From a57388c4da2b4be428a6724d74ad7e5368339dcc Mon Sep 17 00:00:00 2001 From: master Date: Thu, 25 Mar 2021 10:31:47 -0400 Subject: [PATCH 01/49] update to python3 --- 01-python-tools/01.01-python-overview.ipynb | 28 ++- .../01.02-ipython-interpreter.ipynb | 165 ++++++++---------- 2 files changed, 83 insertions(+), 110 deletions(-) diff --git a/01-python-tools/01.01-python-overview.ipynb b/01-python-tools/01.01-python-overview.ipynb index 7e8c33f9..03f184b8 100644 --- a/01-python-tools/01.01-python-overview.ipynb +++ b/01-python-tools/01.01-python-overview.ipynb @@ -52,9 +52,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -65,7 +63,7 @@ } ], "source": [ - "print \"hello world!\"" + "print( \"hello world!\")" ] }, { @@ -102,9 +100,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -148,9 +144,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -221,23 +215,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 -} + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/01-python-tools/01.02-ipython-interpreter.ipynb b/01-python-tools/01.02-ipython-interpreter.ipynb index 1cf4f514..5db2a2ad 100644 --- a/01-python-tools/01.02-ipython-interpreter.ipynb +++ b/01-python-tools/01.02-ipython-interpreter.ipynb @@ -33,7 +33,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -46,7 +45,7 @@ } ], "source": [ - "print \"hello, world\"" + "print(\"hello, world\")" ] }, { @@ -59,9 +58,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "a = 1" @@ -77,9 +74,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -99,14 +94,32 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "b = [1, 2, 3]" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -125,10 +138,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -139,12 +150,13 @@ "SVG": "Other", "bash": "Other", "capture": "ExecutionMagics", - "cmd": "Other", "debug": "ExecutionMagics", "file": "Other", "html": "DisplayMagics", "javascript": "DisplayMagics", + "js": "DisplayMagics", "latex": "DisplayMagics", + "markdown": "DisplayMagics", "perl": "Other", "prun": "ExecutionMagics", "pypy": "Other", @@ -164,35 +176,35 @@ "line": { "alias": "OSMagics", "alias_magic": "BasicMagics", + "autoawait": "AsyncMagics", "autocall": "AutoMagics", "automagic": "AutoMagics", "autosave": "KernelMagics", "bookmark": "OSMagics", + "cat": "Other", "cd": "OSMagics", "clear": "KernelMagics", - "cls": "KernelMagics", "colors": "BasicMagics", + "conda": "PackagingMagics", "config": "ConfigMagics", "connect_info": "KernelMagics", - "copy": "Other", - "ddir": "Other", + "cp": "Other", "debug": "ExecutionMagics", "dhist": "OSMagics", "dirs": "OSMagics", "doctest_mode": "BasicMagics", - "echo": "Other", "ed": "Other", "edit": "KernelMagics", "env": "OSMagics", "gui": "BasicMagics", "hist": "Other", "history": "HistoryMagics", - "install_default_config": "DeprecatedMagics", - "install_ext": "ExtensionMagics", - "install_profiles": "DeprecatedMagics", "killbgscripts": "ScriptMagics", "ldir": "Other", "less": "KernelMagics", + "lf": "Other", + "lk": "Other", + "ll": "Other", "load": "CodeMagics", "load_ext": "ExtensionMagics", "loadpy": "CodeMagics", @@ -203,11 +215,14 @@ "logstop": "LoggingMagics", "ls": "Other", "lsmagic": "BasicMagics", + "lx": "Other", "macro": "ExecutionMagics", "magic": "BasicMagics", + "man": "KernelMagics", "matplotlib": "PylabMagics", "mkdir": "Other", "more": "KernelMagics", + "mv": "Other", "notebook": "BasicMagics", "page": "BasicMagics", "pastebin": "CodeMagics", @@ -217,10 +232,10 @@ "pfile": "NamespaceMagics", "pinfo": "NamespaceMagics", "pinfo2": "NamespaceMagics", + "pip": "PackagingMagics", "popd": "OSMagics", "pprint": "BasicMagics", "precision": "BasicMagics", - "profile": "BasicMagics", "prun": "ExecutionMagics", "psearch": "NamespaceMagics", "psource": "NamespaceMagics", @@ -233,11 +248,11 @@ "recall": "HistoryMagics", "rehashx": "OSMagics", "reload_ext": "ExtensionMagics", - "ren": "Other", "rep": "Other", "rerun": "HistoryMagics", "reset": "NamespaceMagics", "reset_selective": "NamespaceMagics", + "rm": "Other", "rmdir": "Other", "run": "ExecutionMagics", "save": "CodeMagics", @@ -260,15 +275,15 @@ }, "text/plain": [ "Available line magics:\n", - "%alias %alias_magic %autocall %automagic %autosave %bookmark %cd %clear %cls %colors %config %connect_info %copy %ddir %debug %dhist %dirs %doctest_mode %echo %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %macro %magic %matplotlib %mkdir %more %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %ren %rep %rerun %reset %reset_selective %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", + "%alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", - "%%! %%HTML %%SVG %%bash %%capture %%cmd %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", + "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -292,10 +307,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -321,10 +334,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "%reset -f" @@ -339,10 +350,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -365,18 +374,16 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "u'C:\\\\Users\\\\lijin\\\\Documents\\\\Git\\\\python-tutorial\\\\01. python tools'" + "'/home/master/PycharmProjects/notes-python/01-python-tools'" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -395,9 +402,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%mkdir demo_test" @@ -413,9 +418,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -440,7 +443,6 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -467,9 +469,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -502,9 +502,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -547,9 +545,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -582,7 +578,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -608,9 +603,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%rmdir demo_test" @@ -626,9 +619,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -679,9 +670,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "sum?" @@ -697,9 +686,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -734,9 +721,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -757,9 +742,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -786,9 +769,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -822,9 +803,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -845,23 +824,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From f5631a51b2f4d285d920b7a41e1109972e4c405a Mon Sep 17 00:00:00 2001 From: master Date: Fri, 26 Mar 2021 09:37:14 -0400 Subject: [PATCH 02/49] update to python3 --- 01-python-tools/01.03-ipython-notebook.ipynb | 12 ++++++------ payment.jpeg | Bin 10189 -> 0 bytes 2 files changed, 6 insertions(+), 6 deletions(-) delete mode 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zPsKOJ$O$%UeSN@YuI=Jq)&s`$e+++C6%&FVV-=Ly=Iy{LZJ)#4d~$NG+Js8C3DV!J z8z4>ff}v2)>(cq%o3+ko?mxfnEh^N+bWX8Spsq+8$!MS>cCoyqO_Idc5{%K`{IOJ? zMKj7L+&udQ9S=wfxrPLV8EA`Am=b|6N`LrgS3H*z$iT}S>O`8JNT#FV8AR{KK#ep~*NF>^VCrV(5p6Q!xfahi?K1Y1I;O4V z$zb|U{PZdlsbg?|PhD+%6&kD=8j)pG&v0;EK$xA*(BqkFUF=#hzn-d049%%Z{pp01 zxe@%S+wEkZ>G|85R+g{_Z~oAAQy)WzrZQh+701LH#830FwiLfhEcRS<|%t^QCKb| z6KgV+avh}$B{Ndeq&ekYzYa#BS_(94-ydd`11P6uH`90m?INoD+%!&?mIuhA9l^x%fE?;wAye04IWg{bQp26(vo2Su4h^ zwNl|HNE};34QjNmq3%WCHzM{MbiWbk=u)(aa;gN5Hxxq_MqNe4H>ldpo; z+bKft)~KLl4nGr?!QTV$w|_XUa8R-1xUeC2F16A}j0=nj_11v4*1qxhl^6;G*+giw zMD}VPr8+XJCuz^w)3ZldKC#+3F;uU^6!FG5R4dfz@SXA}9wu-jm)vQg#~$GKC?j-M zm(HM}yDzF)IvrA(VC!4%QLJrGBe*IifYC+c;#FX|Y}!%ckopb>=aL1_scxkrv&$p~ zR1C%(9^O30m1>PBZ;32)Qzav(*Tjh=M$-BAX+AJcH|x|5gzev{GNJm8O=^%DJ-8Gc5gv66GEr?BXtLGdnsH{}S;DQ4b_ZsrPd&#{Z4 Date: Sun, 28 Mar 2021 10:08:15 -0400 Subject: [PATCH 03/49] update to python3 add csv --- .../02.01-a-tour-of-python.ipynb | 565 +++++++----------- 02-python-essentials/data/AAPL.csv | 21 + 2 files changed, 244 insertions(+), 342 deletions(-) create mode 100644 02-python-essentials/data/AAPL.csv diff --git a/02-python-essentials/02.01-a-tour-of-python.ipynb b/02-python-essentials/02.01-a-tour-of-python.ipynb index ecdb7610..7dfa6daf 100644 --- a/02-python-essentials/02.01-a-tour-of-python.ipynb +++ b/02-python-essentials/02.01-a-tour-of-python.ipynb @@ -25,7 +25,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -54,9 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -83,9 +80,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -119,9 +114,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "a = 0.2" @@ -144,9 +137,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -167,9 +158,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -196,10 +185,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -213,15 +200,13 @@ "source": [ "s = \"\"\"hello\n", "world\"\"\"\n", - "print s" + "print(s) " ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -235,7 +220,7 @@ "source": [ "s = '''hello\n", "world'''\n", - "print s" + "print(s)" ] }, { @@ -247,10 +232,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { @@ -258,7 +241,7 @@ "'hello world'" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -277,10 +260,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { @@ -288,7 +269,7 @@ "'h'" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -299,10 +280,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { @@ -310,7 +289,7 @@ "'d'" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -321,10 +300,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { @@ -332,7 +309,7 @@ "'hello'" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -350,10 +327,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "data": { @@ -361,7 +336,7 @@ "['hello', 'world']" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -380,10 +355,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { @@ -391,7 +364,7 @@ "11" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -416,10 +389,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { @@ -427,7 +398,7 @@ "[1, 2.0, 'hello', 6.0]" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -446,10 +417,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "data": { @@ -457,7 +426,7 @@ "[1, 2.0, 'hello', 6.0, 1, 2.0, 'hello', 6.0]" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -475,10 +444,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { @@ -486,7 +453,7 @@ "2.0" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -504,10 +471,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -515,7 +480,7 @@ "4" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -533,10 +498,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -544,7 +507,7 @@ "[1, 2.0, 'hello', 6.0, 'world']" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -570,10 +533,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { @@ -581,7 +542,7 @@ "{2, 3, 4}" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -600,10 +561,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -611,7 +570,7 @@ "3" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -629,10 +588,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -640,7 +597,7 @@ "{1, 2, 3, 4}" ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -659,10 +616,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -670,7 +625,7 @@ "{2, 3, 4}" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -690,10 +645,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -701,7 +654,7 @@ "{1, 2, 3, 4, 5}" ] }, - "execution_count": 24, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -719,10 +672,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { @@ -730,7 +681,7 @@ "{1}" ] }, - "execution_count": 25, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -748,10 +699,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -759,7 +708,7 @@ "{1, 5}" ] }, - "execution_count": 26, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -784,18 +733,16 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'cats': 4, 'dogs': 5}" + "{'dogs': 5, 'cats': 4}" ] }, - "execution_count": 27, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -814,10 +761,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { @@ -825,7 +770,7 @@ "2" ] }, - "execution_count": 28, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -843,10 +788,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "data": { @@ -854,7 +797,7 @@ "5" ] }, - "execution_count": 29, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -872,18 +815,16 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'cats': 4, 'dogs': 2}" + "{'dogs': 2, 'cats': 4}" ] }, - "execution_count": 30, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -902,18 +843,16 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'cats': 4, 'dogs': 2, 'pigs': 7}" + "{'dogs': 2, 'cats': 4, 'pigs': 7}" ] }, - "execution_count": 31, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -932,18 +871,16 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['cats', 'dogs', 'pigs']" + "dict_keys(['dogs', 'cats', 'pigs'])" ] }, - "execution_count": 32, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -961,18 +898,16 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[4, 2, 7]" + "dict_values([2, 4, 7])" ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -990,18 +925,16 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('cats', 4), ('dogs', 2), ('pigs', 7)]" + "dict_items([('dogs', 2), ('cats', 4), ('pigs', 7)])" ] }, - "execution_count": 34, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1026,10 +959,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "data": { @@ -1037,7 +968,7 @@ "array([1, 2, 3, 4])" ] }, - "execution_count": 35, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1057,10 +988,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 39, + "metadata": {}, "outputs": [ { "data": { @@ -1068,7 +997,7 @@ "array([3, 4, 5, 6])" ] }, - "execution_count": 36, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1079,10 +1008,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { @@ -1090,7 +1017,7 @@ "array([2, 4, 6, 8])" ] }, - "execution_count": 37, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1115,29 +1042,29 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 41, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 38, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1156,10 +1083,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, + "execution_count": 42, + "metadata": {}, "outputs": [ { "data": { @@ -1167,7 +1092,7 @@ "['1', '2', '3', '4', '5']" ] }, - "execution_count": 39, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1180,10 +1105,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, + "execution_count": 43, + "metadata": {}, "outputs": [ { "data": { @@ -1191,7 +1114,7 @@ "15" ] }, - "execution_count": 40, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1212,10 +1135,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, + "execution_count": 44, + "metadata": {}, "outputs": [ { "data": { @@ -1223,7 +1144,7 @@ "[1, 2, 3, 4, 5]" ] }, - "execution_count": 41, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1235,10 +1156,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, + "execution_count": 45, + "metadata": {}, "outputs": [ { "data": { @@ -1246,7 +1165,7 @@ "15" ] }, - "execution_count": 42, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1264,10 +1183,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, + "execution_count": 46, + "metadata": {}, "outputs": [ { "data": { @@ -1275,7 +1192,7 @@ "15" ] }, - "execution_count": 43, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1293,16 +1210,14 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, + "execution_count": 47, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "d:\\Users\\lijin\n" + "/home/master\n" ] } ], @@ -1319,10 +1234,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": true - }, + "execution_count": 54, + "metadata": {}, "outputs": [], "source": [ "f = open('data.txt', 'w')\n", @@ -1340,9 +1253,8 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1352,7 +1264,7 @@ "[[1, 2, 3, 4], [2, 3, 4, 5]]" ] }, - "execution_count": 46, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1363,14 +1275,13 @@ "for line in f:\n", " data.append([int(field) for field in line.split()])\n", "f.close()\n", - "data\n" + "data" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 52, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1385,7 +1296,7 @@ ], "source": [ "for row in data:\n", - " print row" + " print(row) " ] }, { @@ -1397,10 +1308,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, + "execution_count": 55, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -1423,10 +1332,8 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false - }, + "execution_count": 56, + "metadata": {}, "outputs": [ { "data": { @@ -1434,7 +1341,7 @@ "6" ] }, - "execution_count": 49, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -1457,10 +1364,8 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false - }, + "execution_count": 57, + "metadata": {}, "outputs": [ { "data": { @@ -1468,7 +1373,7 @@ "array([ 6, 11, 18])" ] }, - "execution_count": 50, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1487,10 +1392,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, + "execution_count": 58, + "metadata": {}, "outputs": [ { "data": { @@ -1498,7 +1401,7 @@ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, - "execution_count": 51, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1517,10 +1420,8 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, + "execution_count": 59, + "metadata": {}, "outputs": [ { "data": { @@ -1528,7 +1429,7 @@ "array([ 3, 6, 11, 18, 27, 38, 51, 66, 83, 102])" ] }, - "execution_count": 52, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1539,10 +1440,8 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, + "execution_count": 60, + "metadata": {}, "outputs": [ { "data": { @@ -1550,7 +1449,7 @@ "array([ 3, 5, 9, 15, 23, 33, 45, 59, 75, 93])" ] }, - "execution_count": 53, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1575,10 +1474,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": true - }, + "execution_count": 61, + "metadata": {}, "outputs": [], "source": [ "import os" @@ -1593,18 +1490,16 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false - }, + "execution_count": 62, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4400" + "4803" ] }, - "execution_count": 55, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1622,18 +1517,16 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": false - }, + "execution_count": 63, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'\\\\'" + "'/'" ] }, - "execution_count": 56, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -1661,10 +1554,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true - }, + "execution_count": 64, + "metadata": {}, "outputs": [], "source": [ "class Person(object):\n", @@ -1685,10 +1576,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": true - }, + "execution_count": 65, + "metadata": {}, "outputs": [], "source": [ "person = Person('Mertle', 'Sedgewick', 52)" @@ -1703,10 +1592,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, + "execution_count": 66, + "metadata": {}, "outputs": [ { "data": { @@ -1714,7 +1601,7 @@ "'Mertle'" ] }, - "execution_count": 59, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1732,10 +1619,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, + "execution_count": 67, + "metadata": {}, "outputs": [ { "data": { @@ -1743,7 +1628,7 @@ "'Mertle Sedgewick'" ] }, - "execution_count": 60, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -1761,10 +1646,8 @@ }, { "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": true - }, + "execution_count": 68, + "metadata": {}, "outputs": [], "source": [ "person.last = 'Smith'" @@ -1779,18 +1662,16 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, + "execution_count": 69, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'cats': 4, 'dogs': 2, 'pigs': 7}" + "{'dogs': 2, 'cats': 4, 'pigs': 7}" ] }, - "execution_count": 62, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1809,13 +1690,11 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": true - }, + "execution_count": 74, + "metadata": {}, "outputs": [], "source": [ - "url = 'http://ichart.finance.yahoo.com/table.csv?s=GE&d=10&e=5&f=2013&g=d&a=0&b=2&c=1962&ignore=.csv'" + "url = 'https://ichart.finance.yahoo.com/table.csv?s=GE&d=10&e=5&f=2013&g=d&a=0&b=2&c=1962&ignore=.csv'" ] }, { @@ -1827,40 +1706,43 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, + "execution_count": 76, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close\\n'],\n", - " ['2013-11-05', '26.32', '26.52', '26.26', '26.42', '24897500', '24.872115\\n'],\n", - " ['2013-11-04',\n", - " '26.59',\n", - " '26.59',\n", - " '26.309999',\n", - " '26.43',\n", - " '28166100',\n", - " '24.88153\\n'],\n", - " ['2013-11-01',\n", - " '26.049999',\n", - " '26.639999',\n", - " '26.030001',\n", - " '26.540001',\n", - " '55634500',\n", - " '24.985086\\n']]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" + "ename": "URLError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1353\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1354\u001b[0;31m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[1;32m 1355\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1254\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1300\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1301\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1302\u001b[0m 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host and port specified in __init__.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m self.sock = self._create_connection(\n\u001b[0m\u001b[1;32m 922\u001b[0m (self.host,self.port), self.timeout, self.source_address)\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 787\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m 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last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mge_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mge_csv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[1;32m 543\u001b[0m '_open', req)\n\u001b[1;32m 544\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 500\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 503\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1396\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1397\u001b[0;31m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[1;32m 1398\u001b[0m context=self._context, check_hostname=self._check_hostname)\n\u001b[1;32m 1399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1355\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1356\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1357\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1358\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1359\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mURLError\u001b[0m: " + ] } ], "source": [ - "import urllib2\n", - "ge_csv = urllib2.urlopen(url)\n", + "import urllib.request as request\n", + "ge_csv = request.urlopen(url)\n", "data = []\n", "for line in ge_csv:\n", " data.append(line.split(','))\n", @@ -1878,7 +1760,6 @@ "cell_type": "code", "execution_count": 65, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1904,7 +1785,7 @@ } ], "source": [ - "ge_csv = urllib2.urlopen(url)\n", + "ge_csv = request.urlopen(url)\n", "import pandas\n", "ge = pandas.read_csv(ge_csv, index_col=0, parse_dates=True)\n", "ge.plot(y='Adj Close')" @@ -1913,23 +1794,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/data/AAPL.csv b/02-python-essentials/data/AAPL.csv new file mode 100644 index 00000000..3c750278 --- /dev/null +++ b/02-python-essentials/data/AAPL.csv @@ -0,0 +1,21 @@ +Date,Open,High,Low,Close,Adj Close,Volume +2017-06-19,35.915001,36.685001,35.915001,36.584999,34.890816,130165600 +2017-06-20,36.717499,36.717499,36.235001,36.252499,34.573723,99600400 +2017-06-21,36.380001,36.517502,36.152500,36.467499,34.778755,85063200 +2017-06-22,36.442501,36.674999,36.279999,36.407501,34.721539,76425200 +2017-06-23,36.282501,36.790001,36.277500,36.570000,34.876507,141757600 +2017-06-26,36.792500,37.070000,36.345001,36.455002,34.766838,102769600 +2017-06-27,36.252499,36.540001,35.904999,35.932499,34.268524,99047600 +2017-06-28,36.122501,36.527500,35.790001,36.457500,34.769218,88329600 +2017-06-29,36.177502,36.282501,35.570000,35.919998,34.256607,125997600 +2017-06-30,36.112499,36.240002,35.945000,36.005001,34.337669,92096400 +2017-07-03,36.220001,36.325001,35.775002,35.875000,34.213696,57111200 +2017-07-05,35.922501,36.197498,35.680000,36.022499,34.354362,86278400 +2017-07-06,35.755001,35.875000,35.602501,35.682499,34.030106,96515200 +2017-07-07,35.724998,36.187500,35.724998,36.044998,34.375809,76806800 +2017-07-10,36.027500,36.487499,35.842499,36.264999,34.585625,84362400 +2017-07-11,36.182499,36.462502,36.095001,36.382500,34.697689,79127200 +2017-07-12,36.467499,36.544998,36.205002,36.435001,34.747765,99538000 +2017-07-13,36.375000,37.122501,36.360001,36.942501,35.231762,100797600 +2017-07-14,36.992500,37.332500,36.832500,37.259998,35.534557,80528400 +2017-07-17,37.205002,37.724998,37.142502,37.389999,35.658535,95174000 \ No newline at end of file From 5ad5dff14bbb7944adcbe4eb3d959ffb7ba354f8 Mon Sep 17 00:00:00 2001 From: Jack Date: Mon, 29 Mar 2021 09:38:56 +0800 Subject: [PATCH 04/49] add requirements.txt --- requirements.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..b608c38c --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +jupyter==1.0.0 +numpy==1.19.2 +pandas==1.2.3 +scipy==1.6.2 +matplotlib==3.3.4 \ No newline at end of file From 4811a61b67964e30c30636bfb34115f0bc2da3cf Mon Sep 17 00:00:00 2001 From: Jack Date: Mon, 29 Mar 2021 09:40:04 +0800 Subject: [PATCH 05/49] update to python3 remove unused info --- README.md | 6 +---- generate index.ipynb | 51 +++++++++++++------------------------ generate_static_files.ipynb | 27 ++++++++++++-------- generate_static_files.py | 16 ++++-------- index.md | 3 ++- 5 files changed, 43 insertions(+), 60 deletions(-) diff --git a/README.md b/README.md index b4e87c1f..3fe6990b 100644 --- a/README.md +++ b/README.md @@ -19,17 +19,13 @@ https://item.jd.com/12328920.html 天猫、亚马逊、当当均有销售。 -**打赏一下意思意思？** - -![](payment.jpeg) - --- ## 简介 大部分内容来自网络。 -默认安装了 `Python 2.7`，以及相关的第三方包 `ipython`， `numpy`， `scipy`，`pandas`。 +默认安装了 `Python 2.7` (本项目后面会慢慢升级的到 3.8)，以及相关的第三方包 `ipython`， `numpy`， `scipy`，`pandas`。 > life is short. use python. diff --git a/generate index.ipynb b/generate index.ipynb index 8a3f5299..a7503d1d 100644 --- a/generate index.ipynb +++ b/generate index.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -29,9 +27,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "folders = ['01-python-tools', \n", @@ -59,9 +55,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "chinese = ['01. **Python 工具**', \n", @@ -89,9 +83,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "folders_to_chinese = dict(zip(folders, chinese))" @@ -107,9 +99,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "index_file = open('index.md', 'w')" @@ -124,9 +114,8 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -297,34 +286,30 @@ "for folder in folders:\n", " index_file.write('- [' + folders_to_chinese[folder] \n", " +'](' + folder + ')\\n')\n", - " print folders_to_chinese[folder].replace('*', '')\n", + " print(folders_to_chinese[folder].replace('*', '')) \n", " files = sorted(os.listdir(folder))\n", " for file_name in files:\n", " if file_name.endswith('.ipynb'):\n", " with open(os.path.join(folder, file_name)) as fp:\n", " nb = nbformat.read(fp, nbformat.NO_CONVERT)\n", " name = nb['cells'][0]['source'][1:].strip()\n", - " print ' ' + file_name[:5], name\n", + " print(' ' + file_name[:5], name)\n", " index_file.write('\\t - [' + file_name[:5] + ' ')\n", - " index_file.write(name.encode('utf-8'))\n", + " index_file.write(name)\n", " index_file.write('](' + folder + '/' + file_name +')\\n')" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "关闭目录文件：" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "index_file.close()" @@ -333,23 +318,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/generate_static_files.ipynb b/generate_static_files.ipynb index 0d03cf78..7c0f6eca 100644 --- a/generate_static_files.ipynb +++ b/generate_static_files.ipynb @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -102,8 +102,8 @@ " for file_name in names:\n", " p = nbconvert.export(nbconvert.get_exporter(to_format), file_name)\n", " with open(os.path.join(target_dir, file_name[:-6] + p[1][\"output_extension\"]), 'w') as f:\n", - " f.write(p[0].encode(\"utf-8\"))\n", - " print file_name" + " f.write(p[0])\n", + " print(file_name) " ] }, { @@ -115,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -288,25 +288,32 @@ " with open(os.path.join(\"static-files\", \"html\", \"README.md\"), \"w\") as g:\n", " g.write(text.replace(\".ipynb\", \".html\"))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.14" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, diff --git a/generate_static_files.py b/generate_static_files.py index 99e3d33c..a46e797f 100644 --- a/generate_static_files.py +++ b/generate_static_files.py @@ -1,4 +1,3 @@ - # coding: utf-8 # # 将笔记转化为不同的文件格式 @@ -11,7 +10,6 @@ import nbconvert import glob - # 检查路径是否存在，删除旧的文件： # In[ ]: @@ -19,17 +17,16 @@ if not os.path.exists('static-files'): os.mkdir('static-files') - + for n in glob.glob('static-files/*/*/*'): os.remove(n) - # 文件夹： # In[ ]: -folders = ['01-python-tools', +folders = ['01-python-tools', '02-python-essentials', '03-numpy', '04-scipy', @@ -41,8 +38,7 @@ '10-something-interesting', '11-useful-tools', '12-pandas' - ] - + ] # 遍历文件夹得到所有的文件名： @@ -64,12 +60,12 @@ def convert_to_files(names, to_format): for folder in folders: if not os.path.exists(os.path.join(target_dir, folder)): os.makedirs(os.path.join(target_dir, folder)) - + for file_name in names: p = nbconvert.export(nbconvert.get_exporter(to_format), file_name) with open(os.path.join(target_dir, file_name[:-6] + p[1]["output_extension"]), 'w') as f: f.write(p[0].encode("utf-8")) - print file_name + print(file_name) # 转化 HTML 文件： @@ -79,7 +75,6 @@ def convert_to_files(names, to_format): convert_to_files(file_names, "html") - # 产生新目录： # In[ ]: @@ -89,4 +84,3 @@ def convert_to_files(names, to_format): text = f.read() with open(os.path.join("static-files", "html", "README.md"), "w") as g: g.write(text.replace(".ipynb", ".html")) - diff --git a/index.md b/index.md index 14a45561..0a9a958d 100644 --- a/index.md +++ b/index.md @@ -1,4 +1,5 @@ - [01. **Python 工具**](01-python-tools) + - [01.01 - [01. **Python 工具**](01-python-tools) - [01.01 Python 简介](01-python-tools/01.01-python-overview.ipynb) - [01.02 Ipython 解释器](01-python-tools/01.02-ipython-interpreter.ipynb) - [01.03 Ipython notebook](01-python-tools/01.03-ipython-notebook.ipynb) @@ -150,4 +151,4 @@ - [12. **Pandas**](12-pandas) - [12.01 十分钟上手 Pandas](12-pandas/12.01-ten-minutes-to-pandas.ipynb) - [12.02 一维数据结构：Series](12-pandas/12.02-series-in-pandas.ipynb) - - [12.03 二维数据结构：DataFrame](12-pandas/12.03-dataframe-in-pandas.ipynb) \ No newline at end of file + - [12.03 二维数据结构：DataFrame](12-pandas/12.03-dataframe-in-pandas.ipynb) From eac462f982509a6ecceeef3158c1c8280080e743 Mon Sep 17 00:00:00 2001 From: Jack Date: Mon, 29 Mar 2021 10:51:02 +0800 Subject: [PATCH 06/49] update to python3 --- .../02.02-python-data-types.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index 1407fc22..089d660d 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -41,7 +41,7 @@ "source": [ "| 类型| 例子|\n", "| ------- | ----- |\n", - "| 长整型 | `1000000000000L`\n", + "| 长整型 | `1000000000000`\n", "| 布尔型 | `True, False`\n", "| 元组 | `('ring', 1000)`\n", "| 集合 | `{1, 2, 3}`\n", @@ -52,23 +52,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 -} + "nbformat_minor": 1 +} \ No newline at end of file From 189eb47d022af9af8f2c5ef984a556d395209d34 Mon Sep 17 00:00:00 2001 From: Jack Date: Mon, 29 Mar 2021 10:51:23 +0800 Subject: [PATCH 07/49] fix table.csv not found --- .../02.01-a-tour-of-python.ipynb | 86 ++++++++++--------- 1 file changed, 44 insertions(+), 42 deletions(-) diff --git a/02-python-essentials/02.01-a-tour-of-python.ipynb b/02-python-essentials/02.01-a-tour-of-python.ipynb index 7dfa6daf..2327ac9d 100644 --- a/02-python-essentials/02.01-a-tour-of-python.ipynb +++ b/02-python-essentials/02.01-a-tour-of-python.ipynb @@ -7,6 +7,15 @@ "# Python 入门演示" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -Uqq numpy pandas matplotlib" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1690,11 +1699,11 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "url = 'https://ichart.finance.yahoo.com/table.csv?s=GE&d=10&e=5&f=2013&g=d&a=0&b=2&c=1962&ignore=.csv'" + "url = 'https://notes-python-1257712612.cos.ap-beijing.myqcloud.com/AAPL.csv'" ] }, { @@ -1706,43 +1715,24 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 3, "metadata": {}, "outputs": [ { - "ename": "URLError", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1353\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1354\u001b[0;31m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[1;32m 1355\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1254\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1255\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, 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in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1417\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 920\u001b[0m \u001b[0;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 921\u001b[0;31m self.sock = self._create_connection(\n\u001b[0m\u001b[1;32m 922\u001b[0m (self.host,self.port), self.timeout, self.source_address)\n", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/socket.py\u001b[0m in 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\u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 503\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1396\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1397\u001b[0;31m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[1;32m 1398\u001b[0m context=self._context, check_hostname=self._check_hostname)\n\u001b[1;32m 1399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1355\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1356\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1357\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1358\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1359\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mURLError\u001b[0m: " - ] + "data": { + "text/plain": [ + "[['D'], ['a'], ['t'], ['e']]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import urllib.request as request\n", - "ge_csv = request.urlopen(url)\n", + "from urllib import request\n", + "resp = request.urlopen(url)\n", + "ge_csv = resp.read().decode('utf-8')\n", "data = []\n", "for line in ge_csv:\n", " data.append(line.split(','))\n", @@ -1758,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -1766,30 +1756,42 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 65, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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APffrr0PjxvAf/5GONNhDR2OK1ogRmemJV0j8CR1Wr3bL4cNDH0qed17m02QB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\nghufeyDwP8B477WG3rIM98H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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "ge_csv = request.urlopen(url)\n", - "import pandas\n", - "ge = pandas.read_csv(ge_csv, index_col=0, parse_dates=True)\n", + "resp = request.urlopen(url)\n", + "ge_csv = resp.read().decode('utf-8')\n", + "import pandas as pd\n", + "from io import StringIO\n", + "TESTDATA = StringIO(ge_csv)\n", + "ge = pd.read_csv(TESTDATA, index_col=0, parse_dates=True)\n", "ge.plot(y='Adj Close')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From f5da58462a2da321dd7904db96d959b3ed873b60 Mon Sep 17 00:00:00 2001 From: Jack Date: Wed, 31 Mar 2021 14:56:24 +0800 Subject: [PATCH 08/49] update --- .../02.02-python-data-types.ipynb | 4 +- 02-python-essentials/02.03-numbers.ipynb | 210 +++++------------- README.md | 20 -- 3 files changed, 57 insertions(+), 177 deletions(-) diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index 089d660d..c338f0a8 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -66,9 +66,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/02-python-essentials/02.03-numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb index 7c80e8da..13aa9e4f 100644 --- a/02-python-essentials/02.03-numbers.ipynb +++ b/02-python-essentials/02.03-numbers.ipynb @@ -24,9 +24,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -46,9 +44,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -68,9 +64,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -99,9 +93,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -128,9 +120,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -157,9 +147,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -186,9 +174,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -216,9 +202,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -249,9 +233,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -286,9 +268,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -313,9 +293,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -342,9 +320,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -373,7 +349,6 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -402,9 +377,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -432,9 +405,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -454,9 +425,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -477,7 +446,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -506,9 +474,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -535,9 +501,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -557,9 +521,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -579,9 +541,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -601,9 +561,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -634,9 +592,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -663,9 +619,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -689,9 +643,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -719,9 +671,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -748,9 +698,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -777,9 +725,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -813,9 +759,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -843,9 +787,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -865,9 +807,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -887,9 +827,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -923,9 +861,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -959,9 +895,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -981,9 +915,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1017,9 +949,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1046,9 +976,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1075,9 +1003,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1110,9 +1036,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1139,9 +1063,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1162,9 +1084,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -1199,9 +1119,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1227,9 +1145,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1267,9 +1183,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1296,9 +1210,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1325,9 +1237,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1354,9 +1264,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1390,9 +1298,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1431,9 +1337,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1461,9 +1365,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1492,9 +1394,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1524,23 +1424,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/README.md b/README.md index 3fe6990b..8781308a 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,5 @@ -[![Analytics](https://ga-beacon.appspot.com/UA-80121379-2/notes-python)](https://github.com/lijin-thu/notes-python) - # 中文 Python 笔记 -> 版本：0.0.1
-> 作者：李金
-> 邮件：lijinwithyou@gmail.com
- -由于涉及著作权的问题，对基于本笔记所做的修订、改编，目前不做任何正式授权。 - -笔记内容仅供学习参考，未经允许，请勿用于任何商业用途。 - -`Github` 加载 `.ipynb` 的速度较慢，建议在 [Nbviewer](http://nbviewer.ipython.org/github/lijin-THU/notes-python/blob/master/index.ipynb) 中查看该项目。 - -基于本笔记的实体书：《自学Python——编程基础、科学计算及数据分析》已经出版。 - -京东自营链接： -https://item.jd.com/12328920.html - -天猫、亚马逊、当当均有销售。 - ---- ## 简介 From 28e56bb8c2c64449b13ba8362ad3e3eeb171138c Mon Sep 17 00:00:00 2001 From: Jack Date: Thu, 1 Apr 2021 08:44:12 +0800 Subject: [PATCH 09/49] update data type --- .../02.02-python-data-types.ipynb | 79 ++++++++++++++++--- 1 file changed, 67 insertions(+), 12 deletions(-) diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index c338f0a8..7b549e09 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -11,7 +11,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 常用数据类型 Common Data Types" + "标准数据类型\n", + "\n", + "* Number（数字）\n", + "* String（字符串）\n", + "* List（列表）\n", + "* Tuple（元组）\n", + "* Set（集合）\n", + "* Dictionary（字典）\n", + "\n", + "Python3 的六个标准数据类型中：\n", + "\n", + "* 不可变数据（3 个）：Number（数字）、String（字符串）、Tuple（元组）；\n", + "* 可变数据（3 个）：List（列表）、Dictionary（字典）、Set（集合）。 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 常用数据类型 Common Data Types" ] }, { @@ -23,31 +42,67 @@ "| 整数 | `-100` |\n", "| 浮点数 | `3.1416` |\n", "| 字符串 | `'hello'` |\n", + "| 布尔型 | `True, False` |\n", "| 列表 | `[1, 1.2, 'hello']` |\n", + "| 元组 | `('ring', 1000)` |\n", "| 字典 | `{'dogs': 5, 'pigs': 3}`|\n", - "| Numpy数组 | `array([1, 2, 3])`" + "| 集合 | `{1, 2, 3}` |\n", + "| 自定义 | `Object Oriented Classes` |\n", + "| Pandas类型| `DataFrame, Series` |\n", + "| Numpy数组 | `array([1, 2, 3])`|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 其他类型 Others" + "## Number（数字）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "| 类型| 例子|\n", - "| ------- | ----- |\n", - "| 长整型 | `1000000000000`\n", - "| 布尔型 | `True, False`\n", - "| 元组 | `('ring', 1000)`\n", - "| 集合 | `{1, 2, 3}`\n", - "| Pandas类型| `DataFrame, Series`\n", - "| 自定义 | `Object Oriented Classes`" + "Python3 支持 int、float、bool、complex（复数）。\n", + "在Python 3里，只有一种整数类型 int，表示为长整型，没有 python2 中的 Long。\n", + "像大多数语言一样，数值类型的赋值和计算都是很直观的。\n", + "内置的 type() 函数可以用来查询变量所指的对象类型。" ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(int, float, bool, complex)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a, b, c, d = 20, 5.5, True, 4+3j\n", + "type(a), type(b), type(c), type(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*注意：在 Python2 中是没有布尔型的，它用数字 0 表示 False，用 1 表示 True。到 Python3 中，把 True 和 False 定义成关键字了，但它们的值还是 1 和 0，它们可以和数字相加*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -71,4 +126,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file From 53da87247dc87f389b8bd2dc45cc1645b11ce65f Mon Sep 17 00:00:00 2001 From: master Date: Sun, 4 Apr 2021 19:07:11 +0800 Subject: [PATCH 10/49] update to python3 --- .../02.02-python-data-types.ipynb | 54 +-- 02-python-essentials/02.03-numbers.ipynb | 392 +++++++----------- 02-python-essentials/02.04-strings.ipynb | 372 +++++++---------- .../02.05-indexing-and-slicing.ipynb | 79 ++-- 02-python-essentials/02.06-lists.ipynb | 375 ++++++++--------- 5 files changed, 499 insertions(+), 773 deletions(-) diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index 7b549e09..ce696493 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -51,58 +51,6 @@ "| Pandas类型| `DataFrame, Series` |\n", "| Numpy数组 | `array([1, 2, 3])`|" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Number（数字）" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python3 支持 int、float、bool、complex（复数）。\n", - "在Python 3里，只有一种整数类型 int，表示为长整型，没有 python2 中的 Long。\n", - "像大多数语言一样，数值类型的赋值和计算都是很直观的。\n", - "内置的 type() 函数可以用来查询变量所指的对象类型。" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(int, float, bool, complex)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a, b, c, d = 20, 5.5, True, 4+3j\n", - "type(a), type(b), type(c), type(d)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*注意：在 Python2 中是没有布尔型的，它用数字 0 表示 False，用 1 表示 True。到 Python3 中，把 True 和 False 定义成关键字了，但它们的值还是 1 和 0，它们可以和数字相加*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -126,4 +74,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/02-python-essentials/02.03-numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb index 13aa9e4f..e919ebc9 100644 --- a/02-python-essentials/02.03-numbers.ipynb +++ b/02-python-essentials/02.03-numbers.ipynb @@ -4,90 +4,59 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 数字" + "# Number（数字）" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 整型 Integers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "整型运算，加减乘：" + "Python3 支持 int、float、bool、complex（复数）。\n", + "在Python 3里，只有一种整数类型 int，表示为长整型，没有 python2 中的 Long。\n", + "像大多数语言一样，数值类型的赋值和计算都是很直观的。\n", + "内置的 type() 函数可以用来查询变量所指的对象类型。" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4" + "(int, float, bool, complex)" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "2 + 2" + "a, b, c, d = 20, 5.5, True, 4+3j\n", + "type(a), type(b), type(c), type(d)" ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-1" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "3 - 4" + "*注意：在 Python2 中是没有布尔型的，它用数字 0 表示 False，用 1 表示 True。到 Python3 中，把 True 和 False 定义成关键字了，但它们的值还是 1 和 0，它们可以和数字相加*" ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "20" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "4 * 5" + "## 整型 Integers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "在**Python 2.7**中，整型的运算结果只能返回整型，**除法**的结果也不例外。\n", - "\n", - "例如`12 / 5`返回的结果并不是2.4，而是2：" + "整型运算，加减乘：" ] }, { @@ -98,7 +67,7 @@ { "data": { "text/plain": [ - "2" + "4" ] }, "execution_count": 4, @@ -107,14 +76,7 @@ } ], "source": [ - "12 / 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "幂指数：" + "2 + 2" ] }, { @@ -125,7 +87,7 @@ { "data": { "text/plain": [ - "32" + "-1" ] }, "execution_count": 5, @@ -134,14 +96,7 @@ } ], "source": [ - "2 ** 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "取余：" + "3 - 4" ] }, { @@ -152,7 +107,7 @@ { "data": { "text/plain": [ - "2" + "20" ] }, "execution_count": 6, @@ -161,14 +116,7 @@ } ], "source": [ - "32 % 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "赋值给变量：" + "4 * 5" ] }, { @@ -179,7 +127,7 @@ { "data": { "text/plain": [ - "1" + "2.4" ] }, "execution_count": 7, @@ -188,15 +136,26 @@ } ], "source": [ - "a = 1\n", - "a" + "12 / 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "使用`type()`函数来查看变量类型：" + "~~~\n", + "\n", + "在**Python 2.7**中，整型的运算结果只能返回整型，**除法**的结果也不例外。\n", + "\n", + "例如`12 / 5`返回的结果并不是2.4，而是2：\n", + "~~~" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "幂指数：" ] }, { @@ -207,7 +166,7 @@ { "data": { "text/plain": [ - "int" + "32" ] }, "execution_count": 8, @@ -216,18 +175,14 @@ } ], "source": [ - "type(a)" + "2 ** 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "整型数字的最大最小值：\n", - "\n", - "在 32 位系统中，一个整型 4 个字节，最小值 `-2,147,483,648`，最大值 `2,147,483,647`。\n", - "\n", - "在 64 位系统中，一个整型 8 个字节，最小值 `-9,223,372,036,854,775,808`，最大值 `9,223,372,036,854,775,807`。" + "取余：" ] }, { @@ -238,7 +193,7 @@ { "data": { "text/plain": [ - "2147483647" + "2" ] }, "execution_count": 9, @@ -247,66 +202,34 @@ } ], "source": [ - "import sys\n", - "sys.maxint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 长整型 Long Integers" + "32 % 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "当整型超出范围时，**Python**会自动将整型转化为长整型，不过长整型计算速度会比整型慢。" + "赋值给变量：" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "a = sys.maxint + 1\n", - "print type(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "长整型的一个标志是后面以字母L结尾：" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2147483648L" + "1" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "a = 1\n", "a" ] }, @@ -314,57 +237,59 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "可以在赋值时强制让类型为长整型：" + "使用`type()`函数来查看变量类型：" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "long" + "int" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "b = 1234L\n", - "type(b)" + "type(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "长整型可以与整型在一起进行计算，返回的类型还是长整型：" + "整型数字的最大最小值：\n", + "\n", + "在 32 位系统中，一个整型 4 个字节，最小值 `-2,147,483,648`，最大值 `2,147,483,647`。\n", + "\n", + "在 64 位系统中，一个整型 8 个字节，最小值 `-9,223,372,036,854,775,808`，最大值 `9,223,372,036,854,775,807`。" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2147483644L" + "9223372036854775807" ] }, - "execution_count": 13, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a - 4" + "import sys\n", + "sys.maxsize" ] }, { @@ -395,16 +320,9 @@ "type(a)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "在之前的除法例子`12 / 5`中，假如想要使返回的结果为2.4，可以将它们写成浮点数的形式：" - ] - }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -413,18 +331,18 @@ "2.4" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "12.0 / 5.0" + "12 / 5" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -433,7 +351,7 @@ "2.4" ] }, - "execution_count": 16, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -444,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": { "scrolled": true }, @@ -455,7 +373,7 @@ "2.4" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -473,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -482,7 +400,7 @@ "7.4" ] }, - "execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -500,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -509,7 +427,7 @@ "0.19999999999999973" ] }, - "execution_count": 19, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -520,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -529,7 +447,7 @@ "44.7" ] }, - "execution_count": 20, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -540,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -549,7 +467,7 @@ "6.25" ] }, - "execution_count": 21, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -560,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -569,7 +487,7 @@ "1.2999999999999998" ] }, - "execution_count": 22, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -591,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -600,7 +518,7 @@ "'0.199999999999999733546474089962430298328399658203125'" ] }, - "execution_count": 23, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -609,30 +527,6 @@ "'{:.52}'.format(3.4 - 3.2)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "当我们使用`print`显示时，**Python**会自动校正这个结果" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2\n" - ] - } - ], - "source": [ - "print 3.4 - 3.2" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -642,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -651,7 +545,7 @@ "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)" ] }, - "execution_count": 25, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -670,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -679,7 +573,7 @@ "1.7976931348623157e+308" ] }, - "execution_count": 26, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -697,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -706,7 +600,7 @@ "2.2250738585072014e-308" ] }, - "execution_count": 27, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -724,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -733,7 +627,7 @@ "2.220446049250313e-16" ] }, - "execution_count": 28, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -758,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -767,7 +661,7 @@ "complex" ] }, - "execution_count": 29, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -786,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -795,7 +689,7 @@ "1.0" ] }, - "execution_count": 30, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -806,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -815,7 +709,7 @@ "2.0" ] }, - "execution_count": 31, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -826,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -835,7 +729,7 @@ "(1-2j)" ] }, - "execution_count": 32, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -860,16 +754,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-27" + "-27.0" ] }, - "execution_count": 33, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -894,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -903,7 +797,7 @@ "2.0" ] }, - "execution_count": 34, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -914,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -923,7 +817,7 @@ "-4.0" ] }, - "execution_count": 35, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -948,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -957,7 +851,7 @@ "12.4" ] }, - "execution_count": 36, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -975,16 +869,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "22.0" + "22" ] }, - "execution_count": 37, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1002,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1015,8 +909,8 @@ } ], "source": [ - "print min(2, 3, 4, 5)\n", - "print max(2, 4, 3)" + "print(min(2, 3, 4, 5))\n", + "print(max(2, 4, 3))" ] }, { @@ -1035,7 +929,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1044,7 +938,7 @@ "builtin_function_or_method" ] }, - "execution_count": 39, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1062,7 +956,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1071,7 +965,7 @@ "int" ] }, - "execution_count": 40, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1083,7 +977,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1091,10 +985,10 @@ "evalue": "'int' object is not callable", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'int' object is not callable" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable" ] } ], @@ -1118,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1131,8 +1025,8 @@ } ], "source": [ - "print int(12.324)\n", - "print int(-3.32)" + "print(int(12.324))\n", + "print(int(-3.32)) " ] }, { @@ -1144,7 +1038,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -1156,7 +1050,7 @@ } ], "source": [ - "print float(1.2)" + "print(float(1.2))" ] }, { @@ -1182,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1191,7 +1085,7 @@ "1e-06" ] }, - "execution_count": 44, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1209,7 +1103,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1218,7 +1112,7 @@ "255" ] }, - "execution_count": 45, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1231,12 +1125,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "8进制，前面加`0`或者`0o`修饰，后面使用数字0-7：" + "8进制，前面加`0o`修饰，后面使用数字0-7：" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -1245,13 +1139,13 @@ "55" ] }, - "execution_count": 46, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "067" + "0o67" ] }, { @@ -1297,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1313,11 +1207,11 @@ "source": [ "b = 2.5\n", "b += 2\n", - "print b\n", + "print(b)\n", "b *= 2\n", - "print b\n", + "print(b)\n", "b -= 3\n", - "print b" + "print(b)" ] }, { @@ -1336,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1345,7 +1239,7 @@ "bool" ] }, - "execution_count": 49, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1364,7 +1258,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -1377,7 +1271,7 @@ ], "source": [ "q = 1 > 2\n", - "print q" + "print(q)" ] }, { @@ -1393,7 +1287,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1402,7 +1296,7 @@ "True" ] }, - "execution_count": 51, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } diff --git a/02-python-essentials/02.04-strings.ipynb b/02-python-essentials/02.04-strings.ipynb index ab859a1d..cf333856 100644 --- a/02-python-essentials/02.04-strings.ipynb +++ b/02-python-essentials/02.04-strings.ipynb @@ -23,10 +23,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -38,15 +36,13 @@ ], "source": [ "s = \"hello, world\"\n", - "print s" + "print(s)" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -58,7 +54,7 @@ ], "source": [ "s = 'hello world'\n", - "print s" + "print(s)" ] }, { @@ -77,10 +73,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { @@ -88,7 +82,7 @@ "'hello world'" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -107,10 +101,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -118,7 +110,7 @@ "'echoechoecho'" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -136,10 +128,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -147,7 +137,7 @@ "11" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -190,10 +180,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -206,7 +194,7 @@ "source": [ "line = \"1 2 3 4 5\"\n", "numbers = line.split()\n", - "print numbers" + "print(numbers)" ] }, { @@ -218,10 +206,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -234,7 +220,7 @@ "source": [ "line = \"1,2,3,4,5\"\n", "numbers = line.split(',')\n", - "print numbers" + "print(numbers)" ] }, { @@ -253,10 +239,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { @@ -264,7 +248,7 @@ "'1 2 3 4 5'" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -276,10 +260,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { @@ -287,7 +269,7 @@ "'1,2,3,4,5'" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -313,10 +295,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { @@ -324,7 +304,7 @@ "'hello python'" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -343,10 +323,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { @@ -354,7 +332,7 @@ "'hello world'" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -381,10 +359,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { @@ -392,7 +368,7 @@ "'HELLO WORLD'" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -410,10 +386,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -426,34 +400,32 @@ ], "source": [ "s = \"HELLO WORLD\"\n", - "print s.lower()\n", - "print s" + "print(s.lower())\n", + "print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 去除多余空格" + "### 去除多余空白字符" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "s.strip()返回一个将s两端的多余空格除去的新字符串。\n", + "s.strip()返回一个将s两端的多余空白字符除去的新字符串。\n", "\n", - "s.lstrip()返回一个将s开头的多余空格除去的新字符串。\n", + "s.lstrip()返回一个将s开头的多余空白字符除去的新字符串。\n", "\n", - "s.rstrip()返回一个将s结尾的多余空格除去的新字符串。" + "s.rstrip()返回一个将s结尾的多余空白字符除去的新字符串。" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { @@ -461,13 +433,13 @@ "'hello world'" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s = \" hello world \"\n", + "s = \" hello world \\n\"\n", "s.strip()" ] }, @@ -480,18 +452,16 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "' hello world '" + "' hello world \\n'" ] }, - "execution_count": 15, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -502,18 +472,16 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'hello world '" + "'hello world \\n'" ] }, - "execution_count": 16, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -524,10 +492,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -535,7 +501,7 @@ "' hello world'" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -560,10 +526,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -572,6 +536,7 @@ " '__class__',\n", " '__contains__',\n", " '__delattr__',\n", + " '__dir__',\n", " '__doc__',\n", " '__eq__',\n", " '__format__',\n", @@ -579,10 +544,11 @@ " '__getattribute__',\n", " '__getitem__',\n", " '__getnewargs__',\n", - " '__getslice__',\n", " '__gt__',\n", " '__hash__',\n", " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", " '__le__',\n", " '__len__',\n", " '__lt__',\n", @@ -599,22 +565,26 @@ " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", - " '_formatter_field_name_split',\n", - " '_formatter_parser',\n", " 'capitalize',\n", + " 'casefold',\n", " 'center',\n", " 'count',\n", - " 'decode',\n", " 'encode',\n", " 'endswith',\n", " 'expandtabs',\n", " 'find',\n", " 'format',\n", + " 'format_map',\n", " 'index',\n", " 'isalnum',\n", " 'isalpha',\n", + " 'isascii',\n", + " 'isdecimal',\n", " 'isdigit',\n", + " 'isidentifier',\n", " 'islower',\n", + " 'isnumeric',\n", + " 'isprintable',\n", " 'isspace',\n", " 'istitle',\n", " 'isupper',\n", @@ -622,6 +592,7 @@ " 'ljust',\n", " 'lower',\n", " 'lstrip',\n", + " 'maketrans',\n", " 'partition',\n", " 'replace',\n", " 'rfind',\n", @@ -641,7 +612,7 @@ " 'zfill']" ] }, - "execution_count": 18, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -666,10 +637,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -683,7 +652,7 @@ "source": [ "a = \"\"\"hello world.\n", "it is a nice day.\"\"\"\n", - "print a" + "print(a)" ] }, { @@ -695,10 +664,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -706,7 +673,7 @@ "'hello world.\\nit is a nice day.'" ] }, - "execution_count": 20, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -736,10 +703,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -747,7 +712,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 21, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -761,10 +726,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "data": { @@ -772,7 +735,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 22, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -803,46 +766,50 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 51, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'3.3'" + "'abc'" ] }, - "execution_count": 23, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "str(1.1 + 2.2)" + "str('abc')" ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 50, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'3.3000000000000003'" + "\"'abc'\"" ] }, - "execution_count": 24, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "repr(1.1 + 2.2)" + "repr('abc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* 1.除了字符串类型外，使用str还是repr转换没有什么区别，字符串类型的话，外层会多一对引号，这一特性有时候在 eval 操作时特别有用；\n", + "* 2.命令行下直接输出对象调用的是对象的repr方法，print输出调用的是str方法" ] }, { @@ -863,10 +830,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { @@ -874,7 +839,7 @@ "'0xff'" ] }, - "execution_count": 25, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -892,19 +857,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "'0377'" + "'0o377'" ] }, - "execution_count": 26, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -922,10 +886,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { @@ -933,7 +895,7 @@ "'0b11111111'" ] }, - "execution_count": 27, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -951,10 +913,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "data": { @@ -962,7 +922,7 @@ "23" ] }, - "execution_count": 28, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -980,10 +940,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 39, + "metadata": {}, "outputs": [ { "data": { @@ -991,7 +949,7 @@ "255" ] }, - "execution_count": 29, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1002,10 +960,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { @@ -1013,7 +969,7 @@ "255" ] }, - "execution_count": 30, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1024,10 +980,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 41, + "metadata": {}, "outputs": [ { "data": { @@ -1035,7 +989,7 @@ "255" ] }, - "execution_count": 31, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1053,9 +1007,8 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 42, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1065,7 +1018,7 @@ "3.5" ] }, - "execution_count": 32, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1092,10 +1045,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 43, + "metadata": {}, "outputs": [ { "data": { @@ -1103,7 +1054,7 @@ "'a b c'" ] }, - "execution_count": 33, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1121,10 +1072,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 44, + "metadata": {}, "outputs": [ { "data": { @@ -1132,7 +1081,7 @@ "'c b a'" ] }, - "execution_count": 34, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1150,10 +1099,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 45, + "metadata": {}, "outputs": [ { "data": { @@ -1161,7 +1108,7 @@ "'blue 10 1.5'" ] }, - "execution_count": 35, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1179,10 +1126,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 46, + "metadata": {}, "outputs": [ { "data": { @@ -1190,7 +1135,7 @@ "'blue 10 1.5 foo'" ] }, - "execution_count": 36, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1208,10 +1153,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, + "execution_count": 47, + "metadata": {}, "outputs": [ { "data": { @@ -1219,7 +1162,7 @@ "'foo 5 6.28'" ] }, - "execution_count": 37, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1246,10 +1189,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": true - }, + "execution_count": 48, + "metadata": {}, "outputs": [], "source": [ "s = \"some numbers:\"\n", @@ -1261,10 +1202,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "data": { @@ -1272,7 +1211,7 @@ "'some numbers: 1.340000, 2'" ] }, - "execution_count": 39, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1280,27 +1219,34 @@ "source": [ "t" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.05-indexing-and-slicing.ipynb b/02-python-essentials/02.05-indexing-and-slicing.ipynb index eaad12ad..c72d95c5 100644 --- a/02-python-essentials/02.05-indexing-and-slicing.ipynb +++ b/02-python-essentials/02.05-indexing-and-slicing.ipynb @@ -24,9 +24,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -54,9 +52,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -83,9 +79,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -112,19 +106,17 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "string index out of range", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m: string index out of range" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m: string index out of range" ] } ], @@ -153,9 +145,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -175,9 +165,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -211,9 +199,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -242,9 +228,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -264,9 +248,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -286,9 +268,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -315,9 +295,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -344,9 +322,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -373,9 +349,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -462,27 +436,34 @@ "\n", "综合这两种原因，**Python**使用0-base的方法来进行索引。" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.06-lists.ipynb b/02-python-essentials/02.06-lists.ipynb index 84336636..83aa4dec 100644 --- a/02-python-essentials/02.06-lists.ipynb +++ b/02-python-essentials/02.06-lists.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "在**Python**中，列表是一个有序的序列。\n", + "在 **Python** 中，列表是一个有序的序列。\n", "\n", "列表用一对 `[]` 生成，中间的元素用 `,` 隔开，其中的元素不需要是同一类型，同时列表的长度也不固定。" ] @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -33,7 +31,7 @@ ], "source": [ "l = [1, 2.0, 'hello']\n", - "print l" + "print(l) " ] }, { @@ -46,9 +44,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -69,9 +65,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -120,9 +114,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -156,9 +148,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -187,9 +177,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -230,9 +218,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -260,9 +246,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -289,9 +273,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -320,19 +302,17 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"hello world\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# 把开头的 h 改成大写\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'H'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello world\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# 把开头的 h 改成大写\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'H'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], @@ -352,9 +332,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -367,7 +345,7 @@ "source": [ "a = [10, 11, 12, 13, 14]\n", "a[0] = 100\n", - "print a" + "print(a)" ] }, { @@ -380,9 +358,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -410,9 +386,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -425,7 +399,7 @@ "source": [ "a = [10, 11, 12, 13, 14]\n", "a[1:3] = [1, 2, 3, 4]\n", - "print a" + "print(a)" ] }, { @@ -438,9 +412,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -453,9 +425,9 @@ ], "source": [ "a = [10, 1, 2, 11, 12]\n", - "print a[1:3]\n", + "print(a[1:3])\n", "a[1:3] = []\n", - "print a" + "print(a)" ] }, { @@ -468,9 +440,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -500,7 +470,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -509,10 +478,10 @@ "evalue": "attempt to assign sequence of size 0 to extended slice of size 3", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mValueError\u001b[0m: attempt to assign sequence of size 0 to extended slice of size 3" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: attempt to assign sequence of size 0 to extended slice of size 3" ] } ], @@ -538,9 +507,8 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -555,7 +523,7 @@ "source": [ "a = [1002, 'a', 'b', 'c']\n", "del a[0]\n", - "print a" + "print(a)" ] }, { @@ -567,10 +535,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "data": { @@ -578,7 +544,7 @@ "[1002]" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -598,9 +564,8 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -610,7 +575,7 @@ "[1, 2]" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -637,10 +602,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -653,8 +616,8 @@ ], "source": [ "a = [10, 11, 12, 13, 14]\n", - "print 10 in a\n", - "print 10 not in a" + "print(10 in a)\n", + "print(10 not in a) " ] }, { @@ -666,10 +629,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -682,8 +643,8 @@ ], "source": [ "s = 'hello world'\n", - "print 'he' in s\n", - "print 'world' not in s" + "print('he' in s)\n", + "print('world' not in s)" ] }, { @@ -695,10 +656,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -706,7 +665,7 @@ "[12, 13]" ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -725,10 +684,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -736,7 +693,7 @@ "13" ] }, - "execution_count": 23, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -775,10 +732,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -786,7 +741,7 @@ "2" ] }, - "execution_count": 24, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -812,10 +767,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { @@ -823,7 +776,7 @@ "1" ] }, - "execution_count": 25, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -841,20 +794,18 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "1 is not in list", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mValueError\u001b[0m: 1 is not in list" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: 1 is not in list" ] } ], @@ -885,23 +836,24 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10, 11, 12, 11]\n" - ] + "data": { + "text/plain": [ + "[10, 11, 12, 11]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a = [10, 11, 12]\n", "a.append(11)\n", - "print a" + "a" ] }, { @@ -913,22 +865,23 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10, 11, 12, 11, [11, 12]]\n" - ] + "data": { + "text/plain": [ + "[10, 11, 12, 11, [11, 12]]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a.append([11, 12])\n", - "print a" + "a" ] }, { @@ -947,23 +900,24 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10, 11, 12, 11, 1, 2]\n" - ] + "data": { + "text/plain": [ + "[10, 11, 12, 11, 1, 2]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a = [10, 11, 12, 11]\n", "a.extend([1, 2])\n", - "print a" + "a" ] }, { @@ -982,24 +936,25 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10, 11, 12, 'a', 13, 11]\n" - ] + "data": { + "text/plain": [ + "[10, 11, 12, 'a', 13, 11]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a = [10, 11, 12, 13, 11]\n", "# 在索引 3 插入 'a'\n", "a.insert(3, 'a')\n", - "print a" + "a" ] }, { @@ -1018,24 +973,25 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[10, 12, 13, 11]\n" - ] + "data": { + "text/plain": [ + "[10, 12, 13, 11]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a = [10, 11, 12, 13, 11]\n", "# 移除了第一个 11\n", "a.remove(11)\n", - "print a" + "a" ] }, { @@ -1054,10 +1010,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { @@ -1065,7 +1019,7 @@ "12" ] }, - "execution_count": 32, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1091,23 +1045,24 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 2, 10, 11, 11, 13]\n" - ] + "data": { + "text/plain": [ + "[1, 2, 10, 11, 11, 13]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a = [10, 1, 11, 13, 11, 2]\n", "a.sort()\n", - "print a" + "a" ] }, { @@ -1119,10 +1074,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1136,8 +1089,8 @@ "source": [ "a = [10, 1, 11, 13, 11, 2]\n", "b = sorted(a)\n", - "print a\n", - "print b" + "print(a)\n", + "print(b)" ] }, { @@ -1156,23 +1109,24 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 41, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[6, 5, 4, 3, 2, 1]\n" - ] + "data": { + "text/plain": [ + "[6, 5, 4, 3, 2, 1]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "a = [1, 2, 3, 4, 5, 6]\n", "a.reverse()\n", - "print a" + "a" ] }, { @@ -1184,10 +1138,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 42, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1201,8 +1153,8 @@ "source": [ "a = [1, 2, 3, 4, 5, 6]\n", "b = a[::-1]\n", - "print a\n", - "print b" + "print(a)\n", + "print(b)" ] }, { @@ -1214,35 +1166,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ - "a.sort?" + "a.sort??" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From d9e3fe6872c452421d75472000bda596835563b3 Mon Sep 17 00:00:00 2001 From: master Date: Sun, 4 Apr 2021 20:26:06 +0800 Subject: [PATCH 11/49] update to python3 --- ....07-mutable-and-immutable-data-types.ipynb | 86 ++--- 02-python-essentials/02.08-tuples.ipynb | 89 +++-- ...peed-comparison-between-list-&-tuple.ipynb | 53 ++- 02-python-essentials/02.10-dictionaries.ipynb | 315 ++++++++---------- 4 files changed, 216 insertions(+), 327 deletions(-) diff --git a/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb b/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb index cc103892..e27d5087 100644 --- a/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb +++ b/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -49,9 +47,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -79,9 +75,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -102,9 +96,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -132,9 +124,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -163,7 +153,6 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -172,10 +161,10 @@ "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'z'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'z'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], @@ -192,10 +181,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -207,8 +194,8 @@ } ], "source": [ - "print s.replace('world', 'Mars')\n", - "print s" + "print(s.replace('world', 'Mars'))\n", + "print(s)" ] }, { @@ -220,10 +207,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -236,7 +221,7 @@ "source": [ "s = \"hello world\"\n", "s = s.replace('world', 'Mars')\n", - "print s" + "print(s)" ] }, { @@ -248,10 +233,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { @@ -259,28 +242,17 @@ "bytearray(b'a12de')" ] }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s = bytearray('abcde')\n", - "s[1:3] = '12'\n", + "s = bytearray(b'abcde')\n", + "s[1:3] = b'12'\n", "s" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "数据类型分类：\n", - "\n", - "|可变数据类型|不可变数据类型|\n", - "|--|--|\n", - "|`list`, `dictionary`, `set`, `numpy array`, `user defined objects`|`integer`, `float`, `long`, `complex`, `string`, `tuple`, `frozenset`" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -317,9 +289,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -347,23 +317,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.08-tuples.ipynb b/02-python-essentials/02.08-tuples.ipynb index 3ea45ca9..ba10e5e2 100644 --- a/02-python-essentials/02.08-tuples.ipynb +++ b/02-python-essentials/02.08-tuples.ipynb @@ -24,9 +24,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -54,9 +52,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -76,9 +72,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -105,19 +99,17 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], @@ -142,44 +134,40 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10,)\n", - "\n" + "\n" ] } ], "source": [ "a = (10,)\n", - "print a\n", - "print type(a)" + "print(a)\n", + "print(type(a))" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n" + "\n" ] } ], "source": [ "a = (10)\n", - "print type(a)" + "print(type(a))" ] }, { @@ -191,10 +179,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -202,7 +188,7 @@ "(10, 11, 12, 13, 14)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -228,10 +214,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { @@ -239,7 +223,7 @@ "1" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -250,10 +234,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { @@ -261,7 +243,7 @@ "2" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -287,27 +269,34 @@ "\n", "数据库的返回值……" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb b/02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb index ae80598d..d4cd9c56 100644 --- a/02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb +++ b/02-python-essentials/02.09-speed-comparison-between-list-&-tuple.ipynb @@ -24,15 +24,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1000000 loops, best of 3: 456 ns per loop\n" + "146 ns ± 1.31 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)\n" ] } ], @@ -43,15 +41,13 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10000000 loops, best of 3: 23 ns per loop\n" + "10.5 ns ± 0.0592 ns per loop (mean ± std. dev. of 7 runs, 100000000 loops each)\n" ] } ], @@ -83,9 +79,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from numpy.random import rand\n", @@ -97,15 +91,13 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "100 loops, best of 3: 4.12 ms per loop\n" + "1.34 ms ± 18.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], @@ -116,15 +108,13 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "100 loops, best of 3: 2.07 ms per loop\n" + "702 µs ± 7.18 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], @@ -149,16 +139,13 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The slowest run took 12.20 times longer than the fastest. This could mean that an intermediate result is being cached \n", - "100 loops, best of 3: 3.73 ms per loop\n" + "3.79 ms ± 422 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -169,15 +156,13 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "100 loops, best of 3: 3.82 ms per loop\n" + "3.27 ms ± 34.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -195,23 +180,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.10-dictionaries.ipynb b/02-python-essentials/02.10-dictionaries.ipynb index 9751df50..64dc0400 100644 --- a/02-python-essentials/02.10-dictionaries.ipynb +++ b/02-python-essentials/02.10-dictionaries.ipynb @@ -42,9 +42,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -65,9 +63,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -102,9 +98,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -133,9 +127,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -162,9 +154,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -199,9 +189,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -235,40 +223,36 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'two': 'this is number 2', 'one': 'this is number 1, too'}\n" + "{'one': 'this is number 1, too', 'two': 'this is number 2'}\n" ] } ], "source": [ - "print a" + "print(a)" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'two': 'this is number 2', 'one': 'this is number 1'}\n" + "{'one': 'this is number 1', 'two': 'this is number 2'}\n" ] } ], "source": [ - "print b" + "print(b)" ] }, { @@ -280,20 +264,18 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "0", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m: 0" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m: 0" ] } ], @@ -321,9 +303,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -374,9 +354,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -412,10 +390,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { @@ -426,7 +402,7 @@ " {'first': 'Diane', 'last': 'Chambers', 'name': 33}]" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -457,18 +433,16 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'foozelator': 123, 'frombicator': 18, 'snitzelhogen': 23, 'spatzleblock': 34}" + "{'foozelator': 123, 'frombicator': 18, 'spatzleblock': 34, 'snitzelhogen': 23}" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -492,18 +466,16 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'foozelator': 123, 'frombicator': 19, 'snitzelhogen': 23, 'spatzleblock': 34}" + "{'foozelator': 123, 'frombicator': 19, 'spatzleblock': 34, 'snitzelhogen': 23}" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -530,19 +502,17 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "3.3", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1.1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2.2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m6.6\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3.3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m: 3.3" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2.2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m6.6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3.3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m: 3.3" ] } ], @@ -563,9 +533,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -591,10 +559,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "connections = {}\n", @@ -612,10 +578,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -627,8 +591,8 @@ } ], "source": [ - "print connections[('Austin', 'New York')]\n", - "print connections[('New York', 'Austin')]" + "print(connections[('Austin', 'New York')])\n", + "print(connections[('New York', 'Austin')])" ] }, { @@ -658,10 +622,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "a = {}\n", @@ -678,20 +640,18 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'three'", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"three\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m: 'three'" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"three\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m: 'three'" ] } ], @@ -708,16 +668,15 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print a.get(\"three\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], @@ -734,10 +693,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { @@ -745,7 +702,7 @@ "'undefined'" ] }, - "execution_count": 25, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -774,10 +731,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -785,7 +740,7 @@ "{'one': 'this is number 1', 'two': 'this is number 2'}" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -803,9 +758,8 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -815,7 +769,7 @@ "'this is number 2'" ] }, - "execution_count": 27, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -826,10 +780,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -837,7 +789,7 @@ "{'one': 'this is number 1'}" ] }, - "execution_count": 28, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -855,10 +807,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { @@ -866,7 +816,7 @@ "'not exist'" ] }, - "execution_count": 29, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -884,10 +834,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { @@ -895,7 +843,7 @@ "{}" ] }, - "execution_count": 30, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -925,17 +873,18 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'born': 1831, 'last': 'Maxwell', 'first': 'Jmes'}\n" - ] + "data": { + "text/plain": [ + "{'first': 'Jmes', 'last': 'Maxwell', 'born': 1831}" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -943,7 +892,7 @@ "person['first'] = \"Jmes\"\n", "person['last'] = \"Maxwell\"\n", "person['born'] = 1831\n", - "print person" + "person" ] }, { @@ -955,23 +904,24 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'middle': 'Clerk', 'born': 1831, 'last': 'Maxwell', 'first': 'James'}\n" - ] + "data": { + "text/plain": [ + "{'first': 'James', 'last': 'Maxwell', 'born': 1831, 'middle': 'Clerk'}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "person_modifications = {'first': 'James', 'middle': 'Clerk'}\n", "person.update(person_modifications)\n", - "print person" + "person" ] }, { @@ -983,10 +933,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 32, + "metadata": {}, "outputs": [], "source": [ "barn = {'cows': 1, 'dogs': 5, 'cats': 3}" @@ -1001,10 +949,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { @@ -1012,7 +958,7 @@ "False" ] }, - "execution_count": 35, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1024,9 +970,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1069,18 +1013,16 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['cows', 'cats', 'dogs']" + "dict_keys(['cows', 'dogs', 'cats'])" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1091,18 +1033,16 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 3, 5]" + "dict_values([1, 5, 3])" ] }, - "execution_count": 37, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1113,18 +1053,16 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('cows', 1), ('cats', 3), ('dogs', 5)]" + "dict_items([('cows', 1), ('dogs', 5), ('cats', 3)])" ] }, - "execution_count": 38, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1132,27 +1070,34 @@ "source": [ "barn.items()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From c00123a90a2dd9f60e1fb48efd3722d5d3225991 Mon Sep 17 00:00:00 2001 From: Jack Date: Wed, 7 Apr 2021 09:22:32 +0800 Subject: [PATCH 12/49] update --- 02-python-essentials/02.17-functions.ipynb | 92 +++------ .../02.18-modules-and-packages.ipynb | 186 ++++++++---------- 2 files changed, 105 insertions(+), 173 deletions(-) diff --git a/02-python-essentials/02.17-functions.ipynb b/02-python-essentials/02.17-functions.ipynb index 092c5764..104ad56e 100644 --- a/02-python-essentials/02.17-functions.ipynb +++ b/02-python-essentials/02.17-functions.ipynb @@ -68,9 +68,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -96,9 +94,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -127,9 +123,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -150,9 +144,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -180,9 +172,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -208,9 +198,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -260,9 +248,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -286,9 +272,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -305,9 +289,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -333,9 +315,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -392,9 +372,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -443,9 +421,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -472,9 +448,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -515,9 +489,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -549,9 +521,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -581,9 +551,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -608,9 +576,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -647,9 +613,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -686,9 +650,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -722,9 +684,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -746,23 +706,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.18-modules-and-packages.ipynb b/02-python-essentials/02.18-modules-and-packages.ipynb index 612e1f22..7675d0d0 100644 --- a/02-python-essentials/02.18-modules-and-packages.ipynb +++ b/02-python-essentials/02.18-modules-and-packages.ipynb @@ -24,15 +24,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Overwriting ex1.py\n" + "Writing ex1.py\n" ] } ], @@ -48,7 +46,7 @@ " return tot\n", " \n", "w = [0, 1, 2, 3]\n", - "print sum(w), PI" + "print(sum(w), PI)" ] }, { @@ -61,9 +59,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -87,9 +83,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -106,14 +100,12 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -140,41 +132,43 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.1416\n" - ] + "data": { + "text/plain": [ + "3.1416" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print ex1.PI" + "ex1.PI" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.141592653\n" - ] + "data": { + "text/plain": [ + "3.141592653" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "ex1.PI = 3.141592653\n", - "print ex1.PI" + "ex1.PI" ] }, { @@ -190,21 +184,22 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "9\n" - ] + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print ex1.sum([2, 3, 4])" + "ex1.sum([2, 3, 4])" ] }, { @@ -218,10 +213,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "import ex1" @@ -236,10 +229,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -251,15 +242,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "from importlib import reload\n", "reload(ex1)" ] }, @@ -272,10 +264,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -300,16 +290,14 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Writing ex2.py\n" + "Overwriting ex2.py\n" ] } ], @@ -334,7 +322,7 @@ "def test():\n", " w = [0,1,2,3]\n", " assert(sum(w) == 6)\n", - " print 'test passed.'\n", + " print('test passed.')\n", " \n", "if __name__ == '__main__':\n", " test()" @@ -349,10 +337,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -375,10 +361,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "import ex2" @@ -393,10 +377,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { @@ -404,7 +386,7 @@ "3.1416" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -422,10 +404,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "data": { @@ -433,7 +413,7 @@ "3.1416" ] }, - "execution_count": 15, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -459,10 +439,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "from ex2 import add, PI" @@ -477,10 +455,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -488,7 +464,7 @@ "5" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -506,10 +482,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -517,7 +491,7 @@ "7.5" ] }, - "execution_count": 18, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -538,10 +512,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -637,23 +609,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 326c56f600c58c7f63bf9123fde6003bccb3613b Mon Sep 17 00:00:00 2001 From: master Date: Sat, 10 Apr 2021 12:22:09 +0800 Subject: [PATCH 13/49] update to python3 --- 02-python-essentials/02.19-exceptions.ipynb | 273 ++++++++------------ 02-python-essentials/02.20-warnings.ipynb | 34 +-- 02-python-essentials/02.21-file-IO.ipynb | 247 +++++++----------- 3 files changed, 228 insertions(+), 326 deletions(-) diff --git a/02-python-essentials/02.19-exceptions.ipynb b/02-python-essentials/02.19-exceptions.ipynb index 5b3feac8..b0dfcd11 100644 --- a/02-python-essentials/02.19-exceptions.ipynb +++ b/02-python-essentials/02.19-exceptions.ipynb @@ -26,12 +26,12 @@ "import math\n", "\n", "while True:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = math.log10(x)\n", - " print \"log10({0}) = {1}\".format(x, y)\n", + " print(\"log10({0}) = {1}\".format(x, y))\n", "```\n", "\n", "这段代码接收命令行的输入，当输入为数字时，计算它的对数并输出，直到输入值为 `q` 为止。\n", @@ -41,27 +41,16 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> -1\n" - ] - }, - { - "ename": "ValueError", - "evalue": "math domain error", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 9\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"log10({0}) = {1}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: math domain error" + "> 1\n", + "log10(1.0) = 0.0\n", + "> q\n" ] } ], @@ -69,12 +58,12 @@ "import math\n", "\n", "while True:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = math.log10(x)\n", - " print \"log10({0}) = {1}\".format(x, y)" + " print(\"log10({0}) = {1}\".format(x, y))" ] }, { @@ -100,14 +89,14 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = math.log10(x)\n", - " print \"log10({0}) = {1}\".format(x, y)\n", + " print(\"log10({0}) = {1}\".format(x, y))\n", " except ValueError:\n", - " print \"the value must be greater than 0\"\n", + " print(\"the value must be greater than 0\")\n", "```" ] }, @@ -122,21 +111,15 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> -1\n", - "the value must be greater than 0\n", "> 0\n", "the value must be greater than 0\n", - "> 1\n", - "log10(1.0) = 0.0\n", "> q\n" ] } @@ -146,14 +129,14 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = math.log10(x)\n", - " print \"log10({0}) = {1}\".format(x, y)\n", + " print(\"log10({0}) = {1}\".format(x, y))\n", " except ValueError:\n", - " print \"the value must be greater than 0\"" + " print(\"the value must be greater than 0\")" ] }, { @@ -172,7 +155,7 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", @@ -187,27 +170,16 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 1\n" - ] - }, - { - "ename": "ZeroDivisionError", - "evalue": "float division by zero", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog10\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"log10({0}) = {1}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero" + "> 0\n", + "the value must be greater than 0\n", + "> q\n" ] } ], @@ -216,14 +188,14 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = 1 / math.log10(x)\n", - " print \"log10({0}) = {1}\".format(x, y)\n", + " print(\"log10({0}) = {1}\".format(x, y))\n", " except ValueError:\n", - " print \"the value must be greater than 0\"" + " print(\"the value must be greater than 0\") " ] }, { @@ -249,23 +221,15 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 1\n", - "invalid value\n", - "> 0\n", - "invalid value\n", - "> -1\n", - "invalid value\n", "> 2\n", - "1 / log10(2.0) = 3.32192809489\n", + "1 / log10(2.0) = 3.321928094887362\n", "> q\n" ] } @@ -275,14 +239,14 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = 1 / math.log10(x)\n", - " print \"1 / log10({0}) = {1}\".format(x, y)\n", + " print(\"1 / log10({0}) = {1}\".format(x, y)) \n", " except Exception:\n", - " print \"invalid value\"" + " print(\"invalid value\")" ] }, { @@ -301,10 +265,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -314,8 +276,6 @@ "invalid value\n", "> -1\n", "invalid value\n", - "> 0\n", - "invalid value\n", "> q\n" ] } @@ -325,14 +285,14 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = 1 / math.log10(x)\n", - " print \"1 / log10({0}) = {1}\".format(x, y)\n", + " print(\"1 / log10({0}) = {1}\".format(x, y))\n", " except (ValueError, ZeroDivisionError):\n", - " print \"invalid value\"" + " print(\"invalid value\")" ] }, { @@ -344,9 +304,8 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -358,10 +317,8 @@ "the value must not be 1\n", "> -1\n", "the value must be greater than 0\n", - "> 0\n", - "the value must be greater than 0\n", "> 2\n", - "1 / log10(2.0) = 3.32192809489\n", + "1 / log10(2.0) = 3.321928094887362\n", "> q\n" ] } @@ -371,16 +328,16 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = 1 / math.log10(x)\n", - " print \"1 / log10({0}) = {1}\".format(x, y)\n", + " print(\"1 / log10({0}) = {1}\".format(x, y))\n", " except ValueError:\n", - " print \"the value must be greater than 0\"\n", + " print(\"the value must be greater than 0\") \n", " except ZeroDivisionError:\n", - " print \"the value must not be 1\"" + " print(\"the value must not be 1\") " ] }, { @@ -393,9 +350,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -416,18 +371,18 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = 1 / math.log10(x)\n", - " print \"1 / log10({0}) = {1}\".format(x, y)\n", + " print (\"1 / log10({0}) = {1}\".format(x, y))\n", " except ValueError:\n", - " print \"the value must be greater than 0\"\n", + " print (\"the value must be greater than 0\")\n", " except ZeroDivisionError:\n", - " print \"the value must not be 1\"\n", + " print (\"the value must not be 1\")\n", " except Exception:\n", - " print \"unexpected error\"" + " print (\"unexpected error\")" ] }, { @@ -446,20 +401,18 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "ename": "ValueError", - "evalue": "could not convert string to float: a", + "evalue": "could not convert string to float: 'a'", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mValueError\u001b[0m: could not convert string to float: a" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'a'" ] } ], @@ -476,21 +429,21 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "> -1\n", + "the value must be greater than 0\n", "> 1\n", "the value must not be 1\n", - "> -1\n", + "> 0\n", "the value must be greater than 0\n", - "> aa\n", - "could not convert 'aa' to float\n", + "> 3\n", + "1 / log10(3.0) = 2.095903274289385\n", "> q\n" ] } @@ -500,21 +453,21 @@ "\n", "while True:\n", " try:\n", - " text = raw_input('> ')\n", + " text = input('> ')\n", " if text[0] == 'q':\n", " break\n", " x = float(text)\n", " y = 1 / math.log10(x)\n", - " print \"1 / log10({0}) = {1}\".format(x, y)\n", + " print (\"1 / log10({0}) = {1}\".format(x, y))\n", " except ValueError as exc:\n", - " if exc.message == \"math domain error\":\n", - " print \"the value must be greater than 0\"\n", + " if str(exc) == \"math domain error\":\n", + " print (\"the value must be greater than 0\")\n", " else:\n", - " print \"could not convert '%s' to float\" % text\n", + " print (\"could not convert '%s' to float\" % text)\n", " except ZeroDivisionError:\n", - " print \"the value must not be 1\"\n", + " print (\"the value must not be 1\")\n", " except Exception as exc:\n", - " print \"unexpected error:\", exc.message" + " print (\"unexpected error:\", exc.message)" ] }, { @@ -523,7 +476,7 @@ "source": [ "同时，我们也将捕获的其他异常的信息显示出来。\n", "\n", - "这里，`exc.message` 显示的内容是异常对应的说明，例如\n", + "这里，`str(exc)` 显示的内容是异常对应的说明，例如\n", "\n", " ValueError: could not convert string to float: a\n", "\n", @@ -578,9 +531,8 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -588,18 +540,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "> bad command\n" + "> q\n" ] }, { - "ename": "CommandError", - "evalue": "Invalid commmand: bad command", + "ename": "NameError", + "evalue": "name 'CommandError' is not defined", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mCommandError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mcommand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mraw_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'> '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalid_commands\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mCommandError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Invalid commmand: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mCommandError\u001b[0m: Invalid commmand: bad command" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcommand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'> '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcommand\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_commands\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCommandError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Invalid commmand: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'CommandError' is not defined" ] } ], @@ -607,7 +559,7 @@ "valid_commands = {'start', 'stop', 'pause'}\n", "\n", "while True:\n", - " command = raw_input('> ')\n", + " command = input('> ')\n", " if command.lower() not in valid_commands:\n", " raise CommandError('Invalid commmand: %s' % command)" ] @@ -634,12 +586,12 @@ "valid_commands = {'start', 'stop', 'pause'}\n", "\n", "while True:\n", - " command = raw_input('> ')\n", + " command = input('> ')\n", " try:\n", " if command.lower() not in valid_commands:\n", " raise CommandError('Invalid commmand: %s' % command)\n", " except CommandError:\n", - " print 'Bad command string: \"%s\"' % command\n", + " print('Bad command string: \"%s\"' % command)\n", "```" ] }, @@ -668,10 +620,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -684,9 +634,9 @@ ], "source": [ "try:\n", - " print 1\n", + " print(1)\n", "finally:\n", - " print 'finally was called.'" + " print('finally was called.') " ] }, { @@ -698,10 +648,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -712,21 +660,21 @@ }, { "ename": "ZeroDivisionError", - "evalue": "integer division or modulo by zero", + "evalue": "division by zero", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'finally was called.'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'finally was called.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" ] } ], "source": [ "try:\n", - " print 1 / 0\n", + " print(1 / 0)\n", "finally:\n", - " print 'finally was called.'" + " print('finally was called.')" ] }, { @@ -738,10 +686,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -754,33 +700,40 @@ ], "source": [ "try:\n", - " print 1 / 0\n", + " print (1 / 0)\n", "except ZeroDivisionError:\n", - " print 'divide by 0.'\n", + " print ('divide by 0.')\n", "finally:\n", - " print 'finally was called.'" + " print ('finally was called.')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.20-warnings.ipynb b/02-python-essentials/02.20-warnings.ipynb index a71faef7..77c9d3aa 100644 --- a/02-python-essentials/02.20-warnings.ipynb +++ b/02-python-essentials/02.20-warnings.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import warnings" @@ -39,15 +37,14 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__main__.py:4: RuntimeWarning: month (13) is not between 1 and 12\n" + ":4: RuntimeWarning: month (13) is not between 1 and 12\n", + " warnings.warn(msg, RuntimeWarning)\n" ] } ], @@ -74,36 +71,41 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "warnings.filterwarnings(action = 'ignore', category = RuntimeWarning)\n", "\n", "month_warning(13)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.21-file-IO.ipynb b/02-python-essentials/02.21-file-IO.ipynb index ac126e98..7779c11f 100644 --- a/02-python-essentials/02.21-file-IO.ipynb +++ b/02-python-essentials/02.21-file-IO.ipynb @@ -16,9 +16,8 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -49,38 +48,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "使用 `open` 函数或者 `file` 函数来读文件，使用文件名的字符串作为输入参数：" + "使用 `open` 函数来读文件，使用文件名的字符串作为输入参数：" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "f = open('test.txt')" ] }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "f = file('test.txt')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "这两种方式没有太大区别。" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -97,10 +76,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -109,13 +86,14 @@ "this is a test file.\n", "hello world!\n", "python is good!\n", - "today is a good day.\n" + "today is a good day.\n", + "\n" ] } ], "source": [ "text = f.read()\n", - "print text" + "print(text)" ] }, { @@ -127,23 +105,21 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['this is a test file.\\n', 'hello world!\\n', 'python is good!\\n', 'today is a good day.']\n" + "['this is a test file.\\n', 'hello world!\\n', 'python is good!\\n', 'today is a good day.\\n']\n" ] } ], "source": [ "f = open('test.txt')\n", "lines = f.readlines()\n", - "print lines" + "print(lines)" ] }, { @@ -155,10 +131,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "f.close()" @@ -173,10 +147,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -188,14 +160,15 @@ "\n", "python is good!\n", "\n", - "today is a good day.\n" + "today is a good day.\n", + "\n" ] } ], "source": [ "f = open('test.txt')\n", "for line in f:\n", - " print line\n", + " print(line)\n", "f.close()" ] }, @@ -208,10 +181,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -234,10 +205,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "f = open('myfile.txt', 'w')\n", @@ -254,10 +223,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -268,7 +235,7 @@ } ], "source": [ - "print open('myfile.txt').read()" + "print(open('myfile.txt').read())" ] }, { @@ -280,10 +247,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -297,7 +262,7 @@ "f = open('myfile.txt', 'w')\n", "f.write('another hello world!')\n", "f.close()\n", - "print open('myfile.txt').read()" + "print(open('myfile.txt').read())" ] }, { @@ -309,10 +274,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -326,7 +289,7 @@ "f = open('myfile.txt', 'a')\n", "f.write('... and more')\n", "f.close()\n", - "print open('myfile.txt').read()" + "print(open('myfile.txt').read())" ] }, { @@ -340,10 +303,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -357,7 +318,7 @@ "f = open('myfile.txt', 'w+')\n", "f.write('hello world!')\n", "f.seek(6)\n", - "print f.read()\n", + "print(f.read())\n", "f.close()" ] }, @@ -370,10 +331,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -396,16 +355,14 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "'\\x86H\\x93\\xe1\\xd8\\xef\\xc0\\xaa(\\x17\\xa9\\xc9\\xa51\\xf1\\x98'\n" + "b'\\xe2\\x07\\x03\\xa9e\\x94\\x1e2\\xe4;O\\xa7\\xdb\\xff\"\\x93'\n" ] } ], @@ -416,7 +373,7 @@ "f.close()\n", "\n", "f = open('binary.bin', 'rb')\n", - "print repr(f.read())\n", + "print(repr(f.read()))\n", "f.close()" ] }, @@ -447,9 +404,7 @@ "\n", "- `\\r`\n", "- `\\n`\n", - "- `\\r\\n`\n", - "\n", - "使用 `U` 选项，可以将这三个统一看成 `\\n` 换行符。" + "- `\\r\\n`\n" ] }, { @@ -472,10 +427,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -489,7 +442,7 @@ "f = open('newfile.txt','w')\n", "f.write('hello world')\n", "g = open('newfile.txt', 'r')\n", - "print repr(g.read())" + "print(repr(g.read()))" ] }, { @@ -503,10 +456,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3304,7 +3255,11 @@ "hello world: 2789\n", "hello world: 2790\n", "hello world: 2791\n", - "hello \n" + "hello world: 2792\n", + "hello world: 2793\n", + "hello world: 2794\n", + "hello world: 2795\n", + "\n" ] } ], @@ -3314,17 +3269,15 @@ " f.write('hello world: ' + str(i) + '\\n')\n", "\n", "g = open('newfile.txt', 'r')\n", - "print g.read()\n", + "print(g.read())\n", "f.close()\n", "g.close()" ] }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -3340,20 +3293,18 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'newfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m 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\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'newfile.