwPfF7W48ixD+bPuiC5UZPyl9/fGSoHqQOadL4sgitraRrC88y4kZEiLQqflXdneZAjDBHKsfTqCAAb9Fc9c+L4rN7sXWk6jGlnbpcyuBFINjlguAkhLElGGAO3uK1NP1W01UTNYyebHEyr5g+6+6NZAVPcFXXmmBdooooEFFFFABRRRQAUUUUAUdA/5COvf9hBf/SWCtqsXQP+Qjr3/YQX/wBJYK2qzqfEaR2CiiioKCiiigAooooAKKKKACuR+Kf/ACTXVf8Atj/6OSuurkfin/yTXVf+2P8A6OStaP8AFj6oip8DOR8f/wDJFvDP/bp/6TtWv8SP+R08D/8AYQP/AKMhrI8f/wDJFvDP/bp/6TtWv8SP+R08D/8AYQP/AKMhrtjvH/t78jnez+QaN/yX/wAQf9g9P/QYK9FrzrRv+S/+IP8AsHp/6DBXotctfePovyNqez9WFedfGz/kS7T/ALCCf+i5K9Frzr42f8iXaf8AYQT/ANFyUYb+NEKvwMPiR/yOngf/ALCB/wDRkNGjf8l/8Qf9g9P/AEGCj4kf8jp4H/7CB/8ARkNGjf8AJf8AxB/2D0/9BgreP8L/ALdf5mb+P5r8j0WiiiuA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf8AAP8AyTbwz/2CbX/0StdBXP8AgH/km3hn/sE2v/ola6Cm9xLYKKKKQwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArn/BX/ACAbn/sLal/6XT10Fc/4K/5ANz/2FtS/9Lp6fQXU5H46eELDX/BEus3k1ylxokEslusTKEcuUzvBUk/dHQjvVjwt8IfBUHhXThc6LFfSvAsj3F180jlvm5IwOM4HHQD61rfFn/kk/iH/AK9D/MV0Ggf8i1pn/XpF/wCgCq5nyk2XMeT6r8PvCkPxn0XSotDtVsZ7JpJIAp2s2JuTz/sr+Vdx/wAKn8Cf9CxY/wDfJ/xrK1n/AJL/AOH/APsHv/6DPXota1m0o27f5kwSu/U4/wD4VP4E/wChYsf++T/jR/wqfwJ/0LFj/wB8n/GuworDmfc0sjj/APhU/gT/AKFix/75P+NH/Cp/An/QsWP/AHyf8a7CijmfcLI4/wD4VP4E/wChYsf++T/jR/wqfwJ/0LFj/wB8n/Guwoo5n3CyOP8A+FT+BP8AoWLH/vk/40f8Kn8Cf9CxY/8AfJ/xrsKKOZ9wsjj/APhU/gT/AKFix/75P+NH/Cp/An/QsWP/AHyf8a7CijmfcLI4/wD4VP4E/wChYsf++T/jR/wqfwJ/0LFj/wB8n/Guwoo5n3CyOP8A+FT+BcEf8IzY/MCp+U8gjBHWoNN8G+HvCPjG1/4RvSbfTvtVhc+d5II8zbJBtz9Nx/Ou3rD1IhfF+lsxADWd0gyerF4Dj8lY/hV023L7/wAiZaRM7WPBmm63dTXF3Jcq8yNG4jdQCrJtxgg9CFcf7SKfYkvg+1k1FL2O9vIZoJJJbbyzHi3aUgyFQUOdxBzu3Y3HGK6GiqJMCfRZpdKj0AW0X9jpHFA0n2xhK8ShcqV8sghgCp+bkE8g9ID4B0QyXbeXKFuAdi7gfszHad8RIyrblDZz94k101FAHPL4RjS6ubiLVb6J7hzIxRYAQxAGQ3l7uoBxnB6EEZFRDwPZLEix3tyki3D3BkEUHzMyBD8hiKAYUdFBzknJJz01FAGTL4ftphP5007NPbQW8jfKMiJnZTgKACS5zgY6YAqTSNDtNEe/Nj5gW+ujdPGzZVGKqpC+i/LnHqTjjAGlRTEFFFFABRRRQAUUUUAFFFFAFHQP+Qjr3/YQX/0lgrarF8P/ADXuuOpBVtQGCD1xbwqfyII/Ctqs6nxGkdgoooqCgooooAKKKKACiiigArkfin/yTXVf+2P/AKOSuurkfin/AMk11X/tj/6OStaP8WPqiKnwM5Hx/wD8kW8M/wDbp/6TtWv8SP8AkdPA/wD2ED/6MhrI8f8A/JFvDP8A26f+k7Vr/Ej/AJHTwP8A9hA/+jIa7Y7x/wC3vyOd7P5Bo3/Jf/EH/YPT/wBBgr0WvOtG/wCS/wDiD/sHp/6DBXotctfePovyNqez9WFedfGz/kS7T/sIJ/6Lkr0WvOvjZ/yJdp/2EE/9FyUYb+NEKvwMPiR/yOngf/sIH/0ZDRo3/Jf/ABB/2D0/9Bgo+JH/ACOngf8A7CB/9GQ0aN/yX/xB/wBg9P8A0GCt4/wv+3X+Zm/j+a/I9FooorgOkKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDn/AP/JNvDP8A2CbX/wBErXQVz/gH/km3hn/sE2v/AKJWugpvcS2CiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/wV/yAbn/ALC2pf8ApdPXQVz/AIK/5ANz/wBhbUv/AEunp9BdTP8Aiz/ySfxD/wBeh/mK6DQP+Ra0z/r0i/8AQBXP/Fn/AJJP4h/69D/MV0Ggf8i1pn/XpF/6AKf2RdTitZ/5L/4f/wCwe/8A6DPXotedaz/yX/w//wBg9/8A0GevRa2rbQ9P1ZNPeXqFFFFc5oFFFFABRRRQAUUUUAFFFFABRRRQAVR1bS01W3iUzSW80EolhnjA3RsMjuCCCCQR3BNXqKabTuhNXMQ6dr44TVtPxjq+muSfriYD9KT+z/EP/QW0z/wWSf8Ax+tyir9pL+kieRGH/Z/iH/oLaZ/4LJP/AI/Ve+XWNNspLu81nTY4Y8bmGlyseSAAAJ8kkkDArpKzdV06fUprREuDbwQyGWRkwXLAYQAMpXGSSc9wPwcajb1/JA4q2hkTXGpW7OJtd0tSkSy/8guU71Y4G3E/zZOBgZOSPUU4yaqumXGoHW9NFtb+b5r/ANlS/L5ZIfjz8nBU/XFMTwzqCx2oa5gZtJz/AGaWGd3zceb8vHyBU+Xpy3XAF59GuX8I6lphaIXF2LvYdx2DzXkZcnGeA4zx61o5Lv8Al/kRy+RDPHrFrbyzz65pKRwrukb+zZPlH/f+pBZ662dus6UdvXGnPx/5HqlJ4TmNm0cYtVeS0uopDyPMeSVHjJOOQArAntnjNQ2/hG+WzuYZnty0lvJCGD5DlnDZbEansepbqenOXdW+L8EFvI01s9dY4XWdKJxu405+nr/r6QWuuFVI1rSSG+6f7Ofn6fv6rXHhmdluoba3sFSSRpEmbIcqQP3RAXheNpOT8o6ekM/hK4uW1OVorGFrq2lS3iQkrbyMqBWB2jHKbiQBgnv1KTXf8EFvI0VsteYkLrGlkr1A02Tj/wAj0gtNcKqw1rSiHOFP9nP830/f1Sl8J3BtPLt3t4ZHtrmOV0/5aM8yOmfl5GFYHOcbuhpLfwzeQXEszw2Nx52QI53ysBO35l2xqCTtyQAvIHOcmi6/m/BBbyLdtDrV5D5trrWlyR7mXcumydQSD/y39RUv9n+If+gtpn/gsk/+P1c0WwfTbBraRYlxPK6mM/eVnLAngYOD056da0KzdRp6fki1FWMP+z/EP/QW0z/wWSf/AB+l/s3XWBWXV7JVP8UOnsrD6FpWH6Gtuil7SX9JByIqaZp0WladHaQF3VMlnc5aRicszY7kkk/WrdFFQ227svYKKKKQBRRRQAUUUUAFFFFABXI/FP8A5Jrqv/bH/wBHJXXVyPxT/wCSa6r/ANsf/RyVrR/ix9URU+BnI+P/APki3hn/ALdP/Sdq1/iR/wAjp4H/AOwgf/RkNZHj/wD5It4Z/wC3T/0natf4kf8AI6eB/wDsIH/0ZDXbHeP/AG9+RzvZ/ING/wCS/wDiD/sHp/6DBXotedaN/wAl/wDEH/YPT/0GCvRa5a+8fRfkbU9n6sK86+Nn/Il2n/YQT/0XJXotedfGz/kS7T/sIJ/6Lkow38aIVfgYfEj/AJHTwP8A9hA/+jIaNG/5L/4g/wCwen/oMFHxI/5HTwP/ANhA/wDoyGjRv+S/+IP+wen/AKDBW8f4X/br/Mzfx/Nfkei0UUVwHSFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBz/gH/km3hn/ALBNr/6JWugrn/AP/JNvDP8A2CbX/wBErXQU3uJbBRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/wCCv+QDc/8AYW1L/wBLp66Cuf8ABX/IBuf+wtqX/pdPT6C6mf8AFn/kk/iH/r0P8xXQaB/yLWmf9ekX/oArn/iz/wAkn8Q/9eh/mK6DQP8AkWtM/wCvSL/0AU/si6nFaz/yX/w//wBg9/8A0GevRa861n/kv/h//sHv/wCgz16LW1baHp+rJp7y9Rk00dvC808ixRRqXd3YBVUDJJJ6AVl/8Jb4c/6D+l/+Bsf+NHi3/kS9b/7B8/8A6LauG8A+AfDWteB9P1DU9N8+6m8zfJ58i5xIyjgMB0ApQhDkc5t720CUpc3LE7n/AIS3w5/0H9L/APA2P/Gj/hLfDn/Qf0v/AMDY/wDGsj/hVng7/oD/APk1N/8AF0f8Ks8Hf9Af/wAmpv8A4unah3f4f5h+88jX/wCEt8Of9B/S/wDwNj/xo/4S3w5/0H9L/wDA2P8AxrI/4VZ4O/6A/wD5NTf/ABdH/CrPB3/QH/8AJqb/AOLotQ7v8P8AMP3nka//AAlvhz/oP6X/AOBsf+NH/CW+HP8AoP6X/wCBsf8AjWR/wqzwd/0B/wDyam/+Lo/4VZ4O/wCgP/5NTf8AxdFqHd/h/mH7zyNf/hLfDn/Qf0v/AMDY/wDGj/hLfDn/AEH9L/8AA2P/ABrI/wCFWeDv+gP/AOTU3/xdH/CrPB3/AEB//Jqb/wCLotQ7v8P8w/eeRr/8Jb4c/wCg/pf/AIGx/wCNH/CW+HP+g/pf/gbH/jWR/wAKs8Hf9Af/AMmpv/i6P+FWeDv+gP8A+TU3/wAXRah3f4f5h+88jX/4S3w5/wBB/S//AANj/wAaP+Et8Of9B/S//A2P/Gsj/hVng7/oD/8Ak1N/8XR/wqzwd/0B/wDyam/+LotQ7v8AD/MP3nka/wDwlvhz/oP6X/4Gx/40f8Jb4c/6D+l/+Bsf+NZH/CrPB3/QH/8AJqb/AOLo/wCFWeDv+gP/AOTU3/xdFqHd/h/mH7zyNf8A4S3w5/0H9L/8DY/8aP8AhLfDn/Qf0v8A8DY/8ayP+FWeDv8AoD/+TU3/AMXR/wAKs8Hf9Af/AMmpv/i6LUO7/D/MP3nka/8Awlvhz/oP6X/4Gx/40f8ACW+HP+g/pf8A4Gx/41kf8Ks8Hf8AQH/8mpv/AIuj/hVng7/oD/8Ak1N/8XRah3f4f5h+88jX/wCEt8Of9B/S/wDwNj/xo/4S3w5/0H9L/wDA2P8AxrI/4VZ4O/6A/wD5NTf/ABdH/CrPB3/QH/8AJqb/AOLotQ7v8P8AMP3nka//AAlvhz/oP6X/AOBsf+NH/CW+HP8AoP6X/wCBsf8AjWR/wqzwd/0B/wDyam/+Lo/4VZ4O/wCgP/5NTf8AxdFqHd/h/mH7zyNf/hLfDn/Qf0v/AMDY/wDGj/hLfDn/AEH9L/8AA2P/ABrI/wCFWeDv+gP/AOTU3/xdH/CrPB3/AEB//Jqb/wCLotQ7v8P8w/eeRr/8Jb4c/wCg/pf/AIGx/wCNH/CW+HP+g/pf/gbH/jWR/wAKs8Hf9Af/AMmpv/i6P+FWeDv+gP8A+TU3/wAXRah3f4f5h+88jX/4S3w5/wBB/S//AANj/wAaP+Et8Of9B/S//A2P/Gsj/hVng7/oD/8Ak1N/8XR/wqzwd/0B/wDyam/+LotQ7v8AD/MP3nka/wDwlvhz/oP6X/4Gx/40f8Jb4c/6D+l/+Bsf+NZH/CrPB3/QH/8AJqb/AOLo/wCFWeDv+gP/AOTU3/xdFqHd/h/mH7zyNf8A4S3w5/0H9L/8DY/8aP8AhLfDn/Qf0v8A8DY/8ayP+FWeDv8AoD/+TU3/AMXR/wAKs8Hf9Af/AMmpv/i6LUO7/D/MP3nka/8Awlvhz/oP6X/4Gx/40f8ACW+HP+g/pf8A4Gx/41kf8Ks8Hf8AQH/8mpv/AIuj/hVng7/oD/8Ak1N/8XRah3f4f5h+88jX/wCEt8Of9B/S/wDwNj/xo/4S3w5/0H9L/wDA2P8AxrI/4VZ4O/6A/wD5NTf/ABdH/CrPB3/QH/8AJqb/AOLotQ7v8P8AMP3nka//AAlvhz/oP6X/AOBsf+NH/CW+HP8AoP6X/wCBsf8AjWR/wqzwd/0B/wDyam/+Lo/4VZ4O/wCgP/5NTf8AxdFqHd/h/mH7zyNf/hLfDn/Qf0v/AMDY/wDGuX+JHiLRb74falbWWsWFzO/lbYobpHZsSoTgA5PAJrR/4VZ4O/6A/wD5NTf/ABdc74+8A+GtF8D6hqGmab5F1D5eyTz5GxmRVPBYjoTWlL2PtI2b3Xb/ADIn7Tld7GR431bTrv4R+HrO11C1nuofs3mQRzKzpiBgcqDkYPH1rU8f65pN54u8HTWeqWVxFb3xeZ4rhGWJfMiOWIPA4PX0Nc/4v8LaNpfww0LVrCz8q+u/s/nS+a7b90LM3BJAyQDwK0fG3gvQNI8UeFbTTrDyYNQvPKuU86RvMXfEMZLEjhm6Y611R9neO/2v+CYvm1+Rr+G7u2vvjtrtzZXEVzA+nrtlhcOrYEAOCODyCK9LrC0TwXoHhy9e70aw+zTvGYmfzpHypIOMMxHUCt2vPrTjOS5dkkjqhFxWoV518bP+RLtP+wgn/ouSvRa86+Nn/Il2n/YQT/0XJVYb+NEmr8DD4kf8jp4H/wCwgf8A0ZDRo3/Jf/EH/YPT/wBBgo+JH/I6eB/+wgf/AEZDRo3/ACX/AMQf9g9P/QYK3j/C/wC3X+Zm/j+a/I9FooorgOkKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDn/AAD/AMk28M/9gm1/9ErXQVz/AIB/5Jt4Z/7BNr/6JWugpvcS2CiiikMKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5/wV/wAgG5/7C2pf+l09dBXP+Cv+QDc/9hbUv/S6en0F1M/4s/8AJJ/EP/Xof5iug0D/AJFrTP8Ar0i/9AFc/wDFn/kk/iH/AK9D/MV0Ggf8i1pn/XpF/wCgCn9kXU4rWf8Akv8A4f8A+we//oM9ei151rP/ACX/AMP/APYPf/0GevRa2rbQ9P1ZNPeXqZHi3/kS9b/7B8//AKLasj4Wf8k10r/tt/6Oetfxb/yJet/9g+f/ANFtWR8LP+Sa6V/22/8ARz0L+A/X9GH/AC8+R11FFFc5oFFFFABRRRQAUUUUAFFFZE3iGMXU0FjYXl+8DbJDboqoG7rudlUkZ5wTjp1pqLewm0tzXorF/t+7/wChc1T/AL+Wv/x6j+37v/oXNU/7+Wv/AMeqvZyFzI2qKxf7fu/+hc1T/v5a/wDx6j+37v8A6FzVP+/lr/8AHqPZyDmRtUVi/wBv3f8A0Lmqf9/LX/49R/b93/0Lmqf9/LX/AOPUezkHMjaorF/t+7/6FzVP+/lr/wDHqP7fu/8AoXNU/wC/lr/8eo9nIOZCeKdWm0jTbeWCWG2Wa6jglu51zHbIx5duQOuFGTjLDOeh8x0rUtWmnuNXk8Rl40voYhOGbyRDJqtyhwC5AUhEA/2dq5wK9P8A7fu/+hb1T/v5a/8Ax6mTa1czwPE/hzVwrqVJSe3RgD6MJwQfcHNHs5BzIj8ZT+Rpdp51w1rZSX0KXs6ymLZET/fBBUFtik5HBNSeDZ3ufDKSPPJcR/ablbeaRy5kgW4kWJtxJLAxhMMSdwwc81VsbltPkZ4NA8QOWG0i41GOYfgHuSB9RV3+37v/AKFvVP8Av5a//HqPZyDmRtUVi/2/d/8AQuap/wB/LX/49R/b93/0Lmqf9/LX/wCPUezkHMjaorF/t+7/AOhc1T/v5a//AB6j+37v/oXNU/7+Wv8A8eo9nIOZG1RWL/b93/0Lmqf9/LX/AOPUf2/d/wDQuap/38tf/j1Hs5BzI2qKxf7fu/8AoXNU/wC/lr/8eo/t+7/6FzVP+/lr/wDHqPZyDmRtUVmWeuxXN6tpcWt1ZXDqWRLhBh8DJAZSykj0zng+ladS01uNNPYKKKKQwooooAKKKKACuR+Kf/JNdV/7Y/8Ao5K66uR+Kf8AyTXVf+2P/o5K1o/xY+qIqfAzkfH/APyRbwz/ANun/pO1a/xI/wCR08D/APYQP/oyGsjx/wD8kW8M/wDbp/6TtWv8SP8AkdPA/wD2ED/6MhrtjvH/ALe/I53s/kei0UUV5p1hXnXxs/5Eu0/7CCf+i5K9Frzr42f8iXaf9hBP/RcldGG/jRMqvwMPiR/yOngf/sIH/wBGQ0aN/wAl/wDEH/YPT/0GCj4kf8jp4H/7CB/9GQ0aN/yX/wAQf9g9P/QYK3j/AAv+3X+Zm/j+a/I9FooorgOkKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDn/AP/ACTbwz/2CbX/ANErXQVz/gH/AJJt4Z/7BNr/AOiVroKb3EtgooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuf8Ff8gG5/7C2pf+l09dBXP+Cv+QDc/wDYW1L/ANLp6fQXUz/iz/ySfxD/ANeh/mK6DQP+Ra0z/r0i/wDQBXP/ABZ/5JP4h/69D/MV0Ggf8i1pn/XpF/6AKf2RdTitZ/5L/wCH/wDsHv8A+gz16LXnWs/8l/8AD/8A2D3/APQZ69FrattD0/Vk095epkeLf+RL1v8A7B8//otqyPhZ/wAk10r/ALbf+jnrX8W/8iXrf/YPn/8ARbVwFrd3Nj+zmlzZXEttOmdssLlGXN3g4I5HBIqoR5qXL3kiZO07+R6tRXl+k+BvEeqaLY6h/wAJ7qkX2u3jn8vMjbNyhsZ80ZxnrVv/AIVv4j/6KFqn5Sf/AB2pdKmnZz/Bj55P7J6LRXnX/Ct/Ef8A0ULVPyk/+O0f8K38R/8ARQtU/KT/AOO0ezp/z/gx80/5fyPRaK86/wCFb+I/+ihap+Un/wAdo/4Vv4j/AOihap+Un/x2j2dP+f8ABhzT/l/I9Forzr/hW/iP/ooWqflJ/wDHaP8AhW/iP/ooWqflJ/8AHaPZ0/5/wYc0/wCX8jp9V8QXGm30sO3SVjjjEubrUzC+wkLuK+WcDcduc88fSs/TNWstF+H2n6pqswgt1s4ZJpdhb5nC5OFBJJZvTvXM3WiPpU8tpf8Aju4luo0BPm639lO4kHaUKuQNpzuyc8DAHNR694RvfEXw106XS7vUHuhplpHHp8d0qW8uNpLMrYBIBJzkfdX0rdU4JJX001MOeTbPS6WszQtG/sOxe2/tHUNQ3SGTzb+fzXXgDaDgccZx6k1yN54R8VSabdW1tqMSNIXeGZ9Qm8xJCpCsWCbcA4ONmePvE81zvTY1R6DTTIiyKjOods7VJ5OOuK4tvC+sWzm7jv5ZJEgyyi8mYu+y4DEKeOd8GP8Arn2wM128J69c2dkxuVhaOcyvB9vlLbGijUp5rITy6s5GMHOO5NIZ31Fc5P5zaFDoRnvjqAihikufKnKsRt3Hz1QDJGfm45Pasm68J682oTtBfl7aR2G17+RcxllKKFCHBUDbklgRuJGW4AO3WRGdkV1LJjcoPK/WnVh+GdJv9Ltf+JrcJc3L29uksquzF5EiVXYkgE5YE571uUxBRXH6bazaxf6vd3+t39tHBqJtIo4JdiAbgqjA9SQP1Nbdjp9rJbR3EWt3skcihlMs2CQenBAI6j8xXLTxDqLmUdDTkNWioIrixgW4JvGk+ylhKGO4gqoYgDHOAw6ZqtNJYXdpcXKalcRwwFxK0e5dpUZbjGeBWzm7bC5TQormbrTLGW8itf8AhJtYjmmZlVIpW42uUOcL8o3DGTgGovCyXlh4n8Q6RcaldahBaG3eFrp97r5iMWGfTgcdOPc1mqz51Fx3/wArg46XOroooroICiiigAooooAy9eO2ztZFxvTULTa2ORuuEU4+qsR9Ca6Cuf8AEH/IOt/+whZf+lUVdBUz+FFR3CiiisjQKKKKACiiigArkfin/wAk11X/ALY/+jkrrq5H4p/8k11X/tj/AOjkrWj/ABY+qIqfAzkfH/8AyRbwz/26f+k7Vr/Ej/kdPA//AGED/wCjIayPH/8AyRbwz/26f+k7Vr/Ej/kdPA//AGED/wCjIa7Y7x/7e/I53s/kei0UUV5p1hXnXxs/5Eu0/wCwgn/ouSvRa86+Nn/Il2n/AGEE/wDRcldGG/jRMqvwMPiR/wAjp4H/AOwgf/RkNGjf8l/8Qf8AYPT/ANBgo+JH/I6eB/8AsIH/ANGQ0aN/yX/xB/2D0/8AQYK3j/C/7df5mb+P5r8j0WiiiuA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf8A/8k28M/8AYJtf/RK10Fc/4B/5Jt4Z/wCwTa/+iVroKb3EtgooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuf8ABX/IBuf+wtqX/pdPXQVz/gr/AJANz/2FtS/9Lp6fQXUz/iz/AMkn8Q/9eh/mK6DQP+Ra0z/r0i/9AFc/8Wf+ST+If+vQ/wAxXQaB/wAi1pn/AF6Rf+gCn9kXU4rWf+S/+H/+we//AKDPXotedaz/AMl/8P8A/YPf/wBBnr0Wtq20PT9WTT3l6mR4t/5EvW/+wfP/AOi2ryP/AISnRv8AhR//AAj/ANs/4mn/ADw8p/8An53/AHsbfu89a9wmhjuIXhnjWWKRSjo6gqykYIIPUGuD+JHh7RbH4falc2Wj2FtOnlbZYbVEZcyoDggZHBIq8PON1CXdMirF2cl2Dw98SPClj4X0u0utV8ue3s4YpE+zSnayoARkLg8itH/hafg7/oMf+Ss3/wART/DHhjQLjwjo80+h6bLLJYwO7vaRlmYxgkkkck1qf8Il4c/6AGl/+AUf+FKToczun+H+Q4+0stjI/wCFp+Dv+gx/5Kzf/EUf8LT8Hf8AQY/8lZv/AIitf/hEvDn/AEANL/8AAKP/AAo/4RLw5/0ANL/8Ao/8Km9Ds/w/yK/eeRkf8LT8Hf8AQY/8lZv/AIij/hafg7/oMf8AkrN/8RWv/wAIl4c/6AGl/wDgFH/hR/wiXhz/AKAGl/8AgFH/AIUXodn+H+QfvPIyP+Fp+Dv+gx/5Kzf/ABFH/C0/B3/QY/8AJWb/AOIrX/4RLw5/0ANL/wDAKP8Awo/4RLw5/wBADS//AACj/wAKL0Oz/D/IP3nkcVqvjnwzdX0s9rqc0itGCv8Ap99bgvkDGxEIUbecjqe3Oasy+MLHwn8ONMuJHt57xNOtXjsWuFjklVgq5A5OByc4P3TW7daDcQzyxaRpdnBZlAirDqc1oOoYny449qtn+IHJHGcHFYUvg+x8WfDjTLeRLeC8k061SO+a2WSSJVCtgHg4PI6j7xrdOm0u2hhaabOtsNSsdUtzPpt5b3cSttL28qyKGwDjIPXBHHvWRJ440KLTZNQe4lFpFIY3m+zvtUgZJzjHAB469sZ4rYsdNsdLgMOm2dvZxM28x28SxqWwBnAHXAH5VkXHgfw/dGY3FnK7TI0cjm7m3MjDaV3b87ccY6VzvfQ1XmOj8ZaPJc+SJbgEruDNayBSu12zkrjBEUmPXYfbMNx440m3+zs5mWOaRovniZHVwiuBtIzyrg+w69Di5N4X0uSErHAY38rykcOx2ALIBwTzjzpOv972GIo/B+kJbwRtFO8kL+Ys5upRJv2Khbfu3fdUDr046E0tR6F9tWtTosWqQSrJazJHJHIcgMr42noTzkdqoweL9Jub5LWJ7kyScoTayBWG1yCDt5z5UmPUofUZml0q4lVLQz2x0xAii3eCRpdq4x+983k5GckfXND+GNHkUK9nlRGIsGR/ugSADr6TSf8AfXsMGoFS18caHeSmO3nmd1njgkAtpD5TyMqoHwPl3FhjPvnGDjoa5aTwHp66jFcWMktsnnwz3CebK7TNEyGPJL4wPLUYYMAM7dpJJ6mgDm/C9lb6jb+I7S8QyQyarKGUMVPBBGCCCDkdRW9deH9LvRKLm1DiZdknzsNwyxxwfVyfy9BjI8E/63X/APsLTf0rqa4ML/BXz/M2e5k22i6RGzW1vDgwvE7J5r8GML5Z5POAq+3FaF3ZwX1u8F1GJI3UqQSRwRg9PY1zmtgxawzo2GIVgQeQcf8A1q2dFupbuyZp23Mrlc4xkYH+NcmHx/tMRLDzVmm/u/4Ypxsriy6HYTNGZYXfy5vtCAyvhZN+/cBnGdxP8unFc7pP/JSPFf8AuWX/AKLauxrjtJ/5KR4r/wByy/8ARbV3y/iQ9f0ZD2Z0tZI8S6YdTWw86QTSSNHGTC+yRlIVwrYwdpIB568djWtWNP4U0i4uTcSQTCUuzq0d1KnlszBmKBWGwllBO3Gec9TXYZE2la5a6xNKtkJGjjihlWVkKrIsi7lIz7VVk8ZaJFq/9myXLrc+asODC4XezbAM4xy3GfX6HEkWhHTFjXw89vZKsEduyzxyTgpGNsYA8xcEAkE8k8ZPFSL4d0/7Wt20TLc7g7vFNJGJG3bvmUNgjdkgHIGT6nK1AgtPFmn3+v8A9k2hka4QMZQ6MhjAzg4I7lWH4e4qHxd4wsfCekz3Ej2894io8di1wsckqs4XIHJwOTnB+6atad4V0fSr4XljatHOsYiVmnkcKgLEKAzEAZdug71W8XeD7HxZpM9vIlvBeSKiR3zWyySRKrhsA8HB5HUfeNXC3MubYmV7aEPiPV9LvfCUl5BqEU9lFeWvnT2k27YBPEW+ZDkEA5456Vn6Z478Jafdea2sXGGV1Pm3F5OB8w24V1IyQMk8EdBuBJrT16zsdE8Mn+zbSysYlvrWUqoWCLd9oi+ZiBwMAZbBwB7U7TP7N8RXvkajFoeoiFJJESLUjelSzAk7GQADsD/D90YBrT3eTW9iNebQP+Fp+Dv+gx/5Kzf/ABFH/C0/B3/QY/8AJWb/AOIrX/4RLw5/0ANL/wDAKP8Awo/4RLw5/wBADS//AACj/wAKyvQ7P8P8jb955GR/wtPwd/0GP/JWb/4ij/hafg7/AKDH/krN/wDEVr/8Il4c/wCgBpf/AIBR/wCFH/CJeHP+gBpf/gFH/hReh2f4f5B+88jI/wCFp+Dv+gx/5Kzf/EUf8LT8Hf8AQY/8lZv/AIitf/hEvDn/AEANL/8AAKP/AAo/4RLw5/0ANL/8Ao/8KL0Oz/D/ACD955GR/wALT8Hf9Bj/AMlZv/iK53x94+8Na14H1DT9M1Lz7qby9kfkSLnEiseSoHQGu5/4RLw5/wBADS//AACj/wAKP+ES8Of9ADS//AKP/CqjKhGSkk9PT/IlxqNW0POvH/8AyRbwz/26f+k7Vr/Ej/kdPA//AGED/wCjIaZ8ZoY7fwJYQwRrFFHfRoiIoCqoikAAA6AU/wCJH/I6eB/+wgf/AEZDW9N35H/i/Izkrcy9D0WiiivOOoK86+Nn/Il2n/YQT/0XJXotedfGz/kS7T/sIJ/6Lkrow38aJlV+Bh8SP+R08D/9hA/+jIaNG/5L/wCIP+wen/oMFHxI/wCR08D/APYQP/oyGjRv+S/+IP8AsHp/6DBW8f4X/br/ADM38fzX5HotFFFcB0hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/4B/5Jt4Z/7BNr/wCiVroK5/wD/wAk28M/9gm1/wDRK10FN7iWwUUUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGX4nnv7bwjrE+jiRtQisZntRHH5jGURkphcHcd2OMHNeT/AAa1r4iXdprCajpsdzAlwXj/ALTZrELK8szT7CkDbz5mdwONhAA64Htlc/4K/wCQDc/9hbUv/S6eqT0Ja1OV+J114of4Y66t9o+kQ25tT5kkOqyyOoyOQpt1B/76FbmiXni4eH9OEWiaKyC1i2s2syqSNgwSPspx9Mmm/Fn/AJJP4h/69D/MV0Ggf8i1pn/XpF/6AKf2Q6nmerXPiQ/G/Q3k0rS1vBYsI4V1OQxsu2bkv9nBB68bT0HPPHefbPGP/QC0P/wdTf8AyLXNaz/yX/w//wBg9/8A0GevRa1rbQ9P1ZEN36nP/bPGP/QC0P8A8HU3/wAi1y/xIuvFD/D7UlvdH0iGA+VueHVpZGH71MYU26g847ivSK5H4p/8k11X/tj/AOjkqaP8SPqhz+BkPhi78WL4R0cQaJorxCxgCM+sSqzL5YwSBbHB9sn6mtT7Z4x/6AWh/wDg6m/+Ras+Ev8AkS9E/wCwfB/6LWteon8TKjsjn/tnjH/oBaH/AODqb/5Fo+2eMf8AoBaH/wCDqb/5FroKKm4zn/tnjH/oBaH/AODqb/5Fo+2eMf8AoBaH/wCDqb/5FroKKLgc/wDbPGP/AEAtD/8AB1N/8i0fbPGP/QC0P/wdTf8AyLXQUUXA4TVtW+J8N6F0nwvoM0GwEs2qOcN3GSik9v4RVvT9SXSPhrp18wQ+Vp1vgOxCklFAyQDjk9cfl1rq7i6t7SMSXU8cCFgoaRwoJPQZPesLwt/yJ2jf9eEH/ota1h8L+X6kS3RgeHvHz65qcFo1raxGSeS3ZUug8mU83MgUDGzMWM5PLDnjnS8ReI5dKuXtYfs0b/ZvNV7iYoZCSVxGu07yuMkcYyvrkbZsoCbghCjXIxK6OVY8YHIOQcdxim2dhDYKywPcOGOT59zJMfwLscfhQIwvC3iO51W9vLK+jCPD88UjEDzlzg7QOy8AnOckcAEE9PSBlLFQQSOoz0paYBRRRQIKKKKAMLwT/rdf/wCwtN/Sjxr4xHhm3SG2i82+nXdHvXMagEAluQfXGPTn3PBP+t1//sLTf0rG8eeHr/xF4psLbT4+BbEyTOD5cfJxuIB644HevCqTqwwf7n4np+J0q3NqT/Dlv7U8P38+qObmR7pt0sxy33FHU9OAPyrpLJ7bTp5IUlLxOQQx7Hv/AE5puk+GLHR9JWwtTKVDF2kZhudj3OBj0HToKkbSH8z5JF2Z6nqKyjh61KNNqCcl16jumaaOsgzGysPUHNchpP8AyUjxX/uWX/otq6q2s47UHZkk9STXK6T/AMlI8V/7ll/6LavTTk5U+dWd/wBGZvZmjrurHSLWCRRCPOmEPm3MvlxRZVjudsHA+XA9WZR3rmP+FiG70ovaQ29veCze6kjluN3kKrQj5+OAVmJBOPu/l3dJXcZGP4m1mTQ9OguYYvOaS5SIJ5gQHdnqdp4+grEPji7hhuRd2dpDNHKIoz9pbY7edNE2SVAXmAsMkDnBOevUWemQWLs8El0xYYPn3csw/AOxA/CrlAHIeFPHJ8TXdnH9lgtxd2T3flC53zQ7WiADptGA3m7lOeQAe/FH4pazrum+HLpNLsZY7Xy42fVYbwRtA3mgbQg+Y54GQf4/Y12MOl2dvqEl7FERPICCxdiFBILbQThckAnAGSATmpruztr+1e2vreK5gfG+KZA6tg5GQeDyAauEuWSb1JkrqyOY1DUdZufCL3Oo6F9hvo7+18mz+2JJ52J4ivzgYXJyOemM1SfxB45kljGj6DbebmbclxfwShwHAOApQ/Ifl69/mycGuj8TRmXR441kaIvfWaiRANyZuY+RkEZHuCK1LDTZ7KZnl1W8vFZceXOsIUHPX5I1OfxxzV88Yx2X4i5W5bnE/wBs/FX/AKFrS/8Av4v/AMeo/tn4q/8AQtaX/wB/F/8Aj1ei0Vn7ZfyL8f8AM09m/wCZnnX9s/FX/oWtL/7+L/8AHqP7Z+Kv/QtaX/38X/49XotFHtl/Ivx/zD2b/mZ51/bPxV/6FrS/+/i//HqP7Z+Kv/QtaX/38X/49XotFHtl/Ivx/wAw9m/5medf2z8Vf+ha0v8A7+L/APHqqar4u+JGi6bLqGp6BpcFrDjfJu3YyQo4ExPUivUK5H4p/wDJNdV/7Y/+jkq6dSMpqLgtX/XUmUWot8zOa+KF9Jqfwp0O/nVVlupreZwgIUM0Dk4z25q98SP+R08D/wDYQP8A6MhrI8f/APJFvDP/AG6f+k7Vr/Ej/kdPA/8A2ED/AOjIa3ho4r/F+RnLr8j0WiiivNOoK86+Nn/Il2n/AGEE/wDRclei1518bP8AkS7T/sIJ/wCi5K6MN/GiZVfgYfEj/kdPA/8A2ED/AOjIaNG/5L/4g/7B6f8AoMFHxI/5HTwP/wBhA/8AoyGjRv8Akv8A4g/7B6f+gwVvH+F/26/zM38fzX5HotFFFcB0hRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc/wCAf+SbeGf+wTa/+iVroK5/wD/yTbwz/wBgm1/9ErXQU3uJbBRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc/4K/wCQDc/9hbUv/S6eugrn/BX/ACAbn/sLal/6XT0+gupb8T6FH4n8MX+izzPBHexGNpEAJUZ6gH6VyFv8NNetbaK3g+I2uJFEgRF2IcKBgDmvRKKFJoLJnhep+DdZi+L+j6e/jTVJLiWzZ0vmRPMiGJflHbHyn/vo12n/AArvxH/0UjXP+/cf+FRaz/yX/wAP/wDYPf8A9Bnr0Wuiq2lH0/zM4JXfqef/APCu/Ef/AEUjXP8Av3H/AIVznj7wPrmn+B9Qubrx3q99FH5e63mRAr5kUc49M5/CvY65H4p/8k11X/tj/wCjkqKLbqRXmhzSUWc54e8A6/ceF9Lni+IOtW6SWcLrCiR7YwUBCjjoOlaP/Cu/Ef8A0UjXP+/cf+FdT4S/5EvRP+wfB/6LWtepnJ8zHFKyPP8A/hXfiP8A6KRrn/fuP/Cj/hXfiP8A6KRrn/fuP/CvQKKnmZXKjz//AIV34j/6KRrn/fuP/Cj/AIV34j/6KRrn/fuP/CvQKKOZhyo8/wD+Fd+I/wDopGuf9+4/8KP+Fd+I/wDopGuf9+4/8K9Aoo5mHKjxHxz8H/FGqzWNxHq6eLUhimhNlrcjRJEzjAmQpwWHv6DFdzo9jey/DHT7WznDXMtlEUeV2QAMAcbk5GFOAfYZ9K7WuU03UrLQtNh0rVrhLGWwjWDfctsSVVAVXVjgHIweOhJHatKd2miJaNGF4f0PX9L1Qy6rPJJb2c0khkF3NMXhKy7YljCANjfHyADlMAcCtTUbOfxFLcTafPeQKtsIo1kM9piQtkthkwcrxna3pitL/hKvD3/Qd0z/AMDI/wDGj/hKvD3/AEHdM/8AAyP/ABquSXYnmRlaLbXPhuaeTWd8z3UcQDWiXFzym4EH5TjgrzkZOeBirunaTew682pSXTvBMs+6NpZDwzxmL5G4G0LIO2N3uasf8JV4e/6Dumf+Bkf+NH/CVeHv+g7pn/gZH/jT5Jdg5ka1FZP/AAlXh7/oO6Z/4GR/40f8JV4e/wCg7pn/AIGR/wCNHJLsK6Naisn/AISrw9/0HdM/8DI/8aP+Eq8Pf9B3TP8AwMj/AMaOSXYLorQaBqFhf3s2l60beG8nM7QvbLJhj15J/p6fWtGKHVU/1urLJ/26qP61X/4Srw9/0HdM/wDAyP8Axo/4Srw9/wBB3TP/AAMj/wAawjhYw+FP8SvaeZrRSTJ/rZfM/wCAgVFP9tkz5F4IvT90GrO/4Srw9/0HdM/8DI/8aP8AhKvD3/Qd0z/wMj/xrR0b9H+Ic/mJPZeIHz5PiFYv+3FG/rUOg+H7rStT1LUNQ1Q6jc6gY97+QIgoQEAYBPY/pU//AAlXh7/oO6Z/4GR/40f8JV4e/wCg7pn/AIGR/wCNQsMlJSs7r1Dn6XNaisn/AISrw9/0HdM/8DI/8aP+Eq8Pf9B3TP8AwMj/AMa25Jdibo1qKyf+Eq8Pf9B3TP8AwMj/AMaP+Eq8Pf8AQd0z/wADI/8AGjkl2C6Naisn/hKvD3/Qd0z/AMDI/wDGj/hKvD3/AEHdM/8AAyP/ABo5Jdguh3iD/kHW/wD2ELL/ANKoq6CuYnvbbXpray0uUXSi4huJZ4fmjjWN1k++PlJJUAAHPJPaunrOpokmXDuFFFFZGgUUUUAFFFFABXI/FP8A5Jrqv/bH/wBHJXXVyPxT/wCSa6r/ANsf/RyVrR/ix9URU+BnI+P/APki3hn/ALdP/Sdq1/iR/wAjp4H/AOwgf/RkNZHj/wD5It4Z/wC3T/0natf4kf8AI6eB/wDsIH/0ZDXbHeP/AG9+RzvZ/I9FooorzTrCvOvjZ/yJdp/2EE/9FyV6LXnXxs/5Eu0/7CCf+i5K6MN/GiZVfgYfEj/kdPA//YQP/oyGjRv+S/8AiD/sHp/6DBR8SP8AkdPA/wD2ED/6Mho0b/kv/iD/ALB6f+gwVvH+F/26/wAzN/H81+R6LRRRXAdIUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHP+Af+SbeGf+wTa/8Aola6Cuf8A/8AJNvDP/YJtf8A0StdBTe4lsFFFFIYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVz/gr/kA3P8A2FtS/wDS6eugrn/BX/IBuf8AsLal/wCl09PoLqdBRRRSGedaz/yX/wAP/wDYPf8A9Bnr0WvOtZ/5L/4f/wCwe/8A6DPXotdFbaHp+rM6e8vUK5H4p/8AJNdV/wC2P/o5K66uR+Kf/JNdV/7Y/wDo5Kij/Fj6odT4Ga/hL/kS9E/7B8H/AKLWtesjwl/yJeif9g+D/wBFrWvUz+JjjsgoooqCgooooAKKKKACiiigAooooAKKKKAKt/fLYxRny2lkmkEUcaEAsx9zwOAT+FczbeKNVmvHLWSLCs6weWcBvmuZIsk7jyFjxjpnJ6YrqbuzhvoRFcKxUMHUq5VlYdCGBBB+lVI/D+mwwNDHA4VmVifOfdlZGkB3Zzne7Hr39K1jKCWqIak3oVtY1mezj1RrUJmwtFl+dS252JwMDHAC/ju9qqJ4kubW8aO9gkuIGUFJ1tHtsYDM+VkOSFVQcj1xWxcaTBc3Ny8w3RXUCwzRcjdtJIOQePvEH8PSrE9nb3Ukb3EYkMW7bnoNw2njvwSOaFKCVrBaRl6d4q02/kEbzRW0j7PLWWeM+ZvztAwxy3HK9Rx6isuPxRfxxi6liNxAs0yzRxWUibEQuAwlZtjHKqMd93bFdJY6bbacGFqsg3KqkyTPIQq5wAWJwBk8D1o/sy0/s97Hyv8ARnLFk3HncxY85z1Jo5oJ7BaXcz18SxL811aTW8Id4mlYqQsiIWZeCTwFYZ9R9Mx2fiOS48QCwns5LdZbeKWHeVLfN5pJJBIxhAMeue2K0DotgbtrloSzszOVaRim5l2ltmdoJBIzjufU1XXwxpayCRYp/MAVRJ9rl3AKGAAO7IGHbjpzRenbYLSKJ8Wtc+H5NU07T5pIRDvSSQqF3bQxUjOeM4z6g+xMzeJlilvEe2mmaz3STiMKPKjAHPLfMevT0PtmwnhjSY7fyEtmEPl+WI/Pk2gbNmQN2A23jcOevPJp8nh3TJWcyQOTJkSYnkHmA4yG+b5gcDg5HX1NO9PsFpkNtrp1EmG0t3hllhlktnl2lZPLYIc4OQNzL9Qa0dPvE1HTba9hBEdzCkqg9QGAI/nUJ0m3jjH2MeRNHFLFDLkt5fmEMxwTz8yqfwqzbW8VnaQ21uu2KFFjRfRQMAfkKiTjbQav1JaKKKgoKKKKACiiigAooooAKKKKACiiigArkfin/wAk11X/ALY/+jkrrq5H4p/8k11X/tj/AOjkrWj/ABY+qIqfAzkfH/8AyRbwz/26f+k7Vr/Ej/kdPA//AGED/wCjIayPH/8AyRbwz/26f+k7Vr/Ej/kdPA//AGED/wCjIa7Y7x/7e/I53s/kei0UUV5p1hXnXxs/5Eu0/wCwgn/ouSvRa86+Nn/Il2n/AGEE/wDRcldGG/jRMqvwMPiR/wAjp4H/AOwgf/RkNGjf8l/8Qf8AYPT/ANBgo+JH/I6eB/8AsIH/ANGQ0aN/yX/xB/2D0/8AQYK3j/C/7df5mb+P5r8j0WiiiuA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf8A/8k28M/8AYJtf/RK10Fc/4B/5Jt4Z/wCwTa/+iVroKb3EtgooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACuf8ABX/IBuf+wtqX/pdPXQVz/gr/AJANz/2FtS/9Lp6fQXU6CiiikM861n/kv/h//sHv/wCgz16LXF+LPANz4j8SW+sWWuy6VPb24gUwxEsOWJIYOpGQ5GKzv+Fb+I/+ihap+Un/AMdrrl7OcY3lay7MxXNFvQ9FrC8aaJc+I/CN7pVk8Uc9x5e1piQo2yKxyQCegPauX/4Vv4j/AOihap+Un/x2j/hW/iP/AKKFqn5Sf/HamMacZKSnt5MblJqziQ2nhv4nWNlBaWviLS44LeNYo02A7VUYAyYcngVN/Y3xV/6GXS/+/a//ABmj/hW/iP8A6KFqn5Sf/HaP+Fb+I/8AooWqflJ/8drfnh/NH/wH/gGfLLs/vD+xvir/ANDLpf8A37X/AOM0f2N8Vf8AoZdL/wC/a/8Axmj/AIVv4j/6KFqn5Sf/AB2j/hW/iP8A6KFqn5Sf/HaXPDvH/wAB/wCAHLLs/vD+xvir/wBDLpf/AH7X/wCM0f2N8Vf+hl0v/v2v/wAZo/4Vv4j/AOihap+Un/x2j/hW/iP/AKKFqn5Sf/HaOeHeP/gP/ADll2f3h/Y3xV/6GXS/+/a//GaP7G+Kv/Qy6X/37X/4zR/wrfxH/wBFC1T8pP8A47R/wrfxH/0ULVPyk/8AjtHPDvH/AMB/4Acsuz+8P7G+Kv8A0Mul/wDftf8A4zR/Y3xV/wChl0v/AL9r/wDGaP8AhW/iP/ooWqflJ/8AHaP+Fb+I/wDooWqflJ/8do54d4/+A/8AADll2f3h/Y3xV/6GXS/+/a//ABmj+xvir/0Mul/9+1/+M0f8K38R/wDRQtU/KT/47R/wrfxH/wBFC1T8pP8A47Rzw7x/8B/4Acsuz+8P7G+Kv/Qy6X/37X/4zR/Y3xV/6GXS/wDv2v8A8Zo/4Vv4j/6KFqn5Sf8Ax2j/AIVv4j/6KFqn5Sf/AB2jnh3j/wCA/wDADll2f3h/Y3xV/wChl0v/AL9r/wDGaP7G+Kv/AEMul/8Aftf/AIzR/wAK38R/9FC1T8pP/jtH/Ct/Ef8A0ULVPyk/+O0c8O8f/Af+AHLLs/vD+xvir/0Mul/9+1/+M0f2N8Vf+hl0v/v2v/xmj/hW/iP/AKKFqn5Sf/HaP+Fb+I/+ihap+Un/AMdo54d4/wDgP/ADll2f3h/Y3xV/6GXS/wDv2v8A8Zo/sb4q/wDQy6X/AN+1/wDjNH/Ct/Ef/RQtU/KT/wCO0f8ACt/Ef/RQtU/KT/47Rzw7x/8AAf8AgByy7P7w/sb4q/8AQy6X/wB+1/8AjNH9jfFX/oZdL/79r/8AGaP+Fb+I/wDooWqflJ/8do/4Vv4j/wCihap+Un/x2jnh3j/4D/wA5Zdn94f2N8Vf+hl0v/v2v/xmj+xvir/0Mul/9+1/+M0f8K38R/8ARQtU/KT/AOO0f8K38R/9FC1T8pP/AI7Rzw7x/wDAf+AHLLs/vD+xvir/ANDLpf8A37X/AOM0f2N8Vf8AoZdL/wC/a/8Axmj/AIVv4j/6KFqn5Sf/AB2j/hW/iP8A6KFqn5Sf/HaOeHeP/gP/AAA5Zdn94f2N8Vf+hl0v/v2v/wAZo/sb4q/9DLpf/ftf/jNH/Ct/Ef8A0ULVPyk/+O0f8K38R/8ARQtU/KT/AOO0c8O8f/Af+AHLLs/vD+xvir/0Mul/9+1/+M0f2N8Vf+hl0v8A79r/APGaP+Fb+I/+ihap+Un/AMdo/wCFb+I/+ihap+Un/wAdo54d4/8AgP8AwA5Zdn94f2N8Vf8AoZdL/wC/a/8Axmj+xvir/wBDLpf/AH7X/wCM0f8ACt/Ef/RQtU/KT/47R/wrfxH/ANFC1T8pP/jtHPDvH/wH/gByy7P7w/sb4q/9DLpf/ftf/jNH9jfFX/oZdL/79r/8Zo/4Vv4j/wCihap+Un/x2j/hW/iP/ooWqflJ/wDHaOeHeP8A4D/wA5Zdn94f2N8Vf+hl0v8A79r/APGaP7G+Kv8A0Mul/wDftf8A4zR/wrfxH/0ULVPyk/8AjtH/AArfxH/0ULVPyk/+O0c8O8f/AAH/AIAcsuz+8P7G+Kv/AEMul/8Aftf/AIzR/Y3xV/6GXS/+/a//ABmj/hW/iP8A6KFqn5Sf/HaP+Fb+I/8AooWqflJ/8do54d4/+A/8AOWXZ/eH9jfFX/oZdL/79r/8ZrC8aaZ4/t/CN7L4h1ywutOXy/OhhQBm/eLtx+6X+LB6jpW7/wAK38R/9FC1T8pP/jtQ3Xws1q+tntr3x1f3MD43RTRu6tg5GQZcHkA1UalNSTbX/gP/AABOMmrWf3nGeJ7LxVD8PdHn1nU7W40d/I+yW8agPHmIlMnYOi5H3jz69a7E+CvG+p+IdHv/ABFq+m3cWm3STKEyrBd6lsbYxknaOtM+Kth/Zfww0bT/ADPN+yXEEHmbdu/bC65xzjOOleoUqldqClFLW/Qcaacmn5BRRRXnnSFedfGz/kS7T/sIJ/6Lkr0WvOvjZ/yJdp/2EE/9FyV0Yb+NEyq/Aw+JH/I6eB/+wgf/AEZDRo3/ACX/AMQf9g9P/QYKPiR/yOngf/sIH/0ZDRo3/Jf/ABB/2D0/9BgreP8AC/7df5mb+P5r8j0WiiiuA6QooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAOf/AOEB8Hf9Cnof/gth/wDiaP8AhAfB3/Qp6H/4LYf/AImugop3YrI5/wD4QHwd/wBCnof/AILYf/iaP+EB8Hf9Cnof/gth/wDia6Cii7CyOf8A+EB8Hf8AQp6H/wCC2H/4mj/hAfB3/Qp6H/4LYf8A4mugoouwsjn/APhAfB3/AEKeh/8Agth/+Jo/4QHwd/0Keh/+C2H/AOJroKKLsLI5/wD4QHwd/wBCnof/AILYf/iaP+EB8Hf9Cnof/gth/wDia6Cii7CyOf8A+EB8Hf8AQp6H/wCC2H/4mj/hAfB3/Qp6H/4LYf8A4mugoouwsjn/APhAfB3/AEKeh/8Agth/+Jo/4QHwd/0Keh/+C2H/AOJroKKLsLI5/wD4QHwd/wBCnof/AILYf/iaP+EB8Hf9Cnof/gth/wDia6Cii7CyOf8A+EB8Hf8AQp6H/wCC2H/4mj/hAfB3/Qp6H/4LYf8A4mugoouwsjn/APhAfB3/AEKeh/8Agth/+Jo/4QHwd/0Keh/+C2H/AOJroKKLsLI5/wD4QHwd/wBCnof/AILYf/iaP+EB8Hf9Cnof/gth/wDia6Cii7CyOf8A+EB8Hf8AQp6H/wCC2H/4mj/hAfB3/Qp6H/4LYf8A4mugoouwsjn/APhAfB3/AEKeh/8Agth/+Jo/4QHwd/0Keh/+C2H/AOJroKKLsLI5/wD4QHwd/wBCnof/AILYf/iaP+EB8Hf9Cnof/gth/wDia6Cii7CyOf8A+EB8Hf8AQp6H/wCC2H/4msPwj4J8KXOi3D3PhnR5nGqahGGksImIVbyZVXJXoFAAHYACu8rn/BX/ACAbn/sLal/6XT07uwrK4f8ACA+Dv+hT0P8A8FsP/wATR/wgPg7/AKFPQ/8AwWw//E10FFK7HZHP/wDCA+Dv+hT0P/wWw/8AxNH/AAgPg7/oU9D/APBbD/8AE10FFF2Fkc//AMID4O/6FPQ//BbD/wDE1U1Xwt4C0XTZdQ1PwzocFrDjfJ/ZcbYyQo4CE9SK6uuR+Kf/ACTXVf8Atj/6OSrprmmovqyZaRbOf/tT4O/9AvQ//BJ/9qo/tT4O/wDQL0P/AMEn/wBqro/DHhjQLjwjo80+h6bLLJYwO7vaRlmYxgkkkck1qf8ACJeHP+gBpf8A4BR/4Vs3RTtr96/yISm1fQ4j+1Pg7/0C9D/8En/2qj+1Pg7/ANAvQ/8AwSf/AGqu3/4RLw5/0ANL/wDAKP8Awo/4RLw5/wBADS//AACj/wAKXNR8/vX+Q7T8jiP7U+Dv/QL0P/wSf/aqP7U+Dv8A0C9D/wDBJ/8Aaq7f/hEvDn/QA0v/AMAo/wDCj/hEvDn/AEANL/8AAKP/AAo5qPn96/yC0/I4j+1Pg7/0C9D/APBJ/wDaqP7U+Dv/AEC9D/8ABJ/9qrt/+ES8Of8AQA0v/wAAo/8ACj/hEvDn/QA0v/wCj/wo5qPn96/yC0/I4j+1Pg7/ANAvQ/8AwSf/AGqj+1Pg7/0C9D/8En/2qu3/AOES8Of9ADS//AKP/Cj/AIRLw5/0ANL/APAKP/Cjmo+f3r/ILT8jiP7U+Dv/AEC9D/8ABJ/9qo/tT4O/9AvQ/wDwSf8A2qu3/wCES8Of9ADS/wDwCj/wo/4RLw5/0ANL/wDAKP8Awo5qPn96/wAgtPyOI/tT4O/9AvQ//BJ/9qo/tT4O/wDQL0P/AMEn/wBqrt/+ES8Of9ADS/8AwCj/AMKP+ES8Of8AQA0v/wAAo/8ACjmo+f3r/ILT8jiP7U+Dv/QL0P8A8En/ANqo/tT4O/8AQL0P/wAEn/2qu3/4RLw5/wBADS//AACj/wAKP+ES8Of9ADS//AKP/Cjmo+f3r/ILT8jiP7U+Dv8A0C9D/wDBJ/8AaqP7U+Dv/QL0P/wSf/aq7f8A4RLw5/0ANL/8Ao/8KP8AhEvDn/QA0v8A8Ao/8KOaj5/ev8gtPyOI/tT4O/8AQL0P/wAEn/2qj+1Pg7/0C9D/APBJ/wDaq7ceE/Dh6aBpf/gFH/hR/wAIl4c/6AGl/wDgFH/hRzUfP71/kFp+RxH9qfB3/oF6H/4JP/tVH9qfB3/oF6H/AOCT/wC1V2//AAiXhz/oAaX/AOAUf+FH/CJeHP8AoAaX/wCAUf8AhRzUfP71/kFp+RxH9qfB3/oF6H/4JP8A7VR/anwd/wCgXof/AIJP/tVdv/wiXhz/AKAGl/8AgFH/AIUf8Il4c/6AGl/+AUf+FHNR8/vX+QWn5HEf2p8Hf+gXof8A4JP/ALVR/anwd/6Beh/+CT/7VXb/APCJ+Gz00DS//AOP/CkPhTw2MZ0HShk4GbOPn9KOaj5/ev8AILT8jif7U+Dv/QL0P/wSf/aqP7U+Dv8A0C9D/wDBJ/8Aaq7f/hEvDn/QA0v/AMAo/wDCj/hEvDn/AEANL/8AAKP/AAo5qPn96/yC0/I4j+1Pg7/0C9D/APBJ/wDaqP7U+Dv/AEC9D/8ABJ/9qrt/+ES8Of8AQA0v/wAAo/8ACj/hEvDn/QA0v/wCj/wo5qPn96/yC0/I4j+1Pg7/ANAvQ/8AwSf/AGqj+1Pg7/0C9D/8En/2qu3/AOES8Of9ADS//AKP/Cj/AIRLw5/0ANL/APAKP/Cjmo+f3r/ILT8jiP7U+Dv/AEC9D/8ABJ/9qo/tT4O/9AvQ/wDwSf8A2qu3/wCES8Of9ADS/wDwCj/wo/4RLw5/0ANL/wDAKP8Awo5qPn96/wAgtPyOI/tT4O/9AvQ//BJ/9qo/tT4O/wDQL0P/AMEn/wBqrt/+ES8Of9ADS/8AwCj/AMKP+ES8Of8AQA0v/wAAo/8ACjmo+f3r/ILT8jiP7U+Dv/QL0P8A8En/ANqo/tT4O/8AQL0P/wAEn/2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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Fourier series is expressed as follows:\n", + "傅里叶级数的表现形式如下：\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "f(t)&\\sim\\frac{a_{0}}{2}+a_{1}cos(t)+a_{2}cos(2t)+a_{3}cos(3t)+\\cdots \\\\ \n", + "&b_{1}sin(t)+b_{2}sin(2t)+b_{3}sin(3t)+\\cdots \\\\\n", + "&\\sim\\frac{a_{0}}{2}+{\\sum_{n=1}^{\\infty}}(a_{n}cos(nt)+b_{n}sin(nt)\n", + "\\end{aligned}\n", + "\\quad\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The parameter a_ {0}, a_ {1}... and b_ {0}, b_ {1}... computation formula is as follows:\n", + "其中参数a_{0},a_{1}...和b_{0},b_{1}...