Rlang-QQ 0.1.1.0 → 0.2.0.0
raw patch · 27 files changed
+2700/−360 lines, 27 filesdep +filepathdep +splitdep ~HListdep ~template-haskellbinary-added
Dependencies added: filepath, split
Dependency ranges changed: HList, template-haskell
Files
- Rlang-QQ.cabal +28/−6
- examples/Makefile +2/−1
- examples/ihaskell3.html +1998/−0
- examples/ihaskell3.ipynb +253/−0
- examples/runexamples.hs +7/−4
- examples/test.png binary
- examples/test2.hs +0/−25
- examples/test2.ref +0/−2
- examples/test3.hs +0/−22
- examples/test3.ref +0/−2
- examples/test3_lens.hs +0/−18
- examples/test3_lens.ref +0/−1
- examples/test4.hs +0/−52
- examples/test4.ref +0/−8
- examples/test5.hs +0/−31
- examples/test5.ref +0/−4
- examples/test6.html +48/−0
- examples/test7.html +58/−0
- examples/testplot.png binary
- examples/tests.hs +87/−0
- src/HListExtras.hs +2/−74
- src/RlangQQ.hs +16/−17
- src/RlangQQ/Binary.hs +92/−59
- src/RlangQQ/FN.hs +5/−5
- src/RlangQQ/Internal.hs +36/−24
- src/RlangQQ/MakeRecord.hs +4/−5
- src/RlangQQ/ParseKnitted.hs +64/−0
Rlang-QQ.cabal view
@@ -1,6 +1,6 @@ name: Rlang-QQ-version: 0.1.1.0-cabal-version: >=1.8+version: 0.2.0.0+cabal-version: >=1.10 build-type: Simple license: BSD3 license-file: LICENSE@@ -18,12 +18,11 @@ > R --no-save <<< 'install.packages("knitr")' category: Development author: Adam Vogt <vogt.adam@gmail.com>-tested-with: GHC == 7.6.2+tested-with: GHC == 7.6.2, GHC == 7.8-RC1 data-dir: rsrc data-files: Tree.R parseTreeExample.R extra-source-files: examples/*.hs, examples/*.Rmd,- examples/*.ref examples/*.png examples/*.ipynb examples/*.html@@ -36,12 +35,34 @@ library hs-source-dirs: src + default-language: Haskell2010+ default-extensions: ConstraintKinds+ DataKinds+ FlexibleContexts+ FlexibleInstances+ FunctionalDependencies+ GADTs+ KindSignatures+ MultiParamTypeClasses+ ScopedTypeVariables+ TypeFamilies+ TypeOperators+ ViewPatterns++ other-extensions: NoMonomorphismRestriction+ PolyKinds+ TemplateHaskell+ UndecidableInstances+ if (impl(ghc > 7.7))+ default-extensions: AllowAmbiguousTypes+ build-depends: base ==4.*, containers,- template-haskell ==2.8.*, directory, process,+ template-haskell <= 3, directory, process, trifecta ==1.*, utf8-string ==0.3.*, bytestring, Cabal, syb, mtl, MaybeT,- binary, vector, HList >= 0.3, temporary,+ binary, vector, HList >= 0.3.4, temporary, text, array, zlib, repa,+ filepath, split, haskell-src-meta exposed-modules: RlangQQ.Internal@@ -50,6 +71,7 @@ RlangQQ.FN RlangQQ.NatQQ RlangQQ.MakeRecord+ RlangQQ.ParseKnitted RlangQQ HListExtras
examples/Makefile view
@@ -6,6 +6,7 @@ default: test6.html test7.html cd ../; runghc examples/runexamples.hs+ runghc examples/tests.hs ghc -O2 romberg.hs; ./romberg %.html: %.md@@ -28,5 +29,5 @@ installdeps: R --no-save <<< 'install.packages(c("knitr", "lattice", "reshape", "ggplot2", "lattice"))'- cabal install lens numbers+ cabal install lens numbers hspec
+ examples/ihaskell3.html view
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Name.Variable.Global */+.highlight .vi { color: #19177C } /* Name.Variable.Instance */+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */+ </style>+++<style type="text/css">+/* Overrides of notebook CSS for static HTML export */+body {+ overflow: visible;+ padding: 8px;+}+.input_area {+ padding: 0.2em;+}++pre {+ padding: 0.2em;+ border: none;+ margin: 0px;+ font-size: 13px;+}+</style>++<!-- Custom stylesheet, it must be in the same directory as the html file -->+<link rel="stylesheet" href="custom.css">++<!-- Loading mathjax macro -->+<!-- MathJax configuration -->+ <script type="text/x-mathjax-config">+ MathJax.Hub.Config({+ tex2jax: {+ inlineMath: [ ['$','$'], ["\\(","\\)"] ],+ displayMath: [ ['$$','$$'], ["\\[","\\]"] ],+ processEscapes: true,+ processEnvironments: true+ },+ // Center justify equations in code and markdown cells. Elsewhere+ // we use CSS to left justify single line equations in code cells.+ displayAlign: 'center',+ "HTML-CSS": {+ styles: {'.MathJax_Display': {"margin": 0}},+ linebreaks: { automatic: true }+ }+ });+ </script>+ <!-- End of mathjax configuration -->++ <script>+ // We wait for the onload function to load MathJax after the page is completely loaded.+ // MathJax is loaded 1 unit of time after the page is ready.+ // This hack prevent problems when you load multiple js files.++ window.onload = function () {+ setTimeout(function () {+ var script = document.createElement("script");+ script.type = "text/javascript";+ script.src = "https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML";+ document.getElementsByTagName("head")[0].appendChild(script);+ },1)+ }+ </script>++</head>+<body>++<div class="input">+<div class="prompt input_prompt">+</div>+<div class="inner_cell">+<div class="text_cell_render border-box-sizing rendered_html">+<h1 id="multiple-plots-with-rlang-qq">Multiple plots with Rlang-QQ</h1>+<p>An example of <code>Rlang-QQ-0.1.2.0</code>, which supports multiple figures.</p>+<p>First a pile of imports needed:</p>+</div>+</div>+</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In [1]:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre><span class="kt">:</span><span class="n">set</span> <span class="o">-</span><span class="kt">XQuasiQuotes</span> <span class="o">-</span><span class="kt">XTupleSections</span> <span class="o">-</span><span class="kt">XViewPatterns</span>+<span class="kr">import</span> <span class="nn">IHaskell.Display</span> <span class="c1">-- shouldn't be needed but it turns out that it is essential</span>+<span class="kr">import</span> <span class="nn">IHaskell.Display.Rlangqq</span> <span class="p">()</span> <span class="c1">-- to check it's installed</span>+<span class="kr">import</span> <span class="nn">Data.Monoid</span>+<span class="kr">import</span> <span class="nn">Control.Applicative</span>+<span class="kr">import</span> <span class="nn">Data.List</span>+</pre></div>++</div>+</div>++</div>+<div class="input">+<div class="prompt input_prompt">+</div>+<div class="inner_cell">+<div class="text_cell_render border-box-sizing rendered_html">+<p>Rlang-QQ saves intermediate files. There is some code hidden in <code>IHaskell.Display.RlangQQ</code> and <code>RlangQQ.ParseKnitted</code> which gets those messages/warnings/plots.