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hello world: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero" ] } ], @@ -3373,10 +3324,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4352,13 +4301,14 @@ "hello world: 967\n", "hello world: 968\n", "hello world: 969\n", - "hell\n" + "hello world: 970\n", + "\n" ] } ], "source": [ "g = open('newfile.txt', 'r')\n", - "print g.read()\n", + "print(g.read()) \n", "f.close()\n", "g.close()" ] @@ -4372,10 +4322,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4392,17 +4340,15 @@ " x = 1.0 / (i - 1000)\n", " f.write('hello world: ' + str(i) + '\\n')\n", "except Exception:\n", - " print \"something bad happened\"\n", + " print(\"something bad happened\") \n", "finally:\n", " f.close()" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -5414,7 +5360,7 @@ ], "source": [ "g = open('newfile.txt', 'r')\n", - "print g.read()\n", + "print(g.read())\n", "g.close()" ] }, @@ -5434,20 +5380,18 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'newfile.txt'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hello world: '\u001b[0m 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"metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -6482,7 +6424,7 @@ ], "source": [ "g = open('newfile.txt', 'r')\n", - "print g.read()\n", + "print(g.read())\n", "g.close()" ] }, @@ -6495,36 +6437,41 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "import os\n", "os.remove('newfile.txt')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 49ced4646f39ff3306a21a65041e3f6e5aaa3106 Mon Sep 17 00:00:00 2001 From: Jack Date: Tue, 13 Apr 2021 13:46:36 +0800 Subject: [PATCH 14/49] update --- 03-numpy/03.02-matplotlib-basics.ipynb | 163 +++++++++++-------------- 1 file changed, 68 insertions(+), 95 deletions(-) diff --git a/03-numpy/03.02-matplotlib-basics.ipynb b/03-numpy/03.02-matplotlib-basics.ipynb index b676d75b..475d3abc 100644 --- a/03-numpy/03.02-matplotlib-basics.ipynb +++ b/03-numpy/03.02-matplotlib-basics.ipynb @@ -22,16 +22,14 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Using matplotlib backend: Qt4Agg\n", + "Using matplotlib backend: Qt5Agg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } @@ -40,6 +38,13 @@ "%pylab" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`%matplotlib inline`使用此后端，绘图命令的输出将在Jupyter笔记本之类的前端内联显示，直接位于生成该代码的代码单元下方。然后，生成的图也将存储在笔记本文档中。" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,29 +67,29 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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xcOqpcOKJsHRp6Iiq7/HH4a67fKvkI44IHU1m0Zi+SASZwd13wyGHQOvW8Nxz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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -103,30 +108,31 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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YvUjMNGoEH3zgu+X166Fdu9CJfmnaNGjVCu6+22+9LOGp0IvE0AknQGGh75rX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\ngg==\n", 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\n", 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cpXugWtGbCZcz+t0ZuwmsHUi96vUA+OQTePJJ8PLSWZhOqA3ZsunaFQID4c8/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\nTwg3NL6BJz+bT1wcfPGF/ePyAFJKZm2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\n", 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\n", 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"metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -561,9 +553,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -611,9 +601,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -652,9 +640,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -700,9 +686,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -743,9 +727,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -807,9 +789,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -845,9 +825,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -896,9 +874,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1098,7 +1074,6 @@ "cell_type": "code", "execution_count": 29, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1158,9 +1133,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1203,23 +1176,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 8af59cc19d6e5fdc98f7f0df8686a3618a9887df Mon Sep 17 00:00:00 2001 From: Jack Date: Thu, 15 Apr 2021 14:57:49 +0800 Subject: [PATCH 15/49] update --- .../04.01-scienticfic-python-overview.ipynb | 82 +-- 04-scipy/04.02-interpolation-with-scipy.ipynb | 128 ++--- .../07.02-python-extension-modules.ipynb | 74 +-- .../08.01-oop-introduction.ipynb | 28 +- 12-pandas/12.01-ten-minutes-to-pandas.ipynb | 479 ++++++------------ 5 files changed, 289 insertions(+), 502 deletions(-) diff --git a/04-scipy/04.01-scienticfic-python-overview.ipynb b/04-scipy/04.01-scienticfic-python-overview.ipynb index 908deadf..6c417cec 100644 --- a/04-scipy/04.01-scienticfic-python-overview.ipynb +++ b/04-scipy/04.01-scienticfic-python-overview.ipynb @@ -63,9 +63,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -84,9 +82,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from scipy import linalg, optimize" @@ -104,16 +100,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None,\n", - " full_output=0, disp=1, retall=0, callback=None)\n", + " full_output=0, disp=1, retall=0, callback=None, initial_simplex=None)\n", "\n", "Minimize a function using the downhill simplex algorithm.\n", "\n", @@ -127,14 +121,13 @@ "x0 : ndarray\n", " Initial guess.\n", "args : tuple, optional\n", - " Extra arguments passed to func, i.e. ``f(x,*args)``.\n", - "callback : callable, optional\n", - " Called after each iteration, as callback(xk), where xk is the\n", - " current parameter vector.\n", + " Extra arguments passed to func, i.e., ``f(x,*args)``.\n", "xtol : float, optional\n", - " Relative error in xopt acceptable for convergence.\n", + " Absolute error in xopt between iterations that is acceptable for\n", + " convergence.\n", "ftol : number, optional\n", - " Relative error in func(xopt) acceptable for convergence.\n", + " Absolute error in func(xopt) between iterations that is acceptable for\n", + " convergence.\n", "maxiter : int, optional\n", " Maximum number of iterations to perform.\n", "maxfun : number, optional\n", @@ -145,6 +138,14 @@ " Set to True to print convergence messages.\n", "retall : bool, optional\n", " Set to True to return list of solutions at each iteration.\n", + "callback : callable, optional\n", + " Called after each iteration, as callback(xk), where xk is the\n", + " current parameter vector.\n", + "initial_simplex : array_like of shape (N + 1, N), optional\n", + " Initial simplex. If given, overrides `x0`.\n", + " ``initial_simplex[j,:]`` should contain the coordinates of\n", + " the jth vertex of the ``N+1`` vertices in the simplex, where\n", + " ``N`` is the dimension.\n", "\n", "Returns\n", "-------\n", @@ -174,11 +175,27 @@ "\n", "This algorithm has a long history of successful use in applications.\n", "But it will usually be slower than an algorithm that uses first or\n", - "second derivative information. In practice it can have poor\n", + "second derivative information. In practice, it can have poor\n", "performance in high-dimensional problems and is not robust to\n", "minimizing complicated functions. Additionally, there currently is no\n", "complete theory describing when the algorithm will successfully\n", - "converge to the minimum, or how fast it will if it does.\n", + "converge to the minimum, or how fast it will if it does. Both the ftol and\n", + "xtol criteria must be met for convergence.\n", + "\n", + "Examples\n", + "--------\n", + ">>> def f(x):\n", + "... return x**2\n", + "\n", + ">>> from scipy import optimize\n", + "\n", + ">>> minimum = optimize.fmin(f, 1)\n", + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 17\n", + " Function evaluations: 34\n", + ">>> minimum[0]\n", + "-8.8817841970012523e-16\n", "\n", "References\n", "----------\n", @@ -208,7 +225,6 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -222,15 +238,17 @@ " Change shape and size of array in-place.\n", "numpy.ma.resize\n", " Return a new masked array with the specified size and shape.\n", - "numpy.oldnumeric.ma.resize\n", - " The original array's total size can be any size.\n", "numpy.resize\n", " Return a new array with the specified shape.\n", "numpy.chararray\n", " chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,\n", "numpy.memmap\n", " Create a memory-map to an array stored in a *binary* file on disk.\n", - "numpy.ma.mvoid.resize\n", + "numpy.squeeze\n", + " Remove single-dimensional entries from the shape of an array.\n", + "numpy.expand_dims\n", + " Expand the shape of an array.\n", + "numpy.ma.MaskedArray.resize\n", " .. warning::\n" ] } @@ -249,9 +267,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -260,8 +276,10 @@ "Search results for 'remove path'\n", "--------------------------------\n", "os.removedirs\n", - " removedirs(path)\n", + " removedirs(name)\n", "os.walk\n", + " Directory tree generator.\n", + "os.fwalk\n", " Directory tree generator.\n" ] } @@ -273,23 +291,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/04-scipy/04.02-interpolation-with-scipy.ipynb b/04-scipy/04.02-interpolation-with-scipy.ipynb index 4aff2faf..0a6fbe06 100644 --- a/04-scipy/04.02-interpolation-with-scipy.ipynb +++ b/04-scipy/04.02-interpolation-with-scipy.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -30,9 +28,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "np.set_printoptions(precision=2, suppress=True)" @@ -48,10 +44,20 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "genfromtxt() got an unexpected keyword argument 'skiprows'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m data = np.genfromtxt(\"JANAF_CH4.txt\", \n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# TAB 分隔\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# 忽略首行\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# 读入属性\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmissing_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"INFINITE\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# 缺失值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: genfromtxt() got an unexpected keyword argument 'skiprows'" + ] + } + ], "source": [ "data = np.genfromtxt(\"JANAF_CH4.txt\", \n", " delimiter=\"\\t\", # TAB 分隔\n", @@ -71,9 +77,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -106,9 +110,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -150,9 +152,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from scipy.interpolate import interp1d" @@ -161,9 +161,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "ch4_cp = interp1d(data['TK'], data['Cp'])" @@ -181,9 +179,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -210,9 +206,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -239,9 +233,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "ValueError", @@ -291,9 +283,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -332,9 +322,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -376,7 +364,6 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -430,9 +417,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -461,9 +446,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -492,9 +475,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -523,9 +504,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -561,9 +540,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -657,7 +634,6 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -687,9 +663,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -717,9 +691,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -778,9 +750,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -809,9 +779,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -840,9 +808,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -896,9 +862,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "x, y = np.mgrid[-np.pi/2:np.pi/2:5j, -np.pi/2:np.pi/2:5j]\n", @@ -908,9 +872,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -960,9 +922,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -995,23 +955,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb b/07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb index 562d0be8..957021df 100644 --- a/07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb +++ b/07-interfacing-with-other-languages/07.02-python-extension-modules.ipynb @@ -42,9 +42,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -65,9 +63,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -97,9 +93,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -165,9 +159,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "!gcc -DMS_WIN64 -c fact.c fact_wrap.c -IC:\\Miniconda\\include" @@ -176,9 +168,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "!gcc -DMS_WIN64 -shared fact.o fact_wrap.o -LC:\\Miniconda\\libs -lpython27 -o example.pyd" @@ -211,9 +201,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -239,7 +227,6 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -290,9 +277,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -329,7 +314,6 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -370,9 +354,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -398,9 +380,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -430,9 +410,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -450,9 +428,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -476,9 +452,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -505,9 +479,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -534,9 +506,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import zipfile\n", @@ -560,9 +530,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "!rm -f fact*.*\n", @@ -574,23 +542,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/08-object-oriented-programming/08.01-oop-introduction.ipynb b/08-object-oriented-programming/08.01-oop-introduction.ipynb index c0754a00..653ffd9a 100644 --- a/08-object-oriented-programming/08.01-oop-introduction.ipynb +++ b/08-object-oriented-programming/08.01-oop-introduction.ipynb @@ -42,9 +42,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -71,9 +69,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -100,9 +96,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -130,9 +124,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", @@ -211,23 +203,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/12-pandas/12.01-ten-minutes-to-pandas.ipynb b/12-pandas/12.01-ten-minutes-to-pandas.ipynb index 5bec138e..ef44d8ef 100644 --- a/12-pandas/12.01-ten-minutes-to-pandas.ipynb +++ b/12-pandas/12.01-ten-minutes-to-pandas.ipynb @@ -18,10 +18,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -70,29 +68,30 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1\n", - "1 3\n", - "2 5\n", - "3 NaN\n", - "4 6\n", - "5 8\n", - "dtype: float64\n" - ] + "data": { + "text/plain": [ + "0 1.0\n", + "1 3.0\n", + "2 5.0\n", + "3 NaN\n", + "4 6.0\n", + "5 8.0\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "s = pd.Series([1,3,5,np.nan,6,8])\n", "\n", - "print s" + "s" ] }, { @@ -108,25 +107,26 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", - " '2013-01-05', '2013-01-06'],\n", - " dtype='datetime64[ns]', freq='D')\n" - ] + "data": { + "text/plain": [ + "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", + " '2013-01-05', '2013-01-06'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "dates = pd.date_range('20130101', periods=6)\n", "\n", - "print dates" + "dates" ] }, { @@ -138,15 +138,26 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -160,45 +171,45 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
2013-01-01-0.605936-0.861658-1.0019241.528584-1.524798-0.9221180.9319950.497882
2013-01-02-0.1654080.3883381.1871871.8198180.690332-0.040268-0.631557-0.659915
2013-01-030.065255-1.608074-1.282331-0.286067-2.4205360.730940-0.5358690.239624
2013-01-041.2893050.497115-0.2253510.0402390.2533450.3720060.321703-1.007760
2013-01-050.0382320.875057-0.0925260.9344320.0890620.245964-0.116376-1.386179
2013-01-06-2.163453-0.0102791.6998861.291653-1.320608-0.245361-0.6187430.218368
\n", @@ -206,15 +217,15 @@ ], "text/plain": [ " A B C D\n", - "2013-01-01 -0.605936 -0.861658 -1.001924 1.528584\n", - "2013-01-02 -0.165408 0.388338 1.187187 1.819818\n", - "2013-01-03 0.065255 -1.608074 -1.282331 -0.286067\n", - "2013-01-04 1.289305 0.497115 -0.225351 0.040239\n", - "2013-01-05 0.038232 0.875057 -0.092526 0.934432\n", - "2013-01-06 -2.163453 -0.010279 1.699886 1.291653" + "2013-01-01 -1.524798 -0.922118 0.931995 0.497882\n", + "2013-01-02 0.690332 -0.040268 -0.631557 -0.659915\n", + "2013-01-03 -2.420536 0.730940 -0.535869 0.239624\n", + "2013-01-04 0.253345 0.372006 0.321703 -1.007760\n", + "2013-01-05 0.089062 0.245964 -0.116376 -1.386179\n", + "2013-01-06 -1.320608 -0.245361 -0.618743 0.218368" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -237,9 +248,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -334,9 +343,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -383,9 +390,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -469,9 +474,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -546,9 +549,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -577,9 +578,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -606,9 +605,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -647,9 +644,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -757,9 +752,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -852,9 +845,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -939,9 +930,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1033,9 +1022,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1148,9 +1135,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1183,9 +1168,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1218,9 +1201,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1288,9 +1269,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1365,9 +1344,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1398,9 +1375,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1478,9 +1453,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1540,9 +1513,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1571,9 +1542,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1600,9 +1569,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1638,9 +1605,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1671,9 +1636,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1727,9 +1690,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1789,9 +1750,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1851,9 +1810,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1931,9 +1888,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1960,9 +1915,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2007,9 +1960,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2077,9 +2028,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2171,9 +2120,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2268,9 +2215,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2333,9 +2278,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2370,9 +2313,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2473,9 +2414,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2569,9 +2508,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2672,9 +2609,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2775,9 +2710,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2880,9 +2813,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2971,9 +2902,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3029,9 +2958,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3117,9 +3044,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3219,9 +3144,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3253,9 +3176,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3288,9 +3209,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3322,9 +3241,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3430,9 +3347,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3531,9 +3446,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3565,9 +3478,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3602,9 +3513,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3634,9 +3543,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3670,9 +3577,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3714,9 +3619,7 @@ { "cell_type": "code", "execution_count": 58, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -3843,7 +3746,6 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -3946,9 +3848,7 @@ { "cell_type": "code", "execution_count": 60, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3974,9 +3874,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4054,9 +3952,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4166,9 +4062,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4286,9 +4180,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4403,9 +4295,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4465,9 +4355,7 @@ { "cell_type": "code", "execution_count": 66, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4569,9 +4457,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4681,9 +4567,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4723,9 +4607,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4801,9 +4683,7 @@ { "cell_type": "code", "execution_count": 70, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4886,9 +4766,7 @@ { "cell_type": "code", "execution_count": 71, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4923,9 +4801,7 @@ { "cell_type": "code", "execution_count": 72, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -4969,7 +4845,6 @@ "cell_type": "code", "execution_count": 73, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -5003,9 +4878,7 @@ { "cell_type": "code", "execution_count": 74, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5085,9 +4958,7 @@ { "cell_type": "code", "execution_count": 75, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5123,9 +4994,7 @@ { "cell_type": "code", "execution_count": 76, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5161,9 +5030,7 @@ { "cell_type": "code", "execution_count": 77, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5198,9 +5065,7 @@ { "cell_type": "code", "execution_count": 78, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5258,9 +5123,7 @@ { "cell_type": "code", "execution_count": 80, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5289,9 +5152,7 @@ { "cell_type": "code", "execution_count": 81, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5354,9 +5215,7 @@ { "cell_type": "code", "execution_count": 83, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5453,9 +5312,7 @@ { "cell_type": "code", "execution_count": 84, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df.to_hdf(\"foo.h5\", \"df\")" @@ -5471,9 +5328,7 @@ { "cell_type": "code", "execution_count": 85, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5564,9 +5419,7 @@ { "cell_type": "code", "execution_count": 86, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "df.to_excel('foo.xlsx', sheet_name='Sheet1')" @@ -5582,9 +5435,7 @@ { "cell_type": "code", "execution_count": 87, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5668,9 +5519,7 @@ { "cell_type": "code", "execution_count": 88, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import glob\n", @@ -5683,23 +5532,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From ec6a801f65f5809b690c73fcf24a682bd6766518 Mon Sep 17 00:00:00 2001 From: Jack Date: Thu, 15 Apr 2021 15:34:29 +0800 Subject: [PATCH 16/49] update --- 04-scipy/04.02-interpolation-with-scipy.ipynb | 301 ++++++++++-------- 1 file changed, 167 insertions(+), 134 deletions(-) diff --git a/04-scipy/04.02-interpolation-with-scipy.ipynb b/04-scipy/04.02-interpolation-with-scipy.ipynb index 0a6fbe06..1ede7484 100644 --- a/04-scipy/04.02-interpolation-with-scipy.ipynb +++ b/04-scipy/04.02-interpolation-with-scipy.ipynb @@ -38,30 +38,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "从文本中读入数据，数据来自 http://kinetics.nist.gov/janaf/html/C-067.txt ，保存为结构体数组：" + "从文本中读入数据，数据来自 https://janaf.nist.gov/tables/C-067.txt ，保存为结构体数组：" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "genfromtxt() got an unexpected keyword argument 'skiprows'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m data = np.genfromtxt(\"JANAF_CH4.txt\", \n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\t\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# TAB 分隔\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# 忽略首行\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# 读入属性\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmissing_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"INFINITE\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# 缺失值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: genfromtxt() got an unexpected keyword argument 'skiprows'" - ] - } - ], + "outputs": [], + "source": [ + "np.genfromtxt??" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], "source": [ - "data = np.genfromtxt(\"JANAF_CH4.txt\", \n", + "data = np.genfromtxt(\"./JANAF_CH4.txt\", \n", " delimiter=\"\\t\", # TAB 分隔\n", - " skiprows=1, # 忽略首行\n", + " skip_header=1, # 忽略首行\n", " names=True, # 读入属性\n", " missing_values=\"INFINITE\", # 缺失值\n", " filling_values=np.inf) # 填充缺失值" @@ -76,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -96,8 +93,8 @@ ], "source": [ "for row in data[:7]:\n", - " print \"{}\\t{}\".format(row['TK'], row['Cp'])\n", - "print \"...\\t...\"" + " print(\"{}\\t{}\".format(row['TK'], row['Cp']))\n", + "print(\"...\\t...\")" ] }, { @@ -109,17 +106,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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NdFXMukteEapdXrjFUhjjHTdx68SsfrjFYt0gu3p9LOMmbp2YtYaSQFUckqJodSq6alus\nDAaLOXPm0N/fP2w7lcrur+yxmY2NJCJCeeTlFou13KxZs1i4cOHQeMpgsJg1a5bHTcw6kFss1hYG\ng8nJJ5/M2WefPdTl5daJ2cTIs8XiwGITppEur2nTpo16vZnlx11h1pEa6fIaGBhgzpw5G7RMvGjR\nrP25xWITyl1eZu3JXWEjcGBpDyN1Y+2yyy7u8jJrM+4Ks7ZXq9tr1113dZeXWcG5xWJNU9ntVfno\nXnd5mbUPd4WNwIGlvWRnet19993u8jJrU13RFSbpJEl3SrpL0klp2jaSVki6V9I1kvxnbovV2sZ+\nMD3b7VUZVMBdXmZF1JaBRdKuwInAXsDuwFxJrwUWACsiYgZwbXpsLTTSWMpgN9e0adOG9vyqtlux\nmRVLW3aFSXofcGhEnJge/zPwHPBhYL+IWCNpClCOiJ0q7nVX2ASrNoW4r6/P3V5mHaTwYyySdgJ+\nCLwF+DPwY+AW4NiI2Dq9RsDawePMvQ4sLVC5at7MOkuegWVyHpnkLSJWSToTuAb4A3A78ELFNSGp\nagTp7e0del8qlSiVSk0rq7HBWIpneZm1v3K5TLlcbkrebdliqSRpEfAwcBJQiohHJW0PXOeusIlT\nbSHjgw8+yMc//nEuuugiTyE262DdMivsr9J/dwSOAL4DXAHMSy+ZB1zemtJ1p2oD9fPnz+drX/ua\nt7E3syFt22KRdD3wcuAvwD9ExHWStgEuBXYE+oEjI2Kg4j63WJqo2kC9WyZmna/wg/fj4cDSfB6o\nNyuerugKs/ZUbXt7M7Mst1hsA7V2G16+fDnXX3+99/oyKyC3WKypaq2mB/y8eTMblVssVpUH6c26\niwfvR+DAkh8P0pt1D3eFWdN5kN7Mxsotli7mQXozG+QWi+XCg/Rm1gxusXQ5D9KbGXjwfkQOLI3z\nIL2ZuSvMcuNBejPLmwNLF8sOyvvxwWaWFweWLrFs2bINAsby5cuZPXu2B+nNLFcOLF2i2gyw66+/\nnkMOOWTYdT09PX4mvZmNiwfvu4hngJlZLS2fFSZpM5LHzj+bRyFqfMapwDHAi8CdwPHAlsASYCp+\n0NeYeAaYmVUz4bPCJG0k6QhJ35X0CLAaeFDSI5K+J+k9knIpUPp504CPAHtGxG7AJOAoYAGwIiJm\nANemx1YnzwAzs4lQ7xhLGXgT8EXgNRGxfURMAaanaXsBP82xXE+RPJJ4C0mTgS2A3wKHAxek11wA\nvDvHzyw0zwAzs4lSV1eYpE2rdXtJeiEiJo10zZgLJv0t8CXgT8DyiDhW0rqI2Do9L2Dt4HHmPneF\nVVFrX7C+vj4P1ptZ68dYMgV5MSI2krRVRDydR4HSfF8LXAm8DXgS+C7wfeCcbCCRtDYitqm414HF\nzKxBeQaWyXlkAnwcOGPwQNLmEfGnceT3ZuBnEfFEmt9lwFuARyVNiYhHJW0PPFbt5t7e3qH3pVKJ\nUqk0jqJ0FrdMzKwe5XKZcrnclLzzarHsDTweEasl7QT8S0R8cBz57g5cTDJ282fg28DNJLPBnoiI\nMyUtAHoiYkHFvV3dYqnc4t5b3ptZPSa0K2ywhVDj3GBgeRXwMpJZWvcBP4+IFeMqmHQKMI9kuvGt\nwInAVsClwI54unFNXq9iZo2a6MCynOSH/Ubghoh4KnNuMLBcBVxO0rr4DrBVRKzLo4CNcmBJeL2K\nmTViotexnAScDgg4VdKHq1zzT8DtwLbAfwGX5FE4GxuvVzGzVmp4jEXSuyLih+n7FyNig+AkaWZE\nrMypjA3p9haLx1jMbCxaMt1Y0juBHwEfi4hz0rRhgUXSbOCmiPhLHoUbi24PLJ4VZmZj0arA8hYg\ngAMj4t/StMrA8iFgc2B74LqIyHM1fr3l7OrAYmY2FhM6xiLpQkmfBw4CZgFnjnD55sBNwA0ks7ms\niao9Y2VgYIBly5a1qERmZvUN3p8QEZ8FzgfWAJ8a4drfAfsAOwPbjb94NpJqz1hZuHAhs2bNanHJ\nzKyb5bJAMnO8QXfZROu2rjCvWTGzPLTjXmEXkixY/AvwB+CrrRrA77bAAl6zYmbjN+HPY6lDI91l\nliOvWTGzdlPvtvmjNgPquWYitEkxJoTXrJhZXia8K0zST4GlwA8j4t6Kc68neeDWnIiYnUehxqOb\nAovXrJhZXloRWDYFjgY+AOwKPE2yxctLgLtIdiL+TkQ8l0ehxqObAouZWV5aOngvaRLJnmAAv4+I\nF/IoSF4cWMzMGtfSwfuIeCEi1qSvtgoqReWFkGbWSfKaFWZN5IWQZtZJxrWOpR0VtSvMCyHNrJna\nZoFks6QzzRZnkl4DfBa4CFhC8ojifrrsCZJeCGlmzdJWCyQlHSrpOEmb5FEggIj4dUTsERF7AG8C\n/gj8gOTRxysiYgZwbXrcFbwQ0sw6RR5jLE8CPyd5Pn0zHAjcHxEPAYcDF6TpF5Csnym87MLHadOm\nsWjRomFjLmZm7WTcXWGSPgcMAE8BP4iItXkULJP/ecAtEfF1SesiYus0XcDawePM9YXrCvNCSDNr\ntlYskJwHPEzyA/9kxbk3A78B3k6y+v6YPAqW5r0J8Aiwc0Q8ng0s6fm1EbFNxT2FCyxmZs2WZ2CZ\nXOd1TwFHANMlLY6IZyQdRPKUyFvSay6R9IM8CpXxDuC/I+Lx9HiNpCkR8aik7YHHqt3U29s79L5U\nKlEqlXIulplZZyuXy5TL5abkXXeLJSIuqEjbBHg/cFVEPNGUwkmLgR8Nfraks4AnIuJMSQuAnohY\nUHGPWyxmZg1qxaywl1UmRMRzEXEhcFgeBakkaUuSgfvLMslnAAdJuhc4ID02M7M2Um9geYWkbWqc\n2zSvwmRFxB8iYtuIeDqTtjYiDoyIGRFxcOUaliLw9i1m1unqDSxfB5ZIens2MZ2Z9YbcS9XFvH2L\nmXW6uqcbS3oNycr3rYAy8CdgH+DLEXF5swrYqCKMsXj7FjObaK3eNv+twFuA54FlEXF/HgXJSxEC\nC3j7FjObWK3eNv9nEfGliPiPdgsqReHtW8ysk3nb/Dbj7VvMrNO15e7G49HpXWHevsXMWqHw2+aP\nR6cHFjOzVmirbfPNzMyyHFjMzCxXDixmZpYrB5YW8dYtZlZUDiwt4q1bzKyoPCushbx1i5m1C083\nHkEnBRbw1i1m1h483bggvHWLmRWRA0uLeOsWMyuqtu0Kk9QDnAvsAgRwPHAfsASYCvQDR1Y+7KtT\nusK8dYuZtZOuGGORdAHw04g4T9JkYEtgIfD7iDhL0qeBrf3MezOz8St8YJH0MuC2iHhNRfoqYL+I\nWCNpClCOiJ0qrnFgMTNrUDcM3k8HHpd0vqRbJX1L0pbAdhGxJr1mDbBd64poZmbVTG51AWqYDOwJ\nfCIifinpK8CwLq+ICElVmya9vb1D70ulEqVSqXklNTPrQOVymXK53JS827UrbApwU0RMT4/3BU4F\nXgPsHxGPStoeuM5dYWZm41f4rrCIeBR4SNKMNOlA4G7gSmBemjYPuLwFxTMzsxG0ZWBJ/T1wsaQ7\ngJnAIuAM4CBJ9wIHpMdtzZtNmlm3advAEhF3RMReEbF7RBwREU9GxNqIODAiZkTEwZVrWNqRN5s0\ns27TlmMs49GOYyzebNLM2l3h17GMRzsGFvBmk2bW3go/eF803mzSzLqJA0uTebNJM+s27gprMm82\naWadwGMsI2i3wGJm1gk8xmJmZm3LgcXMzHLlwGJmZrlyYDEzs1w5sJiZWa4cWMzMLFcOLDnyTsZm\nZg4sufJOxmZmXiCZO+9kbGadyCvvR9DqwALeydjMOk9XrLyX1C9ppaTbJN2cpm0jaYWkeyVdI6nt\nmgLeydjMul3bBhYggFJE7BERe6dpC4AVETEDuDY9bhveydjMrI27wiStBt4cEU9k0lYB+0XEGklT\ngHJE7FRxX8u6wryTsZl1qq4YY5H0APAk8ALwzYj4lqR1EbF1el7A2sHjzH0tH2MxM+s0eQaWyXlk\n0iSzIuJ3kl4BrEhbK0MiIiRVjSC9vb1D70ulEqVSqZnlNDPrOOVymXK53JS827bFkiXpNOAZ4CMk\n4y6PStoeuK6dusLMzDpV4WeFSdpC0lbp+y2Bg4E7gSuAeell84DLW1NCMzOrpS1bLJKmAz9IDycD\nF0fE6ZK2AS4FdgT6gSMjYqDiXrdYzMwa1BWD92PlwGJm1rjCd4WZmVnncmAZA+9ibGZWmwPLGHgX\nYzOz2jzGMkbexdjMisSD9yOYyMF772JsZkXhwfs24F2Mzcyqc2AZA+9ibGZWm7vCxsC7GJtZ0XiM\nZQReIGlm1jiPsbSA166YmdXHgaVOXrtiZlYfd4U1wGtXzKyoPMYygmaPsXjtipkVkcdYWsRrV8zM\nRufAUievXTEzq4+7wurktStmVmRdM8YiaRJwC/BwRLwzfYLkEmAqE/QESQcUM+sG3TTGchJwDzAY\nKRYAKyJiBnBtetxUnmZsZtaYtg0skl4NHAacCwxG0cOBC9L3FwDvbnY5enp6hsZT+vv7h8ZZPM3Y\nzKy6tu0Kk/Rd4AvAS4F/SrvC1kXE1ul5AWsHjzP3NWWMxdOMzazI8uwKm5xHJnmTNBd4LCJuk1Sq\ndk1EhKSqEaS3t3fofalUolSqmkXdKqcZu8ViZp2uXC5TLpebkndbtlgkfQE4Fnge2Iyk1XIZsBdQ\niohHJW0PXBcRO1Xcm2uLJTvNuKenZ4NjM7MiKPzgfUR8JiJ2iIjpwFHATyLiWOAKYF562Tzg8mZ8\nfnbDyb6+PhYtWjSUPjjm0tfX14yPNjPreG0ZWKoYbIKcARwk6V7ggPQ4d9mZYINTirMzwXp6ejzV\n2MyshrbsChuPvLrCvOGkmXWTrlkgORZ5jrF4JpiZdYvCj7G0A284aWY2Ng4sVXjDSTOzsXNgSfX2\n9vLggw8C62eCPfnkk/T29nommJlZAzzGknrwwQeZO3cuS5cuZerUqRscm5kVmcdYmmDq1KksXbqU\nuXPncuONNzqomJmNUVe3WKptif+jH/2Iww47jBtuuIF99923WcU0M2srbrHkpHJL/JUrV3LMMcdw\n1VVX8bGPfWxozMXMzOrXlS2WbEtlcAbY3LlzOfLII+nr62PmzJkeYzGzruIWS52ye34NGhgY4Jln\nnhlqqfT09PDRj36Uww47jEsvvZSZM2cC68dczj///FYU3cysYxU6sNR6+uMhhxwytDZl5cqVHH30\n0dxxxx0sXbp0WCCaOnXqsC34zcxsdIXvChtpz6+VK1ey++67c8cddzBz5kxviW9mXct7hY2g2hhL\ntT2/BgYGOProozn99NP55je/Oex5K319fd692My6isdYGlBtz6/BlsnFF1/MzJkzh23Z4i3xzczG\np9AtllpPf5w9ezaHHHLIsO4ut1TMrJsVvitM0mbAT4FNgU2AH0bEqZK2AZYAU4F+4MiIGKi4dyiw\nVFsA6QBiZrahwneFRcSfgf0j4o3ATGB/SfsCC4AVETEDuDY9rmnOnDkbDMJ3eldXuVxudRGayvXr\nbEWuX5Hrlre2DCwAEfHH9O0mwCRgHXA4cEGafgHw7hYUraWK/h+369fZily/Itctb20bWCRtJOl2\nYA1wXUTcDWwXEWvSS9YA27WsgGZmVtXkVhegloh4EXijpJcByyXtX3E+JLXfAJGZWZdry8H7SpI+\nC/wJOBEoRcSjkrYnacnsVHFt+1fIzKwN5TV435YtFknbAs9HxICkzYGDgM8BVwDzgDPTfy+vvDev\nL8bMzMamLVssknYjGZzfKH1dGBFnp9ONLwV2pMZ0YzMza622DCxmZta52nZW2FhIOlTSKkn3Sfp0\nq8tTD0nnSVoj6c5M2jaSVki6V9I1knoy505N67dK0sGZ9DdJujM99x8TXY9aJO0g6TpJd0u6S9In\n0/RC1FHSZpJ+Iel2SfdIOj1NL0T9ACRNknSbpCvT4yLVrV/SyrR+N6dpRapfj6TvSfpV+t/n30xI\n/SKiEC+StS73A9OAjYHbgTe0ulx1lPttwB7AnZm0s4BT0vefBs5I3++c1mvjtJ73s77VeTOwd/r+\nKuDQVtctLcsU4I3p+5cAvwbeULA6bpH+Oxn4ObBvwer3j8DFwBUF/O9zNbBNRVqR6ncB8OHMf58v\nm4j6tbwwEe9bAAAGQUlEQVTiOX6BbwGuzhwvABa0ulx1ln0awwPLKpI1O5D8MK9K358KfDpz3dXA\nPsD2wK8y6UcB/9XqetWo6+XAgUWsI7AF8Etgl6LUD3g18GNgf+DKov33SRJYXl6RVoj6kQSRB6qk\nN71+ReoKexXwUOb44TStE9VaCPpKknoNGqxjZfojtGHdJU0jaZ39ggLVscHFvJ1Wv38HTgZezKQV\npW4AAfxY0i2SPpKmFaV+04HHJZ0v6VZJ35K0JRNQvyIFlkLOQojkT4SOr5uklwDfB06KiKez5zq9\njhHxYiT72r0amF1tMS8dWD9Jc4HHIuI2oOo0/k6tW8asiNgDeAfwcUlvy57s8PpNBvYEvh4RewJ/\noGJ/xWbVr0iB5RFgh8zxDgyPsp1kjaQpAOlC0MfS9Mo6vpqkjo+k77Ppj0xAOesiaWOSoHJhRAyu\nPSpUHQEi4klgGfAmilG/twKHS1oNXAIcIOlCilE3ACLid+m/jwM/APamOPV7GHg4In6ZHn+PJNA8\n2uz6FSmw3AK8TtI0SZsA7ydZUNmJBheCwvCFoFcAR0naRNJ04HXAzRHxKPBUOuNDwLFUWTzaCml5\n/h9wT0R8JXOqEHWUtO3grBqtX8x7GwWoX0R8JiJ2iIjpJP3qP4mIYylA3QAkbSFpq/T9lsDBwJ0U\npH5puR6SNCNNOhC4G7iSZtev1QNMOQ9WvYNk1tH9wKmtLk+dZb4E+C3wHMkY0fHANiQDpvcC1wA9\nmes/k9ZvFXBIJv1NJP+nuB/4aqvrlSnXviT987eT/ODeBhxalDoCuwG3pvVbCZycpheifpmy7cf6\nWWGFqBvJGMTt6euuwd+MotQvLdfuJBNK7gAuIxnQb3r9vEDSzMxyVaSuMDMzawMOLGZmlisHFjMz\ny5UDi5mZ5cqBxczMcuXAYmZmuXJgsUKR9PJ0C/TbJP1O0sPp+1sltdUTUyXtJ+ktTcx/U0k/VWKa\nhj+a4SPp/lg9kr5cuZWJ2Xi01f/RzMYrIp4g2egSSacBT0fEl1tVHkmTIuKFGqf3B54Gbmogv8kR\n8Xydlx8NLI2ISBZMD+VxLPAJYP9IHv/9DeBLwA31lsNsJG6xWNEpfUhROf0L/erMPknl9K/1X6YP\nQtpL0g/SByB9Pr1mWvrQo4uUPCjpu+nWLYyS779L+iVwkqS5kn6etppWSPorJTs9fxT4hzR9X0nf\nlvTeTMGfSf8tSbpB0g+Bu5Tspny2pJsl3SHpb2vU/QPADyu+jCNJnsFxUESsBYiI+4BpyjzwyWw8\nHFis6AR8FXhfRLwZOB9YlJ4L4NmI2Av4BsmP8N8BuwLHSdo6vW4G8LWI2Bl4CpifdqudA7y3Rr4b\nR8ReaWvpxojYJ5IdZpeQPGSpH/gv4MsRsWdE3MiGu8xmj/cAPhkROwEnAgMRsTfJpokfSQPV+kpL\nk4BdI+LeTPK0tMwHRcRjDHcbyTONzMbNXWFWdJuSBIoVaXfQJJK92QYNblR6F3BXpM+pkPQAyU6v\nTwEPRcRgd9VFwCdJHoK0C8mzPKrluyTzfgdJl5I8VGkT4IHMuarb0Vdxc0Q8mL4/GNhN0vvS45cC\nfw30Z67flqSbLesx4AmSDVq/UnHutySBx2zcHFis6ATcHRFvrXH+2fTfFzPvB48H//+RbTkoPR4t\n3z9k3p8DfDEilkraD+itcc/zpL0IkjYiCULV8gP4RESsqJFPtqxZfwTmADdIeiwivlNxrTcOtFy4\nK8yK7lngFZL2geTZMJJ2bjCPHQfvBz5IMsj961Hyzf6ov5T1rZnjMulPA1tljvtJdpEFOJzk2ePV\nLGd9dxySZkjaouKa3wMvqbwxkueOHAp8QdLBmVPbM7zFYzZmDixWdC8A7wPOVPL44FpjCSM9Se/X\nJE8XvIdk2/FvRMRfRsk3m1cv8F1JtwCPZ85dCbwnnQ49C/gWsF+a3z7AMzXyOxe4B7g1nUL8DSp6\nH9KZaHdJen1lHun4zuHAeZLenJ7bgwZmp5mNxNvmm40gHRS/MiJ2a3FRGibpOJLnm585ynUzSLrq\nDp+QglnhucViNrpO/evrO8AcZRexVPd3wFkTUB7rEm6xmJlZrtxiMTOzXDmwmJlZrhxYzMwsVw4s\nZmaWKwcWMzPLlQOLmZnl6n8BKYzjzfpoiFsAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -151,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -178,16 +177,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(39.565144000000004)" + "array(39.57)" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -205,16 +204,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 10.71, 36.71])" + "array([10.71, 36.71])" ] }, - "execution_count": 9, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -240,13 +239,13 @@ "evalue": "A value in x_new is above the interpolation range.", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mch4_cp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8752\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\polyint.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 77\u001b[0m \"\"\"\n\u001b[0;32m 78\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 80\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\interpolate.pyc\u001b[0m in \u001b[0;36m_evaluate\u001b[1;34m(self, x_new)\u001b[0m\n\u001b[0;32m 496\u001b[0m \u001b[1;31m# The behavior is set by the bounds_error variable.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[0mx_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m \u001b[0mout_of_bounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 499\u001b[0m \u001b[0my_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_new\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\Miniconda\\lib\\site-packages\\scipy\\interpolate\\interpolate.pyc\u001b[0m in \u001b[0;36m_check_bounds\u001b[1;34m(self, x_new)\u001b[0m\n\u001b[0;32m 526\u001b[0m \"range.\")\n\u001b[0;32m 527\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbounds_error\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mabove_bounds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 528\u001b[1;33m raise ValueError(\"A value in x_new is above the interpolation \"\n\u001b[0m\u001b[0;32m 529\u001b[0m \"range.\")\n\u001b[0;32m 530\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: A value in x_new is above the interpolation range." + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mch4_cp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8752\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/envs/notes-python/lib/python3.8/site-packages/scipy/interpolate/polyint.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 76\u001b[0m \"\"\"\n\u001b[1;32m 77\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_x\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finish_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/notes-python/lib/python3.8/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_evaluate\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0my_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 676\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extrapolate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m \u001b[0mbelow_bounds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabove_bounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 678\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_new\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 679\u001b[0m \u001b[0;31m# Note fill_value must be broadcast up to the proper size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/notes-python/lib/python3.8/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_check_bounds\u001b[0;34m(self, x_new)\u001b[0m\n\u001b[1;32m 707\u001b[0m \"range.\")\n\u001b[1;32m 708\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds_error\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mabove_bounds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m raise ValueError(\"A value in x_new is above the interpolation \"\n\u001b[0m\u001b[1;32m 710\u001b[0m \"range.\")\n\u001b[1;32m 711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: A value in x_new is above the interpolation range." ] } ], @@ -263,10 +262,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "ch4_cp = interp1d(data['TK'], data['Cp'], \n", @@ -282,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -291,7 +288,7 @@ "array(nan)" ] }, - "execution_count": 12, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -309,10 +306,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "ch4_cp = interp1d(data['TK'], data['Cp'], \n", @@ -321,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -330,7 +325,7 @@ "array(-999.25)" ] }, - "execution_count": 14, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -362,19 +357,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -539,18 +544,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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9sCmhPswjXHptkEMkNyvwG40G1+/dy4GjR9nZtP74kSNcd//9HJ6eXg72Zo5kkdavdKGu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\nAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "ValueError", + "evalue": "Odd degree for now only. Got 4.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcp_ch4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minterp1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TK'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Cp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcp_ch4\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"k+\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TK'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Cp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ro'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/notes-python/lib/python3.8/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0mrewrite_nan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m self._spline = make_interp_spline(xx, yy, k=order,\n\u001b[0m\u001b[1;32m 547\u001b[0m check_finite=False)\n\u001b[1;32m 548\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrewrite_nan\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/notes-python/lib/python3.8/site-packages/scipy/interpolate/_bsplines.py\u001b[0m in \u001b[0;36mmake_interp_spline\u001b[0;34m(x, y, k, t, bc_type, axis, check_finite)\u001b[0m\n\u001b[1;32m 775\u001b[0m (x[-1],)*(k+1)]\n\u001b[1;32m 776\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_not_a_knot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 778\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 779\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_augknt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/notes-python/lib/python3.8/site-packages/scipy/interpolate/_bsplines.py\u001b[0m in \u001b[0;36m_not_a_knot\u001b[0;34m(x, k)\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Odd degree for now only. Got %s.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Odd degree for now only. Got 4." + ] } ], "source": [ @@ -559,6 +568,15 @@ "p = plt.plot(data['TK'][1:7], data['Cp'][1:7], 'ro', markersize=8)" ] }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "interp1d??" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -612,10 +630,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "x = np.linspace(-3,3,100)" @@ -632,19 +648,21 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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ylFUK1HvvwYoVcNppsZMUFhVySVddLfKTgNXu/qa7VwDjgSHVjhkOTHT3dQDurqXx5VNm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\nYv6Xv6i/UQpL167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\n", 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LTjbbLHZEpWXBAjj00LAfzuDBSuKlTIlcGqRnT1i0KAx8jhsHm24aO6LSsHhx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\nJJwSuYhIwv0/gG56R7Cwt98AAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -690,17 +710,19 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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PhNtuC4sNieQ6dzjrrPDJc9AgfQItTyomBLUEZrv7XHdfAwwGTirrtaqYMe+t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\n9/9saTRo/aYs3AAAAABJRU5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+ "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -731,10 +753,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "from scipy.interpolate.rbf import Rbf" @@ -749,17 +769,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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mnruBF8zsUkLDQKP69Z4EADO/ZeTKldC1KxucK1ksru+++2oEkIhIFeIWAJxz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\nwgAAAABJRU5ErkJggg==\n", 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JhXuUXnwR/vY3GDUKDjqI4KCDoq5IRGJC0zIR+cOIEXDxxdC1K4wcGXU5IhIzGrlHZJcxYxJHof7jH9CkSdTliEjMaOQehRkzuBTgqqugV6+IixGROFK416EgCGhoxtyjj+YroMUdd2BmWtMuImmnaZk6FAQBwa67wq9/zenAaq1pF5FaopF7XSopgRtvhOOOS1xhSUSklmjkXpdGjYKVK2HcOPKffDLqakQkxjRyrysffQTjxyeORu3aVfPsIlKrFO51wR1+/WvYdVdQqItIHVC414GnzzwT3n4bfv97aN066nJEpB5QuNe21avpNXky9OgBF1wQdTUiUk8o3GvbmDHsCXDXXdCgQdTViEg9oXCvJUEQsLsZq0eP5nHAjjhCByyJSJ3RUshaEgQBwdKlcN993LBhA64DlkSkDqU0cjez1mb2tJl9ZmafmllvM2trZtPMbEF42yZdxWaVL7+E++6DoUP5IupaRKTeSXVaZhzwsrv/J3Aw8CkwHJju7vsB08PH9c+NN0KjRjBqFPn5+VFXIyL1jCU7XWBmrYA5wD5e4UPMbD7Q192LzSwHeMPdD6jus3Jzc72wsDCpOjLSnDnQvTv89rfwhz9EXY2IxJSZzXL33MqeS2Xkvg9QAjxoZh+Z2QQzawF0cPdigPB2tyqKyjOzQjMrLCkpSaGMDDRyJOyyC1x3XdSViEg9lUq4NwR6AH929+7AanZgCsbdx7t7rrvntm/fPoUyMssD558PL7wAw4dDm/q5u0FEopdKuBcBRe7+fvj4aRJhvzicjiG8XZJaiVnEnf0feghycuCKK6KuRkTqsaTD3d3/D/jGzDbNpx8LfAJMBYaEbUOAKSlVmE1eeIEjAPLzoXnzqKsRkXos1dUyVwCPmtnHwCHA74ExQH8zWwD0Dx/HWhAEmBkfnXwynwONLr5YByyJSKSSXi2TTrFYLfPcc3DKKQwBJmbAbyoi8Vdbq2VkE3e46Sbo0oXHoq5FRASdfiA9XnkFCgvh/vsZWVQUdTUiIgr3lG0atXfqBIMHEzRuHHVFIiIK95S99hq8+y7cey8o2EUkQ2jOPVU33wwdO8L550ddiYjIZhq5p2LGDHjzTRg3Dpo2jboaEZHNNHJPwRfnnw8dOsCFF0ZdiojIFhTuyXr3Xfb98ku49lpo1izqakREtqBwT9bo0ZQAXHxx1JWIiGxD4b6DgiDgIDN48UXGAdaypU41ICIZR6cfSMagQfDMM7RdvZrvMuD3E5H6SacfSKeFC2HSJMjLY3nUtYiIVEFLIXfU7beDGVx9NfmtWkVdjYhIpTRy3xHLlsGECfCLX8Bee2meXUQylsJ9R/zpT1BWBsOGRV2JiEi1FO41VVYGd98NAwdCt25RVyMiUi2Fe009+CAsXQq//W3UlYiIbJfCvSY2bGD5DTdA797Qp0/U1YiIbJfCvSaeeoo2paWJUbtZ1NWIiGyXwn173GHsWD4BOPnkqKsREakRhXs1giCg/047wezZ3AZYgwY61YCIZAWdfmB7BgyAuXNpUlzM2gz4rURENtHpB5L0l4sugmnT4MorWRd1MSIiO0CnH6hGq/HjoWVLuOgi8svLoy5HRKTGNHKvytdfczZAXh60bq15dhHJKgr3rQRBgJlxd5cuOLDXH/+onagiknW0Q7UyixdD5878dc0afpUBv4+ISGW0Q3VHjRsHa9cyNuo6RESSlPIOVTNrABQC37r7QDNrCzwBdAa+Bs529+y5rsWKFay5/Xaannkm53btGnU1IiJJScfI/Urg0wqPhwPT3X0/YHr4OHvcey9N166FESM0zy4iWSulcDezPYH/AiZUaD4VmBjenwiclsp31KlVq+COO3gZoHv3qKsREUlaqiP3O4HrgI0V2jq4ezFAeLtbit9RJ4Ig4PqWLaGkhAAwM62SEZGslfScu5kNBJa4+ywz65vE+/OAPIBOnTolW0baNF2zhqB1azjiCN5//nkyYRWRiEiyUhm59wFOMbOvgceBfmb2CLDYzHIAwtsllb3Z3ce7e66757Zv3z6FMtJjwy23QGkpjB4ddSkiIilLOtzdfYS77+nunYFzgNfc/ZfAVGBI+LIhwJSUq6xtS5ZwFcDPfw4HH0x+fn7EBYmIpKY21rmPAfqb2QKgf/g4I206GvW+Dh1oChzwxBOYLsYhIjGgI1TffhuOPJLbgGsz4LcQEakpHaFalXXr+Or442HvvdFEjIjESf0+5e/YsXQpK4N772XYzJlRVyMikjb1cuQeBAG8/jrcfDNPAJx0ktazi0is1Mtwf76ggJX9+jFv3Tou4ccDlvr27Rt1aSIiaRHbcK9sJB4EAXz4IS8BLffem25FRSwH3B1354033qjbIkVEaklsV8uY2Y9HmW7cyLirr6b1XXcxiMRRVUcCn4evzYTfQERkR1W3WiZ+O1TXr4dLL+V1gAMPhJISWLaMK3/4gTXATsOG8ZNbb2V5GOiaihGROIrVtEwQBPy0cWOYMIFdgcnz5nHfkiX87ocfuArYH7Bbb6WUH6dtNBUjInEUv2mZ116DY4/lGOD1rfq2aaomCAKtjhGRrFe/DmIqKUncVPMSBbuIxF1sw33INdds85ROCCYi9UVsw33YmG3PV6YRu4jUF/EM97ZtoWH8FgKJiNRUPMM9Ay7+ISISJYW7iEgMKdxFRGJI4S4iEkPxCveNG2HZMoW7iNR78Qr3775LBLzCXUTquXiFe7jGXeEuIvWdwl1EJIYU7iIiMaRwFxGJoXiGe7t20dYhIhKx+IV7q1bQpEnUlYiIRCp+4a4pGRERhbuISBwp3EVEYkjhLiISQ0mHu5ntZWavm9mnZjbPzK4M29ua2TQzWxDetklfudVwh6VLFe4iIqQ2ct8AXOPuPwF6AZeZWVdgODDd3fcDpoePa9+KFbB+vcJdRIQUwt3di939w/D+SuBTYA/gVGBi+LKJwGkp1lgzOoBJRGSztMy5m1lnoDvwPtDB3Ysh8T8AYLcq3pNnZoVmVliyKZhToXAXEdks5XA3s52BvwNXufv3NX2fu49391x3z22fjkBWuIuIbJZSuJtZIxLB/qi7Tw6bF5tZTvh8DrAktRJrSOEuIrJZKqtlDPgr8Km7317hqanAkPD+EGBK8uXtAIW7iMhmDVN4bx9gEPBPM5sdtl0PjAGeNLNfAf8GzkqpwpoqKYHmzRObiEg9l3S4u/vbgFXx9LHJfm7SdACTiMhm8TlCVeEuIrKZwl1EJIYU7iIiMRSPcHdXuIuIVBCPcF+9GtasUbiLiITiEe5a4y4isgWFu4hIDCncRURiSOEuIhJDCncRkRiKT7g3bgwtW0ZdiYhIRohPuLdvD1bVqW5EROqXeIW7iIgACncRkVhSuIuIxJDCXUQkhrI/3NesgVWrFO4iIhVkf7hrjbuIyDYU7iIiMaRwFxGJIYW7iEgMKdxFRGIoHuHeoAG0bh11JSIiGSMe4d6uHeyU/V0REUmX7E9EHcAkIrINhbuISAwp3EVEYkjhLiISQ7UW7mZ2gpnNN7PPzWx4rXzJ+vVQWsrrc+fWyseLiGSrWgl3M2sA/Ak4EegKnGtmXdP+RUuXAvDUm2+m/aNFRLJZbY3cewKfu/uX7r4OeBw4Ne3fEh7AVJL2DxYRyW61Fe57AN9UeFwUtm1mZnlmVmhmhSUlOx7PQRCw/8EH8ySwIPF5mBlBEKRQtohIPJi7p/9Dzc4Cjnf3oeHjQUBPd7+istfn5uZ6YWFhKt9HbfRDRCSTmdksd8+t7LnaGrkXAXtVeLwnsKiWvktERLZSW+H+AbCfmXUxs8bAOcDUWvou8vPza+ujRUSyUsPa+FB332BmlwOvAA2AB9x9Xm18F6B5dhGRrdRKuAO4+4vAi7X1+SIiUrXsP0JVRES2oXAXEYkhhbuISAwp3EVEYqhWDmLa4SLMSoCFKXxEO2BpmsqJUlz6AepLJopLP0B92WRvd6/0tLgZEe6pMrPCqo7SyiZx6QeoL5koLv0A9aUmNC0jIhJDCncRkRiKS7iPj7qANIlLP0B9yURx6QeoL9sVizl3ERHZUlxG7iIiUoHCXUQkhrI63OvkItwpMrMHzGyJmc2t0NbWzKaZ2YLwtk2F50aE/ZlvZsdXaD/UzP4ZPneXmVkd92MvM3vdzD41s3lmdmUW96Wpmc00szlhXwqytS9hDQ3M7CMzez7L+/F1WMNsMyvM8r60NrOnzeyz8L+Z3nXeF3fPyo3EqYS/APYBGgNzgK5R11VJnUcBPYC5FdrGAsPD+8OBW8L7XcN+NAG6hP1rED43E+gNGPAScGId9yMH6BHebwn8K6w3G/tiwM7h/UbA+0CvbOxLWMNvgMeA57P17yus4Wug3VZt2dqXicDQ8H5joHVd96VOO5zmH6838EqFxyOAEVHXVUWtndky3OcDOeH9HGB+ZX0gcT783uFrPqvQfi5wX8R9mgL0z/a+AM2BD4HDs7EvJK5yNh3ox4/hnnX9CL/3a7YN96zrC9AK+IpwwUpUfcnmaZntXoQ7g3Vw92KA8Ha3sL2qPu0R3t+6PRJm1hnoTmLEm5V9CacyZgNLgGnunq19uRO4DthYoS0b+wHgwKtmNsvM8sK2bOzLPkAJ8GA4XTbBzFpQx33J5nCvbO4p29d1VtWnjOmrme0M/B24yt2/r+6llbRlTF/c/Qd3P4TEyLenmR1Yzcszsi9mNhBY4u6zavqWStoi70cFfdy9B3AicJmZHVXNazO5Lw1JTMX+2d27A6tJTMNUpVb6ks3hns0X4V5sZjkA4e2SsL2qPhWF97dur1Nm1ohEsD/q7pPD5qzsyybuXgq8AZxA9vWlD3CKmX0NPA70M7NHyL5+AODui8LbJcAzQE+ysy9FQFH4r0GAp0mEfZ32JZvDvU4vwp1mU4Eh4f0hJOavN7WfY2ZNzKwLsB8wM/wn3Eoz6xXuLR9c4T11IvzevwKfuvvtFZ7Kxr60N7PW4f1mwHHAZ2RZX9x9hLvv6e6dSfz9v+buv8y2fgCYWQsza7npPjAAmEsW9sXd/w/4xswOCJuOBT6hrvtS1ztN0rzj4iQSqza+AEZGXU8VNU4CioH1JP5P/CtgVxI7wRaEt20rvH5k2J/5VNgzDuSS+GP/AriHrXbW1EE/jiDxT8KPgdnhdlKW9uUg4KOwL3OBG8P2rOtLhTr68uMO1azrB4l56jnhNm/Tf8/Z2JewhkOAwvBv7FmgTV33RacfEBGJoWyelhERkSoo3EVEYkjhLiISQwp3EZEYUriLiMSQwl1EJIYU7iIiMfT/0B7Ogskh/RsAAAAASUVORK5CYII=\n", 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ERI6Bt8EvIiJR503wb9yoi7dERDyiGb+ISJJR8IuIJBkFv4hIklHwi4gkGQW/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\niCQZBb+ISJJR8IuIJJn/D8ptq7/3lHsDAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -843,10 +869,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, + "execution_count": 42, + "metadata": {}, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D" @@ -861,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -871,27 +895,29 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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KyGazgQenKMRkksQwRbDJQpFKpVAqlTo6t4w3nUZtnCwGdB9Q3WBO/GqP7mJLZ5LwAq7D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\n8fEKn8ltB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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -910,10 +936,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 45, + "metadata": {}, "outputs": [], "source": [ "zz = Rbf(x, y, z)" @@ -921,27 +945,29 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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MST7aF7n1kotfNl1meqoIUb51fV7khhvEakdYzxW3dP9S5ySQDLCowIlVzK5evZp4PM6Y\nMWOcPShbEVqsalyLuojEYrGM9VCtUTzlQy0vLy+6sOvMxVztQ6aSWYW8WeQbWU09/vl6aDNtPxvR\nUqg6otpL615yPS+graanVcy6wS/bHcSgm8nm+GUbyU8Vs6ndv/IVs4lEImmLSRWzr7/+OoFAQIvV\nDGixqnEV1gzOjnyoqR2ZqqqqMmaipovsOUWuwsauZFamfSi0cMp17Epcw5YdsezWXcjjnqsvMpv2\nk8pb6zbcOCY3Yj0vVNS/tLQ0+Xsx/bLdHbeLaSfGl68tJdtWtkqsqm1Zf27atImddtqpU+Pf2tFi\nVdPl5OpDVR7OXEs1FVrsZbP+TFaFrrwZZLNtu7GXl5d3WO6rq/crnxuQNfoWDoe3iMB1deKXW4WD\n20WNopB+2XzErNuPmx6fM61soS3C+vnnn/PVV18xfPhw1q9fzwEHHOD4mM8++2xefvll+vXrx6JF\ni2yXueSSS3jllVcoKyvj0UcfZffdd3d8HE6gxaqmy7D6UJubmwkEAgQCAVsfqp2HM9eLUzGmdNOt\n39oZy+fzEQwGc7YqdFVkVXXEKkS5r64m0w3IMIxk9QgVGUmNpljfrxO/3EuuYsZJv2x391F3h0i+\nG8R0R+eMqnSibADr169n9uzZfPfddyxbtoyHHnqI4cOHJ/+NGDGC3XffnUMPPTTvMZ111llcfPHF\nnH766bavz549m6+//pqlS5cyf/58LrjgAj744IO8t1dItFjVFJV0PlTr/+18qLl2ZLKj2JFV6zS/\nYUirwo6sCrms30lSL/ROjj2XcbvhpqOw3nw6292puwsWTXvy9VGnE7Pq3HHzQ44bx2TF7eMDieKr\nGsRTp05l6tSpAEybNo0HH3yQhoYGli9fzrJly1i+fDkLFizolFj94Q9/yPLly9O+/sILL3DGGWcA\nMHHiROrq6li3bh3bbbdd3tssFFqsagqOdZo/FosBW/pQPR4P8XiclpaWdn2oOyPuUimGWLV6abtD\nZywrVptFNBot6tjdfmzscEqwuCHBZ2un2NHBXH3UQHIGqbNJgU7jpgfIdHT3MTY0NDBw4EB8Ph/j\nxo0r2phWr17NkCFDkr8PHjyYVatWabGq2XbI1oeqEh/UhVplwRdKIBXipqX2U7VuVclSTk+VF0ps\nq4cI9a+7NBxwO/kkfqUm+KTr1tMdpmbdhlvEjJ1fViV/qe+49dzINZFnW3zQ6c5iVX2Xu+p6m3ot\ncetx1GJV4yhWgWqd2k/1oSofpPKh+v3+ZOH4QuH0l1BlxKtoiJriqaqqcnQ7CifFqnpIUDVRvV4v\ngUCAyspKR9ZvpRhe4e5GPgk+qbVlm5ubtxArXR2V7Q6iwe04cW446ZftDp9pdx9jV41/0KBBrFy5\nMvn7qlWrGDRoUNHHkQ1arGo6TTofaupN05pklFpLtKWlpV3bzULghGiyy4hXkeBoNEo4HHZotM5j\n95CgktWUaC0Gbr+puIFMFgPVNjgUCm1hMdBll+xx88OSddYpG5z2y24N9hM3f76QeXzKctUVHHPM\nMdxzzz2cdNJJfPDBB1RXV7vSAgBarGryJBsfKtjXEq2srEyazBXFiL7luw27hC+7af5i+DrzGb+1\nJqrH47FNVit08lY261bLdcebZbHJxWKQTdmlbSHxa2vdr1Q6Ojegred9OvuJ9VxILbnktuOYq9jv\nCqzHNZWamhp69+5dkO2efPLJvPPOO9TU1DBkyBBuuOGGZGvY8847jyOOOILZs2czevRoysvLeeSR\nRwoyDifQYlWTNbn6UJWHM5tEnWIUYM9VkNkVvu/Kov2QfQQh9TPItSZtIdGCtLDkMo28rUTe3Ewx\nvw/W8yKbLk7Wfy0tLa72y7r5vMz0GW/cuJE+ffoUZLszZ87scJl77rmnINt2Gi1WNR2Siw9VCaRc\n63EWK7LakdXATuRlU/herb/Q1QYyrT/bCHA+69ZsPWSaRs418paa+OVW9ANSdtg96ESjUeLxOCUl\nJVn5ZdM95BQqat8dPttM19aamhr69u1bxNF0T7RY1dhi9aE2Nzfj9/uThfitFwarOFJTzJnabqaj\nK20AnRXaHa3fKdKJ7dSaqNm0ni0mqcelu033b0siPtvIWyax0tLS4lhyz7aA278L1vE57Zd1IjHQ\n7cdPkSmyqsVqx2ixqkli50MF2kVU1O9WH6oTU8xdIVatNVGh4/72bkCNP1OiVz6fQbEiq+qYA52+\nSRUDt46rq+goKtvU1JRz4pe2GLibXMRgrn7ZTB3htqbzI9MxrKmpcW0GvpvQYlWTvGCk86F6PPbF\n7p3saV9MG4DyocbjcUe9nMWyATQ3N7dr29odWp9GIpGk5021G0y9SRmGkdwvt/jgNLmTa23ZbOqH\nKnGc73ng5uibm8fmJLn6ZbM9P6wP8G49jh2J1d12263II+p+aLG6jZKtD1VFWePxOH6/n1AolHNP\n+2wopNBT0/zhcDgpukOhULLnu1OodTl90VSR7HA4TCKRwOfzOT7N7/TxV8dclclSNXRViS/rMVI3\nItU7W92sOqob2d2jLdsauSR+WaNuhZxC1nSMYRhFmW3K5/ywniNNTU1F98tmS6Z7wqZNmwqWYLU1\nocXqNoTVh5qpHmrq9Lh6Eq6oqCjKGJ26qFj3w+PxEAgEiMViBSl8r3DKj5kq9vx+P8FgkFgsVtDG\nCZ0ltUyWmhIOBoNpawlaz0HVHMJKuhtURwk/nY3G5UssFmP58uV8/PHHPPvssyxYsICGhgai0Wi7\n8lHWsft8PsrLy+nZsydjxoxh0KBBHHrooRx88MFblHnbWsnVD5nNFHJq9M2NuDkiCO4ZX7rzw9qe\nu9h+2WzJJPg3btxIv379CrbtrYVt4yq4DZPOh5r65bT2hU+dHlftUAuJGk9nL4wqm99uml8JKTeT\nmrBmbZwQjUbbfYZO0pkbeqp/NtVaYbWX5Du2jqaWs+3m45QHrra2luuvv57nnnuO2tpa1FoMwAuk\npsGpC60H8FmWNQwDDINoIkFdXR11dXWsWLYMH/Dnhx/G7qhVVVWxzz77cOmll7LffvvlNf5CUGhR\nk+t5kFpbFuQ657aom6ZzWK1r2fhlc3nYccovqyOrnUeL1a2Ujnyo0CYylBi1djSyLlesyES+27GL\nQqbbj0KTzz6kE9hd0TghF5SwVj7TfCooWMln/3KNxtmVYcrkkQTxlF1xxRW8/NJLSfEYQESnEqV+\n8//qE2s1lzHMv0WBOCJkrbfTBFCOiNgG2sSsYb5fPSIa5t99QH19Pa+99hqvvfZau33dbbfdmDFj\nBhMmTMj+AG4ldHQeqM5yfr8/Y9StKxJ73BK5TIfbxwcdX9sz2Qugc37qbHz1mY6hesDXZEaL1a2I\nXHyo1lqcdh2NrHg8HdcndYJcxUqmKGSm9Rfy4pvtPiiBraLZ6QR2schl3FZhnW2ZLOu6iym684nK\nfvzxx/zkJz9h7dq1eKBddNOLXDQ9iPj0mL8b5r+o+bvP8prX8t4o0AsYAZQBy4Fl5rJey3uqzdcb\ngHVAyFxHzFymJ7DZXFbNFSxesIApkycnx9u7d2+effZZdt999xyP2taH1WaSSjqhks1DTTZCRVNY\nnPDU5uuXzbYrnOr+lW69mo7RYrWbY40Yqd7udhdQNQVu9fdkm6RTjO5SkJ2ISWdXyMbX54bIql1X\nrGzLZXVVZDX1ASdXYe3WG7n1O3LFFVfwl7/8JflaCCgBwubvPkRAVgCbgGZErA4GqoCN5r8EMAYY\nhojPjxCxOQIYbS6zAfjQMg4lTscCg8zX55ivRc2xhIDx5s8vgbVA0Hw9aG5LRV9j5jo3b9zIAQcc\ngN/828EHH8yDDz64TU45ZhI0nREqTngh3R651ONzpr5sOBxOrmPWrFlUV1czdOjQgnUVfPXVV7ns\nssuIx+Occ845/OIXv2j3ek1NDaeddhpr164lFotx1VVXceaZZzo+DqfQYrUboi6esViMlpaW5BSC\n3TR/6vRyPrU4u9oGkK1dIZdtFHpaL/X31Jqo2XbFslLIz8Fu3akPON2hDm22rFu3jqOPPpovv/wS\nP20XQiXs1JFQgtALNAL1iGDc3vxbC/CVudz2iIhdB3xuvq6E4zqgDpnar0VE7WRE2K4FVgMLgbm0\nWQpGAUeYy38JLDLHoNLUyhBxuwSJtvZHRO/X5piVu1lFbd944w1GjxxJAhg5ciRvv/02PXv27MRR\n3DbIV6hkk/jlZhGo0GK1YzLN4CQSCZqbm5P36UQiwaJFi/jyyy9ZsWIFK1asYLvttmPEiBHt/h19\n9NH0798/r/HE43Euuugi3njjDQYNGsRee+3FMcccw9ixY5PL3HPPPey+++7ceuut1NTUsOOOO3La\naae5NqHTnaPS2JLqQzUMqblZUlKSXMbOv9nZMk3FmD63bkdh9UR6vd4Op/nz2YbTWI+P+hw60xWr\n2NgJ6840GwD3eG0TiQTTpk3j9ddft/WbBhHxV4YIUAPYy/z9c0Rw+pFoqgeJgG5ExOseiFAMAe+Z\nyx0L7IRM128EnkME60BgPfA2MB+oRERvPbALMAH4ztzmXUiEF2S6v7+5zCLLeA4G+gKvAwvMMSTM\nfz7a7Achc9xhYOW33zJi2DASwNixY/n3v/+dtlpDtrhBNHQF2VhNUpN6UhO/lNXKzjfblbjhe9sR\n3WGMqRaU2267DYCvv/6aGTNmcPvtt7Ns2bLkv/fff5/99tsvb7H6n//8h9GjRzN8+HAATjrpJJ5/\n/vl2YnXAgAF8+umngPjge/fu7VqhClqsup5MPlT1JVXLqCiqU8JOUawLprpgh8PhZHesUChEZWWl\nY1+iYggnawH8XOwWHVHIsatzq66uLnn+FKKebiYKtX+33XYbt9xyCz5k6j6ICLk+SKQyggjTnsAa\n2oReKRK13IwI1gm0Cdv/muuZADQhkdGPkWhmwlzfi8AbiGDcZC5/DiKIQcTpbHMbqpjat+b6dgdO\nAZ4yxzQcEbCbgVXACeb43gVeQYRoBLEf7AF8YG5zFLAD8B9EMJcjtoVay/H5+osv6N27NwCnnnoq\n999/fy6Ht1vQVUI6G4uBdXo4XeKXnQ+ymJHZrhbNHeHm8WU69zZs2EC/fv0YOHAgAwcOZNKkSY5s\nc/Xq1QwZMiT5++DBg5k/f367Zc4991wmT57MwIEDaWho4Omnn3Zk24VCi1UXkq0PVd3Y6+vrCyLs\nrCghWYh+8+oCrabOgsGgo92xrBRCEKXaFKwF8AtxEXXqxptqEwEKdv4Um40bNzJhwgQ2btyIHxF2\nLbRN85fRNi2/MxItnYsIy4sRkbcIeASJZh6NiM31SIS0PyI8VeXhFxAReSywN2IH2Ai8jCRRjUIi\noQ8ikdJKRCjHEFE6DomcfotEVJ9GBGgcOAg4BBHAXyHC825zrBEk0rovYiP4yFzvFETg/sscW8jc\n9xba7AEJ2pK6gub6nnjiCZ544gkqKipYvHgx1dXVOR55TS6o77Hf79/ie2ctt5Qp8SuTkO3sdaI7\nRMvdPsZM49u4cSN9+/Z1fJvZHI9bbrmF3XbbjTlz5vDNN99wyCGHsHDhwoLWIe8M3f+utJWgpoSy\nKTelEoxisVgyCz4UChX0C1uIJCuVDa+iwT6fL+nnLBROilU7m4K6WXR2StUOpz5fO3tCMBhMRlWd\nRj3oWH8vFG+++SbHHXdcMotfRVO9iDAdiURUP0IE6y7A98CbtGXt34WIwABygdwIPIaISVVaajVw\nPW3lpLxI1PITRLQOQJKoNgLnIQlWIFPwc4G3kKoAG4F/INP4uyCieSkS5T0eWIlESd9BIrK7mO/x\nAZPMZZeY4z0B+DHwPvC8ub2YOa6xiPj2AdOQqOsb5hiGIUJXHa8A0NjYyOihQ4kA11133RbJGZrC\nY43I5puhnq6CgRssBk7QHSwAmcRqTU1NQRIeBw0axMqVK5O/r1y5ksGDB7dbZt68eVx33XUAjBo1\nihEjRrBkyRLXlr7TYrWLSVcPNbXclLXMkdX/2NDQULBsQitOiTyVzd/a2rrFNLlqh1pIOrsfdqWb\nrNHI5uZmp4ZqS74JYqnHPduSU92F888/nyeffDLp71S+TRVJ9SDZ+GuQSGccibauRsTlD4D9kCny\nOea/CwAfrnhZAAAgAElEQVTVsfstZEr+JCRyCjJdfxsSKT0OmVrfAKxAxHDMHMffETG6A1CDREaP\nNbeXMMezGPGcYo71IGBHpALAVMQC8ABiLQggU/1Hm9teiYjtW2mLxgaB/RGBut7c198jtoFnzd9P\nA74x/1ZmjleV44I2T+9tN9/MrTffzIGTJ/Pcc8+l+QTcjZujb/mOrRCJX6mRWTcfN6DdfriVjiKr\nhSgtN2HCBJYuXcry5csZOHAgTz31FDNnzmy3zJgxY3jjjTeYNGkS69atY8mSJYwcOdLxsTiFFqtd\nQCYfqpXUOqLBYHCLbOyuztTPhmyTdoqxL/lswy5pLV01gmLsQ7brTx13R8lShRq73XrtLuD5bP+I\nI47gvX//OzmVHUPE3CgkergZEV0DkAhqGPg5sCciKO9AkqBGA5+Zf1uLTKH/GxFy3yNT+KOQqOZc\nRCQ+Y673fNrqoNYgkdCdgTPM5VcgGfovm2MpAz6lLTFrmPmaHzgdEZfvIZHPfojI/RBJ4LrSfP01\n4BpznMchkdig+VP5Ywcg0dklwCzgCmBXJCL7HvAkEk2tQITxInNMB5j7GEfEexSxD8x76y2qqqoY\nM2YMH3zwwRbXK7cLm22NXBK/1D0ptQi++k6qe1BXtjHurmS6phXKBuD3+7nnnns47LDDiMfjTJ8+\nnbFjx/LAAw8AcN5553Httddy1llnseuuu5JIJLjjjjvo1auX42NxCk8HNwf3x9i7Cel8qNaf0D4C\npvyboVAobfS0qakJn8/XriJAIWhsbCQQCBAKhTpeGPvmAx1VJVD7XUjPTFNTE16vl9LS0g6XtSat\nqYeFUCiUcapcRYcLZWWoq6ujsrIyY0TUWstV2USCwWCHU/zZrDsflFiuqBCHZywWIxaLbTEe63Hu\niAkTJrD8q6+IIOLPoG3qPkFb/dErkanzG5Ho5Xbmsg20RVzLEcG23nz/RNosAZ8gHtAJ5s9mRAxa\n31+GRCoDiNAdBlxCW4mpGPA7872XIdHXT5HM/QZzW15EfFo7hK8H7kcEcACJsp4I9DBfXw08bv5M\nAIchU/xRJBL8T/MYHGuOcSaS1OUzj8MViAieBQxBIskvAfOQiHJvJGJbjVggWhHRqqLWPfr0YfHi\nxUn7SywWIxqNZvXdKjbNzc3J66jbaGpqorS01FUl4ZRgVS2erZ2/nKgt6xTqHlNWVlaU7eVDpuva\neeedxw033MD222/fBSNzLbYnj46sFpBcfKipkcdsE4zcFlntTPOBVG9jIehoP+xq01r73Hd2/Z0l\n3fpTz6Fcx51p3W5i1113ZfmyZUlRCiK+okh09HjgVSQzvwK4FxGZZUjEcDRtkckrkCgjiE+1DrgF\nKS0FMu3fCFyLeF1BxN41iHg9D7ECrEPE50vmcuvMdVchiVQ1SMTzV+Y4tkMirz9EROxYJAJ7M2IX\n2BtJmHoUiQRfjwjdl4BfIsldxyBRX1UZoAWJzv4HOBU4HKnjejciUkEiyBchloL7gN8AZyFJX4+a\n697TXOZ+8/j2RyLDqnWsIgrU1dQwuF8/SquqWLBwYfKBWXm4uyJrPR066psbVouACjRYsfPKWiOz\ndhaDQnT86g6fq/IO21GoyOrWiBarBUBNv3bkQ7WLPOZaLqhYAkO1jLPDqdqcXWUDyCcKnIliCj5r\nkpdba7laj3k8HiccDrerKZlNsseUKVP48MMPqUBEk8pir0aikxVIBPP35muTkAip8njehwjBd5Fk\nqeOQae5Z5t+WI1Pyz5rrX2H+bSKSoV+LRE/vRUTfeeZ2KhGrwSvAUcD/mONtQqbf70cE6nrgfxHx\ntxeS2PQwcCQS+fQggvV9xLs62xzfZbQlZ+2ECN8ngT+bfzvW3C7mul43t1lq7kcMEaRhRLT+Fok2\n/wmpFPAQEqk9ELENvI9YIYKI8F5jrmuiuT+15j7XI5HsGBCpr2fEiBGMHj2auXPn4vF40vZV31oT\nfTpDdxBcqWRjMbCK2dTask6dD93h2GUaY1NTk2uz792GFqsOYY2gxuNxNm/eTHV19RZPkdapZaDT\niS5er5doNOrIPmQiNeqZTuB1RigVo62rdT8KkXRUjEQ36zS/SvJyIlmqUA8L6oZVX19PPB5Pzhik\nJnyoMSgh6/F4uO666/jTn/6UnBeKI37RQ5Go4le0RQDXIkLqUWRq/2IkW34UcDkiKqPmOl6hfb3R\nSUiE1uOBbwyJjv4AEWVvI4K4CRFoC4BLERFaQZvQPdayzwFkin4kEon1IGJvARIhVZYFFRUOItPu\nY5FSU/shF+e7EEE+BYmWfoRk7v+Pue2/IwL1eKQ5wFGI3WEJIqZ7I2WxhpnreNw8FuPN8Y5FotBP\nm+t7ALEp3IEI3PvM8T6DRKZHIeW8Rpv7sAqxB5QgBc4HDBjA1KlTeeqpp9p9/tYIXGot0WJF4TS5\nk48YzDfxK9VikCnxqzPjKzbpxmit2KDpGO1ZdQDDMJKCR52UdXV1yWQou372mXyouVAMnye0+Q7L\nysq2aMEZDAYd8YIZhkFtbW1BTd7hcDiZLKCiwKFQyLGaqNFolJaWFqqqqhwYbRsqWt/Y2IhhGMlx\nO1mLtr6+Pmk/cQKV3KU8W+Xl5QQCgWR5ndSZhkgkQiKRwO/388ILL3DWWWehzirV9tRARGKT+bcr\nEOF2MRIVHGq+VodEBPdE2qAGEJF1gbm8B7EL3IkIs3Hmdj5Fpv2vRqbiQcTwxeb6bkIE7vdI0pTK\n9K8xl6tESmOtQSKV15njVHwM/BE4ExF7r5vvHWiO8x1kWl9FW1vMvz1HWzTzl7RVJIgiiVhP0ubV\nHW6OvwL4m7mNnWizJrwN/ME8Jj2BGYiw/S0iPi839/1eJPp6FPAjJDJci1QZeN18f7W5rwFz2z7a\nKjD8IouSV3aJPlZR61QUrrGxkfLyctcJG8MwaGpqcuXYIDcfuROkOx+sP62fvdIvahbMjQ836TzJ\nhmEwdepU3nvvvS4amWux/QC1WHUIFSlV1NbWUlpamkw6UG1PnS50XyhxZMUwpK1rJBIB5MIQDAYd\nL3qvxGrPnj0dv+BYp8sBSktLC1KbNhaL0dTURI8ePTpeOAusUVRFSUlJQRLqGhoakg8f+aIe3Kyl\nyXw+H62trclzVE0Jpl68I5EIS5YsYdKkScnkpAAwyAM1hvg2x5r/nkF8n03IlL0HEaaTEMH5LhJF\nHIYIrOMR0XWBud5PgKsQ4ad8q6vN109HxJni1+ZrdyNCGUQ0no9ETq+lrf3qEuAec5kWRMwNRabZ\ng4hIPB+JcirWIJaAheb+7o6IWfXIFkOEYj0ihL9BPLMXIhFkEN/pv8xtVCAJXrta1v+geVyqzeMx\nBRGdt5vL32oeq9mImB5i7tcztDUVUNUBmhBxOxw59pjbDZv/lFhVNW5ffvllfvjDH5IP6URLrt5I\nt4tVlXzoNootVjsiVchaZxUzJX51leUk08NIOBzmxBNP5K233irqmLoBOsGqkHi93qQFQN2ow+Ew\nJSUljrU9TbfdQiQlqUieigirL3qPHj0K9oW3lkpxYhuplRVUC9rW1taCVU9wYirdzgNcXl6O3++n\nqanJlTdcqyXE7/e3SxDM1qayy/jxrPn+e0K0Rem8HlhqiEh60AOPGuJDNYCEB7yGCLxnEPH0LJLB\nfhEScVyCRFw95t9nI9PWys/5W7UuJHIaRUTu35GLYysiTEcjns/tkSjlDciYfkFb5YB+iF+0Apk+\n9yDT/u8jYjGOiN04bd2jQITwIsRL2gtpEnAh0iDgWHO7ap1VyDT/nxEv6q6ISK5FLAPDkMSwGxDB\neQ2S0DXeHEvYXNeRiLifaK53OiJgr6atesG5yPT+CeZ7ZprjORP4GRJdvgexXXwKnOGDmXHZ77h5\n3OLAkUceSUVFOatWrc75OpiNNzI1ycfOGwkkH5C0VzZ73DZNnWoxiMfjyWYykF9t2WJYTuzWW1NT\nk2xzrOkYLVYdIhwOt+sHr2pxFvqJ1GmfYTpPLUjkrRiezM7sjxLZ1pqoVuEUi8Vcm/Fu1xEr1QNc\nyCS0XNdt5/lNrQOczXpvuOEG/vj73yc7KEUQ4TjKB4YBnxkQ9MCZhoinX3jhDA88Z8AvDClu/3tE\nxG1CBNIjSETzG0TAnYFEFSsRv+l+HrjaEGHlQQRYpQduMSRy2IhUDJgF/BRY74FlwHuGJEsFEeH4\nG0QI7o9k439Bm2AFidpuj9Q1PQ4RqH8H/mKOaxdkiv9S2qKtuyMC9BlESHoRkajmTsaZ+zsfqSCQ\nMLczkrZarUchyVM/M8fqRaK6eyLWgCsRoXu7+f/DEYH8FiL+T0Y8u79CfMGPAEcgdoi3gL8iwv9n\nSBT6KOD6OEzwyLb+a8BAD6w3I+LRxiaqq6v58Y9/zF/+8hfb8yBXrOIiXYcnq4h1i3BJHaObRXN3\nG59TDzfprCedHZ+VmpoaXQkgB7RYdQiv19suA76pqangZZigTQh05qKSTbkmaxJMIclXjKU2UFBR\n1FyFU2fJdf12x97aEcttpD4M5Fv5AWD16tWMHTsWL3IhUm1RS4BzS2FmGOoMmOSHHwXgphY423z9\n6LgkNiWQiOnOHthoiHiabi5zE9Ld6SHaptTPQKbSrzHa6qD+FolM3mOImK0y3/cM4jk9BJKGqFmI\ngJyBZO+rRgJPIlPlVUjk9xjEy9oMXOKBgww4BxHGZyOJX48jQjVormcf2kRuOWJl2AMRog8jEdNp\niOhdgXhtD0CE7AwPHG/AKUgt1l5Iaax3zHW1IEI+gERkD0FKYh2FiOT3gB4e2N+MXEcQAbs3Em09\n0DxOr5nHdar570BzXC+a+/au+UARBhqNtmYJCXPbs2bNYtasWcyZM4c99tiDQqKEi9frpbW1tV35\npWyEixvqiGoyk8u1NpuHm0yJgEBGIZsuiSrduaLLVuWGO++I3ZBQKNSuVWihRZF1O/lMndtFIDOV\na3JCFGdDLsctncjOJPTcIlbVsVfT5uk6YuW7/nzItO5U72y6h4Fst3PggQeyYMECQESMxyOircmA\nUg/8X4sIt/k9oDUBUxpEDN0P9DYkgeoqD5zjhVID9kyIcPoZbclTTyBlqiJI8fvHkQSnn9FmJfgM\niRD+AhGW5ebyF3jgGMMUqiZfINPet9LWhnUKkmh1LuITbQHmeOAZQ8bfAgw0JHKqPlkPkti0AJju\nge0NeMwDJxkwAomM/h8SKb0DuUj/yNynB5Dp+BbgVA/81IwO/9WQiOcMxAoxxtznnyPi9kWkNeyT\n5roHI2J1OlIZoRcw2xzzaUgE9WBE6D9srvOXwCBz/FHzfR7gHJ9Eo5+Pw69KxEbwxzBMCsLqOHwX\nb7NaRM33HHzggfTpvx1ffbXU7hQpONlGZbOZTrYTLdleI7tb5NKNODW+VIuBFXVdtApZFa3PdE6o\n99kdx5qaGvr06ePI2LcFtFh1iNQTUXlYi7XtbAWMXVembERHsS5YHe1Lqpc2F6GXzfqdwu7i5FSp\nrEKO37puuyiq8s7mcj5Yj/nrr7/Oj445hhBm9ycP7FICKyKwKQH7lMBOQXi0Hnb1wxEN8vdxPris\nFCb7Ye/NcLQHLvGKTeCYhEzNJxBRtxFJRgI4CbnIeRHBNAJ4zSPbbjIkE387JPP9TtrKSAUMiUou\nRATaMMRL+hMkAqoII/7YkxFvJ8DphojJ6Ug0c4P52o6IEBwB/MwDkw042xSb+xtSrP9JpNuWFynP\npS7QfiQK2h+JdJYh4nIsYkHwIMJ5ornP75vbmWy+dgwSab0VicTuiojlfZGI6TWI2L8Z6YQ12zwe\n/2P+XoNEvmvNYzQnBAN9cEorvJiAF0JwtB9+GoZDAvB8JZzQCOP9cGAQ/hqGiSUwPwwtpq1j7dp1\n9Kiq4q677+aMM85IfwJ1knwEV67TyarCxdYWlXW7WC3W+KwPNh1FZe3sJ6pz4ueff87MmTMZPnw4\nNTU1DBw4kMbGRscT7F599VUuu+wy4vE455xzjm1Vjjlz5nD55ZcTjUbp06cPc+bMcXQMTqOrAThE\nPB4nFoslf09tMVlINm/enBQRdthFIPMpnVVbW2vrSXSSdG1d7SJ7HbU+taOQFQcUmzZtorq6Olk3\nNlXwdaZUlvJFF6K9YHNzc9JCkU+71nTE43EaGhrYY4/dWbfme+JI9BRDkqRiBpR44KWBIlKfbYSI\nAfuXwvIoGAn4pBoqPLDPJlhgiNha7YV1CfG57uSDMV7Y0YA/xOAwD/zKKxHMuAG7JMSTeb45pgQi\n1PZDBJzifiR6+TAi0FYA33jhpYREDL1IEf2ByBjmAv08cLfRljAF4ul8BGl5uh0S5XzeA6+Zy/mQ\naGVqkbazPbKdowyzoYAHzjJEhC9Eykpdikz1P4mI7P5IlLQfcKlHhPIjBtzqgXmGTPufa66/HvHn\nrja3/TQixg1EjN+ACN67kajzleZxKAHmBGCIF34Vg8ficGsQzvLDTVG4Owq/CohgPT4sY5hVCRc3\nw/IEXFgGdzTBSD+si0Ndoi3SGvBAsKyM779fm+Esyp9EIkFLS0vBWiCnkksFA5DvR0lJSVG9stni\nxlawVtxa5UFh7eSYSCRYsWIFL730EsuXL2fRokWsXbuW9evXU1FRwYgRIxg5ciQTJ07ksssuy3ub\n8XicHXfckTfeeINBgwax1157MXPmTMaOHZtcpq6ujkmTJvHaa68xePBgt0V5bT9MLVYdQj1ZK4pR\nUkphV3Io3TR/Z0pndSSKnaCpqQmfz0dJSckWWfFOlczatGlTQcVqbW0tlZWVybE7IfgU4XCYeDzu\n+I3XMAwaGxuTkQAnawHPmzePKZMnYwDlXhFqMSQqOqYUPmuGwaaIAbiyB1zRAx7cDL+pg3NDMC8G\nX8RF4OxqRuv6eeBXTfD3EBxsnpLTwzA/Ae96TUEMHBqTaOlTtAnKSxBLwKu0+Sq/QqKODyIiVnGn\n+d43kUjqQiT7/W+I+KtCBN9kpETWN8g0+n2I8LOiGhUM8sAnBoz2wIWGJDNdA3yOCNyeSCWCWUiW\nP8hU+xWITUCxGbjfA08ZclyHIaWm1Df0HaREV5m5nd8h4vlJ4BYPvGhIl6xzzOVXIQllKxEheaIP\nrg/CuVFYEIeZAZjohVfi8rf9fPCPILxtwMlhGOyBnb0wKy7HNYJZ1cH8P8iDSdiAco9Et0s8cqNp\nNeD+++/n1FNPxUlU17RiidVMpEbgYrFYsjFCptJLXRWVdbMYzFQWyi1kKv116aWXctlllzF+/HjW\nrVvHt99+y7fffgvAaaedlvc233//fW644QZeffVVAG677TYArrnmmuQy9913H2vXruXGG2/MezsF\nxPbD1DaAAlGs6ebUbWWbaNTZ7RQKj0daNTY1NTnWGctuG4WYPrIa8hsaGmwT1ZzajlNYKxCoG2Nl\nZaVj4x05cgQbvv8eD+JN9ZuR1Gm9Jfv+kRr5295V8GodHFsOJ1fASevg/VYRp+8YMK4UFjfCv6ph\nvyDEEjBkI5zrbxOqz8fEN/mmD0IGrDVgRkIE4F3AB+aYPkIy95+iTajGkOSr02gvVBcimft/QyKq\nPZDp+SqkfNSLyPT428DrHviTIcJ4EBJ9tfI4ksE/GxhiTvv/DRGTHkSczkaEKubYTjXHcyyy3r95\nYIQhU/qY45luSOKTDxGZj9ImPg9AErWmG1IvdhBS0N8L/J8BRyMe3ucRf+8cJJo8yiOdvIZ5ob8X\nXgjCjBgcG4GLfXBtAOZ54ZgIDGmRsSYQ28OrCbihJ/T0wDW1cEkvmFQKJ6+Gwyqhpx+erIVefojH\nRLgGzdPtggsu4KqrrmLVqlWONBpRuEXM2PkiVWQVtozKqqlkp72y2eDWqimpuOWztSNT6a9NmzbR\nt29fPB4P/fv3p3///uy77762y+bC6tWrGTJkSPL3wYMHM3/+/HbLLF26lGg0ykEHHURDQwOXXnop\nP/nJTzq97UKixapD2HlWi1ENQKGieNkmGuVDIcVqqp+zpKTEkRaidji9H9ZkKSVMKyoqHOsEZcWJ\nC7MS1SpKqyoQqAQSJ7bx+eefs+eee+L1QMKQqV6ASELEysu10JSAi/rBtf3hkCVQn4BXW+DxBgh5\n4IqecGW1CJnhy+GX5SJUAQ6vE5HTaMDUFlhlwAZDhO+UuERAwSwx5ZGsfg8QNUQUBpAoqNdcxkAu\nhv/2wvSElJvaGSlN9TMP7Gk5XRqR5gE/py3Rak/gKgMO9YiALDPEJ9ob8ZT+EElsegCpfwpSWuta\n09JwNTKdPw34MTLd7ze3dRLieb0esSdcYa7jDmRbJyJ+2KeRSPEVSOT0QaQ+7DOIP/UKHzxpiOf2\niYS8NgXxt57vgUlmdPaxUjgmCHNjcEIz/CsOs0NwZQD29cK0MPwzLoJ9LTDID9/HYFYfmFwKP62F\nGXXwXH94ewAcsRbmN8P7w2HqSujjhzsGws/XwJE94a3N8rl4zM+mqamJnj178swzz3DIIdYUt62f\nbLyyqWJWfW+hMFFZt4pBt/tpIfMYN23aVJCp92yOSTQa5eOPP+bNN9+kubmZffbZh7333pvtt9/e\n8fE4hRarBaLQ2fPWaX5VtL+srMzxDllWnBZ5SjRFIpGknzMYDBbMk6lwYj8Mo61Tk2o4oMT15s2b\nHRrplnRm7NYoqopYW6s/WD3XneGQQw5h7ty5lHmhOQGlXpjYAz7aDHjA54X6KBzbQ4TLyEUiXA+u\ngrP7wE1rYJAXbuglVQImficid24ERkVhbVSis8P8sC4E+wTgrw2wrw9+XwG9PNDTCzvVwlQ/3G2Z\n/d2/QaKuL5fK703AUxG4Mgz3hETwfpWALwx4NC5T148CL3thh4REKv8KjPfAuSkfw63I+1/ySRmo\negNeNeT9LyYkKWoN7RsCrESm/+/0ieh8xYCbDPGPHmHAex4pX3WzKSQvMiSZ6w5EwHoRsfu0ub7D\nEWF8MyKWhyD+1KcCcIAPLjPg+gQcmoDzkCoIS4BPDRjrgyUJeCEGR/nhh374tAJObIEdW+A5M9O/\nrxdWJmRf5vWH3YLwQAP8eCP8sgoe7Ql3++HItXBjT1g0GA5fC0ethDeGwk/Xwo1r4eEhcNFq2KEE\nGhPwfRQCCbED+IDjjz+eESNGsHDhws6cjq6OEOZyf8iUra7WlU9UVq0vdRxuF4NuHx9kPvdUa2mn\nGTRoECtXrkz+vnLlSgYPHtxumSFDhtCnTx9KS0spLS1l//33Z+HChVqsbgtYS0il/u7kF8qucLzP\n5yORSHSbBgR2+6CsCspjW0jy3Q87H7C14YB1/W4hNbmuowoEnfl8o9EolZWVSSGW8IhvdHIfmLMR\nBpTA73aEm76G1a3wUj08t1m8q++NgT3L4ecrYWUr/KgaJq6ELyLinRwbgnEVcHwQrvoeHu0ndgGA\nu+ognIDHe0Ifc+OXNkA0AXdYnnn+3AILI/BZpYhgAH8CrmuFm4PwY8vV8Jko/DcOn1TAZgM+isPc\nONwYFQHbywvT4yIwD0Hat/4F+KcpVEEiutM88OcE7OWHKR6YEZVC/Ach0dQTvXCCB04233OkR0Tq\n22algIQh9UytZ1RvJKr7ogeqPbA4IRHb88zXyxGxugHx2VZ6ZSwg/tDbfHCUB86MSAS2Dik5dV05\nLI7BkQ0wvgneLod+XnijDC4Ow2QzXD29Emb0hqs2wf7r4PFecF4l7BSAozfAB63wQh8YH4RjN8Cr\nzTC9QiwBeyyDah+sj8NZK2W/5jdBhVceWMq9bVF4rwHLli2jqqqKFStW0LOnMkjkjpu+k4XCyais\nErDFKFeYL24dVyp2YyzkA9SECRNYunQpy5cvZ+DAgTz11FPMnDmz3TI/+tGPuOiii5L34vnz53PF\nFVcUbExOoMVqAXFK3Nm1DbUWjldCpNBYkwByxU402RW/L5YvNpdt5FpjtJD7kO2686nj2pkL/+23\n3871v/kNAAEftMYhkYCgD15ZD0NK4ObR8NPPoT4Gx/eDswbAKZ/BjYNEoBz2FbzfKFPBL0Zg7x7w\n+QZ4bhgcXinb2XMpHFQKJ5nR0jUxuK4WZla1CdUFUXi4Beb0aEuy2piAy5vhgTLxYCqOa4EdPPBT\ny2nYmIBLYjCjFEab9/49/XBUAl6JwWPlEvl7MQbXtMLFhpn45YGxBu2U5Z1x+NaABSXQxwOXB+CN\nONyVgMlR8CTgJ9727/F4YE5CrBB/KIHfROBZA65JSNH/MHCYFw4OwOMhGcf5LeJnfSghloBfIV7V\nj3vA4xGxSpzig9/5wOuFfTwwxQcvxcUGMc7c/3F++KwapjfCuAb4cyl8b8CTEdgjBItaoSYhNoq7\n+8CeQThlI1wWgZt7wsIBcOgGGP497BOS4zKvFd4PwyE9oU8A/l4Dvx4K2wXg6mVw6nZQF4M5tVAd\ngA0Rqa0b8spPnwdGDBvGiSefzAMPPJD3OepGill6KZeorIrIAskWz+ksBmr9xaY7iNV0Y0wkEgVr\n+ev3+7nnnns47LDDiMfjTJ8+nbFjxya/O+eddx5jxozh8MMPZ5dddsHr9XLuuecybtw4x8fiJLoa\ngINYn1IB6uvrk5G3XEk3RW4nOIpVeSDXTPR8KhIUY1/SlceyklqJIJeSU9msP19isRhNTU306NHD\ndsxKVKdm9GeDepiorKzMaUyDBw+kftMmIgko9cGgMljbAiU+GF8Nc9ZB3yCsj0CZF/49AXavhB3n\nwcow9AxCTUQE68X94ZqB0MMP238CB1bAQ2am0owNcNN6+GIwLI/BB2G4qU78juP90OCBhriIKb8H\nKvwStU0kxA/rAwYHoCwBPQyp9zkvAX8MwPF+qDbv44eF5f2vlrVFYAF2aYCJQXjUcvobBhzYABsS\nMv6lcRjuhRMT4g09zoB/lsAPUz6CGRH4fQyOD8ETYRgB3OmFvc0yWRck4K0K2MMnpbcej8I1Yemw\nlTBgqB9eK2tLTGo04Fdh+EtEkqjWA//tAaNNEfrfmNQ9BXjcB7+JwecemL8dvBmGi2thWgAeLhcx\nCzoAZcgAACAASURBVHB7C/xvswjTvw+AYyrg6wgcskYioe8PkJ/zw+JL3cUPB4Xg7iaJPscN+NNI\nOLonHLEEVkXgw13gnXo4cyn8cqgk1h33OZw+UD6v+76DS0bBH7+BUj80RaHFvKT6AU/Az8aNm3I6\nP6PRaLskJjeRKVu8q4nFYkSj0WRlFrsyXPl2dnICN3+ukLlawcaNG7n44ot58cUXu2h0rkZXAyg2\n+UTYVMF7uylyJ7eTD9luJ9/GA7lsozNk2oadRSHXSgTFrAShOqlYo6h21oRCsGLFCsbsuCM+04ca\n8EBlENa0wEU7Qm0r/H059AnBz7aHGV/Ab0fCP9bB5I9l6v7wvnDGQBEpmyNw0xCJpF29HJrjMK0S\nrv4e5jbB4lYRj4O+g0qfiDYfcFov2M4HPX0wsw6MGNzfv22czzfAE/Xwp34iWjfFYXUM/lIPu4Tg\ntjhc3iJT5H5DaoQe6od/RMW7WeaFG8OwyYC7U6zUz0dECH7WC4b7pFvTU2F4qAV+Z8j0ezzlVPgs\nAbfG4LkecHAQbqqAO5vhhBYYkIA1CbivTIQqyPE4MyhickIDLEO6YjUa4s8FqUH7h1KJZP41AlV+\naT872tzmnn5Y3AMuaYEpLeKf/W6wiM2zKuAHQThqA4xtgLmV8FUc7myBPUuk1u3tm+HwMhgdhIVD\nYNo6GLUK3uoPY4NwXDk80gAfRuEPw2F6P7hsBVy4DAYH4e2xcOa3MG4BzNkJXt8JjlgMx/SGebvB\nlE9hjyqYsSNc9iWcMwJmroShFbCmGZpiEqVvjsaorqri5VdeYdKkSY6ez11Bpmxxt5BtVDZTZ6dC\nRGW7Q2QV7PfPZXVNuwVarDpIvhUB7Kb5c8mEd4NYtWs8kE/Zpq4Qq9laFHKh0DYAu25YnW3YkMux\nv+mmm7j5ppsAmX6JJ8DvhcYIDCiFR7+Bxij8785w5Y6w44tS6P/ab6HcL9O7c38AE3rA09/DvDr4\nZDw8tgEeWQcLwhIxnbYSdquEpVGYUg03D4bhIakEMOITeGwwHGUG4b9thWvWwb+GwD6mqGxOwCnf\nw7194URLwPi4NbBzCbzfXwRw3JBtTFwNZ1fB+gT8MgxntUAPr4jcY4NSF7TS07bu6c0wo0KEKsBQ\nH1xdDh/GoTQh2fHT6qFHXLL6r/HDMa1wQakIVYDtvPC7Cri2FEbXShLTH6KSxLWL5RS8txU2eODd\nfnDzZti+Ea4MwrVmYGlmBB6LwL8GwButMLkWTg7Cg2USLY0CH0VhaABqYnD4enijH5R4pWvYZwPg\nnE0wyoxW/7I3/KafLHvUKhixAuYNgmFBmD0Art0EP1gtJ8CgELw2Fv6wDn69Gg7tAXcNh+1L4Mgl\ncN9weGIU/GoV7LMIntoBPtoV9lsECxvhikHwv8vhswaYVA33fgM/6Cm/V/rlnNkUkbGGEzB16lSm\nTZvGww8/nMsprsmBbMWg8spm29nJKmYh/6is28VqpvHV1NTQt2/fIo+oe6PFagHpSNylTjOXlZXl\nVfC+WGWy7ESeNapnl2GezzYKvS9qG/n4OrNdfyFQxzuRSLB58+ZOnTOdYeDAAdRuqqUsAC1Rma4d\nVgUbmyXLf1NE6qAeM1g8iP3+KclOZ46E80fD1Dnwi5EiVN/eAGd+ZloGFkkSVk0YTuwHNw6HgSF4\naA180gAPj4DepqPmkMViETjK4hY5agWcVNkmVAGmrREhdqpFqL7XAq81w4JBbdP8Pg9cvQnGl4qw\nVX+vj8P4FTCuDL4FhtVJBYOdDViVkAL456bMQr7eCi+3wif9YYcA3FoF/2iGO+vh/lbAgAttZi6n\nN8BAP7zRH26ugx82wF4+KSX1VRxuicDLfWHfELzcD15pgemb4K9NcIlfEsX+th3sWyr/jimFH6+D\nYZvh6Qq4sAkiXlg8VFrY/s/3MHSNrHOvkPh7e/lkDs7raSvi38cPc4fBRetgl5Uwsz+MDsC/mkQ8\ntiTg9L5wcA84qAouWQG7fgYv7yi2jmFBOOVr+CoMF/aH/zTCsV+aNx+PPMTc/B3sVAWNcZi3Gbav\nkrq6CQPWtYotQ9lMfEht21n/eJp//vPZDm0BbhY1W/vYMkVl1b0kl6is9f9uj0pnOn4bN27UkdUc\n0WLVQewiq6mJT6k1OYPBIOXl5Y586Qp94csU1XOqJqoaf6H2RYlU5cdyIiKZitPR4dQEL6AgbW87\nGnd9fT39+vUDRKBG4lDih1PGwD+/hpYY/GYv+GAtPLcM/rUWnvkOAl744FDxrx77rgjZ+XWw3Tti\nFdipEq4aLVUD7lsGf14Bd42GMh80xuDqb+EBi1B9ugY+bYSlO7SNbcY6KWl151DxqHq9MKcJ3m6E\nRcPaxGciASeth2t6wg4Wm+C7LfBWC3w6tL1P9ZF6KW/14mCo8kny1TtNcNtGWNIipacm1cPFQTgx\nJFHR0xrh5h4iVEHsBT8pl6n3M2rh4HIYVwsTgvBAOYz1w2Mt8EYUFg6G/n5JXrqyB1xdJ1PzCeDW\najjAInKnlsI3A+C8WrimCcb44Uelba/vFoLPh8DPa+HAOrEKrBsuEfB+Xnh3MNy0CQ5cD1dUwqdx\nmB+BhWNgfQyOXAbzW+HVQfIZPtAfJpSIyE0YcFQveH9H+LBRpvS/CsNjo+He4TAyCId9IZ/bCb3h\nvO3g1jVw5xoYUQEnDYZZq+GXY+HyHeDwd2FlC3x0GPz0I5i3AV49BKa9A4MrYGSVnFM+jxnJj5vG\ntliMqqoqPv30UwYPHtxO3GjcjfqMconKqqYrKqARj8eJx+O2VoOuPgcy3cM2bNigI6s5ohOsHCQe\nj7erVakSi8rKytqJu1wTX7Khtra2IAJGoabKVWZoZ3vcZ2LTpk1UV1c7ti+piV7qYuZkpyYrTrRE\nTZfg5fP5qKuro1ev1K7ynSdT8tajjz7K+eefT8gPGOJRNUxRGIlD0Avzjoer3oN5a2FIJVy3J1w2\nVwTJ0YNg+nxYUCci43+GQa8APLQUvpoiEdXvmmHcW/DiznCQWaXowE/gu1Y4tQ8sboZlrfB1q/hj\nvV6JtoXjEnlrSbS/YJV7JWpX4pXIbcgrCTvNCRF6u4ckYWrfEhi7Bi7sAb+0VEeqi8GwFfDXgXCs\nJTIbScDAr+Hm/jCxFJ7YDI/XijXBl4D+Plg0QKKTisYEDFsLt/WFc6vhmwjcVANP1cNILyyLSzmu\nEyq2/FxGrRKfrdeAGdVwhmWZ2gSM/x5+UAFLIlAXh+f6wF6mqI0bcPR6+DwKLXEY4od3TK+q4tUm\nOGKNtGT9bpx8LiB1T6cug81xeH+oRF5P+R4+aBGbwDG94G/mA8NXLXDQZzA8CHPHyWfztw1w9rcS\nCa0OwAlD4KmVsHs1zN4X3q+Fqe/BWcPhjl3g+Pfhw03w38Pg+s/h2ZXw/BQ4fx7ghcOHwkOLYf8h\n8PZ3ct55zYirzwNnn3MuN9544xY1RdU9ztruuKuFjKK5udnxe4FTuDn5yzAMWlpakvefVN9sR1HZ\nYnz+mRLA7rzzTvbaay+OPvrogo+jG2L74Wix6iDqyQ/avkwqGlZIcQewefNmysvLHS8ynJpwFI/H\n6dmzZ0G/7HV1dVRWVnb6Ap6a6KUsCtFolGg0SkWFjTJwgHA4TCwWy2v91iiqdczWuoe1tbUF+Qzi\n8TgNDQ1UV1e3+/uUKVN477338AIlAQj5oTUmV5Sh1bB8E+zaBxbXStT07v3hrLEw5Tn4aB30LYWV\njZIg8+vxcMU4WW7gLLhjHJwzTLYz5k2IxmH7UvgmCmvDIkj6lcDICtihTGwDFT74vzFQ7RcRdNan\nIlre3KVtzNd8CzPXwce7SYWATTH4shnO/QYuGyhR2C/C8G2LVCmo8EEfH+wcgEPKRDSetA7K/DB7\nUPvjdMIqWJGAD0a0CVLDgN9vgF+vF+Fc5YdTQ3BDDxHLB2yQkl7/Gtw+crsmCruYgnDPUniiN4yy\naINzN8C/WuGzHeGfm+HiVVIJ4Pk+4o+dUiPNBz4eIwLy5vVw5zo4pRwe6C0e1FfD8NU4mdY/6Vv4\nbxM80x8OLJPappNXQS0y5rURmLc9DDXH0JqA6Svhxc1S4qp/Cby3lxyzAz+GIUGYu7NEazdE4ZDP\noSEGF24Hv10DPUOwPgzTh8Efd5Pku/3ekaS7Dw4QT+qB78LU/vDYRJj+Eby4GuYdCo9+C/cuhWcO\ngusXwupmOHcnuPVjOHQEvLZMjmkkLnYUkGLnn332WbuInLVuc1dmr9uhxWr+ZDp21qisNTKb6pW1\nS/xy6hyIRCLJmcdUrrnmGk4//XQmTpzY6e1shehqAIXGrqsUQHV1dcEvgE5OPadL+PJ6vdTW1jqy\njUx0Zl/UzcnaSjS19Wyhk7hy/axTo6iZ2uUWOyJUXl6GYV7cS4Mwqg+s2AiVIbj3aPjpPyWKVh+H\nkE+iXwcPgR/8AxZvgoHlcOmu8Ny30BiGq3YSgfejt6FfCJY0wq7vwDeN0pVqpx6wQ184tSdc/gn8\neke4epSMZUkDPLESPtgbdjEjnfNr4T/1sGhC25hrInDvanhurFgHegdgOPCzb+CInnDzsLZl17TC\nDh/DQ6MkMvpeI/ypHi7fICJzWABu3ADn94R+fmkbOrsJ/juqfeQ0bsCdG+GmwfCzvvBMrYjXe9fA\nQI9k+C8d1V6oAtxVK8dt8Vi4ZT2MXwMHlcDjfaQ+6ZONMH8HEdM/6QVHV8HP18L4tTDEB7UGLDfL\nIwY8cP12EgU+YQX0+U4eChbvImWhAF7bHv5vPRy5Gs6uhP+0Qr0XPttN7hAXLofxX8HMIXBED4lG\nn9ULZtVBqwduHSqitsoPC34AhyyQ4/fxbtA3AHeNkAjrdSvh9p3hku3h080iSGui8MRe8NFk+X3n\nt2DBZPjwIBGwR8yFP+wKi+pgwquwd1+xPxz9JvQvhRVNcNvH0Pf/2TvvsCjO9+t/ZukdFaVZQFTA\ngg3sBXvD3jCWiN0Ya2JMNLElsX4tUbHFFhsxFtTYNXbF3hW7KDZUEBCWBZad949nl10QrGDI7/Vc\nFxewO7vzTNnZM+c597nNYccdqOwE556CpakYu0otuvXY2dkRExOT/vnRtRDWka73rV7PTUUur3tW\n/6ue0Ld5ZTNbDAw7feXUOfDZs5qz+Kys5iDUajXR0dEZiozi4uI+qvPKu+LVq1fp6/0QvGsmam7b\nDeDD8mkze4HfVOiV21mu75pX+rb2p9kht46BrnArX758xMbG4uLshEYGU2NAI5Q7Y4Xwq/5UHybu\nBzszmNcMjkfC7BPglR/CY4QtYF5d+NIL9j+EVtvgYgCcjYZpV+HmK/F+fgWhiStMuwiL/KCLmxjL\nyPPC73qjvvBLApQ7BLXtYb6XfszFj0CgA0xy1z9W9wLYmcBWT/1jO2Kg0w24XRmcDD4iVS+AmwWs\nM1hWo4HC5+CLgiLvdXM0XEmA/MbCQ9vaHlZn7F5Ij0g4q4JLZcSUtA4nE6DBTUHEPczgt4LQUCu4\nX1VBlfuwpyTU1J4q15NgeCQcfiUI8KzCMDCL77TJUfDzU7A1gS3FoGomx0nIS+jzQFzAJ7jCSKeM\nzx9PgFrXRSzXI1+xv3T4PQqG3YURBaGkGQx8CFO8oLgldD4PA11hurYroyoNOl0VU/qtC0DIc/jS\nTairO57ACX8oaSNuRmodgvL2sKO6sGI0OSo6mU31ht8fwD/PRGveQhbgYCludgK9wNkKZp2Ddt7w\nMgkO3Qcna3gUL9ZvpLWEmChAadD87sKFCxQvXvy9FMLsFLm3tSzVPf6+xDMxMRELC4s8SQqTkpIw\nMTHJlZagOYGEhIQsM0w/FjmlyqpUKoyMjLL8Hmvbti2hoaHvnWn9/wmyPKB57xPyH4aRkRF2dnZY\nWFikRzbpTvzcxocmAqSlpaFUKomLi0OpVGJsbIydnR02NjZZEqd/OwfVELIso1KpiIuL49WrV0iS\nhK2tLba2tpiZmb3xrju3ldU3pUAkJycTHx9PfHw8sixjY2Pz1jF/SqxZswZnJ0FUrcwgVQ0mxlDA\nSlwwTIxg5E5AhuNBcPMFzD8NxkbgXxy8HaCWiyCqKWnQcacguJV2wOCzgqgOLwevvoQjAXAuGsrk\ng0Ct4vlMBQtuw/IKeqK67AE8VMIkD/04p9wRHtSfiuof2/8SzrwSRT46aDTQ5y5MKJaRqG6PgWtJ\nQg00xMj72rakxeGnYnC2EryoAVVsxLZvewUFwoWf82ACXEqCjfGwtnhGogow+jFUs4eH1aF1IRGZ\n5REBa2KFT3RAIT1RBfCygJ2loJiFuDH46SmEZCp2v5cMk6LgtxIw0Bnq34Gg+2I7AS4mQd9IWOwN\nW8rDpKdQ+4aIfAJRHDX1KRQxg6r5wfMiXE3Uv39fR/inrGjC0DcS1paHwW7QohAcrgbLnkDLC2J9\n5kYw3UOQz1XP4HdfmF8ZVlYRpNXvoPCheljD2fpwKwGqHhRNI8rawoNECDoPlhawqyUUsQVHGzgV\nCBOqw/qb0MwdFjaE0HD4sjx0KgMvVbCkHViZQjkXvTVFd75IQIUKFZgwYcJ7qZc60mFsbJxeW2Bh\nYYGVlRVWVlZYWFikTz0bpqEolUoSExNRKpXp9q/U1FTUanU60ckKeVlZzcvI7et3VueApaUlVlZW\nWFpapp8DkiSl282SkpJITEwkMTGRpKSk9OIvtVpNWlpaBjuKSqX6qJqG7LBr1y68vLwoWbIkU6dO\nzXa506dPY2xszKZNm3J8DLmFz8pqDkPnU9Ehp4uFsoNSqUSSJCwsLN66bFaZqDo/7dvwMV253hVv\n6gD1rgrwm5CdNzOnkFWh0oeqqFkhpzy9mSHLMi1btuTQgX2kaiv9TYxFhuq3jWHmHqH2tasAmy9A\nj/Kw/Ra8UELXsvC/xvD3TRi4DVY3ht8uwrEnolFAv3LQyRNWXYNNN+FGR0EsLkRDja2iqMZbu7uq\n7Ib4FGheCG4mwAMV3FcK8iYjFNlktVBvE7VhG5L2x1gSYyxoKnrQO2g7Z91VwW/uggyWsAAXEyh6\nDka6wHAX/T54lgLFz8KOclDH4PSISYFipyC0ItTLD4djYGUUbHwiSJuTCRwoJQigDttiIfAehPuK\ndrMgFMeFT2BchLARLC4KXxTIeBymP4UpUXCjGoQ+h29uQXEzoaA6m0Clm+BhCVvKieUvJUCn62Jf\nLHUWKm9nZ/hNW/wUlQwdLov0gq3usDJW2BRu1BHK7OibEBwBwe7Qw1G85pdImPpI+IVNgHM1hX8X\nRNW+/wmwMoIfikHf66KArrw9/HoN/qoGzZzFspPDYdJ12FAFmjjBqRiocUgcJ58CMK0mzLkEx5/A\nlS5CIa29CSxM4FQnmHcRRofB1jbwIgmCdsHiADj2EEKuwPxW0H8LtCgNu6+LG6ZXyaBKFZYLCShR\nshRhYWG5es2C11uWZlblsppaVqlU79ww5VMjL/tpZVl0h8qtuoMPhaEqq6vz0D3eo0cPzp49i5ub\nG0qlknbt2uHh4UHx4sUpXrw4rq6uH3UepKWl4enpyb59+3B1dcXPz4+QkBC8vb1fW65Ro0ZYWloS\nFBRE+/btP2qbcwGfC6w+BTKT1dwiFpmRlJSELMtYWlpm+byO5KWkpKTnir4vyYOPtxu8CxITEzEy\nMspQRZk5vsnMzAwzM7MP+nAbTnfnBnRk1dbW9rVmAzlx8c+NYjpZlilQID+pyUloZK2CaiwuAF38\n4M/TUNoZQnpDg5nwIlEQgwKWgnhcGQAxSigxT5BKtQbqFIeDdyCsC5QvBI8ToNRS2NkUajsJkuf+\nl+ga5WgBj1LgmVI8XtQa3O3B0x4ORAIyzK8GdqZiynrIKXieBMcbivFrZJhxXfzsrwsPkwTBvfUK\n5t2GyvkgOhliU0TBlTJNbFuHglDPBvyswccKGlwFR3OJjd4ZL33+F4SKt71Sxv02+S7Mvg+V7eHA\nC3A3h+8LwRf5weUK/FgUhmQq0LqaCFXOwWB3WHgfHM3g98JQxwbuq6BMOGwoB021JDYmFUbcEp2/\nCpuAErhfRd8WFUSO7fhImH5fZKU+rZtxnRoZJkWIH40MN+pCMYNLxaan8OVF6OIgCPf0R7DfH7xs\noV0YXHoJJ6vrXxObCqUOQbwapleEwVpivPQODDkLiytDV61S/vtdGHZBqLgnYqCms9gmZSpc7CSS\nJb7YBwcj4WKguEmqswkkBZwNhN+vwMijsL6luEnpugPmNoOrz2HpeVjQBgZugYal4NAdyG8NkS8h\nKUUo4WkakBGWrH8L2U0t66INDe0FmW0G/1YMU162KGg0GpKSknJFncwpZN5/Go2GqKgo7ty5w9ix\nYwkICODu3bvcuXOHu3fvEhMTw7p162jduvUHrS8sLIwJEyawa9cuAKZMmQKIYi5DzJ49G1NTU06f\nPk1AQMB/hqzmTTPKfxiZp4B1AfS5TVYVCkWGaQYdsiJ5H+N3/JQ2gKwKj3Qk7WMu3rm9DbovodjY\n2BxtNpAb0F30CxQogEISRMbMWNsiVAYjI1h2DBysYWpbqDkdlCkwpinUKg5N58OKVtB0DYQ9hCJ2\nMKEhtCkNleZBUBlBVAHabAYPGwi+CkHHIDJOEJWaLlDTFSoVgn57YUhZGK0lhU+VsOwa/NMYqmnf\n5/4r+OcxHG+kL1hKS4Mp4YIklbETPwABR6FWIdhbW7/N8Snguh2GloB7iTD3GTyLhLhUQZRKyjIz\nIqGvsygkOvhSZIlez9TdM0ENU+7BqkrQykmkFyyNFLmw/R+AqQQ9HV/f562uQp+iMMUTxnjAtHvQ\n7DZ4mkN0CnRy1BNVEFFSK0pDOSv48a4g6ydfQXWDhDET7Q1CPhOxT73C4HBlKKS9p1RIUN5a/DYz\ngqArsMdX3FgAtHMCLyuoESZ8oAfrga82HW1nLRh0AXyOwvbKorNU36ugkaC+C/x8DVq5QDFr6O0B\n+U2h2wl4ngxDS4riLgk4Fg1j/WCMn7BvNNoKZf+ES4GwtiF8uR981sGFTnC0PfhvAp/VsDEA6heG\ndlugXEFxjPpvAztziE+GXhvBxRY2XYLyLhD+DIrkh8ex4lw1N4HkVBlbW1uePHnyrxCcrAp+dOqg\nlZXVa2RWN22c19IL8gr+C/aJzGNUKBQ4Oztja2uLnZ0d48aNy7C8bnb0Q/Ho0SOKFCmS/n/hwoU5\nefLka8ts2bKF/fv3c/r06Ty/Dw3xmazmMgxz/nIThgQst0he5vXkFgwr+nVT5tbW1nm6ElfnRdUl\nKAA51ighMz72GBhaKR4/foyPjw9GCqFAWZlDYQd4HA3qNKjlDQcug70lNJkjlKpDw6ByEXAeI8jO\nl1uFb1AhwZ5e4JYP5h6H56/guyow5gisuQFRiWBrBqVt4Iey8M1uWNAQumhnqaafAkmGb8rrx9pp\nLzR21RNVgA4HoV0RKG8gjPc/I1S/9gYqZng87H8G5xtl3P6up6FKAfilXMbHi+0U09f5TWFZJIyO\ngIJmEJcMXZyhSCaHTaeLUMkOWmoJqZM5jCkJbRzB7wiUsgWXE9DQHhaWEn7Z8RGQJMNkbUGXjTH8\nXBIGF4WGp+Bpmpi2T1ILn68O0alCFZ1QBpJlaHgZ2hWAPzyFwro7BoIfwomGYj/0PgMlw2CxJ3R2\ngqsJ8MVV+K0yNHeBgCOiOO2oHxTVqqUn4kRTA+/80OEEnGkgtslIgoUVwdMKmpwBZzNIVcD1jpDf\nDAYeg4p7BMH1yQdti8A2U2h5GGbfFirs3HrgYAGBO0WUWb+y8E9raLwFSq+FK4GwsgH0PgDl18Gc\nWuBhDxtuQ6UQcLQS59y5h9DVV5xvP/wNfWuL827lCfB1h+tPBDG+/Uz4rY0VwhJgYSqUdGdnZw4d\nOkTFihXf8inJfRgWbBkG5Ge1XHbtSnMzvSAvE8K8PDZ4s6f2xYsXWSYBZDcr+q54l/0xbNgwpkyZ\n8knraXIKeU/f/48jq4KkT9UKVaPRkJiYSGxsLCqVClNTU+zt7bGyssoxZS+3yKph4ZFOBX6XYqkP\nge7L4WO3Q0f6EhISiI2NJTU1FQsLi3Svam6p6R86dp2Kqium27p1K5Ur+WBspCeq5YvDkxgxlbp3\nLBwLF2TIszDYW8HXdeHaE8g3SmRbjmgE93+Fhy9hTD1BVG88h+92giyB53LY9RhikuDX+hD1DYS0\ngaMPxDR/oLayP0UNk05BcG2h/AGEPYUzz+A3P/02/PMYwuNgegX9Y4+V8NcDoaoaniadT0APN/A0\nKGAKj4d/oiDY4PUAs2+K7ZldESb5wNUW8LQtVHMAhRGsi4KCB6HlOVH5fjoODr2E38u/HkfV4awI\nuj/dEA7VA7UpFD8FNS7A/yJhlY/ozmUIVZrIfF3gC7ESuByH3x6I52QZgq5LuFvDd17wkzecbABn\nk8D1FGx6Dp2vwa8+UNYObEzgr+oQXAl634A2F6HheejqLpRPZwsIawBNXaDcMdj6FLY/g8FX4a+6\ncLI51HUC791wxqC4q6ebyEeNVEHvUuBgLojiwprwdWmo9Q8cjBLLKiSh9j5Phi6eQmFvWRw2toAR\nx2DeJUHG97YGdzvwDoF7ceBiIc6VXgfghQa294TSTmBrCYcHwW9tIeQclHeFkC9h9UmoUwIG14Ob\nTyFkEFiYQUMfcQ6bmwjympQi7CVmJlDPv+4bi0/yGiRJwsjIKL3gx9zc/LWiLxMTk/SiL51QkZiY\nSEJCAkqlEpVKle7zN4xpyg7/JRKTl5HV91Z0dHSudK9ydXUlMjIy/f/IyEgKF84YW3L27FkCAwNx\nd3dn48aNfPXVV2zdujXHx5Ib+Kys5jJyW1nVZaKqVCo0Gg0mJia5puhBzpJvw2panY/W3Nw8vSVq\nblonPoasZtVu1rBIwlDh/rfv/jMXpJmYmGBtbU2PHj3YsmUTaWlgaiJ8o4kqOH1DfKEPDYCmv0BB\nW1gzBP44CLsTYfUZiEkQZOTQN+DnBsPWCWKSmgYlZsL9GHDPDz82ghbeMPMQRL+CwVrS+TAe0bxf\nAQAAIABJREFU1l+DA530RK/XHihhDzWd4PQzeJAAg44I7+r0axCtEq1Zjz0ThVUdwyQUyChkuBQr\nfLN/3Ic9UVDAFCKVIpd1XTVB9nTrCTwB3YsJL6YOag1MDBdE0dzglLNQiDilFbUgoAgcfAp/3IZG\n58TzRSxEFqshFkdAlAoma1XbyvlgWy3hna22XwTz/3xbqJSFDZTaFuclOrpBLw+ZoOKw6SEMOA1z\nH0O3QnA4RuZeC/3yZe3gUmP49YZE4FUZBzMYbJCUANCtGFTJD2V3axsylNE/Z2oEi32hWj7oclZ4\nXhdUh+baWcTVteGXS+B/CJb6QiNHqHkAClrDmqbQ/G94roK5NcS+nVhZRE4FHIYAFxHs/2NNaOEB\nddYCMixoAE3dYHMAtNkmkiJGVIRl9cDnTygTAsXzwYbusDUctl0D3yJwoC/UWgi+s+HMMPG6gEWw\neyCs7Ao9VsPKnqCuCd3mw9pB0HUBNKsEx66Dixk8jYF4pX77p039lb1797Jv377sPzy5jJy4PrxP\nnqihvUD3eFbxS4bv9W9fv7JDXri2vglvGl92yurHwtfXl1u3bhEREYGLiwvr1q0jJCQkwzJ3795N\n/zsoKIiWLVvSqlWrHB9LbuAzWc1hfAplVXf3nJKSkk5ALCwsSExM/OiphLdB18XqY5AV2TP00WYu\nUsstvM86siJ9lpaWWVor8kIDiKw6YekItbe3NxER95BlsLIAVQqYmUJ+C3iVCOam8M1yoVD9Mxb+\nPAZrjoKdFYzpAHO2gX8pQVTXn4XFR8U6/7wKrSrCwoOwtRd4OUK8CuYehfXthYUAoON68HUSBVfj\nj8PRJ3A0EpLTwG0NWGt9lkmp4OkAESmQz1L4E40UMNwPNLKMWgP34yEsGjp7iuKkc9FiuYh4oTBW\n2ifIqqsF5DOFK3HCR/koCVzMBdEacA6KWEKnohn3Ye9TUMIG2hQVyzVyET9zr8GES1DcQaLkARl3\nSxjlAYGu8P11mFdJVNkb4mKsSDE4GgDTL4PnEWjsAMvLwopH8EQlM7u87vhC+yLQ1BmGnoUpEeBj\nJywDhjBWgLEkY28ufMYldsFBf/20PsCq+6KLVOMi4L0DQmoIG4AODZ0EcZUUsPE+BJUQSrokwU/l\nwdMWgo4KEl8iH4S1F88f7wD+ocKnu76+eK++nrDiJmx5BCOqwA/VxePHu0GtNaIb1rJG0LAobG8F\nLbbAlrtw5jmUcQJLM4iIhmaloHVp6K6BcrMg/Bs41A9qzIcac+H4YHGuNF0I+7+GZV2gxwpR/JcG\ndJ0Pa76CL+ZDSz84dBWKOcKDZ6BMFoWBSclw5swpnJwcefo06o2fpf8q3tdeoCOzhqprUlJStjaD\nfxP/dbKaG8qqsbEx8+bNo0mTJqSlpdG7d2+8vb1ZtGgRAP3798/xdX5KfE4DyGHoctV00JGbnIjY\nyNz61LAVpyznXhtOQ7xr4H1mZEX2sms/m9uh/fDuFfVva3+aHXKzeUJWaQmQ9T42NzdPzwMEsLa2\nRKHQkJIC5mbgUhCePIev2sMf20GpgsZV4cAZ6FIL9l0R/tUv68KsnrBwD0z4C772h+Vh8Cwe6nrB\n7M5Q2gXKjIOGHvCbtqC1xRJ4lQST/OFgBITehItPxBRwPgtRCHP7Bfi5wrLWUEj7MXH5n8SP1WW+\nqiz+12jAaQ5MrQtBBm1VfVZI1C8sM9tf/9i88zDxBET2FSQuIhaOPIbB+8HJEhKTRTKAqUJkfZ6L\nhakVYGAJfcHRg0Tw3g5Hm0NFg2IntQYc18GcmtC1lCgAW3od5l6BOBVYGEFEc7A1CMtI04DTNpjo\nCwO1/tzLMTD8lMSJpzKpaRBSU/hwMx5PaHQAYjWQKktEKWGtr0x9rUf2/EuodQD2toSKDkKJXn8H\nZpSHfsWF3aH1cTjaGioUhMXhMPwYfF0CplaEhFSotAe8HGC+PzQIBYUGTrfQ3zAkpkLlvyEiEbqU\nhOUN9eOLiBcxUx62oiVqk90QrYZf6kG/bTCzHvTXWkNvREPNNdCkqFBm9z0Q/lWlGrpVhMUdRKV/\noyXwKE4QVCMFdA6BY/cgfIQo+qsWDM62cGgQDN4Mf5yCfjVg53W4+wIqFIYLD8WNUZXicOA61C8D\n5yKgSCG4HyW+1NRp4lzXITY29pNXvavV6nTrUF6DWq1GpVJhbm6eZYoBvB6M/ynTC/J6K9g3Hdt5\n8+ZRokQJOnbs+C+M7D+Bz9FVnwIajSZDVb5uiv5DiVfmTFQdWcqKZH2K7lLvSyQNp/nflexllVOa\n03hTXmzmArWsSN/bkJuRZZnJ6rsQao1Gg5WVJUYKoaIZK8DYWFTRf9Uelm0TCtu6iTB6EZwKF1/4\nTvkhJQVuzYGnL8F7uFCmCtmJ4qtNYXB3ChS0gVVhMGQtRIyBx/HwxxmYcVAkDOS3hGIOcO859KsK\nkwPEuC48ghpz4PoQKKrNNZ18GIJPwr2v9GrsuMOw8jLc7idIDMDuu9BhM9zvK1RhsZ3guBBm1IUe\nBtPeiy7Cj8cgsp+oJtdo4MRTCNROR6dqhJJby1GitbPM4jtQOh+EZIqA6ncMjr+AS50ytlu9HQc+\n68HHAa68gDoOsLCyUDn7nIETcRIX28jpY9eh0ma4His8rNMrQFBx/XPr7kP/0/CgO1gZw8zLMP60\nmJJf6Qc+e6CZGyyoo3/N5nvQYz9UzCcybMdWyViwdvoZtNgpukrZKCAiGa51E2ppQgq03Q6XnsOJ\n5uBqCQ33iuzZ0PbgvwYqOcD2AH1s1jOliJmKTBDNIE70EefWrtvQfj1MqgNDta1w77yEGqtFC9eX\nKTCyHvh7QLPfYVJTGFpLHIP6i0UM2tXhgARtV8H5x3D6a9hwGYZsFTchRgqws4DYJKhWChzsYdsp\naOYHsYlw8joUKQgPn4vZA7UG8tvAywSwtQK1WiisGu233KNHjz5pR6G8TFZ11+2sZuqyaldqSGYN\ni76yI7MfC5VKhUKhyLNkVecNziwoAIwfP56WLVvi7+//6Qf230CWJ8jnAqtcxod4VnVkSVe4k5KS\ngrm5Ofb29ulTz1khr3SXyqpLk7W1NXZ2dpibm7+VTP9b25GWlpZegKRrNWhvb4+1tfV7Jynk5jbo\nrCW6cyQuLo60tDSsrKywtbV9bR/HxsZiZSW+dExMSSesKSlCvZu7HpJU8Pc0GPs7nL0BnevD4bkQ\n9RJm9IA208BrOJRwhm1j4FYw7LkAE9sIopqqhmEh4GQD3tOgym8w7yg0Lg33J8HzGTC8vlAZxxhU\n53dbC3199URVrYbpR2F2Qz1RTVHDnDPwWwMykL0Be+H7KnqiCoKQ2ppBV4McbI0GxobBtDqCqIIg\nWwXMRND80W4QPRROfQklHWWm3BAe06NRMO48hMeK1zxLgrX34Pe6GYkqQKd/INBL4kRnONoJzKzA\nc5ewIax9ACtqv05Udz+EG7FwowfMqAMjLoD3TrgaBy+SBVGdUUOotEYKGFkeLnSAhylQeLtIBQiu\nlfE927jDlU5wKhrUEjTPZG3wKwRXOwlP76HnouBJd6pYm8Ku1tChJJTfCvV3w70EuNAHvAvCud5w\nMx6qbBDED4QyGpsiMmjjU8XxBWhaAv4OhNGHYUqYeCwxVRzTlymCpI5tDHU8hGVk9C5YdEI0A9jX\nF2zMoMJcsZ9H1YWYRHCfClMPQ9dawpLS0hce/Q69G8ClCJjTFyZ2g33n4LcB0MwX4pSwbaqYRWhX\nX5z/lmYQlyAIrLmZ/pvR1dWViIgIPhXy8lT2m8amI5+6VqK6oi9dhycrK6sMcX267zPDDk+6oi+d\nlU2XcPA+yKv7Dt68/6Kjo3PFs/p/HZ/Jag7jYzyrhmRJqVSmt2/NrvVpVuv+FCQvu+3RKaKxsbEk\nJydjZmaWnkbwPgH2nzrLNSUlhVevXhEfH49Go8lArPPaBVFXfKZr8ahrj2ttbZ1l4sPZs2dxcnJC\nlsHMTJBBIyMo4gJGxuDoIAqqfL2g0XA4cwPmD4dVY+DLSYAMPedDVJIgDlt/gLpl4IfVgvjVLglD\n/gS7wUKhdHaAWV9CyBDhe1zZEwrnE4Txmw0wqTlYa7s87bgmirHG+evHO3QXuFhDixLwPBHuvoTA\nzaIZQHF7uB4Nt2Jgxil4qYShBiH9KjXMvwhz6mUktePDRPelHqUz7suuu6FrGeHFBChbEIIbg6W5\nxOCqMLo2/P0U/LaB23oxHV7LBao7ZXyf7ffh5kuYWl2csxUKQmgAhPeA+0nCq/r1SbhlkEmv0UDP\nIzC+OhSxgS9LQ0QQNCgGfnug7A4R3dQ7Y/MZStrDzOpiSjwuFXof1LdZ1WHtbUE8+1eGKqGwPDzj\n8xdeQIwKWpSCGuvh4EP9c0YKCK4HVZzgbDSMrKYn+K42cCYIZCMJ7z8lLr2AKusF4bz3AxSylfBe\nKKFMEcvXd4ddX8AvYdBiPVRfDQHl4eJoOBkJPdaK5RqUgk09YcQ2WH5GEN8D/SEuCezHQ7Plorq/\nfDGwsYSVAyFsAuy9BF8vhTlB0LQiVBgKfRvBVy2g9jcwpTeUc4OekyD0F9h1XMwiWJpDjYqixWtK\nKhhOrvj4+BAaGspnfDgypxdk1bLW1NQ0Pb1Adz3LLr0gq5a1eZnow9vJaqFChbJ87jOyx+cCq1yG\nTlnN7uTNqvWptbX1e005G64rt2OyMivFmYulTE1NPzqNwJBI5uYFSUf4ciPLNScJd2YvqkKhwMTE\nBCsrqzeOd+XKlQwY0A8QBFUGChWAcqXh4DEY1gtOX4SnL+DyPbC1FtOmjf2gXE+4/Qja+cOEXtD0\nGxjeEtwd4cYjCN4h1LX6/wPvokKd+/s78NcSwqKDYExzKKD1oE7eJVS1vtXEVG94FPRaB6UKwDd7\nJe6+hAcxMs8SRWcjm/+J5Y0lMW4zE6gTIv7WaETygCyD7VywNAF7c6HcKVMF+YpKFGSvmA3MOQ8r\nm2YksIcfQvgL2J6pecuG6/D0lcyY6kKhHVhZbOePB2HuGTj8GGpslRheRqa1m7BO9D8sptsLZpox\nvfxCEPgzQTD7NJQPhWoFYbU/TL8kCPQwgyl6OzOY5w/lCsDww5ASD6H3oK27fpkkNXyxH4b4iha3\nrTeARwjsbwnutoKI/nwWdneBWkWgTmHovhX2PII19UXMV8e98Et9GFYN5pyGFltF29NB2rGsCIeT\nT2F6AIzaKZTTsVoFN58FHO0m0+wviRobobEn/NVNPLevt0yLPyS8FsCV/mBrDjWKQJMSsPMWtCgL\nCwPFsse/garToc9fsKQTNPGCdT2g80p48BL+DhdtU20swcMJto6EBBXUHAfVxsGJCXB4LNQYBw42\n8McgaDMdfIbCjfliqr/aUDgXDIGTYfBvsH48dBwPY/vC1D+gti8cPg3mFpCohKQkMbaePb/k9OnT\nTJo0KdvPVk4gLxOu3Brb29ILgAyWAsOCL0N7QVpaWvp75ESmbE5Dl7SQFWJjY8mfP/8nHtF/H589\nq7mAzNXsMTExGQqfMkc2fWyveB0SEhLSC5dyC7pCLmtr6/QpnA9t3fomZN5nOQHDGwO1Wo2xsTFW\nVla54ivNiba02XlRU1JS0qf9s0O/fv1YuXIlxsba6m4FpKkhfz5ITIBfR8L+Y7DnKAS1h8a1oPNQ\nqFkWwq5pg+BHQrcmMCMEpq2G1UMheLfEzrMyBWzhh84wMADaTIBkFez9Qax73m6YuBEeTBJT+Mfu\nQOASQXRS0+BZgvAcKiQoU1gUWZUsBLuvgqkx7PkarLVWr06LxfIHh+i3bdZ+mLYPHowXxPVeDFx+\nDD3XQFsfePoKHsUreJkoE50go5ahkqMgbtWdwdcRmoRCJ0/4tU7G/VZ4gcS3VWWG+WV8vNzvUL8E\nTPCHH/+BDVdFdqdXPohMFKqoaabTyHWZxHA/mW+rif/vvIRRhxRsv6FBI8PWVtCkWMbXqNTgsQL6\n+ImuTN/sAt9CsK2xUEuHHYOtDyXuDpTTlx+6F9ZcgXGVYf5VMQW/oJn+PW/FQNN1YCxJmEsyTnaw\nu5v++d23ocMGCCwJPUtD41AI6QqtSsPxCGi6DAK9YXFzsfzTBKi0TESeKVPhwlAxVhDHu80q4UU+\n1Qv67oDzURJzusj0WgE/NBLdzwBuREH1GdC5PCzoIJTUpr/DxUdQuQTs/Qnik6DyKKjkBlu+FbFp\nfj9CcSfY+z2cug31f4Ffu8DAxtDwZ3ieAMcmQ5vJcD0SpvWFoQvAMT+0rQ1zNsIvA2HiEqhfHfYe\ng0IFIeqFIKy6S3e1atXYs2dPlp+vnIDueyI3r9cfirw4NkOPrEqlwtjY+LXmCNl5ZeHT2gZ0NrLM\nM4qyLNO8eXOOHDmSp8h1HsPnAqtPhcxkVVdsI0lSOkHVXQh00yE5geyqxHMKOvKUlJSUnkZgZmaW\nKwVdOVkslpaWlu6P0t0Y6OK3civq60NvHN4lNUGlUr2RrPr7+3PixAlMtX48dZpQKRVGoqDK2UFE\n+CQmwpSR0K8zOFQRxK9yWfE7KQFOL4HHz8G7q74LkE9JOBsOlxZCCVe49QjKD4Bzk8DLFV7EQ6kR\n4GwniMv9aEFALc0hsAbUKwONfKDkYBjXGgbUE2OOSYCi38L+YVBFqyS+SAC30dqOWVrvpUYDTj9K\nzGgl092AUAaugCfxcMiA1CpTwOlHCO4o1LoDt0Rno6hXQvGs5yYR6CXT0A3c7IT6OeUkPPg6I/Hc\neQc6hcL9EaJQTIftN6DDOvF3/cIwrqqYPgeYcAJ+vwZ3v3qdxFZcBvfjhL/z24rwU1X9c2NOSKy+\nAfdHiOvHo3j4crPEmYcyA71gzhU4FQRlMiXf7LwD7TeKG4DoYUKJNkRiiiCYD+LgQA+olil54Npz\naLBKRI195w/jDHzF4VFQd5GIG1vTCqqsgGIFxU1F0BrYdhnOfA3u2tQEdRq0Ww37b0MhG7gwToT6\nH7sFTWbB2Gbwnfb9rz2BGjOhlhuceABO+aBXAxi7DnZ8D3XKwIPn4Ps9tKgAywfCk5dQeQzU9oJ1\nQ2D/FQiYDq394P5zCLsppvatTMWNWooa7G0gMUl4q2VZqOUm2girimXg0nUo7AJPn0FyivicADg5\nOXHz5k1yA3m5oj0vklVDJCQkvJZtnbngy7DwCz5ty1qlUomZmdlr3+2yLNOsWTOOHTuWo+v7P4bP\nBVafCoYnvo60JiYmphfCWFpaYmdnh4WFRY6qerlhAzD0dMbFxaHRaJAkCWtraywsLHIteeBj82kz\nF3lJkpShI9anaIP7Pu+fubvUm7yob7IYFC5cmBMnTmCs7RGfzwHMzcHEDFoEQGoqRMWIu1AvDzHN\nmt8P7Gzg78WwcCKcvQI/94Em34B7J/HFP6EvRO+B2FcQ1FQQVYDOv4KfB2w8BeVHgdMAQcLcXGBk\nJ4hcKUjyX8NhTi9oWxWW/CP8rL1r68fdaznUKCGlE1WAoJVQu5SUTlQBJu8Bc2OZLyrrH4tJFAHy\nM9pk3Bf910FZV4nufjCmMewbBBETRKODr+pCMSeZqecVlF4KTvNg3DH4oszrV8qBeyV+qJORqAKE\nhoOXk8Sd0WBiDQ02gW8IhN6GmRdgUbPXier+CLgZDVdGwJousPAaFF4G++7DrZcw66zMXx31x9bV\nFvZ2l5nXAqZeEOpqsSxCMqxNxD4t4QhuiyRuRGd8/uRjePQK+teGhmtg+YWMz3vkg/zmQoFffUki\nMVn/nLcjnBsiUguKzQdrS0FUFQpY0Q06+0lUmitILYgOVOHPREc0pVpfjFWzJOwYBhN3wuz92vU6\niA5U+28Lf+mVWTAiQCilAVPhwj0oWhCOTITQszByNTjng2PjYc9Fcc4FzgETY9h0CvIXgj3zwNUB\nalaCR/vAqzgULAhX/gYrK2jXAiZ/Dxqgmh9cvikI9oOHYpvMzERSBsDTp09xcHB4a8en/2vI6xYF\n4LVrokKhwNjYOP0GX1f0ZW1tjZWVVYab/rS0NFJSUjIUfSUlJaWLSWq1+oOKvgzHmNX+S0tLy9Vm\nN/+X8dmzmktQq9XpU84gQpl16mpuIScbEBhmumb2dKrV6jyROpAVdKqkYUesrOwJuV3E9S7HOSsV\nVVeM9qbXZzd2W1tbUlJEdYuREZhbwqt4cCgA330P330L5cvBoH4wYIgI/+/7k4iiCp0PvuWgZH3x\nf7sfoWoF8fehBVDWA7YchogncGgqnL0FM0Phwl0xZR+XAm0bQMQ6+HOUiA8C6DYdyhYBf22MlEYD\nk0IlZnaWMdERgljYewXCvtNv0+NY+Oc6nPrW0B8NM/fDwk4Z/adBIVDTQ4FvUf25H5cEoZdh31cZ\n99PKUyJndVproRSDBrUa2i+Bw3dg1TWJxedlWpWEHuVERmtCkszw6hn3dYwS1l2F3X1lXOxgc5BQ\nckf+DV12CfVO56s1PJQ9t8P39cS0uYst3B0FM45A6+0ie7ZWMaiaSfWUJIiMA0db8HKWKBoss6YV\nNNN2rFKmQuAWGFIHfm4B322DystFokKfChCtFMrw6Mbip34J+GIlnHsCc7V2gQG7FCTIMk+nybRd\nDCVmSpwdJKdP77vYQllniYO3ZFQaCbVGxlTbPCC4g4y1mUS1+TLru0L/UJFpGv49dA6GMuMlro6X\nyW8NdUrB30MgYI4Y18aLEspUmbXfQrdZMHsbDAuAoc0h5hX4j4ezU8DTFQ6Mgzpj4Vkc3IoS6vid\nKGhSHdZPgkWb4bt5MOVrOLIEKnWDYdNg7wLw7Qq9f4Ijq8CvExR2gkE9YMk6WDQTBn4Lvn5w5rSw\nAphrixFBqIz29vY8fvw4R9W5N/ka/23k5bHp8L7pLEZGRtk2R8gcv5WamvpWe8GbMmWzI6sxMTGf\n/aofiM9kNRegVCpJSkrCzMwMW1tbkpKS3jv66EPwsQQsq0zXrIqlPoUq+T7r0Kmo2XXEygqfgqxm\n9/6GXlQgXQH4mC8HkVwg/jYxhZRkkDVCKSrqBt8Mh2aNYflCKFJKqKUBAbB/v1CgrCzAsxFERkH3\ntjBmEHQYBJ0bCaIKMHCaRGEHGZ8B8EolyOPg9jBriFhPnyng4QxNtbmaMfGw+TjsH6cf58QNYGki\n00XbBvVlIrQLhlKF4Gk83DkPCcnw6y4Rg3UiAs4+EKR5w3nxmqL54F60aP2ZkAz7bkDYsIw3ab1D\noEpRqOaWcT/9sENiYktZS1T1OHxPYmWQTEsfmWO3YdpuUZikVEM5Rwh/DpUMOj912wg13aGGgRJs\naQqj6sOyU9CjOvTaKeF0GGY1kGnsDtNPCII10sAna2YMo+uBs7XIDz0eCVMOw/cGy9yPhV8Owd8D\noF4pmeAj0CEUWpUQ0/KjDkmYm8HkVuJ8m9laxr84dF0lsmgTUqFYfkFUAVqVg+PDoNF8uBAFQRVg\n41UN18aLG4+dg2R6r5EoO1viUF+Zcs4wZjccj5AJnwYdgsHrV4krP8hYmgrCOq2VjKwRmajeLnB4\njFjXukHQYY5M2QkS1ybI2FtCPS8Y3Rx+3gaerjJ3F2gL9Kwg4Gewt4Ke9WB8J6GaVx0DV2aIGKoC\nNrD+FFQoBU+2w+U70GQorN4FA9vBo2dQpy9c+QsOLoJqQVDEGQ4vhYqBMPl32LcM/L8U6mpAfRg1\nAWb9At/8BL36wLIlYG0LxIMqSX8cXFxciI6OThcFdMqbIanJzWzRzxDIadX3bUVfmcmsLjbwTfaC\n7MYZHR1NgQIFXlvPZ7wdnz2ruYCUlJT06XIQ5FWSpFwPf/6Qzk+Zi73epVjqUxRyvW0dmVXJ9y3y\n+thmDW9DUlISsiyne2Lfp4PX22B4nHXeVSMTkNMEgTQzB2MT0KjFtK4qCezs4Jex8O0YcCwIW/6C\n02fhq6FQoTRcvC4IYfAE+LI97D4M7QdA+Do4fB5+WgxPXoB3cRj8hYj96TceHoYKK0F8Ari2gZ0/\nQy2titpqAigTYUE/uBoJVx7AtC3CT2mkED5VCeEdtLIAYxMFJpIGWRbqWXEn0EgK4TFUyzyJlrGz\ngFSNRHKqjCpFRDhJgG8xKOOioJyjBicb6P0nHBkClQxUyvlHYMJuiPxZ+Gh1GLoe/rktcflHOYMK\nOmknzDsoyNepu+BsKzG8qkzVwlBrKZwfAZ6ZEmhqBksUdYCQXjJqNYzcBMuOgbs93IuDFR2hbdmM\nr1GligzRYQ3ApzB8+Qfks1CwrYuGkgWg0QqQTWDfYP1rrj+FNr+LfvexSXD+O/B0zPi+d16A/1zh\n/b32A7hninZ89goaBIvOT8FdoGcN/XOyDGP/htn/QP8qsOgkhI0XKrkqBVrMgBuP4cposLeEWCVU\nmQ4aI4iKhf0/gJ+2wYE6Ddr9BmfuwfWJsOEcDF4LgfXgz0Owcii0rymW/fsUBE6HNUOgTRUxjrYz\nJPZfktFoIKgl1KsM3SfA5qnQqCpsPgRdx8HWaVDfD4J+ldgVJnM7FC7ehCaDIfh7qOoDVbvD0O5Q\nuzK0GQwrZsCydXDjHgzsDT9Phy+6w5pV4FJU4sE9mZTkjPFgUVFRr13Ls1LnDP82VON0pCYlJQUT\nE5MsG5P828iuQCgv4E0NCz41sjvmOiIrSRJJSUlMmDABd3d3TExMuHfvHrNmzcqRrpaG2LVrF8OG\nDSMtLY0+ffowatSoDM+vWbOGadOmIcsyNjY2LFiwAB8fn2ze7V/F5wKrT4XMLVczE5fcwvt0fsoc\nOfU+xVK5Xcj1pnV8zLgNkdstXXVFUBYWFunEGIQC+i7tWt8E3XG2tLQUGbzaXWRsAmgkPH1MeBqp\n5mW0hvY9zdn8hwprG3jxQlRwH9gliklKVRBfwi2bCxX2ymW4uluoXK7VhKE9LgGsLCFBCUsmQGdt\nJXfhBvBtIAzrJP5vPwaiX8KSIXA8HHafhY3HRI6ltaVQy5QpQkkc0RlKu0HFEtDtV7C3ktgwRn+p\nafg92JnDxh/02zxmJaw/Bjfm6afVn74E9wEwvx88eAGX7sPdZxK3HsrpXYnKuEjUcZeU0BCRAAAg\nAElEQVSp5gaDNklMaSnTy2BKX5UCjqNhQz9oZJDDqtFAwVESC7rJdKoipoT/txsWH4DIWDEtvrMP\nlDbIXD39AOrOh5sTRbas4TpKT4AncVC/lILglhrcDGYCJ+6DJWfgwWTxf4IKvt2sYNVxDf7F4MgD\nePgL2Ga6101MBscfhFo7uy18VTvj8w9jwWsSeBeGO1ESe/rL+Br4f5NSoOwUUKaJ7Qv7DkpkIt/f\nh8Lc/dDbH+b00D+eqoYOwQpO3tQQNgLaLZOQTOHM/2SmboTJG2D/9+BrQFjbzJE4el0mVQ3rf4Tm\nVWHdQeg1E7aMgYYVxLKrD8KA+bDlO7gQIYqt8tsJH+mtv8TvBaHw3Vw4tkgU/c3fCKOCIWwxeLlB\ni5EStyNlbmyANbtgwGTo3Rou3YHjF8DNFV68FM0wynjC+StQ2BmcHOHWXWjTDkJDwcFRIuqxuPFI\n1rZnVRjB1SvhuLq68i7IrtuTrsgT/t3WpVlBqVRm2ynx30ZeIqtZQTc+CwsLZFkmPj6eNWvWcO/e\nPW7cuMGNGzeIi4vDxsYGDw8PPDw88Pb2ZvTo0R+1Tk9PT/bt24erqyt+fn6EhITg7a0Pag4LC6N0\n6dLY2dmxa9cuxo8fz4kTJ3Jik3MaWZ7wee9M/D8IhUKRoQVrbuFtU9tZtRHVdcTKK92ZslpHVqrk\nh4w7u/fPaejU6tTU1HT15F28qO+DuLg4nJwdMTaF1GSdN1LCvZQRz56k8SpOJmR/PoZ0iUUDVKpp\nSviFVOrXkVn7FyxcCu5FYf0aKJAfSvnAruVw9AwMHi+IZ4Vy8Mf38Pce2LkPOmqnkeetFd2vBraB\nO48g9DBsPy4IScXBUDA/xL2CGhUgZIKIDNJowKEpzB8BrbWZnQ+fw/ErcD7YwKsaDcevwflZ+m3V\naGDhblgyMKP/s3cwNKigIKiBXvZ6ES9TrD+ETRZK8cYTMoeuiql5ZbLM6K1w+K6CgNIa6pWEUVug\npKNEQ++M58KYLZDfCjpoLQ3GxvB9C2hYGmpPhmJO4DsbqhaTGNtIxt8DuoeIwi1DogqieUFUAmz7\nBib9raH0TBhUHcY1hDgVTD0E2wbpl7c2h4WBGjpXhPqzhHL5NP51sjrjgIS9lcyiPtBlHuy4LrG1\nt4xCIc6HrqskqpSEf0bL/BIq4z8P5raDIG2U1rBQ0CgkHgbLjFgFladIbBsgU7uUdl8mwLLjUM8H\nlh4GLxf4qqF4zsQYNg3W0GOxhM9kmUL5ZW7MFjc6P3QULUzrT4UD30Nld5FS4GIno06DfPZQv6J4\nn87+QpVtOxn2TYSqntDNH249hpZThK968wyoXRFq9YUqveHMchjYFh69kKj7lczVtSLs/9FzqDMQ\nLq+FUV1lmo8AO39BxPPZw9Kt4FcJun8Bf66HToHiHPkzBBq3kLh4Vub6bZEEEBIiot5AxjYfvIoD\nSSGhUspo0qB0GW+2bvmbunUz9ePNAtlNMyuVSkxMTNLD8Q1J7NummT+FvSCvWhfycvEX6Men+7G3\nt2fQIPEBX7JkCQUKFKBbt248efKEO3fucPfuXV6+fPlR6zx16hQlSpTAzc0NgMDAQLZs2ZKBrFav\nrr9Lr1q1Kg8fPsz8Nnkan8lqLuBTF/PokF0agOE0vy6v08rK6oPVvZws5HrbOnQVmrpxf6y30/D9\nc/qY6FRflUqVXqBga2ub44UK9+7do1z5cigkUKeITlQaDSQnydy/rQYZRk+35uvAOGJjZBZvtOFh\nRBo7N6UQGgsKEwkjSWbVUvD2hAYtwMYKBoyVePBIRpZh2Sz4oh0oldAmCDbOFEQkSQXj5kNBe3Dr\nCPGJ2rzUkrDgR6hSDh4/gxItIHiEIKoAYxaCg51Eq5r6fd5rCjT1k/Ason8saCY08VXgWVh/fo0P\ngXxW0LqKfh88i4WDV+HElIznYZ/5ULOMgvLu4vEyWiXRMQhm9RGWhVUHNAzfqiAqWoOJMbQpL3Mz\nCjy1KqlaDQuPSqzsI5P50AWtUNC/EczuriEmAYb+IdNmuYhJepkEY5rxGgKXQdPyEg3KyjQoC6fv\nQpdgiWWnZTwcoEJRiXqer5+Le8PBzQEC/KDSNPi+EfyoVbbvPIepe2R2/wC1vODyVAj4HxT7ReLw\nIJl/bsLlxzIPgwXB/6kd+BSFrvPg5H1oWRbWnoVLM2SMjOC3nuBeEJoGw+KuEOgLbRZAcWfYNg72\nnheEMlYJo1uJMSgkMDKSkBQyMfHw+KWo3AcY00lMy9WbIhTW3/ZK7Lwgc34FBE2RKD8Iri6QMTaG\n/s0hNlGi0TiZ41Ph9hOYuQU8isLDKPB0EwVP+4KhcjcI+AZ2zIKf+8g8jJKo9KXM7Q0wpCOs3QOl\nOgoyXbUS3IqAar6w8Q8YOV5i+WqZDWugcgUYPQ4OHQeFscTmTTLbD5vSom4KTdqY8eShhhOHUjEy\ngZgXkJwEltYy5paCsMoaaNmqJUuXLKVjx46vH/R3hGEOaHbFP+8Skp/TPtm8TAjz8tjg7d2rvLy8\nUCgUuLq64urqSp06dbJc9n3w6NEjihTRe54KFy7MyZMns11+6dKlNG/e/KPX+ynxmazmAjKfqJ+i\ns5QhdCQsq85YOTGto+sgkhvQqb+64HtdCsGHdPR6E3KKrGZX0a8jrjlNVA8cOECz5oIRaTRgbiVh\nbCKRkqRBYST+NjKS+XVkAgoJpi+xxtEZBnRUYmMnMWqSJasXqahQP41yZWDGHDh6HPLlk2jUQiYl\nBfbthEBtDFSfb8GzuESqWqbTN7BlP5iagpsbBHWEmn5Qqi6smgSltYVYQT9C0+oS3u5y+jgXbZZY\nPkrvCX38Ao5ehnPz9MfgaYx47MxM/WdFo4HgnRKL+2ckjr0XQJ1yCsoV0y8b8wr2XoSjv2b8rP22\nTaQjdKsnlLT2NQA0dJsBp27B1WiJypNlHKwlulWRufUMCueTCSif4W04GA73ojT89KP4P781rBok\nyG3BAcLi4DtF4n/tZNpUECTx3AM4HQHhU/Xb6Vccbs+QGfUnzNsDXi6CLJcy8JzeeyH8ogfGQ9VS\n0K4KdJoFf52H/YOh5xoF/mU01PISyxcrCGd/lRmyAnymCVVw5SCRb6tDa18ImwiNJsGqMzD5C3A3\nWOewFjJFHaD7XJh/CG6/kLi/VIy7UUXYPR6ajhdFcdO7wLTtsPWMhvDlMH4lVBwG52frCeuPnUCT\nBv6/grmpzKVV4OIAu/8nU+dr8B0C5+aJm6BRHWVeJkpU/04ol3NGQ6920O9nBX49NNzYAPa2cPh3\nqNgV+k2CxaNhyfcyDb6Goq1FdrBHcYkyhWReJcKBzRDxACo3gp8mwdSxMncjJPxqy4Sfhzv3oJE/\nnL0kExkp0al5Cpv2mtK8VjJfjTLneZQGdZoCC2sF4edTSFPLJCfLWFhKJCllkKF37948fPiQ4cOH\nkxt4WxV75lzRzFXsb1Jl/6vI62T1TcitAqv32R8HDhxg2bJl/7ms189k9RPgUymruhM2MTGR1NTU\nHOuMldV6cnp7Mqu/RkZGKBSKN3Zp+hh87DZk9s5mruhPTU3N8X20fv16uvfojoSY5jW3UlCiggV3\nLykp6GpC7QAbNi2KwdxKQUEXYxwdNfyzI4VhPVIoXcGIkL22nDqSyt3rafTsBB5lIS4evugG8xfK\nqFTgUQw2LhZEa89BCN0ByakyPcdCjepgYQVLpkCHFmJMTbpD45oSpT3Etj6MgiPn4Nwf+m0fsxAc\nbGVa1RTELuoltB8LpQrD7cdw6Z4gGtPWQyF7OHoNTt0U6tiWE6BJkylWUITDF7QVXs39lyBsckZS\n2nchVPeSqFg8436fHKpgSg8Nxgbf90oVbDkFOydCrTLCk7h0j8yiHXDzEdhbwJy90L2GIKUAfVcp\nGN5cQwGbjMdl2SFBhiNX8//YO+voKq62i/9mruTGE6IkIcGCuxd39+IS3IsVd3cKxaW4FIoUKxBc\nChR3dwgWCCF2k5srM98fw81NILTQEl7e9+tei7XIzNyZM2dmzuzZ53n2w6QNSib94K0yP3wLA7dA\npwoQ9E5ykyzDvusiDUpLxCVAgfEwqBoMqa4kf3VbByVzKEQVoEIeuDMbOiwUyDRaBlkifGHKfWrV\nsKCDkgx25THsvwqNSqTcJk8GyBskcOKWzI+hIiHlJNyS5Xk0LA4PXipxorWLyuiSuSaUygVHJ0GF\n4XDuIZy+B/ung78XLP4eVCqRwt/LnJshE+ilnOPjCBGVSsIkKab8oCTTHZwFJbpAmX5wfCbExsPp\n6zKqtz6nDSop9+DCYRLPXwkUaClzezP4eyuEtXgbcHeB2ESRMzckHB0hZxBcOCgTFwdFq0K1prBv\nI+z5BSo2hODM8PMimbK1oEwV+OMQPHgkUOYbmVPnZKpXgb6dTazboaVJTQNjZzsye3wCHj4q/AJV\nCBoVLx6bSIyXUGvA/Daya9SoUdy/f585c+bwKfinpMs6Tn5o3++SWeuYZE3+/bNqT18zIfya2wZ/\n3r6IiAh8fHxSXfdP4O/vT1hYWNLfYWFhBAQEvLfd5cuX6dSpE6Ghobi7u7+3/mvGvwlWaQSrLRHY\nSpR+7vKhViQnTlYV9XMXHEiOz5Wc9G4MrVarTcqQt56Ps7PzX+/ob+JTS7p+Ska/yWQiPj7+o5Ld\nPgazZs1i0OBBCIKSDKXViWjsBExGCU9fNS2+92De4HBKVneidogL/b99ioOjgJ29QFy0xO5zrmTO\nJlLI9w2vI8DDE0pW1nJ4p5GbdwVcXQVCWkncuAztm8LspfDqNQQFwpL5UKIYjBgLGzfB7SOKGvbk\nOWQrC+c3KMbrAJU7A2YY0xFuPITLd2H5bySponHxYKdVppCdHUBtJ6JRC4DE83CZzAEgI2CRRcxm\nmRevJFzelmk1GJV/MkoEfskckCcQcmcA/3TQ6kc4Mh4KZ7H129xdMH4zhC0lydcVoN0suPEUTs5I\n2c/d5ioVkFpVggXbBZ5FyFTJq6JIBgvT98KTuUo1JiskCdJ/JzIxRKJDNduyIStgwW9KstSOfkr1\npeTYchba/wThPytK9fFr0GSyiFaQ6V5WZtwueLyAFEQSIDYB/DorCU51CisVnJIrzptPQfvFAnun\nyNQfDRncBY6NkpPcD1YdhV6r4M4qaD8NTl6Hk+MVyzFQMvlz9IG2tWD1HqXa2LoBKdvw6wloOg0K\nZ4OTc23LZRm6zxbZdFjm7A8ys3eKrDwkc3mjzA9rRFZukzm/TCbw7bs6IgqKdRII9JJ5HimgtRf4\nY4NE64EC56/K3PlN6ZtEI5Rrq3xgXFyrkNje02HRr4qH8J71SmJU/gpQvhSsXQDPwyF/efi2NiyY\nBtt2Q4tuELoRcueAguWgUEFYsxTKVwezDFt3CHxTVKZwCYF6jdX07mRi1monBnbSU662Ayf2GfDL\nYset8wYcXFTERVtIjH8bV6qGShWqsHnzZj4Wer0+TQurfAip2TG9W+0JSBIMvoaEr+T4mit/wZ+3\nr379+uzYseOzJ4eZzWayZ8/OgQMH8PPzo1ixYu8lWD1+/JiKFSuyZs0aSpQo8Sd7+4/jXzeAL4l3\nS65+7lr3qREnrVZLQkICDg4OaWqH8imuA6khtYID76q/aZ2tDx9f0vXvOBD80z5Kjv79+zN37lxU\nGgGLSUZQAZKSlaxSQVBOHQ+vGyhZ3YlRy3ypk/kekgR9p3ixbvYbylVVU7uRhu9axhLzRqbHUAe6\nD3bgm6A39Okt0eM72LdXplljhWgFZRZp1ErF7EkmTh6GXDkURTR9ZsXqp97bRKvKLUAww8B2cOIS\n7DkJ568qpMXVRcQ9HcTrJbDA5EGQOxvkyqr4t1rMsDOZMtiwJ+jjYc8i27IZq2DGSni0WzlPUBK3\n/KrAxJ7wMhIu34GHz0Xuh0lY3paVzZdJoFwumeLB0HkBTGwNHara9htvAN+2sHM0lElmI2Uwgk9L\ngS1j5KQEoHtPYfAS+O0k2GtgXBNoVw4c3jqqTdkOs/fCoxWkUG4BMrQBT1e4EwYV84jMaSUR5KkQ\nzYzfQ4+6MLSpbXtJgr6LYfEuCPaHUxPA/h3ntj4rBHZfFtg5WaLWEEhMFDk6XCLQU/EhzdQLxneA\n7vXgdTTUGQ4PnsOJUQpZz9kflvSHphWU4/VbKLI8VGbrAJmyOaHCGBE0MkfmyTx4BqW7Q55Agd0j\nlTCMqDjI3VPx5j18Firmh/UjbO2TZegxW2TtfgkBOLMOgoOU5V0niGw5IHN1pYz321jmw+ehQi/w\n84KwYwrxTkyEym1FoqLh0iYJUVSue7Hm4OGiJG/dDoP2rWHBMoWsli4Bd+8rU/79usPIfnD1BnxT\nE8YNhj5dYOZCGD0dju2CP85A1+8hY5ByvGfPFRIsispzZVJqa6CzBy8fgadhMmVr2PPHfgOFyjty\n4ageRzc1cW/MmC0yFqNCWIOzZOfMmTN/9jgnIS4uDkdHx/84+UsOK4m1lgt9l9BaY/H/EwlfVhgM\nBlQq1Vdp+QV/3r4aNWrw+++/p0k/7d69O8m6qkOHDgwZMoRFi5QBtUuXLnTs2JEtW7YQGKgE8ms0\nGk6fPv3Z2/EZ8C9Z/ZJ4l6xGRUXh7Oz8j9XO5Iby1qSj5FZIsbGxScvSChaLhdjYWNzc3D76N6kV\nHEitdrIVn5PsfQh/dk3+qS/q3+mj1NCyZUs2/7oZlVZAkAERRFHA3kmFMcGMZBGUl6wAbYekY8n4\nSDy8RdacDOR4qJ7RHcIpWFzN9ctmBARm/+xClbp2LJyq56fpeiZMFJgxAx49kMmSTWT1dh0ZgkQa\nVErAy8nChtVKOwYNh5274PR2OHISNu+CddsVEpLOQyRDgMSz51AwD2xfpbz0JQl8cinerU3ehg1E\nxYD/N3BsDRR8axUVpwffMnB4ORTJbTv39OUVUtouWRnVkOHw4Cn8vty2LD4BfCrB3gWKarv5gBKK\ncOU26A1K6EDNIlCzMJTPC/2WwbUnIqdmpAwj6DYXTt4ROb9ASuE6sOkItJ8OozrArA0ib6IlulUV\n6F1VJt9gmN8Dmr6TFL5sLwxYBk82KVPcTUbB6RvQo7KSLDb3gMizNe/Hsc/fCaPXviXDFtjUH4q8\nVYpvPoVCA+HUQsibBQyJCjHceFBiQVs4cEPg1D2Za8n6xmyBPvMFVu+VCfIELw+BA9NTDutztgoM\n+UmmSl44flvg8WYZq2Pc8wgo00PA0wmOTZKpMVbkTSKcXSdx/wl8EwJl88LGkbb9rdoL3X4ErQ5u\nbAbftyEQkgSth4scOi1zfbXM43Ao3xMqlYEDx6FdQ/jhrXtPbBx800TA0w0OL1Pa+8NKGDQDfHzg\n7lmljPCshTBqMlw8CJmC4MRpqNIYlsyE5g3hwFGoGwILp4FeD/1HK33i4irgHyRy44qFui0daNjW\ngc61XxPS14XS1expVymcbuM8uX3RyKEtMWTJY8fNCwbFIUAG34waXj83JxFWBDAmKB+Sfr7+3Lhx\n471rmxyyLKPX6786sgq20s+phWClZsGVWmGEtIyT/Zo9YOHD7ZNlmRo1anDs2LGv7pp/ZfiXrH5J\nJLceAcVqyGpf9Kn40HR5aklHX8ID1RrW8DFl45KXP7W262OM+z8X2fszpHZNPpePqyRJREdH/6O4\noGrVqnHk6BFUGgFRFHDy1JIQZSJrERfehCcS8dBA3d4B7F745K2tjoRKhHm7/PEJUNEk/2PMZpmi\nFR3QOap49TCB7afdiYm2UCooktgY8PIVqVhXx7bV8Ry+6ECWbCIP70uUzR3P+ROQJTNcvQ7lqir2\nTXo9pEsHCYlQvBhsWKeQhlevIHtOOL1HmWYF+GE+/LhI4OFROUkZbd4bnr+Aw6ts59lmMNwLEzm2\n0va8LP0VBv8Iz/Yq1bYADAbwrgS/zYayhW2/7zwWLt6C02tT9l9AVRjUAdK5wM+74OItkVevJbQa\nqFcCRjSHHG8TaBON4N0Sfh0NlQql3E/GFtCjMQxopfy97xT0nw03HiiK79WFtml0K3xbCYxuK9O1\nnm3ZmRvQbKzIg+cS39WGWV1TWnHFxEOGEFg4AJpXgV4zYelv0LeWwKjGMuXHiHh5SmydkPJYGw5C\nh6mK+n1tGWT24z10ngE/H4CBTWFkyPvrZ2yE4cuhWSVY9o7dY2QMVOwt8PyVjAw83K0UhQB4+BS+\naQMlcsCWMXD0MtQYAmtnwNb9InuOylzZKOP59jGwWODb/iJnrsjExst0aAYzR8Ol61DmWxjRAwZ0\nUrZ9+RoK14eiecDRXmTHYZmhw2TGT4BBvWHo25ymXoNFNm6Vuf2HjIsLbNoBbXsq8aqiCCHfQdgz\ncPMQadhKy8VTJl48h/3X3AndYqRfm1jWHPbCZJRpUyWCySs9kGUY2u41Cw8GMWfwS149txAy2Jtp\nPZ5SvrknB9e+QjIrtXTNRhm1BlQaFSaTBckEzs7OPH369P2OfgsrWf3cxvCfA3/Xx/RjCiP8mXvB\nx8Kq+qZVmNs/xYfaJ0kSNWvW/K9LbPoP4F+y+iXxLln9O4rnx0yXv4svUS3rr2JwP1VFTQ2fg+z9\nFWJiYrC3t0etVn+26lJWfAqhTw3FihXj8uXLiGoQVCKWRAm1nUg6Py1aexUvHyQwYW8+Fva+w5Ob\nCRSt7k74gwT8MoC7h5oda2Jw81Cx+FAgTi4q6gffZekON04fNTJ/Ujz2TjBiljv1WjnStFQ4mTLJ\nLFyjfODUKqUHE5QrAz+vl3kVoZSf7NpHTbsuKsxmKJQ5kZPHIXt2pb2Nm4JRD6G/2M7BN4/I1IES\nIQ2Vv+Pjwac47PkJSr6dZjcYwLs0/DYXyhax/TZDVZGBIRI9m9uWfTcJjl8WubDO9lyZTOBZAX79\nASoVt237Syh0mwjPDiq2R1Z0HAkHToOLI9x7DB4uAi0rwO0nMvfC4fxC3lNVO86AZ7+lzKw3myFd\ndQjwUghbg9IC41vJZPJVEsVmbYeHG1LGyQIMXiywfLdMYiLkzSSwsq9M5rdEt98S+O0M3Fpn2/7s\nTagzSESNREw8PN+ash3WtuRqA08jIGN6kRM/Srgm40DhkRDcBno0h/m/QINSsGKgbX2iEXJ3EMie\nVebYOWhVFeb1S3mM7ceg6Ujw9oBbWyD5t/Dj51AiBLL7w/nbSqnegZ0VYtq8r8iJczLXf5Vxedum\nM1ehZBtwdoKXF5SPIICjp6BmCCwcB63ekvwjp6B8K3B1gUsXldjU4yegTj1YNhuaNFAU23otRW7f\nhRvHJVQqhaxu2gEIUKaKBp8AFdt/MXLstitqtUD1wtFkzKpixU43Zo/T89PMBPbc8OaPg0aGd3rD\nmuO+HNoez8qZsaw+k4letcLwzagld3EnNs55RdMhGVgz9jFZi7lx90w0hlgLgggaOxFJBrNBQqPR\n8Pr1a1LD/yJZ/TN8qDCC9f/w8YUR9Ho9Op3uqyWrH4pFjomJoX379uzZs+c/1LL/GvxbFOBL4u96\nraZG9FxcXD76wfxSHqjW80l+nslVVLVa/dEq6oeOkdxLMC0gyzKJiYnEx8cnqaify8c1+TE+tf3Z\nsmXj8ePHCCoQVSole1cFskUi+pUJQTbSoG8A83rc4dmdBIatz47GTmRYzWs8f6DCyUNCoxGYvjmA\nTDl0tC35AEEU6Fg3mnTpVag0ArM3eFCyko7bV41cv2Bi6ToHHj+UWPRjIudOyTg4gFFQ890YByYN\njGXBSg1Vair3YNPaRipWEsmeXbnPoqLgwEH4fbvtHBauUNrbvI5tWc8xEOQn4Octc+Oe4tk6YRG4\nOSsZ4lduKxW2jpyF6BiJDsmm/81mWLsLfn7HAWDoHAjwEahYLOWzNXiuyJCOMjo7OcU+Nu2HDTOh\n6ltngmWbZRZtgJv3wdVBURhDqio+sgD9fxIZ1k56jyAOngf+3gLXtsrcfwJth8nk7goNS8LuczCv\n7/tENTwS5myS2bsQCuaARv1l8naHwU0EmpeTWbATjs5L+ZsiOeD+LxLpaoJZgj5zYPE7CU8LtgnE\nJMDzQzKthkDWNgK7J8oUefsh0W22SJ5gmUl9ZNrVhwodoHQfgcPTFZ/ToctELMCOBcp1KRei+Nhu\nHGdrd9sJCgk98IdIrkZwdaOUpK4Gpoc98yFfYwjwVYgqKKrzzzMkGvQQyddE4PpmiUfPoWo36N4B\n/jgrULyewJkdSlxq2eKwdg60eFtYwt0FGvSAsuUEzp6V2bYNunSGUiXhp0XQoauSAFi8MGxYJlGy\nukCxauDqLHL2skymHCoiwmXmrnNCqxV49shCjSIxHLvtwi/7nalaIJrJQ+IYNNGROzfMNCz6kgP3\nfLl3w4l2lV6y66YvD26a6FT+EUuOBhFS7CFBOXQUr+bM1llPqd3Nl9AlL8lR2p1bx6OwWCSMeuX+\nVOsEJMmCm7sbUW+ieBdfc0Z7WrTtQ4URrMd7V5W1FkZILU72XUeDr60fP9R/aWVb9f8F/yqraQSz\n2ZzCi/TPFE9rfKTRaEwiep9S5z45rOpgWn+xW+M9BUH4LNPmqeFzJ6VBylhUa19b1dXPPej9nfYH\nBAQQ8TpC+UMEjZ0KOycNibFGBAQc0mnQv05EY6fCYpJo1M+fGp286ZznIsjQaXomjqx/hbOjhcnr\n/Vk4+iXrZr0hIFhH/9k+7Fj2hvCHRjYeVwIJ6xYKx5RgwclZ4OYVC6JGoHwNHbPXKcUMJg2M5vCO\nBE5et0MQBF5HSOQLSuT3Q5DnbXJS67bw4gmsmgO378Ht+zBysqIAurtBRCRERinT+ZKkkBi1SkAU\nZQxGcNQpSTOS9La4gQlki+IA4OwAbq4CiUaZiEgY0RmyBUFwIGQJgMx1BFaMkalb3taHu49B00GK\nquqUTBwaOAN2HBG4/pucQj3tOwn2nYROTWHeKnj8DMoVECmYWWLhztRVVa8aAmsmy9RK5ud95xGU\nb6ckfnWtJzKuvYRbMjOLTtPg/F04t9627Og5aD5Y5FWkRJ4scH7Z+/fEpDWwYB+E5DIAACAASURB\nVKvAziUydbsI2KkFDs+U8PWAl28gS3NYPQnqV1JiiCf+JDBpiczUThDkAy0mwL1QkqbiI95AtS4C\n0TECc7+TaDQGTm2E3FmV9Q+fQplWAln9YP9MmUp9RCwqmd83KIpw3c4it+7B5Q0SLk6Kglqlm8ir\nWJmISJkyhWDDbFv7jUao1Unk7iOZmDiZb+vBopmKbVrxygKBfrBvre1V89M6+H4MWCTo3ldk5Dg1\n+/dItG5iZt0aqPY2WW7GTIGp0+DiURnPdNB7qMCq9TKevgL7r3ug0wm0qBRDfJzE7nMuJCTI1C0R\ng7uHwIaDLlw6a6ZRuRgmLXaiVmMd35ZRSOWmk970ahLJxTMmloR60a32K9QagY4jvBjd/jndJ/ly\nYGM0ej1kyOnA1d9j8Ayy59XjRPRRJox6C6JGRLZICCoByaSU3EyOP4sL/U/DZDJhNpvTdHbuU/Cu\nImv1r07rwgh/t60fUszPnDnDtm3bmDVr1hdt038h/g0D+JKwWCyYzeakvxMSEpBlOcXUijVZypqM\n9TmI3pewfJJlmejoaFQqVVLZ1n86bZ4aPjZb/2OQWixq8vjftMCntt/Dw4OExAQks4SgFlBrRPzy\nuRNxV3nRNRiXj3V9zqHWiGTI58abx7FUbu3F5pnPcfdSs+BiQcIfGehb8jIt+qRj8+I3mE0ydTqk\no/+P6YmONFM38BZrDnnh4iYwZ2wsO9fH4+kjUqWJCzVaONO+7BP23/TCP1CFxSJRxOslC1ZqqF5H\nUVVbNTCgfyMzYRycOweHj4mE7pIwJICDIzi7qpBlibgYmZDOajJmgizZBXZutXD8gMS5S7YXyMTx\nEuvWwvUrtqn3Yyegbj14fFMhtXfuKgS4ex8oXgTi4uD5c4iNhTfRCsktUwgqFFFiG4vkgnIdBZpV\nlxmTrISpJIFXWYGl42XqV0653KOkwJofZGpVVJY9fgrDZ8CmXYo6OqwtdK5PEvHsOxP2nRO48mtK\n0ms2g1c5RYFcvlEk7JnEuA7QvT6EvYQ8bRSimjNzyut+7jqUaqOM0D0awYROSqIYKGQ0c2PYPB+q\nlVEcE7qOFNlxQGJxf9h+XOTOCzj1c0rFeffv0LS/cn4ju8LADimPaUiEZgNF9p+QaFEHFo9Nuf7l\naygXIhATI2M0Q9gJ29S/0QgNu4lcugFXNkhMWSGyfLvMvQsyLyOgeBVFuV4z3ba/sOeQqQI4OcHr\nuza7rfCXULgClC0G697aYC1ZD71HAio4e02Nf4Cy8erlEoO/t3B4v0yePAox79VbZMtWGY1aRuek\nZsh0B74PiaXPaAc6fe9IdJREzYJvKFhUzYINTrx8IVE1fzS1m2gYP8eJnZsT6RMSx8JfXXnyQGJY\nt1jcPEWMBpnEBBmVFnQ6EZNJRrLIiCoRi1kGWcaYCL6Z7Yh+ZcYtvRZZFpAFgTdPDMo2gMUoIWre\nJ6xfc317k8mExWJJ07yHv4t3E9M+Jk72SxZG+LOPkN27d3P79m2GDx/+2Y/7P4Z/yeqXhNWE2Qqr\n4uno6JgiWepzE73P7e+ZHMmdCCRJQqvVfvZp8+T4pw4Kf5XRn9bJaJ/SfhcXF4wmI4iC8iIXRERR\nRmOvRjJKdN1YikVNj+OVyZH2S4sypdxB1HYqUAmYE8wM35iDotXdaR10mpdhJtx91JRp6sO+pc/Z\n/jgbrunU9Kn5kHuXEwjMrOLKWROiWqB+O2cGzVKML1uXCiM4GKatUObApw+LIXRjPEcu2HHymMS+\nXRKrFpsxJoKrG3j4qIiLk0nnIbB2jwtePsp9UCrLG9p0UtFnsDIPLkkS2bwTmTVHoEFD2z2eKYPE\n1MnQvJmtH4qWgIplYfpE27L5i2HyD/Dgus3CCsA/K3RqD4YEOH4SHj8WeRUhYZGgWkloVgMqFlOm\npscthGXbBO7tSVkJa8QsWB8qcPtASuK5bS+EDIBpI2DqPJFnLyRa1RDp01SiZCdYNxVqlEl5DftN\ng93H4dpehXxv3g09R4EIBPqAnQ4OLX3/2pduC/4BMLwn1G4roFXBhrEyBbNBx8kiFx/C2S0pyeja\nbdBlpGIBdnsnZPB9f7/dxgms2i6TM4vIiVUS74bL95kisnK7hAyELoIS73jBHjkNVTuCjxfc3p8y\nTtVkgiY9RY6dkUgwwMn9tsS6u/fhm2rQoDL8NEEJ9/imiYDOVSA+AezVcCJUSroODx5B0UrQqj5k\nzCAw8geZZZt1bN8osX+XmXM3RJyclI3Hj5RYutDChbMynp4waozAnLkyTi4CZ194IIoixw8a6VAn\nmkW/ulCumh2P71uoVSiSLv119B7uwPXLZhqUiqHXMB1qjcCPYxMwGmVc3EUy5Xfi8u+x1OrkQ4sh\n/nTMd5kS9T1p1D8DPQufo/HwzKi1ImuH36Vy10zsX/wQJAlToozaTiQxzoJfLmciHsZjMUvIFhDU\nAhaTBBJJhPVrJqvJxZOvDZ+iSP9VnOxfFUb4O+/jP7uua9cqGaBdu3b95P3+P0OqHf9l3Yj/H8NK\nnKKjo5OsLVxdXXFycvrbcZ2pwTo98rlgdSKIi4sjOjoai8WCo6MjGo0GjUaTpobWf7fKlCRJGAwG\nYmJikghpan39JSqL/dX+rf1pNBoRtUo8KYKA8Nb63mK0EFjInbn1f8feVcPwk5WY0+AYkixQuk0Q\nmYu4k72oM57+dnTOd4GoV2baTMjEmmclOftbJG2GeOHgJPLLnAjO7I8jIV7GM6sz47cFI1lkOg1X\nYqge3DJy60ICfcY4Icsy1y4YWTNPT2QEZElnoFtbC+tXWwjOo+WPl76cjgxgxxUf9LEyw6c5JBHV\nE4eNvAqXaN/dxioXz7FgZydQN1lm/IplCvlq3Mi27OZNuHMH+vVK2UdTZ8LwQSmJ6vI1irI2bABM\nHge/74NHtyRy5YQ6tcElPYxaLBJcG/wrwQ+roFFVpVKVFZIEC9bDpP4piSpAv8kig7pD51Zw9w+J\no1vhWphEgdZKmILPO6FnRiMs3SLwwzCbSvxtDXh2GkoVh0t3IdEM95+k/N2BU3DlDiyfDnlzwoM/\nZCqVkyndHbr/ILBun8QvP74fg968jhL/K6igWleRyHfCIq/dhVXbZfZsBp0TZK0t8uylbf2Zq/DT\nJonje2BYP4EqHWH7Qdt6QyK0GybQvAVkzCySu4ZIfLxtvUYDUwZKRMeCWgvpvW3rsmaG33fC5j3Q\nYzQ07iWiNwqEHhXZvk/Fqyio1SzZR0sQHNquTP8Pmyqzfo+OCtXUTF+kIWc+FeWL25Jwho0RqFJd\nxTdlBMpXFFi9VmT1YS/cPNS0rx0LQKmKWkbNdKJ7k1ge3DETmFnFsp2uzJ1kYPeWRGKjZfyDVMwc\nm8BPs820HJKekrXTobHXMmVnNsZvycZvi1/y6EYC0/fn4tDacC4djmLUtjysH32PgByOlG7iy/G1\nYQzcUQJJEmgyvRBqrQqds5qX9/SYDRZUahFEGYtRQvXWhNfF1QWLxfJVx6z+r8BKPtVqdZJQYW9v\nj4ODA46Ojtjb2ycl/sqynEQ09Xo9er2e+Ph4EhISksQOs9mcRHY/hL+qXuXl5ZVWp/s/j3/JahrB\nSoQSExOJiYlJCgNwcnLC1dUVnU6XJkTvcxEw6xdsdHQ08fHxqNXqFITvc5Pi1PAp52L9GLCSarPZ\njIODA66urh+sEpPWZPVDg5Y1iS4mJgZHJycssoTaQQOSjKhSIYoCokbEPp0dFqPMg3ORqNQCdYbl\nZGCW3cRFmhjxezkqdM7EjUMvkWXo/c0lXj5KpP2ULDQbGsSB1eFEhiciCFDT7xbzBodTtJo72yIK\nMXhZFhYOeErT7u6k81LUz5FtnxOcS8PCKXqK+rykUcnXIIq0/D4doWGZ2fkgExazwNCZrqTzVH4z\na2Qs3ulVlKlsM78e2Sue9t21uLjYzn3ONJmhw0Clsi2bPAkGD7JlggN81xsa1hdJn0wl3LpDmfoP\naZGyD8dPhsH9bLZWALfuKP9mzoCVK+DmdYmIl1C+ElhkWLEN3ItDoz4iW/fD+AXg6AANq6fc977f\nIfyVxHftbcuK5IcjWxST+Hx5oExbqNQRzl1T1vebDoF+MtXf8VsFOHlJZGBfcPaAvI1gzEIlA1+S\noOcUgXZNwSrEiCIsnARHNsHP+2Q0Wt4j0gBrtyuxsc+uQY6cEFxH4PgFZZ0sQ7sRAnVqQKkScHCb\nROUKkKehwB+XwGiCFoMEQppDzmzQ/zuZedOgxQBY/NbNYeB0EUEtsGAu7NgmkTkr5KwmEq3wQRIT\noX5Xgeq1RcpVUpO/nEhUMsKcIxsc2QnLN8PvZyQOnVEUq3TpBHYfUnHpOrTsYtt+7yEBlRok4PFD\n5ZlUqQRW/KpFawe1KirLBEGgfRd48Vzmxi2ZAw+9KVjCjhX7Pbl42sS4fkoDm3e2p1kHexqViSY2\nRqJoKS3NOuro2TKO1jVjCMzrTPMB6dHHStRo68Hw1UE4OIkMqHqHolVc6TQhgBH1buHkpmLEumws\n7nMPjU6k04ysTG18iW8HZ8Q3sz2r+lyh6/JC/Dr0Is1mFkKyyJTsmguVnQplaFEunsVoQVSLCKKA\nezp3DAbD+xf1K8HXTKQ/V9usRNZq3m9