的计算公式如下：\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "a_{0}&=\\frac{1}{\\pi}\\int_{-\\pi}^{\\pi}f(t)dt \\\\\n", + "a_{n}&=\\frac{1}{\\pi}\\int_{-\\pi}^{\\pi}f(t)cos(nt)dt \\\\\n", + "b_{n}&=\\frac{1}{\\pi}\\int_{-\\pi}^{\\pi}f(t)sin(nt)dt\n", + "\\end{aligned}\n", + "\\quad\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "samplereal = xlnk.cma_array(shape=(256,), dtype=np.float32)\n", + "sampleimag = xlnk.cma_array(shape=(256,), dtype=np.float32)\n", + "outreal = xlnk.cma_array(shape=(128,), dtype=np.float32)\n", + "outimag = xlnk.cma_array(shape=(128,), dtype=np.float32)\n", + "\n", + "for i in range(128):\n", + " samplereal[i] = 1\n", + " \n", + "# for i in range(64,128):\n", + "# samplereal[i] = 1 \n", + "\n", + "\n", + "for j in range(128):\n", + " sampleimag[j] = 0\n", + "dma0.sendchannel.transfer(samplereal)\n", + "dma1.sendchannel.transfer(sampleimag)\n", + "dma0.recvchannel.transfer(outreal)\n", + "dma1.recvchannel.transfer(outimag)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, 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-2.94566154e-04 -2.62618065e-04 -2.23934650e-04 -2.07483768e-04\n", + " -1.16825104e-04 -2.82347202e-04 -1.15275383e-04 -2.69472599e-04\n", + " -2.08199024e-04 -2.27272511e-04 -1.06632710e-04 -2.30967999e-04\n", + " -3.68654728e-04 -2.84314156e-04 -5.05626202e-04 -2.30193138e-04\n", + " -2.58386135e-04 -2.36988068e-04 -3.60608101e-04 -3.11732292e-04\n", + " -2.18391418e-04 -2.89916992e-04 -2.95162201e-04 -3.28958035e-04\n", + " -2.81572342e-04 5.18560410e-06 -3.53723764e-04 -3.40133905e-04\n", + " -3.83913517e-04 -2.85893679e-04 -2.80231237e-04 -3.38971615e-04\n", + " -2.73302197e-04 -3.10257077e-04 -3.85843217e-04 -3.24144959e-04]\n" + ] + } + ], + "source": [ + "print(outreal)\n", + "print(outimag)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pylab as py\n", + "import scipy as scipy\n", + "import matplotlib.pyplot as plt\n", + "import scipy.fftpack\n", + "import numpy.fft\n", + "\n", + "actualreal = samplereal[0:128]\n", + "fig1 = plt.figure()\n", + "ax1 = fig1.gca()\n", + "plt.plot(outreal)\n", + "\n", + "fig2 = plt.figure()\n", + "ax2 = fig2.gca()\n", + "plt.plot(outimag)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/06-SPMV.ipynb b/boards/Pynq-Z2/notebooks/06-SPMV.ipynb new file mode 100644 index 0000000..15e6be9 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/06-SPMV.ipynb @@ -0,0 +1,227 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "spmvol = pynq.Overlay(\"spmv.bit\")\n", + "\n", + "dma0 = spmvol.axi_dma_0\n", + "dma1 = spmvol.axi_dma_1\n", + "dma2 = spmvol.axi_dma_2\n", + "dma3 = spmvol.axi_dma_3" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# As a data structure, **CRS** is composed of three arrays. The values (**values**) array holds the values of non-zero elements of the matrix. The columnIndex (**columnIndex**) array and row pointer (** rowPtr**) array encode the position information of non-zero elements. A column index stores the elements of each column, and a row pointer contains the value of the first element of each row.\n", + "**CRS** 作为一种数据结构，由3个数组组成。值(**values**)数组保存矩阵中非零元素的值。列索引(**columnIndex**)数组和行指针（**rowPtr**）数组对非零元素的位置信息进行编码。列索引存储每一列的元素，行指针包含每一行第一个元素的值。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/crs.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "rowPtr = xlnk.cma_array(shape=(5,), dtype=np.int)\n", + "columnIndex = xlnk.cma_array(shape=(9,), dtype=np.int)\n", + "values = xlnk.cma_array(shape=(9,), dtype=np.float32)\n", + "x = xlnk.cma_array(shape=(4,), dtype=np.float32)\n", + "y = xlnk.cma_array(shape=(4,), dtype=np.float32)\n", + "\n", + "rowPtr[0] = 0\n", + "rowPtr[1] = 2\n", + "rowPtr[2] = 4\n", + "rowPtr[3] = 7\n", + "rowPtr[4] = 9\n", + "\n", + "columnIndex[0] = 0\n", + "columnIndex[1] = 1\n", + "columnIndex[2] = 1\n", + "columnIndex[3] = 2\n", + "columnIndex[4] = 0\n", + "columnIndex[5] = 2\n", + "columnIndex[6] = 3\n", + "columnIndex[7] = 1\n", + "columnIndex[8] = 3\n", + "\n", + "values[0] = 3\n", + "values[1] = 4\n", + "values[2] = 5\n", + "values[3] = 9\n", + "values[4] = 2\n", + "values[5] = 3\n", + "values[6] = 1\n", + "values[7] = 4\n", + "values[8] = 6\n", + "\n", + "x[0] = 1\n", + "x[1] = 2\n", + "x[2] = 3\n", + "x[3] = 4\n", + "\n", + "dma0.sendchannel.transfer(rowPtr)\n", + "dma1.sendchannel.transfer(columnIndex)\n", + "dma2.sendchannel.transfer(values)\n", + "dma3.sendchannel.transfer(x)\n", + "dma0.recvchannel.transfer(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 11. 37. 15. 32.]\n" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write test cases\n", + "写测试用例" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[11 37 15 32]\n" + ] + } + ], + "source": [ + "testx = ([1,2,3,4])\n", + "testm = ([3,4,0,0],[0,5,9,0],[2,0,3,1],[0,4,0,6])\n", + "testy = xlnk.cma_array(shape=(4,), dtype=np.int)\n", + "for i in range(4):\n", + " y0 = 0\n", + " for j in range(4):\n", + " y0 += testm[i][j] * testx[j]\n", + " \n", + " testy[i] = y0\n", + " \n", + "print(testy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare the data, correct 1, incorrect -1\n", + "对比数据,正确1,不正确为-1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "for i in range(4):\n", + " sigma = 1 if testy[i] == y[i] else -1\n", + " \n", + "print(sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/06-SPVM.ipynb b/boards/Pynq-Z2/notebooks/06-SPVM.ipynb deleted file mode 120000 index 5f7c462..0000000 --- a/boards/Pynq-Z2/notebooks/06-SPVM.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/06-SPVM.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/07-MATRIXM.ipynb b/boards/Pynq-Z2/notebooks/07-MATRIXM.ipynb deleted file mode 120000 index 6a5e611..0000000 --- a/boards/Pynq-Z2/notebooks/07-MATRIXM.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/07-MATRIXM.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/07-MATRIXM.ipynb b/boards/Pynq-Z2/notebooks/07-MATRIXM.ipynb new file mode 100644 index 0000000..8c92023 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/07-MATRIXM.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "mmol = pynq.Overlay(\"matrixm.bit\")\n", + "\n", + "dma0 = mmol.axi_dma_0\n", + "dma1 = mmol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " ..., \n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]]\n", + "[[2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " ..., \n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]\n", + " [2 2 2 ..., 2 2 2]]\n", + "[[128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]\n", + " ..., \n", + " [128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]\n", + " [128 128 128 ..., 128 128 128]]\n" + ] + } + ], + "source": [ + "#生成输入数据,并输出结果\n", + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "A = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", + "B = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", + "AB = xlnk.cma_array(shape=(32,32), dtype=np.int)\n", + "\n", + "for i in range(32):\n", + " for j in range(32):\n", + " A[i][j] = 2;\n", + " B[i][j] = 2;\n", + " \n", + "dma0.sendchannel.transfer(A)\n", + "dma1.sendchannel.transfer(B)\n", + "dma0.recvchannel.transfer(AB)\n", + "\n", + "print(A)\n", + "print(B)\n", + "print(AB)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/08-HISTOGRAM.ipynb b/boards/Pynq-Z2/notebooks/08-HISTOGRAM.ipynb deleted file mode 120000 index 963b18c..0000000 --- a/boards/Pynq-Z2/notebooks/08-HISTOGRAM.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/08-HISTOGRAM.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/08-HISTOGRAM.ipynb b/boards/Pynq-Z2/notebooks/08-HISTOGRAM.ipynb new file mode 100644 index 0000000..f1d9651 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/08-HISTOGRAM.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write a driver for hls ip\n", + "给hls ip写一个上层驱动" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pynq import DefaultIP\n", + "\n", + "class SumDriver(DefaultIP):\n", + " def __init__(self, description):\n", + " super().__init__(description=description)\n", + " \n", + " bindto = ['xilinx.com:histogram::1.0']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "hisol = pynq.Overlay(\"histogram.bit\")\n", + "\n", + "# dma = overlay.const_multiply.multiply_dma\n", + "# multiply = overlay.const_multiply.multiply\n", + "\n", + "dma = hisol.axi_dma_0\n", + "# s = sumol.sum_0" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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BNZw26Lq8xRoGRpCxUeRIMHevcfdqjrPgTxFdeGodCtb6Ge0XTBaD/iYT2YglwwL7Y1YzIQVGx2CgJ0OTXp/2OT1X86Pscnqv50Du73/r+tDhZfDetv4qiv4PslnH5sUk11bX9wjSIqgMj2uPKlJwV8xiCFK8ZUZh0vwPc2PjA3sixSWUd9PfRTNqd0zB5Q3yi2yIUIMjDfzkfwgkkegfY5PVfzo+xyeq/nTvb+v6/r5k20t/X9anLXPhlr7xBq93cyqttfWVtBE0TESxSRPK+8cYGC6EHnkdPXn4fCXim38O2di93a3LfaLuW8itr+ewWRpZjIkiyxo0g2gnMfAJbknAz6T9jk9V/Oj7HJ6r+dLdWKu/69LHAad4M1K18OSWE09q0zeHo9KVldipkQSDccrnb849+vFO17wVeavJfss0KibTrOCIea6Ey28zy8soDKpyo3Kdw5IHArvfscnqv50fY5PVfzpttu/9df8AML/193+SOY8I6HNoljdfbIo4bi7uDM6pf3F50VUGZpzuY4UdlAGBjjJ6Cp/scnqv50fY5PVfzovcRBRU/wBjk9V/Oj7HJ6r+dICCip/scnqv50fY5PVfzoAgoqf7HJ6r+dH2OT1X86AKp/1q/wC6f6Vla/pl3e/YrzSnhW/0+czQpcZEcoKMjIxGSuVY4YA4IBwwyDvGyk3g7l4BHU+1L9jk9V/OgDg73wtrWtTSahqb2UF49xZeXbQTPJFDDBcCVv3hRS7tz/CoGFHqxq3Pg3WE0uGC3hsbmZLq9mEi6lc2MsQmnaRds0KliMEboyMEgHPyivRvscnqv50fY5PVfzo6WHdnnGv+A9S1O6gmaePUZJtNj0++km1C5s1baWJkMcBAlDeY+Y2KjgYYZNb+m6PqWl3viKSD7KyX8y3FmzStkOIEj2uNvABjByCxIJ4GOeo+xyeq/nR9jk9V/Ojv/W+oloeeTfDSGGzjt9MvLoGeznsL6S5vZpd0cqHc8aMxVW8wK2FCggt7Us/hfxHq97c3mqf2XbSrbWqWqW00kgaWCfzgXJRSFYgDAzt55NehfY5PVfzo+xyeq/nR1v2/r9Q6W/rp/kjgLTwvr0/iiLWtV/s6AjU1u2gtpnk2xi0eDbuKLubcwOcDj6ct07wPd2Xi5rxhC9ml7cX0Mz6ldOweUN8v2XIhUgyMN+Tlf4QSSPQfscnqv50fY5PVfzoDdW/rr/mzz7SPCmt2kV/FLHp1vbzWT262H224vLSaQ9G8qQAQIBkeXGSMPjPyjOr4R0bVNI+2HUnEUMpQQWY1Ke/EWAdzCWYBhuyBsxgbcj7xrrPscnqv50fY5PVfzp3Dcqyf6p/900TIZIXQdWUgZ+lWXspGRgGXkY6ml+xyeq/nUtXVhrRnm2l/DSLQbXw5qGi2Ok2+v6XEkN3KsQSO7UqFly4Tdu/iV8ZzweGNSzeE/EV34ttdRvLiCaK1vZZllbUp8NC0ciLGLUJ5SMokA35LHaSTzXon2OT1X86Pscnqv505e9v/AF/X+Yv6/r+u3Y87Pgm6TQdNtb64jEVl4bn0u5a3V5H3usQ3IoXLj923HU8DHNZsOna145h1d7+zFgTaWkMB23VusksMzS4zJHHKoJ2gsq/Lu4ZiDj1b7HJ6r+dH2OT1X86bbbu/63f6jvpb+un+SOY8I6HNoljdfbIo4bi7uDM6pf3F50VUGZpzuY4UdlAGBjjJ6Cp/scnqv50fY5PVfzovcRBRU/2OT1X86Pscnqv50gIKZH/qk/3RVr7HJ6r+dIllIqKCy8DHU0Aeb3Pwt0278I6zbXGkaLLrd9JeSQ38lsrMrSyO0ZMhTcCoZRkZxjjNSavpN9oTXWsxqtzONZS+toI4Z5g6/ZVt2V/KidkON5DBWA+XPU16L9jk9V/Oj7HJ6r+dGq2/rb/Id77+f4nnfhXQtVnurXWr+KO28yLUQ0Dh0cfaLhHjO1hkDahzuw3IyoOQNOLwpcSfD/R/DN7MmyCG3gvngldC6RqNwRgAeWUDPynBJ4Ndj9jk9V/Oj7HJ6r+dPpZbf5X/AMxa7/1qcE3gu90zUDcaDIs0UF4moW8OoX00jSTGJ4ZVeRg7BSjKQfmwwPGKZYeGfEWnSxaqg0ubVBPeF7czyJB5dxIj8OIydymNf4fm56V6B9jk9V/Oj7HJ6r+dLrf+v60D+vz/AM2ed6N4CvoNH1Cw1W6tsX2iRaa8ltn5XBn3MAQOP3oxz2NMsPBWpwaPqIuLLT3vLmO3iEU2r3t0sixsW/10h3QnLEpsUlDzlu3o/wBjk9V/Oj7HJ6r+dA7/ANfd/kjgD4X1xvDaWtwtteTLem4jt59Wut1sm3AEd6F87dnJ3FejsnTmul8O2V7p2g21rqlz9puowd7mRpMAsSE3t8z7QQu9uWxk8mtr7HJ6r+dH2OT1X86dxFV/vJ/vf0NPqR7SQPHyvLevsaf9jk9V/OkBBRU/2OT1X86KAK3h6WODwbpcszrHHHp8LO7nAUCMEknsKvNf2aQwSvdQLHcsqwOZABKW5UKe5PbHWqnhn/kUtI/68Yf/AEWKh0/wrpmmao17bLISN32eB3zFabvv+UvRNx6/kMDirqfG/UmHwo2a4qTxL4h1DWZrfT7eLR4oLeJzFqtl5srszSDIMc4AXCAeuc+1drXnereDW8N6hYv4Et4NNttQuoba8t7awDpGoEhM5wRjqqknjpXBjY4iVBrDO09LN+uvfoaw5eb3tjp/CmuXeuaWJL/T5raZFAaZo1SKc5ILRgOzY4/i55HWpNZ8WaNoNysGp3EqP5fmv5VrLMsMecb5WRSIkyD8zkD5W54OGeGfCWl+FrWQadawJdXO03l1HCEa5cEnc2OpyzfnWD4/8K+IfE63Vpp9zEbC5sjAkbalPZ/Z5TuBkIiQ+eCGUbHIUbeh3GuqN0lcSszZ1XxppGnWt8RdRtc2aTM8UiSqF8pFdixVGKrtdDuCkHeuMkgHoFbcgb1Ga881nwLrWpvrd4kmnJeaxp0mmSIWYJHEI/3LB9m4tvLlhjGH77BnopPGel2crW0trrjSQny2MWgX0iEjg4ZYSGHuCQavSy7k62X9dv8AgkV/480y18QWejWwlubm4vhZyN5UiRRtsZziUpsdhgZQNkZOcYNMT4k+F5dvk3l1KZEMkKx6bcsbhQQGMQEeZdufm2Z287sYNUF8L66mpWFvbnTzo9trEmqefLLJ9pYSGRzH5ZTAIaU/Nu5AxgVf0Xwxe6cPDnnywN/ZdhNbTbGY7mfy8FcjkfIeuO1Rry36/wDA/wAxu3Npt/wX+li1eeOfD1lDbTTXzvFc263SywWssyRwt92WRkUiJDz8z7Rw3PBx0AORkcivLdR+H3ii48GWfh6O8tpLePSFsiqancWiwThWVpMRxk3CkFRscqo29DuNdcfGenWZ+zXVprRnh/dyGDQL6SMsODtcQkMM9CODVu13YT0ZFa+MZZ9cggm01YtMvLyawtLwXG6SSeIPuDR7AFU+VJtYOxOBkDPHU1xen+FdTTWLKO4azGjafqVxqdq8bOJ5GlEmI3jKgKFMz/MGJO1cqOa7Sl0QPfT+v6VgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI5P9ZF/v8A/spqSo5P9ZF/v/8AspqSgAooooAyPDUjDwnpP7pz/oUPII/uD3rT8xv+eL/mv+NUPDP/ACKWkf8AXjD/AOixWnV1PjfqTD4UR+Y3/PF/zX/GjzG/54v+a/41JRUFEfmN/wA8X/Nf8aPMb/ni/wCa/wCNSVyb/ETTV1DUbOOw1CWTTUmkuCixgBImw7DLjIBxx19qdribS3Oo8xv+eL/mv+NHmN/zxf8ANf8AGsrw34nsfFFpNPp6TxiGTy3SdQDnAOeCRjn17VX1HxZJaeIJtIsPD+qatcQW8dxM1m1sqosjOFGZZkJOY26A0noNaq6N3zG/54v+a/40eY3/ADxf81/xrKPi7QYtDt9Xv9UtdOs7htiSX8q2+HGQYyHIw4KsCvUYPpUS+NdBPiDUNHbUbaO40+zjvbhnnjCLE2fmzuyMAAkkAAOhzzQBteY3/PF/zX/GjzG/54v+a/41Bpuq6frNkt5o9/bX9qxIWe1mWVCRwQGUkcVS1DxNZab4o0rQriOY3OqJK8MihfLXywCQxJyCdwAwD+FHWwdLmp5jf88X/Nf8aPMb/ni/5r/jXKN8StGW68kwXv8AyGv7F37EC+ds37/vf6vHfr7d6tXPjrTLbVNX07yLyW50oW4dI41PnvOSI448tyxIwc7QM8nAJB/X9feh/wBfodD5jf8APF/zX/GjzG/54v8Amv8AjXNnx1bLblZNJ1OPU/tYs10lli+0PIU8wYYSeVjywW3eZjAIzu4rU0PXYtchuMWtzY3NpMYLmzugvmQvgMAdjMpBVlYFWIwfXIAI0PMb/ni/5r/jR5jf88X/ADX/ABqi+sjyBLbWN3dKS+PJVcFV4LZZgMZ6DOT1AxzTJtft0jElvBcXcXkrcSSQKuI425DHJBPAJwATx06UAaPmN/zxf81/xo8xv+eL/mv+NNmvLa3tftNxPHDBgHzJHCqM9OTTJNTsYbWO6lvbdLeQgJK0qhHz0wc4NAEvmN/zxf8ANf8AGjzG/wCeL/mv+NUG8Q6fHpUV/PMsKTRtJFHI6q8mOoUE8n6etWE1WxfI+1wK6x+a8bSqGRcA5YZ4AyOaAJ/Mb/ni/wCa/wCNHmN/zxf81/xqE6nYCxF6b22+yk4E/mrs64+9nHXiibU7C3to7i4vbeKCXHlyvKqq+RkYJODxQBN5jf8APF/zX/GjzG/54v8Amv8AjTLy8isbNrmbJjXGduO5A/rULaxY/Z47iO5hlgkZh5yTIVG1Sx5zzwp6Z/LJoAs+Y3/PF/zX/GjzG/54v+a/41Stde0y6tLScXkEYvFBhSSVQzHptAzyQeCB3q+8scbIsjqrSHagY4LHGcD14BP4UAN8xv8Ani/5r/jR5jf88X/Nf8ag1LU7fSrdJbotiSRY0VRksxOOP5/QVDdautvcSww2d1eNCoaY24XEeRkA7mGTjnC5P5jIBd8xv+eL/mv+NHmN/wA8X/Nf8ahh1CCe4jijJPmwiaJ8fLIvfH0yM/7wq1QBH5jf88X/ADX/ABo8xv8Ani/5r/jUlFAEfmN/zxf81/xo8xv+eL/mv+NSUUAR+Y3/ADxf81/xo8xv+eL/AJr/AI1JRQBH5jf88X/Nf8aPMb/ni/5r/jUlFAEfmN/zxf8ANf8AGjzG/wCeL/mv+NSUUAR+Y3/PF/zX/GjzG/54v+a/41JRQBH5jf8APF/zX/GjzG/54v8Amv8AjUlFAEfmN/zxf81/xo8xv+eL/mv+NSUUAR+Y3/PF/wA1/wAaPMb/AJ4v+a/41JRQBH5jf88X/Nf8aPMb/ni/5r/jUlFAEfmN/wA8X/Nf8aPMb/ni/wCa/wCNSUUAR+Y3/PF/zX/GjzG/54v+a/41JRQBH5jf88X/ADX/ABo8xv8Ani/5r/jUlFAEfmN/zxf81/xo8xv+eL/mv+NSUUAR+Y3/ADxf81/xo8xv+eL/AJr/AI1JRQBH5jf88X/Nf8aPMb/ni/5r/jUlFAEfmN/zxf8ANf8AGjzG/wCeL/mv+NSUUAR+Y3/PF/zX/GjzG/54v+a/41JRQBH5jf8APF/zX/GjzG/54v8Amv8AjUlFAEfmN/zxf81/xo8xv+eL/mv+NSUUAR+Y3/PF/wA1/wAaPMb/AJ4v+a/41JRQBH5jf88X/Nf8aPMb/ni/5r/jUlFAEfmN/wA8X/Nf8aPMb/ni/wCa/wCNSUUAR+Y3/PF/zX/GjzG/54v+a/41JRQBH5jf88X/ADX/ABo8xv8Ani/5r/jUlFAEfmN/zxf81/xo8xv+eL/mv+NSUUAR+Y3/ADxf81/xo8xv+eL/AJr/AI1JRQBH5jf88X/Nf8aPMb/ni/5r/jUlFAEfmN/zxf8ANf8AGjzG/wCeL/mv+NSUUAQs5aSLMbL83U4/un0NTVHJ/rIv9/8A9lNSUAFFFFAGZ4Z/5FLSP+vGH/0WK06yPDUqjwnpIw//AB5Q9EP9we1afnL6P/37b/CrqfG/UmHwodLFHPC8UyLJHIpV0cZDA8EEdxWBY+Eo7W+ia5vp7yxsm3afZTDK2p9SerleibvujpzzW75y+j/9+2/wo85fR/8Av23+FQUSV4pr9vF9u1RJrO3+0ldTXD6DNJIXeYGH96DhmZeUkHCDg5zXs/nL6P8A9+2/wo85fR/+/bf4U07EyjzHC/Cyaa5h1mefT4tO8y5QrBBbNbxgCMDKoeRkg/U5q9dtq2kfEDUdTtvDmoaraXmn2sKSWU1su143mLBhLKh6SLyAe9dZ5y+j/wDftv8ACjzl9H/79t/hQ9WOKtG39b3PPZ9D8Safptj9ms3a5uJby4u5tKW1e5tZJ5RII0a5wnldQ5wWYqpAFZ+l+G/E2k6SkY0OO4n/ALC022IkaCRUltpX8wBWcAybXDxk/JuUZIxg+pecvo//AH7b/Cjzl9H/AO/bf4UttF/X9XKv/XzucD4X1OLwi2ry+OdUXSn1S+Fxavrl5ZxTzoIIkORCRHkFMfL2IzzTdeZvFWsW2ueCLix1saZY3Co9rexshuRLbyJEWDcbhGwz0APPUZ9A85fR/wDv23+FHnL6P/37b/Cjqn2/ysCdjy4eANXBFr5B8oaqZvtHmrnZ/Zfkeb97OfN7de/TmrNhY+MdDGq61baCt1q2oWNr/o4uYysc7T3DyjLSLuEYlXjcobAAI6j0jzl9H/79t/hR5y+j/wDftv8ACm3f8PwC/f8ArW5wS6RdyeHP9I8Lahezteie/GoXFul9cts2ieCSKbZG6HaB8ybVUhcEDO74RstStrK+W9S/tbWSbNlbajdi6uYE2AMHkDvnL7iAXfAI5H3R0HnL6P8A9+2/wo85fR/+/bf4Uu4jCtzI3hy1sjYXF0iRCC4W2uBFJG6YBHLLwcHkHpjgg1HLFqv2e1066sJp7OOBRO1o8Q849PL+ZlwuOuBz04Gc9D5y+j/9+2/wo85fR/8Av23+FHW4FDV0umtLc2VuWZJAWCLGZYhtIym87A3IHPYnrWXYWGoWcNrPPZS3LxvchoS8Qc+Y+5X6hc4GCAe5xXR+cvo//ftv8KPOX0f/AL9t/hQByiaTqFtoxh/s37Q8+nC18pZExAwLHkkj5TuHTJ+XpVltFvG0jVIhGUlmmikj2su5wiR5AJyOqsPm49eK6Lzl9H/79t/hR5y+j/8Aftv8KO4HOrp14lkZTb6g07Xnng+dbieM7Nu4AAR8jggk8HPXiiWx1BdPs/8ARJ/tCRyqXs2gDKGIO10cbDnALFe444NdF5y+j/8Aftv8KPOX0f8A79t/hQBm39nczeGBa/Z4ZbgRxhoY8LGxBXIGeg4NUhp17calHeG1Nuj3hkMTsu5FEDR7m2kjJOOhPGPw3/OX0f8A79t/hR5y+j/9+2/woeoLRWOVTTdSTT7SGOwnjnW0jt3PmQPE5QniVWydvU5TkhuxAFdWzsrIBGz7jgspGE46nJ6duM9aTzl9H/79t/hQJ0IBG8g9CEb/AAp3uBh+INH1G+ZprKeFuI1SGSEkrhwzENvA5wM8dFxT5oNRsprwW8M8wu2WUTWZiV43CqpBEpxg7Rg/MeT6A1s+cvo//ftv8KPOX0f/AL9t/hSAyYEum1DS4ryRZbu2t2e6kTplgFHYdTk9P4TW1UfnL6P/AN+2/wAKPOX0f/v23+FAElFR+cvo/wD37b/Cjzl9H/79t/hQBJRUfnL6P/37b/Cjzl9H/wC/bf4UASUVH5y+j/8Aftv8KPOX0f8A79t/hQBJRUfnL6P/AN+2/wAKBOhzjfx1+Rv8KAJKKj85fR/+/bf4Uecvo/8A37b/AAoAkoqPzl9H/wC/bf4Uecvo/wD37b/CgCSio/OX0f8A79t/hR5y+j/9+2/woAkoqPzl9H/79t/hR5y+j/8Aftv8KAJKKj85fR/+/bf4Uecvo/8A37b/AAoAkoqPzl9H/wC/bf4Uecvo/wD37b/CgCSio/OX0f8A79t/hR5y+j/9+2/woAkoqPzl9H/79t/hR5y+j/8Aftv8KAJKKj89M4+fPpsb/Cjzl9H/AO/bf4UASUVH5y+j/wDftv8ACjzl9H/79t/hQBJRUfnL6P8A9+2/wo85fR/+/bf4UASUVH5y+j/9+2/wo85fR/8Av23+FAElFR+cvo//AH7b/Cjzl9H/AO/bf4UASUVH5y+j/wDftv8ACjzl9H/79t/hQBJRUZnQAk7wB1JRv8KPOX0f/v23+FAElFR+cvo//ftv8KPOX0f/AL9t/hQBJRUfnL6P/wB+2/wo85fR/wDv23+FAElFR+cvo/8A37b/AAo85fR/+/bf4UASUVH5y+j/APftv8KPOX0f/v23+FAElFR+cvo//ftv8KBOhAI3kHoQjf4UASUVH5y+j/8Aftv8KPOX0f8A79t/hQBJRUfnL6P/AN+2/wAKPOX0f/v23+FAElFR+cvo/wD37b/Cjzl9H/79t/hQBJRUfnL6P/37b/Cjzl9H/wC/bf4UASUVH5y+j/8Aftv8KPOX0f8A79t/hQASf6yL/f8A/ZTUlQtIHkiADfe7qR/CfWpqACiiigDM8M/8ilpH/XjD/wCixWnWZ4Z/5FLSP+vGH/0WK06up8b9SYfCgoooqCgooooAKKK8yvPC/jc63q80d5JcW1z5/wBjC61PCIC7ZjfaBj5Bxt6GmiW2j02iuW8CaX4g0nTruHxNdNcyPPvhLXLT7V2gEbm56jOOnP1rkPHVnY3vxC1OK70m81G8bQIF0421u8vkXJmn2PuUHymBxiU4C4b5hnldbFR1V/63SPWKK8vufDVw1v4vvp9Lee9lv7dJJVtyZbqyEVsbiOI9WVgso2r948daj/sqP+z2dNDuh4P/ALaEp0n+zpBm3+z7Sfsezfs+04fZt6/PjHNP/gfjb/P8GHQ9UorxjxLoUt3c2J0rS7ux037B5ekRTaFNezWc/nsS8WJU+yMQYipkIAVQp2bStbWo6e4+J8V5Hpct7e/a4MyXGkSho0CBWeG/R/LjiA3MYXyWbeMfOKErtA9L+h6aThST29Bmo7edLq1iniEipKgdRLG0bAEZ5VgGU+xAIrzTStOaH4iXNxa6TNJLNLdm4ubnSJre4iVskbrsN5VzHuCqkYBKqUPVDWCnhG4ufCN3Le6FcS6hZeD7FdP821YvDdosxIjBHEqnZ0+YZHrQtVf0/X/Iq2rX9bpfqe20V5P4vs7RNbu59a0m4uNUl1DTjpmoC0Zlig8yFTGJsbYz5nmkoWBbeODkCvV3JCMQCxA4A6n86X2bkrUWivJPDHh8yx6vY2emtAtxprK9/c6RNp86zhsx/aC7FLqXOWMyDAKnnDimazpw1bw7pmqapprTT6kJ754ZtDl1W13SBAitHCwdJVjVFWToB5nPzCj+v6+78u6HbW39bf1/SPVI9StJdWn02OXN3bxRzSx7T8qOWCnOMHJRuh7fSrVeQa14fvpbnWbmDQntNSvtB08SSC1e6JVJW+1QmUYMreXsUpvDSADBOMi1pPho3ei6fZ3NgZ9Kk18yizGjSWEEUP2dwwFu7uyxF85D7QSzfLhhl21t/W9v69GLpf8Ar4b/APAPU3cRxs7BiFBJ2qWP4Acn6CsTTfGWkarq66ZbDUYrx4mmWO80q6ttyKQCwMsajgsB171x2meE49I1LRbzTtGa3uIfEN1C0yQHdFZFbjYmcfLBnZheEyQQMmus8N2k8usa5rF/FJHPc3ZtoFlQqUt4cqgGR0ZjI+e+/wClJa/16f5/mEtNP63f+T/A2dQ1C10rT5r2/lENvCuXbBY+gAAyWJOAAASSQACTUelavZa3Zm50+R2RXMbrLC8UkbDqro4DKehwQDgg96zPGtvcTeHBJawS3LWt3bXbwQrueRIp0kcKv8R2qSAOSQAOTUPhCOWa98Qas1vPbW2p36y20dxC0MhRYIoy5jcBlyyNwwBwAe4ojrf+u3+b+4H0OmrFv/E2l6IIYL6S4aYwCUxWtnNcsidN7CJWKrkEAnAODjoa2q4vXQltrMd0f7b0yWSzjjXUtItWvPOCliYpIhFIF2lgwYrk7iAw+YHqwtKFWpyyv8v+Gb+5N+QnojsLe4hu7aO4tZUmhlQPHIjZV1IyCCOoIqGbUrSDUrbT5Zgt1dK7wx4PzBMbueg69D15x0NVvDcdzF4Z0+O+tIbK4WBRJbwxhEjOOgUEhfoCcetcrrWl69caxe+I7R4lisZ4jBZmyd7iaODdvCMHAG/fMoGxsgrg8jF0sPTnVlCUrJXs+72X+foTd8t+p1i6/prxROJ2/e3LWqKYnDGVc7lK4yMBSckYwM9Oas6ff22qabbX9hJ5ttdRLLDJtK7kYZBwcEcHvXHwxXY8UHxF/Z04s795LRYPIcSwjCqtwyYyN5jCnjhPKJxhq2/BFvNaeAdBt7qGSCeLT4EkikUqyMIwCCDyCPSrxGHp06fNF66dvO6+Vl/TQ09bGnb6la3c15Fby73sZRFcDaRsfYr45HPyup4z1rIPjnQyYBBJfXRuLWO7T7JplzP+6kzsY+XGdudp4ODwayrTQNUudf8AFFxHrOq6TFNfKYo7eC3KTAW0I3gywuTyCvBx8vrmsjwh4c1d5LINfatofl+HNOhdobeIb5F87cjedE+GXIyBgjdz2rohg8NySnOeyi9+61v7r/C/mDb/AK9UdnP4v0O3js5Hvt0d5EJo5IoXkRYyQA7sqkRrk/efaODzwa2q861zRm0a31XR9N068uYdT0JNNsGjiaZRKpmG2RwDsH75W3Pgfe54r0GCMxW0cbtuZEClvUgda5cTRpU4RlTb1vv20+7ez80CbvZ/1/X6ElFFFcJQVHH/AKyX/f8A/ZRUlRx/6yX/AH//AGUUAQx6laS6tPpscubu3ijmlj2n5UcsFOcYOSjdD2+lY9x460G3aFRNeXDztOscdnp1xcOTDJ5cpKxxsQFcgZPBzxmsy40HU7/4lardWurapo9udMs0E1nDAyTsJJyVzNE4yuRwuPvc9q5zw94Y1s6xpMbX+saaYF1nzb+O2hDyb75GTf5kTRjevzjCjOMjihatf13G0lf5fkdzL420GHTLe/a6naK4d0SOOzmeYFCRJuiVC6bSMMWUbeM4yK3IZorm3jnt5FlilUOkiNlXUjIII6giuGudMTwZrGm3sdtqeqWn2a9iuJordrmZ7iZ45N7rGuQHKMCQoRflHAxXQ+DNNudG8D6Lpt+Atza2MUUqg52sEAIz3x0/CmtVf+uv+X4ie5BeeO9BsNQu7S5lvt9i4S6lj0y5khgO0P8APMsZjUBWBJLYAOTipNQ8Z6Npuo/YZWvri4EKXBWx0y5uwsblgrFoY2AztbGT2rAs9aGgeIPFkV1pWsXE13qCzWqW+lXEkdwPssKACUIYxllK5ZgBjkgc1Q8ONL4J1oQa7aanL/xIdPthNY6Zc3cZkjafeu6JHxjcvU9CKS2v6fitfuG1a/8AXVHW3fjbQrRbFhcXF2NQhae1Gn2M92ZI12gtiFGwAXUZOOtaGka1Y67Ztc6bK7okhikSWF4ZInHVXjcBlPIOCBwQehFefWHhrXbjxBpMwudR0FpbfVLlpLaKKQ2/nXUUiQuZEkQHByQMHKnBwDXSfD+2vLLTb+21qK5OsreMb+8mj2pfPtAWaMgBdpRUG1fu42nJGS0tNf61t/X+QnpsdE+pWkerQ6Y8uLyeF5449p+ZEKhjnGBguvGc8/Wqmr+JdN0OaKG+e4eeZS6QWdnNdSlRwWKRKzBckDcRjJAzWH4hvBpPxD0fUrm01Ca0XTbuBpLLT57rY7SQFQREjEZCN19KaNXj03xNca9Np+rS6fqllBFDLDplxJJC8Ty5R4AhlTPmZBKY4OSPl3L7Kfr+bGzoLbxHpF6ivaX8U0b2v2xZI8lWhzgsG6HBHI6im2vibSLyysLuC8zBqCs1szxum4KpZiQwBTAU53Yx061wVzoWqppNnOumXEUurXd7azWqDc1nb3ku/c+3IG3YCecAuevexdeFr+/ufEulW9q8Vvb2dzFpzupSOY3ZWR1DdMBkKn0DUev/AA3l+X4i66d/w7/1+h1+l+MNF1m9S0sbmYSyoZIPtFpNAtyg6tE0iqJVGQcoWGCD0IqOPxxoEupJZR3kpMk5t47j7JMLZ5eRsW42eUWyCuA2dwK9eKx7y9k8W6lottYaVqdi9jcm5u57yzkgFriJ02K7ALKxL4zGXXAJzjbmHRdSNn4Z0nwtceF7641G1WC1mtpbJxaL5eMzfaChiKgLvGCWJwMBsgNa/wBb+f8AXrsJ7f1p5G+/jbw9HoWo6y+obbDTJ3truYwyfu5EYKy7duW5IGQCD2rdUhlBHQjIrxLUtA1pvDGuabDpN60WoDU7x1WBvmkSa4Ea4xks/mwsvqIyRXtcQIhQHg7RR0v/AF/VypaO3r/wPwEP/Hwn+438xVPV9csdDhjl1BpsSNtRLe2knc4GSdkas2AOpxgdzVw/8fCf7jfzFYXiC3voNc07WdP0+bUzbQT2z2sEkaP+9MZD5kZVwDHg85w3ANS7rYEb8Usc8KSwuskcihkdTkMDyCD6VkReLNGm+2FbmQLZqXkZ7aVA6g4JjJUCUZ4ym7kgdSK5Gy8NazpepaTbrba1cWVmlnBcvDq2yOV0iZfNRPNGI1O0OhUGQ7SFO072DwrrNxpen2bWM0LeH7RYI3eWLbqTJPBKCgDkgEW+Dv2cydMZNU7X8v6/rzD/AIH9f1sdc/jDRo9Mjvmku/LkkMYiWwnM6sOSGhCeYuBySVAAIPQip28S6UuqQWH2lnmuFVo3jgkeL5hlQZQpRS3YFgTxgHIrl49O1m215vFK6LeTSXMswOlefB50KvFboGJMnl9bbJw5OJO5yKr6d4T1XSbWLQzBNcQzTafcyakksYjh+zLCGRgWDkt5HGFI+fkjmhW6+X/B+7f8BdDvry8g0+ylu7t/LhhXc7YJ49gOSfQDkngVlP4w0aPTI75pLvy5JDGIlsJzOrDkhoQnmLgcklQACD0IrJ1rxJ4d8UaedK0DxHpGoal58VxDZ22oQvJOYZFlKBQ3cRkenrgVUj07WbbXm8Urot5NJcyzA6V58HnQq8VugYkyeX1tsnDk4k7nIqddf6/r9NxnUN4l0pdUgsPtLPNcKrRvHBI8XzDKgyhSiluwLAnjAORT9O8Qabq15Pa2MsjSwZzvt5I1cA4LRsygSKDxuQkDjnkVxmneEtV0u0i0LyJriGebT7mTUkljEcP2ZYQyMCwclvI4wpHz8kc1p+FdG1KzvNLivrOa2i0PTpLATvJGVvSzRYkQKxIGIcneFOW6dauyv9/5f56ee4un3f1/Wx2VFFFSBHP/AMe8n+4f5VJUc/8Ax7yf7h/lTpM+W2AxODgLjP4Z4pPRAZOkeKdM125eHTRfPtVm86XTriKFwDg7ZXQI3P8AdJz1HFIvi7QZLHUbyHUoprfTLj7LdPEGfZN8v7sYB3tl1G1cncdvXiuV8HaXbWt1aafo0XiH+z47OSHUodeFyVJ+UIqiX92T9/Pk/Jj220RKvhGw8VTw+GZ7uCPVoW0+xtbFipxb26o6KiEhFZTllU7dpwCRin69v1SHvsdHH458PyJbE3skTXV6LCOKe0milE5XcEeNkDJlecsAMEHPIrRtdb0+81q+0m2uPMvtPWN7mIIw8sSAlOSMHIB6E4715rfaTPr1vptzbpqF3qU2qSXF5cT6XcWiRSCylWLYkqArGreWAeeepLGtzwJbag3irVdX1HT7qzbVNPtbgi4jKlWMty3lE4+8iNGpHUcU7f1/XzE9Fdf1rY72iiikAUUVyWgeCbnQ/EF1dN4j1i+092329peX0s3lEnlSWY7lHbPPY5xlgDrajg/494/9wfyqSo4Rm1jH+wP5UPYCjp3iDTdWvJ7WxlkaWDOd9vJGrgHBaNmUCRQeNyEgcc8ipdS1nTtHFudUvYLX7VOtvAJXAMsjHCoo6sT6D61wY8PawNDhs30vVA2jaVJp0L2WoR20l87vFtlikWTKAeTuJfafmI2tyDNd2Ovx+EHtTomq6hqH9txTyk30TiVI7hJTJGJZ/wB3GVTCx5yDwR1YtW0/rr/lqOx1N14t0iy8QDRbh7sXpERIWwneNRIxVC0oQooLAgEsOQRVRPiD4ee3vp/PvlisI5ZJ3k0u5QBYm2SbSYxv2twQucd6zpfDb6t8Vk1rUNOv4beys4DazfaI/JklHnh1eMOSSBOMMV4KthsH5sW28HXsfhDxldro+orrWtJeQR2c13C4KPLM0TJ+8KxjE/zDcOVJxnll0+//AIA7K9vT8d/uPQdI1qz1y1eew+0BY5PLdbm1lt3VsA8pIqsOGBzjnNVLrxbpFl4gGi3D3YvSIiQthO8aiRiqFpQhRQWBAJYcgis/TJ/Dfw/8PWlpqmqQ6Qty8k6rrF/GJWd3Lupdm+cqXxnc3bJPU5dvpdn4r+JkfiaBZbvS7W0h+w6haXsUlrcSobhXUorndgT8EqCCrYI5DPTm02IXwNvfp95sp8QfDz299P598sVhHLJO8ml3KALE2yTaTGN+1uCFzjvSyePdCitIbhzqQE9wbaOIaRdmUyBPM2+V5W8fJ8wJGCASOlcnbeDr2Pwh4yu10fUV1rWkvII7Oa7hcFHlmaJk/eFYxif5huHKk4zy0uteAUufDOg6BbadrD2M1/Nc6hIL6MXFss0UwbfIZMuQ8+ODJlVIO7+JL/L+v6+ZbSTfz/Bafj9/Q9FtLqG+sobu1ffBPGskbFSMqRkHB5HB6GpqbFGkMSxxqFRRgADGKdTe+hCvbUjk/wBZF/v/APspqSo5P9ZF/v8A/spqSkMKKKKAMjw1NEPCekgyICLKHILD+4K0/Ph/56p/30KoeGf+RS0j/rxh/wDRYrTq6nxv1Jh8KI/Ph/56p/30KPPh/wCeqf8AfQqSioKI/Ph/56p/30KPPh/56p/30KkrNl8R6JBM8U2safHJGxV0e6QFSOCCM8GgC958P/PVP++hR58P/PVP++hTLO/tNQhMun3UF1GrbS8EgcA9cZHfkfnWfqvi3w5oV0ttrfiDS9OuGQOsV5exxOVPGcMQccHn2oA0/Ph/56p/30KiCWS3bXSrbi4dBG0wC72QEkKW6kAsSB7n1qwjrJGrxsHRgCrKcgj1FRrdW73clqk8bXEaK7whwXRWJCkjqAdrYPfB9KA6C+fD/wA9U/76FHnw/wDPVP8AvoVJVLVda0vQrVbrW9Ss9Ot2cIs15OsSFiCQuWIGcA8e1AFnz4f+eqf99Cjz4f8Anqn/AH0KoXniTQ9O0uDUtQ1rT7WwudvkXU90iRS5GRtcnByORg9KtR6nYzX32KG9t5LryRP5CyqX8snAfbnO0njPSgCXz4f+eqf99Cjz4f8Anqn/AH0KqW+vaRdyW8drqtlM915n2dY7hGM3lnD7QD8208HHTvVi2vrS9adbO6huGt5TDMIpAxicAEo2OjYIODzyKAKEmheHZtaGsS6Xpj6mowL1reMzAY2/fxu6cdelUYfA/gi2uY7i38MeH4p4nDxyx6fArIwOQQQuQQe9bdxqdjaR77q9t4U3+XuklVRu/u8nr7UtxqFnaPGl3dwQNKcRrLIFLn2yeeooXkHqNvIrDULOW0v0trq2mXbJDMFdHHoVPBFSrLAihUkjVQMABgABUtFAEfnw/wDPVP8AvoUefD/z1T/voUC4ha3+0LNGYdu7zAw249c9MU9WDKGUggjIIPWgBnnw/wDPVP8AvoVW1G00vV7F7LVrezvrWTG+C5RZEbByMq2QeRmrtFAGJpfhjwrod2brRNE0fTrgqUM1paRROVPUblAOOBx7Vr+fD/z1T/voU6SRIkLyuqKOrMcCkeaOJkWSRELnCBmA3HGcD14BP4UAJ58P/PVP++hTIZohbxgyICFGQWHpUsciTRLJC6yRuAyupyGB7g06gDDvvC/hTVLx7vU9D0e8uZMb5ri0ikdsDAyxBJ4AFadsllZWsdtZrb29vEoSOKIKqIo6AAcAe1TySxwx75nWNAQNzHA5OB+tQXWpWNi6Le3lvbtJ9wTSqhb6ZPNaSq1JxUZSbS21FZbkvnw/89U/76FY994X8KapePd6noej3lzJjfNcWkUjtgYGWIJPAAraEiGVow6l1ALKDyAc4JH4H8qdShUnTd4Np+Q9ytbJZWVrHbWa29vbxKEjiiCqiKOgAHAHtUvnw/8APVP++hUlFS227sCPz4f+eqf99Cjz4f8Anqn/AH0KkopAR+fD/wA9U/76FHnw/wDPVP8AvoVJRQBH58P/AD1T/voUyOaIPLmROW4+YegqeigCPz4f+eqf99Cjz4f+eqf99CpKKAI/Ph/56p/30KxNQ8I+D9WvpL3VPD2iXt1LjzJ7myhkd8DAyzKSeABW/RQBWtUsbG0itbJbe2t4VCRQwhURFHQBRwB7CpfPh/56p/30KkooAj8+H/nqn/fQrN1fQvDviAxHXtL0vUzDnyvttvHN5ecZxuBxnA6ela1FAGbpOlaHoNu9vodhp+mwyNveOzhSFWbGMkKACcAc1e8+H/nqn/fQqSigCPz4f+eqf99Cjz4f+eqf99CpKKAI/Ph/56p/30KPPh/56p/30KkooAj8+H/nqn/fQo8+H/nqn/fQqSigCAzRfaEPmJja3O4eop/nw/8APVP++hUlFAEfnw/89U/76FHnw/8APVP++hUlFAEfnw/89U/76FHnw/8APVP++hUlFAEfnw/89U/76FHnw/8APVP++hUlFAEfnw/89U/76FHnw/8APVP++hUlFAEfnw/89U/76FHnw/8APVP++hUlFAEE00Rt5AJEJKnADD0p/nw/89U/76FSUUAR+fD/AM9U/wC+hR58P/PVP++hUlFAEfnw/wDPVP8AvoUefD/z1T/voVJRQBH58P8Az1T/AL6FHnw/89U/76FSUUAR+fD/AM9U/wC+hR58P/PVP++hUlFAEfnw/wDPVP8AvoUyGaIW8YMiAhRkFh6VPRQBH58P/PVP++hR58P/AD1T/voVJRQBH58P/PVP++hR58P/AD1T/voVJRQBH58P/PVP++hR58P/AD1T/voVJRQBH58P/PVP++hR58P/AD1T/voVJRQBH58P/PVP++hR58P/AD1T/voVJRQBC0qPJEEdWO7oDn+E1NUcn+si/wB//wBlNSUAFFFFAGZ4Z/5FLSP+vGH/ANFitOszwz/yKWkf9eMP/osVp1dT436kw+FDZUaSF0SRomZSBIgGVPqMgjI9wRWBYp4nlvorbUZIILazbMl7CFLagP4QEIPlD+/3z93A5roaKgoK8X1OwkhvddvrSLVVa7ttXEzxtbGJ0ScKcBjuCD+L+PONvGa9orCuvBHhi+u5bq60OyknmYvI5iGWY9Sfc+tUnYiUeY5z4VNZNDrQ0v7V9lW6QR/bNnm/6pSd2z5epOMdsVLP4j0Pw/8AFXWf7e1jT9ME2lWXlfbbpIfMxJc527iM4yOnqK6zSdC0zQo5I9IsorNJSGdYhgMfXFX6Td2VFWjy/wBb3PIbu/h03wrpdtdSXGm291Pez6fFPq76PbrB5uYUeVF8wPscFIgMYzuHyjGdperW6C51rWNR1Fb2+8M6W8rQ37Qs+ZXSZ/mO2NFO3fIqgxhmYFWbJ9vopd/67/0yr/187nC/C/VX1Gz1qE3cdzDa6gEtjDqkmooI2hjb5biQBpBuL9ehyOgFT+Nbmy03XdJ1K41iHQruGGeO3v8AUYVexIcx74pMuhDkKCuHUna3JAKnodX0Cz1sxfbJtQj8rO37FqVxaZzjr5Lru6d84qTSdHttGt3hs5b2RHbeTeX010wOMcNK7EDjoDije3l/lYWx5pF4s0HQNM013GkWusXb3kdjc3F20Ni0TTBpblTI3EbkKyopYnIVTty4oKIPDev/AGrw/eLfx2+iWFhb3UO11l89rlI2G07dpmEXTgD2r2iijS1n/X9fr6Id9/66p/oeFaeIPD8ujXyBhBoS6zP8oJIhTUI1kwO/7svxXc/DS3ntU8TLOoF02prLMOn717SB3/8AHmNd5RTvo16/i7g9Xc5qG5s4PDdj513aWV1Pa8XN3HlctgyDJKjJPOM9ecHFUnv9OjsINNd4LK6ubGOK4kvZgPKhAIHDY3McnAwPU9geugt4rZWWFdis5cjJxknJ+nPNSUhGNrlxDa6Vbq0zpE7qgcXPkIflJG+UcqvHUck4Hc1j2t+J9Psv7TvpILIPcq0yXbDLq+I1MvBYbckZ+9gZzXY0UB2OBM0I8Mxpf3s1vD/ZhNrtlaPzpCW3AqMBzgJ8pz1PFXt90ulatcw3E3mQvFEoM7qkURSIuQBkLwWO4Akdq7CijuHY49bt/wCxCZNSgW3+2Yjb+0JtjrtyUNztBHzZIPPTbmlvL4No2nvJetCpjk+W4v3g83BwGWdV+fHYEfMGyeldfRQHUxNXk83wgZJTNFujiZi4AkX5lySAPvfh1rNWcy3MUFleXE+nyXjRxyC5Z/MH2diwD5ywDe/B+nHW0UPW4