</p>+</div>+</div>+</div>+<div class="input">+<div class="prompt input_prompt">+</div>+<div class="inner_cell">+<div class="text_cell_render border-box-sizing rendered_html">+<p>Just some more of the same integration stuff that may be easier to think about because it's written in haskell</p>+</div>+</div>+</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In [2]:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre><span class="c1">-- | trapezoid rule. doesn't reuse previous values</span>+<span class="nf">trap</span> <span class="ow">::</span> <span class="kt">Fractional</span> <span class="n">a</span>+ <span class="ow">=></span> <span class="p">(</span><span class="n">a</span> <span class="ow">-></span> <span class="n">a</span><span class="p">)</span> <span class="c1">-- ^ function</span>+ <span class="ow">-></span> <span class="n">a</span> <span class="c1">-- ^ lower bound</span>+ <span class="ow">-></span> <span class="n">a</span> <span class="c1">-- ^ upepr bound</span>+ <span class="ow">-></span> <span class="kt">Int</span> <span class="c1">-- ^ number of panels to divide the interval into</span>+ <span class="ow">-></span> <span class="n">a</span> <span class="c1">-- ^ integral approximation</span>+<span class="nf">trap</span> <span class="n">f</span> <span class="n">a</span> <span class="n">b</span> <span class="n">n</span> <span class="ow">=</span>+ <span class="kr">let</span> <span class="n">h</span> <span class="ow">=</span> <span class="p">(</span><span class="n">b</span> <span class="o">-</span> <span class="n">a</span><span class="p">)</span> <span class="o">/</span> <span class="n">fromIntegral</span> <span class="n">n</span>+ <span class="n">xs</span> <span class="ow">=</span> <span class="n">take</span> <span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">$</span> <span class="n">iterate</span> <span class="p">(</span><span class="n">h</span><span class="o">+</span><span class="p">)</span> <span class="p">(</span><span class="n">a</span><span class="o">+</span><span class="n">h</span><span class="p">)</span>+ <span class="c1">-- easier to see as [a+h, a+2*h .. b-h])</span>+ <span class="c1">-- but some interval arithmetic doesn't support [a..b]</span>+ <span class="kr">in</span> <span class="n">h</span> <span class="o">*</span> <span class="n">foldl'</span> <span class="p">(</span><span class="nf">\</span><span class="n">s</span> <span class="n">x</span> <span class="ow">-></span> <span class="n">s</span> <span class="o">+</span> <span class="n">f</span> <span class="n">x</span><span class="p">)</span> <span class="p">((</span><span class="n">f</span> <span class="n">a</span> <span class="o">+</span> <span class="n">f</span> <span class="n">b</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="n">xs</span>+</pre></div>++</div>+</div>++</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In [7]:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre><span class="nf">improve</span> <span class="ow">::</span> <span class="kt">Fractional</span> <span class="n">a</span>+ <span class="ow">=></span> <span class="kt">Int</span> <span class="c1">-- n</span>+ <span class="ow">-></span> <span class="n">a</span> <span class="c1">-- O(h^n) estimate with step size h/2</span>+ <span class="ow">-></span> <span class="n">a</span> <span class="c1">-- O(h^n) estimate with step size h</span>+ <span class="ow">-></span> <span class="n">a</span> <span class="c1">-- O(h^(n+1)) estimate</span>+<span class="nf">improve</span> <span class="n">n</span> <span class="n">x</span> <span class="n">y</span> <span class="ow">=</span> <span class="kr">let</span> <span class="n">c</span> <span class="ow">=</span> <span class="mi">4</span><span class="o">^</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="kr">in</span> <span class="p">(</span><span class="n">c</span><span class="o">*</span><span class="n">x</span> <span class="o">-</span> <span class="n">y</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">c</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>++<span class="nf">rombM</span> <span class="ow">::</span> <span class="kt">Fractional</span> <span class="n">a</span>+ <span class="ow">=></span> <span class="p">(</span><span class="n">a</span> <span class="ow">-></span> <span class="n">a</span><span class="p">)</span>+ <span class="ow">-></span> <span class="n">a</span>+ <span class="ow">-></span> <span class="n">a</span>+ <span class="ow">-></span> <span class="p">[[</span><span class="n">a</span><span class="p">]]</span>+<span class="nf">rombM</span> <span class="n">f</span> <span class="n">a</span> <span class="n">b</span> <span class="ow">=</span>+ <span class="kr">let</span> <span class="n">is</span> <span class="ow">=</span> <span class="n">map</span> <span class="p">(</span><span class="nf">\</span><span class="n">n</span> <span class="ow">-></span> <span class="n">trap</span> <span class="n">f</span> <span class="n">a</span> <span class="n">b</span> <span class="p">(</span><span class="mi">2</span><span class="o">^</span><span class="n">n</span><span class="p">))</span> <span class="p">[</span><span class="mi">0</span><span class="o">..</span><span class="p">]</span>+ <span class="kr">in</span> <span class="n">map</span> <span class="n">snd</span>+ <span class="o">$</span> <span class="n">iterate</span>+ <span class="p">(</span><span class="nf">\</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">s</span><span class="p">)</span> <span class="ow">-></span> <span class="p">(</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">zipWith</span> <span class="p">(</span><span class="n">improve</span> <span class="n">n</span><span class="p">)</span> <span class="p">(</span><span class="n">tail</span> <span class="n">s</span><span class="p">)</span> <span class="n">s</span><span class="p">))</span>+ <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="n">is</span><span class="p">)</span>++<span class="nf">romb</span> <span class="ow">::</span> <span class="kt">Fractional</span> <span class="n">a</span>+ <span class="ow">=></span> <span class="p">(</span><span class="n">a</span> <span class="ow">-></span> <span class="n">a</span><span class="p">)</span>+ <span class="ow">-></span> <span