nZ5dUMtvJyQlHR8cUs3AWiwWj0UhCQkISmbUSWaPRmERk\nrYptanj9+vW/ZPUf4F+ymkaQJImoqCgSExOxs7NLSkZKayPj5HE8nworiYqNjSU6OhpJknBycsLF\nxeU9cv0lVMmPOUZyFTUuLu6DKurf3f8/wbv7t1gsxMfHExUVhcFgwNvbG0RQ2akwJ5qxc7VD0IjY\np9NRqH0eEqONBH6THq8srqjUAuv7XcYQY6Te0JwEFXBjarVjmI0yMTEiFbpnxd5ZTc2ufsTHmlny\n/T0MeomtS2KpPygzsizQY2YGRFHkyvFYntyJp90gdy6fTGBYSDg3zify9JHEzdsq+i/KiIOziqHz\nvOk83AMPbzUzB7wiMKuGIqXtkvr9l5/iGTBOl9THN6+aeXjXwnf9bPfJ1g1m4vUWWrSy9Uvobono\nKGjXxrbs5Us4dxaGDUipIg4ZJdK3Z8op6NB9EPkGOrRJsSnde0ODBiJ+yTxP1Wo4fBTGTxJ4+FQk\n9ABIThLdxotMWQoebnDwhEIcreg5VqRPJwGXd8K+h0wEfz+BYweUkARXHyjbDsq1g1U7YMaw94nl\n2m0QEyvRv4dS6jN0MyzbIZCtrsDQOUpM6vRUQthiYpUYrDJlIH8dWLfDti5OD30mwMQRMq6usGmF\nxJDvoWpXmLIU1v4G95/CyvnK9hoNLJmtbFOlM9TvDQaTwNyptn22bgobV8D306B5P1i2WWLbNmWq\nXqeDXzdJ5M0PuaoJRERC73EiCSaBVRtElv8skL+ISL6yIsnziM5fBpVaKWW6ZIGtg9P7Cew+rCb0\nAPQeAguWCYyZKrNqrxuzf3alXxcjxw4rEriDg8Cm/ToePpTp0MrMrxstNKhhplVvF9y9NfRtrjBk\nX38Vy/d5snaRgQ3LEwAYNt2BvIU01CgYxbel37BxRSLBhZywc9DQf1Eg7Uenp2RNN7p+cweVGqaH\nZuXuJT3zBzymUW8fyn3rwXffXKVwVVdaDfdnZI0rlGrgScXWvgwpc4aBG/Ohj0zk7Nbn1OqXlfV9\nztFqXhFO/nSD0t/lRlAJpC/kg9ZJAwJIZultYJ2An79f0hib1uPop+L/A1n9KwiCkERktVptEpF1\ndHTE0dExhdONNeQvISEhibgmJCRgMBi4ePEiW7du5dKlS2lGVkNDQ8mRIwfBwcFMmTIl1W169epF\ncHAw+fPn58KFC5+9DV8C/8aspiESEhKSCN4/9d38FHxqYo91+sNoNCZVxbKzs/vTQSG1hLHPjbi4\nuCQLrOSwTtkYDIZ/5Iua1s4JsbGxSb64yQs66HS6pJgrUacCWUC2WFDbaUCWyNciJ1fW3aBMnwI4\nBzixs9/vOHrYk6W8H/f2P6bPryVY2PosEWHxdFlTnIL1/enru51OP2Qm5pWJ1aMeoFKL9Fmdh+J1\nvRlW7ix+gSqGrc4EQJucVzAazJhNEB8nIckypWq7M2ptRgA2zQnn58kvCH2cCZVKGYzLetxj5s/p\nKFdDyRBePjOGZTPiOPXQDVmGZ2ESbevEolFLNG2t4sUzeBomc2CPBZUAvunBaBRITITXr2VkCby9\nldrvWq1S7tJshLq1IUOA4qcZEwMTp8G544pSZ720eYuLNKgtM3aEbXh6+Qqy5oXTf0BwsO0abN0G\nnbvB/UcCOp3t3lixTGLkCChZVuTEYRkRmU7NIF8O6DAYnpxTnAyskCTwzANLF0CdmrblUVFQpAyE\nh0Pl0iKzRkhkDrStDygJ/XtB72QqIsCQsTBnkaJs7l8LbslCzCUJ8lQRqFBZZsZ0+GUD9OwFtSuI\nLBorMWmRwMY9ArfOpCT2h49Bw9aKajtnCrRvxXuYtwQGjILG9WxkNjn2H4bqjSFfPjj5jne52Qxt\n24scPChhTIQTV9QEBSljjMkk0/JbiRtXZK4ek7h5B8rXhTk/O+HuIdKyeiyTpom062yL5bh6WaJq\nWTMWEyzf5UrJisqH0Io5CUwbHse+0/Zkza7s/8E9ibJ545FlGL/CmxpNnXn2yESjgk9o0dWRfhOV\nDjywPYG+LSJZs9eV3AU0jO8Xzy9L9TilU7Pxfl60OoFBte/z/KGRVVezYzZB15K3cXJRMftQMDfP\n6fmu3G0G/JSR8o3S0avsLRAFZh/Lxfjmd7h6IpaJBwowpt41LCaZ5mOyMLvdVWp+n4Vnt/TcPxdN\n+S7B7J56nUItgzn/812cA1yIDovBZDAjJaa8ZtevX8fNze0/XsI0Ob7mcqZfc/IXKH1nJbqSJLF3\n715WrFjBgwcPePToEW5ubgQHB5M1a1ayZMlC1qxZKV68OJkzZ/7rnacCi8VC9uzZ2b9/P/7+/hQt\nWpR169aRM2fOpG127drF3Llz2bVrF6dOnaJ3796cPHnyc51yWuDfBKsvjeQlV//KSP9z4mMSez5U\nFetjlV+DwYDZbE5Tiyy9Xo8oikkWKp+rupQVaemcIEkSMTExSR6BOp0uibjqdDrlcRQFRK0aEaUy\nkCCAWqsiMcaIfyFv8jXJwv4xpynQNJhaU0owJeta3P11vHqoR1QJNBiTh2p9s7Gy+zmOLL6Ho6sG\nnYsW/ZtEus7PSbkW6Xl6K46+BU+y7HIeHl1PYPWEF9y/EkdAdidqfedPwarp6Jr9JCsv5yQgq/IC\naJjhCt1GudOwo0IA5o2MYO8vsey+5s2DW2aunTcyrncUWq0yVRvxUkJrB8jg7afGwVWNm6eIxQwX\nj8fTa3w6HJ1FHJxEXr8w8+PQSGZvSIckCcTrJaIjJaYNjqZuCwcMCTKvnluIioSHd4zYaZQpZ8kC\n/v4Cvt4yp87C/B+hWmXwS6/0W+NWoDeI7Niakgzkzi/SoiUMGpLy+uQIhp4DVXT6Trnft/xiZtYU\nM9evyHilg0VToVZlW9b6iClKedkbF1Kqp0YjpM8qMGORmlWLLZw5IdG2EYzrC5tDYegPEHYlpTIM\nsHQNDJsg4JNe5NljC6tmQs23bgTrt8N3IwUePZCTwiRehEPNmiIxURKvo+DgdsVf9F107y+wcr1M\nBn+Rk3skkheAk2UoU1PErIab12XqVpNZ9Q5h7dRH5MBxiI6SqV5VZvk7CWGPwyB3XiVZ7Nx1Nb7p\nbc+e0SjTvIHM7esW9HqZpp10DJ2sfJQdCjXSuVEc85eINGysjEsH9kq0bGTGbIHJi5xp1MZmlTRp\nYDwblsdz7JoOTy+Bkf3MrF1qJNEoM3aJN7VbKs/sldMG2ld8xriFbtRrpRxr2Q9xzBkbg04n4+Bq\nR58FQYxqco8GXT3pPMGf+DgLHQrdJGNuHZO2ZCEy3ESbfDeo3MyN3rMCObAhkskdHvPDvmw8u5fI\nhJD7uPtoMRkk9DFmVFoRO3sVZpOEZJHQ6NSYEiwYDYpy6uihRZZAVAsElvDh8ZkIVHYqLEYZQ3Qi\n5nhb4i3AtWvX8PPz+8sSpl/KmulrLmf6NSd/wZ8T/erVq/Pzzz/z4MED7t27x927d7l37x61a9cm\nJCSVsnIfgT/++IMxY8YQGhoKwOTJkwEYPHhw0jZdu3alQoUKNG3aFIAcOXJw5MgRfHx8/tYxvwD+\nLQrwn4R1kJEkKc0rb1inJlI7TmpVsZycnD550PsSMavW/jKbzSlUVAcHh8/invC5wwCscbNWIm9V\nqZOrz0lEVQaVnQbZZAaNCq2TFlOcEY2TDmO8icQ4E7sGnSB9bg8a/1Se6fnWEx9txNXPkeJdcnPl\nlztU7J6Fk+sfc3z5A9x87ak/IT+v7sdxZs19yjRTAj9ntrqGg7OKHiVvgCBiMlmo1SOQ9tMUU81R\nNS5SspZ7ElENXRWBMcFCnRAXXoebOXM4nnWzo0CAfI5P0TmIqDUiJpNA/a6e5CnhSL7Sjoxr+xgV\nEj9uz5B0ri0K36dJFzfa9rNVIWtV6gn1WjtSqa6tT8b3eUOmbBom/GSbdXj6yEy1HC/47YY3fhlU\nPHlk5vQRI5MHxODtB2Mmy/QeoKiyuXMJXL4sM2a0RGIiWN9jJ/6A588kunQTSD7+7dopER0NLdvb\n0uMbNFUTnFOkcrFEytZU07avGa0GBnaHdk1h/iqFIL97yw0aDgFBIg2aqGjYVM31qxJdW5jIWEbC\nzg7GDXufqCYmwtBxMGSCiradNcz7wUSz78zUriQyc6TE92NhQH85RTyvrw+cPyeRv5ASq3r56vtk\n9eZtWLVeZvtRe34YZyFbcTMHt0jkyaWsX/8r3Lgjc/mZPc+eQL1yBirVl9n3q+KQcOQ4rP9V4tA1\nZyxmmbql4vm2iczmDcozIknQpp1IibJqvP3VlC5s4Ph58PFVCKtWK7B6IwT7K8p5/7E28lmhupaZ\nyx3p0U6Pm6uApze0bmKm/zR30mdQ0695BD5+ImWqKBdv8BR7noVZqFwkkTIVRPbuklh9LjN3LhsY\nGfKcDFk15C+uI28xHRNXeTM05BVBwWryFNYSGSFhMkogqlh3NQ9arcjUXdnoW/Em2Qo5UP5bd2bu\ny0rbAjdYPu4Z7Ub4MWNPVrqVvkVQLnt0DiIaO4HeFW7i5KYlWylPHpx9Q/nuwVTpm4OxBXdTvE1W\nynTJzqRCO6gyojBaBw3b+p+k7qyy7Bp8QnECMMnc2vcUi8GCc3oHEqON6NI5kAiYEs1KIDWQO19e\nLp47n6q6llo2u8ViSVMi+zWHAfy3wvqeCQwMJCgoiPLly3+W/T59+pQMGWxjbkBAAKdOnfrLbZ48\nefI1k9VU8W/Mahri71ax+qd4l0gmT/SyWqe4uLjg4uLyl9P9H0Jan4uVpBqNxk+ORf1YpFUymtXp\n4V23BIWoCiCDqFMjatSoHbT4V8mOKdZIgR7fIJstqDQqRAcdGp2aysMLMzn4ZyLuRFFryjf0udyU\nCytvUbJVIBNKHWJJ29MEFfFg2pMGFG+ZkcNzb9N2ejBPbuqZ0ugyj67F4OJtT6tZ+RiwswRmg0TD\nAco8ddRLI1ePvqH9GGXQio0ys3DwM5xcReoEP6R60APGd3uFLAqEjAlkzb1CbHtTHCd3Le2G+9Jt\noh9l6rpipxM5fzCWzqM8k8716QMjD64badvfNr8d8cLMjfOJdBlsU7IlSWLbmgR6jXZJ0aejur+h\nXA0dfhmUD66AIDWlq2iJjZVZd9iTY0/Sc1Xvw7JQD17HCWh0MHGKgJcvlKuoYtp06NwV2rYHN7eU\n98rQISp69NPg4JByed8uFhq11vHjckeuRDjz/Tgds1YIeOeF+AQoXjTldTebYdV6gdFTVEn3Y648\nIkcv29E4RMRghEk/Cuw/nPJ3i1aAnU6kbWdFfenRT8PpO3ZcfSgQ9I1iht+n9/v32bnzEBYG05Y4\nMnCMQEg3kYQE2/ruA0TKVVVRoIiKlVs0hHTV8E0NgU3bISoaegyAEVO16HQimbOKHDiv49lrkfxl\nRSIjoWVn6NhbS4YgkYxZVOw+48jFywLVa4lIEsyeI3DnLiz5zYXpyx0pW01HqcIS4S9savbkseDo\nrCI4nx3VC8WlqKZXu7EdY350pFUTC7UrmWnc2YkW3V2oUMeBITPS0eXbWK5fVlRHQRCYvsKJhASJ\nbRtMrLuYkcCsWio1dKHzKC+6Vn9B+BMlrrVKQye6jkhHhxqRfFvsFb+uMjD7RF6CcjrSu9wt5boU\nd6LfooxMaPuIhzcS8A2yY+pvWVk7OZw/dkVj5yCSJY89c/o+Yf6gF5RoEUSOst44etoz6HB5uq4r\nwZFFd4l7lUif3RU4Ov8WYRde02VLBXYNO4N3DlcKtcxG6PCTdNhVB9lsocyosjj5OOGY3gljgoTZ\nKKF/HovKXo3aTo2ofSsmWCQKFCzAmTNn3rvm1illtVqdFEJkb2+fFDtpb2+fNB5aZ8veTQIyGAxJ\nsZRWJ4L/VnztRPqv2pcWlcE+Bu9e86+5Dz+Ef8nqF8SXKIVqPY5V5dPr9UmJXjqdDjc3NxwcHP6x\nupsWZPXdjH6rOvxnGf2f45h/93epWXolT0az9pEkSbYYK1lG5aBBRkAymnAJ9uLp/luUnlaDh/vu\nYIhJpMzkagiAxkHN+g6HiAnXU6J9HioOLMj6NgeIi0zkwLx7uGRJh0ot0uxHRWLbNOgCpkQLu+c9\npX/RU5zfF0GZVhmZfKUipZpnYGnXS1TrFICbt6Iozutyk8BsOk6FxtC5+G3qeF8iLsaCXx5XWkwM\nZn1MeeydtXSaFMS3vdLjFWDHleMxvHpioEFXm3fT7H5PyZxbR67CNiVtYrdwytZ2xjfAJg+O6x5B\n8QoOBGWxLVs9V4/WTqBiHZv8GBcncfqIkZ4jUoaYjOwRQ8mKDgRmVn4viiIFiqsJfyozbbUHpyL8\nCb3pS4EKDixbq+LZc1i7Grp1kTm4X8Zkkjl1UuLpEwude6a8/8MeSVy9vpq3LQAAIABJREFUaKbP\nME3Svlt3tuPkAxccnAU8fURyFoKOPUQePVZ+M3QUpPcXqFTt/fty9w6BET86U7+tjoZtoH4rkSdP\nIT4eRk+BUVNTHt/HV2TnMQ2iGqJjoVdfkeQJ47IMfb8XqVZfw7etdRy66cbxcyKFKwjcfwg798KF\nyzLz19i9bb/AkHEaZv6kpW0PKFsT/APVtO5om5709hEJPaXD1VtFliKgdRAZPMF2Df0ziOw+48jj\npwLFvhEYM05mznpndDoRURSYvsKRMlUUwvrqpcS+3RJLFphZut+bJXu9EdUidYrrU4x5dZtpsXcU\nSDBAk862j5bGnZ1o+70rzSrE8PyJGUmSGdxRj0qtwsNfy/jO4UnbhvR3p9K3rjQv8YyEBGXfRcrZ\nkaC3cP+WiRV38hNc0InxO7LxMiyRqR0fAFC1lSf1u/nQs/xd4uPM5CvlRNPvfRje+D5t8l1D0jlQ\nOiQjZjM0Gp+LXpuKY4w3s6jFaQrV86dGvxzMqHII72An2i4pxpr2J3APcKTexCIsrbuXqkPz45nJ\nmc1dDtNifXUODT9M5WkVMOtN5AopgMZRg87LEWOcEXO8CVEUEHRv7wMZKlWuxKFDh967lz6E5NZM\n1jCu1IisVbwwmUwYDIYU1kypEdmvmRB+zW2DD7fPZDKlSViFv78/YWFhSX+HhYUREBDwp9s8efIE\nf3//z96WtMa/ZDUN8e5N+yWmzq3kKD4+ntjYWARBSFJRrTGTnwOfk6zKspxqRr+9vX2aJhj8nf0m\nb+ufebha928ymZQwAJWIoBIRNCpkWfFRlWV4feEJbpnScWnWH0TdiqDhztZIkkTEzZeoHe0oMbg0\noiBQaUQh9ow+w9Ut98lcxo/+t1tjjDWRs2J6ggq5c+m3pxxZeBdRLaLxcaXnwRpYTDL1hyvZRo8u\nR/PkegyNBgfy5JaelUPvcS40grDbBnYsjyG4cnq8MjrRcEBGRm7LT4WW6Tm7MwJ9tJGqITbFdH7v\nxzTo4oWTq/KSlSSJgxui6Tratk1cjJkLv+vpOsKmlhoMEn/sS+C7kSnNvJdMi+O7kc6Ioq3fpvSP\nInseLbkLalL8/tg+Iz1HpCwBuWRGPI7OImWrK2Q3QyY1/Se54hOgpkJdZ6at8+ZBuJa2bWX8fGSa\nNYbylUWc3glT7tPZROVaWjJkTDkkrlyQiEYrcPCuN1vPenL5joo8RSGkEyxfKzBm6vvhKMsXmTCZ\nZL4N0dF/nDPHH3nyKl5NzhJQvxW4p1PxbfP3X1wLZkr4+mkIvebFnn0ihYoKXHtrjRW6B27dlpm2\nVCHw3r4iR247kyW/hoLloH1P6NpfnWSeb0WDZhqmzLfj7iPwTv/+8+rkJDBishpDIkRFyTx5lPJj\n2stHZOvvDty8KaO1FyhR3nZNRFHgh5WOlK5iR4kCEu1amOk7xY0sObU4OomsOORFTCw0qagHQJJk\nujfV4+qhpVF3T1qWfsXrlzbj2x6jnKnSwJG6xWIY0D6OI3uNLLuUkzmHs3P1tIHpfV8AynM1dKE3\ngcF2tCz+nO2rY+hY+RkN+2cgcwFX+ldS1FRXDw3T9uXk4C+RbJmvkN3Ok/3JVsiRjkVuM7blQ9bP\nCMcjyBGndPYMCC1Jm/kFyFzEnXElj2LnqGLQ3lJc3v2MPT/eou7InGQt4cGkb/ZTtGkg5bsGM7P8\nHkp3CqZAvSDmlNlB+61ViHsRx5XNd6k0rCg7O++m7sraXF1yjqIDSmOKSSRTw/yoHbTKVL5ZRu2g\nRdCIIIrUq1+PLVu2vHedPhUfQ2S1Wm2Sx6g1f0Gv1yeNcVYia01q+hoU2f9WshoZGYmHh0cqv/hn\nKFKkCHfu3OHhw4cYjUZ++eUX6tatm2KbunXrsmrVKgBOnjyJm5vbf10IAPybYJWmsH6tWvFuwtDn\nQnJDY2tGv0ql+luxqB8LqzXX33U3eLfN1qz/5LGo1iktFxeXv9jb34MkSURHR+Pu7v6X21orYX2o\nralBr9crA5RGhSCIyEYTooMW0U6NbLRgn94d/cNwVHZaZIuF7A1z4xjowsW5J8ndIj8Vf6zGsuxz\n8c3tyotrkegjEshcNoAOoXWJfBTDjJxraDAhP8eXP+DF7Wg8M7sw+GI91Fo1M0v9RmAOezovVcoS\nDS18mLgIA3b2Kl6FGRDVAgG5XBj5e2nUapG7Z94wvvzvrAgrjXM6hZB0y3WKKi3S0Wq48hUedieB\nzvkvseFOTrz9FXV2xcQX7FwaydY7mYmLloiKsPBD3xfcv57I8HmemIxgMsr8uiSaGxeMjF/sjp1O\nwE4Hty6bmDo4mv130uPjJybNPBT1DGfWzy6Uq25TW8f2ieb0URO/nfdM0ccl/CPoM86Jxu1tJDgq\nUqJMhudsOpueLDlt5Grvr3q+bxKBi5sKySLRvI2a1p1EfNJD3gyJ7D7tRPbcKRXPggF6ug91oFV3\n2/4f3DHTqlIEES9kmrfRMHS8Cm8f232QO8BI71EONO+U8jnfu81Ar+YxeHgJrN6iJX8hG7F8EymT\nN9DAgi3ulKmiQ5IkhnSMYecvCYwdLTB3PtRtacfA8e9X7unRPJa9/8feWUdHcf3v/7UedyMJEhII\nGiC4hgSCuxR3CsUJFCvuTiktpTgUd3d31wQSCBHirpvNZvX3x36SsIU60H5/p885HHJm7ty5M3Nn\n9rlved7HVIz7RsaE6cZzUq/X06xmAXZuEsKDNTg5wZnbEsRiw7m1Wj1+Pkoq1TNFKhNzdl8ux66b\n4F25mEwvn6Vi1xY11g5ihHodpx5ZFR0PhsSq2iXSUCrhSqwrNnbFx6anaPmiThIVq4ioXF3Mzg0F\nHHzjhbmliNn9EnhyVc7p1y6YmRWPp7V3AslxGna/qoJLaYOlOPyZgpGNXjF+mSPdRxi+OfIcLW3L\nvEGp0DFlT2UadnYkN0PNSJ+H1Aq0ZvJWTwDun81kTrfXLDvrTZX6FuxalMjWuXGY2UhY8Lg5tq4m\nLPK/iVatY87dpuTnqple/RJla9syem9dQi4m822nO0w814RS1WyYXeMiJavbMHxvQ75teQ15uoqg\n661YWucUMispzaZVZ3uPiwTOqUP8k3Ri7qfQaHpDLky8SN3pftydfw3XQG8Sr0WAUIAmVwkI0Gm1\nCMQi9AUaVq9ezeDB7wj9fiYUljM1MTH5YMIX8MEY2b9T9enPQKFQIJVK/5XJX78sBfsuQkJC2Lp1\nKxs2bPjo5z1z5gzjx49Hq9UyZMgQpk2bxvr16wEYPtwgQzJ69GjOnj2Lubk5W7duxdfX96OP4yPi\nPzWAzw2tVovmnZI5H1vu6dey41UqVZFb+lPhr6obFMbP/pGM/sIwhk9ROrZwLL91DYW6s0ql8k+r\nD+h0OsNzlooRioTo8lUITSXYNvAm6/Zryk9uR8TqM2g1Wkp1rUX0nrtYl7IjKyINWy97hoaN4vqM\nS9xbcguZpZSK/avzYutjxj7ogVNFW1b77CExJB0zWxlV+1Tk+Y6XDD4QQMVAN5LDs1la7QjLggOI\nCc7m1IpIop5k4FTGirqDyuL3lRdT3Y8w8VhdKvkZyN/MuteoWNeS4WsMltiwe9lMD3jM/riaWNqK\n0Wr1BPm9QJWnpd0QW+LC1USHFvD8di7qAh16HYjEAqQmArRaPRZWYsQSIQj0IIDMFDW2DmIQGFQC\ndFo98hw1YpGBzGo0YGFlsKLnZOroM9KM8pXFlCknpoyXkA41M1m21YrmHYoJ7Lkj+UwelMOd5BLI\nZMXPb2LfdJISBGy/bKxpOKh5MtYOYpbtdeX6KTmbFmYQ/lyJUAhOJYRceGyBmXlxP8f3q5g0XMm9\nRGdk78he6XQ66rikM2iqDef2yIl4qWTUBAljJos4sk/L/Ola7sbaI5Uaz6nvFyo4sE1FbX8Zp3fl\nMnSUlKnzhJiYCJg9Scv5s3rOBBuT8RsXlIzrkYVGred2tA129sZkOjNDR73SWQybbc+2JZk08BPx\n447ieNwDO9VMH6/hUkIp8uV6hrdKIiddw6VHUmxshKz/Ts13iw37BQIB38/MYvf3Wew5a0qt+mJe\nPNPSvkEeG6+VplR5KcP8Y9GptJx5UkxYv52jYNeGAirXNyf0voJTL50xtyx+R5LjNXTxTUSerWPb\nQy+8qhieoUajJ6hNDMlvVZx44YRYLGTrylx+nJ+NQ0kZVtYifrxZvqifu2ezmdE1klVH3ajTzJwl\no1I4ty8HrVZPh9FuDFhoSFCKDctjXJ3HDFlYki5jDKK7B79N5Od5cTi6SclM09JlUVX2THhGpxne\ntP3am9z0AqZVvYhvxxIMXudLcoScGTUu0W1BJVqOLcfpleEcXxTGotBWxL/MYXngNTxq2aPIUpMY\nlg1CkJoYCJReDyKpEE2BFk2BFvQgszUp+gX2aOdN5KnX2Pm4khmWilpegFapRigWo1OqQCoGtYb5\n8+YzbtwHgpc/IX6LcBWGCHyIxP4WkS30jn0MIqtQKIqqTv3bUHjvPqSQc+3aNe7cucPChQv/gZH9\nn8N/ZPVzo1AsuBAfQ+7p9+rdA0UWwE8hyfQu/qie6x+xon4IWq2W3NxcbN7V3/nIyMjIeI+sFmq4\nFo5VJpP9qaQutVptuPcyCag1oNMjMpchNJeiy1Ph8aU/sbtuI5KJaHpmPJcaL0Gj0uLWqiqJ54Lp\ncqwnacEpXJt2EedarnQ81ofjHffgVNqERkHVOPLVVRJDUqk1rBotV/hxZtwVEu/GM/lRBwQCAcvr\nHif9TTZ6nR6xiQSNRke1Nu4M3F4fgH1BD3hzJYnFT/wQCAQkR8iZXPUyG17Xx8HdBLVKxzjf+4iF\nerxrWxF2P5eYV3mIpUKs7KRY2MmwdjNBq9YR+SCTeRdr4uptjpmFmAOLI7m0OYHN4bWL7tepn+LZ\nPTeaXXF1iqpYxYQpGFnjCXve1sTWSYI8W0NMWD7T2oZStaEVWo2O5CgN8kw12RkqNGrwqiShThMp\nvvXFVK0lYXinbDr0NWX0O+EGGo2O2vaJrD3uRB2/YmKbk6XDzy2OXfdL4VW5WPZGnqulqUMENvZi\n5NkaegySMjxIShlPEfW8cunxpQVfTTFe9K1fJufntUpOR5VBKBTw6IaC+V+mkJ6kRiKFoLkW9Bth\nbFXNzdFR1z2d5XudadLGnFfPCxjfKRm9Vsui1WKG91Wx87I9NepKjY5T5utp4J6MmaUItVLHluMW\n1KhTbC2eOTaf29d07H9WiqwMDQPqxSGTaNl/ToaVtYAapRWMX2pPty8NC74CpY7JPdN4ekfB1oMS\nerZWsmK/C03aFF/j9pXZrJ2Twfq9JsyZWEDVRhbM2uQKQF6uluEBsWjytZx5asXzh1p6N8ti7TUv\nylUzY0qnaN6+zOdkqDMmJv/TSH2tpqtvInq9gE7DbJnwbXHVBqVCx5eNo5GJdfQbb86sLzNZfKEy\nbuXMGFnjKdWbmDN7l0dR+2M/pbF2chy+Tcx48aCAxQ8akJlYwJyA+wRtKY9fD4N78/GFDOZ1CmHx\nqQpU87Pi+I8prJ0QhVAi5If0jkhlYl5eTmZ1+5tMOt2Qin6OxIZkM6feVQb+WJ3G/UsTfCGZ1Z3v\nMOVcI2TmYpa1voUiRw16PVaulmTFyfEZUJUKHctzqMdR/Fe2wKWmK7ubbKXFz1+Q/Sadh8uu03TT\nF1wZdsAgVScQUJCTjy5fg8zODJ1Gi9TREmVijqFoAEBhUReNjpEjRxbJEX0O/Bbh+r3jgA+S2HeJ\n7N/Vks3Ly/tk+Qt/F4WJth8yEh06dIjMzEyCgoL+gZH9n8N/ZPVz45dk9e/oeup0uiKLJPCbVr5P\n7T4vxO/puf4ZK+qH8Gfc9H8VhYRbIBAUPR+tVls01j+7gi8oKDBYgkVCQ1q3RAyF7j21BpGpFIFY\njF6tJuDyJG52X4cmV0nDnwfx+oer5EclITGTkv46FfsKTvR9PIKUZ0nsrvMT7jWcSHqRjkAswqeH\nNx3WN0el1LDCZR1DDgYgFAs5O+8p0feScalkT8CsOrjVdGS518/MfNYO53JW6HQ6JjkdZNiW6tTq\nYCANCwJuokgvoE57B56czyDiaTZiiRBHD0tcKlji7efIy0vJqLI1zLjSoOhaJ1W6TNM+znSfXkwm\nhpa8Qb/5pQgcWExIBnvep/N4FzqNcS3aNrlZMPYuMqbv8izadut4Bkv6veFQci2kJsVzpEepR7Qe\n4oSphYinl7OIDS0gM0WJqgAq15DRtqcJ9QJkVPCRsGp6DheOKTn5ooTRD+DkvqkkxgvYfMU4sWDp\n+BQeXFPz85NyPLuZyw9fJ/DmeT4VqooJC9ZyL8kJSyvj+VrPNY2xS+xp39/4/Zo/PJnTu7JxcBaz\ndJMFDfyLieeaBQoO/azm5Gvj5IcVk1LZuzYHW3shl8OdjCy4ABuW57HjRyUnojxZPTmJA2uzmLbY\nnEFjpERH6Aj0yWbn/ZKU+5+1UqfTMa5DIs9vK2jYVERYqIhjocbn1On0rPg6k/3rsyjrLWH/45L8\nEoc35bJoXCpmZkLOJ3sZvbMKuY6vmsWSn6MhJ0tNi34OjF5muK+qAh0T2kSTFlvA8RAntBro5JOE\nV11rvphSkqBGzxk4zZ5B05yK+svO0NDfN5L0RBVTdlegcVeDdTn+TT6jaz/ji3EODJlj6F+j1tO3\n0gtS4lSsftkEZw+Dl+rWvkR+HBrMypvVKVvN8H09/n08P8+MolI9C0Lv5zFoe31OzHuBibmYqVcN\ndXEvfBfO0TkvWBoaiI2LCQ8Ox7N+wENm3fLDpZwlqzvfIexmGgKBgBLVnclNVmBXzo4+p7pxZ+UD\nri+6w8hXw4m/l8ChHkfofX0Q6S/TOD/yFL0ej+bOtPOkPkui2d4+HG38I3W+/4LnC88hkEnQ5qsp\nSMlCq1AjdbBAm6cyED21DoFUjF6pAmDgwIGsWbPmvWf0KfBbhOvv4NessYWW2j9KZOVy+Qetvv8G\nFBpkPuQ5Xb9+PSVKlKBPnz7/wMj+z+GDD/fftzz5/wgfkq76M2oAv5Zx/nvZ8Z9LIuvXzvOuCoFa\nrcbU1PQvZfR/ruvIz883Ko37VxUTFApFcciCXo/ARAZ6PUITGdIS9ghEQvQIQK/DqWE5rrX9HlW6\nnOYXgpDZW5B0NYyc2GxMvFwRikUErG1HQZaSw622G95eKwvaHeuPVqWh6ex6AJwacwmNSsv+UXfY\n2PkSsc8yqNypHKMf9KBSew8OD7tClVbuOJczEKuzi19gaiXGsbQZx5a85pua13hzL4OsVA0Pryrw\nalcWj3ouVG/rzoKQlow+2AD/EZ68vpZWlKwFEPUki5RoOa1GFBOhO0eSUcjV+PUqDt4Pvp5JZnIB\nLQcVb8vL0RB6N5fe096piwpsmhZH57GuRkT18aUs5JlaenztSo+Jriw+VYmdkTUoW82S+h3s8Wpk\nz96tGvo2TaO6VQK7fpTj21BGVnrxe6bR6Lh8QsmIOcaLHp1Ox8kduXw5z0CcqjWyZONdb47EVCH8\nlUF7tHvDDC4eV6LTGebhng15aHV6WvV6f8F5+7ySYQvdaNTFhqEds/myUy4JsVpyc3T8tCyPyavf\nX3T1GWuLTgd6gYhWVdMIfV68uM3J1vH9/FwmrjaMb/wyF749UZJVc/MZ2lnBjFH5+DY2LSKqYLBe\nfX/SjZa9Lbl0VoOXz/uxfUKhgIYtTRAIIeKVmsvH8t5rU62hDPQgl2s5syvHaJ+ZhZD1l0uSlakl\nNwe+WlT8HKUyIStOlsHKQULXGinMGJwBIjGTd5SnrI85C09XYuvCNI5tzii+zgwtOZkaBGIhz65k\nF2138zJl0ZlK7Fmewtkd6Wg0emZ2i6KgQEhlf2cWtHpU9D1t2KMEHSaUZVrzYHIyDCSvRqANGrWO\nZ9dzmP+qHdXbuzPulB/xL7PZPeEpAM3HelGjvRtz618zWOW7uNF8hCcLmlxnhPMJ4sIUOFdxwrKE\nFYOufcGgS92IvR3HjSV3qDehFp7NyrC9wQ48W5elwaT6HGy5C88O5ak2uAaH/TYSsKETQrGAh7PO\nE7C1Bw/GHaDe2h4oEzJx/6IOInNTLH3LolGo0CpVCISG74NeqTIsdMUitm3bRt++HyhH9gnwqRKY\nfq986bveK61W+6sSXGD4ffm/VqY2PT0dBweHD+77D38M/5HVz4g/qgbwS91OsVj8pzRGP7dEFhRn\nyWdnZyOXyxEKhVhbW2NpafmXVQgKj/kUElkqlYrc3Nyilf3f1Z3Nz88vTjYTgMBEhsjFDqFMgn0v\nf7RZuYhtLXHq6YcmV0nyrTfotBrKdKsFej2XWn2HqZMVLW9PRatU496wDKlPk9hQagUFOQX0fDCK\nLheGcHvKWWoN8UFmJeXGsgcE7w7D1M6UCn2rM+T5V2jyNTSfXQsARZaSiKtxtJtTFY1ax4tzCZxb\n/oLU6DzmNrnJzX2p5OToKNvAlfkJvQm62Y6ACVWIfZxGm2+8i67t5MJQrBylVA4o/tj+PDYE/35u\nRclYAHtmRtIlqCRSWfFnZdPEKNp9VQJTi2Liv35iFOV9LfCoUmyBeBuqIDEyn85jjLNU1018S4ev\nnDE1Lz4+M0VF+JM8hiwtw1eryrI+uAaHshrQcZwbWr2Qmxc0+LnH07VmEttX5zB/VCbObmJqNjF2\nze/4NgtTcyEN2xpbSNMSVGhUen5+XY3qLeyYPCiHgPJpHN+Tz9qF+QyfZYdEYjxHLhzKJSdLQ8dh\n9oxZ7s7ByMqkZ4sIqJDB0A45OJaQGrnaC/HT3Cy8a1qwP7oKPv7WdKufzvpleeh0ejYsU+DsLqNp\nx2JiXCfAnKMRZQkL1XH3egHDZ384wTE+UoeHjwX3rqgY1jLJ6HugKtAza2gq3Se6MmG9J5N7p3Bg\nQzFJ1On0TO+fSp229nyztyILRyRzeFOmUf8Pr+aRr9Dj5m3BgBoRaDTF/ZuYCll93oMCNVw8qmDZ\n9SpFi9QqjayZvq8CK8clcf14DvJsLaObv6VGG2eW3GvA+e0pHFwVV9RXxXpWTN5ZnhUjYpkQGE7o\nIyWLnvsz7mAtRFIR81s8Kmr7xRxPKvvZE1TnGfdPpzGu9mNqdPPAvboDazveBMDKyYSgc025tiGS\nu/tiEAgEDNjoi5mNhGUtbnFhbQSXNkQiEAsxd7QgKHoYAy93ByHs63YS65JW9D7akesL7vD2Riwd\ntrVGIIKDXY/QeGZDXOu4sqfhVvyWN8emrC3HWm+n07lBJN6OIut1Kj5jGnF7wHb89g4h4ocLVJjR\nEcWreFz7+SM0kSK0tkRgJgORwBBCpNeDSMTx48dp27btB5/1/3X8npasiYmJkWa1RqN5j8gWhpj9\nk0T298iqo6PjB/f9hz+G/8jqJ8Sfsay+S6AKNUYtLCyMdDv/KD6HRFbhedRq9Uexov4aPqZ19d1F\nQH5+fpF0y98N2FcoFMahClIp+nwl2uQMzKp6kHHoJmJ7ayqfmE3q/uuYlHLEc04v9AUaEAo412Q5\nApGQdi/nIrE0IenSS+Jvv+XO/GuIzWTU/toPx2quJN6PJeV5InqdjuWu67ky7w723o6Mih1Po5lN\nODviFN4ty+BY3jCWw8OvYGEn48KKUILs9vFT9+voBQIGn+nA7MxhjLzXDUVqPi1n+hQN/fCEe5Sq\nbkvp6sXXc21DFJ1nli+az9kpSiIfZdB5UumiNm9DckmMzKPtyGIrW0qMkugQOZ3HF2/T6XTcPpxB\nn+nFIQEAa0ZH07iLPbZOxeQ3MUpJ7Kt8ugYZW2B/GBdN5YY2uHkZk8+re9LoO6cMW6PrsTOhPj5t\nnNm1XsmxHXko5Dr2r8smK71YnWPHqmyGzHExks0CWDEqgRZ9HXF0lTJqZWmOpNagWX8nZo3KISFG\njZmFsMjSWjT+qZn0n+qMialhzts4iPnhihcLDpThyX0VCrmWJ7eVRsfER6s5uTuHqVtKIxQKmbKh\nDMvPlGPjSgXdGmSwdbWcGRvfl5ixtBZhZinCtoSUES0TuHpcbrT/wVUFj28qWHq2Ihue+hAXo6dT\n1UQUeYZvz7YV2QiEIgbOKUVgX0dmHyzP0gkZrF9gsHYe2pRLXLSWybvK06CjPTMOVGDF+BT2rTXs\nz87QMqt/Ir3nebL4mi8CiYjBtSOMvm0psWpSE9RY2MtY3PON0fjqtrVjzI9ezOwTz8iAaEztTAja\nU51SVSyZerwm22fGcvNQWlH7hp3sKVvNnJB7cr4+VRcLGykyMzHTztcn+nkuW8a9BAzfirE7q1JQ\noGVepxd0WFqLAdsbM+JEAKnRefw8wiC6X7qGHQM312Xb0EfEh2YjkYloN70CL6+ksHdqCG3XBTI+\n8kt0Gj2HBpxBaiah/9kuRFyK4daqh3j4lSRwQSP2dTqCSq6i95nuRF+J5u639+m8twPqvAJODzxG\n56M9yI3J5P7CK7Q72IfHiy/h3KgMjr7uPJ50hDoruxI64wBVl/cicedVXAc1Q5+Xj4mHK0KZFCQi\nQyiRVgsCATdu3MDf3/+9+fAx8W+ThnqXyBbmOPxbiyL8Hll1cnL64L7/8MfwH1n9xPil7iYYWwq1\nWi0KhYKsrKwiAmVjY4O5uflfLin6qSyShSi0ohZq830MK+qv4e+S1cIP2C8XAdbW1kXxs3+nf7lc\njl2hfp5QADIpaLSAAH1+AfLnUegLCnAZ2JznAd9gVcOT+i++J3bVUbRKNYlXw5FYmOK7uDOaXCVn\nGyxFIBRSdmgTfNf1Q6tUUePrRigz8znZ6Wf0Wh1vriTit7M/IomIpkv8EQgEKDIUxFx5S+Dc2iQ8\nTeXIiKuEnoxCXaAjLVNAz7O9MHe0oOXceni3NJCjczPvYVvSAs/GhtKsOp2OZ4fe0mFmxaLru7sv\nBrVSQ/0e7ui0etLj8vm+10PsXE0IvprB4WVRbJsczqzAR8hMRawe/IZZrV8wrVkwo30fIRTB9yOi\nWNwznFVDIvjaLwSFXENSdAE3j2bw8m4uUS/kvLybS+9vjAnsd6P7+dp8AAAgAElEQVSiqNfWFgfX\n4thPjUbHvdNZ9JlpHHsacjObzGQVrb40EFsrOykD5pel88SSyCzE+A8txdaV2QS6RTKiZTyrp6ai\nzNfRso+xaz41QUXYIwW9pxaTRKFQyMBZ7lg5yPAJsGXFxDS6VI7h1jmDJuXNM3mkJavoMvJ9HcU3\nzwpwcDOlYU9nhrVIZO7wdOQ5BlL34+xsKtayoLR3sRu/ehNLDsZWJTXNMHezM7Tv9Xn9pJy4CDWb\nwmoxfHVZpvZO4sfZGeh0erRaPQu+SqXVICcsbMQ4uEpZ96AKdu6mtCkXz5Nb+WxYlMHk7cWxwnVb\n27LsfCU2L8vhm4EpLP86ndHrPJFKDT8PdVrbMedoJb6bksqOleksGp6Mk4c5ncaXwsxSzKIrNVBr\nBAypayCsBUodUzpFU7tTCZY+aUJ8hJK5XcKMriGwvxOVGlgRFapk1LaqRdur+tszYlMVlg0MJ+xB\nLgBbpsYQ+0pJrS88WdL6HkqFQWHFtoQJ31xowKUt8VzcHINer+fwoigU2Rok5hLSIgzHW9ibMOZ8\nIHd3RnFja4ThmnqUxn9keZb5X2fLkIdsGviAqn0ro9MJkFlKMbGS0f9cF14eDufR5mBsy1jT61B7\nLs26TcydBOqNq0G5lh5sbbQb61KW9DjcmWuzrnF7xV2cqjoRujeEDeXXkJ+u4MXWRxwK3IS2QMPZ\nLtuJvfyajJB4ni04i1AiJHTWQdy+qEPS7us4tKuNOikdsaMNIgcbBGbFxUQAHj16RNWqxffrY+Pf\nRlbfxS/H9qmKInys8b2LjIyM/8IA/ib+S7D6xFCpVEYvQGZmJlZWVkUZ51qttuhF+5jacYXn+ZgS\nH7/UGgUQiUQfTYrrQ8jOzi4i7n8G7yakCQSCooSpX35M5HJ5kaLCn0VWVhYuLi4gFILeUIscmQSB\nTGqozykSIRAJEIhFaHMUCKVi6j3+lsdt51PwNgX34S0R21mQsv0SFcYG8HTWMfRAu5cLsCjjwMmy\nU6jY1weRRMT9JVfRafW0Pj0ct4By3JlwlOSLoQx59iUCgYADHfYSdyMGC0czcpLyEEhE2JW1YciD\nQQBEnI/kULdDzEwcgtTcYL2c77yZL9bWo0Y3Q4LU2UVPub/5FdNvBxAfkk1cSDbH5r1Er9MjkYnI\nTStALBMiEIClowkycxkSCzEiUxFv7yRTs4cHJlYSQxuhgGtrw2jQ3xORVIgyV41KoSHkdAJ2JU0R\ni4QUyA3bcjML0GnA0k6Mi4cJHlXMcK8gY+e8eOYe8qZ2y2I1iK2zYrmyP53Nob5Gz3JU7WdUbGDF\niO+K42oBBnrcp+3YknQMMliBU2Py2T37Dbf2JyEUCPhirBNdRtrh5G4gxJM6RCEQiVlwxNOon2fX\nc5jUJpyf4xthailk65Q3XNiYgEcFGRkpGlr1s2foXGMraH6elvZuLwnaWoGGnR1JjMpnXvsQspKU\njFlgy/IJ6WwProSbp4nRccmxKnp7v6BjUElOroml42Bbxi93RCI1yIJ1KheJX18nBs4rA8Cbp3Km\nt3hB5VoyGrY2ZeOCLPYn+hp5NrRaPd+Pecv57cmUqmjKTw99+CWiQhSMqP0cUysxB5Lrvrf/+bVs\nZrR9gV6vZ0t0I6wdixcR8kw1kxo8wsZOSPlqJtw6m8fqN/4IhULSYhRMrXWD+u1tmbjZ8HxuHEpj\nxcBwfLuW5vmpeNaENsLKobi/I0ujOLIkgg4jXTj2QyJT7rbBubwla1pdITcpjyXP/Iqu7/GpJNb0\neED1QAdeXM9k1LX2aNV61jQ+Tt+NDajdyyBp9exYDFv7XGfy9WaU8bUn/FYqy/wuIjYVMSJkKLal\nrXmy5Tlngy4zOrg/NqWsCD32hoN9TjP0di9K+DhyY9kDbi59wLjwQYgkIlaX24xQKkKdp0FToEGr\nBcf6ZbHydiFi+23q7RmBTqnm4Zdb8XuwkIhlJ0k68xSvxX0IHb0Jka0FFKhR5+ajz1chtjFHjx6x\njSWa7DwEZqZoM3PRF6gMVlYAoRBXFxfCwowXAB8DhQUAiiru/Yug0WiKvHd/B+/Kb30o8aswqevX\nZLh+DUqlsigu95do1aoVN2/e/NcuBP5l+E8N4J+AWq0uco9ptVpycgzJCoXu549tiSzEXyV5v8Rv\nZfR/bN3YDyEnJ6dohfxHxvp7sl6/hFwuL5LS+jNQKBSGGFWJBNRqw/8SEeIKZdFFxiIwkWIWWB/5\nwQtISzigy87FtnElMq6/RJuvpPq+Sdg28+FGiYFoFAWYOFqhFwjw7Fefaku68mbzDe4P3YbYVIK5\nux0apZpyPapTZ3kHdBoNO51m0mFHR8yczLm3/A5vTr3G0s2aCkPrUm18Q7aVWECnnR3wam0gXRur\nbaJy+9K0XGBIzLqz7jmX5z9g2vPOvH2QStStFK6ueUFBnhqRRIiFvSlCEzHZ8bkEzq6Ley0n3Gs7\ncfPbpwQfeMPUF92K7umeodfIDM8h6FrLovtzePJDws4nMPNpcZxd6KUE1nW+zqqkzsjMDPNSp9MR\n5HSUgZvrYGot4c2tFGKeZBF2ORG9Vo+6QIeJuQiv6uZU87Pk8PdJDF1ahlaDXYr6TY1TMqjcYza+\nqoNTqeLnGHw9k1ltQtia6IeZZfF78PpBNtObPuSrjT6cXBFJXGgOtZtZ02WUHTO6R7PmRkXK1zCO\nLx3q+xKfZg4MWl5MYlVKDQs6BfPiRia1/K2ZtM4V55LFhGv3qlQO/pDBlkhj4ndoZQy7ZkdhbiPm\n5+BKWNkav6OLh8QQFaZi2a1aJEYomBHwBCtrWHXMjUfXFKyZmsbuhNpGZDQvV8OEBsFEheQxdHFJ\nek81VgAAeHYtm2ltw9Dp4JsdnjTpamzpeXQxi1mdXyMxE1GpjiXzTlQ02p+bqWGA50Py87T0neNB\n92keRvtz0tUE1X5ARoKSlS+b4lK2WP4oPiyX6fVv0W6YEy0HOzO61jN6r61Nvb4ebOx1h8i7KXz/\nquE7WqV6lnR4zPOLaYw47k/lQIPVXSlXs6jWGUp4mjL5lGEu67R6ZtW/TkxwNqOudaBMHYO79emB\nSPYMvsakO21wq2KwoJ+e94wra0LxH1GOc6tCqTHcl7BDryjjX5LO2wxz9dTw84SfiWBc5BDEYiGX\nZtzi4cZgxkcMRmouYUerwyQ8TkZdoEFiJkOZW0CZL2rScHM/rnfbRFZoEm1fzCZk3ile/XiN1pHL\nCJ1zjLe77+IftoI7TRcgsrXEY2onHndeSqWDM3g99Duk5UuiTkhDFZ+KTq5EaGOBPi8foZMduoxc\n9Hod/E8hALEYa3NzozKaHwOFxpW/snj/1PgcRPpDWrJ/tChCoWf0l7+5er2e1q1b/0dW/zj+UwP4\nJ1BI9nJycoqIqpmZ2d9K5vkj+Lvu8z+S0f85svX/yDn+TAnUD/X/ZyGXyw1EVSAwuPylUsPfQiG6\nyHgQibCfOxL5oYvYdG2GRdemqDNySTv/FKRi7BtVwtavCvdqTkSbr8LtyxaU/2kkmtx8Kk5pRdSu\nuzwauxtzd1tq7RhJje1fUZAux2dKAAD3ppxEnafiypTL7G62k8grb3GsUZI+r6dQc3JTHi2+gqmd\nKZ6tDBal5OfJpIdn0HBcNfLS83m2P5yz0+8iT1cy3W0Pu7+8w93d0egFAobe7cc3iomMjx+Npasl\ndQZVJmBaLcoHlsLMxoQHm0MJ/KZ60X3T6XSEHH1Li2mVje7R/R2RtP7GeNvhKU/x/6pcEVEFuLw2\nHKmpkGrt3ajQ1Jl206sy8mBjhGIxg7Y34EdFD4YfaIK9jwsnt2WiUupZ81UEQys/ZuPkKB5dyGT1\nsAh8W9gbEVWA9UERtBxe0oioAmwe95qAgaVp1NudJY+b8F14M3RmZkzvFo1GoycmTIlGUzznol4o\neBuqoNNEYwIoNRGTk6qlYa+SZOaK6FkhjM1zklHmG1zh2xYk0W9hmffmT4POjqg1esztTOhV7gX3\nzhUnN8VHFnBxTzrjtlUCoISnGRuj6mPvaUmPalGsCEqm79xS78WDm1uKadTVAXNrEXuWJBByyziD\nX6vVs2pYFE2HlGHYZl8WD4jk8PeJRftVBTqWD46k1XhPFj3y583zfCYHvDCKQ103Ngr70pZMvtiU\nvQtjOLgs+r1ry8vSIJKJ2T4u1Gi7WwVLZl2qx/Efkwlq+JwaXUrRoH9ZhEIBQ3bUxb6MJVNq3y86\nX8TDbIKvpGPvZc2+scVZ/yYWEoIuNiP8XiY7JwWj0+r5sd9j0mILqNSpPNu7XUKjMoQJVO9eFr/x\nVVkdcB5FjoHk+Y2pgE6n48yKl/S+1JsWq5rT+3xPQg+95tEmg0JAy++bYe5szs+BhwDwn9cAFx9H\nfvLdya52x4i+EYdWB3bVS9EzaQmBx4bz9sBj0h9E03DHAHRqDbf6bKHqrLbY+5biWuNFVFnaHcty\nTtxruZi6pyaR8yyS9KsvKDe7B6/6LKPSvqkoHoThMKQtIlMTLDo1NXj+hUK0yRnoVQWg04OpicGT\no9GQLc/76HGQ/7YM+8+NQgJaaCEtDC0oVC4wMzMzynEolKvKy8sr8uYplUoKCgo4f/48t27dIikp\n6ZOHV2RkZBAYGEj58uVp0aIFWVlZ77WJjY3F39+fypUrU6VKlc8mh/ax8B9Z/cRQKBSoVCpMTEyw\nsbH5QxbCj4G/ogjwZzP6P4fqwG+R1V8SajMzsz+d3PVnCXdOTs7/Yo8EIJaAiRQEQkAPWh16VQHS\nSmVJGbcUq1b1sRnVjcx1hzGpUIZSu+agy5Zj36YmN8oOQxGdTK2rC6n04wjejN+Ms583Fxst5f6w\n7YjNZQS+WYVb19o8+2orVUY2AqGAR3PPEvrTLUwdLHDuXIvOictBo6POvMCiMb786R5N5jY2PB+N\njmP9TmBiI+Mnv8MscN3C0dHXURdoab21E+Ozp/FV3EQkplIafF0Xt9quCIVC8tIUJDxOovHE6kX9\nBh96g0qhpvoXZYu2XV8TgsxcTMUWxTGk93dFoNVoqdGlVNG2zLg8El5mETC2uCIRwMVvX9NqSiWj\nJKfrm94gFAqo3t4NoVBIxQAXen1bE7FUTIspVVmW1I06gyvw5I6KxX1eE3w9m8QIBSfXxZMWb9Ah\nTn6bT+xLBR2CShmdLz1BSeTTbNp9XWwVtHc3ZdzeGggkQmp1deeHCXF0L/mUo+tSKFDqWDUihqa9\nS2DrYmxtig2VExMqp8d8b+Zca8C0c/U4+XM2Xcq8ZMmwWMwsJfj3ej9BavfcGMrXtWf586a0n1KO\nGd2iWPplLPl5WjbNTMK7rjVu5Yq9FUKhkBlHfKjb0QlVAcS/LkCrMZ6zmSkq9i+PJehoPdp8XY7J\nLUI5vyO1aP+ZLSnkZuvo920VGvZyZ8KRumz6Jo4NU6IB2LcsEb1AxBfzK2Hvbsr8+01IilUT1NBA\nWJ9cyuLm0TTGnWiId2NHJpxuzJ55bzm88i1g+HasGRyGo5cVc1+0J/xBNt/3fWI0xrK+NlRsYo9S\noaNCQPF9EUtFjDnZBI0O5jV/RGpMPvNbPaTx2KqMv9sJgUjIdy0uF7W3dTdn/IXmXPzpLXMa3SD4\nSjqjn/Wl2/ZALF0t+KHp6aK2rebWpExdZ1bUO0PSqywWVT+BtYcdVqVsuLvsHgAO3vZ03t2Rc0FX\nSHyajFgqotfJriQHp3J+2nXSwzMN1e5icoh9nka36Hm0vz+Z9KfxhK6/iVvzClSf3opLbdehU2tp\nfnY08aee83rjDRrtG0pBhpxHw7dS/8gY8qJTeb34GPVOTubt6hOYV3bHobkPrwauosKuSSTO3Yb7\nipEoLtzFdlgXEImQ1K+BwNQUVGooUBkk8aRS0GlRqlQfXYf632r9+6fjaT9EZN+V4AKKknYBLl26\nxIwZM6hfvz6PHj2iRo0adO/enWnTprF582auXbtWpJv+d7FkyRICAwN5/fo1zZo1+2AhCYlEwrff\nfsuLFy+4e/cua9euJTQ09AO9/TvxH1n9xLCwsDAie59LO/TPJA79VV3Uz6E68Mv7VWip/hCh/jNV\npn6t/9+CVqs1WDJEYjAxAb0WdAA6wzaJGL1Wh/LmE4RiESJPN6L8R2Beryrln24nZfpP6NUaImbt\nARMZzm1rY9OgIpFLD5EXlUzy1VeYBfgitbGk8sIvEMkkpN97Q9aLOOSxWewuOYfnq69h4mxNp9il\n1JjfiedzTmDpboO7v8E9HbLhHlqVBomZmGP9TrDMdiXp4RlYlnWk3PDGDEqfj5mrLbXH1adybx/E\nJhJSgpPIjMqg1ohiYnpm3EXKNnLDwas4XvTC7Pv4B1VFLC2Og77x3UtaTqtiRDbPLgwhcEIlRO/U\nj9875gFVW7ph515Mwl7fTCEnJZ+GA43dyWeXhdFyckWEouLj40KySH8rp+mI8ljYmdBiYmUm32hF\n3QFe2JSywqtlaQ6uSmSw1z2GV3rAzDbB+AQ44OBubG3dMCqMaoHOOHkYh64cWfQGK0dThu2sx7eJ\nHWk3uyq7lqbQ2eUxoffltB9nnNAF8MPw1zTq5Y5tCcM5KjayZ01kM9pM9OLq4Wxk5iISIvKNjkmO\nzuf6gWS+2mKIGe0wyYsVIU15clNBT68XXD+SwbhtFd87lyJXw73jaXRdXJXLe9L4umkI2WnFmqw/\nz4zDvZI1lZs60mVGBUbursV3I6PYNC0WeZaGDZPf0mdlsYSUT6ATM6824sTGNL5pH8buJXGM2FFc\nL9zG2YR5d5sgz9Uz0jeYJX1f0XKid9Hz827iSNCpRuyaHc3R1TFc35tM8LVMxpwNwNbdnMm3WvL4\nTCqbRgYX9Xl1WyyvbmXQ9cfG7Bz1kJDzCUX7TC0lfH05gNiwPIJ8blKmgSvtFtVBZi5hxMU2xAVn\nsXPEvaL2Javb4u3nwtvn2XTa1BwLJzPEUhH9T3UgIzqXvV9eBwyasv32+htCNnyO49bUk/6PvuKL\nc32JuhzN7eV3ACjfvhz1J9ZlZ4sDFMhVWDib03VvB26tfMTaattRyyxofWU8qsx8Es6HYeXpiP/e\nQdyfeIT05/H4TA3Eub4H5xqvwsrLCb+9Q3g88SC5UWk0OzuO2H33STz5jCZnJ/J2y1XyY9Op+u0A\nnvdejdeCnoikIpI3nKX01B7ETfiBUj+MI3vdAewm9kP75CXShjURWFqATIZekW8oOiKTgliCVqv9\naAVg/mlC+Fv4N4+tcFxisbiIyC5dupQrV65w6dIlOnXqxKZNm+jatSvm5ubcuHGD6dOnk5f3vsbx\nX8Hx48cZMGAAAAMGDODo0aPvtXFxcaF6dcM33sLCgooVK5KQkPBeu38r/iOrnxi/fLmEQuFn10D9\nED6GLurnCgPQ6XRotdoiQq1SqT6aRNYfvQa9Xm9YPYulIJWBMh9kpmBmCiYmCNu2Aq0WaeO6CG2t\nQCIhY9VuBAIouWkqSXM2oQh9i7lvBUqfXIk2MxeP2T14PXErkfP2YePvQ93YHZh5u6PXaSg9sDG5\nrxO53W4lApGAzLe51L06F7FMSvUFHRH8bx5Fb7tDnfmB6DQ6ok6+5NaUU+Rn5nNm1AUy5SJsqpSk\nZDNvOt0cQ7VxTVAk5JD5KhnfsXWKru3SmLNU610ZcwcDEdFqdEScjqTptGLykvwinfSILBp8VUyk\nXl+OR56moG6/4jjOuOfppEXn0GR4caKTRqUh7HISLScXa7cCHJj4lCZDvTCxKPY2RD1IJzM+j8ZD\nyhq13TvuMTW7l8bC3ti6eWd7FO3nVafbyjrMCe/GivTeVO9XnqRIJcFX0xhT5Q6nfoghO1WFSqnh\n2aUMOk83Tp4CuLAulg6zKhbN+4CvvFgW3Q5Hb2vEMiFT/R5z7LtY1AWGdzctTkn4wxw6ffN+X9ZO\nJphYybApa8tInwfsXRiDWmU4bs/8WDxr2uLiWRzP6VjajFWh/kjMpej0eq5sT0arNZ6TR1fGYu1s\nSstx3iyLaoNaL+HLyo95/TCXuNf5XNyZxMhdxc+rdkdX5t7149TmVAZVeoaNqxmN+hhXqipb04aF\nD5ry6GI2UlMx3o2MNVst7aXMudWY7AwNilwd7acbk+gKfk6MP9GIHTMi+W5IKD1+qIOFnYG4O3la\nMul6C27sjmfXtJckvJKzeXQwPbY0pe5Ab7p824Afu90i5mmxfquVkwklq9uhUelx9S1WVrByMWPk\n5bbc2xXFxdUGmapjM54RcSeN2mPrcKDfeXKSDD/65vamDL7Yhcd7I7i13tD2xYm35KYYCJ6tt6Ff\n6zK2dD7Sk+tzb/L2egwAjWc1wrVWCbY23EXo0dcc7HEcc1drhDIp9df3xLm+B0229eP2iH1kvUqm\nZJvKVJ0QwLnAtWjz1TTZPQBVbj43BmzDvU0VqkxszpUW3yEQCynbrx6PRmzn5ZxjSCxNeDRgHa/m\nHkKdk8+Nal+jypSTdvo+qUfvoNdoSZy1Bdvu/uSsO4DNwPZoHoUg8vZAYGdj+Oao1KDRgVoFMhMQ\nirCyskKlUhXFdv7/5tL/N5PV37rXaWlpuLm5UbNmTXr27MmMGTPYtm0bN2/eLNbm/ptITk7G2dng\nrXB2diY5Ofk320dHR/PkyRPq1n0/kfLfio+Xfv4fPoh/wnX+W+f5ZUb/uzp1f+Ucn/KDqNfr0Wq1\nRWOWyWQfXeHgj1yDTqfDzMwcRBJDbKqqwEBYxRJQ5iPu3wfNjt2YTR6J+nEwugIVJg1row0Nx7pt\nfRLGfEv2lYc4zxhIiXnDCK/RH5MSdjxsOgOdTofE2hyfcwsRSsTEL9pHqQGNedjnJ+JOPAQE+D1f\niaW3K5FrTiMQCSj9hUH0/8XSs6gVBcScesWF/vsQiIToNDraPZ+NbRU3NEoVh5wm0uTC8KJruTHq\nCBW6VcbCxSA0r8hQkPggng7rmxe1ub7gNlILCVILCSFHIsiKyeXayidIzMTs7H0VRWYBiswCMuPl\n6DQ6prjsQ6fVo9eDVq1Dr9PzTdmjSExESM3EKHNVKOUaLq0O58mReGxcTZCaiYh5lkGvNb5GP0L7\nJjyhYX9PzKyLE5UUOSoi7qbxzf3WRs/lzs8GGaJqnYpd/VIzMblJSkpUtmf0zfZcWfGc49+Hs/Xr\n19g4S7Gwl1KmhrVRPzf3xFOg1FC3hzGZUyo0JL6UE3SlFakRORyZ/Ih9C6IYsNiTO4dTqdHaGRdP\n40QsnVbP/lmvaD6xIi0mVib8ZjJbet3k/NZEBi324MqeJJY+afLeHIsPyyUjQcnQA/7sGXqbR2fT\nmXqwCvauMnIz1Bxe8ZYxxxoCIJGJ+eZWAHsnPWOiXzDOpU2o0MQB1/LGVbVKVrZi6oX6zKh9DRMb\nGbnpKiztpUZtkiPyEEtESK1lzKp/k3l3Ghkt/tJiFOSmq3AoZ8tMn0vMf9bMyLJeoakjparZEvkg\nHdX/JKUK4VrZhqCLgaz0P8/lTbFU7eRBtS4GK3q9LyuQk5TP8oDLzH7SEofSFpxZGkrk/XR6HO3K\nvs5HcCpnRa2+hrCREpXtGHK0BZvanyP+WRaPDscy8PYAHCo5II/PY13d/UwM74dYKsa5kj299rVh\nT/dTJIVkcm/rawK398DMyZwjrbZSoo4bZZp7UtrfgyYLmrG/0yFGhH2JhZMFrde34gfPHznU6wS+\nSzpTaVwAt4fs5Ezj1XQNn4lHtxqk3o7irP8aukXPpcac1qTeieZ0k+/o8HASLc+O5Hjt5dyz3Y8m\nS0l+ai4nqs7F1NkGqZsDCRdf4jyuK6rweLIvPcbj+HKSZq5Hm5uP3YR2ZKzdh6SEI+qsHNJ2X4QC\nFVm7zhgq4aVlIBALoKQ7+pg4gzqAXggFSkNYgAYcHBxITk7+y5nt/3ZC+G8dWyE+NL60tLSPIlsV\nGBhIUlLSe9sXLlz43hh+6z7J5XK6devGd999h4WFxa+2+7fhP8vqZ8bnsqy+66L/Ldf531Ej+FRk\n9V3tWa1Wi1Ao/MslUH8Pf+QaAlu2ApEItGpDnqJEYohTVSvB3ALNlp+RNamH5m0c6vPXsFw0BVFr\nP9SxiWT8fJqcOyFIbCxxmTmYtE3HyHv2BnVWHg4LRyKSSvFYNgShREzEN1vJT8zgzXfnkGdpMPN0\nx2NYIJbehkzoqGXH8ZnbHlWWgpcrzhO84DRCiYiU6DzqHJ+EqbsDlYJaYFvF4LJ+Mu0INuUcca5r\nkG1SZilIuh1FnSkNAMO8ODvkGGYOpoQefs3B7sf5wXsTt5bdIy9dyZY2Jzkx8S63N4UjT8mnZMsK\nmPt6ULp3LapOaIIe6HGhH/0eDmfwi9EMeDwckVRE70v96HWlP213daHx0hZodUIq9ahIgbkVr5/l\nc2NnAvu+fopYJmKZ/yVGmO1jeoVTfNf2OtEP03H2tiAzQVF0/w9MekJpX3tcK9sYPZczi14SOKmK\nUbgBwMO90TSfXh2piZiWM3yZ+qoH0yN7kpmiIT9XyzDn8+yf/ZqMBINI/8E54bSdXMGIhAHsm/gM\n10q2lK7lQK0eZZn/tjvtFtZix8y3PL+aSbn6Nu/NnftHElEp9TQPMlghyzVyZuHbzlRqX4YVA8Kw\ntJdi7/a+9M6+GeF4NXbGp10p5sd0AzMzRlS6y/2TaRxYHIOjhwWVm7kYHdNzeTU6z69KYpQSW1dT\ntJr3vyuH54Tj2bgEMlszvvG9SkpUsdtRo9axcdhTGo2twsSHXVAWCJnicw3N/6zAOp2enwY8oUqn\nsoy+1RmZrQkzfC6iURVrv97ZFUNCaC4997ZlX9Aj7u6MNDp/mVr2VO9QigKFlrJNjccfOKM6vj28\nWFj3Io8Ox3Jifgi9TnXHq4UHXXa2Y/9Xt4i4WZwEVj7AjbpDvLm//y0t17bCsbIjAoGAtptaY1HC\ngo1NDxe1rdDGAw8/d+5sDqPFrh6U61oFt8YeNFnelqPdDpATZ0hqqzmuLp6ty7Gt/k5ibsawudZW\nrMs7oxMKEJkZLP51f+yB2MqUC+03AFBrWUcsPew5F/A9AvqtywYAACAASURBVKGQpgcGoUjK5nyH\nn3i+8Dw6jZZX62+SHJFDxT1TEdtZYdmuPr6hm7Gs7on88lM8ds7A1MuNtBW7KHt6FbrMHARmJrgs\nH48uMwfHc5sRmsqQ9e+CwM4WvVqLOjYRfWYO+tdvEHiVNZRjFf3P3qTHIJ8nEODs7IyJiUmR1mhh\nQtC7aikf0hp9V7nmP/w5/BaRTktL+yjVqy5cuEBwcPB7/zp06ICzs3MRkU1MTPzVxDu1Wk3Xrl3p\n27cvnTp1+ttj+pz4j6x+YnzIsvo53DOFltVP4Tp/9xyFMh9/F+9W8MrJySkqgWpmZva7+nZ/B7/3\nPEaOGsWtGzdAqwETM9CoAcH/yKsecgzC45pXUaj2HMVsYHfErZuimL4coZU55kunINRqcVkykqTJ\na4kfvxqLFnUol3AabUomIjMpNk2q8HrQKuK+O4Zlo6pUD99JyZXDUUYm4Dm1IwAx266Qn5hJ/LHn\nHC45leCl5xGaSmmZsoEGF6YjsTFHHpGM91hDhRudTsfbXfeoOas48er6yMOY2JkStu8Fe5psY5XF\nQqIuRKJHSPCxGJRWttg1rYBQJmFI5kIGpMynV+R0nBt54FzTnda7e9J4aWtqTmhM3NUoPAPLUdq/\nLPbejth42PJozT1K1HCljH8ZXGqUwKNZWYQSIXqdno7b2tNhSzv6nOvFwPsDEYhEdD/2BVMVUxgW\nMpxakxoSE5aLxELKuW/fMM3rBKNtDrDc/zIP9sdQMdAFVX6x5e7t43TSY3JpONRYV/XmplcIhAKq\ndChttP3FiRjMbGXMSB5K543NuXMsnTGel5jrf4fUmDz8hhuHHeh0Ou7vj6PNbGM90sbDvPFu4YqZ\ngxlHFr1hRv3bRD81EB+9Xs/eGa9pNMzL6P0SCoW0nFwJrU6PQCZhXPkrvLyeXrQ/PiyXx6eT6LvZ\nYDkVS8WMvdiC9otqsqxXCMfXvKXfOl9+Cb1ez8MD8ZQLLMXTs2ksCLiNPFNVtD/8bgbPLyTTf28z\nxt7qSMl6LnxT8yqRjwyZwufWRKHVCGg9ryZmtjLG3myP1MaMiRWvoJRruLr5LWlxSnpsb4bMXMLw\ni+0xdTBjepULqJQaspOV/DzqMW3XNKVKFy967GrFzuH3eHTobdEYXl5M5OnxGAJXNuPI+Hs8PxZV\ntE8gENBlbQNK1XFiY7/bNJnTEPe6hoVZxc7labbQj43tzpMeZVA2iLqTzL0tr3Dz8+TixMsoswyL\nDZFURI9T3cmMyeXQ4Avo9XouzLzD29tJlPAvz42gM+g0hrnjM7Iu5bpWZXfDreg0WgQCAa02t6dA\nXsAO/914DGlCu5C5NNnzJQ+CDpH+PA6RTEKzUyNJuRfN08XnEIpFBBz9kqzXKdybeJi4sy8RiIUk\nXHpFwrNkqt/6lpKjO6KKTMKxS318zi4gdfsF0o/cosKR2Sgj4oj7ZiNexxaSHxJJ6g8H8Ti5gsyV\nO5CUL41luyZk9pyAw+HvUR08g9ncIJBKEA4Zgl4oBjNT9KGvoKAApBKDl0etMug8C4QgEGBjY4NO\np/vNzPYPEdnC8LAPEdl/OrTg32xZ/acLAnTo0IH/x95Zh0d1b1//M5qJG/EQLCSEhEBwCe5upTgU\np2gLxb20eCkOxV0DFGtwh+CuEUggSnxio2feP4ZMmNLetrfAvff9dT0PD3D0e3TW2Xvttbds2QLA\nli1bfpOIGgwGBgwYQPny5fnqq68+6ng+Bv4hq58YHzuyWhhFzc/PR6fTffTuUn+XfP9WC9TCDl4S\nieSjk/t/tf0xY8awccMGwGAkqnJLkFlArWZgMCBq1BqRlaWxyKHgbZWuqxOZFZojdnHGJeYCupsP\n0avUJH69lLRtxxHLpBQPmw9SKZnLdiOxs+JGwGDehN9B5mBL4LnFWHi7EDt4McX71Edqb0XsT6d4\nNHwjUjtLVHIbqj75CZmdFeWmdkQiN0Z/Ho3YjG/vWiiKGVPBz1eewyAYPyQujTjI9tLf8/LQIwwG\nEVHnk7AKDaR4rzpYeTjSMXYOra5NIHRDb9IjYqk4si4SiyIf1Jf7H1B1Yv2ia6bT8epUNNUn1Cqa\nJghEhj2j5sSiaQCXZ1yk+ihjH/dCRCy4hpWzJSXqG9P3TmUcqdgvGHWWhg472jMibgTjc8fx+bFu\n5Btk6PVwflUUox32MKdaOIdn3Gdr/whq9vbF2tFcw3pq3hMaja9oVpwFcG7BQxpMrIJYIqZCZ19G\n3+vO+Ji+vLivRCwRMyf0HDfDXiO8NV7/Zf5zLO3lBLY0L6zS6QQeHIqn69amTEocgHXpYkyrc4WV\nfe5zYUs8ylQ1bWZWfO9eOjn/KZ5BLkyK7k2V/oHMbXWdjcMfo87XmaKqjt7mkoIGwwKo8nkZxBIx\nO0beIyM+32z+49MpJDxV0ntvMybE9CQ/X8zEiudJiszFYDCwafhDKnUtg42LMZLbd08Tagwqz+wG\nl7m49RVhM5/SZW1dE7G2sJHx5emWFPN3ZIz/WbaOeUiHFfWQvo1cy61kDDnVFht3G6ZWOM3Gfrfw\nqOBCSC9jFDmwgy+dNzZlU98IHoUnkJehZn23S4ROqU21oSG0XdOc7T3Pm0VLBZ1AZlwOIomExzuf\nm70ba4yuQqW+FVhS6wjx99NY2yqcyuMb0P5oH9xr+LCx+hbT8lbOVvQ63Z0HYVFsbHqQqyvu0/ry\naJodGIDM3pKDzbeYtttwdTsUrjbsabINbb6Go71+RtAZEFvKTdHU4u0qUf6rJpxuuhxtnhprb0ca\nHRzC/e9OkHQxCoWzNf4Da/F4+QWuDNuLS99m+K0eiTouFamzLSXmfoGilDsPGk/BJrgUfj+NJHrA\nYvR5Ksof+443qw6Se+sZfsfmkbZkN7rsPLwWjSKx6wScZw9FbG1J7oINOM4fQ96I6dhuWIBh+3ak\nX40yRlE79wGZHFQqY3crmcz4QW0Q3rqTgKOj4+9Wm/+eRVNhO9O/GpH9FET2f5WspqWlffRWqxMn\nTuTUqVP4+flx9uxZJk6cCEBiYiKtWxv9g69cucL27ds5d+4cISEhhISEcPz48Y86rg+Jf8jqR8an\niqy+W9FfaJVV+OL5EFHU38O/czx/1AL11y31/hNkNSwsjFWrVhn/o7AGnc6Y9g8JheunYeAYDJGP\nMajUMGkuIk0BBrGE/PlrQSzC8eAa9MmpqPYcQSyTYjFuKGJLBW6zhyKSSYlrNgJtRg6CQYrXhU2I\nRVB8zgBEUgn5z1+TcycKrbKAU26DeDZlNwaJmBrx2wncP5X8yATUqVmUGGT0XS1IzCDzzgsCxjcl\n495rHsw6yr3JP6POLuDyqMOkROdgEVASS3dHWr5eQsNLUwie04XU8IdUmNzcdL6zo1LIikohcHht\n03l4uv46IqmYkq2KiqNuzLmAjZsNXnWKtKIPNt5FLBVRppWvaVrmy0zSI9Op8qV5VPDO6nvUnljL\n7DrfXH4LuY2c0k2NmkaxWIxPneLkxudR/7tGjEgey7CXI/FpG8jtY29IfZHD9e0xrO18npu7X5Cf\npeZFxBuykvOoOcC8kCv6YiLKlHyq9TcvEDIIoFUJDLvfl9Jt/dg6/B5jih/j7JpozqyIodX04Pee\n3yPT7mDvbUPp+l7IFVJ67GzOmKe9iYvWsHrAPdzK2b/XSiUnTcXFtc/ptLouAC2/q8GYe125eyqD\nUb7nzKKq7yIrMZ8bu2IYfPlzFO72TA06wePTxsIJQTCwc/R9qvYrh1whRa6QMvrWZ5Rs4M2UqufZ\nOf4xb17m0WW1+Xbbza9Bux9qsX7wPaxdFJRvZW7tJVNIGXC4GVJrGXodlAo1T93LLKUMOtEGCwcF\nT86l0H1/a7P5wV39abe8IWu6XGJF2/PYl3Sk7tsPmOBegTT6rh5rW58g6XGG8XyOv0Fupo6BsWNR\n5WnZ1eaA2faa/dgIz2oeLK1zhOLN/Kk5vTEisZiWe7ohUsjY2WyPadli5YpR/rMA4q4lUmNxB5yD\nPJBYSGkRPpjUB8lcGm+0tJLIpbQ72ofUJ6msLrGE5AdpNI9aQN1jX/No7nGSzxrtfIK/bYtjsDcn\n6v0IgHt9Pyp/147T7deyv9z3PFsXQbHOdTEYRHgNbYVnn0Z49GjAg3rjAQPlD04l7/lrXkzejFvP\nRrj3aMjjemOxrlSa0kuG8bLnd8iLu1LihxG87j4N2w51cWhbj/hGQ/E6thT19fvoM5RYt29EwZjZ\nWP8wDf3iH5FOHI/o2F5o1Rms7cDFA8RSI2E1GEDQG7X1gIuLC+np6fxZGAwGk6b1r0RkPwWR/V8l\nqxkZGR9EBvCv4OTkxOnTp4mMjOTkyZM4OBglU56enhw7dgyA0NBQBEHg3r173L17l7t379KiRYuP\nOq4PiX/I6ifAb5GvD/XwvqtFFYlE2NnZmaKonwJ/hUy+G0XNz89HJpOZoqi/12nrP0FW4+Li6NWr\nt/E/Ysnb1L8BNCq4cRY8iiM6tg8S4mDPaTh5CEOBGnG1Ooj9/LHu1BzttXukVeuAxLcUjnERiORy\nDFoNYkc7npdoR971x7itnYb3gzAKLt8FMbj0bEL+45c8afA1CAayn6dS4sB8ZK7OlBj3GVJrY4Qs\n9psN+I5qidRagaDVceOzJYjEYsJrzuNk/UVEbrqGAWiavI6Gr9ZQ4/gU8h7GUW5KG9O9mHj0Hmpl\nAaW6VzMd941ReynTMRgrt6JCnQeLLlBlXD2zSOWT9beoMamOOdlccJWa42qZLXd61An82/ph414k\n4n9xJpb8jHyCepg3DLi19Da1x9c022b89QRyknII/sIYqbT1tCN0ej2KVXDFrUpxul/qj9rSloMT\n7zPBfQ8rWp3Gu7ILeq155uLwNzeoOTgICxvzZ+LQyAv4NS2FUxkHms2txzdJQwmdWouDM56SmZSP\nMkVlJj0QBIGIjS9oMrO62TgdfWxptSgUqUJK6st8ZpY/QvSVN6b5ZxY/o1gZB3yqFXmLFvN1YHxk\nDyTWFggGA5fWRL6nOQ3/9gEewS54VXbji/AONJhWk6Xtr3Do26fcCotH+UZNm4W1zdbpsbUJTWbV\n4PiyF5So7Y5U/v5z5VPdBYNYhDKlgDPz7783/9XNVLIT8/FrV44fKoaREWfeYECn0ZP+Qomluy1r\n6+43mfAXokq/8oT0CeDV/QwazTMvJqv5VVVqjKzK8npHubruKdc2PKPzmS+wdLKiy7n+JNxK5sjQ\nE0UrGAxocrUYDAbyk4v0tlJLGR1P9iflYSrHRxmXv7H0Jk/3P8N3cAMixhwmN94od7Byt6Pl8SHc\nX3mdyL0PAFCl5YMB1EoNAXM6Y+Fkg0tdf4Lnf86Fzj+Rn5yNSCym7r7B5KcouTpkB+qMPDJuv0av\n1pGXnk/1xB0E7pqAS7ta3Kk7AUEQ8F06CJmzLY9bz0Tu4kCFo7OIX/ozGafuUHrpEKTOdjxrPQ33\nQS0p1qEOT2t8CQ5WiO2siQzujaBWo36ZQFzNvshKeKD8fg2CWIwuJQ31+l0o2jbBsGYN0r69EV88\ngSikOiJBD5bWIFUYHUrenrdClCpVisjIyPeu86/xZ96zf2Sa/7GI7H+7s8Gn0Kz+X8c/ZPUT40Ok\nzn8dRS3Uor5bgPQhSfG/wp+xyNJqteTm5pKdnY1er8fa2ho7OztT9PfvbP/v4t3zpNPpSExMxL/c\nO0RK9jbNLJYYfwzEYkiKx5AYBz0Hw5LZcOsqzFuO0H84wpNHqCPuoJy4AEQi7A9vADtbVN8tRZ+Z\nQ9KoH8DTC0VJb+wGdAJAuWATju3rENlmCg+qfok2M5eyNzbge3szYmsFqrgkPEe2AyDnXgx50QnY\nVvTh3oC1HHMcSPb9OGxrl6fsxrHUygxD6mBNmVGtsXA2ks7ko7fRKvMp0aOm6bAeT9lP+ZENkSqM\naU9dvoY3l2OoOK4o3Z9y8xU5CVkE9je6Dwg6PVH7H1GQnkfx+iXJSVCifJ3Ny5PRZL/Kwr9zOYS3\nhEun0RF3IY4a3xRZZAGcn3ieKkMqI7Mqsqt6deU1OW9yCe5bwWzZ02PPEty3EhZ2Ral+QRCIPhxN\n9Yl1cK/sSdvtnRkc+zV9bg9BlaMl43UBM7x2sLppOHf2xJAanUXiw3RCvzZPzWtUOqLPxFN3ShFZ\nF4vF1BgWgsLRCv9O5bi4JprJ3vs4v+IpWrWeC6ueI5KKCOporm8FODPrJmXblmVk/EhKtvFnSfMz\nbOp9ldQXOZxZ9pT2y96PnKZFZ5OdkMdnBz/jyqYY5lU9RuoLowY643UeEVuj6LShSHNcd2wVBl3o\nwukV0azucY1awwJNKfp3IZNLsLBX8OJSMj9/fQ1BMPcp3j/sKn7t/Ol5vi+n5t7j0JhrpmdM0Avs\nGXCRwN4VaL+7I+U+K8+PVfaTFlPUZevYuGvYeNrzxZNRWLjYsrzSbnTvEO2c5Dzu7XhG8cZ+7P3s\nMKlPzSN7DWeH4te2LAe/vkbNbxvjWNao57P1sqPLuX483PmUS/OvGe+BCRdJfZ5J52fTyYhK59TA\n/abtWLvb0vFUf+5tesjPvQ5zbuoF6h8bRZUfuuDTMYTDtZeZiLRLleLUW9+VUwMP8HjTLfbUWo17\nl1qEbBjMrf6byIkxRqx9hzfGs3VFToYuRBAE5A5WND4xmpht19lXYgqpT9Op8mA1cidbnvf5wbjO\nTyMRWch51GkOYrmMCkemkX3zOXFzd2NX3R/fRYN4+vlcNG+y8PqqPRmn7nDdvSspO8+izVQSO3gx\nuLoiyCzIOncf2YBe6HILUOUJiKpXJ293OGJXV7TPX1Cw6xD6xGS0e8IQ1CqIjcJg72CMriKAXTGw\nsDJJAQpRtWo1Ll269N698lv4OwW3H5vI/i9GVnNycj6YD+7/ZfxjXfUJ8GvCVahb/Svp+cICJJVK\nhSAIf2jj9C4p/pgP+O+RycKor1qtNvWaLiyW+hDb/1Ao3LZSqSQ/P5/SZcsZK/4NBrCwhpIVIe4+\nVGoEjy+BRoASARB9D9HhPRiyMxF/1gOhQ1dElUtgEIvQ+4UgsorFqm5FRNaWZNfqgE6Zh7xbB6xW\nfEeOTzWcd89DJBLx5qsFaJLSSNt+Gnnz+shrVMTa1Q6rEGMqO2nkYryHtkbmYEPuw5c8bDMDQaXl\n/rBNKKoFYlW/MuLUDCqenANA3vPX5D2Pp8TxSaZjjJq8C7+RTZEojJHFnOgUlJHJ+A8fbrxOablE\nDN+NRCEl/nQUzzbcJDc2k9cXohE0ejaWmI+2QIug1SOWSRFLYGOF1aYOznqtHrFIxBq/VcZ/S8Qg\nAkEr8MuQ49h52mJXwg4LRwuSH6RQ45vqFGQUYOlkjAKdHXeOkP6VkL8T+czPyCfpbjKtN7U1u163\nlt9EZiOjVDNzf9OI7y5Rsqkf7cO/ICc+m2uzTnPom5soE5TYuluTk5KPQ/GiiPGJKRE4+zriXd3D\nbDvxN5PIfK2kV0Q/LB0tebjtASemnOPYrPsgFtF0Vo339LApTzJ4cTmBUa9HGIupfmxKrbE1COsQ\nxjT/Q9h7WFOmwftNBU7Nuo13TS/KtvBlZNwI9nc5wPfBh+i+uhZR51PwCnHDLdDZbB3vqm40+b42\nR7+6wI2NzwjpXha3gCKvRnWulvCp12m8sjWeNbzYWXsjadFK+uxthNxSyuMjcSQ/zWTk2X5IFVL6\n3hzA9tqbyU1V0W1TPa6tf0ZehobmK1siEolotqoFUoWUJdX2M/JKRwqy1NzeGUnvhyOQWcrofLIP\n+xptYkWlnYy41wOJRMT+/qcpVsGT1oe+4Or4X9gYupMhd/vi4GP8wTYIBtKepiOWS7m3/AaVhtcw\nRYCLBbrR8WgvDrTaStbLLB7ueErbWxOwKe5Ey7MjOVx9EU4BrlQZa5RUuAR7UHV8PW7Ov0Dg5Fa4\nhRoL7qqv6cmpegv5peEq2l0ZBYBv9yrEHXzE2eGHKDupI+WmGT8Ws2++5EK9ebR8uRCJXEqV9f04\nXfVbLrRbReieQTyZdwKRRIxea8B/90SsfL0ICp/N7ZARJK4Lx3NQS4KOzeJ2xWG8Wn4Yn5HtCD4w\nmfutZ2FftwKWfp4YxCKulRmAzMEGi9ohqO88xuHAauTB5UgNboWse3tsO7cko2ILJP6+yMO2kN+h\nF9JlSzBs3oJw/Sbs/AW6toSeI+HnzcaOeYmvjZZ6BXkQVA2iH4OtIyiNMgs0hU0pDLRu3Zrly5eb\nzON/jY8dFCj8Hfr1b1ZhsKDQbqvQV/vX9lsAGo3mD+23/hMolE/81nTgo0rx/q9A9Ac36H937P1/\nBL+2BFEqlSZ/0z/Cr31RLSws/rQvalZWFra2th/c7uld5OXlIZFIUCgUvzlehUKBVCr9t18qBoOB\nzMxMHB0dP+iLSa/Xm77iDQYDNjY29B/0JWF7dgIGkFuBpS2ocqFma3gSAeo8mLoTpnc0PhmBNeD5\nLdhxGEYPhNQU2HrQqG/9oiPWXw0gb/kmEImx3boMefvm5A6fjOj6DVyWTSB9/BLyH0ahaBaK09YF\nCHkFvCnVEL+bG1GUL4XqaSzPK/bGZ0IX0sIuU/DqDYJOoOTOWTh0boggCDx1bYX/5q8p1sZo7vyg\n6WSs3W2ptG0EADnPE7hUaTytYxchkknIuhPH7eHbUCdnYePjTM6LVAx6ASRiLN3skRWzQ+bhiNzN\ngYRt56n00yDsgryx9HRCr9Fx1n8MLaMXYOVlbPGoUeZzxOMrWtyahn2AJ4IgoMnM45fA6fgOqIPC\nzY7cmFTyXmWQfD4SiQQkEjGqrAIkcjH2Pg5kxGRQfWRV/Dv44VbJDbm1nEP9jqCMy6XH2V5m121V\n6RVUG1uLysOLIraCXs9yl0W0PtCL4g2Kop46jY41jt/iHORB1tNk7D1tqDu2IpW6+zG/zHbarG5M\nYCdzN4F1tXfhGuJFs5XmWq7wL4/xePtDrJ0UtF9Rn4A2JU33464eJ1Gma+hxorvZOtp8LYtcfkQq\nFeNd2ZVuWxri6GMkzBmxShaU382Qx4NwLFXULvPx3ieED/4FTb6OoRFd8a5i3q5Vp9Ez32cD1ac2\n5M2dJCL3PqTXrqYEti0JwIkZN7m96wUDI0cCoFKq2F51HQpLGPRLC5bU/JngwVUJnVaUns9NzmVz\nlXV4BjoSez2ZFuvaUv7zouyCwWDgwpTz3Fl1C0t7GSU7BNJoaZFWVZOrZm/9jYj1OkK/CeHIyAv0\njZuM3E5hXHfIQV4cesSwx/2wLmbFxe8juL78Lp2iZnCy+SoMBSp63h5i9oN+a/EVrkw7Q9D4JlSZ\nUbSvpPORnGy9hlZ7ulG6TQBv7iSwr/5aijUOIv3ic9o+moGVp1Gvp0rL4WiFbynZPpC6az7nxf77\nnO+zAwsfV2SWMhrcMX7gCTo9VxvMBkGg0dUpAOS/TudEhWmIJCLkrk74n/mBpAV7yAi7QPUXGxDL\n5aQducbT7vMJufYjNkElyThxm8edv6PyxXlYlfXkQfvvyL7+HLFCjqxedbQPI5FXroDT3qXkLlhL\nzoL1FIs+h/bWAzI7DMX+3G7IziG7wyCsToQhXLmOev4yZBFX0XXsjODpg6FlR0Qzx2FYvAdGfwZ9\np8C2+cZIqqYANBqwtAKpJRTkGDNBGg0YdBgTqQYmT55kKsB5F4UyrcLWof8NeDfrpdVqkUqlCILw\nb/vIfiwUFBQgk8nek7MZDAZatmzJlStXPtlY/j/Ab164f+j+J8Bf7WL1rhY1JyfnPS3qn30IP0UD\ngsJ9fMgWqL/e/oeKrhZGp5VKJUql0nReRSIRq1evIWzfXqNvoVhifPErU43FCnfPgTINZh+E73sZ\nC65+DEf8+jl4ekO31pCWAgtWQq16iMcOApUa1eYDUCUUacniyNo1Q1Cp0O48gPZ1CgmtRlCQq0Ns\nZYnz7iWIra3JGjIduwaVsQgoSe6V+0Q3GIZBEEjZcwWLPp1QNKyBY+s6OHQ22lOlLd2DxNoC51bG\nVLZOmUf21SeUntAOQaMjIyKSm23mg1jEyeBpHPH4imu915EXl45Dy+o4D21HpZvLKLNmJBJrBXUi\nV1LzxgKqHJoEgkCxmv6U6FMPx8qlUbg78HjMNjyaVTARVYCHE/fhVMkH+wCj5ZBYLCbtagyCWkvw\njLYEjGpMtaXdqLd/KGIR1N03lE4pi+lWsJJmEZPRWVpi4WpH5Ml49nU6yEKHxSwtsYLnByJxKudE\nZkyG6dq/uvyKvDd5BPU1T+lfm38FhbMV3vXN27ZGTDuFg68Lna+Ppl/GbEr2qs6ZOfeYWWwd+ZkF\nuAWZRy2Vibkk3n1D9W/e7+oSfzGBSmPq49uvBrv7nGZV7f3E33lD5qscHv0cQ8s17xcq3NtwHxtX\nW/onTUErtWRB4G4iVj9GEAycnn0HjyoeZkQVIPDz8pRt64/EUsr2DkdJup9qNv/2hseIZVJCRtSk\n+caO1F/Siu3dT3Fq1i1yUvI5v+guzda2MS2vsFPQ/9lwJI42fF92N3q9yIyoAti42zD4+XBe309H\nEKB4qHlzBJFIRIM5DSlez4ec1ALK961kNl9uY0GXc/3QI+Hg4DPUWdIWuZ3CtG79NR3walCGnypt\n4eWFOC7NiaDRoSHIbRU0C/8Sdb6OA823mbanVqq4syQCW38Pniy5QE5ckYzAo4EftVd+TniPPcSd\njORA042UGtqEWge/xqtDVY7XnG9K/SuK2dL41FdEbb/NhQG7ON9nBwHrR1L92gLU6bnc+WINAGKp\nhOqHxpL7MpU7o7cDkHY5CkGrQ6NU4bPuG+SexfBZNBR5cVceNp0KQLG2NSk+qj0Pm05Gr9Lg1LwK\n3qPac7fRFC669aLgdSby4HJIvD1xPbgC1xPrKTh+gdx1e7AeNwiLWiFk1uuGRZNQbCd9SU7rL5DW\nrITN5OGoOvRC2r8Hsnq10LVtj2TvLrgZAWlvEHXoinj6IJi7GbbMgeELQKuG9iOMNlZIIF9pLAjV\n642uAVIFxt7QBubMmUv//v3fu1//GwuY3iWfYrH4HO4ZnQAAIABJREFUN6UFcrn8T0kLdDrdR3Mt\n+L1zp9FoPln9yP/v+IesfgL8WUeA39Ki/h0z/HcbA3wM6PV6tFqt6WXwoX1cC/Eh7bFUKhUWFhZm\n5/XgwYNMmDzdSFL1OpDIQf5Oxyp1LgTWgsntQJ0Pm27Bz2sRUpMQZ2VBSENEXiWgTgP4vAVCWioM\nGYv+fCSi+9exXDQd/ZNIlMFNMGi0iJo0RhH9CElmJnazRiOSyRCUOWhOXUJSwp3n/l2JaTHGaGcT\nsROvyF+wH9kT1YWbuEz9wnRcGUv24DO1m7H1qlbHk27zQCzi4cC1hNv14Ubb+eS/TsexexN8Nk+l\nct4JHD5vjG2FUgTunoT38HbYlC9B4vwwSn3THrFUYjpfqT/fxHd8EekRdDpSTz+i7DhzUpaw/zYB\n482nPZp+iHLDGyF5p7jn6Y+nkTtY49bAKG8Qi8XYl/cgPzaDWuv60Or+DDqlLKZL5hKcQsshiMS8\nPBvP+oprWeKymJ+7HuCXAUcI7BmM3Mbcrur+mjtUm9Tgvefs2bZ7hEwyOiaIpVKqTm1Kj5dTsXSx\nw9LDjpWVtrGjzc+8vmbsj310+BlKNyuDw68IZMq9ZDJfZhA8sg41ZzajX9JULEq7sabuAdbU249r\nkMt7pFOv1XPpu8tUm94IuZWcjqf603x7N8Kn32R5zQPc2fWc1uvMO3IBZL/K5sn+p3S+8RXebYNY\nXXsPN356aNR+F+g4OfUqtb8r6jYWPLAqn18ayKUVj1hUYQ9OfsXwaWBO2sViMR1/7oYgQEG2ioTr\nCe/tN+tFJppcLV7NAllfcT2ZLzPN5me/yib2zEtKdKnKvkZbSLmXZDbfwk6BXQkHDMCTtbfMPpJF\nYjFNtnfDMciDHa32U3ZgbVxrlARAbm9Jq/OjSX30hvC+B4yNKvocQOZgS5Pbs/DpWoOjtRajyVWZ\ntlf2i5qUH1qPI5124FjLjwoLeyASiaj0Uz8UXo6cafCDaVnHIC/8RzYkevddSk7tikf3esjsral8\nciYJ+68Tu/aMcRzOttQ6PpGXGy5xue0Sbg3ZTMnNk/GZO5io9tPQZigRSSX4HZpN7pNXvJi8GYAS\ns3tj5V+cB/XHE7/kIPErj2CQSZH6eOEWdRKXE+vRZylJHzQNWRkfiu1YSPbXc9A+icZhxyKEbCVZ\nAydiNelL5JWDUNbvimLScGTVK1HQuCMWG5eDToMwYyayndth6VyE1p0wuLkj2r4Mcb8xiNZNhaHf\nw7G10HowIokYSlc2ypj0WtDkv21mYgliOWAgLCyMJk2K7qP/RRQSWalU+rsa2XeJrFar/WhE9vfI\nalpaGs7Ozr+xxj/4q/iHrP4H8G5ktTCKqlQq/1YU9bfwMfSe7463MDopl8s/io9rIf6OPVZhYVeh\nPZadnZ2ZPdazZ88YNnIMIICFjfEF71UB5ApjlLVcA1AXwKNrxihr7/GwZAycPwANOyNsewgPLmIo\nXwFCg+DebZi2GCbPg+mjENvbotsaRnb1VuiT32BxYDeKDavRHzqGUJCPdb9O6F4n8abW5wj5KnLC\nryPq0x1Z/drYtqyLoloQAGljFmBVwRerykayl33kMurkdASVhkctp3PJrhNZFx4h9yuBtEU9fJ/v\nx6ZLM2wr+1N6/QQcWtZALJWSufssxSd9bjpPOQ9ekB+XgvfAxqZp8T+dQqyQ4dq8yBA/av5hFG52\nFKtTlDZ/ucVYsOHZpijSmfcqnexnSZQdVlSoBRC18gLlJzQzuz8iV11AainDo2mRpZTcRkHGtViC\nZ3ek5bM5dFauosauoSi1FuQk5vBo6z3W+a/g0rSzJN1KICY8ElV2Af49zKOtz3bfR6/WUbqzual/\n8tWXqDLz6fRoKl1efItaYcOWZvtZWWkrMadfUWuSeXU9wMmRJwjoVQXLYsb0qFQhp/mOHrQ5MYDs\nxDxSHqcSseAaem1RZ6dHu54glkoJ7FfVNK1M+/L0i59IZlIBiES8vhT/3n196dsruIR44+jvRv1V\nn9FsX1/CJ15hd9fjXFx4Gws7SwL7hpit4xbiSeezX5CXqaYgW2XqzvQuImZfwqGsKxXGN2Nn421E\nHS2qDjcYDBwfGk7x1kE0DBtA6W5V2VRlI6mPi6K6J4cdx6Vmaepu6UvQ2KbsbbDJjLBGH3rKq/Mv\naXX/W3KTcznSfKPZ/iUyCbbFHUAkIuVCjBmZtfKwp9WF0UQffsbeBhuIvxRHvYuTjAR0ZW/sg304\nUnWRaR1BL5B2+xUiiYSc58mm6WKZlFpHx5LzKpOr/bcCEH/sAc+XncW+RXXifjiEJsNYwGbt703w\n7nE8HLOdzFvGrlvWvm7Y+nmQfOYJpfd/j3OXhriN7Ypdg0o8qTPa2BrZ1RH/I98Tv/QQ6SduIZJI\n8BzZluwbz3k5cyeOm+biEXkCQZlLxqjvENvZ4PLLT+TtOkbe3nCs2jbEfngPMpr2BQs5zr+sR7X7\nKKp9v2C3exn6lDRyhk3BesN8hIRE8vuNQNqrC7qDh9AfOIg4KAjR0O4YOvXAcO8agjILUYVqiI+s\nR9S8B6JL+6ByE0QZ8eDkCa6+RkmTVg06tVEWIFGASMqNGzcICCh69v4bI6uF+Ktj+9RE9vfGl56e\n/o8TwAfCP2T1E+C3Iqt6vf49X9QP3VL0Q8oA3m2BqlarTeO1sLD445X/Jv4KWS3swKJUKsnLy0Mq\nlWJvb/+b9lgajYaOXbobi6n0GmME1c4NspOMkYhhe+H5JXDyhuqdQNAh2r8GLh9CXLURzA2DiZ0g\nLxfRxbNQsxXY2EC3/pCUAEf3oE9KQRX1Bhq3R1rOH0k9Y1W4/vt5KNo2IqvnNySXbYIuNgGb7Sux\njbmOYlR/dOevYj99KPA2Mrz/JK7T+1HwKIbkGeuJ7T4NRCLiV4SjKlEKmyHdkLo5UfrWVlxnDkZa\n3I3c/Wdwm9zTdLypW8IxGASc2xW5Arz4eh1e3UKROxUVH8UtPoLvuDaI3omOv1p7Dv8JrczJ5rxw\nyo1tZlZwdOfr3Xg1DzLpBgHeXImm4E02pXqap9efLzlDwDfNzPaTev0FeclZlP7CSBrFYjGeTQOR\nWskpVtOfdpmr8BnWjMiT8expup0D7XZjV8KRN3cTze6Rm9+eo9LYBkhk5s/S1bFH8e9fG7mtAit3\nOxqFDaLbm3moDTIMBgNHeh/i2YGnGN5W0Ocm55J0O4lK481T5wC3Zp2lVJeqNPp5GNeX32GV30/E\nnovFIBi4OP0iFUe/T3w12WoK0guoOKsdpyeeY3fLveS9MVoyZb9W8nDnIxqsL/qYKNGqPN2jJpHw\nOJPTMyIIHvm+RAHg1rwruNUug12AN5srriHpVlH0VPkqizurbxK6pTeVZ7SixtLP+bnbAe6vv2e8\njoeek/Y0jbpbeyMSiaix7DPKfVmXLXU2k3gzgZjj0by6FEf9/YMBCJ7W0oywqrNVnBzwM0HfdsC2\nrBtNLk8k7VkaxzpuNY3h1clInu++R92I2WjUBo43XmE2fns/N2r82Jmkm4kU71MHuYPxw0AsEVNr\n/wgMUinHmxjXuTXuZzKepFDn5VrE1pZcaTLftB0LZ1tCT08kLuwW17/czqWuaym5bDgBYdNwaFCR\nmzUnmN6JLq2rUnpCZyJazicnKplLtWagyRdw7tuGuH7zEN4W85TcOhlBqyOmp7EHu23N8pRcOJSn\n3ebzpOtcnn2xGMt+nRG0ekQKCySO9rgeW0PuhgPkHT6LPLAszutmkzFwGrq4BOzmfIWsdHEyGvdF\nFlgWx7WzUQ6aRN6yzSg8XFGt301WyVBcHJzwinlFw6cvadqmNa2ylfQIDqZSQAChV09RMqA8imM7\n4MpJhJjHSJ7fwZCTBYkx4ORq/PDOTwc3fyNhFctArwa9CkQSEFuQkJCAm5tRF/3/E1n9V/jQRFan\n0/3uvtLS0j5696r/K/jHDeAT4tcV/QqF4l9W9P9d/F0ZwLsPqk6nQy6XvzfeT6WL/aPjeLewSyaT\nYWVl9YeFXR06deHli1gQGYzpMUEH2W8APbQYAys/B99qMGwLjC0HBhGGCk3g2n6EEQsQLRqO4clN\naNETw+SNiNp7YZi+EHb8BHMmgoMzrD2Mwbc8olruSHcbu+ioZ89DFxePfl8G1K4PbTsje/EIi27G\n1qp5X81EEVIOi5AADAYD6SPnoMvM4fXAueiVeYg93THoBLxizyB1N361J5VoRLFp/UzHm/nTQUQy\nKQ6tiohpytwd+HzT2ZTu1+Xmo7z+lMCl80zLZN2MpiAhHc8uNVAlZ6HLVZF28SkFyZnYlHEl/YYx\nCpUfn0FOTAqu9f0pSM5GZqtAbCEh5cwzGh4dbnae747fT9n+dZHZKEzT0m/FkpeURZl+5oTuzjf7\nKdOnNjJbS9M0QRBICn9IzT3DkCrk+I1uht/oZuS9SueY73hwcOBQqy1I5BLK962CWw0vsmIzKD+k\nptm285OVpN5PoN6ufmbTpQo5quRcqm8eQPaDBMKHhHN+wlkazG/E4+2P8GlUFgdf8x8cVVY+iVdj\naX1jAo6BnnSM+547Uw6xp10YxQKc0eRpqfxN3ffuuTsLL2Hv50bQN03xGxLK2ZYrWeW/hnZb2hJ9\n9AUulbxxDDAvqrIsZoNf72rcnn+Wq9PPYlfCAb/ORQVQ6U/eEPnzEzo8nYmNjxO3JhxkV4PNtN7S\nEf/O5bkw4Qwu1UpQrLKxAYD/gFpYe9tzqssGsuOyuLfhLkGTmiF96xYhEomo8n1bZHYKdjTegUwh\nIWBsUywcrEz7DJ5mlDDsbbCJ4qE+WHo6UW600WbL0sOBplcmcqL6d5zsvZv6y9tzsucuyk7rhH0F\nH+qcn8qFKlM402UDjfcNAECTo+L2lCM41A/ixdqLeLQIxr250cpMam1B/TPjOFVpOofr/EDWwySq\n316M3MmOkBMzuVZxNHeHbyZk5RcA2JXzJHDO5zwavxuXfs1wH2Acq+/WcdyvOoL77eYQctSoOy05\ntQtZ155zrtJErKsE4Ht+JSJBoOBBFJGNxlDu8gok1pb4HV/A48qDSPnpMG5D2qHw90afpyL16A1c\nn59E5u1BXs0Q0nuMQ/7sGPKQ8jgtn0JGnwlYPDmGdffWaC/e4k293rjHnMR5/1ISyjQnI7QbRMVh\nZ2dHg8cJtBk6ikqVKuHr6/unpVS5ubncuHGDx4+fcPGmN+fOnEGv06MvyIXyDSD6OngGQfJz0IqM\nZBWD8X0nllNQUICdvT3paWl/an//CXwqIv1uwdZvjaGwsKvw73eLp/Pz8xGJRGRnZ7Nt2zZKly5N\namoqtra2723rQyEjI4OuXbsSFxdHyZIl2bt3r6khwK+h1+upWrUq3t7eHDly5KON6WPhHzeATwC9\nXk92drapQl4ikaDVarG3t/+o+1WpVCZf07+CwoIptVqNSCRCoVD8boq/UGf7MY8lNzfX9AX8LgrJ\nv1qtRq/Xo1AosLCw+FMv+YGDBrF9xz4jUUVs/FskMlbVit9W1soVMO0sfN8MrOxg3H7EK/si2Nog\nSk3EoMxE3Lw7wqR1sGwc7F2KyNUDg1oN+Tmw8xwEV4OpQ5E+u450whi0M+egfx0PofVh5UawskIc\nWALrrUuQt2qMoNOhdAvGafE49Emp5KwNQ5uaidSvFBajByHv1Zm8pl1RlPbEccN3AOSHXyS92xjK\nJYcjtjQSwmjfjriN7ozbyM4A5N2L4mntYdR4sQldZi4FMUnEzd1D/r0XuDYPQZWQgSo5k4KkTNDq\nEMkkRpsqmQS9RodEIXurQX1rIaPMQywzfgwIWh2CRofhrdemtY8TVh4O2Pg4I3e3JXrdJaos+gzP\nlkFYl3BGLBFzsv4iHMp5UP2nosivJiufMK/xtLwzAzv/os5JTxefJHLZGVq9XGh2D17puBwQU+3g\nGARBIGHfNWKXHifr9gvECim15rambM/KWNgbie+JLlvQFRhocnSI2b3wfP0Vbk/7hfbxixBLjBKd\nRzMPE7PqLFqVhhozmhLyTX2zfZ/stYucpAKanRltti1VWi5hJadg0AvUX9yGCkOqmSLHqqwC1nvN\npcmJUbiHFnX6erryPHcnHUSn0tEpYhSuVcwLnDQ5KrZ6z6bmtoFoswu4NWwblYZUJ3R+U8QSMT+3\n2YHaIKXJsaKPhJgdN7g2ZAeBvYJ5vO0+naNmYO1p/iOWfu814Q2Wggh6pi8wi3AX4myXDbw69oiG\nB4bg3SLwvfnXhu0ieus16uwbhldLc5/cnJg3nKzxHXIbOVJHO+rfLfooyotN5UKVyZTpUZnayz/n\nfLdNpD56Q81HK4hfd5LIsRtpdHUyDkFF5+L13htc77MWly51qLhtTNF+HsVxo9Z4ghd2o/TQJuTG\npHC22nTkQb6oH8YQ8ngdFp5G3aD69RvuVhyKz1dt8Z3ejbzIBG7WmYhWpcW+RS1K7jNGT3WpmTwL\n6kWxXk3w+cF4XrOORRDddSbFujQgbd8FrCYMRb3zMLKAMjgdWGlcpvc4NBF3cIsMRywWk9FvMqqL\nt3CPOo5IpyOlelcMegF5di7OdvY0D63HkMGD8fHxMdk2FRKzX1e3F/7/j0ibwWAgMjKSzVu2cuDY\ncZJeRoFMAfYekJ9lfLfpVMauV4JgjLIatACkpqZ+kmzZX4VKpTL5t/63odBZxtLSEkEQSElJYdWq\nVcTExBAZGcmrV69wcnLC19eXsmXL4uvrS/Xq1T+IZnj8+PEUK1aM8ePHM3/+fDIzM5k3b95vLrt4\n8WJu375NTk4Ohw8f/tv7/oj4zRv8H7L6CSAIAkql0pRa0Ov15OTk/O4X0IdCIZH7M192hZWUKpXK\nFEW1sLD43c5ShfgUx/Jreyy9Xm8i04XT/4rrQEREBA0btwBEYOlojDDoVeDbAiKPgJUTaHOhdFWI\nMpqTs/QJ3DsOP30JCisIaQV3j8H+F5CVCv2rg1wOHUdB7CPEQi7ClhNQUIColhsGjRaxtTVCUC24\ncwHR/WhE1jYIC79Heng3dk8vQoGKnO5foj162phO9C2D3i8Azp/BMfGusRArI5OsEtXwuL0fWTmj\nTVNKxQ7YtamN6/dfGs/XlfvENR1BubM/oopJpOBOFG82HkOfnYdILEJia4XU3hZ1WhaKEH8sgsog\nL+WFxNOF5EGz8bu5Ccsg47a1bzJ4UqIjgU93YFHS6EkqqFQ8cGlL4MUlWIcUaVjvluiBy+BWKMr5\noIp8jSomkYwjEYgFPXJLCzRZuegKNFh5OKBKzaFUzxr4dArBqUoJLN3siBi8lZxnb2h8cYLZ9TpS\neiJlx7XE98tGpmmCTsch55HUDJ+Ic20/03RdropjLoPxGdqcjCM3yU9Ix7dzRQKG1uSX1htoeuxL\n3EPNPVrDfL+lzIhGlPuqqdn021/v4uX2G4h0Whz9XKi7vB3u1X3QaXRscJ1No8Nf4l7P3Poq6Xwk\n5zqsIWTdQB4M34R9CQeabf8cJ38Xrn97hme7HtH+6Yz37skr/bbyct8drFytaXmoP84Vivxfb39/\nmmebb9M6ai4A2c+SON9oIY5lHKg+pT5HPttN59jvURSzMdtm2q04TjRcjIWTNV2iZyL+lSSiIDWH\nPSWnI7aQ4d3Inwa7vjBbJvdVBvsDvsN7SDNerz1Fg139Kd62SAOs1+j4OWAW2NmheplCs4hJ2AeY\n+9ZGrTnP3W/2Urx/fSouM49oKx+95mLtGXjUK03K5RfUilyDhavxPRIzbQevV4fT/NFsLN0dUKfn\nciJwCoqaQShP3yZ411hc2xbZl6WG3+ZBl/lU2z6U+yO2YVm/MiW3z+B1/7nknL1N5ZhNiN++y5RX\nHvOo2UTKzu3Ni5m7sezQBKepg3gV8jkeswfhMqorAPl3nhNddyild07DqX0omoRUHlcdjC4rF6er\n+5GHBKJ7+Zq0iq2xmzMG2xG9MRSoeFO5A7KA0rgcWIZBpSa5ymeIPV2xLlWcvH3HqVK5MnNnzqJS\nJXNXhUL82nP01//+V1ZNv/UOfPnyJdNmzOTC5WtkpyWBtQuoc4yEFZFRHiBoTMtnZGT84Xv/U+O/\nnayq1WqsrKzem/fdd9/RuHFjypcvT1RUFNHR0URFReHi4sK4ceP+9r7LlSvHhQsXcHNzIzk5mQYN\nGvDs2bP3louPj+eLL75gypQpLF68+L89svqPddV/CmKxGEtLy/e6S31s/Jn9/DstUP/qPv4uCqUG\nGo2GnJwclEolBoMBOzs77Ozs/lJhl1KppHvvAbx1/jdO1BdAk7kQ/Quici2hRG1j9OHFHZDKEXcY\nC3EPYNPX4BMAS54gfnUPUcchiLfNhb5VwNUHwhKh8yi4eRJh3Fz4ZR/U8caAGAZMRjiTijg+BvGX\noxFZ2xiNpLetR9qxJQVDJpDpFoz2/DXE3XsgeRqN+EIE4gf3sBw/DNHbl3TeVzOwrBViIqqaqFjU\nkS+x7Vif7L2nSB65iNgWoxAK1ES2nEDCzG2k3opFr9LgvPsHvHNv45V5E+tZI5FYW1Li/Do8Vk/B\nefwXFFy+h03VABNRBUgYswz7+iEmogqQOGMjlr5eZkQ1++J9NOnZeHz9GcU618N7Uk9Krx2LRDBQ\ndssEQl7tpIbyMNWS9iD1L4HU1Yk3j9O5NmQnB0tOYo/z18TuuY2iuCPpt2IRdMZipdQrUeS/UVKi\nt7lc4Ml3R7D0csKpljlZfDRuB46Vy1D+x36ERq+i1s2FZGYaONJ8Ldo8NcqoN+jVWtPyyVeMGtky\nA0LNtiMIAnE7bhD80yAaJa1DUq4kPzday7F2Wzg3ZD/WxZ1wq+vLr/Fg5jHc21XGu0sNWsSvQFrS\ng52Vl3Nt+mluL7pE5R86v7dOQYqSF3tuEXp5Jk5NKrG/5jIer44wZg6UKu7MP0vFH7ualrcv50Hr\nF/PQ6GUcaLmVYnXKvEdUATTZBSAWoxdEHG+yAo2ywGz+nSnHsAsoTqPoFby58YqTLVahyy8iLddG\nhmFfw4+Axf0IXDWY8903Erf/rmn+40Wn0WsM1Lj9A8W/bMmpOvNQRqUU7T8rn/tTDuD6RTNebbpE\n1ELzH0i7oOKErB9EwunnFPs81ERUAUp/2wOXVlU5U2022jwVEZ1XIC/pid/Pcyi58mse9vyRnEdx\npuVdWlbBd1ZPbvRcg8TbnVI7ZiISifD+aRxSd2ceNxxftN86gXhP6ErUpG0o2jTAfeMs5KW98Qj7\ngaTJP5F37REAVpX98V41jpd95pC+5yyPKvRDFByErE4NcgZNBkBaqjgOe5ahnLgQzb0niCwVOP+y\njoLTEShX7USfmY2iXGl0V+/Sx6E4T+7cJWzHToKDzQv/3sW7msrCoIGlpSXW1tYmTWXhx3lhKrqg\noMCkqSwoKECtVqPVatHpdJQoUYJtWzYTG/WE9evX4+1ibySq0rdSG0EDIimFqkAnJyeUSuXvju8/\ngf9VPW16ejqurq54e3vTsGFDBg0axIIFCz4IUQVISUkxaY7d3NxISUn5zeW+/vprFi5c+D/dnOB/\nd+T/Y3j3Zv5Pt0L9rRaohZXyf6YF6m/t42MdiyAIJj1qofFyIZn+q1pfg8HA5937kJz4toBKJAGN\nEoqVg5PfIPJtgiGgIzwPR1SmHtQZabR+ib0Piz4DRw9Y9AAenUNIiMJw8CcMl8JBKoPx640R1/n9\nwMYW0ejuMHUYaLWwaD8MmQ53LiMkv8Yw8EsMBQUYRg5CeJOKetUWVE+SMXQdhsjaGsnSFYjt7BBu\n3kCfmIjFYKMxvqDToTt2GtspQ9C9TiJ380FS6vXBoNLwot4QUiasIeN+PAatHsd74ThlPsI+6iLS\ngDJYVgnC5vOWiN9Gp3PnrcNpbC9E75zDvANnKTa+KC0vCAK54RG4fNPN7DxmbT+Jx/iuZtNeT1yH\n+8BWSKyKdKmpm46DVIJji6KKeHkxewoexlJy2QgCI1ZS8fU+quaF49yvFQaRmIwnaZxv/iP77IZz\ntsFCIvpuwLtdCFJr89Rk7IbL+E5sZ3avCoJA0v4blJzYwTTNNsiHKsemILW3wblNdW5PP85O98nc\nnRVOQWoON8cexHdgfTONLEDMuosgleDevhpShZzKW0bQMHYVuWoJUXseYO3jaCSD7yDjfjypt2Kp\nuNzYIUgsl1Jj32hCT0/m7qprIAJrr/czEI/nn8KunBcOlUpSae0gqoZ9zbUp4Rxvv5nbs09h5e6A\ndxvzKJxUISd4fmckljJSLkURG3bHbL7BYODmqH149G1EaNRK8rM0HK62kLyELOM1fJpM9PbrVNr9\nFXInG+o9+5GceCW/1F+KOiufxHORJJ6NpFLYNwB49WlA0LovudhnCy/23CLnZRoPvg+n/I6xiMVi\nyszphWe/JpysOYfcWKP28c6oXSiKu+G3YhgVjs3i2awDxG44VzRGvUD0/CMoypckeecl0k4UHYNI\nJCJgwwgs/bw4Vmo82c+S8Tu/FIBifVvgPqoztxtOM1X3GwSBrEuPEcmkqF+nIqhUb6+BjNJH55Mf\nnUTM8OUA5D99ReKPBxAX90R17haCxkjQrZvWwnnaYF62/gZdhtFRwalvK6yqluNF3zlIRw7ENnwn\n1nvXoEt6Q/awaQAoWjbAduwg0pv3R8jPR1qqOE5bF5A1bhHp5dvS3cuPmKfPmDVt+t8utvk1kVUo\nFGZE9t0sk16vR6PRmIisWq2mbdu23L0Zwfnz5/Hz8wMMRqJq0AFvJQGAt3dxIiMj/+VYPiX+18nq\n30HTpk2pUKHCe39+ncr/vcj60aNHcXV1JSQk5JMEyT4W/iGr/wF8SKP7f4VfNx/4vUp5Gxubf7vL\nVOE6H/pYdDqdiUwbDAakUum/RabfxbjxEzl//sJbnepbfapeC4m3QCzFIJHDz0MhpBuGfkfg6jJQ\n52FIjAWZBQxaBa8ewbphYOcMfZZhKF4RcfnqEBwKV47AzZOINBoMNbtAve6IfXyhhtEWSrxgBOLm\nrREtWYghqBSEH4PPBmCIyMCw+QySU2FIxo6nMlNPAAAgAElEQVQzEUjDlIlYDeiB2N4OQ0EBeV+M\nRlDmkN57Aon+rciatRYhKwf5+lVYvnmF/PEdxG5uKBrWRlaxPGAkcNr94dhMHGg6D5pHUWjiEnEY\n2NE0LWvTIRCLsGtVFMFMXxmG2FqBbeMispl1+DK6fBVOnYuq47UZSvLuxeA2sogkAiQv3IvX2M5m\nWsg3209jEAw4tikqfhKLxSiPXsd7ai+C7q6jcvphgm6vRSjvR35iFonH7nPYdSS3B20m6Zf7xB+8\nhTanAK+utcz292r9OZBIcG1V2XwcB66hL9BQYe9Ear3egv/mMUSHPWR38Wmk3X2NV/v307FP55/A\nd1IHo2flW1gUs8OtQzXENpYoX+UQVnIqkesvY3j7jD2cHY5LvQDk9ubpQIfKJREEsKrkyy+1F3J/\n9i+myLEqLZenP12kwuoBpuXdW/4/9s46PIrrffufmdW4EIIFd4oFd2uhuBR3L5TiXtydFi1eIDjF\niru7Q3AnBCLEZbNZmXn/GHaTJdDSQmm/76/3deVimTNH5szs2Xue8zz3U5wvn8whJiiea7OPk6tX\n9TTjk2WZ64M2k6F1Vb5Y1pfTnQO4NnqXfSzPt17DEBZHwR87otZrKXdtFro8WdjhP42owJec77MF\nn5rFcM2j+AarnfVUvj0biySyq+xsTnddh1/PWg4qEZlbV6bI6j6c6bKWw3UX4ln5C7yrKH6sgiCQ\nd1YnMrWpyoFSk3gScIag7VcptHucMgdVilBo0w8E9l3Ny60XAEUOzfAqlkIXl5B9Tl9uNJtB3PUn\nKc+FRk3WvvWxxCehzpEFUZ/ywpJ5YlfcqvlzsfRgJIuFJ2M3EHXmHtkf7UGTy48HlfvYz9Wk9yLv\nwR8JW32YoInruFlpILqW9ckQuAfRLyOvvvw25V4N7YxztVI8rtATSZIIHb6IxEv3IGs2pFMXlXF5\neuC2Zw1JAdtJ2rIXAOexfdEUzk9ktfYkn7yIafhsihQtwvH9B5k5eQpeXik6vH8X8RIEAZVK5UBk\nbRHuLi4udrcuQRAoVKgQJ48e4MSJE/iXKqushUggW7FluipVqjTr1q375OP8K/hfJatRUVEf/YJy\n6NAhAgMD0/w1bNjQvv0PEBIS8k5ifPbsWXbu3EnOnDlp3bo1R48epUOHDh81pn8C/5HVz4Q/m8Xq\nU/Vps6LaZLIsFgvOzs54eHig1+s/ybbAp0o+kDpzV0JCAiqVyj7ODwks+D1cvHiRhYuWg9pTsSRk\nbQDIkLEc6DwAAQK3gloHFfvChCyKNuE385C9cyNkyY94bS8MLwNOnrDgJZRsANf3In3VDrFPZRjX\nEgqURV4fBu3HIxxfi9R3mhK4tTMA6eFtpD074MQZxV1ApYYxCxRf1+N7scZGIbRWLJvSiyCsgYHI\nnm4k1m5DlE9hTDuPQMlyWEbMQL4XgbV6bdSFC6Ft3Vx5niwWpMNH0A/rab/u5OUbQaPGqW6K7mlM\n/6l4tqiFyjslKC56+mrSD2rtYGmNmvsrGYa0cZj30LEryNSrEaI2xXfs+eDFuJcrhD53Fvsxw+1n\nGINC8e3ytcN9eDl1E5n6N3Xox3DrKUlBoaTvkiKS71wwO7LRhHu5IhSL3ku2lSOJeGXkcrfVnG22\nEI2XC6E7r2AxJNvrPJ61l5xDGzm0DfB47Gay9U8Zs2+j8pQO/BnPqkUQXXQcrzeXs62WEHtP0Q0N\nPXqXpPA4snZOSxKfzNhNrrFtKB24kLwLenF1xC52Fp3M081XCNobSPHFndPUCVp7BrWznpInplP8\n6BTuLT7DLv+pxNwN4c6PR3DLlQHvso4uBVpPVzI1K4vG243AUdt5+PMxh+9Y2JG7xD4IpcDCHmRq\nW5XSZ2dwf8lpjjVagik2iUsDt5BtUEO7n6YoipTcMwrflpXYVW424RefUuxNSl4bRLWaCpemImu1\nJL1OwLepo5oCQKZm5ck5uCEJwTGkb+IooyUIAvnmdce3SXku9VpHxl710fulaEymq1eGfMv7c7Xj\nYh4vPMD9yTvItW0SolpN+m71yDykNVdqjCEpSNF3TQ6J4lanuXgN6kDys1Ce9Zzl0FfOtSMRvT04\nU7A3z+bsItPRFah9vMi0cx6m8BietptgP9+pSG6yzunLiykbUJUtjtfiiQgaDd47F5P88AXh/abb\n2/VdMxmrLHM3exMiVuxCf/wgTgd+w3zjLoljlTGoixbCZfF0YrsMx/I8GEEUcV89k+TrdzA27cui\nMeM5ffAwhQoVcpijf8qyZSOyGo3Ggcj6+/tz/PBe9u/bi5uHjVBL2Nykvvvue/r374/RaLS7Fvxd\nWaB+D/9mi+DvkVWLxfK3+tk2bNiQ1asVlZnVq1fTuHHjNOdMmTKFFy9e8PTpUzZu3EiNGjUICAhI\nc96/Hf+R1c+ED81i9algi5QHJUDJlgLV1dX1o1Ogvo2PvZbUGq7vyoT1sfJYcXFxNGvZERkNWGIg\n+zcQvBuKDVT8taxmhLxNEJw8IIs/zCsP5iQYcBEK1YW7+5AfX0W+fAC0TtBprpLWcG4LJWHAvD5I\noqeyvvdZDGo1/DIMfDOD0YDQojhM+g4KlYUtz5GWXUI8sQWh+1DQKdvm4uyhqL/7HuLisC5dgqVa\nFZAkzGt3YUz3BfSbBhoNbD4AjVuCKCLu/BX1kP726zT/OA/RxxtN1RSSYZy1BPchXe3WTclgIPnC\ndTwHtbOfY7h2j+SnL3Gp4k/SjYckngskYtkOkp6FoMmUjrhDl4g7cpnobScw3HmKa6XCGJ+FYo6K\nw2oyE7PrHBmHpGiDAjwf+DO+zaqg8XZP6efhS5KehODbvZ7juYMW4dOsmsO5kiQRs/McvkNaIooi\nXvUrkH/PdPJdWAJqNdpyxbg1cD1703XnUtM5PJq7D8PLCPy61HBoO+H+SxIehZDlu7oOxyWTibjz\nDyiweypF764iJtLC/pITON1kIVd6rydnr1qoU0ltAYQfuI4xMo5MnZUo3kztqlM+dC3OlYtxutNq\ntF4uqJzfUqywStwbt40sgxQrtkfZApR7sQptkdzsKjWN2z8dodDctFYOc3wSD2bsJG/AUPJvG8eN\nUds512oplsRkZFnm2qDNZGxbzS435VYkBxUeLSH6cRRbco5CMkvkGtYkTbuF5nZF7e2GJclCxP7r\nacotMQYMzyNwr1+ZyzUnEnE00KHcakgmaPFB3BtW4f7AVbwMOOpQLggCol4LKhWv1xzDHJPgUJ6h\ndTVyTenE7cHrcatbDrfyKQoDGUd3wLt5dS6WHYo5OoGbzWag9y+I76TvyXZ0CRHrjxAye5P9fFGn\nJcu0HhifhaPxL4S+iOK/rPJyJ8vhJcTsOkPo7A3KdUXFETo5ADFzBiwXA5GiFHcIVfp0pNu/gtgV\n24nbtN/etjqDD5aoOITveiAWyIeYMQP6LWsx/rgE06ETAOjaNMGp3TdEV25F8oVrGL5sT5369bly\n9hwNGzRMM7dvz9O/BYIgUKFCBR7cu0O/fjZ1C9t6LvHLL79Qu7aSpc4WTJSYmEhCQoKD5ujfRWRt\n7f2b5iw13kdWPwfBHj58OIcOHSJfvnwcPXqU4cOHA/Dq1Svq1av3zjr/1nn8I/ynBvCZYMuCYUN8\nfLzdef5TwiajYZPJslgsuLm5/a3RnXFxcXan/w+FzeJrk9fS6XTodLp3+qGazWYMBsNfkseSZZmG\nTVpy6MAh5dVMUCuR/1mqKCoAYReh7jq4sxqeHQCdu0IEizVE+vIHmF1KEdKuPQVeP0AIOoY89iTC\nhh+QT61ByFsR+ds1sLwTorsz0phtkJQI7bKAIQHB3Ru5aG04/ytsfgTps8C1kzCkLpx6CW4ecPEk\ndKqBmCsXUlAQQoasyOGvYPUJKKxswYsNCyE3bYncT1mMWLsC4ccJOD+8YbckGgsUx2lcP5y6KP6k\npks3iKnUFN9di5CiYrA8fUlCwG9YHj5Hny8b5ogYpNgEZKsVQatB1GkR1CoEtRpzXCIqZz1qZ72y\n6MpgjopB0KhRqVVIJjOSyYycbAYBtBm90WVOjy6rL+qsPoT/so9sY9qR7puK6HNkRFCruF1vFGp3\nV/JsGGW/P5LRxOX0TdIoC4Qt2UnwhLUUe/GrgxvBw29GgySQfYciz5J0+wlhk1YSv+s0ssVCtu61\n8OtZE7cvFE3Ry7Unok7nSaF1gx2ei0cjVhOx6zJFbi63L96mVxE8aj2JhMv38alSkC/md8ElT4qE\n1sniQ/GuV4Zckx3JpSkillN+nXDO44fpRSjF57UnW4fKCIJA8JaLXO+1mgqha9LsYtztMpeQDcdJ\nVzo3pTb1RZ8pZav4wZTfeL7yJP4PV9n7uFN5AIIpmbx9v+TWuF1UCVuF+FbecYvByDHvdohqkbLH\nJ+BRytFi+2r9Ke72X0nm+QN40XUahaa0JkffFIv27e9/IfzMI/JfX0XEoh0ED11I8Q398a2vPIcP\nfljHq60Xyf/gV2J3nuJ56zEUWtyTzO0VK3Ts5YdcrjqSnJcDiBi1mKRLtyl9ZxFq1xTXiCeDlvNy\n5UFkSabwteXoc6YE78lWK4+bjCbuzE0EjZpcQfvs15h47BLB9fvZo/PNETHcKtwZ1ZeVSN59lHRj\nvsV7UEd7W4ZjF3lZvw+5NowldNxKLFoXXM7sxNCsO9KdB/jc3We/J4ZNe4jpPpIsR5YR0XsK5qgk\n5AlTsHbrjH7PVtRllOs3L1uJeexkPO4cQ8zoi2wyEZunAmJUDIsWLKRF8+b8HmRZJjExEVfXtAFx\n/zQMBgM6nQ6LxUKTb5px+tQJh3JPT0+CgoKAFMWC1EoFqZULUuuVvq1c8GfJ0r95zgB7LMXbv7GS\nJFGvXj1Onz79D43sfxb/qQH8k3iXG8CnevN6VwpUW8rWzxH992csq6nVB4xGIzqd7g8zd32M5Xb8\n+AkcOnhYEf2XAcms+GW9vgHhlxDKDofXgfD8EGKBFlB5NpjikZy9YWpBSDbA0AdQujNcDUDOkAd6\n50A+tQ6xZGPkoYcU0vvgFFLrkQjbfoKWviDJ0HE28rJQiAxC/KqVQlQBcX5/hBbd4eIJxO51oUtN\n8PZFKvsN/BaKXLImYv6idqLKvZtIL58id0jxrVMtmo12YG87UTXt2IXlVShSRDQJnQcTXaw2MRWb\ngiAQ2W4YMSN/JnbLaSzB4YhNm2HuOQhh+RrEC1cQ9E7oj+zD6eUT9M8forlxCQFwPbYV16eXcHt2\nGZenFxG0Otx/W4FXxE3Sxd0lvfERmvy5cR3xPS4rZkHrpiRmzErYjnOg1RKycDfXS/bmrFN9LmZt\nQ+yJm8gqkejd5zC9UoJwgkb/glNePweiChA6eysZB7d0IKqSxULc0aukG9Lafszpi1z4LRqCJEOG\nxaOIuBHCubI/cKboQIIWHyDq9D2yDvkmzXMRuuoomUc4ujhoM/sgujrhVr0USRYNJ4oO5ma3JRhf\nRRF/9yXx91+RpW+DNG29nL8Hlzx+FLy1Br+Fg7g5aAMnyo8n7t4r7o7eQsZutdJ8D62JRsK3niHn\nurGY1E4cLjiIkJ2XAUV+68H038g2O+V+a308KHp7OU7li3B14CbcKxRIQ1QBXi7chz6zD+n6tOBC\ntTGEbr+Q0meymXsDV+E7qhPpWn5F7r2zuDt6E/d/2IAsy8Tfe8nzVUfJtmEcAD7fNSbrwoFca/kT\nrzadJvHBK57N203WjRMB8GhYmezrx3On52JerT2GZLFyq91PuLevh75gTrJsnIyuUC6uFOuD1ajs\n8sSev0vw4t1kOrUG9w6NuFv2O8wRMfYxCioVvgOaYU00IqbzVHYp3sClemkyLhnJk3aTSbh0j8eN\nR6HKlR2vdXPw2rGEyDGLSNyXQgycq5fBd+ZAnrQeT3KsEeeT2xEEAec185FUKqIbprjLOLesh2v3\nFgTX6IY5wQpnLqL6ujbqIcMwNW2LFKOMUd2tE+o6NYmv/A1SfAKWjv3JmS49Rw4c/EOiCv8bvpc6\nnY69e3YRGBiIWp1igIiJScDdXTEY2IinzbXgQ7JAGY1GB8WC1FmgbET398b1b8X7xhcTE/O3y1P+\nX8K/S0zt/xA+ReanD9Eb/ZzSUu+DTcPV5vOk1WrtQV0fgr9K7O/evcv0WfPAowbEHUXI3BI5bMsb\n4X8nJYr/8W6IvIVQpDtSlZ8QVmRGNiUinl+NrPOAij2R3TPCwiqQFIf4+ApSzclwYBjSN4ooP8s6\nKG0N+xJcvBWx7d6roGwTiAyGRxeRxixXzj29C+lRIDy5jbh7I1Le8sp4fj4DmXOCJCEc24w0ZXXK\nhUzrg/hNayQvb+X/509hDXqGKj4BU6tOWC5eRoqKRvD0wLh6D9bs+eHL9vBwIuw/jzVPfqXejk0I\nowegmv8zwpu5t4wZhSpvblTFUgTdTWMmoSlaCHWhFP3S5B+XInq5o6mS4qdouf0A8/NgvAd2RfT2\nhDrVAHjtVx7XBeNxaqNk5JKiY4npNgzOXSP+aRTx38/H/DoKQatFliU8a5Qg/uwtXErlR9RqSLz+\nCGNwOD6dUyx+ACFT16HJ4I1LBUfZn1dDFuBS+gs8OzXEs1NDJKORiCkruT9iPVajiZDlB1ENaIRz\nbsWCF7rpJNZkM97Nqjq0YzEYiT15kzwnF+Hsnx/j/ecEd5zA0bx90fq4k7FFZXQZvBzqWJOSCZqz\ng+zrFO3UdO1q49WsGs/aTOCI/0hEjYoSox3VFABeLt2PxscTr2+q4fVNNcIXbedKu5/J2qIcOr90\n6NJ7kq6ho1yXKIqka16FyF0XiDpxm0cj1pB7Uls7obfEGXg8aTN+AWPxbFQF/Re5uNl+OkljmpNj\nSCOC5u9D1OnI0FchVW5VipPv/FIeVe6FMSSapKAI3L4sjVPBHPY+03Wog+jiRGDHSegzeeH6ZRmc\nSxSwl3s0qqIQ1jZjCd96Dkt8Mtl/VmSiBI0avx0zCarVl6v+ffC/9BN3W07DrUdLdF/kQTtnGFJ4\nFLf9u1P4/hrUznqscYk8bjMRp55tMe0+xqvGg/Db+VNKf+3qYXnyins1BiC6OpPuuUJOddXL4z5n\nDCGthpHt0jq0+ZRrMN18CCoVVoMRLBZQqxGcnXDdt45Y/5rEjp+Px9g+SLHxGA+eQUZAUqfoSwt9\n+iFevoSpRj20l08hiiKahT9hrPQVcdnL0KRBA34+fMSuAf2/jLdJV/bs2YmMjKBLl65s3boFsABK\noGtMTMx7DSEfmgXKRlBtWaDepyH7b/ZXhfeT1cjIyP9SrX5C/GdZ/Uz4VJZVmy+qTW8U+F290c8R\nyPW+a3mf+sCHarja8FcIt9FopFmLjsiabBB3DHIORQ7fC2ig+FIwR4AxDjn6Gaj1yP79YWUuZEME\nFO6OVG4ysmRCLtgAYWElCLoENUYjDXuBELgRsVxrcEsPW0bCw7OI7hmgw2rksp0Q3H2gtELUWNID\nsWQNCDyL2MkfRrWAjLlgzG6kgBBAQiz3tUJUATb9BE4uUPkNUYuJgsBLSEVKwIxxiHUrQKu64OaO\nZct+TGJGpO6TQBSRt9/Euv0GzNkMr54hFi0BNqIKiPOmoerZy05UAYStv6Ia4BhoI2/fiXaQY5Yn\n8+IA9EN6ODxfiYMm4dK0jkJUbfO+9xjWBAP6prVT+vXyQLoUiNvUoXif2YL387OkT7iDfmgPJAkS\ng2O533gMF93rc7t8bx60nIDHlyVRuTtmXotYtof0w9qlkauK23EKr6Ep27+iXo/vhO9Ao8Nr3PdE\nXgvmQpHe3Kg1mqgj13k2bgOZBzVH1Dg+gy+GLcWpYE6c/ZU50+fPTp7zK8i+YybJEXGE7ThH8M97\n7JH8ACErD6PxcsezfkWH/nNtm4Iub1ZkQcWl4n2Jv/Y4ZczJZp5N2kSGCSkKAL7fNaHgrbWEnXjA\nvfFbSN8rrQVXlmWeD12OZ6/mZD8fQPCKI1z7epzdL/T5rN/QZvLBs5Gi1ODdrja5ji7g8bTtBHaY\nz6MJm8k8f4BDm04Fc5D/1hpCD9wg8sJDsv3yQ5p+vZpWI+OwdhhDY3Cu5p+m3KNRFTL/2JfXB67j\n1qm+A0kR9Tqy7ZuD7OrC+ZxdkNVafH58Q2ZFEZ81U9AUyM0d/+5IFgvPu89E9E2Px5wxeB9fT+K5\nQEL6THfoT1MkN7IkIcmikoHpDZy7t8Sla0uCq3TBGpdAzNx1xG7cj+r4WcRceUmokqJxq8qaGbed\nq0mYsQzDr3uJqNoWC05w5gnS6wjMfRU1AUEQEBYtRTJbMHVWLLHS9ZtoIqPp0qo1Kxb+/KeI6r/V\nSvi+9VUQBFau/IUnT2wqDRZAxNPT8y9psf4ZDVkbkTWZTHZXAJuG7IdYZD8X3ndPX79+/R9Z/YT4\nj6z+Q/izltW3t8+1Wu0fbp/b+vnceq62FKwxMTGYzeZPpj7wZ65j0OARPH54H4wPQOMJz+eBNQGq\nXYBbA0EWofgcBFFSFAECioEhAhrsgC8XI5z7QfFfXVgF+dUdxIJ1oNYECLmJ/OIyktYZBvjBgbmI\nRRsgjbsPRRsgnFyI3GaykrL1yXUIPIp0bh/ikpFIfuUUZYBxe6BETTAa4MYRpA4pPpzilnnIPUbB\n4zuwYgY0LAjJRsTZkxCOnEAq+KViuQ24grTuOoxZDmf2IFapAxneRONLEsKhbUjfDUqZkCcPkZ4/\nReiYEq1u3bMbyZiEumGKI75581ZkqwVtw1opxy5cwxL2Gn27lIAdyWTCdPYyTgMco98TR/+E63dt\nEVL5YicfPo0lJg6nlvVTrlMUsWzeh0u/rnhc2o1X+A287h3DXKUKxufhxJ0J5KpnPZ60HE/kpqNE\nbT+JOSYer9aOWaailv2GoNXgUtvRChmzehcyMl4/dCPTmbVkDTqM0TcLN5tOJfHBS0R3Z+RUpFOS\nJCI3Hsd3VKc0z1JUwF6cyvmTbul4nk7czPn8PYg8dA3ZauXZpE34DG2dpk7ipbskPw0hx8vDqKuW\n43KloTwZEYCUbCYk4AgqFyfStXVUStBmy4D3d00Q3ZwJGhNA6PJ9Ds981G9nMUfG4TO5N/rCecj5\ndDeGSCPnivQj+tRtns3eTqZFQx3adCnzBXlvrSd8/3VkUcDtHWRTk94TwcUJWaPhacPhWOMNDuWS\nMZnXP29H16gmoWOWEz5no0O5LMvEbTmOmDMrkXM2EbvtmEO56OKE77TvscYnIXu4O5QJGg2+O+eD\nuxs387Qlav9FPA6vBUDllwnvY+uIXb2byNlrADA9CSak4xhUU6Yg5s5NdPnmDuuoy6wf0JQowvPC\nTQkfMQ9x3SbEHDkQ167HEhZJQoeU9LiaCqVwnjGKqE7DMRskpF0XwdMbAvYgb9+KdZ0yDsHFBdXm\nrVj2Hybp296ILTuy9uefmT1z5r+SeH4M3nc9Pj4+xMXFMWrUaBS1APDz8+PMmTOftO93EVlbCu0/\nmwzhcxDZ32s/MjKS9OnTv7f8P/w5/EdWPxP+imXV9mYZHx9PbGwskiTZxft1Ot0HLZSf0w3A5jcb\nHx9vVx9wc3P7aPWBP6tLu379BlasWAVqb5BlMEaClAx+bRGOl1cyVtUKhNADyKZEeHURnDIjZioN\nOevCrsbICWGIGjeotw2kZKTaM8BshIAGYDUhXt8HteYAMlJDxYePwz+BWgsunojjv4JhpcHNF4Yf\nRZr1AmJDEEvUhMxvgl5WDEbMVQgKloJkI6wYhxQaBLOGQNsKCNvXgcEAU3cjbQ1FXngWwl8glqwK\nfm+yTJlMcOkoUrdUJGXLCmS1GmqkkCFh3BDUdeshpH7TnzEN7XfdEVL5PlpnzkHft6uD9TVp6CSc\nO3yD6JYS4GAYNwdNDj80JVPcByyhrzHfeYhTrxSlAYCEEbNx7dEGIZVOpiU4hOT7j9H3am8/ps6R\nFUwmtEUL4RlxF+fda4nDhRdDl/Go2VhEZz1Ra/ZjSeXjGDF7A95DOqTJax89dSWegzvZfXrVPl5k\nWDsNrf8XaArm4eXk9VzN2pKwJbuQkk2ELd4JWg3uqSykoPjJxu8+i8eoHri1rEOWl8fQtajHrWZT\nuVisL7JFwqdn2qj7sPErcf6qPCpXZzIsHYPfmQBC1p/kfKHveDJqLemHt01TRzImEzJpNT6Lx+K7\nfibPhizjYaspWBOSkCWJZ0OX496juf2FT3R2IvvVDehrVeRyjVGofb1wq14yTbtIMlaDEdE3PffK\ndLf7C9sQuXIvUkIyGcPOY0owc798T4c5Dp+xAUGvJ93a2aTbtZSQUUsJnbHGXh7720kSL93B69wO\n3FbMILjDeOJ2nUrp3mTm1bdTUbdogjU6gZC63zn0Lzrp8Vk5CdOLcIRsWVClT2cv0xTOj9fOZbwe\ns5jYDfsJrt8PoXoNNJ07od64AUusgdim39vPF0QR5/H9sLx6DX7ZEcsr91Pw8ES9bSfJOw+SNFdx\nyZFNJsxb9oBGixwbp7gJAOQrBHNWYx0+FOmWks1KyJkLVbNmqPceZP/27dSs6fjS9KH4N1tWP2Rc\nQ4cO4dq1lCxmderUZcKECb9T4+Mhy7LdNeCvJEMwGAwORNZqtX4yImubt3fNXURExH+W1U+I/8jq\nP4Tfs6x+bArU1Pi73QCsVqt9qyY5ORm9Xo+np6dddupT4UPJanh4ON179AWv70CKB7UXOBcBqwGC\n1iBLRig+Bx4tgtA9iNlbQ+WDkBSEVKgLrC0OQUeg2hyktoEIl8YjFm0Oz87AlCwQFwbNNyL1eQS3\nNyEWqQMZC4DJAAenIkeHIMxpg6TyBa0eem2EAlXBmAB3jiC1GqMMVJIQTm9CylkE1fBGUMcb1s+C\nvKWh/zpYG4NcoQWCd0Yo98YlwGJBuLQfqeOwlAtePhEhY1YoluJLKv4yE3oOAJvF3WRCvnAaevVW\nfMbi4pCuXsX64B5C0cJYT53Bcugo5nUbsTx8hODrg2n3YUz7jpK8+zCWqzdQVSmL9UkQUmQ0ssWC\nKWArTkNSAoAA4gdOQl+tHKqsme3HpPvOnSIAACAASURBVIgozLfuo/++veO5AybjVKsKqswZHI6b\nNu5CO1jZbtVWKoPbhkW4nN+j+Bo2rE/4zE3c9mvE48o9CZ34C8kvX+PR2VEmyHjzAclBIbh1c0xt\nKsUlYLwYiMfm+fiEXEI/qi/BkzdwJVNzXo5fg+/QtmlIb9iklagypENfrbQyt6JIuqkD8As6QuKT\nUMxxiYSNXo6UlKL3anwQROzRK/guHmk/pi9eAL8n+yBHNiwJSVhDY5DNFoe+IlbsQe3mgluberg0\nrE6W+3uIu/WCa4W/JXjaJixRCfhMdCR6AD6TeiGrVCSHRhM+dXWa70n46KVoixUi/b2DCDmzc8+/\nM0m3lW1da4KB4KE/4zp9KCqdjnTXdyJ7enKvdHdML19jCg4nZPpa3FfPUK6jejl89q0gbOIqQib9\notT/djr6Mf0R3d3Qt2qI28KJvGg9ivgD5wCInLQS2SyjX/ojzkd3YLx2n7DWKS9XsiQR9d1EVKVL\nI4XHENPZMRWlrlo5PH6ZTki3CZhjk1CvXgWA4OGOZtdvJJ+4QOwPyvikqBhiGveE2k3hdQSWsaPt\n7Qi5c6NetQbDqBmYjp3B0LY3locv4OALxCw5EVqkkj37uhFC135YmzZGio9HmDoZ31MnOXv4MHny\n5PnbSdC/Gblz5yYmJoZKlaoCMrNmzaJWrVp/WO+v4o+I9J9JhmCL8zAYDPZ7+C4N2Q+9h3+Uveo/\ny+qnw3/SVZ8RyckpP2iSJBEbG2vPbPJ2EJJGo0Gv16NSqT7qTdzW3qeU/bCN1Wg0YrFYUKvVSJL0\nl6SlPhSxsbF/SNZlWaZu/eacvKLGGnsE9HnAdyi86AoaH9BmBTkEUdAgJT5DyN4OueRyhKPFkWPv\nKakG1e6IXtmRWp6DsMuwqQJoXRDUemRJRvRvh1RrNsS9gvl5od8BhLsHkQ/OUup/PR4q94dt3yOG\nXUUa+yYae0U3xIi7SBMPwaXdsGECBN1F9PFDylcNSjWFRS1heRB4KAuc2C0rUteJUKeT0kbAJIRD\nAcjb7ivuBIBY1w9p0DRo1A6sVrhwHLp9DSOnQEIcTi9fwI0rJN27jZOvL8nR0ai1WvRubohqFb4Z\nM6LT6RVLvSwTGxuNb8aMmC0WrBYrRmMS4aFh6J2dSIyLwxAXT1JsHAgCbjmyos3uB5l9MWfxJWHZ\nRpz7dcKpUzNUWTMhiCIxHQfDq3A8D6WIUEuSxOt0JXDfsQxtak3YX/cQ33MEXq+uIaSSQYtv1wci\nY3DatV6pHx5B8sz5mH9ZByYT7nUq4d6rGS5flkFQqQiq0QN1rmz4LB/v8HyEfzsO891neJ3a7HA8\nbuRMDHNXIWrUZBrfDe8ejRF1irX5dpaGeM0YhFvb+g51YhZuIGbKcty2L8XQpg9yYiLZlw/Do14F\ngjpPwfAsnCzHVjg+n5LE87wNEKtXwLr/OBpvN3JumYg+XzYkk5lAvyZ4TemPe7dmb417LPErd+BS\nqzzZ9sxL89yH9ZpK4uUHOC2cSHzt9njUKoPfypGIeh3G+8954N8R31t7UedS5Lyie47BuP43cu+a\nTsKhy0RuPkH6B4cc2oyq3x3z5Zs4FcqJySLic3K9Q3ny2atEfN0FfZ4sWJMseN5z3Po3LttI/MDx\nZJzVl9CBc3A+uA11acUFQXrynISKdXBr+TXpfx5F7IL1RI9bhHDzHkJwMOaa1XHp0x73SSluLEnb\n9hPbYRCyqEF39hRitqz2MunqNZLrNcBj/liMq7ZgjpOQtl2CwCvQuhqqH39E1SIlyE1avhTLhHEI\neifknfeVrf+YSGhcBGo1gCkL35woIXZqALevkTNTRg7u2GEnH28HCaWWbwLSBAnZ/mwShv+2gCwb\niXN2dv7jk1NhwYIFjBgxAlBiJ4KDgz/52JKTkxEEAe071C8+Bn/1Hqb+PbZYLJjNZpycnNK0P2LE\nCNq0aUP58uXTlP2H38U7Cc9/agCfEamtg7bPkiTZrZKyLNvfCj+VVfJTugHYtvpti4dOp8PV1RWr\n1UpiYuIn6eN9+JDrWLFiJceO7Aa0IGrAvRG86A7eDSDjQLhdCQQ1klMhELXIhafBrdHIUTcRvYsh\nFV8Cp2ogVZsPIedgex3QuEDhAchZvoS9XyNVfBOAsrU1yBLMqYXglRvUzsh1J0P5HooF9OYmpO8U\nQXJMyXBlC7JXJmjtg+iWDskQD52WIFVT0qAK02sgVG6J9IaocmGnck6NlB9ZcfdSpJ5vCNjLp7B/\nA1LYS1wObUFcOYOkp49wcnPDLU9eyjy6Sd6sWclRpSwZWzTCw8ODHDly4O3t/ae0fVMv4JIkYbVa\nsVqtREVF8fr1a8LDwwkLCyMkNIRDRYpiOXaVZ8s2Ex0Vg2vuHCS/eImuThWSD5xEXeILVOnTYfhx\nOaKXp4OyAIBx4jyc+nZ1IKqSJGHZfwyn9UtT5sHXB93oQZiWr0GzYT3xa9eR2HYUMjKenRthuBCI\n34JRDm1LkoRh2xHcA2bxNswHz6Dr3hGhRFHCRk0idOJKMk3pieCkRTKacG3xdZo68bNW4TSyN9oy\nxdE+OkXC1IU8bTMB1zIFiTtzk+zXNqWpk7j3FFJsAu5LlYChhNZ9uOvfBb8ZvRB0GkSNNg1RBXCu\nXYnEXw9hOH2N0K4T8P15uJ1Mm1+EEr1qJ16XdqP+Ih9e944RV64Rj8p/S879PxE6ZAG6auXsRBXA\na/EE4nJl5VHdIciyjM+h1Wn69N69jIh63Yg/dh6PeWPSlOsqlMBr2SSiOg9H1y6tG4S+eytkUzIh\n/SejqljWTlQBxFzZcTmynfiqDZBMZhI37kX1yxpEvR7y5EG9ZQeJTRqg8suIS8+2WINeEttpCPLY\nOYg3rmD+shaaa5cQ37yAiyX80S5fSmzX7gguLsgnFC1QipSE2auxDuqIkDc/ov+bMSQmggyyqAXn\nNy/xnulg6QFoUx5KloOmyk6AOrMfXkGPOLhjB15eXlitVgfZpnfFC7xNfGy7T6l3uIxG40frj35K\n/NXfiN69e9OhQwf8/PyIi4vD3d39LwVe/ROwWWTfvoe2ufi9e2i7dzYrrNVqTXMPIyIi/lbLalRU\nFC1btuT58+fkyJGDzZs3v1MqKyYmhm7dunH79m0EQeCXX36hXLm02en+7fjPsvoZkfpht1gsdk1U\nm06dbaviU8IW7PQxVk+bFdVm8X17rFarlfj4+L9VUy4+Ph6dTvfet+s7d+5QqnQlJKsGBAuIzmB9\nDbpsUGAf3KoAKmfIswHxSWukLE0QYi4hx9yFvH2g8BSE07XAGobgng3p2WHFUtr+OejTIf5aBLlg\nfeSi7RCOjER+fAgy+EP1BRBxC070g7GvlHSte0Yi3N2K3Hc74onlSEcWK1vyeWvB1+Mg/B5s+Rbm\nhYBGB4kxMCALzL4MWQsCIPYrgly1CXKXCRDxCg6shaXDcCleDvOTe7i4uJA7X36y+/pQr05t8uXL\nR968eT+bcHbqRTr1gm6zUiQlJfH06VMOHTpETGICFwNvcu/GTUQXZ4xJSajKlcBlTF/UJQojaDRY\nnr4gqtCXeD05j5ghZYFPmrMM49wVuD646PDdMHw/BPnWIzQH9tqPWbbtwDJ0CHJsHC6VSuA+tAtO\nNcsrFt4F64mZvpL0z085bPVbgkN4nfdL3G6dRsyqBKglr1iLddJMzFGxuDarhW/AFEcVhH0nCWs1\nFJ+QywjOKRYVKS6e6MJfIUXGkH5KHzx6p6SvlWWZFyVaIVQpj9vccfY6yXuPkthhAJboOLwn9sZr\nhKNrhSxJBBdoAE0bo/u2I0k1GqF21eG3Zy6abJkI7Toew71gPM5sSxmHxUJ8zbZYbt5BTjbj++w4\nah/vNPcwsnYXko5fwGv6UFz6dXQokyWJ10UbYHb1Rg68ideicbh0SCGlsiwTUaElJpyQbwXi1Lcj\nbpMdt++TFqwiYeQsZIuEy9FtqP0dJccsF6+SWL0h5C+A9uRZhzLp6GEsHdvjuWYWhqmLMbtlQl61\nG6xWxK6NEV48Rn3pnP2l3rp9B6ZevZXv7P5AyJxCzoWfp8CKH1Gdv4h8+iTWvn1gzkGEeYNALSCv\nS9X34e3wQ3vYchzt+mXkf3CDPVt+xc3NTRnXWy5VqeWWAIfPb8Om5CJJkn03KrVl712yTbbPfzeR\n/T0L4YdAlmWyZMlKQkIcIPDiRdAn22kzGo12Pdd/Gm8nQzCbzfZ7J0kSAQEB7Nixg1y5cvHq1Su6\ndOlC8eLFyZ0795+2Wv8Rhg4dio+PD0OHDmX69OlER0czbdq0NOd17NiRqlWr0qVLl0/CBz4D3vmw\n/0dWPyNMJhNGoxGj0Wh/4F1cXD759kZq/FUiaVtYbWO1ZZh610L8tkvD34GEhAQ7UU49RovFQkJC\nAvkLlCQhKQ+SZADLXdDkBusj8KwDUXtA7QL+T+DFaAidByo9aPyABKjzFGKuw4kqIAgI6asjJD5C\nLtAWudR4CD4Ku2si+JVFDr0BggYxe3WkRtsBEFfkQqrSF6r0h6Q4mJoDLIoIupChKPLre9B0Efgr\nmaXEmQWRK7VHbqBsn7G0I2L8C6SJR5WAsNsnYcxX6ErWQPXyAXJCLIWKFiN/Nj9aNG9GsWLF8PX1\n/dvm+mNhW8htVtjUkbnPnz9n165dBIWFcvriBUKePce1bAniwsIRfdLhenCDneABxOatiLpfD3Q9\nHVUH4rMURr1gPqo6KRJZkiRhzpMfadw0OHUc1eE9CM46PAZ3Jm7OWpwGdcOlt2P2qegmPZElFfpf\nf3E4brlyg8QqDRDdXdD4+ZLu51E4VSwBwMui36BqVBuXiYMc6khx8URmLoMwaAjC4oVoMqUjw7op\n6L7Ig+HkFV416Iv362tpxPyTVv1KfC9l2z7Dhpk4f50S5JW44wivu43FOfi23f/c2LQj1jPnyTB7\nIKF9puN14wDqvDnT3IfInBWxvgwj3bYFONV3TENrvv2Q8DLfIMxbBAP74tqzNW7TBttJUeKaHcQN\nnIZw+wEcPIClRzc8Zw7B9U3wnGHTHmK+G4t07THCvTvIzeo5EFbry1CiClRHnrse4e5NWDob51M7\nURdI0e41/bQI4/QFyAYDqjlzHbbqAaRNG7EM6o/g6oJ8/kVKgoAkA0KTyohermj37Ua6f5/k6l/B\n8CWI104gn9mLfOQB2IiXLCMOaIt86QRybAyMXAVftYCYCGhXFKo3hHGL7f2KC8YgBcylUIF87Nq8\nCQ8PD7sl1WZNS70zlprAvA0b0bTNq8Visa+nqfEu/dH3Edm3rbGfgsja/DU/1j1hwIABrFihuL9c\nunSJ/Pnz/0GNP8b7MkT9G2Bz7dPpdMoLXEQEt27d4vHjx6xdu5YsWbLw6NEjnj59io+PD3nz5mXC\nhAlUqlTpo/suUKAAJ06cIEOGDISGhlKtWjXu3bvncE5sbCz+/v6ppMf+J/AfWf2nER0dbU8tqtFo\niI+P/9NpSv8s/iyRTJ1oQK1W28f6ewuiLMtER0fj5eX1t1kAEhMT7YkPbK4TRqMRQRD48af5zFtw\nlqQkL7DuRXAfD8YAZNNjEF0AI+ReAYbb8GoaglM25FwbEB7URi4+FyE5HDlwFDhnhrK/QcIDuNYR\n2gdD9F3YXQtkM2T+GkpMht2loO1F8PkCHmyHg52h+z7ESyuQLq1VFAHKDoSKw+HGajg1Dka/AJVa\n0WtdVA3mBIOLlxJo1ccHuWJznKxGuHEErWAle/bsdGzVgooVK1KoUKHflSf7t8FmcU1OTsZqtaLV\nau0awKmJrCRJREZGcuHCBVavX8+dx4+Ijo5C91UVrHVrIKTzIr75t7gH3URIpUSQvHI9yWOmo7t3\n24HYWlYHYJk0Fa49QFCpFCvY6uWIC2YhhYbh0q0lLqN7o8qipFCVTCbCfUrhvHcT6jIlHK4hsVpD\n5PzFsE6aBSMGIWzbgEvV0rj0aEZYiyH4PD+D6OsY6WuYtQTj4o2IF68hWSxI3/dE3rsL7wHtMZ64\ngiVnDjwCfnKoI0sSUbkrI7frDDo98rTJeHZvitf0gaBRE1ywITSoh9PkkQ71kucuIXnMVMQM6fF+\nejrN98509AyxTXogDxuLMHk0HlMH49o3hahH1uxEstYT9Zr1SA/uI9X/Gue61fD4ZQpysonQbFUR\nRk9A1UGxuNosnW5jv8etV1tCc1RHGjwasbNiCZavX3UgrHH1O5McaUbedBQAYcYoWLcU1/P7EHNm\nx/rgMQlla8Ki3ZAQB4Pbol4VgPhVSpS9dOkiliYNlaQZB65CtlwpFxj5GuqUQlW+NPLVa0jFqsO4\nVWCxIPb+GuIjkHZdVSTkAB7dhTpFlSxyvz1PaefJbehaFkbMhW+6giyjnTmIdKd2cf7YETw8PBye\nVxt5tJFGG4G1/aX+DrxNZG3qLrao9reJ7O9ZZN/lV2lr80N8K/8In4qsAhw5coQmTRQr/Ny5c+nc\nufMf1Ph92NLA/hvXwPf508qyTJ06dTh9+rQ9sOvFixc8fPiQggUL4ufn99F9e3l5ER0dbe/P29vb\n/n8brl+/To8ePShUqBA3btygZMmSzJ0795NbeT8x/iOr/zTe9lv6o63tT4EPIZK2RTQ5ORmLxWK3\nov6ZxSEqKupvJasGg8FuuTCZTHYr661bt6hUqTpWyReIBLeZYDwG5j2Irq2RLKHAfUQEJONT8KwJ\nBfbCo24QuQ5B54NsNYDVCF8/BZ0v4uFcSBnKIBpfI4WcBVTQ7DE4Z4AjDRG1MlLjXWAxworckPga\nNDoE37IQeRO5xlTwVwTfxYU5kaoOgkqK8L6woAJCruJIFTsh3tyF/upWksOeUbnGV9StXpmKFSuS\nJ0+eTxJc97mR+jkCPuhFJ3VdSZIICgriwMED/Hb4MGcOHwY3NzTD+6H5pj6in6IykFi4EkKHDqjf\nCLfbYCpRGql9V4SefR0br1sdyccPVWgQ0sNbOH9TG6eR35O0aivJ+07hcvmIw+lSaDjx+csiHL+I\nkEMhSFJ0FML3XZFPHEFdIA+eZ7YiptIMlU0mIjOVRpgyE1XzFiltXbuG3L4lUlQU7vsC0FV31INN\n3r6f+G9/QLz7WLGcPnwIzRuhcnfCtXtTYiYswfnl7bTpWh8+IaFUDdDpcKpRAde1c+wuCbIsE1O8\nNuaSVRCn/oh09iRi55a4tGmI+/zRmE5fJqJBD1Q37yG6K9cghYUhf1kFbdG8aL7Ii+G344gXrzn0\nKZ09g6VVczR5syElWpBPOZbbCKumelksJy4gn34CHp62G4w4bgDyzo24XjxAUvMuWHzzwrwtSvmW\nFTC5P+odOxBLlkaOj8dSrjRyrfaIyUnIhzchHw1UgqFsePoQahYHdy84GJpyPCEOoV1J5PyFYMkO\niI9DaOCPnPkLeHgZqjaGYYtSzj+1C8a0hpXHUJ/eS9ajWzh5YB/e3mldJ2zz+zaBtf3/bcJou282\nMqhWq+3GCdtvwdu/wTYC+nuZoN4eyx8FCb3LtSA1bML7f8af/fdgtVrx9vZGlmUGDx7MmDFp/Z4/\nFImJifb18N+G97ko2Mjqx+rQ1qxZk9DQ0DTHJ0+eTMeOHR3Iqbe3N1FRUQ7nXb58mfLly3P27FlK\nly5N//79cXd3/9vlxj4S/wVY/dN4W0bqc2SXSq1R+vYC9XbAlF6vx9XV9S8RJNu1fOoFJXUWE0mS\ncHJysm/LGY1GmnzTBqtVBF6DOjeCYTGy9TF4LkDSFIfwskpQlb4mCMGQ42eIvwSRa0DlhJyuO2L0\neuRczZF1vnB3PFL8UzC+RvKpj+jsh5ynPbJzBjBGQMgRpIZbEU7/gHxlAUgWKD4MSo5EfrIVzvaF\nIm90Rh/uRUqMhDJdwJIMd3YjB11GeHmNLM+O0bheber3+JFixYo5+C5/SsmvzwGbpdtkMqFSqXBy\ncvrTRNsW7JAzZ0569uhJzx49SUxM5Pjx42zYsYMDU35CzJcHY7WKWF68RN/eUQpLun4D66tXCK0d\nt/ml1+Fw5xbs34g1a0549gjDmJ4YSjUCjQbd0LeILZA0aDSqilWQc6RY8kQvb6T5y8A/P1aDlcgc\nFXGdMxZ9+28QRBHjhp0Ier0DUQUQ/f2x+pdCvnaDuPpdcOnXBadx/d+kmpUxjJwFbTqkaKfmzYt0\n9RbmLp2IHDgTdf2v3/k8mMdNRyhVEXnhRkxNKxFdoh4e+1ejypEV067DWF+Ewl5FzkmsUAXp8HkM\nDapjefwcy8swaNHGTlQBxAwZkM5fwVS9IkmHz6LauDlNn2KFiqjmLcDcqyc0aJxG91AoXgICNmNu\nVg/KVk0hqsoNRhr3E2KSgXj/Ggg6Lay9klLerCtCTBTWpk3h0BH4aRaCixdyv+lIkoT4+iVCvTJI\nx+6A7eX+zFHQO0F8HOzfALXfJGdwdUdedATa+MP0YYi3r4LODXncbxB0B/qXh/z+0PiNf3DlBggd\nRyB3+wofHx8OHzn0XqKqXMr7A3NSE0aLxWIngbZ6tuNvE9rUdW1t2T5brdY0/acmsn8mSMjWbur6\noijaA4Q+lQ6sSqUiNjaWVq1aMWvWLE6ePMnhw4f/cnv/1pf2982X0Wj8JFbqQ4cOvbfMtv2fMWNG\nQkJC3uka5ufnh5+fH6VLK9J7zZo1e6df6/8C/rd+Ff8/w+cQ7H+7Hxv5S0hIcEg04OHh8cGJBv6o\nj08Bm9ZsTEwMRqPRrqGXWr+1ceMWhIa8Br4DTGB5imx5gKgrCaos8LoqaPKB71lE6T6Cb0fE0Jlw\nqzI4F4bir8CpAJIpFDlDA8Tz9eHhTPCpA1VfQub2SMYw5EL9lUEda6aQzt+aIDw8iKDxRCg+EMpO\nBLUe8cpYhEojlCArQDw2DPxK4Ly9O7qJGSl8+0c6d2zPxbOnuXLmOCOHD6VYsWLKuaksMP+WNIJ/\nBKvVisFgICEhwe5/bZMX+xQ/Li4uLtSrV4+1y5bx8slTVg4bQcWb99GoVGg7d8Xy6xZkg5JtyTxy\nFKqmrRA83vLNHj0UsUwVyPrGpzNHHuSAw8jDZiEnJZM85UeS6rXGej0QAMlsxnrgOFLfwWkHNHIw\nYplqyPsfIQ+dQ+KgycSUrIf58k2Sxs9B7tojTRX56ROsx47ClnPIm85gCPiN6CJfY75xB9PBk1hD\nwxFHjnaoI4oiYtt24OSE9dhpkr8bjGw02sut9x+RvOcg8qwV4OmF9VAg1pyFiSpeF9PRMyQOmIDU\noTtiKh8/MWt2rKdvYnzwAsuzYFT9HNOuAkp0fRF/cHJG/mEYcnh42jlY+QvkLwH79yP98I42DuxV\nMqnduARzJzkWCgLS98PAmIRskZQMbqnnqtsQaPktlppfYtm9C2nhIduEIE1eD96ZERtVUFKs3r0J\nk4bCsA0wdA1M+hYCL6Q0likbLDwAq+cjBV5Dmn1OcQnIURhGbII5A+FGitVLzpILJ42G3zZtIGPG\njGmv+wOQ2jBgSweq0+ns6bBdXV3tL6O2FzyDwWBPR22TApQkCVEU0Wq1aDQa+782lwPAIULd9meT\nxUrt52qz+tmyQdn0R52cnNBqtahUKodgobezQX1sWtONGzeyceNGLl68SKtWrf64wjvwb02kAO8f\n2+dICNCwYUNWr1aUPFavXk3jxo3TnJMxY0ayZs3KgwcPADh8+DBffPHF3zquvwv/uQF8RtgWBBuM\nRiNWqxUXF5ffqfXxiIuLs/t6ppbI0mq1n8yKFxcX99H+t29rzdpS7qnV6jR6sWfPnqV27VaYTIeA\nyig7B32BGaAtBqYrIHpCpmeQuA5ieoKgB3UmsIZAsVugz41wLYuSKMBqBFUGEJKgWhCIGsTT+ZDz\ntEP2KoJwfRxy7EPIUBOKz4ekEDhVA9oHgT4dBB2EQ02h3wsIPofTvbUk3dxCsVJl6dSqKQ0bNsDX\n19d+HSqVyv7Dldoa8y7fuNR+cbbP/9TinfoeSZJkd2P5nONJSkpi9+7dLF67lmuXL6OqVxfDth0I\nB04i5CtgP0+yWBAK5UBetA3KVXNoQ6xZELlBJ+RmPWF8V4Sz+9FULofskw7r5VvIxx3VBySTCeGL\nnMiLd0GpyspBiwVGdYX9mxHUKlTXbiF6OVrkpH59sN5/irzxxJsDEozsjrB7I4KbC3K9xmhmznao\nI8sy1qqVkPxrQKdBiJ2rIrhocNoWgCp3DoytumOKSkYO2OtQj8Wz4aexCHod8p3gNN9t2WyG0l8g\nq3QI5kTUv+1ByJcS8CRdv4alQV3Y8QBxTAfk5/dQ792PkC27Un7wANYe3ZH3BMOrZ9C9CjRohDh7\ngdL+3dvIdavBsotKEoy+NaH3cPh+mO3CEJtXRxLdlLEF3UHaexdSW6AiwqBadiWhxt4gcE2VnjU+\nFqFjGcicBZ4/QvavB32V7Xxhy0zYMAV54w2FqAIc2Pj/2DvrMKnK941/3rOzs013d5d0KI0ICCpS\nkiKCIIg0SHeJ0gjIlxBBUrokpEE6VLq7Ntiu8/7+OHtmz8zOJrvLrr+9r4sLmPfMiZk577nf57mf\n+4EJX0K4Ct8fghJVLLsSG3+A1ZOQv/0N96/hPqYd+3dso0yZMiQExvtCT6fHVwIT0xwQX32sLWLT\nx+rpbJPJZFcbazwXe9rY2Aq9nj17RnBwMPny5Yt2m+g+G39/f9zc3FIkYfX397fbAOf8+fOsXbuW\n+fPnJ9mxPT09adOmDffv37eyrnr8+DHdu3dnx44dAFy8eJEvv/ySkJAQChcuzLJly9LcANIQM2zJ\nalIY9ttCr5ZXVdXSaCApLLLeRH9r6zxgj0jrXrQeHh74+/tTpkw1njxpBKwA8gEHEUoFpOqLEOWR\nXIZMP0P4a/DpCw4ekG4mSsB0yFgbNddIuNUZfPaDR23Iswxxqxqy+HTI3Rkeb4CLbcApI0I4IFVn\nlEzFUd/Voj3Kn5Ugz3uoNWdpD+ENZZHBXjgpYRQoUIAvO7WlZctPyJYtm1WxkR4piYtcIjprKD2N\nZ/vw0lPvSTGp699RcHCwVdTncQIEwAAAIABJREFUbT9AHj9+zOIlS/hp6TLIlg3/Lt3h07YIN3fU\n2TMQq39F7o9sogDAvxegVS3Y+xDSR5BLb08Y1w2O70aULAML/ocoWNjyFnXSaMTuPchtl633BfBR\nBXj2CNQwHL7/AaXlp1qE7dkzQiuWg02noZhNNOO3RTChH6JYMUzLVyIKRFbzqwf/JLxrF+Sh51rK\nW1URg9rCsV04jRxE0LjpcOgqZM9lvc+wMKhWAHx9cGjZBnX6bCvPWrl8CWLmdNSdj2D8l3BgPaY1\n61Cq19AIyfsNCM9ZAib/CoAY1BrOHsC0bRcULEhY5QrIFj2he0Qk+M5V6PYuNG6MmL0I8UFt1GzF\nYMIabfziMejXGPqPhq8GwLoViIlDkOsfgRAogxuDz1PU7X9rlf5SovRohnzpjXDPBI+uoG64Epn2\nB3jxBD4uAk4usM7QNlZKlHm94OQ21M034OFN6FoTuv8P8eIubJuG/PlfyJQjcvsfuiLP78MpPJjf\nV/1CnTp1Yvil2YdRp617Tyfm/BpffazRsQCwW+Rlu39FUQgJCcFkMmEymd5qoZct9AxbUgd0EoKY\niPTevXu5dOkSY8eOfTsnl7qRpll927D9QSeVZtVILPRJzcXFJcEeenFBQmQAts4DemTW3uRm3P+g\nQSN58uQBsAhwivi7GlL1B35Cym1georiOwE15B8wlYDsf0PwbtTgqwj5HpwrCMIEeZdD5o7wdCIo\nzpC1GeLWBOSNKeCcC3J/h8zaEc7kQS01WTsZn39Qva9AvSWIC9Nwvb0cGeZFt27t6f5FVwoXLmwl\ntwAwm824urrGW8dpz67F9mGhL4KSIhqr99kODQ3FZDLh6uqaoixkcuXKxdjRoxk9ciR//vknMxcv\n5sSUcciWbQjdsRX123FRyeWE/ijNOqCmN0RBM2SCRq3gzGEwZUTWq47Sqh3q0JGIrNkRa1Yhh8+J\nuq9Lp+H+Ldj5DHb/ijp4ICxfijJnHnL5MpTCJVBtiSqgbF+LWudTCPYntHYtTNO+R7Rrr/3OJ4xD\nNvkskqQpCvLH9fD7MoLG9YaMmSGzHeuyjStRhIK6/gayRw1EyybIX9YhMmZC+vsjp4xFDpqrbTt6\nCeQpTFjrlpjm/wQmR9Rbt+Cno5bdyRnrYWJPQps0wqFZcwQOyO4GyULBErD8BHxRC9moFjx6BHNP\nRo6XrwU/7oQBTSHAD36ehRywCJy0SKo6bSfi2zooraqg/n4Wtq1Gnj+BXHoPaTKjDK+P0qUa6qqz\nkVX9pw9orhpBwbB6ErSPcEkQAvXr+ShP7yI6vIP094V3u0DNtto98fgKom9V1KU3tc9VCNRO4+DA\nKkZPmBBvomq7eEuITjsuiKs+Vp8D9IyHUc9qj8ga32ucO3S7OePxbfWx0X0ecTHRT0mNEBIL9q7h\n1atXSS4D+P+GtMhqMkKf4HQktkGvqqqWPse6zZOjoyOBgYEWwppU8Pf3t0zaMSGhzgP6Z7V79266\ndOmFECWAR0iZA7gNhAM7gcxARcCEEK2RbISse8BcFZ7mhrCXKE7FUEU5BOeRJa6BBPFvNqRLXvC/\nDoo7qEFQ7RE4uMPVDijiEWrtg6CGIg7VQnpfxNnZlRYtPqJHt05Ur17dUkBhLDYym81JEsmODrFF\nY+09wGyjsdFZT6WWwq+HDx+ycMkS5i74CXPF6gR89Z0mAxACXntDjTyw5qxGtgxQPiqBbNwV2X4o\n3L+GMrkj6t1/EbXrIs+cgcOPIr0+9ff0aoEa7gDTNM9dggIQI1ojz/2peeb+vB1qWnuc8s95aFcb\ntj7W0tz7NyCmd8ehZk1o157wPl8jDz6zTo8D3PwX2lQG9/SIfPmQP/8OWbNrY8HBUKMgdBsPLXtC\nSAjK17WRrx7Aum2I7Zvht9Wom25a73PPWpjYTWta0WkQfGWtnwVgWj9YvwA+/BxGLo46fvU8dKyk\nFS0tPxt1/Oyf0L+JZhu1+pb1mJ8PondNpLsr3LkGvX6Ceh20sYDXiAHVIEcu5ML98PA2tCsPXy6G\nLPlg8vvw7WJo0CFyf4F+0C4HODjC/ww2PmGhKBPqggxFnXsKgvxxHVKHfm2bM2KodTODmGAkqbqU\nJyUt3sB+ww7jH50k6gRT17Tqc0BiyApszyU6aYGRtNqTFiS0DWxyIKao7/z58ylUqBBt2rSx8840\nxAK7D8vU8fT5jyApIqs6+fP19cXHxwcppUXQr2sJk6OQy5h6sgedSPv4+BAYGIjZbCZDhgy4urrG\nKSUuhMDPz4/evQcjxACkrISU3sB9wBVFaQ7cBWoApYG/QDxEcW0I4c/hcW4ID4QMq1EzXEKE7kXm\nmg2hj+BmdWSoF0qYgNx7UEweiHzDNaIaFgBe21AL9sLhykhc9uUnl7sPUyeN5/6d6yxbsoDq1atb\nWs7aFhsld6pcj8aazWacnZ1xc3PDw8PDboFHcHAwfn5+vH79Gl9fX0txha+vLwEBATg4OODh4YGz\ns3OqIaqgRVtHDhvGjb8vM+HjpuQe1wv3jyvD1t9g3LcoZatGIar8cwb1yQNk84giqXzFUReehmm7\nkIcOQnAQbP1V05zquH8L9dg+GGKwQXJ2Rf6wAxp3BikQY7+BKxetDqXMGQtVGkbqMRu0Qq6/g3rv\nGWFdOiIr14lKVAFl7kioUBd+vQfSBRqVh3MRkcw1S1AcnTWiCmA2oy45iaz0PvKDOqhzfkA12jXp\naNwW2n6jkdvXntbXpx/39SvIWgB2/QYLx0Yd37gQkbs4PHsMQ6O2XiUoQCOPz5/Ajv9Zj7mnR84+\nBNf/0QoT6xmIp2s65JSDyDvXYGgbxOCWUKYB1PoMiteC3r/C7K/g8mHLW8SmWQizCyhm+Lln5L5M\njqiDtyM9n8Hkdjh/34EPKpZg+BA7hXR2oM9fvr6+ljoDvZgwpUEnfsbCKn0e0FPWesbN0dERVVUt\n971e6KVHV/UFt22hl/5M0SOxMRV66Yvi6Aq9HB0dLc+P0NBQAgMDLXORrgFOiYWnMRV+pUVWEx8p\n7077j8NIHPV/J6Ta0dYY38nJKVrbKT3il5SI7hh60YHujZrQanEhBKNGTSQsrAFSBgG/IsRHSFkH\n+BYpzwNrgfTAQeA6Uj2CDMoKAYeAcEi/EFzagfeXSAd3lNcbUG+3BEyQay1q+lbwehNq6EvI2Qdk\nOFzvAmG+uP7zNW3btqHXsi2WakpjlAUSlupPLsTk2ahHL0JDQy3bSCkt31tcorEpAfp1hIWF4ejo\nSObMmenZ8yt69OjOnj17mDhzNhfOnkVt3lkjn06RhFBM74f4oDOqh22nN6H96TARMX0I/G8Gcvwi\nqFQL5edpyBIVkZltqsdDgmH/Oui/FHn+D2hdC9G1H/Kb0fDwDurR/bDRJsKYLgNq/7nwdV04sQ8x\nZyTy67GRkdyb/6Ie3QMrb4HZjPzhICwdBe0bIwaNQ86ZjNp/TtQPZeQyePUUzh1EPLiBrN7Ietzv\nNaz/CTpMQv4+BeXlE9SJK0HXul45h7rvd5h3BTyfwJhG4P8aBv6ojV+7gLrzV5hxCUyO8F11xOCP\nkN9v0cYD/WHiF/DJKMhdCma304qnGhlI6fFt2muYYHp7GLI6cixjdph2BPqURwoFRp+JHKv6CcJz\nMnJ0c5h7Gl4+Qq6dCn3/BEdXmFEDcpWAZhFuHu4ZkSMPwJBy5CtWlCVb/4z1N6wv6vRW025ubinS\n7zM26POw3pTGns7SVh+rk8Po9LG6vjU6fayt9ZatfyxEygNsoe9Dtyw02m69SaFXYiKm5/bLly/J\nmjWr3bE0JAxpMoBkhm1jAC8vL4tvaFwQFhZGUFCQZfKMi6DfWJyUVDAWi9lqZp2dnd/YP/Tw4cM0\nafIxqpodKR9rKX45EygEhCFEa2A7kukgGwLvgAgC2RZwRzjuQ2b+B0KvgGcVIBzhWBUpcyNMV5D5\nL2hFH3eKoGZtg3DMhIvnT2RMZ6Jr5zZ8++23llTU2071JxaMJFVP9RsfxPbSifYiJvaKvJILehV2\nSEhInCQLu3btYs6SpZw+f4GgTgM1N4CgAGicD5ZegjxFrLZXvqoCJd5F7T5TK2Ba1BcOrkSp2QD1\nyB/w83EoVsH6IFuWoPxvHOrKB9r/b55DmfAJ0uwIefJDKMi51o0IAJQ+DVBds8MnQxATm0KuPMhZ\nv0OOPCjftkT1DYLJNg4A5/bDuJZal6ftT6wIOADPH0HrotDxe8Sa4YgWXVH7/6Cl/QEx7zvEvs2o\n867A65coAytC/sKoc7aBixuiYzVklmLQXyu64s5FGFkP6n8Mo5YgOlZG5igDfX/Rxl8+0AhrqYrI\nGdtQZvaDo7tRf4hoA3nqd5jfCUashDot4eVj6FgCPv8ZClaG8VWhbnvoNTfyGq6ehOENQJXQehy0\nsE7bK78ORB7+BRkeBg2HwfsR7gPXDsDC5tBvLVT8UHvt2G9kWj+E/bu2UbBgwWgXXrYLn9TofwxR\nSWpCnDvspfFjkxfZ+sfaI7FGsmdLZPXP2l6zAuO52J4XxL0Rwpsipq5fHTt2ZPHixWTPnj1Rj/n/\nBGluACkBtmTV29sbDw+PGFfrttXyus4zrpOnnlpJZzABT2zo56fbTBk1s286Sfj7+1OoUCl8fHzQ\nNKn+wEw0q6r0wCrgF4Q4AOJTpDoXrfDqCpAORG5INxsl/DCq36+g5AO3PaDkRvjmQObZCG4NwPsX\neNIFk6MLTZq2YEC/nlStWhWI1HHqvoPxqepPSbC1nkqoHtVelbL+77hqY9/0OhLaLQu0NoRjpk7n\n6ImTBGXOiZIuG+qMPdYbPbkHnUvA4uuQNW/k697PoWdJCAtGfD4C2X4gOEYUQ4WHIz4pgPy4P3w6\nIPI9qgozPodjG6BxRxg0L/I9AFfPQa/asOQhuGeAsDDE5ObIq8eh91iYMxJ+uQmZc1qfY6AftM4F\nzq6IzNmQP+6CbLktw8rEL5A3ryInHYcntxCjayFKVkSdth58veGTYjB+PxSvrr0hJAhlUCWkWUG2\n6Y2YOwL5vyfWFfkPr8F370H23IgXD5GLnlhreV89hO+qaxZSNy/CxNOQp1Tk+PE1sOhLGLcOZeNs\npF8wctjBiH3/DRNrwcf9oOM4CPRDfFUCWb49lG4Gi5pBj0XwriEyGx4GPXNCcCB876m1O9Zxcjms\n7wsTjkNoMC7TP2DP1t8pVapUlIWXTmj037JunZfaSOqb2Ggl5Fix6WPtuRUYs4rG1L7x2ahfhxCR\n7Uyjyw4Z32NPG6vPS/aisQklsjF1/WrWrBn79+9P0lbq/2GkuQGkBESnW7VHeuJTLR/bMZNK56NP\nKLqhta5zTEwtV/PmrfDxCQemASMBN6AbGiFdA2QFViNlEIItgCuIaUAukM1A+sHrr1FlKcAE7pvA\noTD4d0M4F0fKMNxeNkYGnqXhRy2ZMH4MOXLkQFEUywJBtxzTNVepLYqqL3hCQkIsk/+bPMBiq1I2\nPrxsnQqiI7JxgW1UO6FV2BUqVGDLmtX8/fffdO3Vm1u3LhCycY6mWTVHPHxm9Uap2gzVSFQBFJMm\nI+g8B7FhHGxejBy5FCrVg8NbICQIPuln8x4FxdUdNUtBlOO7kR3KISevhyJlteGfR6GWb6QRVQCT\nCTl6F+xeBHP6Q+bckC5zlOsQm+YgMmRH/eEafP8hdCwLM7ZDuZpw/zrq3jXwg9bwgJyFkXNuIr6r\nguhcFfIWgYIVkDpRBTA7o866jBhZGyb1QrYaYU1UAfIUhwn74NvyyLxlohSdkTkPTD4BvQpBhuzW\nRBWgZjsICYQxrVEdTDDzkWHfZWDwHzCtAXhkQrlzEczpkB9p3bjo+Ass7gwZckIZrXBN2TQBFBMy\neznEjOqoQ85EugdU/xzx4iaMq4eTqzOL5sykUqVKVqdjvDeMBEt3wbBHtlJiJXtyklQdMcmLYnII\niG4uMJlMlm31uUr3ftWfYdG5FQCW/cXknqD/bTwX2wisrbTAHmKSAeitddOQeEiLrCYzdPG5Dlt/\n0oRWy8cEVVXx8fEhY8aMb3z+OnRNoz4xms1mgoODE/UYoJn/N23alqCgDUBX4DGKUgMp7yBEPVS1\nDDACcAYmA1cQYhdSHkGIMUi5EqGUR6pLEaI3wikLqss6UH3ANy+KCCdf/sIMHdybtm3bYI5ogxkW\nFmYR9dumqt6EbCU3jJo7vfDqbZ1vfKOxxgeGUbKgp2UTM6p98eJFhowex7l/rxLQeSzU/hRa5oJp\nh6BIReuNv++A8uo56rC9WsR03QjYNw+lemPknX+RlZpBj++t3+P9Ajrnh1FHIX8F+LkbnFqH6DoS\nWbMZdK8Bi+9Cehud24Mr0L8ieGRGZMyMnLANskcYq/u/hnZ5oM8aqNhUe23DONg+Hfr9iHJiN9LH\nHznqD+t9qiqMrg03T8N3m6FSkyifh9g4Dbl+khalnHwI8lnbbylLvkWe3o0MCUbkLIgcuz+SIALs\nno9YOw4pzIhCFZBDt1sf4PUL6FtIk1Z8dwCK1LAe/2c/zGoBSBhxHTLkiTy3oz8htw2F8cfB7xVM\nawafH4ZMhRGLK0POEshe2yL3FR6KMjIPLRrWZtXKFZaXbbMMtmly2/S3bfRQX7DZk8G8DSlMUESX\ns6Ty0k5MxDQX6DCZTJbPN77+sRB/xwJ70VhbImsks3oQyTZ6KqWkSZMmHD16NEV/BykYaTKAlIDw\n8HDCwsIs//f397foHnXyJ4SwGOMnxo9dSomXlxcZM2Z84/2Fh4cTFBRkMZHWJ0YpZaIT4qCgIEqX\nrsyjRx8ixFqk9EJL/3sCo1CUXKjqYzSieh5wBUoB5YFzgEBRWqCqq9EkAZXA4yiKuhtHdTZZs2Tg\npwU/UK9ePavq1uhS/dE9uIwFCCmhEMnWeiqla+6iSyXqRRU69IpiPXKSFJ/r8ePHGTRqLH9fvoya\nKRfyp3+tvVVDQxHtsyIHbIWStSNf93kOU+rB8zvQdSp81MeKvIllwxEntqNOvhT5nqtHUea3RvXz\ngpLvwviovdOV6a2RPq+RfXciFnyEvH4ERv4G1ZoiVoxF7F8TqQfVcWEPzG0LocEw74YW6bTd7/hG\nqPeuQbA3DN8M5Qz2Wj4voEch6LYBLm2Bs6tg7J5IqcCDKzCwMgw+Be5ZET/WggxZkZOOatfs/Qx6\nF4X2KyB/VcTMalDoHeSQSAKpzGqNfPIAyrRB7h8Lww9rJF6H3ysYXASCA6DbJijd1Pr8d41BHpmH\nFBKq9IO6oyPO/SEsfAeqfAZttIIz8+YBVA3/h11bNloIj5HcJSQCaRs1tKfnjm7xlVgwBjb0ItuU\nTlLtwVbS4+zsbJFjvIk+NjrngOj0sdGdmz1trLEwWp+Ljh49ipubG4ULF+bzzz/n8OHD0e73TeDp\n6Unbtm25d+8eBQydq2wxZcoUfv31VxRFoWzZsixbtsyuZCEFIo2spgQYyaqUWgcM/WY0thdNbHh6\neiaYrMYl2puYhFjH4MEjmDt3HtrP0AEh+iLlB0AzQEVRuiLlZqAHUvYE6gHXUJTiqOpXwHDgGpAX\nRamKKq/j5KTQpEkThg3tS9myZa2uT9cg6TrOuF6HcXK0JbH2CpGSqmWq7aQf3+tIKTBeh/596HIZ\ne9FYew+vN7lmKSVLlixh+twF+Lhlx7/rD1AsolXn8u8QJ7Yhp0btZKVMro8aEIrwvAbZ8iIHr4T8\npbQIaPtc0H+LZr1kxKOr8F1ZLYLZcwHU6xy530fXoV8FmHQdMkUQzgPzYeNQRLPuyJ1LYMBmKGuz\nT0CMr4u8cRqlcAXUYdvAw9AA4Z/DMPlDmPgQji+BHaOg10Ko10m7jvk94Po51MERlfc7xsL+GTBs\nI7zzPsqIOqimzNAjwlvW7xViVm2EizPqlL9Q5nRAPr6P7HdCG/d+CDOrIwpXQg7eAud2wNzPYOBt\ncM+CcmA88siPyDF/Qc7i2jnMbQlPH6JW7Am7v4Wv/4CChuirlDC+ELx+BoOfgrNBj//sMiypCc0n\nQPpcZPtjKGdPHCFjxozJQu7szQXRVdUnRFbwXySpeoAmpmdfdPNsYuhj9f0byWtsRNbf39+yyJFS\nMmbMGI4ePcrt27cJCwujfPnyFCtWzPKnaNGilCtX7o0XLEOGDCFLliwMGTKEadOm4eXlxdSpU622\nuXv3LvXr1+fKlSs4OTnRtm1bmjZtSpcuXd7o2MmENM1qSoD+w9ajqPpNFh9HgIQgJm1sdNBTyMbJ\nJCZ7rMTEtm3bmDt3PkLkRMpMgDdSPgHeR2uvughV3YbWDMANKAEEAktR1RYoyrtAb1Q1EGfnLwgJ\n/ZcO7dsyetRQ8uTJY7k+o/4xoZO+cYVuO9naTq7GtKNtlMA4ucbnHGyvIzWkAe0hPtdh78GlS2ze\nVK4hhKB79+588cUX/LLyV0aO/5jg0nUI7DQFZe8y1M5zo3ayun8Z9eYpGPsQaXaHXzvCN5URnw4E\nkxmRMSeqLVEFlG2TUQu9C7V6IZZ8hfhrM+o3S8E9I8qaMcgiNZGZDJHR+r2heF3kjLqaA0C+slEv\n4Npx5O2zMPIectmHMLAcjN4LeUqClIilfZGV24NLOmgwALIUhoUdUZ7fQa3RCvXgr/CdIQLcbCy4\nZ4OpLaF+F+TdyzDRoDN1z4wceBxm14M+RVFfv4CRhuYDGfJA/5PIH6shpjZF3j4DdUeBu+ZDqdYf\njRLsB+NrIsefgzunkP8cQH59G1wzIQK9kAubwIATkL2k9h0dXQBBvpC3DmJRRdQ+V7XOVgDZy8Jn\nW2B1C5ycHNm0Zwdubm74+vqiKEnXbUpHbHpuI3nVdbJxkRXo2tqk7pqV1LAlqS4uLnEK0MRlnjX+\niYs+VieoRiIbV32s7fc8ZcoUAK5cucKsWbP4+uuvuX79OtevX2fNmjXcvXuX06dPv/Hnt3XrVg4d\nOgRAly5dqFu3bhSymi5dOhwdHS1+2QEBAeTOndve7lIN0iKryYyQkBA8PT0tKXQ90uru7p6kx/Xx\n8YmzibWtN6qzs3OcJkUvLy/SpUv3xlrC0NBQihUrx9On7yJlLeAbtGIqgFC0oqrcQAPAH0XJgqpK\nFKUpqvojsAvojJNTA0ym8/Tu3YNvvulFpkyZLNenF0/Ys2xKDthLfcdkC2UvGpvUOs7kgm1L1ze9\njrhGtuIajfXz8+P7H2cxZ948QkJCYcFTcLXuOqfMa4vq/Rp67op88d5plF9aob68D5+Og5ajrXf8\n6gEMLA5DL0LWouDvibKwIarPQ/j8e/ipF0y4Alny25yQJwzJBxkLg/9jGLIdilbTLx4xqjoyQyn4\nbJn22rpucHEdDFoPIYGIBV8iJz2zLoy6fw4xvxFSqlC4Nny1JeoHcWoVrO4G2YrD8ItRx/094bsc\nYPaAiU+sq/IBvB7A1DIayR7jZT0mJcrW3sjL65FhIdDwR3inW+Tn++d3yHOLkUMvQtBr+KEqNF0P\neeshNtRBiHDU7qcipRfBfjguLEOPz1owcuTwFNttSkdssgKdUAmhGfnrRvopVdpjDzpJDQoKQlGU\nWCOpiXncuEa67eljbYms/v6wsDCrxjv6n2PHjnHo0CGmTZuWJNeTMWNGvLy8LOeWKVMmy/+NWLx4\nMQMHDsTFxYXGjRuzcuXKJDmfJEBaZDUlwGQyRYmiGluwJhVicwQwrtr1YoP4RnuNN/ibYOrUGfj4\nZEXKBkAftNapHYHdCFEDVT0DfAY4AF+jqiWBwajqcOA0Dg79cXRMz4gR9ejefSUeHh5RKn7fdlV/\nbFW0ttX0xmisMa1lNCpPTREWW12t2WzG3d09UR6+cYlsxTUaqygK7u7ujBs9ks/atGLwiDGcGF6a\nwNbToeZnWoT15X3UM1vhOxvtaP4qqPWHw+bBsH06SoAXapvJYNZaEitbpyDzlEdmLapt75YJdeA5\n2DUOFvTUUv8Zo0ZDxJ7piEwFUftchD9GwMT6iA7fIxv1gsv7kI+vQbdDkW9o8z/IUxm+bwUOJmT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gINL4JHUcN4OMqZjsgXB7UGBAW+gRIjI8c9T8GxBlB+GLjlRpwcgKx5F0zpIPAOnKoK+VtC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\nBAKBafl/CyJH4aoVOLIAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -951,6 +977,13 @@ "ax = fig.gca(projection=\"3d\")\n", "ax.plot_surface(xx,yy,zz(xx,yy),rstride=1, cstride=1, cmap=plt.cm.jet)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 3467132e4f2d8698d936d570649e5414fc0992f2 Mon Sep 17 00:00:00 2001 From: lusi Date: Sat, 17 Apr 2021 11:03:40 +0800 Subject: [PATCH 17/49] add array basic --- 03-numpy/03.00-Basic knowledge of array.ipynb | 45 +++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 03-numpy/03.00-Basic knowledge of array.ipynb diff --git a/03-numpy/03.00-Basic knowledge of array.ipynb b/03-numpy/03.00-Basic knowledge of array.ipynb new file mode 100644 index 00000000..e0a0e24c --- /dev/null +++ b/03-numpy/03.00-Basic knowledge of array.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9a603c0f", + "metadata": {}, + "source": [ + "# 数组基础知识" + ] + }, + { + "cell_type": "markdown", + "id": "fc69aab3", + "metadata": {}, + "source": [ + "## 定义\n", + "\n", + "数组是相同类型的数据元素的索引集合。\n", + "1）索引表示对数组元素进行编号（从0开始）。\n", + "2）相同类型的限制很重要，因为数组存储在连续的存储单元中。每个单元必须具有相同的类型（因此，必须具有相同的大小）。" + ] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 6b02cb9d5ac40e9b9e773728077508302a9cb564 Mon Sep 17 00:00:00 2001 From: lusi Date: Sat, 17 Apr 2021 11:18:47 +0800 Subject: [PATCH 18/49] update array basic --- 03-numpy/03.00-Basic knowledge of array.ipynb | 53 +++++++++++++++++- 03-numpy/img/array_example.jpg | Bin 0 -> 20131 bytes 2 files changed, 51 insertions(+), 2 deletions(-) create mode 100644 03-numpy/img/array_example.jpg diff --git a/03-numpy/03.00-Basic knowledge of array.ipynb b/03-numpy/03.00-Basic knowledge of array.ipynb index e0a0e24c..944650ac 100644 --- a/03-numpy/03.00-Basic knowledge of array.ipynb +++ b/03-numpy/03.00-Basic knowledge of array.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "9a603c0f", + "id": "212807e4", "metadata": {}, "source": [ "# 数组基础知识" @@ -10,15 +10,64 @@ }, { "cell_type": "markdown", - "id": "fc69aab3", + "id": "11e7efde", "metadata": {}, "source": [ "## 定义\n", "\n", "数组是相同类型的数据元素的索引集合。\n", + "\n", "1）索引表示对数组元素进行编号（从0开始）。\n", + "\n", "2）相同类型的限制很重要，因为数组存储在连续的存储单元中。每个单元必须具有相同的类型（因此，必须具有相同的大小）。" ] + }, + { + "cell_type": "markdown", + "id": "8c5838da", + "metadata": {}, + "source": [ + "## 例子" + ] + }, + { + "cell_type": "markdown", + "id": "72f4e74f", + "metadata": {}, + "source": [ + "在程序中，经常需要对一批数据进行操作，例如，统计某个公司100个员工的平均工资。如果使用变量来存放这些数据，就需要定义100个变量，显然这样做很麻烦，而且很容易出错。这时，可以使用X[0]、X[1]、X[2]、…、X[99]表示这100个变量，并通过方括号中的数字来对这100个变量进行区分。\n", + "在程序设计中，使用X[0]、X[1]、X[2]、…、X[99]表示的一组具有相同数据类型的变量集合称为数组X，数组中的每一项称为数组的元素,每个元素都有对应的下标(n)，用于表示元素在数组中的位置序号，该下标是从0开始的。\n", + "\n", + "为了大家更好地理解数组，接下来，通过一张图来描述数组X[10]的元素分配情况。\n", + "\n", + "![array_image](./img/array_example.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "e6af5cef", + "metadata": {}, + "source": [ + "从图中可以看出，数组X包含10个元素，并且这些元素是按照下标的顺序进行排列的。由于数组元素的下标是从0开始的，因此，数组X的最后一个元素为X[9]。\n", + "\n", + "需要注意的是，根据数据的复杂度,数组下标的个数是不确定的。通常情况下，数组元素下标的个数也称为维数，根据维数的不同，可将数组分为一维数组、二维数组、三维数组、四维数组等。通常情况下，我们将二维及以上的数组称为多维数组。" + ] + }, + { + "cell_type": "markdown", + "id": "dc16af87", + "metadata": {}, + "source": [ + "二维数组，对应于數學上的矩陣概念，可表示為二維矩形格。例如：\\begin{bmatrix}3&6&2\\\\0&1&-4\\\\2&-1&0\\end{bmatrix}在C语言中表示為int a[3][3] = {{3, 6, 2}, {0, 1, -4}, {2, -1, 0}};" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50be2fc0", + 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z{se*v9C$+-Fw>K=&TMB~SEUQRC{9C<#L!lewX}=-{;n*z;MAcsK*Tv7c8u=yM$qM9 zS!et73MdvyHVHS(vP|5ha<@ixgr!F+nrAV%Mo?-NuQRsx{{#*J@%}f$Z!mtjrM8_x zomBnaVp^E5Z5psqvZH@mHNXHeuwEg!{FC2~|UrC=5y6&*l}PCjg6(hJna6rZ$s84Cp`jA3y)u Dt8oVh literal 0 HcmV?d00001 From 562b85505e2bd14f6e0933f9d3777aef9461656b Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 17 Apr 2021 11:41:26 +0800 Subject: [PATCH 19/49] update version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b608c38c..becd34ab 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ jupyter==1.0.0 numpy==1.19.2 -pandas==1.2.3 +pandas==1.2.4 scipy==1.6.2 matplotlib==3.3.4 \ No newline at end of file From 34c59d72b625675c095e346f4db31bc407e0a236 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 17 Apr 2021 12:06:45 +0800 Subject: [PATCH 20/49] update --- 03-numpy/03.01-numpy-overview.ipynb | 154 +++--- 03-numpy/03.02-matplotlib-basics.ipynb | 623 +++++++++++++------------ 03-numpy/img/Lenna.png | Bin 0 -> 473831 bytes 03-numpy/img/lena.dat | 3 + 4 files changed, 383 insertions(+), 397 deletions(-) create mode 100644 03-numpy/img/Lenna.png create mode 100644 03-numpy/img/lena.dat diff --git a/03-numpy/03.01-numpy-overview.ipynb b/03-numpy/03.01-numpy-overview.ipynb index cdc2c810..9a5c8ef0 100644 --- a/03-numpy/03.01-numpy-overview.ipynb +++ b/03-numpy/03.01-numpy-overview.ipynb @@ -28,9 +28,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from numpy import *" @@ -55,15 +53,13 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Using matplotlib backend: Qt4Agg\n", + "Using matplotlib backend: Qt5Agg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } @@ -89,19 +85,17 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate list (not \"int\") to list", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list" + "\u001b[0;31m--------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list" ] } ], @@ -120,9 +114,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -150,9 +142,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -179,9 +169,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -209,9 +197,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -238,9 +224,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -274,9 +258,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -303,9 +285,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -332,9 +312,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -361,9 +339,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -397,14 +373,12 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4L,)" + "(4,)" ] }, "execution_count": 13, @@ -426,9 +400,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -465,7 +437,6 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -495,9 +466,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -532,16 +501,14 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0. , 0.314, 0.628, 0.942, 1.257, 1.571, 1.885, 2.199,\n", - " 2.513, 2.827, 3.142, 3.456, 3.77 , 4.084, 4.398, 4.712,\n", - " 5.027, 5.341, 5.655, 5.969, 6.283])" + "array([0. , 0.314, 0.628, 0.942, 1.257, 1.571, 1.885, 2.199, 2.513,\n", + " 2.827, 3.142, 3.456, 3.77 , 4.084, 4.398, 4.712, 5.027, 5.341,\n", + " 5.655, 5.969, 6.283])" ] }, "execution_count": 17, @@ -565,18 +532,16 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0.000e+00, 3.090e-01, 5.878e-01, 8.090e-01, 9.511e-01,\n", - " 1.000e+00, 9.511e-01, 8.090e-01, 5.878e-01, 3.090e-01,\n", - " 1.225e-16, -3.090e-01, -5.878e-01, -8.090e-01, -9.511e-01,\n", - " -1.000e+00, -9.511e-01, -8.090e-01, -5.878e-01, -3.090e-01,\n", - " -2.449e-16])" + "array([ 0.000e+00, 3.090e-01, 5.878e-01, 8.090e-01, 9.511e-01,\n", + " 1.000e+00, 9.511e-01, 8.090e-01, 5.878e-01, 3.090e-01,\n", + " 1.225e-16, -3.090e-01, -5.878e-01, -8.090e-01, -9.511e-01,\n", + " -1.000e+00, -9.511e-01, -8.090e-01, -5.878e-01, -3.090e-01,\n", + " -2.449e-16])" ] }, "execution_count": 18, @@ -599,14 +564,12 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 19, @@ -615,12 +578,14 @@ }, { "data": { - "image/png": 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VtDNnhk7zU6tW+Vcdffv6HSklPBV6kRhq3hyeesp3y++/HzrNj/75T1/kr7oK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\nHzC+mVy4trE1AAAAAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -646,16 +611,14 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ True, True, True, True, True, True, True, True, True,\n", " True, True, False, False, False, False, False, False, False,\n", - " False, False, False], dtype=bool)" + " False, False, False])" ] }, "execution_count": 20, @@ -670,9 +633,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "mask = b >= 0" @@ -688,14 +649,12 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 22, @@ -704,39 +663,48 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "plot(a[mask], b[mask], 'ro')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/03-numpy/03.02-matplotlib-basics.ipynb b/03-numpy/03.02-matplotlib-basics.ipynb index 475d3abc..2b92f5a9 100644 --- a/03-numpy/03.02-matplotlib-basics.ipynb +++ b/03-numpy/03.02-matplotlib-basics.