LRHBW93FbaTpflX7NstIttut88cm/cd2wYKzHPy7Dwu3HGa7iUw+ZB5smxy/wC7XzCu87TxjPzcZOOeme1S0U27gct4ua7dRusp5LSFonR43j2tIZB1BYHgcDjGWz2qSe+gsLrVDqAtoprrYYlvphEksWwAoGwQcHfkDPX3zXS0VPSwzCs3LXOiSJbtavJaurwOSWRAF4JPJwdvJ9fet2oxbxC6a42/vWQIWJJ+UHOB6dfx/CpKpiCiiikAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARyf6yL/AH//AGU1JUcn+si/3/8A2U1JQAUUUUAZHhqGI+E9JJjQk2UOSVH9wVp+RD/zyT/vkVQ8M/8AIpaR/wBeMP8A6LFadXU+N+pMPhRH5EP/ADyT/vkUeRD/AM8k/wC+RUlFQUR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUleZXnj3xPba3q9o9hHbw2nn/AGUtpNzKbgo2I03KwHzrzv6D8aaVyXJI9J8iH/nkn/fIo8iH/nkn/fIrmvAniPVPEWnXcmt2a2s0M+xAsDxb0Kg52uSeu4Z9qxvFutazb+KdWhsPEC6Zb6XoSamkBgifzpRJKCHLKW8shFBCkHlcMvOU9Hb+u5UfeV1/WtjvvIh/55J/3yKPIh/55J/3yK89n1/XSniS+/tOa3S2v7bToLfyIdlmJkti8zEruZk85yMtt45B7SHWdaW8bw9/bMxP9tLp/wDbBhi88IbX7RjHl+Vv3fJnZjB6buadn/Xy/wA0HS533kQ/88k/75FHkQ/88k/75FeX6/4l8S2N6bPSNSm1MaZp5uXvLb7DFFcyCZ0K3JmdQqqItrGHaQzMcL8q1sX2t6vb+NEkmvrqPSReQWoW1W1mtkLqo8qcEidZS78FMoAYyRjdQle3n/nYHpe/T/hzuPIh/wCeSf8AfIo8iH/nkn/fIrhLPW9XXxayajqFy9leTXUNqI0tZbGXYGKrGyHz0kVUO/zAV3K4GPlrm7XxD4gHhF7yy1c2MWkeFLLU1tbazgWOaVllLKwKHahEYG1NuOxFCV1f0/G/+Q7O9l/Wy/U9f8iH/nkn/fIo8iH/AJ5J/wB8iuB13XNYF9qN5ba62nR6dqFjZpYeTCyTrKYizsXUvlvNZV2sANnQnNeguwRGZiFCjJJOAKWyuJa7DPIh/wCeSf8AfIo8iH/nkn/fIrznSdb8SNBfRXeoXkuoy6Y19aI0VpJbzlGBLWskXPlNuVcTDfhlIOQ1Ov8AX9e1XTre/wBFv7wWt4bi5ih04Wf2tYF2LE4W5wrRHDMxzvzJGAcZo2/r+uz+5jtrb+v62+89E8iH/nkn/fIo8iH/AJ5J/wB8ivJr7WtQtdX13xFpGo3F291oek+SqQwxoPOmkTzFWQfKVBZwHfaCx3EgDGta6v4luLG30+e+vLC4bXPsRurgWUt0YTbtIQ6wl4lkB4HA4CkqckF2d7f1vYXS/wDW1z0PyIf+eSf98ijyIf8Ankn/AHyK88sNR12K+0v+09bk1G3udZudHmtpbWFEliRZysjbUz5mYxnBCEcbB1q74Q8MaHF4y1fW9J0PT9NitGOmWn2O0SHftwZpDtAyS/yc9BGcfeNJa/1/XdfeD0/rzf8Ak/uO28iH/nkn/fIo8iH/AJ5J/wB8iuf8esB4V2TMFtZr20huiWwDA9xGsgP+yVJB7YJqDwTBFY33iTTtPiSHTLPUxHaQxKFjhBt4mdEA4ADsxwOMk0LW/wDXb/MGdP5EP/PJP++RTIYYjbxkxoSVGSVHpU9cLr+iRa34iA+xaTr0sOnRqdK1ZmjWBWZ/38T7HGWI2nC5+VfmGMHow9KNWVpOy/rzS+9pCeiO28iH/nkn/fIo8iH/AJ5J/wB8is3wrcxXfhPTJ4JLmWNrZNr3bq8rDGMsy8Mfcdetcrruq30fjQapFpt3Lp+kSR273aNEIY0f/j4LZcOQA0R+VSMxfWtKWElUqyp3ta/37JfNicrR5md55EP/ADyT/vkUeRD/AM8k/wC+RXCwC2XxebLBGirczvZDAEZvgu6RBjqF/eMP9vze6LW74A/5Jx4d/wCwZb/+i1p1sJ7Knz37dO9/8tQvrY3fIh/55J/3yKPIh/55J/3yK4iwuNei8ReLl0fTdOuoDqCF3utQeBg32SDgKsLgjGOcj6VyvhvRzq8mmj/hG9D1wx+F9M41eTaIc+d9z9zJnOOfu9B17dMMtvGU5Tskovp1V/5lb57g5W/rzSPYfIh/55J/3yKPIh/55J/3yK8v1fSE0/StStNYmS6vtD8NRS6dcNy0MwM2ZIs5KtuSIZBzgLnrXqEBka2jMwxIUBcehxzXLiMNGjFSjK9/K3RP8n8gUru39f1qHkQ/88k/75FHkQ/88k/75FSUVxlEfkQ/88k/75FMjhiLy5jThuPlHoKnqOP/AFkv+/8A+yigA8iH/nkn/fIo8iH/AJ5J/wB8iuJmn1uH4q6z/YOn6fe50qy837bfvbbf3lzjbthk3d+uMcdc8cTpumTa1qeiQ3fhzRNZnH9uPJZ6nOfIjb7emWVzA5YgnAOxcgnp0oWrX9d/8htWv8vxVz2zyIf+eSf98ijyIf8Ankn/AHyK80i8L2tre6RoPiuGwk082uo3kdivzW1vJ5kbKkYYDPlI7hWwuAWICjgdj4GuLu7+H+g3Gou8l1Lp8LyvJ95yUHJ9z1NNaq/9df8AIRteRD/zyT/vkUeRD/zyT/vkV57beG/DGpa1431LxHpmnyPbagM388KiW3RbOBtyy43Jt5YEEYPI5ql4X0Sw8VeITP4z0ey1O8Xw7prk6haJIyMzXG44YHaTgZpLVX8k/vVxtWv/AF1S/U9P8iH/AJ5J/wB8ijyIf+eSf98ivG0s317VtAiTRNL8TW0VrqosotZmwj26XUSxSB2jl3HZtAJHIOc+vafC5E/4Rm6migh09Zr6UtpEH3NLcYV7ccD+JS5woBLkjIIJaV1f+t7f122FLR2Ow8iH/nkn/fIo8iH/AJ5J/wB8iubvv+SsaL/2B77/ANG21Vb/AEvSvEHxCutP8T2lpqEMGnwy2FlexrJGcvIJpVRsgsMRqTjKgjpu5XRP1/Bv/Ib0Ou8iH/nkn/fIo8iH/nkn/fIryaG8ms9Jgu7K7kmtr5b/AEbTrmRjI0oM3+incc7gAJAGPUYPPd8kV1p1lqGj6OXSTwpY3zW/lrlozKAbcqB3WJpABjtRv+f/AA/4ff5BbX52/r53/DuereRD/wA8k/75FHkQ/wDPJP8AvkVwM+l6Foep+GbnwRFaQ3eoysC1oQf7Qt/JZmklYH96AdjeYxJDMOfnOa+h6R4UbwfoviO/kWLWZpITJrEa/wCmyXZYK8RfBZstujMZyAoK4AXh/wDDf1/W+hPS56N5EP8AzyT/AL5FHkQ/88k/75FeDalJ5Xw/8V6KDxqdzqWoYzztimn3kcdA0UAP/XT3r3qH/UR/7o/lR0uVLR29fwIzDF9oQeWmNrcbR6in+RD/AM8k/wC+RQf+PhP9xv5iuS8b/wBk/wBq6V/wln2L+wPKuPM+37fJ+0/u/Kzu43bfN2989KluwJXOt8iH/nkn/fIo8iH/AJ5J/wB8ivOdN8W6zZXmjaRqesaPFLJFZR3X2iBzNBM8TFoJD5o/eyFcoccfNuBJTfjQ+X/Zcf8AZ32T7X9iH/CWeTt8zzPtEHmefj+Ly/tX3udue1U1rb+v6/4Af8D8T1/yIf8Ankn/AHyKPIh/55J/3yK8qH/CO/a1/tD+yP8AhBPtNx9h3eX9i8zyrfZs/g+/9q27f4s45pLDzftVl/bv2f8A4S/zdN+w+dj7T9m2Q/admfn25+0b8cetCV2vl+P9fdqLoereRD/zyT/vkUeRD/zyT/vkVi+NfM/4RO52f6rzIftPp9n81POz7eXvz7Vwg/4R37Wv9of2R/wgn2m4+w7vL+xeZ5Vvs2fwff8AtW3b/FnHNTff+v68vMdj1XyIf+eSf98ijyIf+eSf98ivKbHzvtNn/bn2f/hMPM037D52PtP2bZD9o2Z+fbn7Rvxx1zWr4D+zf2xZfYfsv9of2dL/AMJH5G3zPtm+Pb52P48+fjdzjPartrb1/K//AAH2Yun3f1/l3PQfIh/55J/3yKPIh/55J/3yKkoqQIJoYhbyERoCFOCFHpT/ACIf+eSf98iif/j3k/3D/KnSNsjZiVG0E5Y4A+ppN2VwG+RD/wA8k/75FHkQ/wDPJP8AvkV5x4I8Pv4dvNPinj8Pz3mrWEr/ANraNp4jmGNjF2mZm85WLg7iqjIHBzgN0vT9K07S/Gdp4h1O8l0+PWomuZriTzJLr/R7ZjGwA+YOfl8tAMhtigAgU/J9v1S/Ufoek+RD/wA8k/75FHkQ/wDPJP8AvkV49rWgy6db6Nc2umLpMMuuNqFjpEQAFt5VlKwUquVVnaPcVXgFu5yT0XgW5TUviJ4k1eFw8Wo2VnPEwOQ0QkuUjYexRFb/AIFTt/X9fP7hPRX/AK3t/Xqd/wCRD/zyT/vkUeRD/wA8k/75FSUUgI/Ih/55J/3yKPIh/wCeSf8AfIqSuS0C48dN4gurPxJFpH2GFt0d5ZwSJ56E/LgNI2G45HOPUjBIB1PkQ/8APJP++RTIYYjbxkxoSVGSVHpU9Rw/8eseOuwfyoeiAPIh/wCeSf8AfIo8iH/nkn/fIryfTdSi0fTzf6Vf6Np+qQaVK3iC4uU3qt95kWwXAjZWLk+cq5O7ngHobmu+KLS88HxajruuWNpNbeIbeJYYrk2v2fZcxh4Zx5pWR1Tczfw4+YDADFpXt/XWw7HpnkQ/88k/75FHkQ/88k/75FeZ3+j2Ov8Ax2jns5tGd7Kys7q6DQo90QDcFWjk5KYJgLccqV+YDhuas9F0lfBPxA15ZdBbTZIdRtoDawxoyv58xxK4zvz+4KcjAwAo6sul/UfLd29Px1/A9x8iH/nkn/fIo8iH/nkn/fIrm/h7oQ0LwqqB9Nf7VNJchtKgWG2ZGP7soi8L+7CZ5PIJ3NnJ5jUtEs9b+PKvbyaP52n2dpc3aSQI13hWuNpjfkphjDu4GVKjcBwzatKxCd4OX9bnpfkQ/wDPJP8AvkUeRD/zyT/vkV4dZ6LpK+CfiBryy6C2myQ6jbQG1hjRlfz5jiVxnfn9wU5GBgBR1abXvCdtb+DvDelLP4VGo6pq881k7WMX2Jw9vMIykPTBHkD+P59pO8/eSV/w/Etxs2vX8F/SPa/Ih/55J/3yKPIh/wCeSf8AfIqPT7OPTtNtrKAYit4liQZ6KowB+QqxTe5C1RC0aJJEURVO7qBj+E1NUcn+si/3/wD2U1JSGFFFFAGZ4Z/5FLSP+vGH/wBFitOsjw1Ep8J6ScvzZQ9HP9we9afkr6v/AN/G/wAaup8b9SYfCiSio/JX1f8A7+N/jR5K+r/9/G/xqCiSio/JX1f/AL+N/jR5K+r/APfxv8aAJKKj8lfV/wDv43+NHkr6v/38b/GgCSsS78IaLqHiT+29Ssob26WGKKIXMSSLCY3dldMrlXzIeQewrX8lfV/+/jf40eSvq/8A38b/ABo8w6WIm02xeK7ieyt2jvc/akMSkT5UKd4x82VAXnPAAqsPDmiDQ/7FGjaeNK/58PsqeR97d/q8bfvc9OvNXvJX1f8A7+N/jR5K+r/9/G/xoAoS+GdBuI7FJ9E06VNOx9iV7SMi1xjHlgj5PujpjoPSnv4f0aTW01l9JsW1RBtW+NshnUYxgSY3Dgkdehq55K+r/wDfxv8AGjyV9X/7+N/jQBSt/D2i2mqz6pa6RYQ6hcgrPdx2yLLKDgkM4GWzgdT2FPXRNKW2kt10yzEMlutq8Qt02vCuQsZGMFBubC9Bk+tWvJX1f/v43+NHkr6v/wB/G/xoA5vVvAen614mt9av7mZ3t9nlQC3tgFCHcoEnlecF3fNt8zBPBGCRViHw3qkVzHLJ401yZEcMYpIbEK4B+6dtsDg9OCD71ueSvq//AH8b/GjyV9X/AO/jf40LQDMh8L6Vp9vepoNnb6JPfD99dabaxRyk84Y5QhiMn7wPU0XHhTQL7S7PTtS0ex1C1sUCW8d7bpMIwABkbgecDrWn5K+r/wDfxv8AGjyV9X/7+N/jQBXuNH0y7uHnu9OtJ5pLdrV5JIFZmhb70ZJHKHuvQ02z0TStOs4LTT9Ms7W2tnMkEMFuiJE5zllUDAPzHkep9ateSvq//fxv8aPJX1f/AL+N/jQBF/ZtjmM/YrfMczXCful+SVs5cccMdzZPX5j61Fc6WradNa6ZcSaQ0shkM9jFEHDM25mw6MpLHOSVOck9eateSvq//fxv8aPJX1f/AL+N/jQBj2fh25iaZNV8Q6jrdrNE0UlnqEFp5ThuDkRwITxkYJxgnIrUsNPstKsY7LS7SCytYgRHBbxLGiZOThVAA5JNSeSvq/8A38b/ABo8lfV/+/jf40ASVmXug6PrlrbDWtKsdREK/uxd2yS7MgZxuBxnA/Kr/kr6v/38b/GgQIAAN4A6AO3+NVGcoPmi7MDKvNCvri6aS18Tapp8JAC21tFaGOMAY43wM3vyTV6302CLSzYXAF3HIrLOZok/f7s7y6qoUlsknAAOTxU/kr6v/wB/G/xo8lfV/wDv43+NXKtNpLt5JfitRW1IBpWnrZxWa2FsLaFg8UAhXZGwOQQuMAg85Hes6bw5c+YF03xFqWlWqKqRWdnBaCKFQAAFDwMQOOmf0rY8lfV/+/jf40eSvq//AH8b/GnGtOLve/qk/wA7hZENjYR2UDLnzppMGe4eNFe4YKF3vsVQWwoHQcACnWunWVkQbKzt7ciJIR5USpiNM7U4H3Rk4HQZNSeSvq//AH8b/GjyV9X/AO/jf41DnJ313GQXmk6dqFxbT39ha3U1o++3knhV2hbjlCRlTwOR6CrdR+Svq/8A38b/ABo8lfV/+/jf40nKTSTewElFR+Svq/8A38b/ABo8lfV/+/jf41IElRx/6yX/AH//AGUUeSvq/wD38b/GgQIM4389fnb/ABoARbW3S7kukgjW4kRUeYIA7qpJUE9SBubA7ZPrUUGl2FtMs1tY20UqeZteOFVZfMbc+CB/EwDH1Iyam8lfV/8Av43+NHkr6v8A9/G/xoAr6no+ma1bpb6zp1pqEKOJFju4FlVXGcMAwIB5PPvWff6BqN5fST23izWNPibG22torMxpxjgyW7N78setbHkr6v8A9/G/xo8lfV/+/jf40AYv/CE+HZrqK91LRtO1LU02FtSu7CBriR1Aw7MEHzcDoBjHGKn1Xwl4c126W51vw/peo3CoEWW8so5XCjnGWBOOTx71p+Svq/8A38b/ABo8lfV/+/jf40ANFlarcRTrbQiaGMxRSCMbo0OMqD2B2rwOOB6Vl6h4dNzdPcaVql1oc0zbrmTT7e23XRAAUyGWFySAMDpx+Fa3kr6v/wB/G/xo8lfV/wDv43+NAGEfBemX9usfilY/FLxsTDLrNlayNCCBlV2RKADjPTPvVu48KeHbvSrfS7rQNLn0+2O6C0ks42iiPPKoRhep6Dua0vJX1f8A7+N/jR5K+r/9/G/xoAjmsLO4jgSe0glS2dZIFeMERMv3WXPQjsR0p8dpbw3U1zFbxJPPtEsqoA0m0YXcepwDxnpS+Svq/wD38b/GjyV9X/7+N/jQBT0/w/o2k3lzd6VpNjZXN0d1xNbWyRvMck5ZlALcknn1pq+G9DTXDrKaNp66o3W+FqgnPG3/AFmN3Tjr0q95K+r/APfxv8aPJX1f/v43+NAFRtC0l42R9LsmR0lRlNuhDLK26RSMdHblh3PJzV8DAwOBUfkr6v8A9/G/xo8lfV/+/jf40AB/4+E/3G/mKkqPyEzn58+u9v8AGjyV9X/7+N/jQBJRUfkr6v8A9/G/xo8lfV/+/jf40ASUVH5K+r/9/G/xo8lfV/8Av43+NAGRYeE9O02/S8t7nWHlQkhbjWryeM5GOY5JSp69xxW3Ufkr6v8A9/G/xo8lfV/+/jf40ASUVH5K+r/9/G/xo8lfV/8Av43+NAElFR+Svq//AH8b/GjyV9X/AO/jf40AE/8Ax7yf7h/lUlRmBCCDvIPUF2/xo8lfV/8Av43+NAFPTvD+jaPcXFxpGk2NhNdHdcSWtskTTHJOWKgFuSevqaL3QNH1K2uLfUdJsbuC5kEs8U9sjrK4AAZgRhiAoGTzgD0q55K+r/8Afxv8aPJX1f8A7+N/jQBnWHhbw/pSKml6FptkqyGVVtrOOMBypUsNoHO0lc9cEirlpptjYEGxsre2IiSAGGJUxGmdicD7q5OB0GTipfJX1f8A7+N/jR5K+r/9/G/xoAkoqPyV9X/7+N/jR5K+r/8Afxv8aAJKKj8lfV/+/jf40eSvq/8A38b/ABoAkqOD/j3j/wBwfyo8lfV/+/jf40CBAABvAHQB2/xoAkoqPyV9X/7+N/jR5K+r/wDfxv8AGgCSio/JX1f/AL+N/jR5K+r/APfxv8aAKOr6Faa2IheTahF5Wdv2LUbi0znH3vJdd3TvnHbrVnT7CHTLGO0tnuHjjzhrm5kuJDk55eRmY9e546VL5K+r/wDfxv8AGjyV9X/7+N/jQBJRUfkr6v8A9/G/xo8lfV/+/jf40ASUVH5K+r/9/G/xo8lfV/8Av43+NABJ/rIv9/8A9lNSVC0YSSIgt97uxP8ACfWpqACiiigDJ8PGQeDdLMKq8g0+HYrttBPljAJwcD3wfpVSx8UyajfRafbaXOt9G3/EwimOxbIepfBDlv4Av3hySoq94Z/5FLSP+vGH/wBFitOrqfG/UmHwoK8ktoLTVfEN5J4zsZJLlLWBYf8AhILW0WUJvm+6IiV25z75z2xXrdYPiTwnZeJLnTJ7lIA9heJcFpLcSGVFDDy8noCXz36dK4MbhniqDoqXLe2q9bmsJcsrmP8ADOW8bR5YZ4tQjs4Qq2n2mG3SHZl/9SYiWK4A++Acbcd6PEviXxDY+ItRtdGTTPsmmaSmpy/a0cvMd8oMQKsAmRHw5DbT/C2eOziijghWKCNY40GFRFwFHoAK5vVPAmm614rl1nVWkmR7OK0FskskSkJI7neUcCRG3gFGBX5ec9uqKskv62/zFdWf9dV+lzKk8Y60x1y5t1sVtbS8t9Psont5C5mnWDZJIwfGxTPyoUEgdR3kPinXFnOhFtOOs/2oLAX3kN9n2m3+07/J83dnYCm3zOvzZxxXSy+HNKnttTt5bQNFqr+Zdje37xtioGBzlSFRcFcYIBHPNVh4O0UaOdN+zz+UZvtBmN5N9o83/np5+/zd2Pl3bs7fl6cVen5fp/wfv6WJ6HLa/wCNfEmiahHp32OG6vrWyW7uksdKu7tbzdI6rHGY8/ZyyxscybgCwHzBS1ad74n1e18aQ2UywWemyTRRRC50+4P2hXUfMLpSYon3kqInXLFeo3jGhN4C8OTwW0MljII7ZWQBLuZfOVm3sJsOPOBbLESbskknqc2JPCOiy61/ar28xuDKszRi6lEDyKAFkaAN5bOMLhiuQVBzkChdL/1/X9dwfW3YxLXxPrB8XT6dqYt7WBmnSC2ksLiJ2CAlHS6yYZiyjcUXaygnklDnn7fxz4iXwyL2xGnJb6b4cs9WuFuUmnknLrIWjVzLkcR8Oxc5PIau6tfB+iWeqNfwW03mlpHVHu5nhiaTO9o4mYpGxy2Sqg/M3qcongzQY9NnsEsMW1xYR6bKnnSfNbxhgiZ3ZGA7cjnnk9KFtr5fr/wCtLu/9ar9LmJq/ifXo72+uNK/s5dP069tbKaG5hdppWlMRZ1dXAUBZlwCpyQeQMV256cVxmueAX1vxRHqL31vBZq8DvbxwTCSUwsGTcfP8pjuA+YxFgvAYHBGlHYeMDMgu9c0KW3LDzY10SZWdO4BN0QCR3IP0NLdWJ9TA07xjr8lvqn9oR2sd7a2TXa6e+m3NvJEUb54wzkpcjHHmxkDO07cMMP1Xxhrf2BL/SEhTTpp51ivf7LuL4BY9qqGihYON7CU+YPlCqARlga3LTwfYaNBO3h8G3vHg8iCW9mmvI7dP7iRvJ8qcD5EKg7V9BSN4G0WXSdNsZkuVGm2wtYZbW7ltXMeACpaFlJU7QSp4yOlH9fn/wAD9bj0v/Xb/h/+Aclf+LdY0zXNc1WK7t7yz/sjTWtLSCKWdFluJXjV12tmQZJJ2oGddgGCvOjbeLfEN3pdtFFBDFfTat9gW7vdKubWKWMwtIJVgkYSDGNpBbBKn5hkEdJP4Q0K4aQvYBVlslsHjileNDCpyi7FIUFSTtYDcuTgin2XhbSLCGGO3t5G8m5N2sk1zLLI0xUpvZ3Ys52nHzE8YHYYel/L/g/5fn8xa28//tbfmcxYeJvEUl5p0esHTGtLzU7jSJVs4ZY5C8aykTK5kOwHysbMEjOd56VY8L6RGvjTVbqwvdVOn6cBYpFdarc3KTTkB5HIlkYfKCqDGOfMz2x0g8PaWGgYWvNvePfRfvG+Wdw4Z+vOfMfg8c9OBQ2ly2WmXEHh2S3sriaZ5/MuonuE3u+5yV8xSckngMAM+gxSWmv9dP8Ag/gDs9F/Wr/4H4lLxpczweHQltNLAbq8tbV5om2siSzojkN1U7WIBHIJBHIqDwhJLDe+INJa4nubbTL9YraS4maaQI0EUhQyOSzYZ25Yk4IHYVKuiazqUM9l4sv9J1LTp4ijwWumS2z7sghg5uHxjGRgAg4IIxWppWkWWiWZtdPjdUZzI7SzPLJIx6s7uSzHoMkk4AHamtL/ANdv8n94Mu0UUUgCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI5P8AWRf7/wD7KakqOT/WRf7/AP7KakoAKKKKAMjw1Gx8J6T+9cf6FDwAP7g9q0/Lb/ns/wCS/wCFUPDP/IpaR/14w/8AosVp1dT436kw+FEflt/z2f8AJf8ACjy2/wCez/kv+FOldo4XdI2lZVJEaEZY+gyQMn3IFYtr4t06+mtLeyWee7uGIktVQCS1C8OZgSNgU8c8k/dzUFGx5bf89n/Jf8KPLb/ns/5L/hUleWX3iPxdBqWsyGS6+wRQ38tk0CWuB5EgTLb/AJti5wf4jxjPNNK5MpWPT/Lb/ns/5L/hR5bf89n/ACX/AArmPAuo67dx6la+J/8Aj9s51UghMqGRWA+T5T1z+NRXa6tq/wAQNR0y28R6hpVpZ6fazJHZQ2zbnkeYMWMsTnpGvAI70noUtVf+ux1nlt/z2f8AJf8ACjy2/wCez/kv+FcU3jbULDwvHeXUWlNLBdXFndXmoaitha74ZDGCGxIQzkZC4IHzAsMDNbSvG+tanrGoXlpYQTaUdEstQtIJLxYmRpjJlnYphVwp3Hc2BGCqsWIB3Y7P+vW35nfeW3/PZ/yX/Cjy2/57P+S/4VgeD/F0XirTr24X7ETY3Jt5JNPvRd27nYr5SUKu4YcA5UYII7ZqVp7240GLUPt1xAJEMwS1tllf5sbFA2tkAdeMk85Aoem4lqbXlt/z2f8AJf8ACjy2/wCez/kv+FYN3eX76TDffb0t2e3TyYbZVk+0TkEleQcjoBtIONxJ9NW+vJrLTxP5UJcY8wzTiKOP1LMQcDtwD1H1oegblny2/wCez/kv+FHlt/z2f8l/wrIg8QSXttanT7aGe4n807ftOIwsbbWYOFO4EkY4Gc9qopr2pSeHw1vCs14libieR3CGPO4LhQpDH5TxwOOvNJ6DWp0vlt/z2f8AJf8ACjy2/wCez/kv+FYf/CRyQ2d3cT28fk2YRDK0+0ySMqEcbcKvz8nPHpQvilH01rhFtGdLjyHcXebdDt3BjKF+6Rgfd+8ce9U9BLVXNzy2/wCez/kv+FHlt/z2f8l/wrHu/ETW1haTmG1Q3KM26e8CQjGOBKAQSc5HTIB6YxVvVbyeDQ3urVWWbCFU+UnJYcdx3xSAu+W3/PZ/yX/Cjy2/57P+S/4VjS6zdCaO3e3WG7E7RNGkwaNv3LOp3FM449AQR3HWrZ+JLpLDTBeRWzT3FvHIzSXQiMu44xGCoDN3K5GMgZOc0bgdH5bf89n/ACX/AAo8tv8Ans/5L/hTnMgZPLVWUn5yzYKjHUDHPOPT+lYmsX15HPfNaXBhXT7RbjYEUiYncdrEgnGE/hweTzQBs+W3/PZ/yX/Cjy2/57P+S/4Vmpro+3y200HlLDGZ5ZWf5ViwCrdOSTkY7bT7ZoJq+oyR6h5h8hzcwQwLsBMKybRk+rANnnIzx0oA6Hy2/wCez/kv+FHlt/z2f8l/wrBF9em4Ol/a38wXvkfa9ib9nleb027d38P3cY7ZpLe+v9QaGyW7a3lT7R5k8cakuYpAi8EEDOcnA7cYoA3/AC2/57P+S/4UeW3/AD2f8l/wrmjrV9eaa99BN9nNrp8d20SoCsrkMSpJBO35OxB561Yn1O786e9jnZbe3uobf7NsXa6vsyxON2f3nGCB8o4p21sBu+W3/PZ/yX/Cjy2/57P+S/4VhRaldrc293JOz29zdzW32fYoEYXftYHG7P7vnJI5OBT9Jvrxrqx+13BnXUbRrjYUUCFhtO1cAHbh8c5PHWktQ2Nry2/57P8Akv8AhR5bf89n/Jf8Kp6bLNNd6iZp2dI7jy44yqgIAinjAyclu5NZ51XULS61h78QlLW0SeKGI5A5k6sQCSdo7AD9SAbnlt/z2f8AJf8ACjy2/wCez/kv+FZVvLPa6nbQ3WtwzzTITJaSmNDyMgxAANjIIwSeO+RRfz3sWoXi21y2EtBcRxMq7QwY5GcZwwGDycZ4xRsC1NXy2/57P+S/4UeW3/PZ/wAl/wAKWCZbi3jmTO2RA4z6EZp9Gwbkflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/AD2f8l/wo8tv+ez/AJL/AIVJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/z2f8AJf8ACjy2/wCez/kv+FSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf8APZ/yX/Cjy2/57P8Akv8AhUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/PZ/wAl/wAKPLb/AJ7P+S/4VJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/wA9n/Jf8KPLb/ns/wCS/wCFSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf89n/ACX/AAo8tv8Ans/5L/hUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/AD2f8l/wo8tv+ez/AJL/AIVJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/z2f8AJf8ACjy2/wCez/kv+FSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf8APZ/yX/Cjy2/57P8Akv8AhUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/PZ/wAl/wAKPLb/AJ7P+S/4VJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/wA9n/Jf8KPLb/ns/wCS/wCFSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf89n/ACX/AAo8tv8Ans/5L/hUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/AD2f8l/wo8tv+ez/AJL/AIVJRQBCyFZIsyM3zdCB/dPoKmqOT/WRf7//ALKakoAKKKKAMzwz/wAilpH/AF4w/wDosVp1keGhL/wiek4dAPsUOMof7g9608Tf30/74P8AjV1PjfqTD4USVFHa28VxNcRQRpNPt82RUAaTAwNx6nA6ZpcTf30/74P+NGJv76f98H/GoKJK861T4b3t5eXjwvobJcfasSz6czTDz5N+4tv5dOit2GeOa9BxN/fT/vg/40Ym/vp/3wf8aadhNJ7nL+AvCFx4Qs72C5uYLj7TMJAYYygXC4xg59M/jVvUfCcl34gm1ew8QappNxPbx28y2a2zK6xs5U4lhcg5kboRW7ib++n/AHwf8aMTf30/74P+NJ6jWisjnJvAtj5WmjTb6+0yXTo5Yo57do5HdZSGk3+cjglmUEtgNnPPJqoPhnpSWKWkV/qKRJZW1mPnibi3k8yGQ7kOWUk8H5SD8ymuuxN/fT/vg/40Ym/vp/3wf8aAMvQfDiaE1/IdQvNQm1CYT3Et35eWcIqZAjRFHyoo4HanwaRPHZwwLfz2xtgY43t2U74/4dyupGQABnGeOvOK0cTf30/74P8AjRib++n/AHwf8aAMv/hHI0uop7S/vLYwwiGNU8twq98b0Ygnue+BVy+05b6OANPLFJA4kjmj27g2CM4YFehParGJv76f98H/ABoxN/fT/vg/40AZqaBFFbxpDeXUcsbyOJ1Kb/3hy4+7twTz04xxTG8M232FLWG7u4EEH2d2jdd0sfPDEqfU8jB5NauJv76f98H/ABoxN/fT/vg/40AUholsLS5gDy4uHWQtuG5GVVCleOo2A9+fypf7Kk+zhBqd8JRIZPP3ruPGMbduzGO233681cxN/fT/AL4P+NGJv76f98H/ABoAof2IqWsUFtfXlsEDhmjZD5m45YsGUrknJyAMZOMDip5tLt5dJGnKXigVFRdjfMoXGME59BVjE399P++D/jRib++n/fB/xoAoR6HAssc0s888yzGZpZCuZG2FOcADAU9AB/Ooh4cjFnHZ/wBoXptliELwlkKyIDwD8vHBxlcEjGTnmtTE399P++D/AI0Ym/vp/wB8H/GgBWQsyFZGQKclVAw4xjByPx4x0qjf6NDqE5kkmmjDx+VMkbALMmc7WyDxyeRg8nmruJv76f8AfB/xoxN/fT/vg/40AUm0Oze6eeUNK8hbzBIFZZFIA2EY+6Nq4Ht7nMK+GNLiW7FtbrbG6KkmBVQxlcbSuBxggNznmtPE399P++D/AI0Ym/vp/wB8H/GgCh/YcX2bZ9puBcef9o+1gr5nmY25+7t+78uNuMdqRtBg+zwRwXFxbyQ7wJo2Xe+85fOQRyeenUcYrQxN/fT/AL4P+NJGZnjVt6DcAcbD/jQBnz+HrSZY0R5oIVhW3eKJhtliHRGyCcdeQQeTzUkuiwS35uTLMEZ0le3BHlu6fdY8ZyML0IHyjIq7ib++n/fB/wAaMTf30/74P+NAFKLRYIr/AO0iWZlWR5Y4GYGON2HzMOM5OT1JHzHAosNFg0+YSJNNL5aGKFZWBEKE52rgA44HXJ4HNXcTf30/74P+NGJv76f98H/GgCrYW09tdXxlEflTz+bGyuS3KgEEY4+76nrUh06Bry4uJAXNzCsMiNjaVXd2/wCBGpsTf30/74P+NGJv76f98H/GjpYChBoiRTwPLeXdxHbHdBDKylYzggHIUM2ASPmJ6+vNMvdPvbm+uniMMcctstuj7yWALHc2NuAQCccnJ9K0sTf30/74P+NGJv76f98H/GgB0UawxJHGMIihVHoBTqjxN/fT/vg/40Ym/vp/3wf8aAJKKjxN/fT/AL4P+NIpmZnG9PlOPuHngH196AJaKjxN/fT/AL4P+NGJv76f98H/ABoAkoqPE399P++D/jRib++n/fB/xoAkoqPE399P++D/AI0Ym/vp/wB8H/GgCSio8Tf30/74P+NGJv76f98H/GgCSio8Tf30/wC+D/jRib++n/fB/wAaAJKKjxN/fT/vg/40Ym/vp/3wf8aAJKKjxN/fT/vg/wCNGJv76f8AfB/xoAkoqPE399P++D/jRib++n/fB/xoAkoqImYSBd6cgnOw9se/vS4m/vp/3wf8aAJKKjxN/fT/AL4P+NGJv76f98H/ABoAkoqPE399P++D/jRib++n/fB/xoAkoqPE399P++D/AI0Ym/vp/wB8H/GgCSio8Tf30/74P+NGJv76f98H/GgCSio8Tf30/wC+D/jRib++n/fB/wAaAJKKikMyRs29DtBONh/xpcTf30/74P8AjQBJRUeJv76f98H/ABoxN/fT/vg/40ASUVHib++n/fB/xoxN/fT/AL4P+NAElFR4m/vp/wB8H/GjE399P++D/jQBJRUeJv76f98H/GjE399P++D/AI0ASUVHib++n/fB/wAaSMzPGrb0G4A42H/GgCWio8Tf30/74P8AjRib++n/AHwf8aAJKKjxN/fT/vg/40Ym/vp/3wf8aAJKKjxN/fT/AL4P+NGJv76f98H/ABoAkoqPE399P++D/jRib++n/fB/xoAkoqPE399P++D/AI0Ym/vp/wB8H/GgAk/1kX+//wCympKhYOJIt7KRu7Lj+E+9TUAFFFFAGZ4Z/wCRS0j/AK8Yf/RYrTrM8M/8ilpH/XjD/wCixWnV1PjfqTD4UFFFFQUFFFFABXOSePvDsd5d2pvJnms/M+0LHZTv5YjOHJKoRgHqeldHXK3Hw58P3F/eXgS8hlvfM+0eTeyoJA5y4IDdGPJHSmrdRO/Q2NE8QaZ4itpbjR7n7RHFJ5b5jZCrYBxhgD0I5qp4g8Ur4e3vLpGp3lvBCbi6ubaNPLtoxnLMXdd2ACSqBmAHTkZm8PeGNN8MW88OkpKiTuJHEkpclsYzk89APyrA8b/D1/GU8vmX1mLea0+zeTfWBuvs7fN+9g/eKI5Du5Yhidq9Mcp7lR294sa14/stPTVraKK4W+062muZVMCyCKNEDLKV8xdyNuAUbgSQw42sR1qNujVvUA1xWp/D641OPU3k1vbdapaS2NxJ9mJTyGTbGoTfwUbLZzzvkGBuGNSXxDqltM8EfgzXLlI2KLNHNYhZAONwDXIbB68gH2p6WSFrZf12/wCCUb/xzIuvafYadply1rPqhsJNRlRTA7Kjl1TD7wwZNuWULlWAJ4ptv8Rorz7N9k8Na7Kb2Fp7ICKAfaVXG/aTKAu3cPvld38O7Iy4eC74apaPBq6QaTbak+prYGzzKZH3l0aXfjbukZgAuR0yRV/S/Cn9mjQ/9M83+ybOW1/1WPN37Pm6/LjZ0561OvL5/wDA/wAxu3Npt/wX+livdePLSDR7bVrbStUvtOlslvpbqCJAltCRnc+913EAElU3sMcjkZ6hSGUEdCMivNtT+Ej6j4fs9Ik1WzuLe30xbD/TtNNx5TKGHnwDzQIpDu5Yhj8q9Mc9OfEGrWjfZx4O1y78r5PtEUtiqS443ANcggHrggGqdruwnvp/X9amfZeLNTl1mymuFszo+palcabbRxxt50TxCTEjSbirBjC/yhFK7l5ODXZ1yll4Omh1qC4n1ESaZa3k2oWlibfbJFPKH3bpQ5DKDLIVUKCNw+Y4rq6XRA99P6/pWCiiigAooooAKKKKACiiigArjvEPiW90q/htE1HStDtfsiyrf6xCzw3EhJHlKwkjVWULk5YkhuF+UmuxrDu9N1iS4ju9F1eG1Elusctve2rXMRxkhkVZEKt8xB5IIC8DGT1YWVONS9S1vP8A4Z/k/wBRPY09NuJ7vS7a4vLb7LcSxK8kAkEgjYjkBhww9x1rJv8AxFJaeKrXT0jQ2nyJdTMDmOSXcIQDnHJQgjH8aVBZzXXhmxt9G0/w3q+o29nEsaXUMlmqycdg0yEemNoHHHFQT+CdP1m1utQvrCGHW7tvPhvbi2je4sXAHlAEM33Nq8K2CQTxmt4U6EZuVVrle1td+tr3Vl31vbcnXlstyeLXtSbW20V47f7bA7TzybCENp/AyjdncxOzk9Uc9MA6XhjU5ta8J6Vql0saT3lnFPIsYIUMyAkAEk459az4/CkqXEN//aA/tUXLy3N0IMLPG4CmLZu4UKqBeTgxqTu5zFp01/4X0ix0O38Parqsen20VuL23a0jSbagGQrzhh9CPz61VSNCpDlpNc2nl3vvbR6WW9um4a3NPRtVn1DVNctpkjVNPvVt4igILKYIpMtz1zIemOMVzV54l8SPoK6pp9xpcC/2s2nNFPYyS5H2026uGEy4wuCRjkg8jPGjZ6N4gh1G+1HTtQs7GLVJ0upbO/04zSwsIkjK747gL0jHQHk9TU3/AAiP/FP/ANl/bf8AmK/2j5vlf9Pf2jZjd/wHOffHarjLDU5p3T1jfRuyt726tv2v5A7tO39b/wDAMXUvFep6V4jfSNU8UeGdKaKyiuPPvrNkFwzvKCEVrlcBQi55bk9uldrp8rT6bbyvc2920kasbi2XbFLkZ3KNzYU9R8x+prGvdC1geJrjV9E1WxtTc2sVtJFd6e8/+raRgQVmTGfMPGD0FbtqtylpGt9LFNcBf3kkMRjRj6hSzED2yfrWGKnSnCPJa+l7K2ttfsrr5vyHZqXkS0UUVwFBUcf+sl/3/wD2UVJUcf8ArJf9/wD9lFAHDeI/GlxpvjW40d/E3hvw9bw2UFwj6xCXadneQMFP2iIYXy17H71W7Hx1N/wjmmXN3pN1f6lfecY7bTUUedHExBuEErqBGw2MBuJ/eKBu61rzeGLa71zVry/KXVrqljDZS2kkeV2oZScnPOfNxjHGPfjntS+G0mp6TpdtfX+n6nc6V5sVvNrGlC8RoHxhXQyDdIuxP3mRnByPmNLVL+v6/r0K0cvL/gf5mnqfj/T7CzS7s7HUNWtzYDUZZLGND5FuQSrsHdSd2Gwqgt8rccV00MomgjlQELIoYZ64IzXnPinwzrdrbjTvCFrKy3mkjTLqZbe2WAKu4I2PNjMRG9ySkUgwwwuQBXottEYLWKInJjQLn1wMVWmtv63/AOARrp/Xb/gklFFFIYUUUUAFFFFABRRRQAUUUUAFFFFAEZ/4+E/3G/mKxte1LUY9UsdJ0SS1gvLqKa4M13A00axxFARtV0OSZF5zxzwa2T/x8J/uN/MVn6zoa6u0Esd9daddW+4R3Vp5fmBWA3J+8Rhg4HbPAwRSd+g15mZpnj7R9RhsAftkdzeRWzrCtjO4HnoXTDhNpXCvlgcLtO4iqFx45u/7T1mG3s41treKzXT5pAT9okuJ3h8wgEfuwyjHQkAkHDA1pnwZbrqdpeW2p6hbfYvKS1hjMWyGFFKtCMxk7H4L5JYlUwRtGMyL4UeG7bXr7VLKFbNruBYRBa2lrFHBtbcroViDhgw3ZLEE4yCAoFOzfkHTTy/4IzVvGupaL4W1xnhttR1zSjIgEETRQybYVm8wqWYqio4z8xyQADlgKuXXie/i1aeSI2o0yxvLawuYnhYzSSz+XhkcPhQvnJwVOcHkUmsfDbRdf8PTabrTPfXE7M76ncW9vJchmCqWUmIoh2oq5VBgKMYIzViy8DWOnvai2u7lLaARGSzSOCOG4kiA8uR1SMYYbV4Qqvyj5aFur+X/AAf1+8TtbTz/AOAafiLVH0fRZLqBVaZpIoIQ4JUSSyLGhIBGQGcEjI47iudTxJrlxqZ8O29xpo1m3kl8+7ezkNu6JHC/yxebuUn7RGPvtjBPPQX5YPEetK2n67o+lWthL964tNXklmiYfMjKjWyqSGCnlsD36F7eDYzboU1jUo9REjyPqiCD7RLuVVYMDF5YBVEHyoMbBjB5qddf69P1v37D0Miz8dXepRRavai2i0hJbO3uIJIWaZ5LlYiCsgYBQvnpwUOcHkVf8N+Jb7U7yxN+bU22s2T39ikMLI8EatGNsjF2DkiVDkBcYIxVtfB1hFfQS209zb2kXlF9PjKeTM0QAidsqXyu1ejAHaMg1No/hi30a8M8V3dXCJG0VrBN5eyzjYglI9qg4JVfvFj8o5q9L/f+X+evp9wun3f1/X/BNqiiipAjn/495P8AcP8AKnuwRGduAoyaZP8A8e8n+4f5U8gMpVhkEYIPek720A4rwd41uPEVw093cWaWU1n9siiNnPbyQrkcea+Y7hQGG549oU4GDuyKTfEa7+w6tfvbW1pZ2mqWNvC9zkZtp2iDSvyNp2uWHTAxkdRWlb+AElVbXXb1L/T7Wyk0+yggie2dLeQruEkiyEu22NAGUJ0PHPFY/C6yWTUfK1C8eO+vbO5Md5cTXQVYHjYofNkbcW8vG48gHHIGKrS6/rr/AJfqHf8Arob3h3XJfEiHVbJrc6HMpFmwBaW4wcGXIOFXggKRu7kj7tblY2m6AdI17ULuxuVjsL8+dLY+V924/ilVs8bhjcuOWG7IJOdml0/r+v66bB1CiiigArm9F+IHhzxDfvZaVeTy3UcjRSQyWM8TI6nDKwdBggg5z0xXSVWj06zh1Ga/itoku7hVWWZVwzgdMn8h+A9BQBZqOA4toz/sD+VSVHB/x7R/7g/lQwOMsPHMkFvBqWvvENN1DT31K1FpZStLbwq0YIkClzIcTIdyquMNkY5FnxJ45jsPCOp6loNvJe3loJ1jint5IlLRKS7ncF3Rrjll4JwoOSKsx+CoYbe4ittY1OAPEYLVomhVrCJmDMkJ8vgHaoy25gAMEYzVPU/hloeseG5tE1AvPbl5DaSSW1s8mno5GYoC0RCLxgcFgMYPAwdP67/5f13pWvckuvE9/Fq08kRtRpljeW1hcxPCxmkln8vDI4fChfOTgqc4PIqtceObv+09Zht7ONba3is10+aQE/aJLid4fMIBH7sMox0JAJBwwNaFl4FsLB7Vbe7uUtYBEZLNI4I4biSIDy5HVIxhhtXhCq/KPlrPi+FHhu216+1SyhWza7gWEQWtpaxRwbW3K6FYg4YMN2SxBOMggKA9Nv6/re3a5K+Hz0/4JveH9Ru7z7fZ6m8Et7ptz9nmltomijkJjSQEIzMV+WQDG48g81zus+LdZsfihZeHrZ7U2t0kDoh0+V2YN5xkBnEoRCFgYrlSWJxjAJGz9g1nQrUJoUNvrVxcSNLeXWq332aR2wqqf3Vuyn5VAwFUAKOuTT9I0a8XW59f1WZoL67tktpbCCdZraNY2YoVcwo5PzsTnj5iOcCjqn/W3+YfZa+77/8AI4y28f8AiOa38Wtv095NBtrqVc6XNEgaOWVI/maY+aGED5KgBSQMkggRah8RtesfBVrrhntPLk1G4tnlOhXBcxxRysSLbzw6NvhYfMwAUhm2YIrsl8GRDwjqfh99X1GSDUnnaW4byfNTzmLSKuIwuCWbqpI3HB6Yt3vh37fJoskuqXyvpEwnQoIf9IfYYyZMxnqrMPk2/eOMYGErben/AAf6/MptXbXn+Who6e9zJpts9+sa3TRKZhH90PjnHJ4z05qxRRTe5C0RHJ/rIv8Af/8AZTUlRyf6yL/f/wDZTUlIYUUUUAZHhoy/8InpOEQj7FDjLn+4PatPM39xP++z/hVDwz/yKWkf9eMP/osVp1dT436kw+FEeZv7if8AfZ/wozN/cT/vs/4VJRUFEeZv7if99n/CjM39xP8Avs/4VJRQBHmb+4n/AH2f8KMzf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/77P8AhUlFAEeZv7if99n/AAozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/AL7P+FSUUAR5m/uJ/wB9n/CjM39xP++z/hUlFAEeZv7if99n/CjM39xP++z/AIVJRQBHmb+4n/fZ/wAKMzf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/wC+z/hUlFAEeZv7if8AfZ/wozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/vs/wCFSUUAR5m/uJ/32f8ACkjEyRquxDtAGd5/wqWigCPM39xP++z/AIUZm/uJ/wB9n/CpKKAI8zf3E/77P+FGZv7if99n/CpKKAI8zf3E/wC+z/hRmb+4n/fZ/wAKkooAjzN/cT/vs/4UZm/uJ/32f8KkooAjzN/cT/vs/wCFGZv7if8AfZ/wqSigCPM39xP++z/hSKJlZzsT5jn7544A9PapaKAI8zf3E/77P+FGZv7if99n/CpKKAI8zf3E/wC+z/hRmb+4n/fZ/wAKkooAjzN/cT/vs/4UZm/uJ/32f8KkooAjzN/cT/vs/wCFGZv7if8AfZ/wqSigCPM39xP++z/hRmb+4n/fZ/wqSigCPM39xP8Avs/4UZm/uJ/32f8ACpKKAI8zf3E/77P+FGZv7if99n/CpKKAI8zf3E/77P8AhRmb+4n/AH2f8KkooAiImMgbYnAIxvPfHt7UuZv7if8AfZ/wqSigCPM39xP++z/hRmb+4n/fZ/wqSigCPM39xP8Avs/4UZm/uJ/32f8ACpKKAI8zf3E/77P+FGZv7if99n/CpKKAI8zf3E/77P8AhRmb+4n/AH2f8KkooAjzN/cT/vs/4UZm/uJ/32f8KkooAikEzxsuxBuBGd5/wpczf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/77P8AhUlFAEeZv7if99n/AAozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/AL7P+FSUUAR5m/uJ/wB9n/CjM39xP++z/hUlFAEeZv7if99n/CkjEyRquxDtAGd5/wAKlooAjzN/cT/vs/4UZm/uJ/32f8KkooAjzN/cT/vs/wCFGZv7if8AfZ/wqSigCPM39xP++z/hRmb+4n/fZ/wqSigCPM39xP8Avs/4UZm/uJ/32f8ACpKKAI8zf3E/77P+FGZv7if99n/CpKKAI8SM6FlUBTnhiexHp71JRRQAUUUUAZnhn/kUtI/68Yf/AEWK06KKup8b9SYfCgoooqCgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/9k=" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The following is an example of a histogram\n", + "以下为直方图实例" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/histogram_introd.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 10 10 10 10 30 50 100 100 0 0 0 0 0 0 0 0 0 0\n", + " 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0]\n", + "[0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n" + ] + } + ], + "source": [ + "from pynq import Xlnk\n", + "\n", + "xlnk = Xlnk()\n", + "in_buffer = xlnk.cma_array(shape=(128,), dtype=np.uint32) \n", + "# our input size is limited by IP core to be 8, and here the buffer size has to be greater than the greatest number in your input array\n", + "out_buffer = xlnk.cma_array(shape=(264,), dtype=np.uint32)\n", + "# our buffer size should be your output size+inputsize\n", + "\n", + "for i in range(0,4):\n", + " in_buffer[i] = 10;\n", + "\n", + "for j in range(4,5):\n", + " in_buffer[j] = 30;\n", + "\n", + "for j in range(5,6):\n", + " in_buffer[j] = 50;\n", + " \n", + "for k in range(6,8):\n", + " in_buffer[k] = 100;\n", + "\n", + " \n", + "dma.sendchannel.transfer(in_buffer)\n", + "dma.sendchannel.wait()\n", + "dma.recvchannel.transfer(out_buffer)\n", + "dma.recvchannel.wait()\n", + "\n", + "inputa = in_buffer[0:8]\n", + "\n", + "print(out_buffer)\n", + "\n", + "result = out_buffer[8:264]\n", + "\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pylab as py\n", + "import scipy as scipy\n", + "import matplotlib.pyplot as plt\n", + "import scipy.fftpack\n", + "import numpy.fft\n", + "\n", + "\n", + "fig1 = plt.figure()\n", + "ax1 = fig1.gca()\n", + "plt.stem(inputa)\n", + "\n", + "fig2 = plt.figure()\n", + "ax2 = fig2.gca()\n", + "\n", + "plt.stem(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/08-SUM.ipynb b/boards/Pynq-Z2/notebooks/08-SUM.ipynb deleted file mode 120000 index 9f071c2..0000000 --- a/boards/Pynq-Z2/notebooks/08-SUM.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/08-SUM.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/08-SUM.ipynb b/boards/Pynq-Z2/notebooks/08-SUM.ipynb new file mode 100644 index 0000000..9988946 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/08-SUM.ipynb @@ -0,0 +1,205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "sumol = pynq.Overlay(\"sum.bit\")\n", + "\n", + "# dma = overlay.const_multiply.multiply_dma\n", + "# multiply = overlay.const_multiply.multiply\n", + "\n", + "dma = sumol.axi_dma_0\n", + "# s = sumol.sum_0" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]\n", + "[ 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54\n", + " 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 102 105 108\n", + " 111 114 117 120 123 126 129 132 135 138 141 144 147 150 153 156 159 162\n", + " 165 168 171 174 177 180 183 186 189 192 195 198 201 204 207 210 213 216\n", + " 219 222 225 228 231 234 237 240 243 246 249 252 255 258 261 264 267 270\n", + " 273 276 279 282 285 288 291 294 297 300 303 306 309 312 315 318 321 324\n", + " 327 330 333 336 339 342 345 348 351 354 357 360 363 366 369 372 375 378\n", + " 381 384]\n" + ] + } + ], + "source": [ + "from pynq import Xlnk\n", + "\n", + "xlnk = Xlnk()\n", + "in_buffer = xlnk.cma_array(shape=(128,), dtype=np.int32)\n", + "out_buffer = xlnk.cma_array(shape=(128,), dtype=np.int32)\n", + "\n", + "for i in range(128):\n", + " in_buffer[i] = 3;\n", + "\n", + "dma.sendchannel.transfer(in_buffer)\n", + "dma.recvchannel.transfer(out_buffer)\n", + "# dma.sendchannel.wait()\n", + "# dma.recvchannel.wait()\n", + "\n", + "print(in_buffer)\n", + "print(out_buffer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# drawing\n", + "画图" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pylab as py\n", + "import scipy as scipy\n", + "import matplotlib.pyplot as plt\n", + "import scipy.fftpack\n", + "import numpy.fft\n", + "\n", + "\n", + "fig1 = plt.figure()\n", + "ax1 = fig1.gca()\n", + "plt.plot(in_buffer)\n", + "\n", + "fig2 = plt.figure()\n", + "ax2 = fig2.gca()\n", + "\n", + "plt.plot(out_buffer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/09-VideoSystem.ipynb b/boards/Pynq-Z2/notebooks/09-VideoSystem.ipynb deleted file mode 120000 index c77863a..0000000 --- a/boards/Pynq-Z2/notebooks/09-VideoSystem.