class="n">a</span>+ <span class="ow">-></span> <span class="n">a</span>+ <span class="ow">-></span> <span class="p">[</span><span class="n">a</span><span class="p">]</span> <span class="c1">-- successive approximations</span>+<span class="nf">romb</span> <span class="n">f</span> <span class="n">a</span> <span class="n">b</span> <span class="ow">=</span> <span class="n">map</span> <span class="n">head</span> <span class="o">$</span> <span class="n">rombM</span> <span class="n">f</span> <span class="n">a</span> <span class="n">b</span>+</pre></div>++</div>+</div>++</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In [3]:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre><span class="nf">xs</span> <span class="ow">=</span> <span class="n">take</span> <span class="mi">5</span> <span class="o">$</span> <span class="n">romb</span> <span class="n">exp</span> <span class="mi">0</span> <span class="mi">1</span> <span class="ow">::</span> <span class="p">[</span><span class="kt">Double</span><span class="p">]</span>++<span class="nf">print</span> <span class="n">xs</span>+</pre></div>++</div>+</div>++<div class="output_wrapper">+<div class="output">+++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>+[1.8591409142295225,1.7188611518765928,1.7182826879247577,1.7182818287945305,1.7182818284590786]+</pre>++</div>+</div>++</div>+</div>++</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In [4]:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre><span class="nf">sse</span> <span class="ow"><-</span> <span class="kr">do</span>+ <span class="p">[</span><span class="n">pun</span><span class="o">|</span> <span class="p">(</span><span class="n">sse</span><span class="p">)</span> <span class="o">|</span><span class="p">]</span> <span class="ow"><-</span> <span class="p">[</span><span class="n">rDisp</span><span class="o">|</span>+ <span class="n">m</span> <span class="ow"><-</span> <span class="n">lm</span><span class="p">(</span> <span class="n">log</span><span class="p">(</span><span class="o">$</span><span class="p">(</span><span class="n">xs</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">exp</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="o">~</span> <span class="kt">I</span><span class="p">(</span><span class="mi">1</span><span class="kt">:</span><span class="n">length</span><span class="p">(</span><span class="o">$</span><span class="p">(</span><span class="n">xs</span><span class="p">))))</span>+ <span class="n">plot</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">which</span><span class="ow">=</span><span class="mi">1</span><span class="kt">:</span><span class="mi">2</span><span class="p">)</span>+ <span class="n">print</span><span class="p">(</span><span class="n">predict</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>+ <span class="n">hs_sse</span> <span class="ow"><-</span> <span class="n">sum</span><span class="p">(</span> <span class="n">resid</span><span class="p">(</span><span class="n">m</span><span class="p">)</span><span class="o">^</span><span class="mi">2</span> <span class="p">)</span>+ <span class="o">|</span><span class="p">]</span>+ <span class="n">return</span> <span class="p">(</span><span class="n">sse</span> <span class="ow">::</span> <span class="kt">Double</span><span class="p">)</span>+<span class="nb">()</span> <span class="c1">-- needed to force a display</span>+</pre></div>++</div>+</div>++<div class="output_wrapper">+<div class="output">+++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>+()+</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>++</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">++<div class="output_html rendered_html">+<img src="data:image/png;base64,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2B0tg8eQjgVuC8duOaXs71/tb9FwIeyDmKarKKX1HZCCPPTn4diAS2PcnuTXYnYXW5seRA4otFBSZImt3HjxgOBy0II85MkuZX4f1oCao9Fv4g4MMOxxMFuzgKOI1YzSZIycvrppy9atGjR4UmS3AD8adbxKJ9qleDHkv+NwDXAo4FPAH/T6KAkSdUtWrTopHK5vDzrOJRvtUrwu9LlVOI87ScB9xBHZpIkNVkI4bTdu3dfkiSJM29qUrVK8I8AG4FXEbtcLAO+SRz8RpLURGvXrp1XLpfvPvPMM+/MOhYpb7yLXlLLCSE8J4Twrqzj0LTlZqAbSVIOrFu3bkFPT8/I7t27fzQ0NPStrONR66mV4GuNamdfS0lqoJ6enk+NjIysP/PMM2/KOha1v15im/w/ZB3INFlFLynXQgjFUqn0vKzj0KzK7UA3lXYA7wFOa1AsktTpjikUCo/OOgi1h6m0wReIMyA50I0kzaJSqbSmUCjckyTJ14i9laQZm04bfGhQLJLUqYZ27tzZyBnmpLZmG7yk3AghHBpCuBxvWm53dpOTpE4QQigCPdu3b79j2bJlJ+AEMWqQ8Qm+Vte4Sn7jlKTpOatcLl9/3nnnfZY4HLjUEOMT/A72Tt5vBp4AbEgfnwE44IIkTVGpVHpeoVC4KkmSt2YdizpDrW5yi4jTxL4ZuD1d3poukqQ6hRAOKBQKz9y2bdvurGNR56jVBt8FLGTvLwHFdJ0kaRL9/f1PKRaLRydJcgGwKet41FlqleB3AtcB5wIHpcu5WEUvSXUpFovzdu/e/cWs41BnqlWCHwFOBv4NuAUYBT4P/GMT4pKklhRC6AY+Pzw8/E9JklybdTxSJ7AfvKSG2rRp00EAIYQVWcei3MjNWPS9xD6Z76n4faJFklQhhPCy7u7ukwGSJPlt1vFIk3WTs7+7JNUQQjhmZGTk7iRJPk9sxpRyYSqzyUmSximXy382PDy8Pes4pKkoAOuBa4EDgG8AjwBvyDKoGbANXtKsCCE8KoRwTtZxqCXkpg2+0iJiMn898CzgbuDpxIFvJKmTdY+MjJyfdRBSLbW6yZWBYWABcArwbmIbfVcT4pKk3Akh/CtwVZIk/5F1LNJkJhvo5l+Ba4jJ/gbgCqAR1VILiFXovcBK4FJgN/AgsXpjvwYcU5LqEkI4OP31DJO7WkVe7pLvBX4NHAlcBNxFbAoYAhJgGfCXMzzGFmAV8Fcz3I+kDhJCOAwYAP46SZLRrONRy7kQOAu4udkHztt88AuBo4AnAbel6/qAH2YWkaSOFEL4/dHR0d9LkuQqLBioBU3WTe5gYrX8ncDRwPuAngbGMpdYkt+/Yv1SYnW9JDVNuVx+drFYvDvrOKTpqlWCLwLvB24EngrcCzwxXfeqWY5jGPgR8F1ikr8AeBpwDHAZ8LZZPp4k7SOEMJd4Q/HGJEk+mHU80kzUSvCLgCcAbwJOIib4NcA3GxDHTuDY9PdV6TICPAC8BLi6AceUpPG6R0dHPz44OHh/1oFIM1UrwRfSZbhi3e5JtpkNd6QLwA8afCxJolQq/W2hUDg8SZI+nBJbbaJWst5JTLAvICb6FUA/cWS7ZjqeeB/AnDpe+xJgbZXnDgN+NltBSWp9mzZt+r1du3ZtLxQKW5Mk2ZV1PFIzHURMrmXgPmL7e2+mEU2fQ9VK+p0QQjGE8KmNGzcemHUsamuZDVVbqwS/BPgO8Awy6L8nSY0QQugpl8vPS5JkK3Z/Uxur1U3uQeIgM+uIo8s12nLi3au3sWfe+duBc4ld5SRpNjyxUCjsyDoIKUu97Em045fZNgf4b+CDwGrigDcLgSOIQ+NeMQvHsIpe6mAhhLf09/c/Les41HFyWUW/g+YNZbuAmMxfkR53zE+II9nd0KQ4JLWp0dHRbw0ODn476zikTjMH+Bp7SvAL0uVwYgn+67NwDEvwUocJITw5hPD5rONQR8tlCb6ZdhMH0ekHLgcela6/E/gscEJGcUlqQW9961t758+fvwT4/rZt2/z/oY6UlwQPcA9wWrpI0rTNnz8/jI6OXjI4OHjb5K+W2lOeErwkTduaNWu6Hv/4x59y0003XZAkyT9nHY+UtfHd5GrdOd/Iu+glaUYOO+ywlcBDW7duHck6FikPxpfgx985/2bihDMb0sdn4DjNknKkVCq9vFAoHJgkyQeAi7OOR8qLWgPdLAJeT0zyt6fLW9NFkjK1du3aeQCFQuGn27dv/0jW8Uh5U6sNvos42Ezll4Biuk6SMhNCWAx8bcOGDS9IkmRb1vFIeTTZbHLXEYeKXZeu24JV9JIysmbNmq7DDz/8sCRJbgKemnU8Up7VqqIfAU4mtsnfQpxwZhT4xybEJUn7WL169UnFYtHELtVhsm5ydxMHoJGkzJRKpTWjo6PfTZLkw1nHIrWKWiV4gIOJE73cCRwNvA/oaXRQklShUCgUVu3YseP/sg5EahdF4IvEseDvBw4FrgE+nmFMM+FY9FILCSE8KYRwTtZxSDOUy7HoFxH7wL8JOAm4l1hd/80mxCWpQ61Zs6Zr9erVheHh4bu7u7sHso5HalW1EnwhXYYr1u2eZBtJmpHVq1d/BnjXGWecYWFCapAu4D+A1wL3EasYLgI+kWFMM2EVvZRjIYRjso5BaoDMqugn6yb3T8Rq+V7gf4CH03WSNGv6+/uPAl6YdRxSO6lV3d5L7P/eB/wZcZKZhcRE//jGhyap3YUQjgMWJknyVeAHWccjtZPJusndA/w9MEisspekWVMul1cB3806DqkdTXbD3H3AnwIfJrZh9zU8IkltLYSwDPgY8PIkSbZmHY/UriYrwUPsA/8yYAFwATC3oRFJalshhPnAQ8BrkyQZyjoeqZ3V2+VtCHgdsQT/isaFI6ldhRA2lsvlHQMDA+8Dbss6Hkntw25yUgb6+/uPTqd3lTpRLrvJSdKMhBAWFovF1+/cubOcdSxSpxlfRd9LvLHuPKA//X0ihUYGJam19fX1Pbarq+vZSZJcgFNMS5kYn+B3sHfyNpFLmrJCofAo4Mqs45A6mePKS5o1IYQPjY6OvntwcPCarGOROt34Nvhe4oh1ky2S9DshhP0ARkZGNg8ODv4463gk7Zvgx6rox5a3EAekODhdLiZ2l5MkAEIIfwK8A2Dz5s23ZBuNpHosIo5Ff2DFugOBX2YTzozZTU6aRSGEP+jr63tc1nFIOZfLbnJdxMllKl9TTNdJ0t+Wy2VHo5Na0Nh88J8GDkqXTwOXZhnUDFiCl2YohNATQnhH1nFILSSXJfgR4GRiW/wtwM3AKPZplTrW8PBw7+jo6JeyjkPS5Gp1k1tCnJ/5GcCa5oQjKY9KpdK6QqFwT5IkHwX+N+t4JE2uVgn+QSAB1gErmxOOpDxZv379UoDdu3f/e5rcJbWIWiPVjQ1bO9Xt8moLsAr4q6wDkVrBxo0bV82ZM+fi+++//6Vbtmx5OOt4pBZ1IXAWsZm7qWpV0Y8ftlZSBwghLANWJ0lyLfDcrOORND1TnU1uEfChRgQiKTdegTNNSi1vsrHoS8R2+ErfbFAskrJTKJVKGwuFwkeSJLkw62AkzVytBL+I2E3uWOD1xDaE44ClTYhLUhOdcsop3cCNSZL8X9axSGq8XuBXxO5ypwAvABYDP2rQ8ZYD7wZuY8+kNrcD5zI7Xyoc6EYaJ4Tw3BDClqzjkNpYZgPd1CrB70qXU4HrgZOAe4gJf7bNIY6S90viTT23pusPIU5usxV4TgOOK3WkEMJ+O3fu3A18G+dtlzrSXwA3EkvQXwKGgdMbcJweYmm9d4LnFgM/m4VjWIKXUiGES5woRmqKzErweTEH+BrwQWA1sCBdDgfOAb4+C8cwwaujhRDmhhBOzDoOqcPkciz6ZtpNHA53J3A58FC6XElsRjghs8ik9vEkYP4Ut1kOvA24AFgLzJ3lmCQ1SS97bnCrtbQiS/DqSCGEvy6VSs+exqYHAt8F/pJYs3Ya8Qv4ZN1rJe2Rm5vsxo9e92bgCcCG9PEZwLeaENd0LSAORzuRXvJTYyE1032FQuGaaWz3T8AAcdpogG3EEv3zgP+cpdgkZWARcZrYAyvWHUi8072ZjidW4dfjhcS78SdafkYsfUhtr6+v77EhhK8ws+GmPwocOm7dXwBvnME+pU6TmxJ8pS5gIXuXeovpuma6ingTXj3+k+oli7HJZqS2dcopp8xZsWLFsh07dtw2b968E5hZk9qPgWeyp9sqxC/cn5vBPiU1Sa0EvxO4jjjQzLp03RbyXUUvdbRVq1Yl5XL5W+edd94XieNYzMT7iF+YVxKr5/+E2GV2Nnq1SMrY/sRBZoaAR4g3qa1o0LEcyU6aplKpdMK6desWNGDXc4G/A95CbAKTNDW5rKIHuJvYfa3RHMlOmqYNGzYsLxQKh/f09FzagN0PEdviJbWZjTSnm5wj2UlT1N/f/7RSqfSmrOOQVFMuB7pZDLwBeH76ukLFMtseBn4KnM2+I9ltJiZ/SXt7pFAofCzrICTl02RV9NuBb9D4wW3GRrLrJ3Zle1S6/k7gsziSnQRACKEbuHx4ePjvBwcHv5d1PJJa10nAeiauOm81VtGrpYUQjgBo0M10khojl1X0vcBFwFnAfbT+ULVSyyqVSi8FXgGwZcuWhzMOR1ILqFVFP37YWklN1t/f/4xisfibJEm+AHwh63gktY6pjs2+CPhQIwKRtK9isfgM4K6s45DUfkrs20XuG5lGNH22waslbNq06dEhhHOyjkPSrMjlQDeLgJOBY4HXE9vij2N2RpWTVN3oyMjIuVkHIam11UrwY8/dCFwDPBr4BHEs+nc0OC6p44QQ3l8uly8bGBj4YtaxSGp9tdrgd6XLqcDNwCuJg9AsaUJcUscY6/4GrDO5S5ottRL8I8Shal8F3AAsA74JvLcJcUkdoa+v7zHAW0IIxSRJHsk6HklqRd5kp9wIIRwWQnh+1nFIarjcDnTzHWDeuHWzMfGL1NHK5fIf4WdJUgNNdJNdF3Ad8Efp4/HVhq3aTU7K1Lp16xb09PScD5yaJMkns45HUnubqAQ/AjwZ2A/4LjCfvWeSO65p0UltYu3atfN6enpGRkdH35Ekyf1ZxyOp/U02VO1TmhWI1K5CCP8A/H6SJBuBH2cdj6TOMFGCPxj4KHGO9hcChwGXErvIXQ68BritWQFKrWrTpk2P7u7uvjdJkguzjkVS5xlfRd8FnA98n9jvvQv4ALFNfhVwPY5FL00qhFDs7u5+++7du53aVVIuLAH+DzggfXwgcC9waMXj3zQ/rFlhNzk1XAhhRalUOjnrOCTlRm66yRWIE8rsTh8fQ6yOvz193A2MNic0qSUdUS6Xf5J1EJI0vg1+J3AT8FrgY8SR7D5HTPgrgfcQ76yXVCGEcMbo6OhlSZJcm3UskgT7luBHgH8EXkYsuZeJQ9MuA34KzCXOMCeJ2P0NYHR09DODg4OOESEpNya6i/4WYtX8eL0NjkVqKSGE/wcE4KWDg4PfyzoeSapUqx+8pAls2LBh/66urp4kSW4AXpp1PJI0kVpj0UuawNy5c0/t6upy2mRJuWYJXqrDKaecMmflypWn33TTTe9MkiRkHY8kTcYSvFSHVatWLQe2bd26dSTrWCSpHpbgpRpKpdKrgAOSJNkCfCHreCSpXpbgpQmk3d8KIyMj1955553vzToeSZoqS/DSOCGExcBXN2zY8OIzzjjjV1nHI0nTYYKXUmvXrp23bNmyo5IkuR44Nut4JGkmrKKXUkuXLj2ROF2yJLU8S/DqeCGEfwCuTJLkY1nHIkmzxRK8Ol2hXC6XgVuzDkSSND3OB6/fCSH8vxDC+7OOQ1Lby2w+eKvo1VHWrl07r7u7uzg0NPSr0dHR9VnHI0mNYoJXR1m2bNmFwHnpnfKSpAZbQKxC7wVWApcCu4EHidUb+83CMayi71yFUqn0gqyDkNSRMquiz8tNdnOB1wA9wMeBR4DHAL9PTPTnZxeaWl1/f/+RwOqs45CkZspbFf1C4CjgScBt6bo+4IeZRaSWVSqVngfMHxgY+BBYr14AABbSSURBVALwo6zjkaRmyksJHmIsc4FfA/tXrF9KLMVLU1IoFLqGhoauzjoOScpCXkrww8QS1neJSf4C4GnAMcBlwNsyi0wtZcOGDfvPnTv3I8ArkiS5LOt4JCkreUnwO9kz9veqdBkBHgBeAlgK06TWr1+/9MEHH7x/yZIlJ5955plDWccjSVnKS4KvdEe6APwgy0DUOkql0gbgrre//e0fAn6TdTySlLU8JvjxjgeuAOZkHYjyp7+///ihoaHvDwwMnJV1LJKUJ3m6ya6aqzC5awLr1q1bUCwWXzF//vxdWcciSXnTCiX4qXgS8Lwqzz0ZuK+JsahB+vr6HtfV1fXCJEnOAd6UdTySlEd5KsEvB95N7P9eTpfbgXOJXeXq8SDwyyrLDmB0dkNWFrq6unpHRka+kHUckqTJzQH+G/ggccSxhelyBHAOsQ1+phyqtsWFEC4JIRyZdRySNAWZDVWbFz3E0nrvBM8tBn42C8cwwbeoTZs2HQQQQliRdSySNEUdPxb9w8BPgbOJJfgF6XI4sJmY/NWBQgh/0t3dfTpAkiS/zToeSWoVebnJbjewBugHLgcela6/E/gscEJGcSkj/f39RwGjSZJcCVyZbTSS1HryUoIHuAc4DTgIKKTLKuBUYHuGcSkDxWLxBaOjo/Z6kCRNyjb4nFu/fv3SEMLZWcchSbOo49vgx+sF7mXim+7UpubNm7cIv4RJ0qzISxu8OlgIoa9cLv9vkiQXE8dBkCTNUF5L8OoAIYSDifda/NvAwMDFDTjE/sA7gc8AZ1D/gEmS1PLymuB3EP8Z78g6EDVGCGEl8IEQwrwkSRpxE+Vy4IvEAZReC3wLuIw45oIktT2r6NVUGzZs2H/u3LlHJ0nyVeBFDTzU3xCHOf6v9PGXgJXAicSbXiSpreW1BK82NWfOnOeMjo7e24RD/R7wi3HrfgEc0oRjS1LmLMGr4UIIReAdw8PD5wwMDHyySYf9PvBc4DsV654HXN+k40tSpkzwaobi6Ojol84444xmDjn8SeLNdecRk/pxxPb3jU2MQZIyY4JXw5RKpRcXCoXnJUlyKnBVkw8/ArycmNgPAy4i3mgnSR3BBK9Zl94h/8j27du/umzZsv/MOJxr00WSOooJXo3wrpGRkYHzzjvv51kHIklqf45F30Dr1q1bEEL466zjkKSccSx6tbbFixc/sVwu78o6DklSZBW9ZqRUKr22UCj8KkmSrwDXZR2PJCkywWtGyuXyjTfddNO3s45DktS5bIOfJSGEw0IIVxEnipEkVZdZG7wleNVt3bp1C3p6eg7ctm3bzatXr/4zoJx1TJKkiZngVbeenp5/Aa7funXrrcSBZCRJOWWC12QKpVLpNQ899NCnkiTZnHUwkqT62E1ONa1fv34/YO7ZZ5/9UNaxSJLqZwleEyqVSs8uFAp/lCTJO4H3Zx2PJGlqTPCa0Ojo6K8feeQRu79JUosywet31qxZ07V69er/HhkZefXmzZsdR16S1BLsB19dob+//w+zDkKS2pD94JWdEMLLgYOBH2cdiyRpdpjgO1ipVHphOo7857KORZI0u+wm19kO3blz5y1ZByFJ0kzYBg/09fU9LoTwwazjkKQOYRu8Gq4QQpgD7BgeHn5b1sFIkhrLBN8hQggfAL6QJMllWcciSdJs6sgq+v7+/j/OOgZJ6mCZVdF7k10b6+vre0yxWPyrEIJ/Z0nqMFbRt6EQwpPK5fJjBgYGLgXWZR2PJKn5LNm1p0OGh4cdR16SOpgl+DYRQlgIfGrXrl0nJUnyH1nHI0nKliX4NhBCmA8MAW98+9vffm/W8UiSspenEvxyYBNwInBQuu7/gM8AbwNMXBMIIbyuXC7vPzAwMAjcmnU8kqR8yEuCnwN8Gvgl8Fz2JKpDgNcBW4HnZBJZToUQVg8NDf0mSZIPZB2LJCl/8lJFvwA4Ajgd2AY8lC4/AfqIiV6ptNvburlz55azjkWSlE95KcE/DPwUOJs4IM3YBCiHAP8E3J5RXLkSQnhUuVw+IUmS84B/zDoeSVJ+5aUEvxtYA+wELmdPCf5K4peQEzKLLF8eVSgUvpV1EJIk5UnLDlUbQvjX/v7+p2cdhyRpyhyqVvsKIewHMDIy8t7BwcFvZB2PJKl1tEKCP55Yhd9R+vv7jwbOB9i8efMtk7xckqS95OUmu1quInaj6wghhINHR0eXDg4Ofg/4y6zjkSS1plZI8FPxcuD1VZ77A2IXvLz7m3K5/IWsg5AktbZ2S/CXpstETgRWNDGWuqVDzQ4AG5MkeXvW8UiSWl8rtMG3vaGhoSXlcvnyJEmGs45FktQe8lKCXwR8D3hcjdcUmhRL05RKpVcXCoUlSZK8h9j/X5KkWZGXEvxO4AnAd4AXEJP5+KVtvPWtb+0FCoVC4fJ0VDpJkmZVXhI8wC7gImKyb1shhMXz58//XAhhaZIktwGOJy9JmnV5qaIf829ZB9AoIYTFwJOTJLkSeHbG4UiS2lyeSvBtrVwuvxBYnHUckqTOkLcS/Jhe4pzwhwI7Mo1khkIIbxwZGfmvgYGBS7KORZLUOSzBN1ahXC7fvnnz5p9lHYgkSXnQC9yb/pwtJ1J9lLtZFUI4JoTw4WYcS5KUa5nNJpfXKvodwNKsg5iqEMLiRx55pAu4kSZ9mZAkaSJ5TfCt6ty5c+e+N0mS72UdiCRJnaIhVfQhhGII4SWzvV9JUlvIrIrem+xm7gjg4KyDkCSpklX001QqlV5cKBS6kiT5PK0xDa0kqYOY4KepUCjsAq7NOg5JkiZigp+CjRs3HjhnzpxPbN++/c+TJPlq1vFIklSNCb4OIYQisBK4C3j5eeedtyvjkCRJqskEX5/Ty+XyHQMDAx8D7s86GEmSJmOCryGE8OfAt5MkeWfWsUiSNBV2k6sihDAf+GPgwaxjkSRpqizBjxNCeCLwoiRJzgRC1vFIkjQdluD3NQw4UYwkqaVZgk+FEC4D+pIkuSHrWCRJmqmOL8GHEA4BuP/++08wuUuS2kVHJ/gQwnOAtQBbtmx5OONwJEmaNR1ZRR9CePLo6OjuJEmuAK7IOh5JkmZbR5bgy+Xy8SMjI3dmHYckSZq5E1/zmtdcNc1tDwH+FFhesW4e8CHgGuCnwCtnFp4kqQ1lNh98R1XRX3311V+b4iZF4APACuBmYCPwReBc4GlAF/BM4A+AK4FPzlaskiTNREcl+F/84hd3T3GTk4BfAaekjwvAZ4GvA98CvpOuPz59nSRJudCRbfBT8Axga8XjMvAZ4DhgF3Bg+vgpwPObHp0kSVV0VAl+Grazd7s7xOr63xDb5S8GTgZ+1uS4JEmqyRJ8bRcBA8AB6eMjgTXAl4EXpo8/B9wIfDuD+CRJ6ngnAq+fxnbHAf9JbG//GBndDSlJakneRZ9j1xJL65IktQyr6CVJakMmeEmS2pAJXpKkNmSClySpDZngJUlqQyZ4SZLakAlekqQ2ZIKXJKkNmeAlSWpDhawDaKLnAecB92cdyBQcRpxzXtNXJF7nI1kH0uLmALuzDqLFdROvw3LWgbSwAvAA8L9ZBzIFvcDTgbuyDkT58mVgftZBtLhXAq/NOog28PWsA2gD5xO/tGv6ng5szjqIVmEVvSRJbcgEL0lSGzLBS5LUhkzwkiS1IRO8JEltyASfb0PAaNZBtLjhdNHM7Mo6gDYwgtfiTA1jl1e1CbvIzVwXsf+xZsZrceY8hzNXAOZmHYQkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVLuLAR+APTWeM0K4HJgd/pzRRPiaiX1nJ9u4IdAuWL5ZLMCzLF6ry2vweq8/maP/w9nyLHo86ELeDNwHXDkJK99F3ALsF/68+zGhtZy6jk/84FlwOHAknR5dbMCzLF6ry2vweq8/mbO/4dqK0Xg2cCrgAeo/o11AXAz8IT08RPSxwsaHWCLqPf8PIZ4nm8mlp7+C1jVpBjzqt5zV+11irz+Zm6m/w+VsgSfD6PAfwNfovZsU3OIF/tv0se/SR/PaWh0raPe89MLfB/4K2AlcA9wfpNizKt6z1211ymayfXX1aQY826m/w+VcpatbJQrft8P2FHndoUq6yZa3+56gfsqHu9X5XUTnZ/vA8+oeLwJuH72QmtJ9V5b1V6niU3l+lsE3N+kuNqB1+IkLMFno1Cx1JvcIc4PvwPYP328PzHJDc1qdK1hB/uex3rPz1OBYyseDxNv0ulk9Z67aq9TNJPrr4ymwmtxEib4/OsGjkt/PgxcC5ycPn41cE26XrXPT+V5PADYChxFbMd7K/EO3E5W77mr9jpFM7n+Hswg3lbjtaiW1Qvcy97tSOPXrQCuIH5TvRxY3swAW0C181N5HovAacAdwC7gEqpX8XeSes5drdfJ6282+f9QkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiS1rZ8D5QmWnwD7A/cCvZlFt0cvM4ul1vY9wLnALcT3fkv6uFnvuzK2mb5PqW0Usw5AanGPAwrAfsB96c8CcAQwlGFcAIuAz6c/dwHnpD9n00HAjcAK4KXAwvTnCuBH6fON0Iz3JknShCXHrEuTs3n8avu6CPgE+xYWiun6i2bh2FOJJ+tzLklqM9US/A5gALgZeBjoJ5bwHwV8MV13F/Bp9pR2VwCXADuBB4FPAcvHHedfgDuAw6vsZwnwQ2KV+Q/TdWPxHQB8CXgE+F/gVWlMACcSmx1GgAfSeItV3t9C4FZgdZVzsjp9fuW4bcfva7JjvhH4RfoeNxKbBKq9t8p91zrHAG8A7k+fX19xDiRJ+p1qCX4E6CMmrGOJ1fjLgP8CTgIWAEuBzcA3gLnAVuCjwGJiFfTFxOQ0lvSGgQ8DT6qxn+5xMY39vhT4DLAlPdbxwHZiEl5ErFY/Nd3+j9Jtemq8v9+y58vHeCvS53+P6gl+smMOp+evK411ongm+n35JOdmCfCb9BweDlxG/MIiSdJeapXgV417zcHE9vnxN+btIpY67wIOrdjPocRktKRinwcRE121/VRLggel+zqkYv9LiQkUYvI7BngdseZgiH2T6JixEvzjq5yT1cCv0/dfqwRf65gTnb9qSb3y91rnuIf45eYa4Argleljqa14k53UWKPAQxOsu5NYsi2kyyLgMGKVPMRkRMXvhXHbPzjJfh6oEs/Y60YrHq8C5gFzgP8ATiN+yRgkNhNU8xBwJbCJPf9LCsREfQxQAq4nVoFXWljx+smOOdH5q8dk52YI+BNiTcYrgW8Sz4EkSXup5ya7scf7AecDHyFWw68CvgJ8jFiS/mz63KJ0uShdN74tvFBjPwX2rq4e+30ZsTr6XcRS63HEEv1KYg3BXcSSd5FYvb0r3abazWsHEUvpHwWOJCbvDewpMa8mlpjvA56Z7ncTseq9d4rHrFYVP9H6Wue4kK67CTgKeCLxy8BiJEkaZyoJfiwR/TuxdPsIMUGOvW5/Ypv7znS5hNieXW2f1fYzH7gW+BV734h2ELFqeoiY2F6Vvr4AnJ4e8xbizYHXA58jVuNPlODHYjgvPU6ZeOPap4Cr09jnEm+c2wX8EngzMeGPfUmp95iV773ae6v3HJMed1d67LUTvC9JkjSBOcBTsg5CkiRJkiRJkiRJkiRJkiRJUpur1l+5Hn9D7DI1k300Y580YH+z7S3Ak9Pfx85Bo9Sz/1cBfzvLxx2/z9k4xkR/16mev2ZeG/Ucq9M+A5NdF424FqWOMN0P/VJiv+JFM9hHM/Y5Zj4Q0p+1VE4rOpvWEPt07wa+S5wedswc4sA0C9n7HDTCRPtfCPyAvc/1IuI47PtN8zj17HOmx4B9r5N631+tfcy2ymtqsmO182cApnddzMZ1ojo4VK3GvIz4oas1NGke9jnmESBJf9bSTZykpHsWj/044IPAa4j/XD9OHNhlbDzzg4mztD1EY88B4/bfRRxE5jriqHKVdqave9kU9z+VfU73GLXU+/6aaSrXVLt+BmZyXTTiOpE6wviRvHYAFxLHLn8HcCZx9K/dxHG/i+lyOfCsCfZRa+rS/YhDf+4EbmfvEcom2me1qVNh6lN3TjQ86WTTih5G9elDx/YxNg3rwel7eTcxUf8P8Nj0tc8gzvA29gV5Vbq/nvTxyen7Hn8Oak1f+nfEEdzmE6dz/SVxZrXeGnGM338ReDaxCvQB9i0p/ilxhrWpTIs61X1O5xiVKv+uU3l/E10PvdQ+59WumwLVr+2D2Heq2mrXdTt/BmZ6XYw9ljQF4z/0u4AXEcfbLgNvIn44n0X88PYQq9l+yZ4ZzMb2sZTqU5d2p79/rOK5DxMn+eitss+Jpk7tYXpTd45/n5NNKzrZ9KFj+/gw8ATiP/gR4j/j+cA7icO7js26NmYucEF6TrrS9/bJCc5B9yTH70r3kaTbv2XceZsojvHneKJzU+kxxMQynWlR691ntWM8mvg3/jFxcpejidPU9ox7XeVxpvL+uonj6ldOk1vv33z8dbMfta/t8ddeteu6Ez4D070uxh5LmoKJvtUvI35Yxz7kVDzuJf4TuLviubHtDqL61KWriOOYj39u7Nv8RPusNvXndKbunKimota0opNNHzq2j8rSzP3pdhCnV72L+I94zAuICWtLRcwHAh8illQqz8FkU7tCHIP+TuCqiv3VimP8OZ7o3FRanr6+Z9xrK+Op1j5c7z4nOgbE5ow1wJ8Tq3XLwDns20xYeZypvr/xz9f7Nx9/3RxE7Wu73muvEz4D070uxh6rgWazTUb5NVLx+3D6s3I60lFiO161m3WqTV1anGA/5Yrfx++z2tSfY1N3Ph94LbGK8OnEfzz1mmxa0bHpQ48FbkvXLSQm1QeI/3jGpmGttv3YtKNdxNLMccCJxCQ/5lhiFWiZvc/BrkmOD7GkWGTP9K1Dk8Qx2d9tvAXEqtnRinU7mH51+kT7nOgYEEuFYy6rc99TfX8TbV/P33yi66bWtT3RcSbaRzt/BiYz2XUx9lgN5E12gvhP6A72fPsf8xCxVPE29kxdmqTr7ibOBZ4QqzEXENsTx6bcrLbPiSwGbiT+0+kjlhjmTPO9VDMEfJl438HY9KGfI1ZRVrMI+Edisn0D8CNim+yxxNL7i4j3MyxOl0K67pvp9pXnYOckx59HnNr0dGKV6Wb2/COtFsdUzjHEKWHvZPKbsqZi/D5n8xhTeX+VVfRjHmLqf/Ox7a6k+rVdecxa2vkzMJnJrouxx2ogE7wgluovIyauSqPA64gf8rvSZQGxhDEM/FP63D3AT4gl2e3Ekk61fU7kQWK19vXAt4AzqF6KmKpdaVz/Q2xLnEP8cnJL+n5OpXrJbCexWnIHsWR1CrE25NnA49PtH6hYDiZWrf423b7yHJSJ7erVjv/PxKrOjxOT/EuIJbpacUzlHJO+7svsqcWZDeP3OZvHmMr7W0SsOansCjbZOa9mlNrX9tg1dQO155Bv58/AZCa7LsYeS2qCxxBLLfU22xSAVxDvUh5zJPHGmQXT3GeeTNbOW6+ZnoPJ4qh3/93p6x4zzTjq2WcjjpHFNVTPtV2vTvwMTHZdNOI60QQswWvMrcB32DP62mTKxDvx30es6juU2C59NXva1qa6z0rjb/4av8w08TbLrUz/HMzm/p+cvu7WWTz2+H024hi30tjzN5F6ru163UrnfQYmuy4acZ1ImsRi9h6RbTK9xD7yI8TqxIvZ95/OVPeZF3OIpbjZaAedyTmoJ4569n8EtauTp2P8PhtxDMjmGqrn2q5Xp30GJrsuGnWdSJIktb//D1Gafm3PU1TTAAAAAElFTkSuQmCC">++</div>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>++</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>++</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>+ 1 2 3 4 5 + 4.741e-02 3.162e-02 1.583e-02 3.381e-05 -1.576e-02+</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>++</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>++</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>++</pre>++</div>+</div>++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">++++</div>+</div>++</div>+</div>++</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In [5]:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre><span class="nf">sse</span>+</pre></div>++</div>+</div>++<div class="output_wrapper">+<div class="output">+++<div class="output_area"><div class="prompt"></div>+<div class="box-flex1 output_subarea output_display_data">+++<pre>+2.4619397033555017e-3+</pre>++</div>+</div>++</div>+</div>++</div>+<div class="cell border-box-sizing code_cell">+<div class="input">+<div class="prompt input_prompt">+In []:+</div>+<div class="input_area box-flex1">+<div class="highlight"><pre>+</pre></div>++</div>+</div>++</div>+</body>+</html>