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -67,16 +67,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -116,10 +116,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -149,17 +149,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -190,16 +190,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -229,28 +229,30 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[,\n", - " ]" + "[,\n", + " ]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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5JCSUBmGLimDmTBg3zmS7oo129CcRUoltSaPUueeG2Sp74VmyhJTiYtfrKbkJ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\nDuf/AbP7N0Qt3HMbAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -362,27 +368,29 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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jt98WSkdHN2lt7SA/+KC/TEpKyrJ/n/6DpYWzr7SwsJV/gAwGWcrBQWfHVmJi\norxx44ZMTEzUaR59Ex0dLb/++hvZf9BQuWTJkucOVV04c+aMtLJylDY2JaSPbyWJ5wqJ21zZsnXn\nHM0THx8vz5w5I+/evSullLJR29bSec4XsqS8IEvKC7Lo0u9l1fpv6qw3r9CbMd6jXcvAGNd56SZ0\nDDAmi7X+Avpkq0kPH6oVEJr2n2Js2mv9gf5pXxcFNpK6X3wG6J7hNyYf8PVXE6VrYVv5UUNbWc3H\nXjZ8s4aMi4vLdty6deukh62ttLW0lA8fPnx13smTpZUQskXDhnmg2jRISUmRvXr2lDWsreVekItB\nOtnaZhqR8O+//8opU6Zo9f19XYiJiZEBAW/IEiXKy/4Dhkg7p0rSzrGc/HbKNK3nCA0NlV7eHtKr\nnIt0cLKTP8/9SZavWV26HfzruTF2P7tBepYtk4efRDf0Zoz3a9cyMMadgPnprt8HZmeyjorUCDKn\n7DTpvD0tpdwCbHnptXnpvr4P5PtCbjdu3GDa999y/rNE3BxBo4E2886zeNEiBn78cZZjy5YtS4wQ\nlC5ZEjs7u1fe/9+IEZT09qZx48Z5Jd/omJubM/eXX+h+9y5Nd+zAxsqKhX/+mWlEQqtWbVCrVdjZ\n2TF48GDUajXden3Evfv3Wbd8ySsllF4H7OzsOH06GEiNaGnVciNWVla0bNlS6zn6D/6I+sNcaf6J\nL/fCYxhXYwxt2ndi6+y/sKpREczMSJy1hKZ16uTVxzAdMrF+Qccg6HiWI3MSOP4WsF9mtVectRyF\nlzlx4gS1y1jj5pgIgJkZdKgYR/ChPdkaYz8/Px5ER2NmZpbh0VsbGxvee++9DEYWLGxsbFizZQsJ\nCQlYWVlleQy5XbsOBAUFUa9ePQAiIiJYs3I55g6FOXToUI4MUEHEzMyM9u3b53jc9evXaNasAgDF\nStnjWtqRj3p8wO1p3xHsEYiZuTn+fn7MW7te35JNj0ysX+Abqe0ZXy54pctNeCFIyYvU7deM6Aos\n00aOaaUJM2H8/f0JCU/icdoBNilh+yVbAqpqd5rLwsLCaFnZEhMTWbBgAd9//z2XLl3K0dizZ89S\nraY/nl7F+O773OchllJy/vx5pJTY2Nhk+71YsWIpd+9GEhsby9dff83BgwcZO3Yc/Xp0L9BPEFkh\npeTbb6fg7V2B8uWrsXbt2hzPEdiwMRu/vMy9a7EcXHKdR5HxVKtWjV2b/uHyydNcOHqM4H+DTDZX\nil7JfWg65x6xAAAgAElEQVRbCOArhPAWQlgBXYANL3cSQjgCDQDt/rPpuveij0Y+2TP+37CPZRkP\nOzmuFbJ5JTtZ2d9XPnnyxNiyXuDQoUPSx6eyLFKkhPzqq29kSkqKrFu/mVS5NpOWxYdIO4eiOTpZ\n5V/JV474xVv+fi5Aeno7ygMHDuRYk0ajkZ9++qkE5IABA2RCQoJW40aP+ESWcrGTn9Y3k03K28tq\nAeVM7vttSN5q305CEQkjJAyWQtjJhQsX5miOmJgY+X6vbtLVs6isWitAhoSE5JHavAN97Rmf0a5l\ntB7Z+MrSrnsCf2mrScnalgOklOzZs4egoCB8fHzo1KlTjite5CWJiYm4uJTgyZPpgB92dt2YMmUI\nY76YSWy5UBDmcPNbPmx2g4W/zXllvEajYevWrYSEhFChQgXat2+Pi5sz80/64OJlzbg2kQztOYN3\n3303R7r69OrO/t3r6FAznuArKuLMvdm990iG++fPuHbtGjUq+3F5UAKFbVOfRN5ZbUvDD79m2Ce5\nP8CRX7l8+TLlyldBakYDz6pHH8K5yDoe3M/sCblgoresbRey7wcg/JSsbSaHEMKkD6Tcv3+f5GSA\nDoAgObkpERERSHUiaJLA3BYzGYOV1auHBNRqNR3btiTiTDCtXWOYtcieH74tRa9eHzK6xVJKlLfh\n5kVo2rRpjjQdPXqUf3ds4NyseFTWIGUcb08JZ+GCBQwZOjTTcWfPnqWGlxWFbVOT8QgBLb3jOXzs\ncI7WLyhs27YNS2szkuLT/+wsefQoW7+QQmYYsL6dNijGuADh5uaGh4cb16+PIiXFDwuLlXTpsoXw\n63fZuqsW2PhgmXCU0Z++Wvx0w4YN3Dp7iCOtY7E0ByljePvfK3h79WbOjL+4c+cObee3zTT6ITOO\nHz9O4wANqrRffCGgbbV4Dh7dD2RujKtUqcKRiCTuxoCrfWr0yrorKtp+XD9H6xcUnJ2dKexhzaPb\nv5IU9xGQiKXNn6hUyp9wrjGxb53iwNMzjx8/5mKols8/esbc3JwDB7bTs6fkrbf2s3HjcqpXr86K\n5YtZ/dcPzJ/WldALJylVqtQrYw/t30cHj1RDDKlGs7NXPMF7dtKiRQt69uyZqwoflStXZs85MxKS\nUq+lhG2nbKlcLevjycWLF2fkp6OpMt+W/v/YUPt3e2IKlefD3r1zrKEg0L59e0SCGb71U3DynI6T\n588UdlczetQoY0vLv5hYbgrFGOsZKysrrK0MmQT1RVxdXfntt5/ZsGHZ86gDIQTNmzena9euFCtW\nLMNxvuX9OPz4xT3c4AdW+FYI0EnPG2+8Qe16zak9xo5Jy6HN13ZceexJn4/6Zjt23OcT2RYUTFyZ\nTjj5NeTtzu9jYfHfX0dycjK58TUkJSWxa9cu9u7di1qtzvF4Y2BnZ8funXtxxYcntx9jniwZ8tFw\nRn86Rue5Y2Nj6da7N280acLBgwf1oDafYGLGWHHgKQCpf5A1K/vzhs0d2hdPZG+UJStvFeLIiTO4\nu7vrNLdGo2Hz5s0cPLCPsuX86NKli9blij4dO545G7YQ16ontgc308jdkU2rVjD5m++Y8MU4Aqq8\nwaF9O7We7/z58wQGtiIx0R0pE3F2TmL//m0UL148+8Emgkaj0WuY5JeTJvHd4YOoWzen0NQZRF2P\n0NvceYG+HHiarGsyPMesiGEceIoxzgMeP37MuXPn8PPzy/EeqzF5+PAhP82aydEDQVSoXJ2h/xup\n9yoPUkqOHDkCQK1atbKspyelxMbegaQ1l6CYByQlYt3Kg/DzZ/GvXJNHdTdjd/h9Ni//iYYNG2q1\nfvXqDTlxoitSDgTA3Pwz2rYNY926v3T/cPmUSV9/zZT9e1G3aobjjJ+4G37N2JKyRF/GODlau76W\njoYxxkaPMX4e81dAiIiIkEWKeMhChQKkk5OrvHLlilF0REZGyi8nTZQfDxkgN2/erJdENfrgi9Gj\nZSkHO1nKwU5OHDc2y74ajUZa2ztINl2XHJOS/bHSupCTvHPnjhwwaLi0UjlJz5K+8vHjx1qtrVar\nJQgJSTJ191pKuCYdHd318dHyLbGxsbJn//7yzRYtZHBwsLHlZAt6ijNOiNWu6WM9rTQZYhFtvjEF\nhalTp0pLy3ckHJNmZt3lhAkTDK7h1KlTsoiLk2wxqJzs9n2ALFmhmBw4uJ/BdWREKddi8qwb8rQb\nsoy7a7b9J0yaLFU+fpKPJ0tV1Tdl5/c/kFKmGuqIiAitD5A8o1ixkhIOpzPGq2VAgOlmKFN4FX0Z\n4+gUK62aoYyx4sDTM97e3lhangEOYmNzKsPIhbxm3BejaPO5Nz1+qkibkWX5LLgOy1csIzQ09IV+\nISEhNGpQg8D61QgODjaItnr1GzAsQcUnCSrerN8g2/4TPxvPn99NZqT9U2YP7s2yxQuB1EdVLy8v\nrK1zFiw6bdpkVKqOCDEVc/MvUakG8sMPE3L1WRTyN2pzc62aoVD2jPWMlJJJk6awfv1WWrduzJdf\nfm7wnBSeJd0YubsKLqXtn782p9MphnaeTJcuXYDUQx7FPYrybe/HWJjDiHmFiLx1Xy9VI7IiMTGR\nP/74A4CePXsapSbcwYMHWbjwL6ytLenf/0MqVaqU/aDXiE2bNrFk0Vyci7ry+cSvdXbg6ht97Rnf\nk/bZdwSKiRid19MGxRgXQJq3aUKJDo9p9FHqXXlSvJpRZXYTtP0A/v7+QGr0RBFnRx5tUmMmoOjb\nlkTcuEvhwoWNKV1Bz/z777+M+XwErVu+xcTPv8q2/86dO/mgW3u+eTeOs5EWbL7gwamzl02qkKq+\njPFtqV0aVncRrRhjhdxx9OhRWrRuSsN+xSnibcO++bepVSGQPxe/GDHQo/s7nDm+HTMBvv6B/L1q\no5EUK+QVNepUxq+zhjUTLnM5NAwPD48s+w8bMgCv6HmMbJd6XfZ/9qzdEvz8n7gpoC9jHCm1O8RU\nXDwwiDE2sQOBCvqgZs2aHD4Ywi+/zuHO/ltMHD4yw+Q+vy9ZyY4dO9BoNDRv3jxXa12/fp3fFv7K\nnTu3KOfrT6NGjahcufILhzMUjEezJi2Y/eVsSpX2zvTAT3q8SpRmzyobhqUkcOUO3ItOwcXFxQBK\nDY8aw+0Ha4NyZ6yQa44ePUqrNk1p9F4hipUwZ+3sKJ48sKZsGX+C/t38WlbjMDWklERGRuLq6qrV\nVkNCQgLvdmzD7j37ADPmzPmFHj175bnOnKCvO+Mwqd1eeGlx+5X1sqsOndYnEJhBatHS+1LKwCw1\nmYIRVIxx/qRxszep0f0urT5MvXOKj1HT2es8yZYt6d6mKIsXzTWyQoXc8ujRI1QqVY6jVQyBvozx\nBVlSq75+4voL6wkhzEnNZdyU1KofR4FuMl1STiGEE3AAaCGljBRCFJWpJegyRQltMyCXLl3Cr4I3\nKpUVk7+eaGw5OnPi+Glqt/nP4Wdrb07FNwuTbF6RQ4ePGVGZgq4ULlzYJA2xPlFjoVXLgFrAFSnl\nNSllMrAceLkGVndgtZQyEp7XAs0SxRgbkM++GEmb9x/x75XCzJjxHRERpp0DIDv8Kvhyeu+T59dJ\niRouHnkCMh6v4lk7ihQUjI0ac61aBngCN9JdR6a9lh5fwFkIsVsIESKE6JGdHsXLYkCsrKx4+hju\n39Wg0WAUJ9fTp0/59pvJDBg4iBIlSug019df/UCnLu24eSURFy9LVs64T1JKERw0i/hx5m49KVZQ\nyBt0cOBps6dqCVQDmgAq4JAQIlhKeTmzAcqdsQGZ8s1MLh4ty+COZkyZ8kO2YUZ5waVLl/hh+nT2\n7Nmj81yNGjVi1/Z9iOuNCf7DlQrFA/l8ZB/OnDpChQoV9KD29eDYsWN80Ls/fy1bbmwprxUpmGfY\nDgfFM2fi/ectA7SpDn0D2C6ljJdSPgD2ApWz0qOzA08fXkXFgWdYbt++jZubW5YZ056xfv16Ll26\nRO/evXOVXP51ICkpieXLl5OQkEDXrl0pVKiQ1mOllBQu5k505cHYHv+JI/t2UrFixTxUm//RlwPv\nkKyiVd864uTLDjwLUh14TYBbwBFedeCVB34CWpBa4Okw0EVKeT6zdXR6Tk7zKv5EOq+iEGJDBl7F\nn0nnVdRlTYXMCQsLY+SnE3B3d2Ha95MzLZaq7fHWLVu20L3nMJLt3mD1um0EH9ipT7kFhlatOnH4\ncAwa6cS0aXM5cyY4R84vjUYNFjYgBBqNJlcakpOTWbNmDQ8fPqRp06b4+vrmap7XidxuU0gpU4QQ\ng4FtpN6ELpBSXhBC9E97f56U8qIQYitwGtAA87MyxKD7nvFzryKAEOKZVzF93aEcexUVcse7Xfpw\nPLQWVuIUbq4z+PzzcTrNFxERgVSVJ9mpAxERX+hJZcHiyZMn7N27i5SUx4AFt+/4c+7cOapVq6bV\neCEE2//ZyLRZc2nzw5Rc5clITk6mQYNWnD2bhFpdFiG+YOvWNdSv/3rWC9SWJHJ/xFtKuQXY8tJr\n8166ngZM03ZOXY1xRl7FN17q4wtYCiF2Aw7ALCnlnzquq5ABarUaiTVgqZdyQu+//z4bNu3iYugU\n5i74SXeBBRA7Ozvs7Qvx+PFyoBjqlLs59gXUrl2bVbWzrgmYFZs2beLs2ThiYvaT6gZqzpAh4zh5\ncl+u53wdSDGxE3i6GmO9eRUnTpz4/OvAwEACAwN1lPb6sXzZfIZ9Mh4PjxJ8+un/su0fHh7OrJ/m\nEn79Ju1bN6FHjx4vZG2zs7Nj88YVeSk532Nubs727Rv46KPhxMcnMH3677i5uRlUQ3R0NFKW4j9/\nfGkeP86+jEVKSgrr1q0jKiqKunXrUqWKdnuohiYoKIigoCC9z5tJDLHR0MmBJ4SoDUyUUrZMux4L\naNI78YQQowFbKeXEtOvfgK1SylXp+hQoB97u3bsJDg6mePHidOvWzSTzNJw7d47a9RqRWK43yY7l\nsbu8iDcrFGHrxtVaOfYUTIeIiAj8/WsQEzMVKI9KNZJhw5rxzTcTMx2TnJxMi9aNeBB7idIB1uxf\n/5gfvv+JD3r0NJju3KIvB94m2USrvm3FLtPP2qYvr2JBMsYzfpzJpOlTcXm3Jk8PheFfuARb123K\ns5zGarWa+b/+SujFs7Rs3Y4WLVpoNa5jlx6su1MJWf3TtImSsFtegV0blvLGGy/vNBV81Go1ERER\neHh45MuTZ8eOHWPw4LE8ePCILl3aMXHiOMyzSIz++++/89PiT5m+ywszM0H4+XgG1g7n0cMnJnnz\nkB59GeP1UrvkWO3FdtPP2pZXXsX8SkpKCmPGjCXwwneoShZDk6ImuNoXbNmSus9ftmxZvXu5Rw4f\nzJHdf/B2rTj6fLCYOfOX0a5du2zHnTl3ARkw+L8XzK3AvQ4XL1587YxxUlISbzZszrnzoRR2sufE\n0QP5LlNZ9erVOXRou9b9b968SblalpiZpdqYUhVsEWaS6Ojo1yaE0dT2jHW+XZNSbpFSlpNSlpFS\nfpv22rz0nkUp5TQppb+UMkBK+aOua5oq8fHxANh6pf4ym1mYY1/KheGDBvJt327UrV5F7+WNVqxY\nzp/D4/i0E0zsGsfKZYu0Glevdk3Mw9f990LiEzTXd1GjRg296ssPhISEcPHaA+Lb3eSRTXXWr19v\nbEl5Tq1atdizMpb7t5IA2PLHA4oWLZKvqpnrShLWWjVDoZzA0yMODg5Uql6Fi6OWkXDnMTdXHeb+\nwVCi7t5lr99TeheJZ9u2ba+MS0hIYOjQ/1GxYg3atHmbsLAwrdcs4VWcf0IEcQmw45Q1JUtlfecd\nHx/PoUOH6NfnA5xvLEO1owscmoDd6hq837WTSSURNxSlSpWC+DuIk+MQUXufH7pQq9WMGz+R3h8N\n4uHDh0ZWqV+aNm3Kx/1H8l65S3QucZk/JySwfu2W18pfoENuijxBSaGpZ6KionivT0+OBB/Gvbgn\nv82ey8jBA7GIuk5ojJqN23dR+6Uwprff7sz27aHEx9fCzCwSZ+fTXLp0TqsSSBcvXuTdd9pw8fJ1\nWjUP5K+/12NnZ5dh34cPH/Jm9eqYPXzI7eRkps6aRUpKCjdu3KRZsyYEBga+Vn+M6Tl58iRr1qyl\nQYP6NG3aFIB169bxXu8JJIkyDP3Qlx+mTTGySt24efMmtra2L9z9RkdH8/DhQ4oXL46FhQX37t3D\n3t4elUplRKVZo68948Xy1YILGdFLrDD9PWOFV3FxcWHHxhdiwfn34GGCgoIoW7YsZcqUeeG9pKQk\nNm1aj1o9DrBEoylFYuJNgoKC6NChQ7brlS9fntPnriKlzNaQLl68GJ/bt/kqMZFzwPhx44i4dy+n\nH9HkSUpK4uDBg5QqVYqSJbXLWVulSpVXQrtKliyJTIrEyuwx5cq2ygupBmPgwKEsWvQHQmiYP38u\n77//HgCOjo44Ojpy6tQper77LuHXr6MBevXqxYyffjJ5Z54umFpom2mpKSBIKUlJSXkes6tSqWjd\nuvXz9wd+MoK7d6NYuWQxZmZmaUY0idSQbAkk5LgApDZ3tObm5iQKkbaCcbLG5TVSSho2bM3ZCw/Q\npESyY/t66tatm6u5qlatyvGQAzx69Ig6deroWanuXL16lW++nU5SUjL/Gz6QqlWrZtjv9u3bLFq0\nmMTE+cANRowY99wYQ+o2WctGjej+6BGNgafA93/8wbfu7nw+YYJBPosxMLWyS8qesZ45e/YsXiVd\nsbOzZfznozPs8+fihaxdvoQnT1LDiIYNG4ZKtRQ4jLX1Ojw9VTRpol0MZE7o3bs3j0qX5m07O0bZ\n2jJz3rzsB+Uz7t+/z/HjR4kRIcSph7Bq1brsB2VB+fLlTdIQ37t3j5q1GrB4Y1GWbPOlfoPmXLp0\nKcO+tra2pP6vvgaEv1IOa9euXbir1TQl1SA4Aj3j41lcAH8/0mNqe8aKMdYzX04aS5fhZmy7XZo5\nc2Zz8+bNV/ocOxzM8ePHn+8Jf//9VH7++Uvee68Io0a14ciR/djY2GS6xt/Ll9O4ZnWa16nNzp3a\nJ+9xcHAg+NQpdoSEcDUyUqsQuPyGs7MzJb1LYyffQmU+l+bNGxtbUp6wZ88eUiyroSnyJRT5lBRV\nZzZv3pxhXycnJxYtmo+b2y+ULXuAFSv+eOF9jUbDy89V5qQ+ZRRkTM0YF7znVCNTqJATNy6puXg8\nAY2aDDOnlStXjtOnTzNt2jTs7Ozo1q0bvXr1olevXtnOv2nTJj7t14ef7eOIldD97fb8E7RH65A0\nCwsLypcvn9OPlW8wNzfn6JEgNm/eTNmyXxXYUD0XFxc0CaGgfgrCCgv1aVxc3sy0f9euXWjfvh2X\nL19+JXdG06ZN6SME+4B6QBzwp60t7334YZ5+BmOTaMCwNW1Q7oz1zJRvppMcVYNfx6pYsOCPDOM2\nN2/eTJM6dTg/bhz/jBhBDX9/7t/XLpndmiV/Mt42jrfsoasD9LWJZ+NrEBebExwdHenevXuBNcQA\n9evXp3uXlljf8MY2siQNa7vTtWvXTPuHhYXh7VeBep26UcK3LCtXrX7+nq2tLZu2b2eVlxe9bG3p\nbW2Nf8eOfJ4uX0xBxNTujJXQNiPg4+7O5Dt3eLYT+YWlJe5DhjD1hx+yHTt80MdYrJjP94VTkBK6\nPrKh9vhvGD58eN6KzmNCQkIYMbAfN2/eonqNGsz+bWG+OwVnDCIjI0lOTsbb2ztLJ+5b73blH49K\naPqNgwsnsO3diJhHD184pi+l5Pr16zg6OmoVVmks9BXa9q38RKu+Y8XMV9bLrqhGWkGN9cCzQwOr\npZSTs1pH2aYwAlGPHpH+aIVfcjIXb9zItH96Ro4bz5urV3Ppceo2xf0i7szv0ydvhGaBWq0mKioK\nZ2dnnXM5hIeH07pJI6a5xvCGG8w/tZ3WjRpy6OTpF7LIKbxK8eLFs+2TmJhIWMQNNA3TIihK+5EY\nH0dycvILPzshBN7e3nmk1PTI7XFobYpqpLFHSqm1Y0bZpjACdWrUYJ6FBRKIBlaoVNRLO2iQHZ6e\nnpy4cJEuM+fRf85CDp44maMyP/rg+vXr+ARUxKdSAEU8PDI8VZgTNmzYQAfHZD5wgXIq+L54Mk/v\nRHLhwsu/2wo55fr165QtW4mrp67AhH6w5Edsh71Nm7c75suESPpEjYVWLQOeF9WQUiYDz4pqvEyO\n7t4VY2wEfl+5kiNly1LDxoa6lpY06NmTj/r21Xp84cKF6d69O507dzbKSaneQ4dwr0s7rG6ehVUL\nead7d5KTk3M9nxCC9KnwJaDW4hDLy1y9epVx4z7Te/6P9KSkpDDmi8+oVLcW3Xv3NPlj0l269OTm\nzTokJiyGB70xmzmTyilxrFryR7ZjCzo67BlnVFTD86U+EqgrhDglhPhHCJFthV7FGBsBd3d3jpw9\ny/lr17jz4AGz5szJk2PIT5484fTp00RHZ59oPCdcDQvDrFXqnbx53VqokTx69CjX83Xo0IGNT62Y\nc0dwLAYG3bDCtVSZHFeY7tDpA6b8eoPGTVtqXUsuMjKSSlX98PbxJCQkJNv+X34zmUV7/kEzpTt7\nrR/ToXuXHGk0NKdPn0Ctbpl2VQ9NfD/iYpNzfKioIJKZ8Q0LusHuifuftwzQxsF1HPCSUlYGZgPZ\nBrwrxthICCFwdXXFwcEhT+Y/dOgQpXxL0bpba0r5lmLv3r16m7tl4yaICd+hPn6KlEnT8PT0pGjR\n3NeZ9fLyYte+g2wq/iZ9YkuRVL8jm3buzjIfb0a4uBTDVp6lcOGiWv9zW7p0KUUrxlC3py2z58zI\ntv+OvUEUGdeVQg0q4/ldP4L3ZvjHajK4uHgA/x0GMTO7RIkS2e8zvw4kYpVhcwssyxsTmz1vGXAT\n8Ep37UXq3fFzpJRPpZRxaV9vIbX0XJYp8RQHXgGlz8A+NPy5EX6dKnB50yU+7P8hVy9c1cvcs777\nDjF6NNsGjMK3dGl+2/xPtsZv3759zPx6MlG3b9Pm3S4MHznyhT3LihUr8k+QbjXb1q9Zys6dO6lT\np47WxjgwMJBvp04CAXN/bpNt/yr+AWxYvB1VZR8eLNlJ2Yo5u3s3NAsW/ES7dp1JTm6ImVk8trYn\nmTnzgLFlmQQ65KYIAXyFEN6kFtXoAnRL30EI4QpESSmlEKIWqZFrWe5pKaFtBRQXTxc67++CU6nC\nxNx+yuKKC4l+oN/time837EjG7du5VBISIZbC3v27KFLm9ZMtoijpDnMUNviUL8xf2/clKv1Ll++\nzMaNG/H399e6sklW3L17l4SEBK2SCsXExNC9Ty+Cdv2LT7myrPpjKT4+PjpryEsuXLjA5s2bsbKy\nonPnzri7uxtbkk7oK7RtiPxOq76zxaiMQtta8V9o2wIp5bfpi2oIIQYBA4EUUs/R/E9KmaUzQzHG\n2RAcHMzZs2cpXrw4zZs3z7PySfpm0CeD2HZyO/59K3Jh8Xnq+9RjwS8L8mStWv4VOHkxlG07d9Ko\nUaNX3m/fpDEdju+mV5qvMVFCiWhbgs+eS80lrCVRUVG0bNqA81cu07CTM2f3JjFtyly6de2ur4+i\nkA/QlzH+WGYf1w8wR4wwSArN/GFZjMS4cRNp0qQzw4Ztp3PnEbRt2xm1Wp39QBNg1rRZ9GvbF7ON\n0LPJB/wy+5c8W2vrvv0cPX48Q0MMcOf2LUqn2/61FuBubUlUVFSO1pn4+Rj87K/g7mbO+D9K0/ET\nJ/4N2qGLdIXXmBTMtWqGQtkzzoSTJ08ya9avxMVtApyBJPbufZ8lS5bQs6fpV9C1sLBg1MhRBlnL\n2dk5y3I9rTp2YuZPP1DHKgFLATsS4K4UmaZ8zIyH9+/SwEfNyVuS4fXPcelMIqv+zvwIsIJCVij5\njPMJoaGhmJtXJdUQA1gRG1uPc+eUgwjacOrUKU6fPo2Pjw+jxo2ja/AhSgYfwsPakhtqWL52bY7D\nqwYM+ZTOHYJo7AfbTibzzTfT9bJnnJc8fPiQJ0+e4OXllePoEIW8xdTyGSvGOBNKlSqFRnMGiAHs\nAQ0qVQhlyrxvZGWmzy+/zGfE6C8wc22EfBBM/95d2bBzF6Ghoal5eGvWzNXpr8DAQPYeDOHw4cOM\nq1qVypUr54F6/fH5F5P47vvvsbB2wNPDhT27/sn3zrOCRBKmFWutOPCyoG/fISxbtp3ExECsrU9T\nsaIN+/ZtV/IlZEFcXByFi7iSFHgS7H0g6RG2QX6cOLKHcuXKGVuewQgKCqLtO32IrXsIrIthcWE8\njb1D2bZ5dfaDFbJEXw68d+VirfquEL0UB56x+fXXH1m+/AcmTSrF3Lkfv2KIpZRcuXKFa9euGU+k\nifHo0SPMrexSDTGAVWGsHMtw584d4wrTE2FhYVy9mn289rlz51C7NAMbFxCCFM/3OHv2XJ7rW7V6\nFZWqlcfJ2Z7WbzXl3Lm8XzO/okNuijxBZ2MshGgphLgohLgshMi4zlBqv5pCiBQhREdd1zQUQgja\ntm3LmDFj6NGjxwuGODIykjq1AmhYrzJv1KhAs8Z1TD5PgSFwc3PD2akQ4upPoEmCW+vRPL1MQECA\nsaXpzOhPhlG7oj9vVgpgWP9+Wfb19/fHLGo7JNwFKbG4uYSAgIp5qm/Lli0MGd6H7lMtmB9akVLN\nr9G4aX0ePHiQp+vmV0wtn7FOxjhdKrmWQAWgmxDCL5N+U4Gt5DCTkanSt3c3WlS+yI21cdxcH08Z\n52OMGD7Q2LKMjrm5Of/u2ETZpMWYrVPhfmMkWzevzTLaIj9w69Yt5s+bxyWbBC7bxLN8yRLCwsIy\n7R8YGMiIIb2x2lkG1U5PSrONRfNn52rthIQErRIxTf/xWz78zp3qzQrjVMySt4e4U6WZPX/99Veu\n1i3oFChjjPap5IYAq4ACURc+KSmJXUEHGfuBGjMzsLCAz3oms2FjxjXI8oKUlBSTrVFWtmxZLp4N\nISUlmVsRl3NdndmUMDMzQwLJQLIEjZTZHgD6auJn3L19gwung7lwJiTHzruUlBQ+fK8rjg72FLJX\nMRh2PoEAACAASURBVP27qVn2j4qKwrXki47RoiUEUffu5mjd1wVTizPW1Rhnm0pOCOFJqoGem/aS\naVqQHGBhYYG9nQ130j39RUZBUWfHzAfpiUuXLtG8QV1sbaxxLmTHuE9HkpKSkufr5oa8yERnLNzc\n3PjfyE/xjbPGJ96afh8PyjIRu0aj4YcfZtGixbv06/c/Tp8+neM1f503j/B9G3nYSc2ltinMnPIV\nR44cybBvcnIytarXZdPP95//k455nMKev57QskWrHK/9OmBqe8a6rqSNYZ0JjElLmCHIZJtiYrp6\nW4GBgQQGBuooLe8wMzPjk2HD6fTZDCZ9FEdiMoz5RcWI0Z/l6boJCQk0a1iP4a732dhWcvRRPAMW\nzuZaxHWWLPs73xzVzq98PmkS/QYPRkqJm5tbln3Hj/+SH3/8h7i4L4Fr7N/fjOPHD1C2bFmt17t4\n9jTtXeKwswQ7S6jnakZoaCi1atV6od+mTZv4sP9HaGwtiI+K4lKFJ1R4w5mjWx/Ss0fvfP9kEhQU\nRFBQkN7nLVChbUKI2sBEKWXLtOuxgCZ9PSghRBj/GeCipCbN6Cul3JCuj0mGtmWFlJL5v85jyR9z\nsbS04qP+/6Nbt27ZD9SBVatWMW9kb3ZUf8qee9DpGDSoCCcjoGrtlqxYvVkxyCaCk5MH0dF7AF8A\nzMxG8MUXjkyY8IXWcyxfvpxJw/qwvFYc9xKg6xEVe4JD8PP7zy1z8+ZNygVUwK5BBeIiH+NY2ZPE\n09ep5V6eaVO/e6FvQUFfoW315Hat+u4XzQ0S2qbrnXG2qeSklKWffS2EWARsTG+I8ytCCPr1H0C/\n/gMMtuajR49wt0rNjTH4PCz8FN56A5JToM6o/7d33uFRFV0cfic9m5AEEkoCCb33GqoUQQN8gCCC\nCCofICigKIogIoKFriBNmooKUkSKoFI/IogUCZDQITRBQocQUjfZ8/2xCwaSkE22ZAP3fZ552Hvv\n3JnfLjdnZ2fOnPMHa9eupVOnzKbsNeyN8UegId2xIcfTNt27d+fv06foNOsLPD08mP/djAzGdeXK\nlYi/H9dSSmMYMJiEFVMpVDSOqKOHHklDbE0cbTu0RcMoEUkFBgMbgCPAMhE5qpQacDecnIb1eOqp\np/jlooFzCRCTAA1Mv3hdXaB2mTRiYmLyVqDGPYYMGYiX1/PAKpT6HE/PxfTsmbPockop3h35Pqf/\nucLhU39n+UWbGHMdw6DPoEZjDK9N4daeUw67uOtIPGreFIjIbyJSUUTKich407m5IjI3k7r/FZGV\nlvaZ37hz5w7h4eFcvWqZM0nJkiX5aNwE6mz3oKCHEyMXQnwS7DkOP+9WNGnSxDqCNSzmww9HMnHi\nAJo1+5pOnfaxa9dWm8Q97tKlC0o5w5blIAJbluHk4sLLPV+0el+PGpYYY1vsr9C2Q9uYxMREGlSr\nhvuNq1xUzuyOjCI4ODj7Gx/C+fPnWbVqFUsWzWffgaMEFPJh+sx5PNu1q5VUazgaIsLy5ctZsHih\ncYrsxT48++yzKKX48ssvGTxsOIaEeJSnjnZPt+KnJcsf2ezP1pozriU7zap7QDW6rz/TvonjQGuM\nKZj+AnqIyH1RxEz1NmFcJ/tGRB66F14zxjZm//79dG/ejBNu8XRO8+LZGXPo1ct6wYYkF1mUNWyH\nXq/n0KFDKKWoVq0aLi7WmZf88JOxzFm2kFIj2yECZz9Zx5u9X2PkuyMAo+/7wYMHKVGiBEWLFrVK\nn46KtYxxZdlnVt2jqs6DxrgR8GE6x4URACIy4YE+3gRSgPrAOs0Y5zHx8fHUrlSR4vG3OZIGuw5E\n5ii7hUb+4fr164SFNeP27fMYDFC4SFl++/V3fH0t8z+Pj4+naIkgnjj4MboS/sZzZ6+yo+4Yrvxz\nCQ8PD2vIzzdYyxhXkEiz6p5QNR80xl2Bp0XkFdNxLyBURF5PV6c4sAhoBXyN0XHhoVO0mh+UjfHy\n8mJ31EGGfruYvTlMM/SoISIsmDuXjq2aM/iVvo9cLI933hlEvQaniIiMZ19UPBUrHmPkyKEWt3vl\nyhXcCnjeM8QAXqUKo1yds4w7cfjwYbZs2UJycrLF/T+qZDVHfCc8gqtj5twrmZCj/RUYXXuz/fJw\nLN+OR5SCBQvSoUOHvJaR5yz8+ms+GzGUT3wS2HhkF8/s38+2veb9VASIjo4mIiKCChUq5DhLiD04\neGgfkz/To5RCKejaLYWJ4/da3G5wcDDOaXB9VzT+DcsBcHXbMXQenpluPvnqq/mMGvUmgUHOeHqW\nJXzrHi3sayZktdXZtUUjXFs0und8c+yXD1b5B0i/8BOMcfdxeuoCS01TiAFAW6WU/mFuvZox1rAb\nv638kQ8KJPCsL3SWFLwiD3Lnzh28vb2zvXfDhg30eOEZGjRIY+9fBp57rg+zZ891qPnyMmUqsGXz\nGRqEGv2L/7fFhbJlLPf1dXFxYe7M2fTu0Jeg50MRgxCzbA8/LPw+0+whM2ZO4NtFiTRuAg3qRLNv\n3z5CQ0Mt1vGoYYGfsU32V2jGWMNulCpfkV/3htNNktl8B3y8deh0OrPuHTToZeYtSKZtO8XZM0Kt\n6vPx8gpgypRxNlZtPlMmz6Zly1B2bE8gzQBXLvsSHj7NKm136dyFWjVrsWz5MpyUE933zs0yNkap\nkmVYs/o8sbGpXLmSxvr1Gxg5egLlyoTw7jtv2MTFLj+SWx9iEUlVSt3dX+EMfHV3f4Xpega3XnPQ\nFvA07EZcXBzdOrRn8/YdFPbzZdnqNTRr1izb+5KTkylSVMeqNUJoQ0VsrBASpHB2LsDZs0cdKpXR\n7du32bZtG0opmjdvbtao/0H279/P4MHDSUnRM3nyhzmO03L58mXeGNKPmJjzxMa5E33Jl4SAfjgn\nHkR3dR779u6gXLlyOdblKFhrAa+InDOr7hVV0i7boTVjrGF39Ho9Li4uZk8xnDp1imrV6uPjm0T/\nAUms/MmTU9HP4OFxnh9//IA2bdrYWLH9SExMJDCwNLGxAwEvvLzGc+rU4Vy5q0VGRtK4RScS6pwE\nJ+OcsfPZ0fR58hbz5ky3snL7YS1j7Jts3o7VWPdALe2SoxIfH09ERARnzpzJaylmc+zYMVq0b0u9\nls353//+l6daXF1dczTXGxgYiFJw5fI4Pv14IEcOjyc5eSzJyYdzFAUtp0RERDB+/Hj+/PNPm/Xx\nIJcvX0avV0BXoB3OzkG5fs7OnTuHS4FK9wwxQJpndU5GmzcifNRJS3Uxq9gLzRhng16vZ0C/F2ne\npA779u3j8OHDVCxVih6tWlGvShXeHDgw13EALly4QKkqlSlSMoRDhw5ZWfn9PNWpA4db1+DCwE50\n6vZcvnIr0+l0jBgxDC+vOYhUB3zR6XrRtWsXSpYsaZM+jx49ylNhLThwbSYdnnmav/76yyb9PIhx\n00YhXF0/wtl5Cu7ut6hatWqu2mrcuDEp13dD/DHjCUMqnte/otN/WllRcf4lLdXZrGIvtAW8bFi/\nfj17/1hFl9B43h/+Blf+uUG369dpJ0I8MPS772jTvj3t27fPcdvr1q3jRpnipBUvyg9LlzDuk0+t\n/wYwBjr/59QZSvbvitJ5kjjiC2JiYhwiFdLNmzdZvnw5ycnJdOjQIUs/7A8+GEGVKuWZPv0bkpOT\n6dPnv/Tr189muvbu3Uvtlr4M+qw4ibf/Yffu3dSvX99m/d3FxcWF3bvDmTJlKikpet58czsFChTI\nVVsBAQHMnjmNgYMb4RbQiLTbR2lQrxqvvWa/SIOOjD0NrTloc8bZcPz4cZo0qoN/AcWzz/dnxhez\n+S45mbt/HnNcXWk4bhzvvPNOjtv++++/afxkKxLj49m6fgM1atSwrvh09Pjvy2w4fACnQr4Uv5VE\nxB9/Wm2rbm65cuUKDRrXo0gdHZ5+rhxcfYEtG/7nED7EFy5coF6DmpSr5cGxvXfYuWMv5cuXz2tZ\nueLq1av8+eefhISEOMRnaynWmjN2unTHrLqGYt7aAp6jcPr0ac6fP0+zZs0IrVGDxkeO0EGEOGCo\nlxezli+nXbt2eS3zoaSlpbFmzRoSEhLo3LkzXl5e965FR0dz8uRJSpUqZdcYuCNHvcefN9by3OyG\nAPwx7zjXf3Zn47rNdtPwMGJiYti9ezd169Z9aHCno0ePcunSJZo0aYKbm2Nlj3gUsZYx5p8k8yoX\n99CMsSNy7NgxWjdrRsLt2yQZDAwYMIDPZ8xwqM0HOeGzqdP54KNPcA2uhf6fg7zxan8mfDrWLn33\nH9iPm5UO0+IN45xo9LYYto+4yN4/99ulf2vww5LFvPXWAIoGuRBQsDKbNv6R6UYMDethNWN8LvuM\n2wCUdNW8KRyRSpUqUbpqPe74d0B8a+IfFGw3Q6zX6zlz5gyDX+tLWOvGbNq0yaL2Dh06xOhPxpP4\nZgS3+20k8e2DzPjqe37//XcrKc6cjRs3Mvj1/tSsWpvt005yfv81rp+NY/0Hh2gflrfbxhd8tZBm\nLdqi15v3h7rgqy+YMM+JX/Y6c/zEQU6fPm1jhRpWI8nFvGIntAW8XHDixHH0gT+gj91GVNQRu/T5\n04oV9O39EipNj5DK8BegW9dOnP/nSq42FoDRGDuXbQoFTT/BvQMwVAojKiqK5s2bW1H9vyQlJdGl\nSwe69xa+X1SB0e9+xKedPyYlOYVePXvxwUjzc8TZguTkFJKTkxER9uzZw5x5XwDw2oA3M13Aq16t\nDovnHuPk0SRS9a4OtQFFIxscLKm6NjLOBRPHj8XlYEt8b3zOyBFv2ry/M2fO8Gqfl9naKJGbHVKZ\nVwtmrYS0tFSSksyc98qE4OBgDBcOQEqC8USaHpcLf1GiRAkrKc+Iq6srAYULsXu7O8HBJRn46kD+\nORvD1ZjrTJ3yRZ4vKg4a2J89O/9HVFQUbds/iX/VTfhX3URYu1ZERERkqD9xwjSahQ7h6qkubNzw\ne66/GDXygFQzi53Q5oxzSWpqKs7OznaZoli1ahXfDOvNz3Vu3ztXcDX0H/ImEydPzXW7IkLP3v34\neds+Ess9jee5bTQqX4QNa1faNMv01atXiYiIoEWLFg4bi/fVgX3QlV5L72FG979vJt0g8WwH5sz+\nOo+VaVhtzjjCTJtT1/L+zEGbpsgl9hzBhYSEEHkjlVg9+LrCsduQ5uLOh2Mt80tWSrF44QJWrVrF\n0aNHKV16MN27d7epIQYoXLgwYWFhNu3DXO7cucMnn4xh/W8rKVq0KO8M+4g2bdrg5ORMarpp41Q9\nODlpC3OPFGau39kLbWScT3jnjcH8tPgb6hZyZvuVVCZ/MZuXevfOa1n5nrZhT+Cr28ObryZz+hy8\n/YEni39YR+HChWnZqjHdBhtH7stnJhG+dSfVqlXLY8UaVhsZ7zDT5jSxz8hYM8b5iF27dnHu3Dlq\n1KhhV3/gR5WjR4/SpnU9zuxP4O4PnYU/wJpNrVi9ZguHDh1iwdfGTA+v9H0t19uSNayL1Yzx72ba\nnOYZ+1NKhWHM5uEMLBCRiQ9c7wR8BBhMZZiIPDQojDZNkYckJyfj6upq9rRAw4YNadiwoY1VPT7c\nuHGDokVcSD/jVDwQrl+/CkC1atWY9vnMPFJnHQ4dOsScebMoX64igwe9rvlApyeXa9+mrM8zSZcd\nWin18wPZoTeLyBpT/erAKuChcUstnhxUSoUppY4ppU4qpYZncr2nUipSKRWllNqhlLLdnt98QkRE\nBPVq1MBbp6OQjw8fjhqV62BDuSEuLo6VK1fy008/ERcXZ7d+HY369esTc9mJdRuMxwkJ8NlsTzp2\nsl727rwkLi6OVq2fIDZgAwuWjWfSlInZ3/Q4kXtvigZAtIicFRE9sBTolL6CiMSnO/QGrmUnxyJj\nnO4bIgyoAvRQSj34+/k08ISI1AA+BuZZ0md+58aNGzzVqhXBBw/SwWAgSZ/AJxPGUblaObtEUrt2\n7Rp161fj8y8HMHXuq9SuW4WrV6/avF9HxM3NjR9/XMfAd/2p0qgAITU9KBrUjiFD/nVXTEtLy7dJ\nPS9duoRySeP590No+ZIfkQczuuY91uTeGBcHzqc7vmA6dx9KqWeUUkeB34A3spNj6cjYnG+InSIS\nazrcDdjOiTUfsHz5ckqlphIMbPZyZnJEQ1YktyY4NJkhQwfavP8Jkz6l3pPxzN3ky9yNvjQMS2Tc\nhI9s3q+j0qRJE86cucSSZduIiormu+9X3IsvsW3bNooW8cPPz5vJk2wTUc+WlC1blhrVajOo6iEW\nvX+RAX0H57UkxyL3xtisn7EislpEKgMdgO+zq2/pnHFm3xAPy3zYF/jVwj7zNbGxsXjo9VwFytYq\nQMlqxvhvYQNLsPAV8zMl55YrV/6hYtN/v4Mr13HiyJZ/bN6vI+Pi4kKtWrUynB818g1mjrhD83pQ\ntv0YBg56874AS46Ok5MTv63bzIEDBwgKCiIoKCivJTkWWbm2HQ6HI+EPu9Oc7ND3EJHtSikXpZS/\niFzPqp6lxtjsiU6lVEugD9Aks+tjxoy597pFixY5zvuVX2jfvj0Txo6lol7Pqcg4LhyPJ6i8ji1f\nx1C9WgOb9/9Umw6M/XQjtRqloBR8OzmF94d3tHm/+RE/v0JEnXDCw82Au5ubTdLdJyUlMefLL1m/\nZg0ly5blrWHDqFSpUo7biY+P5+DBg4SEhNxndF1cXKhXr541Jdud8PBwwsPDrd9wWhbnK7Uwlrv8\nlCFwVrbZoZVSZYHTIiJKqToADzPEmCrkugANgfXpjt8DhmdSrwYQDZTLoh15nJgwbpwU8PCQkp7u\n4uKqxM3DSeqF1pSrV6/avG+DwSCTJo+X4sEBElTCX8ZP+EQMBoPN+82PnDt3Ttq3bS4N61eRTZs2\nWb19g8EgLZs0kRqenvJfkPbOzlLQy0siIyNz1M533y8SnV8h8alcVzx8C0nv/q9JWlqa1fU6CiZ7\nYantEr4V80om/QFtgeMmu/ae6dwAYIDp9bvAIWA/sB2on50mi/yMlVIuJkFPYvyG2AP0kHQuHkqp\nEOB/QC8R2ZVFO2KJDluxZ88efv75Zzp06EBo6P2zL2lpaVy8eJGAgAA8PT1z3Pb58+fZvn07RYoU\noU6dOnmSdSM5OZnY2FgCAgJyvetORKy2JfzQoUP8uGwZo0aPvm8Uum7dOhZ8vwQ3V1cG9+/DE088\nYZX+8ppt27bRq3173r5z597iTbhSOHfsyPLVq81q49y5c1SqVYekib9DqWqQEIfXB08zbUhf+vXr\nazvxeYjV/IznmmlzBthn04dFC3gikgoMBjYAR4BlInJUKTVAKTXAVG00UBD4Uim1Xym1xyLFduLS\npUu0bNuWKYk3adWuHZcuXbp37caNGzSoVpUGlStSKrAYO3fuzHH7wcHBvPDCC7Ru3druhlhE+GjM\nKAKLFqJyxRAqlQ9mw4YNOW5j0Ntv4eruTrUG9a3ikbFyxQrGjRvHhQv/Tr9NmPwZz782lDW+LfjR\ntR5tn+vJokU/WNyXvdizZw+bNm0iJSUlw7Xo6GiKGwzsB1ahiABKiHD86NEMdbNi3bp1qIadjIYY\nQFeA+GeG8u2PK63zBh5lHCxQkEVDfWsVHHCa4tChQ6ILChTvfVvFq0SQREVF3bs2+v33pbevmxgC\nkSUFkYbVquah0pwz58vZUreyTs4tRwzhyMYpSEAhnZw+fdrsNo4fPy66YkUk4HqkeP+3m4z9aKzF\nuvR6vZw9e/becVJSkuj8CglfnBKWirGM3SHFSpa1uC97MHTocPHyKiEFClSXOnWaSHJy8n3Xo6Ki\nxEV5iBslBDqKG8XFF3cZ2L+/2X18++234t20o7BB/i1vzJH/dH3e2m/HYcBa0xRfiHnFCv2ZU7QQ\nmllQpUoV+r7QE/dnXuK/3XvcF5MgMSGeopKKUlDMCRIS4h/SkuOxYN40Jr6SQEhRUAra1IcerVL5\nYfEis9soUKAAkpxCyuY/cIo+R+GAwhbrcnFxuS/b8/Xr1zEoZyiSLklp2fpc/vv03T8ou5GUlMTM\nmTOZNm0a8fHZ/3+npqbyxRdTiY9fTFzc95w4cTPDL6izZ8+iXAJIYQQQRgrvcVsVpGHTpmbr6ty5\nM67Re1Erp0LsNYjYiG7pR7wzWEs6mi0ONjLWtkNngVKK6ZMnM33y5AzXXh38Ok0XLiQixUBkop6Z\nsybkgULzOXPmDLNmzcXT04O33nqDxMREfB/w0PLVpeboSyUwMJClC79l8pzZNGzWildeeSVHmkSM\nwdt37dpFxYoVadOmTYatusWKFcPPx4dLBzdDjTbGk38spnqDxnZPc9WnV3duRm7EzQm2/Pozazc+\nNMwAzs7OeHp6c+fOKSAEg+EGfn5+99U5evQoRjfUu2MiJ5ydqxETE2O2rgIFCvDn1i28+uY77O47\nluIlSzPpy5k2Sw7wSKFFbctEhIMu4MG/O7B0Ot19569du8aePXsoV64cFSpUsLifX375hS1bwqlQ\noSz9+vWzWojOa9euUaFCTWJje+LsfJ2SJSPo+mwbTu6ZydJRSbi4wMVr0GCgjlVrw+2Sjl5E6N37\nVVas2EBaWhiurnuoUsWP8PBfMiyGbt68mU5du0ONp3BKTcbpxA62bvyNOnXq2FxnekKK+bPl6Rt4\nuUK15Tpu3M7+i2vDhg106/YiCQlxjBz5PmPHjrrv+rZt22jXrgfx8cMAdyAJL68p/PrrkgyLlN8t\n+p6lK5dSsWwFPhnzSb7yd7Y2VlvA+9RMm/O+fRbw8ny++O78jSNy69YtCQkpLy4u7rJ48Q8262fm\nzNmi05UQ6C86XX1p3/5Zq7mbrV27Vnx8nhIQAYN4eZWUyMhIafd0cwkJ1ElYYx/x8/GQieM/sUp/\n5rB9+3bx8iojcEUgQeCO6HRtZNasWZnWv3z5ssyfP18WLlwot27dsqm2+Ph4iYmJue/znzlztpQJ\nCZaKRTylRnFvebXvyzlqM6v/S4PBIC++2Ed0usKi0zURna6wvPRS3wz1f/75ZwkoVVjaL+0mVbvV\nkmd7dM3x+3qUwFpzxh+IecVOc8bayPghbN++nbCwHiQkNObpp1NYv948d6OcEhRUjpiYEUBlIAUP\nj2eJjj5I8eIZtrvnmCNHjlCvXisSE9cC19HpehITcwYfHx+ioqI4d+4cDRo0oGjRohb3ZS6TJk3i\n/ff/ITU1/fTO93Ts+Dtr1iy2m470iAijhg9n5owZKBFCgoNZvHIlZcqUwcfHF4OhFRUrxvDFF1NM\nweett9wSERHBgQMHqFWrFnXr1s1w/e1hb7PH/yChI54g9uxN1jRdxJULl63Wf37DaiPj98y0OePz\ngWvbo05oaCjNm9enRIn9fPDBMJv1Y5wrvTuBlYZImtX+2KtUqcLMmRMoUuR5QkLeZvXqpfj4+BAZ\nGcnx48cpXrx4rg1xbGwsrw16i+atOjJu/GTS0rLa0nQ/5cqVw8PjL9Jv4HR330v16uXutdv35R5U\nrRhMm5YNiYyMzJW+nLBo0SJ+mj2br5OSWJmcTFh0NB3atMHNzY2aNevh7Pw7L7zwPE8//bTVM6HU\nrVuXvn37ZmqIARo2aEj0t4c5s/4kf328nQoVyj/W0fasht7MYi/sMfw25yfD48y3334nOl1RcXbu\nKV5eVaVHj9427W/U6I9E5xckPpU6i65QsAwZOlxEjD+bv/56obRo0VHatn1O/vjjjyzbSEtLk9p1\nm4pbsT5C6Z9E599Ehrz5rln9p6SkSNWqDcTTs5PAd+Lm9qr4+wffmx5o9USo9GvjLgcmI/MGIEX8\nC8j58+fv3R8TEyObNm2675yltGvRQkaBbE1XyhYoIHv27JHU1FS77I7MCoPBIFOnT5OgMsHi5qUT\nH59q4unpJ19//U2eacpLsNY0xVtiXrHTNIXmTeEAvPTSi5QuXYo//viDUqXa0717d5v1FRERwecz\n5pHQfj94FoHkmyz4rj6dO7blr7/28+HYuSSkfgxyk/Dfn2HzpjU0btw4QzsXL17k2LETpJT/HZQT\nCR7VWbS4NdOm/hszNyUlhatXrxIYGHjfaNLV1ZVdu7Ywe/aXbN68mpo1KzJ06G6KFSvGxYsXiYqK\nZOOcZJydoWYp2HYijfXr19O3b19GjxrOrFkzqFXRncgTyfTs+SLTps+xeLTq4+fH7XTHacCdtDQK\nFCiAs7MzAQEBnD59msOHD1O+fPlcxY/ILUopAgr6E3u5ICnxa0jBG4hm0KBePPFEM5KTk/Hz89MC\nAeUUe27oMAPNGDsIzZo1o1mzZjbv58iRIzgFNjMaYgD3ghgCW3HkyBEmTpxGguFncDXG/09MSWXi\npFmsWZ3RGHt7e5OWmgCpl8A1CJJPUsDH9971o0eP8lTLZiQlxFO+QgU2hu+4L429t7c37747jHff\nvb9dV1dX9KkG9GkQnwyHz8PlWwZcXFxYv349Py6ZzYllSQT4JXE7Hlq9/gNLljSnZ8+eFn0urw8b\nRqeNG/FNSKAEsNLNjaq1at0zukuWLKVv30G4ulZGrz/O+PEfMmSI/UJSLlq0ivj4XoAbkAyUIy2t\nLk/UrYtbWiqxqak8/fTTLFy2HHd3d7vpytc4mDHW5owfM0qVKoVc/Qv0JtestGScr+2iZMmSpOiT\nQf1rUFG+JCZmHljdz8+P0aNHoztXH5/LHdFdeZn5cz6/d3382FEMKnuDKy8lUfhONIsXm7cwV7hw\nYdqGhfHkp+4E9Xel5ZiibN7vxObNv7Ni+XcM6hxPgMld18cL3uoWz0/LF+bmo7iPxo0bs2TNGv4I\nDeXzEiWo0r8/q377DTC6N/bp05/ExM+5fXsiiYlfMnz4SG7evGlxv+ZSuHBBYB+u1MOFOsCvuOp/\n56PEWA4b4jnplMztzRuZOG6c3TTlexxszlgbGTsIt27dupcGqUOHDpQtW9Yq7V67do2oqChKlChB\nhQoVaNq0KV3at2Ll+lD0RVrhem07bZrWoG3btnTv9hyLlgwg0TAL5AY659H07ZP1H/f7I4fRN27g\n8gAAEntJREFUrm1rzpw5Q5060ylVqtS9a25uHtxKcSLFkMYdPfcCtpvDt4t/pFjREOKTOwGNgERW\nr55E82bliPN0wpjf0UhcAnh6WsfntnXr1rRu3TrD+fj4eFJTU4G7/yeBuLr6cvXqVQoWLJjjfuLj\n45k5cxabN/9BnTrVGDp0SLaLqG+9NZDVPzTkFYMef+BTPsQbPT1cjLsodcDQtCSGL/mB0WMzhHzU\nyAxHS+Bij4lpcybTH2cOHDggRXx95SkvL+nq7i4FPTxkwbx5Fre7e/duCSjiK3WfCBT/It4yacp4\nETEuCK1bt04+++wzWb169T2/1uTkZBkwYIgU8g+WwMDyMnv2nAxtXrhwQd4dMUxGjnrvoYtaf//9\nt9SsVE6cnJR0af90hrgMD8NgMIiTk4vAPIFvBL4RF5e2MnjwYClWWCc7FyCyCznwPRISpJPNmzfn\n8JPJOZUr1xZn5/8KrBalhkixYqVy9J7ukpaWJrVqhYqHR6jAYHFzayNFi4bIzZs3s7331VdekWJO\nThKCktLFS0ohd3e5oUMSvIzle3fkydAGuXl7+QqstYDXQ8wrdlrAy3NDfPeDeZxpVreujAKJMJWV\nIL6enhZvcKgXWl1GLS4jv0sDWXGhlvj4eUpMTEyW9U+cOCERERFZxsKNj4+XkDIlpMWbNaVJv6pS\npWblbOPmpqam5kp7mTJVBF4xGePZ4uVVStasWSPLli6RksEB4ufjLkHF/OSrBZZ/aZnDhQsXpGnT\nNlKgQIDUrt1Yjh8/nqt2Nm3aJN7eZQW+E1gksEg8PZvI9OnTs73XYDDI2rVr5ccff5TU1FR5rn17\n+Y+Xh2z1QBa7IyW8dLJu3bpc6cpPWM0YdxXzSubxjMOAY8BJMo/h3hOIBKKAHUCN7DRp0xR5jIiw\nY98+JqU7VxIo7+pKREQErVq1ynXbV65coVI940Jd4eJu+Bf15Nq1axQrVixD3YnjP+bzKePx0TlR\nuXooq9ZuzBAr4uTJk4hHGs9MDUVEGFNkKZcvXyYwMDBLDblNDb9y5Q+0ahVGaupW9Prr9Or1Ah06\ndEApRdfnunHz5k38/Pzslnq+ePHibN++0eJ2Lly4gEgQ6ZdrEhOLcfbs39neq5TiP//5z73j71as\nYPzHH/PW0qX4B/jz5Qejad++vcUaHxvMc4vPQLpEzK0xpmD6Syn1s6SL486/iZhjlVJhGBMxN3xo\nuyYrnqc46g48exFSuDDjr13jblrtFKCDpyc7o6IoV65crtsd/MYA9p1cQ6/Rhdi3OZ7w74XDB09m\nWG1PTU2lgLcnp6emUtgHao7yZt6i9TRpcn+GrNjYWMpUKM2TY6uSHJfK3plnORv9t03SEQHcuXOH\nI0eO4O/vb7U59Lzm3LlzVKpUk6SkD4BiQBxeXp+yZs1CWrRowS+//MLev/6iStWqdOnSJUdz7Y8L\nVtuB18FMm7P2/v6UUo2AD0UkzHQ8AkBEMo0YppQqCBwUkYcmY9a8KRyA9z/6iFE6HVuAv4B3PT1p\n3rKlRYYY4PMpM2hSowcLhsD1yKps2bQ9U7cnJycnXF1cuHIb7iRBfJIh0+wlvr6+bPx1E/EbvHH6\nqwhbNmy1mSEGo/tbgwYNHhlDDFCyZEmmTZuEh8dYfH3H4eExnAEDXqBZs2b8p21LPh7Zk8RTnzBp\nbB8a1q/OnTt38lryo0vuQ2hmloj5YbELzErErI2MHYQVK1Ywe/Jk4m7fpkuvXgx95x27+osuX7aU\nfn17k5qWxqDXXmXSZ9PtHqbyceLmzZtERUVRvnx5goKCWLJkCTMn9uOJWnqm/uiEe2BJ7pz/m1Yt\nW7JpvXUSql++fJmTJ09SvXp1fH19s7/BQbHayLh1FjbnRjjcDP/3+MzYB0fGzwJhIvKK6bgXECoi\nr2fST0tgFtBERB7qC6kZY417pKSkoNfrLQ7PqNfrSU1NzVVuwMeV1wf1J/n8fH7YW5z4YXvBtxjc\nisF5XAN+/eErnnrqKYva37lzJ+3atCHA2Zl4Nzd2RUQQEhJiJfX2xWrGuJmZNmd7hmmKhsCYdNMU\n7wEGEZmY/jalVA1gJUbDHZ1dN9o0hcY93NzcLDLEIsIbQ4eh8y5AAb+CtGn/zGP1M3vHjh3MmjWL\npKSkHN9btlxlNkZ6E99yuNEQA/gFktZ6KIuW/WSxtvFjxtAqPp5Xbt+m3M2bLJg/3+I28z25n6bY\nC5RXSpVSSrkB3YGf01cwJWJeiTERc7aGGDRjrGFF5s6dz1drwkkd8w9p42+z/bo3r74+NK9l2YW4\nuDhahz3NmO9mMvmzKTm+/+Xevbl+2wWVGHvfeafkOLx0lv/C8C1YkBvOzuiBW66u+OTjaQqrkcsd\neGKjRMyaMdawGhu2bieh4SDw8gcXN5Kbvc2adavwDypKYOkQZs6eldcSbYa7uzv+RQKIP/4PZcuU\nyfH9BQsW5MclS3DePBVObDfmAjj+Ox7bZtO/z8sW65vw2WfcrFCBD52cCGrUiEGDBlncZr4nzcyS\nCSLym4hUFJFyIjLedG6uiMw1ve4nIv4iUttUGmQnR5sz1rAar7/5NnOi0kh9ZhoATvOfomDhS1T7\neiD6m3fY13ECDSvVZdni7ylSpEgeq7U+cXFxXLt2jdKlS2dfOQtWrPiJ198ZztWYCxQJCmbmZ5Po\n0qWz1TSKSL5fmLXanHF1M23OQfsEl9eMcT4mJSUFyFncB1ty+fJlajVozO3CtTB4FkS/fzFND01F\nV8poeC/+sJ0j7/1KsKcbxw/tzzTPX0pKCjNnTOf4sUgqV6nNoMGvZ3Cfi46OZu7s2cTfvk37zp0f\nuY0OIkJiYiKenp753nDaAqsZ48pm2pyj+STTh1IqTCl1TCl1Uik1PIs6003XI5VStS3tU8M4gvIp\n5I9PIX+WLlue13IAKFq0KEcO7GX6a+2Y8nxt/P390d/4dwFPfyOOtGK1uWYowKZNmzLcbzAYeLZz\nGJvXjaZWiUX8tmoU3br+h/Rf1EeOHKFR7drcnDYN76++4tVu3Zg1Y4Zd3p+9UEqh0+nuM8Rbt26l\n+0vPM/8rbeHNajhY1DZL93c7A9FAKcAVOABUfqBOO+BX0+tQYFdm+8Q1ckbxchWFqb8L03dI0VJl\n81pOpsyYNVM8SwRIjUVDpMqMvuLkV1AYslu8Gzwv33zzTYb6+/fvlzIlvSTlCCInkeTDSEhxnURE\nRMiQd96Umg1rS8myIfIKSKSprAXxcne3WgJXRyQ2Nla8/QrIEzM6iX/JIrJ9+/a8lpSnYK3YFMFi\nXrFToCBLR8YNgGgROSsiemAp0OmBOh2Bb00Wdzfgp5SyX/bLR5TAwECcdv+C0651DpvhYfDAQXRt\n2Y5jw37i2PdxGF5eD55+pB7dQJs2bTLUT0hIoKCvM3dnJdzcoKCvCyPHjOKXE+GUnNyMgl3Ls1rn\nSorpnhJAil5PcrKjxUO0Hnq9nrS0NPyrFcPd14Pbt29nf5NG9uTetc0mWGqMzdkWmFmdh+7R1sie\nlYu/o5O6SEfD36z64fu8lpMlCxbMp01oIzxO/YHv5vfwmBXKzGmfZ5r5umbNmty648WkeU6cPAvj\nvnQmUe/Dtq2/0/jr5yjWtAwNJnQg2d+LuxFZlihF1XLl8PDwsOv7sif+/v5M/exzjg3dTudWHQkL\nC8trSY8GDjZNYWnUNnNX3R6c/M5w35gxY+69btGiBS1atMi1qMeB4OBgVjqwEb6Lm5sb61Yt59ix\nY5w9e5bQ0NAsA7J7eXmxafMOBr76EnOWH6dy5cps3PQd9ZuEcvvUNTz8vUiJTSTldgqD3dzwc3PD\n3c+PX3/5xc7vyv4MeGUAA14ZkH3FR5Dw8HDCw8Ot33Auo7bZCou8KczZFqiUmgOEi8hS0/ExoLmI\nXE5XRyzRofFos2z5Mga8/hrBbStzZedZnmv3LCPefpf4+HjKlClj02BFGo6H1bwpCphpc+LygWub\nUsoFOA48CVwE9gA9JF1cT6VUO2CwiLQzGe9pItLwgXY0Y6zxUA4ePMiePXsoVaoUrVq10ly+HmOs\nZow9zbQ5ifnAGAMopdoC0zB6VnwlIuPvbgkU024UpdRMjJHx44H/isi+B9rQjLGGhoZZWM0Yu5hp\nc1LziTG2igjNGGtoaJiJ1YxxDpa87GGMtdgUGhoaGg6AZow1NDQ0HADNGGtoaGjkkOzCQCilKiml\ndiqlkpRSb5vTppYdWkND4zEldzs6zMwOfR14HXjG3Ha1kbGGhsZjSq73Q2cbBkJErorIXnJg8bWR\nsYaGxmNKrvc6ZxbiIdRSNZox1tDQeExJzO2NNvHD1YyxhobGY0pWI+OdppIl/wDB6Y6DMY6OLUIz\nxhoaGo8pWcXHrG8qd5n6YIV72aExhoHoDvTIojGzN4toxlhDQ+MxJXdzxiKSqpS6mx36bhiIo+nD\nQCiligF/AT6AQSk1BKgiIneyalfbDq2hoZGvsN526CNm1q5il+3Q2shYQ0PjMcWeCe6yRzPGGhoa\njym59qawCZox1tDQeEyxY4I7M9CMsYaGxmOKNk2hoaGh4QBoI2MNDQ0NB0AbGWtoaGg4ANrIWEND\nQ8MB0EbGGhoaGg6A5tqmoaGh4QBoI2MNDQ0NB8Cx5oxznelDKVVIKbVJKXVCKbVRKeWXSZ1gpdRW\npdRhpdQhpdQblsnV0NDQsBZ6M4t9sCTt0ghgk4hUALaYjh9ED7wlIlWBhsAgpVRlC/q0O+Hh4Xkt\nIVM0XTnHUbVpuvKKXKddsgmWGOOOwLem19+SSeI9EbkkIgdMr+8AR4EgC/q0O476QGq6co6jatN0\n5RWONTK2ZM64qIhcNr2+DBR9WGVTIObawG4L+tTQ0NCwEo41Z/xQY6yU2gQUy+TS++kPRESM8UGz\nbMcbWAEMeVhwZQ0NDQ374ViubbkOLq+UOga0EJFLSqlAYKuIVMqkniuwDvhNRKZl0ZYWWV5DQ8Ns\nrBNc3n79mYMl0xQ/Ay8DE03/rn6wglJKAV8BR7IyxGCfN6qhoaFxF0e0OZaMjAsBy4EQ4CzQTURu\nKaWCgPki0l4p1RTYBkTxb3rr90RkvcXKNTQ0NB4hHCIHnoaGhsbjjiWubbnG0TaMKKXClFLHlFIn\nlVLDs6gz3XQ9UilV21ZacqpNKdXTpClKKbVDKVXDEXSlq1dfKZWqlOriKLqUUi2UUvtNz1W4I+hS\nSgUopdYrpQ6YdPW2k66vlVKXlVIHH1LH7s9+drry6rm3KSJi9wJMAt41vR4OTMikTjGglum1N3Ac\nqGwDLc5ANFAKcAUOPNgP0A741fQ6FNhlp8/JHG2NAF/T6zB7aDNHV7p6/8O4gPusI+gC/IDDQAnT\ncYCD6BoDjL+rCbgOuNhBWzOMLqcHs7ieV89+drrs/tzbuuTJyBjH2jDSAIgWkbMiogeWAp2y0isi\nuwE/pdRD/artpU1EdopIrOlwN1DCEXSZeB2jS+NVO2gyV9cLwE8icgFARK45iK4YwMf02ge4LiI2\nd4QVke3AzYdUyZNnPztdefTc25S8MsaOtGGkOHA+3fEF07ns6tjjP98cbenpC/xqU0VGstWllCqO\n0eB8aTplj8UJcz6v8kAh0xTYXqXUiw6iaz5QVSl1EYgEhthBlznk1bOfE+z13NsUm0Vty0cbRsw1\nEg+6wtjDuJjdh1KqJdAHaGI7OfcwR9c0YITp/1eR8fOzBebocgXqAE8COmCnUmqXiJzMY10jgQMi\n0kIpVRbYpJSqKSJxNtRlLnnx7JuFnZ97m2IzYywibbK6ZpqYLyb/bhi5kkU9V+AnYJGIZPBjthL/\nAMHpjoMxfvs/rE4J0zlbY442TIsX84EwEXnYT0576qoLLDXaYQKAtkopvYj8nMe6zgPXRCQRSFRK\nbQNqArY0xuboagx8CiAip5RSZ4CKwF4b6jKHvHr2syUPnnubklfTFHc3jICFG0aswF6gvFKqlFLK\nDehu0veg3pdMuhoCt9JNs9iSbLUppUKAlUAvEYm2gyazdIlIGREpLSKlMf6yec3GhtgsXcAaoKlS\nylkppcO4KHXEAXQdA1oDmOZkKwKnbazLHPLq2X8oefTc25a8WDUECgGbgRPARsD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+ "image/png": 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\n", 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xcOqpcOKJsHRp6Iiq7/HH4a67fKvkI44IHU1m0Zi+SASZwd13wyGHQOvW8Nxz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\n", 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\n", 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dtDXEFW9XkJiYaJKSkqyO4RQaGgxXvfI1ReXVfPngRXTy87Y6klKW2n6wlGte3cL9UwZy3xQtgqZEZLsxJrH5cr2y2MV5eQm/vnQwBWVVLPw6y+o4SlnKGMOfPkshItifn02MsTqOy9AicANj+ndnyuBIXv0qg+IT1VbHUcoyXyQfZkfOMR6cNpBAP714rLW0CNzEvBmDOFlbzwtrdLJ75Zlq6hp4asV+4iODufb8PmdeQf2HFoGbGBDRmetH9mHx1hxySiqtjqNUh3s/KZeDJZXMmzEIb714rE20CNzIvZPj8PYSnl+jt55QnuVkTT0vrU1jVHQoF8XrBadtpUXgRiJDApg1LpqlOw9x4IjeekJ5jre2ZFNYXs1Dl8TrXANnQYvAzdx1YSxBfj78bZWOCpRnKK+q5dX1GVw4MJxRMaFWx3FJWgRuJjTIjzsviGHF3sPszjtmdRyl2t0bG7M4VlnLQ9PirY7isrQI3NAdE2LoFujLMzoqUG6utKKGNzZmMmNID4b27mJ1HJelReCGggN8+cVFsWw4UMS3mSVWx1Gq3fx9fQYna+t5YOpAq6O4NC0CN3XL2GgiQ/x5ZlUqrngbEaXO5HBZFYs2Z3PVeVF6Yzk7aRG4qQBfb+65OI5t2Uf56oBnT+Sj3NNLX6bRYAz3T9HRgL20CNzYTxL70Ce0E8+sTNXbVCu3klNSyfvbcrl+ZF/6hAZaHcflaRG4MT8fL+6fMpC9+cdZsfew1XGUcpjn1xzA20uYc/EAq6O4BS0CNzdzeBRxEZ15dlUq9ToqUG4g7Ug5S3ce4tZx0USGBFgdxy04pAhEZLqIpIpIuojMa+H9m0Rkt+2xWUTObfJetojsEZGdIqKTDDiYt5fwwNSBZBRV8OkunQVUub7n16YR6OvNzy+MtTqK27C7CETEG5gPzAASgBtEJKHZx7KAC40xw4A/AguavT/JGDO8pQkTlP0uOacHg3oE8+LaNOrqG6yOo9RZSz1czud7Crh1fDShQX5Wx3EbjhgRjALSjTGZxpga4D1gZtMPGGM2G2OO2l5+Q+Mk9aqDeHkJc6cMJLO4guU6KlAu7IW1Bwjy8+FnF/S3OopbcUQRRAG5TV7n2Zadzh3AF01eG2CViGwXkdmnW0lEZotIkogkFRXp6ZBtdck5kST0DOGlL9N1VKBc0v7Dx/l8z2FuGx9N10AdDTiSI4qgpVv9tXhUUkQm0VgEjzZZPN4YM4LGXUt3i8jEltY1xiwwxiQaYxLDw/U2s20lItw3JY6s4gqW7dRRgXI9L6xJI9jfhzsm6BSUjuaIIsgDmk4H1Bv43t80IjIMeAOYaYz5z30PjDH5tp+FwFIadzWpdjAt4dSoQI8VKNeSUnCcL5J1NNBeHFEE24A4EYkRET/gemB50w+ISF/gY+BmY8yBJsuDRCT41HNgGpDsgEyqBSLC3ClxZJdUsvS7Q1bHUarVXliTRnCAD3dM0GMD7cHuIjDG1AFzgJVACrDEGLNXRO4SkbtsH/sd0B14pdlpopHAJhHZBWwFPjPGrLA3kzq9qQmRDIkK4eV1eqxAuYa9+WWs2HuY28fH0CXQ1+o4bsnHEb/EGPM58HmzZX9v8vxO4M4W1ssEzm2+XLUfEWHu5IHc+VYSH393iJ8k6iTfyrm9uLZxNHC7HhtoN3plsQeaPDiCoVFdeOnLNGp1VKCc2N78MlbuPcIdE2Lo0klHA+1Fi8ADnTpWkFt6kqU79FiBcl4vrEkjJMCH28braKA9aRF4qIsHRTCsdxdeWqejAuWckg+VsWrfEe6Y0F9HA+1Mi8BDNR0VfLwjz+o4Sn3Pi2tto4EJ0VZHcXtaBB5sUnzjsYL56zL0DCLlVPblH//PaCAkQEcD7U2LwIOJCPdOjiOntFKvNlZO5eV1jVcR3zo+2uooHkGLwMNNGRzB4J6N1xXofAXKGTTeYbTxKmI9NtAxtAg8nIhw3+QBZBVX8O/dOipQ1nt5XTpBft563UAH0iJQTEvoQXxkMC99qaMCZa30whP8e3c+s8bpPYU6khaBwstLuGfyANILT/BFcoHVcZQHe/nLNDr5enOnzjfQobQIFAAzhvRkQERnXlqbToOOCpQFMotOsHxXPjeP6aezj3UwLQIFNM5tfM/FA0g9Us6qfYetjqM80Px1Gfj5eOlowAJaBOo/Lh/Wi/5hQbywNh1jdFSgOs7Bkgo+2XmIm0b3IzzY3+o4HkeLQP2Ht5dw96QBpBQcZ01KodVxlAd5ZV0G3l7CzyfqaMAKWgTqf8wc3ou+oYG8uDZNRwWqQ+SWVvLRjjxuHNWXiJAAq+N4JC0C9T98vL2YM2kAew6VsS5VRwWq/b26PgMvEX5+oY4GrOKQIhCR6SKSKiLpIjKvhfdFRF60vb9bREa0dl3V8a4eEUVU1068qMcKVDvLP3aSD5Jy+XFib3p26WR1HI9ldxGIiDcwH5gBJAA3iEhCs4/NAOJsj9nAq21YV3UwX28vfjkplp25x9iUXmx1HOXGXlufgTHwi4tirY7i0RwxIhgFpBtjMo0xNcB7wMxmn5kJvGUafQN0FZGerVxXWeDa83vTs0uAHitQ7abweBWLt+VyzYje9O4WaHUcj+aIIogCcpu8zrMta81nWrMuACIyW0SSRCSpqKjI7tDqh/n7eHPXhbFsyz7KN5mlVsdRbui1DZnUNxh+OUlHA1ZzRBFIC8ua/xPydJ9pzbqNC41ZYIxJNMYkhoeHtzGiOhvXjexDeLA/L65NszqKcjPFJ6p559uDzBzei37dg6yO4/EcUQR5QJ8mr3sDzW9jebrPtGZdZZEAX29+PrE/WzJL2JatowLlOK9vzKSmroG7Jw2wOorCMUWwDYgTkRgR8QOuB5Y3+8xy4Bbb2UNjgDJjTEEr11UWuml0P7oH+emoQDlMaUUNb285yOXDehEb3tnqOAoHFIExpg6YA6wEUoAlxpi9InKXiNxl+9jnQCaQDrwO/PKH1rU3k3KcTn7e/GxifzamFbMz95jVcZQbWLgpi5O19cy5WEcDzkJc8YyQxMREk5SUZHUMj3Giuo4JT33J+X278eatI62Oo1xYWWUtE576kgsGhvHKTedbHcfjiMh2Y0xi8+V6ZbE6o87+Ptw5IYa1+wtJPlRmdRzlwv6xOYvy6jrmTIqzOopqQotAtcot46IJCfDhpS/1WIE6O+VVtSzclMW0hEgSeoVYHUc1oUWgWiUkwJfbxsewcu8RUgqOWx1HuaC3thzkeFUd91ysowFno0WgWu328TF09vfh5S/TrY6iXExFdR1vbMzk4kERDO3dxeo4qhktAtVqXQJ9uXVcNJ8nF3DgSLnVcZQLeWvLQY5W1nKPninklLQIVJvcMSGGQF9vva5AtVpFdR2vb8zkwoHhnNe3m9VxVAu0CFSbdAvyY9a4aD7bU0CajgpUK/zrm4OUVtRw3xQ9NuCstAhUm915QX86+Xrzkh4rUGdQWVPHgg2ZXBAXxggdDTgtLQLVZqFBftwyNppPd+eTXnjC6jjKib3zTQ4lFTXM1dGAU9MiUGflZxfEEODjrdcVqNM6WVPPaxsymDAgjPP7hVodR/0ALQJ1Vrp39ueWsf34dFc+GUU6KlDf9863Byk+occGXIEWgTprP5vYH38fb72uQH1PVW09r23IZFxsd0ZG62jA2WkRqLMW1tmfn47py7Kdh8jUUYFq4t1vcygqr+a+yToacAVaBMousyfG4ufjxcvrdFSgGlXV1vP39RmM6R/K6P7drY6jWkGLQNklPNifm0b3Y9nOfLKLK6yOo5zAe1tzKCyv5r7JA62OolpJi0DZ7ecX9sfHS3RUoKiqrefV9RmMigllbKyOBlyFXUUgIqEislpE0mw/v3fFiIj0EZF1IpIiIntF5L4m7z0uIodEZKftcak9eZQ1IoIDuGl0P5Z+d4gsHRV4tMVbczhyvJq5emzApdg7IpgHrDXGxAFrba+bqwMeNMYMBsYAd4tIQpP3nzPGDLc9Prczj7LIXRf1x9db9B5EHuxkTT3z12UwWkcDLsfeIpgJLLI9XwRc1fwDxpgCY8wO2/NyGucmjrLze5WTiQgOYNbYaD7ZeYj0Qr0HkSf61zcHKT5RzYPT4hERq+OoNrC3CCKNMQXQ+Bc+EPFDHxaRaOA84Nsmi+eIyG4RWdjSrqUm684WkSQRSSoqKrIztmoPP78wlkBfb55bo6MCT1NRXcer6zO4IC6MUTF63YCrOWMRiMgaEUlu4TGzLV8kIp2Bj4C5xphTU1y9CsQCw4EC4NnTrW+MWWCMSTTGJIaHh7flq1UHCQ3y47bxMXy2u0BnMfMw/9ycTWlFDQ9M1TOFXNEZi8AYM8UYM6SFxzLgiIj0BLD9LGzpd4iIL40l8I4x5uMmv/uIMabeGNMAvA6McsRGKevceUEMwf4+PL/mgNVRVAc5XlXLgg2Ns4/pfAOuyd5dQ8uBWbbns4BlzT8gjTsL3wRSjDF/a/ZezyYvrwaS7cyjLNY10I87Lmic2zj5UJnVcVQHWLgpi7KTtToacGH2FsGTwFQRSQOm2l4jIr1E5NQZQOOBm4GLWzhN9GkR2SMiu4FJwP125lFO4PYJMXTp5MvfVuuowN0dq6zhzY1ZXHJOJEOidC5iV+Vjz8rGmBJgcgvL84FLbc83AS2eQmCMudme71fOKSTAl9kT+/PXlansyDmqE5K4sTc2ZlFeXcfcKToacGV6ZbFqF7eOiyY0yI/ndFTgtkoravjH11lcNqwng3uGWB1H2UGLQLWLIH8f7rqwPxvTitmWXWp1HNUOXlufwcnaeu7X+QZcnhaBajc3j4kmPNifZ1elWh1FOVhheRWLtmQzc3gUAyKCrY6j7KRFoNpNJz9v7r4olm8yS9mYphcBupP5X6ZTW2+4V+8p5Ba0CFS7umF0X3p368RTK/bT0GCsjqMcIKekkne35nDdyD7EhAVZHUc5gBaBalf+Pt48MHUgyYeO89meAqvjKAd4dnUq3l6is4+5ES0C1e5mDo9iUI9gnlmVSm19g9VxlB2SD5WxbGc+t4+PITIkwOo4ykG0CFS78/YSHpkez8GSSt7bmmN1HGWHp1em0qWTLz+/MNbqKMqBtAhUh5gUH8Go6FBeWJtORXWd1XHUWdicUcyGA0XcPSmWLp18rY6jHEiLQHUIEeHRGYMoPlHNwk1ZVsdRbWSM4akVqfTqEsAtY6OtjqMcTItAdZjz+3VjWkIkr23IpLSixuo4qg1WJB9mV+4x5k4dSICvt9VxlINpEagO9cj0eCpr6pivE927jLr6Bv66KpW4iM5cM6K31XFUO9AiUB1qQEQw157fm7e3HCTvaKXVcVQrfLA9j8yiCh6+JB5vL52C0h1pEagON3fKQBD0NtUu4GRNPc+tPsD5/boxNSHS6jiqnWgRqA7Xq2snbh0XzdLvDumUlk7uH5uzKCyv5tHpg3RCejdmVxGISKiIrBaRNNvPFm88LyLZtglodopIUlvXV+7nlxfFEhLgy/99tg9j9NYTzqiwvIr5X6YzZXCETkjv5uwdEcwD1hpj4oC1ttenM8kYM9wYk3iW6ys30jXQj/unxPF1eglrUlqc6lpZ7NmVB6ipb+CxyxKsjqLamb1FMBNYZHu+CLiqg9dXLuymMf0YENGZP322j+q6eqvjqCaSD5WxZHsut46L1hvLeQB7iyDSGFMAYPsZcZrPGWCViGwXkdlnsb5yQ77eXvzmssFkl1SyaHO21XGUjTGGJz7dR7dAP+ZcrDeW8wRnnLNYRNYAPVp467E2fM94Y0y+iEQAq0VkvzFmQxvWx1YgswH69u3bllWVE7soPoJJ8eG8tDadH43oTVhnf6sjebwvkg+zNbuUP109RG8l4SHOOCIwxkwxxgxp4bEMOCIiPQFsP1vc2WubzB5jTCGwFBhle6tV69vWXWCMSTTGJIaHh7dlG5WT+83lCZysrefZVXo6qdWqauv58+cpDOoRzPUj9R9cnsLeXUPLgVm257OAZc0/ICJBIhJ86jkwDUhu7frK/cWGd+aWsdG8vy2Hffl6OqmV3tyURd7Rk/zuigS9eMyD2FsETwJTRSQNmGp7jYj0EpHPbZ+JBDaJyC5gK/CZMWbFD62vPM99k+Po0smXJ/69V08ntciR41XMX5fOtIRIxsWGWR1HdaAzHiP4IcaYEmByC8vzgUttzzOBc9uyvvI8XQJ9eWDqQH67bC8r9x5h+pCWDkup9vTXlanU1Rseu2yw1VFUB9Mri5XTuGFUX+Ijg/nz5yl6OmkH2513jA+353HbhGj6ddfTRT2NFoFyGj7eXvzm8sHklFbyxkads6CjNDQYHl++l7DOfsyZNMDqOMoCWgTKqVwQF870c3rw4to0DpZUWB3HI7y3LZcdOcd4dPogggP0dFFPpEWgnM7jV57TeLHZJ8l64LidFZZX8ZcvUhjbvzvXnq9zDXgqLQLldHp0CeDhS+LZmFbM8l35Vsdxa3/8dwrVtQ386eohendRD6ZFoJzST8f049w+Xfnjv/dxrFKntWwPX6UW8umufO6eNID+4Z2tjqMspEWgnJK3l/Dnq4dwtLKWp1bstzqO2zlZU89vlyUTGx7EXRf1tzqOspgWgXJa5/Tqwh0TYli8NZetWaVWx3ErL6xNI7f0JH++eij+PjoZvafTIlBObe6UOKK6duLXS/dQU9dgdRy3kFJwnNc3ZnJdYh9G9+9udRzlBLQIlFML9PPh/64aQnrhCV5bn2F1HJfX0GD41cd76NrJl19dOsjqOMpJaBEopzdpUASXDe3JS+vSySrWawvs8c7WHHbmHuM3lw+ma6Cf1XGUk9AiUC7h91ck4O/txbyPdlPfoNcWnI28o5U8/cV+JgwI46rhUVbHUU5Ei0C5hIiQAH57RQLfZpXyxsZMq+O4nPoGwwNLdmGAP189VK8ZUP9Di0C5jB+f35vp5/TgmVWpJB8qszqOS1mwIZOtWaU8fuU59O0eaHUc5WS0CJTLEBH+8qOhdAv0Y+77O6mq1TuUtkbyoTL+tjqVy4b25JoRuktIfZ8WgXIp3YL8eObH55JeeIInv9ALzc7kZE099733HaFBfnobCXVadhWBiISKyGoRSbP97NbCZ+JFZGeTx3ERmWt773EROdTkvUvtyaM8w8SB4dw2Ppp/bs7mq9TTTnOtgL98kUJGUQXP/ni4niWkTsveEcE8YK0xJg5Ya3v9P4wxqcaY4caY4cD5QCWNE9if8typ940xnzdfX6mWPDp9EAMjO/Pwh7spOVFtdRyntG5/IW9tOcgdE2KYEKdTT6rTs7cIZgKLbM8XAVed4fOTgQxjzEE7v1d5uABfb56/7jzKKmv51cd79HbVzRSfqObhD3czqEcwD18Sb3Uc5eTsLYJIY0wBgO1nxBk+fz2wuNmyOSKyW0QWtrRr6RQRmS0iSSKSVFRUZF9q5RYSeoXw8CXxrNp3hCVJuVbHcRrGGOZ9tIfjJ2t5/vrhBPjqvYTUDztjEYjIGhFJbuExsy1fJCJ+wJXAB00WvwrEAsOBAuDZ061vjFlgjEk0xiSGh4e35auVG7tjQgzjYrvz+PJ97M3XU0oB3tyUxZqUIzwyPZ5BPUKsjqNcwBmLwBgzxRgzpIXHMuCIiPQEsP38oSN3M4AdxpgjTX73EWNMvTGmAXgdGGXf5ihP4+UlPH/9cLoG+jL7re0Ue/jxgq9SC/nz5ynMGNKD28fHWB1HuQh7dw0tB2bZns8Clv3AZ2+g2W6hUyViczWQbGce5YEiggNYcHMixSeq+cW/tnvsXUozik5wz+LviO8RwrM/ORcvLz1VVLWOvUXwJDBVRNKAqbbXiEgvEfnPGUAiEmh7/+Nm6z8tIntEZDcwCbjfzjzKQw3t3YWnrx3Gtuyj/H655811XHaylp+9lYSvtxev33I+gX4+VkdSLsSuPy3GmBIazwRqvjwfuLTJ60rgezc+N8bcbM/3K9XUzOFRpB4u55WvMhjcM4RbxkZbHalD1DcY7l38HTkllbxz52h6d9NbSKi20SuLlVt5aFo8UwZH8IdP97E5vdjqOB3i6RX7WX+giCdmDtGJZtRZ0SJQbsXLS3juuuH0Dwvil+/uIKek0upI7erjHXm8tiGTm8f048bRfa2Oo1yUFoFyO8EBvrx+SyLGwM/eSqLsZK3VkdrF9oOlzPt4D2P6h/K7KxKsjqNcmBaBckvRYUG8ctMIsooruPnNb92uDHbkHOXWhdvo1SWAV246H19v/b+yOnv6p0e5rfEDwnj1pyPYX1DuVmWwI+cos97cSvfOfiyePYbQIL2ZnLKPFoFya5MHR/LqT0eQUnDcLcrgO1sJhNpKoGeXTlZHUm5Ai0C5vcmDI/n7T88npeA4t7hwGXyXc5RbbCXwnpaAciAtAuURTpXBPhctAy0B1Z60CJTHmDw4kldv+m8ZHKussTpSq2w/+N8SWPwzLQHleFoEyqNMSWgsg5SCci57cRO7845ZHem0jDH865uD3LDgm/+UQK+uWgLK8bQIlMeZkhDJkrvGAnDtq1t4+5uDTndvoorqOu5/fye/+SSZcQO688kvx2sJqHajRaA80vA+Xfn3PRMYN6A7v/0kmfvf30lFdZ3VsQBILyznqvlfs2xXPg9OHcjCWSPppqeIqnakRaA8VrcgPxbOGsmDUweybFc+M+d/TXphuaWZlu/K58qXv6a0ooa3bx/NPZPj9HbSqt1pESiP5uUl3DM5jrdvH83RihqufPlrFm3O7vA5DQrLq5j30W7uXfwdCT1D+OzeC3TCedVhxNn2jbZGYmKiSUpKsjqGcjOHy6p4YMlONmeUENW1E/dNieNH50Xh0463bzhaUcPfN2SwaHM2tfWGOyfE8NAl8XrLCNUuRGS7MSbxe8u1CJT6L2MMG9KKeXZVKrvzyogJC2LulDiuGNbLobtojlfV8ubGLN7clEVFTR1XDY/ivslxRIcFOew7lGquXYpARH4MPA4MBkYZY1r821lEpgMvAN7AG8aYUzOZhQLvA9FANvATY8zRM32vFoFqb8YYVu07wt9WHSD1SDnxkcHceUEMEweGExkScFa/s6HBkHqknDX7jvDGpizKTtYyY0gPHpg6kLjIYAdvgVLf115FMBhoAF4DHmqpCETEGzhA41SVecA24AZjzD4ReRooNcY8KSLzgG7GmEfP9L1aBKqjNDQYPt2dz/Nr0sgqrgAgNjyI8QPCGBcbxtj+3ekS6Hva9XNKKvk6o5iv04vZklFCSUXjRWyT4sN5cFo8Q6K6dMh2KAWnLwJ7p6pMsf3yH/rYKCDdGJNp++x7wExgn+3nRbbPLQK+As5YBEp1FC8vYebwKK4Y1ot9BcfZnFHM1+klfJCUx1tbDuIlEN09CO8WdhudqK6joKwKgMgQfy4cGM64AWGMi+2u1wQop9IRM1xHAblNXucBo23PI40xBQDGmAIRiTjdLxGR2cBsgL59dSYm1bG8vIQhUV0YEtWF2RNjqalrYFfeMb5OLybtyAkM3x9Z+3l7MaJfN8bFhhEbHnSmfzApZZkzFoGIrAF6tPDWY8aYZa34jpb+9Ld5f5QxZgGwABp3DbV1faUcyc/Hi5HRoYyMDrU6ilJ2O2MRGGOm2PkdeUCfJq97A/m250dEpKdtNNATKLTzu5RSSrVRR5ysvA2IE5EYEfEDrgeW295bDsyyPZ8FtGaEoZRSyoHsKgIRuVpE8oCxwGcistK2vJeIfA5gjKkD5gArgRRgiTFmr+1XPAlMFZE0Gs8qetKePEoppdpOLyhTSikPcbrTR/U6dqWU8nBaBEop5eG0CJRSysNpESillIdzyYPFIlIEHDzL1cOAYgfGcRW63Z7HU7ddt/v0+hljwpsvdMkisIeIJLV01Nzd6XZ7Hk/ddt3uttNdQ0op5eG0CJRSysN5YhEssDqARXS7PY+nbrtudxt53DECpZRS/8sTRwRKKaWa0CJQSikP51FFICLTRSRVRNJtcyS7JRFZKCKFIpLcZFmoiKwWkTTbz25WZmwPItJHRNaJSIqI7BWR+2zL3XrbRSRARLaKyC7bdv/Bttytt/sUEfEWke9E5N+2126/3SKSLSJ7RGSniCTZlp31dntMEYiINzAfmAEkADeISIK1qdrNP4HpzZbNA9YaY+KAtbbX7qYOeNAYMxgYA9xt+2/s7tteDVxsjDkXGA5MF5ExuP92n3Ifjbe4P8VTtnuSMWZ4k2sHznq7PaYIgFFAujEm0xhTA7wHzLQ4U7swxmwASpstngkssj1fBFzVkZk6gjGmwBizw/a8nMa/HKJw8203jU7YXvraHgY3324AEekNXAa80WSx22/3aZz1dntSEUQBuU1e59mWeYpIY0wBNP6FCURYnKddiUg0cB7wLR6w7bbdIztpnO51tTHGI7YbeB54BGhosswTttsAq0Rku4jMti076+0+45zFbkRaWKbnzrohEekMfATMNcYcF2npP717McbUA8NFpCuwVESGWByp3YnI5UChMWa7iFxkcZyONt4Yky8iEcBqEdlvzy/zpBFBHtCnyeveQL5FWaxwRER6Ath+Flqcp12IiC+NJfCOMeZj22KP2HYAY8wx4CsajxG5+3aPB64UkWwad/VeLCL/wv23G2NMvu1nIbCUxl3fZ73dnlQE24A4EYkRET/gemC5xZk60nJglu35LGCZhVnahTT+0/9NIMUY87cmb7n1totIuG0kgIh0AqYA+3Hz7TbG/MoY09sYE03j/5+/NMb8FDffbhEJEpHgU8+BaUAydmy3R11ZLCKX0rhP0RtYaIz5k7WJ2oeILAYuovG2tEeA3wOfAEuAvkAO8GNjTPMDyi5NRCYAG4E9/Hef8a9pPE7gttsuIsNoPDjoTeM/7pYYY54Qke648XY3Zds19JAx5nJ3324R6U/jKAAad++/a4z5kz3b7VFFoJRS6vs8adeQUkqpFmgRKKWUh9MiUEopD6dFoJRSHk6LQCmlPJwWgVJKeTgtAqWU8nD/D2aHUip1lnQjAAAAAElFTkSuQmCC\n", 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\n", 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vvaT2b5g8WS0dNlLmXtw9Rm8bzcIGC3VHsbTYWGjYEGbNgsyZ1edCQkIICQlJ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\nBACNEnthckIbhmEYKWPLks26QohIoBy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\n", "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot(x)\n", - "plot(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以跟**Matlab**类似用 hold(False)关掉，这样新图会将原图覆盖：" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" + "
" ] }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x)\n", - "hold(False)\n", - "plot(y)\n", - "# 恢复原来设定\n", - "hold(True)" + "plot(y)" ] }, { @@ -600,27 +575,29 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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mX91RjLvYWvQLAjF3fXwq+XMPe02hlA6WP7+NaTxY35p9mbxzMtduXdMdxdI+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\nvFz8ZXrX6E2Z3GUcOqJ3dTlyqIe42rS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\n", 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mX91RjLvYWvQLAjF3fXwq+XMPe02hlA6WP7+NaTxY35p9mbxzMtduXdMdxdI+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\nvFz8ZXrX6E2Z3GUcOqJ3dTlyqIe42rS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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -685,27 +664,29 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0.5, 1.0, 'Sin(x)')" ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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glJlNAvap46Gb3f2FFJ5CC3yIiCRMYhaUMrPXgRvcfXodj3UBBrh7t2j7JqDS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\nQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -726,17 +707,19 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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YeWdYZ52QPP70pzB8NWyYkoeIlAf9Uxazd96BtdcOS4688QZcfnlIHJdcEvYj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\niIiI5EUJRERE8qIEIiIieVECERGRvPwfMOmeP1xnpUIAAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -788,7 +771,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -797,20 +780,26 @@ "array([[162, 162, 162, ..., 170, 155, 128],\n", " [162, 162, 162, ..., 170, 155, 128],\n", " [162, 162, 162, ..., 170, 155, 128],\n", - " ..., \n", + " ...,\n", " [ 43, 43, 50, ..., 104, 100, 98],\n", " [ 44, 44, 55, ..., 104, 105, 108],\n", " [ 44, 44, 55, ..., 104, 105, 108]])" ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 导入lena图片\n", - "from scipy.misc import lena\n", + "def lena():\n", + " import pickle, os\n", + " fname = os.path.join('./img/lena.dat')\n", + " f = open(fname,'rb')\n", + " lena = array(pickle.load(f))\n", + " f.close()\n", + " return lena\n", "img = lena()\n", "img" ] @@ -824,27 +813,29 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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7WT7lWTxO1kW0tATqumGsSRciaYyBvdUo+8tNBqklKwuoHENM0LUq2VRKCX5w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\ncF0fnhfCD8iATdRhDxr0//l8gvl8SAE3kkJX4ma8FK7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TyQFJeMIEgEAYJyiLHEpVMBIc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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -873,188 +864,208 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[u'Accent',\n", - " u'Accent_r',\n", - " u'Blues',\n", - " u'Blues_r',\n", - " u'BrBG',\n", - " u'BrBG_r',\n", - " u'BuGn',\n", - " u'BuGn_r',\n", - " u'BuPu',\n", - " u'BuPu_r',\n", - " u'CMRmap',\n", - " u'CMRmap_r',\n", - " u'Dark2',\n", - " u'Dark2_r',\n", - " u'GnBu',\n", - " u'GnBu_r',\n", - " u'Greens',\n", - " u'Greens_r',\n", - " u'Greys',\n", - " u'Greys_r',\n", + "['Accent',\n", + " 'Accent_r',\n", + " 'Blues',\n", + " 'Blues_r',\n", + " 'BrBG',\n", + " 'BrBG_r',\n", + " 'BuGn',\n", + " 'BuGn_r',\n", + " 'BuPu',\n", + " 'BuPu_r',\n", + " 'CMRmap',\n", + " 'CMRmap_r',\n", + " 'Dark2',\n", + " 'Dark2_r',\n", + " 'GnBu',\n", + " 'GnBu_r',\n", + " 'Greens',\n", + " 'Greens_r',\n", + " 'Greys',\n", + " 'Greys_r',\n", " 'LUTSIZE',\n", - " u'OrRd',\n", - " u'OrRd_r',\n", - " u'Oranges',\n", - " u'Oranges_r',\n", - " u'PRGn',\n", - " u'PRGn_r',\n", - " u'Paired',\n", - " u'Paired_r',\n", - " u'Pastel1',\n", - " u'Pastel1_r',\n", - " u'Pastel2',\n", - " u'Pastel2_r',\n", - " u'PiYG',\n", - " u'PiYG_r',\n", - " u'PuBu',\n", - " u'PuBuGn',\n", - " u'PuBuGn_r',\n", - " u'PuBu_r',\n", - " u'PuOr',\n", - " u'PuOr_r',\n", - " u'PuRd',\n", - " u'PuRd_r',\n", - " u'Purples',\n", - " u'Purples_r',\n", - " u'RdBu',\n", - " u'RdBu_r',\n", - " u'RdGy',\n", - " u'RdGy_r',\n", - " u'RdPu',\n", - " u'RdPu_r',\n", - " u'RdYlBu',\n", - " u'RdYlBu_r',\n", - " u'RdYlGn',\n", - " u'RdYlGn_r',\n", - " u'Reds',\n", - " u'Reds_r',\n", + " 'MutableMapping',\n", + " 'OrRd',\n", + " 'OrRd_r',\n", + " 'Oranges',\n", + " 'Oranges_r',\n", + " 'PRGn',\n", + " 'PRGn_r',\n", + " 'Paired',\n", + " 'Paired_r',\n", + " 'Pastel1',\n", + " 'Pastel1_r',\n", + " 'Pastel2',\n", + " 'Pastel2_r',\n", + " 'PiYG',\n", + " 'PiYG_r',\n", + " 'PuBu',\n", + " 'PuBuGn',\n", + " 'PuBuGn_r',\n", + " 'PuBu_r',\n", + " 'PuOr',\n", + " 'PuOr_r',\n", + " 'PuRd',\n", + " 'PuRd_r',\n", + " 'Purples',\n", + " 'Purples_r',\n", + " 'RdBu',\n", + " 'RdBu_r',\n", + " 'RdGy',\n", + " 'RdGy_r',\n", + " 'RdPu',\n", + " 'RdPu_r',\n", + " 'RdYlBu',\n", + " 'RdYlBu_r',\n", + " 'RdYlGn',\n", + " 'RdYlGn_r',\n", + " 'Reds',\n", + " 'Reds_r',\n", " 'ScalarMappable',\n", - " u'Set1',\n", - " u'Set1_r',\n", - " u'Set2',\n", - " u'Set2_r',\n", - " u'Set3',\n", - " u'Set3_r',\n", - " u'Spectral',\n", - " u'Spectral_r',\n", - " u'Wistia',\n", - " u'Wistia_r',\n", - " u'YlGn',\n", - " u'YlGnBu',\n", - " u'YlGnBu_r',\n", - " u'YlGn_r',\n", - " u'YlOrBr',\n", - " u'YlOrBr_r',\n", - " u'YlOrRd',\n", - " u'YlOrRd_r',\n", + " 'Set1',\n", + " 'Set1_r',\n", + " 'Set2',\n", + " 'Set2_r',\n", + " 'Set3',\n", + " 'Set3_r',\n", + " 'Spectral',\n", + " 'Spectral_r',\n", + " 'Wistia',\n", + " 'Wistia_r',\n", + " 'YlGn',\n", + " 'YlGnBu',\n", + " 'YlGnBu_r',\n", + " 'YlGn_r',\n", + " 'YlOrBr',\n", + " 'YlOrBr_r',\n", + " 'YlOrRd',\n", + " 'YlOrRd_r',\n", + " '_DeprecatedCmapDictWrapper',\n", " '__builtins__',\n", + " '__cached__',\n", " '__doc__',\n", " '__file__',\n", + " '__loader__',\n", " '__name__',\n", " '__package__',\n", - " '_generate_cmap',\n", - " '_reverse_cmap_spec',\n", + " '__spec__',\n", + " '_cmap_registry',\n", + " '_gen_cmap_registry',\n", " '_reverser',\n", - " 'absolute_import',\n", - " u'afmhot',\n", - " u'afmhot_r',\n", - " u'autumn',\n", - " u'autumn_r',\n", - " u'binary',\n", - " u'binary_r',\n", - " u'bone',\n", - " u'bone_r',\n", - " u'brg',\n", - " u'brg_r',\n", - " u'bwr',\n", - " u'bwr_r',\n", + " 'afmhot',\n", + " 'afmhot_r',\n", + " 'autumn',\n", + " 'autumn_r',\n", + " 'binary',\n", + " 'binary_r',\n", + " 'bone',\n", + " 'bone_r',\n", + " 'brg',\n", + " 'brg_r',\n", + " 'bwr',\n", + " 'bwr_r',\n", " 'cbook',\n", + " 'cividis',\n", + " 'cividis_r',\n", " 'cmap_d',\n", - " 'cmapname',\n", + " 'cmaps_listed',\n", " 'colors',\n", - " u'cool',\n", - " u'cool_r',\n", - " u'coolwarm',\n", - " u'coolwarm_r',\n", - " u'copper',\n", - " u'copper_r',\n", + " 'cool',\n", + " 'cool_r',\n", + " 'coolwarm',\n", + " 'coolwarm_r',\n", + " 'copper',\n", + " 'copper_r',\n", " 'cubehelix',\n", - " u'cubehelix_r',\n", + " 'cubehelix_r',\n", " 'datad',\n", - " 'division',\n", - " u'flag',\n", - " u'flag_r',\n", + " 'flag',\n", + " 'flag_r',\n", + " 'functools',\n", " 'get_cmap',\n", - " u'gist_earth',\n", - " u'gist_earth_r',\n", - " u'gist_gray',\n", - " u'gist_gray_r',\n", - " u'gist_heat',\n", - " u'gist_heat_r',\n", - " u'gist_ncar',\n", - " u'gist_ncar_r',\n", - " u'gist_rainbow',\n", - " u'gist_rainbow_r',\n", - " u'gist_stern',\n", - " u'gist_stern_r',\n", - " u'gist_yarg',\n", - " u'gist_yarg_r',\n", - " u'gnuplot',\n", - " u'gnuplot2',\n", - " u'gnuplot2_r',\n", - " u'gnuplot_r',\n", - " u'gray',\n", - " u'gray_r',\n", - " u'hot',\n", - " u'hot_r',\n", - " u'hsv',\n", - " u'hsv_r',\n", - " u'jet',\n", - " u'jet_r',\n", + " 'gist_earth',\n", + " 'gist_earth_r',\n", + " 'gist_gray',\n", + " 'gist_gray_r',\n", + " 'gist_heat',\n", + " 'gist_heat_r',\n", + " 'gist_ncar',\n", + " 'gist_ncar_r',\n", + " 'gist_rainbow',\n", + " 'gist_rainbow_r',\n", + " 'gist_stern',\n", + " 'gist_stern_r',\n", + " 'gist_yarg',\n", + " 'gist_yarg_r',\n", + " 'gnuplot',\n", + " 'gnuplot2',\n", + " 'gnuplot2_r',\n", + " 'gnuplot_r',\n", + " 'gray',\n", + " 'gray_r',\n", + " 'hot',\n", + " 'hot_r',\n", + " 'hsv',\n", + " 'hsv_r',\n", + " 'inferno',\n", + " 'inferno_r',\n", + " 'jet',\n", + " 'jet_r',\n", " 'ma',\n", + " 'magma',\n", + " 'magma_r',\n", " 'mpl',\n", - " u'nipy_spectral',\n", - " u'nipy_spectral_r',\n", + " 'nipy_spectral',\n", + " 'nipy_spectral_r',\n", " 'np',\n", - " u'ocean',\n", - " u'ocean_r',\n", - " 'os',\n", - " u'pink',\n", - " u'pink_r',\n", - " 'print_function',\n", - " u'prism',\n", - " u'prism_r',\n", - " u'rainbow',\n", - " u'rainbow_r',\n", + " 'ocean',\n", + " 'ocean_r',\n", + " 'pink',\n", + " 'pink_r',\n", + " 'plasma',\n", + " 'plasma_r',\n", + " 'prism',\n", + " 'prism_r',\n", + " 'rainbow',\n", + " 'rainbow_r',\n", " 'register_cmap',\n", " 'revcmap',\n", - " u'seismic',\n", - " u'seismic_r',\n", - " 'six',\n", - " 'spec',\n", - " 'spec_reversed',\n", - " u'spectral',\n", - " u'spectral_r',\n", - " u'spring',\n", - " u'spring_r',\n", - " u'summer',\n", - " u'summer_r',\n", - " u'terrain',\n", - " u'terrain_r',\n", - " 'unicode_literals',\n", - " u'winter',\n", - " u'winter_r']" + " 'seismic',\n", + " 'seismic_r',\n", + " 'spring',\n", + " 'spring_r',\n", + " 'summer',\n", + " 'summer_r',\n", + " 'tab10',\n", + " 'tab10_r',\n", + " 'tab20',\n", + " 'tab20_r',\n", + " 'tab20b',\n", + " 'tab20b_r',\n", + " 'tab20c',\n", + " 'tab20c_r',\n", + " 'terrain',\n", + " 'terrain_r',\n", + " 'turbo',\n", + " 'turbo_r',\n", + " 'twilight',\n", + " 'twilight_r',\n", + " 'twilight_shifted',\n", + " 'twilight_shifted_r',\n", + " 'viridis',\n", + " 'viridis_r',\n", + " 'winter',\n", + " 'winter_r']" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1072,7 +1083,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "scrolled": false }, @@ -1080,21 +1091,23 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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U85cp5iVB5FPkFcbA9NqM3a9eZPOvP014x0NsbGxweHjoEJak0R9//HE++9nP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\nq3vUfZsozrjxC7/Gtz71BjY3N4tbHykVHjPpF7GJMo7uUZ2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NwqNSYxn6+3meR6/XY2Fh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KTCMpfDFmrOFESWSxQlWrWpilJsWlhJTK0ugcn+ovRsiN08huURJdIoZufOK8i4DgKT+lqVqakp\nnn32WZaXl/E8j6uuuop2u021WuXo0aN0Oh1WVlYwDIM3v/nNjAwPMT4+zlPHj/Pwww9rm/1rrrmG\nZ555hvPnz/Orv/qrfPe73+XHP/4xExNSgASScHbP3XfzS5MliAIyb/kgUWONzHfvp3phBjvrUdo6\nhuU5YEB7YVWCy46DYVkkUURhfBARxYStLm5vkajVkQ7UUYRpmQjTQEQJdsYlDiLiMCIJIiI/xuu1\n6XvrW5jp20ZmZITxXIZt27Zx+vRpnSZWLpfZsmkDBc/GXT4D52cIF6YRYUC4sggYdNZaVH/1LfzJ\npz+vNw/qGunp6bksNlGpfZvNJuVyWa+8t27dSrVaZe/evdj1iylPRQKKormSOqjbmLYruwOExJm6\n0kFaSq5dbdSithZyqxEArsRnVFaFSsTSVGnjkuWc/a+feBUC7xNCPGMYRgE4aBjGo8A7gEeFEB83\nDONDwIeBDxuGsRt4I7AbGAf+yTCMHUJcrhdVN6PyaVTuyoq1qJKnkyTBwEiTkyPpQGS7GLbUqYsk\ngiiW3gFJfGlGS5F8RXdevxFRqVLr7eiV8KvVaunTQXEk1AWgOhDFljRNk1KpxPz8PKP33sHM3zyI\nX2uTGygiEoPlxw+Qu/IlGHMnWMiOMxpX8EvjeI1FFjoGzWaTjRs3Sp8HFxJTAk2maVBrtqhUVrXM\nW/H2k0SQpNw127bBNIlzvVixzER0RaS1IYmA4yeOc+rUKQ4cOMDS0hJvfOMbGRoa4otf/CKrq6va\n40KFq1x33XVcf921dLo+8/PzfP7zn9df99u/9VusrKzQ6XR4yUtewpNPPsk//dM/EYYhb3/723Vq\n00c/+lHu6u1i9g4TbNxDZvkMK5+9L3UlEhQnhtPVYkJ7cQ0MA7eQk1gSBqYjOz+3XMRodYg6MqhG\nJAmmbZPEMUlXZqZiGJr05JZyjL3xdTzjjbC4uMjVW3pkIQ87FPM5dm3dhGE5WGELp75IfOZxRBwR\npboDs1gmnD1Dt1Jj8fBZZl//y/zXz3xRXxvrRVSqC1UAt1pZZjIZent7CcOQxcVFWq0WN954Ixs2\nbID2EoabSyP7XESrnobR5OTo4GVImnW5lYiiVFWZbuCSWAoOAcNxSVoN+XUqtAYhxVoKqBSAiPVW\nT7IlX1jn8C8uPoUQC0KIZ9KPm8CJ9KZ/LfC59Ms+B7w+/fh1wJeFEKEQ4jxwGnjpT39f1RWYpqln\nv/WJ1Wrk8H2fKI5lZUwkGJOEXcnzbzcxbE8bWqhqmoRB2k5d8ohY79WgmGqqOKjnkM1mKRQKlyVu\nqTVnp9Oh0+loUw8FZKlV59KuW1k6vkLcDnAKORoXK+RHell68MsYjsfgxUO08qO4i1O0y5toNpuM\nj49LO7FCnsTJY4qIMIyYvjjP7OxFRkZGLhONqV16o9FgcXGRQi5HN0o59V5WJi63axSLRS5enGPq\n9Bm+9KUv8cQTT2AYBh/4wAd48skn+djHPsaZM2dYWVlhZGSE2dlZut0ujUaDl73sZaxUVpmZmeG+\n++7TY8T73/9+rrjiCg4ePMjWrVt5/PHH+Zu/+RuKRenXcMstt/Dwww/z13/917y8cQx73520R3fi\nzh1j/r/9Bf7yGpbrUNo8AonAzkufATvj4hazWBmH/OgA2YEyludg5zI6w8IwwM7KRDMhhNxmWBZh\nu0tQb5FEMYM37OPRbTfzJ/98VG8egiBgamoKkSlirs3gnTuAe24/1swxornTEqgzLQwnI701z59k\n7egpzv7DYQY/9n/yB5/4K40xKKByYGBAq3ibzSaNRkNbAQwNDWkezMmTJ7W48I477iAbd7S8GjeH\naFZI2g1ZMEnVld32JZt5y9LrR8XmBCSAGfiS8+DKfFdiWdyEkF2HxC5cSZU3lGNuyqT81ywOP3VD\nbwauAfYDw0KIxfSfFoHh9OMxYHbdf5tFFpPLHsVikU5HovjlcvmygBnVtiuVpHSCcmTLZJgpb1ya\nWYgkkSiu30FF5BkgsZd1kuv16yXVGqpioYJ8gyDQuIMC56Iousx7UkmulWtULpfT9m5ewcXKuCwe\nOkf/7g3MPXkKb2yc8NTTGCTkmguIToPpC+c18Dc2Ngbd1ObLtIijiOXlZQYGBvB9Px2xLG0zprYm\nV0yMg2lQr9V48smf8MADX6HSChHZHtrtNkePHuW+++5jcnKSgYEBbr/9dr7whS/QarU0h+Pmm2/m\n+eefp16vUy6Xue2229i6dStf+MIX+OEPf0i1WsX3fd75zneyedMGjhw9yute9zocx+Gzn/0sd999\nNxs3bqRWq5EJG/zHD7yHrXMHyN7zDmoBZE8/SftH3ySotzFMyI/2Yzg2mdFhgmqdsCW3S1bGIzPQ\ni+nY2PkM2YEeLM/Fcm2tqAxqjZTTIIi7gczTyGcoTIzR+Y3/jT88XqNYlqlg+3bt4KqrrsJ1XUZG\nRghaTbnNSltz0apJSnESk/gd4voq3YuzLB98jsVDZ/He+1u89Tf+vcapAK2kVdocx3EYHR3V4LQy\nDBobG9PhNL29vWzatInh4SForcgDzvYg6iLqK7JrcD1Ep4XhuroTEH4Hw5FuZ0aa5GY6GcC4pI8w\nTekAprgOkdzUoItNpDsUTZ2O/j/ykExHigeB9wghGuv/Tci++/9tT/L/+Lf1M77v+1rfoAqDOq11\nVqVpy0zM1EsPy8bMFyVxJEnWefanL3KSaJaYUmUqU1e1hVA/X2kYfN/XZi7qdAiCgE6no92y1Rik\n1pHFYlEzL6//2//C7FMzZHoyzD0xxcj1kxSv2AEI2qeOEy+cpb7xWtaqcrWlQCzflAay9Wabk6dO\nccUVV1AoFCgWi0RRRL3e0OSsJEkoFwsYIsJKIgYGBpicnGTHjh2cOHWKv3/425w6dYqjR48yODhI\npVJh3759nD17Vs/blmXxhje8gfn5eZaWlnRXsmPHDhYXFzl8+DBTU1MMDw9z9913Y1kW3/qHf+Sq\nq65iYWGBP//zP+eWW27hfe97H0888QQ33ngjzsp5RmYP0tj7KtYim8KFg8x87nOsnjyPW8ziFHPE\nfoDl2iTdDnbOw7RNsgM9GAiSKALbJgkjrFIvdk8PTj6LX29pHMnrLcpxKYwxbYvSdS/lmz3bed/v\nfIjh4WFuuukmRkdHmZqeZawshXWbNm1ifmUVYdrY49uxegakBqexRtJpES3N0p05z9rx84gYwre8\nlQ/86X2AHNsGBwf1OKpIeQq/UlsttdlS+SNra2vqnuGee+6hr68PwyumwKCLaNelNNuy0wAm6ZpF\nGGDYUkAlOg11c6XYQyiLQRxdWl0qx3XlExmla0yBvEeCrta46AzRF/D4mbYVhmE4yMLwBSHEQ+mn\nFw3DGBFCLBiGMQospZ+/CGxc9983pJ+77PHJT35S6yv27dvHtddem74WlxiKKnDGNE0QJkSBzKgI\nurJgRnL0kN7+XTmzZi39eZFiGsqKTnUN6iZTnIZGo6G3F67r6tNV+UoqfEFpNNSYopB+tQE5uBpS\nnihz8cBFxm/YwOqpOQZf4YLtYpfyJLUKSXWBJEnYsnGcII7pKZXkqlK4rKysMDg4qNeq7XZb/ywF\n0OZzMs6PxISgy1onYv/+/XzlK19hz549ABpHGBoaYvPmzZw8eZLp6WnW1tY4ffo073znO9m4cSOf\n/exn9Rh355138uSTT+pOIo5jbUhz7NgxFhcXuXDhAt/4xjfodrt8/Nd/iR/t3082m+X/+OB7oLeH\nYGQ3rmnj7n+Qhe//M3EQYjkWhm3j5hwpfvJDDEsKiNxSgdj3sbwMhm1hlfqBNfzFRdz+PpnvCKlJ\ni0lQa+EWsnjlIqV9L+FU/17ilcd497vfzaFDh5iZmeHee+/l2WefZaS/l7HhIaKUz9KKTfKWIw1z\nPen6RRwT1BosHTpN75Xb+NM5weG//qIOKxoZGdEaCdd19fu9detWpqenNVaTzWa1ClNdK0mSsG3b\nNkZHRzGjAHJliANiy8VorBDXKvIQc1zZ1bRqqZu6l2II0t0s6bakZyqpu3S3jZEt6FgG2Q3JrEw9\nkoBc+1sWP3zqMD/af0jdyT9bVUgfP8u2wgD+G3BcCPFn6/7pYeDfAf85/fuhdZ//kmEYf4ocJ7YD\nT/30933Pe95zGcFIob1qU6FGCsVHUCsZI5PFCHJSY+H7GJmMRHyV6Cq1ykrRKo1jDA4O6mRj1fap\nmz+fz19mNqs6F7ViVTemer4/7XFpGAalUonFxUW2//5/JPrg7zH/9EXGb9zI2mM/oLhrJ/HaCq1z\nF7D2vJJrrh6XVT8IpNMTsJKuGQcHB+XmJpbAYrPZJJPJaHNSz81Qb7aZmZnR+/Zt27bxrne9i9XV\nVdrtNk8//TR9fX1s376db3/725w8eVL7GV533XXceeedfP/736darer1XLFYpFqtcuzYMYaGhhga\nGqJWq/H1r3+dRqPBhg0bGBoa4sCBA1x55ZV4Y9v4i9/5n/nEJz5B/6ZJZqs1RKdO7xN/x8yPn8Ip\nZOWK0ZGXmOU52PmM/lgYNt3lFelZ4Hp4o5shConqdQzLJKrXiJodTNfB8hyCegunkMPrLVE/exFx\n8Gl237OZmYkJzp8/zzve8Q4OHjyox7UnDhzirpfdxNyaxC2Wqh22eHIWF0lMtDxHa3aR1eMXGHrT\nW/jIg9/jzJkzlMtlzdNQrNr1pLgwDJmenqaaros3bNigC4U6SJIkYd++fYyPj0v9i+UQY5CYDk5r\nhai+KjsA00KEksRkZKTRrpHJSSVmJH0gDdfTgKNhmOBmZLccpR1Gus3ANBGGgYGUD5AkGNk8t7/0\nGm6/fp8kEdouv/9f7/vXKw7ALcDbgCOGYRxOP/cR4I+BvzMM412kq0wAIcRxwzD+DjgORMBvCgX3\nr3uoXbNhGJqyDGiptgIkldtzLidfJMN25F7XkMeKCNI5SqSc8ziSs1r6c1QWoSo6YRjqMULd3Pl8\nXt98qtNQz2O9pb36WO2513cjjUaD3t5eDs/V2HP3y1l65IcgoLO8RtA4hOXalF/xag6fn+GGYYfF\n1jj5fP4yYFQVK+lxGeMY0kMzDHyNljfbXbJZKeABOHnyJKurq5r1ubS0xOTkJHv37qXRaGjj2tXV\nVSzLolwu4zgOR44c0XOxbdvMzMzoJG+1Yj1//jzj4+MsLS3R09PDysoKuVyOt73tbSwFFl/49Cfo\n6x/g05//Mq997S/QV5tlbXoat5jDdGytkMwOljEsEzvjYmWzJEGAgWSZWhkPd2CYpCXNVJM4gTDS\nWRJOIYvl2LiFLGGzS/3sRZIwIur4xPNnuPGGV5PJZHjooYf4hV/4BaIoRAhpQ7jaDugvFwniVMvj\neIjKDJ2TR6k8dw4rX8D6jffxvk/erxOv1fs8PDx8me2bAqTV+67A6JWVFT1uZLNZ1tbWKJfLWJYl\nPRsKBRKR0GpLRaTdqSH87qVVo+2mQCsYboakWZMFwkwDok0TIxHgurIjzjhpgJMh/SK9VH/SacrC\nF0kSmOG4Esso9kl/jziQf17A418sDkKIx/gfYxOv+B/8nz8C/uhf+t7qhVZ28Mqb8aeNYOM4lh1R\nSoJKuk1dVUUUSdVaqy4Vmmm7RRIjDEmiWp+svd4kdj2+kT5vvX1QhUNRYYMg0KKa9RZx663rFVEq\neOWrqd//Tfq29RP7odxezC6TG5xgTxRibNxJvLhCu92m2+2mQTe9eF5Gd0vNZhPLsvTF1tvbS6PR\noNVqaYB1YWGBU6dOMTMzw+bNm4miiImJCW0Q8+yzz9JutxkYGNDEry1btjA7O6tJS9lslrvuuotv\nf/vbGk/Ztm0bN998M3fddRdJkjA/P8/g4CDz8/O85jWvYWBggIMHD3LNru10hcktt9xCcf/XidN2\nNzc6gOXa+LWWJDlZJpbnYnoeSeBjZfMkUURmeECSezopey+JUsMWZQMoBXadlRp21sOvNelWaoSd\nkFwi6F44S+910qx13759/OAHP+AN976e1bUq586dY//+/bzzjfdiZWUcgCF8oueYiqAAACAASURB\nVKVZkgRG3/HrPHDwDN/84z+nVqvhOA59fX1YlgR/M5mMjipsNpuX6SjUe6Q8GhSZSo0U4+Pjmkpu\nOw5BOnqWe4qIxbVUOi0QkbyJ1aaNMNTOTySJ7KqyBdkdmBaCSG9YME1tYGzYjuT4YMgCYnuSNZmT\nDlvSx8H4t+PnoArA+ptLte/rnZ2VWxRCSK64ZaVbibYGWBK/I6mm6bpL8me7GKkPXxzHrKys6DWU\nApIUqWm9rkPJtwFtSqtuSPW9lEZj/aiRyWQ0vfnc/DLljf08/93T7LxnB91KjbgbULQSjIGNVOot\nstmsjvqT/I6YbNZMAcg6tVoNgIlNm/Aci7Yv2ZybNozjhxEXLlzg+9//vuZoHDp0iJe+9KVMT0+z\naZNcldq2TbFYpFar0d/fTzYrLeps22b37t3s2LEDz/N44IEHAHRocRiG3H///YyPyyXTxYsXueqq\nq6hUKuzYsUNTvm+5+SbiKGZ7tIC7+xq6pw6RHx/GymaJapIEZDo23uAgZrFH0p39rnSgGhhAdNtE\nq8tYxR6JridCFgwM/OUV6ufndQeRRBFho5MG6UaErQ61qQvknvtnNl/xciqVCv39/Xzowx/hU5/6\nFCdOnGBwcJDv/eQQP3/LdVhOHuF3sYfGKbzlThaqTQ4d/ntWVlYwTZOBgQGWlpbwPE8L6pSf6Prg\nWzVyzM7O6pE0k8ngeZ42j42iiJ07d0rFrQHNVJFpRkE6UoQYxV65YUsS8LuyE065DCKJ05vcRsRh\nCrbLU1+EqfjKy8oiYxgSf0oTrwzXk9aJqRhRiDQeL4lB51n8bI8XzQlKAW3qRVecBiW2UhsMlacZ\nI9cxZq6UOvCW5DcyDNk5KFutJJaorpsFkeibWtGifzowR5GjCoWCPh0Ut2G9yEqdFuvdq9b7TqpN\ni/oarydDeazI4pF56TcQJnQO/5CusHTRcV2X+fl57ULd7Xap1WoYhkGxWGT7tklcxyZOh7IzZ85Q\nazS114WyOU+ShFarxT/+4z8yMzNDEASsrq4SBAHj4+O0Wi1c12XLli20222effZZfv7nf55bbrmF\np59+WvtmKLq67/v09fXpbUaj0eDChQtcffXVvPrVr2Z1dZXz58+ztLyCH0Z4k9dI498kBhHLDBED\n7KyHNziAWSxLoM2ySXTORoOk08Iq9WgloVUexOoZpPb8OVZPTuNXpctT1Pbla+gHJFGCnc8Q+6F0\ngpq7QF+5qK3e9+7dy7333svIyAimafLQQw/RNbMkhokR+9IuzXZot9uasm9ZFktLS5qM19fXR19f\nn7yc0kNjfX7p7Oys7jIVhtVsNmm325TLZY33OCYEUXzJ7KW1Jg+xTC71XpBbNwWOim5HZ05IJyg/\ndYLqogObTGkbJ/zUit609JgByJE6NYKRo0fqEuVmodN8Qffoi1Yc6vX6ZWIoBfQpPcP60FvP87BM\nQ5I6pHu/pJG6sn2SL4hN3KiS+F05x8WRPKlSg1ilklNvqNpWqJtL3ejq+axP4lLPD9ACrEwmo1tJ\nxS5UJ00YhkR+RGe1Q9AMWTtdoTDai3Xj65hfWdNMUN/32bFjhw5EmZ+fT9HvDKODfdI20DQJo1jP\nxNVqlZ/85Cc8/PDD7Ny5k9HRUaanp6lUKtqi7NChQ7RaLXK5HKOjo9xxxx36d221WszOShrKhg0b\nyOVy+mvVtkYR0rrdLtVqFdd12bNnD2/85V/i/PnzFAoF7rrrLjbN/oSeYJX61z5JcO4EQWUFkSQ0\nT5+hvSw7H8PxsHoHAUiaNYkJ+R2SThur3I+3fZ+0sesdJgm6rDz+JJ3lqnwfUmAt6vgk3VDZNkhv\ni3xGag/iCLF8/rIRL5fL8ZnPfIaxsTGuuuoqDh48mI6Z8oCxAhkpsLKyoklvikYP6E5R4U7dbpfh\n4WE8z9OHi+oWlemPupabzSZbtmyhv78fI4m0DYFngqgvysKQLZD4nTSrI9VOhD5muT/lOsguwvCy\nEoS0pLs6piU7CceV7MdEeTmYUmwYRSQp61KkPCCCNHE7CiSF+gU8XrTioJBfZeqiOgj1Iitqtbox\no0gm9iTtJoZpY+YK0o7eTEkjacCNYRpyVywSFGNbdQfruRTKjEO1heoEWc+BWJ99qL6PWl2tH4WU\n9ThcytLMDeSwHJNsb4b6bJ2Bl17DbCO8zMlakZHiOGZtbU3zJorFIgYQJYLK6ppW/B07dow/+7M/\n44tf/CJPP/00Gzdu5Nlnn2VmZuYyYLXValEqlXQ25549e5ibmyMMQ3K5HHNzcywsLDA8PMyv/dqv\nEQSBpo2D5J2ogtPtdnnZy17Gu971LvxQhr/efM0eXvXyn0MEXYIzxzEtIXM1E2n6GrV9Mr1FnHIZ\nq38Ew81gFuTHZqkPf2UVM5vH3rgDEYVYPf10zk1Re+4kUVuGy0Qdn7AV0q02IRFYWQ8RRvjVFlHH\np1OROIBfWUNUl3QSusKD1tbW+PGPf4zrupw4cQI7aMjAH8cj6TR47rnn6Ovrk7yRclnbxav3LwgC\nHV04OjqqsTAVYagA7mKxqLs4dY1ceeWVlHOedH5OORBW0NBBuIaXld1ukOZMpI7RQjEmTQsU8O53\nZAdgIAuc35WdhONJq3oDeRDmShiul+aAmFKj0m6kTEsrTd1+YTZx/78wmBVCaObher9GdaJfFqZr\nSffdpNuS38iwNGJrOJ6MJ/c78vNpwfE8TxcBFaKrZNqqvVQSW8/ztPBKjTrr/SXUx+qhRiHFr1cA\n5vjte0kiQWe1g5118O5+p+YPqO1Jt9vV4KNt24yNjaVzv0+9E2Ckz1MBtydPnuTQoUMkScLk5CRP\nPvkklUpF3xCqoB45ckQHpywuLtJsNtm+fTvT09Ps2bOHarXKAw88wNTUFLfffht//Md/zAc/+EFG\nR0c5e/YszWZT08RvuOEGPvLrb8eIQ37yk5/wmttvxnr0sxiGgXfLLxKcOULUCUiiCL/WpLNcJWi0\nJG9hYFSDYCLwiVcXiZbmsbKeBMu6LaKFC8S1VRnk69hgmTg5D7eUk8EzQsbUdVZqMgE7iPHrXSI/\nojlfxV+rEzdWNXhYKpV0OPOPfvQjMpkMU1NTBGGEGUckKxcRccSxY8dYW5NdnDL1UWOlaZraiEgI\nmfauOlt1vahCUi6XabVaett08803Mzw8jJGEiPR3zxlRGpDrYAxuxuwZxOobvsRcNGXmBKRivjjC\nMG0JKmbzsoP0cpLkZEofSe0HKWSMIUhauVB+DkJg5stStq1UnC/Qnv5FKw7qhhRCaBReteUK/VUn\nvGmashswzDRZO5TrIMDwvHVgTSDFV24G4bcg3USo0aLZbOrOQJ0OygJOFQv1OXVhqBZSgVLr6dUK\ngFI3ryoMmUwG93W/id+UXoC9W3o4PT1HNpvFdV0qlcplBTCOY4aGhi4jXp08eZKFpSWEECwvL7Ow\nsMDk5KTmJNxxxx089thjrK2taZWhIo3Fcczf//3fMzg4SKMhT8nR0VEGBwfJ5/OMj8s16mOPPUYS\ndPnDP/xDvvGNb2h9ifK13L59O3/yoX9PhEmj4/OKl+wi/tZfk7niapKZY7SFjTOyETubIbv9KsJG\nm2x/D/mRfrzRjdK6PhVRGY5DXFvFrzbIbt+Ds3UP8eoSmDZJs07cDdP4OhlXF/khIhYkgWyf/XqH\noOnj1306K13irhzb2stVgrMnyFmCUqlEb2+vfh+jKGJmZobh4WEWVmuIyjThzBSx6WjHJjWOrPfo\nME2T1dVV5ufntY1APp9nZWVFX6cgu1/FmBRCMD4+zo4dO+jJZ8FypY+lYSDqS3KdaLmSwmyYkO3B\n2bgDq28Eqzwgw34yOTlqmOal1LDUml6kZrJG6lliWHYadWekBrSpxkYksmMJA5J2TY4TAKYjR4wX\n8HhRtxVqPZjJZPQIoU5KVckVKKlJI1Ek27JCj6ykUaTzEAzbRbSbiG4bs39MA5KKI6+kz6rqq5+j\npNvrgT7DMHThAPTKVSlGt2ZjZqJLar1MJqPTujqdDmemL5Lty+LXfQb2ThLn8/i+r+3hFDW7Uqlw\n5ZVXkkQhrU6XtbU1arUaTzzxBLlcToq6lpYYGhpi48aNjI+PMzIyctlWx7IsxsbGaLVa+uaem5vj\n0KFDjI+Pc+aMtFC/4YYbqFQqlMtlvQo1QplX+uSTT+rNTLfbZWJigk/98e+RBD4XujFbiyaVv/0U\nvde/VOpAWjUyuRyirw8GBqWICEF+80aClQqAJKolMWYuT1xdAdOi763vgdo80eIFkqCDaLforqyR\nxDFx10+zLKWxi5OzJQ4HWLb0QDC6MZgQdqRrdHupRmdhiWxzid7eXnp6eujv7+f06dMkScLMzAx7\n9+7l2498n9+463oZ0Yek0A8MDGhLeWVmo6475eNRLBaJ41jrTNZHKiq+jDo0FCPV6lQRuTLtToei\nFcvowk4Do8eTRcLLy3HCycjiGUdYXkp+8lsYltRPJO16usZcJxsQQmJsmby0L0hkKpnhuoAtA24M\nE2GastAIIXVJhgHmC2NIvqidgwKQ1qdpqxtXsc00r8C0091uV3LGoxDCgMRPEV4VKGoYEuCJAilk\nSunO60VLatugOgiFRqvRRp0IqjCoAtVut/E8jx1uh9Cw9SiyXiSmaLamabLv999DvRWS270P3/f1\nqZTL5bQSdfv27XJ8CSN6izk9A99www00Gg0ymQxPPPEEzz77LF/96ldZXFxk06ZNzM3N6aK6devW\ny76v53kMDw9z9OhRnn32WQqFAs1mk+XlZa6++mre/va3UywWOX36NGvdiGuvvVaDafl8npGREf7m\nU39O0YpZtnoYzyQ0HvprSldsl69xHBNVFrAQBLNniWsrREsXsRyH2skzuIMDYNsIkSA6TcKL5wDI\n3fEr0FgiWpyRykJDttNBo41pWyRRrHkqludgWmmuJRB1I/yaTxRIPwfDNIiCmKgT0V5cRazM0tvb\nqwvwyMgIYRhy4sQJTpw4wbFjx4gyJezhTSRrS1x99dVacdlsNpmbm9OdQ6FQoFQqMTw8rA+RRqNx\nmfeHwiBUZxrHMVu3bpVW9ZaNsD3yuSyiXSdeW5Rgqt+GfM8lAZTWSHgy87RnCKNvDCPXg2G7WIUy\nZrYo1ZeOK63mbQ9cT+JvqXDLyGRlkYhCjIyMFpT+Dkij2dBP3aFemE3ci1YcVGaEatsUCKgISuvD\nZQAJLgoDw7JlLHn6MCxH00exrLTCyqqMaes30TRN7cyjnKdzuZwuRupnrneMUii2AislMaZIe2g7\na2Zefx9VxFzXxfM8TboKr72VYm+W5R/8UI8MrVaLWk0axW7dulWbkuZzOYTpMDQ0RKVSIZfLsXnz\nZn7wgx9QKpVotVpUKhUymQzz8/PMzc1Rqcjgm71793Lq1Ck2btzI8vKyjs9Tr+Xa2pp2KFpYWGBg\nYIA777yTHTt2cPiZZ/mVX/4lgiDQs/enP3UfQ05ENTtCb8am/eBfkdt+Bd2Ls8TVZZme1ViDyMce\nGMF0s7Rn5whbXcJmJ+UtJPKizhZwNmzD23cH1JcRnabs+JIYEQZ0F5eJ2l26lRqmbcmxohsQNDvE\nYaSzJ0zbkuzJBEQio+tsz0Ikgs5yjXhtkWxGYksTExO0Wi2q1ao+9a+88koqSwsSqLYdenp66O3t\n1erK9b4ZaoxcWVnRh4XneRr/UvhCGIYak9q8eTOTk5MUMjIBTCSxNPmtzslNQpyu2JtrsgtOovTv\nWH8sR+EUh8gUoNCHUejDKA2Ak00j8oz0ek9XmMpSTvmYhF2Ewi7SLQiZvCwq/1ZStlutlr4JVRcB\naAxCCZsgBSYT+cuamQJGNpcavcSIJJLATcoOk71nOhMGbY1g53I5ent79UpzPbC4vvor8osCoBTu\ncAmHQG8c1Fikbnx1YWWzWXK5HPW2z97ffhPLz83S09OjWZmNRoOhwQENgBZyGQSCVrvN8ePH6XQ6\nPP744zz88MNUKhVuu+027fHYbDZZWlpiy5YtXH/99ezevZvvfOc7FItFKpWKboOVHf/OnTsJgoAT\nJ06wa9cujh49yle/+lVtcWbbNls2T+jC+LGPfYxtVp2KJy3z40f+huKuXTJQ17FpnZuWK+NWHWpL\nmH0jGIUSpm3RrdQobh7DLA9iZvOYuSJWTz/WyBZEbZGk3SBuNUhaNUQUkrTqWK6DW8rLwJpaG5Ek\nuMUchmESBwlxEBP5EUErkMxJA+kibRrEfkzQkABzMDeDZyR6rCiXy1oIpUapC76H8LvEnTYTExPU\najXdUYHcNCkmqSr6arRT9Gl1oKhVdr1eRwjB7t27uWLHDmiuSOMhDMwklNu1TJak2ybpNEmaVZm5\n0mnIa1UksiCQHmhqtSkSycnIlWT6dnkYa3AzRnlYhtq4Gcx8j9QSpd4OcoSQqmTtUK3YkUmc/pyf\n/fGirjLVrLbekWk9IKjANiEEJgLiMOU1hCTKdTeS+ZlCVWLTkjHrtnvZi6FaQXXKq7FF7fR/esxQ\nN7vqHtR8r8Jf1GyqnrtS5am4PJCpWOW3vBMn4+ikrVqtxuTkJKZhkHGdFITN0elI1Pz06dPcd999\nHDhwgEqlwkc/+lGpE1hd1a5WZ8+eZcOGDYRhyPHjxymVSrz2ta/V1mR9fX2cPn2a3t5eFhYW8DyP\nnp4ezp07x1NPPcW2bdv44Q9/yIMPPsgf/MEfUBAdduzYwfvf/35umyhSH9whQdWnvkbuymvkujgO\naS9VZXG8OIdZ6CUOfTBdwulTtBdXyY/1k5uYwMzksAbGMPIyUZvmKiIKZNdRXZJgWaeF4biELcl6\nFInA68kT+SFRN8ByLWI/xrQMRCwwLUNmUVgGTtYmiRKSWBB2IjqVNv5aHTfu0NPTo12hR0ZGAElr\nv3jxIk888YR0dK7Oa4vCIAg0bdpxZEdRr9dZWlrSzmAKbygWi7oTVRwHJbrbtWsXGRFI4M/NYogE\nUV8h6baka5NlaUenpNuWYGK3Ce2G3GT4bdlpdJvyOlaPOEKoyDzHw8j1YPZvwBycgEwBQ3UFlqPF\nVdLoxZafS+I0x/SF51a8aMVBocmK36BwAFXt19OSVbFAxBJ4NM1LLZJpSRAmbbNMTxKTRKcOYecy\nnwa1kVA36vrvr7oJxWtQGIjiSKhVZZIkLC4u6uKiioMQQmMTSoiTyWQQlsO193+SSkWCdENDQzJU\nFUHGjInjiHqzSRiGPPXUU3zta1+jXC5TLBZZW1vjwQcf5MCBA2QyGe666y5M06Rer/Pxj3+c173u\ndYRhyG233aYzJ9ToEscxk5OTzMzM8PrXvx7HcXj00UdlxJ4jGYLtdpuFhQUe/tEB/tf3v5+33Xkd\nYmIf7XaHzNmnsMsD0j6t0EOU3giWYxG2fZJOg1gYhJZL9+IChmUycMP12GNbMPvGMUoD2g08rldI\nGmtpZ4cc/YIuIo7wygVMx8L0bESS4OTkCWi6DrnBPJZnYzoWXo+H6ZgkQSIj7QDLlddAd61LZ34R\nsbagO4edO3fqcJlms8nFixcxDAO7bwQC6ZeprOSV14eSXudyOfr6+nQXpjZdiiatbOAqlQpCCG69\n9VauuuoqDBFDpkCMhRV1SeoVDMfBLPRoXwaUzUASEVdXiJZnSbpNkuYaol2XXIRuU+IS7RqiK1Wl\nuFmIA0TQkR6qtodRGsIoDGD0jmEUB2UkgYjTriNzqcgkEes76p/18aIVB0UsUmrC9ei7mu90wjYQ\nJ2nL5GXT3IrUjDVlzClgMmnV5RthWOAVND9BYQnKMVrd/IomrQBLhXOoUUHd8IqwZFmW3kooxWej\n0aDdbtNsNvUKLAgCSnQ4cuQI3z18hlwux9LSEv39fXK291t0hEOYBrJMTU0xPT3N4uIir3rVqzh+\n/DhhGLJ9+3ay2Sxbt27lyJEjFAoFPM9jZWWFL33pS7z3ve9l27ZtPPPMM9TrdWzb1hft7t27mZ+f\n58iRI1x33XWMj48zNTXF9ddfx/bt27Wo7YEHHmBi4xh+zwYuXJhmuLtIeOGkvKhNCzNfxBvZgJkW\nBjvr0VlYQZx+Gru+hEhihn7+lZImnclD0CaeP0u8MofoNKVoKI4R3bZ8f1KvgaTdxspKhyM746Ua\niljKuzOu1GWU89ienXYPJl6Pi5OT/hCGYWDaBtm+DK3FNeKF83iuozdSKovEtm3tEG2ObEUEPgP9\n/dpMyPd98vk87XZbA9ZqbAA5ArdaLe0zUq/X9bWqbOeLxSKG5ZDYLqZpQKeGMl5Rjk4qzs5w5QGo\nCEqiVU9Zoy2SZk1jMsS+zMj0m9BtpKtLZawcad4PIgEvi5Hvw8iVoTBwqYOwHRm5h/Ka/NkfL1px\nUICjIjip1l696Osl3XEcy1j6sCuTe7xsyt9PcQYhjWWxLPknjqQ8N/XvV+pLQANK6wuSGh/We1mu\nN4hR8yeg157q+SubuFKpRD6f1z6D/f39VFpyj/7K63bhHfhOylqsSpGO4WEAtVpNA5hHjhxh7969\nTExM6AvXNE327t1LEATMzs5SLpe58sorednLXsab3/xmWq0WzzzzDLVaTVOf12MmpmmyuLjIrbfe\nSpIkDA8P8+ij/8R3vvMdNm/eTKFQ4Pf+99/FMk2efOppxoou7f2P4m3YTFxfk6dftoDhZYn9gLjr\nS58GwyCpVeg+d4DcxCZIYqyxSamfqC0DAhFHWootOg1pXtKskwRdROBjZjIYbgankMPOZTBdBycn\nRwwhhPw4iiXWAFiOJZ2kYoFhyc7StE26az5BvUvcXCPjuZepehVIrDZSK0kW4XcoehaDg4Pap+KK\nK67AdV16e3v1AaFIcGqDpHQ6aiQBGB4elgXcNsC2CYKQOPARfkeaEmWkGa0I/UsiKuXVYNnpyBaT\ntKXuIW5USTopLhOGsgCkmwiSEMNy5bWuTAlUqlUcSVFWriy7aC+HkStj5PvkiGHLgN0X8njRiwOg\ntQpqNbeejajAIGnp7WLmJbCHIKWJprxzXVHTH5CClAo4XM+dVz9DmdyqTYkiKannpAhPihWnAEw1\ndqjULvX7qK2H8kUonf2JpEYbBXJbJxkpS4qvWpl1ul1Nevrc5z5HvV5ny5YtLC8vE4ahnn9HR0eZ\nmpqiWCwyPT1Ns9nk9ttvZ3FxkaNHj/LMM88wPz+vn0exWKS/v5/nn39eS8K73S5TU1O8/e1v5x/+\n4R+45pprcByH973vfew0Vjl2coobb7yBlU98lNy2nUTz04TnntOvMaZBpk+K3dpLkurdnZtHJDHu\nTfdyLL+dZt8k5qarMDfsxuwbTXMepYVZXKuQNGsyX6QrPTuNFCyLfR8nn5VchrQQJGEMpolTyuEW\nc1ieg2GB5ZjYGVu/j4Zp4PVI5l9cr2OKREvZ1Xvc6XSoVCqS09BsEreaDA70Mz4+rjGmalUWbaXD\nabVaussEdOixwqt836dQKHDdddfR398vtyCmg2ka2HEAQVve/LYNniyChuVIrCzlLIhIulIZjoOZ\nKyI6LcxMqvhNb3oR+ohWVeIRcYyIQ4Tf1B6qJAki7Mj1pWkiWmtyfenmZPKb7WH0jEhgs2fwBd2j\nLxoJSlFO1UNJpdUpr1yYms0mIyMjxIaFabskrRrKTVf65qXeeInUUpjZgqwPQmVrXlJaKl2Ebdt6\nvgQ0zqFudHVCKKGSIr0AugVVz13pLdTXJ49+jYVzpxh689v5yEP7eeyx/6JNQ778pS/Sn3fpBD6+\nbzI/P08ul+Mb3/gG1WqVN73pTeTzee6//35A4hNveMMbeOSRRwiCgB07djA/P4/runzqU58iCALG\nxsY0WDkyMqIl18oRKpvN8qY3vUl3DY1GA9d1efzxx7n++ut582vv4szCGmOjMfVP/B7Dd95O0mrQ\nuTgLSYy3p1cCbI5Hd61J1AkI210yfRGFLRMc3/Zynv7y19mxYwdgaPmzmRmjd/du7G4Va20WZk8T\nVxaImzViP8QSYHouhuHg9vYS1utkh/rortZJghjDlh4QIorAETJHM+sSNNpkemR34eZdDMsg25tl\n5JZ9ZK99OTPVhu6k1AgYRRGFQoGxsTF+/NhjvLbbJm/JkfbkyZNs3ryZSqWiC35vb6/mjMzNzenO\nVr3XKhHeMAxGRkboLZeBmMSwiOMAug2Sxqq8DsMA08ulRrkJpu3KjiLdNogowHDl72nYNkm3jel4\nMoel2JsWkrQQtGvSZdz20gIhJdqEPqLbxMj3XtpaRF1ZINLEbtz8C3afflGzMtVJq05s5d2oCoUS\nNclOQUBqQS/iSKZbWbbcl6dbCixbkmtMOVqIONKKyU6no+naSoWotgzq3xRJSW0t/ntKTNWqr9cy\nqDbecRyqRw/S89Z38ht/8Beajux5HnfffTcDTsypD/wm5c0DtH7pvTz22GMMDAzwox/9iL6+PiqV\nCrfffjsf+9jHKJVK3HLLLTj2JQ/J5eVl7T8wMTHB6uoqmzZt4uJFadGp2JSDg4PceOON9Pb2sn//\nfu2R2Wq1eOSRRzQ/4vc+9H5W29I96hp/Gq7dQ9JtE1WWaC+skO3vwSz1I6KAzunjqVgwxsl55K/c\nR/uWe9liGFx15S5AsFpv0Wg0OHfuHKdOnUIIwcjICJOTk4zvuIX82gWiC8dheQGr2CPHjbiNVejB\n6bUJVuRKUXXMdsZDCAfTCemuNUhChTMp/CFD7+7N9F57HeY1r2CuFXHq+HEtglLdnDIDXl1d5bEn\n9/Palw4j6hVuvPFGTp8+zcLCAtu3b+fs2bN6vKzVaiwtLWkAWLEps9kstVqNIAjYtGmTNNZJR1mD\n1Gu0tSY5DZYjr8sUYzGtdBzwsvrwIhFpvEIEtotVypAEkgavMykSyfHBSgNqkjQ+z3IQQUvSsm1P\nZmimHTWGmZrKKkq1RfICAckXrTiUSiV94ypNvAIn1Zyn2JPyJk0BHIFsr5JYUkZ9mSMoum35744n\nvQVsFyMO9CZCVf71VGc1WyrptbKlU9x8BWqp1lMh2UoXovGQdMuRy2QoTB2YFAAAIABJREFUvedD\niNVZ3vqWt/DFL31Jb102bNiASBIG/8N/Ynp5jcWpKRYXFxkbGyOTybB161a2b9/Oe9/7XorFIkND\nQ+zZs4f/65Of5Oqr9xHHMefPn2f79u1s2rSJCxcu8JKXvER/bmZmhunpaXzf56mnnuLMmTN84AMf\noKenh3K5zLe+9S1GRkaYmpoiiiJ+93d/l2E34tDMRa4pRARnzpPdvJno4lmaMwt0K3UGf+5WsD3i\nmefxl1eI/ADTshj65bfTmriakUIG6kvQWMHIFMl25zCGx2lPbCKXy3Hy5EnOnDnDgQMHcByHm2++\nmZtv/mUypx4nWprGKvViZPOXrP0cG8OyMF15WSZBgJXNENSaeD0FREFSq92ePBt27cTZdwdLIsM/\nPHuU2b/96v9N3XtH2XnW976ft+3epkszKiONJKtXWzY27pYFxOBjTGwgnGBqQooTAiFOThIgAXI4\nyTlphIRLQkJyE59cSABTbAuw5QJCbmpWG5XRFE2fvffsvt/23D+e93lGzk1u8Fr3Li/2Wl4ejUZ7\nZt73fZ7n9/v+voXVq1dj2zY7duzgqquu4rHHHiOZTOo27vTp01QqFZzcOvDbrFmzhmQyqYVvAwMD\nmsk6Pz+vsadkMkm5XMYwDO370dnZybJly1i1ahVGu4JI5HBdjwS+1DQ4cbk4zSQCZFAPQno0CKE5\nCEakpTCdmMTUghDDjjYOhZ9FAbtSmimWcloCX/pJmpZkRvouImNjeIbkTIQBWNKsNjBltuireb1m\nmMOVdFTluafAQDUGVICkEEIq3MIAo6MPENGYxpDQg2WBcouCKHm7DXZMjzJVEpHakBSeoUaYV2Z3\nqopAkZ2uzL5QTEKFXgdBQD5ukZ4/h1mbw8x2YHYNcOToUVzX5YYbbtBEmUa8QKXV5syZM6xdu5aO\njg6efPJJ+vv7uf7663nuueeYm5sjk8nw4IMPMjU1xeHDz3Ht1btJJBJ0d3dryXClUuGxxx6ju7ub\nu+++m1wuR6Uix16KBn7gwAGuv/56ZmdnGR4eZuXKlVy6dInNmzezb/MKvGwfWzddxcJX/4H0xs0E\n81M050pURqZI9XXgXHU1ol6icf4sjekFWnOL5N/3KywObJEaESuO4SQwsj1M+3FI5gknh0kHTbZt\n3cKuXbvYsWMHO3fupL+/nzNnzvDwv3yDxbXX46zbhZmP/AuiRRLrXkaiI6Mt4oIgJPA8zAgQTvd3\n03vjtfQ9+CmO9uzm8RMXGZuaYePGjdx1113s33cH1113HTfeeCODg4MMDAzQ2dmJ7/v6tLdtGyuZ\ngjB4hQvY4uIio6OjLC4uapBcXdMgCMhms/T19RGGIZ2dnQgh6O/vl1L9eBrPl/iGKE1K2zu3pY2J\niIxwwmZD+z5qd6YIVyAMEWGUvJ5IyUzMKIkLry1xBMMEM6qkfU/iDLZ0ykIEckNqlBDturS+D/xI\njxFiRc/0q3m9psIrdZorv0RAi5tqtdorNBfKHotqEZA0atotzERaAjuYhF6LQNSwupbrG6Jo0kEQ\naAGTMgMFiTwHQSBPlAhsurKtUK2HerCudKAWQpBPJzFK43IG3api+C7exZf51bfczKk7bmdLoqnD\nX+r1OquW93Kw1eLgwYM89dRTdHR0sGHDBvr7+3VM+6pVq0gmk3zuc5/DMAwWaw22bt3KpUuXyOfz\nZLNZ5ufnEUIwOztLLpfTMe+e52kZ8YsvvsiJEye48cYbGRoa4rHHHqNQKPC5P/wMxVqFoNZCfPkT\n9L1hP/7cZVqzc9Quz8oTcs0qgqkLeFNj1EfGZEn/a3/AuVKJTlO2DwofkrjJ19m0aRM37tjKidEp\nCoUWIyMj7Nq1i0ajge/7+t8sLi7SsWIzTjwh5/3zU1JwVZzGrTYAA6/eworZWLaFQFBYu4LYyrWY\n19zF+EKVnp4eVq9eDcClS5dwXZdarcb09LTWSShFbaFQ0K1VrVbD7FiO8OVGv2vXLh599FEuXboE\nQHd3t64gi8Wi9tlUMn8VRbB27Vq2bdtGIuZI78vQwDYNcOvy2YzFtULYjCUQQRB5kHhRUratWwbD\nkBMMqbT0peJYhUWDbE9UXoVhSDq070ryn+8CJoYvJ3TEknJTaZQRTjwiA5ryYOXVTStes81BjQMV\n0Held6P6vBLGGIoGGgZSeGXK2HE5C47yK0wLM5GUu7ItCTUySWrJMlyNLhUGEY/Htcz534Kj6sH/\nt+aiSk7e1dVFLqxiOgbCSUCrDs0Kwklg9ywnmJtga/96vNMvcPW1d/NHf/FFzba7fPkyd911F8uW\nLeOaa67Rdm2nT5/m1ltvZXZ2llKppHMrXnjhBTZv3syFCxd0ZWNZlib3XLp0iQ996EN8/OMf18Qd\npUB1XZdvf/vbbNiwAd/3+dVf/VVis8NcDDoZOP0vxDdvIKxXCOo1amPT1MYXSPbkEK0G3vg5SifP\n4fkWZ29+GwuHD5PL5Th9+jQdHR0cP35cX58LFy7wwgsvcOjQOn70ox9pheSOHTuYmJjgumuvpVqT\n4zohBCKewexahZgfxUjnMJ0YwcIUwg8JfXmi+i1peSY1FwGxjVcjklkMo8aJEyc4ceIEY2NjWilb\nKBRIp9NkMhkaDUmRfuSRR14hxRdCyAmC2ySWib3Csi+Xk9OYubk5SqWS5reoTSGbzVKvy43xqquu\nYnBwEKNRhEyXNK1xG1EFEGA6sWjMLvt9wzQlZ0FegOhEl+nZeG05nUCON4Vpyq83LYknhFFr0ZCe\nEIgoAs+0MGLJpXSrRBojngYMKSkwItq035KHZezVOUG9ZpuDOqVVFB2gF2MikdA9nnJIUmMvlURs\nWLa02kpmJJ3a9zBtJ7L5FrLcShUIy3Utv1b0acWvUG7LyuxV4Rsq+EZtTErKrBR7a+NNat/7Msab\n3gPtasR+i0CgyODD2fQ6Dp2+xPINt1MeneLgwYPa9v2nfuqn2LZtG47jMDAwwGB/D48/PkmhUNCe\nDUIIrX3wPE+DqI1Gg76+Pj1WnZqa4ktf+hJ//dd/zWc/+1k+85nP6FM0Ho9Tq9XIZrMcOnSIoaEh\n3vm6jbRXbGNwbozahWGS2zYR1hZpzczSXqwR70iT6uvAq7doL07gtQ0eXb6VxvB52u02pVKJy5cv\n64qq1WppIlkqleLChQts2LCB48ePMzo6yvve9z4+/vGPEzcFRi63NP71W3LMVliGaVqE9UXMVA4n\nk6Q2OU/QdmmXm8TzLvm1/aT33sKldpLP/+ZvMTk5qbEeZSuoxHEqNrC3t5fh4WGt3VGbg+M4mIk4\nQXmWZnKVVqFWq1WGhoYYGxvDsixtY5hOp2XFt2qVplMrjUU6ZmLEkgjk80V9lrBWwkxIHMVMZWXF\nq4JtY0kMEZcbSGMRI5OXtGk7vuTzYNmyWY4WP15L5l96bVkZGBJXI4hs30QAbiBp24pw5cQxkjlp\nyCy9BqVM/FW2Fa8Z5lCr1XQYqQqbUVz1xcVF6vW6zlpoNBpLyK1hyj7VsiISlAsGOgGIyP8fJwFu\nU49FFT++Xq8ThqF2O6rVanqMpcaZqtJQgGWlUtH9/MCFH1H67jc5/0+PslBrLl30RFZr582Bjcy0\nTZ588kn+j7/+Gw4cOKDVmHfddRcPPvigTp/asmULD3zwF9m8eTNDQ0McOHCAndu20G63WVhYwHEc\nWq0Wjz/+OO985zvxPI+NGzfKZO5cjna7zdzcHJ/61KdYs2YNf/RHfyTzIaMNQmEnmUyGT378dyGV\nY2z0Eot/9yd0XHM1WA5hq4lXa9IuN7GTMQLXozFXolVu8sLO25mamaFarXLixAmGh4f1FEcZqyri\n1Z49e/jdh36dn773HoaGhnRb+OlPf5oDB5/BFAGWJR85YUckNrcFiVQ0kQpwqw28WpPGXA234eK3\nfJI33MGff+8Ev/d7v6f5CvF4nDe84Q38zM/8DOvWraNSqWiOQjKZpFgsMjk5STabBdAeFkEQYGS7\nCOo15ufmtJBKtSDq+YMlN/SOjg4WFhZ0q5nL5di1axeJeFwSu0KBHbQR5Sm0yMmOApKSmUjzYEPo\naUWmkcmD6UirOITOtJTxjoEOcVJxdzrWzjAkczKZ09wfYklIZjGU1iKWWmo/4hlwlFflT4jwSmEJ\nCvBTi/LKUy8IAg0QBZ4bGcd6Mmm41YiUZ2Yke1UkpwisFAJiST1iVACnUtkVCgUymYx2mlLf/0oZ\nue/7NBoNLdpav349l7/2CJVTwwy99x3EI2ALoWzzbU4vuIhYikqlQjabpVgs8rWvfY1cLse6dev4\n6K/8Eo888gh/+Zd/yVvf+lY+8pGPcOHCBV5++WV27NjB0NAQHbksTz/9NN3d3di2zfT0NKlUiq9/\n/ev4vs+GdUMaRFU6jFOnTvHnf/7nOhbvoYce0nbp5XKZ3bt3s3dVnjMln7WVC6TWrAYEwfwUQasp\nvRQck9AL8GotTCfG0V13Uq/X6enp0RJxxacoFosUCgWNx9x+++1cd911mF/9Kwaf/zq//+D7uPXW\nWwmCgDVr1vDMM89QrtZpNlsRwSxA2HGMtFwk2I5UH5oG7UqTykSFVqlFz2038oUfDvPss88yPT3N\n1NQUuVyOP/nMJ+iJC+6+fjvvete7sG2by5cvUywWNV7luq7WubRaLTKZjKwI0x3gtghD2X6l02nN\ngFQ+mldqcFQrNz09jeu6rFu3jk2bNmG6dQg8LNNELE7LEFshg50NSyotRasu8QERAoY81GKSMk4Y\nYKay8vePHJ+MVF5yEszouTbNJb2EIJpgRJMLNZFw4rK9sOM6U5ZYEuIZQjty/TasVwq6fozXa7Y5\nJJPJV4iWYCnLQvHbla4imUzixKNyzGtrXkPYbklk23YQoSCo1/DLxaWLqmLJkThGLpfTkxAluwW0\noErjGywRodQ0wzRNTp06RWagC8MIiBdSZFKJpRsfT0JMEqyYG+G5w4cxTVPPzm+66SY+9rGP8Xt/\n8D/4p3/6J9785jfz7LPPatOW7u5uVq2SI8C2MDlz5ox2InrqqaeoVCqUSiUJQGYzdHd3k0wm9dg3\nkUgwOTnJoUOHePTRRzlw4ACmabJ371727NnD7zz068yHCQa6C4x++R9Ir12LNz2OW23gN9rMH7+E\naZv4rTZBy+XSDW8lkZb8gNOnT2sOSn9/P29961spFArcc4+sEFzX5eabb+bLX/4yvmtSPHGe0l/9\nEQ+9443cc889vPOd72Tfvn0sLCxoS0DLjCrBeFaSdyyHoLZIu1xjcbRCe9ElO9DB1I47dGjP+vXr\n9eRpulRFxDO4xw6ybfMmrr/+egqFAsuXL6dcLmspfTwe16xXlYCNaWHG4ixfvly7Z+XzeWZnZ7X/\npLLeU3oLVenm83l27dpFRz4nF6JhSEWw25Q4l2ktmQ+FIUY8FS3ayFVagYuB98qwmUDyHOQOIKSf\nQxjI7xHPyI3AkYvfyHVj2PGI3JTCSEQhvcorMnKs9oMQwzDxQwiCkKb3E0KfViNLRSBSHAS1iBVO\nAESKTQniKNNNfA8zkUQEHmG7RdBsEno+dq4QeepJFdqVaURXJl0p0FORsZTtVzwe19MNVVnE43EO\nHjzIs88+S2pgOc9d82Zq+eWMfPQB3BPPyM3Bdwk6VjE1NcX3Tozy+b/8S77yla+wuLjItm3beOCB\nB3j00Uc5duwY7373u5mbm2NhYYHBwUF6e3tpNpvMzc0xNzdHrCYziQ3D0Pbpg4ODmrZ77OVT3Hrr\nrTpxyTAMzTitRQrPY8eOUSqVeP7559mzZw/dy1cwPDYN3/571vzX+3HW7SSxcRdONkV5eBw7aZPo\nlNe942OfJlnoZGhoiOXLl+v0akX86enp4dd+7dd44oknuPvuu3nooYd47LHHuPmmG1l46QSV8TKV\n0QXm/+lvuOeuN/LCCy+wceNGZmZmaDalUra8WKHVjtitvoto1QjrMvwnDASJjjhX/dLPMj5xmXe9\n612EYcg73vEO3vzmN/OzP/uzfO/Jp1iek2pc22/q1PJjx45RrValBZ5hUCwW9SQqm81K059aHb84\ny4qBASqVCuvWrWPdunU6ErBUKhGLxSiVStrLVG3UyWSSq666inQqtbQ4Az9a0wFmMq3l0cKPCEte\nS7YVpq0rWuIZTdwj9CKQMvpPOUUrEpVhLFUDIpQCrChl24inI2VyUrYTyRy+FafVdhFIGUAQhli2\nRVL8hHhIqnQlQJdv6mYo0EfZpauS3wLpuae1FKE8cdwahmVixuKEbhMziEp9a+nXU2WjElQpL0d1\n8xXnQpGfVATd17/+dSYnJzVX4Le+9KfcYhVIXTqK253nhw/9Gb2bB1jzkQ/zsc/+OidPnnzFmHbv\n3r389E//NJ///OepVCps2rRJpyKpZCTLsti6dQv5XC5ibpo6m7K7u5tGo0FPTw+zkeHs4cOH6evr\nQwhBT08PpVLpFQapasrT2dlJPB7n3W9/G4cPH2bHmn7y8V2Y6TztIwepnD7D4sVJQtcnUcjgt1x6\nfuP3ma57rFixgnw+Ty6X0wrPhYUFhoeHue222xgcHOS2227jwIED2LbNnfvuYPvIYSZMMG2TZEeS\n9OoB8sv6uPnmm4nFYixfvhzLsshmsxqnACKfA6kNKJ6dxqt7WE6ccPdPsW5sjLWrV1KpvImJiQl2\n794tORsrVrCxOYZIpgnjad0e9vb2anxItYTK3Vxt9sW5WTp8MA2Dc+fOcdVVV2FZFul0mrGxMd12\nuq6rx8Pz8/MoR6i+vj7MZkkucIBWBeE15bMJS9wN25H9fuCCFUO0SpKspEBIK2JPOpGjkwh166wc\nnogEhyRzGoMw7BiiXcNIFRBeCyPTGVUxshoLfU+D6gnHwvKaiIVpgvmJV7VGX1P6tKoUAM1rB/SE\nQLEQJVsy2oUtR19E0awTui6mY0uhjgGhOo1CT2rfI66Cek/FcVCTCvU9FPNNceqPHj3KqVOnmJ6e\nZv/+/fT19bFv3z782XE6jHHaMyMcf/QU339+infdeQsf+9w/8IY3vomXXnpJf8+BgQEeeOAByuUy\nq1evZnh4mJGREW0r1t3djRCCqakpOju7WKxUpImpYbBt2zZtc1av1ymVSnpqMjIywvnz5zX7sVar\naTBVaSfK5TJ9fX08+OCDxGypF2j+/Z/T+d6fwz3+LO3pKepTCyAEdipOLJ+Gez9AmSR9fXk9iehO\nS+OQTGYT733ve5menta6l9tvvpE9e/aQCNuIp/43sydPYSds6pU6VjJG/qY3Yvottq4ZYKoiEf9y\nuaz7fyEEPhZ2dPq15xdoV11alTbdGztpt9tcvnyZXC7L7TfdwORcicnJSfbddD3ZE48TlBaIdfRS\nXqxopa2yw1OktmKxSF9fnwafHcehIiyyjSZmuDSxEkIwNDTED3/4Q2KxGO12m0qlgjImVorXzZs3\n09vbCw6RmYqPaNUQzRpmIhVN1owlq8LABSTtWY4dQ8JGReIS6Q6k7110kAXR6FMIHbArcQZJ8DOE\nkB6pCK22NK5weQqA0JOkrrgFTn0KUZ7DL80Q1MqEleKrWqOv6ShTneJK2KJcdhTzUHlKKt0DdclC\nM2KSwCTCqIcyDALXAyPycQilsakZS5JICJ1DoNBnhXarRaxo20EQcFWiyf2/+wd6XLV3715+9mfe\nAQvjiPI0woSJf/oHSBTYdd9ept79Dn7/wPd0itXWrVvxPI/3vOc9XLt3L8VSiXg8rgHCVCrFtm3b\n+Lu/+zuuu+46xsfH2bJlC0YY6BFlc+wMb3zjG5mfn8dxHB1zpx5uJRc2DIPZ2Vmd1J3JZPRcXjkc\nvW5NN9/5wfPs63OYt0yCiXO0Z6dpzpdxyzXMmKQsi7f/IiXfYlXEVtWj30YZw3ZI2g7Xv+46Ri6N\naixocnaO/uXLMecvwd59DHR2489PYWY78KYnqBz8Jvnb7oZ8D/3L+vHDJfKbuq+VSoVOR5bjhmVJ\nUDQIseI22coYu3fvlhkfgcFys87y1V1QW0AMbcXIL8fL93PqhRdky5dKsXr1as1wVdcGoKurS9/j\nIAgx01lEZZa1a9cCMDo6SrvdZmhoiBMnTjA4OKjTy5W6c3BwkO3bt5PNZqBekqV8qyF5ChEvwRCh\nHCfGkxpkRAiMRFpOIgIPM5XFGzuLs74QsXqjViIUkukLS5MFJy4BSTsmE98SafmMB56sJixPCq9s\nCyFCHNvCqUwRzo7gz14mXFzALc4RegFB8yeorXAcR1cQanykJhZqIWvHqCAEMxYFejTlDLndlE5D\n9UZElBFYcVPy1w0T2nWE4BVKOjXqUnNypZ9QmEcslqBSqZDJZPjwhz/Mnh1baX/vy8Sv3o8Y3EXL\nF6x4IMHsU9/nCzMGEydf0rmSw8PD/MFnPsOl0VHOnj3Ll770Je655x4OHDigH7A1a9bQ09OD53n0\n9vby3e9+F5BgGV6Tbdu2IZwGw8PDrFixgq6uLr7zne8Qj8fJZrOauKVs9FWEXTKZZGxsTF+vZDLJ\nxz7yYWZa8kSce/h/sfy/3IM3fg63XKMxXSTEwLIt4h/8CDNNQX+/dKmKOQ5GGJBIxsBIybI2NDEr\nM6xZvQpTBCw25Mk6PnEZcBgYWE012YfAYGJigu0r1xHWq7RPHsLqW4WzrYNq09ceGIr8lk6nQbhg\n2diJOKZt0ggErXIL/9wRUpmL5AbWQ8OHbDfzDR8rn6dYLPKDx57h/PnzBEHA+vXr6enpoVAocPHi\nRT2GVodOT08P9boMQmq6HvHuToTr0tvby4ULFxgYGOD48eNMTEyQSqUk9hOLacFaLBZjaGiIDRs2\nSB5CKk+IiWmYiFpRLthILaxyQaWFYRsS2Yi0Jz1OhdvC7l8LGLK9wIjaD0+2HJFNPfHIft4X8j3j\n6SVKdKogVZ8RA9IgxK7PE86O4o+fxV+YwS0vEnq+jBN0fUmzfhWv12xzUIEwaiNQpKNarUYsFqNS\nqdBoNPRCDoWQ46GYdNEN6xWE6yIi0lPgSlDHdOxI5740tonH4zQaDV3KqvZFEYyuTNf2Ejnuu+8+\ntm7dyg3XXsNivUli1214dgLX9Tj41DNSo9C3g1jpDNlslhdffJFCocC3v/1t1q0ZZP8tN3D27Fke\neOABtm3eyJEjR7j++uspFouMjIzQaDSkDD0I2LRpE0eOHOHOO+9kemGRwcHVuEHI9o421267ijfe\n+w56eqQOX9F6wzDUpa+ieys+hNIKvPvd72Z1zubY5UW2ZTz8G2+QY8tGjaDt4dVbOMkYvOtDHDp5\nkc2bN+tFaxJAbU6eTJ6UxRthAE4CszaHaNdJd65mzaoVVBvSkNWxLAqZNG3fZ/vGdXD+OexVG7FX\nXoWRzElmK7KtS8TjeBE47Lou8VRKjvr7BrATNjHTwKt7+MVZEqs3QjJHmOsjCENMU1ZNhUKBa665\nht7eXsbGxti0aROVqC1T97bZbJLNZmk2m1oPUa/X8YSJaVmEMxdZuXK9bgXn5+e1F4eKBVCVWhAE\nrFy5kv7+fgyvTWBL6rcoTcrrk8xIKXYqK3kJAiCMrOFbclH7rhw7pgtLI/jAlaNLEcj2wZJAo6Q7\ny0rYsJylKYcVIzBt6akqBKEAq1UhnDpPMDeOO3ERr7yIW6ljmKaMFwxCTMvESsRe1Rp9zTYHIYQm\nQMErx5ipVEoz31IpmeXgeh4xyXaKKNQmRiIJ7SZhEBldBKFMFfY9LVm1rKSeUlSrVS3JvTJCTr1a\nrRaVQj8f3H8tZraT4NwhUguzGBv3cmFimu8ffJowDDlz5gy7d+/mXKSsDIKAQqHAzMwM3/zOo3R0\ndnL/T+3DsCwaxPjQhz7E3//937O4uEiz2eTEiROkUinGx8e54447qFarDA8PY9s23d3dJGyTZCbN\nd598Xmsy2u22fnBVf93R0aHfs9lsamwlmUzyiz//QSq1Gr29ceb/4b/Ts/9NNE8cBiGojs8QuB7m\n/f+Vo2NzrF69mkKhIHEeE2g1pKv37MXIXctBmDa4DYmIWw526EGjTEc8DUETvACrViQF0KwQIvAn\nhrG6lhMuXMZcNkQqu4yYIx9sIzLMcRwHLwixnQRho0YsHSMWtwj9kPb8POa5o8RWtzDdJlbnAF2d\n0qmp3mjS29urreFzuRyO4+icUAXIKWq0IkQpINzs6SSsV+ha08X09DS5XI6enh4N5ir3J+UWHovF\nZC6mY4PjEHg+VuTpiOXIcKVYHExHupXFkvKkD31wbOkLqchJIsRwEhJMTHci/DZap04EOIZiaZph\nWUt/b1lYgU9gWBimgVmdxz/3PO74OdqzszRmSnKkGoaYti1jCS2TwPMjm7kf//Wabg6qd71ytgxo\ns9SJiQnK5TKbN2+W6rdWk6BWxlS5gYGP13IxTQMhDEI/wKvWsTt7NchjYmovBmX7ptoItZjUiZxK\npSiVy3R29eKPHMXI91DacDMiEBx7+UeMjIzQ3d3Nm970Jo4dO0atVtMEo0KhQKFQkDwFz+f4xQky\nmQzFYpGjR4/ywgsv6ByEgwcPsmrVKg4cOMCuXbt4//vfzxNPPMHg4CCnTp3CNuDMOeke3dfXp2PY\n5ubmdLWlrPUVA1SF6FqWxe/8zu9gT57i4qLFlpSL2LqJ9rljuNUGpbNjNOdrdN1/L/98fIQ9e/bQ\n1dW15JuhiGVeOyp5gVadoDQtzWajlGgRBrIMrhVlSpNhEJSkPVzYqMmFYtmEs+OEoY+17uoIrDPB\naxGIAD8Il76vYWBmcsQ7Mti2Sbvq0pwrYZiXsHsGoNXA8iVqH+b7NZC8YsUK+vv7dWt6pTmxMgVW\nobdqYuVGJ7s/O0HMMnSgsopFVM9hEARMTk7qBLTBwUFMvwmWiUWIJUKEIZ8zmddqSj1PKi8Zkq16\nZAzry7YhjMRTQYAwIo1Qu77EhHRikZzbwohZEdAIhERVhYMwTJqei2EEJEqjeOePUnnxeVpl6TEZ\netJWz4o5hEGAbUeyAyB0f0J4DirZudlsahq1knEru7hcLrcUae9mwxNPAAAgAElEQVRL63kr3y2J\nJ80oas22MBwptDJtU+6vUTgpvqurhiuTr5Q13JUTkSsZlAtBjPbGW7goOnBdl+PHjzM5OclNN93E\nG97wBl566SW+9a1vAbK8Xb9+PStXruT1r38973//+9m5cydPP/00H//4xwEpob506RK+77N582Zs\n22bv3r2sXbuWT37yk8TjDm/ffyMDAwN87WtfI5HOsHPDWl63eztnzpyhXC7rOf2V+RiKvagSpnO5\nHFu2bOGOnes5H+TJ57KEF44SVoo0JmaoTcxRnSjTef1u/uroKGvXrqWnp+cVwT547SXFoGkiqvOE\n1aI0ZnHbBJUi7dMv4J36EcHkBfzxs4TFGdwLL+PPjOHPT0VkNRdRXySolbB7VoBhkozZGFUZi2dF\nHBOlc5HTp5osf2MywMavS1GcPzsh7dO8NjSrWO0ajiVl9ApYvjJ86MqsEoXtCCG4ePEipVKJSq0R\nJbEH4DYplUqo6LxkMqlbM8XQdRyHdevWyeyRZI4AE2HaMgPT9zESKbkBeNImABFCO6ooQsVmjEaU\nhrnUOhgGEneIwEbTksIrvxXxIVLRtCIiWxkGvh8QiznE587TeulJ6ieP0Zgt4lbq+PUWoedj2FE8\ngyXBXQAMA8P6CZFsKyGM2hxAVhNKiKXainw+r2++kchIlx0h5A2pV7BSKfnQChV6E9MuUGBoGuyV\n0XZqk1AngiJGqRBfz/MYHx9nTcKHZJrM1g3ccVUfZraLly7NcvbsWZ2QlMlk2LFjB+Pj4+zevVuf\nNslkkg996EN0dnby8MMPa1xgenqa/v5+Ojs7ue+++zh8+DBf+cq/kMlkuP3223nLW97C2t4ciUqL\nT33xb3nd617H4uIiMzMztNttradQJ+T8/LwWqgG8773vQZgWntdkFTUwDOqzJdx6E3exRse6Pv54\nvMXKlSs1ceiVqlTJsjPiGakVEBIME76HmZZqTcMwZDiL15YCOEVdN6N8xjAgrC9ixBM4A+vkvXKb\nkRamDcLAuyI3BCCZ7cbqWUno+cTSDo1iC7cuhUNhvUKwuICdyi/lPhoGtiV5B4CegCh699GjR/X0\nS1Un2WxW41rCT+FXqiRM2UJUq1VKpRLr16/X/hvK59RxHPbu3Us2nQavhWHFMQ0Bfpuw3ZRWcKns\nkomLCCNyU8THMaOP7UREhzakI3cYSgwNQ+IKynLeysrrJSQoiWUTSgWVPNxmz9E+9SNq587TnCvh\nVpv6ezsZmTgf+D5OStLRDcOQ5sBt91Wt0f/XzcEwjATwFBAHYsA3hBC/aRhGJ/DPwGrgEnCfEKIc\n/ZvfBN4LBMCDQogD/8F769BZ5e58pQpTnfhKtiwXt4eol2R1EG0GYb2mTTLsuHOFAEv+vfIQVKNT\nhW0A2gFIJk/3YzdKiKlzCN+luzyLs2wQrA5eGhnn7PB5Ojo7ddhMOp2mv79f8w1OnDih3asXFha4\n8847MU2Tz3zmM7odWFxcpLe3lyNHjlAsFrnvvvu48cYbtbDn+eef5w233YQbCD7yB3/Mb/3WbxGG\nIYcOHQLgz/7sz3R4q1oUvu9TLBZpNpusXbuW61dmOVtqk8tmaX7n/8RKJGjOFvHbPq1yk68s28L0\nsBR8KW9MdfoKIfANB5voobYdjGSGsFrCiCflnNyyCL02eC5GIklr9DxOoQCmjZXrkIEtbityOLKi\nnBEHoQxXTckItKw4tn2FlNoAu6uPZE+B0L+E1/JpLdQwnDhmIkWwMI3VMyCpwqaFaRiEoKceSnSl\n2gPl8+k4DqVSiZUrVzIxMYFpmszNzWHlN+F0dWHU5L156aWXZJxALsdLL70kw43DUAPjW7ZsIZ2M\ng2kShCGO3ySsl6Pk69QrNwYzJnEvjReYEq9BgKlYjt4Vz6n8HYTXjNTHAhI5WW20Gwg7jmmACAXO\n/EW84Repnj5Nc65Mu1zDMKWlv2nbGKZB6PrYiVjE0gzxWu0Il/v/UJUphGgBtwohdgLbgVsNw3g9\n8BDwXSHEBuD70Z8xDGMzcD+wGXgD8HnjP/CmqlarmhBTqVRYWFjQFm3K7blWq2madfQDScVlpEsX\nQYAZk4vEjDlLoIvl6Dgwy7LwfZ9KpYLnebqEtCyL3t5ehtauYTBnYZQnEbEU/uAe/PU30Nz+Jk57\nee7/uQ/zhS/+DYlkkiNHjuisyY6ODsbHx9m1axelUolCocCKFSuoViq8/nXX0tXZyRe/+EWNmSST\nSXK5HOVymTAMmZyc5Omnn+Yb3/gGx48fR4QhG9et4fY3vpmbb7uDhYUFMpkMAz0F9u7dy/DwMPl8\nXmscFMNS9dWZTIYPvO99tANZzXSl48TyGaqjk4ReQOncDIv73sA3HzugDUxUmwIsjZWjKkD4nvYm\nxLLl9QSC8jyGYcqchcUSdr4gWwi/jb8wLVWWgS83CMOMXJEa8kGtLche3JQ6AANjyfrfchCR+MtJ\nOsTTDl7Dk61jvgt7+SCiUZWjwZaMkjMMU0vvVYuoDgLla6GYkWoj1KG4novV0YexMMm6desQQtDR\n0cH58+fp7OwkCAIuX75Mo9Ggv7+fnp4eLBnUKTfS0uSS1sdtSgwm0uXgtvSJTyggcCUOodK1o/Gl\nYSciZ29ZtcmNL3KOMqRpMgmZveJ7PkargnfxONWTJ2gtLBK6PqZlYccdrJiDFZfX0Ii8Pv1Wm8Dz\nCFwf4fkErVdXOfynmIMQQhkuxAALKAFvAb4cff7LwH+JPr4beFgI4QkhLgHngb3/3vuqtqFarery\nT0mpVZq1soDXbYeyhms15M4c5RtYcSkzNiwLM5kCIsaZWMpuUGM6RaGVobg5SuVFLjdNJurgGg4t\nV24go6OjzM3NsX//fvbt26dP/r6+PiYnJ6nX6+zfv5+9e/fyve99j+3bt1Mul3jdrq0cfuElXnzp\nJV5++WVisRjFYlFrNNQ0IZlM8txzz+E4DidPnuRTn/40zxx6ToNi27dvZ2ZmBoQ0Rt2yZQu2bVMs\nFnV6l0LnS6USfX193LBtHQvxbhKJBKXPfQK/6WHFbNrlGvmbr+GTDz+ixV0qS/JKf8xmsxk5d0dz\n9YhpaiaSkgoMMgrAa0smXrMWMf/iURJ0XOY1iBAznsJIpmUwjmFKmbIdJT77HhZC40BhGMrRnWni\nZFKyyqm5+C2pRjQMU25WpuSugAGtuiaxtdttPXXyfV+mh0f6CFU59Pf3axGVnEjIJC8zJy3qFeZV\nLBa1M7nS9wwNDZFMRgpI3yNmiigpW8iqAUNSpoWQRixOTP69YjzacenroERWoTzAhJCt1RJQGWks\nTEuOOE2TMKqibcvAP/sjWsMv05qXG4NhW7KNMA0wDcyYpTco4QcIPyBouQjPx601cRcbvJrXf7o5\nGIZhGoZxFJgBnhRCnAT6hBAz0ZfMAH3Rx/3AlQTuCWDg33vfxcVFqtWqPgGV246Ko1OuTZlMRnMQ\nFDPSsGzJHLPMCIX1cFJx+VBbEfIbl3JqNZVQva0ScylfQVU2KgpttVrl9OnTPPnkk5RKJVatWsWq\nVVJQtX37dkZHR+nrk3qB3bt388///M8IIVjT3wsYHBse4YknnuDFF1/U4Jbycujr63uF6Yzv+8zM\nzHD8+HEdvqLwEZWqhJ1g3crlKDcqZZjabDYBOX5dXFzkoYcewm3WZd5jaw7huVQujGHFHLyGy/8Y\nntcg8ODgoMZY1OapSGAAgWlDulPeqMgPwEznloBKITASCYx4Ar9SpTExGSkQE1j5TqxsB8JrYSYz\n0eJB9tvqNFSsYFtWdbYtw2DJduFk08QyMjWqVWpiOfaSj0GUeiZaMiDHQGBG1aHa4FQbqu779PS0\n/DWiqYU6bIx8D0KEGNku1i3r1KY4lmXpSkJVrJs3b6ZQ6ABAxFLQWIwyP9tLtGg7EbEhI3l1GEj8\nIJnTCkzDji1JqwM3cocGHaYrlAdqKMFIwHUjjGziFO0LJ2nMLkRWekQAo4wOtOOObq8D1ydwfUI/\noL1Yp1ms4tVauPVXx5D8cSqHMGorVgA3GYZx67/5+4j/+R+/xb/3yVQqpUdPismm2gtAx8Eroo/j\nRDx234O4dPJFCIx4Eiubw0xmsLMZ2VI4MZk7eIVWQ1G12+02rVZLL7Lod8D3fUqlEgsLCzz77LO8\n9a1v5bprpdz5hRde4NZbb2XDhg3s3r2be++9l/Xr1/PZz36WU6dOUSgUOHr6HN1dXTQaDVatWkU8\nHmfNmjVMT09jWRa7du3SgKLaDBXJZnp6mrGxMZ0Xms1m2b59O47jMHzhIqnyOLdevxchZGK0kg9f\nunSJcrnMtm3bWJ9sMRdKR6uZv/srYh1ZgpbL3LGLeA/+NpOTk6xcuZLu7m6mp6c1zqNOXkCnmhum\nBe0qRjwdzeuTYDuYuQ6srj7MXJe0QQsFTiFPPJ+RrUSrKceYThyro08uPoB0h6xEUvmI1RdHGBbN\nZktPBWQ74hFfPoCdtHHiNr4b0izVEb6LaNYiT4RIBu21JPAZLShFjVab6pVGQerQAcnMnZ2dJVyc\nR1RKELTp68hqIPpKg2Ml0Fu/fj22cUU1OjdK0KhL1aUVTT2UFZsyYLFjUm3qNvQ0QqDMWqT+x4il\nJOM/npHvoxSW6QK+MCQD0zSxvQbe+Bmalyfx6q2oIgYRCEJVHYShXmkiDPGbbVoLFbxai6Dt0664\n+O3/n/wchBCLwLeBPcCMYRjLooW3HJiNvuwysPKKf7Yi+tz/4/WP//iPPPbYY3zjG9/g/PnzmnOg\nHpYrpxiqBVGZg7Jfky6+IJWahvLfC3xJq3YiY03Qo1CFzvf29rKwsPAKbYf6vtVqlY994F0sz9jM\nTIzyt3/7t1x77bWEYcj3v/997rzzTjKZDF/4wheIx+MMDQ1x7NgxaYNueLRaLdavX8+xY8f0HL6r\nq4tMJkO1WtUmq57nsbi4yKFDh/joRz9KMpnkK1/5Ct3d3aTTaY4cOYJt21SrVZ67XCVx8vv8+R9+\nms2bN+tT0TRNenp6+MAHPoDfsVI6I5sutulTvTRN6cIchdddy8d+53eZn5/X/gSZTEYT0BTbUjlh\nGYaBGbiRB4ElY+zaDYxEFiudl2HFmRxW5zKJhMcSspUzTMxsXk40RBjZrcclxhD5C2DHMJJZ8NxX\nJIQZRjTOA6xsB10b+6XztBsghIERT0mn6lQW4ml5YhtGhN+jNzrXdSmVSpr+rFyelCmN8gipVqu0\nQgPheRiF5XTks+RyOU3dV1VtIpFg9erV9PX16dNfCCFFTPOXMbMd8pkLA/mTGKaWVWu/hijdSoLo\ngZzWWDGMiExGIiu/1neX2gvAtpciG8X4y7QvnqFVrOA3InDRMjEMsFNx7GREwUYQer60+Cs3aJaa\n/HBkhr84dpYvDF/gC2cu/LjLHfhPNgfDMLoNwyhEHyeBfcAR4BHg3dGXvRv4evTxI8DbDcOIGYax\nBlgPPPfvvffdd9/N/v37ueOOO3ROoTL0bLfbGoNYXFxcsvCyVAxapJf32rI6sB1ZqkajNTOTl8Cl\n7+mHAmQrU6vV2LZ1C5lMRo8yLcuiK5tkIFjghvXLeOzQS3zyf32ev//fX+Wmm25i79b1PP/887z5\nzW9GiJBPfOITTE9P84EPfIA1a9bQ2dnJddddh2HHWLlyJbu3XEUYhhprUD4CavNTrYxSEL744ous\nXbuWtWvX6s1r79695PN5dm5cx9FTZzmZ30L6h//CJz/+O/pnVmDkNRtWMj5blGDb49+kNj5LY64K\nhsmfnJnS9nsDAwM6l1NVZc1mU28K6mEMTcn4IxYZjGCAJ+f2ZiIdoeCuLH9NU6ZpawKPgWE70tfA\niWLjnTgimSM0bEjlIbrHykVKfV8j04kQIXbSIZaUgGRzbhEzk8NMZCKTVl86J8UkthSKULNGgyBg\nZmaGyclJjTf09/dHi83WatIwDPFSBcxsnnYAYbqLtWvX0mg0yOfzuqItFousW7eOjo4O7S5m1hcg\n9HFWbZC4F8gTWymG/Ygn4kjhlWHaS+NXy9Gflz+UI5OrnISkqoOsOjB0wLJdnca9dIrG5BxetYlh\nGthxB9M2ZeXgBdKQ1zTwG23cSh230sJr+rgVl135HD+3cR2/vGcjv7B1/Y+3K0Sv/6xyWA48EWEO\nh4FvCiG+D/x3YJ9hGMPAbdGfEUKcAv4v4BTwKPALQjWy/+alNBSdnZ36hFSMP2WkWqvVdFAtEDnh\nROScMMAQQmYv+h5epSb7uuj9RRhALKFPKPUyDAPba2pwavXqVayqnCMVNGh0ruHUfJv/+cd/ysTE\nBPv27WPPzm00QpudO3dy9uxZPv3pz5DJZNi2bRu1Wk3nUty0aYDZco3OQp6zI+Ncc801Wj3Z3d3N\nwsICnufpHEyVe7F582a2bNnCjh07uPrqq1lYWOCDH/yg9Iicfhlx5HF27NjBww8/jHjTz5EvdOjs\ny0qlwtve9jacRArDMOlI2lRf+AHtxSaBG8DP3MvBQ8/pRdHR0YHjOBSLxSsUiksp4+r6YBiQzEQE\nn8gD0bQlKh94mJk8ds8KrM5embGQ65LX3rK0dV/YaqLckMh0S+Nlw0AI8LC12lH954dChsDGEiBC\n4rkYfjugfG4K4bYJa+UIe7BA+FFCtY1hmDrjUm26ly9f1i1kT0+PdqNWqthsNouX6iSoFHFbDcxm\nmT179mgvj3a7TblcJhaLsW3bNmIm8pkzI4ISYCTTMhDYa0nNj8IOwiByepIYlyAys8GI2KFNaTmv\ncljMKOTWsuUmY0tLAt/3sURAOH6GxvlzuNW6PACDUAKQhgQhDcvEtC28agO/5dKutPFbPm5VVmeG\nZWgswzB/7EYB+M9HmSeEELuFEDuFENuFEH8Yfb4ohLhDCLFBCHGn4jhEf/cZIcQ6IcRGIcTj/9F7\nK/677/taHq3MUFXkm3L+Va4+ol1f0rbbskwVnrQvd7q6o7xM1XghrewjPKHVamGapiQUVVtcv3Mz\nmzZuJDE7zMLyncyFSZKJBJcvX2bt2rXcdddd3Hj9tZQqdb7wxb+mVCppZ+VMJsNtt93GD37wA9b0\n98jKZ2Ajly9fppCwuTgywtvfdo/OS5ifn9fVi9KPmKZJoVCgq6uLG264gfXr13P06FHy+TwAN954\nI5Wjz1M/dZR1vVlOnz7NEwef5uzwOemkVC5TKBS4986bKbYkTmGPn0UE8vTuvu0GfuF//o0GdpVl\nu2VZ9PX16ZEfvNISD8D3XFk5pDukSa8VeTzmuvXkwEiksHoGooc6xMx3Rn10AivfpQ2AZVqTi0mo\ngVh1nxUYqslQvouZSJFdtYxER4KSG1C7XMTs6JMkI8OWy81OQFtuVIr9qq7J9PS0luSXSiWaTanB\nmJub04G5pVKJ2eIiVr6brGMQVEpSHRrdn8nJSQCWLVtGX18flpBjXQOgVpQJa56HGUtKTCaZ0+NZ\nLCfCQ0LpKamf2SCaAiXkweV7SyPLSAqAZUksQbVY1WnaI6dozZcJ2h4iCDEdW1KjPTmyFH4gq4Vq\ni1apReCF+O0gGoyY+K1AhsS5V6yNH/P1mobaqNGlQplVVmWtVtOTBOUZKISAZBZKU3KurvEHpEQ7\nIu3Ioynq364YZSrPBiEEJ0+eJL57F6lUkhl3Gc1qVfogRMQmwzAYHR2luFjjmWee4eabb2ZkZISN\nGzdy4MAB9u3bx/T0NPfffz9nzp6NOA8T0kp+/iKTk5MYdoznnnuORCKhtRxqjOl5nlabJpNJvvWt\nb2ka85/93kNUrSwjIyNUVlzNtukjpCOy2COPPEKxWNRt1+tf/3ryuSxT9YC4bXPhT/8Ut9IgvbyD\nX/zuC1qSbpom9Xqdzs5Ourq6tD2fKucV4Uz934lFLUK7Jntivy0Ze2GIke/GbFRk6xBPYS4bJGxK\narWRlACmaNUx43LhGBHq7gVCj0tt23qFIlZdHywTI5EmsbwXwzTxQiGBtEoFO2ZFhCIQzUWMVF4e\niIakX6upzcTEhA5OVvaD1WqV5cuXa3xD5V8K4RNE3ImBgX5tE1gulzFNk/7+fro6OpYYjsq4ONoE\ntbmwE5dcDsuR2EFcmrpg29HoVqoxRVsG9hhRa0YsJTkbkSU9ELEbAxIW+BeP056axKu1JKch5ujq\nS7YxJl69hVtt0q65BO0g6l4kHmHFpICNQOAHPqb9ExKHZ5qm9liIxWKk02ldSSiAUOEP6vTXN8ay\nIndfCFt1tDOv21rKj3DlTVGzajWqUw7Ex4+fYHpGpjsVCgVWDfRjXkFH7unp4ejRo5owtGrVKp59\n9lnuu+8+wjDk3LlzdBbynDp1CsuyuHjxogRQI8uyk6dOSzux6PdUzs35fF5bsNVqNW655RbOnDnD\nyMgIt9xyC7nu5SwsLHDixAm+9Z1Heb5zKx/7gz/RrkgqjOf+++/n/e9/PwueNLIpVEYRvsv69/00\nhzZfx9jYmKYAZ7NZEokE+Xxe+2MqdeKVPb96Kb/OwEnJkXDgRaJAAaYT2ZJZsmf2oxRpQwKHot3E\nTOdkq5HKywVg2joMxjRNYpFS8kqHLtM0Ze8dRcGlerOEgN/28RstjGxXxCQU0jcxAjt9X9Lim80m\nJ0+eZG5OhvFe6WZeKBSYm5tjYGBAC9fGxsaw+1Zihh5mLM7qFStYtmyZbk2EEJFXZEKe/qYNvkvY\nqOmpgGE70gi2viixBMOKsiSMKMeyGf2ckVTaMMEyZVSdCKFZkRtK5JgemFJY5fk+FCfwJy9Rn1zA\na7TkiD6ULMegJQFZr9akXanTLDbx6p7cCADTsbATtmZECiEI/RDhh7ya12u2OaiH3HEczWVQuRL1\nulT5qZ5dcRRE1JMJ30eEAUKEWNnCEvEjCAhrFcJWPRqZRcQdYyneLBaL0dfXx9WRGnFxcZG5uTkm\nZ2aZmZ3lkUceYePGjeTzeZ599lk2btxIoVDg6aefZv/+/Rw5coQjR46w7447+P4TT/DMM8+wfv16\nEomE/LnTBTZs2MDDDz/ML//yL+u5u2ma7N+/n3g8zsjICGNjY6xcuZJz585Jk9OhtTLe7dJLbNl4\nFRs2bODtb3871WqV8+fP6+yJqakpbrzxRv7bbz7EsmXLmJycYmpqisbzT7Dxv/0mqV2vZ8223dpV\nSnE8lO5AidkUIKiqCLUxu64rGYCmJVWQvidHkqkOueGq/MZoMzCSGYQIsLuWE11sRCSkkmrEOLWW\nnABIbkUcI/SxjZCMtURzBqBVw0jml8JmgVAIRCAwMh1y1IelGYZCoAVorVaLM2fO6Pags7NTT7kU\ng3RkZEQ/W0eOHIFUTgKtYUAyEdO4RTKZxHVd9uzZQ4xozOo1JV5gIPkbZkR2EpFng2ku8UAcOco0\nUnn5e7RqSy7lWort6EpC+TiYpoXvy5YoLE5SGb6AW5PtlzwUTYK2J5mh9RZ+28Nv+NI5K2bhpBzs\npKMrhNAPCX2hiW2vtq14zTaH7u5ubQp6ZRReoVAgn89rXrwaMUrxDK80lzUtwnpVSmAjVyjldmNY\nlvR3iMrleDzOhg0b2LN7F7u2baHeaDA2NqbJRcqfcfXq1czPz5NOp7n22mvp6upieHiYDRs2cOrU\nKXK5HOPj41y1fi2jY+PMzMxgmibdnR08++yz4DUppONMTU2xc+tmenp6GBoaYt++fTz++ONUq1Wu\nvfZafN+nXC5TrVb57ffcSyao88lPfpK/ePIUZ89fYHR0lN/4jd/gs5/9LLOzsziOI0872+YPf/4+\nrGOPUVi8yNYOi4F8Euf6t9A4+gMq8/N88YtfZHZ2lmQyeYXDVSwyst1KpVLRrNErK7UrnbmWVIEC\nIxYF9xT69GgO05YEICF5ByLwEIEvyVLIysOwHGmLFlVPlmUt9drRA2+3FjXTs4UE40QoiOfirOpK\nEk87WEnpfYAQsmRPZiEMltS6wIkTJxgdHdWp7YuLi3qDUM5YjUZDP3cjIyMQSxMUZyDdQeD73H77\n7Tp1O5VKsXz5ch2MhGESVheWBFVCyPwIlWgVBvr3fsXkwonLnxcwnAQgn1sCT4K9IgC3LqsOJB5j\nLE7jT13Cb8kNxYo5Eog0JKhpOvJrvVpbAs+hkJuWCgwKlwBIK2ZKJynL5FXuDa8d5qAMTDo7O1lc\nXKTdbmsWm2VZ9PT0sLCwQG9vr7aql6o16e1vODF5wpgWRjwBbltayHmh/LwlQ1Ks0JIjwZ076e7u\nprxY4fLklAbpDEMmGpmmqd2v5+bmsG2bNWvWaFnwpUuXOHHiBJZl8Su/9AuMXBrjwoUL2LbNyZMn\n2b9/v2RDhjZD3Tlp6xZ32LlzJydOnGBkZIQgCFixYgWDg4M89dRTzMzM0Gq1+L2/+Qovv/wy7Xab\n3t5eDRyqE1UJii5fvszOHduxy1OIwKdx+LtYqTThzAxNEZK++S187tuHGB0d1aM9xX68kpWpAnGW\nNl1DA3tqcxACjKAtH3DTkp4C7foSU9GJyxK6VZUiLMuW7YUIMdN5iQmlJb/BxNRRdIZtE1o2xWKJ\nVCKGaacxjSUJeizbS2zNJhJdp+jd1kMskyQ+sEICea267PWbFch06Z95YWFB2+0pFaZKGpcBOhLT\narVadHd3MzMzQ7FYJEzkEfUKuE38ULBixQo9Pdm0aRNJx4K2dI5GBNCqSTDWsiNiU5RjGYZRRJ0H\nnieB00DyGQg8QBKeRKsmg5aiUFzDsCSz0rIBg0ARJKcv4E5eIvR9Qj/EitmYjo1hm+CHuNUGbiWi\nuhsGTsqRICRErYWQYGRb4gx+28eOW6/aJu41qxxgqdxXHwOamGTbNp2dnXR2duoRnOG1ovLKWZK4\ngvR28NqEzaZ0ghKhLGmjPIpUKsXo6CgHDx7k5MmTutRXcuVGo6GnJCpqXeUudjnyZCoUCly4cAHH\ncdjdm2Bs4jKmaTI4OEi5XEYIwQ033EC15REfkdTpZ37wA3bv3q0t8Pbt26f7X9u2qdVq/OM//iMT\nExO86U1vor+/n0/t20Sj0eDTn/40CwsLOnClWq0ihOCDbyK1uREAACAASURBVL+XYGGK2qkTiHaL\nxVPD+M0Wmd3X8/bf/EO++tWvUq/XdaugxFWxWExb8yWTSQqFgpY5qzwMBXQGgUS4sSJeglLzRale\nCF+WypHBr5HMoEg4AGG7IdWEgQTxEraxJPIScPDgU/z8z/88v/27n+Dpp5/m8OHDGpSs+WD1DZJe\nsYxUZ4p1D9wrDVRb9SjZLIB4WnoqRJvdoUOHIpfqnL6vpmmSyWTo7++nXq/rMaayl2+32wSmQ1Cv\nEJoOsaClrfuWLVvGvn37pBKy3ZCcDreJaDckzyOuks6i/Ih4YgmQTKQlcJrMRw+5JTcGkG7TYYDR\nMSA3GBFqTYXaGKzAhXaD5lxZiqjMiGsuBKEbSI1EtYnf8vEbMrHbsAztgeE1Pfx2gNuQGITf8rEc\nE78d6Irix329ppiDir5TM3hA39Tu7m6WLVvG0NCQ5LwbIJoV+Z/nSszBi041w8QwbWn8Ev05bMgT\nAaQCVE0/FDvRsizq9bp+wBTlds2aNTp81TRNFjybqakpDh8+jGVZfPTDv8Jcqp9//dd/5Z577pGl\nJ/J0X1hYIFHowehYzr1vvYeR8csMLe9kenqa8fFxHSnX1dXFLbfcokHXixcv8vzzz/O2t72NxPbX\n88QTT9Db28v69etZs2aN5oCsWLGCW29+PbGNV5MaHMSttWgVK7j/N3VvGiXZWZ95/t67xZ6REZH7\nVln7rkJbIYR2MMMis9nGuPEyYwwGm3E3TdvGbhub4/bY2J5mevDYYGhjW6JBSBYgAUKAQAiEkFRS\nSapFtVdlVlbuGZGxx93nw3vfN6vGHjf6pEOco1NVUiorM+Pe9/6X5/k9rR4f+gdJqAb0Ll/FwClu\nhnJiqkjAfD5/hUpRuT11CplITEK+K41TfUOaY0i+Im+O0Je/ikR2bRjy8E7gJnGnBoGL4ziyWvQD\ndu/ezR/+4R/ym7/5mxw4cIBSqcSpU6dYXZX+j2h4O7kduyjtnIQwIKqvSPl1ppAMOE3trG02mzz0\n0EP09/drIZfrunr7peIEVcvUbDYZHByk1WoxM3cpQbsZRPVl/TEHDx5k06ZN2L062Gk5eAylV0KD\nXJxMImpKy2rBzmwE0ATyBid5iMWKTh0FUiHaWgWEbjcQAkNRyfwuQXWRsNNL5gsCYQj8rkvQcYmD\nELcuRU5xFBO6AUE3IPRCgm6AZZsE3YDI25BTh26ElZJDypfyetnainQ6TafTwbZtnbegKE2qtJ2c\nnKSvr498Po9tmRvrJJEYTnJ9xIEv/RbEYJoEzQZOTirqiKMrYu5U361KTzXXGBgYoN1uMzY2pmGj\nvV5PMxoeeughZmdnefe7383Q4CBf/+a32bFjB+vr65TLZU6cOEGpVNLyXXNsJ7dMOXwPwabhQX3D\nnTlzhlarxaf/7PeZa8vW6oknniCdTjM7O8tNr76R+773GA8++CB9fX1MTExw4403Uq/XKZVK7N69\nm5mGz/jkVYih7fRvOUP65GG+2izw1AOfwvd9RkZGuO6661hdXeXEiRPaP1Eul/VqU91EalCq+AdK\nL0Did7EMO4ljy0GrKn+vNA+ZotzXJ09yhCVXn56PyBWJ3Y6s9MhAp0GxMIgbyrVjPp9ncHBQi9Mc\nx2FoaEjOQfCIjnyXtcd/SN+mYYxcHiOdk2IjKw2pDLHpYEQxvZ7MF7kcA+e6rqZGh2HI9PQ0tVpN\nb6yiKNK298OHDzM9bGEEPaJ8GcNY1UCYoaEh8NflwWeliOtLklOqsAFBkkehTWHJzCEZNgLgtWXO\nhNoECZFUtIloKvDk/5PKI/weIQZGfYlgdYnA9SQYNmE1gCDoSZ9E4ErMXZxk3sgWBQzbwO/KSlcY\nAsM2CL0QYcphZvSTsq2I41iXd6lUinK5rDX+Chiq/snn85iRHGRFrTrCdmRWILFU6GVzspKIkkFk\nJA06csiTfKPJ0E1Jj9VkPp1O6zTqer3Ogw8+yNLSEul0mlKpxHe/+12Wlpa44YYbeNOb3sTn7/1n\nvv3tb7Nz504OHTrE4OAgw8PDWmTT19fHMy+ew75wiImJCQoT27j++uuJ45hKpSJvCCfH6Moxbr31\nVu3YfNvb3sa99/0ztm2TzWZ57Wtfyzve8Q6mp6d5y5tez0+NWuQt+XN78dwsrpXlud/9U06fXOC/\n/e2n9TpYtSurq6tJepbJwsKCdh0qizygJ/sqSFjFAiozUxwnKUx2SuodbBm/JgyTuL22ERvvuxK7\nbjngpIk7Ddn2BR70msTNVURjiZTXIO8YpBKpfBzHGN06hZTJQD5F/8nvsPyx32X+83dJQGq+Lxno\nJQpZ00xuKBJvSkMnbkVRpNeYagOiXKxTU1N0u11KpZIeTJqmyfPPP4/RP0DkucSGzNAYGBigUCiQ\nT0mjlLBS8vv0e4kKVK5T5VYilXgiQvngUjh6xWhINB4S7pIMKZP5AqY0Y6lVb2w5RKEkaHnrdSLX\nx7BMQs+XqLc4Juj5cgCprulkyKhUkHJoCVbKIo5iIj/CzliYKTOZlf6EtBWGYdDpdK5YowHaJKOw\n9CppWslSjUKJyOuBCg0NQ+IgkOYry5BoeiDudoi99hUKPEUoVpBZ0zSpVqvEccx4Xjonu92uLk0v\nXrxIKpViYGCAj/7B7/Hwww/zpS99iV6vp7Ft6XSa1772tXQ6HXbt2kWv1+P06dPExWHK5TJGp8Z7\n3vMehoeHdQ7oTK1Lc9tN2LbNxMQEV111Fblcju9///ssLy/zsY99jDvvvJP+/n72bxnHfOSzBI11\nLiyusrCwwMTEBM7hhwgj+G8nFunvl+vTTZs2MTg4SBzHeutTKBQYHBxkYGBAWpXFRrqXojOrwaea\nPaiSHWHIJ18YbIBnTUv20VYGka/INzOQjIa4104szGlJOlIRb6msXNt11onXF3FEREpEmK0V/Bce\nofe1T2OszxOsLuDVZbZE0HUJmw0MJTZKbPiRnSEIZIVz/vx51tfX9dxIDZnX1tYYHR3Ftm1KpRLz\n8/OMjIzQ7XbZsWOH5pTOzs5iDm8mWl/SHI3t27ezb98+RLsqWyOQFWivI1sQlUkRJQ8fw5IHh5PR\n9CdhpxNbdiANgnZa6nLCQNrNDSMRlnVkxioCPwhJOxZxa53I9YgjebNb2TSmYxEFIVEQE/QCwl4A\nhhw+WmmZehUFiRnLEIS+rBbiKJbKyCjG7/j4bZ+X8npZcyuUdVq9ueqmVDHrIyMjWigDyIs18DBS\nGfl0C+VaM/Y9om6bsNdDGCZGTllpYwyBHkp2Oh2pq/d9jVnL5XJMDg8QLJ1jZWWd0dFR+vv7dYjM\nVVddxW++/9d54tBhnnrqKTKZjIaujI6OcvLkSYpJyMrQ0BCO48h8y+Ik9dUaQ7kK+/bt481vfjPL\ny8tMTk6yuLhIo9HgyJEjHDx4kMnJSe6++24qlQrbtm1jz46t+K6LcfEFeo99C2+9ztP5AjbywOxb\nO82pf/g8C+95P/V//Ec9ZFSot1arxdjYGAsLC6yurmpeRafTIYoiSqWSVqMqkZiSThuGoWcOglg+\nGdXGQiU8mRL/hrJRW86GySj0idyOdGQaZqIlcJOnqYWwHJkt6fWIFs6w9r1H6a7WSU1uJmw3CdyQ\nlBCEXoCZTssWJl2QvoZeGyPdh2FEuFHEXXfdpQ9o1aLEsdw6KPKToonVajVdhU5MTHDx4kUZpJQr\nEXfbUhUK7Nq1i927dyczgki3DIaTlj28293wQgiR+H2SiiY5IOQBYCJSOTm7STYWkvEgMymwk6oi\nScQyDANcGSYctCUT0rBMIl8GOnnNLpEfJtJogZH4K+TaWaohAzfESpkEoOfDcRRLq7bYGPr/uK+X\nrXJQq7Ver8fy8rIuaZWDUA0OIdH+x6H0TngJFUqBTj0Xkc4ikkm4mUmoRXGEsByCMNKGLpB9fqPR\n0ClYowMlRKfGgijiui5XXXUVpmkyMzPDa1/7Wu644w6+/Z1Hufvuu/UTqNFoaMVhp9Nhfn6eF198\nkbm5OWZnZ8nn89x1z31MTYyxvLxM6PbYtGkT+Xyea6+9lnq9zsmTJ3Fdl2eeeUYPSu+44w5ubJ+Q\nYqBj38U99iSNEycJtr0Cw5bmIsMwqP3oR8zc8SY+9rGP6Uj7c+fO0W63dY5GNpvVg0cVO9doNKhU\nKnoqrwRSyi2qfA+qigsuE+wQJoPJtIyUF+m8VEoqZaBI3LG5fozikCyXExGaRMOZScsXypVeu0a4\nvkLt1EW6Kw16509hOA5REOLVW4Suhyj0S5t0FCFy/TIcJwYMOb+Zn5/X2xXf93VwrrKmDw8P6zZK\nUa5932dpaYl2uy21Jl0ZJydSks61e/duCoVCIrKLk0OppdeGKlRJmhcMqcqNla7BS3I+LDmcTecS\n7U2yXTMtOVhVhOpEFyG9hJJyFdbX8LuuFDx5Uk8RdF2iIMbvJavNVJJjEcdyrawUkFGMsIyk6ogJ\n/VCmwDlJWxH+hLQVsOGvcF1XJxkHQUA6LRmRqiUIwzCRzsqeiijSU2YjLSW7wpSI+bDb2dhmhD6m\nkENOVX6qQ6Hb7cob1q1ycrlFPp/n4sWL+qIZHh5m9+7d3HPPPXz2s59ldHSU0dFRxsfH9RBV5Ssu\nLy+zurpKo9HQeRR79uwhly9gFAaIApdrr72Wc+fOsW/LBIcPH+auu+5idXWVMAy5cOECH/7wh3nr\nW99K9g2/StxYIWquUz91nsj1OBJXKBaLpFIpdo5W6KTzfPzu+zRibc+ePeRyOQ2yUQg8ZQ9PpVK0\nWi1arRbtdpuhoSHNNlAH9OUhP+qAsCw1XBNyRRcnyc+91objMluUCkEnvTG9j+VwOO5J2bFI5WVQ\nSzKDoNckaq4RNao0Zht4LRdvdRVhWgQ9qfhzijnp1xBCHgqtVSBGhC6dbo9PfOITuv28HBOnNBxq\npqVWpK7rksvlWFxcJJVK6blEL4gwCv1U6y3t4JTRCIZ0VibbGuGkpdEvCDbmMIEntw8qCkHRnBJB\nGF4PhCm/76xkYWjFZJQ4Sy0bIeQ8jMAn9gM9PzAseUB4bQ+/7eG3fblxSOYMgRcS+hFhooS0UrLF\nsFImkRdd4aUwLIP432Qy/cvXy1o5qMmxmj8oOAdI+atCrHW7XelUM2yMVC6h3sQyDi/wtNTWSKcw\n0mm5vQh8CRXB0E9JFUQL0hU6Wc5xriNBIdlslrW1NUzT5C1veQubN2/GdV0ef/xxBgYGmJmZ4cCB\nA1iWxVVXXUUYhjz33HPcdtttNBoNSqUSTz/9tCZNP/LII1yaXyCXzVLv+hQKBd77nl8jXLvEl7/8\nZcbHxzl27Bi/8zu/w/vf/34qlYqkYNlp4uYqXnWF7kqN+ts+QDabpVqtsn37dpr3f5oP3fMQvu+T\nTqfZsmWLhtGqVkgh66enpxkaGtLSaQXQsSxLw1MvF0Gp2Y/acMQxsnWzHOg0EhOWJ7cTCe5MKBSa\nKdkPwnJk3JuVwugfkW92HCWbjQSC4nv6JrOz8udvFfqIPA/TNujVuhiOTdxpIYqD8vM6ebDThFHM\n3Nwc9Xpd2/FVS6U2T+p7BPQcYnl5WR96SiDWbDaJEcRej9CXMutMJkPKlrkSwrQRUbDBfOw05OzA\nTuYLviuvP2Ekq0tLPsQMU+ZigNyyOBlwW8ShJ69NO51sflJEpkPPlYd01K1LendyPQc9D7+TqCAR\nmLaZzBbkPEG1DdLJnsBeooigF+rDPo5iOXvwVUTfj/962Q4HtfNXijSVrK0zK5P5g5L+ClkXSV99\nmOCuDEPKpwECD29dqvUQyDctWdUpbqNqVYrFItfv28nFaktzJer1OkEQsH//fh555BEWFhZ44YUX\nmJmZoa9PKh4XFxdZWVlhaGiI9fV13vGOd7C6ukq5XObUqVN6rXnnnXeyurrKX/7lX9JaX2Owv4Db\n67J/osKf/9OXqVarHD16lHe/+93cfvvtXD2cZfvUCFOTk8QLp+m9+Cy1518k86a3Uq/XNcuy8Pi9\n3BsNU6vVcF2Xffv2ceutt2oephrwLi0toRiIKgBHCMHKyorWljiOQ6vV0q2FUkeqGYRMkU8IgH5P\nzg6iQN4ITnpjOBnLHlhk8lJmnZT9on9UltHpgrQ7Z4vyaStEwl70icKY4Wu3kC71MfvwU8x+8ynS\ng2XGbzuAlU4R9lxEfoDYycgKJfAIhckXv/hFqtWqxtpfe+21+jAAKaS7fHWr3vNsNsvOnTvxPE8z\nS6u1GggDS8hZhWVZGJe5JGNVvqubPI5l9Wpa8oAEeVikC+i8ikR9Hvu9DeVkDAIjSeM29DrTiEOd\nFUsUEwWBBMP6IX7L1RVDr97DsOTKUhiC0AuJ/OQhGcl/FwN+S4qfhJCDySiKiPyEafKTIp/udrvk\ncrkrAkvVvlq1Eyr5SogNYEXUXkeYJlHXlTOG8jBhbZkoDDEzToL2DmUZGPramXj5wO3gwYNUGy0u\nXJghlUpRLBa1YOaJJ57g2Wef5QMf+AC9Xo+pqSkqlYqeZqtUrlQqxfDwMHNzc+zbt4+RkRFarRZr\na2vMzc3xoQ99iM985jN8+h/u4pZbbmHz5s3khge47bbbuO+++xgeHuamm25ieXmZieVTVD/3BfKb\nN3H80HH6Ng3Q/5rX82TYT6rXo1KpMC0aNPbcxFc+9bsA2uG5fft27rnnHs08VCpBlRnpOM4VWLjl\n5WUtLVZQk8szJJXV2Q8CHNVb2xkZ3NrrSOtxjAzWjUNwssRCzhN8M8AyQCjycrJuhjhxIsaQ7sMo\njmLYDua+28i8eo2xKCS4dFru6vuHMAoVovoS9Do0IhvR6VGwgShkbnGFQ4ekHT2Xy1EqlTh27BhC\nCO24VKTwvr4+hoaGME2T8fFxTpw4ocnjIKuKQ4cOsfvaCUK3S7E0QBTKFCw9RPW6aP1CHEm5s0rC\nNpUa10UownS7ishX5O+jINEhGHLVq3B5ImlDokg6S11JX8fvyhvbMIgSpLzkMwSYdrKWN9QWIkwO\niQhhxBi2QeRHcu2ZzB2l9DrZRPkvLQoPXsbKQQghQZ9J2pDS+Su2oppDqKeaBop2WtIKiyDqtghX\n5xOwhk/sh8m6SaK2FPNPmW8ajYbEumVtDh99UfehoyMjWKbBzMwM586dY3p6mrvuukuX4YVCQZff\njuPw+OOP63nBNddcQzablU/2QoFOp0Oj0eDYsWO8613vot1uUygUWF5epuNHjI+Pc/fdd/Nf/ugP\n2DUo7d0i149TyHLx4afprrbJbZriybDEpk2b6Ha77Nw8Rfd7D/DO//Bh7WZNp9McPnyYEydO6G2P\neiKqr2NpaYnh4WGWlpZ0jkMQBFdAalUUQLfb1UY3NcCLogg3kHOGUJjS9OQn4qZOA9rrEEVcWlzi\nuedfYHVtjVbXxTNSROkCUa5CmC3jZcq0nBLrVj+tzCDrcYqlpsfyepuqyBOUJrH33oKx5xZOu1nO\n1DxqxS0camU4eeqUFMB1G8R2hi9+8Yt6uKy0ErVaTaMEfd9n27ZtLC0tkUqlaDQajIyMUK1WmZub\no9vtMjIyopka1WoVkevHth2sOMAmgO66XMUqjDzJ4A8SuXRuYwib2NjjKJlFZIrEnfrGfEZF4Bnm\nBgDH78l5RBQQJfEKvu/L2VkcEroevVpXrx9DTypQQzci8iL8jpw9WGl541sZM3FgyipBDR4jP9L/\nXvrcXtq24mWrHNRNp3QOjuNQq9W08aq/v18/9WzbBr8JpoWZ7ydcW5BiKMsmCl0IumDbmHEsh1gK\nh27aOgbPNE127NjBq3dNcOT0BdrtNsVika0TwzjV86xEUm48OjrK29/+dj7xiU8wOzur8eSu67J7\n925qtZomS42MjDA+Po7v+ywvL7Nnzx6ZGVGr8fjjj5PJZPjABz5AoVDgz//8z3nzm9/MCy+8wOTk\nJK+7bg9tK0clbVL9H18i9AICL2T0lZu5cO1PM5pKsba2Jg+IL/w1dy25eJ7HyMgIhUKBhYUFKe9e\nW6O/v5/5+Xk9T/E8T69Wle6h1WppjoGyMC8tLV2xLlbwGbWtUHoHQYSRZIZgp/WFjWEShgELCwvU\n63WOHz+O67qMjo6Sy+W0KCuXy2lOgvraCgUJdR0dHdV08dnZWer1uhYvTU5OknIc0kJe3Isrq3z/\n+9/X4TWpVErPEFRuaDqd1oI01S5ms1na7TbVapXh4WFGRkb01zc/Pw+FQfKZPEa3TpjKJz1BRNxZ\nl9+raROuXsIcnEr+WwJ5icKkAgiBZKUb+NJDEUvOZux7ckOhU61cOey0bLBShFGc/NzTxN0WQUda\nsUM/JOjK9sKwpLpRDRittLUhukyqB3UgCFPOjqIw0hwHwzaIvAjhvLRa4GU7HNTqTEFe1brRsixt\nNVblfhBsUHlj39UDmziKJGvAdsB1icOIKAixKmkZtGKntTx4dHSUn75mKxfqAcePH8cwDEZGRkgT\n0O0bpzE7x8rKCjfddBP3338/uVyOEydOcPXVV2tFXbVapdPpcM0113D+/HlyuRyB19PItVarRblc\n1gG19Xod2xQQyaHYoUOHmJub44YbbsDqq2ApInMqw5F7vsvQjjJDr3s9LyYX9/j4OGPHHuHiyGa+\n9eA9ejWpDFStVosLFy7Q399PrVbTvXUURVQSTL7qu/P5/BWzHTWHUNsMxTBQh+nlh3YYgWVaxMKE\nAOitynmC5WAYJnv27NG27PX1dQ1LUQe7Mo+pFbUaGEr0ex7H70Dksm8wTbxnN67nk3VsYmJE6EO3\ngSgM8F//7A802atUKmnFp5JMG4bB0NAQ58+fx7Isut0uYRhqnYeaOY2NjWkJfafToRdBpteAbBHT\nMOSswDA3dA6Bj1keldVA0rZKvUIgZePdOmT6EMTEAokn9CRDU9iSiakOHEEsCVExxMJACPn9GIDf\nbeHWm3j1jhQ7+aHMx04GjnEQY6QN/LaPmTZllRDEiCiZKxkCgo0WwkxZUjIdSzrU5SzVH+f1snor\nVB+fTqdpNBqaSjQ0NKQ3F1IGm/R2QOz1MOwUURI/Fnsuwknh1VsYSSwYkKQvdfCRDsRrUnVahXG+\n+8CD2hK+ZXqKbqdJ0+3SbDbZvn078/Pz7Nu3j0OHDrFjxw5+8IMfMDo6ysjICLt378bzPKanpzl0\n6BClvjxpr8HKygqXLl1i69at+iml1mn3f/kB5ufn+Y3f+A3uuece3vve9zI9PY2YPwqTV+EHIc47\n/j3WXz3E9J038EJ6is2jZVkeV49RO3+e//pijXw+r9eTanCrnp6tVotutyuJUP392jOi4gYLhYKe\nkygjkkq8Uq2ESpZWWZzq79HiItvGFonJKpWVNw4gOlVyThbXyGNZFgMDA1qBqjQU5XJJ9vCGSXTu\nEMHSRaLaEl6tRpxOc+mZ48RhSGXfFvJveR+ZvkFEt47wEoNTtsiRE6f0oa6clwoGBOivXxG1R0ZG\nsBLiFECtVmNiYgLP8zaAxaA3ZqnOIubW6xNMvCvDfMKePAiMZPZiJdQmrwsZO5lLOBtuUSWMEoa8\n/tRA10kTt+uyojVt+bFODhEFRFGiK3EsYreH1+gSBTHNhRbpvhSBJ4VN0taJlk+r9iEmJuyFxDEY\nShXpS2KUYcq1pqG0Dz8psBfV5ypun3oj1dDs8sqi13P1DtnoS+ClliO99YYgRmAXZCshLBMMQdRp\ngu9imiY7xyp0pq/nc5//gnbrjY2Nke6ucWF5o5XpdDqcO3eOt7/trYyPj2tkXK1WY3p6Wg+zhoeH\npRS5XSOOJRfx4YcfZmZmRj/xlYlsdXVVw2lvu+02xsfG5EZGGIhQGrUylmDk6hHaP/VO+or95NMO\nO1pnaB19gfuzW2g0GvqCVvMA5bI8ffo07XZb+wfm5+dZWVlBCKGriMnJSZ3H4DiOdpKePHlSrzTV\nZkgpVdXPX2PjBbghsr8WyY0iRAJLdUlZBplk0Ke2CIqX0Wq18WKDuNtA2A5WZQQME7tQILVpB+Wr\n99C3YzP28BhxYQDDa0tic7sKlkNg2PzFX/yFnkmpSiCdTmsRWLlcJpvNcurUKdbX17V0HNDD2svf\nx2KxqAnVfq8jtwimJXM6wnBDGq3s/3EkB5FBMosAWT30mpDtlxoFty1DgzsN4iQQV4NzhJCfz3bk\nr16H2LA3dDzdJlGvS9hz6VY7WI5J6IWYSSsQ+Umb4MtDOegFek2p1JBymCkwHQPDFJgpE2FJS7dh\nGdhZ+yXdoy/b4dDr9fRFXq1WtXVbpWKr7YFWNwrkmxImqVdKDioMifFKyqrIC4jdHlF9ld6z3yXn\n1Vhp+/zdpz+jb5pUKsX+HVvoxZamXx85cgTLsti9ezcDcVOLipQf4+DBgxw5coRbb72VZ599lhtu\nuAEuvUhgpjl48CCVioSPnDp1ii984QscOHCA4eFhXvOa13DHHXfQ6/UkYShskcEj9rp0A1lSvvib\n76Vw+62stT0mx8cYbF7CX6/zg6EDPPDAA1q34Pv+FU981R6oQWOxWKTb7TI9Pc3KygqdTodarab5\nnJ7nUalUaDabV8TpKSiK53mazKUYjKqCiCJpbQ8NC5xccvVI4RmmlE2bhtAbpl6vp/0xapXsm2nE\n0DbE4CZSB99A+to7MLdeTeENv0L57e8mfdsvSLOX2yHutRC5ErFp85UHHtQ4/csHw6pCU6xR5cRU\nKDhlvR8fHycIAjZt2sTMzAyA1nU4joNvpIjDkLi+CJ11KW4i2rBgq00FbPg81AzGchLGZHItBpI5\nInmm8muNe+0kXTzYmNfYKfm9qpeTJg4j3HqXoCudl6EvB5BxFGNYhiQ9xeD3AgmcUtVDBFEoB48k\nYFkrbWGlLDKlDFbKJFVM/eQcDt1uV5ef6omgrLWaNg26Z5Q0njiZJUixjQwnlYdCHEaJMERSf82B\ncVJ7D+JmKpy9MEMcx5pxcPPNN8PFI7x4UQZ1FQoFlpaWWFpaYnp6mrgwwBNPPKEdozt27GBoaIjZ\n2Vnd8lQqFaJzR5hdbVBfr3HnG15Pr9dj3759rKysWKBqWQAAIABJREFU8PDDDzM9Pc2uHdu58cYb\nueP226hUKrRWl/Cf/CrG2G48V6r3ihP9lN7ys1y1fx/WD7/I7Kf+ls7Wq/ncPfcyPT0NSFGYck9m\nMhlGRkaoVCoMDQ3p8B+ljDxz5oy2RKtqIJvNambk2tqabjk6nQ6Li4ua31mv17XWRHks1CGUyaTl\nz9tyJMzETkG2JJ+0CTvDFOinuiJQqYPIC2NI54j7xxCFAURxGJHrJ86V8QrDRKk8RmOJOHDlEK9v\niOVqnSeffFI/MHK5nF5FqvehUqnoKqdQKOiqRYXTqBmFIn7ncjmCIGBgYIBut0uxVJY3t2FqSlMc\n+MnQNQnx1ZqGlPzYONwIy1UBP2r1q1FyiSQ9SQ+TKlJHDyfVAkSu6iPCbpsoCAm8KInBiBPOS0yY\naB/iBPoSeqFMBUsIUMQxhm1ipkycnI1TcHDyNqYtKwbTNvRa88d9vWyHg+M4+umWy+V0aa+eVuoJ\npGLrwiiSEl7Lkf1c4EmLqmy2MNMOVsqW+n3PlZkGfRWCMNJTd9VSVHrLnBWD+L5PNptlaWmJmZkZ\noiiS6sdYrlnL5TKzs7OUy2Vt8PnkJz9JqVSiXC7jN5uUy2Ue/OrXeM1tN/GqV71KQ1qOHj3Kn/zJ\nn9BX7Gd0oMyp02fYsWMHX33qOObYZi6uVDHdJlkL6heWKdGleddfMfuV79D/htfwJ5/4jB7GKr2C\nZVkUi0Vs29ZPZWXLjuOYgYEBpqamME1TrzPjWEbLKx/I6uoq58+fZ3x8XINZFxYW9IT/8uRztUpW\n/wRBKJOY1LQddA4kdgoRupIMLgSplMTDK9Nbo9Gg2WyyvFplZa1K3Y1YC2yWGj2WVqUyVcwchva6\nxNln+uh4IZ/61Kf016NMea7rSgYk8saqVKQ7tF6v64fN6OjoFQeAakdUElmhUNB5ml5sEDVqiFxJ\nbhISwphKcheZJLLOMJItRcKJVD18Op9oI3rJ1R1pM5Y0cPlJ8nYyWE/lwM5o0FCv15NQm06LoBvg\nd6RALI7jDZt14o2IQqmMDHoBUXJYWCkTJ++QKaVJFVISK2cZyUJFbiy8jo/X9F7SPfqyHQ6e5+lt\nxPLyMs1mk/n5ebrdLuVymf7+fr0GUwYYYcuYd2E7CNOSZqtEwRb0PIKeJ+k5hinflMt2u2qAl8/n\niTt1XaUEQcDDDz/M/Py8nq6fujBHpVLB8zxKpZIOnFEuv0OHDnHTwWtJb9lNprMsczbWJZDFNE1K\npRLbtm2jXC7T63YIfI+xsTGOHj1KtVqFzddy7MWThHaa6OyzRJFBnOnHGRojv/8AH77/h8zNzek5\nQbvdptPp6HJdkbnn5uY0GzGVklBbVf0899xzCCFYXFzUqdwqZl7Jo9UqUEnHlYJSDelWV1e101Pp\nHoQQBDGEoVIDxomwx9D8DAMIApmhoBSqgwMVSsl6WmV45DIp+jM2w5aLsfCiVFoOTsuBp5PhS1/6\nEoZhsLKyouPtlDxcrbqnp6cJgoCRkRE8z2NyclLL5ZW2JZ/P6wpoeHhYu3J3794tr40wJKytENcX\nidrrkMoh0nkp3BJIL4Sdlu1rlKRom3KmgDDktiJOBpVeRx4CCUIvDgNZKUSJ0YpIflws6ek69StV\nAGFKVWOQ5ISGUhrtNtyNNsMPZVamSL6MlJXAXCTcBWTKdtD1cRsuXsvD70paVNB7aUG6L9u2Qk3c\n1YDNsixs29ZvsIKsgqwyZE6CIxV5qu8DuR4yLaxMmshzUfIw6d6MNKhVcQviOMYolIlr67iuy+zs\nLLlcTu++Pc/jU5/6FH19fVqMlclkOHXqFPl8npGREc6dO0eutYh9/U8TBT1+9Zf+HQ88/Ai//Iab\ncZ08W7du1QlT3/7Odzl48CCO42hhkiBm69atkoswOEm32mHtc3/N2tEZ7pvao+XNpmnqZChVGTSb\nTc1DVKW7+rOywVerVY3dUyu7iYkJXnzxRS5duqRj+Wzb5ty5cxSLRT3/ieOYYrGoiU2+72t8Gsjp\nvm2Z0jKfIPl0P26YiEBCX+xkpWdYFjSXMQMPM45JuS3iXheRySV9uZlYmpHJUaYNvSY9K8/f//3f\nc+edd5LP52k0GrTbbUqlkr750+k0c3NzBEGgEXMq9AfQ1ac68FSbE4YhhUJBA4K73R5GrgDdppwl\nODKVSjgynCZGqj5FKgcq1SoMdDiwDK1JrlGEDPaJJE5PmIkaUqkr4xhMiygWesOigoAj35f4tzjx\nQiTzA9O2MAyB1/YwbVNyIw2BnZMYeqV/CF15sESh5DyE3gYwOEpI7C/l9bJVDpcz/zzPY319Xd+I\nxWJRD7LURSC9FaAF4oEvU5fSWU3nkWE3Bpim7Flj9NBJCXKq1SrtvglGR0cpFot6Pbhz506uuuoq\nLl26RLPZ1AeVekqpaDV14yx2I4I4otqLaHsBN930asmSSIxAjuPwwx/+EMMwOH36NCKOGR0dpVKp\n4McGzzzzDMK0qP7z3ayeqbL4xDEuvfFtnDt/gVarpdOn1GBQ8RgUJFWpIZU5TTkUPc/TCdPqwkul\nUnroqOTSp06d0mvXRqPB2bNnqdVqWjGpNkXq71ElcBRFRIlGArWqU7ZuZV9uLEO7BvUFWL8kb7rm\nKtGlEwRzp/FnjhMtzcgnc+DK2ZEpoShxcwVMm7/91Kc0zWl1dVXf3ICmOY2PjwMwNjamtysKqFOp\nVK5ABObzeUqlkt4kKcBuX18f3XaL2O0Rrq8QuR1EpiirAUiGiSaxuyH8it3ula1Vvqy5D8ShPBiU\nycyQdvcY5DDdkjMHRSZTX7fneTLvNY4hTOjRSeugvBRmysJMW6SLaeycnVQWQrYiXT+By0qeZOiG\nCZJeVhRW2iJVTL2ke/RlOxyUrl8NIPv6+shmswwODpLNZq+Q8OqgV9UqWDK9mTiWga2G9NDHodz3\nCtNKzFmCOAy0Ak+V5DMzM/oNOX36tAaAlMtlDV5dW1vTOn3FJjx79qxOw2rGNi+eOMW5c+fIZnOk\n0xleXOlgddb0E2p8fBzP8zhx4gQDpT7K5TK/9Eu/iGVJRFm1WsV4x2+w92f2Ef7ae/jsPz+oZczK\nnTo2NqYPNpUxCRJSow5PtQJWCdGNRoPl5WXiONYDxlarpYVOlx98ruuysrLC4OAgjz76KOfOnWNt\nbU1DYtThpIaU8imXvIlxhOYaREl4rNsk9rvE6wvE64sSI++7ROsrRJ02UWtdKgEXZ+RTudeR5Tpx\nYnPu4MeCb3zjG1iWpVsdQK8fHcdhYmKC1dVVpqamOHHihKZbX36YqhV5JpMhn8/r9W86ndb4uF6v\nR68rvwYVr4hpSQOfArQIgcgVkhs+SIJzBWDIFiHwNriRQsjr8zLpdRwnWRW5finqSkyEStEaBAHZ\nlEOv1iToBMSAnZXbhhgpgLKztsTLC/Shh0CLnNRMIg5jLMfCylhkKmmsjLUBl/1J0Tmo0k+tntQN\nDOgLXc0EoiTARu6dQ4kcS9RqRr6POJS0HCOTTVLbkr1yHBFcpl1XPn7Vx6uyfXV1ldOnT2uX6Nra\nms6uvHxVmE6nWV1d5cCBA3hBxEqig7j33nt5/vnnGR0dZdW3GRwckCKrLVvYs2ePLOPXavS68sku\nLh7h7NmzLC4uksvlcPqyPHZ8hp07d+oeVA3g6vU6hmHQ7Xa57Ta58Wg0Gtp5qZ7onueRzWal5Thh\nc6qf3czMjGZTqidpEAQ0m02tn8jlcnS7XQ4fPszRo0c5d+4cjUaDarWqDxxF6xZCYAhBZMjQGsIA\nOnX5+yiWluxek7C6THDpNFFjRY6GfJfY7WH2DyJyfYTry4T1VfkktpzkfrM4duqsls6r9aXC7Ckm\nh4LuqEP8wIEDNBoNut0uAwMDelXpuq5epSqvhfLFZDIZ2UolIbZAEsQTyOF36MmHTmLPxjATlqZk\nO2CYshVyMonRLA1RTNyqys/lZIA4SSrPyoPEThMn4F4tTxcCmiv0qi38nhxIKiGTnbYwHWNDr2Ab\nRKE0XmlsXBBh2iZWxiLVl8LKWXJ7F0t5tbwX+MnJyvR9n8HBwX9B8VEl49zcHLVaTSviBMg3MAwS\nKk8yVwh8hJPGsG2tZot9T578dlr3yyrRSd18isa0tLSE4zgcP36cYrGo07hLpRL5fF4nIAEaMVer\n1eQUPOnvn3/+eb7yla9w3333IYQgLWKyaWkV3rJlC7fffjsLCwuU7AjTtBCmxW//9m9z+PBhTp48\nyenr38js7Cw7d+7UKsNyuawHhkpe/uKLL2rDkMLVqb5aUbQuD5Btt9tkMhkmJiY0vapQKLC+LkPR\nlWBrbW0N3/fZvHkz3W6Xp556imeeeYbjx4/TbDZZWVmh0WgAaFZjEMqBI2pNB9JP4Hch8nXfHa4u\nEK4uSGt94MtELCEv2LC2QrB0UTIgnIy02acLfOfR72mNyfz8vG6RVIuoYMCTk5PaYzI3N6ft5opJ\nUSqVrlCBKvdpKpXSUJ/BwUGaXY+4192YncShbHF8Vyse4yDhWujsjngjYNhtS9s2STReTs574iAR\n76lHe0KAkoPISH+tQgji+hJes0voBlrQZDoGRiJ7FoZIyGeRrhKEkOxIy5H2bytlkuqT7bSdc7DT\ncmthpS2cgkNmIPeS7tGXHfaiErAB7RJcW1sD0ENE0zQxxEb0mBysbOC+YxVJRkzoJSsn0wSvo/0E\n6gJRffq2bdv4xje+wdzcHGfPnqVer9Pf38/w8LCe0m/bto1er6eHd9VqlbW1NZ5++mny+TzXXX89\nV199NWEYMjk5yezsLEeOHOHkhYu0OlLOnHVMKpUKxXKFnl2gYnmIXInz58+zf/9+JiYm+NyXv0al\nUuEb3/gGfX19+smmSmUFZVFPGhVAMzg4SKVS0S2HOnABLbpaXl7WyshSqaQDeFTV1Gw2WV9fp91u\nk8/n9XxhZWWFU6dOceLECarVKisrKxqw0u126fV6uK5HrLYU2X7iwEPYmUQJmMYa3YpZGcUsVpJk\ndHfjhvM9RDqH2T8gKdOGvOG6ttSc+L7P1q1bdSBNpVLRN7qavyiTWH9/P6dPn9bCtdHRUbLZLOvr\n61qi325LcG06ndaqz7FErZpOOWBZsvxPZimx10XkiohMAWEYspWwHFklaNVjMmg0ncR5qVLeXYmH\ny5U2pNfq52JJjY7n+7TbbV0ZhStzeC0Prxdqn4RhGcmTH0iETn5PzhcURNYpOJhpqW2wMtJLYWct\n7IxFupylMNZPadsIIwd3MfX6G1/SPfqybSsU3UglLhmGQbFYZHV1VYNLstmsllf7QYR9mY1WmJZk\nR9opyRmIQsJeW+YKul0Z5gqIhI6zuLio07UOHjyomQBKDKTWWktLS/T39+uvRzEbQB5olUqFS5cu\nsbK8zM4d24kCn/379xPHscw6QDIvjxw5wtTUFEYhxVhfitgLmWs0yA/0Yyyf4bbrruKf7v8qlmXx\nvve9j89+9rNalKTaGfUz6PV6eiYzPDzMmTNndGzf2NiYNmIpopYqvfv7+/UBoG6c7du3MzMzo/0W\nijep/s5rrrmGs2fP0uv1uHDhAp7n0e122bx5s3afqtAhlXXh+z75tIPRN4zwe5It6baJQx9rZBNh\nbVm+X9k+onYdI5snTueIfU8GIadkFqfoG9JzgVwup52zSiSXzWavOOiFEGQyGTZv3syxY8fI5/M4\njqODglUb1W636e/v11WXEILh4WHdMlq2g+GkMLJFTdqOK5MYcSzR+rnL1JGhn8Bt+qTU2s4k9mxL\nBth4XWmsUhg9Yml1D31ZNRh2Qm1Cw3Usy8I9fYTQDTANAZax4bpMqgNFgDJtI5GtJ8PGhDgtnZux\nrhRSxSyFqWGKV+2HHdczH+X41lNPv6R79GW1bCsBj6LyqJJ+dnaWXbt2EYah1smX+4vEfo/Ybes1\npkiMMMK0iAIPI+1AGGFkC1ps4gfy4FFT+Z/7uZ9jV9lmYcHmd3/3d/nQhz5Eq9XiFa94BZcuXeLI\nkSNs27YNz/NYWlpiz549XLx4UZvCFN59ZnaWPdOjmMih4fHjx9m8eTOPP/44YRjKVSXw/aee4/ar\nd/KDo+e47rrr5LC026acT/Gbv/5rmKGHmcpyaP9+vvKVr+iKYX19XYuelHgrm83S19fHwsIC3W5X\n05TVjEGlhSmyUBiGuh1Qn1fxJfr7+/X8oNVqcfHiRaampjRfUuWGNhoNvalRa82BgQEqlYre+JRK\nJcJSSWZU5vKYcXKIdxuQ6cO0HMLVORktYFoY+bIE1cZA4CKKI3Lyb6dZu3SB1dVVhBCcO3fuCpqT\nqhzW19d1rKBhGCwuLlIoFGg0Gto3EoahNu4pRoWT5GUo0Z0ifJXzaaKFtkS75YoEVpZWR8J8c7kK\nxBFmAoCNe015MLjthOhkadZIrLIrDNVKIK/VoCf/mM4RITCTg029R6VcmrULl5JAGnmDm440TKmD\n3kwnXEg7OVDScv1pZ23pLLBNMuUc6XKB3MQQub1XE2+/nm88+QLf+j8/zczMzE8Oz0FlEs7OzmrO\nn7oglSe/XC7TbDZpNpty9ZnKSWxXMleQE2YVrCtzAqJQilSMXAFhp+l2utRqNcIw5I9//3do/tVH\nOfz2X8RxHAorpzRX8vz58+zYsYMLFy5ocRZICbGCkqbTaWnDtm1ZTQTXgBC87c43sHXrVnq9HocP\nH2ZpaYk77ridcH2Ze154gaGhISqVijSadRqQzsDwNtxWi8yzX8eY3sO/e92NXLhwgcXFRT0AVSAc\nkOXwxMQEgAanrq+vX7G3V/zKTCajtz3qhlI3isLHra6u6tmF0j2USiVGR0fZs2cPR48exfd9Dhw4\nwM6dO+Us5bKtknoqq0FyoVCQX3PoYyqFYBwTry8jbAth2YTNdYRpETariHadOPAwx3cQp3IyOi6O\ndG6GIncp0E6pVGJpaUkrWgcGBrSXZGFhgU2bNrG2tkaxWNS6BhVko16qclA3ZaFQkIeJaRH5riSa\nVzaxWq1ppoXig8p5Rg7LzhAKkzhdxBKxPgDjMEDEgbwmbSdZYfpJVZQEQMdyRen5gX6PhRCwOktn\ncU37JOysnBEoKKyZNpOMJoFlWxiOIWcOyIohN5gnVcpTmB6heOMd+Ntu5EsPfo1H/8dfsLi4qJWj\nl/8sfpzXy3Y4lMtlarUa2WxWK/eUfFqtzVSf29/fz9jYmPRW2BJBL2wn0cB7sr+LIsgWMDOxNLzY\nKYgCfF8ePB/90P/O4Z/7Few/+TAPfutRXnHVfgZHi+RyOdbX15mZmeHaa69lcnKSRqOhL/4TJ06Q\nSqX0mkxH1AP12MHzAga8Jebn57Xq87HHHqNYLGoQbblUYsLqct+jj3LrK69lwHMRxHzx3vvo9Xq8\nKh9xIDjBf/nwB/m7z93HF++9F5Bl5/DwMI1Gg3q9zvz8PFNTU3qLMTk5qTc96smqCFqNRoNyuay5\nCb1ej3q9ztjYmN4QqUGtArCqNmLz5s0cPHhQVmzlsq5Q+vr6EvaArNZYX5DS4mQF6PgNEFnibl2S\nogIvwaOlEJk8pmkTt9Yl+DcKiXsdouM/JFhZwF2rEbS7TE9M8Ocf/iDPnr7At77zqNZddDob8yOF\nuVtfX6fZbGqth1LUzs/Pa8rX3Nwcu3bt0sRwpfkIw5BLly4RRRFZPKyBMQmaTRepXjxNpVJheXmZ\nYrHI2tqazvYwDINsNqvt8qT6JLPDsKRGw0pWi74rtxpRIH8FiEOiyNRqXUgUvAtn6K62JOEpY5Hq\nT2FnpGNTmCIxFEbYCePCtAyEIw+G7GCB4tZxClunSF//Ok62LP7pT/9MxwSqymlsbIzp6Wm+/vWv\n/9j36I81kBRCmEKIw0KIB5M/l4UQ3xJCnBJCfFMI0X/Zx/6eEOK0EOKEEOJ1/3+fM5VKMTo6quXG\nyoGpkG4K5qr29KYaCCkbbULgESQmmWQLQBTJdsOQQ7JsNsu1WZ8XfuFXCP/zB1lsyEGeF4Q8N1e7\nQjikErbV0yufz+O6LsVikU2bNumLr6+vj02bNpHNZnEch3WjwJtfd7vuH2dnZ3nooYeIoojbb7+d\nyXTAmlORVch6i2BgC6zO8LM/8zNs3ryZv/nkp5gzyrTv/xve96ZX8653vQvHcejr69MTdTUX6Ha7\neutQqVT0xkf14r7v6xsauGKXrqzLKiQ4lUolg0VXawqKRZkOfXnKeaVS0dF5nU4HP8kCifqGIZUj\nNhwiYcg1n2EgjGTnn+2Xa7ykRzcKFcypPZjj27FGprHHt0hVpZOCKKKzUmPliWeY/+hvs/nhu/lP\n123hg7/6LiYnJ/VqUlGjFxcXZSZI0jq1Wi0sy9KzB3XNpFIpVlZWtKhObcAGBwd1fknKAGtsC8JJ\na0CO53la36KEegr9rzB7yo6/Wl2XAjKRwc0N4aeKRIVBCcVN5UGYsuI1ZVYnbMCOLMsirC7hNqSb\n03SkT8LKWJhOQujqhWBII5ZpmRiWiWGbpPrSWNkU2bFBnN3X08iN8b3vfY+TJ09egV7csmUL+/bt\nY+/evT/2wfBjHw7AvweOoxspPgx8K47jHcAjyZ8RQuwBfh7YA7we+BshxL/6d+zYsYN0Oq1l0kor\nH4ahRpp1u12iKNpQ7ZkpqXtPZa7g8hnpnFxxem7SBya0ntYamRPfp/7NrzLz3vfy6FPPUa1Weeyx\nx3j7q/fz9a9/XSciqcGdaZoMDw/TarX0mtOyLN74xjeyfft2XfL+6Ec/4szZc9i2Td0NiUyHt771\nrSwvL5NOp/mpn/op0uk0B3ZvJ6pe4rOf/Szj4+PMz89zbGaBsDLNsePH2b17N3v37uX5xSaF1/08\nYXmS//WmXdx66606X0P1+QqHpspRBWmpVCpawOW6Lo1Gg+HhYS0muzzlqlqtksvlmJqa0kYktQFQ\nkm8lsFKrXEVZUhWdmr8IIYhiCRwxokROnIjVRK5fXi6FMqI0Jk1NuX7ZPmTykMoTdVpErQYEsh2M\ng4jQ9TEdG7fRYubue6n/6R/znrzLX/z+B7npppvo6+ujVCppzFsmk2FoaEgLwgYGBiRpKwnW1X6Q\nRDylJPmGYciBsWEgakuIbB+iJJF5lYrMCRkYGKC/v19rQZRoTHEg1EaoXq9rLcja2hrtnst6o0m9\n2WK92abrh/hhhJ+YANX7p1aufr2BsEziKMbJ2dhpGyPRNAhDUp7M5L/7XR+nzyY30kdhapjC5BD2\nwDBeZTPfeuQ7fPvb39Zw3cnJSbZt26ZTvBTy8Md9/U/bCiHEBPBG4E+B/5j86zcDtya//0fg0eSA\neAvw+TiOfeCCEOIMcBD40f/3827evFmX4sp/r/ryyclJut0uZ86cAdAo+Fwuh20mVJzQR9h2Ip2G\nODAQqUyitoOo16b+hf+bVsPg/C0/w7FvfpPx8XHOnj3Lb/3WbxGvztFoNPRN1m63dfVw//33s337\ndi5duqRLyYGBAXbv3q3tvmtra3z84x/nIx/5CGOjo3z9oYd4w+tfzx/90R8l2DKbVOxjnDvEj+oy\nE/PMmTPcfPPNzM7OMjs7yysP7OGvP/OP3PiqG9i2dSsiqFHrhRw/t8p/euttrK+vc/ToUYIgYH5+\nXq85VUKY0mM0m029iVGEJJX6rTBtKttCrfa63a7+3lRUXLVapVarUS6X9Y2n6Erq56R+1aQoITAt\nE4STgFAsOe33ezLPIl2Q6dupHPTackbk9STMxeslk/dEJiyEJIt7PsI0SRdTBL2A6uHnCX/0LD+1\nc5x3/McP8ndfepgfPvEECwsLGs+nqjbbtmk2mzQaDbZv3069XieTyehhdzqd1k7UV7ziFTz//PPk\nHNmiKqekNOBJJWilv4+eH5LP56/AC1SrVf15FXpAMUnU4ao2RMo2rpSzSoOi1phRGGFaBk7OJtXn\nyEi7BPWmMPN6c28I/HZAfiRDfmKA7P6DuHtfwwMPPMDf/d3fEQSBRhVmMhmGh4e1Xf2lvn6cyuHj\nwG8Dl+d3D8dxvJT8fgkYTn4/Bsxd9nFzwPi/9klvueUWbr/9dq677jpuvPFGduzYoVdkYRhqa+7i\n4iKnTp1iYWFBrulMR6YEOelE596Tach2Sl5YvS7Noy+w/r1vkr/uZorv+7BWx6VSKe584xu4dc8m\nPvvkeQ0fVf/ta1/7Gt1ul1tvvZXrrruOs2fPsrKywtraGvPz82zdupWJiQlM0+SVr3wlTz/9NH/z\nN3+D5TW5+eab+fYjj/DRj36UJ598kiNHjmKHLiupAQ6/cBTLstizZw/PPPMMe/fulfbjWotbb72V\nbdt3kFs6ReP0SWq1Go/86FnWuj6/9LNv0ZoGlU6lpMCqd1aT+MuzMNXmR2kjlPxYMR9M09RPQzWU\nVNPzlZUVfeGqKklVcKq0V7MK3/cRcaCTr1UUHKbMtySdl+u/VE6u9VTCtCEg6CX6FPnkjHwfI+Ng\nJCQvqf6Uqr7Ij3EKKVaPz3Hit/4DP1s9wd/+wQe58cYbNYei2WzqClNVNWoTpr5utbpcW1sjm83y\n2GOPJTeucu8KLNMgZRmYyWzF8Dp6oKtMbv39/QwMDGhuhapy1SG6vr7O4uIi8/Pzuv1ZXV3l4sWL\ndDod6vU6CwsLLC8vs7KyQnp4iHR/CitjYdgmUQJ3iZOgXMMx9OpScRnSg/1kdl7FpaH9fOQjH+EL\nX/gCxWKRbdu28YpXvIJCoUC5XNbvr5rNvJTXv1k5CCHuBJbjOD4shLjtX/uYOI5jIcS/Jdr+V//b\nJz/5Sa35n5qaYteuXYCEwAwNDdFqtVhfX9dTezV4wzKToaSi/5rErbqGdobtNoZlUrjhZsyxLVpF\nODg4yFvf8mb8R7/K544e5utf/4aeaCffh2453vve92KaJo1Gg1wup9sM9fTftWuXvgC///3v8087\ndvCut/80t912G0NDQzSbTR577DH2793Dd599kVKpxCtf+UoWFxfZs2cPTz75JLfffjtra2uUy2XC\nMGS1MM6EY3L48GGy2Sw/vLDKa67by5133smS+DqnAAAgAElEQVSRI0e0Z0AxCGq1GiBveED3smpL\noWYMqnxVlYJSoCos3+TkJKurq+r9Zm1tTYfjqATyy3MeAL3JEUIQqX46iuWOPtpwaBIm7AfDgkBK\n3uNOXa792nXiQFK9wp5HFISyAslnMFO2pCIFIbEppBu65WFn5eW6dPgc/uO/x6++4dW85vd+mz/9\ny4/ra0W1Tps3b9aiMVUpqaeqHiaSgHQtGXqLaZIiQPRkarZBTGza2KELTgaRHAYKKgPQarVIpVIa\nShTHsa4WFOBWEav6+vr00DiOY44fP8709DS7SvLZatqGFj6FfojfCZK1poFhmxi2QW6gQHa0TPGa\na/H23Mp//7/+Hw4fPszExATXXnut/DzJVkqBf771rW9p6/xLef3P2oobgTcLId4IpIE+IcRdwJIQ\nYiSO40UhxCiwnHz8JWDysv9/Ivl3/+L1kY98RBOLG40GJ0+e1E8513UplUpUq1UNPFEDS8xYavET\nUKcwDe0OFE4auzJAqlCSF2kqr38ot9xyCxdmZhl49jG+MeNpXLoC3So3Zblc5rnnnmPv3r0afKvs\ny4MDMtzm6quv5uGHH2b//v2cPXuWY8eO8c2ENv0LP/tWjp8+z/ve9z6yZszZs2d1qfrKV76SVqvF\n9PQ0j//gB9y2e5zVXJmhwQHmFxY5Gwzyjhty/OIDDxBFEbt27eL9r30Fv/KjH+k11Pz8vHZLqvmA\n2iKMjo5Sq9W0qSoIAvbu3cvhw4evSDPP5/N0OvKJqGCsajCpRERhGOoNjZKgq8g5QJu94jgmk0oR\nEYMrDVSySjCRhWkMkS/XhE4SctNtyMqgJQ8KM5PCdH1iZStOO/idHqlSAeIYd72F6Zj4bQ8rbelS\n++LXHyf35GG+9OlP8P4//D84cuQI1WqV8+fPa06oCttVmZlqSxPHMe12W64y/Z5URJYnMNw2sdtB\nRD5YGflrUvWYiSXbdBJjXBzqwSKgq7nLVaxKbq6EV8VikWazqQ/oer1OnDfx2tJPYToRYRzjrrtS\nGp2x5LbCD0n3Zxi6YR/ZfdexNno1n/nkf+fZZ59ldHSUAwcOaBGYMp7Zts2rXvUqbr75Zs21+Pzn\nP/9jHw7/ZlsRx/Hvx3E8GcfxZuCdwHfiOP4l4AHgV5IP+xXgy8nvHwDeKYRwhBCbge3AU//a5zYN\ngdGtk3FsBgcG2LNnD1dffTWbNm1iamqK8fFxPUFXfW+ceOGFZSeedksSnxwJm43aDUQqLQk86RzE\nIa7rcnDXNGNP3isxab/2O9RqNWq1GoVCQd9oii/5wQ9+kHf+/M8zMDDAG9/4Rvbu3csv//Ivc/2B\nvdz/pS8ThiFHjhzRac1jY2P8+q//OgsLC3znO9/hnvsf0OvQj/zZX+G6LpOTkxw5coT5+XnK5TKF\nQoHJqSmElaKYsWl3umRSNqdPn+HpJY9P/Nkf8+ijj3L//ffTHtrFu975Djqdjp6BqNJWZVsahkFf\nX582k6nNgtIE7N27V9vW1X5fmbbyecmfUMpM1UMrDYPyaqi/x7ZtbWRT2gcRh5h+N1EBXp4O5SQM\nAxCmIduIwJcUr24iIhISXmtYJmHPQzgSXCtMQ/5jCAxHGYkk1ci0DayshWEbdNc6HHrn/8bH3/5q\n7rzzTgqFgtaLKD2DmgWoQ00xNBU34/Jov7i+JL0SUQR+V/7qZGWVqlazvouII+JYkM/lyGYl3FgN\n2JUwbHh4mP7+fnK5nB4YX47Ij+OYyclJ3IU5TYh2Gx5BN0hSsqPEjh1gmAbpcp78q/4XDoeD/Oc/\n/EMeeeQRSqUSr3/965mamiKfz+shZ7FYpFj8f6l78yDLrvrO83PO3d7+Xi4v96ysrF21qFBJQrIl\nAUKAkeixwGaxjRtjNHS0gTCDw25s2m7bPTPYjTGDF2yzDIZxWxgZaNltJIHEJiQkIVQCSrUvWVVZ\nlfvy8u3LvffMH+eek1Xd7WhplqjoG6FAlEqprHz3nvtbvt/Pt2gPDMdxmJ+ff9EHw3/3cPhvXKZF\n+EPgtUKIU8Crk/+PUuoY8AB6s/Ew8B71z/CwVULKYeU8Tm2JwT49HR4eHrY3YLFYtNmPZi2lcVpV\nLYJq13Vp2tNsB1wP1Wraqbnw02R9l+X/+Ke4/cMEQcDMcpXBwUH7IGQyGaSUlEolVlZW+OhHP8q3\nv/MdGrUq73vve/j0pz/NrYeu52+++GVSqRSXL1/m5MmTRFHEzp07+dM//VNmZ2f59re/zdDQEK++\n4yf5/ve/z0c+8hGCILDp3nfccQfPPPMM9XpdcwXGxlCBzqFIu5K+TMBTTz3Fhz70IYq5HL/5v7yX\nJ598kjPnL/Lqvs29eLvdvorvYNaQxt1nbN1hGNo8DSMearVaFvnm+z5DQ0P2sDBVRaPRsKG7TpI1\nqrFvgZ1hmNVpJq0Phk3gKgkIWFqvkaafhtp/0W1qo5IQ4LqIIIUs9CODgKCQxc0EeNlUglWXuGkf\nJxPgpgPctI+fC5COPjik2MyNVJHihf/wOf71RIZbbrnFzqqWl5dt4M7q6iq1Wo10On3VSrS/v18f\nYiQFqBCJE1PPUVS3qQ+9TmPz0Ij0P5MqRCYp7gbZZ7gTRjQ3NjZmN3E6hxNrsy+VSuzcMkZj5jyt\n9VaSTKXoNXuJv0ILn1LFFH4+oG//LpqDO3jggQe4dOkSk5OT3HPPPUxMTNhIASNQM7M0k3Wi1Z4v\nzXj1okVQSqnvAN9J/n4NeM0/8/s+DHz4v/8VhR1kqdYGRF1KhRGk1Bgvk+BkSTbJCkg5WuOgkjRi\n1W1DkESmhz2tUJMSpSJwA7ovfJ/D/8dDDH/hr1hbXubEiROMjIwghOZEmgegVqsxMjLCzp07eeSR\nRzhy5AivfvWrGRwcpF6vU61WrThq3759doB68uRJPv/5z3P27FnuvvtuCjLkr/7qr9i2bZvlBhw/\nfpwDBw6we/duzXYYHGRqagq/MEAchTinniQcmuYXf+Zf8PDDD/MfH/wqPxOscMstt9BsNlkKPW65\n5RZ++MMfbgJXzM8jGbKZ1ZrJjzR/pnK5TKlU4vnnn7e/DpqNYOIBh4eH2bdvH4899phd1xl7u9mV\np1Ipy1806z7HtBDSRaULiLBtSU4qXdIHRxRpnH2vCr2W1qREkXZiuj50WjjZPEo2SA+WtHEuk8Lx\nPXr1lq4ehMBNBTiBR9yLiLo6Rs5NuYSdCNETxL2Y43/xZd5+3920X/lK5ubmCMOQ4eFhe5gZVL+R\nWmcyGaYnxzbBLIYfaSjnoqvvqyjUNOx0XisiXT8ROfkIIRHdFr7r0+r0rHjOOHkNWMakePVC/VLM\nZDKMjIzQFze5cHlRZ0/EOpzCsCKFAMdz8PMe+ckhsgdu5AcnTnHkyBFSqRQ33ngjg4OD1kxoXMdD\nQ0N2JiKltKtNM596sdc1c2XWGk1rbSWOdCZAp04+n2