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/09-VideoSystem.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/09-VideoSystem.ipynb b/boards/Pynq-Z2/notebooks/09-VideoSystem.ipynb new file mode 100644 index 0000000..0c68d9b --- /dev/null +++ b/boards/Pynq-Z2/notebooks/09-VideoSystem.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "\n", + "vsol = pynq.Overlay(\"vs.bit\")\n", + "\n", + "dma0 = vsol.axi_dma_0\n", + "dma1 = vsol.axi_dma_1\n", + "dma2 = vsol.axi_dma_2" + ] + }, + { + "attachments": { + "Diagram.JPG": 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tq3hvTtbmR9SF3IqrsMCX00cMi5zh4lcI4PQhgcjg8VqAYGBwK1aKAMuitSigDLorUooAyD/rV/3T/Sn1qUUAZdZMfhvTU1r+1WF1NdBmdPtF9NLHExGCUjdyiHBIyqjAJHQmuqooAy6K1KKAMuitSigDLorUooAy6K1KKAMiP7p/3j/OitOH/Vn/AH2/9CNFAGZrJ1q2mjvdFVb5EUpLp0jLH5mejpIRww7g5BGeh62tKgv4LEf2tdrc3bsXcxoFjjz/AAJxkqOmWyT1PoLtFAHG/Ebw5e69pIksLfTZ3tYJ22Xdo80xJUYEJV1COdvUhudvHHPN3MUvju8gh0mwh22M0yTvrWnSSQI6gKVwHQ7wff1r1aqmn6XZ6WtwthD5Qubh7mX5i26Rzlm5Jxk9hxXJVwlOtWp1pXvC9vmUpNJruR67dCx8O6jdE3QEFtJJmzRXmGFJ/dhgQW9AeM9a8j03xFcyDWbXQdWWadY7KXSEh1+XVFmumklBR5HwcEKokjBKquWBzzXtdFdVhX0scH4D1CS7166iGo3l5Cmj2Tt9qlLMs5kuRKSpJCvuXBA6bQOigVqeOrn7PYacLu8lsNJkvlXUruK5a3MMOxyCZVIMamQRqWBHDYyM1tarpFtrNssF5JeRor7wbO+mtWzjHLROpI56E4qLSNAs9EaVrObUZDKAG+26ncXWMenmu23r2xmqbu7i2/ryt/wTzXwrbW+rzWdhaaxqj6XNe6tJ5lvfSwvcBZY9hMiEO2M5DZye5IJzLpd/d6XaaPf3Gq61dtqPh28ubwrOZ5GeIQ7GiibMauAzAYUBictk816xRSWiS8rfhYaavd97/jc83+Gmsi98SaxZ21/Hd2KWltNB5WuSaqNxaUOTLIMq2FjzGCVHBHWt/wAaXNvp914e1HUpY4NNtNT33M8vEcOYJUR3Y8Ku9lGTxlhW3qukW2s2ywXkl5GivvBs76a1bOMctE6kjnoTiotI0Cz0RpWs5tRkMoAb7bqdxdYx6ea7bevbGad7i7+f+VjL+HysPCrSAFbee/vJ7QHvA9xI0ZH+yVII9iK6eiikHmFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBHD/AKs/77f+hGiiH/Vn/fb/ANCNFAB5EP8AzyT/AL5FHkQ/88k/75FSUUAR+RD/AM8k/wC+RR5EP/PJP++RWZrOo6jpU0d1DYtqGn7Ss0NrGWuEb+F1GcOvYjgjrkjIFrSn1GSxEmsRwQ3EjFhDCSREp6IWz8zDuRgZ6DuQCz5EP/PJP++RR5EP/PJP++RXJfEiyTUtJ0yykmkgWbUAPMjtXuGGIZW4jT5m6dunXtXmIW2S60W4a8S1uo7TT2hhj0uWAzs0hDMXJwWAwfM6P0HSqUbmcp2dj3vyIf8Ankn/AHyKPIh/55J/3yK574j8/DDxLn/oGXH/AKLNZU2i6L4Y8SeHP+EZ02y0q6vJpEuo7GBYRNbLCzO0irgEK/l4Y5wWwPvHM+pq1pf1/Cx23kQ/88k/75FHkQ/88k/75FeL+IvEGraj4d1m0k1XU59P1Pw7eX0F1Pa2sMUoQxkfZ0XMixssjAiYFsYIOcmuh1fxDq2latbix1bU76CzvbGwu8WtqltGZDEG89mxI0hWXcDCAi5UFeGy0r28/wDO35g1b8fwt/mej+RD/wA8k/75FHkQ/wDPJP8AvkUl1/x5zf8AXNv5V4RpUmfAnhHRM8afd6ZqBGedss1vszx0LSz494/aiPvS5fT8f6uD0Vz3jyIf+eSf98ijyIf+eSf98ivBtSk8r4f+K9FB41O51LUMZ52xTT7yOOgaKAH/AK6e9dzqeiaZrPijytJtvteuR3FvPc6tIATpMa7CIUcYILqp/dqf+WjM/DAMLW39f11+4JLldvX8P8z0DyIf+eSf98ijyIf+eSf98ivLJtKs7W9k8QG1sdSiXWstr0Eph1S2c3Hl/ZyrxneikiIjeuYzgJkDd6JekS67ZQTcQpFLPycAspUA/gGJ/I9qS2T/AK2uJ7tF/wAiH/nkn/fIo8iH/nkn/fIrm/stnZ6m3lNFY2N1ZSYuLab5pQNpMjsR1AY4bLZySSKvaHZxR3M93Y2i2NlKirFCqbPMxn94V7ZyAM84HPoADW8iH/nkn/fIo8iH/nkn/fIrAGqXQ1238ue4ntZ7mSA5ijWEbVf5V/5abgUxk/KecdqoNfXSSWOqSXRuZnsbm4W1KqBGcKdo2gHA6HJJ4o6DtrY67yIf+eSf98ijyIf+eSf98iuRuZbyz1RzFqrXE8sFqnnGOPcgebacADGMEkcfiauTahqcWttDG88kNtNDCSxgVJQwG5mJIbcd3G0YyuMdaFqI6LyIf+eSf98ijyIf+eSf98isS2v7tdaAurmQ280kqRbRE0DBckBWU71YBTndkZDD0pdEvLx73ytQnld5YPNQMsRjbBGWiZDnZ8w4cZ5HPWhahsbXkQ/88k/75FHkQ/8APJP++RXPXU09rq+o3UF4UEc1uptgikS7tq8kjPOeMEcjnPSqMEk/9pR3Md1HA8cF/tDqiRZE4ALfLnHIJOc8e5yDOv8AIh/55J/3yKPIh/55J/3yKzNEubh5Lm3vZLlpo9jbLpIg6hgf4ovlIJBxwCOc9qvXErWOnzTETXTRhnCKmXbuFAUfh0/Oh6CWpL5EP/PJP++RR5EP/PJP++RXGwXb/Z9Z8ya5DPPA1zK0MkWyM7BJjcAVABOO4Az71rw2ul2+tNp8GnNbJdWzZEQjWCdRjJ2qc5G7GSB1PXigDb8iH/nkn/fIo8iH/nkn/fIqrok0k+i2zzMXfbtLnq2CRn8cZq9QBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUARwACPAGAGbAH+8aKIf9Wf99v8A0I0UASUVH5K+r/8Afxv8aPJX1f8A7+N/jQBJRUfkr6v/AN/G/wAaPJX1f/v43+NAFTV9Gttat4orp7iMwy+bFLbTvDJG20rkMpB+6zD6Gue/4Vh4fM1vK7ahI9qEWHzL13EaocqoBJGAeg6V1nkr6v8A9/G/xo8lfV/+/jf407sTSYlzbQXtrLbXkEdxbzIUkilQMjqeCCDwQfSqGk+GdB0CSV9C0TTtMeYASNZ2kcJcDoDtAzWh5K+r/wDfxv8AGjyV9X/7+N/jSGZlv4T8OWk081roGlwS3IcTvHZRq0ob7wYgc575601/B/hp7iGdvD+lefbxrFBN9hiLxIv3VU7eAOwHArV8lfV/+/jf40eSvq//AH8b/GgDCi8Namlwjy+NNcnjVgWhkgsdkgzypxbA4PTgg+9aa6HpKIqJpdkqosSKot0AVYjuiA46ITlR/CemKteSvq//AH8b/GjyV9X/AO/jf40AVG0LSXjZH0uyZHSVGU26EMsrbpFIx0duWHc8nNVJ/Bnhe61M6jc+G9Imvi4kN1JYRNLuHRt5XORgc57VreSvq/8A38b/ABo8lfV/+/jf40BuUG8N6G+uDWm0XT21UdL82qeePl2/6zG7px16cVcntUnmglJZZIG3Iyn1GCD7H/Cn+Svq/wD38b/GjyV9X/7+N/jQBAmk6bGsyx6faqLj/XBYVHmf73HP40tpplhp7M1hY21sXGGMMKpu+uBU3kr6v/38b/GjyV9X/wC/jf40AQjS7AXhuxY2wuScmbyV3k+u7GadHp1lDdPcw2dvHO5JaVYlDMT1ycZNSeSvq/8A38b/ABo8lfV/+/jf40AQRaTp0GfIsLWPcQx2QqMkHIPA7Hn61I9jaS3iXclrC9zGMJM0YLqPQN1HU/nT/JX1f/v43+NHkr6v/wB/G/xoAjj0+yhu3uobSCO4k4eZYgHb6tjJ6UWun2Vk0jWVpBbtIcuYoghf64HPU1J5K+r/APfxv8aPJX1f/v43+NAEbWFm94t09pA1yv3ZjGC44xw3XpQ2n2bbd1pAdhYrmIfKW+8Rx3yc+uak8lfV/wDv43+NHkr6v/38b/GgBlpZWthEY7G2htoydxWGMICfXAqWOKOFdsMaxqSWwq4GSck/iTmozGPOVdz4Kkn529R7+9O8lfV/+/jf40AKsMau7LGoaT75CjLcY59eKrRaTZWsM0en20ViZl2s9tEqN7HgdRnvVjyV9X/7+N/jR5K+r/8Afxv8aAFghjtreOCBQkcahEUdgBgCn1H5K+r/APfxv8aPJX1f/v43+NAElFR+Svq//fxv8aPJX1f/AL+N/jQBJRUfkr6v/wB/G/xo8lfV/wDv43+NAElFR+Svq/8A38b/ABo8lfV/+/jf40ASUVDLGFQEM/3lH329R707yV9X/wC/jf40ASUVH5K+r/8Afxv8aPJX1f8A7+N/jQBJRUfkr6v/AN/G/wAaPJX1f/v43+NAElFR+Svq/wD38b/GjyV9X/7+N/jQBJRUfkr6v/38b/GjyV9X/wC/jf40ASUVH5K+r/8Afxv8aPJX1f8A7+N/jQBJRUfkr6v/AN/G/wAaPJX1f/v43+NAElFR+Svq/wD38b/GjyV9X/7+N/jQBJRUfkr6v/38b/GjyV9X/wC/jf40ASUVDFGGhRmZySoJ+dvT607yV9X/AO/jf40ASUVH5K+r/wDfxv8AGjyV9X/7+N/jQBJRUfkr6v8A9/G/xo8lfV/+/jf40ASUVH5K+r/9/G/xo8lfV/8Av43+NAElFR+Svq//AH8b/GjyV9X/AO/jf40ASUVH5K+r/wDfxv8AGjyV9X/7+N/jQBJRUIjHnMu58BQR87ep9/aneSvq/wD38b/GgCSio/JX1f8A7+N/jR5K+r/9/G/xoAkoqPyV9X/7+N/jR5K+r/8Afxv8aAJKKj8lfV/+/jf40eSvq/8A38b/ABoAkoqPyV9X/wC/jf40eSvq/wD38b/GgCSioTGPOVdz4Kkn529R7+9O8lfV/wDv43+NAElFR+Svq/8A38b/ABo8lfV/+/jf40ASUVH5K+r/APfxv8aPJX1f/v43+NAElFR+Svq//fxv8aPJX1f/AL+N/jQBJRUfkr6v/wB/G/xo8lfV/wDv43+NAElFR+Svq/8A38b/ABo8lfV/+/jf40AEP+rP++3/AKEaKIBiPHozdf8AeNFAElFFFABRRRQAUUUUAc7418WDwfo8F8bVbrzrkQbXn8oLlGbOdp/uYxjvXLp8Xk/tHTbZtPspRfLAxa11PzWh8xtu1l8sfOvUrn05r0h40lQpKiup6qwyDUP2Cz/59IP+/YpqxLUr6Mp+J9Tm0Xwjq+qWqRvPY2U1xGsgJVmRCwBwQcZHrXIXni/xPplq9veJpL3832CS3eOKQRRLcz+SyOu8linUMCu7+6uOez1/Sv7d8N6lpPneR9vtJbbzdm7ZvQruxkZxnOMis6x8D6FYWP2ZLWSUGSCVnluZZG3QsGiALMSqKwyEB2jJ45NJb67af8Et7K3n+lv1Mi68Ua5Z31zI7afLY6XeWmn3qC2eOW4lmEW6SNjIRGo89CEIcnaw3DINPHijW103VPEhTT5NEsftgNmEdbkC3Lru83cVbc0Z+XYuAw+YkYO9c+FtIvNcTV7i2drtCh4uJFidkzsZ4g2x2XPDMpIwMEYGI/8AhD9D/taTUWs2eaQuzRyTyNAGddruIS3lqzAkFgoJ3Nk8nKd7eev6f8H0Hpf7jmdM8Y+JbrQ9UdtOe4vLaOCWGcaJd2seHbEgEMp3ytEAXwjDeCoGDU48V63L4TF3aNb3N0l+ba5uIdHu2NvHgsHexJE4b7i7Ax4cPkrxWzB4F8P2+mz2KWtw0VwYyzy308kq+Wcx7JWcugU8qFYbSTjGTTm8E6G2mpZeRdBY5zcLcLfzrc+aRtLm4D+aTtO3lvu4HQAVTtd/1/X9XJW39dv8zldS8cX9rp9vqkX2O6nTw/qF6xjM6RNLC8QwYmIK8k5VwXQ5UN1zek8V69pzapYaj/ZtxqEUdk1pJBBJFCjXUrQqsgLsWCMuSQV3DgBTW9J4L0CWxW0ksN0K2c1ljzpMtFKVMoLbsszFQS5JYnJzknK6z4YtdStNS+zxwR3moW0drJNcJJKmxCxX5FkQ5G9iCrKQSDngUO1v67tj7f12/wCCM8M6jq91eaxY669lLNp9ykSTWcLxLIrRI+SjOxByxGNx6VW8aa3rGjR2raSixwyCQzXb6ZPfiNlA2IYoGDgNlj5nIXZgj5hVPRvB2u+HbOZdI13T/tV5OZ7ya806e4EhCKi7d11vGFTku7kk9QAANCTwm+sRo/iy6ju7uLckVxpP2jTSI2xuRtk7MwJUHBbHA44zSfl/X9f12CNluVh4ru5NXsrCAWckmoJbT2zxbnjaJg7TENkbsCM4bA++mRXL6v4t8SXXg3xNHc3Nvpmow6VLcxw/2fc201qy8OquzbbgAHHnRsAG2naQwFd5F4diTXYLzFstrZ2TWdnax223ylYrvy2SCMRoAoUYGeueK9r4F8PWlrc2yWcssNzbNaPHcXk04SE9Y497nylPHCbRwvoMPT8/+B+gRdrX8v8Ag/qc6/jfW4PFS6WtsbyKzmtbS7Fvo10/ntIiM8qzKWjhVBIrbH3EhW+YZBqZ/EniuW9jNo+jJa3OrT6XEkltKzx7PM2zMwkAb/V4MYC5671zgdAfBmiHULa9e3uHntljC772ZlkMf+raRC+2V17O4ZhgHPAq6mg6anlbbbHk3b3qfvG4mfdubr33tx056Uev9bf8H0v907Rt/Wz/AFt/W/D6peS654LN5fPcrrcd9NpFuNO1C6s4ZZxcGFZCkcoJXjeQSxADAGu90qw/svSLWx+03F2beJYzcXMhkllIGNzMeST1qvF4d0uEQiK2KiG9kv48SvxO+/c/XnPmPweBngcCq+oWniqS+kbS9Z0e2tTjy4rnSJZnXjnLrcoDzn+Ef1pdP6/re43vp/Wv+VvxOV1i9vY9U1TWUvrxLjTdbstPt7ZbhxC0Mn2cOrRA7GLee5DMCw+XBGBXotYaeFNPm1ODVtTiSfVUVDLLA0sUEsiAhZDBvKlhn5S25l4weBW5T2jb+tl/w/zDd3M3XHnj0m8azvIbG4FpL5V1Pjy4XwNrtnjAODzXN+HEn07xJDa3S61YvNbOHh1G9N9DduhX95FIZWMeMngqm4MDtG3A6+4giucwXMSTQyxOkkcihldTgEEHggjtWPF4Vg0yNpdBkaK+CeXBPqU1xfJCuRuVUeUFQQBwrL0XOcAV24etCNKVOWl/L89eno/Kz1Jkr2LXibVm0Tw1e38Kl5449sCBC5eVjtQbVyTliOBzXJ2WszDwo0sVzqMl7ol8sUSXaTQy36uRsR0kCsS6yBQzDAZd3aumtdM1qW8hfX9Q029ggbzI47XT5bdhIBgMWM7ggAngr1we1W5dC06bXI9Xlty17EoVH8xtowHAOzO0kCRwGIzhiM1pTq0KMOR+897rb01SdrXu/PZ2E7s5jQ724m8R6A51Ga5S/wBMvLudfMby/M823+UIT8oTcygdRz3JzqeOr250/wANR3Fl55mXULIBLdwjyA3MYKAkgfMCV5IHPPFWJPDcFtM97oSwWeo5lMc06yTRJ5rI0v7oSKPmKA8Ec5Pc5Ymj6tfMsXiW/wBNv7NXSZYrXT5bdxIjq6NvM78BlBxjn6cG/bUJVYVb6R3TWr1b6abO26+QWaTRyPi/xFq7zXgWx1bQxH4c1GZBNcRDfIvk7XXyZXwy5OCcEbuO9Pk/tnSLG/1bTLHW9MgtdKuZJf7X1QXnmy7QY2RTPKBtwxJ+XIOOe3canoGmayzHUrbzi1rLZn94y5il2714I67F56jHBFXpIIpbdoJY1khdSjRuNyspGCCD1GKtZhTjShCMFpe61t+evzvboPl1T/rp/kcz4fSTTPFl/o0d3dXVothbXam7uXndJHeVW+dyTgiNTtzgYOAM11NZuj6Bp2gxSR6ZFIvmbQzzTyTOQowq7nYnaB0XOBk4HNaVefiakalTmj5b7vTfqEU0tQooornKI5v9WP8AfX/0IVhePrmey+HXiC5s5pIJ4dOneOWJyrowQkEEcg+9bs3+rH++v/oQqLUtOtdX0u507UIvOtbqJoZo9xXcjDBGQQRx6UFRaUk2cRqniTV7jXPDFvJoms6NFLdyiSW4uLbZMBaTMEIhmcnkBuRj5c9cVjeB4dbutM8M6na2PiOJzbpcahe6nrRuLe8QwnISE3EhDM5VhlEwB1H3T6beaTZX8tnJdw+Y9lIZbc72GxijRk8Hn5XYc56+tP07T7XSdMttP0+LyrW1iWGGPcW2oowBkkk8DvQ+tv63JWyT7HC+D7q7i1bw5I+oXd4df0aW/vVuLh5VEymEho1JIiX98y7UwvTjitv4gvKPDMMcNxcW/n6lYwO9tO8L7HuY1YB0IYZUkcEdasQ+ErbR0uZvCyw2F9MNqS3YluooVL7mRIjKuxScnahVc4ODgCoZPDurazA9l4u1LTr/AE9iriPT7KeylWRHDI4lFyxGCueADnHNO6bWmz/W9vu0DY5jxj5/hKx1Wz0XUtS8q60G9usXGoTXEltLCECyJJIzOufMIxuxlVIAOSda7sR4V1LRX0q+1OQalM9rcW95qU92GXyJJA6+azlGUxjlSAQxzn5cbMXgrQ47G/tXt7i5TUYDb3Ml3fT3ErxYPyebI7Oq/MTgEAEk9TT9N8IaRpd39qgS8nnEbRJJfahcXbRq2NwQzO2zOBnbjOBnoKl6xt/XX/hh3Wn9djzHwte3ksPg6OKDxNZ6vqKQ3B1DU9Zaazu41VWnAjNxICzoWKqY1Yfe+XaSPZbglbWUqcEISCO3FZkvhfSJdCstINriz08xG0USuGgMWNhVwwbIx1zkjIPU1UjsPGBmUXeu6HJblh5kceiTIzL3Ab7WcHHfBx6GnP3k0vP+v6/US0dzL0/Wr+z+BtprSytPfx6Elx51wxctJ5IO9yeTzyc0zUNIk8P3ujrp+tarK2q3DWV2tzfyz+cGhkbzUDEiJlK7v3YVcZGPu42dJ8E6Lonlrp/9oiGKIwpbT6rdTwKmMbfKkkZMY4Axx2qXSvB2iaNepdWFrIJYkMcAmupZktkPVYUdisS8AYQKMADoBTnaUm+/9W/r9AV0v6+/5HF2XiK/a50jWr29lW0022htNTiWQlDPIsgkLDOGKukOCQSNzepq7oVje+ILmPT/ABDqGpRi10y3u2itdQmt3ea4aVnLPGyuVXaEVc7RzweMdTJ4R0OXS9R06SwDWmp3DXV3GZH/AHsrEEtnORyo4GBxT9X8M6XrcsM17FPHPApSO4s7qW1lCHqnmRMrFSQDtzjIBxkCle97/wBf0w9P6/pbnG6ZdX/iC/sPD+patdGzhbUBJc207W81/wDZ50jQGSPay4D5YoRuZfTINfxSbzw4+lW2l+Iry6it9YM7W7XTPNHElrLMYJXL7pFOzI384I6gCu4ufCeiXOlWmnGxEFvZHNr9kke3e3OMZjkjKumQSDgjIJBzk0228IaHaQ20cNkT9muHukeSeSSRpXRkZ3dmLSMVcjLE8Y9Bg2Xn+fmGjb+fy3MHwjql1qfxE8SyPeTS2LW9s1rCZS0cYElxEWQZwNxi3ZHXI9K7isvSPDek6CV/sm0FvttYbNcOzYhi3eWvJPTe3PU55JrUpu3QN23/AFsRw/8AHrHj+4P5V53peratp9jZ6paq9/d6ho82oX1tf6k8cMcyPF8ql96wgB5RhQFO0ZxjI9Eg/wCPeP8A3B/KqDeGdBdb1X0TTmXUHEl4DaRn7SwOQ0nHzkHkE55pLe/9f11+Q9OpyzapdeKbzToL9ZdM02cXkqvZahNFI5hMap5jKI2jb5pCYznG3Dc5AzdM1XVtQs4dbvpLpNSgn0yCO2W4dInjnSAylogdjkmWXkqSNnBGK7qbwvoFxbTW9xoemywT3H2qWJ7SNlkm/wCerAjBf/aPNWJNI02bVYdTm0+1kv7dDHDdtApljU5yqvjIHJ4B70L/AC/r57eguh5uNS1S30nTbmK5vWm1uyWbUy91I4tXNzbxsY1LHytqzTD5No+QHqM12vhfzIJNX0/zJ5bWwvvJtpLi4edyhhjcgyOSzYd3HJJ4x2rQi0LSLdr5oNKsom1Ak3pS3QG5zkHzMD5+p656mqlx4bVLG2svD+o3Hhy1t922DSre2VGyc/dkicDnJ+XHU5zTTt/Xnf8ADZDepz+p3N4PEGoXyzXS3FhqdlZ2tuly6xSQS+V5haIHY5PmS8kEjZwRisUalqlvpOm3MVzetNrdks2pl7qRxaubm3jYxqWPlbVmmHybR8gPUZrv7Pw9Zwz2t5fhdU1S1Rkj1S8t4ftIUk/KGRFCjkjCgcdc81NFoWkW7XzQaVZRNqBJvSlugNznIPmYHz9T1z1NJaef/Dfru/P7wv8A1/X9fkcTGk0/iKTw3PealHo9tNcGK4XUZhMxSG3dVacPvYBppjhmP3ADkCux8M3l1qPhPSb3UF23VxZxSzDGPnZAT+pofwxoMmkQ6VJommvp0Db4rNrSMwxtknKpjaDknkDua1OlPpb0/wCH+f6CCiiikBGP+Ph/9xf5muU8aabr2qappUWj2rz2UaTPdY1ubTRv+QRgvADI3VzjGOOT0rqx/wAfD/7i/wAzWfq/hzTtclhlvluUmhDLHNaXk1rIFOMrviZWKkgHaTjIBxxSGjkNdvdQufCnhzU9F1S40/Tvtlgklsd0k8265jQq87MSVwSDxlj1bBINnxBbTt4oSPT9X1CfXJbiGWC2t7mSO3srQFQ5mjDeWwbEmC6lmLYXGzK9ZcaLp11ptvYTWq/ZbaSKSGJSVCNEwaPGCOhUHHTjms0+B9F/tu41aP8AtGC7uplmnNvq11EkrqAAWjWQIeFAxjGBiqT1+d/y/wAvu06i6HmV/wCItXg8AeKrVdVvRfTXl7Pa3IuG8yCKOS4yqNuyFAttvGMeYK9qiJMKE8naKw5fA/h2eKSOXTgyyQ3MDfvZMlLl/MmGd2RuYZ9u2BW8oCqAOgGBR0sOWruvP8RaKKKQjnfGOv6z4csIb3R9AXWYA5F2Bd+S8C9nC7G3r1zyCODgjJXX0y5urzTori+s/sU0g3GDzN5Ue5wOfardFAEZ/wCPhP8Acb+YrnfEqNfa9pelz3F1a2E0FzPJLa3Uluxkj8vYu9GBxh3bGcHbyDXRH/j4T/cb+YqDUtJ07WbUW2safa38CuJBFdQrKoYdGwwIyPWk1ca0OS0bxhrkzaHZXmlWbT6ha2sxme/KHDRs0pZBEQrgqdqZ+cZII2Ptq6nPqEfhT4ipLqt9NJYtK1rP5vlSQD7HFKqq0QXAVmOO57k5JPYzeHtFuLtrq40iwluGmjnaZ7ZGcyRjEb7iM7lBIB6jtUC+EPDSreqvh7SgNQ5vALKP/Sed37z5fn555zzzVOzv53/T+v6sOLUWn6HnGs6V4ptfB+kwRrd/2vdatcCKxHiG7CMgt5zGhuPM8xlPlRvhivzEj92Ccak2la2fijp9pZ3d9PaWdlZSXkz6tOvl4+0Dd5AcRybzFGrFs+uCSWXvJdB0ecWIn0qxkGnEGy32yH7KRjHl5HyYwOmOgp6aRpsWrS6pFp9qmozII5LxYFEzoMYUvjJHA4J7U763/rbQj7NvK343ZmeO3kj+HviCW3mmgmi06eSOWCVonRljLKQykEcgdK8/1nSvFNr4P0mCNbv+17rVrgRWI8Q3YRkFvOY0Nx5nmMp8qN8MV+YkfuwTjvB4SluGMet+IdR1uwfIl0/ULWyaCYdgwW3Vjg4I56gVqS6Do84sRPpVjINOINlvtkP2UjGPLyPkxgdMdBUq35F36dr/AIqxwc2la2fijp9pZ3d9PaWdlZSXkz6tOvl4+0Dd5AcRybzFGrFs+uCSWWx8PLLWP+Eh1q9vZbptOW4uYLV5tUnufOIu5VIMcjERlFjQDaOQxJYk4Xt49I02LVpdUi0+1TUJkEct2sCiV0GMKXxkjgcE9qXTdI03Rrd4NI0+1sIXcyPHawrErOerEKACTjrTv+v4kvVW9PwX+ZbooopARw/6s/77f+hGiiH/AFZ/32/9CNFAB5bf89n/ACX/AAo8tv8Ans/5L/hUlFAEflt/z2f8l/wo8tv+ez/kv+FUdQ12z0q+gg1ItbRTqdl3LgQ7xzsLZ+VsAkZABxwSeKl0rU49XsReW8M8UDsfKaZNplTs4GchT2zg45x0oAs+W3/PZ/yX/Cjy2/57P+S/4Vz3je71u202yj8Mk/b7m7ESqBHllEcjkfP8o+5n8K4tfFPjASabdRyXJsjbWU94blLbBM7lcrs+bY2MAfeHOccVSVyHJJ2PVfLb/ns/5L/hR5bf89n/ACX/AArH8bajdaT4D1zUNOl8m7tbCaWGTaG2OqEg4IIPPqKzQ2q+HNW0cXevXms2uqTm1eO9ht1aJ/KeRXQxRx8fuyCCGzkEYwcyW9Ff1/A6ry2/57P+S/4UeW3/AD2f8l/wrzXX/iVqUWneILGyt9MtdXs9MuLyFIdVS5ltfLKgi4jEZEbgOCFBdWII3Ac1s6l47utD1bTbLV7LTIPtj28O06uv2iV5WVC0EOwGSNWYAsSh4Y7eBlpXtbqNq39drf5nY+W3/PZ/yX/Cjy2/57P+S/4U9s7TtwTjjNeeaFrOvprenQ6zqt3FqNwzpe6VqNgIbRW2FgtrcJFhyCBhTK5KbicMpwuthdLnoHlt/wA9n/Jf8KPLb/ns/wCS/wCFeaa1rPiXQ7LWPK8QtqN7aaNcXV8FtofJ024CBoljIQHBy2Ek3sVVScZ+ay/izWW1jwnZx3W0yebFqv7lf30ghnC4JXj57dz8uO3ah6K/9df8h2/X8Lf5noXlt/z2f8l/wo8tv+ez/kv+FeUWHjjxBP4F8LSSX2dVub+1+3z+Qn72CSSHIxt2glbmIZA7HBzzXpt1cy/2raWcDhN6vNI2AcquBt/EsPwBqnFr+vmIteW3/PZ/yX/Cjy2/57P+S/4Vixz6jFqk1kl4bmVrZnVrqARRrKCMbMKCy/Nzy2MDnJqxpU1wdQuoDeyX9vCqgzSIg2y5O5AVABwMduCcZ7CQNLy2/wCez/kv+FHlt/z2f8l/wrLOumPXI9Pnit4/NdlQC6DTABS25owOFIU85J5GQM8VD4huRcW13PAINNktprgFZA7SKoBUkbRtOOcAnr14oDrY3/Lb/ns/5L/hR5bf89n/ACX/AArnpdd1O1vpPtdlGuYYfLgScMpaSTZkvtBBGRkYI4qabxRHbastjcLaoyvHFMPtQ3h3AxsQqC6jK5PHU8cUbgbflt/z2f8AJf8ACjy2/wCez/kv+FZlprhutYlsvKhUIzqV+0jzl2n7zREAhT2IJ6j14XR9bOqzSIYoECKGxHch3jOcbZEwCje3I4PPHIBpeW3/AD2f8l/wo8tv+ez/AJL/AIVkS6teWuq3YeBZbKGSJWfzNrR7gBwuPm5OTkjjpnpVSPXL7+0EKxGa2WK7d03AyMY5do2gIM9gBn+LnJHIB0XkncG818gYHA/w9qPLb/ns/wCS/wCFUtH1U6rDK5FsQjAB7W6E6HIzjOAQR3BHp1q20/kW0k15siWPczEMWAUd84Hbt/OjYFqO8tv+ez/kv+FHlt/z2f8AJf8ACufTWdQ8rUWkVI5PtEMVtG6/6oSBQN3qRuyR+FXIGlXUJ7BddS5lMJOyQxefC/YhVUArg9x6etAGp5bf89n/ACX/AAo8tv8Ans/5L/hUOmXbX2mwXEihXdfnA6Bhwce2QatUAR+W3/PZ/wAl/wAKPLb/AJ7P+S/4VJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/wA9n/Jf8KPLb/ns/wCS/wCFSUUARtCWGDK/UHoP8KPLb/ns/wCS/wCFSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf89n/ACX/AAo8tv8Ans/5L/hUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/AD2f8l/wo8tv+ez/AJL/AIVJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/z2f8AJf8ACjy2/wCez/kv+FSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf8APZ/yX/Cjy2/57P8Akv8AhUlFAEawlVCrK4AGBwP8KPLb/ns/5L/hUlFAEflt/wA9n/Jf8KPLb/ns/wCS/wCFSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf89n/ACX/AAo8tv8Ans/5L/hUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/AD2f8l/wo8tv+ez/AJL/AIVJRQBH5J3FvNfJGDwP8Pejy2/57P8Akv8AhUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUAR+W3/PZ/wAl/wAKPLb/AJ7P+S/4VJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/wA9n/Jf8KPLb/ns/wCS/wCFSUUAR+SdwbzXyBgcD/D2o8tv+ez/AJL/AIVJRQBH5bf89n/Jf8KPLb/ns/5L/hUlFAEflt/z2f8AJf8ACjy2/wCez/kv+FSUUAR+W3/PZ/yX/Cjy2/57P+S/4VJRQBH5bf8APZ/yX/Cjy2/57P8Akv8AhUlFAEflt/z2f8l/wo8tv+ez/kv+FSUUARwcR9c/M3J/3jRRD/qz/vt/6EaKAJKKjxN/fT/vg/40Ym/vp/3wf8aAEubW3vbdre8gjuIXxujlQMrYORkHjqKlqPE399P++D/jRib++n/fB/xoAyPFGhS67Z2iQfYme1ufPEV/bGeGX926YZQR/fyD6qK4Q/CXUWvNPnN5pMP2JIUxbWbRmXy2zuY7jl26Fu/HFepYm/vp/wB8H/GjE399P++D/jTTaJcU9yrrekwa9oN9pN28iQX0DwSNEQHVWGCQSCM89wazbDwn5GowXuqa3qWsy2qsLYXvkIsBYbWZRDFGCxHGWyQCcYyc7mJv76f98H/GjE399P8Avg/40ijjovhhpqWn2SbVdUuLRNPm02C3doVWC3lChlUpEGJGxcFyxyOScnM1x8PLe4uJJW1vVcTS29zcRA24FxPBs2SM3k7gf3aZClV44UZNdXib++n/AHwf8aMTf30/74P+NF3/AF/XfX11AwY7rxhLMsd3oGhLbOwWVk1qZ2CHqQptACcdsjPqKitPBEVo1tGdb1aeysjmxspniMdqdpVSrCMSNtViB5juOhOSAR0eJv76f98H/GjE399P++D/AI0AcjY/DiKz8PXmhv4j1m6068glhlhmW1yTJndIXWAOz8k7mY5J5zV5fAulpqx1FJLoTG+N7jeu0OYGhKgbeFw7Nj+8xOecV0GJv76f98H/ABoxN/fT/vg/40bgcpb/AA10W2jgSOa8PkQ2UKEyL0tXDofu4yxVQx7hVxjFdHdWjyX1tdQlQ0YaNwSRujbGcEdwVUj8fWrGJv76f98H/GjE399P++D/AI07tgZzaEHRll1G+kbymiicuoaEN12kKCTwOWyf1qfTdNOmQiFbyeeFVCpHIkahAPTYi/rVrE399P8Avg/40Ym/vp/3wf8AGkBnJ4fhjvEnW6udkdw1wkHybA7Z3HO3cc7j1J601PDlqsymSe4mhjieGO3dl8tEfgqMAHoMDJJFaeJv76f98H/GjE399P8Avg/40B5mWPDkJmMtxe3lw/7oBpWTgRvvUcKO/XufWrT6UragbqO6uYQ7K8sMbAJIyjAJ43dAAQCAcDOatYm/vp/3wf8AGjE399P++D/jQBSTR1F6txLeXU6xszxRSOpWMtkEggBuhIGWOM+ww6z0lbS6FxJd3N3IsflRmdlOxSQSMhQTnA5bJ469at4m/vp/3wf8aMTf30/74P8AjQBRn0SG4vnuHuLjZK6PJbgr5blMbc8Z4IB4Iz3yOKZ/wj9urI0VxcxMvnjdG4BIlbcwzjIwcEYweO9aOJv76f8AfB/xoxN/fT/vg/40AV7HThZyzTPcTXU8wUNLNtB2rnAwqgcZPbPNTNbJLbPBdf6TG5O5ZlUggnOCMYIHT8O9KTMJAu9OQTnYe2Pf3pcTf30/74P+NAGdF4a0qD7WIbSOJbvaHWNQm3AGNpUAjkZ+vNOj0qSCU3X2ua9u44mSA3TKqpnGfuKOpAySCeOKv4m/vp/3wf8AGjE399P++D/jQBHYWgsbCG2Vi/lrgsR949z+J5qxUeJv76f98H/GjE399P8Avg/40ASUVHib++n/AHwf8aMTf30/74P+NAElFR4m/vp/3wf8aMTf30/74P8AjQBJRUeJv76f98H/ABoxN/fT/vg/40ASUVE5mRQd6HkD7h7nHrS4m/vp/wB8H/GgCSio8Tf30/74P+NGJv76f98H/GgCSio8Tf30/wC+D/jRib++n/fB/wAaAJKKjxN/fT/vg/40Ym/vp/3wf8aAJKKjxN/fT/vg/wCNGJv76f8AfB/xoAkoqPE399P++D/jRib++n/fB/xoAkoqPE399P8Avg/40Ym/vp/3wf8AGgCSio8Tf30/74P+NGJv76f98H/GgCSio8Tf30/74P8AjRib++n/AHwf8aAJKKijMzxq29BuAONh/wAaXE399P8Avg/40ASUVHib++n/AHwf8aMTf30/74P+NAElFR4m/vp/3wf8aMTf30/74P8AjQBJRUeJv76f98H/ABoxN/fT/vg/40ASUVHib++n/fB/xoxN/fT/AL4P+NAElFR4m/vp/wB8H/GjE399P++D/jQBJRUQMxkK704AOdh759/alxN/fT/vg/40ASUVHib++n/fB/xoxN/fT/vg/wCNAElFR4m/vp/3wf8AGjE399P++D/jQBJRUeJv76f98H/GjE399P8Avg/40ASUVHib++n/AHwf8aMTf30/74P+NAElFREzCQLvTkE52Htj396XE399P++D/jQBJRUeJv76f98H/GjE399P++D/AI0ASUVHib++n/fB/wAaMTf30/74P+NAElFR4m/vp/3wf8aMTf30/wC+D/jQBJRUeJv76f8AfB/xoxN/fT/vg/40ASUVHib++n/fB/xoxN/fT/vg/wCNABD/AKs/77f+hGiiDPl88nc2cf7xooAkooooAKKKKACiiigDO1rXtN8PWcd1q9wYIZJBEjCNnyxBOMKCein8qzF+IHhtrm1t2vpY5LsIYPNs5oxIHOEYFkA2k9G6e9aOv+HtO8S2CWerRPJFHKJU8uVkKsARnKkdmI/GsQfDPw99otpm+3SPahFgMl7I4jVDlFAJIwD0HSmrEvmvodFrGpw6Jod9ql2sjQWNvJcSLGAWKopYgAkDOB61zp+ItimnzXFxpOrQSRm3MdrJCnm3CXD+XE6AORgtxhiGXHzKOM63i+wudU8E63p9hH5t1dafPDCm4Ludo2CjJ4HJHWsJPA+oXtukmuaxHPeK9lseKz8tUit5hLsK7zl2IILggdMKMYKWr120/wCD/X/ALeyt5/p/wTQk8bW0GoJbXOl6hDGrww3dywiMVlNKBshkIkJLfMgJQMo3rlsc0o8bWX2m6L2F+umWpmV9W8tGtt0IPmDhi42lWG5kCkqQCcjMN/4Onu9auZYtSSHS766gvL2zFsTLJNFs2lZd4CqfKj3Ao2dpwRu4b/wht20V/pcmsj/hH703LPZJa/vz5+4upmLEbA0jMAEVh8o3EAgp7eev6f8ABt+I9L/cPi8eWh0y7urzS9SsZ7YQEWVwsQmmE7bISpWQphnyvzMMEHdtHNTzeLhBpcFxNomqx3lxdmzi02RYlmeQKzcMZBERsVm3CTBxjO75ayNK+HT6Vomo2lvNocFxepHGzWfh6GGCRE6rNFuJl35Ib51GD8oXkl0Xw+lt/Cr6RBdaTsluzczWkujK+nkEY8tbYyZRQQH4k+/k9DiqdruxK21/rT/MuX/ji305Ybq9try2g/sq51Ca1ltMTIIWjDKTvwCN5GAGDdQwAG6SPx1afZdQkvNM1GxnsUhk+yTpGZZ1mYrDsCuwy7AqAxUg9QKy5vhoJdDi05dV2BNIvNNytt8q/aHRsou75UTZtWPJwuBu4ybniHwtLK2ranavcXF1Pa2kdvBbRx745beV5EceZIqsNzjKll4UjPNDtb+u7/Sw+39dv+CbGg+IV11r2M6de6dcWMwhngvFTcrFFcYKOykYYcgmma74kXRLi2tYtNvtUvLlJJUtrLyt/lx7d7fvHQHBdRgEsd3AODjnfD0/ifSBqGoax4d1DUbvVLoSmKz+yRGBUiSMble5IGSpICyScdSCcCzrejX/AI5sI1utNg0uOJmVrLxDptvfo5IGJUEU/wAjD5gDu7n5ehpO/T+v6/ruEbdTZfxRaJcCA290JWa3CRtGFZxNna21iCANr7gQCNjcHFYOpfEOYeCtW1vSNC1DFtYNe2M1zHGYbtOzgrLwBwxRyj7eikggXLfwy0fizTJdt2YdJ037OL6eVGNzJjajdSSyqZclgBmXjPOM2H4Zu8GtLe6hYrLqthLZyTadpa2hmMnWacByJZAehAQDc/HzcPT8/wDgf15hHpfy/wCD/Xka48cWy6lDZz6ZqEY3QRXVyREYrOeYApDIRITuO5BlAyjeuW5qGb4gW8d6YI9C1mdDdy2MU8cUWya4Td+7XMgIzsJDsAnqwIIFOT4bx3HiqLW7p9HnmdoJruSXRUknaWJQuYZXdjEh2L8uHIwSGBOa2IvCvlfZf9Mz9n1ebU/9V97zPM+TrxjzOvt05o/r8v8Ag/hfzn7Pn/wH+tv62zNV8U6jceFX1/Q7q206K3MkM9lqGmtcztcLIY/KHl3CKGLjaMFgSRg4rqdK/tD+yLX+2vs/9oGJTc/ZlIiEmPmCgknGfU1hw+DfLtbe3e+EkUetS6q6mHiTdJJIsf3uNrup3c52dBni5qGvajZ30kFv4S1jUI0xtubaWzCPxngSXCt7cqOlLp/X9b/kN76f1r/l+Zj6j4s1O11e7uIVszo+n6jbabcRGNjPI83lgyK4YKoUzINpQk7W5GRXZ1yEnhO51XUGu5bp7LTb65t9QvNKngVphPEE2gSpIVVcxx7lw+SpwwzXX0/s67/8BfrcOpR1W6msbG5u7W0e9ngtpZI7aM4aZgAQgPPJIx0PWsDwv4jvtV1Q28upaPqsXkb5jpyGCWxkBA8uWJ5XfJy3UKQUII546K/tft0Elr581v50Dp51u+ySPOBuU9iOorD/ALL1i0vYdY1aZNbubGF4raDTbJLaRxIV3lmlmKn7oOAUHXgnGO3Duk6UoytzPa/4dNNfNed1oTK+ljd1TUItJ0m6v7gM0dtE0hVerYGcD3PQVgJ4kv4fD6ajfpbI2n3DRaxGiN+7UdZE+bgAFZMHd8pPepLjz/FSJpureHNQsLEyLLP9tNrJHOqnIjIjmc8ttJypBAIPWmR+B7S01Erpv2ax0aSWK4n0u3tFRJJo84bKkAAnyyRt5MY5wSDpTjh6cOWq/e30107XV9Xr+GqE23t/X9f1cdpviDUbnXtMtbuGCKDUbO5vEUKfMjRHhEYJ3YyVkJbjrgDpk3PFmuHw7oQ1HdAiLd20Ur3BwiRvMiOxORjCsTk8DHNZ0Ph240G7t9Qt2uNUj0+G4t7WwgjRZRFLJEwXfJIqkJ5ZAzj5cDqPmkvPtvimGOwutE1LR0juILr7TdG2kQmKZJNmI52bLbcZxgfob5aDqwmrci36dX0eu1tk/mGqTv8A1/TMvXPiRaRyXS+GNR0nVBbaNeX7+TOJtkkXl7A2xuFO9sjqccEVDb+PpYHuJDrGieI7eDT57uU6RGY/s7RhSqu3myj58kDODle/ON/xN4T/AOEjkkb7b9m8zS7vT8eVvx5/l/P1H3fL6d89RitfUNOh1PRrnTbot5NzA0EhQ4bay7SR6HmrVbBRpQXJe979/vt+VvO47O6/rt/wTL0PVNTbWbrR9da0luobaG7Sa0iaJGSQuu3azMcqYzznkEcDFb9Ymh6Fd2F9cahq+oR6hfzQxW/mxW/kIIo9xUbdzfMS7EnODxgDFbdefiXB1Pc2020V7a226hG9tQooornKI5v9WP8AfX/0IVmeLdWn0Hwbq+rWixvPY2cs8ayglCyqSM4IOOPUVpzf6sf76/8AoQqj4j0f/hIPDGpaP5/2f7dayW/nbN+zcpGcZGcZ6ZFBUbcyvsc7cfEfR7nV9EsPDut6Nqc19cSJcRW92kzxotvJJuARuPmRRk5HPrWH4X+I97qzaFJLr3h3VX1JQ13pumQMtxYKYmdnc+fJwrAKdyLyw5B4PcavoH9q3WjzfafK/sy4abHl7vM3QSRY6jH+sznnpjvmpvD+jjQvC+m6M032kWNpHbGXZt8zYoXO3JxnHTJofW39b/8AAJWyv2MDw14o1W/1HTE1dLPyNc099Qslto2VrdVMf7uRizCQ7ZVO4BBlTxyK1PGOrX2jaCk+km3W6mvLa1R7mJpETzZkjLFVZScBs43CsrTvDN74aMF7JJPr40u1ay0yztYY4ZUhdkzvaSUJI4CIN3ycKeCTTtVGreMLD+zf7E1Hw/JHPBdxXuoLazxb4ZUkClIbksc7fbjPNPRtW7/hf/IPUq654n8QeFdP1JdWk02+uV0u4v7G4trSSCMtCBujkjMjn+NSCHGfmGBjJvjVPEOi31imv3Om39tqLNDFJZWUls0EojaRdwaWQMpCMM5XBx1zxBqPgnUNes9R/wCEh1m2nvLnTptPtpLSwaGK2SXG9vLaVyzEquTvAwoAA5Jtw+GtWvL+0uPEur2d6lhua1hstPa2USMhTzH3SyFiFZgANoG4k54xLvy6b/8AD/8AAHpp/Xa36nJ+H/iRqGp2+h3A8Q+GdWudQeAXGiadbst3AJMbzn7Q+PLyWbKDhT92vUJnMcEjr1VSRn6VyqeCDaaJ4disNQ8nU9AhjhivhBnz4woWSN03DKOB03cMFOcrVpfEOqXLi3k8F65bpKdhmkmsSsYPG4hbknA68An2pz1TUfO36f0/8hLe72G2Hip/+FY23irUYVaRtLS+lhtwQGYxhiq5Jxk8DJNVZdU8VaRcWa6udJuV1Jmgi+y28sf2Ocxs6Byzt5qEqVLARkHBxz8rdH8IaxaeG4fDmsaxp19o0dj9iKQaZJBOyBNgPmG4YA98hPpirdr4a1WbU7GfxFrkepQaa5ktIorLyGeTaUEkzb2DsFY42LGuSTt6AOdnJ22/T/P+u4LRf18ihZeOLm91fQVSGFNPv7KKW6dlIaOWVJGjUHdgD9y4IIJyy8jummeIvEXiRYIdJbTtPl+xx30813aSTLsmZ/JjWMSId2xMsxbg4AHPD3+H27w7rGmJqrpJf3n2mC4EOTaoGBWMDd8wX5hnI+90rSvPDl9BqCX3hfUrfTZvsyWs0V1Zm4hkjTJQ7VeNlddxAIbGCQQcKQt7/wBf1bYPT+v+HMu38WazrDWelabbWljq7m5F7Lcq88FsIJBGxVQUMm9mXb8y4BJPI2mDWvFfiXw2ulx6ta6e7Tar9nnu4lZY5rURPK0iIXJjYBCCGLD5SRnPGgngqawt7GbRNWaHVbRp2e8u4BMl0Z2Dy+bGrJnLAEbWXaVA6ZBbP4JutSFrJrmtve3CXctxNthKRFXt5IBHEm8+WoEmerEkHJ54Nl5/1sGl387fja/9fqT6F4mutW8ca/pLRwCx06OBraRAd7lmlSTcc44eIgYA6HrXT1zXhfwi/h28kuptSa+mmsYLaZ3i2mSSN5XeU8n77TE47Y6mulpuy0QdW/62I4Di2jP+wP5Vxlh45kgt4NS194hpuoae+pWotLKVpbeFWjBEgUuZDiZDuVVxhsjHI7OD/j2j/wBwfyrno/BUMNvcRW2sanAHiMFq0TQq1hEzBmSE+XwDtUZbcwAGCMZpLf8Ar+u34j06kV34om1S/g03wtLHFcubgvPqFhMYwICiuqqTGWy0ijeCVGG64xWfZ+OrvUootXtRbRaQktnb3EEkLNM8lysRBWQMAoXz04KHODyK1F8EwwWccFlq+o2hgmka1lhEG61ikxut0BiKiPgYBBYYGGGBUy+DrCK+gltp7m3tIvKL6fGU8mZogBE7ZUvldq9GAO0ZBoX+X/B/DT11F0/r+v6+Rgr451KKxtLu4Fm663bLcaXGkDqYN00MSiUlz5n/AB8RkkbOjDHcdN4f1G7vPt9nqbwS3um3P2eaW2iaKOQmNJAQjMxX5ZAMbjyDzVKPwPYJDPDJd3k0JjMVpG5jxYJvD4iwgPDIhBfefkXtwZPsGs6FahNCht9auLiRpby61W++zSO2FVT+6t2U/KoGAqgBR1yaat/Xrp9y3/pjZUv/ABNfW+tXTxG1Gl6fe21hcxPCxmkkn8vDI4cBQvnJwUOcHkVmr451KKxtLu4Fm663bLcaXGkDqYN00MSiUlz5n/HxGSRs6MMdxtweHJb69j1LV2NlPJIkt1ptncLNbTSRn93IztEshICoeNoyoyDjJbH4HsEhnhku7yaExmK0jcx4sE3h8RYQHhkQgvvPyL24KWm/9af56rsvuDT+v6/r8TPTxJrlxqZ8O29xpo1m3kl8+7ezkNu6JHC/yxebuUn7RGPvtjBPPQdPomqJregWGqRIUS9to51QnO0MobH61lN4NjNuhTWNSj1ESPI+qIIPtEu5VVgwMXlgFUQfKgxsGMHmt20tYbGygtLRBHBBGscaD+FVGAPyFPpr5f8AB/T/AIAiaiiikBGP+Ph/9xf5muU8aeKbzRNU0rTtNmSGW9SaV5DpFxqJCx7BxFAysMlx8xOBj3rqx/x8P/uL/M1n6vaa1PLDJoWrW1iVDLJFd2JuY5AcYOFkjYMMf3sYJyM4IQ0YWueK9V03S9EuNNtLfUob24tI7nUwwjgCyypGdke9nLHfkA8AdWJG0ya7rXiLSr/7UqafHpv22C0hs5Y2e5vt5UM6Or4TG5sKUY4jJJUHIuXPhOKXwvp+iW9y0UdlcW04lZNxfyZllIIyMbiuPbPTtVOTwvrx8aS66uuadNGSEt4LvS5JHtIsDekbidVBbGS+wk8A5CgClvr3/DT/AIP59hdDn7r4l6rb+CPEepfZ7H+09PvbiGyiKtskijdwGYbsk7YZScEZ28Yr0uNi8asepANcFd/C8XVldW/9sMi3MF/GwFv8u+4kkdHI3cmNZpVx/FvzxgCu+Rdkar12gDNHT+v68/mOW+nn/wAAWiiikIxPEfi/RfCS2ja/cy2qXkhihkW1llUuBnaWRSFJGSAcZwcZwcaWn6hbapZJd2LtJA/3XaNkz+DAGnX1ja6lZSWl/Ak9vKMPG4yDzkfiCAQexGalREijWONVRFAVVUYAA6ACgBp/4+E/3G/mKxte1LUY9UsdJ0SS1gvLqKa4M13A00axxFARtV0OSZF5zxzwa2T/AMfCf7jfzFZ+s6GurtBLHfXWnXVvuEd1aeX5gVgNyfvEYYOB2zwMEUnfoNeZmaZ4+0fUYbAH7ZHc3kVs6wrYzuB56F0w4TaVwr5YHC7TuIqre+KdYh8O+Mbr7NY215oLSfZxueeORVt0mUt9w5IfBAxj1bGTePg23TU7S8ttT1C2+w+UlrDEYtkMKKVaEZjJ2PwXySxKpgjaMQy+B0ntPENtNr2qyR+IM/aARbjysoIz5eIePkVV+bdwM9cmqdne3n+lv1/rUcbJq+2n/BOS1D4ja9Y+CrXXDPaeXJqNxbPKdCuC5jijlYkW3nh0bfCw+ZgApDNswRWnc+MPEVr4+0nQZjYj7Zb20jxpp0r5ZhMZR5/mhI8CBioKsTnGDhmHVXvh37fJoskuqXyvpEwnQoIf9IfYYyZMxnqrMPk2/eOMYGJYdDWDxTd64L66eS6to7ZrVvL8lVQsykYTfnLv1Y/e6cDDur/12I+z52/G/wCiGeLNSu9G8H6vqeneT9psrOW4jE8ZdCUUtggMpOcY4IrgNQ+I2vWPgq11wz2nlyajcWzynQrguY4o5WJFt54dG3wsPmYAKQzbMEV1d/Z+JfEen3Wja1p+nabp19C8E91p+rNNOiMpB2pJahTnocngEkcgVpXvh37fJoskuqXyvpEwnQoIf9IfYYyZMxnqrMPk2/eOMYGJXn5F3X5/lp+Jytz4w8RWvj7SdBmNiPtlvbSPGmnSvlmExlHn+aEjwIGKgqxOcYOGYWfBXi3Wde8Uavp2ovayRae8qN5Ony2+0i4kijId5WEoIhcnaoCnAyTkDpoNCWDxTd64L66eS6to7ZrVhH5SKhZlIwm/OXfqxHzdOBg8P6Gvh+wmtUvrq9EtzLcl7ry9ytI5dgNiKMbmY9M89cYp6fmS9rLy/LX8TUooopARw/6s/wC+3/oRooh/1Z/32/8AQjRQAZm/uJ/32f8ACjM39xP++z/hSfarf7Z9k8+P7T5fm+TvG/ZnG7b1xnjNEF1b3XmfZp45vKkMUnluG2OOqnHQj0oAXM39xP8Avs/4UZm/uJ/32f8ACsbxZrt1oOjT3FjptxeSi3ldZURWigZVyDKC6nbnn5cnAPtWMPEfiLTtUto7+GHV4bhXHk6XYmKVSADuzJOVx69+lc9TE0qVWFKb96V7edilFtNo7LM39xP++z/hRmb+4n/fZ/wpLm5hs7WW5u5UhghQySSyNhUUDJJPYAVjWnjXQ71WMdxcQlZYomS6sp7dgZTtjJWRFIVmBAfG0njOa6CTazN/cT/vs/4UZm/uJ/32f8Ky9I8SWWt6hNDp8scsKW0VyjjzAzrI0ihtrIBtPlkghjnrgDBa3q2sWWiWP2vUZHWMusarFC8skjscBVRAWY+wBPU9qHpuHWxZzN/cT/vs/wCFGZv7if8AfZ/wrlLD4k6Lc6f9quftEXmXVzBbww2s08swhcKzeWkZcHkEqRlec9Di/YeOfDmpNJ9k1IGNLZrvz5IZI4XhXG50kZQjhdwDbSdp4ODR0v8A13A3Mzf3E/77P+FGZv7if99n/Cs7RvEmma+0yac9wJIArSRXVpLbSBWztbZKqsVODhgMHB54NTa1qy6Lpcl4bO7vWX5Ugs4TJJIx6DjhR/tMQo7kUP3Vdgtdi3mb+4n/AH2f8KMzf3E/77P+FUfDmr/2/wCGNM1fyPs/2+1jufJ37vL3qG25wM4zjOK0qbTTsw3I8zf3E/77P+FGZv7if99n/CpKKQEeZv7if99n/CjM39xP++z/AIVJRQBHmb+4n/fZ/wAKMzf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/wC+z/hUlFAEeZv7if8AfZ/wozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/vs/wCFSUUARETGQNsTgEY3nvj29qXM39xP++z/AIVJRQBHmb+4n/fZ/wAKMzf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/wC+z/hUlFAEeZv7if8AfZ/wozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/vs/wCFSUUAR5m/uJ/32f8ACjM39xP++z/hUlFAETiZ1A2IOQfvnsc+lLmb+4n/AH2f8KkooAjzN/cT/vs/4UZm/uJ/32f8KkooAjzN/cT/AL7P+FGZv7if99n/AAqSigCPM39xP++z/hRmb+4n/fZ/wqSigCPM39xP++z/AIUZm/uJ/wB9n/CpKKAI8zf3E/77P+FGZv7if99n/CpKKAI8zf3E/wC+z/hRmb+4n/fZ/wAKkooAjzN/cT/vs/4UZm/uJ/32f8KkooAjzN/cT/vs/wCFGZv7if8AfZ/wqSigCKMTJGq7EO0AZ3n/AApczf3E/wC+z/hUlFAEeZv7if8AfZ/wozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/vs/wCFSUUAR5m/uJ/32f8ACjM39xP++z/hUlFAEeZv7if99n/CjM39xP8Avs/4VJRQBHmb+4n/AH2f8KMzf3E/77P+FSUUARATCQtsTkAY3ntn296XM39xP++z/hUlFAEeZv7if99n/CjM39xP++z/AIVJRQBHmb+4n/fZ/wAKMzf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/wC+z/hUlFAEeZv7if8AfZ/wozN/cT/vs/4VJRQBERMZA2xOARjee+Pb2pczf3E/77P+FSUUAR5m/uJ/32f8KMzf3E/77P8AhUlFAEeZv7if99n/AAozN/cT/vs/4VJRQBHmb+4n/fZ/wozN/cT/AL7P+FSUUAR5m/uJ/wB9n/CjM39xP++z/hUlFAEeZv7if99n/CjM39xP++z/AIVJRQBHBny+eDubOP8AeNFEP+rP++3/AKEaKAKOs6Hba3DGJpJ7eeFi0N1ayeXNCTw21uwI4I6H8BVqxsbbTLGKzsIVgt4V2pGvQD+p7knknmpPMb/ni/5r/jR5jf8APF/zX/GgDH8T+EdI8WWXlarZW81xFHItrcyxB2tWcD50z0OQp/4CK5fSPB0viaa4fx1DDqFtY3txDbWlzYBFkUHak3JOcjOCOOa9A8xv+eL/AJr/AI0eY3/PF/zX/GspUac5xnKN3HZ9hptKxDqkN5caRdw6XcraXskLrb3DIHEUhB2sVPBwcHFeeQ/D/XLs6vDqn2WO11q3t7S7V9XuL+RY0eRpCjTRjBYSYCgBVOSPQ+k+Y3/PF/zX/GjzG/54v+a/41pYLuxxmm2t14S1CXVNf3TpLYWunoNLsp7ly0LTHeY4oyUBV1PoDkZ6E2r27/4Sk2d14ehulvtHuhdRxarY3VjFNujkjK75IgfuuxyqtggZGDXU+Y3/ADxf81/xo8xv+eL/AJr/AI1Tbbuxbbf1pb8jkPDvhbWLHVLbUNWex80TX80qWrsVH2iRGQLlRnAUgk49e/EUXg/Wraz0QWN9a215pmj3VkJ8Fwk0gi2OqlcMoMZJzjtwa7TzG/54v+a/40eY3/PF/wA1/wAaWySXTT8LDTs7nBaHYX/g/WNQ1rxDFcGK/ggtxFZ3F9rMokRpWLf6rcinzOgUKCOOtdXp2u2mvxXMVjDqETImCb3Tbi0BznGDLGu78M4rS8xv+eL/AJr/AI0eY3/PF/zX/Gh+8rMWzuZvhXSp9D8H6RpV20bz2NlFbyNESVLIgUkEgHGR6Ctao/Mb/ni/5r/jR5jf88X/ADX/ABpttu7AkoqPzG/54v8Amv8AjR5jf88X/Nf8aQElFR+Y3/PF/wA1/wAaPMb/AJ4v+a/40ASUVH5jf88X/Nf8aPMb/ni/5r/jQBJRUfmN/wA8X/Nf8aPMb/ni/wCa/wCNAElFR+Y3/PF/zX/GjzG/54v+a/40ASUVH5jf88X/ADX/ABo8xv8Ani/5r/jQBJRUfmN/zxf81/xo8xv+eL/mv+NAElFR+Y3/ADxf81/xo8xv+eL/AJr/AI0ASUVH5jf88X/Nf8aPMb/ni/5r/jQBJRUfmN/zxf8ANf8AGjzG/wCeL/mv+NAElFR+Y3/PF/zX/GjzG/54v+a/40ASUVH5jf8APF/zX/GjzG/54v8Amv8AjQBJRUfmN/zxf81/xo8xv+eL/mv+NAElFR+Y3/PF/wA1/wAaPMb/AJ4v+a/40ASUVH5jf88X/Nf8aPMb/ni/5r/jQBJRUfmN/wA8X/Nf8aPMb/ni/wCa/wCNAElFR+Y3/PF/zX/GjzG/54v+a/40ASUVH5jf88X/ADX/ABo8xv8Ani/5r/jQBJRUfmN/zxf81/xo8xv+eL/mv+NAElFR+Y3/ADxf81/xo8xv+eL/AJr/AI0ASUVH5jf88X/Nf8aPMb/ni/5r/jQBJRUfmN/zxf8ANf8AGjzG/wCeL/mv+NAElFR+Y3/PF/zX/GjzG/54v+a/40ASUVH5jf8APF/zX/GjzG/54v8Amv8AjQBJRUfmN/zxf81/xo8xv+eL/mv+NAElFR+Y3/PF/wA1/wAaPMb/AJ4v+a/40ASUVH5jf88X/Nf8aPMb/ni/5r/jQBJRUfmN/wA8X/Nf8aPMb/ni/wCa/wCNAElFR+Y3/PF/zX/GjzG/54v+a/40ASUVH5jf88X/ADX/ABo8xv8Ani/5r/jQBJRUfmN/zxf81/xo8xv+eL/mv+NAElFR+Y3/ADxf81/xo8xv+eL/AJr/AI0ASUVH5jf88X/Nf8aPMb/ni/5r/jQBJRUfmN/zxf8ANf8AGjzG/wCeL/mv+NAElFR+Y3/PF/zX/GjzG/54v+a/40ASUVH5jf8APF/zX/GjzG/54v8Amv8AjQBJRUfmN/zxf81/xo8xv+eL/mv+NAElFR+Y3/PF/wA1/wAaPMb/AJ4v+a/40AEP+rP++3/oRoogOY+mPmbg/wC8aKAJKKKKACisjWdCbUJo73T7xtN1OFSkd5HGHyh6o6nh17gHocEd82tK0q20exFtaBiCxeSWRt0kzn7zu3dj6/0AFAFbxH4jtPDGnx3l/HPJHJMIVECgnJUnuRxhTWM3xI0uK7sYLmy1CA38UU0LukZBjkOFc7XJA4PGM+1TePEVrHS3nhjlto7/AHTGawe8jRfJlALRJyRuKjPYkGvKEMkM2mQWukW0nmW9is866NLDJbukhL/O3G7B+d+jDHTFWkmZSk0z3LW9Wg0HQb7VrtJHgsYHnkWIAuyqMkAEgZ47kVn6d4pa61GCy1LQ9T0aW6DG2a98hknKjJUNDLIAwHOGwSAcZwcO8bafdat4D1zT9Oi867urCaKGPcF3uyEAZJAHPqazCur+I9W0c3GgXWj2ulzm6d7+W3Zpn8p40RBFJJgfvCSTjoAM5OINnsvn+li9qfjzw1pmlatfNrFlcjR4y95BbXUbyxEHGwru4Yt8oBxk8Vcj8U6BJeWdomt6cbq+iWa1t/tcfmTowyGRc5YEA8jI4rzKfwt4u1SC6iuNLngzoF7p6W5NnFaQyyCPatsIiZPLPlkfvTkcdOa09e0zxRqWpI0Oj3dpbG9sbwpAbFYpFjaFn+0uzGUyrsYDy/lwqDLc01bS/wDWv9MHbp5/p/wT02RxHGzt0UEnFcpb/EfR7jwvo2upBeC31i8js4IiieYju+0FxuwAOpwTwR9Ksnx74PvM2lp4s0Oa4m/dxxR6lCWdjwFA3cknjFcNp/gfxHDpOkWc1iBHYppkoXz0JEolt/tA+9j5Ftt2R18wgZ7kVeWu11/wfwB7f1/W518vxI0eHwtrWvPBefZ9Gu5LSeIInmO6PtJQbsEE9CSOh6Vau/GtraajJB/Z2oTWkE0dvc6hEkZgt5X27VYFw5++mSqMBu5IwccLqHgbxHPomqWMNiCl5HqUxT7Qg3TGW4+zr97Hzpc7snp5Yzg9Okvzr2o+KkttV8NalNoVnLEbYWs9r5dzIMHzpt8yvtRvuoF6ruO47Qoun9f11/DpcJaXt5/8D+vU0z46tBe4/szUTpv2z7CdW2xfZxNv8vbjf5uPM+Tds25745roLi7S3lhiYMzzMVVUGTwMkn2H+HrXCT6PrA1x2sNDvNO1Br4SnU7DUFj06aPzMl5bfzctI0eVYmIndjDAAMOvvMw69ZXDf6topYAT0DsVZfpnaR+XrSWyE92ImvQAy/a7e5swkJnUzoB5iDqQASRjI4IB5HFT2WpC7neCS1uLSZED+XOFyVOQCCrEdQeM59qzNuoDUJNRg0uZJ1t2V4pblGWZuNqxncdq5BycLnIyCelnRY7jzJp9QtLmO7lA8yaYx7SBnCIEdsKMnr65JJoQMvrf2b3rWaXcDXSjLQCQFwPdeveoV1mxk1NbCCdJpzv3CJ1byyuMhsHIPP6Gsf7Hqj65bO1tJHBDdySER+SsO1lcBxz5hf5gTnjOeKhGj6hNFbWf2P7ObeyntjeF0IdmAAYAHdgkZOQDR0H1sbUfiDS5Z5EjvYGSKMO84lUxjJIxuz1yOnuKsrqVi80MK3tu0s6b4kEq7pF9VGeRweRXO3GnX1/eeb/ZP2ZAtqhVpIzuCTBm6E8Benf27VPd6TeSeIJXAumt554Zg0bwrGmwD725S+QVzheDuxxyaELubqX9pLePaR3UD3MYy8KyAuo9SvUdR+dFvf2l3JLHaXUE7wnEixSBih9CB06H8qxrWzvY9aDLayR26yyyMJnieIbs8xsB5gLE5IPABYelO0OzvLe8Hm200FvHB5arcNE5TkYWNk+YoAD9/n7vvQgZpDVrP+0nsZJ0juFICpI4BkyM/KM5PWoU16xbUYrJpFSeVZGVTIh4Rtp6MevJx/snOCCKoXtjdy6jeJFY5W6lgZbvcuECYJyM7sjBxgHk9utV10m9QjzLEzo8d9G8YmVeJJdy5OeAQMZGSCRnFAzorS+tb+IyWNzDcxg7S0MgcA+mRUscscy7oXWRQSuVORkHBH4EYrK0SC8jkuXvEmVX2BGuhF5xwDnJi+UryMZ56+1XrhZ7vT5o4GazmcMiSMoYp2DYB/Ec/lQxIrrrtiy3zh22WLiOVguQzY6LjknJx9aItX3vLHNYXdvOkRlWGQIWlUddpViM5xwSDyKyINB1KNL+Mta7fMgktgkRRXMYUgH5mIHy49e/tV6G41Eai95eC4s9PjhZpYrloSA3GChTJwMNnce4wKANeCaO5t454GDxyKHRh3BGQafVHRIZINFtkmUo+3cUPVcknH4ZxV6gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAI4f8AVn/fb/0I0UQ/6s/77f8AoRooAPOX0f8A79t/hR5y+j/9+2/wqSigCPzl9H/79t/hR5y+j/8Aftv8KkooAj85fR/+/bf4Uecvo/8A37b/AAqSigCPzl9H/wC/bf4Uecvo/wD37b/CpKKAI/OX0f8A79t/hR5y+j/9+2/wqSigCPzl9H/79t/hR5y+j/8Aftv8KkooAj85fR/+/bf4Uecvo/8A37b/AAqSigCPzl9H/wC/bf4Uecvo/wD37b/Cka4RbtLYrJvdGcERMUABAOXxtB+YYBOTzjODiWgCPzl9H/79t/hR5y+j/wDftv8ACpKKAI/OX0f/AL9t/hR5y+j/APftv8KkqtqWo2ukaXc6jqEvk2trE000m0ttRRknABJ49KA3JfOX0f8A79t/hR5y+j/9+2/wp6kMoI6EZFLQBH5y+j/9+2/wo85fR/8Av23+FZ2seI7HQmQX8WouHUsGtNMubpVA67jFGwX8cVb0zUrXWNLttR092ktbqJZYXaNkLIwyDtYAjI9RRuBN5y+j/wDftv8ACjzl9H/79t/hWbP4o0i21xNImuWW7cqvEEhjR2GVRpQuxXbsrMGORgcitajzDyI/PTOPnz6bG/wo85fR/wDv23+FMuJ4rbM9zKkMMUTvJJIwVUUYJJJ4AA71Q0rxPpetXTW1lJcLMIxKI7qzmtzImcbkEirvXOMlcgZGeorSNOcouUYtpbvsJtLc0vOX0f8A79t/hR5y+j/9+2/wp5YKpZiAAMkntWdB4g0y5tbC5guC0Ootstn8pwGbBODkfKflIw2ORjrxSjCUleKv/X/AHsXvOX0f/v23+FHnL6P/AN+2/wAKpW+vabd6gtlb3G+4bztq+WwDeUypJhiMHDOo69c46HFi/wBQtdMt1nvpfKjaWOENtJy8jhEHA7swH403TmpKLTuwJfOX0f8A79t/hR5y+j/9+2/wqlqev6ZozMNSufJK2st4f3bNiKLbvbgHpvXjqc8A1WsvFukXs7QrJdWrrC0/+n2M9oGRcbmBlRQwGRnGcZGapUK0o86i7d7Owro1vOX0f/v23+FHnL6P/wB+2/wqlpGvafrkcjadJKTFjek1vJC4BGVbZIqttPOGxg4ODwa0aznCUJcs1Z+YJp7EfnL6P/37b/Cjzl9H/wC/bf4VJRUjIzOg67x/wBv8KPOX0f8A79t/hRN/qx/vr/6EKi1LUbXSNLudR1CXybW1iaaaTaW2ooyTgAk8elAbkvnL6P8A9+2/wo85fR/+/bf4VXvNWsrCWzju5vLe9kMVuNjHewRpCOBx8qMecdPWsnSvHeg6zNaR2Ut6ovRm1ludMubeKf5SwCSSRqjEqCQAckAkUB0ub3nL6P8A9+2/wo85fR/+/bf4Vm6T4n0jW7ua2025aSWIFvngkjWVQdu+NmUCRM8bkLLyOeRVjWdZsdA01r/VJJI4FdI/3ULzMzOwVVCICzEsQMAHrQHWxa85fR/+/bf4Uecvo/8A37b/AArFi8a6HJY3909xcWyadAbi5ju7Ge3lSLB+fypEV2X5SMgEEgjqKfpvi/SNUu/ssD3kE5jaVI77T7i0aRVxuKCZF34yM7c4yM9RQBr+cvo//ftv8KPOX0f/AL9t/hXPWHxA8P6k9oLeW/SO9ZVtri40q6gglLDKgSyRqnzdueSQBkkV0jMEQsxwqjJND0V2HWwzzl9H/wC/bf4Uecvo/wD37b/CobLU7PUdIg1S0nV7K4hW4jmYFAYyNwY7sEcc84rJs/HGgXzssF1MpELzxmezmhFxGoyzwl0AmAGDmPdwQe4oem4bm55y+j/9+2/wo85fR/8Av23+FUV8QaY2pWNgt1m5v4DcWyiNsSRgA53YwOD0JBPOOhqpc+NNEtoYJPPuLg3G8xxWdjPcSFUbazeXGjMFDDG4jHTnkUPTcN9jZ85fR/8Av23+FHnL6P8A9+2/wrLufFmiW2lWmom+E9venFr9kje4e4OM4jjjDO+ACTgHABJxg1Cnjfw/Jb2covmUXt79giSS3lSQXGCfLdCoaM4GfnA4we4o62A2vOX0f/v23+FHnL6P/wB+2/wqpa63p95rV9pNtceZfaesb3MQRh5YkBKckYOQD0Jx3q/QBGJ0IBG8g9CEb/Cjzl9H/wC/bf4UQf8AHvH/ALg/lVHTvEGm6teT2tjLI0sGc77eSNXAOC0bMoEig8bkJA455FAF7zl9H/79t/hR5y+j/wDftv8ACqer65Y6HDHLqDTYkbaiW9tJO5wMk7I1ZsAdTjA7mom8S6UuqQWH2lnmuFVo3jgkeL5hlQZQpRS3YFgTxgHIoA0fOX0f/v23+FHnL6P/AN+2/wAKyovFmjTfbCtzIFs1LyM9tKgdQcExkqBKM8ZTdyQOpFXdM1W11izF1YtIU3FGWaF4ZEYdQyOAynvggcEHvRuBY85fR/8Av23+FHnL6P8A9+2/wqjP4g0221mLS5pZBdSYxi3kaNSfuq0oXYjHHCswJ4wDkVBF4s0ab7YVuZAtmpeRntpUDqDgmMlQJRnjKbuSB1IoA1fOX0f/AL9t/hR5y+j/APftv8Kx38YaNHpkd80l35ckhjES2E5nVhyQ0ITzFwOSSoABB6EVtRSxzwpLC6yRyKGR1OQwPIIPpQA3zl9H/wC/bf4Uecvo/wD37b/CpKKAI/PTOPnz6bG/wo85fR/+/bf4UD/j4f8A3F/maoaz4h0/QTbrfm6Z7ksIorSymuXbaMk7IkY4HHOMc0AX/OX0f/v23+FHnL6P/wB+2/wrPvPEmk6dZWN1qN39kTUJYoLVLiNo5JJJCAqeWwDBueQQCOc4wahvPF+i2GsDTLq6kWfekbuLaVoYnfGxHmCmNGOVwrMCdy8cjJ1sBrecvo//AH7b/Cjzl9H/AO/bf4Viv428PR6FqOsvqG2w0yd7a7mMMn7uRGCsu3bluSBkAg9q3VIZQR0IyKAGecvo/wD37b/Cjzl9H/79t/hUlFAEfnL6P/37b/Cjzl9H/wC/bf4VJRQBH56Zx8+fTY3+FHnL6P8A9+2/woP/AB8J/uN/MVT1fXLHQ4Y5dQabEjbUS3tpJ3OBknZGrNgDqcYHc0AXPOX0f/v23+FHnL6P/wB+2/wp0Usc8KSwuskcihkdTkMDyCD6Vkv4s0NNL1PUU1CO4tNKdkvZLVWn8llALAhASSoIyBnHOeho23Ba7Gp5y+j/APftv8KPOX0f/v23+Fc/J490KK0huHOpAT3Bto4hpF2ZTIE8zb5Xlbx8nzAkYIBI6VIPHGhHU7ewWa7aa4jhlQrp9wYwkxxGWkCbE3EEfMRyCOooDpc3POX0f/v23+FHnL6P/wB+2/wqDVdTtdF0m51LUXeO0tYzLM6RNIVUdTtUEnHXgVjSePdCitIbhzqQE9wbaOIaRdmUyBPM2+V5W8fJ8wJGCASOlAHQecvo/wD37b/Cjzl9H/79t/hWGPHGhHU7ewWa7aa4jhlQrp9wYwkxxGWkCbE3EEfMRyCOoqbR/Fuka7fS2mnPdmWIyBvPsJ4FJjfY4V5EVWKtwQCcUAa3nL6P/wB+2/wo85fR/wDv23+FSUUARwHMef8AabqP9o0UQ/6s/wC+3/oRooAkoqPz4f8Anqn/AH0KPPh/56p/30KAJKKj8+H/AJ6p/wB9Cjz4f+eqf99CgCSio/Ph/wCeqf8AfQo8+H/nqn/fQoA5/wAc6VreraJBD4buza3MdysjkXLwF02sCu5RnqVOOnFcinhjx2uo6bNHdywR2ywC5B1qabz2RsyPtYY+YcbOg/GvTvPh/wCeqf8AfQo8+H/nqn/fQpp2JcU3cw/H/wDyTbxL/wBgq66f9cmrzwaBBfaTLFoWg39tpc8ulJd281nJEbiZbkGaQqQC/wC7I3yjIb+8dua9buBZ3dtLb3XkTwTIUkikwyupGCCDwQR2p6ywIoVJI1UDAAYAAUlo7+n4Ft3SXr+Nv8jzXU9CWDxVNDaaJKNTW8sv7FvbayYRWdkgjEkQmC7IlAWfMZI3BwADuApBpR8zWY4NFvh4zla/8nV0tnRQjh/ILXOFR0CmJRHlipA+UbSw9M8+H/nqn/fQo8+H/nqn/fQpbqz8/wAbf5a9x3s7+n4HkOi6G0XhHXIltbqCwnS0VrWz8MS2qeYrZdmtpJWecEbVl2gb1GFLHOLJ0xZPAMVrcaMbeyj1ZpIY4fD1xLaTJsPzSaazmVIyxYBB0dVk4Br1Xz4f+eqf99Cjz4f+eqf99Cqbu3/X9f1uStFb+trHkWqaTqs/haBLfRbmAr4X1S3jht4JcAtJD5SqjFmjLKuViJ3KPl/hrSv/AAy2ly+IrPRdHkh0qaz06We3tICBdATSfalGB88jRABhyzZAPJFel+fD/wA9U/76FQX1vp2qWUlnqcNreWsoxJBcKsiOM5wVOQeaG9P67t/qPol/XT/I5P4dppiX3iUaDp76dYC/jEVu1s1vs/0eInETAGPk52kL1zjnmD4nafDfnT/tNk92scc22ObQ5dVtWYhfvRRMHSTj5ZOgBcdSK3pfB/g64srezn8O6HLa2u4wQPYwlItxy21SuFyeTjrV7SdK0PQbd7fQ7DT9Nhkbe8dnCkKs2MZIUAE4A5pPUIuxyS6feyeJtGguLLyIryyhvb22M3mvHLajAUuTljvli+YnnyuTzXIxeG2ufC/ifT7HQHuRcaLKGln0WayuJJwd0azb2KXc2ct5yDhlOD84r2JINOivpb2OK1S7mRUlnVVEkirnaC3UgZOAemTU/nw/89U/76FO/X1/H+vwXzUfdt5W/D+vxPJJdDupPHtrcWdo1rH5ti+lSJ4dmaS2tVRd8Qn3olsuRKHidd2H4DEgDRPgqzu722u7zQvMuZ/EV0LqV4W3SWredhXPeFjtO0/ISehzz6V58P8Az1T/AL6FHnw/89U/76FH9fl/kLpb+tmv1PPbbSb0eCV8OR2VxbWl3rtxaNGsLKsNj9okcgAD5Y2jXYDwMOMdq9FVQihUAVQMAAYAFM8+H/nqn/fQrE1Dwj4P1a+kvdU8PaJe3UuPMnubKGR3wMDLMpJ4AFK7G7N3/rf+vuOY1izvZNW1XRUsLx59R1ux1C3uhbsYBDH5BcmUDarL5DjaxDH5cA5r0aq1qljY2kVrZLb21vCoSKGEKiIo6AKOAPYVL58P/PVP++hT2Vv62S/QNyjrkH2rSbyD7CuoebaSobRpPLE+QBs3fw56Z7Zrm/DrzTeI7Y2j6veWcVvIrnWtNMMllkrtSKVo0d84IbJkzsUlgfvdeZovtCHzExtbncPUVFfW2m6pZvaanDa3ltJjfDcIsiNg5GVOQeRmuqjXUIOElv8AhfR6flZrzbWgmrlHxZb3l/4em03Tvln1Ai280xl1iRvvswBHAQN3GTgZGa5r+ztYhivfDF0vnyX10lzb6haWTw29ojHc7KSzhXR42ZQWJLSIcYzjptN8O+GdGujc6Ro+k2FwVKGW1tY4n2nqMqAccDitXz4f+eqf99CtYYpUY8lPVb6pJ30167W2v3FZvVnF6Db3g8S6Ek+mzWo03TbyynYQsIt4kt9rKxGCHVSw5PcHlTjV8dWVzqHhqO3svPEzahZEPboHeMC5jJcAgj5QC3II454rXvrbTdUs3tNThtby2kxvhuEWRGwcjKnIPIzVLTfDvhnRro3OkaPpNhcFShltbWOJ9p6jKgHHA4p/WoOpGs9JR2Vrp6t737vswtZWRxni/wAO6uk14VvtW1wSeHNRhQzW8R2SN5O1F8mJMs2DgHJO3jvW1qvgy5utBv2k1bUNW1BtNntrRbvyEWMyJggCONB82FGWzgdMZNdd58P/AD1T/voUefD/AM9U/wC+hT/tGrywSSXL5L+l5236hyq9/wCun+RzXh55NU8VX2tR2d3aWj6fbWoW7t3gdpEeV2+RwDhRIo3dCc4JxXU1H58P/PVP++hR58P/AD1T/voVx1qntZ8yVtl9ysNKxJRUfnw/89U/76FHnw/89U/76FYjCb/Vj/fX/wBCFYXj62nvfh14gtrOGSeebTp0jiiQs7sUIAAHJPtW1LNEUGJEPzL/ABD1FP8APh/56p/30KBxfK0zg9U8N6vb654YuJNb1nWYoruUyRXFvbbIQbSZQ5MMKEckLycfNjrijwX4Luf+ES8Ny61q+ryyWVlFJDp1ykMaWk3k7eiRK5KhmADsffJGa7zz4f8Anqn/AH0KPPh/56p/30KHqmu//B/zEtEl2OA8HW15Nqvh1JNOvLI6Bo0thetcW7RK0xMKhY2IAkX9yzbkJXG3nmtv4gpKfDMMkNvcXHkalYzultA8z7EuY2YhEBY4UE8A9K29RtNL1exey1a3s761kxvguUWRGwcjKtkHkZqjpfhjwrod2brRNE0fTrgqUM1paRROVPUblAOOBx7U+Ztpvo7/AI3/ADDY43xi1z4tsdVutF03UvItdBvbbNxYTW8lxNME2xpFIqu2BHnO3GWUAk5xrXd+fFOpaLHpNjqcaadM91c3F5p1xaBR5EkYRfNVC7MZOi5ACnOPlz2fnw/89U/76FHnw/8APVP++hUtXjy/11/zHd/152/yPKdK8Pa7pnhrwZNqt5rGp6RDHa/btGa1jD2koCmJwI4hKyRuBuQknoxJ2kH1a4Ba1lCjJKEADvxQZoGUhpIyDwQWHNc/D4H8EW1zHcW/hjw/FPE4eOWPT4FZGByCCFyCD3pz9+6fW/4iWjucxo1x/a/wkt/CUVlqttqraILVku9KureISCHBQyvGEGSMZzz2zWtPfN4q1fQoLPR9StDp92bm8kvrN4FtgInTy1ZgFlZi+3MZdcBjnG3PY+fD/wA9U/76FHnw/wDPVP8AvoU5Pmd2HS39anltvperafoU+pw6XdSah4fnhsbGLySz3EMO+PeoIywZZ2OR/d9q3LC3HgXVEa7s7+5spdKtLOO5srSW6KSQ+ZlWSMM43b9wbGM7gSDjd23nw/8APVP++hR58P8Az1T/AL6FK718/wCv+CD1PO9MtL3w/f2HiHUtIvBazNqBktreFriWwFxOkkZMUe5jkJhtgO0t6ZIj8R21z4rk06607QLiyU6nK8dxNbNHLLssplSaRCoaMCTaq7+eBwMgV6R58P8Az1T/AL6FHnw/89U/76FHSy6bDvq33vf5nDeBLbUG8Varq+o6fdWbapp9rcEXEZUqxluW8onH3kRo1I6jiu9qPz4f+eqf99Cjz4f+eqf99Cm3cXW4QjNrGP8AYH8q86Hh7WBocNm+l6oG0bSpNOhey1CO2kvnd4tssUiyZQDydxL7T8xG1uQfQoZohbxgyICFGQWHpT/Ph/56p/30KXW/9f1qNOxwdjpuu6ZdQarNpWpahd20t5BLC17G/niVoys8XmzERxYiA8ssGXJ4bkmLTvCWq6XaRaF5E1xDPNp9zJqSSxiOH7MsIZGBYOS3kcYUj5+SOa9B8+H/AJ6p/wB9Cjz4f+eqf99Cj/gfh/wf6sI85HhXWbjS9Ps2sZoW8P2iwRu8sW3UmSeCUFAHJAIt8Hfs5k6Yya3bLWdO8Otfaj4svrXw++s3nnwWuqXsMboEiiiIyHKk/Ju+VjjcOa6nz4f+eqf99Cjz4f8Anqn/AH0Kd3/XrcdzhjbSa3fXb6IV1HS9X1G1v11azuYXghEBiDxkh9xJMGBtUjLnJGDVIeFdZuNL0+zaxmhbw/aLBG7yxbdSZJ4JQUAckAi3wd+zmTpjJr0bz4f+eqf99Cjz4f8Anqn/AH0KS02/rS34BdnDR6drNtrzeKV0W8mkuZZgdK8+DzoVeK3QMSZPL622ThycSdzkV1nh3TZNG8M6Zpk0nmyWdpFA75zuKqAT+lXfPh/56p/30KPPh/56p/30Keyt6fgIkoqPz4f+eqf99Cjz4f8Anqn/AH0KQAP+Ph/9xf5muT8Z2Wly6xpl5rMfiPZBFMkT6L9p2guUyH+y/vc4Xj+Dg55xXUiaL7Q58xMbV53D1NP8+H/nqn/fQoA4bWNEu7vwfod1qGni612G609ZZxAHnSMXUTuCwGQAFy2OOCaZq2qRah4sm0S40zU7DS4rqKW4mg0a5k/tKYbWX97HGUWMFVDMTltuPlUZbvPPh/56p/30KPPh/wCeqf8AfQprT77/AJf5B0PFdS0DWm8Ma5psOk3rRagNTvHVYG+aRJrgRrjGSz+bCy+ojJFe1xAiFAeDtFJ58P8Az1T/AL6FHnw/89U/76FHSw5au/r+JJRUfnw/89U/76FHnw/89U/76FIRg+MfC9x4lsIRp+s6jpF7auXils7uSJJM9UkVSAynHXqp5HGQ2vplidO06K2e5nunQfNNO5ZnPryT+VT+fD/z1T/voUefD/z1T/voUAB/4+E/3G/mKwvEFvfQa5p2s6fp82pm2gntntYJI0f96YyHzIyrgGPB5zhuAa2jNF9oQ+YmNrc7h6in+fD/AM9U/wC+hSauM88sfDes6VqekW622tXFlZJZQXLQ6tsjldImHmonmjEanaHQqDIdpCnad8t9Za5eaN8QLZPDt8kmq7xY7p7bFxuto4OMS/Lyhb5sfKR3+Wu+8+H/AJ6p/wB9Cjz4f+eqf99Cqbvf+t7f5DUmnzen4HnGteAUufDOg6BbadrD2M1/Nc6hIL6MXFss0UwbfIZMuQ8+ODJlVIO7+LUfwr9u+Kiapd6feRWenWUAs7hbiMQSyL54ZWiDEnCzjBKDBVsEA/N2fnw/89U/76FHnw/89U/76FF3e5PS3lb8b/icZ4m8VaD4k8KaxonhrW9L1jVr6ynt7axs9RgaWV2QjgFx05J56A1l614BS58M6DoFtp2sPYzX81zqEgvoxcWyzRTBt8hky5Dz44MmVUg7v4vR/Ph/56p/30KPPh/56p/30KFoO7/P8dDjH8KfbviomqXWn3kVnp1lALOdbiMQSyL54ZWiDknCzjBKDBVsEA/M/wABeGW0y71fWL+xvbLUNQu5sxXVwkqiL7RLLGyBXYJkTYIBAyM4zknsPPh/56p/30KPPh/56p/30KLieqt6fgtCSio/Ph/56p/30KPPh/56p/30KQBD/qz/AL7f+hGiiAgx5ByCzYI/3jRQBJRRRQAUVkaydatpo73RVW+RFKS6dIyx+Zno6SEcMO4OQRnoetrSoL+CxH9rXa3N27F3MaBY48/wJxkqOmWyT1PoACa8v7TT4RLqF1Baxs20PPIEBPXGT34P5VUi8R6JPMkUOsafJJIwVES6QlieAAM8msX4gWCapZaTYyRXUqz6hgpZtGspxBM3ymQhf4ec9s98V5fcNEs2hnUf7V+1f2fpos/M+z+VsMpBzs+bZj7ufnznd2qlG5nKbTPd7m6gsrWW5vJ47e3hQvJLK4VEUckkngAetUNJ8TaDr8kqaFrenam8IBkWzu45igPQnaTis74j8fDDxLn/AKBlx/6LNZU2vaP4j8SeHP8AhF9Rs9UurKaSS6msJkm+z25hYMrsucbn8vCnG4rkfdOJNWtE/X8Lfmdrc3MFnay3N5NHBBCpeSWVwqoo5JJPAA9alByMjkV4DqOsrrOm63Z2eoXU9teeHr2eWJ9dkubnzkMbKJoVAS1cZfMcZ2kZUjAxW3rXiW0tNfsBousTMYLnT4kNx4gkBkt3MW54bYbhcxlHbdLId27cQTtGGle3n/nYJK34/hb/ADPY6zbPxHomoarPplhrOn3WoW+7zrSG6R5Ytpw25AcjBIByODWhJ/qm+Vm4PCnBP0ryjQr/AEqG48P6bZ6rYapZ6e7LFp7xmDVNJXyXDNOY5MYXlGDRpyy5YsBum+v9f1/XQOn9f1/XU9Fi8UaBO18sGuabI2nAm9CXcZNqBnJkwfkxg9cdDVltV05TaBr+1BvQTagzL/pAC7iU5+b5eeM8c147qOteG/E3g/VrjQdQ0uG00vw/d2umadDdI91JEUAaSRMlkX5Fwp553NgnaLoLS+LfD9s6ts0K/m06MspGN1pcSYBzyPK+z/jmm9Ff+v629Lhb9fwSf+Z6kmuaTJYW99Hqlk1pdSLFb3AuEMczsdqqrZwxJ4AHU1ceRI9vmOqbmCruOMk9B9a8G0osfCHhfSSreTp8uk6iuVOAZ5oFQg5/vfauPp7V7Rd/N4isllGYlgmdRjOXyg6eu0t+ZqpRt97X3JCLcF/Z3UkqWt3BM8JxKscgYoffHToevpS2l/aX6M9jdQXKqcM0MgcA+hxXNS3mlyX0krXFvNpsdjJHJHbqU+zR/LlZADnJxgDCkYIwecaGhXdtqd9PfxXFqZHiSNbeCVXaOMEkF9p+8cnjoOnPJqUDNymtLGsqxs6iRwSqk8tjrgd+tcoup/8AFUwJBcPl7qSKWOW9JfAV8DyANqLkLhupGM9aqR3DG5tpbS6muNXFlcNNA8pfy5sD5dp4TngKAMgUdLj62O282MzGLevmBdxTPIHTOPSnVwsktsb6QaVqVzMrx2kckouWdl3T4Ybycg4PI7Z6CrlzPdQeJhbJdLGY5YUt45b2XdJFgbv3W1hJn5wXJyMZJGM0LUXc66iuYsr0f8JMyC7a5ZpJQyx3TlkUZwHtyMIBgAMvJOP71L4bu/N1CSMXZvCYdzyx3bSqTu6ujD9yxyfkBxwfQULUHodIsiO7orqzJjcoPK56Z9KYl1byuEjnidm3YVXBJ2nDfkeD6GuZvpY4vEF4YruVL8zW4gt0lIEgwu75M4cYznOcDnjrVQSZv4rq4vZoZfJ1BI5i7ttKy8YTPzYXJxjoo/ujAOx21FYPhm5E32pEl85EKHfHetdxZI5CuwBB45XkDI9a03lMemzyaYv2yRd+xPO3bnycruJ4wcjGeMYoeglqWBNEzSKJELR/fAYZTjPPpxzUEGq6fdQyS219bTRxDMjxzKwT6kHiuTginNvrMMlldf6+CS5EmxmlX5DIMIzZyMnA7ce1bFrqVpqPiHbaxWt1GbZlN1b3Bk2rkfJIoXAyc4yT0PvQBvUVnaAzNoNoWJYbMKSc5UHCn8sVo0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBHD/qz/AL7f+hGiiH/Vn/fb/wBCNFAElFR+RD/zyT/vkUeRD/zyT/vkUASUVH5EP/PJP++RR5EP/PJP++RQBX1PSNP1q0+y6tZw3kAYOEmQMAw7j0PJ59zWVH4B8KxSLJFoVmjoQysseCpHQg1u+RD/AM8k/wC+RR5EP/PJP++RRcVkSUVH5EP/ADyT/vkUeRD/AM8k/wC+RQMkoIypB7+hxUfkQ/8APJP++RR5EP8AzyT/AL5FAGHD4K0u3uY547rXC8bhwJPEF86kg55VpiCPYgg10FR+RD/zyT/vkUeRD/zyT/vkUASUVH5EP/PJP++RR5EP/PJP++RQBJUctvFO0bSrlon3ockFTjHb2JFHkQ/88k/75FHkQ/8APJP++RQBJRUfkQ/88k/75FHkQ/8APJP++RQBJRUfkQ/88k/75FHkQ/8APJP++RQBJRUfkQ/88k/75FHkQ/8APJP++RQBJRUfkQ/88k/75FHkQ/8APJP++RQBJRUfkQ/88k/75FHkQ/8APJP++RQBJRUBhi+0IPLTG1uNo9RT/Ih/55J/3yKAJKZNEk8DxSglJFKsAxBIPuOaTyIf+eSf98ijyIf+eSf98igB6IsaKiKFVRgADAApaj8iH/nkn/fIo8iH/nkn/fIoAkoqPyIf+eSf98ijyIf+eSf98igCSio/Ih/55J/3yKPIh/55J/3yKAJKKj8iH/nkn/fIo8iH/nkn/fIoAkoqCWGIIMRoPmX+Eeop/kQ/88k/75FAElFR+RD/AM8k/wC+RR5EP/PJP++RQBJRUfkQ/wDPJP8AvkUeRD/zyT/vkUASUVH5EP8AzyT/AL5FHkQ/88k/75FAElFR+RD/AM8k/wC+RR5EP/PJP++RQBJRUfkQ/wDPJP8AvkUeRD/zyT/vkUASUVH5EP8AzyT/AL5FHkQ/88k/75FAElFR+RD/AM8k/wC+RR5EP/PJP++RQBJRUfkQ/wDPJP8AvkUeRD/zyT/vkUASUVBDDEbeMmNCSoySo9Kf5EP/ADyT/vkUASUVH5EP/PJP++RR5EP/ADyT/vkUASUVH5EP/PJP++RR5EP/ADyT/vkUASUVH5EP/PJP++RR5EP/ADyT/vkUASUVH5EP/PJP++RR5EP/ADyT/vkUASUVH5EP/PJP++RR5EP/ADyT/vkUASUVAIYvtDjy0xtXjaPU0/yIf+eSf98igCSio/Ih/wCeSf8AfIo8iH/nkn/fIoAkoqPyIf8Ankn/AHyKPIh/55J/3yKAJKKj8iH/AJ5J/wB8ijyIf+eSf98igCSio/Ih/wCeSf8AfIo8iH/nkn/fIoAkoqAwxfaEHlpja3G0eop/kQ/88k/75FAElFR+RD/zyT/vkUeRD/zyT/vkUASUVH5EP/PJP++RR5EP/PJP++RQBJRUfkQ/88k/75FHkQ/88k/75FAElFR+RD/zyT/vkUeRD/zyT/vkUASUVH5EP/PJP++RR5EP/PJP++RQAQ/6s/77f+hGiiAAR4AwAzYA/wB40UASUUUUAFFFFABRRRQBzfjnXNW0DRILvQ7MXczXKxyA27zBEKsd21CD94KM9Oa5FPiH4m/tHTY1sIriG4WA3RGlXMPkMzYkTczEfKOd/Q16lRTTJcW3uY/i+/udL8Ea5qFhJ5V1a6fPNDIVB2usbFTg8HkDrXD3eveIbG0a0i8R/bZbkaXKt6ttDm3+03HluqgLtKFeU3AsOcs3FeiavpkOtaJfaXdNIkF7byW8jRkBgrqVJBIIzg+hqvY+GtE02yNpZaRYwwtKs7olsih5VIIkYAYLgqDu65ApLe78v+CW9lbz/T/gnI32sa1Y6neSrrM7ppN/Y6eLGaGHF8s3lBpnKoGDnzW27Cqgx/dIyKVtf1aHStU8SNra7oJL+GDQ5IY/LkaASBFQ4EvmERbz8zDBbCgYI7SXRdLuNYh1afTbOXUrdCkN48CmaNeeFcjcB8x4B7n1pE0PSYtZk1ePS7NNTlTy5L1bdBM64A2l8biOBxnsKW6t6/p/XlfQd1f7jgNM13xU2ianBPc3AufKs5LWfUJtOW4ZpWKukawsY/mC/ujIMFmwSwFWV1rXLjw2traahqLanFqjW1xFKljFqOwRmTy0yTbSOAUYkYHl7uAwzXX2/hbw/aaXcaba6FpsNhdHdPax2caxTHjlkAw3QdR2ofwt4fk0VNHk0LTW0uNtyWJs4zApyTkR42g5JPTvVN3b/r+v18usrb+u39eh55qnirUIdLg1K0uxLe2/hzVJPtNxZRLMs8MsK4bG4AhgQwQ7GIyBjbjTuda13Rpda02bWpLt0g0+SO+uIIlNobmZ4nYKiBSqBdwDBv8AaJFdu2h6S1tHbtpdkYI7c2qRG3TakJxmIDGAh2rlenA9KZqeiWupWV5CNtrLewC3muYoIndoxnCESIysvzMMMpHzHjmhtW/ru2Pt/Xb/ACf3mV4Sub5tS1/T7/V31YafeJDFNLHEkigwxuVby1VScsew7cVX8cXmswy2kOh3V3GPKmlnj0w2hu/l2hXCXPyNEMndjDZKYPWl03wAmiacLTQtf1PTA0hkme1gs1844CjKG3KIFCgAIqjqTkkmr/8AwiFhe2a2/ihh4oCSGSJ9Ys7aQxZAGFCRKo6dcZ96T1CLsYsfiW+udY0u1tr7zINVtre+huRAsaiFVZp/lbJAOIxySV87rxXKazqetXngXxJZ63qt8JrrQpruL93ZvbyhfvNbSRZPkkMoxKu8qykMCCa9VXSkGuf2m880jrAYIYW2+XCpILFcKDltq5yT90YxzmKy8M6Dpy3a6fomnWi3wxdiC0jQXA5+/gfN949c9T60/wDg/wDA/T+noRfLbyt+H9P+t+Fm8QeJbXxcbS3e+uLPTrmys3MjWMcFysqIXklLlJfMPmHaIgFJQDByQJ2vPElzewzReJZ4IrvXLnTPIW0gKxQr5uGUlC3mDyxgsSuMZQ8k9mvhrQkvbO8TRdPW5sYhDaTi1QPbxgEBEbGVUAngYHJq2NPsl27bSAbJjOuIh8shzlxxwx3HJ68n1o/r8v8Ag/eTa0bf1s/1t9x5tqMFv4j8FiPW9PsNZ1o6pPotnfX1hDK6AXDp5uNm0FY0LkAAEp0Ga9G0rTLTRdItdM02IQ2lpEsMMY/hVRgUq6XYJs22NsvlztcJiFfllbO6QccMdzZbqdx9aztQ0HUby+knt/FusafG+NttbRWZROMcGS3Zvflj1pdLf1/V7/eN6u/9b/5WOK1uKFtb1fVNkZ12z1/T7Wym6yxQP9nzGp6hGDzEgcHLZ6V6hWbDoOnreWt/d28N9qtrD5KancW8X2nbzn51UYzk5CgDk8VpU9o2/rZL9L/MN3cyvEbIug6gZb6TT0+xTbryIEvbjb/rBjnK9ePSuY8N6XBoPimzhGk6dpr3dnII5tGmHk3yoUPmTRFFIYbgVIZ8b2BY5Ge4P/Hwn+438xWVF4W0uwtrhPD1tBoM1xt33OmWkEchwc87kKnv1B6mu3D4iMKUqTe/3fO3bfZ+ST1Jkr2F8VX9zp/hq6k05HkvpVEFqkeNxlc7VxkgcE55IGAcnFcfayvb+Hzpw06ax1bSdQRdGtrpo2lIkBKAmNmGzb5qNgnCRseorsLHQrm2vEnvte1DVVjyY4ryG1Co2MbwY4UYHBI69Cavtp1k+opqD2du16ieWlyYlMirz8obGQOTx7mtKeIp0Iezspdbq+/bW2lrp6dWJps4jw6bd/FPh6a3LNLNpmoPdtIMObjzrcS7vQhwy46DAA4ArY+IRlXwtEbZEkmGpWJjSRyis32qLALAEgZ74OPQ1rXWiW0vmy2ONNvZSSb61gi84Z27uXRgdwRQcg52j0GK9r4fmjmDanrl/rEIIYW99Ba+WHVgyv8Au4VO4EAjng8+lX9Ypyqwr3+Ho73erfRW623QWsmu5wnjmbWJp73+2rCxtMeF9U8v7JevcbuYM53RR47eufarNz4cv9H0nUdZ0/RtG8ONbaRdKF0WUl7hmQFWYiGPGzaSOGOTxjnPol1p1lekm9s7e4JieE+bEr5jfG5OR904GR0OBVgDAwOBVrM+WlCEIWte+9vz/O/lYfLdpv8Arb/I5DwxaWWmeLtQsdAihg0s6baXHlW6gRiVmmG8Y43Mipk98Amuvqpp2k6do9u0GkWFrYws5do7WFYlLHqSFAGeBzVuvPxNVVqnOvLfd2VrsIqyCiiiucojm/1Y/wB9f/QhXPfEf/kmHiXH/QMuP/RZroZv9WP99f8A0IUlzbQXlrLbXkMc8EylJIpUDK6nggg8EH0oKi+WSZ59rVz4im17wkut6Xplpbi9lKSWmpSXDlvsU/BVoIwBjPOT9Kx/h/4Qum0Lwrq1n4f8P6Obe0juH1CxlJu74GArskAhTAYsGbLvyvc8j1iaztrhoWuLeKVoGLRF0DGMlSpK56HaxHHYkd6db20FnaxW1pDHBBCgSOKJAqooGAABwAB2oez8/wDgkrZLsrHm3gaK2ttX8MT6WqCXVdCmudWkjA3XMwaDEsp/ife8oyeeWFdB8SbeC88KQW13DHPBNqunpJFKoZXU3cQKkHggjtWu/hyyiivToqpod3fOJLi9062hWaRs53MXRlYnJ5YHqe9VI/CfnpJB4h1m88RWMq4ex1S1s2hY5BDEJApJBHGTj2p3u1ps7/je36BscT450vTPD9preneGbG20+3uPDd7Nf2lnEscQI2iGQouFDHMq5xlgpB+6MbN9omjeGda0AeF9MstMu715Y7mKxgWET24gdmZ1XAYK/l4Yg4LYH3jnqrLwxoGm6dcafp2h6baWV0CLi2gtI0jmyMHcoGG4457UaV4X0DQWlbQ9D03TWmULKbO0jhMgHQNtAyOe9S1eNv66/wCY77f12/yPH/C2kpa2HgTzPC+haGb1baWLxFZSbriR1QP5L4hQq8y7lOXK8sMscA+33X/HnN/1zb+VQS6Rp02lppslhatZRhRHbGBTGmzBTCEYG0gEccYFZUXhrU0uEeXxprk8asC0MkFjskGeVOLYHB6cEH3p1PfTj3v+Ilo+YxNKnvLX9n+yn0tnS7j8PRtE0a5ZWEA5A7kdR71Xu9C8NWF94cHhWG1ifWJHimNmR/xMbRoHLySsD+9AyjCRiSGYc/Oc9Xp3g3wxo98t7pHhzSbC6UELPa2MUUgB4PzKoPNWdP8AD+jaTeXN3pWk2Nlc3R3XE1tbJG8xyTlmUAtySefWnO05Nvr/AFYFdLT+vP5HmdpqLW4sPE19Jv8A+EZii0u8OMfOVdJ+o/vmA/8AAa09D8OaffatBo3jC0tNQa30iC5t7O9iWSNpZHkNzMqNkFt5QE4yoIHG7nvTpGmta3Fs2n2pgupDLPEYF2zOSCWYYwxJA5PpTNW0LSdet0g1zS7LUoY23pHeW6TKrYxkBgQDgnmldu9/67/i7h6f12/A850aOz1y80TStal/tHw/I+ojT4rxvNjvfLmUQh9xIlCx+YU3ZyF38lQRB4p07SLGfS7TwrIxTT9eecWUQ/c2s8dnLL5UQ24UEgEqMgFjwM4r1C+0jTdU046fqen2t5ZHbm2uIFkjOOnykEcYGKbbaJpVna2ttaaZZwW9mxa2iit0VICQQSgAwpIZhx/ePrRrbT/h/Nho279b/K99jivAtympfETxJq8Lh4tRsrOeJgchohJcpGw9iiK3/Aq9DqtaabY2BBsbK3tiIkgBhiVMRpnYnA+6uTgdBk4qzTdugdW/62I4f+PWPHXYP5V5PpupRaPp5v8ASr/RtP1SDSpW8QXFym9VvvMi2C4EbKxcnzlXJ3c8A9D6xB/x7x/7g/lUlJaO/wDX9f8AAHc8vuNbsNdktLjxvc6PLo0U97BPBMiqlpcqYxDFNud1MwXzTkEckbc8E1bHzvtNn/bn2f8A4TDzNN+w+dj7T9m2Q/aNmfn25+0b8cdc161RQtPw/r+umguh45D5f9lx/wBnfZPtf2If8JZ5O3zPM+0QeZ5+P4vL+1fe5257V3ngf7H9l1P+w/sv9h/bf+JZ9i2+R5flR7/L2/Lt83zOnGc109Zur6Faa2IheTahF5Wdv2LUbi0znH3vJdd3TvnHbrQnb+vO/wDww3qcXrH2f/hNbrzfsn/CRf2jZ/2R5m3z/sf7rz/L/i2f8fG7HHrWFD5f9lx/2d9k+1/Yh/wlnk7fM8z7RB5nn4/i8v7V97nbntXrWn2EOmWMdpbPcPHHnDXNzJcSHJzy8jMx69zx0qzQtP68rf8AD92F/wCv6/pHkw/4R37Wv9of2R/wgn2m4+w7vL+xeZ5Vvs2fwff+1bdv8Wcc16L4Y+2/8InpP9rbvt32OL7Rvzu8zYN2c985rUop30t6fgIKKKKQEY/4+H/3F/ma4/xt4bl8T6/pFusugPFbQ3Er2msWZvN5PlqHWHemQvILbuNwGOa7Af8AHw/+4v8AM1U1bQdI16GOHXdKstSijbeiXlskyo3TIDA4NJq40cFr9uuqeCfC+qxtPp1vDfaaI9LtWWO2BN1EoO0KCQB91SduCDtyARb1rRNM1nxhJFpNt9r1yO6gnudWkAJ0mNdhEKOMEF1U/u1P/LRmfhgG7uaztrmFIbi3iliRldEdAyqykFSAehBAIPYgVl3Hgzwvd6odSuvDekTX5cSG6ksYmlLjo28rnIwOc9qpPX53/L/IXQ8c1KTyvh/4r0UHjU7nUtQxnnbFNPvI46BooAf+unvXvUP+oj/3R/KqbaFpLxsj6XZMjpKjKbdCGWVt0ikY6O3LDueTmr4GBgcCjpb+v6sOWrv6/iFFFFIRzvjGfxXaWEN14Oj064eJz9qtryF2eSP1jKuvzD+6Qcg8EEYbX0w37adE2riBbthmRYAQq+3JOat0UARn/j4T/cb+YrkvG/8AZP8Aaulf8JZ9i/sDyrjzPt+3yftP7vys7uN23zdvfPSutP8Ax8J/uN/MVJSauNOx5tpni3WLK90XSNU1jR4pJIrKO6+0QOZoJniYtBIfNH72QrlDjj5twJKb87UtS0GXwl8UrNdWtbyOJpWKT332gxk2kYA+diQPNDKB0DDaMYwPWqKpu9/O/wCn+Q4y5Wn2t+B4hr3hO2t/B3hvSln8KjUdU1eeaydrGL7E4e3mEZSHpgjyB/H8+0nefvbDeF9PuvjPaWtg+jg6Pp1lJcRyQo92FT7QE8t+qYPkbuASu0bgOG9Xop31v/W1kR9nl8rfjc5j4km1X4X+JPt5hEP9mzj9/jbu2Hb14zuxj3xXm+veE7a38HeG9KWfwqNR1TV55rJ2sYvsTh7eYRlIemCPIH8fz7Sd5+96pYeE9O02/S8t7nWHlQkhbjWryeM5GOY5JSp69xxW3Uq35fgXfp6/irHlD+GNPuvjPaWtg+jg6Pp1lJcRyQo92FT7QE8t+qYPkbuASu0bgOGsfCrRLQazrmvadJo72cl5dW0H9nQIkin7VKzCV1zvyvkleRheAo6t6fRTv+v4kvVW9PwQUUUUgI4f9Wf99v8A0I0UQ/6s/wC+3/oRooAPJX1f/v43+NHkr6v/AN/G/wAazNZ15dBmjm1C3ZdMZSJL2Ml/JfsHQDIU9AwzzgEDINWtKvLm/sRc3di1iZGJjhkfMmz+EuMfKx/u849c5AALPkr6v/38b/GjyV9X/wC/jf41xvxPmv4/D0kdnHqb20trcLdfY4bd4gmwf64ykMFwW/1eTjd3xXNX1tY6XrGnzeDrEx3jCVZP7BtrRpimB1EpC7c475ziuKti1Rr06PLfnvr2sXGN4t9j1fyV9X/7+N/jR5K+r/8Afxv8ai1G5Nlpd1dBrdDBC8ga5l8qJcAnLvg7V9Tg4HNefQfELVYLTWpbk21//Y8VtdyNHpdxYmaKR3V440mc7mxHlHB2uSFwOtdlybM9G8lfV/8Av43+NHkr6v8A9/G/xrlfCus32p+IrmPUfs+86TZXf+jSSNGDK85woLleAqjcAC3U8bQun4l1S+sV06z0kwJe6neC2imuYmkiixG8jMyqylvljYAbhyRzVNNOwlr/AF5XNfyV9X/7+N/jR5K+r/8Afxv8a860PWfFbyLpcV7pxvri/wBS826uopZ44xDKgUIgkUhTvxtLfL6nGDa0nxnrkgsLjV/7Hig1PSJ7+FAXiW1aHy8iWZiQUPmZJCLtA/i60lqk/K/4XHZ3t8vxsd35K+r/APfxv8aPJX1f/v43+Ncn4M8U32taxqem6jLDctZwwTrcQ6ZcWIYSGQbQkxYuB5eRIp2tu6cVp+LUtjpKTanq0+madDKHuvs0jxSzjBCxq8ZDglyvCfMxAUfewR6CWrsbPkr6v/38b/GjyV9X/wC/jf41jeDodQg8ORrqhuQxlkaBLyTzJ44C5MayMckuFxnJJ7Ek5NbtAEfkr6v/AN/G/wAaPJX1f/v43+NSUUAR+Svq/wD38b/GjyV9X/7+N/jUlFAEfkr6v/38b/GjyV9X/wC/jf41JRQBH5K+r/8Afxv8aPJX1f8A7+N/jUlFAEfkr6v/AN/G/wAaPJX1f/v43+NSUUAR+Svq/wD38b/GjyV9X/7+N/jUlFAEfkJnPz59d7f40eSvq/8A38b/ABqSigCPyV9X/wC/jf40eSvq/wD38b/GpKKAI/JX1f8A7+N/jR5K+r/9/G/xqSigCPyV9X/7+N/jR5K+r/8Afxv8akooAj8lfV/+/jf40eSvq/8A38b/ABqSigCPyV9X/wC/jf40eSvq/wD38b/GpKKAIzAh67z/AMDb/GjyV9X/AO/jf41JRQBH5K+r/wDfxv8AGjyV9X/7+N/jUlFAEfkr6v8A9/G/xo8lfV/+/jf41JRQBH5K+r/9/G/xo8lfV/8Av43+NSUUAR+Svq//AH8b/GjyV9X/AO/jf41JRQBH5K+r/wDfxv8AGjyV9X/7+N/jUlFAEfkr6v8A9/G/xo8lfV/+/jf41JRQBH5K+r/9/G/xo8lfV/8Av43+NSUUAR+Svq//AH8b/GjyV9X/AO/jf41JRQBGIEAAG8AdAHb/ABo8lfV/+/jf41JRQBH5K+r/APfxv8aPJX1f/v43+NSUUAR+Svq//fxv8aPJX1f/AL+N/jUlFAEfkr6v/wB/G/xo8lfV/wDv43+NSUUAR+Svq/8A38b/ABo8lfV/+/jf41JRQBH5K+r/APfxv8aPJX1f/v43+NSUUAR+Qmc/Pn13t/jR5K+r/wDfxv8AGpKKAI/JX1f/AL+N/jR5K+r/APfxv8akooAj8lfV/wDv43+NHkr6v/38b/GpKKAI/JX1f/v43+NHkr6v/wB/G/xqSigCPyV9X/7+N/jR5K+r/wDfxv8AGpKKAI/ITOfnz672/wAaPJX1f/v43+NSUUAR+Svq/wD38b/GjyV9X/7+N/jUlFAEfkr6v/38b/GjyV9X/wC/jf41JRQBH5K+r/8Afxv8aPJX1f8A7+N/jUlFAEfkr6v/AN/G/wAaPJX1f/v43+NSUUAR+Svq/wD38b/GjyV9X/7+N/jUlFAEcAxHj0Zuv+8aKIf9Wf8Afb/0I0UASUVH5bf89n/Jf8KPLb/ns/5L/hQA6SNJonimRZI3UqyMMhgeoI7isLwz4SsvDTXz28du0t3ezXKyJbrG0ayNny8jOQPw+grb8tv+ez/kv+FHlt/z2f8AJf8ACgBt5Z2+oWM9newrPbXEbRyxOMh1IwQfwrEtPAug2d0LlILuacNG3mXeoXFwx8slkBMjtkKzEgHgHnrW75bf89n/ACX/AAo8tv8Ans/5L/hR1uG6sYR8LnS1aXwi9npt1IqRSSXkEt2hiVnZUVBMm3DSNjBwBxjAGFXQdR1S0ltfF17Y38JZXgOnWk1jJC4P3hJ57sD6FSpHPJzW55bf89n/ACX/AAo8tv8Ans/5L/hQBl6V4T0XRPJOm2hiMBmZGaaRzmUhpCSzEksVBJOf1NI3hLQ3tba3ksFeG1tJbKKN3ZgIZNu9CCec7F5OTxWr5bf89n/Jf8KPLb/ns/5L/hQC02OdTwgdIZ7nwpdLa6hMqxTXOrNc6jviXcQg3zqRgsSPm4yeOaZP4Sude8keM762vxaSiezbSo7nTnhkwVLbluWJ4bA6Y59a6Xy2/wCez/kv+FHlt/z2f8l/woAg03TYNKsltbV7mSNSSGurqW4fn1eRmY/nxVuo/Lb/AJ7P+S/4UeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf89n/ACX/AAoAkoqPy2/57P8Akv8AhR5bf89n/Jf8KAJKKj8tv+ez/kv+FHlt/wA9n/Jf8KAJKKj8tv8Ans/5L/hR5bf89n/Jf8KAJKKj8tv+ez/kv+FHlt/z2f8AJf8ACgCSio/Lb/ns/wCS/wCFHlt/z2f8l/woAkoqPy2/57P+S/4UeW3/AD2f8l/woAkoqPy2/wCez/kv+FHlt/z2f8l/woAkoqPy2/57P+S/4UeW3/PZ/wAl/wAKAJKKj8tv+ez/AJL/AIUeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf8APZ/yX/CgCSio/Lb/AJ7P+S/4UeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf89n/ACX/AAoAkoqPy2/57P8Akv8AhR5bf89n/Jf8KAJKKj8tv+ez/kv+FHlt/wA9n/Jf8KAJKKj8tv8Ans/5L/hR5bf89n/Jf8KAJKKj8tv+ez/kv+FHlt/z2f8AJf8ACgCSio