+ examples/ihaskell3.ipynb view
@@ -0,0 +1,253 @@+{+ "metadata": {+ "language": "haskell",+ "name": "",+ "signature": "sha256:77784b243dcc073ca234e267b10dd773decbb010b9dbb529bf20bf1714830221"+ },+ "nbformat": 3,+ "nbformat_minor": 0,+ "worksheets": [+ {+ "cells": [+ {+ "cell_type": "markdown",+ "metadata": {},+ "source": [+ "# Multiple plots with Rlang-QQ\n",+ "An example of `Rlang-QQ-0.1.2.0`, which supports multiple figures.\n",+ "\n",+ "First a pile of imports needed:"+ ]+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [+ ":set -XQuasiQuotes -XTupleSections -XViewPatterns\n",+ "import IHaskell.Display -- shouldn't be needed but it turns out that it is essential\n",+ "import IHaskell.Display.Rlangqq () -- to check it's installed\n",+ "import Data.Monoid\n",+ "import Control.Applicative\n",+ "import Data.List"+ ],+ "language": "python",+ "metadata": {},+ "outputs": [],+ "prompt_number": 1+ },+ {+ "cell_type": "markdown",+ "metadata": {},+ "source": [+ "Rlang-QQ saves intermediate files. There is some code\n",+ "hidden in `IHaskell.Display.RlangQQ` and `RlangQQ.ParseKnitted`\n",+ "which gets those messages/warnings/plots."+ ]+ },+ {+ "cell_type": "markdown",+ "metadata": {},+ "source": [+ "Just some more of the same integration stuff that may be easier to think about because it's written in haskell"+ ]+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [+ "-- | trapezoid rule. doesn't reuse previous values\n",+ "trap :: Fractional a\n",+ " => (a -> a) -- ^ function\n",+ " -> a -- ^ lower bound\n",+ " -> a -- ^ upepr bound\n",+ " -> Int -- ^ number of panels to divide the interval into\n",+ " -> a -- ^ integral approximation\n",+ "trap f a b n =\n",+ " let h = (b - a) / fromIntegral n\n",+ " xs = take (n-1) $ iterate (h+) (a+h)\n",+ " -- easier to see as [a+h, a+2*h .. b-h])\n",+ " -- but some interval arithmetic doesn't support [a..b]\n",+ " in h * foldl' (\\s x -> s + f x) ((f a + f b) / 2) xs"+ ],+ "language": "python",+ "metadata": {},+ "outputs": [],+ "prompt_number": 2+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [+ "improve :: Fractional a\n",+ " => Int -- n\n",+ " -> a -- O(h^n) estimate with step size h/2\n",+ " -> a -- O(h^n) estimate with step size h\n",+ " -> a -- O(h^(n+1)) estimate\n",+ "improve n x y = let c = 4^(n-1) in (c*x - y) / (c - 1)\n",+ "\n",+ "rombM :: Fractional a\n",+ " => (a -> a)\n",+ " -> a\n",+ " -> a\n",+ " -> [[a]]\n",+ "rombM f a b =\n",+ " let is = map (\\n -> trap f a b (2^n)) [0..]\n",+ " in map snd\n",+ " $ iterate\n",+ " (\\(n,s) -> (n+1, zipWith (improve n) (tail s) s))\n",+ " (2,is)\n",+ "\n",+ "romb :: Fractional a\n",+ " => (a -> a)\n",+ " -> a\n",+ " -> a\n",+ " -> [a] -- successive approximations\n",+ "romb f a b = map head $ rombM f a b"+ ],+ "language": "python",+ "metadata": {},+ "outputs": [],+ "prompt_number": 7+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [+ "xs = take 5 $ romb exp 0 1 :: [Double]\n",+ "\n",+ "print xs"+ ],+ "language": "python",+ "metadata": {},+ "outputs": [+ {+ "metadata": {},+ "output_type": "display_data",+ "text": [+ "[1.8591409142295225,1.7188611518765928,1.7182826879247577,1.7182818287945305,1.7182818284590786]"+ ]+ }+ ],+ "prompt_number": 3+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [+ "sse <- do\n",+ " [pun| (sse) |] <- [rDisp|\n",+ " m <- lm( log($(xs) / (exp(1)-1)) ~ I(1:length($(xs))))\n",+ " plot(m, which=1:2)\n",+ " print(predict(m))\n",+ " hs_sse <- sum( resid(m)^2 )\n",+ " |]\n",+ " return (sse :: Double)\n",+ "() -- needed to force a display"+ ],+ "language": "python",+ "metadata": {},+ "outputs": [+ {+ "metadata": {},+ "output_type": "display_data",+ "text": [+ "()"+ ]+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": []+ },+ {+ "html": [+ "<img 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\">\n"+ 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\">\n"+ 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C2B0tg8eQjgVuC8duOaXs71/tb9FwIeyDmKarKKX1HZCCPPTn4diAS2PcnuTXYnYXW5seRA4otFBSZImt3HjxgOBy0II85MkuZX4f1oCao9Fv4g4MMOxxMFuzgKOI1YzSZIycvrppy9atGjR4UmS3AD8adbxKJ9qleDHkv+NwDXAo4FPAH/T6KAkSdUtWrTopHK5vDzrOJRvtUrwu9LlVOI87ScB9xBHZpIkNVkI4bTdu3dfkiSJM29qUrVK8I8AG4FXEbtcLAO+SRz8RpLURGvXrp1XLpfvPvPMM+/MOhYpb7yLXlLLCSE8J4Twrqzj0LTlZqAbSVIOrFu3bkFPT8/I7t27fzQ0NPStrONR66mV4GuNamdfS0lqoJ6enk+NjIysP/PMM2/KOha1v15im/w/ZB3INFlFLynXQgjFUqn0vKzj0KzK7UA3lXYA7wFOa1AsktTpjikUCo/OOgi1h6m0wReIMyA50I0kzaJSqbSmUCjckyTJ14i9laQZm04bfGhQLJLUqYZ27tzZyBnmpLZmG7yk3AghHBpCuBxvWm53dpOTpE4QQigCPdu3b79j2bJlJ+AEMWqQ8Qm+Vte4Sn7jlKTpOatcLl9/3nnnfZY4HLjUEOMT/A72Tt5vBp4AbEgfnwE44IIkTVGpVHpeoVC4KkmSt2YdizpDrW5yi4jTxL4ZuD1d3poukqQ6hRAOKBQKz9y2bdvurGNR56jVBt8FLGTvLwHFdJ0kaRL9/f1PKRaLRydJcgGwKet41FlqleB3AtcB5wIHpcu5WEUvSXUpFovzdu/e/cWs41BnqlWCHwFOBv4NuAUYBT4P/GMT4pKklhRC6AY+Pzw8/E9JklybdTxSJ7AfvKSG2rRp00EAIYQVWcei3MjNWPS9xD6Z76n4faJFklQhhPCy7u7ukwGSJPlt1vFIk3WTs7+7JNUQQjhmZGTk7iRJPk9sxpRyYSqzyUmSximXy382PDy8Pes4pKkoAOuBa4EDgG8AjwBvyDKoGbANXtKsCCE8KoRwTtZxqCXkpg2+0iJiMn898CzgbuDpxIFvJKmTdY+MjJyfdRBSLbW6yZWBYWABcArwbmIbfVcT4pKk3Akh/CtwVZIk/5F1LNJkJhvo5l+Ba4jJ/gbgCqAR1VILiFXovcBK4FJgN/AgsXpjvwYcU5LqEkI4OP31DJO7WkVe7pLvBX4NHAlcBNxFbAoYAhJgGfCXMzzGFmAV8Fcz3I+kDhJCOAwYAP46SZLRrONRy7kQOAu4udkHztt88AuBo4AnAbel6/qAH2YWkaSOFEL4/dHR0d9LkuQqLBioBU3WTe5gYrX8ncDRwPuAngbGMpdYkt+/Yv1SYnW9JDVNuVx+drFYvDvrOKTpqlWCLwLvB24EngrcCzwxXfeqWY5jGPgR8F1ikr8AeBpwDHAZ8LZZPp4k7SOEMJd4Q/HGJEk+mHU80kzUSvCLgCcAbwJOIib4NcA3GxDHTuDY9PdV6TICPAC8BLi6AceUpPG6R0dHPz44OHh/1oFIM1UrwRfSZbhi3e5JtpkNd6QLwA8afCxJolQq/W2hUDg8SZI+nBJbbaJWst5JTLAvICb6FUA/cWS7ZjqeeB/AnDpe+xJgbZXnDgN+NltBSWp9mzZt+r1du3ZtLxQKW5Mk2ZV1PFIzHURMrmXgPmL7e2+mEU2fQ9VK+p0QQjGE8KmNGzcemHUsamuZDVVbqwS/BPgO8Awy6L8nSY0QQugpl8vPS5JkK3Z/Uxur1U3uQeIgM+uIo8s12nLi3au3sWfe+duBc4ld5SRpNjyxUCjsyDoIKUu97Em045fZNgf4b+CDwGrigDcLgSOIQ+NeMQvHsIpe6mAhhLf09/c/Les41HFyWUW/g+YNZbuAmMxfkR53zE+II9nd0KQ4JLWp0dHRbw0ODn476zikTjMH+Bp7SvAL0uVwYgn+67NwDEvwUocJITw5hPD5rONQR8tlCb6ZdhMH0ekHLgcela6/E/gscEJGcUlqQW9961t758+fvwT4/rZt2/z/oY6UlwQPcA9wWrpI0rTNnz8/jI6OXjI4OHjb5K+W2lOeErwkTduaNWu6Hv/4x59y0003XZAkyT9nHY+UtfHd5GrdOd/Iu+glaUYOO+ywlcBDW7duHck6FikPxpfgx985/2bihDMb0sdn4DjNknKkVCq9vFAoHJgkyQeAi7OOR8qLWgPdLAJeT0zyt6fLW9NFkjK1du3aeQCFQuGn27dv/0jW8Uh5U6sNvos42Ezll4Biuk6SMhNCWAx8bcOGDS9IkmRb1vFIeTTZbHLXEYeKXZeu24JV9JIysmbNmq7DDz/8sCRJbgKemnU8Up7VqqIfAU4mtsnfQpxwZhT4xybEJUn7WL169UnFYtHELtVhsm5ydxMHoJGkzJRKpTWjo6PfTZLkw1nHIrWKWiV4gIOJE73cCRwNvA/oaXRQklShUCgUVu3YseP/sg5EahdF4IvEseDvBw4FrgE+nmFMM+FY9FILCSE8KYRwTtZxSDOUy7HoFxH7wL8JOAm4l1hd/80mxCWpQ61Zs6Zr9erVheHh4bu7u7sHso5HalW1EnwhXYYr1u2eZBtJmpHVq1d/BnjXGWecYWFCapAu4D+A1wL3EasYLgI+kWFMM2EVvZRjIYRjso5BaoDMqugn6yb3T8Rq+V7gf4CH03WSNGv6+/uPAl6YdRxSO6lV3d5L7P/eB/wZcZKZhcRE//jGhyap3YUQjgMWJknyVeAHWccjtZPJusndA/w9MEisspekWVMul1cB3806DqkdTXbD3H3AnwIfJrZh9zU8IkltLYSwDPgY8PIkSbZmHY/UriYrwUPsA/8yYAFwATC3oRFJalshhPnAQ8BrkyQZyjoeqZ3V2+VtCHgdsQT/isaFI6ldhRA2lsvlHQMDA+8Dbss6Hkntw25yUgb6+/uPTqd3lTpRLrvJSdKMhBAWFovF1+/cubOcdSxSpxlfRd9LvLHuPKA//X0ihUYGJam19fX1Pbarq+vZSZJcgFNMS5kYn+B3sHfyNpFLmrJCofAo4Mqs45A6mePKS5o1IYQPjY6OvntwcPCarGOROt34Nvhe4oh1ky2S9DshhP0ARkZGNg8ODv4463gk7Zvgx6rox5a3EAekODhdLiZ2l5MkAEIIfwK8A2Dz5s23ZBuNpHosIo5Ff2DFugOBX2YTzozZTU6aRSGEP+jr63tc1nFIOZfLbnJdxMllKl9TTNdJ0t+Wy2VHo5Na0Nh88J8GDkqXTwOXZhnUDFiCl2YohNATQnhH1nFILSSXJfgR4GRiW/wtwM3AKPZplTrW8PBw7+jo6JeyjkPS5Gp1k1tCnJ/5GcCa5oQjKY9KpdK6QqFwT5IkHwX+N+t4JE2uVgn+QSAB1gErmxOOpDxZv379UoDdu3f/e5rcJbWIWiPVjQ1bO9Xt8moLsAr4q6wDkVrBxo0bV82ZM+fi+++//6Vbtmx5OOt4pBZ1IXAWsZm7qWpV0Y8ftlZSBwghLANWJ0lyLfDcrOORND1TnU1uEfChRgQiKTdegTNNSi1vsrHoS8R2+ErfbFAskrJTKJVKGwuFwkeSJLkw62AkzVytBL+I2E3uWOD1xDaE44ClTYhLUhOdcsop3cCNSZL8X9axSGq8XuBXxO5ypwAvABYDP2rQ8ZYD7wZuY8+kNrcD5zI7Xyoc6EYaJ4Tw3BDClqzjkNpYZgPd1CrB70qXU4HrgZOAe4gJf7bNIY6S90viTT23pusPIU5usxV4TgOOK3WkEMJ+O3fu3A18G+dtlzrSXwA3EkvQXwKGgdMbcJweYmm9d4LnFgM/m4VjWIKXUiGES5woRmqKzErweTEH+BrwQWA1sCBdDgfOAb4+C8cwwaujhRDmhhBOzDoOqcPkciz6ZtpNHA53J3A58FC6XElsRjghs8ik9vEkYP4Ut1kOvA24AFgLzJ3lmCQ1SS97bnCrtbQiS/DqSCGEvy6VSs+exqYHAt8F/pJYs3Ya8Qv4ZN1rJe2Rm5vsxo9e92bgCcCG9PEZwLeaENd0LSAORzuRXvJTYyE1032FQuGaaWz3T8AAcdpogG3EEv3zgP+cpdgkZWARcZrYAyvWHUi8072ZjidW4dfjhcS78SdafkYsfUhtr6+v77EhhK8ws+GmPwocOm7dXwBvnME+pU6TmxJ8pS5gIXuXeovpuma6ingTXj3+k+oli7HJZqS2dcopp8xZsWLFsh07dtw2b968E5hZk9qPgWeyp9sqxC/cn5vBPiU1Sa0EvxO4jjjQzLp03RbyXUUvdbRVq1Yl5XL5W+edd94XieNYzMT7iF+YVxKr5/+E2GV2Nnq1SMrY/sRBZoaAR4g3qa1o0LEcyU6aplKpdMK6desWNGDXc4G/A95CbAKTNDW5rKIHuJvYfa3RHMlOmqYNGzYsLxQKh/f09FzagN0PEdviJbWZjTSnm5wj2UlT1N/f/7RSqfSmrOOQVFMuB7pZDLwBeH76ukLFMtseBn4KnM2+I9ltJiZ/SXt7pFAofCzrICTl02RV9NuBb9D4wW3GRrLrJ3Zle1S6/k7gsziSnQRACKEbuHx4ePjvBwcHv5d1PJJa10nAeiauOm81VtGrpYUQjgBo0M10khojl1X0vcBFwFnAfbT+ULVSyyqVSi8FXgGwZcuWhzMOR1ILqFVFP37YWklN1t/f/4xisfibJEm+AHwh63gktY6pjs2+CPhQIwKRtK9isfgM4K6s45DUfkrs20XuG5lGNH22waslbNq06dEhhHOyjkPSrMjlQDeLgJOBY4HXE9vij2N2RpWTVN3oyMjIuVkHIam11UrwY8/dCFwDPBr4BHEs+nc0OC6p44QQ3l8uly8bGBj4YtaxSGp9tdrgd6XLqcDNwCuJg9AsaUJcUscY6/4GrDO5S5ottRL8I8Shal8F3AAsA74JvLcJcUkdoa+v7zHAW0IIxSRJHsk6HklqRd5kp9wIIRwWQnh+1nFIarjcDnTzHWDeuHWzMfGL1NHK5fIf4WdJUgNNdJNdF3Ad8Efp4/HVhq3aTU7K1Lp16xb09PScD5yaJMkns45HUnubqAQ/AjwZ2A/4LjCfvWeSO65p0UltYu3atfN6enpGRkdH35Ekyf1ZxyOp/U02VO1TmhWI1K5CCP8A/H6SJBuBH2cdj6TOMFGCPxj4KHGO9hcChwGXErvIXQ68BritWQFKrWrTpk2P7u7uvjdJkguzjkVS5xlfRd8FnA98n9jvvQv4ALFNfhVwPY5FL00qhFDs7u5+++7du53aVVIuLAH+DzggfXwgcC9waMXj3zQ/rFlhNzk1XAhhRalUOjnrOCTlRm66yRWIE8rsTh8fQ6yOvz193A2MNic0qSUdUS6Xf5J1EJI0vg1+J3AT8FrgY8SR7D5HTPgrgfcQ76yXVCGEcMbo6OhlSZJcm3UskgT7luBHgH8EXkYsuZeJQ9MuA34KzCXOMCeJ2P0NYHR09DODg4OOESEpNya6i/4WYtX8eL0NjkVqKSGE/wcE4KWDg4PfyzoeSapUqx+8pAls2LBh/66urp4kSW4AXpp1PJI0kVpj0UuawNy5c0/t6upy2mRJuWYJXqrDKaecMmflypWn33TTTe9MkiRkHY8kTcYSvFSHVatWLQe2bd26dSTrWCSpHpbgpRpKpdKrgAOSJNkCfCHreCSpXpbgpQmk3d8KIyMj1955553vzToeSZoqS/DSOCGExcBXN2zY8OIzzjjjV1nHI0nTYYKXUmvXrp23bNmyo5IkuR44Nut4JGkmrKKXUkuXLj2ROF2yJLU8S/DqeCGEfwCuTJLkY1nHIkmzxRK8Ol2hXC6XgVuzDkSSND3OB6/fCSH8vxDC+7OOQ1Lby2w+eKvo1VHWrl07r7u7uzg0NPSr0dHR9VnHI0mNYoJXR1m2bNmFwHnpnfKSpAZbQKxC7wVWApcCu4EHidUb+83CMayi71yFUqn0gqyDkNSRMquiz8tNdnOB1wA9wMeBR4DHAL9PTPTnZxeaWl1/f/+RwOqs45CkZspbFf1C4CjgScBt6bo+4IeZRaSWVSqVngfMHxgY+BBYr14AABbSSURBVALwo6zjkaRmyksJHmIsc4FfA/tXrF9KLMVLU1IoFLqGhoauzjoOScpCXkrww8QS1neJSf4C4GnAMcBlwNsyi0wtZcOGDfvPnTv3I8ArkiS5LOt4JCkreUnwO9kz9veqdBkBHgBeAlgK06TWr1+/9MEHH7x/yZIlJ5955plDWccjSVnKS4KvdEe6APwgy0DUOkql0gbgrre//e0fAn6TdTySlLU8JvjxjgeuAOZkHYjyp7+///ihoaHvDwwMnJV1LJKUJ3m6ya6aqzC5awLr1q1bUCwWXzF//vxdWcciSXnTCiX4qXgS8Lwqzz0ZuK+JsahB+vr6HtfV1fXCJEnOAd6UdTySlEd5KsEvB95N7P9eTpfbgXOJXeXq8SDwyyrLDmB0dkNWFrq6unpHRka+kHUckqTJzQH+G/ggccSxhelyBHAOsQ1+phyqtsWFEC4JIRyZdRySNAWZDVWbFz3E0nrvBM8tBn42C8cwwbeoTZs2HQQQQliRdSySNEUdPxb9w8BPgbOJJfgF6XI4sJmY/NWBQgh/0t3dfTpAkiS/zToeSWoVebnJbjewBugHLgcela6/E/gscEJGcSkj/f39RwGjSZJcCVyZbTSS1HryUoIHuAc4DTgIKKTLKuBUYHuGcSkDxWLxBaOjo/Z6kCRNyjb4nFu/fv3SEMLZWcchSbOo49vgx+sF7mXim+7UpubNm7cIv4RJ0qzISxu8OlgIoa9cLv9vkiQXE8dBkCTNUF5L8OoAIYSDifda/NvAwMDFDTjE/sA7gc8AZ1D/gEmS1PLymuB3EP8Z78g6EDVGCGEl8IEQwrwkSRpxE+Vy4IvEAZReC3wLuIw45oIktT2r6NVUGzZs2H/u3LlHJ0nyVeBFDTzU3xCHOf6v9PGXgJXAicSbXiSpreW1BK82NWfOnOeMjo7e24RD/R7wi3HrfgEc0oRjS1LmLMGr4UIIReAdw8PD5wwMDHyySYf9PvBc4DsV654HXN+k40tSpkzwaobi6Ojol84444xmDjn8SeLNdecRk/pxxPb3jU2MQZIyY4JXw5RKpRcXCoXnJUlyKnBVkw8/ArycmNgPAy4i3mgnSR3BBK9Zl94h/8j27du/umzZsv/MOJxr00WSOooJXo3wrpGRkYHzzjvv51kHIklqf45F30Dr1q1bEEL466zjkKSccSx6tbbFixc/sVwu78o6DklSZBW9ZqRUKr22UCj8KkmSrwDXZR2PJCkywWtGyuXyjTfddNO3s45DktS5bIOfJSGEw0IIVxEnipEkVZdZG7wleNVt3bp1C3p6eg7ctm3bzatXr/4zoJx1TJKkiZngVbeenp5/Aa7funXrrcSBZCRJOWWC12QKpVLpNQ899NCnkiTZnHUwkqT62E1ONa1fv34/YO7ZZ5/9UNaxSJLqZwleEyqVSs8uFAp/lCTJO4H3Zx2PJGlqTPCa0Ojo6K8feeQRu79JUosywet31qxZ07V69er/HhkZefXmzZsdR16S1BLsB19dob+//w+zDkKS2pD94JWdEMLLgYOBH2cdiyRpdpjgO1ipVHphOo7857KORZI0u+wm19kO3blz5y1ZByFJ0kzYBg/09fU9LoTwwazjkKQOYRu8Gq4QQpgD7BgeHn5b1sFIkhrLBN8hQggfAL6QJMllWcciSdJs6sgq+v7+/j/OOgZJ6mCZVdF7k10b6+vre0yxWPyrEIJ/Z0nqMFbRt6EQwpPK5fJjBgYGLgXWZR2PJKn5LNm1p0OGh4cdR16SOpgl+DYRQlgIfGrXrl0nJUnyH1nHI0nKliX4NhBCmA8MAW98+9vffm/W8UiSspenEvxyYBNwInBQuu7/gM8AbwNMXBMIIbyuXC7vPzAwMAjcmnU8kqR8yEuCnwN8Gvgl8Fz2JKpDgNcBW4HnZBJZToUQVg8NDf0mSZIPZB2LJCl/8lJFvwA4Ajgd2AY8lC4/AfqIiV6ptNvburlz55azjkWSlE95KcE/DPwUOJs4IM3YBCiHAP8E3J5RXLkSQnhUuVw+IUmS84B/zDoeSVJ+5aUEvxtYA+wELmdPCf5K4peQEzKLLF8eVSgUvpV1EJIk5UnLDlUbQvjX/v7+p2cdhyRpyhyqVvsKIewHMDIy8t7BwcFvZB2PJKl1tEKCP55Yhd9R+vv7jwbOB9i8efMtk7xckqS95OUmu1quInaj6wghhINHR0eXDg4Ofg/4y6zjkSS1plZI8FPxcuD1VZ77A2IXvLz7m3K5/IWsg5AktbZ2S/CXpstETgRWNDGWuqVDzQ4AG5MkeXvW8UiSWl8rtMG3vaGhoSXlcvnyJEmGs45FktQe8lKCXwR8D3hcjdcUmhRL05RKpVcXCoUlSZK8h9j/