fr1q1s2bKF4eFha64xaznhBsnGwtfZFHGk\nFXe9DgiJSOcSgrBE1ZZpnz9DdjjL2Jat3HnDXubm5njyySc5deoU2WyWbdu2sWvXLsteAK3eNMKn\ncrlsseimTHv00Uf54z/+Yz7wgQ/wpS99iXq9npRxBUI/y/T0NCsrKziOw3XXXcfw8DCPPfaYFfOc\nPXuW1dVV3PYGQkjkjptYjXx27zvAu9/9bo4cO0Hu5a/kt95wG7t37+appTZvumGXNaIVCgVKpZK1\ndJdKJTzPY2RkxM4f8vk8IyMjGqibUKpd17W+imKxaGcMpVKJAwcOUC6X7cFsSNXmbdhsNlleXkYp\nxfDgQDKMTByHKtbBulGk15RBVkNNYgWtms6fiHXLodoN4kYVVEzcaWomR3EAkc7gpANAkBoogpQ4\n6QDpusjAQ/o6MEZIiZd1kY4g7GosmkoOijiMOfKJf+I9k9rt++Mf/5gzZ84wNzfH0tISS0tLzMzM\n2DVjrVajXMghMgXwM5o2JnSrQ9jZZIi4nmYzCKEhQ15KVw9hV7ceAoTQq0rTrplNxuZqdBM2ZH6u\nY2Nj9M7+kHalQdgKdRUUxyD1cFLFOiA33V+gf/8unO2HOHr0KAMDA+zevZsdO3bY1s7cs8Z9a4Rz\nRsdinLkv5bpmh8Pi4iLdGERpBJEp6ZO7uoiYP0nWlzayzJzCBiuuei1wHD1Aki4qjok7Lei0UZ0m\ncbuhS9xY6/uLN9/C5Ct28p3vfIf1hUXLUDTDxvn5eWZnZ20Zvba2RiaTYXBwkG9+85s8/vjjHD9+\nHM/zuPvuu6/q3zqdDnNzc9x+++2EYcjnPvd5HvnWd/mFX/gFms0mZ8+e5cyZM4RhSDqd5rnnnmN6\nepp0Oq37Py+FFArl51DSZWOjyr++75d4y1vewkJuK93xHVy8eJGvfe1r9K+cs2KbWq3G6uqqHXz5\nvs/g4CDr6+ssLi6ytrbG3NyclV0bQKsxVBlLdCaTob+/34qdCoWCpU8bPYNpJTY2NuxNFyNwpUAq\nbRTSm6NkMxElYJSopw1JjqtbiTgGJMJPaUJ4HCNzJd2ChD1AILMF/FIOXI/MUAkvExD05fDSCUQl\nim2atHQdvJRGrgMJI1FXmT/4wwf5Nz91iw38Mej9YrHI0tISq6urlh9Z7ishMzlEvpy0DclsIVVI\nZlyJRyJZ0SKk1m3o/6hunaSLEgJHCoIrhFaw2UKYysFNuAy+7zMyNEj9yPN0qi2NnY81CjHqasCs\ncCRu4JIaLOJv389Kz6FWqzExMcGhQ4esCvbKUKLR0VEKhYL9/MyvZ7NZ6xp+sdc1OxxOnz6tDVGu\nPl3j9UXi1Tni9TloN+zbzuyoza7epg4BUbORbCz0ECtq1vU/E9LSp+NOh/5ffh8333wz//D0Yfvm\nMGpCw45YX1+nXC7bN41xMJqDY3JyklKpxOrqKjt37rR77larxe23325NWcePH0dKybvf/W4uXLhw\n1S58Y0NTr0dHR/Vqst2g04totlqM5DxSmQxqY4lLly5x4cIF/uxzX2DL43+PUooj3zjNTQcPMD8/\nb2E1rVaLZrNpoarmoDBtgWEhOI5je+7V1dWrdv/pdNqG2hpVpUHMmcGjmZPk8znNjQj1oaDQZbiS\nziYExRiNlNJvVaX0r4GeM/gBIpXVhHA/0JVgKoNIIKsiyCA8T682U3pl7QQuwpU4Sdyhlw1sByAc\naQNfrA8hUpz993/BB3755xkaGrJr4KWlJRYWFmg0GhZMO9SfkJxQes3qpXSVoKLEzNfVq9deZzNx\nWzqbYNwo1BVTAiGK4hgnCVwy8BkTT+i6LvRa+MlQOSViahfn6VS7xGECb4kUMpm5eBkXJ5Bkx8uI\nXS/n2LFj9Ho9du7cyfCwXn8amb/neXYIaV4axpRnzHEmB/XFXtfscDh27BiLi4u0O11AEXdaqG6H\nuFlH1VbIZDLWz2AYkp7nJaxIfZJLqXmRRCEqjoi7IcL19AcpJRATdkIaR87ZN2C5XLYlnvnaJk9y\nYGDAmmxMbL0ZBO7YsYNsNsvRo0fZsmULU1NTVmffarV43eteR6vVYmZmhlOnNFvy/e9/P8vLy0xM\nTHDw4EFWV1d56KGHrP16rqorj8XFRa0BWJqh/b1/5Gf+pzfw3e9+l0cffZThn/ufee9734sckLzx\nX/y0lUdLKVlZWbHqUsDOZgy7wMjC5+fnmZyctPF05mFJp9MMDg7agaZ5kAxc1qSem/RqDCJO6r5c\nOPpmF516EvZyReS8IR+RWOuV0lCUIKejBTxfb53SGUAg0jmdCpVUHdJzka5Dr95GOA7S93QL5rm4\nKR8/5yM9x2ZBCjdhGiS5DvW5Gvm/v5/bbrvNrhqNJ2JtbY0o0qpHUVvSFmw3lYTeRrqVEA6qUdEt\nhIHAhN0kKk8mGRUiEX5JRBwihcBTIUpIuz41ikrXdZEq1rAXAanAR106Rq/eoVvr2pT1OIx15mXi\n4EwPFkntvJ7L603Onz/P2NiYFTeZdta0KaVSib6+PttGmBeJ4zhW9fpSrmt2ODz77LMcPXpUC3Di\nCOKYuLGBatUhbONIQT6ftwMVk7dgb75u27oBVfJrbj5H3O1okVQy3EnteRmFPVMEnstbf/ZNXLhw\nwVrDu90ug4ODVKtV7r33Xl544QXa7TaXL1+2kJH+/n76+vqsxl4IwY9//GMAqyS8//77OXToELt3\n72ZjY4OBgQErXHrta1/LpUuXKBaLHDx4ECEE3/jGNywXstFoMDU1xdmlKiJb1BV6bdla1d/5bz/M\n/r3XkR7tZ3T1+H8FOpFS2rbLukUTJWQ6nWbHjh2MjIxQqVRsnmQul2Nubs4a3szNaxD1pgw3Wabm\nLWgwao6T+CyU0tF4SRoZiRzaTPM1mCfUABSTOdlLmI1J6yeESGTwDiKVRub7NE9S6tYjKOU0iQmQ\nvnuFfFshXY2bk67ES+v1tp/1iCNFtxdx/rET/PLLd9FqtahUKrzyla+0TE7Tg6tWXWPlzSo26iVE\nKHR2RZKWrV84rs7UEFInfiXmP6IkjTvSMXfmMo5Y0PySThjpbM3VWdyNedpHvkev0dbDxyT4Vkit\nIPXzvgbLZlO4U/vs/ZTL5SxH1Ii5zIrZKCEXFhaoVCpWMm22fle2Oy/mumaHw9zcHN/61rdYXV1F\n+Vm9++71dPXQ2ID6qtWsr6+v2yGZCLJaOu1oX4VSsdY0SJNjgQbOdprQaxHFivXiGH3rM/zd33+Z\nTCZjjTsmKGXXrl0sLCxw7NgxuyExjINWq2WluCZJypCRzN76zJkzfPKTn+Rd73oXu3fvtklSR48e\nJQgChoeHkVKyf/9+3v72t+O6Luvr69Yy7LSrZHxXOxeDFIfPLbBjxw4KhYJ+M2zMMzI2wvp3v2Wl\nvGZgWCwWGRkZscxJs10wh0+1qlPAjUHIbDXa7TZbt24ljmNbiZi1pRDCpmxVq1U9CBaCZrurmQRC\n4ElBpNCzBtfT7Z6bwFmNejXsJg+Vo9/A0tFKQy+wD5HqdbX0PaUPARWF+mCIIqKOriCEK3FSAU7K\nTyzLDlIK25u7gQ6dFYkMWjqCdFZb+7//nt/hXe96F2EY8sUvfpEgCJibm2N9fV0PvIMMcV0zNVWn\nmRxosR4MqighSSeOX5EoQIXQa1npEgc5LQePeighiRO/iXljX+nCDEREfPk4nee+xsaDn2bhuz+g\nW2trnYfnEHUiu5oVQFAMKF63k2q6TKvVolwu22rWDKLNwNMwJ4yB0KDxrtz4GfDPi72uaZCueTup\nblP/8FWsH+xmHZHIUA230Ny4Colq1TW1R8Wodou400YgiFstZJDSfouwpynIwNraGmfpY9u2bRYs\nc2U/tn37dk6cOGHXXK7rWrm2WRcODg4iVWzNSboH18DZyclJLly4wNe//nXuuecehoeH2b59O6dP\nn+bb3/42W7du5fDhw7aFOXjwIJVKhWq1yuzsLHSbDLfncP0Uwd6XEwQBn/nMZ3jTm95Eq9Xi/JFj\nZDsVctNb7Fq13W6zbds2FhYWLF/RfK/m52Zi6ExepZSSAwcOEAQBxWLRHiZRFFnTmRHvmD7WrOaM\n16Lb62mkoQIpxWYfbv0TSdCLkcTEkV73+elE1ar0ARGkkekcTqmc2LyTTMpkW6CAYKCPOIz0liOO\ncAIPmRiVzJd3XEm3ngQrJ7g0N+XY31Obq/P6dMPec41Gg3q9jlKKiYkJZN+IHmIncBrMoeB4SVJV\nhEqqCT1rkBYlh3SQUYhyA/1nFgIlJCrJyDSeFIC8J4hOfo/2C8+w9twPWTs2Q2OxaucNejPhEHcj\nnEADYwsTg2SufzkXZi9Zsrip5AxXQkppQ3HMnOFKwZrRP1zJSXmx1zU7HDKZDJ1Oh/X1dVSQ1VxB\nCyHtQrNiJ/KgSyORrJiQDqrXpVet6bDRbpu4lUyQDYo7yVJwwg7Dw0MMlss2CMXEoxUKBbZs2cLl\ny5ftSW+kx2ainMvlmJqaYqIvTSNRU164cAHf921FYKLzVldX+cxnPsPnPvc5K3Petm0bp06dYmpq\nikajwblz51hZWbHS5VKppMvm4hBsLBBWltk1USYMQy5dusTi4iKRhO7aCulX/xwAhUKBdDpNqVRi\ndHSUKIrYsmWL5UEWCgXrJzDrSWP1Pn78ONlslu3bt1sRlKFClUoly3swSDyAwHPtYNVxHD2gj7qI\nKEx+zrGuGtykpDYDSRUjMkVUHKMiHfgCCjqtRP/gEHfbNvtUZgp2ruFkc4CgV2uiYoWXTSMd7TFA\n6HEoShF29TxKhUk8fXJIuCk3MYfFLP3d17n55pstSNcki+/duxcVx7hS6IAd10ta3FCrJUNtdhMq\nRrgpPWdobmzCZXsdPWSNeiBcFLpiiBVW8q+UIuM7cP552keeZunpH9KYX6O5VKVT7dCpdYi6ISpW\nhJ0Qku89PZChsGuK9uh1llthNk5mrmSqSDN0bjQarK+vW7WriWd0HId6vW61Dy/2uqZZmel0WmvL\n3RQy15fIovWgS2X7LcKs0WhYkrAI0skKSeFmUsgg0KlXqQxISW9Dqyft4Mj1yP3gH3FdlwceeMB6\nN2q1mnWALiwsWBqVCfc1op+VlRX27dtHIBSrq2tWbm1i9FKpFENDQ4yNjZHJZPRm4cgRa8IZGRnh\nyJEjjI+PU6/X2b17t863WFnh2LFjCU0og8oNMN8LoN3gwnKV97///Rw/fpxut8vS6jLCcfm//unr\njIyM2JzPwcFBQGv6z549a3+25kEHPRfZsmWLzWwYHx+3GxOj8R8eHmZ0dNRO182w0jgRw1hZCI/r\nuojY9NbK2ucjoVPHsNoA3fqpdgOE2uQtOp7Gt3u+TSaz5bzjIIKUjsoTgrjXJTVYQEhB2O7qVZ8Q\nuIGHm9oEoriBa5mLmxFySQy9I1g/s8Sb903awWu327UaFOEFkOtDmlmBnwI3pVfm6IMBIfWswfV1\nhZTOa51GkCWKAccjTtaoJkPEbAbiOMZvrbP29f/Mxcd+wNqpBdrrdV0pOHo7QdIKJWNlhBQUtpQp\nvPwOVuodm/FqKrggCCxnwyggTS7olWpWM3NotVoEQWCduC/2umaHQ71e58yZM3rQKF3wU4hMTm8j\nOi19Wl/xBjc/dNWq6RvN9Yh7ekuhuu3kdHeRiRFLB5g6CCFxiyVLlTYRcIVCgbGxMRYXF6lWq3bd\nZGYEVqjkurz1LW8mzvRRrWrpdV9fH8899xyVSsVWIfl8nsuXL9tS3KDLjKrRyKgLhQIXL160H+jM\nzAzRzA/pyhSlXAanrINgf+bG7fzcz/0cO3bsYP/e68i/4R1Mbpniw7/32zbQ15Ss6XSaxcVFS4oy\nBCvf9zl16pRFpRktQ6FQsEq6sbExW0FIKalUKlftycMwtG8f3/evKL0VsUje4n4GR23u/PWmQrd9\nwvMR0tWfj+MlXAJdVag4+VpegPBTqFYDGSTcAengpNNIz8Mv6FLaGLOMID/qxtZz0a11k4h5TWsO\nOyFuoKvOXjNk4IVjFr1vyu6+tKvXmI6Pqq0k1Q66cnASDqQQuuJRSbK2n07CaQIkIImJYmUBMObn\nZrgf6XSK5lMPsfzDM6wcX6Y+X6e11tZoeSmQSe5l1IuRnoOX8UiVUhS2jcPWgxa2aypoM4g2K0yz\nhTN2feOvMXMic1gYdOBLua7Z4QD6RFteXqbdTfbFidpRxXqqa05L48F3XRe8JEg30mYUAQjXT4aZ\nXe37ySQnZJJt6O24gQcffJDDhw+jlGJ8fJx9+/Zx8uRJWwmY7yefzzM3N2eNMR/84AdJRS26Slpk\nfrvdZmFhgeHhYauN8DyPM2fOWHqUAX6cOXOGTCbDM888wxNPPEFfn559PP7447YnlZkiKQeyvQ1U\nq8Y3vvEN4uEd1Ot1/vf/9fdJnf8BKlPi9uYxhtZ1hWD0CCah3HhRXNe1nEiD/e92u/YArFar3HDD\nDezfv59CoWBNWFe6C43k2BiwjBhKxj1cJ9laRHqWgOMj4kgf8FJsemYiHc5CkNFVgRug4hDhZ3T7\nIYTWNLiu7eHd4S3298a1qt5cOC5Ru6vbRUjCXYynIsbxtT1ZHwyJbSPlkioGtppQSnH52z/ihv17\nbVuRy+VwVeL5aG2gKou6Guq1k6Qq9KEQR8k2JjkYeh2Nfms39GxC6EGuK4U1VAkhbOsilKL6oyNU\nL23QXmsTNkN6jR69elebrDoRUVsPceNQHxi5sX5yh25lrSMsGPZKrodZw5sDwfBBDfovCAIraIui\nyK66jU39xV7X7HAQQtgcgmazCaAxb4n9Gse1slDD/9MDlWRYZDiSaNhs2GxhwkDiTsu4aHR5mtbR\negay2dfXx9LSkhU4GWKPMatUKhWGh4f54Ac/yIEDB/joX/01CwsLfOtb37oqtr5arXLzzTdbQrRS\nivX1dbLZLFu3biWfz7O2tkYul7OCqsXFRcrlMrt27eL1r389W7duRUzs02/cTgNVHOY9/+o+lio1\nbvvJnyB74nGCl72Cv/unr/OfGmV+6/7H2LlzJ5OTk3S7XVZWVqhWqxYC22w2WV9fZ2RkhMnJSavx\nN1bxoaEhSqWSdXVeKZIxfoOhoSE7aTc3n2Zp+Ppn6uqWQiSrvys+VS1BDrubwztbaYSbuQ6On0it\n9Wfm5BMZvdlWOBKZ0QNOFWoqlJdJmf+EbS+CQkCQD3R1WfATGIqj72qh38Z+Vr/925UO73vzvbZt\nKhQKSBNr56b0wRBHiTRaax2E6yeJXq5e2Rr0fLsKXoASkjDW3pY4mTcYfYNZYWZ8h9ZalagTEUU6\n4k56EhVD2Na8Buk7miyd8sgOZSntnMSZ2k+tVrMDYVMNGG6pWVUbJonRp5h20PApAQvf/f/blfn/\n2XVlKaSU0qWl69sAXFC2nzLDsV5vU5GmzFAINOI8nbK5gEJKRCqtrcKdOhuhXkEODQ1RLBaZmZlh\ncXHRTuRNHoPp6Xbu3Mnu3buZmJjg137t15idnSXstLn11luvOqyMAEUpxcrKCoODg3ZqbIhECwsL\n3HjjjczOzhIEAbOzs6ysrPCe97yHg9Lifi0AACAASURBVAcPMjExoVOjmhXWvT5+588/D0IymnPZ\nl26y8cwTVL0Szz77LB/72Mdot9vcdtttFAoFZmZmLBrNyIF9X0NMFxcXba9qZgWFQoHx8XHrs0in\n09Z41uv1rCTczGIAe2Dqn02ocWChRtKZ4TBSIsJWsrkgwbCjDwE9IQQnSEhQyRVktWcBhQpDZJDS\nCddJlL1IpYlbDYTn42ZTehAJif1bQ1adwKPX6uGm3M1qApUAXHT4rEiUhgjoffULFls/MDCg51RR\nFxV2kIWytvp7KUBtbiuE0IPKsJ20TMK6UIWKcYVCJqsTQ083P1Pf94lXLxE2OsSRwkt5BMVAC7gS\nulPYSoJ1EXhpl9xoicz+G6Fv3LbUxitjthPmEHBd1x4KVw7TTRsI2D+vGVC/lOuaHQ7m5ltbW9Pl\njnT1TRak9AfS0WDVqakpWzLHsR4OmdRi4QU4/cPIXAm3fwh/bBp/eAKZK+lgmyCjIR2Ow913300U\nRRw9etSKfjKZjC2fM5mMNSa97W1vI51O85GPfISlpSVGR0c5euIkpVLJaguWl5eZmpriiSeeoFwu\nU6lUrNDEdV1yuZwNqjW5jceOHeNTn/oUn/rUp5idnaWvkGW4PEg3DFHSQfgZ3v72t/P1b3yLi+st\nRLFM8I7fJHP2GT7ytju5/2//lk9/8pMcOXKE/fv302g09MByaclq57du3WrnDWaFubGxYQ+DbDZr\np9bm5ovjmCAImJ+ftzfXwMCAZU5eieO3OgZivesPuwnDwUU5LrF0UY6no+adJPkpGeDplsHX/67r\n68MkVdCtRRwjgwwyk9eyaj+NTGdxCv2gBFG7Z2Gr0ndRsS7DncC0DrqiMJmSQgh6zR5+Vvsvom7E\n5ceP8rrXvY44jrU0X+p1pD6gutpxKROKthlQSkcPVjt6QGnj7xLVZ4RESmFb0yvVqqlUCupr9Jpt\n6wvx874+zNBfSgeViySlKqC4exp35yHWKlX7cjSbCNDMCBMQbAaNRkpv5kIGR2CqQQNVeqmrzGuW\nW2Gsxo7jaLVeObBbCJFkEAaBb1drFqKaz+qdeByhui1ix9GiGVw9Na+t4eRKqE5bl7xeiqKrqG5U\n2LNnD48++qgN7L0SDmr8FkopDh8+zHPPPfdfJUOZJOtGo8H09DRPP/20zZgcGhqi3W7T19dncWSN\nRoPbbruNUqnEyZMncV2XEydOMD09zcmTJ9m3cxpHRIRJ6nLgS+bm5njqqaf43d/9XcbGxrjvvvt4\n9NFn2LVrF29+861UNjb4iz//M6QUzC0s0eu0OHr8BNu2befYsWNcvHiR5eVlOyAzppvJyUkrADMV\nU6fTwXEc0um0jZ4zN5d585nSVPMioNtu4Se2bFLJbKeXvFWF0R8IbddOVoK0G0mGg44xtGlRQmpl\nYlLSx616YpHWK0+RyugVtRRaABVFuKmAXqOF47tJj65bh069i5/z6dY2S3rHlYTtCBAEeZ/2epvf\n/rcf4sknn2TLli10w0j/WQzsJcjqw05IiyAkmW9ZmnYUJv6dNHTqOOkCKGGrBlMJGwdktLpA2Aqt\n/8MJHFvlgOY1CCGQriA3UqB4062E+RHC9Yo9lM1nYipbA5cJw5Dl5WWbp2LoX0b7YA4MYJNd8RKu\na1Y5mD+AGbQgJDLfp4lO7QZxdRWR3Nj9/f2WHCwcDxBaWBNkiOtV4maNuLpKXF1D9XrErbq2c6sY\nais0K6ucv3CR2dlZO6Xvdrts2bLFzj6MxLZWq/HMM8/YKX6r1bKmpRMnTtgKwagLoyiyke/GEFWt\nVpmZmbEW70wmw+XLlzl06JCVKWcyGXo4fPWx7+BFLY6cnqG2qpWR9913H9lslnq9zk033cThw4d5\n+OGH+exnP8sb3/Qmfv3ffJC/+dsv8IlPfIJwY4Utfbo6MetZQ3cy7YRxX+bzeVuemp7UvGVN/sPo\n6KidgBt5thDCHty+n8x7zMEgpB7UCYFgcw4UJ7OJ2MvogyE08wmpyUrm1vNTljXp5Pu0a9PzkVcE\n1grHxS9k8QpZnMC3LYV0JI7nEPc2A3CF1HMH6UiclIt09VtZupKoF5N59LOEYcj27du1R6TT0tWD\nlIh0XusdkkqIOFmHS7m5rZDO5pA1ldNzEcfTS43kQb6S1RhtrCbtjSAKY2Ty/cWRotfuaRRc4JAt\nZ8ltGcaZ3EO90bQiPXOPXWmoM7LsWq1mV/21Ws0OIo1pbn5+Htd1qVartj15Sc/o//vH/P/5ZSTK\ntVpNh58opU06cZQE00gbFGskw0ihe0VA9TrIVFoPssKIuNsh7nZRYZj4K2JUKg9xZB2JZs3k+77N\nnDThs6a16O/vZ2RkxIJXgyCwBGrTrx49epQ4ju3DaAarps831uBMJmNFUEeOHOE3fuM32LdvH+l0\nmrn5Ba6//nqWqm2mpqZYWK8zOTbMWLmf++67z7YGUkr27NnDQw89ZA/Ls2fPcurUKb76+DN8/P/8\nG+6//34mJyfZunUr2WyW2dlZ6vU6YRgyPT1t0XLFYtH6M8zNYsAws7OzHDp0iGKxSD6ftyWtUkpb\nVSLNeSRhZ2iNQxfadYh6xEhEHCHi0D6sMkp+bypny3Hhp5OHLKuHgYm3RvV6Wm7tesS9jmZO9nrI\nfAk8X4up4lh36J6bpF9LnEDPFMJ2hJvSIFYrhkq7OnDW0wrIxSeOUSwWreALgTbuhXr2gOMmM5No\n0+SnVLLSjK8wXSnbCuufoWPf9OZt7UiBOzxBZqSPVDEgKOoZjPQdG2dnVJ2p/gzpsRGi7IBt5Yxx\ny2w//suNRaVSsYnwAwMDtg0xLFZT+QVBYKvil3Jds8NhaGjoqt6JTAlncAwRpJOoO3CSMslIfeM4\nWaFJJxlg6pJQKHTfCsiUnlmodkP3hWuXEVGPLVu2kMlkGB8fp1Kp2OGMCYUxg5tSqcSePXtQSlGp\nVOjr62N9fZ3V1VUbFSeEsPBb413YunWrlWZPTEwwNzdHJpNh7969PPLII/zu7/4uQ0NDrKxowtP6\n+jpf/epXrftzcXGREydOMLe4gu8I3v6WNzI8PMypU6dIpVLMzs4ihGB8fJxer8fly5fZ2NigXq/T\n399v1ZlPP/207S3Nemt0dNS6UQ2QxAytTDK34T8Y9WU2m7XtlBQCJ2wTCd2fE2T0gRC2N7cVYQ8p\nEpfilUatONlyGDxcHCYPnK5ECNvaaJXO6aDkxJQlXI+43UQODINhRijwMoGePYSRxtijn1cv41m0\nuyE4qzgm6sb2792US/X8Cr/0S7+kV35uIpkOkkpGSF2ZRj19kMWhHlDGIfS6m6wHFWsvSaQ5FELF\n9hAHzalM+R5SSNxtL2Pghr2Utpfp3zlIppxDOHoGEvUihEC3GsSkJqaIvJR1zRp37JWGKTOAXlhY\n4OTJk/YeNZQvs2o3xiuzqTBbj5dyXbPDoVKp2NNsfX2dXqS0dLqrcyi0wUdTmcxcIDYyXaFXZioJ\nzdWCG4VMZyHs4eRLiHyflVGnCCknOC2T/2hOV1NJZDIZXvOa1/COd7yDmZkZLly4YA1SY2NjNkB3\nbm6Oubk5pqen7aF19uxZXvGKV9Dtdm3IrREYXb58GSEEzz77LD/1Uz/FZz7zGbLZLC/bv5d/dd8v\nMzw4wMbGBsPDw6RSKf7gD/6Af/jaN6l3Yv7wf/v3uK7LxMQEd9xxB71ej1KpZENcHMdhZWXFekEM\nhMYkhDuOw8jICMBVclqjfzDCMiGElXvDZnnc7XaRKkaGbei1cCy/IEzeoo5uEVwf/BQqjrTmAf12\nVca1iUjexvHmijDJGtGGLL0VkJkC0k8nmSQSmSvpwbPrI7NFC5oNCrqlcTKBJlFLbRGXjqBb7+p1\npgA3cJMyXlcXwhHEYcSdxURx2K5BHBJ6We338DO6RYp6OlcjcZOqZgW6jUT+jRWBaZaF1mkYDUHa\n94iiEEcmbVamRP71v8jkz/404684xOjtB3FTPnEvEWqlPLysh5fL4I5sodvTn0u9XrdDxEqlQhzH\nNmpgfX3dVhBXBhubA8oY7EzlYQBFLzXY5podDoVCwVqWV1ZWiBK3m8zkodchbjfxXZfp6WkGBwdJ\np9O6DHTNvlsiHCcx82glnk7dTnbpob6RRSpPvHiOmfPnmZqaYmRkxA43TTk2OTnJW9/6VqSUfPzj\nH+fcuXMW8Z7P5y3JZ2VlhY2NDfL5PFu2bLGzg3q9zvz8PP39/UxMTDA7O8utt97Ka1/7Wh555BE7\n5Ny9ezf79u3jne98J9XVZS6fPUnZ13F/S0tLFv565MgRYqXoD9c4ePAgv/6ed/OWV72cMAxZWlqi\n1dLJ4SYJbG5ujlqtZnULnU7nKu29icJLpVK2rTB9sQmkPX/+PIODg5TL5auk00Zzgkhi5V1fPzjt\nWgJJASWcZJCMVkUSb0qlhSCWuuzH8TATf+EFWu3qaDAwjvnLQfgpfR8IkWRbdCHs4eWLyMDD8V3c\nQFu0vUyAQuH6nh5c+g5hZ5PebJOiACEkcagozOntDqEOu11dr1BzcoTZQaJMP8rPQrqgU907DWyi\nYxRtei/iEDpNrfVINheBKyHu4UZdPbCNI30fZvvwbvlpMne+mdTulxG2e/Z7lJ6w3AaZ67sqTMjI\n283GwUCDarUac3NzV31eZhBqMjLNy8/ocgyK/6Vc12xbYcwhnudRqVRYW1tjNHlryHwfMsgQRzpa\nzFCTdbmsrA5fxZHuO72AuNMkanc1azCpPFCg8oNEjTqlgSmmp6etzNg8AIbn8OCDD171wRQKBZrN\nJsVikcuXL3P99dfbDE/jb8jlckxMTFiT1MGDBzly5IilQ5tp8urqKuVymU6nww033MCuXbv4wIf+\nHYODg+zatYu3ve1t7Jssc8z3WVhYoNfr8cADD/DzP//zpL/9ebb7aT783XMWWmsyGnfu3Gl9Jwb+\nYeYzpkoaHh62oFXzdjNlqpnqLy8vc/ToUe68806azSaFQgHXdXFF0mN327r0jnp6u+CmgKo2IaXz\nutWI46TkBhx0hECieJVekEBck4pDKW2J9oJNE1PYTmIH/OQlgVYNqhjVCYi7HW1KSgf06i2k7xMm\nW4+gkKNTqemVZQeiTgQCpKNTrMJ2pHFyQrsf5x57mr33/Sqq16IqMpw/f55SqUQURXZjUygUcFIl\n3FRe4+Oirm6Jem0t9Y7jTRGYlIheR992jYqemSmlWyk/DTJF5AS4uX6idpNeQ6sjdeulW6LCzino\nn6RTbdkBsRGiGal9EASsr69bjEChULCuS+PUNYfAlZ4Lo/w1YsMXe12zysFkRrTbbdLpNAMDg7rk\nzOT0gDGOkK4erkxPT9uVphJCpxGlc7Y8jXttkBK31JesOU1ynyLsdvF2vIyVBI56/vz5RJ+vaDab\nzM/Pc/nyZarVqjVlmYrCcRz27t1LOp3m7NmzNBoNSqUSe/fuxXF0OtXs7CzZbJb19XWee+45azHf\ntWsXX/nKVxgcHCSOY2699VbGx8e59957OXnyJAcOHGB8fJxnn32W97znPZw4fZY9W8fxfZ8DBw6Q\nSqX45Cc/Se/2X2Bh91189eFHbIydiZhXSrG6usr6+vpVidlGwWnUdObvDb7ehKyY3AtjJx8aGmJi\nYsKi6kHYqkCb2AII8hq+mimgHZYJst2s+YR+ewJ6WOe49gEyB4OeFpIQqBOClPR1X+9r8pLqtIhb\ndT18dN2E3QFOJoP0ddXj+J710kDSYaY9GwRjNA/SExYOo6KY5koD98IJ6J9kdnaWM2fOcOnSJS5e\nvMjp06eZn5/nwoULrK6ustFo0yCgm+rX1UShrCvUVF6naEc9/fPQ6qxkPtFB9VooU2GAnseEXXoL\nl+g1u/Z7lp5DejBPML2HlvDtPWjQAOYgMCbEubk5ms0m5XLZ3qObL07s1iKVStkBu5Fanz9//iU9\no9esclhdXbV03lqtxvLKCuOuR7S+TFxd0+Km0TV78pXLZX3DOoGGiHRaSQvRtnAQ1axpYYynrcSq\n00IK6JYmcT3dm2/dupVnnnnG2pvN3n9hYcHq0I0BaevWrRw5coQdO3Zw6tQparUa4+PjdLtda182\noTv9/f1WANXX18fp06epVCrcfvvtpNNpzp8/T6fTIZVK8aY33ssz33+WSqVCvV5naGiILz38Dfbs\n2WNnFm94wxs4ceIETzz1tM0fuDK2PZvNcuTIEZt6Zeg/ZlW5c+dO+89GR0eti89IfI2fYm1tjRde\neIGBgQE8zyOfz5MOfKJYESGQKmklomQQZ+AtvS54mrWITAZmcagPkOT7FCZ5O9ZvclKOnvQnTAgF\nycPVS6TLSaBtwm8QrgdhqFeFqQxuKktUryC9hgbACD1cDVsdvFyaXrtG3ImJw03eQ7fZ1W5NpSsI\nhKDX6LHyT18m7J/ixz/+MUtLS5asNDQ0xPLyMgMDAywuLtpA4Ww2a9eIxaL2rnjJxkWqWOP4W9Uk\nDKeNCDKQ6SdytczaUTGqUaF6/BSdSps40mlW2XKG3HgZd2o/9QTEYyDAxgRXLpeJ45i5uTkbKG3E\neMYbZFoJs3427ePi4qJFC16+/N8Mn/tnr2tWORiOo+M4nDt3jkajTrxyWadWSYlq1iHsanx3kmoc\nhiExQu/CM/lN7b6T7KRdnbGo4kj/+66LjLp2i9BoNDh48KCNeDM49/n5earVqi3hMpkM09PT+L5v\nZchGAGWyKkwvD3ole+7cOYvSz2azPPDAA4yPj7O8vMz73/9+3vnOd3L27FlyuRxvuGk3tVqNSqXC\nhz70IZrNJq1WiyeeeIJ6vU4ul+PizFkuX77Mnj17+Id/+AcboLu4uEitVrPor06nY/0Tpi9VStl0\n5ZGREcbGxqzeXil1VfDrE088wdmzZ7nxxhutyrIb6nbNId7EtYddCBN1pCFt+alEw5Ag2h1fP/xu\nMuEXQld6xtod9rQdOmzrg4VkJJBUg6BQtTX9Z8kUkkNHaYOToX45rnZrSs2TFFJubi2E3kponkOS\nxq7Q1YVUeg7R1e7Rxe+f4PSpU7YE37ZtmwXPXrx4kZmZGc6dO8fZs2dZWlri1KlTzMzMcPr0aY4e\nPaoH0/MLLK2sEQoXsv06b7OxTrRyGdXcQLWrtLvawBcLSbx6mfVTl+nUu4TdCD/jkR3OU7rpRsTg\npJ1dLS4u2ti+KxWShm86PDysZfdKUa/XbdttPl/zkjItZhRFdjb1Uq5rVjkYXYHZ3bquhxABMsgQ\nrc0jE6JxPj9iteRhqAm/uD4q7Oo3i58iblY1ScoLkL2uHlD6KV22Rj3WNuqMjo5Sr9ctmRegVCpx\n9uxZG4ZqnJhGS/DEE09c5dE3klSz5jSDIBOtt7i4yO7du1la0tGhr371q/nrv/5rfud3foc///M/\n5/Tp0xrjJYt8+H3/ki8/dYz777+f22+/nXK5zI4dO1heXuZl+3bzve8f5pvf+hZPPfWUDdwxA1Fj\nya7X6xQKBWuQMpNrw3kwfw7ApiEZNV0cx1y6dIkHH3yQUqnE1iQrRAiB7zqEcazpZwocmegUWskQ\n0uRUmP2/kUKbFoJYfza9rp4/GLqTn0U0K4hUQT/wsRZUiSAH6BtbFMu6LK8sJXJrT/9vElqEq7Fn\nTqCHnb1aU3MYwwjpaMGTipNQGFfg53yibqQXDQkV2vEl1dkNitmUrpSS+dPU1JRVwtbrdVKpFIuL\ni3bjdCVa0Pd9W81ubGxQLBYplweRnab+eWVKEHZJLTyHzA9AKkd3boawFdKpdHBcSVAMKO0cJ3X9\nK1itNrh48SLz8/MsLy9rbH65TF9fH5VKxQYFl8tlxsbGSKfTtuUwYsIwDDWxLBEYmvu52WzazcVL\nua7Z4QDY9Zs2YTUQhYz+AI2AqbqMlxu1Qa9KaVMNcYT0U4Qba0nl4Gk3H11UGGn4RqsOvQ7dGL7/\n/e9zzz33MDk5aR8M43NfXFy0w0czkDp06BDnz5+n29WBu9ls9iqtugmKCcOQvr4+Wq0Wi4uLTE1N\nsbq6alOIvvCFL1iwzBe/+EWuv/56FhYWdCBNcZB/+bafxfc8RKK4NHbuj370oxw9epShoSGOHj3K\n6OiobWdarRbDw8M2NMfMOIz1OpVK4bqufdCNH8RYt6+ceH/uc59jY2OD1772tTYZy/P0WtiRgigG\nRyTKwGSyrx2XUrcLnbreSoSdTUdjKrdp107AsJESOCTsxVROezIAvQnxdRoWiQozkVXLwgBxbU2v\nMTN5/Xk6WsmoXJ+gr0B7dQPpe/iFDN1KQ6dzK4BQw2chaSlASJ2x2al29QYljDk0UuR7zx62askt\nW7ZYOpl54xr1rJktGUWicRQrpdi1axflcpn9+/drgdXEOPLSUVrPfQsnlyfYeytqY4nKkaM0V5oI\nR+KnXYpTRUqHbiTMl7l06pxdoa+urrJt2za63S7z8/NWU1MulxkfHyefz9PpdOwa2xwGRohnXh7m\nUGi328zOzr5kheQ1OxwGBgYArNMMEhZAHBNWa7j9AXGrTtaX5HI5e0qqOEKE3UQl5xM1q3pfbloL\npZIczSaq26bW2kStr62tUalomKhJ8a7X63ieR71eZ3h4mNtvv50f/ehHtpzrdrusra3Zw2H37t0c\nPXrUGmCiKKJcLgN6+r+xsWGR75VKhdHRUdtmmJN/cHAQ4UTMr1Z4yz13cWG1xtLSshW1vOMd7+Dj\nH/84i4uLVtwyMzNjVY31et2CRcz3YOYRjuMwOjpqzVMmDdvIpA3s5OGHH+bw4cOMj4+zd+/eTVl1\ncnAS9ZBusDlrcBObta+DcenpWHrrQdBpLLpySOWSHAtPo/ocBxWhE6uVCaY1HgWh25F2bdPLkPyl\nU6ZAxSHST2kOS72qw5NTaYRbx01k1N2NpsXDRd2I2LAeYgURqJ5K1NjKxs5d+LM/onD9q+0Mxqxv\nDQXMGNsMJGVpackOCTc2Nrh06ZJtMwcHBzl9+jQ33ngjtxzcR/M//x3N+SVS5T76B8cBRXt5HQDp\nSYJCQG58EGdsO/OrG5w8edJqaBzHsW2jaS8MRLhUKuE4jk3pNrgAww8xvhhzf9TrdS5cuEC327X3\n/ou9rtnM4ZZbbuGOO+5g79697NmzR9+QfloLoeKY3soS4eVzxLGGuxptuELorUYSlosQ0OsigjRx\np0XY7prVNnGnTTPUlKaSq8v/p59+2n4tw04sFovs27ePTCbD448/bonLrVbL9mvZbBbXdS3RyWDr\njClsz549NsD2+PHjXLp0iRtvvJGNjQ2CIKBcLvP444/z2c9+lkuXLhFLl3J3lflaly0jGhiTz+e5\n4YYb7GzDJGWZKbUxShm9vRlGmTdHNpu1EJd0Ok1fX5+tJAwExHVdDh8+zEMPPUQ+n+e2225jbGyM\n4eFhXVEllZlQajMkV7r6Ly+wLYJOpVZ6xqCSSX0Uba40k0tJVx8CieaBKNQxegnNCyfJv/AzegMQ\nx3bLIfwAkFqlKITeTnm+/npK4fguTjpAKfCyOnBXGtVjrKsG6UoNH47iJAhH6wqkK1g8fJEdO3bY\ng+HKB+xKSrmhZ01MTDA1NWWzNlOpFCsrK1y8eJFTp05x+PBhbZD6wUOsHTtHY2ENYoXMFYjrFeoL\nFcKOXqtmBtPkp0aJRnbx3HPP8cILL3D58mWCIOC6666zL6eFhQXa7bZ1/Bqpe6PRsLoFM78z0FnA\nOjE3NjbsvO5/GIbkoUOH2LVrF/v372ffvn1aT14a0ZqFMKKz0dASagmTk5NXIblJPPdxt4UQUg+9\nwh7C1/9uHKONPMUB2u2OXs2lsrYUNNWCCZ296667tNchERMZ12ahUKCvr49ms8ng4CATExPMz8/b\nAed1111nA2ReeOEFq90YHR3l4sWLnD17lpGREe666y6GhobI5XIcOXKED3/4w5yaW6PZt4XxQoDw\nAnbt2sXk5CRf+cpX2LVrFy972ctsVWUAMkbHYHI3jEPPzD727NljRV0DAwN2Z28s2VEUMTMzw/33\n308YhuzYsYNbb72VkZGRq1K6lZAoT4fY0Gnot323rdsHpRLClotNfjJDRyOlNjkVKk72/1IfNNIB\nFSWKy2TbkdCqhRcAGkirPRuaDCY8DxmkQEjiBPwi833IdA7pODi+ZjaoWOnQXcchyKdwAqnPol6E\nQtlcC+lov4VS0Nnocv1Izs6UTN6HWQGbudjg4KC9VwxZvFQqWZFcuVy2w8Th4WEqP3iWxlKN1mqL\n7MQwIluieuTHdKvakxGUAkrbhij+1Fv4+hPPcOTIES5evEgQBAwMDFjFq3kpjY6OWh5JEAQ2aqDd\nbtvWwRwMnudZr4+RUU9MTFAqlf7H0TkYnPrIyAh9fX1Jjp9ApLM6ySitoR+qusLAwIAVTLmuq8Uo\nYQ/pp/S/oxI/fhQRh5FuT6QkbtYsjbnS0DkNa2trFm6ybds2CoUC3/ve965CvGezWc6cOWNvmLW1\nNVqtFn19fVy8eNGuLc1BYgwtBhzT19fH2NgYMzMz3HTTTTz++OM2B8LoO770pS9x8eIsLTdL9Pwj\nHLhuN2fOnOH1r389USKDzeVyVCoVC7pxHIdUKoXv+zQaDTuEklJakrYB6Jjhk2E2KKWYmZnh93//\n91leXmbfvn3cfffdbN261QJr9DwmqcaMRNisKb2EitRLQC+OazF8+vckmgYDKhAJldrMF+IEXuD4\n1rcQG3hKMsxUSmwOIb0gOXRARRHCD5BBWq+nc0W9TUnehHG3h5MoJlUcI1yJihRIgZdy8dIublp/\nb27atdyHOIqpPvj3NkHdAH/M1sr8dWWWh3mpTE9Pc91119m2zVQdnU6HzOggqWKaVCmFTGfoXTxO\nd6OGE7hkh7OM3DDJ2FvfwvMrEY8++igXLlyw60nDuVxaWqJSqVx12Jv8VeOpMapYs2EzyVcmhsBY\nA4xQqlgsvqRn9JrNHAxwxZTcmUwGOlVN43EcoEdc20B1GhSLAzr8Jdk0ZJKbV/V6+ibr9RBKg02l\nm8A7oh5IxzojO52OdVGCzjC4/ZSeZQAAHT5JREFUcOGCpgMnD49FsQcB/f39NhTGfFhra2v2h20o\nT8vLy+zZs4djx44B+mFsNBrs3r2bIAh48skn2b59O319fdRqNXbs2GH5lU8++SRf+MIXeN/73odX\n2+Dg9QdwPZ/f/nf/jpmZGZtR0O126e/vZ2VlhWw2S7VavYojEcexJikLwejoqI2+M/g7gB/+8If8\n0R/9EQsLC+zevZu77rqLvXv3ksvl7M9WKYWTWI+Rji7fpbdZEZiDwFQPRsMQ9iBpAYiSuYLj6YMk\nWVECKOkhVMdqI6RxQXopLUU2n1sc69mGdBKDU2iHlU7/iI5ORCGkJO71SPXlCdtd4jDCy6ToVOq4\naY841MlYwhFEbb3CVKEiKPh0Gz26tS6VH52kf/8dNjrQCIdM/KIxnxl6uBDCDqENos2wI8vlMo1G\ng9T26/B+cJxes83S9w7jF7KEzTaFyQH8vgLle+7lhBzii/d/wZb8qVTKBuOura3hOA7Dw8MMDg5a\nILCR2Zt72sybzPdkKsher2fJT/l8nqWlJUtUfynXNUXTF4tF28/lcjn95m+3tBffTzT3vbZFipuV\nHdLRq8xUWg/FEypy3NYPgvQ9REIRPnnyJP39/VZXcOnSJbvTNnFwqVSKKIp0hoQQ9oM3KK5SqWQJ\nSoVCgYWFBdbX1zl9+jS5XI4bbrjBxuVlMhnL6vvFX/xFWq0Wc3Nz3HzzzdTrdc6dO8f09DSlUokz\nZ87wute9jiAI+NsHvkK3F9LfuITjOFaXYTYmZsNi1rEms8K0PMPDw7iuawNWr8TrPfbYY/ze7/0e\nrVaLV73qVbzxjW9kenqavr5NLT+QRNzprYLG9QEkVYBpMxT6wZea9EyvrR/oKLqCGalQDT18o1PX\nhCWldM6FIUchbPyc/tC0alW4PmRLiZdDJAlZvm5N/GT24OuVN1ISDJXxi1nNnfRcnLSPk/JxPEdT\nojyHOFTaoCUESkAcxqgwRrqS2vll9o2W7MNUqVRsmW4GvCYoxvM8+vr6MLTxkZERdu7cyb59+/iJ\nn/gJrr/+eprNpjYAKkWn1mXt1DzNxTWK06OMvuF1jL7rVzkTTPDsc4dZWlqyK+axsTG7OjVirPHx\ncavzMX4aA7A1aEMpJcPDwxSLRVKpFOvr69TrdTuDmpubs5st82J8sdc1qxyMPdqE1YRhiBNkNMo8\nSOOls1bHAFhcuu/7UAfhBUSLsxr1BeDoN550NUjDvAkNSFUIYYGdtVrNSogBWwkMDw9b44phOJh0\nJCmlnQXMzs7a/s20GDt37uRrX/sa4+PjSCnZsWMHAK95zWs4deoUDz/8MNPT0+RyOb75zW9y4403\nMjc3p4Gv7SXq9Tof+9jHyOfz3HPPPfzJn/wJvV7PMh0MCcj4O6SU1qq7Y8cOPM+zwi1jEjt16hSf\n/vSnrWrzzjvv5NZbb7WzFKPpMFsQ13VRwtE8BqOCDDuQKmxuIrzUFRsFPTcQxnHZbepKQ8XarCQl\n4G9uMzp1jWDrNJLKQLeF+pDwEKkk56LX2vzvdBqa+9BMDhjH1YlmKJy+IaKNVeJWAzcd6BeKUoS+\nfvO7UUTY6VjykhACmSRhCUcgIq2WTM9eoNns2nvFVFvmQMhms9ZwZ9SHhglqVLfmxTU9PQ0LP6Tb\n7NGYbyA9ycDeLNlDtxPtu5NvP/U058+f5+TJk4DW2pTLZbZt28b8/DypVIpyuczw8LC9t0zS+fz8\nvA1ONgNmw3Ew8nlTAUspWVtbw8TgmWT3l3Jds8rBCDSMdXh5eRkVZHGK/chUgp9PSlXDkDReABwv\ngchmwPD/ej2thDN27rAHCQlpaWnJAlRXV1epVCrWhGScijbFG6yX/sCBA/i+T7PZxPd9ZmZmrInJ\n0JQMfWfnzp1s2bLFmqyeeeYZer0eTz/9tM3o+JVf+RXOnTtHuVzmiSeeoFgs8t3vfheRKXLvvfdy\n3XXXcfHiRS5duoTrumxsbFhruwG3GDFOEATUajW2bdvGyMgIuVwOKSXPP/88jz/+OL/+67/OX/7l\nXzI0NMTdd9/Ne9/7Xl71qlcxMDDAwMDAVSQu05/KJFMiNrdFkEmUjEqX90LqA8BQmEEfCmFHVxC9\nrm4lQt06qKaWE6tmRaPiTKvhapoXYc9mTmqIay9ZS2vwLCZmLg61+jARRKk4IVm3mxDHOKUBhBQE\nRZ0iLV0Hx9WVh5f2cNOeRdSTbC6kI3ES7kPnx8/al4epEsw8x6gOrwwwNveN7/tWmm6Ym1LqaD/H\n0yKnwX0jDN/1SpaG9vOV//Qgzz//PKdOnbJr7enpaUZHR221OjIywo4dO5ienqZQKNiwGqOXqVar\nthIwBjmz2boSCmz8GRcuXKC/v9++8F7K9aIqByHEeaAKREBPKfVyIUQ/8EVgCjgPvFUpVUl+/28B\n70p+/68qpb7+X35NkxhVqVSsoajcX8LL9aHmLxC3NJ2Jdo18/7RVKsZxnMSeJ+EpoNFyPZ1jASQp\nWFlQsd0wBEHA6dOnKRQKVKtVxsbG7IDv3Llz9vQ1YIxLly5xww03WHejmeYbWaoZRJk+MYoi3vzm\nN/OJT3wCIQTnzp1jaGiI/v5+Tpw4wU033cSZYz/mFXfcznplc8h58OBBFluKXW6VhSTP4stf/jL7\n9+9nYWHB3iC1Ws26PrPZLI7jWMT+2toaGxsbzM3NWafl0NAQr3jFK9i+fTvZbJaBgQE7sDIiGoMf\nM1e3F+J7Ho4UCSsxSsp79JtcgHIzCGNsMwNJU0kIiQrbCKnVkcI82H5GDxfb9U2Aaxzpg8JJyM6J\nGYsgi7DJ3Mq2NCJZkWplrI/q1bH4+zjGzaSIuj2E5+Kk/CTKXhIa41NCh9IHg0Ov2dMp1imfpadf\nYOutr2N5o0Gz2bT9vJEhGz6kiQTsdPQGzMyECoWCvY/r9TrOxG7GXnED3fUNstftp7L7Tr7/7CaT\n1LTIxWLRMhharRYDAwNs3bqVUqlkGaRra2vWMOX7Pq1Wy4KK+vr6yGQy1qlrDgej4DXbl/7+fhYW\nFl7Mo37V9WLbCgW8Sim1dsWv/SbwqFLqI0KIDyb//zeFEHuBtwF7gXHgMSHELqVUfOUXNBJRA02R\nUtKLwXN9rZ/P5LQ1tqe3AaatcF0X0VWaGBVkiOsb+qaMY+LECtxd3yDwfES2wMBUH//4j/9IqVRi\nY2ODgYEBLl26ZN/IYRgyPq7dkMa0YgxSW7dutYAVE993+vRp9u3bx8zMDJ1Ox2Y83HXXXbRaLX7y\nJ3/SVgoPP/wwd955J/l8nhdeeIFarcYf/4c/4L3v/4C9kSYmJuj2QsTgFm4fSfHkkzt55JFHCMPQ\n7q6bzSb9/f1WdGN6zhMnTlx1g+3fv5+hoSF27dqF7/u2ZDXcCfPmMOEnRmJrbiotoEkISsLBlY4e\n/Blyk0xs2Citqw6yyRxCQWtDv/FdP5kjJKRm4aA6dS2ZNnmaxg8jMtr27Xr68PEzSYXiILyU9m84\nrqUuqU5Lzx48DyELRLWKrlZUhIoipKs5D2GzjZv2CTtd3DBChTFu4NJpd5JvSyPaNI1J6x92DA1y\ncWHF/ozM5gGwGpcrq4dS6f9u79xi4zivO/47szu7w13eTPEmihQlXizTMlMStgW4jiA7apy4KGI/\npIARpAgMJC3QAi1QwDGSPBpogr64fSiKArWDIjFip7DrKLEKy4FlNkpdy6oki5ZqWbIpUiTFO0Vy\nucu9zHx9+C5cqSyiwDa1AfYAwg5nl7uHq5nzne+c8///G910ou/7roCdy+XIdd1J+vAfU1vfwsxa\ngVOnTrnZBNsetdu65eVl1tfXHW9IKpUinU47TZJsNus4Im1W093dTX9/P83NzY7v03awbKt7ZmbG\nZROTk5N4nsfi4uJnEhzA0WY4+wpwyBz/C/AWOkA8BvxEKVUErojIZeAA8F/lv2yVmpqamhzzTWtr\nK+l0I15DM2E4o/kGY3E3Z14sFslms9Qk01AsojayehXJreP5cYiUYQUCr7EFSQb4GyvMzMy4CrOl\nbe/u7mZubs6pV/X09Lg0fXp62q2yloV5Y2PDdVXa2tqYnp4mnU5z8uRJ+vr6XMpXX1/PyMgI6XSa\nXbt2cejQIV588UU+/vhj7r33Xv7+H/6R733vezz//PNcvHjRka1cvDhLU1MTTzzxBEtLS1y9epX2\n9na3MlhtjPLVwXZBLHx3586dNDc3k8/n6ejocB0Lu12ybVc7BGZrF+XTk4lEwopLEWLGnhHN6BQz\nHYzihqlBmBfGkygvrjkMwryeYBQ99YqfRDBV8lIRTUapdCAIi5A0zEtRaGoaCQ3sKppZCcyodrzk\nCpIqp0VvvaAGlaojylwnLGhQWGmjQMyPEyqFn9I3ehQp2CiRqPWJwoiooCnjvIRHVIwoZkvUjZ9j\nYWHVMSdZhKv9ru1QkgW3hWFIbW2tq20BrvNzbWaGRCJNbnqRpSW9njY1Nbkg09DQoDVi1abehS0s\nWhm9jz/+mNXVVYeqtOQtQRDcBKvfDGRW+KZcn8KCs4Ig+K3JXm615qDQGcApEfmWOdemlJo1x7NA\nmznuACbLfncSnUHc+IaGlGJ8fJxMJuOYl4j7qFyGcG2FaH2FKLvmimye5xlshdlamCk7pUCFEWG+\nSLhR0DP2G1lEwYYXOAh2d3e3w7dbsJV9tIy+FiMfRRHnz59n//79bnWdmppynQ9bx1haWqKuro5X\nXnmFvXv3Mjw8TBRFjI2NOcWrp5/+Np7n8fbbb9PQ0MDk5CQ7d+5kz549vPHGG4RhyPDwMO+++y5R\nFHHw4EGy2SyZTIaVlRU2NjaYnJx01enOzk7uueceBgcHOXDgAAcOHKCnp4fe3l4H4LJs03Z1A5yu\ngS2wWnVmO35teS6UeW0sKuqszHIuWAKTYl7fsFbbwTA74RsRG883HA1murKkOQ/07ENJE7m6seyk\n3rqI6EzB1jAUhijFs9BNPUnpeZpvsmAYy2vSeHV3kNjRTDwVkKjXIrzxIEksmSDmxwka9DnxPDMd\n6ZlW6Cb+Yubo67S0tDiKNXut2dXagrNsMdCu9DeDmayu6+LiIuvr6649mkqlHBNZTU3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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "\n", + "# 打开一个jpg图像文件，注意是当前路径:\n", + "im = Image.open('img/Lenna.png')\n", + "plot.figure()\n", + "plot.imshow(np.asarray(im))\n", + "# 获得图像尺寸:\n", + "w, h = im.size\n", + "print('Original image size: %sx%s' % (w, h))\n", + "# 缩放到50%:\n", + "im.thumbnail((w//2, h//2))\n", + "print('Resize image to: %sx%s' % (w//2, h//2))\n", + "# 把缩放后的图像用jpeg格式保存:\n", + "im.save('thumbnail.png', 'jpeg')\n", + "plot.figure()\n", + "plot.imshow(np.asarray(im))" + ] + }, + { + "cell_type": "markdown", + "id": "de135931", + "metadata": {}, + "source": [ + "其他功能如切片、旋转、滤镜、输出文字、调色板等一应俱全。\n", + "比如，模糊效果也只需几行代码：" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0a015680", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image, ImageFilter\n", + "\n", + "# 打开一个jpg图像文件，注意是当前路径:\n", + "im = Image.open('img/Lenna.png')\n", + "# 应用模糊滤镜:\n", + "im2 = im.filter(ImageFilter.BLUR)\n", + "plot.imshow(np.asarray(im2))" + ] + }, + { + "cell_type": "markdown", + "id": "08081000", + "metadata": {}, + "source": [ + "PIL 的 `ImageDraw` 提供了一系列绘图方法，让我们可以直接绘图。比如要生成字母验证码图片:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "82053b00", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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OmYv/GvMuha5dEsf/EC5/uIK7Q0GYaSBe/2MWMrcRlu7hA7ZjvPyll6mUgrQ/9QCjwSzam0UKvUZ+MjxAra+TPbkinz0X5/Jdh+luDtJjiTFzS4MtPwthXbDROhMg96E36E6coy+eZkfcwS9HD1AuKDjObZK+/ztcaYhUJ23sXx0ivuvT+PdvMjT2Ns2vimj6zBQMVqZ+WqfR2aR3X4179xSInrrARD5HMVbjpnOnUKc/yeyQhY1AgV2uLNvODZPtDXHtY9vwPV9CKuRJSTlUTSNvXgoyInj5dCXA62dzzGjybLSKLF2PkhmKoZKs+K/fgsXiJNLv4NyWIOy8Bf2EjUhVx4ZV5EvJc8R2LLJy53UWfHfh/rslKtowqzuv8O7FvQiCkQ5fCXPhBXI3BQiourk7eoT87G6Obn2bTdMyHyTGcOh1DJ1W7vXs5vvflVHpbJR1VhblFVrxRyiUWuij5+i59XbWd+qpo2PomR7euauB91IC/6k4vgc/jtMlo9XKpBMyH/usgYW1HD840aBfs4Oba6d4uCNM9TM9KIU10sUFxDk78vYnOTL0Aq5YBeWpI3xz60GkjBq9UqFt5E7ua5QxlQucs/0D+vAG46ZOjo0GaLu4yef7fAx3KFjuS3NnrMyvnGkup2XufUPFWNspDP1tlHZ9htOn2mh2XiQQWGHX3/fR9oUkCanCxZNRHk/exmsfMnGpQ8fQN9rY8YgasVliekJF6OwsFr8eXUcXqaievtJL9Ky1qJ9oJz0+jfajZewjelT6Ni7dLOBcnSOwMo97u5kS91K+LrP/1F8xpd5Nbf8C2X0XaF3+V4hGLTaTlrGhNWZn1fjqnfgNdr5hq3LrT1oYzZ28+vDd+Jp/jC6Txq7IDGi2cjJzhaW6mzfrD/GtSzu4YDrHuPg9vh6OELd6mCpoqM80ibwdRlFtQ+k4zF/eezP90y/SvXyJ5fUaxVtk3muPMevJYUbF4xd+QWnOxYtX3Ri2bHCl2sa02M9uzz1EMq/TK9Q54h3gW7+hqf/Lt1AABEG4D/gbQAT+UVGUv/of/b6to1354n96EmtTYttZK9c63k++oaZRiDPUfor6vlsptJlYrfyM22bvpse1iCNwjr947nbaikU8RRFntJutnzjNXFXgesLEI7esUaoGKRd6yG8cxnL5J7gObMO0+zDnf38WUT2NOKBC+MhOls78hNByC2/OwJsPici5AOa0QmckjnLAizA+jGY+iGfrOS5Xk3gLVh5Mj/LSbUvIeT1ytcbSwLOIO29lONfLfWdC/FOtSqmowihXOOI/Q4pNNkUDS6oA929sYJH8iIqLgseIbdut6IolNIszJB06zh8+xpKlSOVnn+cLY+vEs1VOzDexU2HoplHahCCuvzfwevfTOHaIDO7zktefxLTxQaj0krFFSAZURK4biF4U6DjwPbTkUZs60HR/Huvb8yzMp1naSHLQeRWL+xPIugZp+VvcsefLnO10cszVpDJ/CfXJNVRRDzTvYbT9q2izBowZJ3socbajyNqAithBGx9f+RjOpXmkzCY/3noHTyQdyPXrnOcHUN6OGBrDoTFz76s/5hnzH6NrdLO72KJp/C6ZrUNUe13ovWexGgMsySnOV+Z4YMXDXGedlEGDZ2oY/YXDbDXnONg1yXu11xGKI9S1btZ35GgYdUhZO5W4Dcl5Hf9GBEdVg81zE6ngKMFcHV88y19LWdp3DiI2JDZefY3P3xFGv9JGYzyIXfcOL97qRWOx8+SKg/8wnmSHvcXhgJFo910IZgW1WYXOqeXlFR2WyHHaKuc4dL/IDzYGwNDG7f0dvHu5F580i02IcNU/ypF5A57yJLSe54eRLvTDavTOOrl3Fhnc1o+xOwjtbXT8UYMVfz9Sp5vbh6KshSZQixkcUo34O0fwGgrYHAmEwXH+aeorhFolbrOcJ9k+jWkVmgk/y5X3kTC/gLWcxV7U8tzNj3D4pVO0r0lUO+6nfNcS9oYGX9aEzbXB9fV1MgYTupvv4qE/ex6j3kzB5+GbG8s4PlpkAAv3/dN2/vqmm4hGw7Ae4XHdXqIf+weaVQnb9+5E6ngev9aC0+hnbWCQ7ROTRCp6njdu49YVA+5OPTq/mli6zr6eV4iX4dX5MRY+M0hgQiR0vcGWyVXGvvoG05YCz6yYeH96Gxf0OuabGgyTNm797AZVkizPZ7it/FneKwosFZtYwi3u7a0R5Trnaqcxb9uOPiyiLuZRrBdQ+W4nnU6RWl3ifZkH2DzoJO0pUlx/B98rn8WtdWAJNpjeP8Ge1AZCVMOJ8d1sGFPcpFaxFw1/G1P4PBsEnRkmxgrERD+deRV9GXCvNsltbbDs1HJc5WZofY6Om8sEdxsIzX+Cb2fyxFrLBPSnOfLrATZtGlaDAlM7dCQXFxCLLjy1A3xlHt7bpmWpXcWDcyXSmQl0UgUnAsnfrTD5nJH8lJkDH/Eh/vwFtMP9GD7+QcbcA5cVRdn9/9/Uf84JHEVRXgNe+59YgaQ1IWtNzGsteNtWsCSdZNaczDs6aJuK4QyXaVhzFNvUJOUQytJubJkA8XwGScjSPmzDYWjiVrvxtPxUG5dY0/dSKmnQr03QNiAjGVQkY02UA0sYFxQsaidOW4C4xUnJZKJe8WFJtQjX41S1FQzdIvl8AIumjM07S7NcR+1qUHWmuei+grJRoWbsomJ2IE05qQwbcBq0pP0GHGcb1N05ao4cm3UTy6kyRasWTZcLd2Meo1aLpDKzUc6hr89RLQnkcqDyraCOm9BVnWzsVTO3EkfKOdGII1hq09jlJGpzi6mhbegLPViSGjRRG9X+BtqMgpxqEKmukklAudiJ2N5GxObD26ojtCSii9Ns1XupipvUKgv0yVo03RUknxFBPIClvYbeEMNYqbAjk0dQjOQFK8uSgEUzRMPSpCgaWBXH6FAVcYgxNpxzxDej+G11grKe/nUFUmaSop9F2xB3udcpNYcRKxbsoQqDmlWa2jJoq5SvuMnUgpQVKy6dgi8fw6xJYrRnsXR4MBTNqLJGpC4DukgZpV6nVtZwtbOGu6lDozJRENMUxoPoSyKWZo2F2hYsBgmdrUzC1qR7CZqpHIuZFUoqLfW5KlqNQMsdglQNRTQjd4rUog2qdRMNyU6tZmanb4N2gx214MW9sESmo0VGbSattBMLZPEWsrQX8qRmTRiENVpyidyiRJfYhUKLXLNBWqgTjxeoFco09G66lK24mzL6eoXregvWlhFjzoHQdKFxRtAU09TXBap9fqTWBWhpaJbbWXaZSWpmcWjX6M5a8MaXUDkrrAZyhAJJpBUd2arIsuEkgzkXDrsKXV+WbvVF3NpVtE4D0T0C+ZxAS5dA58xjWG2SiqpJWMGWv86EuY8eScGRkvGZLBCFbEPN+UaKQOIK9mYG0VrBKuupKypaKrDaYDnQgJIVsREk2p2k70qNXMHOsqaDIy0TZbdAeUBCXbmOJiVjLAh4tXkcbddRyp2oJCNrrGKqtSHjY5cgYB80I45rYF3GbZineFWkorXTqNup+66jyw/iKPiwGmXStSp5qQtVRcIU1GIu2TFWAhiTZiKtfsSqGaO6RabLT1VTxVCU6Np00DBFqBcNRJd8ZDzLpN0hZL+XxbwZf2YGxW0n5XOyxQVqSaJqzaIJZRAKPqx2AWdAZHxyiKFIgY5KBb+lQLo9jqvspj7Zxmn1MlWXjEmq4EzqqDolcj0mJL+eQ5MRrjRMVM1mzAMyJqOPDpsadbWOvhSj5Rdo1eto8jl0ioXeQZG6Q4e+3UvYswNzS4dlce43FvWfFfD/WaKmhUlrpagYeXd7g6/sfBf1bD+L07fyk4Vb8Fz/IT3yNIOaYd79jzYyq510vjXGwVUDb5uvEu5aJvlYiUKqjYCxH7PLzzjPcN3XT150Yq/9ktBDY6xHbMzNp+j5yC+x/3gPfsVDUKiQH/JwUgwxIwzw8RMWXt3zPPnhFrVt/Uy/ZKUzcAWxY47FRD8dhz0UnTGeq/6K279hInmTndX+ENl/bEdJd5EMOVkYrdL9th1lzzU2huf59buj5FMBfI52duzaQ3thgXq7i6LGyPQ7lwnlFilU3Fx3hBCdi1jX+vAbAkw9usbRd+doL+6gz9NPq28ejIvETJs8c8dW9h7dgjvdRBqvstmVRjUdpbio8Kv2aaqxMGP7d7DrXh1nwsNYJAPNbITVhb9Ht+1PKFolTKoYN+nvZaNNTXzYgqb9I+iTVwlkZ9mWXeP975gxDwZZs2p4cX4FUdhPwtgkqdeRtR3gfYY0XvNliuE5XshN0N7mpj0QYOsPdEzrFFYMHnLSLYzs+wHRjRbFioXKfU7uWbpKzt5kaSDNwuYHyRTNCIsS3fU66nAUpzXHSHcF73CWxIsh6lErus83MdjzKOs1MitNrtub2NtCmFp9KKdLpE6MMmZIsTuwwunkw0h78+Q8S0yns9zxDkxJOd7UL+Cq6mm7bkHsChH/xBEqL3tId+aJHyjQeq6d2OogLjFEPeLlM4fnmZeHWUxvY+sbf8XKoTozxU6uJNzYRwu4rVVsWRWvXBvA1LtBoxXl/NUGX9w2wEX1Ou+pYyRN/axMvoO6IJEZGuQu3UN0NxqY6wUcwTgayzqGlAXLhSDV3RXEyRkaGZH0xr/BIkVoCEGK9ds4o5nA1FjGl4/A5TEOxs+yOKhwVGzyeb2KRY2ZabXERe2Pebj4DYLdBeR9ZylefY4uu4mGr4eJB4rUnjcQ04TJOt5D+0wv5VaIRqCEmDzPO93/lgMLCW4KL7Lr/mHykRliiQw/r8zx/skLuJxGdJ4A1bYiW4pGdK0GtR1xzuyQaK54yKS6ONf7I3qbdiotA6o2Mx6tkfmhNOFtqxw2v4r4PS++hplbdufwNqcZH9nBdLeXOd9lcqmPMVpz8NFageqeGhdOaNFPFBje/ibJXx+mZu3B1hcg3v2nuDJP4kv20X5Iy/T4Kkp2iKHWAdzqn6Nq60VX7cT5lovIjmXsajttLR/SISfFVAzXssBjx5281D/LXE3L2pqZ+uuzLH7qCMJQPzXvKrve2kTodBPe38MRaRLVqkxeJ8IOM8b5OnjKZDQ6Xv35zdji89jyEdrMFdY+o5C6FqJyspdfHX6aHbv92FIGWid0zJmXWOtpp+Ef4rMvLGHuCZAJadDsnEc6ZMe/nMO8mmLDdZrKqI5aLkdzch3PvB/fVjW4fayX+zjZHaJ98zKdv3jpNzb1nzVC+Z/VM2pT3vfZT5Eqe+j5UpmpX9cpOYdoDuzB9LXn+Mj7h9ln6sXx1UFO/l/z6Lov4NRd5ORbnyG90UPFXEB+7B8Z+GqV7L464ftahD/j5rZ9Y2wfbbJz+1ucmfgGc44YqcBVfj9XYep5ibCcIfbIIk8s3sqrayneXSvw7fCnqTgvsrTVxFsfvZum7Snu+LmO7W87+dHOAMt3DWBVWdkzLlI7dpmNW3Sk94h4s/M8NpBEv5Rm9bkYT49s5dGCnsOJEuWrb3Dq9v+A3Sazo36Kry0H6Lmrh+7RFsHNb/O8Z4zuVRv3XzTzzSeeoPONn+G6vkKBTzEtaenpTXN45zL+1lFipgDFnBfVKwG+32Ok0cjhKkcxji6zetBJsx6i/2/2MV2cIHTEzcCd3ay/bOTDw2qC5g2mk89jH3wCyZij1lyn/0U9lbibSMHK6ZKVEdcp5rqWWXaVOfjSF9n9lWkc29eoaZb5y+ZH2ftTNbe+mqTD8x3+/ssWtOkQ7//rrbz4O9OoL8SQp+D46BPU+wdpizXZf3SNwZueZ37TQSbVzkHjYXoe+RbaepLmtI3oB++m8tI1hMUcvZ/6IKefewtdRUvQ2cXXdsUxnbTRXa7z4d87zt/V/iOFYhNnZpLfm9fzhs9BrtbgQ29e56sf3kFh045tQo/n95OYXhIILVi4z9bLpGWJjTpkRIU7/+1xfnL0TtJrPm4jQvfht9k0icyIdk6o21BdFxiLCfx+SUVZXeDCmImLPQZap5aInz+JkrfiNj9On+UaEfcCmVCZm+77Em1ny+RLCa5bFglMHCayZZVkzwqOWAlB2Ep/PcSdZT/zu05TuNggs9Hi0h1N7rPto351isnnnkb5d3sIdGqw6BxkZ+/ifapfYrjWJP62n6f/MkDuLQ+sF/Hf80Pc1ffREemgb9FFOWcndNc05bFZnva8x4MrdtpUe7Cq97H5zDf4UX+EaZ8Zm/lejrj30pQbVJopbglO86Nnu1lelwi1HUX7KQel8A6UxZ18/gE16+WnySwskX9Rh12ys5HcyUZjJ+rfcXGP+CPMkoqFzPt543KDkf2vsX34JO0/9nN9zwEico745FuUcttxDEt4R5qM7BwgumojU2qRlkoo3xHwBlP4PQnGirM8Zf9X9Ft9fMFV5J9u/ya+o6MYl7qY3i9hXOzCr1fobc/i7HudH710KwtzIR4fuEDg0J0UBQvhrEy0GWXrG1asYT2zI01sw0/hi2twT7fxy+oGkY98mNqYD6fwGiPHjzDQrNOp3WBc+h7GoAepamL+rIDxnocxtdnQqJvM/M1xVvq20tYn8eldR7ko/D6XL1aYuxinr15G3jDibGwyanmXYHY3Vx6pcO3mEtK7NgITCcimydQTtOQ+OnYO4+tsRzoj8ejwMpvlGP80E0H6cCfDfQ66nFa0RT/Z5hs4NjroGX+INf2fs3b1IeprwxwwvcokZloGG2abi4/+1w//7x+h/M9SayzsFw8jxdVU3nyVyet66h0aWjYvnuodaFNuagYV8YfOUdFfprBhI5d4H/qNSwyULTTNDlY0e/Ht86HJyUg/LNDe/hpbd6TxhIKshD/Fpfaf47Dm2WGC7+UeYOS2HP5mGUtjO8srRuS6g5C/yGrHFKXNJJuRCvIz09wjpEgsjPFMYYju2QXaNF5q/iQJ9yydpCjmt1HZaCMxU+GSpwUqM2F3HwfcA2RdMr/y56k6h9gycA5TpkR4ag2bYRj/4hz2dJTZkglNpou838OFIzqOvPZrVuoS8347XD+GZm+AJFZOvdfOof1+3lN6yclBDskWbt20IQ7KGI50cp086hkr9k0zd9f0LO2z4uo2sQ0NYwHQdinENG7S8UdJv5Gl3rFCtX2dM5UBRrqu4Ebi3rSW1mEPlngvg0sqTKYsxosy9aSNsH+EB2PnsWwaiZv1vNJ6mOlVAwGLmvXHVUxbsrR3lAnUVewKhwnsi2LpMmLqseJ8PYAmNYSi78XxpJbKVTUFoZtC/yFOlSZJinkEa5Vdql9ztexGnazgLV/C/ciDDB/S0lMv0vQMsff5JFV9DVVPmdeCK1iUKl1yE71P5APdRYrOBrLYghk1OebBJBKNm1ke1WALJOk1bzL+dAFn+zqm/hKJcJZ8bRcZq5qYton6V+e4z1hjzBdE3HUnR9eeYn7dTHzRjdubYKfFgq2hxiReRHvnEpZFJ9XxHpyGo2T6dxO2aVhRJwn6f4FWJeEs67hn5S6aVR+5QIVf7DuPIgbp15joFhtcip/hxJkIUktP6cG7+VhPP+tKms1sHU85TNmrsGoWOadtIp86SYdmBOugG1PrEIIqRkmpMVsrIYnrSJU6LJsxv+bizA4nHb5VBvTzWPMlDiya6Ctq0W+Zw/SeCVuvAcMgvPuzdlQmM6NbHOxRHmc5V2dzJUrl8stYlMdRB3ajLw9itVhZ2WHAnZMJlcPMmnS4olUMBRWaapF24ziGxDq5Vouoy0JTjtCpGLjF+SCzO4zkViKU3onzbmuZ7v4BdN4yYm6aLvUofrELk2mA2TY/zrQLs5IlrrpA/Z0GBauEsC/DSGmFq7kujDoBnbHGS/GPkRVstG+B0cPD1G2rhJMwnVVTpBOT9RI93XEOjXl5Z+huTMNWRnZo8Dd+gVdfoBJxsKLdQSRhQWcQ0Zn19Gk7abSPktW6MZpaWMVZ0uku0o1ufMZdbM2rCayCXb2D2aaWVDKJwZilnX6WD51jI58nfW4vv6s6gHo6R66eo+720HfvBvZInMCpDQyP5Om2RbHUdbwuDHJVo6JhsNFtttMzfoD2a1psQp2MewlzbRvlYg8T6TbaO/W0QiYy7gAxx/30rbwNskJd7P3NTf2t1RtQtXT4Nd00RZn8ihZjyknVJtKqZtD5OmgU1OSyGUo7p0mxiJDcgXG5h1J1Ap9YQoOVVLydtn4/uut66jMSne2vYW6vUXJrCSd3s9LxNHo1mGo9zFe6aPcVsNRkNKsG5jZT1DwVfJ0VsqEI1eMt6pslDPE5Rs0GFhQXZ5xuPlh5j75YjKI5w/jgKepuLVY5hBB1EVvIM78h0xLVKB4TY1UL80GBNYuelmWEQ841jMUUq8UKBqMNV2UKa2uR9aIT1USAptpEIlBi99QMKwNWMiEDjeVldIMShUgX+QkXY/2dhAU3CUFHl6uML+nGYTRj3mJiPuzEsaLDEREI1HK4XWY8AvjjGSw2CIt64k2RdM2GMb5GyZgla6sTqVvp8s6hNkbxmuok+w7QVrKilfREB2KoMhqKdRvLZYnBzDXqVQ0pg5s58QGUkgbFnCLSMUFBblAx2FA5TeyINehtpGnYdax11sn8WE1e9iC5gph35ame1FI12Cj3htjMXWBTbtHUKRg0MTLNDpotmbhYRm8S8bpbeBsCCU2QYLpO3VSnKCucESPcXMtgVynEuztot5VRmWTEVovMcoOoaYGKq8FsNMCmqwNPMEWXcYbXV5p4QkvYDJvUlDrL1XvIV0VKjRydF+sMDa7SZquTDclMrFbIrNcg3sT2oSZD7gCuZg2lMU11m4At7qCV8lFNv05tZCsVnZZCXabQNoW6YMcR78AR9mFvuJixF5i0TdARfhip7EZXk9CnU6Qn45S7bDTuG8Bac6At16FUQK7nybVMbOhVTLm1WKdy+DrCBDwtysUQEgtkxDxZlwFrQsYoCeiTLRxTPta3aBGFTczKMg6xwYDspKtqRUmp2IjMYbVb8ZXsTKy66Tos0eXV0Z7dxmp+EVt4GtfiOKgOUFa3IakN2DQWREcMp24Nr5Rk3WRGLJsQ82AkTq9rAXW9TDNuoRhy41bKeOo6gtp2sp4mLGepJzWkokkCI0lU6jIUsnQ5jWjdPmoeK5lgC3+5glNJU6qWcc46qB3QUHE1GYqpmC5VkapN4mKZBTGEy9kg5KkjubrIisdJ1CGeduNpr9HsKNCqZehXaXhJv4+y3gVOgaYcpGutilLIknbpqGTKRPUW1Bort1qMFFs+1FofqoBEfXOTZrGMUqugsxkIFCv4MyLqXD/FbBa5VsYsqmg3WVkLZKhrJTSMUejQopJtONZNVHzd2IIN3I0WBqGBcQRsTT3KpkCtVSdR1SIKJvROC365C/+6iDFTYjO4gVqyIAt6EvoyWzUmVF1QUkOuNoxbvkIrr0bKVX5jU3+rARcyDS4bIxTvdPB+z0O01fsxdF6AkT8luvN2Vl6xUJ1sET8sMZfsZkcrwY7Qs/zbgY9zoFKnI5PA/v1N+u97Efttgyj37KTjxa28ktKwaa7gPTSFHHFwZSPI7OZWvpQq817KxJsZLfFNLdVcgK7hIoO3FnF1bcV77QrlyDJ2/QT1z3+MltZEs5TjtalNPtRTw92WY7QzwstPBLhv/iKPrE6iViX4i7d0mLwtPtRd49mf1El9cgeNbf0EpgO0TA10piU6ey7x2ooP1c0WrFtNlC91Umn5aGOSQ6ee5WX3k+hCMdraipzv7UA71sQ2kSCwJGN9fR8DN02g9J7lmQfO0vPiB9km29ieV3Cu9eDatkmzbY2XLk1w2zsfxaSeZVN3AuP9Oq683UOqVEavfY17P3GAmVY3E+l9/Cf1ARSVmxVxme9pljG8ssBYq0FfH6x9og31uXtJxYqca/6Utx/sp++tVUbDEb7Z/gFmRkxsxJZZ+dYr7LtzG8n4LSwXR3nsjihcMLAwM8+l0Ve5UK4ib7cS3C7QmjFjGDJg8m/iHfkZD718C9HYOVLNFpvuj7HTOMum08eFA3eSmf8rHEehnreyZtiC785+slGJ9StNdAt6ku0BCu0aZm6xQSnDPl0vd3R30jT/J9zZAuvxJk9ln8Kt+QzK+Cza6Dkm/+IwQ0dPM3I5zZ0xDV9vu4XIkgYh2eIr5T/g3Pk/42JsFr3zKUq//F38lXlCvhn6Dm4nUAyhYo7c6km0wpdRWRvUeiNc+FyYgz/UYJ90Ek+OcOxWhT09W+hWt/ONuV/xOwcew66qsfO1MDsjCX69IjNeKnDAMskdVj/xYI1TwSLf/GqBD4x5ubvPyfcsWpxre6nLTXy7imROfRz3mWP4xbMc3dKNqNvCSq+NmQNGPvCf/fgrb+H0xLE/fBNb246SKjnYjD3AwOgPEXZ3IUjbqX/7PpJPfAFlXUfjWD/FP4qwV1XG1/By3OrhWOUp7iqpeLjhYdb5dS73fJ6KPMzWayUOvvSnFFAIO93Ub8kw0XoIl9zE73iNwXYD2UInUk3D+/dsozBZZCa1wn/b+D7y/BDDW9Ts+KCOHbg4WjlPctKJ+IM78d59F8cGwyz6lnm0EUdv/yXm2CCWic/xkHSCM5MF4psGXHyWe5UfcCkb5XupBo/96Vl2ij1UYr386+/toeOxJXTZLmyb2/njT71IPHsfjfEu5K/N47jrHaRWiIn0Dl4pPsrn++YZGbwCO+ZZebaX6FyA8LiZ3TvnqM7LSLKBaivNe6E/5g7TNE9ofs5/9YV5vXSQDmM/XzgQZO+lXzAzv5VU4iBDhyc4d8VEf8nAl3ZU+PKH/oodzX4+l95N7OI+qsfCCJl1tFkNueOf4G2Vm6VilYHiN9g6rifmKPF6d4zjT1i4czpIz4yXn689Tl/lD3H6crRv7yNg8CP4NimVT6N8rYs3/t37SG++R/YX//E3N/W3OgNv71a+8Kk/JGlRSH/Yzue/dg5J28n82GF6dFampy9Qi81zUNkgfN9e2kw1eptxXqwX2Cr3YdJ2MePfijdSw68s45cXUf/KxlP2DOvdKrbcHaL67QlWjBaiviAPzoxxruMoKAUOLvdydOt1hHwX7kIXj+xeI7w2i8rYIrSrkxedO9gmr9JRi/An6X52Ri6ik01kTYd48t1+lrUiKX2eBxuv8KZ0PyZdmIP2n3JR/DrnD8+wENpAfGWA2/tfRhcJkDx5H1cd/8RWAji9Hbz2uSHufv5tQp4wrptyBP/6EcIPbFJtS9H94zpXSh0saWTWzCUOaSy0hoJkNQauvJehoTjZ2lHgpv48i9I9hPquY2pmqbztJ6F5Aat6FJ9xP+7Hvsvycx1Imzo8vVGub25DUMLYdRHu2fIBNKKJenGF9NKP0dx3J5rCMJp0gIb3Oucuh5ldz3I9ksO6r8mRjR72ZAZJvG8vqo5nyWvrrJW3Efi6luxOLcpuife7X+UXUj8rBgdlu55HnjqDxeygFXAxf7OFW749SbVqYbJ3COfhq0yI/YTrBqybr3C/+HGm9GmesxznKxqJmZUis2Unk72f5nPuFp3zC1gvXKf0+EeImCqsJNY4/pMXqHeYcLYPEwjtor7s49FkjZ4spCIWjilVFNU4VusM/r/4PKnZX6FdjjI8E+K1u5YoO/ZibO3jyeeOke+bo6htkI8H6XSNsH6twuJyksTvzzG4fjPKMixeuYxx78N05bKYa2G+/8gSnzTupDcloLm+xlr33VQ8JZKWFM/XVtjysocezQYDe07wWuur7FDCdGjzXO85zNqb19ErOTraZEbfHGT6wFnWRmdoaGFY92U61hOErr/OM65hrJccqLN21m8KIaU9pOpFMuIaX7r1eVbPDbGWc7F0IMvvTV3DHNpGfexOtqhLhDthRpPm2ZUT+GNb2Vp10980841AlvdXJuiPlJFOt3HGvBPBNonLfob7PBvU1IeoOfqoDXdS/vbP0XTvhJHdTGycYbCrSNlk5r3GADf9wMPszhybWzb53IWjLM0HKCki+Ktoxj7MhnSeVOUqNkVD8qF7aNe2c2TFyLM8hc6wC5vGR3fhKWbO6bBXLWy1eLH3PcRzuUkuM0v/3hVYu53+SItD65sYvH28Xb3GXLmFNfNvqB6ewedI0q0rcFG1na73zuKcW6eVNDO49QCzfU7Od4ncdekc2iUPeYOLiw+1sTf+Or6yH6c0hNtS5e9PvM5CMkOHd5hLXgiho7dhYrU8yM2HIrT1pNH50lyJ1sielJAvNNjb5uK6Nodb3cbj2vv41pHLxNKz1NeS9F26g8ruFG2VOgcvqvmrNi8HBkPs6zAi6I9TmGhQb+WRvDHetdzM/p4evHY937p+lLvCc7jrPrS6m9h7f4052UC6rjDQWKN2rU50ZY3F5XH+9Q9e+j8/A6fZxKq2oLJaUSoKHr1A2KQmbNTTnZrGq0/R8Nao1uq07DlyZS8rawFc1Veo9mUR7H66mnaur8XZMDVxulpYuvoJhk/iWpewJgbJ6CpYbUbqNg2eYglnvEbNXEbuCuMT7Bg0atyGAurYOolmDdkgYnIUqc9Z0BetuBo53A4j1uoIckmhFM/jNM1TNIrUDXUSzRYmcwZrRYUqth3xJiemjIA3VsJXSmNY1SLFdeSL4HY40Cs9tKr9aGY9uDtcqBxN1hsONKMBNuQKpayMdsxJ4cwmimzBqu+nzRtD7SngrkMp24vKtYRBr2He2MU68zS1BiyoqSkZXN4YtUKQhVQDSzyEVldCsjRYamwjGh2g21yjx7ZByXmNxIaNWlVG7DmAXpslnytQXnHTX19FZ6nga6+ySyyzMb8bQRFoWiOsh89Qdy4i2/yodSOod0ax9Gpo2fTMloYRXHqMTTXSoh5B68RlMqHWi0zGiizVR1EEC2gtRBxFCvUMSkGH8XIK69gSpooW9XIvyo4ykq5AQ6XC75lCrfWjOArIPQVkWSHV0JKXdfSKMrGwBzQ2sjYV/piZaq5CrlVAGIqi0igUCgJStYebZzyszTmJlIqYhlr0lTPIxgItfYMzRSPBXBctRKLLfqQ9dprZDIZsjIiiQiPoMAs2rMpuOrx5muYCK/UGRZ2elnWeQksm5q1h2PTTcpRRWVW0xTN0FNewOdMUuvzUVQ2SZSuquoWGFKA0ehxzSaG3OIR6mxpbTU3bjBFDl49YXYOl6WSsfwy/2UJeclMpG3EPJZg/GaEl1LG7KkjBJrpmElNVpNzZw8zEOMaUFmHNRdtgEzkh0VJL5J1J2loK5TzEcgr91wxofBoUtARkC26VRMtfwtBXY7U0SkNRU9cmqGozBGt2jFUNSiWPvljG3KpSF0TyrQqJ6iLZWJOyvYrsV1NKm0DR0+m1sFDWk5X0FMomhqIudNc7MHf6SXe3MNY60SVaCNlNZtwFqt0jOOJOxJid6NIatSBoA3a0tgzlnEShbmHT3YbU7CGjjaBWp2jTzBPNBnE3bPSZE8SDDexqDSqjjURXgD6NjZpWS97YINas0dUdxa4q47xkZK3HhcamwqVNshDxklSBZNSgbvZiE1OYxSpabQ7BESdlAV1DILSSoN2yC69lnqplnUtZH4HdBvz2Kmu1izjMLkylAA2DwGKPjFUwU9bWSPSXGQtEcWryVFMmhFY/CesytFSYy22U3W3EBRGxUmF0TYVBvRutxY3F4ESzkcPZVKNS1aA9j77uQS+70Rh8vzGpv90RSqOO39OOpnuILZvztNyjLPt1vNe/gi/zHDvaAxj1Bs5pRaTAGpGrPagu3E2o9i4LQy1s9joPXRF5aXKD1cEy5SEnHTdt48OvHCeUKbIQ8ZDbkcVpt+LUGhhq5kgteYm0aVh9KEvg/AN0+CXahqII8xXyTS+5WoNmLkzohAdV0kC25WDLTTWGhXspyJtsRp+lujtBl8pEQDYzUzGg3XIGY7STxtqjlAYVXKcEPONq9nvzFCJbiBZ0GOR1OjmAO9gDRjedr1dwf2wnZXWKtXCS6D3trFzNk81pufbEfjS5v8actxEy3Mxu7wkaviXy5STq1ghuzSwz5i2cce8inPo6a8Id6CUbQuoYnx0WmYvlOD81S6htJ3Xj22T8DY7H9uGVvDh9Ej3BBFH1Fc5FZNKaYYyHP487/kVWpprEr0o8dstlHNt2EDBpMKTXefqvH6QaPEbc9yYbx1us+VUY5B6GMi7kDy5iLPtpZvycmhtix7YpnMkUl9/NM61zYrDbCNh1mCbWuaq/B7tToLdvkbdUIq3NZUxTdUxnDLS0pxDze7HOvI+pvivkSyVsYoKRtlcg+T6irhZ1g5r6VIRJsw5JaXB3t4vp87somH3I7Va2p+OUSlNM2xeRD0xSGd5JfboDzdkeHG/B2rSGFZeC8fMxbruoR2g0iTsKfHN9K/dkS5hlFdcTDjZcOra2T9CfmyHv2M/qpouOVpB96kH2HHyF4xWJqXwTTbCJUllkuSXzjMnNrWcFTH0+VDYzw2/FuLV4lkobvNe7Dbc2z9RcHxfSXrbGi6huXcFaCNF2aReX7n2X9lcMjF0fQjAd5h+WNWj7nFRuG6Rjc5l5t0JTnWew4wxzJ5fRuSy4DrQT0Q7SrZ7HaamzvOUTnLt8lGZYj+qikc6uDcyzEqZmFt+oBVdwE0musrFs5/BJN+oHdTS9eiw9oxgs53ANr+AdlTl++QhhY4GafhNT8iKfKBxAH04jiRHUugpizoUgaVHkNeK2q1RiblQNL/XPjdA0dWCsqvBZsrx0JUFSDwbBz46VfjZeMxPZpfDWYw326N9PcuoskfBVrg8a2OHrxHa1B9P5NqbPPY3208N0D3YyWAkSvhYnqVHzi9FeGi07ve52BnUy1tRxUmsfwrTZTbDVwfs+/2ui/T42DcOUxRAbhQI5uUar3OCNjI7PPjxHn7pK9WsRfuTcRcqUR2jOcP1KjWZHHZfXjCHRQ4c1QLfpMl36q2yG5jgn7cW/oeaJeIWte7ehuEokupf4p2kfXxwt4g6GuRY7hsX1Oww3QlhlP78zbGTsrERJaTB+U5aPdZW5fD3L6Qkt5tUvUHlsHo3Kgnn+JlL7dCzWN5DXUtx6opfE/p0onha2tjC60xJaQQVmiWijhFq4lYwpQMks/eam/jZHKB1bdyo//M/fw5NapNg1z09ffIi8bhpD3y8Z3lbhbtUODC0zTzWusrXWhXp8GPm9fvypFyh9qQNDt5Hdr2Vpzn+Kymia3M45rv5K4WLARqIpIU5O8c17bVhZJp1e5T8eu5+QXU1rq4qlT4pUfn0KoeswYs9tGOdUDKTmKecuczHzc/5Qa+Fqx0PM+e7mC0kHM6ObbBTirJ7eIHgmxM5QAJfLxLen5vlI7ee0RoKce98nWBWu8MS5M+ydKXJV+E+8J00iNatYWiIjg6usySqa9hqPf2CS6LNfRiXF0Hlf5Vd7LZjKh1FlrWwsP8stqodYNtY4rQ9z38V9XHr4AlVThvv+yyiXnngV4+JOPGceY/JDVZIXfkiTWbxP+vm9t9yUPSJzg2rOufei2yzhCWvYeS1EuedFVggRrw3ymUwKt+U88YqRV2cf5v7DX6WV3U+tei+dv+tipVlhNZdgcmGKx76bQmgbodLWS2oxg7JnjoJ3mYh2gv2v5hC2dVEJ9TD541Ee0psoaTY5aniPuY+tsq27l16DC2l6kv3vWGjmmyyrJY6v/B4+tx6nuYq6MsNEj0SPrHBnSYX4CRWa8AxxCf5+y2foKH2fxgWZ8tFOHvq9T/Ny5ijR+AZ3Lw1wePIa5W0WFu4N8r2+PMYX1Rgu5zEtXUT67K3s16q4KdXg2Re2Yve9QKe6yJH0nfztJ54nMthHJbiftnwHfZethKbm6T/3N+T3DnLMnmTaneLr94YQnlJRmxkh1/wYbx54huExM1tCVswzMWYqd1Ne0KI9Ok/gjzd5w6hwPFkn89fzuFyrjGl93Kc7iPbza9RPj6LM+/HcPM3T4zqy8RSm/ByXt8cY6XYw2GmnZ7uJrvkDKLVFKuLr9GVvZmnFT66hw3swxtz3oWAKUx5b5qLrCZy9brQtSL+2zv31cebzQ1yo3cpf/6WT+K9eJrMYp2LczetFD3tUs9wvzFFZ/jKv73uWKWeMtcQOvFdNHLrlEodvPY344yovTX4FrCM8+lCBuZea1KwXEJ2LbAl/hbz2DEvmOMfcTb7w5gpvPq7nzdu8OL/zMJ8cVjFgW8agfoMv988QzJcYLIlsse+n9N8Ok3eXyH78HA/93WFeiJm5rjfw0U84eMN/km7yfLCosLC8RLQisVwx8fPNOxgovoQ+EEIZu5MB8wsMVc1YanpmahLnsksY6i16RD23PGbgytwok5t9XKyOclOuQKZ7nPDoBT4yr8LTP0xZo+P4zATL0znGjLvYb7uZ9YkS3LxM2t/kXL6XgGaKfZFxDqxsoF74D7y85S3mBhfJ7E3z6fN+Gt5hcrYRlO+NcLE/irpWZu/VJrGeaVK5KJJSYewPdyK9lkEyNZDuFvjEswbe0PZw1Gam4H2eJ7vuRJ0OMPOejsdP57A8/h7ZfSn+9uxnaG9E8Psm6Rg8zeDPP8C7ua1M5LSIC0+jeuBOTDojtlyVf/Pvd/6fH6GoMhnyx96gVpWozgbQ++bpKmvYfv1RxsU13lBkZKMAo/cQP9vA12ziOzJPo30XXbopWkmJXx8a5M59P0XryKGYKmwa7iO/JqBRDGzr2El2LMKC3s5a3s8D4dOsjwyQtJowvVbgnqEDLEfVzL1wCnVqncgRF6b2MnctCFj1H2FbsY/2FMRDcbLxBIb8BoebUwQYwNBjRhk0cseyjVpFjT61zs1TzxIeKtKsyeTLVo6LJ5AyXpruJtUtU/gHGwQrFmxqA6J2H9rukxSSCrFqD0eka0TWZ8mlOhnUHMHe8rJdKTAglOgUJdZOySQ1GQLdL+JpRKg5vOR2zZAXztBbncJYb5FJDvJr1waqnIn6GQeZI2aGrGa6LApD8QrL97qwJZMo6xmuHrmbkWoR4hE6Mz9DiLWhqydRCa8xN/4Q4ZVzbChh1tryXAy002zOUY9PY+obRBGtSDkrjZrE6vYa/oYaw6wNdaCHyVoWAQcuYS9bz5nwrjlQGTWIK3q0g9uo1MxEIgrbPRoaumsIljDem5yMnpMxiU2ioTrpCRX6+FZyLRV587N0KYNYMwLNTIOZEy/S6/fRL/Ygqt/lDVM77pIb77id3pNO2pbX0EstMvt2spCJsuAso/ZJ3NSbZD7fZE6nJnb7Rewbj5BONgkbItTtizjaGxicOVQmG73aLIdSfgaX+1hMXaOk205mrIt1WU+XNIp8ZoFZcR7LrpvJzGfYLBmZ2zpET8COXpXmULPCr/oHGKmU8YdybBzapCeeJN+rItHv4qjTxfZrRuS6RExp4kruJjbWxlWXitlLp9nZdZZAo4ArYeFEtIdoh0heX6U5Vaanf4B82sTkBS3ZT55FW7FjSQvY8qs4d4fYJsew5V7g6ROPcHO8G6tFy9sHjzP6k7vIt3L8xLnCkzd/nxG9hF9jRTtk4PjJdhY36xQiIrdoFbaZrlDSX+Jcs4YYvJuIrYui386hA9dZaZyjkGngXdzOKe8Y5fwmA7MbVDuPMrk0yIJVRWHLEPtPTxK2H+A9Sz/TG9PYXJcIqbxsvXg3P9g+RWoqj1io8UpTh21chSoX4HJuiOPvU2gugxKXORA4xtpMBqliw30xhr3ZYl6lQ+U0s+VWLZuxu8EoYbDn0P20jfZSGaNtnbF7WszOlbArLYKr24m7argzPYQKNR6dynLZOkyyPcNr7meQ3tzP3fIUbjJcTa1gGrgdU1lAt65GesxFZlahOV1mayOP6KxjqHgRi03euleD97yMoQqJfVZ2BA6iTp2kVJnljcoQh7pyaFpLrC6/x2vuB3CqF3jCXOBSx/tw6jJk1UWitgDlB36FUJOonnZhYJle4RJ2UaGlvY3ze7QYJk+yVVVl6bHdHJbdpLUpLg5O/Oam/hb7jVKSkOKblOtFqusinf4l+o1luqO9qNa3s7ZhZDkp4mUXzWQPKkWHrTONcosZt6qMLVtk0emjsmsDaSBG1VFH6jBgE/OEWiW6XQESIT1TPi9XnT0EQimcPgGrXsQcK9Gh9eAqKBgWIvgn1ykXmzQ1Ngbd/eDoxl/TMxjJsJiaJx2NQTFL0F6iM1jFaiui0ZUI+rVUTGaa9QahlVkshVUqGh1Rh4eUeAm9IY/enkcKrlPqKWKxCATqZqRNJ5IQR5IlGskQjngOaziONZ6h3WLDUlXwtVT0m0xYzWV8KQPeDT2CdoW2VA1js0zOHcWsuUano0mn0YNmKURUyZJqVKjn1QSaJcz1FoZaDUdzFdFqwWwTcJuzJEwVwjo1abOMMTRLTNNDtdlAV7xCcn6D7KVZyhOzCOk1ljo0rHg3iBov0NAoSGYbVYObquIlE+hG1rgw1/R4u41UulXUQ3p8riBDxQ50yQClhBtL1UbJ7STlC5C29+AdKGEJZdD4MqiGJAxqE6Joo2K2sLxYJFwykVPZsNVXcaYddLTcDHv0ZDYiWLNa2lsGbL45Vjp0JMwimnyF4ese+qsG2p16tCNttKplivUyKUONAfc0ogwZNKyPLNFeG8C/ZsE4HsayMA3SeYqWZZZ72pEdHkLVEGMrPSxPupk1uJgOmplxNLDbdJSiZWavJIhb3CS0MeLmOJshPXM6N/qWhWHFhG77ABZjF1qDi1IQSgUjxYCBxE49pzwWnIJCl1aD1+9lr74Hj6kLSQiyekEkJubICiKV5CDLm3qWbLAUbLK61qCkE6gIRvI5B4JxEyGxgbgUxZxt0rD7cLqrjNgucGp1hUxeBYqBvDWKvVij2ZJZc9ZI9J5Hq+QIFGRGG1lcDRN1ycFmxUPDHMTtXMRkPs189SJFT4aMWUdSdKPqWKDmWUa0pulWG9kcNqBW9PSuqvC3z7NRn+NSMcopWUvnhgtdYYh4fQeXFsyEgzFkZ47AqoPLgRKVYBi7e5qY6STGWAYxAtGcmUWnkU2ribJRRdC6iDmgYDIqWNNlSrMi6xGIlNS0mQM4tb04TN3YnSESkV5MBS395DnsWMHRkSGk1TAWa6fq8CJJdsSEhb6EmZDZjNpYJy1GqAslxHwMc2KdQHYJYxOElo+6eghpu4jcFDGsCPRea5KvWVDXtfjqDfKeBFatgl0nIHsqtHUZCQYtOBwmYvkmRo0GS0ODPCezJmiglaK9tkmvtBu5XqTZTGNtqamFVknlDaSnO2irpAgpURwNhVZ+mPWBBtq2CO1tYXy3OBmwCDjUNVK60m9s6m/1BF5ruRAfeoTQ9ijldy9zb3+eeXmF5/RLeGqfY58zi1NfYmS1g8nRbkylOUzzBaqBV6HiRZcZYctzA0Sf2E/DaqKoEhn9xGm6Js8jLNeZn4OZlpflmV7Wz2v5G4+WT717Mz1+DW9+4RI/+LvXkL0jWO4+xAN/+2le+haUBwTCH/g4deUjmF0hquVOXvrJRTrv6Ke8PUDj7tup7r3EwAsarC9a+eG/HmPoXBeVQpDNmp2e8V8S3R0ieXsvD77yFPoRiVRLZD7VAEuJwlk36hNOLJ0/Yrz5e3TlHdyVTvMXmxIHRBOjzgbN4Nfxv/cE41tsvLFXxluU6HftpD2zk5PfNfCwN4nQCDApa/n07+8i/1g3sYiL+lN6PupeonlbnsInLdzSnOQfftbG9bUyg2NHia48ybX2g6wcdPKJL3+VhV6RUrcT3Y7buRq4jftfe4d7j65gt52gWtRjyDkZmp9m8g+ymLuzeBp1Rj53K8VvZFnrd7GQaKPnZ1twdJ8juGuSUf0pSn4/DalKK7qO3KnhBU03y2o7f67L8qs3jhPPaWha2lm7U4e3fCuGsodr0jOcvedmduadfDRR5Xzkzwk9MUtwj5vR2h8QefolciEjPQ+O0HX6A8yFT6FvjfPJj7uR03Ua1atkqyvcGvpjVrqGWNbnmYvOoU34GCm5ORizUyk8jaqmpl8n8nheQdl6FW9kkeDaOB/Mt3HmaYFZtZO5gVtpb9+BTTNHS7/C2u1/QUV8hmbyKr5rDpJ/9nNmxRaxszb2d85xamQNw5LIl14p8W7ajG46j5CsYPvcOG9u/JjhcR9PXP8E049ocGhaeMQEJX2Lq7WLtLW7Md3/ATprl7FZ19i+qeHyq7dw4GYvUsrD9CtOOhv/mciIF1ln496Eiakrp9CGqtx1p8xSdCfaS2a00x506/u50FshVD9KKH6J+u2vcjFpZHTKwe/MPsgP40723tXBJx7by59MZFGPJxGXNhCibzHa3s32zusE+n5NsC5zVm1go2xBG9OSDC5gmm/hPlMncVmDPWHG77bRe5uGywd/SfO9QzD+ONW2cxy77SXKhTzqhJvEnifoVIO1OM0bp44w9McXCCkrlN74U3bNfQC73Yh/0MSTO9/j8mIGbdcFOg+f5ubrKppSjFJF4vwrQzz5OQ3mLUGSSw6+9ZaOroE420MyrsuPUZv7KTZVjXa3i6/9cZFHKka2xq0UXkxx2+OH0LcEtGs56p1F1kt5lo1WHB0fY9z0TXqm9nHv6qfQ3nuZYxc8VMpqHh5xcf7a10n67mbmIw/jCUcYShppxL3YsiWeK32FB+5vcd+BTR744x+x8KknkSUDW37wIrG+MEfL27kYvpl9hj+jNNtBND3EYun3eb/1HOc0QV5SjfHgiTxnH1qgq6Tjj8Y7iL3t4Zx+P1HzMPdvnkLp3EI+qqEyn8D92SVqW2+iWnJzS8+bREo9rE0Fab7xPuD3/rtN/a3OwD29W5X7fv1rfGKSL/7wWRT3h7mWaHIissmur+wm/dJbFMbL1JV/xTbn32Id0cDufl7yjWB87efos9Pojwj0XtiHZNRTcDTQnriMQbJjMXkIDA6S7R/jSjDHVUcS11kL729boelb50dt0/zhX3QijGqJ7lJ4biaKyuTH6xphZ/d97Fl4l+mFCuNxgeyQnbssavKWNC86ZxDkQez5dlx5Df2Z54nMFahmO2iVb6ZrxxITkR7Waxp8j36XxzvuwFFzIMVUqLs7OTXrIjVb59NXX6VyyzUElwyCiXefziIXDyN5+ln/XJMtT+foJEBnVz/C/XNsZINkYi3q06foL25ntT7JBMe4/3APSx13sZz1Mf6DZd6f/x6dB0Qsd3VwxvtREqtNWEgSPHEZX9fd6J0JDJ45ug4sca6xm/XlDjKvBLhvjxkxVaIYCXO2+Q0e2PFJ6nInr0yWiX7Jyv71SfqvLfD91x309R6h1W4n3FOksuDBum+VUG+YJ9+s8xOxF0u9xL2VKX55h4amoEVOVdl84QS/GwhRV49xoX6Im2MvsTa6Sa67QqfJi1f9AWJLSa5dPMnlI1t4ckuWXnWLn/1iiHtv+QUbaw5OvrcL8a797Mlu4pHniLT9gojp97DPRmgfP0PbRwyk/fexJA/wyqQWVfdF7rk4w6OnZ4g8FGGppEMX7eDmq/fz9J9MYL26gvtokjdG78JcaMOqKeJvO4O6VqDbcJg2ZS+rb1zn5U9NkQzacETvwtNTZceFJYbm15Aea/KSzY0UdTNwqg3BMkfMESaqLbA02U/3RC8Op4LjcJytegu6podCUc0vzp/FPBphrNnP7bEH+cVeNV7nAh5hGfPUPLERiUxmkNzsTYz2/DkX5h9jtdKHYeebfL54FaPGQMrcyVHNXgqVUwjaBP6BrWwXTWQy7UQSQR5r/1umjvcQzjrJ7FLoX+0k1gqyqfXzIZfEM4JEPT/P56a+z6V8N9GBXeSGRhheOY5Nt4BbytK70eBE61as3Qq+7homrYfG0RKFlsz6XpHy/CSDhUN4Gjv5rv8yBw90U7TkOF4+x1fNd1C/GqEUW0f8eArVsXspr8WJJl9kfNu/58DOE/jcS3z7jY9wOPUWo21VhvZZ+WbjKkOLIvaFIG+c/wyPD4XRNMPEMmvUD+yiqpIRKlq88/2oVXosg+dx7HmTxMoX6MwrWIp18ok8ax88isEyikk+wg9VF+m5roaUiXNmL0Ozf0PGOUbOfzd/dj3GcmGOiKrEutvOUvoiXX3tjIwM4BFbXNS2Qc3O7qSJ432XKOfi1PIFIkM+PjB/C0q5zOuaV7j7yuNkvDrK7QL39uh5+cLf0koI7Kw+jCTaSW7JkwtVEOZcTJr1eBKb3Hn9Aj1Papme6SObbeOBD+s58+ICRSWDti/PcGcG+8kAwryDhXaBc08YiUpN0lMVnn7/Z/7Pz8BbokRd3kCqV4naVYi+MDWXBXOHA1O6QquupUGd2eYaW7RJZEML2SAwIGwno5hoqbUYXDlq4jrGhh6H1EIXTDKd7SaudaI3bGK+7qGzFqOp30Bv6WctYKbmsBGo1aln28jVY6xootSbfezOL9MlaumQyizrbGQCaQzWLB53NyZBh9BqsqMgIqcqVOQE6pqG/lUPFdlMTWsmL+cxRtyosjUQMwQlEW1Bh7Gowb7RYKKhxSZvYg/FaRgj2I0u8lUD4aodl0mgqjhQmRs47BrqhgAFSSJdOU1lMociW3FVRCyqNYruHtDYCWm2I5R0KBtWDAUtQ4YCptYgjUKVwqSIZjiNIBqpWSCjaTJoVuHK59BlNrmybxsLJS/FqhWj0UZw9jppoUbSVGRFrKN0JLEITpw5N4UZFcKygLwo0CX4GYgvIJga2EZLyBoT0bqWdKlJUZhBzIlU1XoWPB7aG3UaiplKzUy6ZGcmNYrKF0TpLZDZkIm1vOTEFm59nOW1Iq28SKcrSLM7jinSoJZQsNWn0K0OINccFDvNeCslLJUIzuYmzYIGi2ERrRP0fb1cLzRQyhs0qDNo6qWRSaNU1Cyo+zCWRylas+T0Aiss46QDsV6mUkvRSKTR2uw43Tp6O8cYr75J2VSmpSjIig5rrJuS2kFR40AfaaK0nBhcTRqaTWzjndiyBrqsaWRLjbhHIKfWY0pa0LZy2AWR/rqVdpWPdFZEyjfo8QfZbC9SlYqoK5dp27wdb0rBprTIp0YpyHkkpxrN8AxutY3+ogEhCzOGArG8iaDkxyn0Mjpc5XrZQwUdIXUTldygIBaJ6OOoZiQMShlBr2N1WUWwtU41r6NScFESN3HWjdRLIlHMpEcsaBQt7VMiKjpRQkC1inZWj84YQygakMMWdBYV89UWGyaJiLvBwEkjpVqGqmGOrNPM5lIWoyKwU95P2WWmLEsUHCka+RzBjQSFSJbrlRbZDRULITcxVYtipBd6XCj+BLWaRGx+N1viIm1NK1u3y8y5MtQ389SSNTrTJTy2Amih4NHSqWtR0uRYDrvomNERrcls6CRSoxnKU2YaljKSc4KwfRW7V41Ho2bX5hpJ+x5UITf+zjiF1Vmkbi1SI0h+wY5ni4V2pYhvcYUru4fJCDG02gox/QilzCb5JQkpbMEjD2Msm6gYJSrdJkpXGtRsEnVPlc3xFrKnC4uuRnB+nml1ACNpdOoc4WEVjssuVJstptUupF6JmFxBTsRp6XIYFCMtQwsxFGGlYGVIVmHRNMk0s0greqyKhbaa9zc29bd7D1wu4dy8gEUUudgBbWMvIdsH8KkOYvxJAVfTgj6k8Hb9Eq0eHQ1nBFle4f2N3Vw3WEmqejAZNskH87jqdYb0WoSPqDg36Wc9bUYnnmbPJRWDhQ26hTVy/hbHTEeoCy52FS5zrdTPXCPNqlxgZOpT3JX5S3r9UdS2KH+qzePuXaJTu45zaSc52YBFMPEhjRvN9SQztRUSdTXdq/cS7yhSsmVIWpawTPZh98UI+JLcuqhHV6wjJHKYr+c522XntsBVRtqmWTqyQM+LX6Y838vCho2h7hpKYxXZlWDAKpFyHiHbPMOK83vEX+zjtp4eBqwWPMUp3u33oPbsoNN2K8rEFTSbNnyVFnutKUze3WQrGTbPRxlVn6XgGkUSGwjeML6+LJbJAsWZGi9evZlUIopdrnJoi4Lw63dpOjJUOuukOizkgjO0CRIj+V003tHQTOYpFGs8obqVLr5F0zTFRncB34qaN9J7GFfayHtO0V+pk3EOMbFzhN+pxVlsdbKmM+ILJXkjcRC1qcLwkWtMRxSi3iNUzA4s5r/i7EyefZouPr+tm7Dvj8ieNpOf17L1QBbVqT9AN2Ih+FAe35tp9JmLGMV59vtt6ExvUghuIbz7EM8d1+EJv4lfd5F7b95H6r0FMlI/5/v2cGS9i83+KbLtl9He8iwHV77NWrHOtGqarWvTmPbk8AVG6O99nJP6WTJyg2IuQrK3i7bZPsSYmdk+B6rsOHn0RNy92PUqfMeHsNeLHLzlKqudOuZsdtQ1LQFND4rledxNB/fNP0alr5tYaY2ikmb0nn0UtDWUxhJ158vc/sZhjKUi1UqLN9KPUN6bQgyNY+89TvBiNy6VSECdZVEqcjoSZGurj/2OQW7rPUc8NsRmwsLg1QhrLoEUG2Rqx5k41UTdl8agbVJ4N8DG0BL6okJ7RuCS711C893IJYUTLi/SoV3smVDYe2aWhaEg2U4n6aqRyKkQmtD/j/KGH+lqkM52kev5PNPuErUu2G+2kBTWCZvX0R66l7PfeZ3+tTYe1/8u851r5LfUSXcXuP6Wmfenpkg1ylyqa3FOVDmp64GOPkItA8LOACVzkXgsTObYJ1Fpmvh9BQ4/sMZ/EafJXqzjumpEO7XG4NgG1o46+fYaOnmS6aiHX53ZwcNhNSltnUxvkdx903j+rx1E5U0Wul5Gc2+T5KCJQEbhk+/F+beH/o6OnjyHvReJrpxibfs9pPJ9cNnJlkfGGT6TxP1Wgq/dfT++heewNevMh3TE302jWfbiifWz/eo25HtkSu0FXC439bYFcqEGSVuBwqsXsP27z+LxNdBff5mSYRFHOotRl2PzzjIDv4Z8bIwp//uJO88gD9ex+hIUMm/i076PqgOKgQSvXguB00m/USBrn0V32Ym3sZ0R1W/+1PBv95+Yrh3Kk4e/Rlqew+cNE1Kvo7LVaQYN1J74Cw5Fy7TCaf58YoF/99ExkrYoV+uLHHzvEMb5bgyWJs5PneZr/06m2eXGcasHSR3hyNVJXIspwtMtFkpPYt+5hOfgBFL7Lg5qVGQbG3w3+2sOjHydUO0k/vAJPH/2KX7UUccyvMrv3XqU175hpcvThr2/jf9rXwf73jhNr1VD78N7uDrRR0X3Ks3qHPzdf+PCk1q6O+b4qPbXVFwmiuP7KS30UyulET78T2hW1Wh/uocrXxnG2nwHKb7M8+/czL8VJVJSkGO1Uf78zw0cLS4TWYxzz88k5u+7ikWs440beepX9/DxLzvo3qXi15NJHv6lwvngEj/fMU/7u/ejPqig8a1iiP0jGasf9YUm5lMKQ4NPMFjpw1ENg/SXrH5hK9cWRliZ7OEPPzvBM2+bqSZk7rYnWbylC1dwE58ljHbazIQpRNIgUTXOUqgcwn20k7ZzIvttf85KYgeVASPyk0WePdZBbcqGqtzE8PnX0WU+Sm8jwK1infrNad7LV0gs1PlXX9eS+9daEoMu1kwdxP5yE62niqAtUj6zQnDsMQZ7RLZ1bfDMdy7T+MhFpJtyrCh7ufXELlpLamLrVc4qKXruOY5xWGJB9xEGPcfoPraL7pfvQfyz/4o5txfDIhie+xnPHPorOrtbjLSv8R3bImvfauC4nuVDqimu6baz6ZMpheo82j/Cr7qnKKULbHvRyNV/9SCejvO0Oa6ibnyShb+5hqWR4fD7wfvrEtexMq/yMhzZikn/PGlrjmuBABFZRhUMYFG3s/Uft7Ey9vc4Sw12Tezk9BdduL1FvAUBz58fQnWrGd2uJNrdczz1qZvwNC/i0cUwmQ5yJfcafbty3Pm4yPe0Nj68ehs9q108fXGdKcPfUKGOphngjzI9XPhgk3m7jeR/u4Wtn83TEMcp567yyI77uLDyOnJJ4hHnRzAUjpGcbWdltpcLoe+we81Nt8uL75NdnH0+j2dnAdu+HH/yVBJfcTvmRhMdZ/js3q9yLqzm7GqZ27QtxvzjrLHAtzMT7PoDM6q1OoaIzL33d/Dz17LMbRpJy93co/k0zsWXqMXO8XT7+zB+fAOXy0xXegDfa8+RCfdS1XTS/UUDq4kw/guL7H/5GtX7eplPhkjJXTgP34bq4i/xWWbpHtng2uh2LmxuZSMHes13+crcDmYrJZ6vbXDXJR+rvu3QI3DnXc/yjz9uJ5tuYNRX+eTndqJJbqW1oKX28nWe/qM+Gh0KASHHH//p63zH+XlSHTt5+BYdLfc/EAtXSa5a2GLextX549jKKj5suRth2zibmRorcYn3DOMY+3V05Czc9HYAofIJcroCOXuSxiGFX3m60ISbHHxjjbfU53gwV2SftspbX8qjP9FGRmtlaZuR33EdRzB1UdC2MaNW4Tsmkd/QsRi3UqscQy4b0bvU7Pj3EX6S+iKJqJnWaoJXP3TH//kRiuJU2P7BLBU5zVteIz2/bNAoqkgUzPh/fQx1MIiEDSm7lZlXzqF1gtfuYUpVoT9xGtWSRPSnGrwpN0VrASLr3O/zsT6gpqDVsW9CwejdoOop0LLq8BVl1pNTrNdKpLUHUZcWCCfVLEUHGf74O/TZRSSLnR8LTxKW1hALbkx5B3eLSdJaH+sZE8JbRlIDBRIqA7myn4ZSIrSqIiibibtvYfnkMvo2M0qfzPSrpzC/2Y9VErGbi6yf+xm9fVb0ri5Mw1cR1xXq8jx54RKaf/o026xgEyq81nOVomsvYqmGpp6g9gGIBtcQmzkk2yYvDNvJJ0Tc7wxTG4nRpg1jqySoyyGWqhqqoRqGI02OxFe4bC2Q1NaQqru5M9OPkOslWvbwfO0NtMURNIKZ06NNhMxWWnIeQZfHNvUo7brz2I1RwjaRa9VhLI00+uA8L3prWNvG0DZCiD8ucEcmSiHgIevWs3i+xc60C79bT3HbBlM/NWIOJmjTxhDaSryjHCI2J6HMn6O7UqG+0UkdB6LxBO3bLqFudDB9zEewuEHl+C4asxraamdI9UQRrTswuW4mf3gK3Yob+1QLURwl9oE4BV+D5QOn6HurRUY2oqv42Rv6GG1BBUtyEunaRUbEQ6gNp2n0p5iY68M3ZKHHmcJgy9GsQ2W6F1ndxPsA3LVxluV0hnG1C+ZWCVZTdNHA+7absOwhm9QhVc1c2uFgsHYbemmTofUw6tvVDM7XCczmuaBpECjvQaHKhU4t4do08mUfunQb/Q+LyLZ30BhKaGQdWwbX8WzYEXVw5hMv4Tu6k55AhU7NCh++3k3PzAqGYoy9B4aJJntRW5sYAk5KqkmGVnbSc8JJpfAuJ0wimtQagWsFipkOGqHD5NVF3jrW5NaxGGueGBeVafT6CkJ+g1StwtSbHvq7yyxT5/UZHYbRuxm+vIEmX2VSPMBm80VSPUHSXR2cWPFQdnZRFTyYtNuYfvd5vLmdhKTt5M6uEm0EqYsZhvIX2XtvBauql2pJZikxi6KocFcK9C4t8fah7VQmQLuWpv9nKoRdW4j3dnL6iXYe3ejE7FNIuBtE7G/RJ/mRqg0uaEpcOu8mWrFRbpgQWg9QLF5FGdLgvGUbhi09+C+vUMymOVod5iFfC31bC9lpZe6YkUTyCrZMlfdpG+wx72dZUyZfzbAi3YFrZAqxf4Zlwcri2R0EWgYGdQpdxtOo+xu0RAPr7hXOWzJowil0CzX0zgewm9eRsw0ubZhwjcWxJefwxxdoDQS545Ur1HIi6mEzzpkFSlUn81Iblxf2Y+4+g7HeoG2mm2eMt9LdU6fd1GTriSZrqx1UcjKGSgKhK8iy1kdWpyU8IZNvjiOstiFebfuNTf2tXiOUTS0s2+u4tghkhl0YdaBVmamLnbgjMfKNMjFRwVoykbwQIX+thLJuI6pqUpKiVKJrbJzK0CFIBMUqNqnI1kqWnNbMhsmLXXTQZy1hcrbI2wz41ooUY2vkCxms6nYKCylya2oyhTYmhtYRBheptxU4oRpkpbOLDZebmEqPJ65QcWhI6AQ2ZsrktVVyGhN5lQtBXcBeTCIk6yxuhJhY9pIstmgKMaq161QWvWRzTjaCCQrLi4jpJhbZjNGSQ2UA0ZxDY5sgM7+BcyFLMFlmsadCvTlIodXLmtWKq32DljJHPjNFNj3PRfs8SSVLaFNPxVlCrK5hjUfwp5y4BBdqi4WqX4PbCosdKc4HK8zJY6TTPZQLesq1NGeVJOW6nrroYKLDQKQcZGPVzfq0nVq5G1uliatYw5Cyop9tYSnlMTlSrHdbKXZ2I2l6kMYD7JTsbLPY6XVaEafV+CMGnJkmUmWVxCUNrYgOIyLV3hQbtRaJaB5laRyPM49ZVUVdb6Cxq9CbVsg301yPObCYtNhyQ3iWtrNtTqFOgpwmS0mU0Y46UZWtCHNWTCs+kCxUdFkS9qvMTmtZ3ywTa9aJj/RjN+Zp5NfYWFgguAwDYhK/Lcu62odsduC2m+h3aDDoG4iSG1HpwtjVx45qBHukSGpaTebYBuZqA1NNQ+6qhpxFR0WvIAk10h0iRX0ARRXCoXUxbLEzXJbpjKaoGjaxC05ErY9Fh5FaS0KdVTDlVDj6CqgCl1AJixhXDASGqnS5bATMLsL7llF16NE4PKAJ0BO2oixukEvMYNqhw9/hwB30YAh5SHRuYi9oGY4Y2GMcxyBMYVZy+BoOpFkZalakppVrk1kKmRY5Y4pU3zwBW4uqW8umRs/6lIjsqpETVUQyDvqDO/E6m1gsLdS6PSxVJ0hYN6j2NVm2l9iQLVRrPYSEQyQmWxRWOqknDhOP9kBtBIeqjS5djQ6DhM3kxGTqZme9zvZskb5MBFv+KjVXFyW3lbKxgWpDwib4EYKDbOzdilRzY9U4CNgNaO1R3CYHghhgueJl85yAvFTFGmvgXxigVq+hUWtoN/cj7uhF6xKRWzLTxUE6lHa2OXzs6HDQXIS16Arh6jz+ANgzdcy5EppamUVhC25Dk4BhkcXWScIzFqorXnRZO7KphNWix2wxUDRJzBZbxNNlmskircoezDEnRI3M5H1UdBlaZBBrBQxilt7MPMHaMpWeKviK1LwyBaeRamWEUtBK3WxEt+RmammAzUUP1XkdmgkD5YSVckOkZS3hcnUj+9pI2vxEVgawFDJ4s0V8YetvbOpv9QQuNVu8MRGEqAOb0Y1Pd5WGd4BG76Ps2V/i73UyC5s5HuEE5dogiyonc3Y7o3VouG8mKcUYz32Xj+9zUx66g3D3h0lO/Rm1Mw+TjbdzTJPkiKDloqvIyUCKD/3BJYwPZwluU7NjcIG//Z0t3N8j8L4xid/98RgcWEUIRVHZn8b5n/cxu7HJ+mSFye/egv+P/hsWIUH1pS6qzY9hsnnpcat41BznR71xJioy9RN6Nj+9A//xcfreHGdoLEbWV+N6IM2b7WfZdfzjdK8tYFpcoz77MPKuLXiCcXbqLvDi+/6O4fc8WFOdbO/+DPe+YqIy0mTt/ybuv+Msu6o7b/h7bs45162cq6tDdU7qbkmtVpaQUEAiG7AJNtjG2OCZsT1OOAxjzAsYhAkGgSQkUE4tqaWWOsfqrpyrbt2cc77nvH+YZ96Z56MWnud9Psz6p+6tc/Y+n/27e/322mutvc6tNvo+9/fYblSzbjZy8ucefIPn6HDB5kNW3gh+glYKqqkyD6wb2PU3O7lyaZWrLy5hfuhhWhtmsYeKPPyGm+8GBMrpl1HLnkdj0XNF66Yh2ajrx5k0VTGfPkjX8nYO/tkCCVM7yZqT/LqM//Ts3yGzbKc+uJ2HDyqJvGWjoCtS2X0V45Y70U0kkb+4jCLjZOYmCb+0RtfTL9HbtpXTzp28Yd/Ox244xs0TYYzqMK4HF2g6bie5eJpkIEYg/iEWnn6DRV+Js7dp+PTqH2K9LoOls4Av+Lu40xXOnZ/i4rm/5/aDXyHsU7HuLuDZINHedYG2VwNYftTiz/Zex+dH32Kgq8jJLhueX25gQlbl8l4jD2z/Gfv/LUN+2sijCjm/uNhG9z4/YzvK3Otb4ZxGRyhoI/KLfnbctoInNk5XYhp33YI+t535loLHk8t87kCEgDxHoqRiR7ob/dIrpL1Goh+/i88/GeKK9jwnbpzEvjyFMOihXOogNTPKkO0QR25fZXflMsIP5lj/eBjd8jYcP7yH4NeLdNfr2NdVNGIHOJP7CSntRorKh5g1vIpBHUKmUhEc1XCrLszqnIpjL5p566oa8aYm/R8tYVqu8DvRGRr+G6l85gGCT/0MbWkOfaVGWRqkeX4X3v0lDu6Pc/OlNb45/DDpfD8PTRZ5Nvs0g/0O/tQ/iG/JwPd3iESrGsbiOzgaGKGi3YggH8AkPsvo6T564m3IJTULB9W0qJOnxOxmP7edn0BuTBLbu4Hs90Wu1AqE5C3u7foo6gt/y5J/ide2qrnnmMT5gp2A1Qi/02CnWKGUb7EYLvEjxzfpXL4JV2wPqt0PI+6LoxTqOMwDjEZOoR2bw203cPCcgfDt3XgWPXR/3c2FBzPMeA+RMGkwLzT4xQkr3d0rbFBd5ndcaX7glZG2+skabua17z6BZazEphuMvG3fz8cf+zgt3zKPfu7LPBR5jWRIw1NyFV37PkDz3CIdi/C+zACt1jIqrYqWJ86bozL6rl5FWNeRq3+MnZPniej3Met4H46Xr5C+103IrGei1sv01jSDhQLbynFs2gZ586dZT7YYX09yxBug63kTyoqHb9/cQYf8BNWOBNEtCr40foiMNEW9nuWO4EdQjVyi0VJQWRL47jU49df6wAVBaAd+DHgAEXhEkqR/FgTBBjwBdAGrwAOSJGXeqy/3hnbphq/chSIKtzoeIKX4BeuVIpGshr+VqXjZ0cNVeTfF4Fb6zqqR785Rvz1I6Rs/5+ANLvx+GbXkMuPhe+nujjLcs8DJJ29Ce/A0mmYD9Y+v48y+V1kt7iKSvA6n6SWOVIcxahTM+ZcpBi6Tbcpo1XU8tGjh+U4jSq+GWztUfC+oxtm8SnttHf3kHYwbnqHYZUa39z4ODB0jekFJespAv8FDeN8o5fUlmo8/yt6xbUylPRRQ8oU7L/CT2Y+giUXYGfsFZ5ZH2dXjp9vbIqJ8gjd9X0Lu8NDmqJK/+iqLPUvkTVU6QoMUugWKlgw1eYyPflJCeedOyn1KZtZ+xqUXH6bhKqMbCTLy9m7kh16noKgw8bN7+KMHTqN3tMiq9RwtdLOY0oGhxsY9y7i/fj+i8i2yzuf5eXkEp7GCskdL8bZOer5yga7BA3Rs2IHzl99GfNjIUp+CZxQSlm/dii51HkPrCh3OLZzfqERZtrLpcieVG16gVPYgxYwMnnkdxwO7WS+JvPP6Al+8N81aaBPh9T6sYQ3lznEMGyJ49mTQvvCHZFsSIWWaN2wX2BRoorRWqHdVOPL0JladGWKGItWCSEi2BXtvgq4N01SO+vF1uVEodSxN1hj5LR3RpJ7JKxpODYTpOhbFk2rhHWtj3eGiqEqDGOOjxxxMz88gigW2bbCyUn0IuX8S0fM2L51vsntMR0Xp4Bczw3QE6+xlhX5TnK/ecyte0UfHaoKxo6/i+7xAol4lu16l90mR+kdj1Lx+qtkjDD75A57r38jbvn4c56cRbh1AHu9E/VwPh//6GxSODlBcGELzhQ5MrXWMp7Xof+blaz9+i1tX4/THYzzXPY7+KQu6iheDsY96tJe2ZAGjFKcwfBHHhv2obEUaugCPnjpMNZ1EY67TfZ+Pnf9SZ920yHT/LA+/tp+feBe44lQi+e/kk7/8PlaXHja5aW16ESnTibE4ylD5fVzuqVOJ5CAQR687wXO2DdTrem5YTtLovUCktoVobQzZwAqx1zvoxsFHd1p487k/Zm7MQXDEh+Zskuv25CgKfo7NXU/fuceojRrR9Dj4aMxKYrUPmT6EcfBVnrjgYKztRhw93fzLoVe5YfwAukUdxakSfSkJXV2NZFcS+ARsjD1OZTbAyoUyl+/+ffa5ZjGqEnw/2Umr+Ar7hD7ull3P45dfJb6rF6Osjbu+Z+f0rc9Q9PhoaTdQvfyXJFcNWDW93Hvbgzz/zbdpM7jp6Rrk6yUDh2+coru3iMLo4ke/0NOZnGVHaZa+6Bae/sBpqs4ChxZ8uNRucqwTKGX50etf5NDgMxgactav3kXaILHfq2VPlwLdoSDrq/Os5Y1M1bdg4Y8Y8XhwKIc49ssbGNkzja49RMMZZmrChquWRahr+Vn+0/xuR4KKtMaJ5FVueivBeoeHSJuGqnOBtGcnG/Jxbg2co/8zP/x/7ANvAl+UJOmSIAhG4KIgCK8BHwPekCTp7wRB+DLwZeBP3qsjVVWNsapHlGewzucpjm3GJBVpikUWZBLeog+pZeJEfg5P7xaK3jQx5VWaWzwsdelpWVpsyJvRydspSjEW6utMxLT4NVXabCk6ts1QbMVoyRax2HW0ja4jXbAgigbafAK6rIdz2SQzYobUUD+dDgtY9ESMegw+AUGcplnNsimQZbHgRZlXM9xYp3R1lVSwjVjRSqOSpBpZQEymUdY9WDNmbO4ykr1BuuZFyTSWZhZPUYdh9xJhmYucYKSgTqJuBTEk5ViSWrSWAdatWQR9CnvYxaS+TqVSQxdVE7L1kFf0QqNOX03DjNhE1bBgrWixWNewZWtUxDpJwywptRNBlkMjptF4VCjinSjyStw1A5r+SdSSDL1qL+ZmJ/rOGewO8C9Y8ft7ULRpkVmy1JSwVDYRSsuxiDnWSwGMrRq2pg3TTDv0T2Kylena4iGi1CJz5MGQoStUIyBboVSx4i/2YW6dhUSTYrhMxbpIvmrEFazjuVBEWhfQGJU49AY2rluJaa0YjQE61KtExRLluA4po6CuidI9NIXdDo6GjbBCiZYWMnmdnAOWZy3k4k3qwQx71W70dhuSWmAlYELwmFEVmqhDcQwoMI+YQVBgFjUUNsXRi3L0qV4wSuhbMdSUcGozRJUpIoo6dqMVtVZEb1RhamkxevVMrOgxWxL49DV0+W5Iu5FZ1agtUa5qummqNXRq0gi6BNlYNxopRdeuJGvTLVSVOmiLBC4XMTiruGUC7T0VrDMtosY48q5VxqQcWpefStZKtmmmvaJB1VFGLlPQP+lgxlFHXdNjVoxQsvpxFHXYy1mskwnU1o3YbGnajSUu2cLENXLkcjl9zFDSKKk21DTTKozqjUg2HzK5h0augjpoR7meQYpGWTicQL+ewJ7NYGARh1DDYcjhMMSYiOlxeYt4lBVUiiBD2b00Q3HqmgSBFT8zu5QoGiZ8Mzoq1l4sGgMu0UBVLJJxmKgYZKDbiMO8iqGVQ8yWiOe8RGeD+LJmnF4XvkUJmTZEUZEhldRSzzvRSXI6rHkWlpJUdHJEhx1dy4I548SukZCZAxhSYFJW0dvK5L0OtIUyClUeyVAi0tqLIydhqthJ1mUIFjstSU6zkGG7WaBDiGBKZihfqjMs347RqKImlZnNhHBl4pTVBa7UJA6b/WiMWvTOGkKnBO0dGGt1RuNBzlgi1G0iKaPEjCWPUZfGlvUwGi1h0W9EtFRYkSWIKpdQSzp0ZSPEazQ1axhSCkw5DZtMIRyyKNlWA63Yz2JqmWS3gYy5Qb6yRFpjwFQosJzJXpNTfy2BS5IUASK/+lwQBGEGaAPuBg796rZ/A976dQSuLpjpKGyhKJ9AfjlK56ab8CgUlOVZLvniHCz30JkqMBf47wzf7uCqe5qF4jEcH/oSZ8p1QvEMo9Em3XIrS5KG86UWk8kM8YKOenuag0dOYnpCRrVjHNnYRe71V1ldWkOUuxjb14N5dgdR2SWmLNMcu87LYbWcilzLs3In/j02apVzpKJluqdSGDWDOJUpblp5gW9fzRKwdJJTm0lenEGUXUVVdmKq3ECq4cHb9Q6u9mVmz+zFKT6PRQZFcwemT00zcXGEtRkjkZKBjyvW8cQbyFfMmP54CL88jqFgwi/sopAs0QqHcUxamO7YyIpkRhdNs23Bh8q4hl01yMbSCJkNP8I91cSYqWH3/JJQ+z+Qy01hWbvC8B1mggERRVTPtrcHOH3oeayZLThC99BrlRBHZHTWy3zgLTOy6z/MjDLBQm0VxUYPpxs91FYldsVXSYSfRaUdQq3eixjupjN3ka7eAmP7QnjPbiOvv0TLNk9bj8CxahhlRs0dhS1I4QjRqJ3FShPVnUfJX/xt5DMmlOejtKwSGmUBU7PG6NQYf3K9D6PBxIBilctqE45cN5aqEpP1NNv2vUE94SV8cjcNj49Cc4VaM8PS9g3MvySin1vFFZjn1r6Pof6ElVWbkq//oMqBmgbmA7TOJpAfUdGzUU2zoiTxnJILW6fpWOxkZO0IQ9uB1uvIKym2m3KcGDnHMl6K8g561mbwbdFjdLaIdel59YKJHTvCuDqrpE13Ur5URpCCqN93hZe77qHffo69utMs25cpT7Sh61vF88Er/OTvdnJ4sMKIb5GFf86x9OAK3TYnbdt0DL1lYfWGJomOAp+OKJE6PAStvWQKI3QHiwS25Mnqm2w8exOPpU7RinTjiR4kfX2Jg64+NifjKB//FrL7t+D2uxnRt/GZ4FUEWRd+sclNS08QsO8kKjeSras4VL+ZaeMAqoYCT3iBdF6ONxPBzgovb1PTdXkKZ6iCrjeLN+fC05fH5Qxy5scD3Lo9RJ8miXw5RWf5dxHHTyCbucyqeAPnU7N0luHWpSqTN9xFb7NF11qZWmWewnCZJYeWKfN+PltOU1pOMLekpjUwROTya9jcdjrvbEP92hyi9SwN0zqBaDubC3djMclw7F7H+sjrhE39yIzt7Axo6S0NYKkkKVavYKnb6ZE3Eex53jxQxf+GDq++iNW/hL352zSlBZrNAoF0nbRPi1COYRDmuNu8Gddiikx6lVfOHud9h0cIyCXG5Q2mzOs8vFhHCsG/5atcp7ZisjfwecF8YwOjogtfNkd3bhXB/SwyeZ4FbYM3RImbXQ56yyMMzwwgGu7mYuM1ZlqXqA81mbZ9mFrKQ2Uiwq5tZ/HOD9G97sF09zSWxiQVRRdW9xHm9WdJdTaodFdQX6xSNY2zGtfy6vK188D/t9IIBUHoAt4GRoGAJEmW/+laRpIk63u1d/lHpT++5af0KAPs/sL3ifEFAicNrDydZFv705x8MEnCIbH5Rx7mi6PoqgJWUeLsntvYUVDRlZdhiMl5/ZNh2h0rbGktM/hSjnPxLbQkgX3ekzy5lme8y8jcqA31RiPtwVnEcpI1oc4Xf/45ZnZWWTiQ4I/qNTInTpNYyLK2amL2S39JeP4F8pdeYGTSiuXDnyUnzzB3+juwdz8682GsBh/bnF9kj9mEdambxss7+YvbJxEutDBdadAVDLBUL2IbVDB2s43dPV/mZesiJ1Mhlv5KxV1/bMKvj2COTIAiTufUVswxEynLMRLdQ2inzRjfNvLzL3oYe2UVeyLM8m1RjncfYUctxftjk6jfCfC6ezuTKhVrybe5dehrLPhiXNZf5UN/fQ9X97+B1nSV+2cmMD+4A7mnl7po5/xfPMEvnFmy5iabTUq6KvfQtlWDbqDKz56+zFjbIQwVL8UpgYO/WyBwqp3gmxra1/+ChVuHyHbqqRka1H7iZEs5xpBURlLtQ/VHDZKKZSZPvsXUvJIjg2ZG2n2cbt6Nv/0YsnqNctzF3OAW1o/G0a7m+fIREz/dqcUkqdkeVlN5EhoqPcqmFv9VA5U7/oBaeYTi+sd5/bfWGTjlQVhP8ZL9H/jswgCpaDvn415WlZcY+pMEhk6R8BNetMlJIpYs0XYZN+/5C5KpMpFakFnlO/zllT8lWZjgSuUsp0sfwt+eROxYYGX0ZTZINTre3kjn2UHGhif52fscTKIhdlTGX7zYYqanwrk+BXOFmxGCV+g0lblui4GnRruxFY5hTZ0hsVDCeraH0kYna3/kxhlq8cD6LvYE+kicSfKL9n8kTQZd1cfGj2+gum6gvl5BH3qZ4NRfUZdqqM1n+ZTuThaHJWY9AU40HkFcP4ikCtCyniXEnag79fQqC3x2+iTfkt9LX6Wd/Rkrfz4RZWiwgEnbIrpooP2zL6NhK8bwPu4/9cf8aP4AKaWOIwfO8e3bRTa82cvOZ/p4RSoy5JtB21UitdOMeOoIsw4FYTN88vleZlpXURoibO5MkNfJKU+OIp/t5aCU5PHhMJFtetS3DpD54ldxpPP4UXGwYxui7QjqETOGW/KcNM5w6i0TwYUy23b+M5d+9Nv0uYe4+x4H/+nML1EKdTQIUNDy4Mh+hNUlAm8/z0r/HHfvuQ2XfIhvfy3K2G+XSRusTOVcBFaO8gm9mX1WA+ZuDQlOIV/ejnbhHlo3azgpfJPZUJLVpz5L+OAkd0X93L/Uyd+PfY+NChVmX5nM4XkqJ7ehWZFjCZTYkTjFE0c+y5qmG/35MB/YZ0FjWaUoLvP2Y1l6GhvIdnfz9t2D/NXzP8E0XKTaUeXiU0s8c2AQsbuH7d4RBv7yx7yhVLCqMfL1gJ3gzjtYUVu4mi0wrC4x7bYQ15rYs6yhmnwLZOsIphQzQ34O6GGDpoRkmuHPjn+MdY0GeV+M1/cc+P8vjVAQBAPwC+D3JUnKC4LwH23328BvA+hcbko70lTzKpJvf4CEYYnyehF5M8uFsIyuqIJutcSkT0/AXMGiqiEpauQaGcqFIjFNjXNjCtaOdSMb6cG1RYf3QBnza0qiIYlHQjvY3z5Pd4efmLWf2JyWQKITSbfCkdGz1DQXEOVW9A09issylHktSp8W2e7NHNZf4YRNz5Xu67FqosRc6+jqBW43WTkedOPKa+g0igzndaz6tjHZslNsL7IzP4LcHUO5t4xOsQNL8i0SLSvHV3ex2/QUPcUsYlbLgPVufHoVjVidlVebbDl8Lyd9VhKWOt5qD9b2EIZ6AkPSzoIwgsI5QX8zzNhcFXPCRk3n522FnxEeIe7oJqHtIhp3U9K08EVTqApJ3vxIi61uDX05DfpJEdnpzUw6XFy0SMx/oBPn2xtwl1rktiZZWxWRLTtpW7FyKFwllJ0k0pymWnExHO1jWpQx5ZVT7RllQdeDrCnSroiw4tdRWJUIFRu8sdnHvpYMhQEUOwJcN5/BYDWQc5kZeduFKe9hXrfEq+pzlC816chW6BAVLF7pxdZ1BqNKoFV2ke9KcbFdTaEp5/qlGhe7y5SzMsS4yOqrMcQ2B+4RJ9edv42zowbWdVnW85Ps3zFIbspJcanJbpuVxPJGrJ4AQ/5F+o6+TMo4hqgy0iEzsJYMsVJ2sVC/hUMNCzLfRdTmPAcn7mQ28hbuWpm2gTBP4WBtYRWnycYtG64nnj6KQ3BwsOSksXQUzQ12bG4jokLAW1GQsxuIu3wcqg1iN04h81coiWZedqxzXneSpOMMA7UcGwo+lvMeruYk2i/ViEYSFFJl3IoRNh4cJ2QVOG9WMftKjpVwhHQ6wabiXuYOjWIvaelZTfNm/2ZaS7O0ai1O6H6Lbc0wQiTJpSUl7m2v0RXdiq2qxXDzca44HfSIJXqk87yuHWR1zxJ5ocjJRJruE4cQKjLmti4znM3icxVJGwXOrQsciM6hZBBJ3cPA3jri1QRJjchU/06GfEEMmlnklikavbeSqR8nZ9Djjg5gaPaQNs6S0JZJ0Ms2t0hNEWH26hIbtQYOTkdorRSQtPdh3rKEva2AzjrC3cZZ5KsWpKqB0OYkltkSuogWi7qDzbvX6KytIzQqjH06xWbFFiaXrFTmNQzsv425wGVKyRj7dzo5P+Una7HTurnJ/e41BM02nJKCnaKLiHmdfE3kEZsOwZGl2urA0DLjXoaoNI9gH6Cl34B6yxyuwBqUwal1Ej/rJ9KXI96mYcg0iNhfoaAeJ/XWGR7vsGFR6pGH88yv1tluG6RZGWClaiVSLmEtaGlHxWvWDZjmX6LVXcKxXwHP7cNey6GyKKjKNxAYrmMRWgy25JzqGOT8mXFiKzFGOm5gX/8kVVMSpSbC69fg1/9QGqEgCMpfkfdPJUn65a/+HRMEwfur614g/m5tJUl6RJKk7ZIkbdda9ci789R8agLj15FegkY5ita5QERuw56z0pW2UZTaaHTrqPXJKLVXaUgryMRFWsISAX2Q4oqBXMRETNSx4O0ha1KRk0ssFUy0Gb2MKdvZX+7EG+jBmNiMuzrKHpuTmnkFeSuEOV6mGHBQLfRQ0faS39CGU5nEYVNi7u1C1teNZMxgNJQZ9ffRK2vRI+XorufwhbwEs13MVDwsSWq6Uk7adWYcPVaUo1vp6bRjUTsJB3sRVgKoY0ksxTLDmjLqBJRXBWJLcizKbjKaNpY1bnKqbsLyBklzmoo/RiopsWaWEXULuKMN2sI55DEt69lhllp+okY/BVMH6koneSGBORli82yMcFcEQVtBIygoa50k13uZWbDzdkDJdKcfn95Lv85Dvd2HTK+hUNUSj5ux5bzEAykCqRXK5iCpeoyIGCOoSJG1mUjpzBRVdpRyDw6bEq1BoKEVWWkrkq+qoWHGZnUyrO/AaLbTNOqwVVWocnYadQVJwzq50DpaqYTZJDBbaaEvB1DkF4mmlsgIUeYNc4xbJlhWrzKrbjKvaLEiVsgt5SjKM2Cq0BtzEO3oYq1HSdQfwtrrQ5UcRL7Qj66hQyFtwqzegs84gD4dx5xM4U4XGcoKpORZ8jIZSsHOmCOKwb+OwVpkW6yPxoKBUq1GwZFkvSpSDSbQp9N0u7REhstoLCqGa3raGuN02os47QqaGPBGoljyVbQtI+3iVsweG167gh3NJi2pwLJ2gXHnZTJD65jd3RhMQ1TkLuIzOhKRPOlSnHJVj6NzCd1ggEx3nSvKGCkxiKKRwt9qw+Zo4pXL6Y05MFqtWLJNFKt1ZtJttGvl6DQNovISvuEgBmMdvapBV8cCtaSPVlyDopjgnKef2oYCmr4ghXoDa3QYVdNJ1VlHMyQguHU01DrKWRWkI9hiWTpSTZq+Bj6XDKvLRMDfS9nYQt22gGnDWUIHZdBbRaWq0VqRY1D5qThdhNqsrGq9pNU5osSZzldozAi4UwW6xSKy6iYs3Wn07fM0ZVN0m2OMlqtsykCbT4ZxrYIhpcBha8dqUlJs1km0ypg2Sbi1Nmx1DZpslTa7i6pWT1CpYkVvZjlgZ7HRZKl7mYguTFnyIpf14fLV2GaRaPkqvNWXROWsIhpFGqIOaWkYWUmHIFfQNCkJe7uxZYt0RGMYW3VqISXxjI6QaMZr8aH1C0j6LJWlVc4ZBC7U9ExEzSyrBCxxF/aAm0pIS0itxtSS6Km1mGizsiobJyWcoWVcQMit4Kiu4G4tEy/Nk7S3yDuU1NRymiYDK7USE+E8lelRRjVZ9hgD7FWuXZObf60FLvy7qf19YEaSpP/+P116Dvgo8He/+vvsr+vLXiwx2myQdLg4njLRY34IW+crOK6bQrm+m7Jyjfq8nK2v30V0OE4zFKN1KYBF/nNGZTa8JTept0YJHtagsU2RWT/Oo9kHqKvMdPuy/GnoZRTR+6kv6amUovysp4tbbHo26troye9mcuANjOFFfMtJxi33Ul/dSzS5wKXmz1nbcwse7xq7OtY5fuJ27pZfxOZzs9L/AL+r+CItlqmVhtG/fjM5ZQ550MS2V/aT2fUGSbeWXN5F80KG668K+KoRxlRvoJ34IuceWuPswAQ3FD9P8NuHqZq0yMcMeIb+jYd+di+Vq0MoN6X581SCSVmIbfIE5hfO0riui8SAh4X0izy++Tt0JLeyY+5mfpn5AisqI2pVmNvCjzOV1WBPxNk+H2fff/45z1tzPO81cuvme8kHncwqkqTqCTr/2cjOra8iH6xxrn0Xd11t4/LOBK/5Zsj9vYlUph+XrcL294k0xh7FHGyn/4qHoewV8l+8nUz7FuYS+/mjwpNoLVqq9iba2vfoit+NIdFAUc4SGvokPttlNNoIswfK1A1WrFYvn3V6OZbQURF2c9loIzT0Ah9U5UnOlTl+KsGWyfuQTRxDbSwRaf5ndh/7CxSFAs1IhKUbZOwMXsUXjZIJvsgBxTcwbLdyfETPc/MV7mwfxpAq8cM3foB9uIikdSKWtzH0h/vY+eRjOCJzlAbljPebOZC4ij9zHOVNazzmGyFXcaBOHuNCFd5sybDFCnxr9jLPiwJTSoGvmo+y2dyGM2DFnJeQ3ZVE+eJVpNI28o57GKx8mH11GYpaB6eKWi5ab8U+tsbBjgtE5ss4SmbUyn6K225lfsxANpjHey7Ilbc78QwIdJkvYT/1BuMTY6QXZNgbAR5rS/PJDXa2dGs41XaRza+dRLokZ3HJS6r7LfyJLPpUlOgvP0vgm3+DYo+RtmwGm/w2rvibrGbC3PqWm5vP7aBWUbNoLDHzj00+EFxiNFHD0KnkG3uH2HC+wdZzXfzdpwbwz1/GV4jxYXOJd5Jp9oQusXlpgn/Kf4hPDhzG51OQcoo8++xjHHTk2NBj5Rn3KjfkDpJP23jlkoXtbQbiHTvRO9Q8GNdRnz+GbcBJ/0P3cuZrxzjTb6M6YKTac5WlFTudyTWuT/yYVwbbuHFWx2DBi9y+A3l6maxWYHajn5883cS+fxtGz05ajypwPwhKzxqjAxMU6s9x/dhe9PpdHK17MV74KYOh4/i0QX4weC/a4zmqRYnHPpXjvyhuRm6poDavow3JkK2vkIrJOB/4LdzF27GKTyOIP+Xrpa/wcXcQty7JSzOXuHu0jNFhpFfagNUewzZlo9zsRWobRr/+PSw5H+amG9VH7Dx9WsKULzGWFahvGUWbWiMnxdHfMcEloU55qQPF9w7zAfEnaLaNkOh08uqf/Jge2x+zrjQwFztN/47LpG5OotmoZORJO81XboLeMLIta8Bj787P/4E0wv3AO8AE/55GCPCnwFng50AHEADulyQp/V59tfe0Sd/5k7/AmvMTSG5jr6LAeG2CX5TfwUeQ2w816O5qJ1L9HE/rv019Ton1bDcHggbiyiXkgpJNzVt5/E+fpOruRq/Yw7aWF+n0BUzrawyW4jw7/QHWRqaJ7TpBdCjNFx7dxeC6hqxjib8uaGnfFmJ4MEflqY8zepsFybLMdPgxziS3Iw2IKO11FF+T+NADt1NtV/F8a4GbHymir3vQmr34b2jn5+YZltMNMhNahNgjbN8xiq+rh+OzCW6/PE20YORF2QZ0Yx68tTq96hJ37Qnyl/90GGVVy05fBsp/y4RyEEnRze8oXNTvPIpSVUedtFL84b0U2kM09AEUsUsoC79NzRel0HuFC6mHSLYvY/JUuKOtC8PcVabCccaDGbZfyrJ6MI1gszESuIPv9+5joC/B5o4lno0kuCU0T13e4qf97bgxU8quIJbS3N/xn/H+awlxPce6Ncqrpjo3NG3sUsp4be8jqLI2xJqSqijA/s10XxExBqs888EEe3/ShcEko3hHkfBjR5D1nsNsnuK6F+ApY4HU1gyqu5IMX+kg+LqLYthBzx1d7B6O0xwPEv7ZGnL1Zt7Y7CdrF/jY7FnWenOE425CSz3kb1tCFHdiyeo4MPky7W4zZyWRY40W/2gdwz5rImGs8Ohnxrn/27dTk5QEnXXGLX4sm/4Fk2Ie24SbfMaIYsSAZqsZn3WU/Mo62SmRyEs+0rvjBJzrZE1J7tNuYH4xjSmt4GDZR6qvB3/iNKbSGZ66Oce+N3chlS3M+mqY+hcpH3dSW7Zh/WKVcxcmKUS8aJK30FUK0jy0imxjmd7aPl4ulWnJc3So03Q/0UPX5gkcjjCyo22cDx9iulFnQrOE8ytX2fvODdhCHRzbP82WagWZKkrVsE6XZgNvWMcJxZL0/VM77R8dRKlph4oN4/D3KEyUyTa9rB78EBv/NUTGu0h8LMCw7xa2/OsK9bUaL4160B5epFsrx16z87cXPoZldgWl7gqNA0/wwUfGEHYMk9zezks/XmGHcgutrjTnbnoa66KbUZuONpeJV5xbUPwgxKBmmdvfP8751gfxrqjRJ1o81V5E+Xad0WKB95lilA6YmatYWMxJTMWOszahxOXpYtvuHfhfuEi1MopM4aave4o3LBkcKhs7pF4uPCOR2SURG61yqjfIDc+fxV/qwKTfyk+OfB2DuBVDawCt6Ke98AybNHpG5L088l0/GzwmDJo8S8ljVJRrlBpmcgoXS1+Ahx/tomPSwpqoxLBrC6quOST7BOfO+rjbNozHkyQ2+BO+s7oJ22yezpUaVtMDSMkULd8CzdvfwPl4Lyq9hYrFxk/SI/Q338Kns+H1vY83uv8eJwo8ZSfW6V0cO7xCprWM/uosn3z+81zecopV/xxjZ7eyoWeCQtnM3OpGdnfAyd0B4u0tHlq7ix+uvYQu72Zb6QCH/qv//5kPXJKkE8C1HN43/rr2/4uIkGoJNHQyNMNqQulFopU8JbWGynofgiaGzNYknZvAZU4gt6gx2kyEVP1E03W0TQWSy46vFSYVstAsCOg2tUgGFyjMTlNq1FiLvEFMvUJOPY0ulmZ9RqQZNZKPlclv6qOq0tGqylDr9ASMTRrIKSx1wFoMMVymqS2hXiyxetxC1aGg3Fxk4YoOTTOK2rBIXGdD1ybgUJspuy0M64w42lQ0DEpyq51MagqINgV+t4Kl/kVGCiK+usRCQY20OYBYVZKR1wmf3kN6axZt+wor2TrORRG5SU5dK7DYXMSbUqIvuVlo7MCoiCM41mj2xVDqVTiaGfTlGnlvG7X8OSRsOKROqsFjaFQ91LR6Zvun6PXCoKxERyKDXWNCHu9DVi1gVQWoaW+iIUrIMSHEDPiMcXBmyCjLRFJpptR5BJXIRKmFIZZGqLSoCXWy6yaCWQF9tcHyUgVFaQl9S05rFqK58xBYw2It0zZkp6ZUY1a68M9tYC1ZpJwtIxTXkRQCQZkcSWmgbGhD5aqikTewyyS8e1c5oXSQt1kwtXnRmuTUo3XklQrrLjf+9AJdFZH9dSORloI1Y4mit4pP70DW1yIbjBAMrtEmqGgtWSjhIhGt48jH0Ley6PV55ldt1IlTsdXJ+YsM6hpo9XXWbFom/G1YLlcx5+Tk2uykhQwWcwGlrkFiSSJXasekt+Dti1Io7UYvljArcxBawaXwoVb3UhGdJEbP0dYSsUwbCMYypAZFVJoyUjZD0NvEVS7SCCZZULVYU18gLWsgKqNIoVUCmQtE8wGyyyFWFQIyfYqGGEMb0aPTarHbvOQONNlUd1JVy4jaYwRLXoYLZdSCj7fUXYxYKlTs7cQtanqmgyQqZko6JUEzbJpZpm6QEdOUEQ0ZXN5llFKcUMSPckMbCX2DtdAKzmqSkBSnUkkjJTNsl12PtZxFFi4yEtMzLq9QBtoX/KScEeR5kXK2Tspao73HQiMOiwEtJYVAqpWiXmhSXx7C59FgVhpojmcpmOJEupogaNh4VYvrwCzylpxIUEfDqkFqZFEnRToMOkpaHZFqiWxunsZyk1wrQrUpYmlFiWvSzMsalBsJckIRs2UHPkFDZTXEiiKNSabHZtRCdZ1Io5uGQobZnqDuOUnNlqKpL9JIFZjpFyh1Q3t7ltKKFn9Zoj2nIEOOWl6JVi3QGYiQK99Myawlp4bqUpiurTbsfgMZzzpFde+/nx6u6hDrk1SvVjCYqwy3CxivkzDlNTiWzAx3mHBkWwipAqZMAk+/HHnTTSGrJ7sQxZMV0MhkKEzNa1Lqb/QkprwJ62o1DYeWkR4N78yESBDHpBaQgvdQ1ZwirVxmLnKCYY8avVOk1RXi35xadBf78OY1BHoMdC+rUaxVCaxHSbcrGL98nvjZc796yimECFhO//u305xGLtei1XlRfdCLQjAhLvlxuLt5sxomlTCif2cMdewoQn4VoZxABE4+PvM/AJr9Vc8SwIrA9dtvxjSwG+Wohw8Nb2XBo+FKUUfm7F5O7VbQt6nIgZ0KmrWz9ChbWHMKnn3GiubmOURgLmAgdvWz+Dc9j2PTOBOrDWzfMWP1tjDsr/Jj5cs8ULmZtsYoF7VmhP6/wTuSoGPYiFyvxDRTRpkpsVSUSPkj9GgGOKgY453oOzRq+8nRYvbG7/KFwjj2BQ3NFRNtG65HP9uPOh9kQHyNkvD7pDv7qDoTpN4oUdVPoh2OoDcKVK6e4WizwdEqcI7/VV69yOSvPqqelRFXSWiyMszPKEnJJ5EX5JgKFoyf2YelZWBk3cnek14+0ngFT30WryVM0rFEUtiATOdG3jWC1T2PuRDEUK9ju6XC4rIMo8bJHvsmGtMtdNMvUkmscrG7g7FEkuFygy21Jo+EDET2JzB0Vbl5yUloT4wrp88xtXSWzw25yL7jZD3X4qr1HA6lEk81gjtf4M3jSwSvVyIMi3Qoywyk3VgVZjRyB6dNcu5cqaCvqzh7p55i+BhyfYpm3czSGwW8LT99w2109VuZfLSH9uIJ7Io5xh8P0jb2fmzmDlK6Mi89fJp7nx+k7dVeTqXyFAeNmJvQmK9x2q9AMV0gE13l57bL4D8GgBrIvQ05ZsAIhGD6fwAvENQU2Gq4CV+XneP3v4H/4lYihgihrpPMTtzA5lYFp9JEOWVBZuijKRsml5RYevu/UTYdoNFpot45g+bVEtVqk5weVPcuM+w5hSlWQ3vpRnI3OAhfuUz82ASDygEumcNkNRU0SQd3KobIJd8ilp9gIHcza/11hKwH4dGbGN39Yy6U0oxXGgiSja69bpQOF8dCI4QScYz5aZSZMs30pxi6x4B2bQbxx88x+ZkFwn09aIrt1H/pZ/Omo6yV9Zyc1ZAfiqNVlNGF4brlLtb3NQnGJ0mMv4DupOzfASJEDsgBSyRBWEY2IiLr6sKUN2AsrGKXO9G4vZjb/PTn3+RZcy/jfWruHMqS6HieilKiUjSRXTTy+t0FurpKPNRoYYk76K2o2ayocaGyiCh2o0zo0By1cK41Rk4tUFDHkMeepLfvYRSbNVx1XqQ1eR3ZjJVyIkvW9F2qb1npajey5z4/uk3jdD2uwHmxH+9tdbJvusgmirRa8yhG7ZTU1xFf9RN++gQH/D6UIwYaY6FrcupvtBqhZ2CT9Lk/+VP08kV098CRXwww7i/y+O4QxZ+Y2DIyS79FSX/gw5QmvYz3zDK+4zR/niwiFXZQkpkJepfY+VULl7VuXu80IIW/hXh1BimZveZzJa0efF3c89HNxNq2EpFvYstzDmZuTFOrnMf+zLe4ci5Ko95C4Np4SGoZ4qiZjw4NExHH+FHoCB/0FOmtPE89P8VXAjfy5185R2/fRuTKTyA79lOmJjrIrarYF32WyxtHiVQcJMM62g88SXlgI1rNMAfObGB5Touq+zzG/c/zjrOE900/1ZCFZ3okPp8/g9JZJ9WjwbW/k/w/NBAv1HC0FZBv7yM1miXWlkT+pf3M6bvQeUTu2XYV+ekVss0DBM1H+Nd7C6i+v4RuvoRdqWbpJhX1wHksqav87T9v5l/+WmK2bEO6v5+lpx+lVa38b/22gsT/QE4hyHCqdNzVZsZnqqA2xTk6VMau7ERXNxBYmyU/8wVGpEEO6Ux8f/6j7NvxaYZte5G/s0DtyFX0yzU0bwr89b4QdzebbLG3UN/V5K9erxNp9aCVb+Lb33OQ+vTjhLpnuXTMRPhQP7sSbexZaeev2zZht5/BmZ+l9+1ZrPdZ0efcyBc8TL5spCLTU3EL5HdXmDmyF/NZka4TST6h/RmhyRtp1RwYXQW+fOd3cV9t4ZnQ8Y7Tx36bE317isyWixwp38jSmetYueKDyg94yCZRGkrz8o1rnDJ8iRuOzrIzmML2n+7j5Ld+gELy0r3hXqRjf8Up0yqz+hTNWuGa29v/dfKBXjSyrfk79Mo24DVdQf2RW8lNLBF8+izPVR5C+8kwnaMN7km3Y538B2rj3RRmxwje/A1Oig9Q0XjZ6p3B3j6K78oqmjMTfCm/iLm5F7fZzobhAldffwezeCcW7V0suCYo9UWR9zWw7tBgFhv4Mmcx5aa4jBeb+sNoQ3ZUJyNc6vgOLX8PWPtoZo0cjGRpK0Qw15YI3v4ZSsfVECji/+QZHlXcQ00Vo0v5HFu/32ByWz9hWxudj2qY9JmobpCh2lbH9DvPcVOfmY0OP8bwIZ4bC7FQvMJa4PQ1YVJIsL2qQxroxiYo2XdigSf6lFh3XUfv7oOkMsdgeTORcoPXzc/xqZ4Z1pZ2MDWxi8H02yx/cC9qWzc7xt08HvohQ7EWu8tmdh8YQt82xXhQxd8/MYbf9A57B2tsGFAQaO+h7pjBJJjpLOzlnekYa/4OKmqBW77xe3jvu49oQcaJlybxxPOobuhGN9zDwOI25gqvUuuwod51kL1HjURVz7NWPsXF54LEfvcBtruHeajZjWu7/f98NUKkMi5PBaPMwfyEnuVcBlfGwaeWRwmnE+gvWVEYG6z41jBvPMWgUUdnYZTXKi02JTuxJotY3rxAtWDDWd/ArtVeTgRTUGq8pxKICmgYJDhpxb4jgHp0GeVBBTHFANloktJiilaz+Z59NDwOGn4T9c010n1WKi0TKquCE5KRauYAXbVePiNfRrm2gatGBYvdj/MhmZPuSphEpcwbno2oazK8bQqG97sxNgYoa1SgqyAMm5CKcXKOHCmDiCWnpDXQTaW7F63ShJiuYorVsbxsIBPrwqC+SGVDhklRhcsYxiKpGSj6ebRLi2vMiMJm4Om8nY1HzJRFLRlpnfcJWk5sW0c7XOYuTy9XT40zj4pI314eC6nwNQzYyhLrU/OsNpu0/nd/2v8JvBYimXqF47EmXQkrw+zgQMdFwuU2ws1OooMDkD9NSFHkku927u26nzZNHUE2yTu7BtlWP0+tUiYu03PLpAnjDjsrbQ3WZi+yp3Q9C4Y8C57jZD+xk7pmJ4rlDdgTIaKnoxSNAlGXhqZeSyznpBKpoY7qkVsvExO1VPUuBj6sJZjzIhTSdC6/Tvd4EM2yCWtFQf6uMipjGUUyjFk+zqfMfUS3Bcm2pXjwLQtXxT4yRgcDjQV0iwrk8RxNtZzM+2WQNCBVGpTfUPIJpQ1BbyC9LYom9DY5uYpWTY0sGkNpqFGQV2k2q/8x8v6V1IQKdcd5qo4wmeEcfc+bUa1EaZRDFDfmUReuULqS5eTiJjqUR3B61rHoX0ZzYQS9fQZj+zKbPSZ+dnKcsTUF+8q9PGxSM1XfQbKp5+TMNI76vVSHHCSHF7kzXiMVtFGhQmM4wuXQJJ6ID09qA5LiHGuqM9RrndTNQ7RtaMOzIGEbj6HbXKM7YqehMLLe28alq15Qh9G3BzA/v8i+3rPoFWW85QZvpg+iiNnpKGuIRCoMNFVUtGskLJcJHzpAvTOIZK6wEJXTyK5B4V2T3f6HCHIB56gcyeVALnmYH+tn/8JxGrOXKeuDNPI91M0aTHaJh40CS4ox1OIA++puLIWdiE0bclkFp3Ka3tpWetSz+BUhhIXDTCqMxJtBbvQ/C6Zh2lMqjOcVvNVuxnaxH0vVREpuY/j1Y7R1z1DpaKHeY0G4nERTN+DubEcxoEcuapFPGGikdMzq9lKJJ7GefItQcy+NnhYVrYbkspM9c2v4akYWNrRfc7y/2TfytFromy3sggrZjJHVZInBmpEdUj9xs4r4upqEMkvSnqZmXKdb5mWk2M6Pon6cMZFmJEx5Ok7JnUauMWMR7MjyZcTme9ONXK1E4bFQr4AhmcOQTtHsKVGcapFZjUP0va1NQaFG1mZENuJA4ZPRcqpQyiu4LAmSEw6KCgcqvcgB42UuBdqZs5aZ7pyl0LoRrWoWgyFJwHIbG8yL2PwNnMM6aqFN2LRpRH2LtQGRaHINUZtDVbXgiIqkfX7yxnaEchMx3YcsLaKeNyAJg9T6YxT9ZVKpKkWnmpGWDU/MydyQBXevGUFt43JRg2fDKmIF6ukcnYUaSkMItblKd5eX6gt1MlY3IbubyUKY+xwelFKRTHACofW/S9//q0hAlRaz5RalmgxdzcORfC/JUi9NejCZFJTav0dBYSVgFzlibUNeWiRRjxBzeWgt15HEGtVugbHZDootJ1GxxsrqGu8vDKI2XyFlm2NJ148s3E81r6ahMaIqhyjIE6wp5OjTWaREE3lMR6nci0x7lppBIGU20eo00GqCbq1Gx0weUovUyl5EwULcXcbWV0arbyEkVtjfHGbGZmRFX8JzWc5UUUErrccQd6HNatCKWVTGEvVuBWhMKNckdPMO+lR1lgZbhHQiYjBFVumEqgp9aIGqtkpZrCG0Gv9xUAVoCk1EUxRcAlK7msZP09STVZo6NcpNDZRCHjGaIjmfprJlCxVNHuQ5qtH9KJlE60xjsQgkwmESBT8lbTeD7SpCaTPJnEQqXWNUs5dye5rmhiB9lSLGkok8NUq1EqZEBUugA2t0IxrvOTK6NUpKgYavn3ZZL5ZCAnMyy4i8hbHQQdJgZskuJ3jJitw0i023jrSooacRwSiTY6x6KGPBmdFgyCvJFzU4bCKNfAF5cI2Vre+jaCmSkeVZqEUpBCM0yrn3hkkOim4FMqWber2DJW+Zfee0VJYS5BQpiuqNVLQSJnmTIaWMS4Vu/BUvbZIBp95NWC7QkGUQdKtYFTuwKzVYZFVyZR2RkoVqvciQbJGqegvGpJpqSWK90EAT9qEsGGjIWrQXY5jyRQwVCfmIj9KFOoLUomu7m6U2K7KpKtJ6g5w+iSAZqGWyBMNx5J15SkqRuk6P0G1m42wNoVlmwXVtffzNViMsmSm9oGOkMc2t6ghPhvaS2W6Au5dpD2iZODXOWjJK58JuXp36fbaMXMa561U+9OKXedr2NM/ppqls6uAP7lsm4FnlTEtE+6kq9XIL8T10wWx3MHzzLZS738B0fAe2R29n8aNzWJ96AnFq6X+k1ry7CKit7Vg2g3ZnE/PSpxmK/BDJP4Vq7Dyxf/4ku32v0O8/z/omDyfnz1GSdTHYdysXYxn0HTHU7Vn2rZoYfThGPNni9Cu9XLnvZo40pzEq4vxd/xpN1WtcN+XgA8duwV4s8PSgl1lPkLDqv6F99Q+JixaWOiV2Wgd4caOdtHeC61P/nR/2/Bm5iw3k56KUPruFyksWdHE5vt4iltwl9AsbKE2O8o/6N1Gfm8OXFnnd4EJwfJhiPYgycpWDnefZ8OE9RJaNzL0S4Nohk/99WdeEiRpSqMN/hrLLRq8Jrru8xOM7N1IpujHNrXM8/jWKXSokuZaNr5ymNr8V+3UVen5/mvJ//RRtV9P0LpXRd4zSWSigUstpOe389Csa2vaMQ6+OK5sOsHNgicxr00Qef5vhvIqtzRRanYm5vkPsUBnJuEZZqN7Gdx/J4b7zJVzqOtXKZyntbnHV4mDtHDzwyLP49tTQaDXELxrQyybppY925U6+8VsN9v78SVoBgYXAKDd+QI/dE8YeLdD4rhX1qBmHzsRNm418w3iK7IUIrXdkmG/7AFptjo7kBN3TL/HaliAFofzugElcO2UA0LU24cnsZPObHl7o9rLmbJKUldl1k454+nosK2U+OC/n64eusvRmG7Znv4xum0Q924W8Os8V7cvct0+iUJBzXNSSHmoneexl7ATZ7wixvbwdmRVKUpOT4ssUPl5G4XLgzO/hK47P0lqwkCkVaTu0TK9vIxpNHWPzZey/fzfP7bvM/Mev8P+RMihkWpw1PbvKVc4ZdRQCqwjNZUYe/AqT35pkSu4msWM79/o+xXzMSj45zMesn+T7+xewap0cLm9kqetN1gP9lJb1rFz4FgXBRk1eB/m18REFgXW7HjHaTyWpJRL/DucaQ/Sv9DMYN7N0WyfCQoHYcpQVp0QupUYs6UCh4XcfuMQTbiVL8ioFe5hQp4hnuUk618WVOwP0YcA8pWH57dvZ3D9F3BEh0pNn32ofPZ23oUtJNK6e45GvGtEbnLSLRm7Kulkw53Cj5JDTxuP+JXqW87Rpi8ze/jqfuqRmOraRr1c+wF+YM7x4UuJyUUPnru04z1VYf8vF6aPXpunfqA/cPuKRbn/m62iKnXhflKE9XESWmEGcvkziE0fYczmKMankWPcNdD/+KiMjfkZv2M7CIzqyR16FrilchQj+lft5RaHmSTFF11f/Fqlcg9a7j0Ou0SK5+qltegDPvU52LsfoH1/hx9NvUozFaZYrcA0MZHIVap2RW76gQT7ST8U0wtLLd9E1nUdvi6PauUzbK2VeM+s4bxYos4z5fBfOZh/trhEKv/c3dL5iRHuhi9Oy+7n/U2Us7haVBrzwIxm6kQq1zhxv65cYjS9jXpChndQj8+aI52xkZC7Sff18pLCAvWOZRu8SP3pdh+LeKn6NjLu/ZeXsvk8TV10m33wDa+4eUnYXurKGredFxI9WmE4oWF5q8LDtlzx/IUQz0+QWo43HfztPu7rJBrFJlz7DwuP/lclVkddk/0S59f8mhYNMkDFqGsC3exNtvUa2l16j8uo6ha5+crdez46lcS6fM7FcblG8b5zkT1R0xZxsbrXz2mEz7f42HDoZmuw4eztTLMYKnJgsIz5tQz+mx97vYUPPfp53h+mXBdkhLXNVOE/tiduxz46wr+nivKeForeFxiOw8iM3l3ecQTkscXBsJ8m5o2jlOsxaKy5Ng7fXJmkuqNlwZicjf56ndUJN9IKcv3v/OqOu5+ijwGjWw0xfibbjt2G7NEbQeRJptEFIanAxXsf0vs0Mpg0Y4zKeT8e5Z15DU5XiqneW0MrTtMQa0v893iLx774oQbomid8kXscQHhTCGlJ5O2vFDIvNKL0f/iOqoTLapMBIzs9XpThddjkHzAq8z65xfFMHaUeVdvEk6U0WwmE92VUlH+6ZIpdYopEtoEjLMfX9Z6aNK6wYJ7i308e46igJnYO65Xc48tg/onfqkbp9rPd28kbxKIYpG4efvo/X1FeoHJlFHAij+lcNrVYZ0SNHGDVz/8/+kEndWwS0lxgsmEjE5NS2VGl9sEgycpDO5AWcrSiywUNsfluJymWmvNvBhWCcyddlpFZF/N050pV3qEtZRFn9GvoKBoOKr3zwPl7NZphuZshaYqiycrrUcjoFFcef8rOvYx9tdSu8EyJ1eIpixEUj0M3Y9SZqW5+h6JgmSRRvcju2aBf6iJVg+C0iooZ0xkNqcSPv11VYUagIyCRGW7NMeg6h65bYtm2cJ05Noyz14qoNcGO6h1zouzQcJcSdAwQzTuSuJqK5wkolTK42CDkjupjAbdq3aZWKZIpWziV+i7ZP/TNxUcnUzGYe+/Td/+d94NqqHHuuQVlVo2noR914GXOhjD7WR+rUOg6dFXOPlUp7gZSljSAOVDkZMx1NDIUWpgUdytQmVIsmLCY5XkMFqdYE8dqLkCAzodPo6fCWSKwPsJJepNycIB8IIbaa1yRvSRAwGdT0ttsRvWUquRLlUBZDXWIh40aVN+FSGIi6poi0V5HpRDad6yXnH8IgGLAoIyxc1VPFiqZbT0sZoDqvRSyqUbUpiRiW0Cdb6Io19gsVMjUnikwWnybMtHcLLkscT3OdQFOBqRFBUMQp2jMUNvXSLVvFkSqRzevxzZ0n3Vcn3D2A3TCNIlFDTKvIaKIYq/04ShoUZRnmjiIbjZBGYN5TJKmqM6DR0YkKvSwFmXUaaaha33tB16r1dFYN2JpaVgx1SlKDmlil1ixcs40kiWQaUTYUHbTnPSSbXWQVeuooEMuTpFJlDGUn3rqa1ZIVuzyBoNESkkbIh7MEG2vUzDmGTSGCjR4i1QL5VgTpuhii2YilpaVjrkmP14zHqkSvMsNqhIZDTn0wCYYVWpdvpKRtUFQVGNkokW7ZqKXytPJXKa7IkDtKNNV5VtdqmCxpVIMO9E0DK3NliAhUKgJDayb6PGqMosBKpIep0hQNmQJh0IhCHCBYDZNV1DDbJDzRZbpbI9hFJ8HlJVqKFdLaJClZkCYNeLdguSBg0nqoNHI0Wu9uoddtGRpyEWM8zJVchJSQQ2aK0UyuYknUULYkVjenGb5iwqhSk7e3aCrkmJw1jM4qnqiIdjZGNaanmNaysEOGXa0AuZlwoIOGMoPRIGPI4kOrV6IMbEJfcuOQ2QhoM0gGCzLJjvd8CH1di25eg341TbpXSWvdhgoRRZ8C/2yTXEHNXMmKfHMAWbVFs2YloKjT5vZRaMuzmMrSWDSg9/ox2pXMNPLs80mUtCKTSS1eFphttlOrOpAXLYiyd5CEaxsXUkuFWLaQvOyAkga13IjYoWc4qEFrCpO1rlP3GTDmF3Cm2hBqfhYDC8hqFSz6NGVFF2K6h6asTMVXoGBRgqxFVVFHM9FGVJGkUMniaESY22ghr63RalRZm8pSCs+jsjaouxaRZHmKCgVN0cSZmpq6QoHQLKOMrlAsjyB3AsoGsmU3yaoMY61FX7OCtp5HYQGNTcVGpRadvECrpESbfPcFC37DBK5P69n0ZolU9wIy/XbUV67QketltPk+8t/+Fq6PdKEYcCE4lrjivZVwK09wdZmVvjb8pxs4lk3kk4exypdw9MrY1N5ktim9qy78XyKTWXHZDNy4M8Xjx6pMZIJczE6ha7534BOVAofTzHWbOzhDiNh4mvryPJu605yR5NTiRtoTTsbvqtEzeoId2hgP//Jh/u3IBiRPCLP8GJFv9bBwkxL1HU22ts5Re9JAYd1Hc0cnM9sW8F+sMzQtcHdLxT+oPTgNZXb7kswO3MaQ7RkczXNox0/hDLaTyNZYqVtx3Pwg3csv4ohOsmgN0Rt8ArXvFirue4hu/kuM3xQRVuVMbDnHvnqVzdlhzGkXa8oyYwZYVwt8v7NBIzaAVmvHrJVT86xiVl7FLAfx14QvLSYH+1t+NjbsPG8ps9oqkqrGqRXfg8CBkrxBZy7DxpCN1xV3c2ljAqVinLa5Y0xNy+kTzTi1bhrjdny1JBm/kZX2HqzP5mnyKHXnDLbDHawUDxKsB6nYs+TvmEMXLGFY0sCbWTYfViKoXKQrXTTfSqM1FJGPjRPachHD8lZSFSPJvJr99xQpnjCRiqQpn3yR9MotlFox0sICgaNn+eDvD9O23UW2I8fr/y2GtqnEotNxx5KDrjY7yyUNj759iCRZ6jfWKR5M4zu/kXxUgcFcY7hPRf3kt3Hr9HhFG7ddFjl68DwB1RrlUALEd3PaCcjkClyWPuK5BRqVdyfwYneGsqHBcB7eKdYR7NDVrqBSuoCzISBZ6kwcyHIot4+YysasoCXrNrDPlaDbk8ChC9N8YhJL3oxkauPloR4OF81oBC2zJ7ehFOfZYvUy0DnCbPEkmuW9GBa9DK/AS5/TEBOHYGmMjx39I0Yse1CXdZhlJ9DW9pAaV9EIWtny+yI3f62X1ayVtbyezIffpnK5QmPZTKZDyfW2TYQyRU6dVTN8IYvv4U7MbV1kXo5RORhhLVPl+KUCn9pxDIvtThKGHhRxNZITJOV7ODzrOhoFB6dfVCBr7cSmVhL2T7LzfBtx59uMD00iP1hH+cxxVGs+RPdvMT6tottdpq87gsIC8+Ex0hUT9Y4sIbUVo72KXV5nsHqYoOMUQjXKvsJVXrplGJM1g7kU52w1hm88iyxXJU2Qut9MSiFSQs4VZQkhYsReDdIbmiTgfBhjS4WxKMMQdqEKn8Aoz+K2i+h0MvJeBU2Xkn2ba+ijcoxzSlYvqq855N+sD9wVI19fhFNd1Pe9wB5zJzGTkW/plrH88vcIx1/HeHyJsf593P6BOo6IGuuUl1DpHULNUVY1As8NPc/FDxlRV2YRZy/BryEc09YWBVOTH31DpGR4AyE0jS74a1LkBAHlrq0oN25GGDvAPae0XA6WWFFEaX/on+mVb6YxaaRzRWRvSiJ4sZusYRNv3FugeCpKIbZO8LolTB1x9nc5MPR08rj6dqyqNYqlKSYbT/Kxd/6aIBmCW8J8cf8VpCtXaGm8ZH2foOGXsWz206g3OZCs8+S2N6hN78L43QfZcchO0CEQsRbp2RPldPte/Fc6+Nyfu/ih5cukkq9ikloMzvwVY/e+wGV+ydPLYe79gciz228kabGzcyZB4kAfwWKYH2VXqVs6cGnnyChrvGd5HEFA09fOmjuN3BridvM2fh4wkl1uwfjye0LaqkkkE0mWRAMXe61s2noW1VU5jcduo8NyDnIXyJurqG9WMMtvUyumEDNfI/qNjbyvKrE5vhHdqQ/ju2cR+dQ5os+f5mM/d/HCvR9kwmHmedP3eP1v3RgOb6Dzth6G2x/DceaTxE7s57+8uJuHq1na3BXcFpFP9L7BHcc70Qlmzu26k1Frli7PImbvEiHzPQjW/cTm5bzxrzN85KGtqHynKaovcfH453n0rc+i703ziX8OMCkzo3/mMvpvHqMulbhTtRmzdhQxtJ1vGMZQD8hQtyVY7u7HX1mglSoRK8feFR+1RofB7MSqkMgK17ZIEsf1XC5ZOVVQ8XHvYarFHLHTE9x4zxn+5foHmCtsZfR3vXyv8CqG2+ZxHRDxndrG8dMtztocHDj4AXa7f0pXl428o5srj+iRuVQoSKDpehZnI8OF6BCPlweYiYpsNb5Bj0tL5mon+iEt8rkstdgaym1jbOqSk5fFmUgH+Ki7g9fzU1xOR8l960P894/1oVGJ7Ftc5zqtnpopRMmX55YD+3jlpTyqy3U+dNnEm8PnCCyE6F8z8uV3/ppYqoCxmeW2TJqxQ//KRCPOUjPIythlSObfU90d2hydjhJDHyzR0D9MIDxE8N86+ZuPFWjX38Cg7Fbukb5H9oF1TqmneFZ/gf3pIrsWXWxZ7Cd99hYulX9JyzfNgZ4K518eZiEl52oWbltIY79pjLSyRaieZYtzhivnBlgZ38c3736eHz10K9l1MwPfSfLhlBvFtotIoy+RuO0Ia+rLaMIr9FyUU7/zu2gS21Cs+ykKx1grg86fYWD/Gu8EVrlwqU5B5eCh39tGpPFJ6ltW6br7DDz97mP+jRK4oa7DsXkHQbWeZvRV7KlOwkKdBdkKQ5kYcauRjNXAZMcKjZid+GQK1dlZRl3rrFmVyBxGDhjt5NcjNNMCQqAdpBTvZYJnlSOo1N1YtTKa66/TygYR3mMRlyuUuNr6WO+ok5NVqJ418tzyS5QUSpRmDYapbSjHBMoDZdLxJnsHygiX3bBqofT+E8iLQexmI3bfTbgH4jSrURKLJfTOWRZ03dhbWnZXjZw/9Cb5qTjNUpXOugO3IovWVkTtj6H95Sh19TJztmkmNnqZXdKir+aQ+a5SP7WfskOLxtuDYet1BMsu0o4U4d0/R9l1C4pXMxjLCZxj06w+liJmtVLe18bxnSpQb8Qt1bE4xwm8kaBeL6Bultk43clqXUbEloNrWH4AMkFis2cZj6KKPdVCe3EaXe9BNAYJQTh/LW8UAJKsQTiWQ5/LsMmjRP1MAnllDeVwnivdeTbqm+iaDmZevZPN7n7M7WUUtgy6xkW6V4aQx9v4pTVCr1hA4XYxuu8g7VMF7rRNULSbUG07wl61D6O7gTka5LjfiroVwJhTc6dtgI1bFpFKETLrEUZLa/iNKuRNCXvAjsdiQ601UKsqMS22c2w6g0YvYb5PTUa4jKE2h7yxRpvulzi2jqKUTBie7CYnWQhkp6FniaFBFbKYkbh+nfHeK8R0N/JWIcxkYhJff43IO1MkUtFr4mM2qOhpN5GN91MnBrw70Re0BrTabja1D+NXnSXflFOvuWjOebH39NLWpsB6ywTG8Bz9TT3b3nRQ7Pw5Yn4raksHA2549HoduWgIIRvkg2M3Yj7vpyz34jnUTk33JE5zEpPaDeWN9OteRBQKvLpnCwdlt0JTR6AuY7mgoWkaplEVqS/aSLeX8bc7UQy7CHY52FlYpphWcGXWjH1gDrW1nwGdnhdyGqqbFvHZJUw+A+VSGzrZEDZJS3HoDSatNqRKFUs1zRNTYZT9fnZ2y1iorJHNNHivBCk7PgalHprJEPXKW8gVF+i9Gw5eMpNvNojqCkz3rxJWGZHlbXxm0kKj9wqiLMG0X87p5HlqbXYsnh0oZApakoEB1yIGzzKvz9nZcVqBwmKh4upC/69qoskrFMunOPeOkTblRRIVGc+3mnRgpzdyFaeQJu01k5ffQbmQR6FQUJrI4jNqcWugb2kjt7qfR9YTp+YX6PU/hCx3lmhsjQv1F9m2vhlFNks+n7+2Tl4bjv/3RVHXoPa4EUflNEtrVGolKFQwxYu08gEKMiUFvQOZrkmjmqCUCpFfD2MMKKhoMjQ70ox02/FX65hiIAR0v/aZMr0RpU6LWiogT60iK137rW+SWoZg12MY24HO6aBVlwhPx5kvxkgaU7TcVRSxIURdg1p7g9JGK01HBbNMhbduQmMsIHeGMDkb9NgH6BjyYNLJUZdL2Go54koNtZaTtlQ7i91TJJQTSMVleuoG/MhpazXwV3J0hFrUFiusr+WYF+JkAw5otjD2zJGp12nmRaSygaJpN8qGkYoyQsR1CtOGGDKHSF0PZUOa6HKNRtGE3tXL1C4XYo8Mo6uG1lRDmJlDvZrAlBJwzWkotjRk1Mpfi6dbXcMn1HBUqySDJagpUKB6z8wJAEEhkG+0yJYquKphZMtlxFqV1sYSxY0l2AKKPgvF1HasLTsulQq3qU5HOYwQVhMNGrkkLTEfjFIUZdgGupFv7KHHWqFfn0Xq66Bzhx+304A606LU6CZZbSK2CuwxqjHb6jTUKTLVdbxLZVrNKhWhhibdxFi0QcZGKWmj0XQyFQkxXQpS79exJBRYrUvEKko0wgydnUXsWjnJ02Yq77QRadpZGTRT8XWQ7LOwMtTkwtAq5m43JY2OtUqVcm2RcDZIqpR993knl6E0yDC4ZNQEJS3h2mkWDYOAzGak17oNjSKGWplHrzFTUzgxNrR4JIm2tizW4TptRjUDKTMG7Sq6thIGTw17NcZct455t0hUnaTLFUPerCDWNZgt3bR8VjSeBm57gg0y8Dca6BVV0u0V2tbceLJKTEKOilQjXFAQyxppZrzUsg10kgunaRBlNzjWw6jn44TiMBMREIo+3LVBEitaRG0epTeLqqdBpd2JzNqFxuSl1T9F3VNA9DRQtNU5FT2PwhRioKOETswg+zW7bWPTQEfFg7oqog6H0GWmcQ5eYGsuTXtyESl7hpJMR77cDqFuho87kacMVOpa0nKBGecqCocem7ELZX2IasWOE4kBbYJzwiyWxCJ9iXU8zSyqdzToA3k0zXXm5xTYluew5a+Q8SywooiRqiUopZOsLiRpRXqolbcS0G2hmvWSz2go5CVMaOjuTuBrL6FQOTAY/HQrLQzU5ZTX4phjy1hCSfTzmmuO+TdqgecraqLBq6BbpJg38sytM2zV9vP32Z385KocWSOLuyLx0MxdRLb8Am1ehnVuB9LVYdKd51k3ZhntMPPhARdXk2Fem7p8zSAkgCAI7Oo/jUxxltNPr9OoVt/TXy51GWjt6KH4pS9y6LllMqvn+Fn4H/nAzn8iNZal1JOgmDcSe26aSrMLd8eDfHfxa9zRq+X6nR6y5+8muBRE2afEZw7x1JEn2NlIM9a0kmh+iAvZGKGrCYSjIWQuCeuqgD8lY6NCwcmWiu6LGna/aKT2e0XeLu4kmTRw25uPcmLhM4xtTXPT/nm+s7uMqrhMqZLmqTU5/0n+MrXVNGsTLRref+QVx21cLvVx/mkNH/6DGN1hFV3HS/zo8Jtk21uoWnZML43RrQ3Q6e3D5xvisvktMkczlJN1ULzHmi7JiYRuZKhtEm9nmP9ivBF5YpFGNID0HrsaAdBoTWhsWtAKLIhfxXbzGKX220n2dnEP/xVhXE05rWDvF1eo/3yYhXEZmQUF9kN6XmwsEcuvo1pZ5uzLWexbbHQc7qV+4BPUCk0SzQjnnG8w6lATSw8Si2/nzx7rRYqqUJgaWEYnOXFOydX2PhY3u2n/hzlOuD2IPgu+Nqg+5UPUbCLeZeLc5zuwqX9OPSjywnMfwdK1E709jlURYldiDdHdyaqo5E1ZgJsaJcybUlR3Koj8xS6WPgcp5xDhxD08kjtPxjBIUDbM0jf/hZpZoqF8dwISTRoSjiYNVxD7TBIF164Jp3fEsLCO8VKTiLCHpkpAM6yh8qUfIZx9CNdrO7np8vWs3+dCdZ1Azqbk/N89xMoDImb1ImuPXubew4eZswwzs6HKv4w/gjCiwlTy0/vjG9F/4P2EHefISSe5feFtGvEdOEQ5I4afYPq5A+dwnsZoji09LR5NL1FQWOm/roteu5rjNT8zM14etJzl8RdqxKQS1i1lvGd6aa7pIVXjz3RGvtvVSVlKoChEKH1qA1HpMsl6lM0NA52KMaJaHbP6COWjpzDMTmDNaSgkJVoqrm1ySqDPRvCVK7T1xpCeuY1AVMvZ4uNMD4yBfJoRY5hDO/4L0YUwi5FV/ka6SHVkK9fPdnDgsp/hP1hn4GweT7CBtS4QurIVc0mLEzMzXODVGzexvaLi+kdW+KlriPjALSjb9CjXTiAcLDDaU+Y6p56nvroLfVeFsucUR3/yV3z+7gPUvBZeVbT4kk/NxBMaxq9UcHz4+1waLuMpb2Tfwh389NQX8dQN+IRuPvTNO1m/5yXqchOkDgJffddh/0YJvKFTMuaXo3EZ+W973s/1F2dRh9QEIwp23enh4kQ/b0VzPP3Qt9hSjWHVajFtiLLz4FG2njBifdHMyTviHL9cRhyvIFau7cuW5AKtTh3TV40IlRaNcu09yds0ZEKh2QBrY2R+EqTSPoVyVE975r+wwfJPvBbp4lxoiMhyG4LyP6EVNKSDIncdWGdSEeHtVif3NNpJdryOWg/62SHckwJl+ybWjH6iYpg7X1firytx7TLRueswsaUKleg6F374GIev+zz5bXpeVKbZ9M2jOA5KNLokljY8yHDwLMGYg7++sBvPjrM0VjrxrNv5ePkJlh9c5XLbFmY3H+QzZ+pctg2gGtFyyBtBISUpmzSIXgX88BMc0L+DUojxTGWaux76A5Yi53h74nmun9hCWTtDVnHtZHq1pMQu6dm/8Bp9q3p0rXZuXpUT3T7DsiLKtas1/HtWXE+wwTZnJx1mN1MHWhxQbiRZVHPhdJZThj+mX0yAusjLb6e4d8uPqNUbBHJb8F2+hU2Wl2DnLLKKnZ9p7yWarsE7Ue7b8zOUdi3JsgrxhTauGDpwOOocMozTrOhI328jI4+SePJnDG7uZ7NOib0uELrXjUs4hy3lYduLn2HboQRNyUKiuYO1NzOsxttoyRMMdL3MkVenyUpyIpoGR03r5Npi9HmM/GXSyXedC/SGV9jx4yKrVhcu/QVGkz0MvvgwZk4z7jnNeU2TsO0iFaF4TXz6+9vQmY3IgmoUkRSK+rW3zHUqlBUlykaRn2zsZKCjwC2OHLqv7cfZU6foWGTRDNHCUzSfkSNO2jhVmuPhkocBs5lq9xYcRzeS7I5hG11Ce5OGZHwDqpIVTc8iLzmM9Gfa6As+wPfDOfq8JpzOGg4vCD13MKeLcko9x2vxZ9mh7KbL2CJru4B2+OM0l3zkIjLy5jk+ePMNhBt1Thmu0DdawUIbYkbHS69O4paVkZwtJvcr+FLgEqs9XoL2MeLzBR4PPItcLaPHK+eug3XyU5s5seyikJ9CvNYuTwKaJmZlVR6VLSB9u0FGUcHk1vOpNitr7+hYG3CxNtrGxD/8nJbZRrlRJdOR57fEOEqdnwmLB9Vjr3BGdhuathajHT+l4hUpjQZpbg3wyVkzW8ZHicoU/O7nC+Sk7bT0b6JXvckhfYtV5UdYm9ZjmZjj5iMaphv7uZToxdN8lV/qr2L1eBi1b+LS4iUyaFEZJezBJeYyHyAV76J3psbkqJ505lYC+UGu+o5C8P14SgUGK1euOR9+owTeUsoop/Qoy3rMCTUZUYbKqkQ0aDHlo1RlA5TQUgzGEQoumlQo9IdomTqwrxUoJOJMrJVoLMoQkiB/j/RBAElSk1/LIZQbSNe4V5CBQiOjW9yFpqWhJaYYN8yTThmQihJiW4RLzhLpRgVNoUG+aMDXnqKqCRKRJTG0D1HOtwimp1jJ1ek1qbEWVSQW6lDqoJhoQ9Gw0qeI0JaSMAhqqioH0qqDWscyeSlB/nKCSGcUrBrUlTQiHpzmGnJHnXq9hqXaRb7cpFoJYl50UFWqaBpMzM1qaag1aC2dOEQt82Ujqg05jPYgscI02xJpWro+Cr5O9kaLFKwyWiY5Xc4CwTwI4RZta1UWhTx5oUnzPd6ypBLB0ZLhLg+RrspYKktkcqtE41kyVHkvH4oMgUFJTylfZjqeI57pYSJkpl6X0ZQEygYvoUIcZbOM3+AlKUtS0iURZClmV0TUGiV2hZHdFS+jVR8VKYPZkaRqKJCpNclXjHhs7agdq5TldfJ1OcpRJ+HuFDHZGuLWKGVTJ7mWk1TBiWoj9JV6scq1CB3LTHYnaFbU1AoGhrRmtM0aGVKUtFGWDEbKxTYyVQtp5Tz1gBa1okWPMkk9302lVKfcSBHqbUOVD6NvmXF1SCwtjhLKrJFRBSjKs7Skd7O+BeQyLbIVNxWZklKjjEzVTqVVAPHdSVzWkJA3m8grOTR2GU2lkWRGjUlYwhFUolHB0sAqeVsCWb2FvrPMaG0ATd5ITa2g1dXAVgmhV+XR5VsM+yqsOTSUJRO5KyIlW5P8epXSLPgTOWwWJWalEXPHVhaUVSLJGmJCZNQj4Su40Fe1tEWMiINaTAI4m1WCC0lc3ijtZRU7A3rMBzJEcJJQ6qltzpCSdVBXKFE0a9wM5OsG4mUV0bSK1ZUclmaOobUYtfZ+FAk72oKc1ns5ewVQWQSUHX4ErZnqibeotycpqBosq0uQi2IO1Ggz6pjQrtBhFDC2zBQMO1kNFEAmUu+5hHHZhLOrhcLZoplzII3VSDlkhFpW9rWbKeUarNWLrMmgu67C05DjVcixtLugWKWSM5JXbeCqfgpBLNMnZJHtL5AQrCjjGsRcg8JZG46MAaNR4qrXiLKlR2dT0uprssnvQmbxUim7qLRktMfl2Ooa5DbTNYf+mz1KrxQIzBuxRPW0t1IEtuYobNEi+oxUv3qGSpsNlVFL5xUlXmkTitElGrvXkBIfw7D5dSyRObQXSngjmykV5LznoVpJgKKKWi6MrF675m1ylRydzcBw6X3ouUBFdZFLG5VEn7qTVi1FdvQpnvC66Sxp6ZVVyOhkdDsukrDMMKlboWH6EvLKNFLzHFcykzwkv59WzshELkutc5jKFT2GxRaHLEHqvgzpRhfBlQFCb8uI3zhNces5FDU5pzefpbsmMDrToDnwJeztMQzWIKnVaZTFWzEZL9ChfB7P6YfIHcoSNWh45Jk93NdU0yFP41bHeVXuw9Mzg7ZrkXeyZ3nfL/RkB9vIDWzjXuUP+I65QcWl4f2+Mj+cXmLvTI2D63b+fHeAdKoK14YJlShhrYsomncxnl/nYm6WoOYSgWCJquK9HeAKQc5mg5tjqRBXCymcE2OMvyPHYpTRMaqhWa+zMruEXoxzwwMHOROx0VSfwmg8y6ncOCo9DFU7eCjcw/UxJfURCfl1sOZysDxfpllUMbzDzy19P+f1VT0vzA5zz80OAsolIoo1+j8sEj1pYS64gfnSMDvaptgVGUIrz3O+/S2OOZvU0qBXavlI1+10V7Ms1GI8L9ZZHJVQpp2oM25Uybewn/Zh04FKv4J25Q5yDht1X4jlDVtpxo0oLVA8UuVE6P2Ei29QlTK0pGscPBEUaNQ2Cqd8lIolYrII4h1b0YhhlI3Iu8/XqgxVq4WmGGeDRkczaWdxyYRj6DkG3xyl1jJw/qOTVA1yil0VCnsz3Lv4GRaiZeYLUbq3nENpT6GOmtAnFWxKVDB2wXrSwPSLZjTmGMWVKLGpKLeV45AcRO2zY3Vcx1OlR0nMZ/AE83x6r56l6XaUMR89AQWLw3JslQK91QLzl7L4Hhynu+nnlvPdhB+4wmRZYqqk4PDeOhflw5TXdVgvZxB7uzDlg1RSKYIhK+VAG7bkAvrCAuvGOxgxqbDq0qB6zymGxivi3DxCz8D1xF8+ha4eJkuSX5LgiLiAd05Nd1jF4t0FNmsqaLKdlELX8+xkCfPos3SMPcf2wgfoGc2g0CsoBPYj268lvmpBPqHh/o1dPLdvgquJJIazNba3ymzTO+ixbiGyw4v3zRlSjQITo3fwbOGHPGgLc5O7zJlNSnLnx8jM2IgmK2hOj9JvtWDrafKNoRD9tRJ+IYWo1HFXtpuAzMQyChzldva+nEQtV5DvunYtlN/oScyRTUPSv/3eQyTmijxt+jIft8Tp8MvQ9cmZ136DxYKM1bSFqflRblo/ic8zhq3jeoRffAGz4jBINqLpMxTLHpYKU8wX/u+1Tv9/IgEIAkjSe8bXdO2b8d74BWRaB3OzedbXogxtepPe2TVSowZOfGkjH39HoCtTxZpukLuiINPvIKaqE6gk+d35XZy/p48zu6ycXw9xw4W3yRmDXNiQ4MCrJUIyBRqHhb86vI/msomwus6Mq0xSvIfU9FO0KpP03NCL3ahDXIqRu7DAI6YE2pgBZUhF+YKE+JGb8TvrDBHnhhM+TvRmiUpZHO8kOO5/EOuGKr39cXYe7WbmljRlIYXnp2ukittYuq6TtUMu/IlZzFkZ1ZUqq2/E+NyDYzjnJJqzOR49fILLV2bIZK69JArSv7sflYKKliQhSiKi0PoVztfG1yAz4lO18Qf7P0r1nWPIcivs+osb+RdTBHNknp2zV/nbg/ex/dnL9KwGKR+xclP4MyQyQa4kX8N12oji0wL63Q56Sod4+oQKlXYKr/Mc82e91BxVTAY3uxUf4Hs9R0k3PVDqwqJ/gu0zN6NaKXNp8m9J/PFfUowK1OdjeLes8olxiYG8nobJh3Dri5yoRbmabPHA0r14yw4mwzr+YVzBb//N66zo5lhPRbju+0YULRMqxQh64x2k7v8pXa9vxnVpgPVdARL3lSnYyxQyBaa/piarPk7ZMI5I/V0xUup1eLZtIjDnZ7jU4qZGjn8UasgbCyhb7160Sam04LP3cO+u+7m0pMeAjXaVDctSBpuzRFMpMZM04LqoJdS/zsL2BeolOXuzWtoKWYTMaf5S12LQLbLDqeVc4y7uUA+gSIV57ex3iOTvYXd9I1vMfp7/xwts+t4knWt22qx388Ev/B59wPZAO11/9jtU+48Squq4MHcz4gNWais/phF9FcPudgZXtVgjg+jDNzH0yXkaUxmi0TQ/PjTDR05LFKwdHN+1HaM6jGNlGlM4iqJgIv8hCUdcpP/tBh1/GODMWR/jUxquxFbfQ4tBbutkdNjIjl4d839g5ZzMgLKjwCfvPc63bR+lthTFMr1Ir+3jKGQBhHoSKZ3jqNHNpl1n2bPtIrHzv8VsNYZV3eB9HgOPP72fjk3jdG46w6m3C+w868Mva2LftkZo+E947YqDhWCVr3/u+8T+u4yryRIv9kf4+HoFTVuDgk3Lc0u38aFNAqWSk6OTgwx9fA7nO9MYx6OIOhfSZ6+y7IMzVTcD/3Q9I30eZN0mvm0QuN+qpGg4zWXDz/ie88X/8ycxqy0oF0RszRo7vGEmOU+pqmRo3cKcUSSgN1N1m9jXVsJ4XoHcKKfVBskH7SwolShFPf35HSy+PkV2uQDXPjvy7/ryH1icyrUSK7F53l9O4tC1s7xtAzVTA/mOEdqsGW67mCCn3Mtqr5r8aIPusasoT0VIFRss2eo8pztFI3QR3UUj7tQQWzan0DRsbJjfQ3Z0iYRZSbap5Pv/VuTW3i7oqqEx1mlXyyjJncRSFqZOjHM4Z0SUe6mbD2PccgbpxQa2Rov7P1zhydoaRsFMe7eTt2bilOZ82DK9jOZ0nNizTLu1yX5BRmD7KnWXHn1dQV9vAVczilVw4pvXku71Iru0gjlRYudBExVthqS2SdVQIJCtUW2+d0UYSfj3FNzW/0VG/4EyejoJ7KoibZY1NME0q0IPYZ2V4ESQdFeM/LKV2ul7eV/3DrTNFtWmm+kT26k7p7A2wSXuI9I3i9KYRmw1OVeM4LHUcVgNWJw3Ey9c5koixbpVxHoozt7zQyiKBiRkPHcgT9oZQlUzkInegW3lFBur0KERcGhk5GJOjooyFm8IcPeMhW6lAw9yzKkLyIWNWPRKtg+HkZW0uFfkKNabXOkYoapK4GoU2F4M4LT0M3eTkVOjWWyLF1id6EXbLNO9Ps07rSZVIYTENeIKMgGNzM6m8of+v8z9dZRk93X2i39OMTNXVzPD9DBKM6MRM1oyxk5iJ3EcO3kdxzGEHLAdBjvGmEG2ZEsWszSjIQ3PNDNUdzEzn3P/cN77rvVbnnHyu1m+91mrV1efXudU7V3f89Su/d372WTbn8GYauAJubh7q4OplTSR+FVU92RN6mKC9cxrmDNbySngjE7BnbXTvO4UCHssyBvbeKipx6pro62gQFh9hlWzlTWnFXX3A9yxFsRu0mO1W+h7Mo9+4BwyY4bAbiubqRmSmzHWK26y+W00tClWhSYvrEwwFrqX5kqapckK21znqG/bIK5SE+86wxZvPyZXEx0OjP4uQpNelmpqSn0X2dJsw1SZQ1ZfpcO6DbtymkpVQSpiop69hE6rxWpy0HllgYvZ+6kkFUjLIZrf3CRarLNZ+S8sNnmD9JqWlZU2BjoLJBqbxLQlXoi6cWx4sSb9OArt5MOP4bxdQ12m5Px58LzzIqIxxey6jZW5CkqbhORVEvLqOaBIYL/UhnLyPmLVApnGCRQaJZul99HmeB5Djw1J4eWpU/cw2lalR7/Ag9E1OqJakuM6KlttBCrXk5pLUVXJMY026HzxKHmHl9hte/C3HNjFBcSFBuF5ExvZEu35CToqDe7osaM9paLkzKDf5b+q2b/SMsJqs0VMoUW0GdjSsUpMHyfYzJJJVWkuO6gW9Mg1Mrb2NNCYtUj6OqIpQn6wk/UBNRuDSpRb+kjpsxTkV9/o+e+g2ShRyCxiKy3R68owvlVJW1sfxr5xPK5e9mZVlOQ+1rVWFs0apICBml6iqK2TMlZZd5TIteZQJt+iLxfFbqvj1OloT3dSU/Rg8gxi8nZzZrNORqagJFOTr6lRKEvo/Qp0fj31DYHyUplyzkDFPEafpw230YXXbOPgkB2LRkIrE9DK1BTUdWoFkVZaR1Pqx6SvYZXVMVUFKt4QDVUOpVzEZ7eicxbx1sr0LzVRlCSEYhODXGJoyEi6VSIjxCiqgsTTeeqN/0n9EwmlQsKsNWExWzB7tIjCMnWjhpy+nSvLWWrJDI24hnxwlI4NOeqmh4JiiGBsN/PJFZK1Emr9CCG7gYRaIFlvsLJZwWiMY7DIEXRdmG01mtU8mVKONUcZ84aAN1bDXcsizysRq0Vq6hqFwXY6wquMV0LstlYYFBXUm0ZW5UrOuEKUYkoMKR+eai9JIkRZp6AKYnVFUG7aUQf9aGJt1IwWUiYNBXMFlWUBY0FF2Fvh7PYYEX2RcLZIIVpGG61RVq7QUGSvqmuiFfXYGl4Cpf30uGUYzWrStU7G223YDVfvvIMGtVaZjVyMSjNNvJxjPlugockSFNZZUm+S65chbaujcOvQln2MJCOQz5KWFGTat7K7MU53aQRZqQtdvUKyvEBC2kTn86Hy5Uh7VlixTmMMVhBacuLqBkeVq7Snu5CvDhJe6aDVmUJv1qC2ipS7Z1AZNrB3CviGPejlfoSGk7oM8o5FSrICTUMVtV2iX++kZrFQUSvQlQpYlzVIOQ81wYfDWKWWMJCNW4nl7aydtRHdkJMrXb2V/H9DaFUo5pWEE078w3X8nQXU9goXWwZkm+Cv6hm22mmk51GJQdTqJC1dk66OGDqDnEzOT30ti7KpQBBUpPIV9J4walGLsDGGubWVcpuVmMtOMLuVhCJEy76MyhVkPm4nbm1H7nAxioQo2GjZ2xA7e1A4fBQKTpplGR7jJtbgItpWHdFlIdFjRlOwYV4xYr4sQ6jHqFUWaBan6GgG0a9voA3VMZf/PyInW2kVOLp9gCGnmd/tfIql195FPdvOesXIg28Mc+L6J8krFhhUHeLolBXJsYZBP0vr3D2ohCxqFdRMbkpzG9STqf+R16RrlfDUlzl2d5SukQQ9gRluzexleaaC3OjEt/tBzh81M3fqx+Q3z9AVfg9H3zNCwrCJLXiKh3o/wuW+o0TcE7wzleKVyzauUOZi4Dl0X+7m13/dxZbbNPzkq5sUmlE2gkpev6yjf/ACW12z7NXLUIz/C08bjpNXaNHKNfx2xMjMXgeVqJ7Wj9w4P2qhHg6z/NQSf5gP8O9DxzkxXOLZ9Ru4a2oHaVuLr7oTPOK4SLwcpFzpxbv5B3yx/iKObJnO/DRr2Xna9w+gNnWgjwlc2IjTlVvCLZ2jHgaxBf+lsPqXQQKFApwWiS7f9ejahmj0txGxfIhtZ25l19wopzJKCsoKQ5YwB93n+dO1F9DK70UM7CJryXD3j1cxdJkJHeli9UUPA5KK9rKaXU91UP1wkRVVjGQ0y/hvxNn+ZoVIycamxc4J0xdojajQ7Gtn6z/uZHTaSqEtwyt/8RL3/Gsb6gE/8Z0ups5l6B6ssV3KUDgTI6ATWBTsnK2buDDiwbVxETHpIR5/mJsvBzi1fT+Tu9N8+s0vMln2IPfW8B54HX4ioeyyIXX6ELvuxN/xDGq9iiC3o/rhX9OoXl1xcLAwRH9uP0qLgg/5hjhZ9PDZ9GE+0fwEghS+qnsbtQY5uYEZ8UGi7hj1jQzqJSXr976X8Sv/yvjyDKrfvIWlQwnOXXZy5aSRf9bl2H2li+0ralqeDPYL4wTzl1m0HOfMP7uJvFxAG1aws7qDvkSM5YDA2o4Wf/Lm14mmjGzYdJTuNCCXP4u853rq7hs55ilw448s6HNz0PF93toTp72pwVTr4uhP2nin5iIHrRmUuTrHzT/AdvgmPLIj3FJ7na/19aJsxXin8G0Gk//EE5s55v2r7P27NWrf+S7LCQ9nRrYjcx9AVplEXt2geY39GZkEiliC2nAnyb1atK4qlpkA5oKE6ApyrnwS/3CKri0JnvrHu1l47BxOX5RH/lqGSeEmLnWTkHl5KP4jjluOUCw2aP7NU3z9s520KeuM5gU+FBZ5deBhMqtVfF88y1ciDyCvZrEJEUaOfJrjbz6AtlVg2946m+tjtNvG0JfbeLWxyLuG6/Sn5nAce57IgxYG35il/sYSf/SID0t0kHIsSShziVsFkURJ5PSmjmhKzdtsKnQmI+WQ76q2/5cJXBAEOXAeCEmSdJcgCDbgx0AnsAY8LEnS1btkALfMxu+Vgsia67xEO+ZjRrzFK3TrTrLo3I84qCXfqeIzqy9yW+09CItKVpZzpDJFcnecQnIUUZ/M0cyladWu8Y7+N9AoFskszJPxd1EMmshN1ZFujPPoZB65wcmNQ6NIPzlDW8ceaiOHWRxRoZA1CYT9OOYOcrRNy/TRLmqtOtwbpGvdT4/NxPt26PjHvz1H3iYjVPfTjLVw/TRGqtpG1jqMdjGJY+gyqo4ML21dZfm1LaQzJZq1MA+vztA2BJLfgOHeHjJz3XRX7dzSZuPy5Z8QDQVoVT04Q2PcKi5Q0LuYD/awad7P+kASZS3DxdN/hNchYd7Wg218iM8mBpHX54jmN3lDv8aaqQepkKUkM1JvFq5VYfnfwqjYT7u2k/QDLjoyJdTyM1D4CfIlC7Fzc+RXk1y0DPFbP8xgKJa4klzHFfoUKw41cl+L/+WLYvvMb2CYSCJ//Hs8dv8NBE4dQ1vfYMsjIm+dN+BqBhlUrGBM380FzTTZupL9X27w/uptXJgs8WqwyoVQHHtHEnO/xBZhCz+QgUJIoRI2CFjsvNqeRr+p5J2PH0b1a01c3jLblOt0H3+Ydo+IXhlDXH6dU85BEpvdGEN6fqhTUq1KlAoeHlvYAb/VQ017CUVrFU0kz9zSVurFCPris7TqV4kaJdCgo189To84xDPhx3HN9+Nv+XlkF6SbCSqt6jU8LKJUS/i71DQkCxrBiaPqJXjsUcbGY5h9MqLzC5x95hibYROymotnPyDQtW8PFI0shL7EvkcszF/aweW5B/CklDw0MYm8HOXM8DJvuKY5aBrmPZZxzrxzlPKj8zhCFf7tFSPBghJ5wYpNWUT7my+zOayiJMjYOryXRXUnYx4bQ3I5wb4LnL+hSNTpYqcwTv+Tp1nTh5m0JOh26HENXmQ9m+D7yymU7/8R0dcHEOaMXPzzIe6/8x6W+pM8t3YFqThDpdKk0bjGlCyZgpbOxN3Vt7OokHijFOav/+RWAqPPgWsOabnBbZfb8UdcrGyY0drsHHjX/Th1ZcJvLCKaL9Fp09ChV/CV6zdJvr6BNWOlR7YdeXor0uwSzYX/wPB7cuqFJjWVBe3dI3xgcJxE5mUyayeofKvG4W0BkuMtnunoJv+PuxjenMWnepa2VI51QUGmqkLb6kNZCpPZokbXb+c9zcMkggaUnikO3V/lRdOfIJ6exxlJ8ju7d/Hy6e9ju+Dhltduvar9/50I/PeBWeB/17R8AnhNkqTPC4Lwif/8+4+vdQGhpSCTtlJs5Vled7A9sIy8VWW9FYCVKdQFB8aUFXfoCulBB8VQjuRaiN6tTYouC3W0VArTiM3qNfPbEjJQKpEPeZA2MlCoIDV/cS5SEkVa5QqGKwKNlopIzUD5uIW11CxyqcSl0CCaVgZvw4NQtpAtqKnk8tibcvoVItVYmeWknErTiGpeR6SpQ5XTY7popF0zRqxDQ8VUpXe9i8R8Clkjw27fWRS9YyQaOgrhAtVIie76KiqMrDfsrAoGiCsR1AbKQ3IGz+Uw1A0EZT6aBOhplnFKdeQWPUKygN5WpWOgTKPWw6BLR77U4KxyDVu3CtFuIiLZ2F9aZ63eIC2JaBo5dtmUKCsStWIVKfc/EHn/J2rWBiVLAyatKAbziIYqeXmdamEHKZWSlFpC71jHFq1QQcuC2UUaNQ2VCoVcpNYqsLSqob1YoNczgWvNT1UZJmJLE3LoWDlbxp+DAXkb8s529KkCslKRYGCWQLAbrdAiIJZZK0fJ+adpaiUIbaPV10QtxtFfKOOo1KiKRfJlC7NCJ/Jai83NdTZLYXbkIuS63OTEFk31JiulfVTcVWSuHCi6sORkmNBhsaioa9sRawWErER1JYlWo6fVbJFOriKKV+8aFGkRlK9QkYmEWsucCXYjk1tI1zRE1hJki9cOTnRShRFxllpSTb0pp+FSEVYpaGuzISlVbFw2knX6MGlyuKuLrNc1kDAipm3MFry0hE1EVw6zuUlSpyXRK6GJq5GWOtlmvIi2JLBStLDe3ka5Z5ouWwN/3cv5pTgGqYjfWEA+ZcdaU6HTaxlsGpldXURRr2P16AiMzRCW+0jHDawlUuwL6JAMNSp6CdQ2Bta0aNJupFSE6aZAVR3G2K2n4Rpms6amKg/T6YhRrzsIV7Nkpav3ewiSgKyuJpeygzWDPZek3JmkURJRrFkxNlyM6UrUii0mpgQ0lhabzRjxVpaElGCgrKdsalGV5TBlt+NyKZE5Sqy2KjjPu/BvlHBms6zVBXxrMiwxLaVCk/ryBM36KsZGiUB3FwZdC0FVZdSkpSVItGWUGONaVnszqBbU6LQm3NttnIuEqOtk2JRqNqNG3MoQZqOA3XQYn2qDULFOKmVkvSWnInmJW9Rc9G1e1f7/EoELgtAG3An8DfDR/zx8L3D4Px9/BzjKLyHwalPkWKKTdNGKYlbihk+eJqka4dL63dwx9QfUg7+BojJAf1jg9Yc0rM0vkCuc47771cRyd5FZU5AufpGWeO0FLlMokJsNaG8fp/HiHM2VGM3CNYoORfA/qaDUbSTucnDqu16qvjUUFiWXl0c50lHCJaVQrcmZXhygrKjg9tQYGImgO5tgU9WkrtYhO+njsttEI6khd0pDr/QQZ2+7wNJgjE8u7+dUZh5b5QIPtk7x8rv/jvmwHWG1geFMneHrv8uCYRcZ4f1cHAigD1ogoiNrbHJnMcVqyclRmZNdyv0c5EWMyhAZh0Ci2MQwvE77vVMo5/8IV4+S+UyBxzxy3n2gyGY1y9pKjMPx45xU30K27qc/HufgfT6WmhneyuUhpOVa6RNBJkcQAKGJ2Lw22Ye9cQp68H+3l+bfV6gElKxL7fQob2RhTU2ikWB3/5eQYyBdHWJB2E1IuYlB40ev1zNdE1h+PMGung16jiwx8qXn2HxHlvltAoqynYnUBsrNblSNPSh+zYRjroNwNcSph07Q+vEgDrOdUQ+0ZBqq2lcoSRXyc7fSvbuB44SA9UWJtu4MunSejZaBR7s9fCAHU0sZFpfXuGv4OCfVYyyQJ29I0VweorV7Ee31C+xa24J7LotNVcPRk6MSk5OM9xKPGJi8dBTnLW4Uxhzx2i8uAfy5M6FOjdOK1//vQ8+uHvs//7/6vfp/w9DMszd1gsR0H2uGCrH2AuztYq2uRL8qZ+aUF+HTB+kWz9G9cYrH5m1I5xU0UlYWjbexHH6SXfsLjB9c5jFDF6UbW9jmLLh+vIe3eV/iqErJYxo1lqKF7LY4cl2LUqKbqTfm2W2SM2qTIby8A7VXT82uQLveonH8m1Qy3bS2uujYfZ7KmfeTn5AzeeEUd/xjF16TCamlpplwMf6zEdryRWyWORZeE5DtuYL5+jqa4Y9y7LU3sclXGG9Lkc7dTr58hWxt/aq+kLVkaKpqJkJ59OosI/4MlfecR/W4GmlynDZxH0Pjj3I2kuOtFRU7emocWzpJxr6JukuGObeVtKpFVJXlwOzD+H9tirB3ne8sRTn8uJF++RhOX4Dj0SKHLrkRQ2VO1qe5GH4CrylNn1PBtgf2sHA5iz2f5tcqEoImiTznoKgxMPeAEsuqjw6rltHbJB77xxZapwy5Wcmrmw3e672EzdiJIf1ebqx+lJdDHUxHu3kyWWRQtZNoX4QXDpz/P6z7/4P/agT+L8DH+fms7P8NtyRJEQBJkiKCILh+2UVSlgytAy8xkqmytKvIX2j1tF24xL7nJrF95H7AwrKsxNf3DvDRi1/inHcbT37knzD+RYWaO0dSNstGrkTzlzTwdO7TMHyrkZG+MJdSXlaUOpbPX77mOcH6MgR6qO1sUlf/OX1tA2w1jXKnbhA+7OLVJ17n/LFT2Lcd5h1mHW5TE2wWzo1OkRwaJesb4U83ofvZFzGKeaSDGk7ev8qW0BkGKpepP/I67Wolm+FOvtX4IAn7N2gL3oTZsJf0X17iu3/RgOrLWDtOkLptkI4tIr5Wjdr5DK/c46ZzxsnvHG9S2D2HfHWApQ0Dj5+r8mGdH3elC3lIx3LuDN+LKEjH9dwdvJeXZ94kodtJRXUrX733IOmVLO6ynsDIA2he/zqLlTyv1K1I1yoABzzdBzC7KxjsbzLxkpZrlNXDTAmLLM379lWYUq5TFGuoNUa8E88w10hScaiptT5FIr2OGEvRVznLnX/5CNJkClW4TldxB/+uepNlpZwfK63c9LCft4J11mbSrKom+ECzQVif4pvVBd7/ao4Z/2+RrI7yJ5+5QiXxY2QHOhH3tLPwx2H2v25Ekbby2oEaS+mXiRb6cOpHsb3vz2icvANzs5fdt6dRhtfYWcmyVWnj1Q/dju7KKoHZPPNzLnZ8/Ch1bQfp6F6+88JRHgn0k1D6efZ1OXtGTrBRkZOsS+zQ6Di9+CSxa+Sv/6dQz5mIvHyIC8YjlJoGzGKNA0M/ZvZSO41Ejf3X/zHuihPn6naUZz/Gxuke7t9bRtuW58qxOvc5H0JVTJJK5nn4LzuZekCDvHeN7j9/lD+6sJ/xmRAPx7/K9z7qpqFcIZLt5c28h/CWFK+UN5iW5jg8PMYp7W7EEnSfn6f/zhLpvl6OWreSOPY8zb5VjJ15rNdfIPW9m3lpo8VqrsjHTGFeuDlPaChFunOBQ7/fiXKoieQUiXzqRd5/9wXMKR2Fb93Ln45tUKuXrukLDTp62YrhnscQ1GMohbdzo/EtvvsBC8W8h49d6eXN5QCx21UMbFUgPHqYklxJ2juDcHCKk8dlFOsZpGKBP7zXyXfNNyIamnzp5hJSXclyRcUlrUTQ/+d8Jvc2HCi484Zj3PC8jc1pGctKJX+jGWNw9jKD+XlUL03yKc9BHJ1W7F4dq/EGfQ/oiLVS1GIneCSooiCOITi7+fdPX6Hy1fdQXm6RVJ+ib8v9PPR2D3vqFt7UwLbu11nPVlh9ve2q9v9SAhcE4S4gLknSBUEQDv831xqCIPwW8FsADpeOPa0YRoWWkaNOvtYbILNFwYQFdjm3ciqUZ6UVprMrSdM6grxYwjj9Tc7sqxBsU5PJZUhOlGldS3gDKETsbFzpxrC7hN3WpG5ssvxLXme9UKVc26SltHJofITFoooL+RTl5FtcV1ylp2HEsH0nL+5/g9PPO9BE6sgGk+zUd9KlqCBTVskPKNgtVHGmNLTyFo6lnqNuhZZzN/JND+qBFt2Sht6LLd5QpVHk4tTXdUgzVaTSI1RlEnV5nbc3g9QmGiw3lCzcqCT5lJ10PkPGcJz+gSm8kSFcChNjw1mOG1YZ68ky5G7xSvw+5Esr+ONZZBYZ5eItyAoOzGKYtGGF6+xetFZ4uXya4sY+wnIZov7qCnn/G21xP1q9ntSoBlsrSE5MUZH94tbwmiCRUIm80lFn23P7ELpKLB+IsnQqRaqtQtPeQBZbQ3X7CXJ5LdHlfnLNOKVqDnUpSTn5PDfc20604GHz+HawaulM3oij3MLomkAnbZKudTCTH+Ki+SUK8SWapSqL7WOoVGnIZKmfEOEeL3FqyFshFJbjjOVGSHTqCdnytMSHKRvk5MrrRGOX6aBB1ailXDTh+VoYBhz4fFp2bVWiqAc5vZ5nM6LhyNZFxLQVNDHGh+eolLYTLqbZKObQSi6SxSpl4ZdIFf8PIC/IOaowsrfxGgZ3J8aObnyPr6JS6qkbVXS69VgNt2JoGmiUkyje3cGKI4+6UUavtTBz5hKj5wTGIhZ021Wcb/SgXNKwJeVkw5zF5NHSkHejf65J1e/GZFDS5riMZ49IcdFJMmElvDfJYHMWeUUFgTqmzFaa+SY1wyTXq8o8/1oPGXcJDq9gFjLcEGunO+7gW/NHua7tMnKtwGrIgvxuDYZyAdnZKPV6jkTnQapqgboqiqEVRxQrVyvEBKCirDNn3USjSDKai7I9vwaRCNaVTSqZOV5QXGK84qFypZOFJR1HSjJ0oz5ilibVky38yihqmwy10cnC7Bl2Zj3oKyrk0yV+qjRirhnxVkSuJGVc3x7Hr7TgGRvgNZkW21txnLNNXj5uZDwgkFN08Pj6NuRVGa7KNJ5sluWhNhSnZNRTBuL1bQwcHqNhrlNTxJF/Pc3K2HmkigH7ggp5WQG9SZqaFKlndEy468RNIoVrDFn5r0TgB4B7BEG4A9AAJkEQvg/EBEHw/mf07QV+YeGqJElfA74GMNLplJxFPbKGGsesmraMjnW7grWAjGJBTbScISXF2V6WaGjbETaW0Ycnmb0uQ8tlRrtRJ1eoIF4jApfJZVSKFuLRNizaRXo0KqxKJQIypGtMv2zVmgj5LOpsErthG/OyDdK1BHPJBo50EK9jGEuvCdG3QEyRRlkGVbVMzTqITmhgr2Ro6cHcmcGhdSBf0aNILZHzdxIT3HhmhygNirg0ZXqySa5U5DSII6KAmAq/Z5SYWiTlTNOVzzOdkojVJbLFCvVTHiJ2FZldKTxGOW1aAYumRqc8yQvOPAZHlh5TnUSuhnw1jTadoaE1oNf1YckVMSRnkYgxssWDaKlzggk2KkdIq5Q0NC1+Xk36i1MjMpkMQ1WLqmJBbLjRK5tUmjUq/GICb8oE8nKJCaHMwVkPoihHvTNCcKVFVadBZZIhqeYpuRJUHVYkQ5WQYpOssoJcFUUjO8vhcRvlOQ3F01ZEdQuPvBO5Vo9VWaOmL6AouZCXuwlb9EiZLPKamniHFo1RpJpuUF6pYoyKJFEhaQU0qgiG/I3EDBUy7gTN5jCG1iyaeoR0OkhKBQ1jDzVJj/7VIMqOLZitDlx9VXJiFjFZRYwU8W6PIY83UVTKqBVz1CsH0dSjqMU4EbmaYr1KQ/b/bCD0fwU1QWBVo6SnOEebSobD2YZqPk++q0XerKbY9KJM+6mKFcrOGJbRDUoyaDRk2HUKQleWaCvqkCVVtG4soo+40KRFVGmJ7TY5UZONcN2IfK2AriFD424hBa7gb6uSTLZTK3aSbV9jOLSKgIpNnQUmxyjXMkjCCm5DC92mgppgwKi30zInsVsslNoNnKzIuF62grZio14dJdOhQjgjR73coqEvULIMUBcr5AKbKKUssqt0scLPV2xTKSflUuORG1HJatiFVcSVGObpPLkihNtcHCn3Y8sa0JVkBBwrVNUtzBojjaCDNvUcJpUBhdrMGfkyW2JZ7GUt9ZCCyY48vXU9poZIdbWTUX2DgFWibBpkY6SEJaHAExexBasY+xTUVH5mGnvxcwlnrYihnMGQ6KIyX6WWV9EyjbBzWx9V3RWKuSCFN0usHAqhrnqwXuwkKI9Q62ogySQsM3kaGh2CuoXjGpu4v5TAJUn6JPBJgP+MwD8mSdK7BUH4e+C9wOf/8/dTv+xaOSnA6bV3oyhNkxFfZv/ra1hyco4uKSh3BNm1I0qfzUTipd9A5plAvRJAc66T4C2XuCkZxbAc5ZX1yjVHqBl0WgSPg0a3j6psglZjGKFRRimbpy5WuJai1dCChK0o8VhFYuftc3QatbRt9PLvo0NUrWcwq1/k/rMSxodzeGp6ehbb+PPRQ+ijMYzz07TORHi2eZEOzTBjVjmO+jAzU5tMxrPwZhfLD/vpi+VwFFPUL2/BNriMfmuYfOrtvP22Gc4zxY+yk6z+7P2sHHAgNJv84Z9OcUq1m9ndTlbv1FO7dD+C8zzK0iSyH58meuRONqo3kWwYedcLf8N3qnrWBR0avYEbbirhm13G8+oq2teup3o5T7Ojym/tD7Lc/VdUMnLiedVVfSIIAjq9hpQ3hklYwf3C62z63w7JLGSvEbmXGwhvBmn4TuIcqHDEBa/Le9Gf6sOzAsW/eZJX/+J38QXWOPRr32HBfQt5WZ10AE5obiRZq5IV1oi0r6OwjODasoha7aK+dj1mY4jDRZHBXJSQxYWnvY1Ko4rF/mU29/SxvLaP6KWtfPKTT/Da7dvIjR5iazHJc8fM1P0tDONN8vKT3DkfIpKTeGJrD5c3LjKy00/nzdfxz2N/j3vVgJAzkXZlcDh+h22eDLc1FniM0/xZbpDcqox/fjHFZw4fZJddS92Z5gvNx5FqxWsKpv1PQSUHr07k2XU7A3kDB1QStv1eQqoeZso+3nrBTl/1J5Ru1lH4LSfXfeO7bOn6DVS2NrTGCU6OrnFuoMzlnYs4ZRbefVaGriDy4/3P8gCf4aKk4Ul1HfONZjrnX6IemeGJ0hnuNMiRybdStoyznO5k4plvkSgVmOwdxia/nbL1FIJvGlPVzsANz+JXGxnc9PDqZ9e59MAJVg6Dyvp7vPDMGWRuH8qb7+T1b5zEEBlD3xjFOObi9ryBqEvkpV/zkHy0QEu8en+CQlCi0AZodH6It5XPohqe5Yp3mkN/lsLeW0Tj7eNQ/jOEC0fplh9lq3mC3GySmOcwpS0uArck2P6ROg11mI3BGM/et40Xv/o8Q7MKfp2b6bnyKnFngUWblsjUx1D0nKbl0bDEAW4yvkrnloP4err4h5/+lBXBQ6LhY6tMw/XX2VkcPcCKvknHP6zxSucmtRs9BK4bxPSDp/G1T1KxJDjlHOTiaj82tR9Ht5Vvd/6II20H2Ct08onOk2jvu438WpXoT+b51tV88P9gHX0eeEwQhN8EgsDbftkJdbHCZPcbGGTr7HllGzL5Xlr7FUjvLmJ+axPHvA55U0Dff5Z/iK/S0fLwPkc3b77gZnHdTiloBnHims/hGfXhuk6LZnuFp572MKztomO8QVdzkOUzkzTrV/9CtpFOkVDOsv+OGLQsqK0dbHmPj39QD7Oid5JUDdGTewXDxT5C2jpf2LOKbvMlehp+OvVeujQdnPNr0VSKSOs/Y2XiMNcfPsHg0DzJ+E8Ij1hZUQ1wyrADY7uA7/ISttQKwV0/Yu3SPRgXMtx2cZpXLbu5vlLG4y9z+sP72Lf4OgeDJYof13BOdy/T928iv65MOrGHTx06wORcgy99L8l1XR/g/vQc6WaBnxr0yCeLCE474q8bSecGUDVPYi036Y/t5vLyIGVpDUm9cXVnKuSIPh89lg7sihZVa4Gty0c5XitzUaZCEn9xdNQU6oQ1C3xTdhOmkAX9q02uy12m8/oNHIMOxBc/R+Hgo4TyAq99/700PrKFlO4kCl2eh+K3Yz/5U1TGLK1xiS+FbuTgWw76CyrkYoWn3ztAt2GGA/nH2XzCiry9Qs1gYTJyC0VDGkGZp92/Tu3+PiqjSgRrhPb4Gxw8FCRubCNhsfJDWYz32fsZtxvo3FVH9ZlbmDOruNJxjsCkiuLs02i1Anv2+bi3dSPV7EkykZe5PytQ8CSIt3fg6ryPWmaKC+UQwVydcK1A45ek9v6noJKl6ND8lJUPPYRJ7qQjXeHfZj+BYncewVRlfMaP9ua7cGWn0PzDNGP6UZbCIslIgXIBbg3+NrniBKH4JHMDkyzU5chVVd5S+pi+8hjWdi8HO41UvnOcW4+0IanaeOO1bh6/rRuhmsGQ/hEHZwv01RoEO5WEH17G8qefZWdXFZOvzItfr+G+s0WtJ0Cn/U6soz/GPBnAuaLiut3PUBtrQ7NhYsvnj3P+tmfZk3HTEfIxe8aMddeLZKYMKI65EQ3CNVsTZFoJizLFgfDPWG93kF/SU3vOw07jb2CQasgUdQqjJzDm+7jksXAx4OHe6Dep+FeJ+7LMCU1OezPsMYyzx7yNz//Tq3yvr5/ULjOvtwsceEHNYq/IRCfYf/gohd4MZptIW2yCH1zahaVtEZt9hjWzmbyxG8HdxOh9hY6ZE5hTebb5RRq/42etUKGSL+P/WYtzx+9j7IG7cHXk8NcX2eWeRl1fwDTl5m1bDmKtD1EV5VwYXOL5b51Do+pgYHz3VX3w3yJwSZKO8vNqEyRJSgE3/nfOl0lyfAkbzmaJABZkGR+9G1oQayz2ahjMraPKVXhLq0eSEpQsedareezODJkZI/XELy9309jbqKhk5Csx+sWt1MoSCaFGrbsL6cIMXIPA67UyVNQEfOOYEi00VZFLQhJv9BL6pgqxqWWjpGFrsIlkk7GUdzF4JUdR4SWkN9LjddDSnUVWjmPK1vHbo6gzIlLLRFuPB13Rja6hot0epLnhwxwbxpqQ03FllXq7HMmmwdovUApH0VRXsUgt1L79bNCOppVACJVJWYJkii0EpQ7JpyPaqJESQpSMSzQ0h8gZJPL1DHKKdK6sguhhQ9NFp1JFpeqBhhJB1k9EXCbPNaolABkiFnK45XG8ggVqvVi6IswnNehTSoq5X0zgkiBRE6rE1CXKcgu2mp2wpolghIK2jma2gmlfG6K2SCqeZueVKiHRioiA33OJ1TcsGC39OD0VLIU5gkofTYOa3cUwZn0StVlJXREgGmigkaIY0xXW4i6EySABbZlOtcSVZgbNihWnWYasP4VJp6EeMtGaNeLQFpApW5RUIs31IsEeGXG5AmGpwcByiZZfh9kjp9/Rok3IE9bnSJsq6BrDaBQObHUTfRsC5/tKFCMpstkQ9auRtwQySaDNakeh1lKVa0hnOwkoMzg0FWROM5fDCWqSHI3KyJa6m6XSNLH6JoLwi9eqIJNQaOvsjLrpkuy0CqDK11EnYsitdYTOToplEIod1OtqkvYS2lYGHSVWdZc5ELibRjmPuBbDoJVQGrLIBFDF3KRNBSzqLNZ6g0JMgXbTS1N0UcprSG7o8NdWCGgWcdo1RHUu4ooyutYSBsMIUrBGSRRJV8bpVIXQqXSIoomiqYlWJcdtUKCuSnhLnVQEBZGODTSeFvp4Fl1Ji2HHXialOptZkVqigKT/xV2s/xtiS6RWLZJNTJKVuSglijQzWd5SLpOoN2nkK2STCZRSisVajUQhh06zD0kmoylpUYhuVtUiNqMfl9WKTttOh0JCI0K8lGSBFnKlnz6Dk2B/kFV1P/myQCORpJFYpaasU6krMKWup+AWUaoiuAwXWB9QoaENZV2DMqjGYE5gUFexaTZICmkW1G4yOh165QolRYusvoRssIK/NkJ4TsNywYC1sR1Bs0FFUWX1Gg3zv9JOTIWgZtfkTrqyLkyaHLWEgrZ1A/sqNv7+Cyb8+RjNWIUv6Xq5y/A0BdUsL6nr3LHfj+mKm3KrdJXM6/+BTNfDRj5FbHmD3/X+GqH5o4QbKVL+PkS5ErhGk0SjCrU6GuNBhiPnCGcy/HBplvHFJboSY+hyAU7X5IyaoiiqZsqqTuxHV5h3CEx3KOjptVNqrWEuBbFn9ezacY74hJyVjI/77j6Ma6UTf36JuwLHeeP8e3FUDThqTkzHLrL66y1qOxVwgwr1P5xEKl1AVtDSZryFl33XIVUjGCqrhDuXqEZUCFErZkODxY1NBNMl3EcuoZo9woSqn5SQx9aYYOdCkKnyAFOVQ9ypOMVqejctwULF4WBFv0q6fu3v+3Kxib+wjts2S6AxiCk3SvWOIK7FLM5mkWLumn1bNLXzNI0tmpYtvOVtoVVoMJYbtBePs8t+M1X9PGL6pxx8yc2yy0qmQ0Ax/k3OlT6MqelhS6LO/vxf8lr3ICGtiZtWF9gt6JHEQVKK/SwfOsbI2VXUESWF1D7skQTDPQ72D8j5vewSN0120e3QUdlbpyn0ojzWhudxiVtdBdZuzLIkq5N+PcPRW6rYM+20T9jpXE7hvXsP9n497kqMSixDyyLR9LvIcoAt9T7sm0rqyzG+9Dkt6kIcY2vuqj4QEFCgZMTfgdbiIqdyM79wD7s1M2xxRNHs7iV87ALJpg6rpZdHCtv40caXideScBUCl2QK6lob977gAqWVlL3CSOMyjbU1yjUN1dFBMm9WKGuHELuPoFZ+jusrQeyqGkfbXgXNvTQnvdSv9BBQ53F2p5HL5PSsu9k8IEdXlRBCVXLCDlLTW2hgJSlIyOcWGXBE2e+NYRwe5CfTPUQbMXSrZzB17Ce9epHM1BrVtlvxKV/FWddTRSSuEdFuq6FvV1G62EHfajuL3gxvPTjNcNNHIRwjHk3Cp7t4ftJJvrSOqL3ML8tHNWoi2VqRy/lZiM/+/KAanmAZKvz8Jw5wCimhxJCyYtB/BKpTKPMiPdZdbGpdrBgEsDVxbd9HV+oirrUIl+cKvJATOVwaYHt9B8/seo6p9J3IUlqKiQsESt/BGlNjzXbQu97DeecsCuU0w4oLXDjyboqJflorVhwvzKE+LGL2ZXEO5Zk9fYqL2l1o5W7u1JRYqhvJ+BpEOiJ0nPKweLbKYtTHvtFHGNvyKaLlFaaCV9HF4VdM4CqdSMXYwyZOBt5xhpDuDOqIG/Oil+6jX0LfacPu6uH9U048ng9h7zqB9dApsrW/YUH+rxQ4+0uavQWW5rvxuK30jQhcSL9Mj6GDznU/yVMb1GoyGoLAtRQYhVIB5ZPf5Bm/m1RLgKlNQttyqA450XdtZykb5Ny/3UzfTJC/T3+H11vXo0ldRKU6QZvhnXTbVFRMTmYdVmTHryew+zRS2wpPLexhKf4zykY/Z8Y/wFvTatS7XqPRdoG3DAOov69Be74dtf8gNd9xiGyh0vBw+u7vMfrJ12BIS/5dvbx47l1cd+a7dESKyNUfpfu9RYrKPKlMFVv6DW6JVAh5qnz7/WW++ik9GvkZ5IF5vnbzLTjTb2LPVyHXg+RcRshmrvl5JqvJMV50UXPtJDw2xNx9fo5O5Bm5/CIPTszxDy7dzxUfr4JKMIrPKmffThO6nbsIKlQ0cg1ujE/x8voEBruXI/v+lL89r6fQ9yRN7XlUX1bzqTs6abX0zBSDHH2XguSLA9Qu+/h0W4HCk7uxtwVobzMz/qMW0ZucVO6uc7DwGrte24rBViLjfI67Vgfo+c0geneKTLLFW/pncIgGuhVyFrJprizqyegHUJf9/Mn34NQtdV55JMHLlhwHj5/BcCZAIrCHOx+345Bvw+R08K93XmHgDRdYrFz6fJ3h+ZfJbMxRKF+91E1Uqig6Bnhuh4gbGcMbbj7au8r3zuf5wVyJO3yvc4emwZy4g1fl17Nw6xM0n13CGK9SuIokSqUsMD+p4NZEHFpnqCVP09j6TmRKJ/5ciJv/+RPoDvwJb40ZOdMd5Y+eaVE2PM+mpOXI7D1MPPRtxls7ual4E98R/5rIxK10t8v4g99+iWfW9azNBFib8rN/t4wn+ouksjp0r3TxsM7Ioq/BN9p1vDPRyy6Zm5xOScy6m8CFJOz2kzrg4lLvPM8f8/JSzo9sp4VP7drDa8/LWI3Axz+/wbP7BKITXka+8E4+Ypjl5OAFrhyM0J79OtKsHnUljmZ8gWqi9V/Ro/svQVVWIUkmPnWdkxtWt7GnkKSj9wx7flfB4lKVlXkRQ+Uw4o4JrKUSD7ycJpp7P2GpyGvCm7z3q+OkkBNuh5kb2jH8REGrr0VpoES7sID2zp/