/Lb/ns/wCS/wCFHlt/z2f8l/woAkoqPy2/57P+S/4UeW3/AD2f8l/woAkoqPy2/wCez/kv+FHlt/z2f8l/woAkoqPy2/57P+S/4UeW3/PZ/wAl/wAKAJKKj8tv+ez/AJL/AIUeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf8APZ/yX/CgCSio/Lb/AJ7P+S/4UeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf89n/ACX/AAoAkoqPy2/57P8Akv8AhR5bf89n/Jf8KAJKKj8tv+ez/kv+FHlt/wA9n/Jf8KAJKKj8tv8Ans/5L/hR5bf89n/Jf8KAJKKj8tv+ez/kv+FHlt/z2f8AJf8ACgCSio/Lb/ns/wCS/wCFHlt/z2f8l/woAkoqPy2/57P+S/4UeW3/AD2f8l/woAkoqPy2/wCez/kv+FHlt/z2f8l/woAkoqPy2/57P+S/4UeW3/PZ/wAl/wAKAJKKj8tv+ez/AJL/AIUeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf8APZ/yX/CgCSio/Lb/AJ7P+S/4UeW3/PZ/yX/CgCSio/Lb/ns/5L/hR5bf89n/ACX/AAoAkoqPy2/57P8Akv8AhR5bf89n/Jf8KACH/Vn/AH2/9CNFEHEfXPzNyf8AeNFAElFFFAGbqGu2elX0EGpFraKdTsu5cCHeOdhbPytgEjIAOOCTxUulanHq9iLy3hnigdj5TTJtMqdnAzkKe2cHHOOlWLm1t723a3vII7iF8bo5UDK2DkZB46ipaAOa8b3et22m2Ufhkn7fc3YiVQI8sojkcj5/lH3M/hXFr4p8YCTTbqOS5NkbaynvDcpbYJncrldnzbGxgD7w5zjiu+8UaFLrtnaJB9iZ7W588RX9sZ4Zf3bphlBH9/IPqorhD8JdRa80+c3mkw/YkhTFtZtGZfLbO5juOXboW78cVatYykpX0O68bajdaT4D1zUNOl8m7tbCaWGTaG2OqEg4IIPPqKzQ2q+HNW0cXevXms2uqTm1eO9ht1aJ/KeRXQxRx8fuyCCGzkEYwc9Brekwa9oN9pN28iQX0DwSNEQHVWGCQSCM89wazbDwn5GowXuqa3qWsy2qsLYXvkIsBYbWZRDFGCxHGWyQCcYycwbPZfP9LHI6/wDErUotO8QWNlb6Za6vZ6ZcXkKQ6qlzLa+WVBFxGIyI3AcEKC6sQRuA5rZ1Lx3daHq2m2Wr2WmQfbHt4dp1dftErysqFoIdgMkaswBYlDwx28DJF8MNNS0+yTarqlxaJp82mwW7tCqwW8oUMqlIgxI2LguWORyTk5muPh5b3FxJK2t6riaW3ubiIG3AuJ4NmyRm8ncD+7TIUqvHCjJpq2l/n9/+X4/eDs9vP9P+CdXOxS3kZThlQkH8K8osPHHiCfwL4Wkkvs6rc39r9vn8hP3sEkkORjbtBK3MQyB2ODnmu5S68YTSrFdaFocdu52yPHrczOqnqQptACcdsj61St/hrottHAkc14fIhsoUJkXpauHQ/dxliqhj3CrjGKI6Su9rr8Nwe1v6/rqcjeeOfENv4C8TzLfY1WG+uv7Pn8hCIoI5JsDG3acLbSDJz1Ga6fXbvXbLVbaaHWyJ7q6ijsNGghjdLmEbPOeUsnmAgM5LK6qoCcEnDT3Hw00W6gmikmvcTQXsDESLnF1IZHP3cblLMFPYM2c5qdPBLQ+JrrW7TxJq9vNdGMSwhbV02IOIwXhZ1TqdoYcsx6nNC6f1/X/BYS628/xMObWNetPEOdU1a806Y6kI4rKewB0ya2MmxP8ASViYrIycgNKp8zA2hSM9tdXMv9q2lnA4TerzSNgHKrgbfxLD8Aax28E25uHQatqa6XJcfaW0kNF9nL7/ADD83l+aAX+baJAM8Y2/LW1dWjyX1tdQlQ0YaNwSRujbGcEdwVUj8fWktkv62E92/wCv6/q7M2OfUYtUmskvDcytbM6tdQCKNZQRjZhQWX5ueWxgc5NWNKmuDqF1Ab2S/t4VUGaREG2XJ3ICoAOBjtwTjPYK2hB0ZZdRvpG8poonLqGhDddpCgk8Dlsn9an03TTpkIhW8nnhVQqRyJGoQD02Iv60IGVTrpj1yPT54rePzXZUAug0wAUtuaMDhSFPOSeRkDPFQ+IbkXFtdzwCDTZLaa4BWQO0iqAVJG0bTjnAJ69eKvJ4fhjvEnW6udkdw1wkHybA7Z3HO3cc7j1J601PDlqsymSe4mhjieGO3dl8tEfgqMAHoMDJJFHQelyjLrup2t9J9rso1zDD5cCThlLSSbMl9oIIyMjBHFTTeKI7bVlsbhbVGV44ph9qG8O4GNiFQXUZXJ46njipx4chMxluL28uH/dANKycCN96jhR369z61afSlbUDdR3VzCHZXlhjYBJGUYBPG7oACAQDgZzQhdytaa4brWJbLyoVCM6lftI85dp+80RAIU9iCeo9eF0fWzqs0iGKBAihsR3Id4znG2RMAo3tyODzxzKmjqL1biW8up1jZniikdSsZbIJBADdCQMscZ9hh1npK2l0LiS7ubuRY/KjM7KdikgkZCgnOBy2Tx160IGU5dWvLXVbsPAstlDJErP5m1o9wA4XHzcnJyRx0z0qpHrl9/aCFYjNbLFdu6bgZGMcu0bQEGewAz/FzkjnUn0SG4vnuHuLjZK6PJbgr5blMbc8Z4IB4Iz3yOKZ/wAI/bqyNFcXMTL543RuASJW3MM4yMHBGMHjvQMk0fVTqsMrkWxCMAHtboTocjOM4BBHcEenWrbT+RbSTXmyJY9zMQxYBR3zgdu386hsdOFnLNM9xNdTzBQ0s20HaucDCqBxk9s81M1skts8F1/pMbk7lmVSCCc4IxggdPw70PyEvMwU1nUPK1FpFSOT7RDFbRuv+qEgUDd6kbskfhVyBpV1CewXXUuZTCTskMXnwv2IVVAK4PcenrT4vDWlQfaxDaRxLd7Q6xqE24AxtKgEcjP15p0elSQSm6+1zXt3HEyQG6ZVVM4z9xR1IGSQTxxQBZ0y7a+02C4kUK7r84HQMODj2yDVqq9haCxsIbZWL+WuCxH3j3P4nmrFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARw/6s/77f+hGiiH/AFZ/32/9CNFABib++n/fB/xoxN/fT/vg/wCNSUUAR4m/vp/3wf8AGjE399P++D/jUlFAEeJv76f98H/GjE399P8Avg/41JRQBHib++n/AHwf8aMTf30/74P+NUta17TfD1nHdavcGCGSQRIwjZ8sQTjCgnop/KsxfiB4ba5tbdr6WOS7CGDzbOaMSBzhGBZANpPRunvTsxXSOgxN/fT/AL4P+NGJv76f98H/ABqvrGpw6Jod9ql2sjQWNvJcSLGAWKopYgAkDOB61zp+ItimnzXFxpOrQSRm3MdrJCnm3CXD+XE6AORgtxhiGXHzKOMofmdVib++n/fB/wAaMTf30/74P+Nc9J42toNQS2udL1CGNXhhu7lhEYrKaUDZDIRISW+ZASgZRvXLY5pR42svtN0XsL9dMtTMr6t5aNbboQfMHDFxtKsNzIFJUgE5GQLM6DE399P++D/jRib++n/fB/xrmovHlodMu7q80vUrGe2EBFlcLEJphO2yEqVkKYZ8r8zDBB3bRzU83i4QaXBcTaJqsd5cXZs4tNkWJZnkCs3DGQREbFZtwkwcYzu+WnZh5m9ib++n/fB/xoxN/fT/AL4P+Ncxf+OLfTlhur22vLaD+yrnUJrWW0xMghaMMpO/AI3kYAYN1DAAbpI/HVp9l1CS80zUbGexSGT7JOkZlnWZisOwK7DLsCoDFSD1Apf1+gHR4m/vp/3wf8aMTf30/wC+D/jWZoPiFdda9jOnXunXFjMIZ4LxU3KxRXGCjspGGHIJpmu+JF0S4trWLTb7VLy5SSVLay8rf5ce3e37x0BwXUYBLHdwDg4AWprYm/vp/wB8H/GjE399P++D/jWS/ii0S4EBt7oSs1uEjaMKzibO1trEEAbX3AgEbG4OKwdS+Icw8FatrekaFqGLawa9sZrmOMw3adnBWXgDhijlH29FJBABpXtb+rnaYm/vp/3wf8aMTf30/wC+D/jXODxxbLqUNnPpmoRjdBFdXJERis55gCkMhEhO47kGUDKN65bmoZviBbx3pgj0LWZ0N3LYxTxxRbJrhN37tcyAjOwkOwCerAggH9f1+H3om+lzqcTf30/74P8AjRib++n/AHwf8a4/VfFOo3HhV9f0O6ttOitzJDPZahprXM7XCyGPyh5dwihi42jBYEkYOK6nSv7Q/si1/tr7P/aBiU3P2ZSIhJj5goJJxn1NAyfE399P++D/AI0Ym/vp/wB8H/GuQ1HxZqdrq93cQrZnR9P1G2024iMbGeR5vLBkVwwVQpmQbShJ2tyMiuzo6X/rv+odSImYSBd6cgnOw9se/vS4m/vp/wB8H/Gquq3U1jY3N3a2j3s8FtLJHbRnDTMACEB55JGOh61geF/Ed9quqG3l1LR9Vi8jfMdOQwS2MgIHlyxPK75OW6hSChBHPHRDDznTlUWy/r+r2vsrsTdjqcTf30/74P8AjRib++n/AHwf8ah1TUItJ0m6v7gM0dtE0hVerYGcD3PQVgJ4kv4fD6ajfpbI2n3DRaxGiN+7UdZE+bgAFZMHd8pPelTw9SpHmj3t8/6/NA3Y6XE399P++D/jRib++n/fB/xrndN8Qajc69plrdwwRQajZ3N4ihT5kaI8IjBO7GSshLcdcAdMm54s1w+HdCGo7oERbu2ile4OESN5kR2JyMYVicngY5qvq1T2kafWW332/NBdNXNbE399P++D/jRib++n/fB/xriNc+JFpHJdL4Y1HSdUFto15fv5M4m2SReXsDbG4U72yOpxwRUNv4+lge4kOsaJ4jt4NPnu5TpEZj+ztGFKq7ebKPnyQM4OV7846I5biXBSta/TX/Ky+bV+guZf18v8zvcTf30/74P+NGJv76f98H/GsTQ9U1NtZutH11rSW6htobtJrSJokZJC67drMxypjPOeQRwMVv1xVabpy5X/AFcadyPE399P++D/AI0Ym/vp/wB8H/GpKKzGROZkUHeh5A+4e5x60uJv76f98H/Gib/Vj/fX/wBCFZni3Vp9B8G6vq1osbz2NnLPGsoJQsqkjOCDjj1FA0m3ZGnib++n/fB/xoxN/fT/AL4P+NcfcfEfR7nV9EsPDut6Nqc19cSJcRW92kzxotvJJuARuPmRRk5HPrWH4X+I97qzaFJLr3h3VX1JQ13pumQMtxYKYmdnc+fJwrAKdyLyw5B4ILpc9MxN/fT/AL4P+NGJv76f98H/ABrkvDXijVb/AFHTE1dLPyNc099Qslto2VrdVMf7uRizCQ7ZVO4BBlTxyK1PGOrX2jaCk+km3W6mvLa1R7mJpETzZkjLFVZScBs43CnZqy7/AOdvzDc2cTf30/74P+NGJv76f98H/GuJ1zxP4g8K6fqS6tJpt9crpdxf2NxbWkkEZaEDdHJGZHP8akEOM/MMDGTfGqeIdFvrFNfudNv7bUWaGKSyspLZoJRG0i7g0sgZSEYZyuDjrnhPRXHZ/wBf15nT4m/vp/3wf8aMTf30/wC+D/jXmHh/4kahqdvodwPEPhnVrnUHgFxomnW7LdwCTG85+0Pjy8lmyg4U/dr1CZzHBI69VUkZ+lEvdTbEtXYTE399P++D/jRib++n/fB/xrnrDxU//CsbbxVqMKtI2lpfSw24IDMYwxVck4yeBkmqsuqeKtIuLNdXOk3K6kzQRfZbeWP7HOY2dA5Z281CVKlgIyDg45+Ul7raYLU6vE399P8Avg/40Ym/vp/3wf8AGuOsvHFze6voKpDCmn39lFLdOykNHLKkjRqDuwB+5cEEE5ZeR3TTPEXiLxIsEOktp2ny/Y476ea7tJJl2TM/kxrGJEO7YmWYtwcADnhtNB6nZYm/vp/3wf8AGjE399P++D/jXHW/izWdYaz0rTba0sdXc3IvZblXngthBII2KqChk3sy7fmXAJJ5G0wa14r8S+G10uPVrXT3abVfs893ErLHNaiJ5WkRC5MbAIQQxYfKSM54Nw2v5HcYm/vp/wB8H/GjE399P++D/jXOaF4mutW8ca/pLRwCx06OBraRAd7lmlSTcc44eIgYA6HrXT0gejaIozM8atvQbgDjYf8AGlxN/fT/AL4P+NEBxbRn/YH8q4yw8cyQW8Gpa+8Q03UNPfUrUWllK0tvCrRgiQKXMhxMh3Kq4w2RjkG7sOzex2eJv76f98H/ABoxN/fT/vg/41zN34om1S/g03wtLHFcubgvPqFhMYwICiuqqTGWy0ijeCVGG64xWfZ+OrvUootXtRbRaQktnb3EEkLNM8lysRBWQMAoXz04KHODyKP6/r56CO2xN/fT/vg/40Ym/vp/3wf8a4VfHOpRWNpd3As3XW7ZbjS40gdTBumhiUSkufM/4+IySNnRhjuOm8P6jd3n2+z1N4Jb3Tbn7PNLbRNFHITGkgIRmYr8sgGNx5B5p2f9fc/uDY1MTf30/wC+D/jRib++n/fB/wAa5e/8TX1vrV08RtRpen3ttYXMTwsZpJJ/LwyOHAUL5ycFDnB5FZq+OdSisbS7uBZuut2y3GlxpA6mDdNDEolJc+Z/x8RkkbOjDHcJXe39dfxQ7HdYm/vp/wB8H/GjE399P++D/jXHJ4k1y41M+Hbe400azbyS+fdvZyG3dEjhf5YvN3KT9ojH32xgnnoOn0TVE1vQLDVIkKJe20c6oTnaGUNj9afS/wDWoi1ib++n/fB/xoxN/fT/AL4P+NSUUgIgZjIV3pwAc7D3z7+1Lib++n/fB/xoH/Hw/wDuL/M1ynjTxTeaJqmladpsyQy3qTSvIdIuNRIWPYOIoGVhkuPmJwMe9K49zq8Tf30/74P+NGJv76f98H/GuS1zxXqum6Xolxptpb6lDe3FpHc6mGEcAWWVIzsj3s5Y78gHgDqxI2mTXda8RaVf/alTT49N+2wWkNnLGz3N9vKhnR1fCY3NhSjHEZJKg5FW/O35f5i6HU4m/vp/3wf8aMTf30/74P8AjXm118S9Vt/BHiPUvs9j/aen3txDZRFW2SRRu4DMN2SdsMpOCM7eMV6XGxeNWPUgGl0uD0dn5/hoNxN/fT/vg/40Ym/vp/3wf8akooAjxN/fT/vg/wCNGJv76f8AfB/xrI8R+L9F8JLaNr9zLapeSGKGRbWWVS4GdpZFIUkZIBxnBxnBxpafqFtqlkl3Yu0kD/ddo2TP4MAaAJCZhIF3pyCc7D2x7+9Lib++n/fB/wAaD/x8J/uN/MVja9qWox6pY6ToklrBeXUU1wZruBpo1jiKAjarockyLznjng0m7BubOJv76f8AfB/xoxN/fT/vg/41zmmePtH1GGwB+2R3N5FbOsK2M7geehdMOE2lcK+WBwu07iKq3vinWIfDvjG6+zWNteaC0n2cbnnjkVbdJlLfcOSHwQMY9Wxk001fyGk27HW4m/vp/wB8H/GjE399P++D/jXluofEbXrHwVa64Z7Ty5NRuLZ5ToVwXMcUcrEi288Ojb4WHzMAFIZtmCK07nxh4itfH2k6DMbEfbLe2keNNOlfLMJjKPP80JHgQMVBVic4wcMwLO9hdL+V/wAbHf4m/vp/3wf8aMTf30/74P8AjWZ4s1K70bwfq+p6d5P2mys5biMTxl0JRS2CAyk5xjgiuA1D4ja9Y+CrXXDPaeXJqNxbPKdCuC5jijlYkW3nh0bfCw+ZgApDNswRSHyvT5/hqepYm/vp/wB8H/GjE399P++D/jXAXPjDxFa+PtJ0GY2I+2W9tI8aadK+WYTGUef5oSPAgYqCrE5xg4ZhZ8FeLdZ17xRq+nai9rJFp7yo3k6fLb7SLiSKMh3lYSgiFydqgKcDJOQHb9fwE9Ff0/HY7bE399P++D/jRib++n/fB/xqSikBHBny+eTubOP940UQ/wCrP++3/oRooAkoqPM39xP++z/hRmb+4n/fZ/woAkoqPM39xP8Avs/4UZm/uJ/32f8ACgCSio8zf3E/77P+FGZv7if99n/CgChr/h7TvEtglnq0TyRRyiVPLlZCrAEZypHZiPxrEHwz8PfaLaZvt0j2oRYDJeyOI1Q5RQCSMA9B0rqszf3E/wC+z/hRmb+4n/fZ/wAKd2KyZl+L7C51TwTren2Efm3V1p88MKbgu52jYKMngckdawk8D6he26Sa5rEc94r2Wx4rPy1SK3mEuwrvOXYgguCB0woxg9jmb+4n/fZ/wozN/cT/AL7P+FJaO/8AWg3qrf1rb/I5m/8AB093rVzLFqSQ6XfXUF5e2YtiZZJotm0rLvAVT5Ue4FGztOCN3Df+ENu2iv8AS5NZH/CP3puWeyS1/fnz9xdTMWI2BpGYAIrD5RuIBB6jM39xP++z/hRmb+4n/fZ/wo6WHd3ucVpXw6fStE1G0t5tDguL1I42az8PQwwSInVZotxMu/JDfOowflC8kui+H0tv4VfSILrSdkt2bma0l0ZX08gjHlrbGTKKCA/En38nocV2eZv7if8AfZ/wozN/cT/vs/4UPURxE3w0Euhxacuq7AmkXmm5W2+VftDo2UXd8qJs2rHk4XA3cZNzxD4WllbVtTtXuLi6ntbSO3gto498ctvK8iOPMkVWG5xlSy8KRnmurzN/cT/vs/4UZm/uJ/32f8Kbbf8AXzH5f10/yRw/h6fxPpA1DUNY8O6hqN3ql0JTFZ/ZIjAqRJGNyvckDJUkBZJOOpBOBZ1vRr/xzYRrdabBpccTMrWXiHTbe/RyQMSoIp/kYfMAd3c/L0Ndfmb+4n/fZ/wozN/cT/vs/wCFJ67iV1scvb+GWj8WaZLtuzDpOm/ZxfTyoxuZMbUbqSWVTLksAMy8Z5xmw/DN3g1pb3ULFZdVsJbOSbTtLW0Mxk6zTgORLID0ICAbn4+bjuszf3E/77P+FGZv7if99n/Cnfr6/j/w4LTby/Db8jipPhvHceKotbun0eeZ2gmu5JdFSSdpYlC5hld2MSHYvy4cjBIYE5rYi8K+V9l/0zP2fV5tT/1X3vM8z5OvGPM6+3Tmt3M39xP++z/hRmb+4n/fZ/wov/X9egdLf1s1+TOch8G+Xa29u98JIo9al1V1MPEm6SSRY/vcbXdTu5zs6DPFzUNe1GzvpILfwlrGoRpjbc20tmEfjPAkuFb25UdK18zf3E/77P8AhRmb+4n/AH2f8KXl/Xb9Aeru/wCtb/qcpJ4TudV1BruW6ey02+ubfULzSp4FaYTxBNoEqSFVXMce5cPkqcMM119R5m/uJ/32f8KMzf3E/wC+z/hR0sBBf2v26CS18+a386B0863fZJHnA3KexHUVh/2XrFpew6xq0ya3c2MLxW0Gm2SW0jiQrvLNLMVP3QcAoOvBOMdERMZA2xOARjee+Pb2pczf3E/77P8AhW9OvOmrK1vT79d1frZoTVznrjz/ABUiabq3hzULCxMiyz/bTayRzqpyIyI5nPLbScqQQCD1pkfge0tNRK6b9msdGkliuJ9Lt7RUSSaPOGypAAJ8skbeTGOcEg9Jmb+4n/fZ/wAKMzf3E/77P+FafW6sVy0/dXZbeut9fPddBcqe5zEPh240G7t9Qt2uNUj0+G4t7WwgjRZRFLJEwXfJIqkJ5ZAzj5cDqPmkvPtvimGOwutE1LR0juILr7TdG2kQmKZJNmI52bLbcZxgfoejzN/cT/vs/wCFGZv7if8AfZ/wp/W5tqUknJbPqtb9NN9dUw5TB8TeE/8AhI5JG+2/ZvM0u70/Hlb8ef5fz9R93y+nfPUYrX1DTodT0a5026LeTcwNBIUOG2su0keh5qfM39xP++z/AIUZm/uJ/wB9n/CsXXqOMY30jt5FdbmRoehXdhfXGoavqEeoX80MVv5sVv5CCKPcVG3c3zEuxJzg8YAxW3UeZv7if99n/CjM39xP++z/AIVNSpKrLmlv935CSS2JKKjzN/cT/vs/4UZm/uJ/32f8KzGE3+rH++v/AKEKo+I9H/4SDwxqWj+f9n+3Wslv52zfs3KRnGRnGemRV1xM6gbEHIP3z2OfSlzN/cT/AL7P+FA02ndGXq+gf2rdaPN9p8r+zLhpseXu8zdBJFjqMf6zOeemO+am8P6ONC8L6bozTfaRY2kdsZdm3zNihc7cnGcdMmr2Zv7if99n/CjM39xP++z/AIUdGu4u3kcjp3hm98NGC9kkn18aXatZaZZ2sMcMqQuyZ3tJKEkcBEG75OFPBJp2qjVvGFh/Zv8AYmo+H5I54LuK91BbWeLfDKkgUpDcljnb7cZ5rrMzf3E/77P+FGZv7if99n/Cnd/19/5gcjqPgnUNes9R/wCEh1m2nvLnTptPtpLSwaGK2SXG9vLaVyzEquTvAwoAA5Jtw+GtWvL+0uPEur2d6lhua1hstPa2USMhTzH3SyFiFZgANoG4k54x0eZv7if99n/CjM39xP8Avs/4Ut1Yd/6/r0OWTwQbTRPDsVhqHk6noEMcMV8IM+fGFCyRum4ZRwOm7hgpzlatL4h1S5cW8ngvXLdJTsM0k1iVjB43ELck4HXgE+1b+Zv7if8AfZ/wozN/cT/vs/4US969+ottjktH8IaxaeG4fDmsaxp19o0dj9iKQaZJBOyBNgPmG4YA98hPpirdr4a1WbU7GfxFrkepQaa5ktIorLyGeTaUEkzb2DsFY42LGuSTt6AdFmb+4n/fZ/wozN/cT/vs/wCFNtt3YHIP8Pt3h3WNMTVXSS/vPtMFwIcm1QMCsYG75gvzDOR97pWleeHL6DUEvvC+pW+mzfZktZorqzNxDJGmSh2q8bK67iAQ2MEgg4UjdzN/cT/vs/4UZm/uJ/32f8KXSwHLp4KmsLexm0TVmh1W0adnvLuATJdGdg8vmxqyZywBG1l2lQOmQWz+CbrUhaya5rb3twl3LcTbYSkRV7eSARxJvPlqBJnqxJByeeOqzN/cT/vs/wCFGZv7if8AfZ/wo30Hd3uc94X8Iv4dvJLqbUmvpprGC2md4tpkkjeV3lPJ++0xOO2OprpajzN/cT/vs/4UZm/uJ/32f8KbbbuxBB/x7R/7g/lXPR+CoYbe4ittY1OAPEYLVomhVrCJmDMkJ8vgHaoy25gAMEYzXQRiZI1XYh2gDO8/4UuZv7if99n/AApAc6vgmGCzjgstX1G0ME0jWssIg3WsUmN1ugMRUR8DAILDAwwwKmXwdYRX0EttPc29pF5RfT4ynkzNEAInbKl8rtXowB2jINbmZv7if99n/CjM39xP++z/AIUAc9H4HsEhnhku7yaExmK0jcx4sE3h8RYQHhkQgvvPyL24Mn2DWdCtQmhQ2+tXFxI0t5darffZpHbCqp/dW7KflUDAVQAo65NbuZv7if8AfZ/wozN/cT/vs/4UAYEHhyW+vY9S1djZTySJLdabZ3CzW00kZ/dyM7RLISAqHjaMqMg4yWx+B7BIZ4ZLu8mhMZitI3MeLBN4fEWEB4ZEIL7z8i9uD0OZv7if99n/AAozN/cT/vs/4UAYDeDYzboU1jUo9REjyPqiCD7RLuVVYMDF5YBVEHyoMbBjB5rdtLWGxsoLS0QRwQRrHGg/hVRgD8hTszf3E/77P+FGZv7if99n/CgCSio8zf3E/wC+z/hRmb+4n/fZ/wAKAAf8fD/7i/zNZ+r2mtTywyaFq1tYlQyyRXdibmOQHGDhZI2DDH97GCcjOCL4EwkLbE5AGN57Z9velzN/cT/vs/4UAYVz4Til8L6folvctFHZXFtOJWTcX8mZZSCMjG4rj2z07VTk8L68fGkuurrmnTRkhLeC70uSR7SLA3pG4nVQWxkvsJPAOQoA6nM39xP++z/hRmb+4n/fZ/woWgdLHCXfwvF1ZXVv/bDItzBfxsBb/LvuJJHRyN3JjWaVcfxb88YArvkXZGq9doAzTMzf3E/77P8AhRmb+4n/AH2f8KOlgeru/wCv6sSUVHmb+4n/AH2f8KMzf3E/77P+FADL6xtdSspLS/gSe3lGHjcZB5yPxBAIPYjNSoiRRrHGqoigKqqMAAdABTczf3E/77P+FGZv7if99n/CgAP/AB8J/uN/MVn6zoa6u0Esd9daddW+4R3Vp5fmBWA3J+8Rhg4HbPAwRV8iYyBticAjG898e3tS5m/uJ/32f8KNwMA+DbdNTtLy21PULb7D5SWsMRi2QwopVoRmMnY/BfJLEqmCNoxDL4HSe08Q202varJH4gz9oBFuPKygjPl4h4+RVX5t3Az1ya6XM39xP++z/hRmb+4n/fZ/woGm1sZV74d+3yaLJLql8r6RMJ0KCH/SH2GMmTMZ6qzD5Nv3jjGBiWHQ1g8U3euC+unkuraO2a1by/JVULMpGE35y79WP3unAxoZm/uJ/wB9n/CjM39xP++z/hQLpb+u5zF/Z+JfEen3Wja1p+nabp19C8E91p+rNNOiMpB2pJahTnocngEkcgVpXvh37fJoskuqXyvpEwnQoIf9IfYYyZMxnqrMPk2/eOMYGNXM39xP++z/AIUZm/uJ/wB9n/CgN/67mfBoSweKbvXBfXTyXVtHbNasI/KRULMpGE35y79WI+bpwMHh/Q18P2E1ql9dXoluZbkvdeXuVpHLsBsRRjczHpnnrjFaGZv7if8AfZ/wozN/cT/vs/4UBv8A18iSio8zf3E/77P+FGZv7if99n/CgAh/1Z/32/8AQjRRBny+eDubOP8AeNFAElFFFAEX2q3+2fZPPj+0+X5vk7xv2Zxu29cZ4zRBdW915n2aeObypDFJ5bhtjjqpx0I9KpazodtrcMYmknt54WLQ3VrJ5c0JPDbW7AjgjofwFWrGxttMsYrOwhWC3hXaka9AP6nuSeSeaAMjxj4oXwlo8V81sLkSTiHaZfLAyrHOcH+7+tcyvxah+36fbmxtpBepDIWtr8SGHzDjaw2D517rn05rpPF0V80Gm3GnRXsjWt75kv2ARGZUMMqZUS/KfmdQevBJ7V5sdM8XltOtYNP1b7ILaxgvFuYrfA8iQthSnzbFzkH7x5znirSVjKTknoer+JNY/wCEf8L6nrAg+0fYbWS48nfs8zapO3dg4zjrg1nWniHV4NTsrXxLpNlYpqDGO1nstQa5UyBS+xw8UZUlVYgjcPlIOOM3fFukz694N1fSbNo0uL2zlgjaUkIGZSBkgE459DWZFpniLV9U02bX7fTdPttMkaeOOyvJLlp5TGyKWLRR7VUOxx82SR0xzBs9l8/0sQ6p8S9EstB1PULH7ReyWNo91HF9knjW6RSBuicx4kTJXLpuUBgScc1ojxtof2y3tXnuY5p/KHzWM4SJ5ACkcjlNsTncuEcq3zLxyM8XN8PfFGpNeNqt5bPPcaPd6fJcvqdxOJ5JQmJRC0apbjMfKR5GD3xV7W/CnivWdRgnuZbXyobi0uo1TVrhI7YRNG7xeQkQWfLIxEj4PzDCjApq2l/n9/8Alr/wdAdunn+lv1PRGO1STngZ4FcjoPjPUdZWwvzoSDRtS3/Z7m1u2uJoiASBPEIwI+FKnDvtfCnrmrsfjTS7mZbeK211JJWCK7+H75FUngEs0O0D3PHrWLaeE9Z/4SC11K60/Qbe/tWLTa1ZuyXOpgIUCSoIlCq3ysRvkAKgAHghf1/X9feHT+v6/roPvvH+p6RYT3er+HVtlbTbjUbOIX2ZXWIKxSZDGPKbDr90yAHIJ6ZtyeOwl34Zt104s+uJI0v77H2QrEz7T8vzElGXt90n2rFXwp4t1HQNdttettHfVNXsJbd9Sj1KWTaSCEjSI26iOIZ6Bie53MSatp4E1FPEcN+bi0MFvqLzQoMhlgaCcbfu8t51zIfTbjnPFD20/r+v0DT8/wAlb9RbX4mi58H6Brf9klZtYvYbV7T7R/x7rI4XzC235gAyHoM7gM967O4vPJure3jj8yWYk4zjai/eb9QPqRXnVj8NNWtrHTreS6sStnFpxCqW/wBbFJCbg528grbRbffdnFd9dxSJrNndorMvlyQNgE7CxUhiPTK4P1FVK3Tu/wBP1EQrrFzHPJFe6eY3+ztcRRwS+a7AEAqRgANyOhI688VNY6hcTXstpfWsdvOkayjypvMUqSR1KqQcg9se9Uhp+qfapb2OHT7e88lk3xuxW5c4CtINoIAxwPmPOM+tnRbS8tFk+3QwiWQhpJ0uWleVvfKLgegHA6ACpQMnTV7KS++yJKxl3FAfKYIzDqofG0kYPAOeD6Gqy+IrOXU4rSEllZJHaZ1ZFATGSCRhhyeQcDFVTpGpy63BdXEqPHDcPIH+1ScoVZQoi27FIDD5sknHvUX/AAj99NFBZXD26WlvaS2qSxuxkcMoUMQQAOByMmjoPqW18U2DXEqkyJBHEkhleJ1JLttUBCuTnjBHXNXP7ZsftUVuZJFllC4DQuApblQxIwrHH3WwfasqbSNVvbzz7v7HFtFuoWKRmz5cu9jkqOo6D9e9SXOgSy65JcqEeCaWOZy11KmwoAMeWpCt90EEng9jjFCF3NOLVrOa+NokjeblgCYmCMV+8FcjaxHcAkjB9DRZ6tZ38zR20jMwXeN0TIHXONylgAw9xkcj1qhbaXfRawZwYYId8jP5U8jLNnOMwn5UOcEsDkkH1NO0fTL2yumebyoIPL2+RDcSSozZ+8quMRgc/KuRz7ChAyc65bR6tLYz7o3RkVX2MyksOMkDC5PAyee1QL4kt/7Qjt5F2o0c7tMocogjfackqB657DAHOQS270u+uL25VDbi0upIndy58xAmMgLtwc4x1GOvPSoE0K9iZWT7JL8t2jrKWK4lk3rxjnpgjjr1NAzYstRtr/zPszSboyA6SxPGy5GQdrAHB9anjkWVSyhgMkfMhU8HHQ/z71naNY3dl532ltkb7fLgF1JcBMDk73APPHy9Bj3q3cW73thNbzs1uZQyFoHyQp4BBI649uPfrQ/IS8zPj8RwSQ30qQu0drKsUeCMzlsbdo9yQAe/WrEV5qO+WK506NJREZIjFOXjcj+EsUG09Ox/SqEXhu4T7cDqDuJXie3LonyNGFwSFVe64wOMe9TQ22oRai+pXv7tEhYNbW1zLOJW45CEAKRjgAHO40Aalpcx3lnFcw52SoGGRgjPY+9TVT0i2ktNJt4ZwBKFy4BzhickfgTirlABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUARw/wCrP++3/oRooh/1Z/32/wDQjRQAeY3/ADxf81/xo8xv+eL/AJr/AI1JRQBH5jf88X/Nf8aPMb/ni/5r/jUlFAEfmN/zxf8ANf8AGjzG/wCeL/mv+NSUUAR+Y3/PF/zX/GjzG/54v+a/41JRQBH5jf8APF/zX/GjzG/54v8Amv8AjRcXENpbSXF1LHDBEheSWRgqooGSSTwAB3rPj8TaDNZXF5FrenPa2svkzzrdxlIn6bGbOFbkcHnmgDQ8xv8Ani/5r/jR5jf88X/Nf8aox+I9DlvbSzi1nT3ur6IT2sC3SF7iMgkOi5yy4BORkcGpE1zSZdZk0iPVLN9TiTzJLJbhDMi4B3FM7gORzjuKALXmN/zxf81/xo8xv+eL/mv+NULXxLoV7pdxqVlrWn3Fhakie6iukeKIgZO5wcLgEE5Peg+JtBXQxrR1vThpROBfm7j8gndt/wBZnb97jr14oAv+Y3/PF/zX/GjzG/54v+a/41QbxBpqyW5F3btbXFo94l2LmLyvKXb833slcODuAKjuRkZda+ItEvrO6u7HWLC5trMsLmaG6R0g2jLb2BwuByc9KNgLvmN/zxf81/xo8xv+eL/mv+NV9M1nTNatzcaNqNpqEIODJaTrKoPXGVJFN1XXNJ0G3SfXNUstNhkfYkl5cJCrNjOAWIycA8UBuWvMb/ni/wCa/wCNHmN/zxf81/xqJtRskZ1e8t1ZHSNwZVG1nxsU88FsjA75GKx9R8d+G9O0bV9SGr2d3Ho6FryO1uY3kibJAQjdwxIKgEjJ4oGtdjd8xv8Ani/5r/jR5jf88X/Nf8aoReJNDmvLO0i1iwa6voRPa24ukMk8ZBIdFzllwCcjI4NMm8V+Hbe6mtZ9f0uK4gjeWWJ7yMPGiEhmYE5ABBBJ6YNGwtzS8xv+eL/mv+NHmN/zxf8ANf8AGsPVPFsVlpaapp2m3Ot6a0LTtfafc2vkoi8klpJkyMAnIyOK1dKv/wC1NItb/wCy3FoLmJZRBcqFljDDOGAJAPtmgCfzG/54v+a/40eY3/PF/wA1/wAaw7vxhZ2euHT5LS7aKOeK1nv1VPIgnkAMcTZYPk7k5ClRvXJHOOgo6XDyI/OO4L5T5IyOR/j70eY3/PF/zX/GoNQvrfTLeW+vpPKtraCSaaQgnai4JOByeB2rP0vxML/UksbvStQ0q4mhM9ut6Iv36AgMVMbvgruXIbB+YcdcaxoznFzS0X9fO3W23UTdtzX8xv8Ani/5r/jR5jf88X/Nf8aWeaO2t5J7h1jiiUu7scBVAySfwrJt/E9pc6bp98ILmOC+m8jdIqj7PJkqFkG7g7xs4z8xH1pRpTmrxQNpGr5jf88X/Nf8aPMb/ni/5r/jWVY+J7O/1O3soIbgG6jnlgmZV2SJE6IzA5zgmQbTjkAnpjNzVtVg0azS5ukkdHuIbcCMAndLIsankjjLDPtnrTdGopKDWr/4b8wuWfMb/ni/5r/jR5jf88X/ADX/ABrJ13xTZeH3db2K4cpYXF+fKVT+7h2bhyR8x8wY7deRVePxhFFMU1rStQ0Zfs0t0sl4YXV0jAL48mR8EBgcHGe2cGrjha0oqajo9vP0W7+QX1sb3mN/zxf81/xo8xv+eL/mv+NZujeII9Ylngeyu9Pu4ESR7a7Cb/LfOx/kZhg7WHXIKnIFa1ZTpypy5ZbgmnsR+Y3/ADxf81/xo8xv+eL/AJr/AI1JRUDI2mKjJifqB1H+NHmN/wA8X/Nf8aJv9WP99f8A0IVV1vVoNB0G+1a8SR4LKB55FiALFVGSACQM8eooGk27IteY3/PF/wA1/wAaPMb/AJ4v+a/41R1HXLbTLjTYbhJWbUZmhiKAEKRE8p3ZPTbGRxnnH1rF0jx7Fqi6XNNoOradZ6ttFneXRtzHIWQuoxHM7rlVJGVA7HBoF0udR5jf88X/ADX/ABo8xv8Ani/5r/jWJofjCz129W3htLu2E8BubOW4VAt7AGAMse1iQPmThwrYYHFXPEOuxeHdJ+3TWtxd5migSC22b5HkkWNQN7Kv3mHJIoDrYv8AmN/zxf8ANf8AGjzG/wCeL/mv+Nc5L44gsrHUZ9Y0fU9LmsLN702twIWeaFPvNG0cjISDwQWBGRkAEEz2XiwzXsVrqmh6no8lwjNbm88hlnKjcyq0MrgNjnDYyAcZwcHmBueY3/PF/wA1/wAaPMb/AJ4v+a/41ymn/EGO9tNNvrjw7rFhpupNEsF/cG2MeZcCPcsczONxKj7vBYZxXWyOI42duigk4oeiuw62G+Y3/PF/zX/GjzG/54v+a/41Qsdfsb7wrB4hLNbWE1mt4WnABjjKb8tgkDA64JrMh8axmaOO+0TVtO+0RvJZG6ij/wBL2qXKqFkYo+0ZCyBCeeOGwP3b36AtTovMb/ni/wCa/wCNHmN/zxf81/xrFi8YabNrGladEJWfVLX7VBIAuxVKllDc5ywVyMAj5G59a/8Awm0FxHb/ANkaRqWqzzRmfyLUQqyRb2RZGaSREAYqdo3biMnHBwPTcNzovMb/AJ4v+a/40eY3/PF/zX/GsB/G2nvp9jPptte6lcagZBb2NvEFnJjOJdwkZFj2Hht7LzgckgGtJ8Q9Mt/saXtlf2l1c6kumyWsyIJLaVlLgyYcqUKjO5CwIPHfB1sB1HmN/wA8X/Nf8aPMb/ni/wCa/wCNZlh4ks9R8TarodvHMLnS0hed2UCNvNDFQpzkkbTngfjWvQHWxGsxZQyxOQRkcj/GjzG/54v+a/40Qf8AHtH/ALg/lWTo/ie31m8MEVpdW6PG0trPN5ey8jUgF49rE4BZfvBT8w4o62A1vMb/AJ4v+a/40eY3/PF/zX/Gs/WdcXSGgijsbrUbq43GO1tPL8wqoG5/3jqMDI755GAapr4xsJb6CK2gubi0l8oPqEYTyYWlAMSNlg+W3L0UgbhkijcDc8xv+eL/AJr/AI0eY3/PF/zX/Guej8cWDwzzSWl5DCIzLaSOI8X6bwmYsOTyzoAH2H517cjW0jVl1a2kc2s9lPDIYp7W52eZC+A2DsZlOVZTkMRgijcC35jf88X/ADX/ABo8xv8Ani/5r/jWTc+J7a21v+zzaXUkaSRxT3qeX5NvJJjYj5YPlsr0UgbhkiqkfjiweGeaS0vIYRGZbSRxHi/TeEzFhyeWdAA+w/OvbkAHQ+Y3/PF/zX/GjzG/54v+a/41gN4yjFugTR9Sk1EyPG+loYPtEW1VZixMvlkBXQ/K5zvGMnit20u4b6ygu7RxJBPGskbj+JWGQfyNADvMb/ni/wCa/wCNHmN/zxf81/xqSigCPzjuK+U+QMnkf4+1HmN/zxf81/xoH/Hw/wDuL/M1l634gOj3NnawaVfapdXe8xwWZhDBUA3MTLIi4G4DrnnpQBqeY3/PF/zX/GjzG/54v+a/41hat410vQv7Mh1UTQX+pSQxxWACyTRmR1Tc4VioVWYAtnbngEkgFL7xna2OqS2x07UJrW3njtrrUYkjMFvK+3arAuHP30yVRgN3JGDgA3vMb/ni/wCa/wCNHmN/zxf81/xrlZfiRo8PhbWteeC8+z6NdyWk8QRPMd0faSg3YIJ6EkdD0rrVbegYdCM0eYbf12GeY3/PF/zX/GjzG/54v+a/41JRQBH5jf8APF/zX/GjzG/54v8Amv8AjUlFAEfnHcF8p8kZHI/x96PMb/ni/wCa/wCNB/4+E/3G/mKz9Z1xdIaCKOxutRurjcY7W08vzCqgbn/eOowMjvnkYBovYDQ8xv8Ani/5r/jR5jf88X/Nf8abaXcN9ZQXdo4kgnjWSNx/ErDIP5GsWbxlYR6NruoxW19NHoTul1F9n8qQlEWRtgkK5G1gQeAe2eMj0Ba7G55jf88X/Nf8aPMb/ni/5r/jXI3HxItrbTIryTQtTy93JaNF51mDE8cbSMWcz+WFCo+Tv4KMDgjFTN4/tV1uz006RqQN1FbyCdjbqiCbdtBUy7yR5b7gqtgIT05p2YdL/wBdjqPMb/ni/wCa/wCNHmN/zxf81/xqprmrR6DoN9q08E1xFZQNPJHBt3lVGTjcQOgJ5IrnLj4kW1tpkV5JoWp5e7ktGi86zBieONpGLOZ/LChUfJ38FGBwRikOzOu8xv8Ani/5r/jR5jf88X/Nf8a5dvH9qut2emnSNSBuoreQTsbdUQTbtoKmXeSPLfcFVsBCenNWNA8aQeINXuNPTS76zkg8357loCG8uUxNgRysw+ZWwWAB2tjODTsxefp+J0HmN/zxf81/xo8xv+eL/mv+NSUUgI4DmPpj5m4P+8aKIf8AVn/fb/0I0UASUVH5y+j/APftv8KPOX0f/v23+FAElFR+cvo//ftv8KPOX0f/AL9t/hQBJRUfnL6P/wB+2/wo85fR/wDv23+FAHP+OfCsni7RILKG6jtnhuVnBli8xWwrLgjI/v5z7VyKfCe/XUdNulvtLgNgsCj7LZNE0vlNu3sdx+du7fTivTvOX0f/AL9t/hR5y+j/APftv8Kak0S4pu7MLx/z8NvEv/YKuv8A0U1cgfDeqazax3Efh7+zY1Gk24tXlhbzY7e5Ejv8rEbFQkKDhjz8o4Femecvo/8A37b/AAo85fR/+/bf4Ulo7+n4FvVW9fxt/kcNq+g6vPruo2kGmvNDqepWV8mqebGEtUh8rdGw3CTd+5YrtUjMvJHzVG2gatLpWqeHDoi755L+WHXZJo/Lj+0CQqyDJl8weYEOVUYDYYjAPe+cvo//AH7b/Cjzl9H/AO/bf4Uracv9dP8AL/MfM07/ANf1qeaad4Z1f+xdUmvtM1uW4kjsoo4Li9sI5wYHLh4fJQRZQkFfMb59uGCgc2hpGvnw7HLc6bqcl6mqtdQS20ljFqMQMZTzJBj7NI5yykZ+4ynlhXoPnL6P/wB+2/wo85fR/wDv23+FU222/wCv60JWit/W1jzDVPCPiO/8PJE2nxm4bw9qdoUQwxnzZpI2iVwpCb2CksV+TduwcEVe8T6PNYya5qUqWttpkdjphTz7iOGKU29xI7wksQFypVQWwvzAZxmvQfOX0f8A79t/hR5y+j/9+2/wou7af1q3+o+ln/W3+R554T8beHo7nXdZ1O/03RbXVL9WtTdX8Ci42QRK7B1co5BGCUZgDxnIIFnxHeReJ/s2oeDZZtY+zxz27XugX1lK0RcITFIlxmNkbapJzuG0YGCa7rzl9H/79t/hR5y+j/8Aftv8KTswTadzhrfTp38ZaTZvLYmW20yKbUrO0O2OOWIFYQFx8qM0rlcjpCMDjjHi8LeIbnw7rOmRaZdWsDaHNY2kGpS2kjROR8kVvNF85hGCCZiGOEP96vUfOX0f/v23+FHnL6P/AN+2/wAKd9b+v43FH3bW8vwPNrjwxrl54wN1Jb6xFZ391ZXpWO4slitjCqZSUsry7lMZOIiVbfjK5Y1qxeFbndYtNp0RaLxLcajITsJCN5uyTr1+ZPcceldp5y+j/wDftv8ACjzl9H/79t/hQnb+vT/JCtpb+tmv1OIi8N6o/h8aRPalbe68Qz3F0okQgWhuJJl79HwilRzhzkda6DUPG3hXSb6Sy1TxNo9ldRY8yC5v4o3TIyMqzAjgg1r+cvo//ftv8KPOX0f/AL9t/hS6W/rov0Kerb/rdv8AU4G60u/1a6vLbTLdbvSNZ1Oz1WPV4bmN4Ujj8kspG7cWPkfKVVlIcZIwa9CqPzl9H/79t/hR5y+j/wDftv8ACn0t/Xb9BFbVIp57K4itEtpJ5LeRY0u1JidiBgOBztPQ+1cnpW7Q78X9za3nhnRoYWW7TVtUjkt9xKiMQjzXEaqdw48sYKjaeNvZmQecrbXwFIPyN6j29qd5y+j/APftv8K6KWIdODg1dPff/hvS6dnqhNXOW1XWNI8Y6c+heHvEGn3Ut4QlwbK8hlkigzmRguTnI+XoRlxkYzVQ+G9Wia/0FpLm/wBM1SZJ59TnaFWiUjEsYWMJhj5aYKqMeYzZyvPaecvo/wD37b/Cjzl9H/79t/hWscY6ceSktN9dXfTW9l2Wm3dMXLfVnF2sV3ouraZf+Izb2NhpFjdWMl/NPHHE4aSDyX+98u5UOQQMMCOhUmTxDqejeNNJXSPD2s2GpXJu7WaSGx1FPNWJLiNpHBVwwwoJyCD0xziuw85fR/8Av23+FHnL6P8A9+2/wp/XFzxqOPvR2s7Le+3q+jQctlZHn/iXwNcGW7OhR310J9Bv7T/S9Tln/eyeV5ajzpDtztbkYHHJ6VtXngWxfQ9Qhszctf3VhLaxT31/Pc+VvXoDI7bQSFztxnA9BXTecvo//ftv8KPOX0f/AL9t/hQ8xxLjGPN8Prr666hyq9/66f5HPaBbajdeIbvW9U06TTDJZQWaW0sscjko0js+UZhtJkAHOeDkDiulqPzl9H/79t/hR5y+j/8Aftv8K5atV1ZczVttvLQaViSio/OX0f8A79t/hR5y+j/9+2/wrIYTf6sf76/+hCsfxtp11q/gPXNO0+Lzrq6sJoYY9wXc7IQBkkAcnua1pZAyABX+8p+43qPanecvo/8A37b/AAoHF8rujib7wKLbWvD15pJ1O4+y3MjXP2vWLi4SNDbSoCEmlYZ3Moyozz6ZqTwb4As9L8NaKdUhvTqdrZIjx3GpT3EdvL5e1zGjSNGhGWAKAYBIHBrsvOX0f/v23+FHnL6P/wB+2/woeqa7/wDB/wAxLS3kef6Lb3WiS6Re+K0g0Sx8N6ZJp7Xl1dxCK6d2iUSI24lUxF/HtbLgY4NWvEviHSfFWkJZ+EdU07X9Qt720vDZafqEDyvHFcRu5ALgdB3IGcc123nL6P8A9+2/wo85fR/+/bf4U7ttN9Nfxv8AmG2x5/4n0nXfGFjqdwuh3GmtDo13ZWlrdzQGa5mnC945HRUAjUDLAkscgAAnSmj1jxNqGlC40C70e20yVrl3vpbdmmfynjREEUkmB+8JJYr0AGcnHXecvo//AH7b/Cjzl9H/AO/bf4VLV1Yd3/Xnb/I8y0vwFfaDo/hO/SK+vZ9OihXUtHutUklhB2gGWNZJDErxMNygfLjOOdpHWnx74PvM2lp4s0Oa4m/dxxR6lCWdjwFA3cknjFdB5y+j/wDftv8ACjzl9H/79t/hTl710+v6iWjv1OB0ex1i++G9v4O1Lw5qOmyjSRZvfTy2skCyLFtziOZnKkj+709K02XXPEeqaQmo6C+kQabc/armaW4ilWZ1RlVYNjFipLZLOsZwANuSdvV+cvo//ftv8KPOX0f/AL9t/hTb5ndh0secxeFdfs/Dl81nZqNT0+6jh0hBMo320JZUOc4XKSycH/CtiHTL7wfqST6Ro02sWUmn21k0dpLCk0LQbwpxKyKUIfnDZBHQgkr13nL6P/37b/Cjzl9H/wC/bf4UtfvB67nC2Oha5oN1Z69/ZqahdyNeG+sLSZA6C4lWRfKaQorbCgDbiuckjoFJrmk+IvFiadJeaYunxR30zxwmVGmt4jaTRq8hVypYyODhCcAjk847rzl9H/79t/hR5y+j/wDftv8ACjpZfLyH1b77/M47wVomsWOvX2qa3aLby6hYW7ShZVcLOZbiSSMEEkhPNVc9COldrUfnL6P/AN+2/wAKPOX0f/v23+FNu4utwgGbaMf7A/lXAy+BtQn0P+zbzT9Hv4NP059N0+K6uJClxG7RnfMBHlCohQgLuJP8S9a7yKQLCisrghQD8jen0p3nL6P/AN+2/wAKXUabWxw+neEdX0Ro7zTLHSGv4J7tCxuGiF5FOyMZpSsPE2Y0yoUrxwVGAHWfgW702KLSLU20ukPLZ3FxPJMyzJJbLEAFjCkMG8hOS4xk8Gu285fR/wDv23+FHnL6P/37b/Cj+vu/4OojhV8DalLY2lpcGzRdEtlt9LkSd2M+2aGVTKCg8v8A494wQN/VjnsdS1vP+EYW6vdftrgXur3JuJIdKs7m/jiKxxxgb44s/dRTllXJJwOK6bzl9H/79t/hR5y+j/8Aftv8KLv+vvf3jOOGkXGuXk1xYxhdH1S9t7+4e8Sa2uYpIDGAqwPGCQ3kJyxXAJIB4zWXwNqUtjaWlwbNF0S2W30uRJ3Yz7ZoZVMoKDy/+PeMEDf1Y57HuvOX0f8A79t/hR5y+j/9+2/woWm39dPwQXZxyeG9ct9TPiK3t9NOs3Ekvn2j3kgt0R44U+WXytzEfZ4z9xc5I46np9E0tNE0Cw0uJy6WVtHArkY3BVC5/SrXnL6P/wB+2/wo85fR/wDv23+FPpb+tBElFR+cvo//AH7b/Cjzl9H/AO/bf4UgAf8AHw/+4v8AM1y/jHRrLUdQsLnUvBa+KUt45URd8LGAsVyfKnZIyDt+9ksMAAYJNdMJB5zNtfBUAfI3qfb3p3nL6P8A9+2/woA5O+8P6o/gPR9M/wCPq9tbyxkmPmDhY7iN3+ZiM7VU+5x68VX1R9d1Pxh9n1Pw1qU+hWc8bWotZ7Xy7lxhvOm3zK+1G+7GF6ruO47Qvaecvo//AH7b/Cjzl9H/AO/bf4U76/O/5f5B0seS6h4G8Rz6JqljDYgpeR6lMU+0IN0xluPs6/ex86XO7J6eWM4PT1yIFYUB6hQDTfOX0f8A79t/hR5y+j/9+2/wo6WB6u/r+JJRUfnL6P8A9+2/wo85fR/+/bf4UgMHxj4H0bxvYQw6xbqZ7VzJaXIX54GPXB7qcAFehwO4BGvpmmWuj6dFZWMeyGIYHqT6n3qfzl9H/wC/bf4Uecvo/wD37b/CgAP/AB8J/uN/MVja9puoyapY6tokdrPeWsU1uYbudoY2jlKEncqOcgxrxjnnkVrmQecrbXwFIPyN6j29qd5y+j/9+2/wpNXA8/g+HklhrWmT22laLcW+niziZ53YS3IhjZfPYeWcSx5AjGSCC25gQmy3d+HvEl5pfjW0e10pDru4WbC/kYDdAkH7z9yNvCbuN3J29tx7Xzl9H/79t/hR5y+j/wDftv8ACqbb/r+uw1Jp366fgcPq3w9t77QdA0ODQtJGkW149zf2LXDiNN8cisIcR84eZmX7mNoxt/h0T4T+0/Ew+Ir/AE+xkhtbOKHT7kTObiF180P8uwAKyzEY3H7inGcben85fR/+/bf4Uecvo/8A37b/AAou73F0t5W/G5yGva3F4q8O6noGi2uoLfajaS20TahpN7awKWQglpWg2gAZPucDvVbVvh7b32g6BocGhaSNItrx7m/sWuHEab45FYQ4j5w8zMv3MbRjb/D3HnL6P/37b/Cjzl9H/wC/bf4Uth3f5/icwfCf2n4mHxDf6fYyQ2tnFDp9wJ2NxC6+aH+XYAFZZiMbj9xTjONq+CfCp0BtUvr6wsbbVNSvJZZ5rKZpBNG0skke8sifMvmlc4PAHOMAdN5y+j/9+2/wo85fR/8Av23+FFxPX8PwVkSUVH5y+j/9+2/wo85fR/8Av23+FABD/qz/AL7f+hGiiA5jz/tN1H+0aKAJKKyNZsNTkmjvtBvFivIlKGC6ZjbzIf7yjow6hhz2OQeLWlWEmnWIiuLye9nZi8s8zcu564Xoi+ijgD8SQCDXfEWmeHLF7nVLqKJhE8kUBlRZLgqMlYwxG5uQMZ6kVmR+O7KK8ih1qyudEjmDbLjUZ7ZImI/hysrHP4djTvG3h6/8QaPJHpupS2skcEy/Z0hhZbosowjNIjFBxjKlT8x54GOXs7fVvGl8GtxcaDFptzPbTXK/Z52Mi4UoEdXGM98dq4q1TExr04043g78z7duv+ZcVHld9z02iuf8f/8AJNvEv/YKuun/AFyavPBoEF9pMsWhaDf22lzy6Ul3bzWckRuJluQZpCpAL/uyN8oyG/vHbmu1au3p+JL0V/X8Lf5nsdFeYanoSweKpobTRJRqa3ll/Yt7bWTCKzskEYkiEwXZEoCz5jJG4OAAdwFINKPmazHBot8PGcrX/k6uls6KEcP5Ba5wqOgUxKI8sVIHyjaWC6XXn+n9P06jtrb0PUKK8d0XQ2i8I65EtrdQWE6Wita2fhiW1TzFbLs1tJKzzgjasu0DeowpY5xZOmLJ4BitbjRjb2UerNJDHD4euJbSZNh+aTTWcypGWLAIOjqsnANU1Zv+v6/Ulbf12/r0PVGuEW7S2Kyb3RnBETFAAQDl8bQfmGATk84zg4lrx7VNJ1WfwtAlvotzAV8L6pbxw28EuAWkh8pVRizRllXKxE7lHy/w1pX/AIZbS5fEVnoujyQ6VNZ6dLPb2kBAugJpPtSjA+eRogAw5ZsgHkihqy/ru0Ppf+un+Z6fRXEfDtNMS+8SjQdPfTrAX8Yit2tmt9n+jxE4iYAx8nO0heucc8wfE7T4b86f9psnu1jjm2xzaHLqtqzEL96KJg6ScfLJ0ALjqRSelgirs76q2paja6RpdzqOoS+Ta2sTTTSbS21FGScAEnj0riV0+9k8TaNBcWXkRXllDe3tsZvNeOW1GApcnLHfLF8xPPlcnmuRi8Ntc+F/E+n2OgPci40WUNLPos1lcSTg7o1m3sUu5s5bzkHDKcH5xT6/f+H9fl3CNna/l+P9f1Y9sUhlBHQjIpa8gl0O6k8e2txZ2jWsfm2L6VInh2ZpLa1VF3xCfeiWy5EoeJ13YfgMSANE+CrO7vba7vNC8y5n8RXQupXhbdJat52Fc94WO07T8hJ6HPJb+vu/z/An7N/62b/Q7nWPEdjoTIL+LUXDqWDWmmXN0qgddxijYL+OKt6ZqVrrGl22o6e7SWt1EssLtGyFkYZB2sARkeorg7bSb0eCV8OR2VxbWl3rtxaNGsLKsNj9okcgAD5Y2jXYDwMOMdq9FVQihUAVQMAAYAFLpf8Arv8Ar+Y3o7f1vb9PyMqfxRpFtriaRNcst25VeIJDGjsMqjShdiu3ZWYMcjA5Fa1ec6xZ3smraroqWF48+o63Y6hb3Qt2MAhj8guTKBtVl8hxtYhj8uAc16NQvhT/AK2X/DfIHuFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAEcP8Aqz/vt/6EaKIf9Wf99v8A0I0UAHnw/wDPVP8AvoUefD/z1T/voVJRQBH58P8Az1T/AL6FRW8djaCQWq28AlkaWQRhV3u3LMcdSe571ZooAr3As7u2lt7ryJ4JkKSRSYZXUjBBB4II7U9ZYEUKkkaqBgAMAAKbeX9pp8Il1C6gtY2baHnkCAnrjJ78H8qqReI9EnmSKHWNPkkkYKiJdISxPAAGeTQFy958P/PVP++hR58P/PVP++hSXN1BZWstzeTx29vCheSWVwqIo5JJPAA9aoaT4m0HX5JU0LW9O1N4QDItndxzFAehO0nFAGh58P8Az1T/AL6FHnw/89U/76FJc3MFnay3N5NHBBCpeSWVwqoo5JJPAA9alByMjkUAR+fD/wA9U/76FQX1vp2qWUlnqcNreWsoxJBcKsiOM5wVOQeasvIke3zHVNzBV3HGSeg+tQwX9ndSSpa3cEzwnEqxyBih98dOh6+lAGPL4P8AB1xZW9nP4d0OW1tdxggexhKRbjltqlcLk8nHWr2k6Voeg272+h2Gn6bDI2947OFIVZsYyQoAJwBzVu0v7S/RnsbqC5VThmhkDgH0OKnoAqJBp0V9LexxWqXcyKks6qokkVc7QW6kDJwD0yan8+H/AJ6p/wB9CnNLGsqxs6iRwSqk8tjrgd+tHmxmYxb18wLuKZ5A6Zx6UAN8+H/nqn/fQo8+H/nqn/fQqSigCPz4f+eqf99CsTUPCPg/Vr6S91Tw9ol7dS48ye5soZHfAwMsykngAVv01ZEd3RXVmTG5QeVz0z6UAQWqWNjaRWtktvbW8KhIoYQqIijoAo4A9hUvnw/89U/76FIl1byuEjnidm3YVXBJ2nDfkeD6GpaAI/Ph/wCeqf8AfQo8+H/nqn/fQqSobW8tb6IyWVzDcRg7S8UgcA+mRQA7z4f+eqf99Cjz4f8Anqn/AH0KdJLHDHvmdY0BA3McDk4H61G17apeJaPcwrcuu5ITIA7D1C9SOD+VADvPh/56p/30KPPh/wCeqf8AfQpq3tq949olzC1zGNzwiQF1HqV6jqPzogvbW5kljtrmGZ4W2yrHIGMZ9CB0PB60AO8+H/nqn/fQo8+H/nqn/fQqOHUbK4tXuYLyCWCPO+VJVZVwMnJBwMUn9o2X2H7b9st/sv8Az381fL64+9nHXigCXz4f+eqf99Cjz4f+eqf99Co5tQsre2juLi7gigkxsleVVVsjIwScHIp9xdQWkJmup44IgQC8jhVGenJoAXz4f+eqf99Cjz4f+eqf99CnSSxwxNLM6xxoNzOxwFHqTUV1f2ljGsl9dQWyMcK00gQE+gJoAf58P/PVP++hR58P/PVP++hSSXdvDa/apbiJLfaG81nATB6HPTFOjmim3eTIkm04bawODjP8iD+NACefD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUAR+fD/z1T/voUefD/wA9U/76FSUUARwEGPIOQWbBH+8aKIf9Wf8Afb/0I0UASUUUUAZGsnWraaO90VVvkRSkunSMsfmZ6OkhHDDuDkEZ6Hra0qC/gsR/a12tzduxdzGgWOPP8CcZKjplsk9T6C7RQByfxAsE1Sy0mxkiupVn1DBSzaNZTiCZvlMhC/w857Z74ry+4aJZtDOo/wBq/av7P00WfmfZ/K2GUg52fNsx93Pz5zu7V7hqekafrVp9l1azhvIAwcJMgYBh3HoeTz7msqPwD4VikWSLQrNHQhlZY8FSOhBqlKxnKDbuR/Efj4YeJc/9Ay4/9FmsqbXtH8R+JPDn/CL6jZ6pdWU0kl1NYTJN9ntzCwZXZc43P5eFONxXI+6cd1RUmr2t6/jY8B1HWV1nTdbs7PULqe2vPD17PLE+uyXNz5yGNlE0KgJauMvmOM7SMqRgYrb1rxLaWmv2A0XWJmMFzp8SG48QSAyW7mLc8NsNwuYyjtulkO7duIJ2jHsdFNO1vL/O/wDwAbv+P42/yMy7+bxFZLKMxLBM6jGcvlB09dpb8zWNLeaXJfSStcW82mx2MkckdupT7NH8uVkAOcnGAMKRgjB5x1EtvFO0bSrlon3ockFTjHb2JFSUhGHoV3banfT38VxamR4kjW3glV2jjBJBfafvHJ46Dpzyazl1P/iqYEguHy91JFLHLekvgK+B5AG1FyFw3UjGetdbRQHQ4eO4Y3NtLaXU1xq4srhpoHlL+XNgfLtPCc8BQBkCmyS2xvpBpWpXMyvHaRySi5Z2XdPhhvJyDg8jtnoK7qijqmD2ORuZ7qDxMLZLpYzHLClvHLey7pIsDd+62sJM/OC5ORjJIxmp7K9H/CTMgu2uWaSUMsd05ZFGcB7cjCAYADLyTj+9XT0UAcz4bu/N1CSMXZvCYdzyx3bSqTu6ujD9yxyfkBxwfQVFfSxxeILwxXcqX5mtxBbpKQJBhd3yZw4xnOc4HPHWuroo7B3OJEmb+K6uL2aGXydQSOYu7bSsvGEz82FycY6KP7oxseGbkTfakSXzkQod8d613FkjkK7AEHjleQMj1reooWisD1dzM1NBN4b1COwczlopVG1zIS3OVByec5GO3SsSe8We4u7vRJw26G2jtzE3yyThnIQ4PI2kbh2H0rrqKAOJ1aW5n0W1b7Pd3VrCIn87zEy03mANvDOCCDkAYwCe2BVu8lU3d3at8moT6hbTQxH75QeXlh6gBXBI4GDXV0U+twOUgcPeWlpCy/b4NQuJZlx8yIwkwxH907kwe+RVNf8AS9Mhg07557bSJoLpEGWRzsARh1DEq/HXrXb0Uv6/QfW5yFzcW93Ld3ti6y6eiWfnPGNy/JKWbp/dXGfQdaeLiEXx1Eyp/Zn9qbxPn93/AKjZv3dNu/jPTNdZRR/X5f5COQtJ4LNrW61BlisZBeCFpOFw8oZRk/3lBwO46VeSOaHwtosdyGWVZbUMrdQdy8H3roagu7OG+g8m5DFAwcbHZCCDkEFSCOaFpb5fgwet/mZni2zguvDV606l/Jgd0G8hd204JAODjqM5weajurqLTtbe6vXhiV7NUtZbiTy492WLIXwdpPyH3A6cVvUUAYVpMs/g+5eO1W1j8qbYiOXQj5vmUkDKnqOMYIxUmn/LrCCPo+nxNKB6gkKfy3fl7VqXVtHeWslvPuMcg2sEdkJH1BBpILSC2ZzAm0vjcSSScDAHPbA6f40Le/8AXX/MHt/Xl/kTUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBHD/qz/vt/wChGiiH/Vn/AH2/9CNFAB5EP/PJP++RR5EP/PJP++RUlFAEfkQ/88k/75FHkQ/88k/75FSUUAR+RD/zyT/vkUeRD/zyT/vkVJRQBH5EP/PJP++RR5EP/PJP++RXP+Odc1bQNEgu9DsxdzNcrHIDbvMEQqx3bUIP3goz05rkU+Ifib+0dNjWwiuIbhYDdEaVcw+QzNiRNzMR8o539DTSbJcknY9O8iH/AJ5J/wB8ijyIf+eSf98iq2tXyaZoN/fS3SWiW1vJK1xJEZViCqTuKKQWA67QQT0rzhPFPiPT01yF7jUprjT4bO7tIdVhtFmvTJJIhhX7OCAr7FC5AdWbnjipuXZ2v/X9anqHkQ/88k/75FHkQ/8APJP++RXGeDNQub7xJdG6u4b1pNFsLhrlLdIy7SPcEjIUNtGAArZxj1LE6/iy8vIV0qysLyTTzqV+ttJexojNAvlu/wAocMu5igQZB+90ziqaadhf8P8Ahc3PIh/55J/3yKPIh/55J/3yK8u8NNq008ekWHiSWFJ9R1V7i+treFpJWjmQKRvRkByxzhcdcAcEWNN8T6zZW2m6hrevhodS0K6vpjJZJ5Nm8Iiw6IgDsMOSylzk/d29KlapPur/AIXHZt287fjY9J8iH/nkn/fIo8iH/nkn/fIrjfBOsatP4k1bSdXl1OVLe1trmFtVjtUmbzGlUkC3+XZ+7UgMA4JOe1XfGkFve3Xh7T9Tjim0281Py7mCcAxzYglZEdTww3qpwe4WqaF38v8AK50vkQ/88k/75FHkQ/8APJP++RXOfD5mPhVowS1vBf3kFoT2gS4kWMD/AGQoAHsBXT0g8iPyIf8Ankn/AHyKPIh/55J/3yKkooAj8iH/AJ5J/wB8ijyIf+eSf98ipKKAI/Ih/wCeSf8AfIo8iH/nkn/fIqSigCPyIf8Ankn/AHyKPIh/55J/3yKkooAgMMX2hB5aY2txtHqKf5EP/PJP++RWd4jZF0HUDLfSaen2KbdeRAl7cbf9YMc5Xrx6VzHhvS4NB8U2cI0nTtNe7s5BHNo0w8m+VCh8yaIopDDcCpDPjewLHIz10sOqlKU76q+luyu+q/C/noS3Y7jyIf8Ankn/AHyKPIh/55J/3yKy/FV/c6f4aupNOR5L6VRBapHjcZXO1cZIHBOeSBgHJxXH2sr2/h86cNOmsdW0nUEXRra6aNpSJASgJjZhs2+ajYJwkbHqKuhg3Wp897a2/wCD6Xa/HsDlZ2PRPIh/55J/3yKPIh/55J/3yK4Pw6bd/FPh6a3LNLNpmoPdtIMObjzrcS7vQhwy46DAA4Arc8d/8i/bf9hXT/8A0riolhOWtClf4vLbVr9A5tGzoPIh/wCeSf8AfIo8iH/nkn/fIrnPFkUc+teFYZ41kik1ORXR1yrA2dwCCD1FcdfeGtCsdB+I9xZaLp1vNaiaO3khtERoVNjESqkDKglmOB6n1rShg4VUuaTTaT2vvLl7r1HrzJL+t/8AI9U8iH/nkn/fIo8iH/nkn/fIrmPC3h5dMvftJ8I+HNGYw7VudLl3ytnHyn/R48A9fvHoOK6uuOtTjTnyxd/u/RtfiKMuZXI/Ih/55J/3yKPIh/55J/3yKkorEoglhiCDEaD5l/hHqKf5EP8AzyT/AL5FE3+rH++v/oQrnfiR/wAkv8S9/wDiWXH/AKLNJ7FRXNJI6LyIf+eSf98ijyIf+eSf98ivMNE0lNN+I2k2zeF9D8KSpbSXEc+kSb/7UXaVaAkQxfdJSQhs9FIBwxDNA8aeKW0271W+t7qZJtHuNRjgu2swkcsZBVIVhfzmj+Yq3mDcCq8gkim/6/H/ACJjeW3l+J6l5EP/ADyT/vkUeRD/AM8k/wC+RXJ+GLrUYvFV3pl74ifW4V0y2u0aSGFGRpHkBI8pVG0hAQCPxNdhTasJO/8AXzI/Ih/55J/3yKPIh/55J/3yKkopDI/Ih/55J/3yKPIh/wCeSf8AfIqSigCPyIf+eSf98ijyIf8Ankn/AHyKkooAj8iH/nkn/fIo8iH/AJ5J/wB8ipKKAI/Ih/55J/3yKPIh/wCeSf8AfIqSigCPyIf+eSf98ijyIf8Ankn/AHyKkooAghhiNvGTGhJUZJUelP8AIh/55J/3yKIf+PWPHXYP5V5PpupRaPp5v9Kv9G0/VINKlbxBcXKb1W+8yLYLgRsrFyfOVcndzwD0It7f1/X/AAB2ueseRD/zyT/vkUeRD/zyT/vkV5J438V6jqHwt1mSZ45Lctd294+nyQwPYFRtS3m8yfl2YjcY85Hyqp3BqtpqFnqutJf7LVdduLuwk0ZZJInuPsJEXneWyM2Ux9o3FWK+pppXf9df6/MT0X3/AIHqHkQ/88k/75FHkQ/88k/75FeLt4gu7rxj4ms9Rtri3vZ7Oxe9t57u1cW1slw/moFjmZtvkvknHOWZgoZQfRfA/wBj+y6n/Yf2X+w/tv8AxLPsW3yPL8qPf5e35dvm+Z04zmha6/1/X+TB6O39bXOj8iH/AJ5J/wB8ijyIf+eSf98ivPdY+z/8Jrdeb9k/4SL+0bP+yPM2+f8AY/3Xn+X/ABbP+Pjdjj1rCh8v+y4/7O+yfa/sQ/4SzydvmeZ9og8zz8fxeX9q+9ztz2pLX+vK/wDw/Ydv6/r8D1/yIf8Ankn/AHyKPIh/55J/3yK8qH/CO/a1/tD+yP8AhBPtNx9h3eX9i8zyrfZs/g+/9q27f4s45r0Xwx9t/wCET0n+1t3277HF9o353eZsG7Oe+c07aX9PxEaHkQ/88k/75FHkQ/8APJP++RUlFICAQxfaHHlpjavG0epp/kQ/88k/75FA/wCPh/8AcX+ZrivHWlXOveItK0vbpb2b2l1M0eq2puYGkUxBT5QdMsFZ8HcNuWPNJuw0rna+RD/zyT/vkUeRD/zyT/vkV494s8UG/wDBejR/Y9YsbFbewvG8uyubhZmMqbYjMqMCFAJO5tzEoOeQbUuoXa+JPERvLVl8KSazF/adwshSUB7SAKHQgEQg7fM5Bww42h6q3vOK/rVL9RdLs9X8iH/nkn/fIo8iH/nkn/fIpYYYre3jht40ihjUJGkagKqgYAAHQAU+kBH5EP8AzyT/AL5FHkQ/88k/75FSUUAR+RD/AM8k/wC+RR5EP/PJP++RWD4xn8V2lhDdeDo9OuHic/ara8hdnkj9Yyrr8w/ukHIPBBGG19MN+2nRNq4gW7YZkWAEKvtyTmgCQwxfaEHlpja3G0eop/kQ/wDPJP8AvkUH/j4T/cb+YrkvG/8AZP8Aaulf8JZ9i/sDyrjzPt+3yftP7vys7uN23zdvfPSk3YaVzrfIh/55J/3yKPIh/wCeSf8AfIrznTfFusWV7oukaprGjxySRWUd0LiBzNBM8TFoJD5oHmyFcoccfNuBJTfzreILu68Y+JrPUba4t72ezsXvbee7tXFtbJcP5qBY5mbb5L5JxzlmYKGUGmrOwW0+78T2jyIf+eSf98ijyIf+eSf98ivHPF2oLZfDXxHH4fhUeFZnmFtdaXPbxwRx+RGNiF5EGx5zID5e4nDKBlgRqJqFnq2tR3+21TXbi7sJNGEkkT3AsSIvO8tkZspj7RuKsV9TQldr5fj/AF+DE9F9/wCB6f5EP/PJP++RR5EP/PJP++RXOfEk2q/C/wASfbzCIf7NnH7/ABt3bDt68Z3Yx74rzfXvCdtb+DvDelLP4VGo6pq881k7WMX2Jw9vMIykPTBHkD+P59pO8/eS1/Aq2ifr+Cue1+RD/wA8k/75FHkQ/wDPJP8AvkV5Y/hjT7r4z2lrYPo4Oj6dZSXEckKPdhU+0BPLfqmD5G7gErtG4DhrHwq0S0Gs65r2nSaO9nJeXVtB/Z0CJIp+1Sswldc78r5JXkYXgKOrO36kvRfd+Kv+B6X5EP8AzyT/AL5FHkQ/88k/75FSUUgI4ABHgDADNgD/AHjRRD/qz/vt/wChGigCSio/JX1f/v43+NHkr6v/AN/G/wAaAJKKj8lfV/8Av43+NHkr6v8A9/G/xoAkoqPyV9X/AO/jf40eSvq//fxv8aAJKKj8lfV/+/jf40eSvq//AH8b/GgBzoksbRyqrowKsrDIIPUEVlWHhPw5pTK2l6BpdkUYOpt7KOPawzgjaBzyefc1p+Svq/8A38b/ABo8lfV/+/jf40eYGXd+HIWjP9jXMmgzu2ZLjTbe3EkgyzbW8yNwRudm6ZySc8nLLfw3utLm01/VLrxFaXKhWt9Ut7Voxg5+7HCgPb72egxitfyV9X/7+N/jR5K+r/8Afxv8aAK1no2l6csS6fptparCGEQggVAgbG7GBxnAzjrgU5dK05VhVbC1AgiaGICFf3cbY3IvHCnaMgcHA9Kn8lfV/wDv43+NHkr6v/38b/GgNjD/AOEN02ytvK8MKnhh2YF5tHsraNpBz8p3xMMZOemeOtSQeGRJZ3Nn4h1O58SWlwFDW2q21q0Ywc/djhQHnB+bPQYxWx5K+r/9/G/xo8lfV/8Av43+NABBBDa28dvaxJDDEoSOONQqooGAABwAB2qSo/JX1f8A7+N/jR5K+r/9/G/xoAkoqPyV9X/7+N/jR5K+r/8Afxv8aAJKKj8lfV/+/jf40eSvq/8A38b/ABoAkoqPyV9X/wC/jf40eSvq/wD38b/GgCSio/JX1f8A7+N/jR5K+r/9/G/xoAD/AMfCf7jfzFZUXhbS7C2uE8PW0GgzXG3fc6ZaQRyHBzzuQqe/UHqa0jEv2hBl/ut/GfUe9P8AJX1f/v43+NXCpOGkX/k/VdQMux0K5trxJ77XtQ1VY8mOK8htQqNjG8GOFGBwSOvQmr7adZPqKag9nbteonlpcmJTIq8/KGxkDk8e5qXyV9X/AO/jf40eSvq//fxv8aqVWcnfb00/IVijdaJbS+bLY4029lJJvrWCLzhnbu5dGB3BFByDnaPQYqJ4YMyyQ65q93r1nIuHstStrRoWOQQSEhUkgjjnHtWz5K+r/wDfxv8AGjyV9X/7+N/jVRr1Iqyf4K/ye6+QWKFh4Z0HSmB0vRNOsiriQG3tI48PtK7vlA5wzDPoxHerb6dZSRXUclnbvHeZ+0o0SkT5UKd4x83ygDnsAKk8lfV/+/jf40eSvq//AH8b/GolVqSfNKTb9QSS2M3TvCvh7R7v7VpOg6ZY3G0r51rZxxvg9RlQDitao/JX1f8A7+N/jR5K+r/9/G/xpTqTqO83d+YJJbElFR+Svq//AH8b/GjyV9X/AO/jf41Awm/1Y/31/wDQhSXNtBeWstteQxzwTKUkilQMrqeCCDwQfSmyxKEHL/eX+M+o96f5K+r/APfxv8aAGXFhaXbQNdWsE7W0glgMkYYxOAQGXP3TgkZHrVWDQNLspb6fS7G1067v8m4u7S2jSWRjn52O35iCSfmB5q75K+r/APfxv8aPJX1f/v43+NAGB4U8Eab4QNy9g7yy3IUO7QQQgKpJACQRxoOWYk7dxzyTgY6Oo/JX1f8A7+N/jR5K+r/9/G/xoAkoqPyV9X/7+N/jR5K+r/8Afxv8aAJKKj8lfV/+/jf40eSvq/8A38b/ABoAkoqPyV9X/wC/jf40eSvq/wD38b/GgCSio/JX1f8A7+N/jR5K+r/9/G/xoAkoqPyV9X/7+N/jR5K+r/8Afxv8aAJKKj8lfV/+/jf40eSvq/8A38b/ABoAIP8Aj3j/ANwfyqSoIYlNvGcvyo6OfT60/wAlfV/+/jf40ASUVH5K+r/9/G/xo8lfV/8Av43+NAElZur6Faa2IheTahF5Wdv2LUbi0znH3vJdd3TvnHbrV7yV9X/7+N/jR5K+r/8Afxv8aAItPsIdMsY7S2e4eOPOGubmS4kOTnl5GZj17njpVmo/JX1f/v43+NHkr6v/AN/G/wAaAJKKj8lfV/8Av43+NHkr6v8A9/G/xoAkoqPyV9X/AO/jf40eSvq//fxv8aAAf8fD/wC4v8zVbVNG0vXLVbbW9NtNRt1cSLFdwLKoYAgMAwIzyefephEv2hxl/ur/ABn1PvT/ACV9X/7+N/jQAlxa293b+RdQRTQ5B8uRAy5BBBwfQgEfSmHT7IpdIbSAreEm5XyhiclQp38fN8oA57ACpPJX1f8A7+N/jR5K+r/9/G/xoDYIIIbW3jt7WJIYYlCRxxqFVFAwAAOAAO1SVH5K+r/9/G/xo8lfV/8Av43+NAElFR+Svq//AH8b/GjyV9X/AO/jf40ASUVH5K+r/wDfxv8AGjyV9X/7+N/jQAH/AI+E/wBxv5ipKgMS/aEGX+638Z9R70/yV9X/AO/jf40ASUVH5K+r/wDfxv8AGjyV9X/7+N/jQBJRUfkr6v8A9/G/xo8lfV/+/jf40AZFh4T07Tb9Ly3udYeVCSFuNavJ4zkY5jklKnr3HFbdR+Svq/8A38b/ABo8lfV/+/jf40ASUVH5K+r/APfxv8aPJX1f/v43+NAElFR+Svq//fxv8aPJX1f/AL+N/jQAQ/6s/wC+3/oRoogGI8ejN1/3jRQBJRRRQAUUUUAFFFFAHO+NfFg8H6PBfG1W6865EG15/KC5Rmznaf7mMY71y6fF5P7R022bT7KUXywMWtdT81ofMbbtZfLHzr1K59Oa9IeNJUKSorqeqsMg1D9gs/8An0g/79imrEtSvoxNRuTZaXdXQa3QwQvIGuZfKiXAJy74O1fU4OBzXn0HxC1WC01qW5Ntf/2PFbXcjR6XcWJmikd1eONJnO5sR5RwdrkhcDrXol5Z2+oWM9newrPbXEbRyxOMh1IwQfwrEtPAug2d0LlILuacNG3mXeoXFwx8slkBMjtkKzEgHgHnrU6l6W/r+u5S8K6zfan4iuY9R+z7zpNld/6NJI0YMrznCguV4CqNwALdTxtC6fiXVL6xXTrPSTAl7qd4LaKa5iaSKLEbyMzKrKW+WNgBuHJHNQnwudLVpfCL2em3UipFJJeQS3aGJWdlRUEybcNI2MHAHGMAYVdB1HVLSW18XXtjfwlleA6daTWMkLg/eEnnuwPoVKkc8nNU7N6C2/ryt+epy2h6z4reRdLivdON9cX+pebdXUUs8cYhlQKEQSKQp342lvl9TjBtaT4z1yQWFxq/9jxQanpE9/CgLxLatD5eRLMxIKHzMkhF2gfxda6fSvCei6J5J020MRgMzIzTSOcykNISWYklioJJz+ppG8JaG9rbW8lgrw2tpLZRRu7MBDJt3oQTznYvJyeKlaJLy/S352GrX17/AKmR4M8U32taxqem6jLDctZwwTrcQ6ZcWIYSGQbQkxYuB5eRIp2tu6cVv69BqFzo80Wk6gunTkc3BgErKvfaCQA3oSGA7qelZSeEDpDPc+FLpbXUJlWKa51ZrnUd8S7iEG+dSMFiR83GTxzWlpdrriCZfEGo6dfI6gRiy0+S229c5LTSZ7dMY96JLmVkJaO7KngO4nu/h34euLuaS4nl023eSWVizyMY1JYk8kk9636radp9tpOmW2n6fF5VraxLDDHuLbUUYAySSeB3qzVzacm0JaIKKKKkYUUUUAFFFFABRRRQBm6488ek3jWd5DY3AtJfKup8eXC+Btds8YBwea5vw4k+neJIbW6XWrF5rZw8Oo3pvobt0K/vIpDKxjxk8FU3Bgdo24HX3EEVzmC5iSaGWJ0kjkUMrqcAgg8EEdqx4vCsGmRtLoMjRXwTy4J9SmuL5IVyNyqjygqCAOFZei5zgCu7D1oRpSpy0v5fnr09H5WepMlexa8Tas2ieGr2/hUvPHHtgQIXLysdqDauScsRwOa5Oy1mYeFGliudRkvdEvliiS7SaGW/VyNiOkgViXWQKGYYDLu7V01rpmtS3kL6/qGm3sEDeZHHa6fLbsJAMBixncEAE8FeuD2q3LoWnTa5Hq8tuWvYlCo/mNtGA4B2Z2kgSOAxGcMRmtKdWhRhyP3nvdbemqTta9357OwndnMaHe3E3iPQHOozXKX+mXl3OvmN5fmebb/KEJ+UJuZQOo57k52PGdzPa6HbyWs0kLnUrFC0blSVa6jVhkdiCQR3BNSSeG4LaZ73Qlgs9RzKY5p1kmiTzWRpf3QkUfMUB4I5ye5zBJoOqatC9n4p1CwvbFir+XY2c9nKsisGRhKLhiMFQeAD05q/a0JVYVb2Ud1bV6t6W02dtWtugWaTQeKJJRq3hu3jnmiiutQkimEMrR70+yznBKkHqAfYgHqK5G7sDp+i+Pbu21LWBNpQlSzaTWLqQRD7HG/RpCCdzsQSCQTx0Fdxa+FtMtJoJV+2zyW83nwtd6jcXBjfYyZHmO2Pldhjpz6gVPNoGmXFrqlvNbbotWJN6vmMPNzGIzzn5fkUDjHTPWnRxlOjaMb2sr9L+9fa/bT/AIBVrtN/1v8A8AxfC1n5V95raP4ksT5P+t1TWDdRNnHAT7TJz3ztHfmurrJ03w1Y6VdC4tZtTdwpULc6rc3CY/3JJGX8cVrVxYmqqtTmTfzv+rl+ZMIuKswooormLI5v9WP99f8A0IVheP7mez+HPiG5s5pIJ4dOneOWJyjowQkEEcg+9bs3+rH++v8A6EKi1LTrXV9LudO1CLzrW6iaGaPcV3IwwRkEEcelJ6oqLSkmzzvQJpl8e2FhY2fiPSJIrd7m+i1zV2u0urcqVAiU3EwLCTYSRt2jgn5gC/Q/iVqV3YXmq32mzSWA0ybUYdml3NsIAnKxNPLmOUsrAh0wPlbggg13l5othf3lhd3MBa40+QyWsqyMjRkrtIypGQR1U5B4yOBWfbeDtI05rybTLYJPcxSRD7TJLcQxK53MqxM+1ELclE2g4HoMNt/n/X9frYmOm/l/wSt4f1LxAfEVzpXiKXTZ/LsYbpJbGCSLBdnVlIZ3yBsGDkZz0FdPXJ+DfBLeF7q7u7q+W8ubiKOBTGswWOKMsVUedNK/Vz/HtAwAo5z1lN26CV7/ANdv8wooopDCiiigAooooAKKKKACiiigAooooAjh/wCPWPH9wfyrzvS9W1bT7Gz1S1V7+71DR5tQvra/1J44Y5keL5VL71hADyjCgKdozjGR6JB/x7x/7g/lVBvDOgut6r6JpzLqDiS8BtIz9pYHIaTj5yDyCc80Le/9f11+Q9Opw3jPxrqjfDu/ubS3udNkuI7ryLuxhuLpoY4lPL7Yh5EjHjD42DcS25dtWn1O5vdQuNXhl1BJbXULC1tLaR5oFeCbyfMLwNgM37yXll3DZxjFdhJ4b0OaxurObRtPktb2Yz3UDWqFJ5CQS7rjDNkDk88Cli8PaLb3VpcwaRYRT2UXk2sqWyBrePGNiEDKryeBgc01vf8Ar+unzYnt95xfh3xFe6/431+0lutYsBPp0D2kT6bND9iG+YE/vothcgKckEE5Ubgma6nwTdXF94C0K6vZnnuJ9Pgkllc5Z2KAkn3JqxN4X0C4vrq8uND02W6vIjDczvaRs88ZABR2Iyy4AGDxwKry+F4oLGCy8N303hm1hLN5OkW1qiOW55WSFwO/THU5zS6fd+o3q2/62MHU7m8HiDUL5Zrpbiw1Oys7W3S5dYpIJfK8wtEDscnzJeSCRs4IxWKNS1S30nTbmK5vWm1uyWbUy91I4tXNzbxsY1LHytqzTD5No+QHqM139n4es4Z7W8vwuqapaoyR6peW8P2kKSflDIihRyRhQOOueami0LSLdr5oNKsom1Ak3pS3QG5zkHzMD5+p656mhaef/Dfru/P7wv8A1/X9fkcTGk0/iKTw3PealHo9tNcGK4XUZhMxSG3dVacPvYBppjhmP3ADkCux8M3l1qPhPSb3UF23VxZxSzDGPnZAT+pofwxoMmkQ6VJommvp0Db4rNrSMwxtknKpjaDknkDua1OlPpb0/wCH+f6CCiiikBGP+Ph/9xf5muP8bf2tfa5puj6XDJLFLa3FzIsepy6flkaJVzNEC/HmN8oBBJGeBXYD/j4f/cX+ZqjrGgWGuxxLqCzgwsWjktrqW3kXIwRviZWwR1GcHAyOBSauNOx5r4q+JFl/wjWk2+n+JI9PleCzvbiW8uY4rmWJpEAQDjJIDs5UYCqRxuGLZ8U3snirWNLea9h0e61eKE6yjho7dZLWEpDGd2U8xifnAwpcfxOCPQ7nRtPu9JTTJrVPsSeXsgQlFUIwZANuMAFRx7VFL4d0qe21S3ms1ki1Zy96jMxErbFTPX5flRRxjpnrzVaczf8AW6/y/wCA9RLRWNCGJYII4kLlY1Cgu5diAMcsSST7k5NPqO3gS2to4Ii5SJAimSRnYgDHLMSSfckk1JQ9xLYKKKKQznfGOv6z4csIb3R9AXWYA5F2Bd+S8C9nC7G3r1zyCODgjJXX0y5urzTori+s/sU0g3GDzN5Ue5wOfardFAEZ/wCPhP8Acb+YrnfEqNfa9pelz3F1a2E0FzPJLa3Uluxkj8vYu9GBxh3bGcHbyDXRH/j4T/cb+YqDUtJ07WbUW2safa38CuJBFdQrKoYdGwwIyPWk1ca0OS0fxhrkzaJZXmlWbT6ha20xme/KNho2aUsgiIVwVO1M/PyQRsfbzp8Y6jfa/ryF9Wt1uLewWG3l0+5thZRPcvHK250UByh3bweSMKT5ea9Jm8PaLcXbXVxpFhLcNNHO0z2yM5kjGI33EZ3KCQD1Hakbw5obalc6g2jaeb26jMNxcm1TzJkIAKs2MsuABg8cCqbTd7B0svL8Dzzxbr2oeH/CHiPS9Mm1VFt5Jo7XUVjuL14Y1to5GBlAdlYvIyh5GAUBjn5AK6qG/uLjxzoskN9e/YL7SLmU2U8PlKro8ADlWQSBsSEEMeOwHOdk+HNEbQxozaPp50odLE2qeQOd3+rxt689OvNRx+FfD0V7a3kWg6Yl1ZxiK2nWzjEkCAEBUbGVAycAetCff+tLf8ET6fMreO3kj+HviCW3mmgmi06eSOWCVonRljLKQykEcgdK8/1nSvFNr4P0mCNbv+17rVrgRWI8Q3YRkFvOY0Nx5nmMp8qN8MV+YkfuwTjvB4SluGMet+IdR1uwfIl0/ULWyaCYdgwW3Vjg4I56gVqS6Do84sRPpVjINOINlvtkP2UjGPLyPkxgdMdBSVvyKv07X/FWODm0rWz8UdPtLO7vp7SzsrKS8mfVp18vH2gbvIDiOTeYo1Ytn1wSSy2Ph5Zax/wkOtXt7LdNpy3FzBavNqk9z5xF3KpBjkYiMosaAbRyGJLEnC9vHpGmxatLqkWn2qahMgjlu1gUSugxhS+MkcDgntS6bpGm6NbvBpGn2thC7mR47WFYlZz1YhQAScdad/1/El6q3p+C/wAy3RRRSAjh/wBWf99v/QjRRD/qz/vt/wChGigA8tv+ez/kv+FHlt/z2f8AJf8ACpKKAI/Lb/ns/wCS/wCFHlt/z2f8l/wqSigCPy2/57P+S/4UeW3/AD2f8l/wqSigCPy2/wCez/kv+FHlt/z2f8l/wrK8TeJrTwrp8N5fw3EyTTiBVt1UtuKs38TAYwp71it8TdMivNPt7rTdStzqCQywO6xFfLlbajnbISBntjPtTsyXJI6/y2/57P8Akv8AhR5bf89n/Jf8KS5leG1llhge4kRCywxlQ0hA4UFiACenJA964a0+Jyva6tNdWunSjRxDNfPpWqfbUhhdnViSI1PmJsYtHj7vQk8Ui7M7ry2/57P+S/4UeW3/AD2f8l/wrnfDviC61fXrm3vLWWzK6da3Yt2lVxH5rzADhAQ2EG7LEZ4HQlr/AIg1qbSIbOKxtY7y/v7kW1rBLP5KM21nYs4VioCIx4UngDHNNpp2Yv6/C5p+W3/PZ/yX/Cjy2/57P+S/4VwekeMPEkyrZtpEF3qlxe36CKe9EMVukEigLvWNiww2A23J4yBkkXtG8c32pzWfn6AYI9S06W+sEjvFkml8vYGjdSqohJkG07yCOu3pSWqT+f4XDrb+ux13lt/z2f8AJf8ACjy2/wCez/kv+Fcz4R8ZjxLqOo6fKum/abBInkbTNSF7EA5cbWbYhVwYzlcdCOTmtPxGt81ghtNWTR7aNjJe3x2b4YVUklPMVkByFyWBAXd3wQPTUOtjT8tv+ez/AJL/AIUeW3/PZ/yX/CsbwdeX994cjn1OWSdjLIILiWERSXEIciORkAABZcHgDrnC5wN2gCPy2/57P+S/4UeW3/PZ/wAl/wAKkooAj8tv+ez/AJL/AIUeW3/PZ/yX/CpKKAI/Lb/ns/5L/hR5bf8APZ/yX/CpKKAI/Lb/AJ7P+S/4UeW3/PZ/yX/CpKKAIDG32hP3r/dbnA9R7U/y2/57P+S/4VV1V72OxuX0mKOa+W2lNtHKcI8mBtDcjgnHcVzfhnU71teSzutYv7ovbM09rq9gLWaORSozCUiRJU5IYhnA+QhsHnpp4eVSnKomtPW/5fnYTdjrvLb/AJ7P+S/4UeW3/PZ/yX/CqXiDWItA8O32qz7NlrC0gDsFDN/CCT0ycDPvXL2HiuW48I/2rBrEGoto92Yb57Uxul6vH3So++VdGAXGX+XvVUsJVqw9pHa9vn/Vl80DdtDtfLb/AJ7P+S/4UeW3/PZ/yX/CuV0nV9Tn8QaLHc3iywanp11fNEioVT54PLUMBkhVkIznkkn0A0/FmoXWmaPBPYy+VI2oWcJbaDlJLiNHHI7qxH40nhZqpGndXl/m1+aFzJq5r+W3/PZ/yX/Cjy2/57P+S/4Vi+I727t9S0G1s7lrdb++eCZkVS237NM4xuBAIZFPTt6ZFctcXWv2GleM73/hKNRuH0MSpapNBabWP2RJQzbYQSQznoQOBkHnOlHBSqpNSSvrrf8Am5eifUfVL+v60PQ/Lb/ns/5L/hR5bf8APZ/yX/CuZ8L3TXV8c654jvsQ5MWqaQLWLqOQ/wBmjyfbce/FdVXPWoyoz5JfqvzSYoyUldEflt/z2f8AJf8ACjy2/wCez/kv+FSUViUQSxsEH71z8y9h6j2p/lt/z2f8l/wom/1Y/wB9f/QhWP421G60jwHrmo6fL5N1a2E00Mm0NsdUJBwQQeexFJ6IcVzNI2PLb/ns/wCS/wCFHlt/z2f8l/wrz3w/r93c+MLOw03Xtd1gLGZNTttY0pbQW0JVvLlU/Z4SSZFCgfMCCxxxkSaH8XdK1Z7x5DZCCKxm1CEWeoLcz+TGcMJogoMUhDKQuWzkjOVpiWu39XO+8tv+ez/kv+FHlt/z2f8AJf8ACsHQfEGq3+tXGma5o0GmzxWkV0pgvftCsrs67TlEIYbDngjngmuioAj8tv8Ans/5L/hR5bf89n/Jf8KkooAj8tv+ez/kv+FHlt/z2f8AJf8ACpKKAI/Lb/ns/wCS/wCFHlt/z2f8l/wqSigCPy2/57P+S/4UeW3/AD2f8l/wqSigCPy2/wCez/kv+FHlt/z2f8l/wqSigCPy2/57P+S/4UeW3/PZ/wAl/wAKkooAghjY28f71x8o4AHp9Kf5bf8APZ/yX/CiE4tYz/sD+VcLpXijVbe2stSuU1DWU1bTJNSGn28UO+22NF+7i+5u+WbkOzMSnBycE62/r+tB2b2O68tv+ez/AJL/AIUeW3/PZ/yX/CvPvGPxLj0/wPdX2myw6XeXCTjTZtRnhCTrGuTNFh2Dg5ARerFlO3bk1aPie6vbyfVtN1QzadZ31nZLb24ikhu1n8nMpfaW48/gqwHycg807a2Edv5bf89n/Jf8KPLb/ns/5L/hXnWmePj4m8R65ZaR4h0mO3W0t5bLy7qJpIo/MkE8p4bDhFDBWBC5j3AZauq8IPqcmn3TalfTahbm5JsLu4iSOaaDavzOqKq/e34IUZXacc5KWv8AX9f1cHo7f1tc2/Lb/ns/5L/hR5bf89n/ACX/AArkNR1vUo9cvrqG7mS10zUbSwNgscZS4E3lbpGJXeCPP42sB8nIPNZA8Vazb6Xp9419NM3iC0WeNHii26azzwRAIQgJAFxk79/MfXGRQtdv60uOx6N5bf8APZ/yX/Cjy2/57P8Akv8AhXDR6jrNzrzeFl1q8hktpZidV8iDzplSK3cKQY/L63ODhAcR9jk11nh3UpNZ8M6Zqc0flSXlpFO6YxtLKCR+tPdX9PxEXfLb/ns/5L/hR5bf89n/ACX/AAqSikBAI2+0P+9f7q84Hqfan+W3/PZ/yX/Cgf8AHw/+4v8AM1yXjTWNVg1Sw0nRRqayXFvPcvJpcVu8w8sxqAPtH7sL+8JOeTgAdTSbsNK51vlt/wA9n/Jf8KPLb/ns/wCS/wCFcJ4g8Z3q+GNNk8O3cNzcTJaXF1eiHaqQySomQhJwzksApJwAxzlRmI+N7t/GOreF1uzDdSamtta3Utv+6tYzbRybQ23a0rEyFFY84JOQApfW39dF+oltc9A8tv8Ans/5L/hR5bf89n/Jf8KWFGigjjeV5mVQpkcAM5A6nAAyfYAe1PoAj8tv+ez/AJL/AIUeW3/PZ/yX/CpKKAI/Lb/ns/5L/hR5bf8APZ/yX/CsPxb4ui8H2ltdXek6nfW00nlPNYxo625ONvmbnUgE8AgEZ4OCRnV0zUBqmnRXiW89uko3Kk6gNj1wCaAJDG32hP3r/dbnA9R7U/y2/wCez/kv+FB/4+E/3G/mKwvEFxfT65p2jafqE2mG5gnuXuoI43f90YwExIrLgmTJ4zheCKTdhm75bf8APZ/yX/Cjy2/57P8Akv8AhXKaR47e+j0iGfQtRF1qNtbzq0fkiNldN0jrukDbYzgNkZG5MA7hnnI/ieup63rUGl6zpskLRWUemxW88cksfm3DQyTMOfmGUfac7Rs3AEkVTTvYLaXfl+J6d5bf89n/ACX/AAo8tv8Ans/5L/hXmvinx3c+FPDGt2Emswf2vaySw2V7qbQxNKFt45i20BUdx5oRVVeTtyMbjWxdahqt/q9hc6F4gjnS4lgkgsrXyZreez+XzZpW2b1PMgUq+0kIMElhQtbedvxE9Fr5/gdl5bf89n/Jf8KPLb/ns/5L/hWR40u7vT/Aut3unXL2t3a2M08MyKrFWRCw4YFT07iuA1TW/GVl4M0+8WbxB9uuNTuIRZiGy+2yxJDMVU/uTFy0IcFFJ2uQN525Q+V6fP8ABXPVvLb/AJ7P+S/4UeW3/PZ/yX/CvPLi/wDFcXxI0zSI7/Ubi3+yWkl20UNt9njJ8/zGlzGZBv8AIAG0gBmPI+VTY8B6j4j1HxJrC6rdalcafazTwxSXcVusUpFzLGvlGKNWyqxANvbJL5wBgs7fqJ6K/p+J3flt/wA9n/Jf8KPLb/ns/wCS/wCFSUUgI4OI+ufmbk/7xooh/wBWf99v/QjRQBJRUeJv76f98H/GjE399P8Avg/40ASUVHib++n/AHwf8aMTf30/74P+NAElFR4m/vp/3wf8aMTf30/74P8AjQBBqWk6frNqLbVbOC8hDBwkyBgGHcZ6Hk8+5rLj8C+F4ZUlh0KxjkRgyusQBUjoQexrbxN/fT/vg/40Ym/vp/3wf8aBWRDqmm2+saRd6behmtryF4JQrFSVYEHB7cGucg+H1qLl5tQ1jU9SMqwRypcC3VJY4WZ442WOJBtDMT79CccV1OJv76f98H/GjE399P8Avg/40D6WOag8N3PhpmuvDMSajcyQQ2Zi1K+MCRwRNIyBWSFySPM28jkAc5BJlfTdY8QW/l+ILa20eW2lWeyu9K1E3EscgDAtiW3VR8rEYIYEMciugxN/fT/vg/40Ym/vp/3wf8aN9wMLRfBllok8E8d5e3U0L3MhkuZFYyNOytIWwo7qMYwBk+2IpfAekz6dZ2M8l08Fpp0+mqvmAF4pggfcQB82EGCMdTwa6LE399P++D/jRib++n/fB/xo8hptO6OXt/DWp+HrqXUdGuX13ULiGK1kGrXUdqiwxlyu3yLYjILkfd6HrxTNS8Paz4vt4YPEhTRI7SdLiH+yNQW6EzrnAkSe1CkKcMBg8gHggV1eJv76f98H/GjE399P++D/AI0eovQg02znsbJYLrUbnUpASTcXSxK7Z7ERoi8f7tW6jxN/fT/vg/40Ym/vp/3wf8aAJKKjxN/fT/vg/wCNGJv76f8AfB/xoAkoqPE399P++D/jRib++n/fB/xoAkoqPE399P8Avg/40Ym/vp/3wf8AGgCSio8Tf30/74P+NGJv76f98H/GgCC/srfUoJLK9j823uIHilTJG5TgEZHI+o5rITw/faey30OoXWu31uhjtI9UnjhjjDEb/mhhySQByyseOMZJrdMcpcN5iZAI+4e+Pf2pcTf30/74P+NbU604LlW39fNX2drXQrXMSODW9VuYI9f0zT7a0gkWcG01J5y7qcqGVoE4Bw2Q2cqKnuPDdrc6+uqST3HBjdrUMvkySRhgjsMZLDf64yqHGVBrUxN/fT/vg/40Ym/vp/3wf8ap4iSfue6trK/63C3cwB4YGk3K6hogNzdQLOltaXVz5UCLNIjuoZY2YAFMgYOMkdMbWXena54jtWsNdtLTS7ffHOl1pupGeZJI5FdMLJbBcZXnOfpzXRYm/vp/3wf8aMTf30/74P8AjVLFTupNXktnrdde9t9dUwsY8XhlzdWdxqOt6jqUljc/aIDcLAu1vLeMj93EmRiQn1yBz1Bdc+FrK6sdetJJbgR66WNyVZcpmFYjs44+VAec85+la2Jv76f98H/GjE399P8Avg/41P1mre6dvSy636eeoJWM3TNGvbC5Elx4j1PUYwu0QXUdsE+uY4UbI+ta1R4m/vp/3wf8aMTf30/74P8AjWU5ubu/wSX5AkloiSio8Tf30/74P+NGJv76f98H/GoGE3+rH++v/oQqrrekwa9oN9pN48iQXsDwSNEQGCsMEgkEZ59DVlo5XGDInUH7h7HPrS4m/vp/3wf8aBptO6M698PWt5rOm6qJZre808MiSQlR5sTD5onyDlCQpxwQVBBFZ1r4Mh0/T7y0S9vdRspLWS1t9MvJ1S2hjbP7sbEDY6KGfeyqMDvnosTf30/74P8AjRib++n/AHwf8aXQS02OS8F+Gtb0rUrzUfEl79onlt4rWJTd/aSscZc5LiGEZ+fGNmeMlmzx2NR4m/vp/wB8H/GjE399P++D/jVN3ESUVHib++n/AHwf8aMTf30/74P+NIZJRUeJv76f98H/ABoxN/fT/vg/40ASUVHib++n/fB/xoxN/fT/AL4P+NAElFR4m/vp/wB8H/GjE399P++D/jQBJRUeJv76f98H/GjE399P++D/AI0ASUVHib++n/fB/wAaMTf30/74P+NABB/x7x/7g/lWM3gzRHgvoWguPLvl2TKL6cBULbiifP8Au0J6qm0HoQa2EjlRFUSJhRgZQ/40uJv76f8AfB/xo8wMmbwjpE+i6hpMkVz9i1GV5bmJb6ddxc5cBg+UUnOVUheTxyadH4V0mO9tbsR3LTWqKieZezOr7RhWkUuRI47O4Zh68VqYm/vp/wB8H/GjE399P++D/jQBiah4J0TU7+9vbxL5p76JIZzHqdzGrIjblAVZAFwcn5QOrf3jlv8Awj+oaXbLD4U1KG1DyNJcPq6XOpPIcADDtcKy4A6EkemO+7ib++n/AHwf8aMTf30/74P+NAGPbeG0lu7fUtbeO61SL70tr51vbyEE7GMBldSyg8M24gjII4AfF4T0aH7YFtpCt4pSRXuZXCKTkiMFiIhnnCbeQD1ArVxN/fT/AL4P+NGJv76f98H/ABoAx38H6NJpkdi0d35cchkEq384nZjwS0wfzGyOCCxBAA6AVtRRRwQpFCixxxqFRFGAoHAAHpTcTf30/wC+D/jRib++n/fB/wAaAJKKjxN/fT/vg/40Ym/vp/3wf8aAAf8AHw/+4v8AM1na1oX9rvbzQaleaXdW4ZUubLy9+xsbkIkR1IJVT0zlRgitARyhy3mJkgD7h7Z9/elxN/fT/vg/40Acxqfwy8IatpcFldaFYn7PHFFFcG2jeZI4yCqb2UnHGD6gn1q3J4L0qXT9WsiJVh1SdZ2CEKbd0jjRDFgfKV8pGHXBH4VuYm/vp/3wf8aMTf30/wC+D/jR3ALeJobaOKSaSd0QK0sgUNIQPvHaAMnrwAPapKjxN/fT/vg/40Ym/vp/3wf8aAJKKjxN/fT/AL4P+NGJv76f98H/ABoAdJGksbRyoro4KsrDIYHqCKdUeJv76f8AfB/xoxN/fT/vg/40AB/4+E/3G/mKp6vodjrkMcWoLNiNtyPb3MkDjIwRvjZWwR1GcHuKtmOUuG8xMgEfcPfHv7UuJv76f98H/GgDJk8I6NLqCXpgnSeOSGSMx3kyLGYlZUVVVwFUB2BQDa245BzRN4S0i41a91KeK5e5voBbz7r2fYyD7oEe/apBJIKgEEkggkmtbE399P8Avg/40Ym/vp/3wf8AGgDKl8J6TN4cn0KWO5axucicG9m82bPXfNv8xsjg5bkcdOKrp4H0WPUEvVOpmZPKOG1e7ZHMYATehl2vjA+8Dnqc1u4m/vp/3wf8aMTf30/74P8AjQHkc5PoGvavBNp/iXVtLvNJuUaO5t7PTri1lkQj7olF0SvbPHIyO9adz4c068bS2uBdM2kyCS0IvZgVYLtyxD/vDtJHz7sgn1OdDE399P8Avg/40Ym/vp/3wf8AGgNynFodjD4guNajWYX1zCsErG5kKFFJKgRltgwSTkAH5m9TRo2h2OgWkltpizLFJM87Ca5kmJdzuY5kZiMkk4HGST3NXMTf30/74P8AjRib++n/AHwf8aAJKKjxN/fT/vg/40Ym/vp/3wf8aACH/Vn/AH2/9CNFOjQomCcnJJIGOpzRQB//2Q==" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "\n", + "Rin = xlnk.cma_array(shape=(16,16), dtype=np.uint8)\n", + "Gin = xlnk.cma_array(shape=(16,16), dtype=np.uint8)\n", + "Bin = xlnk.cma_array(shape=(16,16), dtype=np.uint8)\n", + "Rout = xlnk.cma_array(shape=(16,16), dtype=np.uint)\n", + "Gout = xlnk.cma_array(shape=(16,16), dtype=np.uint)\n", + "Bout = xlnk.cma_array(shape=(16,16), dtype=np.uint)\n", + "\n", + "for i in range(16):\n", + " for j in range(16):\n", + " Rin[i][j] = 2*i;\n", + " Gin[i][j] = 255-i*i;\n", + " Bin[i][j] = i; \n", + " \n", + "dma0.sendchannel.transfer(Rin)\n", + "dma1.sendchannel.transfer(Gin)\n", + "dma2.sendchannel.transfer(Bin)\n", + "dma0.recvchannel.transfer(Rout)\n", + "dma1.recvchannel.transfer(Gout)\n", + "dma2.recvchannel.transfer(Bout)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Do a filter test\n", + "做一个滤镜测试" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "vstest = Image.open('./data/vstest_32x20.png')\n", + "\n", + "testarray = np.array(vstest)\n", + "\n", + "red = testarray[:,:,0]\n", + "green = testarray[:,:,1]\n", + "blue = testarray[:,:,2]\n", + "vstest" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ContiguousArray([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 40, 41, 41, 45, 56,\n", + " 70, 102, 142, 143, 120, 110, 110, 108, 96, 0],\n", + " [ 0, 48, 51, 55, 60, 68, 76, 105, 143, 143, 128,\n", + " 127, 130, 125, 110, 0, 0, 56, 61, 69, 74, 78,\n", + " 86, 113, 145, 155, 156, 160, 160, 153, 138, 0],\n", + " [ 0, 68, 73, 84, 93, 100, 113, 135, 151, 159, 166,\n", + " 170, 168, 167, 166, 0, 0, 79, 85, 99, 112, 120,\n", + " 134, 157, 168, 172, 180, 185, 180, 183, 194, 0],\n", + " [ 0, 100, 109, 117, 125, 128, 141, 159, 162, 171, 184,\n", + " 190, 190, 196, 211, 0, 0, 120, 132, 135, 137, 136,\n", + " 142, 146, 146, 175, 202, 205, 204, 208, 211, 0],\n", + " [ 0, 109, 115, 128, 132, 114, 107, 118, 140, 174, 209,\n", + " 223, 220, 220, 210, 0, 0, 99, 98, 121, 125, 90,\n", + " 74, 99, 138, 159, 173, 190, 193, 190, 184, 0],\n", + " [ 0, 114, 114, 112, 112, 88, 78, 99, 128, 145, 142,\n", + " 145, 147, 129, 128, 0, 0, 157, 163, 139, 135, 123,\n", + " 123, 134, 126, 128, 129, 128, 123, 100, 102, 0],\n", + " [ 0, 182, 192, 185, 178, 163, 166, 169, 145, 127, 116,\n", + " 108, 104, 97, 98, 0, 0, 177, 189, 190, 164, 137,\n", + " 140, 151, 146, 124, 106, 97, 92, 92, 90, 0],\n", + " [ 0, 159, 169, 168, 143, 108, 106, 128, 135, 112, 101,\n", + " 103, 97, 92, 90, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 102, 108, 108, 98, 96,\n", + " 101, 99, 96, 96, 95, 93, 92, 88, 83, 0],\n", + " [ 0, 94, 94, 94, 93, 97, 97, 90, 81, 83, 93,\n", + " 98, 98, 93, 85, 0, 0, 88, 87, 86, 90, 97,\n", + " 95, 81, 63, 66, 88, 98, 96, 91, 86, 0],\n", + " [ 0, 86, 86, 87, 90, 95, 98, 86, 65, 70, 86,\n", + " 90, 87, 84, 81, 0, 0, 81, 80, 80, 83, 85,\n", + " 89, 81, 63, 66, 79, 83, 80, 68, 62, 0],\n", + " [ 0, 70, 62, 60, 67, 66, 59, 54, 51, 52, 53,\n", + " 65, 72, 58, 50, 0, 0, 70, 46, 41, 55, 57,\n", + " 48, 45, 57, 66, 52, 54, 76, 80, 74, 0],\n", + " [ 0, 76, 58, 61, 68, 64, 63, 57, 57, 68, 60,\n", + " 56, 72, 78, 76, 0, 0, 71, 72, 81, 72, 57,\n", + " 59, 54, 45, 46, 48, 53, 56, 50, 54, 0],\n", + " [ 0, 73, 69, 66, 58, 50, 54, 56, 53, 50, 47,\n", + " 49, 51, 50, 56, 0, 0, 75, 63, 60, 59, 60,\n", + " 65, 62, 57, 56, 53, 51, 51, 55, 57, 0],\n", + " [ 0, 62, 53, 60, 61, 62, 67, 57, 47, 50, 53,\n", + " 50, 48, 49, 48, 0, 0, 53, 49, 46, 47, 54,\n", + " 55, 47, 43, 46, 46, 45, 47, 48, 45, 0],\n", + " [ 0, 51, 50, 39, 38, 47, 45, 38, 41, 45, 40,\n", + " 40, 47, 47, 44, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pynq.lib.dma\n", + "\n", + "vsol = pynq.Overlay(\"vs.bit\")\n", + "\n", + "dma0 = vsol.axi_dma_0\n", + "dma1 = vsol.axi_dma_1\n", + "dma2 = vsol.axi_dma_2\n", + "\n", + "\n", + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "\n", + "Rin = xlnk.cma_array(shape=(20,32), dtype=np.uint8)\n", + "Gin = xlnk.cma_array(shape=(20,32), dtype=np.uint8)\n", + "Bin = xlnk.cma_array(shape=(20,32), dtype=np.uint8)\n", + "Rout = xlnk.cma_array(shape=(20,32), dtype=np.uint)\n", + "Gout = xlnk.cma_array(shape=(20,32), dtype=np.uint)\n", + "Bout = xlnk.cma_array(shape=(20,32), dtype=np.uint)\n", + "\n", + "for i in range(20):\n", + " for j in range(32):\n", + " Rin[i][j] = red[i][j];\n", + " Gin[i][j] = green[i][j];\n", + " Bin[i][j] = blue[i][j];\n", + " \n", + "dma0.sendchannel.transfer(Rin)\n", + "dma1.sendchannel.transfer(Gin)\n", + "dma2.sendchannel.transfer(Bin)\n", + "dma0.recvchannel.transfer(Rout)\n", + "dma1.recvchannel.transfer(Gout)\n", + "dma2.recvchannel.transfer(Bout)\n", + "Bout" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newimage = np.zeros((20,32,3), dtype=np.uint8)\n", + "newimage[:,:,0] = Rout;\n", + "newimage[:,:,1] = Gout;\n", + "newimage[:,:,2] = Bout;\n", + "img = Image.fromarray(newimage,'RGB')\n", + "img.save('./data/vstest_after.png')\n", + "img\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/10-SORT.ipynb b/boards/Pynq-Z2/notebooks/10-SORT.ipynb deleted file mode 120000 index becc4e4..0000000 --- a/boards/Pynq-Z2/notebooks/10-SORT.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/10-SORT.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/10-SORT.ipynb b/boards/Pynq-Z2/notebooks/10-SORT.ipynb new file mode 100644 index 0000000..23e3c03 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/10-SORT.