QqBRQvnIdHzq+jR9te44Xthxj9u8MfPDf3k9/tB/NvTpelj6B9y0j/ktKYutajK+MU5BlCaUmSWtWeGBsGcc+N8q1D/Bm5z4y0jRR8+RV7fqVEnhJynC692nMMj0FfRcdqw0MkRKywgz1WwKUlkLUk2c5JsvSZlGiTDeRrW/BPv4sucYG8qZ4jTqSn0/hyfecw6wLIGZHKViDJOYVqGxG2j7gJfaDLhrrQUhdfWRVo9lkcSmIcV8XkqaNfNOErh7FsZrAlHqRallFovMEBrUCKXg7Y41Fkj6RcIcKsfQaV6y9VPIS4myJ2eQqFNKopDq2PgGXlMBVEjAlIvQNdWKoWWHBj61R50qnjXI6gWylTO9ekcsLFqayVjyJGLbyDZQrbnJVL7t9C5RlMqImkX0PPUGgNsSkUWJm1IFco+LWhRD9pST3HC1w6V12qhteXHM2rh97g7X0bvIyGfrABbJCjuo1dvgBZDoVijsHEMMx5MoC1h4VxoYNjdeHqjGMLREnL6vRvMqUaKkpIi5XEH+YYpfNgLPSJFhW8kL2drasXMJu1qMKDBC/7gWuG+mkxzlIzJHEbNEQiStJrLno124ju82L3KbjbfNqnuiTk5OnWE1vcueNBYYGLCQbKs4dK3JJuwN/YQb/hTUsyZ0sLpxAaBTxDNxP/LUKWYWJ+tuMbLU7WFpOU6qcQBVQUvMNoakFcD/bQaP0LgYGDJgaZqQZP6/dJTK6qMG96qHykowKIZzKaXatrPBybJNm+RoNYhJQB+V6BdV8AK0ox7C+jlJW5UCgSlevBuP5G6n05fHJGry9+Dzur3aiL3pQyDfgKqPVWpJArqFk9g4FxYyP9fXt7K8W0BcHkSv9VO9fZWFbGp0W7mo0iDXA7O7CZNZhqrRoHKtSsYZIXy/DMpFkfuAkacmI7VtelKZhdJEErdoiHeFbOKdy4pYrONwf4YTFjTrfoutUFHsyyYU970Awe9kZMqG9IcGsI0y0VuBgeBvdrmlKLLO4FqW5kGLYrKLD3KJ4Zgn9tgaB/j5MMgsvnniLYlKGuuQilV7C3zxIQWiQKsn+x8gbwO3S4HeoeN36HS7b1VTQEbjs5PVkCe1qnc6NEvG2H6JSLVP15oiYRLqu5HDXRIbPtvB2v8Ax635aBgP2VAyH208hV6G+UEe56yWyx/cSy5lJS3JmDi+zEXXhfelO9oScqMZXKGjXkCXNFGsHUan60XtUzN68QFxjpWK1onJez4VjMyzWrXhTGu5YeZbMEyBTjTEg38GL/PQX2vUrVSOUhCalwCr59hDFlIXyXIt0tEZIUaPZ6aShqFAvhygI6wS1MSZaWk4kRsgU5mlWMwjNayjmyQTkGgUaR52mXEc256eq1BKSxUhak9j22Gh1+hHNV59uAdBqicRjWbSyBmabBXXnDmTZdqRYAjF8EdWmmrw+TVgHi6VxDDoNgs5MVWOgKp9jiQDBxgCNkodyK0Mt1aIVkqMrJ7H5itiMJSzpEj5ziUoVEiEDppkOmoKaXKtJLFNC2d4iolezLmrR1yTqbQM0LX2oaw7GhUXQaUk6HVQHYzTUSXJSgQ1RYslhJW3WIVcqGEw1kW1tQ2EZwLAxiC4cRp5w08p4KeUT5OvFn+u3XAWCJEeh1uLcF0AlylCHYtiXzuCWsqhMCpouKzalDYVw7RiglqiRPpdGtSgQmITAlJL1sh9TVIlmBbLz4BpeptOookfqo8exg5pTRdkgIUkyLHjRBEDbXaOzYqNLo8JIgXJhHZ2ngUUtoSspySw7iCut5JtqmqkW2oaSQhlSVQN1zXY0sQBNnYXsdjW63R4c6hzWWhDBEKbsy9EslVFNg78xhsa5C6W5H2OqQWRvjjVvhmApT2PFhKBbQ6+epG1thno2TLN2FcEqQJCrkSmMSAoX6oQTc9iMOypHCpcZcIrsHdCijrYRK3lAEhlVzaBeVKPIq1FcI7aSgCYS8S0loqOQ9lmxZZVoRQ2i1Uhmp4tkh4jWkqNXHidt1ZNx9VO2D6GSLDTiLkrNIgX7JDpvnahnkTX5CskrKspRD6qqDJsqQU1TwlIX6VSU2Da4SMlVoFWXoV7XULyYouGpgl+JMuum1lGiYKxQrDaxpxUMCGEGhCV8xUUq6yG88gLDZqhe0KEJbmCTh/Bsq7Epq9OoatFnjMhXa3QYVNh1KqrXkoP5/wMarRqTwwiaDSI9q6x6QySnKsTmEijWqnTGBdT5MygbMWpqkbkOO/mAAmNNon25Qlk/QdExT8u2hlWZwNXVj9PQgalhZNO1SjbsJb7RyxRWgltyKCQL7dND+M57kenz1CxLVMtnsZo7kaksFNQNkns2DXghIQAATJhJREFUEXVFzDYVXbv8ZDw6FqR2prIdFPNxGpsa9GE3ncneq9r1K43A/XoPH+i4jdpalK3fzfBn4f9gcfsOKne8l9ubj2K6UaR9m58/e/aPeLK5xvyADWmXh5uPFXg5vky2dPUMuFwjxxDQs7f0R8yq6pySggzHbuVS+1ewClEenDpA3O5HZtjgWhQuSCCvikjPT6C6zobzoftI/fbzHD8SRralzOCSlcKZO4llIOtYJ73tA6zOTFG6MEnkfcuksm5cDifjt9kxv9zCP6/FcCzLcviH5L/gw+Sy4bzUzub0ZV4Sz5EQqjxS+U1u/3GavDrKZnuRZ/15rm+v0J0XyLd7OPWZDTpKE+yIxXA+kyDafhNTmjG+9EQf17/3UbKn5+CrGQTD7RzbHGCyI4ntTybpvnIA34oLW07BX6xdx62mGL7NDOUvFUh3Vqlc492XiQaMMhf39NpJb9uG5sIibZ/7ItmPW4mXVomHM7R7bieRPE31asJNQExd5JhpncK+vbxtw8L1sRZl6acc336IUEpHZjPGdwx9PHX6KN+aPc5Y6f0kPq7D0oixMz3Dy8U4WtkSddHEv6hu4/dCEhFtibdkOV593MxST464xkiufTe/2TqB2Zeh6XYhX/xHtt74IA1PD4l4jYcOmsjVT5ArnCCUDjC6KOBJ9/G8+Gs05yus62eY7jjPB/1OnoruoVYKscP9BY6Y72BKmuF0dYnW1uvwuC9gbEhcDneTk71KQ7i67Qq9H8k1RuzIb2C+cIW2uJmD5jGy6dNotCJNR5ON9tc4+ayEbUBO8YE2LH96lNKrcyjn0le9O+WyFkZNAUPjKD5XnqHdeab/9l94870/ojAWZE92J4dOj1DuuMhc3+voHQd4rbWF3Jwbx3fULPx9BX3wpwy8/DjCe1wI3y8jxMtIt1zikkzHSFqir2nh3973WR5I7GVAoUDmW+KTpzZ52jrKK+7f5criaT5kuEjB5OZf1IcRLj9Fr/tBXJ5+fqq4SPa0CrtDh+bIGOnXHkeRHERZPYhqox3Zwkdo7NZSvivAg7m/Jjz2CiXfJL3GbZh2xjkbCnM6cu0OzP8WJNjMmEg0B5DMv4dd9wKywhQvnHyRX7s9hMuzD31yD+Ppk4hrVoKRDporB9Hrt9Eovc5aY4FPndbQ59zAvV3A+b6tDFX24ZDFKVbX+a0T57lv30WE7CWCizI+p9pJ05xiyTTB59af56NP/x7m3hzVAz/m/W/fwk/ezHFiosKhWzPs/ImJvg042L3AT/f+jI25AzQ2d3Gp+0P47p6GxhwsHL36Gvuf89IvRzoYI/Kt4yjb9Hz2YRMPv24lHZ/j8hf/joh1nK8P3oMgU2J+fYab++Lcls1CPMvEW2fIpK4tH6ura+lP9LG9ESHgK9MTCJL/yjnK992MIlAmXfo6H5fdySVZmHOyKRCvXfI1La/SWV3gvo1vY35HkxcD1zEt+Ln9mRGeu/dF5Oo44yGBzIFO9t4wjCHj4fG/UpOLFxC3Bkk8MsEbC0v4N7rornjY33EQ98RhluQZvuX8Mfewh+HtAouuEqeDaW7KLEAhSK1a5J8K96Ff81AspDitOs7W8D7Ssh08qfER3XyUR/a1MWYt8MUf/wF7P+0g2ClQv9PIg6EXedVSJilPY31+GUWnn42+EmFU/O2qh6CjzoZYYGYgT72l5Fpb/CaZhK8kYPqKhZ9sfB15rMUd9QeR3Glqse2UdFaEtpMIBRGhcfV9haYoUW7UEQonuGLoIqozYDHHGUtf4jp1iQ51GM2P9iI0utE4kwx/8KvonszRzA5TU17Pu1NKxGNZsss6zis6+cbhv8TtiTImqPGc+xD9nmdZH67z1C07uBI5gVE7gMJ4iIh8jX1TenSno+Six3AGRimk1aynBin2/TqBd6XIKYssxI7RStUYU+S4S53Dpp4iohxhuW5jMj9MIv4a6oCDPdfvY2xqgKi6m4uqIGeVx6lfo0YZoHtNh2UVironabMbEcoZzq6ucvLdFzmgN9Kec6JQj/Ng43FswSzWY3r+7bo/QKErIBnT0Ej+Yp8KddKKMOcDw8gVB6krx4j9RRAzd9C2WUC6/ArP5c6QfR2yzb00xvtwz5+mLSpje/Mm3E8+gc+0jN3s5DtLvZjXC4ibFb6armL5rR3MzmcJX1jnz/92Cw7dXjblBf5DmuCjnccwdRiw9nXzSOEmXj72DJHOIOxKM3ZIwv30a0iPvYbacpC2HOiHjGy2xpDdr8Vw8hzG6S+y7P5N9LJuqkUD6UgU+eKzHDdXySg0fDjbxU8jM6xeYzLUfxvSz4MypX8FZ3+cD57fIO3OkSnmSeTyWJqfIFHr4KwkcebdefY5vGjW6uiff4N9nzCRvlgmltbx6YfVeI121go+vvU5P3Olb7D3Vj/tI1akny6g+nUNSn8b3uWd/OwLCewFAbXo4W27RtjQvUK22I3zh5/lVMWMW3mK97lX6K68kx8rZLy5EWfx+xXU5Q9SNUPdl0N+46PoFsPop+uYz2muat6vlMBRQ6PhRsxoUY/NElYYadplWNwGSqoESesYYtGCr7JGcsVBNWlC3lSR8JXR71Vi2vTimhmi3B0krRHJyOX4ZA20SjmqsgvF6h42OtZRmg20SR42h2ax5AUqIYFVQwJtqYilu499vvupzeaYMKUpCUqMZQ9yxQmqTh1VnZnaaQ3aPgnJJ1GrTGLoc+HNKUluGpnrkJOuqVAo1Sj8LTTai7TUvVQkA/pqkt7UKM6VLNXzUbTaNsxWHxa1GSVhmhvQ9Ig0u+vEZhWUogItSUShb6LPxqnZcggmBWWlm6zSTa2ew32xSK4cIqNVEdO26O8GpzSPLqViR6adaGyTCAbKaheSIoVGl0ZNjlqlRod9jZq6RE2lR3oxjrbLjVphoxK2sH8oizZhQ4ibOe9fRWvXYFCrcIgaJmZidJpcDOh7iSxO4bRZkXfbKHj7qEXnEWUe5B4XhpYanX4MnQba1CpWqtBoFNHUi4zlS4TNZjSU8LSipGUylCoBrVLBbKcbQyyNRhCpuG1ET21ClxqDy4tQMNJcSdEqhZDME+QumKlqWuS6qjQiGyQXA7iyIg5Dhklnnr7NCj3VGvvtMeJiJwo3OBXThBpKmmo7QlVJppgmiRUxl8C0WWNjl0TWZqcpNdBunsPWVOLRGnEabCxLYYS1BNosiJoy+QtbGWwY6NNrsfWWONUukJKJuLRy/E4763EZ8agGe9xPoX+RBnXEhAaTwYBBtOGsqdkt2KlrcyRMGgSLGe1SkU2rjrhGZDGwwYipiE4lkkaFJ7uI3hFAqbwRf3GNtYIJQZlDo4sRFG1IsRzaQokueQ1dqJOcykO6oWZwwII/XUaSxZnuztJbXsKyKKe8rOREZpA2nRacMiZlEoGyF0lWJtYC7UYHle556o4mzYobq91EVqElWpMh16wzVbWxKChZVih4I9FFUyVnuz2M+aCINaRCVtbTXquT1oWotuUQtrYwaPWUxSIVg4rQ8iQqqZdCZRNDs4yzdoZZixO5oKOtlCSvrGIVRfQ1gWgjSathQCF6UcuVNPRm/JU8KrHBrM6Mox7BagSbRUf1spWorkRL06RXo0XnrRKOy1hZVeKQKki2KHKVDH15C9GyApc2Rp8/RuhomkioRUbQkB3p4kQgBJKBUtGNfKafuT3DWExqOtvDrDiqxNQySoKFe7sUKOQ9bKidlJwFUrEF8ok6tWwT9XYFypQPih1osj6CqRBReQydLUF/n4aKXkk9qSdYaGBohNB0byDrCiNle+neuUwwo2Gu4sCb6MYibmDRhfDJl8lKUYSWDV39/yMpFI1TC207qEaqjGue5eXGVrQjXXS/y49P/DGN+BjCvIlRU52zM1spyjWgLGN/WEubzEJg1cb2L/8GyZueZ9rVYFavYYfQwKFVUdnwcvHbhzgx/hT90hD9mVGSD76G7ukkhbU6c1t1rBXXuHXPAe4ffw+Fby+z1DtFRq7DEtmLqNygttND2dtHPubEu28ZlWud5fgi+k4tnskS9Zki374zh/p4D069ncbuGPbiy0QLSiqlDra2T3GweB+NdIWFZ+oMDh+k12bCWyiSDb5BMDRK1amkrd/N5bdE1s4pqVjUtO+V0G/mqQTqyPvUvNkSKFqcmGI6bn1K4FXnMknzJi2rlvfsNVNJH6UesnF37ff5jvKvCG8aqEf8TB9YRSMVcKor1K1mtrStIUgFskYTE0+v4W1rxyTzozzTxdsOX6LtnAsh2sPnB5ZwjesIWM1saVn58to6w4EAI76tTE/9Lbt2fQLB28WmPEfxihPBo8PYKcc44cRmG8FpdHCb3cxT4SbZ6gbmYoj3p0OctHdhFSLsyJ/mE0oPTsFIQGPgSy43o4XjpLEQ9AwzVHwMyb4Fk72f/Ck/jWQDVXMNjfgiF2dvIPHBCqUOEfE7ZdSv3Y5FP43Z9zpPDp7n104k2BEWeYf5HP86uhP1+Gn8xicpJK+j0e+lbrWSDKtR6gI46yEC2RxXOpdZbfShy0h0xqbYnmrDobKR1gQ4ls2juxxFXSlgH06SP/pxnI4Uw21LBI/MMmWWUNbK3BNzYdxn5fmzKjbTVpzRmyje8CMarQytdQcmTwCtoY5ZoeYW0w5+JnuFsFFGq7OPbT9+kwtjFma3qEnsPseDLgVVpZ1Zt5/9m8+i6n0E5cgd6FNzPBvsQKlfxOU5xUZzDOHkPLbcBodkCfKXhpjXakgYV3hkx1Zs8qdZMk7zva4SD4qrdOgrtFIiZxTbMe/ooirqeHpCxW/rbqJYt5EoX6Kt3sGVm6dpaEW6rgzgNcqpChaymFna9jwvhFRsSEZEu5HvzNzCneUqh0pzFB9aYfzVPhTFNoRQgb+LvUi1X4PygBZr4xlSCg2l5RrrLz+B1Pwk6eQY6paZ3809z0XnA7gEDXuLCabtHsYVJTT1AhNM4hEPohQC1JVZMrYexnPLWBsl5u2jeHJHGWiDoSEX5TeHOa7bpGqocJfDg3N3gjcvKdm4qKe7FaPlK6A0qvHm7uFk1Ui34hzb7cf4t5ac8KqCqt2K5iY3VwYfp9N4Hf2l2xj7wBFedfdh6m5x4wEF/yI1KYgqrEo3BlMP8eogRbuE/sgC5lQMRVZGc0NE/9t2pK9uQ5hvR1uykZbqpDRTYJxgqM+DR7+duFfGhOksR6YLRHavEOzKoLs8wd5bcuiqfs4vBGiFjRzO1tkqJvGH45wQUpT1dgRr11U59Vc71Hj7kPSvX7qftaUwjy88zJFxH7l+DeuDSr5WU7IylWYpscEVwxv0+o6wubbG0sJFPvrbd6NeeZHSYoq50wfYunwJ5S4oXu/ksZ/9CcHoDNXcIpbsFQbe9WH29J5mwPgyf/aVnVS2D+LUeTgccjB4/+fZqN9AOHkL+6fOcGY0ikKTZm8mzh/NRVHK9ditDkz37GVP43FcEwm0P7Qw39OPu7IVvdDNqetKDG6o8Wgu4+r8Ia+c+RyZeSXyPOzf5+TMntNYzRvsaUZwfCHGc4PbuDzqxHzdGTaf+E064wb2tBJ8v+sxwo07Mevb+V8HXuTHrZspnnsLw3Pf5Dq1Dou2F0muYaM1y5GBj5BigSvlo7yw8ZcY91+ko2uKuwxv8e+vv4+0UEPQR/BXzeyaK+NxCCg/ZMQSvYXk2SyJmRW6f/MUz3y/TnLVScB0gIE/8nDh8gwT5+Y4eLCHXsUwSpNEyj/Pgd/8ESfNRi70t7P9hvsoXKoQic8yU3yOI6Wv0Dtcx2cro/t2J8//VRWxFwaqAtYvfJent9mZCJi5/o0CHQdCNOsC0QULk3EFxhsm0VhF8n/1Gbr/8Ic4mMQ5t0L/2Dt4/WdThEIbHHi3wHTweryOTra1t6F6Vs9G7zxrzTpnHt3FyA2n6G0V6UjVOWq8yEYsij2v5ncqByhsC7KirbGuaHLE3uKJ6VuZN/VR/zUL+//2GyQU21jvPELpoRJbl5SoWmusBB5D/oUPo93zFtptJ5F9/Da8v92Jp2am66saPvIvCupFAW86yx8HvszFmXdTb+lx+ObJjBVJxM9RyoTwtj3MdHYTy7kme76vJnJLA//wBIJyk699yc0nAw9jG4wR2vEi5//qL5k1v0K5bY7b9+1k4+IQXp+csd1lHv1GA9tdBVS9SoIXd2IWNhhoLDFcn+Wzcht3XZlh10aFQHE7cx8tMLviZPmEg/f8/beQwkdYW3Pz3bcW+Cu3G200TDgR5gu/fw/bvjeMreUg84dyin+coXdLhcDBFP/+2r8xfKQbT06H+cs1jv/TIVzzS/RNrnD41kNEDWrW9HDZVOftXynxYruTE24ldz72Yyz+AYL9Hbyyr4uP/eEnmT7UztzuNhzTs9znG8KhF6gpEphnU8Si9xCs7uW1/gac/StMWQvexkPcEKty0h7gnL/M5X2f5sNnPoe3WaOpP84TK0sceUeNLV1ejD/5fX5WO8uqPkPWVuPTfg1v5huEUdLf7mfq6CWUdiWOISO9prOkNWMkYyaWH99E95FH6A8XGTs3h37hm/z01j9gw2zAdfFfmFbZsUpKPDUL0uWP0vrQIkJ3DVXITsfPXkQx7KPa5+PVr55kn24rnbIcvvrzrO1oYyIaYL5sov6750hHD9AeaXDnynkuel1UXRsobWm2GMeoLm/BZCzRPjDJ3P86QtgSQO4s8hvqDzOf/BQz8ipnda/Tdfb9FGwydINZ3vOHJ/nqJ/qx5NY56DvG6Od+9P/+UON8XUJmGmJvoBfXlT6W8yVsq1F82QxvtO+jGM5RXm/iLt3LYEcUlT1OfHeaH0XP0n3ainqmj/XwjVjaM6BOkl3LEwl9lfE9alR+HUvK62jKI2TCckK5UcqPDDB8eR1HZo5crxJzPUW2dQoVMWqbZlraLmqil5WFFurUMttGAuzyjaJRWVnYPEK4toBi7DwDe+RoL8iRVmR4chIk3mTJkOVVz/VIthDJQ2kERR6prKNvoY7JUkPvUlDsvpekbIZYbBPVwm20D62hoMml4zU0USPtW2fQKIKc/56aeutN9IUiNu31vDkapKNqxJAVSC3VKd6ZJpKUsbjmpdeySWk+QjYj8PqRu6l4LtK9ITEQ0aDd56G+4mUto0B2sULW+Qr2Tg9t6k48zxhxbUxhUue5buwcT06oscX9PKDcQ/D4m5y0JLF2NdkeCPK8zYjG52Kn00b0mRW6jTo8Xidu//0sv5nHrJnCbN/k0iO3MbpcRlpMkC6v4LLB9uUsztkChYTERKhFTaeiapMjWtQk9G5ashKKHU9TRkdKsZuSfw+71wdw6NRkbRZW3jqBPaZCGNczO67HsUvDZhbijQzivafoyAVI+Ytc3J/k9smHyUdXKLZiRG2rHGsPk8q5qW70UQqVsapVjKiKnDuf4WShE8FlQLSGyemeopAyIm+UCHem8SvzGK/04Alq8d85wQWriqV0gomuNW563cHSkIm8RwPfuwNvTklVvonMfo6BfpGOyTYqZ3eQ1XdzpFlHpa4ju1/HxeVNzKpeuow+PI4o4sAaEWWNiRNtHJCnGPTVKPjqaNcnSalcqEQNrViFG24dJr+0QH0hhv+GV/FPFFAodeQ8u9mitENxgDl5itOhFXa+tcjW2joDThvN6k6CFh/hfjOdpmEUghZVsYY+myJxLMNEXxKtIkXp1UtoPRlqzREicwN4jMOMGn345FpkgQbnaxoidoFUfwtxpkZ75zyKRo3SvJHjHRVssTXuWFAwcf0edte6UJqLyA3P01D1Ug4VKU7P4zC6CAV0VHQFFLUYyzSZCrRYUsBmrcad/gFMChuVTS9z5iTxPVcwDqzzIZmF3sZbZEt1ZmRRNh65iXxtkfTxFmfmUvhu0COWqtSXSuR2VMgJBfKiRKENQhkDLbTEizpKTRO+nBxLyYhq9CCyfIh8bJ711BV2d3koeCLUVHLGBDXFxb0kxhIsH0hyjydB0JlCkY4yfPwkccMU9lUTypCX8Hv3okpMUWtFed1gxNmxC9N0jY7lJKlLSWxliUDehbW0j/qygU7DFly2MgvPX0JhSpJvZMgGV5jb4aXobqBXw+rLGl5urSAOFNl7KEZhMYV2zwJsC/FM3Eq40EMi26IiXH1G6q+0jLDeEMlm2lHVx9itM2EtRHElIvRE85ypJ0i26sirerTRfoRFCW0GLDoNZ0sbzBTNrFd7yCr9JPoNJBwK0s06Mvt5etuijHQ1sPUbaRizlIpK8psB6gEj3nQSX2KNuiNGNu2kWKpSEZYICUoqKSeFoIelZQsIdVx2HQMeJ93rdarxEZLNUTI+E7JAnZa6Sa0ioVDJQUqRFuVcke1G4U/SHFiiMjSLpAuj3Sii2ZAhVGws+HspympoUiGksA2rOYPaFqSoWKQjLNFVXsBRusjSsSr2M8v4o1Vslj5iHQbWnCbWzHqS5jplY4q6SoUo9eMwFXBlG+jXtGyuDlBX5XAoCozWRXyyGhm7nA2NkuKUwGJplqwpidqvI3G5C6Gqw6yt4deFWdtIQkVBt8YLa7AU3GRjcxFzZJ1pfTtVvQu3oKA6HcRUlnCbPHgGdxN3bbCpDLJGiMnRWayry5guLhFZnEU0ibTX8ozEI1RbaRINE6mag3LZAko98oYPdcOF0b8AZSWlWgcx7W5aVQ2oehD1o+SqOmxVNVSUrFQVLHbXSSkrNKQc2s51xHyTiKLBVH8Fv8mLXtOOJHdTkre4qCmwIahoVX2cqOrBosKvBv/FHHF6aMitOMnjEWZpCZdp1JaxhmVYvYs4MmVc522YuisUSinWyhucb5siENvEl41gKuWQzg4iJlqI2TDSxixCbRF3SkZn0EdpsUz7Uou2Fsh2Qz1dRBaxos11Yxu2Uh0qkXAoWM8OYNFXGPM12eYXKTUK5B0RquoMigT0eeS403rMa2pcjiid2XlM2RTllomAzYaqq59szyBXzBqa2RZ2IUmXd5lqzEs1I9Bo1lB4NNTVLmjzoR/3Yp5pEbcVWHJGCV6cReu8Qk4dZS4jI6Ay4kaPQamn3GbCnikjKGVE3UYuLcsI5VMU0hGE6QQX/HXERoTB4DKyUTMNvxH0VUyVK0QUbRQLCmSxAgpLgKRZIqqpkmrUSZQNLLSUzCpqKFmiw2LGY7chmUXW2xWUurNY/GluzQew6mKUNSHWWyUqO600cgbKl5UsZqqI5iZWfYv2lkimlSNlSJO1pGmSJi/JydS1pIo64k0dhWqTlgC2/i5s2Sit/BwxYR55Ry8yewWNLo+PDsyiE8mqJ9fXQj+yRF2RgFScrvlZUtoozVQB44xEqUuH1LNBeSDJ/HgXGYeFlleBxt1AP62iLanE1zKjM/diLLtxZ3tpiw9DUAsliVqlRSrXou4LIW8LI3MWWKwOMK2LkzJFcJlayNw5rG1JTK48V/JO9A4tGpuRlNx7VU79lUbg2lKdl78h8ETLwC0PhLl++gcUrMPMbXkbU+5vcGjrA3S3Bfhu5CTnLprwnh6l44yX6T9fpfJ+B8WqiGXlVSruBRyKKj65Fs07dqH5ohMeSzGS+mfOvP+TtKwyLM0gub+ZoJhu4XSO4U2+mx+EK6QdSYqeIjwwQMe5TWT5FHPtEPmQmllfFV1lE+F/LaO/7R62GXfSngnw9LH/wLcQwN7oZvGghcPGR+iXNFS9Fm4JhLlyLkd4SURh/SDP5R/FqTJwRLWPzzte4cZ0g7vKFqYNXyS19C46DVVu+a0XcD2vZTpWYX5JIl5I8PYth2m41cw789xeT3NGPsRMjwrj22uo3kiyp3WA8Z17+PfEEgd7zLjjcTb+7DQ/fs9DVPtT1PassPL4Rc49eAWlXM8N/+El6fQiaSVWagt8xVrBbTlNd62O8+mDKG98F+cdc8wyweHSX7Caew7V9DTi5RYdineSXbrAhdlzOPs1FJq3s5Sxc7S8SWPbExxf28axSzfiM32flRkrCZOXFw8cZGvrEtpDWTBWiMzJ6dt3D6rJdqrfglXTGrtvaDHQUUexEGGuUiCmSlKWSVy4aZ6zPzxApLiDw38ewbpkIxfSsPKmivgdUxyyrzOcKxJ/dpTHl74KzTJep5qI+nnodmBRe3GfuxXREWdAa2ZfwMnHdx9nor6TnSud/PEJJ1/f66dNhG1zJfwHHuAn45NIC00+9sU+Zv7564jP1aj/xMFTL38GQ3WCTl2Jpa4Bjh3px/amxOjPRCTbKq/dU6dhSrMzHuFn5Qa3j9UZ6rrM852rqKZHCaj0WLRNPlJcR+4YoODsQHqonwJeykUXYr+f7z2W57buCVzdGt703kDWdYGRjU563jrC5PkTyHu3I9u6hZfDcQpnP40htYLauIrw4S46B1yovA7KK3cTfSBASrhMK34Z8cnTbAuqSNTlPOoX2B/9OP23dWG7fRt/LK7z9VWROUGDJXszv7PtLCcdMl5SLbN78kUqs3uY1Np4oqvIH71VZLjDT9C1g1Or7byyv4jLn2ZU0+SHveOQW6DGHH8sPcEzPT2kCnV6Tsl4uuKh3apgxNCJ3noLrdiPKKVzsDjE9XPtZDc9yGVJHvrgP9G+MkzU6iIXSFDsGEATOYzr1TtRnlkl/FCOeD1CY2mZoeTnMC3ZUa8NsLXNzcvRNxnxNnjgLiMnjk6xdLucokPPoWdtSO1ZzHIN3RUbHYfgoi0HMoGH0ot4Tqwzp8ww3a9AGriL3Y4EhZSAvHAX1Xv+A7M1j2FN5MX6Y0yvBPBtGtjfGOGZapUDqhj75K/BZ04QuqOFMzBKIPZeIgtfZLXTQWJ3J+N/3o7+AS+GUQFtd57bjycoXm5SOaPgXTffylpWRUOVQ+N0YTg7RyvWIqNx8eyWv0XR/QXqazkuf6wP1QMx3Kt3oVrvRXM7/PbvvYElV6UUuo3PfuPzv5BTf7U5cJtV+tg//CE5t599z+RwdyhpVr0UUj2khmQ0AysUjSUWaz4aqjW2VNRsrVr5qyMmzJWX6Uxmuf3KdTxauEhe3QaWIZrXlXnw6V6EzSRfc3yV99QO0jdiwdaj4ti/pJDlOzD4fQQe6OGt736CBY+ZsH+IrtP3oe3/FmIlS+XsFno/skB8IUJ2JcfWAz5C6wY0DQXdBoEv39iBEN7EH8nwEdk4hj4/M/o1fiw+j+H473FDNsRIOU2qOUCs5xiJrjLRPhPWZ7bjl6+hVa4xLy6TG3kvg8IyN+Yfo8wIz72pZmG1jt+6wo69hyihZi1bYtM2w7B8J9p6J5dCWsbbXkQKuMk7d7D0l1vJvS0F7TG8E9PMyM7Q3zbCLud+ml/J89o+HXp/jI/JnuYHPX9A6eQEshPHUD7opWK/CbumjX0CPLc8y0LWTbpo5N76F5HcDwJVhORjvGZ6L/3qFCPNOI5jLtabdmS9LTx3ZIi9OIpdNYPCNsFT4xf4wDf2kk218bjHzu4PXqG8FkaWrnHDnq38x0kLlpCOGxMqPis+jWlsF1ZdG/pvT7C97WYau2tEb1/h0OQIsVchngqTePi7+F+WY3d2Yh0f52I6wE55GmdaYPWVfr7tbNJCwidWebD9Z8iKDZQtCzbDLr5WyuLrSjPcleP1Z/vJVbZiLeu4IRXEuL3Kel5PMKFjyB5H15lC79Jgcw0hRSY4HzrHRGyODrbSOaTBSAvZWpHofVZOXdzC3HwbBx98naXMCNVqC31zgfeNXaYw00VuoQ1DoUrb9nlCKi1vZPvwlGcxV3ei0neSvH+CA0+dI67Tc2zrCJYfrFJ37Ebb7ePAfRdZ/CcNtVULirKNQcM84UaBdbuRtx65j+2rf4IkC1GywN3tLi5faicb1zBsjzCj7Mfhr9DpSzP/fS04BQwNgb6zCt7Yex8ljQ4ZdbpcS7y25CVNke6R1xl5zUajd4V6/zJjjw8Q6biOjc4ml8Zf4d5j2+nKJdAVwnzrrJYdu7cjBiQm7NPE3jqMJpXEWg0z4izzliJIQpUDXYWDKz10q9tQOY18/YHL7PtZGU+0C6NwA57OfyDUsZ2kchjFU03StwwhCasoN17BMPb7rCVeIpWdwcAwXXtvwLWQwvniWeY+v0xHWImupGOhI4B+tkF5wUZ8xslF2RkO3aKh02OhcLEXbXQVTc2OVu7DcPhVNkUrG3IHk7YubumcY+OSlalTHrp2TdG9fRuyqp6FJ1YpdCtJZvTk0wK79z1Kd3w7pmoJmeKnZCvvQFdtIG8lOOmcYNysQjC3seDaz+zpk4ys7KU9NsiT8q+yb7cSg85LLruNypSOoR1FOnvy1M7HSa34aFULyBWLvDbez/5ygb50mtnZeaIaBSVjP3XHQfTpGFb9WeyBMp3v+0Ne+/rTqEw2evbt5ZZBx//7OXCFSsJRtSBLe6CtRqOzRKoQJiRUGWnbz5JDT0FTwVmLodJXUCUsRFM+nOcvUmwVCVdVTLecqBUW9EaoW5Okr0QhZ8RUL9KXapDQa6mJckxihZGtYTYv12hVslRXauQbBpxJGa56kpT8JApnFVm1iWRYIDnnIxwtkq+nabgb1AQzigIoykm2Ni0UZRVkOjhvVWLXblCpJxhPGVj05UgllazGjcz4QjhzaQypFl0OLfalIopRqDpt5M86sahW0GrjlKR2SPrQSQZ03hrl3iz5Li0aQUZ7UcGljQOk0n5sZScGwYmQdlD0lYnapmk4LVgsZbSWGi5XBxOLR5EaIrKckRVvmUBRhy9jRjXgQsqJGJtFrNostbkOsjtF8s40ZfsalcY6ykYdZ9ZDj7VBpb1KuVYnFxMJCEFEd4qwOU93pI3A5jJZiiyWa1g17cidDZRtAu3OFiujflIpPVXdGuqKgZrRT0NskH2riUyToNAOkw4JbW4AT8WIvQTl4S5M5iVS7hIRe5Zztg5U4wJSto66oiOmM1LRWairFPQapqgru9loOkloazS8HkwVFe5ki9XMIA6rhEmmopiQGNMWadXVxNMBvHYFKuUCrVKDS7UofUMmWnXQZuCS1kdb3IQ6r2Nyj4aOcApRr8HT1kePtRODT6LSbLEuWhlWyGi3J0l1lVhzuKnFltEWanilBpHFYcg1QJGkqRxjOpenbFHg85hQN1rkFtaobdbgfA/JmTcpBhSoNCZqI2kaaQ3KqB/FTBhZVIVWW8MyuopeYySzGCEoy1GwhLCLIJOpyCjlFNbbkeoyREpshBqsqJtkKy1Itgg4lCTVZWQtAYvGQNB6glbTh6vsw9ktsmUpSSibI1UsQdcwLk0UXbZMdayORaFH0NapmfOEh9YRzssxB60IlgpanYaGuUqxPY76MQNtmgx+j4RKN47XEkeSV4jU7Xj6+1BrZDS1LXrWBnAozmPTVrGVNGQ0QxRlOpqNLJZolqXUEEazgT6bgwtyBXl3lbopzdp6DP1iCnWiiclsRaMbpKSCUl4iG6nSSIjElE3WhloMKXJYFBqEshKNtYYrlEaXV6GuN2hGi5SadkpiHUPoLItkEVJlOipNwnMlxMAkCklFNibS3OpEXdTgqGowCjkCuRTUW1xyW5BWLcjUeWQGAV/VgDZhJm+xEDZJbOvuR7lWIZG6TM/9NTymKOVWmWW1lYJrP+2AKlFkaTFBzt9AUWtgjLRo+mrkkw2S1QpJ/xKKwn6cdge6bRKmKSVZrZmkVknrUoHNzSJyQ56G9RrNYr86+gbBoMC4OkqtOEL5PaDVv8pKrcDJATXbdg1RrXmpVdV0Fo9hFw1Ei0ZOLzrxXP4ZU/oAm5YBVjq83KKx4/OHKTnn2PxKhHKtQVtRwT2hGt+8LUCxXsIWD/H5G5bJrJ8htWIicnQPQcVBtqcjjMZW+crex7B270BVFqi0H+X09EcomvKoOrJUNdDcsxV5QsR86jRv3xRJFtpYFjr49/1atgdfYkcU7l08zOPvXGJtXc+VKSVTeyPccCXOjoKZ/UoPK3PL5LfIqNgdJFdvZSj+52jdTVbbxul4w4F72EJppMXcYJm0y86Ars6oTOKbK29jYraMvdrAe4sW3UonxeISZfMM0k4VO+wSAZkFkX08H3Ij1bRotQ1O7o/zcEhkPCvQkG2ldjlERyvDSK+Ks8+7iduWSFtibLpPE2rvQJOu0h5JsNO6lVlvnlQqgzwuY59ymQvaMFc6c9x8qIfA8VkuSBl+mhW526sm32FC2eZjQKPjzT0dhBpFiorz2JduQD7cRtImcuxvZ9B+TiLsKnMmWmQs+tfsmlmmrZRi+v59GMW/Yc1R5pLYwfnhOXqHvATyagLnhpgdbWdJZUch6vnAwE+4WO9lVeGl6r5Iw2vA1rTQadRxKj6Mv8eIW2wROLbG3usnWSxuYyJ3APfBk2g9V0gmkpxJiSSu38WwIcmAmOQr5ruJ/JucVklg2V3jYPtJBvJ+DrAb765DxHLrzDSq/LBDw5f0eca730LuCvIGf4Cy9kV85Qg7m26eXX2IUfcJOjuWmFRfx5XlTjr0Eo/s0LC5VOLy1HGC6yZ0pa+gCP8M3FYEwyDZg2/iPFnHviASfqabjVaOjh0ptj48RyF3iNirataySRqeCwyGGugkI5GqjaXwfnTdUwjKGKef9LHZC6GVApl8gj+918RaLEW20CLsMBLhUTzacXr0N7HFXsUvLjCZyPIfl+Ced7gIbPjRr3iYeSjM4GIZhSRiEwu8sPsEkdXd6HLbaQ5EKA+oaPZmoTOGXGZgyN9gz1iRDf1WHB3nWK5CdqEfU+BmysZLVJtB7n/u7Yg9FzE4kriXChyVPUAktEEju8RW5TNcCfsxaW1sGRzha80mzoABEzaykzlyC8vIdCaKQ07Msl5SKZHKXIVMeJP1ZoTkVhn5m+V8urzJpZCZYLFBb2Ad1dwKuoaIoeggmkiyTBvJapZ98Z/wZtROX1POTknBY1d2cXngReSCRNvsDdQeqWGtS7gzRrREUWVlFMo2ZvU7SU03UXqamNpk7Fq3IS63kXW5WPWa+f0dg7ww/RJvLh3n/Te1Y13dYLlWJuJWkHbfQmmhCeflLM/lKd+eRa9QIT9rItAdoy5TsdhScamjRf/aAG0BB9t2BlHL9JwVtnOlpOSVx+NYyjXILzKZevqqnPorJfCCqENV2mRbJoh14TKqEw8RtGnIj6ZYKn6NydY9ZNSDODpuo3PzLYqV09B+gvN738MN2jB2eZLJ1hdR/cU7KaZkhNULRPeLfH1Yh9k4iCN/E6qGllu6NzkQcCO+/Hdoyv+AkQTqlJ37mgnOjy/wD1s2UBfez3rrJOVmiqBpjI/9eosz82EurcXIS39JZakIS0qspx9hObmK7/qX2T4QxHrsbzlgr1LOb/JHEyGG5UMk+hep7CrycOIw0jscpDUdnBUPcmnkL+hb1NK+1oXXU6Nq+FPOy87ySuKnvOdvLbwV3Eu10M7HFbv5XDLC8lSc208lsNzyMzq2XsaqkFM88vt80fYiO60W3r55CNeJfUS8T1EQg4gXb8eoeDv+epOuYoliRuCrQoIRqcGfrJXY95yRRcnL036Jd3w3ys5XzeSu6Ik4t+D5m21oHRUcnjr5b9yG/j0TaKUM6mAXP/vtEbouH+TQyzWM27/Ko9aPUitW+YPJ75Gc6iEqekgZjDQOXMdNM0uk2mZ4/s4Uk29ssLkZoiFr8e4d7+PMxivILucInKkxeFOQRdUVXlXMsel+g7x3N4llP7Jvu3n7Lf8IzhGqrQ42zvXSb30Gmd5ArTTI4607WCh0EE9WaNVP8rEXGuh2eYjdrWWq65+JyOx0Lrbhsu5iIuzA5oeb+3M8uu1G1oM51GEnNxXvRPODCex9nWi9Ot7/zDfwVCpkevp5Rf5B1obeibyyjKo+h7k0iC2SYW+lTpuijnlnjYVihmRrmesGnyHj+XVqaw3ePHOG3aUlRO0w651j0PFD7osZkS55OPqylZ/xPqq+80gjGYiL5NzvoyIuEjz1Q+55TI9d91Mcnq+zbVeBp2tjvNrRztfkY3S9+WWU5TsYtmzlpvRRmu1bWZtsI3K2A9kfJMkmgmhLG/zmoUFecgyTUluQyY3/V3t3HiTnXd95/P3r+77POXvuGWkkzaHTliXfF9gYJxw2WdgNJGFDUrAJybJhs8tmIZUQchCSkGw2EEgIBHxhC9tIsmRLlnVYGkmj0dxXz0z3dE/f993P/uGhQlFS9p9EQqhfVU89T/+6q57f86lvfavn10/3EAsepK37BUzyaV69sMETh0cQu6qIO19i5fc6OX/gHNGeKg9d2M9GeIITtDLv/SQfMsxR7niW4ESR7/32AZp/KcPoLgnf8Bxf/mo/33bNodiw0vonX2byD36A6y07svM7Ofquz/PkX/vRuxSsf7zOTH4fu+ND9CZa0DY/xeXjVlbYxqxPg4gfp2M5T1Uqcu5/+6jVjpLR27lg9tH+xTiKe7pQ72yi/1MWlKEOklKZRRHjyd/W4R7NUhjI8OLQMntnZtA3yVHanJjVn0JhaCVVSHMy9nV+rimK9k459WEF6b+roYlUcXgVeP7Ixn/5/hAX42mecQTYeOoN7P77YG47c2zB/hcvkLnvOUo/F+Do0ie5vOvbdFqivLfncXQXX2HSsMK4PsrLOQV3KR/GFbbxkefO8dWvdZMdHMJ1r5boRwpccDthsMwjj1aYmAkSntZzLNSK6ld30P9DF2sVO8+0uviCapp8aR3/WojMdB+GUYEul6V4KMeXpp3sbZazWxtjrPrPPKL+DLXBGjMPXIY/P3zzG3i5VObN0SSSykx1qo1HqnkcpgrvaqsyoTQT1J9CqbtMi76FN2bbUaKnxbXCnH2MxYkqy1ETYc0j+NonaBpYoMVcxPV2DqPyEurmInLLQ7x66gShdJKAqPGqbZruoS4USz4WzjgQLjMtITOe3CDHBzRsFRrqRify9h7iL2/QlfXgqhhZuPgt6pod1ISB8q5llkwFJvVlshU56lgI41oTyrIV4xYz68NGemxNeJQKEjID/uXt2LJy9NWrXBqtE4+34Cs00+k7xcSSk7qxzLv6tqKX9lBfSBJamOQbk9Ps0R1Ea9Ey8+4K+1KnacmAXGniaul1nlo3Iot0Mh7pR9ekZtoiqEoJdi69jt6Up2oNk7aG+M/UmfYpEcLBxPowdW2OqipPWp/k2fP9HFDaMOdLrD2TY8cWH6UmFRVHjZPqIOqiHy0lTA/0smfWQC5pJKGy8+L8k7iGUrhI45v3EUyv4XauEOlQcnzHDoqXfGjyJbqMkxjv6sYwnyQTDDNrypOeVWMzWBi4D+ZzAqdeg1ftRZHcRkguw1LLcl/LWWbzBfaNpWkKBljIrdISz5BxWFlvMdF1Rk48M4NcZOn1rXH1TgU53KSmXIx0vw9DWotSVDj78BQjk6vIlFWyKhVr+Q3aShIal4Gpj5Y4UNNjW1lBcyTKycpjyLozSK0atME1oi0W+tc7cSx4efU+BwdfG8OyksGrbGdRGwNtMx12J5WlIXzyAIFCnqNZPalCE1azDpNbiW7mYdq6K9TsCYrLZ9hLBW82gSZc42Tk77nXnSLQYiU4cgfWS5ew5lXoambCUyN0WepgqhOIFGgaej/1E1pUoShhkSPUqsUYgLZ8nNNvRBnwbsNg3MpJg5wmq5W+qEAdSPMPrZfYK9dgqXailZdw14eorYVIX17kpe0OcgUVzvUE98+fZ2y/jh6vjC0aN86xAG8vqKhl3Dyx9w786gxrhkWChhVa3zeDb3wFU8pFa38f8Su9qFWTqLdc4D1HH0A9kKPUlkafj9JTnaA6UWL+Shqrcgh/0U3O4UPdpSdgruGxa1Ft6Jg4WsRpLOByO+lu38Pr3iw+cw1zXcXpSRNPaJ9hvVbheM5OpO0gxr7LSJ1hFNG7iY7YqVuT2MsFljRH0EY66VuRoblUYMG6k2VTGzpFK52DLiLpCv5qjCPP1HmwmMDtaOUhyy4m3hYUuydR9Y2xc2Yr00JgLAisMdBE0gT9cvxFE9tMzVQORBGuNqzmvez2n2TG/SrhYAstM32EhuapbLmE2TGDe5sH1bCRjM1OKmjlHtvrxPvN5FxGenIyfKMGVEod7bo6P0iMkTauk90mcHU/hb4jQ5QSC3EFkZlVTlvT2OxFRhf2sLxDQa5dToSm6/bUG3obYVWqsNEJy1v0HIvJiOollLo8zVKcomhFXttAVx7HWFrhyoqO1ZgDtcxBW2GD/KKMwIyDZLqPuGuJSkcAY1eF4bKSvYl1dkZW6YzU0ITnyAb9LAcjXJTGiTWZKXqbSJdK5HQCR8HCtjU3ykwRZ65Ec7lGk0JBYT6AM2VmUN6PMvgm5VSWnLxG2hej0pxkuVZlKijRtrGAfM6EOtJGq8NHUWPALWuntz5ArWpnPWgnGqxA8iqlDhcRt4WAUSKlvchydZq6Jsdoezv62jDGmASBJd6KTtO2WESfFSy4FLRIKbQ6JzK1B+fqEluDOvRLOubnJS62B1mkTiSjoFqPYzNuIPNGyHbEuEsVp1sXQ6vLsCDXUGoCmaOA0GQ5G/eS0jcjkzWjONdMp8OBy2NCuFTMDRYJFbKk0oKyo4O2pRqyeppVb5ap9A5ksggabZKiqR+vtoDPvUh7+zh1Y5qE2UJO2YQ624TCo0Zr0aPSaQk4gpQTFeQFJSqnllXZBiqZEp/koTfhpRypI8+kaatH8AchPatAuSyj3Z7EqPagklqh4MGUMdIc26Att4K7XUe03c+ydo7lWABzdSfOxA7MZQ+lXfMUjGVy8goZKU8pt0aHTIbPqSG0J0PVIBDZCDL/NKlaDzOmDpY1OgyRaYyJAvKYkmxcz5iIEY+kKK0nyGcDzKdXKdTArPBSzbpRBFdRRVeQyWQEJRmZUg7SCZRLNrRGE6qOOtUhP13bY3Rrs3QkEtjTJzHzNgZtCJXdS6HbQd1uolK3Mb+wC2/QQUdY4I5k6Grfh0duQpvMEg5WCNaKZPVlNM4qtfkk9byFqqKVtDCgkWdx1mo0F3QsaK+yVJYRLLaQN/Qi6R1IuCjnWpi3OChFnCj9ZlQZgaEIrboy/e4s+ZU8/kkTsfUmfB2dyBVesikL0ZgK+e4CA6tpBucjWGxLOCaUmKUS+s4E9qCFYn8ntV4fLTkT2nCeZDzGUjZNqr6VvKKdnMlMsq1MvlNDud9OtcNLbc2J1q9DHzagK9nRmoNYNEms1QLV5TSujUlMwSUqCyXqphxFXZCKPIC9qKWutSMVPKjnPBRSlzFnFvGm4ujW9Mzbu5jUeZjPK5F3W7E6tajqSi5PaAhG49QrBtyKEVqmfHhKcZp1frYbypgNckxlHbaQFktxFXXISHnKQvBIjJBNTa7NgaK5mU6VnrJvjVh7kLxGj65/Fq17FY0mgdWaRt8NSqeeetSLTxbGY1zEaFhBu2ak2pRH3xdlsGudaDXLij5KqCmGa8BJyS4naRDEdXL0ilXWRZQZWRWHaZBCn4q4VRAO/ZT8FkrNUuM+XRfmcAu/a/wbbJ2PsZZa4vCzb/Grg59i/pKedGGBjf15NpamSFu6UWkf4MmAnEvzu5k3WVh6cJpXvhNBN5PDq4APfGgYRzKCYsVA4iUNT77bwJK+wvhqjoGVV4luvZ+ipKJbdg695EHWGmdDn0N/5W5WUhdRShvo1k7iOWBE5m6nqunhQ1dr/KWhjzlZD55klDu7j2E4nSEwFuB9+ee5XLgPyWTigMhRO1Gl2hMk6syguzJC2n4MW+8ErsF13lf9PCnHYYrlYwR/mCVlTWNRdiLRj1EXZci+jGUgyOWnPPi/+V2CZ1RcfNVC668/zJsH9iFL6/n8Hx7mH8szJMRlqsYxAo/HuPv0dnzR3cgffZihzr9DZdhFQTFAaPUkY1MbrKoL+B44RHP/VmyzVYohmHyyDf+aC1lBh6cwincqTpXDRKWLyCf/ByIQw7ChpPfIVpaHv8aZA1OM9aW487Wf5+Rzs6RU7ZR2fJA/u/MNVL4QKds6nmeLTN+VJFbSsPLMHWhcLyO5fOhHhqkrXsGWVeBfUnPogob4r/894s0Hwe/jiva7ZCoJZLPdBN+4n5jJz/9tGaG9e5Dff+ggZ6utJCfUlE7D5Adh+/hxKsUqX93yCZ7+whdwtwYZe0DDK6Uw+wJGRioJ3n9wij+zvxtDZQvt2Va6CuPsaFMjlwkuLcW4/LcpsuYK3UNKHrt0ltevLlOKBunqWWbn9x/lzd4SX9keh4sBFjUPEhjRcfbgP1NrAu+CCuuSlblH1Rz60gItuRof26vk9cBV2o+t4jkaZbVTTYAWAv06Xn3Ixmjpv3J8/SVWrhzHKyvwp5ki0vwKmjdOcqj1IEPFMs7kBqf9XTwVLDGQ9WMzr3GwXeDXaVgp6Vj+czUGxxjZrbu5/PF93P/CJV6UL1NOV/mC38JfNU+x6OnD8eAwvzA7iT+/izFNL+M77qaQex75QAfF7Q8x/DtruCN3kVAp+eTWfXx5pULEtMgZzQJntP0MqtbRVKw8sy4R182y7wdy+q4O8el/0DMiNJSSEf7+7bfoz1Toch/EPHiQX3/413hg21a6lV7eP+/mxHMPULlHh+npKsPr4/R9d4EZe5iTPQGeKLSSbPVRcBn5WLORxSsTZGxJznS9RvfJF0hnO4ibHOwpbXD1/ChLoS2UAoPs8HyLeD5OwZTk/vLvko1bMQbvxB14AN1jJ5CGe5jvGeRLtXXke4to06cwL59Ge4cW0zMfQD07Sv6xNKHnTzEzmSLggK3RP6b/NTUmRQfZfARnjx51xY00W+KN3lf54LankQwSX3/9t+h59tPI9o0jur7Bkb9tovmhg/jsRsT+OX7B+zqhjW6Si/ejOXKeN21Fqo4ce1NRQmv34Jq6inc5yfcroyRMf8HW0SAf3mKmkPgob0WPMhs/hWftNzi28iF6azY+pilzUX+FE5cfZl6+k4t3KviAx8HMfJizr1z7R83gBt9G6PaYpd/8xNcQpp287pjH1tuJXl3BJCU5IJeTix0lmVpjNdOG4vyDdPTo6dtdZ/KUEk2bwOrM0W9c5PN/3YUjmmIbfsr9h+nbdT+S3cLbuWkWIzvIm41obGp+M1Ih1/kiS1KRI3P7kB06BAU1kt5G8VEXH9iyQHPJRn7sIVryefJdZpJNZUKTn2Uh8i7MtkF2j/bwnerr2I9VaHpbQzuDTP0nGcFsmPUXxmmPjyM16Sm2a1nZXWLYsJeiv4XFM270TwWwr19AW1gltctBXywAfguZyR7a1/ZQGH2FYusE5ayGwsIeYuoSMXeMjz30NKXDqwT8BQ75fGwdOo+qVEaKa7HaewnLXoXVCt3f+Cix3j/FaR3FbX+c2TvjNP/T98gXAhx/uoP5NWjTWWjXWjh+JUertIpVZ8Ds3Y8nsMz8goxlf40R7VuUZRJWuZxBlZZ1Xz/EnRRScl5uf5GeS0XkbXriH26j7exOfMurmLJp3tzzIKGlMk2pK+wvvsjXD3yWEdUp2moTnIy3kElcpU9Z4B6Tgk+v1djRJcNnUhEadxBLyegymbiryUXBbSJ8bDv5vArz7zyDKthKfUxQO5Gj13WFK9VhAs4eCu/vYOfn/wxlPUKmRc03PZ/gHs8MbbV5MpfHUcV+ieXBBDPDC5THurijPYpMWeFI0MivjAyj0k9RKlzE8ZcGJsMjpJ0u1I8o6PY4UNhK5JUhnj/+RQ4YuzDZZERbwkTP/jJt8QD2zDrHoqNE10yIlkUsj71ANFbGvijHGrMgOh6nKxNHVJNERZyc7xfpqjyDLXWeucn7SRhOYG+F3m0tjGnkGM+HMc2WMaYHMG7rYjJp5vi8kt/60FGunlUQnHKiT96LfzckXDHyljD3ybtZtZxAKa3xxLKbb62WkbqzeHdm2BUuseb0sWDp4ph1P62f+wqaZiWGPR4eNMZ5SdvNYrUF2UYnxrGXKEYN1Cpe7jogY3fwGeqxECcjLVyoPsVdWzbY1THLPx2doRZ14fTJGXk6y4WECl9+D02xLiZe/kccv3qA5l4L/aoCly6YuHzJQGg+y77M51nZ10va5KRUtnCXdJzLuRaSyhY+eZ+XK98UXM2qGfNq+A+Pv81cxko0IKP/+NvIHnVTixuonjExUNvJaZOWHIJfXEyT/q2/JEaV8Ewb5lc/zBu7y8RaSuwpFNDufB5JXaZasZBYewDVmTkU2SwMewgcOYTMsQWTbxfvuvoV4jt15NqaqVjvRqW2ojw9Qf7kNP9T/17ufaiTjpY0xtpR/F8eot0o4fYVebkvhfOiRM2mIvykkXtX9RTnjFT8MFo5zBc9W1nTFmgW49wz+yvIF2cRhSVcTzfz3a4w6kiSO88kOPPEALqjduRXNUzvSdGzxcO2api9K+f4w5fPUzO/B1nzEPX7Frg7vkpap2TcbeMPtjx9828j1EkCT2yNRM6FzjiAUSdw6CI01aexV+qU7UpKWh/hcicHTaDPh1mZjrO6sQejbxK5PIlm1cF+j4EkacLZHIOtOZIeAxGthfl4CW3QgDavxSBqqJtTrKwZiMiVMFBEdmgUlayEXFOnqjYQsXajKyqxqYIoixkqlSDZYpWCspekxY7cJgNTnNbpFAadHEOnnlrAjS7sB3mOUL+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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image, ImageDraw, ImageFont, ImageFilter\n", + "\n", + "import random\n", + "\n", + "# 随机字母:\n", + "def rndChar():\n", + " return chr(random.randint(65, 90))\n", + "\n", + "# 随机颜色1:\n", + "def rndColor():\n", + " return (random.randint(64, 255), random.randint(64, 255), random.randint(64, 255))\n", + "\n", + "# 随机颜色2:\n", + "def rndColor2():\n", + " return (random.randint(32, 127), random.randint(32, 127), random.randint(32, 127))\n", + "\n", + "# 240 x 60:\n", + "width = 60 * 4\n", + "height = 60\n", + "image = Image.new('RGB', (width, height), (255, 255, 255))\n", + "# 创建Font对象:\n", + "font = ImageFont.truetype('img/NotoSans-BlackItalic.ttf', 36)\n", + "# 创建Draw对象:\n", + "draw = ImageDraw.Draw(image)\n", + "# 填充每个像素:\n", + "for x in range(width):\n", + " for y in range(height):\n", + " draw.point((x, y), fill=rndColor())\n", + "# 输出文字:\n", + "for t in range(4):\n", + " draw.text((60 * t + 10, 10), rndChar(), font=font, fill=rndColor2())\n", + "# 模糊:\n", + "image = image.filter(ImageFilter.BLUR)\n", + "plot.imshow(np.asarray(image))" + ] + }, + { + "cell_type": "markdown", + "id": "24cb2e1d", + "metadata": {}, + "source": [ + "要详细了解PIL的强大功能，请请参考Pillow官方文档：\n", + "\n", + "[https://pillow.readthedocs.org/](https://pillow.readthedocs.org/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0af76721", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/13-image-processing/13.02-Opencv.ipynb b/13-image-processing/13.02-Opencv.ipynb new file mode 100644 index 00000000..197d2ddd --- /dev/null +++ b/13-image-processing/13.02-Opencv.ipynb @@ -0,0 +1,76 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4bf0ebeb", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plot\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "0df1f40e", + "metadata": {}, + "source": [ + "# Opencv 简介" + ] + }, + { + "cell_type": "markdown", + "id": "2ea18e70", + "metadata": {}, + "source": [ + "## OpenCV开发环境配置" + ] + }, + { + "cell_type": "markdown", + "id": "9077e333", + "metadata": {}, + "source": [ + "做科学计算需要安装下面的一些工具包\n", + "```shell\n", + "pip install jupyter numpy scipy matplotlib\n", + "```\n", + "安装 OpenCV \n", + "```shell\n", + "pip install opencv-python\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "278ed9f7", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/13-image-processing/img/BMPvsGIF.png b/13-image-processing/img/BMPvsGIF.png new file mode 100644 index 0000000000000000000000000000000000000000..d2196f8c21b3657bca7bf6a66608ec8f518fae74 GIT binary patch literal 20070 zcmaI;Wk8i%+cu2SN{P}b-Q5i$-6<_yQ@W8B1f{#XTck_6yO9QA(kb117Xs^ke(ZOz zABtQf&pgg!jIja~`M3f*Ppu8a%d8Du%A+hTKLZ zf&xT*Eu|60q+@<>|QJLp>(8cT}slK}l0Kp-O?Zf-ViRwhm+Zua*a z%*>)pT%uy{#hBT+*;zO^xrA6mp8JYe8#-DVTRA-UHTqv)=Kp8k2Sc#50cI94wgWjC z8;RLjTN3@-Fc0YexfTx5|L1s5eU1K~YvB<4fA(br7Q^_U*8ijC=U)M~^YG(;MhpDo zKeIQs0&Lw5FzO0ZI0*;{sd-5eAr+U|y_6R&%9Hn9im#1hV&%T2n{Zer?z0Wvq@67PP0R)*=7PGe zKM#%PSz7ILo&VanU9=z0*N1^{p<|a85`b6^n0SW?_$VAii#H?$lOBX#3j_o<69fbt z5`;G-^#AL_HADRhYF4YaJJ4nmtHt}v4pX$*5r;MBrb7<`Hj}p}BocP*tJC2)V}7_# zRtrL^!00E4dMz!EV2{HHu1v=}$~011V6H1zCcR}Ew!{Hb3ix)Va*6?_?b9BV=Mj7& zm+sN?oc=(ekqy?Hjg5s911r3R!e+98{cJ9XDq0DCANlnS{juOe0MeJol?cTcjzk*? z@P9!J#uAv9!A?x(wnIIFfkR5y1vc{`^y;W|j3dlm2%ZA+8=0i$`N)kU-V78p+z6vL zq;Da)bvtF-cU6&km6pt&smmlm;p(TArz0@u2ePK6v|V;5PG}|8E_W1{DTjPwXL<2F zv}kV?UgRr}JJRJAmo3spc1|l2`==c$d;s4sQMNHDFCkOt=}VMTWg543$wzQM?Is@; 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25/49] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E8=89=B2=E5=BD=A9?= =?UTF-8?q?=E8=BD=AC=E6=8D=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 13-image-processing/13.04-cvtcolor.ipynb | 132 +++++++++++++++++++++++ 1 file changed, 132 insertions(+) create mode 100644 13-image-processing/13.04-cvtcolor.ipynb diff --git a/13-image-processing/13.04-cvtcolor.ipynb b/13-image-processing/13.04-cvtcolor.ipynb new file mode 100644 index 00000000..90e5c901 --- /dev/null +++ b/13-image-processing/13.04-cvtcolor.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "baf4536b", + "metadata": {}, + "source": [ + "# 简单图像处理" + ] + }, + { + "cell_type": "markdown", + "id": "89d889a4", + "metadata": {}, + "source": [ + "## 色彩空间 todo http://www.1zlab.com/wiki/python-opencv-tutorial/opencv-intro-color-space/" + ] + }, + { + "cell_type": "markdown", + "id": "03f948ae", + "metadata": {}, + "source": [ + "颜色空间 ColorSpace跟向量空间其实是一个东西. 举例RGB色彩空间, 我们用Red红色通道, Green绿色通道, Blue蓝色通道三个值来表示一个特定的色彩. 如果我们把颜色当作向量, 那所有向量的集合就是 色彩空间, 那RGB的色彩空间长成什么样呢?" + ] + }, + { + "cell_type": "markdown", + "id": "1321c0c4", + "metadata": {}, + "source": [ + "就是这样的:\n", + "!()[./img/]" + ] + }, + { + "cell_type": "markdown", + "id": "46b649c4", + "metadata": {}, + "source": [ + "## 通道交换" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "71c6da83", + "metadata": {}, + "outputs": [], + "source": [ + "## todo 演示 pillow rgb to bgr ,然后cv2.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04f1cc66", + "metadata": {}, + "source": [ + "## HSV变换" + ] + }, + { + "cell_type": "markdown", + "id": "45c66d76", + "metadata": {}, + "source": [ + "## 灰度化（Grayscale）" + ] + }, + { + "cell_type": "markdown", + "id": "b1a884b8", + "metadata": {}, + "source": [ + "## 二值化（Thresholding）" + ] + }, + { + "cell_type": "markdown", + "id": "9308f7c1", + "metadata": {}, + "source": [ + "## 大津二值化算法（Otsu's Method）" + ] + }, + { + "cell_type": "markdown", + "id": "1ac12448", + "metadata": {}, + "source": [ + "## 减色处理" + ] + }, + { + "cell_type": "markdown", + "id": "a60539d1", + "metadata": {}, + "source": [ + "## 平均池化（Average Pooling）" + ] + }, + { + "cell_type": "markdown", + "id": "13442625", + "metadata": {}, + "source": [ + "## 最大池化（Max Pooling）" + ] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 559c1f27f6c54ce816276a8401d84cf5e7d06b1c Mon Sep 17 00:00:00 2001 From: Jack Date: Mon, 26 Apr 2021 15:44:08 +0800 Subject: [PATCH 26/49] add pooling --- 13-image-processing/13.05-pooling.ipynb | 36 +++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 13-image-processing/13.05-pooling.ipynb diff --git a/13-image-processing/13.05-pooling.ipynb b/13-image-processing/13.05-pooling.ipynb new file mode 100644 index 00000000..f5746948 --- /dev/null +++ b/13-image-processing/13.05-pooling.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From ec0a2135c5e6029d609348ae0e40563c6fcc2c4e Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 1 May 2021 11:57:03 +0800 Subject: [PATCH 27/49] =?UTF-8?q?update=20=E9=A2=9C=E8=89=B2=E5=A4=84?= =?UTF-8?q?=E7=90=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 13-image-processing/13.04-cvtcolor.ipynb | 723 +++++++++++++++++- ...1280px-RGB_Cube_Show_lowgamma_cutout_b.png | Bin 0 -> 322299 bytes 13-image-processing/img/750px-HSV_cone.png | Bin 0 -> 182892 bytes 13-image-processing/img/HSV-Color-Space.jpg | Bin 0 -> 54453 bytes 13-image-processing/img/HSV_cylinder.jpg | Bin 0 -> 45184 bytes 13-image-processing/img/Triangulo_HSV.png | Bin 0 -> 17307 bytes .../img/cat_threechannel_image.png | Bin 0 -> 215734 bytes 13-image-processing/img/imori.jpg | Bin 0 -> 12638 bytes 13-image-processing/img/imori_noise.jpg | Bin 0 -> 14696 bytes 13-image-processing/img/rgbyuv_tango.png | Bin 0 -> 161181 bytes 10 files changed, 714 insertions(+), 9 deletions(-) create mode 100644 13-image-processing/img/1280px-RGB_Cube_Show_lowgamma_cutout_b.png create mode 100644 13-image-processing/img/750px-HSV_cone.png create mode 100644 13-image-processing/img/HSV-Color-Space.jpg create mode 100644 13-image-processing/img/HSV_cylinder.jpg create mode 100644 13-image-processing/img/Triangulo_HSV.png create mode 100644 13-image-processing/img/cat_threechannel_image.png create mode 100755 13-image-processing/img/imori.jpg create mode 100644 13-image-processing/img/imori_noise.jpg create mode 100644 13-image-processing/img/rgbyuv_tango.png diff --git a/13-image-processing/13.04-cvtcolor.ipynb b/13-image-processing/13.04-cvtcolor.ipynb index 90e5c901..d122ed7b 100644 --- a/13-image-processing/13.04-cvtcolor.ipynb +++ b/13-image-processing/13.04-cvtcolor.ipynb @@ -1,5 +1,18 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "id": "66c96dd3", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plot\n", + "import numpy as np\n", + "import cv2\n", + "%matplotlib inline" + ] + }, { "cell_type": "markdown", "id": "baf4536b", @@ -30,7 +43,28 @@ "metadata": {}, "source": [ "就是这样的:\n", - "!()[./img/]" + "![1280px-RGB_Cube_Show_lowgamma_cutout_b](./img/1280px-RGB_Cube_Show_lowgamma_cutout_b.png)\n", + "每个颜色在这个空间都有一个自己的坐标.\n", + "\n", + "把一个彩图的R通道, G通道,跟B通道剥离开, 我们看看是啥样的:\n", + "![cat_threechannel_image.png](./img/cat_threechannel_image.png)" + ] + }, + { + "cell_type": "markdown", + "id": "78d7fe61", + "metadata": {}, + "source": [ + "### 色彩空间变换 cvtColor" + ] + }, + { + "cell_type": "markdown", + "id": "f6ea7951", + "metadata": {}, + "source": [ + "![rgbyuv_tango.png](./img/rgbyuv_tango.png)\n", + "我们在做图像处理的时候，或者是显示图像的时候，经常需要从一个颜色空间变换为另外一个颜色空间。" ] }, { @@ -43,12 +77,127 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "71c6da83", "metadata": {}, "outputs": [], "source": [ - "## todo 演示 pillow rgb to bgr ,然后cv2.show()" + "def BGR2RGB(img):\n", + " \"\"\"\n", + " 将 BGR 转为 RGB\n", + " \"\"\"\n", + " b = img[:, :, 0].copy()\n", + " g = img[:, :, 1].copy()\n", + " r = img[:, :, 2].copy()\n", + " out = img.copy()\n", + " # RGB > BGR\n", + " out[:, :, 0] = r\n", + " out[:, :, 1] = g\n", + " out[:, :, 2] = b\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "7bc3870a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Read image\n", + "img = cv2.imread(\"./img/imori.jpg\")\n", + "\n", + "# BGR -> RGB\n", + "img_rgb = BGR2RGB(img)\n", + "\n", + "# imshow 需要的是 RGB\n", + "plot.imshow(im_pillow)\n", + "\n", + "# cv2.imshow(\"BGR2RGB\", img_rgb)\n", + "# cv2.waitKey(0)\n", + "# cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "39071d47", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from PIL import Image\n", + "im_pillow = np.array(Image.open('./img/imori.jpg'))\n", + "plot.imshow(im_pillow)" + ] + }, + { + "cell_type": "markdown", + "id": "1d5104b8", + "metadata": {}, + "source": [ + "使用 opencv 转换" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ba50bd79", + "metadata": {}, + "outputs": [], + "source": [ + "im_rgb2 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + "cv2.imshow(\"result\", im_rgb2)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" ] }, { @@ -59,6 +208,251 @@ "## HSV变换" ] }, + { + "cell_type": "markdown", + "id": "04204452", + "metadata": {}, + "source": [ + "### HSV色彩空间介绍\n", + "![HSV-Color-Space.jpg](./img/HSV-Color-Space.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "309de12e", + "metadata": {}, + "source": [ + "\n", + " * H通道: Hue 色调/色彩, 用这一个通道代表颜色. 将颜色使用$0^{\\circ}$到$360^{\\circ}$表示，就是平常所说的颜色名称，如红色、蓝色。色相与数值按下表对应\n", + " \n", + "| 红 | 黄 | 绿 | 青色 | 蓝色 | 品红 | 红 |\n", + "| :----------- | :------------ | :------------- | :------------- | :------------- | :------------- | :------------- |\n", + "| $0^{\\circ}$ | $60^{\\circ}$ | $120^{\\circ}$ | $180^{\\circ}$ | $240^{\\circ}$ | $300^{\\circ}$ | $360^{\\circ}$ |\n", + "\n", + "\n", + " * S通道: Saturation 饱和度, 饱和度越高,这个色彩越纯.饱和度越低则颜色越黯淡（$0\\leq S < 1$）\n", + "\n", + " * V通道: Value 明暗, 数值越高, 代表越明亮, 越接近白色，数值越低越接近黑色（$0\\leq V < 1$）；" + ] + }, + { + "cell_type": "markdown", + "id": "419faba9", + "metadata": {}, + "source": [ + "### HSV色彩用途\n", + "\n", + "HSV模型通常用于计算机图形应用中。在用户必须选择一个颜色应用于特定图形元素各种应用环境中，经常使用HSV 色轮。在其中，色相表示为圆环；可以使用一个独立的三角形来表示饱和度和明度。典型的，这个三角形的垂直轴指示饱和度，而水平轴表示明度。在这种方式下，选择颜色可以首先在圆环中选择色相，在从三角形中选择想要的饱和度和明度。\n", + "![Triangulo_HSV.png](./img/Triangulo_HSV.png)\n", + "HSV模型的另一种可视方法是圆锥体。在这种表示中，色相被表示为绕圆锥中心轴的角度，饱和度被表示为从圆锥的横截面的圆心到这个点的距离，明度被表示为从圆锥的横截面的圆心到顶点的距离。某些表示使用了六棱锥体。这种方法更适合在一个单一物体中展示这个HSV色彩空间；但是由于它的三维本质，它不适合在二维计算机界面中选择颜色。\n", + "![750px-HSV_cone.png](./img/750px-HSV_cone.png)\n", + "HSV色彩空間还可以表示为类似于上述圆锥体的圆柱体，色相沿着圆柱体的外圆周变化，饱和度沿着从横截面的圆心的距离变化，明度沿着横截面到底面和顶面的距离而变化。这种表示可能被认为是HSV色彩空间的更精确的数学模型；但是在实际中可区分出的饱和度和色相的级别数目随着明度接近黑色而减少。此外计算机典型的用有限精度范围来存储RGB值；这约束了精度，再加上人类颜色感知的限制，使圆锥体表示在多数情况下更实用。\n", + "![HSV_cylinder.jpg](./img/HSV_cylinder.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "479eeff6", + "metadata": {}, + "source": [ + "### 转换公式\n", + "从$\\text{RGB}$色彩表示转换到$\\text{HSV}$色彩表示通过以下方式计算：\n", + "\n", + "$\\text{RGB}$的取值范围为$[0, 1]$，令：\n", + "$$\n", + "\\text{Max}=\\max(R,G,B)\\\\\n", + "\\text{Min}=\\min(R,G,B)\n", + "$$\n", + "色相：\n", + "$$\n", + "H=\\begin{cases}\n", + "0&(\\text{if}\\ \\text{Min}=\\text{Max})\\\\\n", + "60\\ \\frac{G-R}{\\text{Max}-\\text{Min}}+60&(\\text{if}\\ \\text{Min}=B)\\\\\n", + "60\\ \\frac{B-G}{\\text{Max}-\\text{Min}}+180&(\\text{if}\\ \\text{Min}=R)\\\\\n", + "60\\ \\frac{R-B}{\\text{Max}-\\text{Min}}+300&(\\text{if}\\ \\text{Min}=G)\n", + "\\end{cases}\n", + "$$\n", + "饱和度：\n", + "$$\n", + "S=\\text{Max}-\\text{Min}\n", + "$$\n", + "明度：\n", + "$$\n", + "V=\\text{Max}\n", + "$$\n", + "从$\\text{HSV}$色彩表示转换到$\\text{RGB}$色彩表示通过以下方式计算：\n", + "$$\n", + "C = S\\\\\n", + "H' = \\frac{H}{60}\\\\\n", + "X = C\\ (1 - |H' \\mod 2 - 1|)\\\\\n", + "(R,G,B)=(V-C)\\ (1,1,1)+\\begin{cases}\n", + "(0, 0, 0)& (\\text{if H is undefined})\\\\\n", + "(C, X, 0)& (\\text{if}\\quad 0 \\leq H' < 1)\\\\\n", + "(X, C, 0)& (\\text{if}\\quad 1 \\leq H' < 2)\\\\\n", + "(0, C, X)& (\\text{if}\\quad 2 \\leq H' < 3)\\\\\n", + "(0, X, C)& (\\text{if}\\quad 3 \\leq H' < 4)\\\\\n", + "(X, 0, C)& (\\text{if}\\quad 4 \\leq H' < 5)\\\\\n", + "(C, 0, X)& (\\text{if}\\quad 5 \\leq H' < 6)\n", + "\\end{cases}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "92f9bb00", + "metadata": {}, + "source": [ + "### 题目:请将色相反转（色相值加$180$），然后再用$\\text{RGB}$色彩空间表示图片。" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "33f511ed", + "metadata": {}, + "outputs": [], + "source": [ + "def BGR2HSV(_img):\n", + " \"\"\"\n", + " BGR -> HSV\n", + " \"\"\"\n", + " img = _img.copy() / 255.\n", + "\n", + " hsv = np.zeros_like(img, dtype=np.float32)\n", + "\n", + " # get max and min\n", + " max_v = np.max(img, axis=2).copy()\n", + " min_v = np.min(img, axis=2).copy()\n", + " min_arg = np.argmin(img, axis=2)\n", + "\n", + " # H\n", + " hsv[..., 0][np.where(max_v == min_v)] = 0\n", + " ## if min == B\n", + " ind = np.where(min_arg == 0)\n", + " hsv[..., 0][ind] = 60 * (img[..., 1][ind] - img[..., 2][ind]) / (max_v[ind] - min_v[ind]) + 60\n", + " ## if min == R\n", + " ind = np.where(min_arg == 2)\n", + " hsv[..., 0][ind] = 60 * (img[..., 0][ind] - img[..., 1][ind]) / (max_v[ind] - min_v[ind]) + 180\n", + " ## if min == G\n", + " ind = np.where(min_arg == 1)\n", + " hsv[..., 0][ind] = 60 * (img[..., 2][ind] - img[..., 0][ind]) / (max_v[ind] - min_v[ind]) + 300\n", + "\n", + " # S\n", + " hsv[..., 1] = max_v.copy() - min_v.copy()\n", + "\n", + " # V\n", + " hsv[..., 2] = max_v.copy()\n", + "\n", + " return hsv" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "8524f988", + "metadata": {}, + "outputs": [], + "source": [ + "def HSV2BGR(_img, hsv):\n", + " img = _img.copy() / 255.\n", + "\n", + " # get max and min\n", + " max_v = np.max(img, axis=2).copy()\n", + " min_v = np.min(img, axis=2).copy()\n", + "\n", + " out = np.zeros_like(img)\n", + "\n", + " H = hsv[..., 0]\n", + " S = hsv[..., 1]\n", + " V = hsv[..., 2]\n", + "\n", + " C = S\n", + " H_ = H / 60.\n", + " X = C * (1 - np.abs(H_ % 2 - 1))\n", + " Z = np.zeros_like(H)\n", + "\n", + " vals = [[Z, X, C], [Z, C, X], [X, C, Z], [C, X, Z], [C, Z, X], [X, Z, C]]\n", + "\n", + " for i in range(6):\n", + " ind = np.where((i <= H_) & (H_ < (i + 1)))\n", + " out[..., 0][ind] = (V - C)[ind] + vals[i][0][ind]\n", + " out[..., 1][ind] = (V - C)[ind] + vals[i][1][ind]\n", + " out[..., 2][ind] = (V - C)[ind] + vals[i][2][ind]\n", + "\n", + " out[np.where(max_v == min_v)] = 0\n", + " out = np.clip(out, 0, 1)\n", + " out = (out * 255).astype(np.uint8)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b3e31275", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[228. 225.23077 223.125 ... 240.90909 223.125 215.2381 ]\n", + " [225.21739 228. 232.88135 ... 234.2857 224.15094 221.31148]\n", + " [225.88235 229.84616 232.88135 ... 232.88135 223.56165 223.29114]\n", + " ...\n", + " [224.81012 226.0274 230.4762 ... 225.21739 219.04762 215.78947]\n", + " [223.29114 225.19481 228.75 ... 228.26086 222.85715 221.01266]\n", + " [222.85715 223.84615 228.75 ... 224.83516 219.75 215.84415]]\n" + ] + } + ], + "source": [ + "# Read image\n", + "img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# RGB > HSV\n", + "hsv = BGR2HSV(img)\n", + "print(hsv[..., 0] )\n", + "\n", + "# Transpose Hue\n", + "hsv[..., 0] = (hsv[..., 0] + 180) % 360\n", + "\n", + "# HSV > RGB\n", + "out = HSV2BGR(img, hsv)\n", + "\n", + "# Save result\n", + "# cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8127e127", + "metadata": {}, + "source": [ + "### 使用 opencv 解决" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "4a4a6400", + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\")\n", + "\n", + "hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n", + "# Transpose Hue\n", + "hsv_img[..., 0] = (hsv_img[..., 0] + 180) % 360\n", + "out_img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2BGR)\n", + "cv2.imshow(\"result\", out_img)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, { "cell_type": "markdown", "id": "45c66d76", @@ -67,6 +461,115 @@ "## 灰度化（Grayscale）" ] }, + { + "cell_type": "markdown", + "id": "f2d98a10", + "metadata": {}, + "source": [ + "灰度是一种图像亮度的表示方法，通过下式计算：\n", + "$$\n", + "Y = 0.2126\\ R + 0.7152\\ G + 0.0722\\ B\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "8c68fc3b", + "metadata": {}, + "outputs": [], + "source": [ + "def BGR2GRAY(img):\n", + " b = img[:, :, 0].copy()\n", + " g = img[:, :, 1].copy()\n", + " r = img[:, :, 2].copy()\n", + " print(b.shape)\n", + " # Gray scale\n", + " out = 0.2126 * r + 0.7152 * g + 0.0722 * b\n", + " out = out.astype(np.uint8)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1bd88407", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "uint8\n", + "(128, 128)\n" + ] + } + ], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\")\n", + "print(img.dtype)\n", + "# Grayscale\n", + "out = BGR2GRAY(img)\n", + "\n", + "# Save result\n", + "#cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "48ac398b", + "metadata": {}, + "source": [ + "### 使用 opencv 解决" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "a2580cb6", + "metadata": {}, + "outputs": [], + "source": [ + "# Grayscale\n", + "out = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", + "\n", + "# Save result\n", + "#cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "868d6df4", + "metadata": {}, + "source": [ + "或者" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "f366c7b2", + "metadata": {}, + "outputs": [], + "source": [ + "# Grayscale\n", + "out = cv2.imread(\"./img/imori.jpg\", cv2.IMREAD_GRAYSCALE)\n", + "\n", + "# Save result\n", + "#cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, { "cell_type": "markdown", "id": "b1a884b8", @@ -75,6 +578,79 @@ "## 二值化（Thresholding）" ] }, + { + "cell_type": "markdown", + "id": "d38cc564", + "metadata": {}, + "source": [ + "二值化是将图像使用黑和白两种颜色表示的方法。\n", + "\n", + "我们将灰度的阈值设置为$128$来进行二值化，即：\n", + "$$\n", + "y=\n", + "\\begin{cases}\n", + "0& (\\text{if}\\quad y < 128) \\\\\n", + "255& (\\text{else})\n", + "\\end{cases}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "edea7723", + "metadata": {}, + "outputs": [], + "source": [ + "def binarization(img, th=128):\n", + " \"\"\"\n", + " 二值化\n", + " \"\"\"\n", + " img[img < th] = 0\n", + " img[img >= th] = 255\n", + " return img" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "c46d3ff1", + "metadata": {}, + "outputs": [], + "source": [ + "# Binarization\n", + "img = cv2.imread(\"./img/imori.jpg\", cv2.IMREAD_GRAYSCALE)\n", + "\n", + "out = binarization(img)\n", + "\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "45648b11", + "metadata": {}, + "source": [ + "### 使用 opencv 解决" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0245e427", + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\", cv2.IMREAD_GRAYSCALE)\n", + "#二值化处理，低于阈值的像素点灰度值置为0；高于阈值的值置为参数3\n", + "ret,thresh1 = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)\n", + "cv2.imshow('BINARY',thresh1)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, { "cell_type": "markdown", "id": "9308f7c1", @@ -85,26 +661,155 @@ }, { "cell_type": "markdown", - "id": "1ac12448", + "id": "ae04628d", "metadata": {}, "source": [ - "## 减色处理" + "大津算法，也被称作最大类间方差法，是一种可以自动确定二值化中阈值的算法。\n", + "\n", + "从**类内方差**和**类间方差**的比值计算得来：\n", + "\n", + "\n", + "- 小于阈值$t$的类记作$0$，大于阈值$t$的类记作$1$；\n", + "- $w_0$和$w_1$是被阈值$t$分开的两个类中的像素数占总像素数的比率（满足$w_0+w_1=1$）；\n", + "- ${S_0}^2$， ${S_1}^2$是这两个类中像素值的方差；\n", + "- $M_0$，$M_1$是这两个类的像素值的平均值；\n", + "\n", + "即：\n", + "\n", + "* 类内方差：${S_w}^2=w_0\\ {S_0}^2+w_1\\ {S_1}^2$\n", + "* 类间方差：${S_b}^2 = w_0 \\ (M_0 - M_t)^2 + w_1\\ (M_1 - M_t)^2 = w_0\\ w_1\\ (M_0 - M_1) ^2$\n", + "* 图像所有像素的方差：${S_t}^2 = {S_w}^2 + {S_b}^2 = \\text{常数}$\n", + "\n", + "根据以上的式子，我们用以下的式子计算分离度$X$：[^1]\n", + "\n", + "[^1]: 这里原repo配图里的公式好像打错了。\n", + "\n", + "$$\n", + "X = \\frac{{S_b}^2}{{S_w}^2} = \\frac{{S_b}^2}{{S_t}^2 - {S_b}^2}\n", + "$$\n", + "\n", + "也就是说： \n", + "$$\n", + "\\arg\\max\\limits_{t}\\ X=\\arg\\max\\limits_{t}\\ {S_b}^2\n", + "$$\n", + "换言之，如果使${S_b}^2={w_0}\\ {w_1}\\ (M_0 - M_1)^2$最大，就可以得到最好的二值化阈值$t$。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "d7015a64", + "metadata": {}, + "outputs": [], + "source": [ + "# Otsu Binarization\n", + "def otsu_binarization(img):\n", + " max_sigma = 0\n", + " max_t = 0\n", + " H, W = img.shape\n", + " # determine threshold\n", + " for _t in range(1, 255):\n", + " v0 = img[np.where(img < _t)]\n", + " m0 = np.mean(v0) if len(v0) > 0 else 0.\n", + " w0 = len(v0) / (H * W)\n", + " v1 = img[np.where(img >= _t)]\n", + " m1 = np.mean(v1) if len(v1) > 0 else 0.\n", + " w1 = len(v1) / (H * W)\n", + " sigma = w0 * w1 * ((m0 - m1) ** 2)\n", + " if sigma > max_sigma:\n", + " max_sigma = sigma\n", + " max_t = _t\n", + "\n", + " # Binarization\n", + " print(\"threshold >>\", max_t)\n", + " img[img < max_t] = 0\n", + " img[img >= max_t] = 255\n", + "\n", + " return img" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "acf5b6a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "threshold >> 130\n" + ] + } + ], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\", cv2.IMREAD_GRAYSCALE)\n", + "# Otsu's binarization\n", + "out = otsu_binarization(img)\n", + "\n", + "# Save result\n", + "# cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" ] }, { "cell_type": "markdown", - "id": "a60539d1", + "id": "1ac12448", "metadata": {}, "source": [ - "## 平均池化（Average Pooling）" + "## 图像彩色量化（减色处理)" ] }, { "cell_type": "markdown", - "id": "13442625", + "id": "34edae78", + "metadata": {}, + "source": [ + "$\\text{RGB}$ 的像素值在 0~255之间，我们想要用更少的内存空间表征一张图像时怎么办呢？首先是减色处理，将图像用 32， 96， 160， 224 这 4 个像素值表示。我们将图像的值由$256^3$压缩至$4^3$，即将$\\text{RGB}$的值只取$\\{32, 96, 160, 224\\}$。这被称作色彩量化。色彩的值按照下面的方式定义：\n", + "$$\n", + "\\text{val}=\n", + "\\begin{cases}\n", + "32& (0 \\leq \\text{var} < 64)\\\\\n", + "96& (64\\leq \\text{var}<128)\\\\\n", + "160&(128\\leq \\text{var}<192)\\\\\n", + "224&(192\\leq \\text{var}<256)\n", + "\\end{cases}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "c38c53a1", "metadata": {}, + "outputs": [], + "source": [ + "# Dicrease color\n", + "def decrease_color(img):\n", + " out = img.copy()\n", + " out = out // 64 * 64 + 32\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "6941ce2d", + "metadata": {}, + "outputs": [], "source": [ - "## 最大池化（Max Pooling）" + "# Read image\n", + "img = cv2.imread(\"./img/imori.jpg\")\n", + "\n", + "# Dicrease color\n", + "out = decrease_color(img)\n", + "\n", + "#cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" ] } ], diff --git a/13-image-processing/img/1280px-RGB_Cube_Show_lowgamma_cutout_b.png b/13-image-processing/img/1280px-RGB_Cube_Show_lowgamma_cutout_b.png new file mode 100644 index 0000000000000000000000000000000000000000..e05c9718738b863b94bb6052ee83af878cd3f45a GIT binary patch literal 322299 zcmce;bySpH*f%**wql ze&74o`R^Roat({y(R=p2_jUd1B3wy9>M_Q13)JdD|C-AUcv-VT4Rb-v&o?xJ}4 z*xA-~vT%?uE3o8U8De0EAv+Nk%0Dj)JM3J@|9<=LPY|d6??o&WYQM+--(N(o{9iYO zB?)=)-@X0w^8d%hCnE2e`&aUrX4~|KA51qWWmC!Bnd&9t98e 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z4Kd$rwsrt)B6A_5L?%k#qbaem6sOhprU?KVX%>R8F%v&Q&mk67uIlra^bJt>d zI&)_B$y?bP_^ih)fO&Rkf-WuerZFFyDjV9?{z?HxO9jEm_upE?xSUjr zfn~G8dOr#?w91$k=^k+p1FpE8lXCHKOqV!pdrN|P#p7)gph&m3F0nt4L(8@`IM>Dw zT}79+QjH9&sI*KxR~^M|{w#vH_zF4$zIPep@K4a0JszUd^4zt*e+0m{8xXoAfQ6M# zQ~3@D2;LM>);dOpY}Gx4s-`|dclU_^0tE)g=+ZlP(798;Z0{Wlt9FFYfqf!OldhF& zwDf0n*q+LMr@|C@&5@orvUi)tPVLy-2!yEFp-!le`EK+~|D6g8#yjzKJ2!XjwU;qp zzOMi&1@KbV33?so9P_lIY|EX;7HJd8H5hv0faol*T_yf7c-=KHbafnq848boYbR!Y z-g#<%omC8Kyy@au=bqD?coW5GG0%|ceAHK|Y}3yL%D63S!?El*a_vbL>7B|pZx*=< zQ>d@!c^uo{2kqJ3uV6_5cV(VM)si)8-OmNSmV8^+uZI`r`EEW>vVyH!?~C zd+iMi^hXGlR3iaIV*_Q+7#P|Uz`zQ$19bYN%K{vg$;kQSXmwuW7$7}QT18emhK6ZG z0-+jRYU?I~uhIr$4VIp#T7V$elFSUXM_;t#+(6J?X==~62vOFF+8>IrmC33cV+O>H zV-*zYzJR!8%o+?m6+?y88blwaT4~WZa_KFCi@W_U(Cu0m^INQyET+5UGjs?CW#D;)OADy_sr(QvN#nlERrpDS3 zbZr9oHH5NsGD38UK5;;dsjL&ko<{^1_W*nbadT3F4NpZAz`Sm3&w&6h5B~P-=RE@j z8v;O{aEV?OP|tNe}uqA@zdL Date: Sat, 1 May 2021 12:36:45 +0800 Subject: [PATCH 28/49] update --- 13-image-processing/13.04-cvtcolor.ipynb | 72 +++++------ 13-image-processing/13.05-pooling.ipynb | 157 ++++++++++++++++++++++- 2 files changed, 186 insertions(+), 43 deletions(-) diff --git a/13-image-processing/13.04-cvtcolor.ipynb b/13-image-processing/13.04-cvtcolor.ipynb index d122ed7b..0d85a897 100644 --- a/13-image-processing/13.04-cvtcolor.ipynb +++ b/13-image-processing/13.04-cvtcolor.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "66c96dd3", + "id": "182bf9c5", "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ "id": "89d889a4", "metadata": {}, "source": [ - "## 色彩空间 todo http://www.1zlab.com/wiki/python-opencv-tutorial/opencv-intro-color-space/" + "## 色彩空间" ] }, { @@ -52,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "78d7fe61", + "id": "c512d20f", "metadata": {}, "source": [ "### 色彩空间变换 cvtColor" @@ -60,7 +60,7 @@ }, { "cell_type": "markdown", - "id": "f6ea7951", + "id": "1c8cbc31", "metadata": {}, "source": [ "![rgbyuv_tango.png](./img/rgbyuv_tango.png)\n", @@ -101,7 +101,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "7bc3870a", + "id": "b85e12f6", "metadata": { "scrolled": true }, @@ -147,7 +147,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "39071d47", + "id": "bd84f03f", "metadata": {}, "outputs": [ { @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "1d5104b8", + "id": "ae84ac0a", "metadata": {}, "source": [ "使用 opencv 转换" @@ -190,7 +190,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "ba50bd79", + "id": "b9c67772", "metadata": {}, "outputs": [], "source": [ @@ -210,7 +210,7 @@ }, { "cell_type": "markdown", - "id": "04204452", + "id": "76a33b93", "metadata": {}, "source": [ "### HSV色彩空间介绍\n", @@ -219,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "309de12e", + "id": "f08b651a", "metadata": {}, "source": [ "\n", @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "419faba9", + "id": "8cbd0135", "metadata": {}, "source": [ "### HSV色彩用途\n", @@ -252,7 +252,7 @@ }, { "cell_type": "markdown", - "id": "479eeff6", + "id": "120e942a", "metadata": {}, "source": [ "### 转换公式\n", @@ -299,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "92f9bb00", + "id": "724b6104", "metadata": {}, "source": [ "### 题目:请将色相反转（色相值加$180$），然后再用$\\text{RGB}$色彩空间表示图片。" @@ -308,7 +308,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "33f511ed", + "id": "a075c844", "metadata": {}, "outputs": [], "source": [ @@ -349,7 +349,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "8524f988", + "id": "436c8ac3", "metadata": {}, "outputs": [], "source": [ @@ -389,7 +389,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "b3e31275", + "id": "e944b92b", "metadata": {}, "outputs": [ { @@ -429,7 +429,7 @@ }, { "cell_type": "markdown", - "id": "8127e127", + "id": "b0274a81", "metadata": {}, "source": [ "### 使用 opencv 解决" @@ -438,7 +438,7 @@ { "cell_type": "code", "execution_count": 41, - "id": "4a4a6400", + "id": "24e01565", "metadata": {}, "outputs": [], "source": [ @@ -463,7 +463,7 @@ }, { "cell_type": "markdown", - "id": "f2d98a10", + "id": "0a3fa263", "metadata": {}, "source": [ "灰度是一种图像亮度的表示方法，通过下式计算：\n", @@ -475,7 +475,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "8c68fc3b", + "id": "04b9d915", "metadata": {}, "outputs": [], "source": [ @@ -493,7 +493,7 @@ { "cell_type": "code", "execution_count": 56, - "id": "1bd88407", + "id": "58f06e32", "metadata": { "scrolled": true }, @@ -522,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "48ac398b", + "id": "2883e065", "metadata": {}, "source": [ "### 使用 opencv 解决" @@ -531,7 +531,7 @@ { "cell_type": "code", "execution_count": 57, - "id": "a2580cb6", + "id": "bb7aa69d", "metadata": {}, "outputs": [], "source": [ @@ -547,7 +547,7 @@ }, { "cell_type": "markdown", - "id": "868d6df4", + "id": "a4af4bd5", "metadata": {}, "source": [ "或者" @@ -556,7 +556,7 @@ { "cell_type": "code", "execution_count": 62, - "id": "f366c7b2", + "id": "735f7c9e", "metadata": {}, "outputs": [], "source": [ @@ -580,7 +580,7 @@ }, { "cell_type": "markdown", - "id": "d38cc564", + "id": "dc0ce5f2", "metadata": {}, "source": [ "二值化是将图像使用黑和白两种颜色表示的方法。\n", @@ -598,7 +598,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "edea7723", + "id": "59a1dc9c", "metadata": {}, "outputs": [], "source": [ @@ -614,7 +614,7 @@ { "cell_type": "code", "execution_count": 65, - "id": "c46d3ff1", + "id": "a6961d36", "metadata": {}, "outputs": [], "source": [ @@ -630,7 +630,7 @@ }, { "cell_type": "markdown", - "id": "45648b11", + "id": "5d810f14", "metadata": {}, "source": [ "### 使用 opencv 解决" @@ -639,7 +639,7 @@ { "cell_type": "code", "execution_count": 74, - "id": "0245e427", + "id": "bcd0b949", "metadata": {}, "outputs": [], "source": [ @@ -661,7 +661,7 @@ }, { "cell_type": "markdown", - "id": "ae04628d", + "id": "a0a5bedc", "metadata": {}, "source": [ "大津算法，也被称作最大类间方差法，是一种可以自动确定二值化中阈值的算法。\n", @@ -698,7 +698,7 @@ { "cell_type": "code", "execution_count": 72, - "id": "d7015a64", + "id": "4a14240c", "metadata": {}, "outputs": [], "source": [ @@ -731,7 +731,7 @@ { "cell_type": "code", "execution_count": 76, - "id": "acf5b6a2", + "id": "8a553c58", "metadata": {}, "outputs": [ { @@ -764,7 +764,7 @@ }, { "cell_type": "markdown", - "id": "34edae78", + "id": "dbafec6c", "metadata": {}, "source": [ "$\\text{RGB}$ 的像素值在 0~255之间，我们想要用更少的内存空间表征一张图像时怎么办呢？首先是减色处理，将图像用 32， 96， 160， 224 这 4 个像素值表示。我们将图像的值由$256^3$压缩至$4^3$，即将$\\text{RGB}$的值只取$\\{32, 96, 160, 224\\}$。这被称作色彩量化。色彩的值按照下面的方式定义：\n", @@ -782,7 +782,7 @@ { "cell_type": "code", "execution_count": 81, - "id": "c38c53a1", + "id": "36445238", "metadata": {}, "outputs": [], "source": [ @@ -796,7 +796,7 @@ { "cell_type": "code", "execution_count": 83, - "id": "6941ce2d", + "id": "ecf3d86e", "metadata": {}, "outputs": [], "source": [ diff --git a/13-image-processing/13.05-pooling.ipynb b/13-image-processing/13.05-pooling.ipynb index f5746948..cd217336 100644 --- a/13-image-processing/13.05-pooling.ipynb +++ b/13-image-processing/13.05-pooling.ipynb @@ -2,13 +2,156 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plot\n", + "import numpy as np\n", + "import cv2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 池化操作" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "将图片按照固定大小网格分割，网格内的像素值取网格内所有像素的平均值。\n", + "\n", + "我们将这种把图片使用均等大小网格分割，并求网格内代表值的操作称为**池化（Pooling）**。\n", + "\n", + "池化操作是**卷积神经网络（Convolutional Neural Network）**中重要的图像处理方式。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 请把大小为$128\\times128$的 `imori.jpg` 使用$8\\times8$的网格做池化\n", + "![imori](./img/imori.jpg)\n" + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": true }, + "source": [ + "## 平均池化（Average Pooling）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "平均池化按照下式定义：\n", + "$$\n", + "v=\\frac{1}{|R|}\\ \\sum\\limits_{i=1}^R\\ v_i\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# average pooling\n", + "def average_pooling(img, G=8):\n", + " out = img.copy()\n", + "\n", + " H, W, C = img.shape\n", + " Nh = int(H / G)\n", + " Nw = int(W / G)\n", + "\n", + " for y in range(Nh):\n", + " for x in range(Nw):\n", + " for c in range(C):\n", + " out[G * y:G * (y + 1), G * x:G * (x + 1), c] = np.mean(\n", + " out[G * y:G * (y + 1), G * x:G * (x + 1), c]).astype(np.int)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\")\n", + "\n", + "# Average Pooling\n", + "out = average_pooling(img)\n", + "\n", + "# Save result\n", + "# cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 最大池化（Max Pooling）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "网格内的值不取平均值，而是取网格内的最大值进行池化操作。" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# max pooling\n", + "def max_pooling(img, G=8):\n", + " # Max Pooling\n", + " out = img.copy()\n", + "\n", + " H, W, C = img.shape\n", + " Nh = int(H / G)\n", + " Nw = int(W / G)\n", + "\n", + " for y in range(Nh):\n", + " for x in range(Nw):\n", + " for c in range(C):\n", + " out[G * y:G * (y + 1), G * x:G * (x + 1), c] = np.max(out[G * y:G * (y + 1), G * x:G * (x + 1), c])\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ - "" + "# Read image\n", + "img = cv2.imread(\"./img/imori.jpg\")\n", + "\n", + "# Max pooling\n", + "out = max_pooling(img)\n", + "\n", + "# Save result\n", + "# cv2.imwrite(\"out.jpg\", out)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" ] } ], @@ -21,16 +164,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "nbformat_minor": 1 +} From 7565f25116f089ac91efc99d5590855b9c905532 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 15 May 2021 11:18:21 +0800 Subject: [PATCH 29/49] affine --- .../13.06-Afine-Transformations.ipynb | 1390 +++++++++++++++++ 13-image-processing/img/20170323174605746.png | Bin 0 -> 8738 bytes 13-image-processing/img/answer_31_1.jpg | Bin 0 -> 12287 bytes 13-image-processing/img/answer_31_2.jpg | Bin 0 -> 12514 bytes 13-image-processing/img/answer_31_3.jpg | Bin 0 -> 13438 bytes 5 files changed, 1390 insertions(+) create mode 100644 13-image-processing/13.06-Afine-Transformations.ipynb create mode 100644 13-image-processing/img/20170323174605746.png create mode 100644 13-image-processing/img/answer_31_1.jpg create mode 100644 13-image-processing/img/answer_31_2.jpg create mode 100644 13-image-processing/img/answer_31_3.jpg diff --git a/13-image-processing/13.06-Afine-Transformations.ipynb b/13-image-processing/13.06-Afine-Transformations.ipynb new file mode 100644 index 00000000..827cdd6e --- /dev/null +++ b/13-image-processing/13.06-Afine-Transformations.ipynb @@ -0,0 +1,1390 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "db115b88", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plot\n", + "import numpy as np\n", + "import cv2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "c98780e6", + "metadata": {}, + "source": [ + "# 仿射变换（ Afine Transformations ）" + ] + }, + { + "cell_type": "markdown", + "id": "5469cd63", + "metadata": {}, + "source": [ + "图像上的仿射变换, 其实就是图片中的一个像素点，通过某种变换，移动到另外一个地方。\n", + "\n", + "从数学上来讲， 就是一个向量空间进行一次线形变换并加上平移向量， 从而变换到另外一个向量空间的过程。" + ] + }, + { + "cell_type": "markdown", + "id": "33781fe8", + "metadata": {}, + "source": [ + "## 平行移动" + ] + }, + { + "cell_type": "markdown", + "id": "f689a3d0", + "metadata": {}, + "source": [ + "原图像记为$(x,y)$，变换后的图像记为$(x',y')$。\n", + "\n", + "图像放大缩小矩阵为下式：\n", + "$$\n", + "\\left(\n", + "\\begin{matrix}\n", + "x'\\\\\n", + "y'\n", + "\\end{matrix}\n", + "\\right)=\n", + "\\left(\n", + "\\begin{matrix}\n", + "a&b\\\\\n", + "c&d\n", + "\\end{matrix}\n", + "\\right)\\ \n", + "\\left(\n", + "\\begin{matrix}\n", + "x\\\\\n", + "y\n", + "\\end{matrix}\n", + "\\right)\n", + "$$\n", + "另一方面，平行移动按照下面的式子计算：\n", + "$$\n", + "\\left(\n", + "\\begin{matrix}\n", + "x'\\\\\n", + "y'\n", + "\\end{matrix}\n", + "\\right)=\n", + "\\left(\n", + "\\begin{matrix}\n", + "x\\\\\n", + "y\n", + "\\end{matrix}\n", + "\\right)+\n", + "\\left(\n", + "\\begin{matrix}\n", + "t_x\\\\\n", + "t_y\n", + "\\end{matrix}\n", + "\\right)\n", + "$$\n", + "把上面两个式子盘成一个：\n", + "$$\n", + "\\left(\n", + "\\begin{matrix}\n", + "x'\\\\\n", + "y'\\\\\n", + "1\n", + "\\end{matrix}\n", + "\\right)=\n", + "\\left(\n", + "\\begin{matrix}\n", + "a&b&t_x\\\\\n", + "c&d&t_y\\\\\n", + "0&0&1\n", + "\\end{matrix}\n", + "\\right)\\ \n", + "\\left(\n", + "\\begin{matrix}\n", + "x\\\\\n", + "y\\\\\n", + "1\n", + "\\end{matrix}\n", + "\\right)\n", + "$$\n", + "但是在实际操作的过程中，如果一个一个地计算原图像的像素的话，处理后的像素可能没有在原图像中有对应的坐标。[^2]\n", + "\n", + "[^2]: 这句话原文是“処理後の画像で値が割り当てられない可能性がでてきてしまう。”直译大概是”处理后的图像可能没有被分配到值。“我也不知道该怎么翻译才好……你们看输出图像左下角黑色的那一块，就是这种没有被”分配“到的情况。\n", + "\n", + "因此，我们有必要对处理后的图像中各个像素进行仿射变换逆变换，取得变换后图像中的像素在原图像中的坐标。仿射变换的逆变换如下：\n", + "$$\n", + "\\left(\n", + "\\begin{matrix}\n", + "x\\\\\n", + "y\n", + "\\end{matrix}\n", + "\\right)=\n", + "\\frac{1}{a\\ d-b\\ c}\\ \n", + "\\left(\n", + "\\begin{matrix}\n", + "d&-b\\\\\n", + "-c&a\n", + "\\end{matrix}\n", + "\\right)\\ \n", + "\\left(\n", + "\\begin{matrix}\n", + "x'\\\\\n", + "y'\n", + "\\end{matrix}\n", + "\\right)-\n", + "\\left(\n", + "\\begin{matrix}\n", + "t_x\\\\\n", + "t_y\n", + "\\end{matrix}\n", + "\\right)\n", + "$$\n", + "这回的平行移动操作使用下面的式子计算。$t_x$和$t_y$是像素移动的距离。\n", + "$$\n", + "\\left(\n", + "\\begin{matrix}\n", + "x'\\\\\n", + "y'\\\\\n", + "1\n", + "\\end{matrix}\n", + "\\right)=\n", + "\\left(\n", + "\\begin{matrix}\n", + "1&0&t_x\\\\\n", + "0&1&t_y\\\\\n", + "0&0&1\n", + "\\end{matrix}\n", + "\\right)\\ \n", + "\\left(\n", + "\\begin{matrix}\n", + "x\\\\\n", + "y\\\\\n", + "1\n", + "\\end{matrix}\n", + "\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4e88647e", + "metadata": {}, + "source": [ + "### 练习: 利用仿射变换让图像在$x$方向上$+30$，在$y$方向上$-30$" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "33420d76", + "metadata": {}, + "outputs": [], + "source": [ + "# Affine\n", + "def affine(org_img, a, b, c, d, tx, ty):\n", + " H, W, C = org_img.shape\n", + "\n", + " # temporary image\n", + " img = np.zeros((H + 2, W + 2, C), dtype=np.float32)\n", + " img[1:H + 1, 1:W + 1] = org_img\n", + "\n", + " # get new image shape\n", + " H_new = np.round(H * d).astype(np.int)\n", + " W_new = np.round(W * a).astype(np.int)\n", + " out = np.zeros((H_new + 1, W_new + 1, C), dtype=np.float32)\n", + "\n", + " # get position of new image\n", + " x_new = np.tile(np.arange(W_new), (H_new, 1))\n", + " y_new = np.arange(H_new).repeat(W_new).reshape(H_new, -1)\n", + "\n", + " # get position of original image by affine\n", + " adbc = a * d - b * c\n", + " x = np.round((d * x_new - b * y_new) / adbc).astype(np.int) - tx + 1\n", + " y = np.round((-c * x_new + a * y_new) / adbc).astype(np.int) - ty + 1\n", + "\n", + " x = np.minimum(np.maximum(x, 0), W + 1).astype(np.int)\n", + " y = np.minimum(np.maximum(y, 0), H + 1).astype(np.int)\n", + "\n", + " # assgin pixcel to new image\n", + " out[y_new, x_new] = img[y, x]\n", + "\n", + " out = out[:H_new, :W_new]\n", + " out = out.astype(np.uint8)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c6efabb0", + "metadata": {}, + "outputs": [], + "source": [ + "_img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# Affine\n", + "out = affine(_img, a=1, b=0, c=0, d=1, tx=30, ty=-30)\n", + "\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "5a86cd86", + "metadata": {}, + "source": [ + "### 使用 opencv 解决" + ] + }, + { + "cell_type": "markdown", + "id": "e7eb7880", + "metadata": {}, + "source": [ + "平移空间变换表达式" + ] + }, + { + "cell_type": "markdown", + "id": "09c37203", + "metadata": {}, + "source": [ + "
[x′y′]=[1001]×[xy]+[b0b1]
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f49fcbf3", + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\")\n", + "height,width,channel = img.shape\n", + "\n", + "# 声明变换矩阵 向右平移30个像素， 向上平移30个像素\n", + "M = np.float32([[1, 0, 30], [0, 1, -30]])\n", + "# 进行2D 仿射变换\n", + "shifted = cv2.warpAffine(img, M, (width, height))\n", + "cv2.imshow(\"result\", shifted)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "6a6dce33", + "metadata": {}, + "source": [ + "## 放大缩小" + ] + }, + { + "cell_type": "markdown", + "id": "63411d35", + "metadata": {}, + "source": [ + "### 练习: 使用仿射变换，将图片在$x$方向上放大$1.3$倍，在$y$方向上缩小至原来的$\\frac{4}{5}$" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3f8674d3", + "metadata": {}, + "outputs": [], + "source": [ + "_img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# Affine\n", + "out = affine(_img, a=1.3, b=0, c=0, d=0.8, tx=0, ty=-0)\n", + "\n", + "# Save result\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "0bdab4b0", + "metadata": {}, + "source": [ + "### 使用 opencv 解决" + ] + }, + { + "cell_type": "markdown", + "id": "39479044", + "metadata": {}, + "source": [ + "opencv 其实有专门进行图像缩放的函数 resize。\n", + "```\n", + " resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) -> dst\n", + "```\n", + "参数解析\n", + "\n", + "* src 输入图片\n", + "* dsize 输出图片的尺寸\n", + "* dst 输出图片\n", + "* fx x轴的缩放因子\n", + "* fy y轴的缩放因子\n", + "* interpolation 插值方式\n", + "* INTER_NEAREST - 最近邻插值\n", + "* INTER_LINEAR - 线性插值（默认）\n", + "* INTER_AREA - 区域插值\n", + "* INTER_CUBIC - 三次样条插值\n", + "* INTER_LANCZOS4 - Lanczos插值" + ] + }, + { + "cell_type": "markdown", + "id": "95e0d919", + "metadata": {}, + "source": [ + "#### 传入指定的图片的尺寸dsize" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "811a1f17", + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread('./img/imori.jpg')\n", + "height,width,channel = img.shape\n", + "\n", + "# 声明新的维度\n", + "new_dimension = (400, 400)\n", + "resized = cv2.resize(img, new_dimension)\n", + "cv2.imshow(\"result\", resized)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "ea435dd6", + "metadata": {}, + "source": [ + "#### 指定缩放因子 fx, fy" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eeabd613", + "metadata": {}, + "outputs": [], + "source": [ + "resized = cv2.resize(img, None, fx=1.3, fy=0.8)\n", + "cv2.imshow(\"result\", resized)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "f10ea545", + "metadata": {}, + "source": [ + "#### 分辨率 从5*5 放大到1000*1000, 选择不同的插值算法，对应的演示效果。" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a4a67e84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\"\n", + "差值算法对比\n", + "\"\"\"\n", + "img = np.uint8(np.random.randint(0,255,size=(5,5)))\n", + "height,width= img.shape\n", + "\n", + "# 声明新的维度\n", + "new_dimension = (1000, 1000)\n", + "\n", + "plt.subplot(231)\n", + "plt.title(\"SRC Image\")\n", + "plt.imshow(img,cmap='seismic')\n", + "\n", + "plt.subplot(232)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_NEAREST)\n", + "plt.title(\"INTER_NEAREST\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "plt.subplot(233)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_LINEAR)\n", + "plt.title(\"INTER_LINEAR\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "plt.subplot(234)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_AREA)\n", + "plt.title(\"INTER_AREA\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "plt.subplot(235)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_CUBIC)\n", + "plt.title(\"INTER_CUBIC\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "plt.subplot(236)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_LANCZOS4)\n", + "plt.title(\"INTER_LANCZOS4\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f7ced68c", + "metadata": {}, + "source": [ + "## 旋转" + ] + }, + { + "cell_type": "markdown", + "id": "b94b823f", + "metadata": {}, + "source": [ + "使用下面的式子进行逆时针方向旋转$A$度的仿射变换：\n", + "$$\n", + "\\left(\n", + "\\begin{matrix}\n", + "x'\\\\\n", + "y'\\\\\n", + "1\n", + "\\end{matrix}\n", + "\\right)=\n", + "\\left(\n", + "\\begin{matrix}\n", + "\\cos(A)&-\\sin(A)&t_x\\\\\n", + "\\sin(A)&\\cos(A)&t_y\\\\\n", + "0&0&1\n", + "\\end{matrix}\n", + "\\right)\\ \n", + "\\left(\n", + "\\begin{matrix}\n", + "x\\\\\n", + "y\\\\\n", + "1\n", + "\\end{matrix}\n", + "\\right)\n", + "$$\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f5425a6", + "metadata": {}, + "source": [ + "### 练习: 使用仿射变换，逆时针旋转$30$度" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fd09fc39", + "metadata": {}, + "outputs": [], + "source": [ + "# affine\n", + "def affine(org_img, a, b, c, d, tx, ty):\n", + " H, W, C = org_img.shape\n", + "\n", + " # temporary image\n", + " img = np.zeros((H + 2, W + 2, C), dtype=np.float32)\n", + " img[1:H + 1, 1:W + 1] = org_img\n", + "\n", + " # get shape of new image\n", + " H_new = np.round(H).astype(np.int)\n", + " W_new = np.round(W).astype(np.int)\n", + " out = np.zeros((H_new, W_new, C), dtype=np.float32)\n", + "\n", + " # get position of new image\n", + " x_new = np.tile(np.arange(W_new), (H_new, 1))\n", + " y_new = np.arange(H_new).repeat(W_new).reshape(H_new, -1)\n", + "\n", + " # get position of original image by affine\n", + " adbc = a * d - b * c\n", + " x = np.round((d * x_new - b * y_new) / adbc).astype(np.int) - tx + 1\n", + " y = np.round((-c * x_new + a * y_new) / adbc).astype(np.int) - ty + 1\n", + "\n", + " # adjust center by affine\n", + " dcx = (x.max() + x.min()) // 2 - W // 2\n", + " dcy = (y.max() + y.min()) // 2 - H // 2\n", + "\n", + " x -= dcx\n", + " y -= dcy\n", + "\n", + " x = np.clip(x, 0, W + 1)\n", + " y = np.clip(y, 0, H + 1)\n", + "\n", + " # assign pixcel\n", + " out[y_new, x_new] = img[y, x]\n", + " out = out.astype(np.uint8)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "14ebc9e7", + "metadata": {}, + "outputs": [], + "source": [ + "_img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# Affine\n", + "A = 30.\n", + "theta = - np.pi * A / 180.\n", + "\n", + "out = affine(_img, a=np.cos(theta), b=-np.sin(theta), c=np.sin(theta), d=np.cos(theta),\n", + " tx=0, ty=0)\n", + "\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "b803ea77", + "metadata": {}, + "source": [ + "### 使用 opencv 解决" + ] + }, + { + "cell_type": "markdown", + "id": "3d5c7055", + "metadata": {}, + "source": [ + "使用getRotationMatrix2D (内置API)与wrapAffine (矩阵运算)两种方式完成图像的旋转." + ] + }, + { + "cell_type": "markdown", + "id": "a3d5bcf6", + "metadata": {}, + "source": [ + "#### 利用getRotationMatrix2D实现旋转" + ] + }, + { + "cell_type": "markdown", + "id": "fcbf79fb", + "metadata": {}, + "source": [ + "opencv 中 `getRotationMatrix2D` 函数可以直接帮我们生成 `M` 而不需要我们在程序里计算三角函数．\n", + "参数解析\n", + "\n", + "* center 旋转中心点 (cx, cy) 你可以随意指定\n", + "\n", + "* angle 旋转的角度 单位是角度 逆时针方向为正方向 ， 角度为正值代表逆时针。\n", + "\n", + "* scale 缩放倍数. 值等于1.0代表尺寸不变" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4c8585de", + "metadata": {}, + "outputs": [], + "source": [ + "def rotate(image, angle, center = None, scale = 1.0):\n", + "\n", + " (h, w) = image.shape[:2]\n", + "\n", + " if center is None:\n", + " center = (w / 2, h / 2)\n", + "\n", + " M = cv2.getRotationMatrix2D(center, angle, scale)\n", + " rotated = cv2.warpAffine(image, M, (w, h))\n", + "\n", + " return rotated" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c81ea37f", + "metadata": {}, + "outputs": [], + "source": [ + "_img = cv2.imread(\"./img/imori.jpg\")\n", + "\n", + "# Affine\n", + "rotated=rotate(_img, 30)\n", + "cv2.imshow(\"result\", rotated)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "436bc725", + "metadata": {}, + "source": [ + "#### 利用wrapAffine实现缩放" + ] + }, + { + "cell_type": "markdown", + "id": "a7e6c8a7", + "metadata": {}, + "source": [ + "围绕原点进行旋转\n", + "![20170323174605746.png](./img/20170323174605746.png)\n", + "\\begin{align*} x &= r * cos(\\phi)\\\\ \\\\ x' &= r * cos(\\phi + \\theta)\\\\ &= r*cos(\\phi)*cos(\\theta) - r*sin(\\phi)*sin(\\theta)\\\\ \\\\ y &= r * sin(\\phi)\\\\ \\\\ y' &= r * sin(\\phi + \\theta)\\\\ &= r*sin(\\phi)*cos(\\theta) + r*cos(\\phi)*sin(\\theta) \\end{align*}\n", + "由此我们得出\n", + "\n", + "所以对应的变换矩阵为\n", + "\n", + "\\begin{equation} { \\left[ \\begin{array}{c} x'\\\\ y'\\\\ \\end{array} \\right ]}= { \\left[ \\begin{array}{cc} cos(\\theta) & -sin(\\theta)\\\\ sin(\\theta) & cos(\\theta)\\\\ \\end{array} \\right ]}\\times { \\left[\\begin{array}{c} x\\\\ y\\\\ \\end{array} \\right] }+ { \\left[\\begin{array}{c} 0\\\\ 0\\\\ \\end{array} \\right] } \\end{equation}\n", + "\n", + "**注意，这里我们进行公式推导的时候，参照的原点是在左下角， 而在OpenCV中图像的原点在图像的左上角， 所以我们在代码里面对theta取反。**\n", + "\n", + "我们可以利用`math`包中的三角函数。但是有一点需要注意 ：**三角函数输入的角度是弧度制而不是角度制**。\n", + "\n", + "我们需要使用`radians(x)` 函数， 将角度转变为弧度。\n", + "\n", + "```\n", + "import math\n", + "math.radians(180)\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2cdaffc6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# -*- coding: utf-8 -*-\n", + "'''\n", + "围绕原点处旋转　(图片左上角)　正方向为逆时针\n", + "'''\n", + "import numpy as np\n", + "import cv2\n", + "import math\n", + "from matplotlib import pyplot as plt\n", + "\n", + "img = cv2.imread('./img/imori.jpg')\n", + "\n", + "height, width, channel = img.shape\n", + "\n", + "def getRotationMatrix2D(theta):\n", + " # 角度值转换为弧度值\n", + " # 因为图像的左上角是原点 需要×-1\n", + " theta = math.radians(-1*theta)\n", + "\n", + " M = np.float32([\n", + " [math.cos(theta), -math.sin(theta), 0],\n", + " [math.sin(theta), math.cos(theta), 0]])\n", + " return M\n", + "\n", + "# 进行2D 仿射变换\n", + "# 围绕原点 顺时针旋转30度\n", + "M = getRotationMatrix2D(30)\n", + "rotated_30 = cv2.warpAffine(img, M, (width, height))\n", + "\n", + "# 围绕原点 顺时针旋转45度\n", + "M = getRotationMatrix2D(45)\n", + "rotated_45 = cv2.warpAffine(img, M, (width, height))\n", + "\n", + "# 围绕原点 顺时针旋转60度\n", + "M = getRotationMatrix2D(60)\n", + "rotated_60 = cv2.warpAffine(img, M, (width, height))\n", + "\n", + "plt.subplot(221)\n", + "plt.title(\"Src Image\")\n", + "plt.imshow(img[:,:,::-1])\n", + "\n", + "plt.subplot(222)\n", + "plt.title(\"Rotated 30 Degree\")\n", + "plt.imshow(rotated_30[:,:,::-1])\n", + "\n", + "plt.subplot(223)\n", + "plt.title(\"Rotated 45 Degree\")\n", + "plt.imshow(rotated_45[:,:,::-1])\n", + "\n", + "plt.subplot(224)\n", + "plt.title(\"Rotated 60 Degree\")\n", + "plt.imshow(rotated_60[:,:,::-1])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "093159b8", + "metadata": {}, + "source": [ + "##### 围绕任意点进行旋转\n", + "\n", + "那么如何围绕任意点进行旋转呢？\n", + "\n", + "可以先把当前的旋转中心点平移到原点处， 在原点处旋转后再平移回去。\n", + "\n", + "假定旋转中心为 (c_x, c_y)\n", + "\n", + "M_{translation} 为平移矩阵 M_{translation}^{-1} 为平移矩阵的逆矩阵 M_{rotation} 为原点旋转矩阵\n", + "\n", + "其中\n", + "\n", + "所以\n", + "\n", + "\\begin{align*} M &= M_{translation}^{-1} \\times M_{rotation} \\times M_{translation}\\\\ &= { \\left[ \\begin{array}{c} 1 &0 & c_x\\\\ 0& 1& c_y\\\\ 0 & 0 & 1\\\\ \\end{array} \\right ] } \\times { \\left[ \\begin{array}{c} cos(\\theta) &-sin(\\theta) & 0\\\\ sin(\\theta) & cos(\\theta)& 0\\\\ 0 & 0 & 1\\\\ \\end{array} \\right ] } \\times { \\left[ \\begin{array}{c} 1 &0 &-c_x\\\\ 0& 1& -c_y\\\\ 0 & 0 & 1\\\\ \\end{array} \\right ] }\\\\ &= { \\left[ \\begin{array}{c} cos(\\theta) &-sin(\\theta) & (1-cos(\\theta))*c_{x} + sin(\\theta)*c_{y}\\\\ sin(\\theta) & cos(\\theta)& -sin(\\theta)*c_{x} + (1-cos(\\theta))*c_{y}\\\\ 0 & 0 & 1\\\\ \\end{array} \\right ] } \\end{align*}\n", + "\n", + "完美．\n", + "\n", + "**旋转效果**\n", + "\n", + "围绕图片中心点旋转30度至60度。" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "08b80002", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# -*- coding: utf-8 -*- \n", + "'''\n", + "围绕画面中的任意一点旋转\n", + "'''\n", + "import numpy as np\n", + "import cv2\n", + "from math import cos,sin,radians\n", + "from matplotlib import pyplot as plt\n", + "\n", + "img = cv2.imread('./img/imori.jpg')\n", + "\n", + "height, width, channel = img.shape\n", + "\n", + "theta = 45\n", + "\n", + "def getRotationMatrix2D(theta, cx=0, cy=0):\n", + " # 角度值转换为弧度值\n", + " # 因为图像的左上角是原点 需要×-1\n", + " theta = radians(-1 * theta)\n", + "\n", + " M = np.float32([\n", + " [cos(theta), -sin(theta), (1-cos(theta))*cx + sin(theta)*cy],\n", + " [sin(theta), cos(theta), -sin(theta)*cx + (1-cos(theta))*cy]])\n", + " return M\n", + "\n", + "# 求得图片中心点， 作为旋转的轴心\n", + "cx = int(width / 2)\n", + "cy = int(height / 2)\n", + "\n", + "# 进行2D 仿射变换\n", + "# 围绕原点 逆时针旋转30度\n", + "M = getRotationMatrix2D(30, cx=cx, cy=cy)\n", + "rotated_30 = cv2.warpAffine(img, M, (width, height))\n", + "\n", + "# 围绕原点 逆时针旋转45度\n", + "M = getRotationMatrix2D(45, cx=cx, cy=cy)\n", + "rotated_45 = cv2.warpAffine(img, M, (width, height))\n", + "\n", + "# 围绕原点 逆时针旋转60度\n", + "M = getRotationMatrix2D(60, cx=cx, cy=cy)\n", + "rotated_60 = cv2.warpAffine(img, M, (width, height))\n", + "\n", + "plt.subplot(221)\n", + "plt.title(\"Src Image\")\n", + "plt.imshow(img[:,:,::-1])\n", + "\n", + "plt.subplot(222)\n", + "plt.title(\"Rotated 30 Degree\")\n", + "plt.imshow(rotated_30[:,:,::-1])\n", + "\n", + "plt.subplot(223)\n", + "plt.title(\"Rotated 45 Degree\")\n", + "plt.imshow(rotated_45[:,:,::-1])\n", + "\n", + "plt.subplot(224)\n", + "plt.title(\"Rotated 60 Degree\")\n", + "plt.imshow(rotated_60[:,:,::-1])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6532f19e", + "metadata": {}, + "source": [ + "## 翻转" + ] + }, + { + "cell_type": "markdown", + "id": "0f621b9d", + "metadata": {}, + "source": [ + "翻转图像是通过在水平轴或者垂直轴上对图像进行镜像反转而生成的静态或动态图像" + ] + }, + { + "cell_type": "markdown", + "id": "34a105fa", + "metadata": {}, + "source": [ + "### 利用 numpy 的索引实现翻转" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c89f41da", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "'''\n", + "使用numpy的索引进行图像反转\n", + "'''\n", + "\n", + "img = cv2.imread('./img/imori.jpg')\n", + "height,width,channel = img.shape\n", + "\n", + "# 水平翻转\n", + "flip_h = img[:,::-1]\n", + "\n", + "# 垂直翻转\n", + "flip_v = img[::-1]\n", + "\n", + "# 水平垂直同时翻转\n", + "flip_hv = img[::-1, ::-1]\n", + "\n", + "def bgr2rbg(img):\n", + " '''\n", + " 将颜色空间从BGR转换为RBG\n", + " '''\n", + " return img[:,:,::-1]\n", + "\n", + "plt.subplot(2,2,1)\n", + "plt.title('SRC')\n", + "plt.imshow(bgr2rbg(img))\n", + "\n", + "plt.subplot(2,2,2)\n", + "plt.title('Horizontally')\n", + "plt.imshow(bgr2rbg(flip_h))\n", + "\n", + "plt.subplot(2,2,3)\n", + "plt.title('Vertically')\n", + "plt.imshow(bgr2rbg(flip_v))\n", + "\n", + "plt.subplot(2,2,4)\n", + "plt.title('Horizontally & Vertically')\n", + "plt.imshow(bgr2rbg(flip_hv))\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "67e6997f", + "metadata": {}, + "source": [ + "### 使用 opencv 的 flip 函数实现翻转" + ] + }, + { + "cell_type": "markdown", + "id": "d0fe4746", + "metadata": {}, + "source": [ + "**flip 函数原型**\n", + "\n", + "flip(src, flipCode[, dst]) -> dst\n", + "\n", + "**参数解析**\n", + "\n", + "- \n", + "\n", + " `src` 输入图片\n", + "\n", + "- \n", + "\n", + " `flipCode` 翻转代码\n", + "\n", + "- \n", + "\n", + " `1` 水平翻转 **Horizontally** （图片第二维度是column）\n", + "\n", + "- \n", + "\n", + " `0` 垂直翻转 **Vertically ** （图片第一维是row）\n", + "\n", + "- \n", + " `-1` 同时水平翻转与垂直反转 **Horizontally & Vertically**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8ab5b7be", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = cv2.imread('./img/imori.jpg')\n", + "\n", + "def bgr2rbg(img):\n", + " '''\n", + " 将颜色空间从BGR转换为RBG\n", + " '''\n", + " return img[:,:,::-1]\n", + "\n", + "# 水平翻转\n", + "flip_h = cv2.flip(img, 1)\n", + "# 垂直翻转\n", + "flip_v = cv2.flip(img, 0)\n", + "# 同时水平翻转与垂直翻转\n", + "flip_hv = cv2.flip(img, -1)\n", + "\n", + "plt.subplot(221)\n", + "plt.title('SRC')\n", + "plt.imshow(bgr2rbg(img))\n", + "\n", + "plt.subplot(222)\n", + "plt.title('Horizontally')\n", + "plt.imshow(bgr2rbg(flip_h))\n", + "\n", + "plt.subplot(223)\n", + "plt.title('Vertically')\n", + "plt.imshow(bgr2rbg(flip_v))\n", + "\n", + "plt.subplot(224)\n", + "plt.title('Horizontally & Vertically')\n", + "plt.imshow(bgr2rbg(flip_hv))\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aa6e49db", + "metadata": {}, + "source": [ + "### 利用 wrapAffine 实现翻转" + ] + }, + { + "cell_type": "markdown", + "id": "5ffd5e68", + "metadata": {}, + "source": [ + "**水平翻转的变换矩阵**\n", + "\\begin{equation}\n", + "{\n", + "\\left[ \\begin{array}{c}\n", + "x'\\\\\n", + "y'\\\\\n", + "\\end{array} \n", + "\\right ]}=\n", + "{\n", + "\\left[ \\begin{array}{cc}\n", + "-1 & 0\\\\\n", + "0 & 1\\\\\n", + "\\end{array}\n", + "\\right ]}\\times\n", + "{\n", + " \\left[\\begin{array}{c}\n", + " x\\\\\n", + " y\\\\\n", + " \\end{array}\n", + " \\right]\n", + "}+\n", + "{\n", + " \\left[\\begin{array}{c}\n", + " width\\\\\n", + " 0 \\\\\n", + " \\end{array}\n", + " \\right]\n", + "}\n", + "\\end{equation}\n", + "\n", + "**垂直翻转的变换矩阵**\n", + "\n", + "\\begin{equation}\n", + "{\n", + "\\left[ \\begin{array}{c}\n", + "x'\\\\\n", + "y'\\\\\n", + "\\end{array} \n", + "\\right ]}=\n", + "{\n", + "\\left[ \\begin{array}{cc}\n", + "1 & 0\\\\\n", + "0 & -1\\\\\n", + "\\end{array}\n", + "\\right ]}\\times\n", + "{\n", + " \\left[\\begin{array}{c}\n", + " x\\\\\n", + " y\\\\\n", + " \\end{array}\n", + " \\right]\n", + "}+\n", + "{\n", + " \\left[\\begin{array}{c}\n", + " 0\\\\\n", + " height \\\\\n", + " \\end{array}\n", + " \\right]\n", + "}\n", + "\\end{equation}\n", + "\n", + "\n", + "\n", + "**同时进行水平翻转与垂直翻转**\n", + "\n", + "\\begin{equation}\n", + "{\n", + "\\left[ \\begin{array}{c}\n", + "x'\\\\\n", + "y'\\\\\n", + "\\end{array} \n", + "\\right ]}=\n", + "{\n", + "\\left[ \\begin{array}{cc}\n", + "-1 & 0\\\\\n", + "0 & -1\\\\\n", + "\\end{array}\n", + "\\right ]}\\times\n", + "{\n", + " \\left[\\begin{array}{c}\n", + " x\\\\\n", + " y\\\\\n", + " \\end{array}\n", + " \\right]\n", + "}+\n", + "{\n", + " \\left[\\begin{array}{c}\n", + " width\\\\\n", + " height \\\\\n", + " \\end{array}\n", + " \\right]\n", + "}\n", + "\\end{equation}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "23c477b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "'''\n", + "使用仿射矩阵实现反转\n", + "'''\n", + "img = cv2.imread('./img/imori.jpg')\n", + "height,width,channel = img.shape\n", + "\n", + "# 水平翻转\n", + "M1 = np.float32([[-1, 0, width], [0, 1, 0]])\n", + "flip_h = cv2.warpAffine(img, M1, (width, height))\n", + "\n", + "# 垂直翻转\n", + "M2 = np.float32([[1, 0, 0], [0, -1, height]])\n", + "flip_v = cv2.warpAffine(img, M2, (width, height))\n", + "\n", + "# 水平垂直同时翻转\n", + "M3 = np.float32([[-1, 0, width], [0, -1, height]])\n", + "flip_hv = cv2.warpAffine(img, M3, (width, height))\n", + "\n", + "def bgr2rbg(img):\n", + " '''\n", + " 将颜色空间从BGR转换为RBG\n", + " '''\n", + " return img[:,:,::-1]\n", + "\n", + "plt.subplot(221)\n", + "plt.title('SRC')\n", + "plt.imshow(bgr2rbg(img))\n", + "\n", + "plt.subplot(222)\n", + "plt.title('Horizontally')\n", + "plt.imshow(bgr2rbg(flip_h))\n", + "\n", + "plt.subplot(223)\n", + "plt.title('Vertically')\n", + "plt.imshow(bgr2rbg(flip_v))\n", + "\n", + "plt.subplot(224)\n", + "plt.title('Horizontally & Vertically')\n", + "plt.imshow(bgr2rbg(flip_hv))\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b74a7bd2", + "metadata": {}, + "source": [ + "## 倾斜" + ] + }, + { + "cell_type": "markdown", + "id": "67494dd6", + "metadata": {}, + "source": [ + "1. 使用仿射变换，输出（1）那样的$x$轴倾斜$30$度的图像（$t_x=30$），这种变换被称为X-sharing。\n", + "2. 使用仿射变换，输出（2）那样的y轴倾斜$30$度的图像（$t_y=30$），这种变换被称为Y-sharing。\n", + "3. 使用仿射变换，输出（3）那样的$x$轴、$y$轴都倾斜$30$度的图像($t_x = 30$，$t_y = 30$)。\n", + "\n", + "原图像的大小为$h\\ w$，使用下面各式进行仿射变换：\n", + "\n", + "* X-sharing\n", + " $$\n", + " a=\\frac{t_x}{h}\\\\\n", + " \\left[\n", + " \\begin{matrix}\n", + " x'\\\\\n", + " y'\\\\\n", + " 1\n", + " \\end{matrix}\n", + " \\right]=\\left[\n", + " \\begin{matrix}\n", + " 1&a&t_x\\\\\n", + " 0&1&t_y\\\\\n", + " 0&0&1\n", + " \\end{matrix}\n", + " \\right]\\ \n", + " \\left[\n", + " \\begin{matrix}\n", + " x\\\\\n", + " y\\\\\n", + " 1\n", + " \\end{matrix}\n", + " \\right]\n", + " $$\n", + "\n", + "* Y-sharing\n", + " $$\n", + " a=\\frac{t_y}{w}\\\\\n", + " \\left[\n", + " \\begin{matrix}\n", + " x'\\\\\n", + " y'\\\\\n", + " 1\n", + " \\end{matrix}\n", + " \\right]=\\left[\n", + " \\begin{matrix}\n", + " 1&0&t_x\\\\\n", + " a&1&t_y\\\\\n", + " 0&0&1\n", + " \\end{matrix}\n", + " \\right]\\ \n", + " \\left[\n", + " \\begin{matrix}\n", + " x\\\\\n", + " y\\\\\n", + " 1\n", + " \\end{matrix}\n", + " \\right]\n", + " $$\n", + "\n", + "| 输入 (imori.jpg) | 输出 (1) (answers_image/answer_31_1.jpg) | 输出 (2) (answers_image/answer_31_2.jpg) | 输出 (3) (answers_image/answer_31_3.jpg) |\n", + "| :--------------: | :--------------------------------------: | :--------------------------------------: | :--------------------------------------: |\n", + "| ![](./img/imori.jpg) | ![](./img/answer_31_1.jpg) | ![](./img/answer_31_2.jpg) | ![](./img/answer_31_3.jpg) |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "335928a3", + "metadata": {}, + "outputs": [], + "source": [ + "def affine(org_img, dx=30, dy=30):\n", + " # get shape\n", + " H, W, C = org_img.shape\n", + "\n", + " # Affine hyper parameters\n", + " a = 1.\n", + " b = dx / H\n", + " c = dy / W\n", + " d = 1.\n", + " tx = 0.\n", + " ty = 0.\n", + "\n", + " # prepare temporary\n", + " _img = np.zeros((H + 2, W + 2, C), dtype=np.float32)\n", + "\n", + " # insert image to center of temporary\n", + " _img[1:H + 1, 1:W + 1] = org_img\n", + "\n", + " # prepare affine image temporary\n", + " H_new = np.ceil(dy + H).astype(np.int)\n", + " W_new = np.ceil(dx + W).astype(np.int)\n", + " out = np.zeros((H_new, W_new, C), dtype=np.float32)\n", + "\n", + " # preprare assigned index\n", + " x_new = np.tile(np.arange(W_new), (H_new, 1))\n", + " y_new = np.arange(H_new).repeat(W_new).reshape(H_new, -1)\n", + "\n", + " # prepare inverse matrix for affine\n", + " adbc = a * d - b * c\n", + " x = np.round((d * x_new - b * y_new) / adbc).astype(np.int) - tx + 1\n", + " y = np.round((-c * x_new + a * y_new) / adbc).astype(np.int) - ty + 1\n", + "\n", + " x = np.minimum(np.maximum(x, 0), W + 1).astype(np.int)\n", + " y = np.minimum(np.maximum(y, 0), H + 1).astype(np.int)\n", + "\n", + " # assign value from original to affine image\n", + " out[y_new, x_new] = _img[y, x]\n", + " out = out.astype(np.uint8)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a9c02ac1", + "metadata": {}, + "outputs": [], + "source": [ + "# Read image\n", + "img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# Affine\n", + "out = affine(img, dx=30, dy=30)\n", + "\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", 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yOl*S=3(GShfg7F3^aM)3Omv9sn$#F%`K~sJ~;s)=zB5@cAOw7H#-s9%rrl15&_5g5C za8X00Z0QOhA#f4odLB*0P(Uab$HIkSOS|Iw+aF;k+X|cx_A6F}qyb$g4*+U#k7Ecg zJWcDaun|Iurb2kmOA?JM?=B2;<$M3=^FH^WqRRa1PD=C9j_`ZNg!9IU+#~nN2$ho@ z*aE5$afP&k>qa$am>^Bg;OmDMnvc#~rXY9Jq0W?@oN6^g3?EsWonSSq64Xj%tL4OR zbNR8>B2t-a@!yiKkox>3JCx)p8;cf4okq%flvgfXc)2{{oKuZ%Y6G literal 0 HcmV?d00001 From 250ab9571d63424d0929f368ef25e98929c04d79 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 15 May 2021 12:07:16 +0800 Subject: [PATCH 30/49] affine --- 13-image-processing/13.06-Afine-Transformations.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/13-image-processing/13.06-Afine-Transformations.ipynb b/13-image-processing/13.06-Afine-Transformations.ipynb index 827cdd6e..b026d330 100644 --- a/13-image-processing/13.06-Afine-Transformations.ipynb +++ b/13-image-processing/13.06-Afine-Transformations.ipynb @@ -1063,7 +1063,7 @@ }, { "cell_type": "markdown", - "id": "5ffd5e68", + "id": "83d05936", "metadata": {}, "source": [ "**水平翻转的变换矩阵**\n", From a39e12c253dbb5d0d08f04bc4ac168abdc8415de Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 15 May 2021 12:08:07 +0800 Subject: [PATCH 31/49] interpolation --- 13-image-processing/13.07-Interpolation.ipynb | 425 ++++++++++++++++++ 13-image-processing/img/chazhi-01.png | Bin 0 -> 74700 bytes 13-image-processing/img/chazhi-02.png | Bin 0 -> 1386 bytes 13-image-processing/img/chazhi-05.png | Bin 0 -> 97991 bytes 13-image-processing/img/chazhi-06.png | Bin 0 -> 11666 bytes 13-image-processing/img/chazhi-08.png | Bin 0 -> 41954 bytes 13-image-processing/img/chazhi-09.png | Bin 0 -> 7243 bytes 13-image-processing/img/chazhi-10.png | Bin 0 -> 106287 bytes 13-image-processing/img/chazhi-11.png | Bin 0 -> 133339 bytes 13-image-processing/img/chazhi-12.png | Bin 0 -> 30992 bytes 13-image-processing/img/chazhi-13.png | Bin 0 -> 11449 bytes 13-image-processing/img/chazhi-14.png | Bin 0 -> 35650 bytes 13-image-processing/img/chazhi-15.png | Bin 0 -> 35808 bytes 13-image-processing/img/chazhi-all.png | Bin 0 -> 14385 bytes 13-image-processing/img/inner-nearst.gif | Bin 0 -> 51354 bytes 13-image-processing/img/speedtest.png | Bin 0 -> 24987 bytes 16 files changed, 425 insertions(+) create mode 100644 13-image-processing/13.07-Interpolation.ipynb create mode 100644 13-image-processing/img/chazhi-01.png create mode 100644 13-image-processing/img/chazhi-02.png create mode 100644 13-image-processing/img/chazhi-05.png create mode 100644 13-image-processing/img/chazhi-06.png create mode 100644 13-image-processing/img/chazhi-08.png create mode 100644 13-image-processing/img/chazhi-09.png create mode 100644 13-image-processing/img/chazhi-10.png create mode 100644 13-image-processing/img/chazhi-11.png create mode 100644 13-image-processing/img/chazhi-12.png create mode 100644 13-image-processing/img/chazhi-13.png create mode 100644 13-image-processing/img/chazhi-14.png create mode 100644 13-image-processing/img/chazhi-15.png create mode 100644 13-image-processing/img/chazhi-all.png create mode 100644 13-image-processing/img/inner-nearst.gif create mode 100644 13-image-processing/img/speedtest.png diff --git a/13-image-processing/13.07-Interpolation.ipynb b/13-image-processing/13.07-Interpolation.ipynb new file mode 100644 index 00000000..2d40306b --- /dev/null +++ b/13-image-processing/13.07-Interpolation.ipynb @@ -0,0 +1,425 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e8fe06b2", + "metadata": {}, + "source": [ + "# 图像插值算法(Interpolation)" + ] + }, + { + "cell_type": "markdown", + "id": "b624102f", + "metadata": {}, + "source": [ + "## 何为插值?\n", + "\n", + "一个图片从**4×4** 放大到**8*8**的时候， 就会产生一些**新的像素点**（ 如下图红点所示），\n", + "\n", + "如何给这些值赋值， 就是`interpolation` 插值所要解决的问题。\n", + "\n", + "![chazhi-01.png](./img/chazhi-01.png)\n", + "\n", + "## 插值方法一览\n", + "\n", + "全部的插值方式 请见文档 [InterpolationFlags](https://docs.opencv.org/master/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121)\n", + "\n", + "这里我们只讲解五个插值方式。 我们随机生成一个5×5的矩阵， 然后用下面五种方式进行插值， 效果如图所示：\n", + "\n", + "![chazhi-all.png](./img/chazhi-all.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7178453c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import cv2\n", + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "\n", + "\n", + "img = np.uint8(np.random.randint(0,255,size=(5,5)))\n", + "height,width= img.shape\n", + "\n", + "\n", + "# 声明新的维度\n", + "new_dimension = (25, 25)\n", + "\n", + "plt.subplot(231)\n", + "plt.title(\"SRC Image\")\n", + "plt.imshow(img,cmap='gray')\n", + "\n", + "plt.subplot(232)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_NEAREST)\n", + "plt.title(\"INTER_NEAREST\")\n", + "plt.imshow(resized,cmap='gray')\n", + "\n", + "\n", + "plt.subplot(233)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_LINEAR)\n", + "plt.title(\"INTER_LINEAR\")\n", + "plt.imshow(resized,cmap='gray')\n", + "\n", + "\n", + "plt.subplot(234)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_AREA)\n", + "plt.title(\"INTER_AREA\")\n", + "plt.imshow(resized,cmap='gray')\n", + "\n", + "\n", + "plt.subplot(235)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_CUBIC)\n", + "plt.title(\"INTER_CUBIC\")\n", + "plt.imshow(resized,cmap='gray')\n", + "\n", + "\n", + "plt.subplot(236)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_LANCZOS4)\n", + "plt.title(\"INTER_LANCZOS4\")\n", + "plt.imshow(resized,cmap='gray')\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ff578f40", + "metadata": {}, + "source": [ + "为了更加直观的观察，我们将 `cmap` 换为 `seismic` 分辨率 从 5*5 放大到 1000 * 1000\n", + "![chazhi-02.png](./img/chazhi-02.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7cbd79d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "img = np.uint8(np.random.randint(0,255,size=(5,5)))\n", + "height,width= img.shape\n", + "\n", + "\n", + "# 声明新的维度\n", + "new_dimension = (1000, 1000)\n", + "\n", + "plt.subplot(231)\n", + "plt.title(\"SRC Image\")\n", + "plt.imshow(img,cmap='seismic')\n", + "\n", + "plt.subplot(232)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_NEAREST)\n", + "plt.title(\"INTER_NEAREST\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "\n", + "plt.subplot(233)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_LINEAR)\n", + "plt.title(\"INTER_LINEAR\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "\n", + "plt.subplot(234)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_AREA)\n", + "plt.title(\"INTER_AREA\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "\n", + "plt.subplot(235)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_CUBIC)\n", + "plt.title(\"INTER_CUBIC\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "\n", + "\n", + "plt.subplot(236)\n", + "resized = cv2.resize(img, new_dimension, interpolation = cv2.INTER_LANCZOS4)\n", + "plt.title(\"INTER_LANCZOS4\")\n", + "plt.imshow(resized,cmap='seismic')\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6d0ab530", + "metadata": {}, + "source": [ + "关于插值方式的不同与缩放因子的不同对应的时间消耗， 可以查阅下表：\n", + "\n", + "![](./img/speedtest.png)" + ] + }, + { + "cell_type": "markdown", + "id": "d534f785", + "metadata": {}, + "source": [ + "## 最近邻插值 INTER_NEAREST\n", + "\n", + "最近邻插值法， 找到与之距离最相近的邻居（原来就存在的像素点， 黑点）， 赋值与其相同。\n", + "\n", + "![](./img/inner-nearst.gif)\n", + "\n", + "\n", + "![](./img/chazhi-05.png)\n", + "\n", + "**效果展示**\n", + "\n", + "![](./img/chazhi-06.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f66c03c9", + "metadata": {}, + "outputs": [], + "source": [ + "def nn_interpolate(img, ax=1, ay=1):\n", + " H, W, C = img.shape\n", + " aH = int(ay * H)\n", + " aW = int(ax * W)\n", + " y = np.arange(aH).repeat(aW).reshape(aW, -1)\n", + " x = np.tile(np.arange(aW), (aH, 1))\n", + " y = np.round(y / ay).astype(np.int)\n", + " x = np.round(x / ax).astype(np.int)\n", + "\n", + " out = img[y, x]\n", + "\n", + " out = out.astype(np.uint8)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f2eb704d", + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# Nearest Neighbor\n", + "out = nn_interpolate(img, ax=1.5, ay=1.5)\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "bba9d215", + "metadata": {}, + "source": [ + "## 线性插值（**默认**）INTER_LINEAR\n", + "\n", + "这里的线形插值`INTER_LINEAR` 其实是`Bi-Linear Interpolation`(双线性插值)\n", + "\n", + "双线性插值考察邻域的像素点，并根据距离设置权值。虽然计算量增大使得处理时间变长，但是可以有效抑制画质劣化\n", + "\n", + "我们先来看一个简单的**一维线形插值**的例子。\n", + "\n", + "已知两点（红色） ，在给出一个蓝点的x坐标， 求y。\n", + "\n", + "所以需要根据两个红点确定一条直线，求出直线的表达式， 然后再将x坐标带进去。\n", + "\n", + "![](./img/chazhi-08.png)\n", + "\n", + "接下来我们来看`Bi-Linear Interpolation` 的例子。\n", + "\n", + "![](./img/chazhi-09.png)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd449c70", + "metadata": {}, + "source": [ + "双线性插值考察$4$邻域的像素点，并根据距离设置权值。虽然计算量增大使得处理时间变长，但是可以有效抑制画质劣化。\n", + "\n", + "1. 放大后图像的座标$(x',y')$除以放大率$a$，可以得到对应原图像的座标$(\\lfloor \\frac{x'}{a}\\rfloor , \\lfloor \\frac{y'}{a}\\rfloor)$。\n", + "\n", + "2. 求原图像的座标$(\\lfloor \\frac{x'}{a}\\rfloor , \\lfloor \\frac{y'}{a}\\rfloor)$周围$4$邻域的座标$I(x,y)$，$I(x+1,y)$，$I(x,y+1)$，$I(x+1, y+1)$：\n", + " \n", + " \n", + " \n", + "3. 分别求这4个点与$(\\frac{x'}{a}, \\frac{y'}{a})$的距离，根据距离设置权重：$w = \\frac{d}{\\sum\\ d}$\n", + "\n", + "4. 根据下式求得放大后图像$(x',y')$处的像素值：\n", + "$$\n", + "d_x = \\frac{x'}{a} - x\\\\\n", + " d_y = \\frac{y'}{a} - y\\\\\n", + " I'(x',y') = (1-d_x)\\ (1-d_y)\\ I(x,y) + d_x\\ (1-d_y)\\ I(x+1,y) + (1-d_x)\\ d_y\\ I(x,y+1) + d_x\\ d_y\\ I(x+1,y+1)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fc8af741", + "metadata": {}, + "source": [ + "**效果展示**\n", + "\n", + "![chazhi-12.png](./img/chazhi-12.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "29ede889", + "metadata": {}, + "outputs": [], + "source": [ + "# Bi-Linear interpolation\n", + "def bl_interpolate(org_img, ax=1., ay=1.):\n", + " H, W, C = org_img.shape\n", + "\n", + " aH = int(ay * H)\n", + " aW = int(ax * W)\n", + "\n", + " # get position of resized image\n", + " y = np.arange(aH).repeat(aW).reshape(aW, -1)\n", + " x = np.tile(np.arange(aW), (aH, 1))\n", + "\n", + " # get position of original position\n", + " y = (y / ay)\n", + " x = (x / ax)\n", + "\n", + " ix = np.floor(x).astype(np.int)\n", + " iy = np.floor(y).astype(np.int)\n", + "\n", + " ix = np.minimum(ix, W - 2)\n", + " iy = np.minimum(iy, H - 2)\n", + "\n", + " # get distance \n", + " dx = x - ix\n", + " dy = y - iy\n", + "\n", + " dx = np.repeat(np.expand_dims(dx, axis=-1), 3, axis=-1)\n", + " dy = np.repeat(np.expand_dims(dy, axis=-1), 3, axis=-1)\n", + "\n", + " # interpolation\n", + " out = (1 - dx) * (1 - dy) * org_img[iy, ix] + dx * (1 - dy) * org_img[iy, ix + 1] + (1 - dx) * dy * org_img[\n", + " iy + 1, ix] + dx * dy * org_img[iy + 1, ix + 1]\n", + "\n", + " out = np.clip(out, 0, 255)\n", + " out = out.astype(np.uint8)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "162537f8", + "metadata": {}, + "outputs": [], + "source": [ + "img = cv2.imread(\"./img/imori.jpg\").astype(np.float32)\n", + "\n", + "# Bilinear interpolations\n", + "# out = bl_interpolate(img, ax=1.5, ay=1.5)\n", + "out = bl_interpolate(img, ax=9, ay=9)\n", + "\n", + "cv2.imshow(\"result\", out)\n", + "cv2.waitKey(0)\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "68d3b2d7", + "metadata": {}, + "source": [ + "## 区域插值 INTER_AREA\n", + "\n", + "![chazhi-13.png](./img/chazhi-13.png)" + ] + }, + { + "cell_type": "markdown", + "id": "2342adc9", + "metadata": {}, + "source": [ + "## 三次样条插值 INTER_CUBIC\n", + "\n", + "![chazhi-14.png](./img/chazhi-14.png)\n", + "\n", + "由相邻的4*4像素计算得出，公式类似于双线性." + ] + }, + { + "cell_type": "markdown", + "id": "5e86e905", + "metadata": {}, + "source": [ + "## Lanczos插值 INTER_LANCZOS4\n", + "\n", + "![chazhi-15.png](./img/chazhi-15.png)\n", + "\n", + "兰索斯插值：由相邻的8*8像素计算得出，公式类似于双线性" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ae5e149", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/13-image-processing/img/chazhi-01.png b/13-image-processing/img/chazhi-01.png new file mode 100644 index 0000000000000000000000000000000000000000..7f0aaedf82b81aae30dc94df203e9373fd864109 GIT binary patch literal 74700 zcmagFV{~L))GZv_w(V4G+ji2a*tR>i)nUiBZB(3ejP8zYtG_(&d+!+c-}j?xRP9yg zjJ5Y(XYRSyTotXVEQ5?dfB*&thAby5sSXAPzV-DIgoF4RIS9`r0|Tc_m6H_H^xil( zf(<4dUCyZMSn@+ggJY)p2?G}-PC!LY^{jj_yA6YsWy}9Fl$}_SUFaV8WAL7UZPa~9 ziD#_i?ebTt1IJwuhm%+9%C;QiiK3im5!|+3dv|m5-Av9^?^ACQ+VtJ7MNrK%^yMDn zGRd`HKNyLjs5AH=^!w_}8R;c-nB@F-oR&i93)m$HRTTaIzJR5L{x`yG4NeEkY%PYU z4+i>r`+qH92mhJ>A4{<7|K5f9|5`-({xg3K{$EQW(f{014DtWh@_*eZg#JI*{Er(V zEn@+JasevZvUh=x@2O8qL4S##7d)c8NYQrg3Ss{H=?7U^?OjvHh~DMrbf;Ane?Qc# z{;i23xjPMBYX5*0=0D(Mkb2Niw(r9N_N_1!lD-4h9^MW+(6N-1xJ9|QP z6~ML<9cYWL)!+W6{BiW?blmnn@k;PDkxzvgGd~EoZuN!_Kgab3#ijJlrL89NX#
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z&bBs6NL3nxSt-hlOe?-anwq&fi|=r6bmBpKDm9t7T+5Q7rVCp;$5J;wkR~yYa{wIx+X#TdDXFy9in9q5igENm!RJx} zfFmj4a)bP@|Cz=IQcA4@?itaq0BFNVRKWfJRO)Ws(tknjC#ceQfBlOsPp6lH&q0jE zKsFr3XHJgeM@9ohTuCc_z5y;cU4MX%qc_2~_!}|qsTMxRwX#l%cnS+Ub@-SZH29@o zE*6%F{$P+-nc;9MtIOkfKBW8CbEa0C{2sV@+RXw$zTW>;)4n{ZNj7U9SvCGT>`}F+ zL*FWwJ%eSr!)_xdf1;$IspocW6g z7Wui8wuVOM_^KnTCvi;nDGAsHm`?dfk4K+*_mW@k6&204$rmn{xF3F69j7?%H5zw+ zdeu*e*wfwo!FQ9h=CxZm0k@gW4kq_#-YURew<+RcPFN-VNfCK7Q74#Dq~hoy{Au1V zI*Ske>D!MZiyJ8u3zkla%qpjHv)7u8FXaV9J4q$^bNn`zxT;j`cWtXre&8guR)EXJ z)*Z=L`5JkH8jp+~XK*8YTs5R7L6>q=K%_jn*(FD{bUdH6`g1(rK`N<&t>xNQP|o@h+YiRr#GX!7Lx0ux6?Gx-@HtG{bLK%mm1{;TyyuPcohwFE z{97sxWO(E{jytS9?zmJ|G9u@>T3i*#O_q^sPsmf=z34Qq1X1ArEPtHbb-Ot$nr#` IAtvg70CbMe#sB~S literal 0 HcmV?d00001 From 66ce6fdd0e4b740ef42418bb3face719c1a8c269 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 15 May 2021 12:24:33 +0800 Subject: [PATCH 32/49] add filter --- 13-image-processing/13.08-filter.ipynb | 41 ++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 13-image-processing/13.08-filter.ipynb diff --git a/13-image-processing/13.08-filter.ipynb b/13-image-processing/13.08-filter.ipynb new file mode 100644 index 00000000..7c1a1574 --- /dev/null +++ b/13-image-processing/13.08-filter.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "79e022a9", + "metadata": {}, + "source": [ + "# 滤波器" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be81f811", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1aff2cbca5a008567e294e946e26a2cd9658ee06 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 29 May 2021 12:04:41 +0800 Subject: [PATCH 33/49] update useful --- 11-useful-tools/11.01-pprint.ipynb | 30 ++-- 11-useful-tools/11.03-json.ipynb | 110 +++++------- 11-useful-tools/11.05-shutil.ipynb | 102 +++++------ .../11.06-gzip,-zipfile,-tarfile.ipynb | 167 ++++++++---------- 11-useful-tools/11.07-logging.ipynb | 24 +-- 11-useful-tools/11.08-string.ipynb | 114 +++--------- 11-useful-tools/11.09-collections.ipynb | 122 +++++++------ 11-useful-tools/11.10-requests.ipynb | 105 +++++------ 8 files changed, 317 insertions(+), 457 deletions(-) diff --git a/11-useful-tools/11.01-pprint.ipynb b/11-useful-tools/11.01-pprint.ipynb index 6fd82b12..58b34ad7 100644 --- a/11-useful-tools/11.01-pprint.ipynb +++ b/11-useful-tools/11.01-pprint.ipynb @@ -17,9 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pprint" @@ -35,9 +33,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "data = (\n", @@ -58,9 +54,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -71,7 +65,7 @@ } ], "source": [ - "print data" + "print(data)" ] }, { @@ -84,9 +78,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -113,23 +105,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/11-useful-tools/11.03-json.ipynb b/11-useful-tools/11.03-json.ipynb index 0af58cc7..e4268624 100644 --- a/11-useful-tools/11.03-json.ipynb +++ b/11-useful-tools/11.03-json.ipynb @@ -79,9 +79,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import json\n", @@ -114,29 +112,21 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{u'age': 24,\n", - " u'ages for school': {u'high school': 15,\n", - " u'middle school': 9,\n", - " u'primary school': 6,\n", - " u'university': 18},\n", - " u'coding skills': [u'python',\n", - " u'matlab',\n", - " u'java',\n", - " u'c',\n", - " u'c++',\n", - " u'ruby',\n", - " u'scala'],\n", - " u'hobby': [u'sports', u'reading'],\n", - " u'married': False,\n", - " u'name': u'echo'}\n" + "{'age': 24,\n", + " 'ages for school': {'high school': 15,\n", + " 'middle school': 9,\n", + " 'primary school': 6,\n", + " 'university': 18},\n", + " 'coding skills': ['python', 'matlab', 'java', 'c', 'c++', 'ruby', 'scala'],\n", + " 'hobby': ['sports', 'reading'],\n", + " 'married': False,\n", + " 'name': 'echo'}\n" ] } ], @@ -156,9 +146,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -184,23 +172,21 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\"name\": \"echo\", \"age\": 24, \"married\": false, \"ages for school\": {\"middle school\": 9, \"university\": 18, \"high school\": 15, \"primary school\": 6}, \"coding skills\": [\"python\", \"matlab\", \"java\", \"c\", \"c++\", \"ruby\", \"scala\"], \"hobby\": [\"sports\", \"reading\"]}\n" + "{\"name\": \"echo\", \"age\": 24, \"coding skills\": [\"python\", \"matlab\", \"java\", \"c\", \"c++\", \"ruby\", \"scala\"], \"ages for school\": {\"primary school\": 6, \"middle school\": 9, \"high school\": 15, \"university\": 18}, \"hobby\": [\"sports\", \"reading\"], \"married\": false}\n" ] } ], "source": [ "info_json = json.dumps(info)\n", "\n", - "print info_json" + "print(info_json)" ] }, { @@ -229,10 +215,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "with open(\"info.json\", \"w\") as f:\n", @@ -248,22 +232,20 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\"name\": \"echo\", \"age\": 24, \"married\": false, \"ages for school\": {\"middle school\": 9, \"university\": 18, \"high school\": 15, \"primary school\": 6}, \"coding skills\": [\"python\", \"matlab\", \"java\", \"c\", \"c++\", \"ruby\", \"scala\"], \"hobby\": [\"sports\", \"reading\"]}\n" + "{\"name\": \"echo\", \"age\": 24, \"coding skills\": [\"python\", \"matlab\", \"java\", \"c\", \"c++\", \"ruby\", \"scala\"], \"ages for school\": {\"primary school\": 6, \"middle school\": 9, \"high school\": 15, \"university\": 18}, \"hobby\": [\"sports\", \"reading\"], \"married\": false}\n" ] } ], "source": [ "with open(\"info.json\") as f:\n", - " print f.read()" + " print(f.read())" ] }, { @@ -275,30 +257,22 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{u'age': 24,\n", - " u'ages for school': {u'high school': 15,\n", - " u'middle school': 9,\n", - " u'primary school': 6,\n", - " u'university': 18},\n", - " u'coding skills': [u'python',\n", - " u'matlab',\n", - " u'java',\n", - " u'c',\n", - " u'c++',\n", - " u'ruby',\n", - " u'scala'],\n", - " u'hobby': [u'sports', u'reading'],\n", - " u'married': False,\n", - " u'name': u'echo'}\n" + "{'age': 24,\n", + " 'ages for school': {'high school': 15,\n", + " 'middle school': 9,\n", + " 'primary school': 6,\n", + " 'university': 18},\n", + " 'coding skills': ['python', 'matlab', 'java', 'c', 'c++', 'ruby', 'scala'],\n", + " 'hobby': ['sports', 'reading'],\n", + " 'married': False,\n", + " 'name': 'echo'}\n" ] } ], @@ -318,10 +292,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -331,23 +303,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/11-useful-tools/11.05-shutil.ipynb b/11-useful-tools/11.05-shutil.ipynb index 3d78c6a5..8fb6f204 100644 --- a/11-useful-tools/11.05-shutil.ipynb +++ b/11-useful-tools/11.05-shutil.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import shutil\n", @@ -35,10 +33,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -52,7 +48,7 @@ "with open(\"test.file\", \"w\") as f:\n", " pass\n", "\n", - "print \"test.file\" in os.listdir(os.curdir)" + "print(\"test.file\" in os.listdir(os.curdir))" ] }, { @@ -64,10 +60,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -81,8 +75,8 @@ "source": [ "shutil.copy(\"test.file\", \"test.copy.file\")\n", "\n", - "print \"test.file\" in os.listdir(os.curdir)\n", - "print \"test.copy.file\" in os.listdir(os.curdir)" + "print(\"test.file\" in os.listdir(os.curdir))\n", + "print(\"test.copy.file\" in os.listdir(os.curdir))" ] }, { @@ -94,10 +88,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -111,7 +103,7 @@ "try:\n", " shutil.copy(\"test.file\", \"my_test_dir/test.copy.file\")\n", "except IOError as msg:\n", - " print msg" + " print(msg)" ] }, { @@ -137,10 +129,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "os.renames(\"test.file\", \"test_dir/test.file\")\n", @@ -156,10 +146,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { @@ -167,7 +155,7 @@ "True" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -194,10 +182,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -211,7 +197,7 @@ "try:\n", " os.removedirs(\"test_dir_copy\")\n", "except Exception as msg:\n", - " print msg" + " print(msg)" ] }, { @@ -223,10 +209,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "shutil.rmtree(\"test_dir_copy\")" @@ -262,10 +246,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { @@ -273,10 +255,11 @@ "[('bztar', \"bzip2'ed tar-file\"),\n", " ('gztar', \"gzip'ed tar-file\"),\n", " ('tar', 'uncompressed tar file'),\n", + " ('xztar', \"xz'ed tar-file\"),\n", " ('zip', 'ZIP file')]" ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -296,18 +279,16 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'/home/lijin/notes-python/11. useful tools/test_archive.zip'" + "'/home/lusi/PycharmProjects/notes-python/11-useful-tools/test_archive.zip'" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -325,36 +306,41 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "os.remove(\"test_archive.zip\")\n", "shutil.rmtree(\"test_dir/\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb b/11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb index 2a4244b2..285777e0 100644 --- a/11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb +++ b/11-useful-tools/11.06-gzip,-zipfile,-tarfile.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os, shutil, glob\n", @@ -42,27 +40,25 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x�+��,V\u0000�D����\u0012�⒢̼t\u0000S�\u0007�\n", - "this is a test string\n" + "b'x\\x9c+\\xc9\\xc8,V\\x00\\xa2D\\x85\\x92\\xd4\\xe2\\x12\\x85\\xe2\\x92\\xa2\\xcc\\xbct\\x00S\\xe9\\x07\\xcd'\n", + "b'this is a test string'\n" ] } ], "source": [ - "orginal = \"this is a test string\"\n", + "orginal = b\"this is a test string\"\n", "\n", "compressed = zlib.compress(orginal)\n", "\n", - "print compressed\n", - "print zlib.decompress(compressed)" + "print(compressed)\n", + "print(zlib.decompress(compressed))" ] }, { @@ -74,10 +70,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -88,15 +82,13 @@ } ], "source": [ - "print zlib.adler32(orginal) & 0xffffffff" + "print(zlib.adler32(orginal) & 0xffffffff)" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -107,7 +99,7 @@ } ], "source": [ - "print zlib.crc32(orginal) & 0xffffffff" + "print(zlib.crc32(orginal) & 0xffffffff)" ] }, { @@ -128,13 +120,11 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ - "content = \"Lots of content here\"\n", + "content = b\"Lots of content here\"\n", "with gzip.open('file.txt.gz', 'wb') as f:\n", " f.write(content)" ] @@ -148,16 +138,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Lots of content here\n" + "b'Lots of content here'\n" ] } ], @@ -165,7 +153,7 @@ "with gzip.open('file.txt.gz', 'rb') as f:\n", " file_content = f.read()\n", "\n", - "print file_content" + "print(file_content)" ] }, { @@ -177,10 +165,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "with gzip.open('file.txt.gz', 'rb') as f_in, open('file.txt', 'wb') as f_out:\n", @@ -196,10 +182,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -211,7 +195,7 @@ ], "source": [ "with open(\"file.txt\") as f:\n", - " print f.read()" + " print(f.read())" ] }, { @@ -241,29 +225,25 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "BZh91AY&SY*\u001c", - "�v\u0000\u0000\t��@\u0000\"�\u001c", - "\u0000 \u00001\u00000\"zi\u000f�\u0015\u001b�FLT`�軒)„�P�˰\n", - "this is a test string\n" + "b'BZh91AY&SY*\\x1c\\xd9v\\x00\\x00\\t\\x91\\x80@\\x00\"\\xe1\\x1c\\x00 \\x001\\x000\"zi\\x0f\\xd2\\x15\\x1b\\xb1FLT`\\xa5\\xe8\\xbb\\x92)\\xc2\\x84\\x81P\\xe6\\xcb\\xb0'\n", + "b'this is a test string'\n" ] } ], "source": [ - "orginal = \"this is a test string\"\n", + "orginal = b\"this is a test string\"\n", "\n", "compressed = bz2.compress(orginal)\n", "\n", - "print compressed\n", - "print bz2.decompress(compressed)" + "print(compressed)\n", + "print(bz2.decompress(compressed))" ] }, { @@ -282,10 +262,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "for i in range(10):\n", @@ -301,10 +279,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "f = zipfile.ZipFile('files.zip','w')\n", @@ -325,22 +301,20 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['file.txt.9', 'file.txt.6', 'file.txt.2', 'file.txt.1', 'file.txt.5', 'file.txt.4', 'file.txt.3', 'file.txt.7', 'file.txt.8', 'file.txt.0']\n" + "['file.txt.0', 'file.txt.1', 'file.txt.2', 'file.txt.3', 'file.txt.4', 'file.txt.5', 'file.txt.6', 'file.txt.7', 'file.txt.8', 'file.txt.9']\n" ] } ], "source": [ "f = zipfile.ZipFile('files.zip','r')\n", - "print f.namelist()" + "print(f.namelist())" ] }, { @@ -352,31 +326,29 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "file.txt.9 content: Lots of content here\n", - "file.txt.6 content: Lots of content here\n", - "file.txt.2 content: Lots of content here\n", - "file.txt.1 content: Lots of content here\n", - "file.txt.5 content: Lots of content here\n", - "file.txt.4 content: Lots of content here\n", - "file.txt.3 content: Lots of content here\n", - "file.txt.7 content: Lots of content here\n", - "file.txt.8 content: Lots of content here\n", - "file.txt.0 content: Lots of content here\n" + "file.txt.0 content: b'Lots of content here'\n", + "file.txt.1 content: b'Lots of content here'\n", + "file.txt.2 content: b'Lots of content here'\n", + "file.txt.3 content: b'Lots of content here'\n", + "file.txt.4 content: b'Lots of content here'\n", + "file.txt.5 content: b'Lots of content here'\n", + "file.txt.6 content: b'Lots of content here'\n", + "file.txt.7 content: b'Lots of content here'\n", + "file.txt.8 content: b'Lots of content here'\n", + "file.txt.9 content: b'Lots of content here'\n" ] } ], "source": [ "for name in f.namelist():\n", - " print name, \"content:\", f.read(name)\n", + " print(name, \"content:\", f.read(name))\n", "\n", "f.close()" ] @@ -406,10 +378,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "f = tarfile.open(\"file.txt.tar\", \"w\")\n", @@ -426,37 +396,42 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "os.remove(\"file.txt\")\n", "os.remove(\"file.txt.tar\")\n", "os.remove(\"files.zip\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/11-useful-tools/11.07-logging.ipynb b/11-useful-tools/11.07-logging.ipynb index ce13ffd2..5741c2df 100644 --- a/11-useful-tools/11.07-logging.ipynb +++ b/11-useful-tools/11.07-logging.ipynb @@ -45,9 +45,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -79,9 +77,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -107,9 +103,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -130,23 +124,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/11-useful-tools/11.08-string.ipynb b/11-useful-tools/11.08-string.ipynb index 76609e8c..deeb6137 100644 --- a/11-useful-tools/11.08-string.ipynb +++ b/11-useful-tools/11.08-string.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import string" @@ -28,9 +26,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -58,9 +54,8 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -68,14 +63,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\n", "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\n" ] } ], "source": [ - "print string.letters\n", - "print string.ascii_letters" + "print(string.ascii_letters)" ] }, { @@ -87,47 +80,22 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcdefghijklmnopqrstuvwxyz\n", - "abcdefghijklmnopqrstuvwxyz\n", - "ABCDEFGHIJKLMNOPQRSTUVWXYZ\n", "ABCDEFGHIJKLMNOPQRSTUVWXYZ\n" ] } ], "source": [ - "print string.ascii_lowercase\n", - "print string.lowercase\n", + "print(string.ascii_lowercase)\n", "\n", - "print string.ascii_uppercase\n", - "print string.uppercase" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "print string.lower" + "print(string.ascii_uppercase)\n" ] }, { @@ -139,10 +107,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -150,7 +116,7 @@ "'0123456789'" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -168,10 +134,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { @@ -179,7 +143,7 @@ "'0123456789abcdefABCDEF'" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -197,10 +161,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { @@ -208,7 +170,7 @@ "'This Is A Big World'" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -217,55 +179,33 @@ "string.capwords(\"this is a big world\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "将指定的单词放到中央：" - ] - }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "' test '" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "string.center(\"test\", 20)" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/11-useful-tools/11.09-collections.ipynb b/11-useful-tools/11.09-collections.ipynb index b5a43807..108f5196 100644 --- a/11-useful-tools/11.09-collections.ipynb +++ b/11-useful-tools/11.09-collections.ipynb @@ -10,9 +10,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import collections" @@ -36,16 +34,14 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Counter({'two': 2, 'one': 2, 'from': 1, 'i': 1, 'tree': 1, 'three': 1, 'china': 1, 'come': 1})\n" + "Counter({'one,': 2, 'two,': 2, 'three,': 1, 'tree,': 1, 'i': 1, 'come': 1, 'from': 1, 'china.': 1})\n" ] } ], @@ -54,9 +50,9 @@ "\n", "sentence = \"One, two, three, one, two, tree, I come from China.\"\n", "\n", - "words_count = collections.Counter(sentence.translate(None, punctuation).lower().split())\n", + "words_count = collections.Counter(sentence.translate(punctuation).lower().split())\n", "\n", - "print words_count" + "print(words_count)" ] }, { @@ -75,42 +71,59 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deque([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", - "9 8 7 6 5 4 3 2 1 0\n", + "9\n", + "8\n", + "7\n", + "6\n", + "5\n", + "4\n", + "3\n", + "2\n", + "1\n", + "0\n", + "\n", "deque([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])\n", - "9 8 7 6 5 4 3 2 1 0\n" + "9\n", + "8\n", + "7\n", + "6\n", + "5\n", + "4\n", + "3\n", + "2\n", + "1\n", + "0\n" ] } ], "source": [ "dq = collections.deque()\n", "\n", - "for i in xrange(10):\n", + "for i in range(10):\n", " dq.append(i)\n", " \n", - "print dq\n", + "print(dq)\n", "\n", - "for i in xrange(10):\n", - " print dq.pop(), \n", + "for i in range(10):\n", + " print(dq.pop()), \n", "\n", - "print \n", + "print() \n", "\n", - "for i in xrange(10):\n", + "for i in range(10):\n", " dq.appendleft(i)\n", " \n", - "print dq\n", + "print(dq)\n", "\n", - "for i in xrange(10):\n", - " print dq.popleft()," + "for i in range(10):\n", + " print(dq.popleft(),)" ] }, { @@ -122,17 +135,15 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "100 loops, best of 3: 598 ns per loop\n", - "100 loops, best of 3: 291 ns per loop\n" + "737 ns ± 164 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", + "278 ns ± 10.8 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -160,10 +171,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -171,8 +180,8 @@ "text": [ "Regular Dict:\n", "A 1\n", - "C 3\n", "B 2\n", + "C 3\n", "Ordered Dict:\n", "A 1\n", "B 2\n", @@ -190,13 +199,13 @@ "regular_dict = dict(items)\n", "ordered_dict = collections.OrderedDict(items)\n", "\n", - "print 'Regular Dict:'\n", + "print('Regular Dict:')\n", "for k, v in regular_dict.items():\n", - " print k, v\n", + " print(k, v)\n", "\n", - "print 'Ordered Dict:'\n", + "print('Ordered Dict:')\n", "for k, v in ordered_dict.items():\n", - " print k, v" + " print(k, v)" ] }, { @@ -215,10 +224,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -233,37 +240,44 @@ "source": [ "dd = collections.defaultdict(list)\n", "\n", - "print dd[\"foo\"]\n", + "print(dd[\"foo\"])\n", "\n", "dd = collections.defaultdict(int)\n", "\n", - "print dd[\"foo\"]\n", + "print(dd[\"foo\"])\n", "\n", "dd = collections.defaultdict(float)\n", "\n", - "print dd[\"foo\"]" + "print(dd[\"foo\"])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 -} + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/11-useful-tools/11.10-requests.ipynb b/11-useful-tools/11.10-requests.ipynb index fb067ae2..aa2dc99f 100644 --- a/11-useful-tools/11.10-requests.ipynb +++ b/11-useful-tools/11.10-requests.ipynb @@ -9,10 +9,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "import requests" @@ -31,10 +29,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "r = requests.get(\"http://httpbin.org/get\")\n", @@ -61,10 +57,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "payload = {'key1': 'value1', 'key2': 'value2'}\n", @@ -80,16 +74,14 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "http://httpbin.org/get?key2=value2&key1=value1\n" + "http://httpbin.org/get?key1=value1&key2=value2\n" ] } ], @@ -113,23 +105,21 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{\"message\":\"Hello there, wayfaring stranger. If you’re reading this then you probably didn’t see our blog post a couple of years back announcing that this API would go away: http://git.io/17AROg Fear not, you should be able to get what you need from the shiny new Events API instead.\",\"documentation_url\":\"https://developer.github.com/v3/activity/events/#list-public-events\"}\n" + "{\"message\":\"Hello there, wayfaring stranger. If you’re reading this then you probably didn’t see our blog post a couple of years back announcing that this API would go away: http://git.io/17AROg Fear not, you should be able to get what you need from the shiny new Events API instead.\",\"documentation_url\":\"https://docs.github.com/v3/activity/events/#list-public-events\"}\n" ] } ], "source": [ "r = requests.get('https://github.com/timeline.json')\n", "\n", - "print r.text" + "print(r.text)" ] }, { @@ -141,10 +131,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -152,7 +140,7 @@ "'utf-8'" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -170,18 +158,16 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "u'{\"message\":\"Hello there, wayfaring stranger. If you\\xe2\\x80\\x99re reading this then you probably didn\\xe2\\x80\\x99t see our blog post a couple of years back announcing that this API would go away: http://git.io/17AROg Fear not, you should be able to get what you need from the shiny new Events API instead.\",\"documentation_url\":\"https://developer.github.com/v3/activity/events/#list-public-events\"}'" + "'{\"message\":\"Hello there, wayfaring stranger. If youâ\\x80\\x99re reading this then you probably didnâ\\x80\\x99t see our blog post a couple of years back announcing that this API would go away: http://git.io/17AROg Fear not, you should be able to get what you need from the shiny new Events API instead.\",\"documentation_url\":\"https://docs.github.com/v3/activity/events/#list-public-events\"}'" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -201,19 +187,17 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{u'documentation_url': u'https://developer.github.com/v3/activity/events/#list-public-events',\n", - " u'message': u'Hello there, wayfaring stranger. If you\\xe2\\x80\\x99re reading this then you probably didn\\xe2\\x80\\x99t see our blog post a couple of years back announcing that this API would go away: http://git.io/17AROg Fear not, you should be able to get what you need from the shiny new Events API instead.'}" + "{'message': 'Hello there, wayfaring stranger. If youâ\\x80\\x99re reading this then you probably didnâ\\x80\\x99t see our blog post a couple of years back announcing that this API would go away: http://git.io/17AROg Fear not, you should be able to get what you need from the shiny new Events API instead.',\n", + " 'documentation_url': 'https://docs.github.com/v3/activity/events/#list-public-events'}" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -238,18 +222,16 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "407" + "200" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -269,18 +251,16 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'text/html'" + "'application/json'" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -288,27 +268,34 @@ "source": [ "r.headers['Content-Type']" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 40cec8eab3198f5e58ac15ebf406de8a35a24658 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 29 May 2021 12:19:32 +0800 Subject: [PATCH 34/49] add tracking file --- 13-image-processing/13.08-filter.ipynb | 4 +- 13-image-processing/face_tracking.py | 25 + 13-image-processing/haarcascade_eye.xml | 12213 ++++++ .../haarcascade_frontalface_default.xml | 33314 ++++++++++++++++ 4 files changed, 45554 insertions(+), 2 deletions(-) create mode 100644 13-image-processing/face_tracking.py create mode 100644 13-image-processing/haarcascade_eye.xml create mode 100644 13-image-processing/haarcascade_frontalface_default.xml diff --git a/13-image-processing/13.08-filter.ipynb b/13-image-processing/13.08-filter.ipynb index 7c1a1574..f5508b90 100644 --- a/13-image-processing/13.08-filter.ipynb +++ b/13-image-processing/13.08-filter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "79e022a9", + "id": "98de914b", "metadata": {}, "source": [ "# 滤波器" @@ -11,7 +11,7 @@ { "cell_type": "code", "execution_count": null, - "id": "be81f811", + "id": "cc8dc2cd", "metadata": {}, "outputs": [], "source": [] diff --git a/13-image-processing/face_tracking.py b/13-image-processing/face_tracking.py new file mode 100644 index 00000000..58fa3ed9 --- /dev/null +++ b/13-image-processing/face_tracking.py @@ -0,0 +1,25 @@ +""" +脸部和眼睛追值踪 +""" +import cv2 + +capture = cv2.VideoCapture(0) +eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml') +face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') +if capture.isOpened(): + while True: + ret, frame = capture.read() + if ret: + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + eyes = eye_cascade.detectMultiScale(gray, 1.3, 5) + faces = face_cascade.detectMultiScale(gray, 1.3, 5) + for eye in eyes: + x, y, w, h = eye + cv2.rectangle(frame, (x, y), (x + w + 5, y + h + 5), (0, 0, 255), 3) + for face in faces: + x, y, w, h = face + cv2.rectangle(frame, (x, y), (x + w + 5, y + h + 5), (255, 0, 0), 3) + cv2.imshow('camera', frame) + key = cv2.waitKey(30) + if key == ord('q'): + break diff --git a/13-image-processing/haarcascade_eye.xml b/13-image-processing/haarcascade_eye.xml new file mode 100644 index 00000000..b21e3b93 --- /dev/null +++ b/13-image-processing/haarcascade_eye.xml @@ -0,0 +1,12213 @@ + + + +BOOST + HAAR + 20 + 20 + + 93 + + 0 + 24 + + <_> + 6 + -1.4562760591506958e+00 + + <_> + + 0 -1 0 1.2963959574699402e-01 + + -7.7304208278656006e-01 6.8350148200988770e-01 + <_> + + 0 -1 1 -4.6326808631420135e-02 + + 5.7352751493453979e-01 -4.9097689986228943e-01 + <_> + + 0 -1 2 -1.6173090785741806e-02 + + 6.0254341363906860e-01 -3.1610709428787231e-01 + <_> + + 0 -1 3 -4.5828841626644135e-02 + + 6.4177548885345459e-01 -1.5545040369033813e-01 + <_> + + 0 -1 4 -5.3759619593620300e-02 + + 5.4219317436218262e-01 -2.0480829477310181e-01 + <_> + + 0 -1 5 3.4171190112829208e-02 + + -2.3388190567493439e-01 4.8410901427268982e-01 + <_> + 12 + -1.2550230026245117e+00 + + <_> + + 0 -1 6 -2.1727620065212250e-01 + + 7.1098899841308594e-01 -5.9360730648040771e-01 + <_> + + 0 -1 7 1.2071969918906689e-02 + + -2.8240481019020081e-01 5.9013551473617554e-01 + <_> + + 0 -1 8 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+ <_> + + <_> + 1 6 22 10 -1. + <_> + 1 6 11 5 2. + <_> + 12 11 11 5 2. + <_> + + <_> + 15 17 9 6 -1. + <_> + 15 19 9 2 3. + <_> + + <_> + 0 18 18 2 -1. + <_> + 0 19 18 1 2. + <_> + + <_> + 3 15 19 3 -1. + <_> + 3 16 19 1 3. + <_> + + <_> + 0 13 18 3 -1. + <_> + 0 14 18 1 3. + <_> + + <_> + 15 17 9 6 -1. + <_> + 15 19 9 2 3. + <_> + + <_> + 0 17 9 6 -1. + <_> + 0 19 9 2 3. + <_> + + <_> + 12 17 9 6 -1. + <_> + 12 19 9 2 3. + <_> + + <_> + 3 17 9 6 -1. + <_> + 3 19 9 2 3. + <_> + + <_> + 16 2 3 20 -1. + <_> + 17 2 1 20 3. + <_> + + <_> + 0 13 24 8 -1. + <_> + 0 17 24 4 2. + <_> + + <_> + 9 1 6 22 -1. + <_> + 12 1 3 11 2. + <_> + 9 12 3 11 2. + From 482c026e45200145623dad8ef3c10b58aa19171b Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 29 May 2021 12:25:47 +0800 Subject: [PATCH 35/49] car tracking --- 13-image-processing/cars.mp4 | Bin 0 -> 698693 bytes 13-image-processing/haarcascade_car.xml | 3654 +++++++++++++++++++++++ 2 files changed, 3654 insertions(+) create mode 100644 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16 18 3 2 -1. + <_> + 16 19 3 1 2. + 0 + 0.0178245995193720 + 0.0100319497287273 + -1.0000309944152832 + <_> + + <_> + + + + <_> + 1 18 3 2 -1. + <_> + 1 19 3 1 2. + 0 + -4.5915339142084122e-03 + -0.7925583124160767 + 0.0804975628852844 + <_> + + <_> + + + + <_> + 11 15 2 2 -1. + <_> + 12 15 1 1 2. + <_> + 11 16 1 1 2. + 0 + -4.2610289528965950e-04 + -0.4836213886737823 + 0.0490311309695244 + -1.0918780565261841 + 11 + -1 + From 609ff2eea98a88e6890c84575fff6dbeed5827b1 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 29 May 2021 12:27:53 +0800 Subject: [PATCH 36/49] car tracking --- 13-image-processing/{ => data}/cars.mp4 | Bin 13-image-processing/{ => data}/haarcascade_car.xml | 0 13-image-processing/{ => data}/haarcascade_eye.xml | 0 .../{ => data}/haarcascade_frontalface_default.xml | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename 13-image-processing/{ => data}/cars.mp4 (100%) rename 13-image-processing/{ => data}/haarcascade_car.xml (100%) rename 13-image-processing/{ => data}/haarcascade_eye.xml (100%) rename 13-image-processing/{ => data}/haarcascade_frontalface_default.xml (100%) diff --git a/13-image-processing/cars.mp4 b/13-image-processing/data/cars.mp4 similarity index 100% rename from 13-image-processing/cars.mp4 rename to 13-image-processing/data/cars.mp4 diff --git a/13-image-processing/haarcascade_car.xml b/13-image-processing/data/haarcascade_car.xml similarity index 100% rename from 13-image-processing/haarcascade_car.xml rename to 13-image-processing/data/haarcascade_car.xml diff --git a/13-image-processing/haarcascade_eye.xml b/13-image-processing/data/haarcascade_eye.xml similarity index 100% rename from 13-image-processing/haarcascade_eye.xml rename to 13-image-processing/data/haarcascade_eye.xml diff --git a/13-image-processing/haarcascade_frontalface_default.xml b/13-image-processing/data/haarcascade_frontalface_default.xml similarity index 100% rename from 13-image-processing/haarcascade_frontalface_default.xml rename to 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zpOED=KYZWtkuzy`@i7KDp<)2zA1f(hRE%Kp z(2#rw$6pF;bjdEX*zUCtv*~)2`hB@FCgp@~nHTQkCbZ;F567q`88scqoXVUc3op$S-10FAvr!o9&fA0DBu$7n9^t>I0$ z5IUh8KiPq+E+nvJ{{YX+?( zD<#Ih9YD8bWoMk}ZuN;qWf79!ditOfk9&akM1n`x+To&^uU!8%$ z9R2TaZ`9R0uL;}lRr4m=R19T%kEgft^b}E3DKW+>=PBZk3qtX?@2)!!ygPyI?Os#k z-v-;yr`>Lv>*k5U!CrVH`?OJ6xjQq|!bNk(L*-8w{5QAPz9mm255]=255 + result[back==255]=255 + return result.astype('uint8') + +finnal_img=dodge(blur_image,gray_img) +cv2.imshow('2',finnal_img) +cv2.waitKey(0) +cv2.destroyAllWindows() \ No newline at end of file diff --git a/13-image-processing/turn2pencil.py b/13-image-processing/turn2pencil.py new file mode 100644 index 00000000..30a8ed96 --- /dev/null +++ b/13-image-processing/turn2pencil.py @@ -0,0 +1,28 @@ +import imageio +import matplotlib.pyplot as plt + +img = "263697.20.jpg" +start_img = imageio.imread(img) +# start_img.shape(196, 160, 30) +import numpy as np + + +def grayscale(rgb): + return np.dot(rgb[..., :3], [0.299, 0.587, 0.114]) +gray_img = grayscale(start_img) +plt.imshow(gray_img, cmap='gray') +plt.show() +inverted_img = 255-gray_img + +import scipy.ndimage +blur_img = scipy.ndimage.filters.gaussian_filter(inverted_img,sigma=5) +plt.imshow(blur_img, cmap='gray') +plt.show() +def dodge(front,back): + result=front*255/(255-back) + result[result>255]=255 + result[back==255]=255 + return result.astype('uint8') +final_img= dodge(blur_img,gray_img) +plt.imshow(final_img, cmap='gray') +plt.show() \ No newline at end of file From d247c3b4d7bb09aea2c0497d4be7bfe705894661 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 29 May 2021 13:07:40 +0800 Subject: [PATCH 38/49] update --- 13-image-processing/image_to_pencil_v2.py | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 13-image-processing/image_to_pencil_v2.py diff --git a/13-image-processing/image_to_pencil_v2.py b/13-image-processing/image_to_pencil_v2.py new file mode 100644 index 00000000..fd3696ae --- /dev/null +++ b/13-image-processing/image_to_pencil_v2.py @@ -0,0 +1,10 @@ +import cv2 + +img=cv2.imread('263697.20.jpg') +gray_img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) +blur_image = cv2.GaussianBlur(gray_img, (25, 25), sigmaX=0) + +finnal_img=cv2.divide(gray_img,blur_image,scale=255.0) +cv2.imshow('2',finnal_img) +cv2.waitKey(0) +cv2.destroyAllWindows() \ No newline at end of file From cbb0e6992336a7b7baff34d9ad2b99c24b4de3c5 Mon Sep 17 00:00:00 2001 From: master Date: Fri, 1 Jul 2022 23:18:33 +0800 Subject: [PATCH 39/49] update to python3 --- 01-python-tools/01.01-python-overview.ipynb | 10 +- .../01.02-ipython-interpreter.ipynb | 6 +- 01-python-tools/01.03-ipython-notebook.ipynb | 4 +- 01-python-tools/01.04-use-anaconda.ipynb | 20 +- .../02.01-a-tour-of-python.ipynb | 232 +++++------------- .../02.02-python-data-types.ipynb | 38 +-- README.md | 1 - 7 files changed, 91 insertions(+), 220 deletions(-) diff --git a/01-python-tools/01.01-python-overview.ipynb b/01-python-tools/01.01-python-overview.ipynb index 03f184b8..d624e1d2 100644 --- a/01-python-tools/01.01-python-overview.ipynb +++ b/01-python-tools/01.01-python-overview.ipynb @@ -20,9 +20,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`Python` 的创始人为荷兰人吉多·范罗苏姆（`Guido van Rossum`）。1989年的圣诞节期间，吉多·范罗苏姆为了在阿姆斯特丹打发时间，决心开发一个新的脚本解释程序，作为 ABC 语言的一种继承。之所以选中 `Python` 作为程序的名字，是因为他是 BBC 电视剧——蒙提·派森的飞行马戏团（`Monty Python's Flying Circus`）的爱好者。\n", + "`Python` 的创始人为荷兰人吉多·范罗苏姆（`Guido van Rossum`）。1989年的圣诞节期间，吉多·范罗苏姆为了在阿姆斯特丹打发时间，决心开发一个新的脚本解释程序，作为 ABC 语言的一种继承。 之所以选中 `Python` 作为程序的名字，是因为他是 BBC 电视剧——蒙提·派森的飞行马戏团（`Monty Python's Flying Circus`）的爱好者。\n", "\n", - "1991年，第一个 Python 编译器诞生。它是用C语言实现的，并能够调用C语言的库文件。\n", + "1991年，第一个 Python 编译器诞生。 它是用C语言实现的，并能够调用C语言的库文件。\n", "\n", "`Python 2.0` 于 2000 年 10 月 16 日发布，增加了实现完整的垃圾回收，并且支持 `Unicode`。\n", "\n", @@ -215,7 +215,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -229,9 +229,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/01-python-tools/01.02-ipython-interpreter.ipynb b/01-python-tools/01.02-ipython-interpreter.ipynb index 5db2a2ad..746f1f63 100644 --- a/01-python-tools/01.02-ipython-interpreter.ipynb +++ b/01-python-tools/01.02-ipython-interpreter.ipynb @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "通常我们并不使用**Python**自带的解释器，而是使用另一个比较方便的解释器——**ipython**解释器，命令行下输入：\n", + "学习时我们并不使用**Python**自带的解释器，而是使用另一个比较方便的解释器——**ipython**解释器，命令行下输入：\n", "\n", " ipython\n", "\n", @@ -824,7 +824,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -838,7 +838,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/01-python-tools/01.03-ipython-notebook.ipynb b/01-python-tools/01.03-ipython-notebook.ipynb index d5e928e9..e853c911 100644 --- a/01-python-tools/01.03-ipython-notebook.ipynb +++ b/01-python-tools/01.03-ipython-notebook.ipynb @@ -28,7 +28,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -42,7 +42,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/01-python-tools/01.04-use-anaconda.ipynb b/01-python-tools/01.04-use-anaconda.ipynb index cd0587d3..9540a929 100644 --- a/01-python-tools/01.04-use-anaconda.ipynb +++ b/01-python-tools/01.04-use-anaconda.ipynb @@ -35,9 +35,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -226,9 +224,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -300,23 +296,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.01-a-tour-of-python.ipynb b/02-python-essentials/02.01-a-tour-of-python.ipynb index ecdb7610..9407dbe3 100644 --- a/02-python-essentials/02.01-a-tour-of-python.ipynb +++ b/02-python-essentials/02.01-a-tour-of-python.ipynb @@ -25,7 +25,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -54,9 +53,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -83,9 +80,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -144,9 +139,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -167,9 +160,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -197,9 +188,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -219,9 +208,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -248,9 +235,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -278,9 +263,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -300,9 +283,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -322,9 +303,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -351,9 +330,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -381,9 +358,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -417,9 +392,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -447,9 +420,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -476,9 +447,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -505,9 +474,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -534,9 +501,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -571,9 +536,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -601,9 +564,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -630,9 +591,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -660,9 +619,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -691,9 +648,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -720,9 +675,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -749,9 +702,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -785,9 +736,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -815,9 +764,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -844,9 +791,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -873,9 +818,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -903,9 +846,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -933,9 +874,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -962,9 +901,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -991,9 +928,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1027,9 +962,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1058,9 +991,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1080,9 +1011,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1116,9 +1045,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1157,9 +1084,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1181,9 +1106,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1213,9 +1136,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1236,9 +1157,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1265,9 +1184,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1294,9 +1211,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1342,7 +1257,6 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1370,7 +1284,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1398,9 +1311,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -1424,9 +1335,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1458,9 +1367,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1488,9 +1395,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1518,9 +1423,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1540,9 +1443,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1594,9 +1495,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1623,9 +1522,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1704,9 +1601,7 @@ { "cell_type": "code", "execution_count": 59, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1733,9 +1628,7 @@ { "cell_type": "code", "execution_count": 60, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1780,9 +1673,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1828,9 +1719,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1878,7 +1767,6 @@ "cell_type": "code", "execution_count": 65, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1913,23 +1801,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index 1407fc22..6a4d812b 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -25,50 +25,38 @@ "| 字符串 | `'hello'` |\n", "| 列表 | `[1, 1.2, 'hello']` |\n", "| 字典 | `{'dogs': 5, 'pigs': 3}`|\n", - "| Numpy数组 | `array([1, 2, 3])`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 其他类型 Others" + "| 元组 | `('ring', 1000)`\n", + "| 集合 | `{1, 2, 3}`|\n", + "| 布尔型 | `True, False`|\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, - "source": [ - "| 类型| 例子|\n", - "| ------- | ----- |\n", - "| 长整型 | `1000000000000L`\n", - "| 布尔型 | `True, False`\n", - "| 元组 | `('ring', 1000)`\n", - "| 集合 | `{1, 2, 3}`\n", - "| Pandas类型| `DataFrame, Series`\n", - "| 自定义 | `Object Oriented Classes`" - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/README.md b/README.md index b4e87c1f..ed729106 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,6 @@ https://item.jd.com/12328920.html 天猫、亚马逊、当当均有销售。 -**打赏一下意思意思？** ![](payment.jpeg) From 8ae61b1656550b788569f9fe7d06d0c94f515a9a Mon Sep 17 00:00:00 2001 From: master Date: Sat, 9 Jul 2022 15:23:37 +0800 Subject: [PATCH 40/49] update to python3 --- 02-python-essentials/02.03-numbers.ipynb | 442 +++++++++++++++-------- 1 file changed, 300 insertions(+), 142 deletions(-) diff --git a/02-python-essentials/02.03-numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb index e919ebc9..e6bd3ec8 100644 --- a/02-python-essentials/02.03-numbers.ipynb +++ b/02-python-essentials/02.03-numbers.ipynb @@ -4,17 +4,61 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Number（数字）" + "# 数字" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Python3 支持 int、float、bool、complex（复数）。\n", - "在Python 3里，只有一种整数类型 int，表示为长整型，没有 python2 中的 Long。\n", - "像大多数语言一样，数值类型的赋值和计算都是很直观的。\n", - "内置的 type() 函数可以用来查询变量所指的对象类型。" + "## 整型 Integers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "整型运算，加减乘：" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 + 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3 - 4" ] }, { @@ -25,7 +69,7 @@ { "data": { "text/plain": [ - "(int, float, bool, complex)" + "20" ] }, "execution_count": 3, @@ -34,29 +78,41 @@ } ], "source": [ - "a, b, c, d = 20, 5.5, True, 4+3j\n", - "type(a), type(b), type(c), type(d)" + "4 * 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*注意：在 Python2 中是没有布尔型的，它用数字 0 表示 False，用 1 表示 True。到 Python3 中，把 True 和 False 定义成关键字了，但它们的值还是 1 和 0，它们可以和数字相加*" + "在 python3 中除法返回浮点" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 2, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.4" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## 整型 Integers" + "12 / 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "整型运算，加减乘：" + "取整" ] }, { @@ -67,7 +123,7 @@ { "data": { "text/plain": [ - "4" + "2" ] }, "execution_count": 4, @@ -76,7 +132,14 @@ } ], "source": [ - "2 + 2" + "12//5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "取余" ] }, { @@ -87,7 +150,7 @@ { "data": { "text/plain": [ - "-1" + "3" ] }, "execution_count": 5, @@ -96,66 +159,96 @@ } ], "source": [ - "3 - 4" + "13%5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "幂指数：" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "20" + "32" ] }, - "execution_count": 6, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "4 * 5" + "2 ** 5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "取余：" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2.4" + "2" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "12 / 5" + "32 % 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "~~~\n", - "\n", - "在**Python 2.7**中，整型的运算结果只能返回整型，**除法**的结果也不例外。\n", - "\n", - "例如`12 / 5`返回的结果并不是2.4，而是2：\n", - "~~~" + "赋值给变量：" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 1\n", + "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "幂指数：" + "使用`type()`函数来查看变量类型：" ] }, { @@ -166,7 +259,7 @@ { "data": { "text/plain": [ - "32" + "int" ] }, "execution_count": 8, @@ -175,61 +268,97 @@ } ], "source": [ - "2 ** 5" + "type(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "取余：" + "整型数字的最大最小值：\n", + "\n", + "在 32 位系统中，一个整型 4 个字节，最小值 `-2,147,483,648`，最大值 `2,147,483,647`。\n", + "\n", + "在 64 位系统中，一个整型 8 个字节，最小值 `-9,223,372,036,854,775,808`，最大值 `9,223,372,036,854,775,807`。" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2" + "9223372036854775807" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "32 % 5" + "import sys\n", + "sys.maxsize" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "赋值给变量：" + "## 长整型 Long Integers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当整型超出范围时，**Python**会自动将整型转化为长整型，不过长整型计算速度会比整型慢。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "a = sys.maxint + 1\n", + "print type(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "长整型的一个标志是后面以字母L结尾：" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1" + "2147483648L" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a = 1\n", "a" ] }, @@ -237,59 +366,57 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "使用`type()`函数来查看变量类型：" + "可以在赋值时强制让类型为长整型：" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "int" + "long" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "type(a)" + "b = 1234L\n", + "type(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "整型数字的最大最小值：\n", - "\n", - "在 32 位系统中，一个整型 4 个字节，最小值 `-2,147,483,648`，最大值 `2,147,483,647`。\n", - "\n", - "在 64 位系统中，一个整型 8 个字节，最小值 `-9,223,372,036,854,775,808`，最大值 `9,223,372,036,854,775,807`。" + "长整型可以与整型在一起进行计算，返回的类型还是长整型：" ] }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 13, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "9223372036854775807" + "2147483644L" ] }, - "execution_count": 28, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import sys\n", - "sys.maxsize" + "a - 4" ] }, { @@ -320,9 +447,16 @@ "type(a)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "在之前的除法例子`12 / 5`中，假如想要使返回的结果为2.4，可以将它们写成浮点数的形式：" + ] + }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -331,18 +465,18 @@ "2.4" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "12 / 5" + "12.0 / 5.0" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -351,7 +485,7 @@ "2.4" ] }, - "execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -362,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -373,7 +507,7 @@ "2.4" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -391,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -400,7 +534,7 @@ "7.4" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -418,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -427,7 +561,7 @@ "0.19999999999999973" ] }, - "execution_count": 24, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -438,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -447,7 +581,7 @@ "44.7" ] }, - "execution_count": 25, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -458,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -467,7 +601,7 @@ "6.25" ] }, - "execution_count": 26, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -478,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -487,7 +621,7 @@ "1.2999999999999998" ] }, - "execution_count": 27, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -509,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -518,7 +652,7 @@ "'0.199999999999999733546474089962430298328399658203125'" ] }, - "execution_count": 29, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -527,6 +661,30 @@ "'{:.52}'.format(3.4 - 3.2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "当我们使用`print`显示时，**Python**会自动校正这个结果" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2\n" + ] + } + ], + "source": [ + "print 3.4 - 3.2" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -536,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -545,7 +703,7 @@ "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)" ] }, - "execution_count": 32, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -564,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -573,7 +731,7 @@ "1.7976931348623157e+308" ] }, - "execution_count": 33, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -591,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -600,7 +758,7 @@ "2.2250738585072014e-308" ] }, - "execution_count": 34, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -618,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -627,7 +785,7 @@ "2.220446049250313e-16" ] }, - "execution_count": 35, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -652,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -661,7 +819,7 @@ "complex" ] }, - "execution_count": 36, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -680,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -689,7 +847,7 @@ "1.0" ] }, - "execution_count": 37, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -700,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -709,7 +867,7 @@ "2.0" ] }, - "execution_count": 38, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -720,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -729,7 +887,7 @@ "(1-2j)" ] }, - "execution_count": 39, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -754,16 +912,16 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-27.0" + "-27" ] }, - "execution_count": 40, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -788,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -797,7 +955,7 @@ "2.0" ] }, - "execution_count": 41, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -808,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -817,7 +975,7 @@ "-4.0" ] }, - "execution_count": 42, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -842,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -851,7 +1009,7 @@ "12.4" ] }, - "execution_count": 43, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -869,16 +1027,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "22" + "22.0" ] }, - "execution_count": 44, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -896,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -909,8 +1067,8 @@ } ], "source": [ - "print(min(2, 3, 4, 5))\n", - "print(max(2, 4, 3))" + "print min(2, 3, 4, 5)\n", + "print max(2, 4, 3)" ] }, { @@ -929,7 +1087,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -938,7 +1096,7 @@ "builtin_function_or_method" ] }, - "execution_count": 47, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -956,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -965,7 +1123,7 @@ "int" ] }, - "execution_count": 48, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -977,7 +1135,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -985,10 +1143,10 @@ "evalue": "'int' object is not callable", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: 'int' object is not callable" ] } ], @@ -1012,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1025,8 +1183,8 @@ } ], "source": [ - "print(int(12.324))\n", - "print(int(-3.32)) " + "print int(12.324)\n", + "print int(-3.32)" ] }, { @@ -1038,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1050,7 +1208,7 @@ } ], "source": [ - "print(float(1.2))" + "print float(1.2)" ] }, { @@ -1076,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1085,7 +1243,7 @@ "1e-06" ] }, - "execution_count": 53, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1103,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1112,7 +1270,7 @@ "255" ] }, - "execution_count": 54, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1125,12 +1283,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "8进制，前面加`0o`修饰，后面使用数字0-7：" + "8进制，前面加`0`或者`0o`修饰，后面使用数字0-7：" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1139,13 +1297,13 @@ "55" ] }, - "execution_count": 2, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "0o67" + "067" ] }, { @@ -1191,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1207,11 +1365,11 @@ "source": [ "b = 2.5\n", "b += 2\n", - "print(b)\n", + "print b\n", "b *= 2\n", - "print(b)\n", + "print b\n", "b -= 3\n", - "print(b)" + "print b" ] }, { @@ -1230,7 +1388,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1239,7 +1397,7 @@ "bool" ] }, - "execution_count": 58, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1258,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1271,7 +1429,7 @@ ], "source": [ "q = 1 > 2\n", - "print(q)" + "print q" ] }, { @@ -1287,7 +1445,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1296,7 +1454,7 @@ "True" ] }, - "execution_count": 61, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1318,7 +1476,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1332,7 +1490,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, From ece993e14eacd9db6d7236972b03736aef65c38c Mon Sep 17 00:00:00 2001 From: master Date: Sat, 9 Jul 2022 15:35:48 +0800 Subject: [PATCH 41/49] update to python3 --- 02-python-essentials/02.03-numbers.ipynb | 264 +++++++---------------- 1 file changed, 72 insertions(+), 192 deletions(-) diff --git a/02-python-essentials/02.03-numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb index e6bd3ec8..baaa28c1 100644 --- a/02-python-essentials/02.03-numbers.ipynb +++ b/02-python-essentials/02.03-numbers.ipynb @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -293,7 +293,7 @@ "9223372036854775807" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -303,122 +303,6 @@ "sys.maxsize" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 长整型 Long Integers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "当整型超出范围时，**Python**会自动将整型转化为长整型，不过长整型计算速度会比整型慢。" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "a = sys.maxint + 1\n", - "print type(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "长整型的一个标志是后面以字母L结尾：" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2147483648L" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以在赋值时强制让类型为长整型：" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "long" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "b = 1234L\n", - "type(b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "长整型可以与整型在一起进行计算，返回的类型还是长整型：" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2147483644L" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a - 4" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -428,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -437,7 +321,7 @@ "float" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -456,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -465,7 +349,7 @@ "2.4" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -485,7 +369,7 @@ "2.4" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -496,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -507,7 +391,7 @@ "2.4" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -525,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -534,7 +418,7 @@ "7.4" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -552,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -561,7 +445,7 @@ "0.19999999999999973" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -572,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -581,7 +465,7 @@ "44.7" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -592,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -601,7 +485,7 @@ "6.25" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -621,7 +505,7 @@ "1.2999999999999998" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -661,30 +545,6 @@ "'{:.52}'.format(3.4 - 3.2)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "当我们使用`print`显示时，**Python**会自动校正这个结果" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2\n" - ] - } - ], - "source": [ - "print 3.4 - 3.2" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -694,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -703,7 +563,7 @@ "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)" ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -722,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -731,7 +591,7 @@ "1.7976931348623157e+308" ] }, - "execution_count": 26, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -749,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -758,7 +618,7 @@ "2.2250738585072014e-308" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -776,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -785,7 +645,7 @@ "2.220446049250313e-16" ] }, - "execution_count": 28, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -810,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -819,7 +679,7 @@ "complex" ] }, - "execution_count": 29, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -838,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -847,7 +707,7 @@ "1.0" ] }, - "execution_count": 30, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -858,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -867,7 +727,7 @@ "2.0" ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -878,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -887,7 +747,7 @@ "(1-2j)" ] }, - "execution_count": 32, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -900,7 +760,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "##交互计算" + "## 交互计算" ] }, { @@ -912,16 +772,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-27" + "-27.0" ] }, - "execution_count": 33, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -946,7 +806,27 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.8" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "14/5" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -955,7 +835,7 @@ "2.0" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -966,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -975,7 +855,7 @@ "-4.0" ] }, - "execution_count": 35, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1000,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1009,7 +889,7 @@ "12.4" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1027,16 +907,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "22.0" + "22" ] }, - "execution_count": 37, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1067,8 +947,8 @@ } ], "source": [ - "print min(2, 3, 4, 5)\n", - "print max(2, 4, 3)" + "print(min(2, 3, 4, 5)) \n", + "print(max(2, 4, 3))" ] }, { From 891248d8f9f01397094717bd2d3544fd7561ef81 Mon Sep 17 00:00:00 2001 From: master Date: Sat, 9 Jul 2022 15:40:34 +0800 Subject: [PATCH 42/49] update to python3 --- 02-python-essentials/02.03-numbers.ipynb | 72 ++++++++++++------------ 1 file changed, 37 insertions(+), 35 deletions(-) diff --git a/02-python-essentials/02.03-numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb index baaa28c1..267977dd 100644 --- a/02-python-essentials/02.03-numbers.ipynb +++ b/02-python-essentials/02.03-numbers.ipynb @@ -967,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -976,7 +976,7 @@ "builtin_function_or_method" ] }, - "execution_count": 39, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -994,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1003,7 +1003,7 @@ "int" ] }, - "execution_count": 40, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1015,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1023,10 +1023,10 @@ "evalue": "'int' object is not callable", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: 'int' object is not callable" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [53]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable" ] } ], @@ -1050,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1063,32 +1063,34 @@ } ], "source": [ - "print int(12.324)\n", - "print int(-3.32)" + "print(int(12.524))\n", + "print(int(-3.32))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "整型转浮点型：" + "字符串转换" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "2343\n", "1.2\n" ] } ], "source": [ - "print float(1.2)" + "print(int(\"2343\"))\n", + "print(float(\"1.2\"))" ] }, { @@ -1114,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1123,7 +1125,7 @@ "1e-06" ] }, - "execution_count": 44, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -1141,7 +1143,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1150,7 +1152,7 @@ "255" ] }, - "execution_count": 45, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1163,12 +1165,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "8进制，前面加`0`或者`0o`修饰，后面使用数字0-7：" + "8进制，前面加`0o`修饰，后面使用数字0-7：" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1177,13 +1179,13 @@ "55" ] }, - "execution_count": 46, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "067" + "0o67" ] }, { @@ -1195,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -1204,7 +1206,7 @@ "42" ] }, - "execution_count": 47, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1229,7 +1231,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -1245,11 +1247,11 @@ "source": [ "b = 2.5\n", "b += 2\n", - "print b\n", + "print(b)\n", "b *= 2\n", - "print b\n", + "print(b)\n", "b -= 3\n", - "print b" + "print(b)" ] }, { @@ -1268,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1277,7 +1279,7 @@ "bool" ] }, - "execution_count": 49, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1296,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1309,7 +1311,7 @@ ], "source": [ "q = 1 > 2\n", - "print q" + "print(q)" ] }, { @@ -1325,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -1334,7 +1336,7 @@ "True" ] }, - "execution_count": 51, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } From a7bc6dfd7c88b51179f96cc7235f058562168c51 Mon Sep 17 00:00:00 2001 From: master Date: Sat, 9 Jul 2022 15:54:10 +0800 Subject: [PATCH 43/49] update to python3 --- .../02.01-a-tour-of-python.ipynb | 154 ++++++++++-------- 1 file changed, 84 insertions(+), 70 deletions(-) mode change 100644 => 100755 02-python-essentials/02.01-a-tour-of-python.ipynb diff --git a/02-python-essentials/02.01-a-tour-of-python.ipynb b/02-python-essentials/02.01-a-tour-of-python.ipynb old mode 100644 new mode 100755 index 9407dbe3..fbf4a3cb --- a/02-python-essentials/02.01-a-tour-of-python.ipynb +++ b/02-python-essentials/02.01-a-tour-of-python.ipynb @@ -114,9 +114,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "a = 0.2" @@ -191,18 +189,18 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "hello\n", - "world\n" + "ename": "SyntaxError", + "evalue": "Missing parentheses in call to 'print'. Did you mean print(s)? (43688260.py, line 3)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Input \u001b[0;32mIn [7]\u001b[0;36m\u001b[0m\n\u001b[0;31m print s\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(s)?\n" ] } ], "source": [ "s = \"\"\"hello\n", "world\"\"\"\n", - "print s" + "print(s)" ] }, { @@ -222,7 +220,7 @@ "source": [ "s = '''hello\n", "world'''\n", - "print s" + "print(s)" ] }, { @@ -1476,10 +1474,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "import os" @@ -1494,16 +1490,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4400" + "20913" ] }, - "execution_count": 55, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1521,16 +1517,16 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'\\\\'" + "'/'" ] }, - "execution_count": 56, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1700,10 +1696,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "url = 'http://ichart.finance.yahoo.com/table.csv?s=GE&d=10&e=5&f=2013&g=d&a=0&b=2&c=1962&ignore=.csv'" @@ -1718,38 +1712,42 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 11, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close\\n'],\n", - " ['2013-11-05', '26.32', '26.52', '26.26', '26.42', '24897500', '24.872115\\n'],\n", - " ['2013-11-04',\n", - " '26.59',\n", - " '26.59',\n", - " '26.309999',\n", - " '26.43',\n", - " '28166100',\n", - " '24.88153\\n'],\n", - " ['2013-11-01',\n", - " '26.049999',\n", - " '26.639999',\n", - " '26.030001',\n", - " '26.540001',\n", - " '55634500',\n", - " '24.985086\\n']]" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" + "ename": "URLError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:1346\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1345\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1346\u001b[0m \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1347\u001b[0m \u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_header\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTransfer-encoding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1285\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1285\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1331\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1330\u001b[0m body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1331\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1280\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1279\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1280\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1040\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1040\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1043\u001b[0m \n\u001b[1;32m 1044\u001b[0m \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:980\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[0;32m--> 980\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 981\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:946\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[38;5;124;03m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 946\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 947\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 948\u001b[0m \u001b[38;5;66;03m# Might fail in OSs that don't implement TCP_NODELAY\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/socket.py:823\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 822\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 823\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mSOCK_STREAM\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 824\u001b[0m af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/socket.py:954\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 953\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 954\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 955\u001b[0m af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n", + "\u001b[0;31mgaierror\u001b[0m: [Errno -2] Name or service not known", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01murllib\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m ge_csv \u001b[38;5;241m=\u001b[39m \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m ge_csv:\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:214\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:517\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 514\u001b[0m req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[1;32m 516\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[0;32m--> 517\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[1;32m 520\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:534\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m 533\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[0;32m--> 534\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[1;32m 535\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:494\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[1;32m 493\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 494\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:1375\u001b[0m, in \u001b[0;36mHTTPHandler.http_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttp_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[0;32m-> 1375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:1349\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1346\u001b[0m h\u001b[38;5;241m.\u001b[39mrequest(req\u001b[38;5;241m.\u001b[39mget_method(), req\u001b[38;5;241m.\u001b[39mselector, req\u001b[38;5;241m.\u001b[39mdata, headers,\n\u001b[1;32m 1347\u001b[0m encode_chunked\u001b[38;5;241m=\u001b[39mreq\u001b[38;5;241m.\u001b[39mhas_header(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransfer-encoding\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[1;32m 1350\u001b[0m r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[1;32m 1351\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n", + "\u001b[0;31mURLError\u001b[0m: " + ] } ], "source": [ - "import urllib2\n", - "ge_csv = urllib2.urlopen(url)\n", + "import urllib\n", + "ge_csv = urllib.request.urlopen(url)\n", "data = []\n", "for line in ge_csv:\n", " data.append(line.split(','))\n", @@ -1765,38 +1763,54 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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APffrr0PjxvAf/5GONNhDR2OK1ogRmemJV0j8CR1Wr3bL4cNDH0qed17m02QB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\nghufeyDwP8B477WG3rIM98H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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "URLError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:1346\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1345\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1346\u001b[0m \u001b[43mh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselector\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1347\u001b[0m \u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_header\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTransfer-encoding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1285\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1285\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1331\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1330\u001b[0m body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1331\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1280\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1279\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1280\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:1040\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1040\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1043\u001b[0m \n\u001b[1;32m 1044\u001b[0m \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:980\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[0;32m--> 980\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 981\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/http/client.py:946\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[38;5;124;03m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 946\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 947\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 948\u001b[0m \u001b[38;5;66;03m# Might fail in OSs that don't implement TCP_NODELAY\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/socket.py:823\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 822\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 823\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mSOCK_STREAM\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 824\u001b[0m af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/socket.py:954\u001b[0m, in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 953\u001b[0m addrlist \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 954\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m res \u001b[38;5;129;01min\u001b[39;00m \u001b[43m_socket\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetaddrinfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mport\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfamily\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 955\u001b[0m af, socktype, proto, canonname, sa \u001b[38;5;241m=\u001b[39m res\n", + "\u001b[0;31mgaierror\u001b[0m: [Errno -2] Name or service not known", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ge_csv \u001b[38;5;241m=\u001b[39m \u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\n\u001b[1;32m 3\u001b[0m ge \u001b[38;5;241m=\u001b[39m pandas\u001b[38;5;241m.\u001b[39mread_csv(ge_csv, index_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, parse_dates\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:214\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m opener \u001b[38;5;241m=\u001b[39m _opener\n\u001b[0;32m--> 214\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mopener\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:517\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 514\u001b[0m req \u001b[38;5;241m=\u001b[39m meth(req)\n\u001b[1;32m 516\u001b[0m sys\u001b[38;5;241m.\u001b[39maudit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124murllib.Request\u001b[39m\u001b[38;5;124m'\u001b[39m, req\u001b[38;5;241m.\u001b[39mfull_url, req\u001b[38;5;241m.\u001b[39mdata, req\u001b[38;5;241m.\u001b[39mheaders, req\u001b[38;5;241m.\u001b[39mget_method())\n\u001b[0;32m--> 517\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;66;03m# post-process response\u001b[39;00m\n\u001b[1;32m 520\u001b[0m meth_name \u001b[38;5;241m=\u001b[39m protocol\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_response\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:534\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[1;32m 533\u001b[0m protocol \u001b[38;5;241m=\u001b[39m req\u001b[38;5;241m.\u001b[39mtype\n\u001b[0;32m--> 534\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_chain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_open\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[1;32m 535\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m_open\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result:\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:494\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handler \u001b[38;5;129;01min\u001b[39;00m handlers:\n\u001b[1;32m 493\u001b[0m func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 494\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:1375\u001b[0m, in \u001b[0;36mHTTPHandler.http_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhttp_open\u001b[39m(\u001b[38;5;28mself\u001b[39m, req):\n\u001b[0;32m-> 1375\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhttp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mHTTPConnection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/lib/python3.9/urllib/request.py:1349\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1346\u001b[0m h\u001b[38;5;241m.\u001b[39mrequest(req\u001b[38;5;241m.\u001b[39mget_method(), req\u001b[38;5;241m.\u001b[39mselector, req\u001b[38;5;241m.\u001b[39mdata, headers,\n\u001b[1;32m 1347\u001b[0m encode_chunked\u001b[38;5;241m=\u001b[39mreq\u001b[38;5;241m.\u001b[39mhas_header(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTransfer-encoding\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m 1348\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err: \u001b[38;5;66;03m# timeout error\u001b[39;00m\n\u001b[0;32m-> 1349\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m URLError(err)\n\u001b[1;32m 1350\u001b[0m r \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39mgetresponse()\n\u001b[1;32m 1351\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n", + "\u001b[0;31mURLError\u001b[0m: " + ] } ], "source": [ - "ge_csv = urllib2.urlopen(url)\n", + "ge_csv = urllib.request.urlopen(url)\n", "import pandas\n", "ge = pandas.read_csv(ge_csv, index_col=0, parse_dates=True)\n", "ge.plot(y='Adj Close')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 8bdfbc40644c7bcb5f93ef79f1cf3e7fbb8463ad Mon Sep 17 00:00:00 2001 From: master Date: Sat, 9 Jul 2022 16:28:57 +0800 Subject: [PATCH 44/49] update to python3 --- 02-python-essentials/02.04-strings.ipynb | 219 +++++++++++++++-------- 1 file changed, 143 insertions(+), 76 deletions(-) diff --git a/02-python-essentials/02.04-strings.ipynb b/02-python-essentials/02.04-strings.ipynb index cf333856..22f4daa0 100644 --- a/02-python-essentials/02.04-strings.ipynb +++ b/02-python-essentials/02.04-strings.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -82,7 +82,7 @@ "'hello world'" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -110,7 +110,7 @@ "'echoechoecho'" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -137,7 +137,7 @@ "11" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -192,7 +192,7 @@ } ], "source": [ - "line = \"1 2 3 4 5\"\n", + "line = \"1 2\\n 3 4\\t 5\"\n", "numbers = line.split()\n", "print(numbers)" ] @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -239,16 +239,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1 2 3 4 5'" + "'2 3'" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -260,16 +260,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1,2,3,4,5'" + "'2,3'" ] }, - "execution_count": 11, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -295,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -304,7 +304,7 @@ "'hello python'" ] }, - "execution_count": 13, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -332,7 +332,7 @@ "'hello world'" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -368,7 +368,7 @@ "'HELLO WORLD'" ] }, - "execution_count": 15, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -433,7 +433,7 @@ "'hello world'" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -452,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -461,7 +461,7 @@ "' hello world \\n'" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -472,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -481,7 +481,7 @@ "'hello world \\n'" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -501,7 +501,7 @@ "' hello world'" ] }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -594,6 +594,8 @@ " 'lstrip',\n", " 'maketrans',\n", " 'partition',\n", + " 'removeprefix',\n", + " 'removesuffix',\n", " 'replace',\n", " 'rfind',\n", " 'rindex',\n", @@ -612,7 +614,7 @@ " 'zfill']" ] }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -637,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -664,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -673,7 +675,7 @@ "'hello world.\\nit is a nice day.'" ] }, - "execution_count": 24, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -703,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -712,7 +714,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 25, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -726,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -735,7 +737,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 32, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -766,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -775,7 +777,7 @@ "'abc'" ] }, - "execution_count": 51, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -786,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -795,7 +797,7 @@ "\"'abc'\"" ] }, - "execution_count": 50, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -830,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -839,7 +841,7 @@ "'0xff'" ] }, - "execution_count": 35, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -857,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "metadata": { "scrolled": true }, @@ -868,7 +870,7 @@ "'0o377'" ] }, - "execution_count": 36, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -886,7 +888,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -895,7 +897,7 @@ "'0b11111111'" ] }, - "execution_count": 37, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -913,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -922,7 +924,7 @@ "23" ] }, - "execution_count": 38, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -940,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -949,7 +951,7 @@ "255" ] }, - "execution_count": 39, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -960,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -969,7 +971,7 @@ "255" ] }, - "execution_count": 40, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -980,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -989,7 +991,7 @@ "255" ] }, - "execution_count": 41, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1007,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, "metadata": { "scrolled": true }, @@ -1018,7 +1020,7 @@ "3.5" ] }, - "execution_count": 42, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1045,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1054,7 +1056,7 @@ "'a b c'" ] }, - "execution_count": 43, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1072,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1081,7 +1083,7 @@ "'c b a'" ] }, - "execution_count": 44, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1099,7 +1101,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1108,7 +1110,7 @@ "'blue 10 1.5'" ] }, - "execution_count": 45, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -1126,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1135,7 +1137,7 @@ "'blue 10 1.5 foo'" ] }, - "execution_count": 46, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1153,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1162,7 +1164,7 @@ "'foo 5 6.28'" ] }, - "execution_count": 47, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1189,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1202,7 +1204,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1211,7 +1213,7 @@ "'some numbers: 1.340000, 2'" ] }, - "execution_count": 49, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1220,6 +1222,71 @@ "t" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 深入数字格式化" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| 数字 | 格式 | 输出 | 描述 |\n", + "|:-------------------|:-----------------------------------------------------------------------------------------|:---------------------|:--------------------------------|\n", + "| 3.1415926 | {:.2f} | 3.14 | 保留小数点后两位 |\n", + "| 3.1415926 | {:+.2f} | +3.14 | 带符号保留小数点后两位 |\n", + "| -1 | {:-.2f} | -1.00 | 带符号保留小数点后两位 |\n", + "| 2.71828 | {:.0f} | 3 | 不带小数 |\n", + "| 5 | {:0>2d} | 05 | 数字补零 (填充左边, 宽度为2) |\n", + "| 5 | {:x<4d} | 5xxx | 数字补x (填充右边, 宽度为4) |\n", + "| 10 | {:x<4d} | 10xx | 数字补x (填充右边, 宽度为4) |\n", + "| 1000000 | {:,} | 1,000,000 | 以逗号分隔的数字格式 |\n", + "| 0.25 | {:.2%} | 25.00% | 百分比格式 |\n", + "| 1000000000 | {:.2e} | 1.00e+09 | 指数记法 |\n", + "| 13 | {:>10d} | 13 | 右对齐 (默认, 宽度为10) |\n", + "| 13 | {:<10d} | 13 | 左对齐 (宽度为10) |\n", + "| 13 | {:^10d} | 13 | 中间对齐 (宽度为10) |\n", + "| 11 | '{:b}'.format(11)　　　　　　　　　　　　　　　　　　　　　 | 1011 | 二进制 |\n", + "| 11 |'{:d}'.format(11) 　　　　　　　　　　　　　　　　　　　　 | 11 | 十进制 |\n", + "| 11 |'{:o}'.format(11) 　　　　　　　　　　　　　　　　　　　　　|13|八进制|\n", + "| 11 |'{:x}'.format(11)　　　　　　　　　　　　　　　　　　　　　|b|十六进制|\n", + "| 11 | '{:#x}'.format(11)　　　　　　　　　　　　　　　　　　　　　|0xb|十六进制|\n", + "| 11 | '{:#X}'.format(11)　　　　　　　　　　　　　　　　　　　　|0XB|十六进制|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ```\n", + " ^, <, > 分别是居中、左对齐、右对齐，后面带宽度， : 号后面带填充的字符，只能是一个字符，不指定则默认是用空格填充。\n", + "\n", + "+ 表示在正数前显示 +，负数前显示 -； （空格）表示在正数前加空格\n", + "\n", + "b、d、o、x 分别是二进制、十进制、八进制、十六进制。\n", + "```\n", + "此外我们可以使用大括号 {} 来转义大括号，如下实例：" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "goodman 对应的位置是 {0}\n" + ] + } + ], + "source": [ + "print(\"{} 对应的位置是 {{0}}\".format(\"goodman\"))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1230,7 +1297,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1244,7 +1311,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, From 62c5c629df337e9037fc2822bee6ee6384a4ac32 Mon Sep 17 00:00:00 2001 From: master Date: Sat, 9 Jul 2022 16:31:36 +0800 Subject: [PATCH 45/49] update to python3 --- .../02.05-indexing-and-slicing.ipynb | 38 +------------------ 1 file changed, 2 insertions(+), 36 deletions(-) diff --git a/02-python-essentials/02.05-indexing-and-slicing.ipynb b/02-python-essentials/02.05-indexing-and-slicing.ipynb index c72d95c5..8ce37f15 100644 --- a/02-python-essentials/02.05-indexing-and-slicing.ipynb +++ b/02-python-essentials/02.05-indexing-and-slicing.ipynb @@ -403,40 +403,6 @@ "第二种表示方法从`-1`开始，不是很好，所以选择使用第一种`[low, up)`的形式。" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 使用0-base的形式" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> Just too beautiful to ignore. \n", - "----Guido van Rossum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "两种简单的情况：\n", - "\n", - "- 从头开始的n个元素；\n", - " - 使用0-base：`[0, n)`\n", - " - 使用1-base：`[1, n+1)`\n", - "\n", - "- 第`i+1`个元素到第`i+n`个元素。\n", - " - 使用0-base：`[i, n+i)`\n", - " - 使用1-base：`[i+1, n+i+1)`\n", - "\n", - "1-base有个`+1`部分，所以不推荐。\n", - "\n", - "综合这两种原因，**Python**使用0-base的方法来进行索引。" - ] - }, { "cell_type": "code", "execution_count": null, @@ -447,7 +413,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -461,7 +427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, From f5a5e394ca1c57b0d4d3b8caf252167bbb1bf225 Mon Sep 17 00:00:00 2001 From: Master Date: Sat, 23 Jul 2022 10:24:04 +0800 Subject: [PATCH 46/49] update --- .../02.02-python-data-types.ipynb | 83 +++- 02-python-essentials/02.03-numbers.ipynb | 153 +++++-- 02-python-essentials/02.04-strings.ipynb | 224 +++++----- .../02.05-indexing-and-slicing.ipynb | 70 ++-- 02-python-essentials/02.06-lists.ipynb | 212 +++++----- ....07-mutable-and-immutable-data-types.ipynb | 2 +- 02-python-essentials/02.08-tuples.ipynb | 63 ++- 02-python-essentials/02.10-dictionaries.ipynb | 242 +++++++---- 02-python-essentials/02.11-sets.ipynb | 385 ++++++++---------- 02-python-essentials/02.12-frozen-sets.ipynb | 28 +- 02-python-essentials/02.14-if-statement.ipynb | 212 ++++++---- 02-python-essentials/02.15-loops.ipynb | 179 +++----- .../02.16-list-comprehension.ipynb | 44 +- 13 files changed, 1076 insertions(+), 821 deletions(-) diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index 6a4d812b..c0cdda63 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -30,6 +30,85 @@ "| 布尔型 | `True, False`|\n" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1.])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a=np.ones(3)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0.])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b=np.zeros(3)\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: RuntimeWarning: divide by zero encountered in true_divide\n", + " a/b\n" + ] + }, + { + "data": { + "text/plain": [ + "array([inf, inf, inf])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a/b" + ] + }, { "cell_type": "code", "execution_count": null, @@ -40,7 +119,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -54,7 +133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.03-numbers.ipynb b/02-python-essentials/02.03-numbers.ipynb index 267977dd..549d925b 100644 --- a/02-python-essentials/02.03-numbers.ipynb +++ b/02-python-essentials/02.03-numbers.ipynb @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -99,7 +99,7 @@ "2.4" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -126,7 +126,7 @@ "2" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ "3" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -262,7 +262,7 @@ "int" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -293,7 +293,7 @@ "9223372036854775807" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -321,7 +321,7 @@ "float" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -418,7 +418,7 @@ "7.4" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -436,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -445,7 +445,7 @@ "0.19999999999999973" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -456,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -465,7 +465,7 @@ "44.7" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -476,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -485,7 +485,7 @@ "6.25" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -496,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -505,7 +505,7 @@ "1.2999999999999998" ] }, - "execution_count": 20, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -527,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -536,7 +536,7 @@ "'0.199999999999999733546474089962430298328399658203125'" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -545,6 +545,107 @@ "'{:.52}'.format(3.4 - 3.2)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "float division by zero", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero" + ] + } + ], + "source": [ + "1.0/0 " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "128" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2**7" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "240" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0b11110000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "248" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0b11111000" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "249" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0b11111001" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1358,7 +1459,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1372,7 +1473,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.04-strings.ipynb b/02-python-essentials/02.04-strings.ipynb index 22f4daa0..4af1281b 100644 --- a/02-python-essentials/02.04-strings.ipynb +++ b/02-python-essentials/02.04-strings.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -82,7 +82,7 @@ "'hello world'" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -101,22 +101,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'echoechoecho'" + "'echoechoechoecho'" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\"echo\" * 3" + "\"echo\" * 4" ] }, { @@ -128,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -137,7 +137,7 @@ "11" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -180,21 +180,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "['1', '2', '3', '4', '5']\n" - ] + "data": { + "text/plain": [ + "['1', '2', '3', '4', '5']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "line = \"1 2\\n 3 4\\t 5\"\n", - "numbers = line.split()\n", - "print(numbers)" + "numbers=line.split()\n", + "numbers" ] }, { @@ -206,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -239,16 +242,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'2 3'" + "'1 2 3 4 5'" ] }, - "execution_count": 15, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -260,16 +263,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'2,3'" + "'1,2,3,4,5'" ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -295,23 +298,23 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'hello python'" + "'hello kitty'" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"hello world\"\n", - "s.replace('world', 'python')" + "s.replace('world', 'kitty')" ] }, { @@ -323,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -332,7 +335,7 @@ "'hello world'" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -359,22 +362,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'HELLO WORLD'" + "'HELLO KITTY'" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\"hello world\".upper()" + "\"hello kitty\".upper()" ] }, { @@ -386,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -424,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -433,13 +436,13 @@ "'hello world'" ] }, - "execution_count": 21, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s = \" hello world \\n\"\n", + "s = \" hello world \\t \\n\"\n", "s.strip()" ] }, @@ -452,16 +455,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "' hello world \\n'" + "' hello world \\t \\n'" ] }, - "execution_count": 22, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -472,16 +475,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'hello world \\n'" + "'hello world \\t \\n'" ] }, - "execution_count": 23, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -492,7 +495,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -501,7 +504,7 @@ "' hello world'" ] }, - "execution_count": 24, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -666,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -675,7 +678,7 @@ "'hello world.\\nit is a nice day.'" ] }, - "execution_count": 27, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -714,7 +717,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 28, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -728,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -737,7 +740,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 29, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -768,42 +771,42 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'abc'" + "'ab\\nc'" ] }, - "execution_count": 30, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "str('abc')" + "str('ab\\nc')" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"'abc'\"" + "\"'ab\\\\nc'\"" ] }, - "execution_count": 31, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "repr('abc')" + "repr('ab\\nc')" ] }, { @@ -832,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -841,7 +844,7 @@ "'0xff'" ] }, - "execution_count": 32, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -859,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 24, "metadata": { "scrolled": true }, @@ -870,7 +873,7 @@ "'0o377'" ] }, - "execution_count": 33, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -888,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -897,7 +900,7 @@ "'0b11111111'" ] }, - "execution_count": 34, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1047,22 +1050,58 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'a b c'" + "'a=b+c'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a='a'\n", + "b='b'\n", + "c='c'\n", + "'{}={}+{}'.format(a,b,c)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "请输入姓名\n", + "请输入年龄\n" + ] + }, + { + "data": { + "text/plain": [ + "'你的名字是:，你的年龄是：'" ] }, - "execution_count": 40, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "'{} {} {}'.format('a', 'b', 'c')" + "name=input(\"请输入姓名\")\n", + "age = input(\"请输入年龄\")\n", + "# \"你的名字是\"+name+\"，你的年龄是：\"+age \n", + "\"你的名字是:{}，你的年龄是：{}\".format(name,age)" ] }, { @@ -1074,22 +1113,22 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'c b a'" + "'c b xsssa'" ] }, - "execution_count": 41, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "'{2} {1} {0}'.format('a', 'b', 'c')" + "'{2} {1} {0}'.format('xsssa', 'b', 'c')" ] }, { @@ -1101,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1110,7 +1149,7 @@ "'blue 10 1.5'" ] }, - "execution_count": 42, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1128,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 67, "metadata": {}, "outputs": [ { @@ -1137,7 +1176,7 @@ "'blue 10 1.5 foo'" ] }, - "execution_count": 43, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -1155,7 +1194,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -1164,7 +1203,7 @@ "'foo 5 6.28'" ] }, - "execution_count": 44, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -1191,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1199,25 +1238,14 @@ "x = 1.34\n", "y = 2\n", "# 用百分号隔开，括号括起来\n", - "t = \"%s %f, %d\" % (s, x, y)" + "\"%s %.2f, %d\" % (s, x, y)" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'some numbers: 1.340000, 2'" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "t" ] @@ -1297,7 +1325,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1311,7 +1339,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.05-indexing-and-slicing.ipynb b/02-python-essentials/02.05-indexing-and-slicing.ipynb index 8ce37f15..6f8a5662 100644 --- a/02-python-essentials/02.05-indexing-and-slicing.ipynb +++ b/02-python-essentials/02.05-indexing-and-slicing.ipynb @@ -23,23 +23,23 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'h'" + "'d'" ] }, - "execution_count": 1, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"hello world\"\n", - "s[0]" + "s[-1]" ] }, { @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -60,7 +60,7 @@ "'o'" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -78,22 +78,22 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'l'" + "'r'" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s[-2]" + "s[-3]" ] }, { @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -113,10 +113,10 @@ "evalue": "string index out of range", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: string index out of range" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m: string index out of range" ] } ], @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ "'hello world'" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -173,7 +173,7 @@ "'el'" ] }, - "execution_count": 6, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -207,7 +207,7 @@ "'ello wor'" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -227,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -236,7 +236,7 @@ "'hel'" ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -256,7 +256,7 @@ "'rld'" ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -267,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -276,7 +276,7 @@ "'hello world'" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -303,7 +303,7 @@ "'hlowrd'" ] }, - "execution_count": 11, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -330,7 +330,7 @@ "'dlrow olleh'" ] }, - "execution_count": 12, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -348,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -357,7 +357,7 @@ "'hello world'" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -413,7 +413,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -427,7 +427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.06-lists.ipynb b/02-python-essentials/02.06-lists.ipynb index 83aa4dec..50a08433 100644 --- a/02-python-essentials/02.06-lists.ipynb +++ b/02-python-essentials/02.06-lists.ipynb @@ -18,19 +18,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1, 2.0, 'hello']\n" + "[1, 2.0, 'hello', '32']\n" ] } ], "source": [ - "l = [1, 2.0, 'hello']\n", + "l = [1, 2.0, 'hello','32']\n", "print(l) " ] }, @@ -113,16 +113,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3" + "4" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ "[1, 2, 3, 3.2, 'hello']" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -176,16 +176,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[1, 2.0, 'hello', 1, 2.0, 'hello']" + "[1, 2.0, 'hello', '32', 1, 2.0, 'hello', '32']" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -226,7 +226,7 @@ "10" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -254,7 +254,7 @@ "14" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -281,7 +281,7 @@ "[12, 13]" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -309,10 +309,10 @@ "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello world\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# 把开头的 h 改成大写\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'H'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"hello world\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# 把开头的 h 改成大写\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'H'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -357,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -366,7 +366,7 @@ "[100, 1, 2, 13, 14]" ] }, - "execution_count": 12, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -411,22 +411,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1, 2]\n", - "[10, 11, 12]\n" + "[1, 2, 11]\n", + "[10, 12]\n" ] } ], "source": [ "a = [10, 1, 2, 11, 12]\n", - "print(a[1:3])\n", - "a[1:3] = []\n", + "print(a[1:4])\n", + "a[1:4] = []\n", "print(a)" ] }, @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -448,7 +448,7 @@ "[1, 11, 2, 13, 3]" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -468,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -478,10 +478,10 @@ "evalue": "attempt to assign sequence of size 0 to extended slice of size 3", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mValueError\u001b[0m: attempt to assign sequence of size 0 to extended slice of size 3" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mValueError\u001b[0m: attempt to assign sequence of size 0 to extended slice of size 3" ] } ], @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": { "scrolled": true }, @@ -516,13 +516,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "['a', 'b', 'c']\n" + "[1002, 'b', 'c']\n" ] } ], "source": [ "a = [1002, 'a', 'b', 'c']\n", - "del a[0]\n", + "del a[1]\n", "print(a)" ] }, @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -544,7 +544,7 @@ "[1002]" ] }, - "execution_count": 19, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -575,7 +575,7 @@ "[1, 2]" ] }, - "execution_count": 20, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -602,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -610,14 +610,14 @@ "output_type": "stream", "text": [ "True\n", - "False\n" + "True\n" ] } ], "source": [ "a = [10, 11, 12, 13, 14]\n", "print(10 in a)\n", - "print(10 not in a) " + "print(100 not in a) " ] }, { @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -637,7 +637,7 @@ "output_type": "stream", "text": [ "True\n", - "False\n" + "True\n" ] } ], @@ -656,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -665,7 +665,7 @@ "[12, 13]" ] }, - "execution_count": 25, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -684,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ "13" ] }, - "execution_count": 26, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -732,23 +732,23 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2" + "1" ] }, - "execution_count": 27, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a = [11, 12, 13, 12, 11]\n", - "a.count(11)" + "a = [11, 12, 13, 12, 11,11]\n", + "a.count(13)" ] }, { @@ -767,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -776,7 +776,7 @@ "1" ] }, - "execution_count": 28, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -794,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -802,10 +802,10 @@ "evalue": "1 is not in list", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mValueError\u001b[0m: 1 is not in list" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mValueError\u001b[0m: 1 is not in list" ] } ], @@ -836,23 +836,23 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[10, 11, 12, 11]" + "[10, 11, 12, 13]" ] }, - "execution_count": 30, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [10, 11, 12]\n", - "a.append(11)\n", + "a.append(13)\n", "a" ] }, @@ -865,16 +865,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[10, 11, 12, 11, [11, 12]]" + "[10, 11, 12, 13, [11, 12], [11, 12], [11, 12], [11, 12]]" ] }, - "execution_count": 31, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -900,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -909,7 +909,7 @@ "[10, 11, 12, 11, 1, 2]" ] }, - "execution_count": 32, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -936,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -945,7 +945,7 @@ "[10, 11, 12, 'a', 13, 11]" ] }, - "execution_count": 33, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -973,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -982,7 +982,7 @@ "[10, 12, 13, 11]" ] }, - "execution_count": 34, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1010,23 +1010,21 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 60, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "12" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "11\n" + ] } ], "source": [ "a = [10, 11, 12, 13, 11]\n", - "a.pop(2)" + "b=a.pop()\n", + "print(b)" ] }, { @@ -1045,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -1054,7 +1052,7 @@ "[1, 2, 10, 11, 11, 13]" ] }, - "execution_count": 36, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1074,7 +1072,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -1109,8 +1107,10 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, + "execution_count": 70, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1118,7 +1118,7 @@ "[6, 5, 4, 3, 2, 1]" ] }, - "execution_count": 41, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -1136,9 +1136,16 @@ "如果不想改变原来列表中的值，可以使用这样的方法：" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "如果不清楚用法，可以查看帮助：" + ] + }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -1146,24 +1153,17 @@ "output_type": "stream", "text": [ "[1, 2, 3, 4, 5, 6]\n", - "[6, 5, 4, 3, 2, 1]\n" + "[6, 4, 2]\n" ] } ], "source": [ "a = [1, 2, 3, 4, 5, 6]\n", - "b = a[::-1]\n", + "b = a[::-2]\n", "print(a)\n", "print(b)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "如果不清楚用法，可以查看帮助：" - ] - }, { "cell_type": "code", "execution_count": 44, @@ -1197,7 +1197,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb b/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb index e27d5087..46d661ab 100644 --- a/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb +++ b/02-python-essentials/02.07-mutable-and-immutable-data-types.ipynb @@ -331,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.08-tuples.ipynb b/02-python-essentials/02.08-tuples.ipynb index ba10e5e2..1d37345d 100644 --- a/02-python-essentials/02.08-tuples.ipynb +++ b/02-python-essentials/02.08-tuples.ipynb @@ -29,7 +29,7 @@ { "data": { "text/plain": [ - "(10, 11, 12, 13, 14)" + "(10, 11, 12, 13, 14, 15)" ] }, "execution_count": 1, @@ -38,7 +38,7 @@ } ], "source": [ - "t = (10, 11, 12, 13, 14)\n", + "t = (10, 11, 12, 13, 14,15)\n", "t" ] }, @@ -106,10 +106,10 @@ "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -188,7 +188,7 @@ "(10, 11, 12, 13, 14)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -198,6 +198,27 @@ "tuple(a)" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 3, 4, 5]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b=(1,3,4,5)\n", + "list(b)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -270,6 +291,26 @@ "数据库的返回值……" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"hello jack , I'm lu\"" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"hello %s , I'm %s\" % (\"jack\",\"lu\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -294,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.10-dictionaries.ipynb b/02-python-essentials/02.10-dictionaries.ipynb index 64dc0400..cbc88913 100644 --- a/02-python-essentials/02.10-dictionaries.ipynb +++ b/02-python-essentials/02.10-dictionaries.