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "require(['notebook/js/codecell'], function(codecell) {\n", + " codecell.CodeCell.options_default.highlight_modes[\n", + " 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n", + " Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n", + " Jupyter.notebook.get_cells().map(function(cell){\n", + " if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n", + " });\n", + "});\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "import random\n", + "sortol = pynq.Overlay(\"sort.bit\")\n", + "actualin = np.array\n", + "# dma = overlay.const_multiply.multiply_dma\n", + "# multiply = overlay.const_multiply.multiply\n", + "\n", + "dma = sortol.axi_dma_0\n", + "# s = sumol.sum_0" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Insertion sort is a basic sorting algorithm whose core idea is to insert a new element into an ordered array and keep it in order. In each step, insert a record to be sorted into the appropriate place in the previously sorted file according to the size of its key value until all the records are inserted.\n", + "插入排序是一种基本的排序算法，其核心思想是将一个新的元素插入到一个有序数组中，并继续保持有序。每步将一个待排序的记录，按其关键码值的大小插入前面已经排序的文件中适当位置上，直到全部插入完为止。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/10.1.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input is:\n", + "[ 53 27 41 57 5 59 100 17 8 34 74 33 90 25 68 78]\n", + "sorted output:\n", + "[ 5 8 17 25 27 33 34 41 53 57 59 68 74 78 90 100]\n" + ] + } + ], + "source": [ + "from pynq import Xlnk\n", + "\n", + "xlnk = Xlnk()\n", + "in_buffer = xlnk.cma_array(shape=(32,), dtype=np.int) #the length of the \n", + "#array has to be twice the number you wanna sort\n", + "#here we set the sorted array's length to be 16\n", + "out_buffer = xlnk.cma_array(shape=(16,), dtype=np.int)\n", + "\n", + "for i in range(16):\n", + " in_buffer[i] = random.randint(0,100);\n", + "\n", + "dma.sendchannel.transfer(in_buffer)\n", + "# dma.sendchannel.wait()\n", + "dma.recvchannel.transfer(out_buffer)\n", + "\n", + "# dma.recvchannel.wait()\n", + "\n", + "\n", + "actualin = in_buffer[0:16]\n", + " \n", + "print(\"input is:\") \n", + "print(actualin)\n", + "print(\"sorted output:\")\n", + "print(out_buffer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/11-HUFFMAN.ipynb b/boards/Pynq-Z2/notebooks/11-HUFFMAN.ipynb deleted file mode 120000 index 2db3b33..0000000 --- a/boards/Pynq-Z2/notebooks/11-HUFFMAN.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../Pynq-Z1/notebooks/11-HUFFMAN.ipynb \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/11-HUFFMAN.ipynb b/boards/Pynq-Z2/notebooks/11-HUFFMAN.ipynb new file mode 100644 index 0000000..5ee3d4d --- /dev/null +++ b/boards/Pynq-Z2/notebooks/11-HUFFMAN.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Overlay\n", + "导入Overlay" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pynq.lib.dma\n", + "import numpy as np\n", + "hmol = pynq.Overlay(\"huffman.bit\")\n", + "\n", + "dma0 = hmol.axi_dma_0\n", + "dma1 = hmol.axi_dma_1" + ] + }, + { + "attachments": { + "Diagram.JPG": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Address%20Editor.JPG](attachment:Address%20Editor.JPG)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Canonical hoffman codes as follows:\n", + "Canonical霍夫曼编码如下：" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![title](./data/canonical_huffman_flow.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ContiguousArray([1048560, 3016, 2281, 7, 7112, 1992,\n", + " 10473, 2055, 1031, 3079, 15850, 519,\n", + " 6088, 4040, 6377, 65517, 12267, 14569,\n", + " 32234, 1002, 2567, 8136, 17386, 524271,\n", + " 131054, 40, 4136, 2088, 6184, 1064,\n", + " 9194, 5160, 3112, 1257, 45035, 7208,\n", + " 1543, 3591, 552, 9449, 263, 4648,\n", + " 2311, 1287, 2600, 25578, 6696, 5353,\n", + " 3335, 13545, 775, 2823, 1799, 3847,\n", + " 1576, 5672, 3305, 11497, 135, 196589,\n", + " 3624, 5098, 7720, 28651, 393198, 2183,\n", + " 7401, 15593, 296, 4392, 1159, 2344,\n", + " 6440, 745, 3207, 1320, 5416, 647,\n", + " 2695, 21482, 3368, 7464, 808, 4904,\n", + " 2856, 8937, 13290, 4841, 6952, 1671,\n", + " 1832, 3719, 391, 13033, 29674, 5928,\n", + " 3880, 61419, 7976, 168, 3050, 2439,\n", + " 19434, 1415, 4264, 2793, 2216, 8171,\n", + " 3463, 6312, 11242, 903, 2951, 1192,\n", + " 10985, 27626, 1927, 5288, 7146, 3240,\n", + " 7336, 680, 6889, 4776, 2728, 3975,\n", + " 6824, 1704, 23530, 15081, 71, 32748,\n", + " 5800, 3752, 7848, 15338, 2119, 1095,\n", + " 424, 1769, 4520, 2472, 3143, 9961,\n", + " 6568, 5865, 583, 14057, 1448, 5544,\n", + " 3817, 2631, 1607, 3496, 7592, 3655,\n", + " 98284, 327, 936, 40939, 5032, 12009,\n", + " 2375, 2984, 7080, 7913, 1351, 31722,\n", + " 2026, 3399, 16105, 839, 489, 8681,\n", + " 18410, 1960, 2887, 6056, 4008, 1863,\n", + " 4585, 10218, 2097136, 24555, 3911, 199,\n", + " 12777, 26602, 8104, 262126, 6122, 104,\n", + " 2247, 4200, 2152, 57323, 6248, 2537,\n", + " 1128, 10729, 5224, 6633, 3176, 7272,\n", + " 616, 22506, 4712, 1223, 2664, 3271,\n", + " 14825, 1513, 14314, 711, 6760, 2759,\n", + " 30698, 1640, 4074, 5736, 1735, 16363,\n", + " 3783, 9705, 5609, 455, 3688, 13801,\n", + " 7784, 360, 4456, 2408, 6504, 1384,\n", + " 2503, 1479, 5480, 3432, 7528, 872,\n", + " 20458, 4968, 2920, 3527, 3561, 11753,\n", + " 7016, 1896, 5992, 7657, 3944, 49131,\n", + " 967, 8040, 232, 4328], dtype=uint32)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#生成输入数据,并输出结果\n", + "data = []\n", + "frequency = np.zeros(256, dtype = np.uint32)\n", + "with open(\"./data/huffman256.txt\") as f:\n", + " for line in f:\n", + " data.append(line.split(\"\\n\"))\n", + "\n", + "for i in range(256):\n", + " frequency[i] = int(data[i][0],16)\n", + "\n", + "from pynq import Xlnk\n", + "xlnk = Xlnk()\n", + "inputvalue = xlnk.cma_array(shape=(256,), dtype=np.uint16)\n", + "inputfrequency = xlnk.cma_array(shape=(256,), dtype=np.uint32)\n", + "encoding_v = xlnk.cma_array(shape=(256,), dtype=np.uint32)\n", + "num_nonzero_symbol = xlnk.cma_array(shape=(1,), dtype=np.int)\n", + "\n", + "\n", + "for i in range(256):\n", + " inputvalue[i] = i\n", + " inputfrequency[i] = frequency[i]\n", + " \n", + "\n", + "dma0.sendchannel.transfer(inputvalue)\n", + "dma1.sendchannel.transfer(inputfrequency)\n", + "dma0.recvchannel.transfer(encoding_v)\n", + "dma1.recvchannel.transfer(num_nonzero_symbol)\n", + "\n", + "encoding_v" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/boards/Pynq-Z2/notebooks/data/10.1.png b/boards/Pynq-Z2/notebooks/data/10.1.png new file mode 100644 index 0000000..e57ecfd Binary files /dev/null and b/boards/Pynq-Z2/notebooks/data/10.1.png differ diff --git a/boards/Pynq-Z2/notebooks/data/8ptFFT.jpg b/boards/Pynq-Z2/notebooks/data/8ptFFT.jpg new file mode 100644 index 0000000..c4ebf43 Binary files /dev/null and b/boards/Pynq-Z2/notebooks/data/8ptFFT.jpg differ diff --git a/boards/Pynq-Z2/notebooks/data/block.png b/boards/Pynq-Z2/notebooks/data/block.png deleted file mode 120000 index 9fbfecc..0000000 --- a/boards/Pynq-Z2/notebooks/data/block.png +++ /dev/null @@ -1 +0,0 @@ -../../../Pynq-Z1/notebooks/data/block.png \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/data/block.png b/boards/Pynq-Z2/notebooks/data/block.png new file mode 100644 index 0000000..9fbfecc --- /dev/null +++ b/boards/Pynq-Z2/notebooks/data/block.png @@ -0,0 +1 @@ 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+16de +7f6b +3a97 +7fc8 +0c85 +500f +0c1f +3811 +7745 +063d +728c +2529 +1d22 +627a +349c +19e4 +506e +4d5e +39d8 +3bbb +4c8f +5148 +6d73 +6a1f +5002 +5c50 +4874 +300e +17a0 +5e7d +4718 +73e3 +1f20 +1839 +4d71 +5972 +4ade +2d01 +3f54 +0a54 +5eda +4e61 +4f72 +5880 \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/data/huffman256_golden.txt b/boards/Pynq-Z2/notebooks/data/huffman256_golden.txt deleted file mode 120000 index f71f229..0000000 --- a/boards/Pynq-Z2/notebooks/data/huffman256_golden.txt +++ /dev/null @@ -1 +0,0 @@ -../../../Pynq-Z1/notebooks/data/huffman256_golden.txt \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/data/huffman256_golden.txt b/boards/Pynq-Z2/notebooks/data/huffman256_golden.txt new file mode 100644 index 0000000..f71f229 --- /dev/null +++ b/boards/Pynq-Z2/notebooks/data/huffman256_golden.txt @@ -0,0 +1 @@ +../../../Pynq-Z1/notebooks/data/huffman256_golden.txt \ No newline at end of file diff --git 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/dev/null @@ -1 +0,0 @@ -../../../Pynq-Z1/notebooks/data/vstest_after.png \ No newline at end of file diff --git a/boards/Pynq-Z2/notebooks/data/vstest_after.png b/boards/Pynq-Z2/notebooks/data/vstest_after.png new file mode 100644 index 0000000..a4ebdac Binary files /dev/null and b/boards/Pynq-Z2/notebooks/data/vstest_after.png differ diff --git a/boards/Pynq-Z2/spmv/spmv.bit b/boards/Pynq-Z2/spmv/spmv.bit index 9ea0b89..d44b071 100644 Binary files a/boards/Pynq-Z2/spmv/spmv.bit and b/boards/Pynq-Z2/spmv/spmv.bit differ diff --git a/hw/ip/cordic/cordic.cpp b/hw/ip/cordic/cordic.cpp index 25aa327..b1d50c0 100644 --- a/hw/ip/cordic/cordic.cpp +++ b/hw/ip/cordic/cordic.cpp @@ -11,34 +11,34 @@ void cordic(THETA_TYPE theta, COS_SIN_TYPE &sine, COS_SIN_TYPE &cos){ #pragma HLS INTERFACE s_axilite port=sine #pragma HLS INTERFACE s_axilite port=cos - // Set the initial vector that we will rotate - // current_cos = I; current_sin = Q - COS_SIN_TYPE current_cos = 0.60735; - COS_SIN_TYPE current_sin = 0.0; - - COS_SIN_TYPE factor = 1.0; - // This loop iteratively rotates the initial vector to find the - // sine and cosine values corresponding to the input theta angle - for (int j = 0; j < NUM_ITERATIONS; j++) { - // Determine if we are rotating by a positive or negative angle - int sigma = (theta < 0) ? -1 : 1; - - // Multiply previous iteration by 2^(-j) - COS_SIN_TYPE cos_shift = current_cos * sigma * factor; - COS_SIN_TYPE sin_shift = current_sin * sigma * factor; - - // Perform the rotation - current_cos = current_cos - sin_shift; - current_sin = current_sin + cos_shift; - - // Determine the new theta - theta = theta - cordic_phase[j]; - - factor = factor / 2; - } - - // Set the final sine and cosine values - sine= current_sin; cos = current_cos; - } + // Set the initial vector that we will rotate + // current_cos = I; current_sin = Q + COS_SIN_TYPE current_cos = 0.60735; + COS_SIN_TYPE current_sin = 0.0; + + COS_SIN_TYPE factor = 1.0; + // This loop iteratively rotates the initial vector to find the + // sine and cosine values corresponding to the input theta angle + for (int j = 0; j < NUM_ITERATIONS; j++) { + // Determine if we are rotating by a positive or negative angle + int sigma = (theta < 0) ? -1 : 1; + + // Multiply previous iteration by 2^(-j) + COS_SIN_TYPE cos_shift = current_cos * sigma * factor; + COS_SIN_TYPE sin_shift = current_sin * sigma * factor; + + // Perform the rotation + current_cos = current_cos - sin_shift; + current_sin = current_sin + cos_shift; + + // Determine the new theta + theta = theta - sigma*cordic_phase[j]; + + factor = factor / 2; + } + + // Set the final sine and cosine values + sine= current_sin; cos = current_cos; +} diff --git a/hw/ip/fft/fft.cpp b/hw/ip/fft/fft.cpp index f65825a..b09ed31 100644 --- a/hw/ip/fft/fft.cpp +++ b/hw/ip/fft/fft.cpp @@ -44,19 +44,19 @@ void bit_reverse(DTYPE read_real[N], DTYPE read_imag[N], DTYPE stage_real[N], DT void fft_stage(int stage, DTYPE read_real[N], DTYPE read_imag[N], DTYPE stage_real[N], DTYPE stage_imag[N]) { - int FFTpts = 1 << stage; // DFT = 2^stage = points in sub DFT - int BF = FFTpts / 2; // Butterfly WIDTHS in sub-DFT - int step = N >> stage; // Perform butterflies for j-th stage + int FFTpts = 1 << stage; // DFT = 2^stage = points in sub DFT + int BF = FFTpts / 2; // Butterfly WIDTHS in sub-DFT + int step = N >> stage; // Perform butterflies for j-th stage -butterfly_loop: - for (int j = 0; j < BF; j++) - { - // Compute butterflies that use same W**k -dft_loop: + butterfly_loop: + for (int j = 0; j < BF; j++) + { + // Compute butterflies that use same W**k + dft_loop: for(int t = 0; t < step; t++) { int i = j + t*FFTpts; -#pragma HLS pipeline + #pragma HLS pipeline int k = j * step; DTYPE w = -(2.0 * 3.14159 / N) * j; DTYPE c = hls :: cosf (w); @@ -74,11 +74,11 @@ void fft_stage(int stage, DTYPE read_real[N], DTYPE read_imag[N], DTYPE stage_re void fft(DTYPE sample_real[N], DTYPE sample_imag[N], DTYPE out_real[N], DTYPE out_imag[N]) { -#pragma HLS INTERFACE axis register both port=sample_imag -#pragma HLS INTERFACE axis register both port=sample_real -#pragma HLS INTERFACE axis register both port=out_real -#pragma HLS INTERFACE axis register both port=out_imag -#pragma HLS INTERFACE ap_ctrl_none port=return + #pragma HLS INTERFACE axis register both port=sample_imag + #pragma HLS INTERFACE axis register both port=sample_real + #pragma HLS INTERFACE axis register both port=out_real + #pragma HLS INTERFACE axis register both port=out_imag + #pragma HLS INTERFACE ap_ctrl_none port=return DTYPE temp_real[N]; DTYPE temp_imag[N]; @@ -86,9 +86,9 @@ void fft(DTYPE sample_real[N], DTYPE sample_imag[N], DTYPE out_real[N], DTYPE ou DTYPE read_imag[N]; DTYPE stage_real[M][N]; -#pragma HLS array_partition variable=stage_real dim=1 complete + #pragma HLS array_partition variable=stage_real dim=1 complete DTYPE stage_imag[M][N]; -#pragma HLS array_partition variable=stage_imag dim=1 complete + #pragma HLS array_partition variable=stage_imag dim=1 complete read_loop: for (int i = 0; i < N; i++) @@ -102,7 +102,7 @@ void fft(DTYPE sample_real[N], DTYPE sample_imag[N], DTYPE out_real[N], DTYPE ou stage_loop: for (int stage = 1; stage < M; stage++) { -#pragma HLS unroll + #pragma HLS unroll fft_stage(stage, stage_real[stage-1], stage_imag[stage-1], stage_real[stage], stage_imag[stage]); } fft_stage(M, stage_real[M-1], stage_imag[M-1], temp_real, temp_imag); diff --git a/hw/ip/fir/fir.cpp b/hw/ip/fir/fir.cpp index ccbe0bf..168f987 100644 --- a/hw/ip/fir/fir.cpp +++ b/hw/ip/fir/fir.cpp @@ -14,10 +14,10 @@ typedef ap_axiu<32,1,1,1> data_t; typedef int acc_t; void fir(data_t *y, int x , data_t *input) { -#pragma HLS INTERFACE axis register both port=input -#pragma HLS INTERFACE axis register both port=y -#pragma HLS INTERFACE s_axilite port=x bundle=x_axilite -#pragma HLS INTERFACE ap_ctrl_none port=return + #pragma HLS INTERFACE axis register both port=input + #pragma HLS INTERFACE axis register both port=y + #pragma HLS INTERFACE s_axilite port=x bundle=x_axilite + #pragma HLS INTERFACE ap_ctrl_none port=return coef_t c[N] = {1, 0, -1, 0, 2, 3, 2, 0, -1, 0, 1}; diff --git a/hw/ip/histogram/histogram.cpp b/hw/ip/histogram/histogram.cpp index 6f0557a..89a98b9 100644 --- a/hw/ip/histogram/histogram.cpp +++ b/hw/ip/histogram/histogram.cpp @@ -2,8 +2,6 @@ #define VALUE_SIZE 256 - - #include #include "ap_int.h" #include "ap_fixed.h" @@ -11,14 +9,10 @@ typedef ap_axiu<32,1,1,1> data_t; - - void histogram(data_t* in, data_t* hist) { - - -#pragma HLS INTERFACE ap_ctrl_none port=return -#pragma HLS INTERFACE axis register both port=in -#pragma HLS INTERFACE axis register both port=hist + #pragma HLS INTERFACE ap_ctrl_none port=return + #pragma HLS INTERFACE axis register both port=in + #pragma HLS INTERFACE axis register both port=hist int tempI[INPUT_SIZE]; int tempV[VALUE_SIZE]; @@ -62,22 +56,21 @@ void histogram(data_t* in, data_t* hist) { // old = val; // // } - for(int j = 0; j < VALUE_SIZE; j++){ - tempr = in->data; - current = tempV[j]; - hist->data = current; - hist->keep = in->keep; + for(int j = 0; j < VALUE_SIZE; j++){ + tempr = in->data; + current = tempV[j]; + hist->data = current; + hist->keep = in->keep; - hist->dest = in->dest; + hist->dest = in->dest; - hist->id = in->id; + hist->id = in->id; - hist->last = in->last; + hist->last = in->last; - hist->strb = in->strb; - - hist->user = in->user; - } + hist->strb = in->strb; + hist->user = in->user; + } } diff --git a/hw/ip/huffman/huffman.cpp b/hw/ip/huffman/huffman.cpp index ba48c6f..b6c5520 100644 --- a/hw/ip/huffman/huffman.cpp +++ b/hw/ip/huffman/huffman.cpp @@ -2,13 +2,6 @@ #include #include "assert.h" - - - - - - - void create_tree ( /* input */ Symbol in[INPUT_SYMBOL_SIZE], @@ -27,20 +20,16 @@ void create_tree ( ap_uint in_count = 0; // Number of inputs consumed. - - assert(num_symbols > 0); assert(num_symbols <= INPUT_SYMBOL_SIZE); for(int i = 0; i < (num_symbols-1); i++) { -#pragma HLS PIPELINE II=5 + #pragma HLS PIPELINE II=5 Frequency node_freq = 0; - - // There are two cases. // Case 1: remove a Symbol from in[] @@ -79,8 +68,6 @@ void create_tree ( } - - assert(in_count < num_symbols || tree_count < i); intermediate_freq = frequency[tree_count]; @@ -108,24 +95,16 @@ void create_tree ( parent[tree_count] = i; tree_count++; - } // Verify that nodes in the tree are sorted by frequency assert(i == 0 || frequency[i] >= frequency[i-1]); - } - - parent[tree_count] = 0; //Set parent of last node (root) to 0 - } - - - //functions // Postcondition: out[x].frequency > 0 @@ -138,13 +117,13 @@ void filter( /* output */ int *n) { -#pragma HLS INLINE off + #pragma HLS INLINE off ap_uint j = 0; for(int i = 0; i < INPUT_SYMBOL_SIZE; i++) { -#pragma HLS pipeline II=1 + #pragma HLS pipeline II=1 if(in[i].frequency != 0) { @@ -153,16 +132,12 @@ void filter( out[j].value = in[i].value; j++; - } - } - + *n = j; - } - void sort( /* input */ Symbol in[INPUT_SYMBOL_SIZE], @@ -175,31 +150,26 @@ void sort( ap_uint digit_histogram[RADIX], digit_location[RADIX]; -#pragma HLS ARRAY_PARTITION variable=digit_location complete dim=1 + #pragma HLS ARRAY_PARTITION variable=digit_location complete dim=1 -#pragma HLS ARRAY_PARTITION variable=digit_histogram complete dim=1 + #pragma HLS ARRAY_PARTITION variable=digit_histogram complete dim=1 Digit current_digit[INPUT_SYMBOL_SIZE]; - - assert(num_symbols >= 0); assert(num_symbols <= INPUT_SYMBOL_SIZE); - copy_in_to_sorting: +copy_in_to_sorting: for(int j = 0; j < num_symbols; j++) { -#pragma HLS PIPELINE II=1 + #pragma HLS PIPELINE II=1 sorting[j] = in[j]; - } - - - radix_sort: +radix_sort: for(int shift = 0; shift < 32; shift += BITS_PER_LOOP) { @@ -207,19 +177,16 @@ void sort( for(int i = 0; i < RADIX; i++) { -#pragma HLS pipeline II=1 + #pragma HLS pipeline II=1 digit_histogram[i] = 0; - } - - compute_histogram: for(int j = 0; j < num_symbols; j++) { -#pragma HLS PIPELINE II=1 + #pragma HLS PIPELINE II=1 Digit digit = (sorting[j].frequency >> shift) & (RADIX - 1); // Extrract a digit @@ -228,28 +195,23 @@ void sort( digit_histogram[digit]++; previous_sorting[j] = sorting[j]; // Save the current sorted order of symbols - } - - digit_location[0] = 0; find_digit_location: for(int i = 1; i < RADIX; i++) -#pragma HLS PIPELINE II=1 + #pragma HLS PIPELINE II=1 digit_location[i] = digit_location[i-1] + digit_histogram[i-1]; - - re_sort: for(int j = 0; j < num_symbols; j++) { -#pragma HLS PIPELINE II=1 + #pragma HLS PIPELINE II=1 Digit digit = current_digit[j]; @@ -258,14 +220,10 @@ void sort( out[digit_location[digit]] = previous_sorting[j]; // Also copy to output digit_location[digit]++; // Update digit_location - } - } - } - void compute_bit_length ( /* input */ ap_uint parent[INPUT_SYMBOL_SIZE-1], @@ -293,20 +251,15 @@ void compute_bit_length ( #pragma HLS pipeline II=1 internal_length_histogram[i] = 0; - } - - child_depth[num_symbols-2] = 1; // Depth of the root node is 1. - - traverse_tree: for(int i = num_symbols-3; i >= 0; i--) { -#pragma HLS pipeline II=3 + #pragma HLS pipeline II=3 ap_uint length = child_depth[parent[i]] + 1; @@ -321,13 +274,11 @@ void compute_bit_length ( // Both the children of the original node were symbols children = 2; - } else { // One child of the original node was a symbol children = 1; - } ap_uint count = internal_length_histogram[length]; @@ -337,14 +288,10 @@ void compute_bit_length ( internal_length_histogram[length] = count; length_histogram[length] = count; - } - } - } - void truncate_tree( /* input */ ap_uint input_length_histogram[TREE_DEPTH], @@ -362,11 +309,8 @@ void truncate_tree( for(int i = 0; i < TREE_DEPTH; i++) { output_length_histogram1[i] = input_length_histogram[i]; - } - - ap_uint j = MAX_CODEWORD_LENGTH; move_nodes: @@ -379,7 +323,7 @@ void truncate_tree( while(output_length_histogram1[i] != 0) { -#pragma HLS LOOP_TRIPCOUNT min=3 max=3 avg=3 + #pragma HLS LOOP_TRIPCOUNT min=3 max=3 avg=3 if (j == MAX_CODEWORD_LENGTH) { @@ -387,7 +331,7 @@ void truncate_tree( do { -#pragma HLS LOOP_TRIPCOUNT min=1 max=1 avg=1 + #pragma HLS LOOP_TRIPCOUNT min=1 max=1 avg=1 j--; @@ -395,8 +339,6 @@ void truncate_tree( } - - // Move leaf with depth i to depth j+1. output_length_histogram1[j] -= 1; // The node at level j is no longer a leaf. @@ -407,25 +349,19 @@ void truncate_tree( output_length_histogram1[i] -= 2; // Two leaf nodes have been lost from level i. - - // now deepest leaf with codeword length < target length // is at level (j+1) unless j+1 == target length j++; - } - } - - // Copy the output to meet dataflow requirements and check the validity unsigned int limit = 1; - copy_output: +copy_output: for(int i = 0; i < TREE_DEPTH; i++) { @@ -436,12 +372,9 @@ void truncate_tree( assert(output_length_histogram1[i] <= limit); limit *= 2; - } - } - void canonize_tree( /* input */ Symbol sorted[INPUT_SYMBOL_SIZE], @@ -454,29 +387,22 @@ void canonize_tree( assert(num_symbols <= INPUT_SYMBOL_SIZE); - - - init_bits: +init_bits: for(int i = 0; i < INPUT_SYMBOL_SIZE; i++) { symbol_bits[i] = 0; - } - - ap_uint length = TREE_DEPTH; ap_uint count = 0; - - // Iterate across the symbols from lowest frequency to highest // Assign them largest bit length to smallest - process_symbols: +process_symbols: for(int k = 0; k < num_symbols; k++) { @@ -486,26 +412,21 @@ void canonize_tree( do { -#pragma HLS LOOP_TRIPCOUNT min=1 avg=1 max=2 + #pragma HLS LOOP_TRIPCOUNT min=1 avg=1 max=2 length--; // n is the number of symbols with encoded length i count = codeword_length_histogram[length]; - } - while (count == 0); - } symbol_bits[sorted[k].value] = length; //assign symbol k to have length bits count--; //keep assigning i bits until we have counted off n symbols - } - } void create_codeword( @@ -519,68 +440,58 @@ void create_codeword( Codeword first_codeword[MAX_CODEWORD_LENGTH]; - - // Computes the initial codeword value for a symbol with bit length i first_codeword[0] = 0; - first_codewords: +first_codewords: for(int i = 1; i < MAX_CODEWORD_LENGTH; i++) { -#pragma HLS PIPELINE II=1 + #pragma HLS PIPELINE II=1 first_codeword[i] = (first_codeword[i-1] + codeword_length_histogram[i-1]) << 1; Codeword c = first_codeword[i]; // std::cout << c.to_string(2) << " with length " << i << "\n"; - } +assign_codewords: + for (int i = 0; i < INPUT_SYMBOL_SIZE; ++i) { - assign_codewords: - - for (int i = 0; i < INPUT_SYMBOL_SIZE; ++i) { - -#pragma HLS PIPELINE II=5 - - CodewordLength length = symbol_bits[i]; + #pragma HLS PIPELINE II=5 - //if symbol has 0 bits, it doesn't need to be encoded + CodewordLength length = symbol_bits[i]; - make_codeword: + //if symbol has 0 bits, it doesn't need to be encoded - if(length != 0) { + make_codeword: - // std::cout << first_codeword[length].to_string(2) << "\n"; + if(length != 0) { - Codeword out_reversed = first_codeword[length]; + // std::cout << first_codeword[length].to_string(2) << "\n"; - out_reversed.reverse(); + Codeword out_reversed = first_codeword[length]; - out_reversed = out_reversed >> (MAX_CODEWORD_LENGTH - length); + out_reversed.reverse(); - // std::cout << out_reversed.to_string(2) << "\n"; + out_reversed = out_reversed >> (MAX_CODEWORD_LENGTH - length); - encoding[i] = (out_reversed << CODEWORD_LENGTH_BITS) + length; + // std::cout << out_reversed.to_string(2) << "\n"; - first_codeword[length]++; + encoding[i] = (out_reversed << CODEWORD_LENGTH_BITS) + length; - } else { + first_codeword[length]++; - encoding[i] = 0; - - } - - } + } else { + encoding[i] = 0; + } + } } - - void huffman( /* input */ Symbol symbol_histogram[INPUT_SYMBOL_SIZE], @@ -588,14 +499,10 @@ void huffman( /* output */ PackedCodewordAndLength encoding[INPUT_SYMBOL_SIZE], /* output */ int *num_nonzero_symbols) { -#pragma HLS INTERFACE axis register both port=num_nonzero_symbols -#pragma HLS INTERFACE axis register both port=encoding -#pragma HLS INTERFACE axis register both port=symbol_histogram -#pragma HLS INTERFACE ap_ctrl_none port=return - - - - + #pragma HLS INTERFACE axis register both port=num_nonzero_symbols + #pragma HLS INTERFACE axis register both port=encoding + #pragma HLS INTERFACE axis register both port=symbol_histogram + #pragma HLS INTERFACE ap_ctrl_none port=return Symbol filtered[INPUT_SYMBOL_SIZE]; @@ -613,14 +520,10 @@ void huffman( int n; - - filter(symbol_histogram, filtered, &n); sort(filtered, n, sorted); - - ap_uint length_histogram[TREE_DEPTH]; ap_uint truncated_length_histogram1[TREE_DEPTH]; @@ -629,11 +532,9 @@ void huffman( CodewordLength symbol_bits[INPUT_SYMBOL_SIZE]; - - int previous_frequency = -1; - copy_sorted: +copy_sorted: for(int i = 0; i < n; i++) { @@ -647,26 +548,20 @@ void huffman( std::cout << sorted[i].value << " " << sorted[i].frequency << "\n"; - previous_frequency = sorted[i].frequency; - } - - create_tree(sorted_copy1, n, parent, left, right); compute_bit_length(parent, left, right, n, length_histogram); - - #ifndef __SYNTHESIS__ // Check the result of computing the tree histogram int codewords_in_tree = 0; - merge_bit_length: +merge_bit_length: for(int i = 0; i < TREE_DEPTH; i++) { @@ -677,21 +572,15 @@ void huffman( std::cout << length_histogram[i] << " codewords with length " << i << "\n"; codewords_in_tree += length_histogram[i]; - } #endif - - - truncate_tree(length_histogram, truncated_length_histogram1, truncated_length_histogram2); + truncate_tree(length_histogram, truncated_length_histogram1, truncated_length_histogram2); canonize_tree(sorted_copy2, n, truncated_length_histogram1, symbol_bits); create_codeword(symbol_bits, truncated_length_histogram2, encoding); - - *num_nonzero_symbols = n; - } diff --git a/hw/ip/matrixm/matrixm.cpp b/hw/ip/matrixm/matrixm.cpp index 5897a76..91d9f8d 100644 --- a/hw/ip/matrixm/matrixm.cpp +++ b/hw/ip/matrixm/matrixm.cpp @@ -1,69 +1,55 @@ - #define N 32 #define M 32 #define P 32 - - typedef int BaseType; +void matrixm(int A[N][M], int B[M][P], int AB[N][P]) { + #pragma HLS INTERFACE axis register both port=AB + #pragma HLS INTERFACE axis register both port=B + #pragma HLS INTERFACE axis register both port=A + #pragma HLS INTERFACE ap_ctrl_none port=return -void matrixm(int A[N][M], int B[M][P], int AB[N][P], int test [N][P]) { -#pragma HLS INTERFACE axis register both port=test -#pragma HLS INTERFACE axis register both port=AB -#pragma HLS INTERFACE axis register both port=B -#pragma HLS INTERFACE axis register both port=A -#pragma HLS INTERFACE ap_ctrl_none port=return - - int tempA[N][M]; - int tempB[M][P]; - int tempAB[N][P]; - - for (int ia = 0; ia