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 字典 " + "# 字典 Dict" ] }, { @@ -188,22 +188,22 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'this is number 1'" + "'this is number 1,too'" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "b = {'one': 'this is number 1', 'two': 'this is number 2'}\n", + "b = {'one': 'this is number 1,too', 'two': 'this is number 2'}\n", "b['one']" ] }, @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -240,14 +240,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'one': 'this is number 1', 'two': 'this is number 2'}\n" + "{'one': 'this is number 1,too', 'two': 'this is number 2'}\n" ] } ], @@ -259,12 +259,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "因此，**Python**中不能用支持用数字索引按顺序查看字典中的值，而且数字本身也有可能成为键值，这样会引起混淆：" + "因此，**Python**中不能用支持用数字索引按顺序查看字典中的值，而且数字本身也有可能成为键的内容，这样会引起混淆：" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -272,10 +272,10 @@ "evalue": "0", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m: 0" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 0" ] } ], @@ -284,6 +284,37 @@ "a[0]" ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'one'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d={0:\"one\"}\n", + "d[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "key： 键\n", + "value： 值 " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -353,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -365,7 +396,7 @@ " 4180: {'mag': 0.03, 'width': 30}}" ] }, - "execution_count": 11, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -390,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -402,7 +433,7 @@ " {'first': 'Diane', 'last': 'Chambers', 'name': 33}]" ] }, - "execution_count": 12, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -433,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -442,7 +473,7 @@ "{'foozelator': 123, 'frombicator': 18, 'spatzleblock': 34, 'snitzelhogen': 23}" ] }, - "execution_count": 13, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -466,22 +497,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'foozelator': 123, 'frombicator': 19, 'spatzleblock': 34, 'snitzelhogen': 23}" + "{'foozelator': 123, 'frombicator': 3, 'spatzleblock': 34, 'snitzelhogen': 23}" ] }, - "execution_count": 14, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "inventory['frombicator'] += 1\n", + "inventory['frombicator']+= 1\n", "inventory" ] }, @@ -501,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -509,10 +540,10 @@ "evalue": "3.3", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2.2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m6.6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3.3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m: 3.3" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1.1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2.2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m6.6\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# 会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3.3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 3.3" ] } ], @@ -559,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -578,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -622,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -640,23 +671,44 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": {}, "outputs": [ { "ename": "KeyError", - "evalue": "'three'", + "evalue": "'four'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"three\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m: 'three'" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"four\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 'four'" ] } ], "source": [ - "a[\"three\"]" + "a[\"four\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'one1'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'one1'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 'one1'" + ] + } + ], + "source": [ + "a['one1']" ] }, { @@ -668,20 +720,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 36, "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print a.get(\"three\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" ] } ], "source": [ - "print a.get(\"three\")" + "print(a.get(\"three\"))" ] }, { @@ -693,22 +744,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'undefined'" + "'not found'" ] }, - "execution_count": 23, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a.get(\"three\", \"undefined\")" + "a.get(\"three\", 'not found')" ] }, { @@ -731,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -740,7 +791,7 @@ "{'one': 'this is number 1', 'two': 'this is number 2'}" ] }, - "execution_count": 24, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -758,20 +809,21 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 57, "metadata": { "scrolled": true }, "outputs": [ { - "data": { - "text/plain": [ - "'this is number 2'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "'two'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"two\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 'two'" + ] } ], "source": [ @@ -780,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -789,7 +841,7 @@ "{'one': 'this is number 1'}" ] }, - "execution_count": 26, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -807,22 +859,22 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'not exist'" + "''" ] }, - "execution_count": 27, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a.pop(\"two\", 'not exist')" + "a.pop(\"two\",\"\")" ] }, { @@ -834,22 +886,42 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'one'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"one\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 'one'" + ] + } + ], + "source": [ + "del a[\"one\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{}" + "{'two': 'this is number 2'}" ] }, - "execution_count": 28, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "del a[\"one\"]\n", "a" ] }, @@ -873,7 +945,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -882,7 +954,7 @@ "{'first': 'Jmes', 'last': 'Maxwell', 'born': 1831}" ] }, - "execution_count": 30, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -913,7 +985,7 @@ "{'first': 'James', 'last': 'Maxwell', 'born': 1831, 'middle': 'Clerk'}" ] }, - "execution_count": 31, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -933,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -949,7 +1021,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -958,7 +1030,7 @@ "False" ] }, - "execution_count": 33, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -969,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -978,7 +1050,7 @@ "True" ] }, - "execution_count": 34, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -1013,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1022,7 +1094,7 @@ "dict_keys(['cows', 'dogs', 'cats'])" ] }, - "execution_count": 35, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -1033,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -1042,7 +1114,7 @@ "dict_values([1, 5, 3])" ] }, - "execution_count": 36, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -1095,7 +1167,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/02-python-essentials/02.11-sets.ipynb b/02-python-essentials/02.11-sets.ipynb index 158086d0..7651a3dc 100644 --- a/02-python-essentials/02.11-sets.ipynb +++ b/02-python-essentials/02.11-sets.ipynb @@ -32,10 +32,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { @@ -43,7 +41,7 @@ "set" ] }, - "execution_count": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -62,10 +60,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { @@ -73,13 +69,13 @@ "{1, 2, 3}" ] }, - "execution_count": 2, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a = set([1, 2, 3, 1])\n", + "a = set([1, 2, 3, 1,3])\n", "a" ] }, @@ -99,10 +95,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -110,7 +104,7 @@ "{1, 2, 3}" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -129,10 +123,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -140,7 +132,7 @@ "dict" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -166,10 +158,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "a = {1, 2, 3, 4}\n", @@ -194,10 +184,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { @@ -205,7 +193,7 @@ "{1, 2, 3, 4, 5, 6}" ] }, - "execution_count": 6, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -216,10 +204,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { @@ -227,7 +213,7 @@ "{1, 2, 3, 4, 5, 6}" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -238,10 +224,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { @@ -249,7 +233,7 @@ "{1, 2, 3, 4, 5, 6}" ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -276,10 +260,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { @@ -287,7 +269,7 @@ "{3, 4}" ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -298,10 +280,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { @@ -309,7 +289,7 @@ "{3, 4}" ] }, - "execution_count": 10, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -320,10 +300,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { @@ -331,7 +309,7 @@ "{3, 4}" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -342,16 +320,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "set([3, 4])\n" + "{3, 4}\n" ] } ], @@ -384,10 +360,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { @@ -395,7 +369,7 @@ "{1, 2}" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -406,10 +380,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "data": { @@ -417,7 +389,7 @@ "{1, 2}" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -435,10 +407,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -446,7 +416,7 @@ "{5, 6}" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -457,10 +427,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -468,7 +436,7 @@ "{5, 6}" ] }, - "execution_count": 16, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -495,10 +463,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [ { "data": { @@ -506,7 +472,7 @@ "{1, 2, 5, 6}" ] }, - "execution_count": 17, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -517,10 +483,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -528,7 +492,7 @@ "{1, 2, 5, 6}" ] }, - "execution_count": 18, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -539,10 +503,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -550,7 +512,7 @@ "{1, 2, 5, 6}" ] }, - "execution_count": 19, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -575,10 +537,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "a = {1, 2, 3}\n", @@ -594,10 +554,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 43, + "metadata": {}, "outputs": [ { "data": { @@ -605,7 +563,7 @@ "True" ] }, - "execution_count": 21, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -616,10 +574,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 45, + "metadata": {}, "outputs": [ { "data": { @@ -627,7 +583,7 @@ "True" ] }, - "execution_count": 22, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -645,10 +601,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 46, + "metadata": {}, "outputs": [ { "data": { @@ -656,7 +610,7 @@ "True" ] }, - "execution_count": 23, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -667,10 +621,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 48, + "metadata": {}, "outputs": [ { "data": { @@ -678,7 +630,7 @@ "True" ] }, - "execution_count": 24, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -696,10 +648,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "data": { @@ -707,7 +657,7 @@ "True" ] }, - "execution_count": 25, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -725,10 +675,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 50, + "metadata": {}, "outputs": [ { "data": { @@ -736,7 +684,7 @@ "False" ] }, - "execution_count": 26, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -772,10 +720,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 52, + "metadata": {}, "outputs": [ { "data": { @@ -783,7 +729,7 @@ "{1, 2, 3, 5}" ] }, - "execution_count": 27, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -803,10 +749,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 53, + "metadata": {}, "outputs": [ { "data": { @@ -814,7 +758,7 @@ "{1, 2, 3, 5}" ] }, - "execution_count": 28, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -844,10 +788,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 54, + "metadata": {}, "outputs": [ { "data": { @@ -855,7 +797,7 @@ "{1, 2, 3, 5, 6, 7}" ] }, - "execution_count": 29, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -883,20 +825,19 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 56, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{2, 3, 5, 6, 7}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "1", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 1" + ] } ], "source": [ @@ -906,10 +847,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 57, + "metadata": {}, "outputs": [ { "ename": "KeyError", @@ -918,7 +857,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyError\u001b[0m: 10" ] } @@ -943,20 +882,19 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 63, + "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{3, 5, 6, 7}" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "'pop from an empty set'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 'pop from an empty set'" + ] } ], "source": [ @@ -965,29 +903,25 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 64, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "set([3, 5, 6, 7])\n" + "set()\n" ] } ], "source": [ - "print t" + "print(t)" ] }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 65, + "metadata": {}, "outputs": [ { "ename": "KeyError", @@ -996,7 +930,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# 报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# 报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyError\u001b[0m: 'pop from an empty set'" ] } @@ -1023,10 +957,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, + "execution_count": 68, + "metadata": {}, "outputs": [], "source": [ "t.discard(3)" @@ -1034,18 +966,16 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 69, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{5, 6, 7}" + "set()" ] }, - "execution_count": 36, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1063,10 +993,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, + "execution_count": 70, + "metadata": {}, "outputs": [], "source": [ "t.discard(20)" @@ -1074,18 +1002,16 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 71, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{5, 6, 7}" + "set()" ] }, - "execution_count": 38, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1109,27 +1035,66 @@ "\n", "从a中去除所有属于b的元素：" ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "a={1,2,3}" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "b={2,3}\n", + "a.difference_update(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.12-frozen-sets.ipynb b/02-python-essentials/02.12-frozen-sets.ipynb index 054e74a7..51501f78 100644 --- a/02-python-essentials/02.12-frozen-sets.ipynb +++ b/02-python-essentials/02.12-frozen-sets.ipynb @@ -19,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -51,9 +49,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -87,9 +83,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -109,9 +103,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -131,23 +123,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.14-if-statement.ipynb b/02-python-essentials/02.14-if-statement.ipynb index dcbc780e..afd7744c 100644 --- a/02-python-essentials/02.14-if-statement.ipynb +++ b/02-python-essentials/02.14-if-statement.ipynb @@ -23,25 +23,23 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hey!\n", - "x is positive\n" + "over\n" ] } ], "source": [ - "x = 0.5\n", + "x = -0.5\n", "if x > 0:\n", - " print \"Hey!\"\n", - " print \"x is positive\"" + " print (\"Hey!\")\n", + " print (\"x is positive\")\n", + "print(\"over\")" ] }, { @@ -66,10 +64,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": { - "collapsed": false, - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -86,10 +83,10 @@ "source": [ "x = 0.5\n", "if x > 0:\n", - " print \"Hey!\"\n", - " print \"x is positive\"\n", - " print \"This is still part of the block\"\n", - "print \"This isn't part of the block, and will always print.\"" + " print (\"Hey!\")\n", + " print (\"x is positive\")\n", + " print (\"This is still part of the block\")\n", + "print (\"This isn't part of the block, and will always print.\")" ] }, { @@ -101,10 +98,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -117,10 +112,10 @@ "source": [ "x = -0.5\n", "if x > 0:\n", - " print \"Hey!\"\n", - " print \"x is positive\"\n", - " print \"This is still part of the block\"\n", - "print \"This isn't part of the block, and will always print.\"" + " print( \"Hey!\")\n", + " print( \"x is positive\")\n", + " print( \"This is still part of the block\")\n", + "print (\"This isn't part of the block, and will always print.\")" ] }, { @@ -146,27 +141,33 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "x is zero\n" + "输入数字-1\n", + "-1\n", + "\n", + "\n", + "x is negative\n" ] } ], "source": [ - "x = 0\n", + "x = input('输入数字')\n", + "print(x)\n", + "print(type(x))\n", + "x=int(x)\n", + "print(type(x))\n", "if x > 0:\n", - " print \"x is positive\"\n", + " print (\"x is positive\")\n", "elif x == 0:\n", - " print \"x is zero\"\n", + " print (\"x is zero\")\n", "else:\n", - " print \"x is negative\"" + " print (\"x is negative\")" ] }, { @@ -182,10 +183,58 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "今天是星期三\n" + ] + } + ], + "source": [ + "a='3'\n", + "if a=='1':\n", + " print('今天是星期一')\n", + "elif a==\"2\":\n", + " print(\"今天是星期二\")\n", + "elif a==\"3\":\n", + " print(\"今天是星期三\")\n", + "else:\n", + " print('今天不是星期一')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b > 0\n" + ] + } + ], + "source": [ + "b=1\n", + "if b==4:\n", + " print('b is 4')\n", + "elif b>2:\n", + " print('b > 2')\n", + "elif b>0:\n", + " print('b > 0')\n", + "else:\n", + " print('b !>0')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, "outputs": [ { "data": { @@ -193,7 +242,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -206,10 +255,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { @@ -217,7 +264,7 @@ "False" ] }, - "execution_count": 6, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -228,10 +275,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -239,7 +284,7 @@ "True" ] }, - "execution_count": 7, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -257,28 +302,26 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "This is not a leap year.\n" + "This is a leap year!\n" ] } ], "source": [ - "year = 1900\n", + "year = 2000\n", "if year % 400 == 0:\n", - " print \"This is a leap year!\"\n", + " print(\"This is a leap year!\")\n", "# 两个条件都满足才执行\n", "elif year % 4 == 0 and year % 100 != 0:\n", - " print \"This is a leap year!\"\n", + " print(\"This is a leap year!\")\n", "else:\n", - " print \"This is not a leap year.\"" + " print(\"This is not a leap year.\")" ] }, { @@ -304,10 +347,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -320,9 +361,9 @@ "source": [ "mylist = [3, 1, 4, 1, 5, 9]\n", "if mylist:\n", - " print \"The first element is:\", mylist[0]\n", + " print (\"The first element is:\", mylist[0])\n", "else:\n", - " print \"There is no first element.\"" + " print( \"There is no first element.\")" ] }, { @@ -334,10 +375,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -350,38 +389,57 @@ "source": [ "mylist = []\n", "if mylist:\n", - " print \"The first element is:\", mylist[0]\n", + " print( \"The first element is:\", mylist[0])\n", "else:\n", - " print \"There is no first element.\"" + " print (\"There is no first element.\")" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 39, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ - "当然这种用法并不推荐，推荐使用 `if len(mylist) > 0:` 来判断一个列表是否为空。" + "a=()\n", + "if not a:\n", + " print(\"True\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.15-loops.ipynb b/02-python-essentials/02.15-loops.ipynb index 399ca7de..0e264924 100644 --- a/02-python-essentials/02.15-loops.ipynb +++ b/02-python-essentials/02.15-loops.ipynb @@ -36,25 +36,26 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "499999500000\n" - ] + "data": { + "text/plain": [ + "499999500000" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "i = 0\n", "total = 0\n", "while i < 1000000:\n", - " total += i\n", + " total =total+ i\n", " i += 1\n", - "print total" + "total" ] }, { @@ -67,17 +68,15 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Perform Hamlet\n", "Perform King Lear\n", - "Perform Macbeth\n", - "Perform Hamlet\n" + "Perform Macbeth\n" ] } ], @@ -85,7 +84,7 @@ "plays = set(['Hamlet', 'Macbeth', 'King Lear'])\n", "while plays:\n", " play = plays.pop()\n", - " print 'Perform', play" + " print('Perform', play)" ] }, { @@ -116,25 +115,25 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Perform Hamlet\n", "Perform King Lear\n", "Perform Macbeth\n", - "Perform Hamlet\n" + "{'Hamlet', 'King Lear', 'Macbeth'}\n" ] } ], "source": [ "plays = set(['Hamlet', 'Macbeth', 'King Lear'])\n", "for play in plays:\n", - " print 'Perform', play" + " print('Perform', play)\n", + "print(plays)" ] }, { @@ -148,57 +147,22 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4999950000\n" - ] - } - ], - "source": [ - "total = 0\n", - "for i in range(100000):\n", - " total += i\n", - "print total" - ] - }, - { - "cell_type": "markdown", + "execution_count": 13, "metadata": {}, - "source": [ - "然而这种写法有一个缺点：在循环前，它会生成一个长度为 `100000` 的临时列表。\n", - "\n", - "生成列表的问题在于，会有一定的时间和内存消耗，当数字从 `100000` 变得更大时，时间和内存的消耗会更加明显。\n", - "\n", - "为了解决这个问题，我们可以使用 `xrange` 来代替 `range` 函数，其效果与`range`函数相同，但是 `xrange` 并不会一次性的产生所有的数据：" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "4999950000\n" + "499999500000\n" ] } ], "source": [ "total = 0\n", - "for i in xrange(100000):\n", + "for i in range(1000000):\n", " total += i\n", - "print total" + "print(total)" ] }, { @@ -210,35 +174,14 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 40.7 ms per loop\n" - ] - } - ], - "source": [ - "%timeit for i in xrange(1000000): i = i" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10 loops, best of 3: 96.6 ms per loop\n" + "23.7 ms ± 587 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -246,13 +189,6 @@ "%timeit for i in range(1000000): i = i" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "可以看出，`xrange` 用时要比 `range` 少。 " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -271,18 +207,16 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "3\n", - "2\n", - "1\n" + "3.0\n", + "2.0\n", + "1.0\n" ] } ], @@ -292,7 +226,7 @@ " if i % 2 != 0:\n", " # 忽略奇数\n", " continue\n", - " print i/2" + " print(i/2)" ] }, { @@ -311,10 +245,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -371,10 +303,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -388,10 +318,10 @@ "values = [7, 6, 4, 7, 19, 2, 1]\n", "for x in values:\n", " if x <= 10:\n", - " print 'Found:', x\n", + " print('Found:', x)\n", " break\n", "else:\n", - " print 'All values greater than 10'" + " print('All values greater than 10')" ] }, { @@ -403,10 +333,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -420,32 +348,39 @@ "values = [11, 12, 13, 100]\n", "for x in values:\n", " if x <= 10:\n", - " print 'Found:', x\n", + " print('Found:', x)\n", " break\n", "else:\n", - " print 'All values greater than 10'" + " print('All values greater than 10')" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.16-list-comprehension.ipynb b/02-python-essentials/02.16-list-comprehension.ipynb index df732d31..014d1aff 100644 --- a/02-python-essentials/02.16-list-comprehension.ipynb +++ b/02-python-essentials/02.16-list-comprehension.ipynb @@ -17,9 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -47,9 +45,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -82,9 +78,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -110,9 +104,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -140,9 +132,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -169,9 +159,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -214,9 +202,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -233,9 +219,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -252,23 +236,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 6abc9fb350776141c4cbfd127b165c2e626ffc4e Mon Sep 17 00:00:00 2001 From: master Date: Sat, 23 Jul 2022 21:39:45 +0800 Subject: [PATCH 47/49] update to python3 --- 02-python-essentials/02.04-strings.ipynb | 130 +++++------ .../02.05-indexing-and-slicing.ipynb | 9 +- 02-python-essentials/02.06-lists.ipynb | 87 ++++---- 02-python-essentials/02.08-tuples.ipynb | 29 +-- 02-python-essentials/02.10-dictionaries.ipynb | 132 ++++++------ 02-python-essentials/02.11-sets.ipynb | 204 ++++++------------ .../02.13-how-python-assignment-works.ipynb | 78 ++----- 02-python-essentials/02.14-if-statement.ipynb | 126 +++++------ .../02.16-list-comprehension.ipynb | 60 ++---- 02-python-essentials/02.17-functions.ipynb | 114 +++++----- 10 files changed, 390 insertions(+), 579 deletions(-) diff --git a/02-python-essentials/02.04-strings.ipynb b/02-python-essentials/02.04-strings.ipynb index 22f4daa0..6f4c49d3 100644 --- a/02-python-essentials/02.04-strings.ipynb +++ b/02-python-essentials/02.04-strings.ipynb @@ -180,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -239,16 +239,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'2 3'" + "'1 2 3 4 5'" ] }, - "execution_count": 15, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -260,16 +260,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'2,3'" + "'1,2,3,4,5'" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -295,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -304,7 +304,7 @@ "'hello python'" ] }, - "execution_count": 17, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -332,7 +332,7 @@ "'hello world'" ] }, - "execution_count": 18, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -368,7 +368,7 @@ "'HELLO WORLD'" ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -386,7 +386,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -433,7 +433,7 @@ "'hello world'" ] }, - "execution_count": 21, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -452,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -461,7 +461,7 @@ "' hello world \\n'" ] }, - "execution_count": 22, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -472,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -481,7 +481,7 @@ "'hello world \\n'" ] }, - "execution_count": 23, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -492,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -501,7 +501,7 @@ "' hello world'" ] }, - "execution_count": 24, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -526,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -614,7 +614,7 @@ " 'zfill']" ] }, - "execution_count": 25, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -666,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -675,7 +675,7 @@ "'hello world.\\nit is a nice day.'" ] }, - "execution_count": 27, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -714,7 +714,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 28, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -728,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -737,7 +737,7 @@ "\"hello, world. it's a nice day. my name is xxx\"" ] }, - "execution_count": 29, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -777,7 +777,7 @@ "'abc'" ] }, - "execution_count": 30, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -788,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -797,7 +797,7 @@ "\"'abc'\"" ] }, - "execution_count": 31, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -832,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -841,7 +841,7 @@ "'0xff'" ] }, - "execution_count": 32, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -859,7 +859,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": { "scrolled": true }, @@ -870,7 +870,7 @@ "'0o377'" ] }, - "execution_count": 33, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -888,7 +888,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -897,7 +897,7 @@ "'0b11111111'" ] }, - "execution_count": 34, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -915,7 +915,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -924,7 +924,7 @@ "23" ] }, - "execution_count": 35, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -942,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -951,7 +951,7 @@ "255" ] }, - "execution_count": 36, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -962,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -971,7 +971,7 @@ "255" ] }, - "execution_count": 37, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -982,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -991,7 +991,7 @@ "255" ] }, - "execution_count": 38, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1009,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": { "scrolled": true }, @@ -1020,7 +1020,7 @@ "3.5" ] }, - "execution_count": 39, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1047,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1056,7 +1056,7 @@ "'a b c'" ] }, - "execution_count": 40, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1074,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1083,7 +1083,7 @@ "'c b a'" ] }, - "execution_count": 41, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1101,7 +1101,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1110,7 +1110,7 @@ "'blue 10 1.5'" ] }, - "execution_count": 42, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1128,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1137,7 +1137,7 @@ "'blue 10 1.5 foo'" ] }, - "execution_count": 43, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1155,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1164,7 +1164,7 @@ "'foo 5 6.28'" ] }, - "execution_count": 44, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1191,7 +1191,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1204,7 +1204,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1213,7 +1213,7 @@ "'some numbers: 1.340000, 2'" ] }, - "execution_count": 46, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } diff --git a/02-python-essentials/02.05-indexing-and-slicing.ipynb b/02-python-essentials/02.05-indexing-and-slicing.ipynb index 8ce37f15..a60b09f2 100644 --- a/02-python-essentials/02.05-indexing-and-slicing.ipynb +++ b/02-python-essentials/02.05-indexing-and-slicing.ipynb @@ -115,7 +115,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ms\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m]\u001b[49m\n", "\u001b[0;31mIndexError\u001b[0m: string index out of range" ] } @@ -402,13 +402,6 @@ "\n", "第二种表示方法从`-1`开始，不是很好，所以选择使用第一种`[low, up)`的形式。" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/02-python-essentials/02.06-lists.ipynb b/02-python-essentials/02.06-lists.ipynb index 83aa4dec..38985236 100644 --- a/02-python-essentials/02.06-lists.ipynb +++ b/02-python-essentials/02.06-lists.ipynb @@ -311,7 +311,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello world\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# 把开头的 h 改成大写\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'H'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [10]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhello world\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# 把开头的 h 改成大写\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m s[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mH\u001b[39m\u001b[38;5;124m'\u001b[39m\n", "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } @@ -480,7 +480,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [16]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m a[::\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m []\n", "\u001b[0;31mValueError\u001b[0m: attempt to assign sequence of size 0 to extended slice of size 3" ] } @@ -507,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "scrolled": true }, @@ -535,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -544,7 +544,7 @@ "[1002]" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -564,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -575,7 +575,7 @@ "[1, 2]" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -602,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -656,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -665,7 +665,7 @@ "[12, 13]" ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -684,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ "13" ] }, - "execution_count": 26, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -732,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -741,7 +741,7 @@ "2" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -767,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -776,7 +776,7 @@ "1" ] }, - "execution_count": 28, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -794,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -804,7 +804,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mValueError\u001b[0m: 1 is not in list" ] } @@ -836,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -845,7 +845,7 @@ "[10, 11, 12, 11]" ] }, - "execution_count": 30, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -865,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -874,7 +874,7 @@ "[10, 11, 12, 11, [11, 12]]" ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -900,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -909,7 +909,7 @@ "[10, 11, 12, 11, 1, 2]" ] }, - "execution_count": 32, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -936,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -945,7 +945,7 @@ "[10, 11, 12, 'a', 13, 11]" ] }, - "execution_count": 33, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -973,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -982,7 +982,7 @@ "[10, 12, 13, 11]" ] }, - "execution_count": 34, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1010,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1019,7 +1019,7 @@ "12" ] }, - "execution_count": 35, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1045,7 +1045,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1054,7 +1054,7 @@ "[1, 2, 10, 11, 11, 13]" ] }, - "execution_count": 36, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1074,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1109,7 +1109,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1118,7 +1118,7 @@ "[6, 5, 4, 3, 2, 1]" ] }, - "execution_count": 41, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1138,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1166,24 +1166,17 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "a.sort??" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1197,7 +1190,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/02-python-essentials/02.08-tuples.ipynb b/02-python-essentials/02.08-tuples.ipynb index ba10e5e2..73d79cd3 100644 --- a/02-python-essentials/02.08-tuples.ipynb +++ b/02-python-essentials/02.08-tuples.ipynb @@ -108,7 +108,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# 会报错\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m t[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -188,7 +188,7 @@ "(10, 11, 12, 13, 14)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -223,7 +223,7 @@ "1" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -234,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -243,7 +243,7 @@ "2" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -269,18 +269,11 @@ "\n", "数据库的返回值……" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -294,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/02-python-essentials/02.10-dictionaries.ipynb b/02-python-essentials/02.10-dictionaries.ipynb index 64dc0400..49b82b88 100644 --- a/02-python-essentials/02.10-dictionaries.ipynb +++ b/02-python-essentials/02.10-dictionaries.ipynb @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -240,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -274,7 +274,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# 会报错\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43ma\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n", "\u001b[0;31mKeyError\u001b[0m: 0" ] } @@ -308,14 +308,7 @@ { "data": { "text/plain": [ - "{'immutable': ['fixed',\n", - " 'set',\n", - " 'rigid',\n", - " 'inflexible',\n", - " 'permanent',\n", - " 'established',\n", - " 'carved in stone'],\n", - " 'mutable': ['changeable',\n", + "{'mutable': ['changeable',\n", " 'variable',\n", " 'varying',\n", " 'fluctuating',\n", @@ -326,7 +319,14 @@ " 'fickle',\n", " 'uneven',\n", " 'unstable',\n", - " 'protean']}" + " 'protean'],\n", + " 'immutable': ['fixed',\n", + " 'set',\n", + " 'rigid',\n", + " 'inflexible',\n", + " 'permanent',\n", + " 'established',\n", + " 'carved in stone']}" ] }, "execution_count": 10, @@ -501,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -511,7 +511,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2.2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m6.6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# 会报错\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3.3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m data[\u001b[38;5;241m1.1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m2.2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m6.6\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# 会报错\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3.3\u001b[39;49m\u001b[43m]\u001b[49m\n", "\u001b[0;31mKeyError\u001b[0m: 3.3" ] } @@ -532,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -541,7 +541,7 @@ "{3.3000000000000003: 6.6}" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -559,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -578,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -622,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -650,7 +650,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"three\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [20]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mthree\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n", "\u001b[0;31mKeyError\u001b[0m: 'three'" ] } @@ -668,15 +668,15 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", + "evalue": "invalid syntax (2637741548.py, line 1)", "output_type": "error", "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print a.get(\"three\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + "\u001b[0;36m Input \u001b[0;32mIn [21]\u001b[0;36m\u001b[0m\n\u001b[0;31m print a.get(\"three\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], @@ -693,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -702,7 +702,7 @@ "'undefined'" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -731,16 +731,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'one': 'this is number 1', 'two': 'this is number 2'}" + "{'one': 'this is number 1'}" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -758,20 +758,21 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { - "data": { - "text/plain": [ - "'this is number 2'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" + "ename": "KeyError", + "evalue": "'two'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtwo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyError\u001b[0m: 'two'" + ] } ], "source": [ @@ -780,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -789,7 +790,7 @@ "{'one': 'this is number 1'}" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -807,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -816,7 +817,7 @@ "'not exist'" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -834,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -843,7 +844,7 @@ "{}" ] }, - "execution_count": 28, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -873,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -882,7 +883,7 @@ "{'first': 'Jmes', 'last': 'Maxwell', 'born': 1831}" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -913,7 +914,7 @@ "{'first': 'James', 'last': 'Maxwell', 'born': 1831, 'middle': 'Clerk'}" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -933,7 +934,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -949,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -958,7 +959,7 @@ "False" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -969,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -978,7 +979,7 @@ "True" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1013,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1022,7 +1023,7 @@ "dict_keys(['cows', 'dogs', 'cats'])" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1033,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1042,7 +1043,7 @@ "dict_values([1, 5, 3])" ] }, - "execution_count": 36, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1053,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1062,7 +1063,7 @@ "dict_items([('cows', 1), ('dogs', 5), ('cats', 3)])" ] }, - "execution_count": 37, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1070,18 +1071,11 @@ "source": [ "barn.items()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1095,7 +1089,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/02-python-essentials/02.11-sets.ipynb b/02-python-essentials/02.11-sets.ipynb index 158086d0..d5db3857 100644 --- a/02-python-essentials/02.11-sets.ipynb +++ b/02-python-essentials/02.11-sets.ipynb @@ -33,9 +33,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -63,9 +61,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -100,9 +96,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -130,9 +124,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -167,9 +159,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "a = {1, 2, 3, 4}\n", @@ -195,9 +185,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -217,9 +205,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -239,9 +225,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -277,9 +261,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -299,9 +281,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -321,9 +301,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -343,15 +321,13 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "set([3, 4])\n" + "{3, 4}\n" ] } ], @@ -385,9 +361,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -407,9 +381,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -436,9 +408,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -458,9 +428,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -496,9 +464,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -518,9 +484,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -540,9 +504,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -576,9 +538,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "a = {1, 2, 3}\n", @@ -595,9 +555,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -617,9 +575,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -646,9 +602,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -668,9 +622,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -697,9 +649,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -726,9 +676,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -773,9 +721,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -804,9 +750,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -845,9 +789,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -884,9 +826,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -907,19 +847,17 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "10", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m: 10" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [31]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyError\u001b[0m: 10" ] } ], @@ -944,14 +882,12 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{3, 5, 6, 7}" + "2" ] }, "execution_count": 32, @@ -965,39 +901,35 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "set([3, 5, 6, 7])\n" + "{3, 5, 6, 7}\n" ] } ], "source": [ - "print t" + "print(t)" ] }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'pop from an empty set'", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# 报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m: 'pop from an empty set'" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [36]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# 报错\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mKeyError\u001b[0m: 'pop from an empty set'" ] } ], @@ -1023,10 +955,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, + "execution_count": 37, + "metadata": {}, "outputs": [], "source": [ "t.discard(3)" @@ -1034,10 +964,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "data": { @@ -1045,7 +973,7 @@ "{5, 6, 7}" ] }, - "execution_count": 36, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1063,10 +991,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [], "source": [ "t.discard(20)" @@ -1074,10 +1000,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { @@ -1085,7 +1009,7 @@ "{5, 6, 7}" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1113,23 +1037,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.13-how-python-assignment-works.ipynb b/02-python-essentials/02.13-how-python-assignment-works.ipynb index 330adc2e..3b4a146c 100644 --- a/02-python-essentials/02.13-how-python-assignment-works.ipynb +++ b/02-python-essentials/02.13-how-python-assignment-works.ipynb @@ -17,9 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -96,9 +94,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -119,9 +115,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -151,9 +145,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -182,9 +174,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -205,9 +195,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -236,9 +224,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -259,9 +245,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -282,9 +266,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -313,9 +295,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -336,9 +316,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -359,9 +337,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -440,9 +416,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -474,7 +448,6 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -494,9 +467,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -524,7 +495,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -551,9 +521,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -579,9 +547,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -601,23 +567,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.14-if-statement.ipynb b/02-python-essentials/02.14-if-statement.ipynb index dcbc780e..f4c1ebca 100644 --- a/02-python-essentials/02.14-if-statement.ipynb +++ b/02-python-essentials/02.14-if-statement.ipynb @@ -23,10 +23,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -40,8 +38,8 @@ "source": [ "x = 0.5\n", "if x > 0:\n", - " print \"Hey!\"\n", - " print \"x is positive\"" + " print(\"Hey!\")\n", + " print(\"x is positive\")" ] }, { @@ -54,8 +52,8 @@ "\n", "上面例子中的这两条语句：\n", "```python\n", - " print \"Hey!\"\n", - " print \"x is positive\"\n", + " print(\"Hey!\")\n", + " print(\"x is positive\")\n", "```\n", "就叫做一个代码块，同一个代码块使用同样的缩进值，它们组成了这条 `if` 语句的主体。\n", "\n", @@ -66,9 +64,8 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -86,10 +83,10 @@ "source": [ "x = 0.5\n", "if x > 0:\n", - " print \"Hey!\"\n", - " print \"x is positive\"\n", - " print \"This is still part of the block\"\n", - "print \"This isn't part of the block, and will always print.\"" + " print(\"Hey!\")\n", + " print(\"x is positive\")\n", + " print(\"This is still part of the block\")\n", + "print(\"This isn't part of the block, and will always print.\")" ] }, { @@ -101,10 +98,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -117,10 +112,10 @@ "source": [ "x = -0.5\n", "if x > 0:\n", - " print \"Hey!\"\n", - " print \"x is positive\"\n", - " print \"This is still part of the block\"\n", - "print \"This isn't part of the block, and will always print.\"" + " print(\"Hey!\")\n", + " print(\"x is positive\")\n", + " print(\"This is still part of the block\")\n", + "print(\"This isn't part of the block, and will always print.\")" ] }, { @@ -146,10 +141,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -162,11 +155,11 @@ "source": [ "x = 0\n", "if x > 0:\n", - " print \"x is positive\"\n", + " print(\"x is positive\")\n", "elif x == 0:\n", - " print \"x is zero\"\n", + " print( \"x is zero\")\n", "else:\n", - " print \"x is negative\"" + " print( \"x is negative\")" ] }, { @@ -182,10 +175,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -193,7 +184,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -206,10 +197,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { @@ -217,7 +206,7 @@ "False" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -228,10 +217,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { @@ -239,7 +226,7 @@ "True" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -257,10 +244,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -273,12 +258,12 @@ "source": [ "year = 1900\n", "if year % 400 == 0:\n", - " print \"This is a leap year!\"\n", + " print( \"This is a leap year!\")\n", "# 两个条件都满足才执行\n", "elif year % 4 == 0 and year % 100 != 0:\n", - " print \"This is a leap year!\"\n", + " print(\"This is a leap year!\")\n", "else:\n", - " print \"This is not a leap year.\"" + " print(\"This is not a leap year.\")" ] }, { @@ -304,10 +289,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -320,9 +303,9 @@ "source": [ "mylist = [3, 1, 4, 1, 5, 9]\n", "if mylist:\n", - " print \"The first element is:\", mylist[0]\n", + " print(\"The first element is:\", mylist[0])\n", "else:\n", - " print \"There is no first element.\"" + " print(\"There is no first element.\")" ] }, { @@ -334,10 +317,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -350,38 +331,31 @@ "source": [ "mylist = []\n", "if mylist:\n", - " print \"The first element is:\", mylist[0]\n", + " print(\"The first element is:\", mylist[0])\n", "else:\n", - " print \"There is no first element.\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "当然这种用法并不推荐，推荐使用 `if len(mylist) > 0:` 来判断一个列表是否为空。" + " print(\"There is no first element.\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.16-list-comprehension.ipynb b/02-python-essentials/02.16-list-comprehension.ipynb index df732d31..f23584b4 100644 --- a/02-python-essentials/02.16-list-comprehension.ipynb +++ b/02-python-essentials/02.16-list-comprehension.ipynb @@ -17,9 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -34,7 +32,7 @@ "squares = []\n", "for x in values:\n", " squares.append(x**2)\n", - "print squares" + "print(squares)" ] }, { @@ -47,9 +45,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -62,7 +58,7 @@ "source": [ "values = [10, 21, 4, 7, 12]\n", "squares = [x**2 for x in values]\n", - "print squares" + "print(squares)" ] }, { @@ -82,9 +78,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -97,7 +91,7 @@ "source": [ "values = [10, 21, 4, 7, 12]\n", "squares = [x**2 for x in values if x <= 10]\n", - "print squares" + "print(squares)" ] }, { @@ -110,15 +104,13 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "set([16, 49, 100])\n", + "{16, 49, 100}\n", "{10: 100, 4: 16, 7: 49}\n" ] } @@ -140,9 +132,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -169,9 +159,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -203,9 +191,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "x = range(1000000)" @@ -214,15 +200,13 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1 loops, best of 3: 3.86 s per loop\n" + "204 ms ± 2.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -233,15 +217,13 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1 loops, best of 3: 2.58 s per loop\n" + "194 ms ± 2.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -252,23 +234,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.9.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/02-python-essentials/02.17-functions.ipynb b/02-python-essentials/02.17-functions.ipynb index 104ad56e..4964e57b 100644 --- a/02-python-essentials/02.17-functions.ipynb +++ b/02-python-essentials/02.17-functions.ipynb @@ -26,9 +26,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def add(x, y):\n", @@ -80,8 +78,8 @@ } ], "source": [ - "print add(2, 3)\n", - "print add('foo', 'bar')" + "print(add(2, 3))\n", + "print(add('foo', 'bar'))" ] }, { @@ -101,16 +99,16 @@ "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"foo\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m\u001b[0m in \u001b[0;36madd\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;34m\"\"\"Add two numbers\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfoo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36madd\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd\u001b[39m(x, y):\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m\"\"\"Add two numbers\"\"\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m a\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ - "print add(2, \"foo\")" + "print(add(2, \"foo\"))" ] }, { @@ -127,18 +125,18 @@ "outputs": [ { "ename": "TypeError", - "evalue": "add() takes exactly 2 arguments (3 given)", + "evalue": "add() takes 2 positional arguments but 3 were given", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: add() takes exactly 2 arguments (3 given)" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mTypeError\u001b[0m: add() takes 2 positional arguments but 3 were given" ] } ], "source": [ - "print add(1, 2, 3)" + "print(add(1, 2, 3))" ] }, { @@ -148,18 +146,18 @@ "outputs": [ { "ename": "TypeError", - "evalue": "add() takes exactly 2 arguments (1 given)", + "evalue": "add() missing 1 required positional argument: 'y'", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: add() takes exactly 2 arguments (1 given)" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mTypeError\u001b[0m: add() missing 1 required positional argument: 'y'" ] } ], "source": [ - "print add(1)" + "print(add(1))" ] }, { @@ -184,8 +182,8 @@ } ], "source": [ - "print add(x=2, y=3)\n", - "print add(y=\"foo\", x=\"bar\")" + "print(add(x=2, y=3))\n", + "print(add(y=\"foo\", x=\"bar\"))" ] }, { @@ -209,7 +207,7 @@ } ], "source": [ - "print add(2, y=3)" + "print(add(2, y=3))" ] }, { @@ -229,9 +227,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def quad(x, a=1, b=0, c=0):\n", @@ -259,7 +255,7 @@ } ], "source": [ - "print quad(2.0)" + "print( quad(2.0))" ] }, { @@ -283,7 +279,7 @@ } ], "source": [ - "print quad(2.0, b=3)" + "print( quad(2.0, b=3))" ] }, { @@ -300,7 +296,7 @@ } ], "source": [ - "print quad(2.0, 2, c=4)" + "print( quad(2.0, 2, c=4))" ] }, { @@ -319,18 +315,18 @@ "outputs": [ { "ename": "TypeError", - "evalue": "quad() got multiple values for keyword argument 'a'", + "evalue": "quad() got multiple values for argument 'a'", "output_type": "error", "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mquad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m: quad() got multiple values for keyword argument 'a'" + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m( \u001b[43mquad\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mTypeError\u001b[0m: quad() got multiple values for argument 'a'" ] } ], "source": [ - "print quad(2.0, 2, a=2)" + "print( quad(2.0, 2, a=2))" ] }, { @@ -350,9 +346,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def add(x, *args):\n", @@ -384,8 +378,8 @@ } ], "source": [ - "print add(1, 2, 3, 4)\n", - "print add(1, 2)" + "print( add(1, 2, 3, 4))\n", + "print( add(1, 2))" ] }, { @@ -398,15 +392,13 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def add(x, **kwargs):\n", " total = x\n", " for arg, value in kwargs.items():\n", - " print \"adding \", arg\n", + " print( \"adding \", arg)\n", " total += value\n", " return total" ] @@ -435,7 +427,7 @@ } ], "source": [ - "print add(10, y=11, z=12, w=13)" + "print( add(10, y=11, z=12, w=13))" ] }, { @@ -460,7 +452,7 @@ ], "source": [ "def foo(*args, **kwargs):\n", - " print args, kwargs\n", + " print( args, kwargs)\n", "\n", "foo(2, 3, x='bar', z=10)" ] @@ -495,7 +487,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "5.0 0.927295218002\n" + "5.0 0.9272952180016122\n" ] } ], @@ -508,7 +500,7 @@ " return r, theta\n", "\n", "r, theta = to_polar(3, 4)\n", - "print r, theta" + "print(r, theta)" ] }, { @@ -532,7 +524,7 @@ } ], "source": [ - "print to_polar(3, 4)" + "print(to_polar(3, 4))" ] }, { @@ -563,7 +555,7 @@ ], "source": [ "a, b, c = [1, 2, 3]\n", - "print a, b, c" + "print(a, b, c) " ] }, { @@ -593,7 +585,7 @@ " return a\n", " \n", "z = (2, 3)\n", - "print add(*z)" + "print(add(*z))" ] }, { @@ -630,7 +622,7 @@ " return a\n", "\n", "w = {'x': 2, 'y': 3}\n", - "print add(**w)" + "print(add(**w))" ] }, { @@ -656,7 +648,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4, 9, 16]\n" + "\n" ] } ], @@ -665,7 +657,7 @@ " return x ** 2\n", "\n", "a = [2,3,4]\n", - "print map(sqr, a)" + "print( map(sqr, a))" ] }, { @@ -690,7 +682,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[12, 8, 7]\n" + "\n" ] } ], @@ -700,13 +692,13 @@ "\n", "a = (2,3,4)\n", "b = [10,5,3]\n", - "print map(add,a,b)" + "print( map(add,a,b))" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -720,7 +712,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, From 3944654e1ee7babf6afef2e2d357769081f17084 Mon Sep 17 00:00:00 2001 From: master Date: Sat, 23 Jul 2022 22:11:33 +0800 Subject: [PATCH 48/49] update to python3 --- .../02.18-modules-and-packages.ipynb | 145 ++++++++++++++---- 02-python-essentials/02.19-exceptions.ipynb | 106 ++++++------- 02-python-essentials/02.20-warnings.ipynb | 13 +- 02-python-essentials/02.21-file-IO.ipynb | 77 ++++------ 4 files changed, 197 insertions(+), 144 deletions(-) diff --git a/02-python-essentials/02.18-modules-and-packages.ipynb b/02-python-essentials/02.18-modules-and-packages.ipynb index 7675d0d0..a120520e 100644 --- a/02-python-essentials/02.18-modules-and-packages.ipynb +++ b/02-python-essentials/02.18-modules-and-packages.ipynb @@ -105,7 +105,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -141,7 +141,7 @@ "3.1416" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -161,7 +161,7 @@ "3.141592653" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ "9" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -242,10 +242,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -290,14 +290,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Overwriting ex2.py\n" + "Writing ex2.py\n" ] } ], @@ -337,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -361,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -386,7 +386,7 @@ "3.1416" ] }, - "execution_count": 18, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -413,7 +413,7 @@ "3.1416" ] }, - "execution_count": 19, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -439,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -455,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -464,7 +464,7 @@ "5" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -482,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -491,7 +491,7 @@ "7.5" ] }, - "execution_count": 22, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -512,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -590,13 +590,61 @@ "- struct" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Python 从哪些路径导入模块" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* 1、程序的主目录\n", + "* 2、PYTHONPATH 目录（如果已经进行了设置）\n", + "* 3、标准链接库目录\n", + "* 4、任何 .pth 文件的内容（如果存在的话）" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 主目录" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python 首先会在主目录内搜索导入的文件。如果程序完全位于单一目录，所有导入的会\n", + "自动工作，而并不需要配置路径。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PYTHONPATH 目录" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "之后，Python 会从左到右搜索 PYTHONPATH 环境变量设置中罗列出的所有目录，可以是\n", + "用户定义或平台特定的目录名。因为 Python 优先搜索主目录，当导入的文件跨目录时，\n", + "这个设置才显得格外重要。" + ] + }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ - "## PYTHONPATH设置" + "###### PYTHONPATH设置" ] }, { @@ -605,11 +653,50 @@ "source": [ "Python的搜索路径可以通过环境变量PYTHONPATH设置，环境变量的设置方法依操作系统的不同而不同，具体方法可以网上搜索。" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 标准库目录" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "接着，Python 会自动搜索标准库模块安装在机器上的那些目录，这块通常不需要在单独\n", + "配置" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### .pth 文件目录" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "最后，Python 有个相当新的功能，允许用户把有效的目录添加到模块搜索路径中去，\n", + "也就是在后缀名为 .pth （路径的意思）的文本文件中一行一行的列出目录。他是\n", + "PYTHONPATH 的一种替代方案，我们也可以把它放在标注库所在位置的 sitepackages 的\n", + "子目录中扩展模块搜索路径" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -623,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/02-python-essentials/02.19-exceptions.ipynb b/02-python-essentials/02.19-exceptions.ipynb index b0dfcd11..9fc05415 100644 --- a/02-python-essentials/02.19-exceptions.ipynb +++ b/02-python-essentials/02.19-exceptions.ipynb @@ -41,16 +41,25 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 1\n", - "log10(1.0) = 0.0\n", - "> q\n" + "> \n" + ] + }, + { + "ename": "IndexError", + "evalue": "string index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 4\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m> \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtext\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mq\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 7\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(text)\n", + "\u001b[0;31mIndexError\u001b[0m: string index out of range" ] } ], @@ -111,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -120,6 +129,10 @@ "text": [ "> 0\n", "the value must be greater than 0\n", + "> 4\n", + "log10(4.0) = 0.6020599913279624\n", + "> 5\n", + "log10(5.0) = 0.6989700043360189\n", "> q\n" ] } @@ -170,15 +183,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 0\n", - "the value must be greater than 0\n", "> q\n" ] } @@ -221,15 +232,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 2\n", - "1 / log10(2.0) = 3.321928094887362\n", + "> \n", + "invalid value\n", "> q\n" ] } @@ -265,17 +276,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 1\n", - "invalid value\n", - "> -1\n", - "invalid value\n", "> q\n" ] } @@ -304,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -313,12 +320,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "> 0\n", + "the value must be greater than 0\n", "> 1\n", "the value must not be 1\n", - "> -1\n", - "the value must be greater than 0\n", - "> 2\n", - "1 / log10(2.0) = 3.321928094887362\n", "> q\n" ] } @@ -356,12 +361,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "> 1\n", - "the value must not be 1\n", - "> -1\n", - "the value must be greater than 0\n", "> 0\n", "the value must be greater than 0\n", + "> 2\n", + "1 / log10(2.0) = 3.321928094887362\n", "> q\n" ] } @@ -401,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -411,7 +414,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43ma\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'a'" ] } @@ -429,21 +432,15 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> -1\n", - "the value must be greater than 0\n", - "> 1\n", - "the value must not be 1\n", "> 0\n", "the value must be greater than 0\n", - "> 3\n", - "1 / log10(3.0) = 2.095903274289385\n", "> q\n" ] } @@ -513,9 +510,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "class CommandError(ValueError):\n", @@ -531,7 +526,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -540,18 +535,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "> q\n" + "> sd\n" ] }, { - "ename": "NameError", - "evalue": "name 'CommandError' is not defined", + "ename": "CommandError", + "evalue": "Invalid commmand: sd", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcommand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'> '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcommand\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_commands\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCommandError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Invalid commmand: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'CommandError' is not defined" + "\u001b[0;31mCommandError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m command \u001b[38;5;241m=\u001b[39m \u001b[38;5;28minput\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m> \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m command\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m valid_commands:\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CommandError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid commmand: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m command)\n", + "\u001b[0;31mCommandError\u001b[0m: Invalid commmand: sd" ] } ], @@ -620,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -648,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -665,7 +660,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'finally was called.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfinally was called.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" ] } @@ -686,8 +681,10 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, + "execution_count": 14, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", @@ -706,18 +703,11 @@ "finally:\n", " print ('finally was called.')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -731,7 +721,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/02-python-essentials/02.20-warnings.ipynb b/02-python-essentials/02.20-warnings.ipynb index 77c9d3aa..45b1f281 100644 --- a/02-python-essentials/02.20-warnings.ipynb +++ b/02-python-essentials/02.20-warnings.ipynb @@ -43,7 +43,7 @@ "name": "stderr", "output_type": "stream", "text": [ - ":4: RuntimeWarning: month (13) is not between 1 and 12\n", + "/tmp/ipykernel_5427/2502358554.py:4: RuntimeWarning: month (13) is not between 1 and 12\n", " warnings.warn(msg, RuntimeWarning)\n" ] } @@ -78,18 +78,11 @@ "\n", "month_warning(13)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -103,7 +96,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, diff --git a/02-python-essentials/02.21-file-IO.ipynb b/02-python-essentials/02.21-file-IO.ipynb index 7779c11f..d792b49f 100644 --- a/02-python-essentials/02.21-file-IO.ipynb +++ b/02-python-essentials/02.21-file-IO.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "scrolled": true }, @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -303,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -355,14 +355,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "b'\\xe2\\x07\\x03\\xa9e\\x94\\x1e2\\xe4;O\\xa7\\xdb\\xff\"\\x93'\n" + "b'g\\x86\\xad/\\xca\\x9d\\x9aD}\\xe25\\xd2z\\xeb\\xf3\\xf0'\n" ] } ], @@ -380,9 +380,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -427,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -456,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -3253,12 +3251,6 @@ "hello world: 2787\n", "hello world: 2788\n", "hello world: 2789\n", - "hello world: 2790\n", - "hello world: 2791\n", - "hello world: 2792\n", - "hello world: 2793\n", - "hello world: 2794\n", - "hello world: 2795\n", "\n" ] } @@ -3276,7 +3268,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -3293,7 +3285,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -3303,7 +3295,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'newfile.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hello world: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "Input \u001b[0;32mIn [20]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnewfile.txt\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3000\u001b[39m):\n\u001b[0;32m----> 3\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhello world: \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(i) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero" ] } @@ -3324,7 +3316,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -4300,8 +4292,6 @@ "hello world: 966\n", "hello world: 967\n", "hello world: 968\n", - "hello world: 969\n", - "hello world: 970\n", "\n" ] } @@ -4322,7 +4312,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -4347,7 +4337,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -5380,7 +5370,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -5390,7 +5380,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'newfile.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m 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\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3000\u001b[39m):\n\u001b[0;32m----> 3\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhello world: \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(i) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero" ] } @@ -5411,7 +5401,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -6437,25 +6427,18 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.remove('newfile.txt')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -6469,7 +6452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.12" } }, "nbformat": 4, From 2b4bf86109cf84e8666a54926ac0737c92957108 Mon Sep 17 00:00:00 2001 From: Master Date: Sun, 31 Jul 2022 10:33:07 +0800 Subject: [PATCH 49/49] update --- .../02.02-python-data-types.ipynb | 48 +++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git a/02-python-essentials/02.02-python-data-types.ipynb b/02-python-essentials/02.02-python-data-types.ipynb index c0cdda63..79b7b41e 100644 --- a/02-python-essentials/02.02-python-data-types.ipynb +++ b/02-python-essentials/02.02-python-data-types.ipynb @@ -7,6 +7,54 @@ "# Python 数据类型" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "变量是存储在内存中的值，这就意味着在创建变量时会在内存中开辟一个空间。\n", + "\n", + "基于变量的数据类型，解释器会分配指定内存，并决定什么数据可以被存储在内存中。\n", + "\n", + "因此，变量可以指定不同的数据类型，这些变量可以存储整数，小数或字符。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 变量赋值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python 中的变量赋值不需要类型声明。\n", + "\n", + "每个变量在内存中创建，都包括变量的标识，名称和数据这些信息。\n", + "\n", + "每个变量在使用前都必须赋值，变量赋值以后该变量才会被创建。\n", + "\n", + "等号 = 用来给变量赋值。\n", + "\n", + "等号 = 运算符左边是一个变量名，等号 = 运算符右边是存储在变量中的值。例如：" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "counter = 100 # 赋值整型变量\n", + "miles = 1000.0 # 浮点型\n", + "name = \"John\" # 字符串\n", + " \n", + "print(counter)\n", + "print(miles)\n", + "print(name)" + ] + }, { "cell_type": "markdown", "metadata": {},

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13-image-processing/img/gray.png | Bin 0 -> 88137 bytes .../img/image-data-sturcture.png | Bin 0 -> 2765 bytes 13-image-processing/img/image_types.png | Bin 0 -> 254444 bytes 13-image-processing/img/pixel.png | Bin 0 -> 151117 bytes 13-image-processing/img/rgb.png | Bin 0 -> 258522 bytes 13-image-processing/img/two_value.png | Bin 0 -> 118700 bytes 10 files changed, 416 insertions(+), 8 deletions(-) create mode 100644 13-image-processing/13.03-image-data-structure.ipynb create mode 100644 13-image-processing/img/flower-rgb-3channel.jpg create mode 100644 13-image-processing/img/gray.png create mode 100644 13-image-processing/img/image-data-sturcture.png create mode 100644 13-image-processing/img/image_types.png create mode 100644 13-image-processing/img/pixel.png create mode 100644 13-image-processing/img/rgb.png create mode 100644 13-image-processing/img/two_value.png diff --git a/13-image-processing/13.00-image-basic.ipynb b/13-image-processing/13.00-image-basic.ipynb index 601ab17b..64f86b54 100644 --- a/13-image-processing/13.00-image-basic.ipynb +++ b/13-image-processing/13.00-image-basic.ipynb @@ -9,6 +9,80 @@ "# 图片基础" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 图片基础知识" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "图像都是由像素(pixel)构成的，即图像中的小方格，这些小方格都有一个明确的位置和被分配的色彩数值，而这些一小方格的颜色和位置就决定该图像所呈现出来的样子。像素是图像中的最小单位，每一个点阵图像包含了一定量的像素，这些像素决定图像在屏幕上所呈现的大小。\n", + "![pixel](./img/pixel.png)\n", + "图像通常包括二值图像、灰度图像和彩色图像。\n", + "![image_types](./img/image_types.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. 二值图像" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "二值图像中任何一个点非黑即白，要么为白色（像素为255），要么为黑色（像素为0）。将灰度图像转换为二值图像的过程，常通过依次遍历判断实现，如果像素>=127则设置为 255，否则设置为 0。\n", + "![two_value](./img/two_value.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. 灰度图像" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 灰度图像除了黑和白，还有灰色，它把灰度划分为256个不同的颜色，图像看着也更为清晰。将彩色图像转换为灰度图是图像处理的最基本预处理操作，通常包括下面几种方法：\n", + "\n", + "```\n", + "(1) 浮点算法：Gray=R0.3+G0.59+B0.11\n", + "(2) 整数方法：Gray=(R30+G59+B11)/100\n", + "(3) 移位方法：Gray=(R28+G151+B77)>>8;\n", + "(4) 平均值法：Gray=（R+G+B）/3;(此程序采用算法)\n", + "(5) 仅取绿色：Gray=G；\n", + "(6) 加权平均值算法：根据光的亮度特性，公式: R=G=B=R0.299+G*0.587+B0.144\n", + "```\n", + "\n", + "通过上述任一种方法求得Gray后，将原来的RGB(R,G,B)中的R,G,B统一用Gray替换，形成新的颜色RGB(Gray,Gray,Gray)，用它替换原来的RGB(R,G,B)就是灰度图了。改变象素矩阵的RGB值，来达到彩色图转变为灰度图。\n", + "![gray](./img/gray.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. 彩色图像" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 彩色图像是RGB图像，RGB表示红、绿、蓝三原色，计算机里所有颜色都是三原色不同比例组成的，即三色通道。\n", + "\n", + "![rgb](./img/rgb.png)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/13-image-processing/13.02-Opencv.ipynb b/13-image-processing/13.02-Opencv.ipynb index 197d2ddd..3d11fb05 100644 --- a/13-image-processing/13.02-Opencv.ipynb +++ b/13-image-processing/13.02-Opencv.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "4bf0ebeb", + "id": "cfa7ab9f", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "0df1f40e", + "id": "48911668", "metadata": {}, "source": [ "# Opencv 简介" @@ -22,7 +22,22 @@ }, { "cell_type": "markdown", - "id": "2ea18e70", + "id": "dcfb5cad", + "metadata": {}, + "source": [ + "OpenCV(`Open Source Computer Vision Library`) 是一个基于BSD许可（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows、Android和Mac OS操作系统上。OpenCV于1999年由Intel建立，如今由Willow Garage提供支持。它轻量级而且高效——由一系列 C 函数和少量 C++ 类构成，同时提供了Python、Ruby、MATLAB等语言的接口，实现了图像处理和计算机视觉方面的很多通用算法。\n", + "  OpenCV致力于真实世界的实时应用，通过优化的C代码的编写对其执行速度带来了可观的提升，并且可以通过购买Intel的IPP高性能多媒体函数库(Integrated Performance Primitives)得到更快的处理速度。OpenCV在以下领域有着广泛的应用。\n", + "\n", + " 1、人机互动  2、物体识别  3、图像分割  4、人脸识别\n", + " 5、动作识别  6、运动跟踪  7、机器人  8、运动分析\n", + " 9、机器视觉  10、结构分析  11、汽车安全驾驶\n", + "\n", + "OpenCV-Python 是 OpenCV 的 Python API，集成了 Python 语言和 C++ 语言的最优特征，致力于支持Python解决计算机视觉问题。" + ] + }, + { + "cell_type": "markdown", + "id": "349b1c9e", "metadata": {}, "source": [ "## OpenCV开发环境配置" @@ -30,7 +45,7 @@ }, { "cell_type": "markdown", - "id": "9077e333", + "id": "6acc84d8", "metadata": {}, "source": [ "做科学计算需要安装下面的一些工具包\n", @@ -39,17 +54,217 @@ "```\n", "安装 OpenCV \n", "```shell\n", - "pip install opencv-python\n", + "pip install OpenCV-Python\n", "```" ] }, + { + "cell_type": "markdown", + "id": "db99a500", + "metadata": {}, + "source": [ + "## 导入 cv2" + ] + }, + { + "cell_type": "markdown", + "id": "def2350b", + "metadata": {}, + "source": [ + "OpenCV-Python 在 python 中的包名称叫做 cv2\n", + "```shell\n", + ">>> import cv2\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "21eaf4ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'4.5.1'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cv2\n", + "cv2.__version__" + ] + }, + { + "cell_type": "markdown", + "id": "ced04f6e", + "metadata": {}, + "source": [ + "## 读入图片" + ] + }, + { + "cell_type": "markdown", + "id": "1a32ee2b", + "metadata": {}, + "source": [ + "我们读入一张图片的时候, 使用的是 cv2.imread 函数, 传入的第一个参数是图片的路径. " + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "278ed9f7", + "execution_count": 5, + "id": "235a2cab", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "img = cv2.imread('./img/cat.jpg')" + ] + }, + { + "cell_type": "markdown", + "id": "3a881eb8", + "metadata": {}, + "source": [ + "help(cv2.imread)" + ] + }, + { + "cell_type": "markdown", + "id": "f8a4e455", + "metadata": {}, + "source": [ + "### 导入RBG彩图 还是是 灰度图?" + ] + }, + { + "cell_type": "markdown", + "id": "2e039c57", + "metadata": {}, + "source": [ + "第二个参数是图像颜色空间, 默认就是BGR彩图 cv2.IMREAD_COLOR\n", + "\n", + "上面这个语句跟下面作用是一样的." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5885a9cc", + "metadata": {}, + "outputs": [], + "source": [ + "# 导入一张图像 模式为彩色图片\n", + "img_color = cv2.imread('cat.jpg', cv2.IMREAD_COLOR)\n", + "\n", + "# 如果你想导入灰度图, 就需要传入 cv2.IMREAD_GRAYSCALE\n", + "\n", + "img_gray = cv2.imread('cat.jpg', cv2.IMREAD_GRAYSCALE)" + ] + }, + { + "cell_type": "markdown", + "id": "aeb9904e", + "metadata": {}, + "source": [ + "## 显示图片" + ] + }, + { + "cell_type": "markdown", + "id": "32bdf2ef", + "metadata": {}, + "source": [ + "opencv 中在窗口展示图片需要用到 HighGUI 中的 API。" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a693800c", + "metadata": {}, + "outputs": [], + "source": [ + "# 创建窗口并展示图片\n", + "cv2.imshow('image', img)\n", + "# 等待任意一个按键按下\n", + "cv2.waitKey(0)\n", + "# 关闭所有的窗口\n", + "cv2.destroyAllWindows()" + ] + }, + { + "cell_type": "markdown", + "id": "446977d5", + "metadata": {}, + "source": [ + "## 图像属性" + ] + }, + { + "cell_type": "markdown", + "id": "618a5417", + "metadata": {}, + "source": [ + "Image的属性,其实就是 numpy 的 ndarray 数据格式的属性.\n", + "\n", + "我们可以直接获取img对象的诸多属性, 在这里我们将其打印出来." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d75227df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "================打印一下图像的属性================\n", + "图像对象的类型 \n", + "(182, 277, 3)\n", + "图像宽度: 277 pixels\n", + "图像高度: 182 pixels\n", + "通道: 3\n", + "图像分辨率: 151242\n", + "数据类型: uint8\n" + ] + } + ], + "source": [ + "# 导入一张图像 模式为彩色图片\n", + "print(\"================打印一下图像的属性================\")\n", + "print(\"图像对象的类型 {}\".format(type(img)))\n", + "print(img.shape)\n", + "print(\"图像宽度: {} pixels\".format(img.shape[1]))\n", + "print(\"图像高度: {} pixels\".format(img.shape[0]))\n", + "print(\"通道: {}\".format(img.shape[2]))\n", + "print(\"图像分辨率: {}\".format(img.size))\n", + "print(\"数据类型: {}\".format(img.dtype))" + ] + }, + { + "cell_type": "markdown", + "id": "b6960429", + "metadata": {}, + "source": [ + "## 番外篇-为何是BGR不是RGB" + ] + }, + { + "cell_type": "markdown", + "id": "863c56f5", + "metadata": {}, + "source": [ + "我们当下普遍使用的是 RGB 格式, 但是为什么 cv2 返回的数据结构是 BGR 格式的呢?\n", + "\n", + "早前windows下, 不管是摄像头制造者 ,还是软件开发者, 当时流行的都是 BGR 格式的数据结构. 后面 RBG 格式才逐渐流行, 所以这个是 opencv 在发展过程中的历史遗留问题. " + ] } ], "metadata": { diff --git a/13-image-processing/13.03-image-data-structure.ipynb b/13-image-processing/13.03-image-data-structure.ipynb new file mode 100644 index 00000000..8a7d8c4a --- /dev/null +++ b/13-image-processing/13.03-image-data-structure.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da9ff162", + "metadata": {}, + "source": [ + "# 图像的数据结构" + ] + }, + { + "cell_type": "markdown", + "id": "15c84e24", + "metadata": {}, + "source": [ + "opencv图像读取(imread) 读入的数据格式是 numpy 的 ndarray 数据格式. \n", + "\n", + "下面是以 BGR 格式为例介绍 Image 的数据结构.\n", + "\n", + "![image-data-sturcture](./img/image-data-sturcture.png)\n", + "\n", + "第一维度 : Height 高度, 对应这张图片的 nRow行数\n", + "\n", + "第二维度 : Width 宽度, 对应这张图片的nCol 列数\n", + "\n", + "第三维度: Value BGR三通道的值.\n", + "\n", + "BGR 分别代表\n", + "\n", + "B: Blue 蓝色\n", + "\n", + "G: Green 绿色\n", + "\n", + "R: Red 红色\n", + "![flower-rgb-3channel](./img/flower-rgb-3channel.jpg)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5df12c91", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[206, 218, 228],\n", + " [206, 218, 228],\n", + " [206, 218, 228],\n", + " [206, 218, 228],\n", + " [204, 216, 226]],\n", + "\n", + " [[205, 217, 227],\n", + " [205, 217, 227],\n", + " [205, 217, 227],\n", + " [205, 217, 227],\n", + " [203, 215, 225]],\n", + "\n", + " [[204, 216, 228],\n", + " [204, 216, 228],\n", + " [204, 216, 228],\n", + " [204, 216, 228],\n", + " [203, 215, 227]],\n", + "\n", + " [[204, 216, 228],\n", + " [204, 216, 228],\n", + " [204, 216, 228],\n", + " [204, 216, 228],\n", + " [203, 215, 227]],\n", + "\n", + " [[197, 210, 224],\n", + " [198, 211, 225],\n", + " [199, 212, 226],\n", + " [199, 212, 226],\n", + " [200, 213, 227]]], dtype=uint8)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import cv2\n", + "img = cv2.imread('./img/cat.jpg')\n", + "img[100:105, 100:105]" + ] + }, + { + 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K#K0K1K9K9KMKuKlKbKUKMKKKBKQKkKCK%K%K!K(K8KLKKKK|KxKhKVKJKLKTKbKKKQK|KKxK8KAKeKpKaK`KcKZKcKgKfKcK|KKpKuKoKtKpKlKpKsK_KKKKRKBKWKJKQKNKKKiKKdK3K/K4K:K5K1K?KLK[K@K5K/K.K'K%K,K:KPK_K:K1K-K.K8K2K1K-K-K5K1K/K/K6K=K7K8K8K:K2K4K1K0K.K-K1KK=K7K:KEKIK@K7K4KKAKBKGKEKDKGKKKNKPKOKVKTKSKSKRKYKUKSKSKTe]r(KKKKKKK}KxKlKaK\KVKWKOKGKBKCK?KNKKK;K?K8K9K2K6K/K7KBKFKVKgKrK~KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKyKjK[KQKQKaKKhKKKK;K2K.K%K'K.K&K(K(K(K&K*K0K)K*K*K4KMK_KwKwKGKKK!K&K*K2K9KDKvKxKjKSKIKOKLKbKrKAK%K"K#K$K*K9KRKuKKKKKwKeKYKkK]K@KZK|KKiK?K3KTKwKKgKgKdKiKtKlKTKZKKK}KnKsKnKpKwKKkKKKK~KZKiKDK5KSK_KPKKKWK3K7K6K4K8KDKFKSKBK3K1K0K,K'K+K8KRKOK5K5K-K+K5K4K/K0K.K0K-K(K,K4K8K7K=K6K2K3K4K3K.K.K2K0K5K9K;K:K6K>K=K>K?KLKJKAK8K7K8K6K2K1K2K7K>KFKQK_KbKgKgKsKtKrKwKzKxK|K|KzK{KzKxK~KK}KKKK}KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKoKJK,K+K)K!K$K%K8K5KNKGKHKWKRKhK`KFKHKJKVKGKOKFKHKJKK9KKEKCKHKMKCK=K6K?KK8K3K:KOKGK2K3K2K2K0K5K4K5K1K:K5K:K:K=K;KAKFKLKCK8KAK9K9K5K0K8K>KOKPKYK_KcKmKoKnKpKvKvKuK{KzKyKK|K~K|KKKKK~KxKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKcK:K.K"K&K+K-K>KK:KCK:KrKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK{KK{KxK~KyKrKuKrKtKnKjKnKhKgK\KfKcK\KVKYKVK`K_KjKvKyKKzKwKvKqKfKYKOKLKLKcKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK`KPK;K.K-K3K1K@KFKHKLKNKVKPKJKPKMKRKSKSKUKUKXKYKZK\KYKRKSKYKXKTKUe]r(KKKKKKKKKKKyKvKnKgK`KZKUKRKTKSKOKFKGK8K2K/K+K/K@KHKTKhKoK}KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKtKoKzKpKKKUKlKKK;K.K-K'K&K,K+K(K.K,K'K*K*K-K%KAKiKcK]KXKvKiK@K'KK!KK#K*KAKqKKKcKBKIKUKnKeKDK.K2K:K/K#K+KDKeKYK]KCK9KFKWKuKmKjKyKKKaKMKBKAKSKUKEK>K]KKzKrKvKpKuKbKVKnKKKKuKKKK{K_KeKqK~KKKbK3K)K3KOKeKmKKKCK2K9K9KEKKK?K3K7K,K.K(K.K/K9K_KIKDK1K1K3K8K8K6K6K3K:K/K1K.K1K5K9K6K:KFKEK4K3K7K3K5K,K2K7K5K6K2K6K>K>K;KFKLKSKLK:K8K8K9K3K1K7KDKTKQKXKcKeKjKwKvKwKrKsKtKtKzKxK}KzKK|KKKK|KzK{KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKsKBK1K.K1K.K;K:K4K9K]KcK_KGK@KLKMKNKPKHKDKQKIK[KTKJK@KHKLK?K4KFK=KtKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKxKzKyKzK~KuKpKpKrKiKhKfKlKlKeK]KZKVKYKhKjKiKrKKK~K}KsKlKjKVKIKFKQKiKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKyKYK@K/K/K*K2K6K>KEKKKOKQKPKJKPKOKTKNKRKRKVKWKYK_KWKbK^KYKUKTKSKWKQKVe]r(KKKKKKKKKKKK{KwKoKsKcKbK^K`K[KSKOKFK>K0K,K+K2K@KKKUKeKqKyKKKKKKKKKKKKKKKKKKKKKKKKKKKKK}KyKKK~KKK`KtKKtK.K/K,K+K)K0K)K(K&K$K'K)K!K$K,K6KkK~K|KuK|KmKXKDK.K-K+K3KKKpKKKtKGK;KHKZKYKJK=K@K6K-K-K*KK5K3K-K.K-K+K0K=KZKOKHK:K2K3K;K4K3K8K2K6K1K3K1K5K1K=K7K7K@KKKDK5K3K7K4K:K4K4K;K>K=K4KBKAK8K?KMKQKQKEKKNKGKGKPKBKAKQKEKPKSKCKDKEKFK5K6KFK;K{KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK|KK|K{KyK{KyKxK{KsKuKnKoKoKoKrKfKdKfKZKbKcKlKtKxKKKK}KrKoKbKTKNKEKVKuKKKKKKKKKKKKKKKKKKKKKKKKKKKKKlKVKKFKCKQKDKFKPKYK0K'K-K:K_KxKvKOKK8K7K6K7K4K2K3K6K>K@K=K>KDK9K7KJKOKOKHK7K4K6K=K8K2K=KXKRK[KcKdKqKqKuKsKtKxKxKuKtKxKKKK~K{K|K~KzKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKLK0K.K7K/K+K.KNK\KXKAK=KFKDKEKFKKEKBKHKWKCK)K,K=KgK}KK^K@K9K;K>KIKfKKzKbKHKK`KOK>K:K6K0KK8K5K4K3K2K.K2KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK}KxKwKwKvKyKtKqKtKqKmKkKeKeKeKmKwKxKKKK}KxKpKhKYKSKUKjKKKKKKKKKKKKKKKKKKKKKKKKKKKKKZKDK0K+K6K.K2K=KKKTK^KZKXKVKNKNKNKTKLKOKSKYK]KaK]KcKfKaK`K`K_KTKTKSKTKXK[e]r(KKKKKKKKKKKKKKKK|KsKpKjK`KZKRKJKDK7K1K*K-K6KEKVKcKvK}KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKFKtK`KpKUKDK9K8K1K'K'K(K)K*K(K)K/K,K&K+K'K2K=KCKDKK-K.K2K@KJKzKKVKMK:K9KEKEK7KKNKfKcK_KLKLKaKzKsK~KK~KKKKKKKKKKKnK3K*K&K7KKKKaK.K+KAK\K^KKKBK/K?K;K8K1K.K(K1K*K0K/K5K^KKKDKBK1K0K@K6K4K6K7K6K2K+K-K1K.K4KAK=K@KK;K9KIKJKTKIK@KQK?KEK,K/K6KFKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK~KK{K}KzKzKyKtKvKyKnK~KkKgKiKnKrKuKKKKKKxKlKeKZKVK^KzKKKKKKKKKKKKKKKKKKKKKKKKKKKKxKUK:K,K*K*K;K:KOKZK\K[K^KZKZKWKUKQKRKNKQK[KaK_KdK`KeKcKbKaK_K[KUKOKSKPK[K^e]r(K~K~KKKKKKKKKKKKKKKzKvKpKdKZKQKGKCKK5K2K>K?KFK=KAK3K/K2K9K5K7K5K6K9KNK5K?KCK?K?KUKVK^KcKIK=K2K0K/K*K7KWKSKWKZKpKsKqKsKqKvKyKKKK~K|K~KrKnKvK{K~KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKUK1K)K6KAKPKTKBK2K>K9KBK7K7K@KJKGKPKCK?KKK;KDK)K.K5KLKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK{K|KK~KKxKwK~KwKzKwKuKtKnKkKmKqKzK~KKKKKwKpKjK`KZKcKKKKKKKKKKKKKKKKKKKKKKKKKKKKKgKGK0K%K+K1K7KFKPK[K[K^KZKWKSKVKYKVKQKQKSK\KaKaKgKdKoKeK`K\K\KYK\KZKSKWK]K`e]r(K|K|KKKKKKKKKKKKKKKKzKtKkKcKVKGKBK;K8K+K'K6KIKUKfKpK|KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKcKZKdKGKYK:K:K>K@K8K2K/K,K-K1K)K/K)K&K*K0K4KCKQKRK9K K%K%K3KFKNKCK>KBK@KCK:KKAKVK>KAK=K?KLKeKjKgKSKVK]KXKdKqKsKKKKKKKKKKKKgK1K#K%KKqKUKLKKKGK/K2K.K)K*K+K0K-K*K2K5KVKHKDK3K4K3K9K6K/K7K4K8K6K5K-K4K7K7KKEKBKDK3K7K3K1K5K8K5K9K;KSK;KEKCK9KOKOK^KcKgKfKKK6K1K,K-K+KCKLKNKSKpKvKqKxKtKwKwK~K~KKzKqKpKrKxKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKvKDK-K9KAKPK?K0K;K4KK8K9K?K8K?K@KBK4K6K8K;K1K4K,K/K>K2K2K/K)K-K:KVKKKrKPK3K)K8K:KDKRKQKMKHKHKAKDKUKoKmKZK[KPKUK\KiKKKKKKKKKK]KKKsKGK5K8KIKKKKKXKyKUK>KKKkK8K:K5K8K/K2K4K5K3K8K;KXKIKCK8K>K7KK7K4K@KDKOKFK;KMKJKNKGK)K0K8KaKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK{KxKyKzKtKxKwK}K|KKKKKK~KqKoKnKKKKKKKKKKKKKKKKKKKKKKKKKKKKKgKHK5K5K0K9KFKVKZKYK[KWK]K\K_KaK\KZKWKUKXKdKiKjKuKnKjKiKhK^K_K]KWKVKSKWK_K`K_e]r(KyKyKuK~K~KKKKKKKKKKKKKKKKyKmK[KYKFK2K-K-K>KIKQK^KnK~KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKuK\KtKKKiK$K%K!K'K#K%K0K1K8K8KKCK9K/K/K9KNKpKkKpKhK=K)K-K8KGKRKJKLKSKHKOKKKNK`KyKgKeKeKSKUKgKvKKKKKK|KKK_KuKKKnK]KTKEKKKKKsK`KBK9K[KKK@K3K.K,K,K-K1K7K4K3K=KbKKKEK;K=K9KFK5K>K6K=K:K8K3K9K4K7K8KKHKNKOKKGKFKUKIKIKKKoKvKfK`KVKOK]KvKKKKKKKK~KdKiKKKKuKnKcKyKyK{KKK\K0K2K@KKKWK.K0K2K2K0K.K4K,K2K:K^KGK?KBK=K8KGK4K6K7K1K5K2K,K.K/K3K5KK:KK8K8K9K/K1K2K.K4K8KBKFKCKCK?KKK\KiKvKvKgKdKOK6K)K0K+K6KQKLKWKpKzKvK~KtKrKqKlKnK|KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKGK1K:K0K*K'K-K3K2K?KKKBKKIKLKcKvKtKeKWKQKrKKKKKKKKKKsKKKKlK;KiK}KPK8KdKKKUK.K4KqKKK7K$K(K&K$K-K(K&K.KKK&K&K0KIKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK~KKKK}K~K}KK}KKKKKKKKK~K}K|KKKKKKKKKKKKKKKKKKKKKKKKKKK{KbKTKFKGKEKMK[K_KcKcK]KTK[K]K_KbK_KaKdKcKdKjKmKtKpKoKmKjKdKeKbK[K_KYK_K`KcKbKgKgKhe]r(K3K3K8KOK`KiKvK|KKKKKKKKKKKKKKKwKgKUK6K*K'K5KBKSK]KmKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKzK}KKMKTK?K,K(K*K'K*K(K3K+K+K+K+K6K,K.K.K;K_KOK4K/K0K;KK+K9KdKKKZK;KUKKKfK,K(K%K&K+K&K+K.K7KYKBK=K@K6K:KDK6K7K8K6K8K3K,K2K6K5K:K5K>KAKDK;K5KK?KEKTKiKoKzKxKsKiK`KIK3K/K+K1KPKTKVKbKsKhKsKgKuKKKKKKKK~KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKTK6K0K-K,K0K1KKDK7K7K>K8K4K3K3K)K.K2KBKJK;K5KMKXKoKuK|KvKqKkKcKKK5K-K)K.KJKNKVKdKlK`KuKnKzK}KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKJK,K(K*K0K3K0KK>K.K/K.KAK;K7K7KOK`KhKvKxK~KqKqKkKQK2K&K)K(KOKIKSKfKcKkK{KvK~K|KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKfK1K&K'K/K-K2K=K;K.K-K3KFK5K%K&K9KaKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK~KKKKK}KK}KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKjKVKIKIKPKPKTKTK[K`K^KWKXK^KcKcKaK_KcKfKjKoKrKrKsKoKlKaK^KZKWKaK[K_K\KbKeKdKhKhKlKdKfe]r(K&K&K*K4KCKSKcKnKwKKKKKKKKKKKKKKKrK^K>K-K(K/K:KKK`KsKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKsKIK{KK(KK&K 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KK+KHKK}KNKKKK5K"K3K$K)K$K)K%K:KOKEK>KKNKOK2K6K4K/K5K-K/K3K6K4K.K2K9KJKTK:K2K/K8K6K9K/K*K0K7K;K7K;KNK\KhKsKqKoKqKuKlK]KOK.K(K'K%KBKJKYKhKkKuKpKqKKKKKK}KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKDK:K%K-K+K3K2K/K*K4K0K'K!K(K=KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK|K}K}K|KzK|KxKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKjKXKNKPKVKXK[K`K\KZKWK[K\K`KdKgKkKjKlKoKrKrKpKnKiK`K_KaKYKXKSKXKZK^KfKgKiKhKgKiK_KbKhKce]r(KKKK%K0K4KK,K(K'K+K1K-K,K'K-K0K?K3K9KKK?K5K0K-K-K-K/K4K2K/K4K-K+K0K*K0K1K0KRKtKKsKDK+K.K/KHKTKLK?K'K'K(K"K'K/K$K-KJKcKcKZK=K?KgKKK8K.K;KOKMK[K[KXKLK^KsKtKqKvKmKnKLK?KSK[K^KWKPKhKKKKyKKKzKKKK\K>KKEKsKKKKKK?K,K,K+K)K K'K(KDKUKDK4K;KEKOKOK2K0K4K6K4K+K/K3K0K9K4K;K8KGKBK7K*K2K.K/K2K5K2K-K6K5K6KKMK\KhKaKiK`KDK-K1KNKAK;KVKiKVKJKrKsKlKnKfKjKbKMKSKRKZKRKIKYKvKKKKKKKKKKKK0K6K4K.K5K.K/K,K.K2K:K,K9K1K.K*K,K3KMKlKKKqK6K$K&K+K:KTKNK2K1K(K(K)K2K5K2K)K8K7K6K@KOKUKVK[KJK:K2KYKOK6KHK[KcKWK`KsKjKiK[K^KSKPKQK[K]KOKPK\KvKKKKKKKKKKKSK'K$K-K[KOK1K2KjKKKKKK.K$K)K$K&K"K$K)KKKFKJK1K4KOKhKYK1K+K0K8K.K*K*K4K2K-K*K6K7KNK>K3K4K.K/K0K9K0K2K4KK:KXKjKZKVKfKqKeKQKcKUKGKRK]KYKLKVKbKvKKKKKKKyKKKKjK(K K1KCK5K-K1KgKKKKKK/K 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Date: Sat, 17 Apr 2021 12:27:24 +0800 Subject: [PATCH 21/49] update --- 03-numpy/03.03-numpy-arrays.ipynb | 506 +++++++++++------------------- 1 file changed, 189 insertions(+), 317 deletions(-) diff --git a/03-numpy/03.03-numpy-arrays.ipynb b/03-numpy/03.03-numpy-arrays.ipynb index 86fd3bc1..808a04d2 100644 --- a/03-numpy/03.03-numpy-arrays.ipynb +++ b/03-numpy/03.03-numpy-arrays.ipynb @@ -17,9 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from numpy import *" @@ -42,9 +40,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -73,9 +69,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -110,9 +104,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -139,14 +131,12 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dtype('int32')" + "dtype('int64')" ] }, "execution_count": 5, @@ -169,14 +159,12 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4" + "8" ] }, "execution_count": 6, @@ -198,14 +186,12 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4L,)" + "(4,)" ] }, "execution_count": 7, @@ -228,14 +214,12 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4L,)" + "(4,)" ] }, "execution_count": 8, @@ -257,14 +241,12 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(4L,)" + "(4,)" ] }, "execution_count": 9, @@ -287,9 +269,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -309,9 +289,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -338,14 +316,12 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "16" + "32" ] }, "execution_count": 12, @@ -369,9 +345,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -405,9 +379,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -451,9 +423,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -481,9 +451,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -511,9 +479,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -534,9 +500,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -556,9 +520,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -585,9 +547,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -607,9 +567,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -635,10 +593,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ "od = array([21000, 21180, 21240, 22100, 22400])" @@ -653,10 +609,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { @@ -664,7 +618,7 @@ "array([180, 60, 860, 300])" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -708,10 +662,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { @@ -720,7 +672,7 @@ " [10, 11, 12, 13]])" ] }, - "execution_count": 24, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -744,18 +696,16 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(2L, 4L)" + "(2, 4)" ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -780,10 +730,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "data": { @@ -791,7 +739,7 @@ "8" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -810,10 +758,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 29, + "metadata": {}, "outputs": [ { "data": { @@ -821,7 +767,7 @@ "2" ] }, - "execution_count": 27, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -846,10 +792,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "data": { @@ -857,7 +801,7 @@ "13" ] }, - "execution_count": 28, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -877,10 +821,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "data": { @@ -889,7 +831,7 @@ " [10, 11, 12, -1]])" ] }, - "execution_count": 29, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -908,10 +850,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "data": { @@ -919,7 +859,7 @@ "array([10, 11, 12, -1])" ] }, - "execution_count": 30, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -952,10 +892,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "data": { @@ -968,7 +906,7 @@ " [50, 51, 52, 53, 54, 55]])" ] }, - "execution_count": 31, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -992,10 +930,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [ { "data": { @@ -1003,7 +939,7 @@ "array([3, 4])" ] }, - "execution_count": 32, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1021,10 +957,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "data": { @@ -1033,7 +967,7 @@ " [54, 55]])" ] }, - "execution_count": 33, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1051,10 +985,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { @@ -1062,7 +994,7 @@ "array([ 2, 12, 22, 32, 42, 52])" ] }, - "execution_count": 34, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1094,10 +1026,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "data": { @@ -1106,7 +1036,7 @@ " [40, 42, 44]])" ] }, - "execution_count": 35, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1131,10 +1061,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1147,7 +1075,7 @@ "source": [ "a = array([0,1,2,3,4])\n", "b = a[2:4]\n", - "print b" + "print(b)" ] }, { @@ -1160,9 +1088,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1189,10 +1115,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 39, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1206,7 +1130,7 @@ "a = [1,2,3,4,5]\n", "b = a[2:3]\n", "b[0] = 13234\n", - "print a" + "print(a)" ] }, { @@ -1222,10 +1146,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { @@ -1233,7 +1155,7 @@ "array([0, 1, 2, 3, 4])" ] }, - "execution_count": 39, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1275,9 +1197,8 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1287,7 +1208,7 @@ "array([ 0, 10, 20, 30, 40, 50, 60, 70])" ] }, - "execution_count": 40, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1306,10 +1227,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": false - }, + "execution_count": 45, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1322,7 +1241,7 @@ "source": [ "indices = [1, 2, -3]\n", "y = a[indices]\n", - "print y" + "print(y)" ] }, { @@ -1334,10 +1253,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, + "execution_count": 46, + "metadata": {}, "outputs": [], "source": [ "mask = array([0,1,1,0,0,1,0,0],\n", @@ -1346,10 +1263,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, + "execution_count": 47, + "metadata": {}, "outputs": [ { "data": { @@ -1357,7 +1272,7 @@ "array([10, 20, 50])" ] }, - "execution_count": 43, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1375,19 +1290,17 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": false - }, + "execution_count": 48, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0.37214708, 0.48594733, 0.73365131, 0.15769295, 0.30786017,\n", - " 0.62068734, 0.36940654, 0.09424167, 0.53085308, 0.12248951])" + "array([0.56450396, 0.18642879, 0.0958086 , 0.6009389 , 0.59648863,\n", + " 0.88710848, 0.10216209, 0.09341995, 0.49085839, 0.83004556])" ] }, - "execution_count": 44, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1400,18 +1313,16 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0.73365131, 0.62068734, 0.53085308])" + "array([0.56450396, 0.6009389 , 0.59648863, 0.88710848, 0.83004556])" ] }, - "execution_count": 45, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1437,10 +1348,8 @@ }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false - }, + "execution_count": 58, + "metadata": {}, "outputs": [ { "data": { @@ -1453,7 +1362,7 @@ " [50, 51, 52, 53, 54, 55]])" ] }, - "execution_count": 46, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1477,10 +1386,8 @@ }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": false - }, + "execution_count": 59, + "metadata": {}, "outputs": [ { "data": { @@ -1488,7 +1395,7 @@ "array([ 1, 12, 23, 34, 45])" ] }, - "execution_count": 47, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1506,10 +1413,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, + "execution_count": 60, + "metadata": {}, "outputs": [ { "data": { @@ -1519,7 +1424,7 @@ " [50, 52, 55]])" ] }, - "execution_count": 48, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1544,10 +1449,8 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false - }, + "execution_count": 61, + "metadata": {}, "outputs": [ { "data": { @@ -1555,7 +1458,7 @@ "array([ 2, 22, 52])" ] }, - "execution_count": 49, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -1589,9 +1492,8 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 62, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1603,7 +1505,7 @@ " [20, 21, 22, 23, 24, 25]])" ] }, - "execution_count": 50, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1622,10 +1524,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, + "execution_count": 67, + "metadata": {}, "outputs": [ { "data": { @@ -1635,13 +1535,13 @@ " [40, 41, 42, 43, 44, 45]])" ] }, - "execution_count": 51, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "condition = array([0,1,1,0,1],\n", + "condition = array([0,1,1,0,1,0],\n", " dtype=bool)\n", "a[condition]" ] @@ -1655,10 +1555,8 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, + "execution_count": 68, + "metadata": {}, "outputs": [ { "data": { @@ -1684,7 +1582,7 @@ " [60, 61, 62, 63]]])" ] }, - "execution_count": 52, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -1697,10 +1595,8 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, + "execution_count": 69, + "metadata": {}, "outputs": [ { "data": { @@ -1726,7 +1622,7 @@ " [62, 63]]])" ] }, - "execution_count": 53, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1775,10 +1671,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": true - }, + "execution_count": 71, + "metadata": {}, "outputs": [], "source": [ "a = array([0, 12, 5, 20])" @@ -1793,18 +1687,16 @@ }, { "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false - }, + "execution_count": 72, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([False, True, False, True], dtype=bool)" + "array([False, True, False, True])" ] }, - "execution_count": 55, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1822,18 +1714,16 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": false - }, + "execution_count": 73, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([1, 3], dtype=int64),)" + "(array([1, 3]),)" ] }, - "execution_count": 56, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -1855,18 +1745,16 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, + "execution_count": 75, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 3], dtype=int64)" + "array([1, 3])" ] }, - "execution_count": 57, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -1886,18 +1774,16 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, + "execution_count": 76, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 3], dtype=int64)" + "array([1, 3])" ] }, - "execution_count": 58, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -1916,10 +1802,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, + "execution_count": 77, + "metadata": {}, "outputs": [ { "data": { @@ -1927,7 +1811,7 @@ "array([12, 20])" ] }, - "execution_count": 59, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -1953,10 +1837,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, + "execution_count": 78, + "metadata": {}, "outputs": [], "source": [ "a = array([[0, 12, 5, 20],\n", @@ -1973,19 +1855,18 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 79, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "(array([0, 0, 1, 1], dtype=int64), array([1, 3, 2, 3], dtype=int64))" + "(array([0, 0, 1, 1]), array([1, 3, 2, 3]))" ] }, - "execution_count": 61, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -2003,10 +1884,8 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, + "execution_count": 80, + "metadata": {}, "outputs": [ { "data": { @@ -2014,7 +1893,7 @@ "array([12, 20, 11, 15])" ] }, - "execution_count": 62, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -2032,10 +1911,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": true - }, + "execution_count": 81, + "metadata": {}, "outputs": [], "source": [ "rows, cols = where(a>10)" @@ -2043,18 +1920,16 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, + "execution_count": 82, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0, 0, 1, 1], dtype=int64)" + "array([0, 0, 1, 1])" ] }, - "execution_count": 64, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -2065,18 +1940,16 @@ }, { "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false - }, + "execution_count": 83, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([1, 3, 2, 3], dtype=int64)" + "array([1, 3, 2, 3])" ] }, - "execution_count": 65, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -2087,10 +1960,8 @@ }, { "cell_type": "code", - "execution_count": 66, - "metadata": { - "collapsed": false - }, + "execution_count": 84, + "metadata": {}, "outputs": [ { "data": { @@ -2098,7 +1969,7 @@ "array([12, 20, 11, 15])" ] }, - "execution_count": 66, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -2116,10 +1987,8 @@ }, { "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": false - }, + "execution_count": 85, + "metadata": {}, "outputs": [ { "data": { @@ -2131,7 +2000,7 @@ " [20, 21, 22, 23, 24]])" ] }, - "execution_count": 67, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -2144,10 +2013,8 @@ }, { "cell_type": "code", - "execution_count": 68, - "metadata": { - "collapsed": false - }, + "execution_count": 86, + "metadata": {}, "outputs": [ { "data": { @@ -2156,10 +2023,10 @@ " [False, False, False, False, False],\n", " [False, False, False, True, True],\n", " [ True, True, True, True, True],\n", - " [ True, True, True, True, True]], dtype=bool)" + " [ True, True, True, True, True]])" ] }, - "execution_count": 68, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -2170,19 +2037,17 @@ }, { "cell_type": "code", - "execution_count": 69, - "metadata": { - "collapsed": false - }, + "execution_count": 87, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4], dtype=int64),\n", - " array([3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=int64))" + "(array([2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4]),\n", + " array([3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4]))" ] }, - "execution_count": 69, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -2190,27 +2055,34 @@ "source": [ "where(a > 12)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.8.8" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From 096c95d57fff5c6651695086557c5df6e558ce84 Mon Sep 17 00:00:00 2001 From: Jack Date: Fri, 23 Apr 2021 13:45:19 +0800 Subject: [PATCH 22/49] update requirements.txt --- 13-image-processing/13.00-image-basic.ipynb | 48 +++++++++++++++++++++ requirements.txt | 4 +- 2 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 13-image-processing/13.00-image-basic.ipynb diff --git a/13-image-processing/13.00-image-basic.ipynb b/13-image-processing/13.00-image-basic.ipynb new file mode 100644 index 00000000..a40de44a --- /dev/null +++ b/13-image-processing/13.00-image-basic.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# 图片基础" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 几种常见的图片格式" + ] + }, + { + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index becd34ab..4f89516e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,6 @@ jupyter==1.0.0 numpy==1.19.2 pandas==1.2.4 scipy==1.6.2 -matplotlib==3.3.4 \ No newline at end of file +matplotlib==3.3.4 +opencv-python==4.5.1.48 +Pillow==8.2.0 \ No newline at end of file From b71bd6e55846e57a6fb7eab0188e1cd1840ad9e0 Mon Sep 17 00:00:00 2001 From: Jack Date: Sat, 24 Apr 2021 10:50:18 +0800 Subject: [PATCH 23/49] update lesson --- 13-image-processing/13.00-image-basic.ipynb | 234 ++- 13-image-processing/13.01-Pillow.ipynb | 297 ++++ 13-image-processing/13.02-Opencv.ipynb | 76 + 13-image-processing/img/BMPvsGIF.png | Bin 0 -> 20070 bytes 13-image-processing/img/GIFvsJPEG.png | Bin 0 -> 58576 bytes 13-image-processing/img/JPEGvsGIF.png | Bin 0 -> 274454 bytes 13-image-processing/img/Lenna.png | Bin 0 -> 473831 bytes 13-image-processing/img/Mahuri.svg | 1429 +++++++++++++++++ .../img/NotoSans-BlackItalic.ttf | Bin 0 -> 483668 bytes 13-image-processing/img/PNG8vsGIF.png | Bin 0 -> 27197 bytes 13-image-processing/img/SVG.png | Bin 0 -> 609456 bytes 13-image-processing/img/SVGvsPNG8.png | Bin 0 -> 9107 bytes 13-image-processing/img/UydAT.png | Bin 0 -> 18743 bytes 13-image-processing/img/cat.jpg | Bin 0 -> 7123 bytes 14 files changed, 2031 insertions(+), 5 deletions(-) create mode 100644 13-image-processing/13.01-Pillow.ipynb create mode 100644 13-image-processing/13.02-Opencv.ipynb create mode 100644 13-image-processing/img/BMPvsGIF.png create mode 100644 13-image-processing/img/GIFvsJPEG.png create mode 100644 13-image-processing/img/JPEGvsGIF.png create mode 100644 13-image-processing/img/Lenna.png create mode 100644 13-image-processing/img/Mahuri.svg create mode 100644 13-image-processing/img/NotoSans-BlackItalic.ttf create mode 100644 13-image-processing/img/PNG8vsGIF.png create mode 100644 13-image-processing/img/SVG.png create mode 100644 13-image-processing/img/SVGvsPNG8.png create mode 100644 13-image-processing/img/UydAT.png create mode 100644 13-image-processing/img/cat.jpg diff --git a/13-image-processing/13.00-image-basic.ipynb b/13-image-processing/13.00-image-basic.ipynb index a40de44a..601ab17b 100644 --- a/13-image-processing/13.00-image-basic.ipynb +++ b/13-image-processing/13.00-image-basic.ipynb @@ -17,11 +17,235 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "在大多数的web页面中，图片占到了页面大小的60%-70%。因此在web开发中，不同的场景使用合适的图片格式对web页面的性能和体验是很重要的。图片格式种类非常多，针对几种 web 应用中常用的图片格式进行一个基本的总结。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 有损vs无损" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "图片文件格式有可能会对图片的文件大小进行不同程度的压缩，图片的压缩分为有损压缩和无损压缩两种。\n", + "\n", + "* 无压缩。无压缩的图片格式不对图片数据进行压缩处理，能准确地呈现原图片。BMP格式就是其中之一。\n", + "\n", + "* 有损压缩。指在压缩文件大小的过程中，损失了一部分图片的信息，也即降低了图片的质量，并且这种损失是不可逆的，我们不可能从有一个有损压缩过的图片中恢复出全来的图片。常见的有损压缩手段，是按照一定的算法将临近的像素点进行合并。\n", + "* 无损压缩。只在压缩文件大小的过程中，图片的质量没有任何损耗。我们任何时候都可以从无损压缩过的图片中恢复出原来的信息。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 索引色vs直接色" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "计算机在表示颜色的时候，有两种形式，一种称作索引颜色([Index Color](https://en.wikipedia.org/wiki/Indexed_color))，一种称作直接颜色([Direct Color](https://en.wikipedia.org/wiki/Color_depth#Direct_color))。\n", + "\n", + " * 索引色。用一个数字来代表（索引）一种颜色，在存储图片的时候，存储一个数字的组合，同时存储数字到图片颜色的映射。这种方式只能存储有限种颜色，通常是256种颜色，对应到计算机系统中，使用一个字节的数字来索引一种颜色。\n", + " * 直接色。使用四个数字来代表一种颜色，这四个数字分别代表这个颜色中红色、绿色、蓝色以及透明度。现在流行的显示设备可以在这四个维度分别支持256种变化，所以直接色可以表示2的32次方种颜色。当然并非所有的直接色都支持这么多种，为压缩空间使用，有可能只有表达红、绿、蓝的三个数字，每个数字也可能不支持256种变化之多。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 点阵图vs矢量图" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "\n", + "* 点阵图，也叫做位图，像素图。构成点阵图的最小单位是象素，位图就是由象素阵列的排列来实现其显示效果的，每个象素有自己的颜色信息，在对位图图像进行编辑操作的时候，可操作的对象是每个象素，我们可以改变图像的色相、饱和度、明度，从而改变图像的显示效果。点阵图缩放会失真，用最近非常流行的沙画来比喻最恰当不过，当你从远处看的时候，画面细腻多彩，但是当你靠的非常近的时候，你就能看到组成画面的每粒沙子以及每个沙粒的颜色。\n", + "* 矢量图，也叫做向量图。矢量图并不纪录画面上每一点的信息，而是纪录了元素形状及颜色的算法，当你打开一付矢量图的时候，软件对图形象对应的函数进行运算，将运算结果`图形的形状和颜色`显示给你看。无论显示画面是大还是小，画面上的对象对应的算法是不变的，所以，即使对画面进行倍数相当大的缩放，其显示效果仍然相同(不失真)。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BMP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "BitMap的缩写，是无损的、既支持索引色也支持直接色的、点阵图。\n", + "\n", + "这是一种比较老的图片格式。BMP是无损的，但同时这种图片格式几乎没有对数据进行压缩，所以BMP格式的图片通常具有较大的文件大小。虽然同时支持索引色和直接色是一个优点，但是太大的文件格式格式导致它几乎没有用武之地，现在除了在Windows操作系统中还比较常见之外，我们几乎看不到它。\n", + "![BMPvsGIF](./img/BMPvsGIF.png)\n", + "从上图中可以看到，在同样的图片质量下，BMP格式的图片文件大小是GIF格式的很多倍。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### gif" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "全称Graphics Interchange Format，采用LZW压缩算法进行编码。是无损的、采用索引色的、点阵图。\n", + "\n", + "GIF是无损的，采用GIF格式保存图片不会降低图片质量。但得益于数据的压缩，GIF格式的图片，其文件大小要远小于BMP格式的图片。文件小，是GIF格式的优点，同时，GIF格式还具有支持动画以及透明的优点。但，GIF格式仅支持8bit的索引色，即在整个图片中，只能存在256种不同的颜色。\n", + "\n", + "GIF格式适用于对色彩要求不高同时需要文件体积较小的场景，比如企业Logo、icon、线框类的图等。因其体积小的特点，现在GIF被广泛的应用在各类网站中。\n", + "![GIFvsJPEG](./img/GIFvsJPEG.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### jpg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "JPEG是有损的、采用直接色的、点阵图。\n", + "\n", + "JPEG图片格式的设计目标，是在不影响人类可分辨的图片质量的前提下，尽可能的压缩文件大小。这意味着JPEG去掉了一部分图片的原始信息，也即是进行了有损压缩。JPEG的图片的优点，是采用了直接色，得益于更丰富的色彩，JPEG非常适合用来存储照片，用来表达更生动的图像效果，比如颜色渐变。\n", + "\n", + "与GIF相比，JPEG不适合用来存储企业Logo、线框类的图。因为有损压缩会导致图片模糊，而直接色的选用，又会导致图片文件较GIF更大。\n", + "![JPEGvsGIF](./img/JPEGvsGIF.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### png-8" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PNG 全称 Portable Network Graphics，PNG-8是PNG的索引色版本。PNG-8是无损的、使用索引色的、点阵图。\n", + "\n", + "PNG是一种比较新的图片格式，PNG-8是非常好的GIF格式替代者，在可能的情况下，应该尽可能的使用PNG-8而不是GIF，因为在相同的图片效果下，PNG-8具有更小的文件体积。除此之外，PNG-8还支持透明度的调节，而GIF并不支持。 现在，除非需要动画的支持，否则我们没有理由使用GIF而不是PNG-8。当然了，PNG-8本身也是支持动画的，只是浏览器支持得不好，不像GIF那样受到广泛的支持。\n", + "![PNG8vsGIF](./img/PNG8vsGIF.png)\n", + "可以看到PNG-8具有更好的透明度支持。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### png-24" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PNG-24是PNG的直接色版本。PNG-24是无损的、使用直接色的、点阵图。\n", + "\n", + "无损的、使用直接色的点阵图，听起来非常像BMP，是的，从显示效果上来看，PNG-24跟BMP没有不同。PNG-24的优点在于，它压缩了图片的数据，使得同样效果的图片，PNG-24格式的文件大小要比BMP小得多。当然，PNG24的图片还是要比JPEG、GIF、PNG-8大得多。\n", + "\n", + "虽然PNG-24的一个很大的目标，是替换JPEG的使用。但一般而言，PNG-24的文件大小是JPEG的五倍之多，而显示效果则通常只能获得一点点提升。所以，只有在你不在乎图片的文件体积，而想要最好的显示效果时，才应该使用PNG-24格式。\n", + "\n", + "另外，PNG-24跟PNG-8一样，是支持图片透明度的。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SVG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "全称Scalable Vector Graphics，是无损的、矢量图。\n", + "\n", + "SVG跟上面这些图片格式最大的不同，是SVG是矢量图。这意味着SVG图片由直线和曲线以及绘制它们的方法组成。当你放大一个SVG图片的时候，你看到的还是线和曲线，而不会出现像素点。这意味着SVG图片在放大时，不会失真，所以它非常适合用来绘制企业Logo、Icon等。\n", + "![SVGvsPNG8](./img/SVGvsPNG8.png)\n", + "![UydAT](./img/UydAT.png)\n", + "SVG是很多种矢量图中的一种，它的特点是使用XML来描述图片。借助于前几年XML技术的流行，SVG也流行了很多。使用XML的优点是，任何时候你都可以把它当做一个文本文件来对待，也就是说，你可以非常方便的修改SVG图片，你所需要的只需要一个文本编辑器。\n", + "\n", + "SVG并非只能绘制简单的Logo类的图片，它可以绘制出精致的图片的，比如下面这涨\n", + "![SVG](./img/SVG.png)\n", + "链接地址：https://upload.wikimedia.org/wikipedia/commons/1/14/Mahuri.svg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### webP" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "WebP是谷歌开发的一种新图片格式，WebP是同时支持有损和无损压缩的、使用直接色的、点阵图。\n", + "\n", + "从名字就可以看出来它是为Web而生的，什么叫为Web而生呢？就是说相同质量的图片，WebP具有更小的文件体积。现在网站上充满了大量的图片，如果能够降低每一个图片的文件大小，那么将大大减少浏览器和服务器之间的数据传输量，进而降低访问延迟，提升访问体验。\n", + "\n", + " 在无损压缩的情况下，相同质量的WebP图片，文件大小要比PNG小26%；\n", + " 在有损压缩的情况下，具有相同图片精度的WebP图片，文件大小要比JPEG小25%~34%；\n", + " WebP图片格式支持图片透明度，一个无损压缩的WebP图片，如果要支持透明度只需要22%的格外文件大小。\n", + "\n", + "想象Web上的图片之多，百分之几十的提升，是非常非常大的优化。只可惜，目前只有Chrome浏览器和Opera浏览器支持WebP格式，所以WebP的应用并不广泛。为了使用更先进的技术，比如WebP图片格式，来压缩互联网上传输的数据流量，谷歌甚至提供了Chrome Data Compression Proxy，设置了Chrome Data Compression Proxy作为Web代理之后，你访问的所有网站中的图片，在经过Proxy的时候，都会被转换成WebP格式，以降低图片文件的大小。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 总结" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "结合以上的介绍，我们了解了各种图片格式的优缺点及适用场景，我们再来通过一个图表来做一个抽象总结： " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| 格式 | 优点 | 缺点 | 适用场景 |\n", + "|:----:|:------------------------------------------:|:----------------------------------:|:--------------------------:|\n", + "| gif | 文件小，支持动画、透明，无兼容性问题 | 只支持256种颜色 | 色彩简单的logo、icon、动图 |\n", + "| jpg | 色彩丰富，文件小 | 有损压缩，反复保存图片质量下降明显 | 色彩丰富的图片/渐变图像 |\n", + "| png | 无损压缩，支持透明，简单图片尺寸小 | 不支持动画，色彩丰富的图片尺寸大 | logo/icon/透明图 |\n", + "| webp | 文件小，支持有损和无损压缩，支持动画、透明 | 浏览器兼容性不好 | 支持webp格式的app和webview |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### " + ] } ], "metadata": { @@ -45,4 +269,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/13-image-processing/13.01-Pillow.ipynb b/13-image-processing/13.01-Pillow.ipynb new file mode 100644 index 00000000..ba6db0d3 --- /dev/null +++ b/13-image-processing/13.01-Pillow.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "58d56cea", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plot\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "9f59c0d3", + "metadata": {}, + "source": [ + "# Pillow 简介" + ] + }, + { + "cell_type": "markdown", + "id": "41773d38", + "metadata": {}, + "source": [ + "PIL：Python Imaging Library，已经是Python平台事实上的图像处理标准库了。PIL功能非常强大，但API却非常简单易用。\n", + "\n", + "由于PIL仅支持到Python 2.7，加上年久失修，于是一群志愿者在PIL的基础上创建了兼容的版本，名字叫 [Pillow](https://github.com/python-pillow/Pillow)，支持最新Python 3.x，又加入了许多新特性，因此，我们可以直接安装使用Pillow。" + ] + }, + { + "cell_type": "markdown", + "id": "d0bb973c", + "metadata": {}, + "source": [ + "## 安装Pillow" + ] + }, + { + "cell_type": "markdown", + "id": "74312836", + "metadata": {}, + "source": [ + "```shell\n", + "$ pip install pillow\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "7f3033e3", + "metadata": {}, + "source": [ + "## 操作图像" + ] + }, + { + "cell_type": "markdown", + "id": "918ee057", + "metadata": {}, + "source": [ + "最常见的图像缩放操作，只需三四行代码" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c661cfbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original image size: 512x512\n", + "Resize image to: 256x256\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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