X5KkWZGXEvxO4AnAd4AXEJP5+KVtvPWtb+0FCoVC4fJ0VDpJkmZVXhI8wC7gImKyb1shhMXz58//XAhhaZIktwGOJy9JmnV5qaIf829ZB9AoIYTFwJOTJLkSeHbG4UiS2lyeSvBtrVwuvxBYnHUckqTOkLcS/Jhe4pzwhwI7Mo1khkIIbxwZGfmvgYGBS7KORZLUOSzBN1ahXC7fvnnz5p9lHYgkSXnQC9yb/pwtJ1J9lLtZFUI4JoTw4WYcS5KUa5nNJpfXKvodwNKsg5iqEMLiRx55pAu4kSZ9mZAkaSJ5TfCt6ty5c+e+N0mS72UdiCRJnaIhVfQhhGII4SWzvV9JUlvIrIrem+xm7gjg4KyDkCSpklX001QqlV5cKBS6kiT5PK0xDa0kqYOY4KepUCjsAq7NOg5JkiZigp+CjRs3HjhnzpxPbN++/c+TJPlq1vFIklSNCb4OIYQisBK4C3j5eeedtyvjkCRJqskEX5/Ty+XyHQMDAx8D7s86GEmSJmOCryGE8OfAt5MkeWfWsUiSNBV2k6sihDAf+GPgwaxjkSRpqizBjxNCeCLwoiRJzgRC1vFIkjQdluD3NQw4UYwkqaVZgk+FEC4D+pIkuSHrWCRJmqmOL8GHEA4BuP/++08wuUuS2kVHJ/gQwnOAtQBbtmx5OONwJEmaNR1ZRR9CePLo6OjuJEmuAK7IOh5JkmZbR5bgy+Xy8SMjI3dmHYckSZq5E1/zmtdcNc1tDwH+FFhesW4e8CHgGuCnwCtnFp4kqQ1lNh98R1XRX3311V+b4iZF4APACuBmYCPwReBc4GlAF/BM4A+AK4FPzlaskiTNREcl+F/84hd3T3GTk4BfAaekjwvAZ4GvA98CvpOuPz59nSRJudCRbfBT8Axga8XjMvAZ4DhgF3Bg+vgpwPObHp0kSVV0VAl+Grazd7s7xOr63xDb5S8GTgZ+1uS4JEmqyRJ8bRcBA8AB6eMjgTXAl4EXpo8/B9wIfDuD+CRJ6ngnAq+fxnbHAf9JbG//GBndDSlJakneRZ9j1xJL65IktQyr6CVJakMmeEmS2pAJXpKkNmSClySpDZngJUlqQyZ4SZLakAlekqQ2ZIKXJKkNmeAlSWpDhawDaKLnAecB92cdyBQcRpxzXtNXJF7nI1kH0uLmALuzDqLFdROvw3LWgbSwAvAA8L9ZBzIFvcDTgbuyDkT58mVgftZBtLhXAq/NOog28PWsA2gD5xO/tGv6ng5szjqIVmEVvSRJbcgEL0lSGzLBS5LUhkzwkiS1IRO8JEltyASfb0PAaNZBtLjhdNHM7Mo6gDYwgtfiTA1jl1e1CbvIzVwXsf+xZsZrceY8hzNXAOZmHYQkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVLuLAR+APTWeM0K4HJgd/pzRRPiaiX1nJ9u4IdAuWL5ZLMCzLF6ry2vweq8/maP/w9nyLHo86ELeDNwHXDkJK99F3ALsF/68+zGhtZy6jk/84FlwOHAknR5dbMCzLF6ry2vweq8/mbO/4dqK0Xg2cCrgAeo/o11AXAz8IT08RPSxwsaHWCLqPf8PIZ4nm8mlp7+C1jVpBjzqt5zV+11irz+Zm6m/w+VsgSfD6PAfwNfovZsU3OIF/tv0se/SR/PaWh0raPe89MLfB/4K2AlcA9wfpNizKt6z1211ymayfXX1aQY826m/w+VcpatbJQrft8P2FHndoUq6yZa3+56gfsqHu9X5XUTnZ/vA8+oeLwJuH72QmtJ9V5b1V6niU3l+lsE3N+kuNqB1+IkLMFno1Cx1JvcIc4PvwPYP328PzHJDc1qdK1hB/uex3rPz1OBYyseDxNv0ulk9Z67aq9TNJPrr4ymwmtxEib4/OsGjkt/PgxcC5ycPn41cE26XrXPT+V5PADYChxFbMd7K/EO3E5W77mr9jpFM7n+Hswg3lbjtaiW1Qvcy97tSOPXrQCuIH5TvRxY3swAW0C181N5HovAacAdwC7gEqpX8XeSes5drdfJ6282+f9QkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiS1rZ8D5QmWnwD7A/cCvZlFt0cvM4ul1vY9wLnALcT3fkv6uFnvuzK2mb5PqW0Usw5AanGPAwrAfsB96c8CcAQwlGFcAIuAz6c/dwHnpD9n00HAjcAK4KXAwvTnCuBH6fON0Iz3JknShCXHrEuTs3n8avu6CPgE+xYWiun6i2bh2FOJJ+tzLklqM9US/A5gALgZeBjoJ5bwHwV8MV13F/Bp9pR2VwCXADuBB4FPAcvHHedfgDuAw6vsZwnwQ2KV+Q/TdWPxHQB8CXgE+F/gVWlMACcSmx1GgAfSeItV3t9C4FZgdZVzsjp9fuW4bcfva7JjvhH4RfoeNxKbBKq9t8p91zrHAG8A7k+fX19xDiRJ+p1qCX4E6CMmrGOJ1fjLgP8CTgIWAEuBzcA3gLnAVuCjwGJiFfTFxOQ0lvSGgQ8DT6qxn+5xMY39vhT4DLAlPdbxwHZiEl5ErFY/Nd3+j9Jtemq8v9+y58vHeCvS53+P6gl+smMOp+evK411ongm+n35JOdmCfCb9BweDlxG/MIiSdJeapXgV417zcHE9vnxN+btIpY67wIOrdjPocRktKRinwcRE121/VRLggel+zqkYv9LiQkUYvI7BngdseZgiH2T6JixEvzjq5yT1cCv0/dfqwRf65gTnb9qSb3y91rnuIf45eYa4Argleljqa14k53UWKPAQxOsu5NYsi2kyyLgMGKVPMRkRMXvhXHbPzjJfh6oEs/Y60YrHq8C5gFzgP8ATiN+yRgkNhNU8xBwJbCJPf9LCsREfQxQAq4nVoFXWljx+smOOdH5q8dk52YI+BNiTcYrgW8Sz4EkSXup5ya7scf7AecDHyFWw68CvgJ8jFiS/mz63KJ0uShdN74tvFBjPwX2rq4e+30ZsTr6XcRS63HEEv1KYg3BXcSSd5FYvb0r3abazWsHEUvpHwWOJCbvDewpMa8mlpjvA56Z7ncTseq9d4rHrFYVP9H6Wue4kK67CTgKeCLxy8BiJEkaZyoJfiwR/TuxdPsIMUGOvW5/Ypv7znS5hNieXW2f1fYzH7gW+BV734h2ELFqeoiY2F6Vvr4AnJ4e8xbizYHXA58jVuNPlODHYjgvPU6ZeOPap4Cr09jnEm+c2wX8EngzMeGPfUmp95iV773ae6v3HJMed1d67LUTvC9JkjSBOcBTsg5CkiRJkiRJkiRJkiRJkiRJUpur1l+5Hn9D7DI1k300Y580YH+z7S3Ak9Pfx85Bo9Sz/1cBfzvLxx2/z9k4xkR/16mev2ZeG/Ucq9M+A5NdF424FqWOMN0P/VJiv+JFM9hHM/Y5Zj4Q0p+1VE4rOpvWEPt07wa+S5wedswc4sA0C9n7HDTCRPtfCPyAvc/1IuI47PtN8zj17HOmx4B9r5N631+tfcy2ymtqsmO182cApnddzMZ1ojo4VK3GvIz4oas1NGke9jnmESBJf9bSTZykpHsWj/044IPAa4j/XD9OHNhlbDzzg4mztD1EY88B4/bfRRxE5jriqHKVdqave9kU9z+VfU73GLXU+/6aaSrXVLt+BmZyXTTiOpE6wviRvHYAFxLHLn8HcCZx9K/dxHG/i+lyOfCsCfZRa+rS/YhDf+4EbmfvEcom2me1qVNh6lN3TjQ86WTTih5G9elDx/YxNg3rwel7eTcxUf8P8Nj0tc8gzvA29gV5Vbq/nvTxyen7Hn8Oak1f+nfEEdzmE6dz/SVxZrXeGnGM338ReDaxCvQB9i0p/ilxhrWpTIs61X1O5xiVKv+uU3l/E10PvdQ+59WumwLVr+2D2Heq2mrXdTt/BmZ6XYw9ljQF4z/0u4AXEcfbLgNvIn44n0X88PYQq9l+yZ4ZzMb2sZTqU5d2p79/rOK5DxMn+eitss+Jpk7tYXpTd45/n5NNKzrZ9KFj+/gw8ATiP/gR4j/j+cA7icO7js26NmYucEF6TrrS9/bJCc5B9yTH70r3kaTbv2XceZsojvHneKJzU+kxxMQynWlR691ntWM8mvg3/jFxcpejidPU9ox7XeVxpvL+uonj6ldOk1vv33z8dbMfta/t8ddeteu6Ez4D070uxh5LmoKJvtUvI35Yxz7kVDzuJf4TuLviubHtDqL61KWriOOYj39u7Nv8RPusNvXndKbunKimota0opNNHzq2j8rSzP3pdhCnV72L+I94zAuICWtLRcwHAh8illQqz8FkU7tCHIP+TuCqiv3VimP8OZ7o3FRanr6+Z9xrK+Op1j5c7z4nOgbE5ow1wJ8Tq3XLwDns20xYeZypvr/xz9f7Nx9/3RxE7Wu73muvEz4D070uxh6rgWazTUb5NVLx+3D6s3I60lFiO161m3WqTV1anGA/5Yrfx++z2tSfY1N3Ph94LbGK8OnEfzz1mmxa0bHpQ48FbkvXLSQm1QeI/3jGpmGttv3YtKNdxNLMccCJxCQ/5lhiFWiZvc/BrkmOD7GkWGTP9K1Dk8Qx2d9tvAXEqtnRinU7mH51+kT7nOgYEEuFYy6rc99TfX8TbV/P33yi66bWtT3RcSbaRzt/BiYz2XUx9lgN5E12gvhP6A72fPsf8xCxVPE29kxdmqTr7ibOBZ4QqzEXENsTx6bcrLbPiSwGbiT+0+kjlhjmTPO9VDMEfJl438HY9KGfI1ZRVrMI+Edisn0D8CNim+yxxNL7i4j3MyxOl0K67pvp9pXnYOckx59HnNr0dGKV6Wb2/COtFsdUzjHEKWHvZPKbsqZi/D5n8xhTeX+VVfRjHmLqf/Ox7a6k+rVdecxa2vkzMJnJrouxx2ogE7wgluovIyauSqPA64gf8rvSZQGxhDEM/FP63D3AT4gl2e3Ekk61fU7kQWK19vXAt4AzqF6KmKpdaVz/Q2xLnEP8cnJL+n5OpXrJbCexWnIHsWR1CrE25NnA49PtH6hYDiZWrf423b7yHJSJ7erVjv/PxKrOjxOT/EuIJbpacUzlHJO+7svsqcWZDeP3OZvHmMr7W0SsOansCjbZOa9mlNrX9tg1dQO155Bv58/AZCa7LsYeS2qCxxBLLfU22xSAVxDvUh5zJPHGmQXT3GeeTNbOW6+ZnoPJ4qh3/93p6x4zzTjq2WcjjpHFNVTPtV2vTvwMTHZdNOI60QQswWvMrcB32DP62mTKxDvx30es6juU2C59NXva1qa6z0rjb/4av8w08TbLrUz/HMzm/p+cvu7WWTz2+H024hi30tjzN5F6ru163UrnfQYmuy4acZ1ImsRi9h6RbTK9xD7yI8TqxIvZ95/OVPeZF3OIpbjZaAedyTmoJ4569n8EtauTp2P8PhtxDMjmGqrn2q5Xp30GJrsuGnWdSJIktb//D1Gafm3PU1TTAAAAAElFTkSuQmCC\">"+ ]+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": []+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": []+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": [+ " 1 2 3 4 5 \n",+ " 4.741e-02 3.162e-02 1.583e-02 3.381e-05 -1.576e-02"+ ]+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": []+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": []+ },+ {+ "metadata": {},+ "output_type": "display_data",+ "text": []+ },+ {+ "metadata": {},+ "output_type": "display_data"+ }+ ],+ "prompt_number": 4+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [+ "sse"+ ],+ "language": "python",+ "metadata": {},+ "outputs": [+ {+ "metadata": {},+ "output_type": "display_data",+ "text": [+ "2.4619397033555017e-3"+ ]+ }+ ],+ "prompt_number": 5+ },+ {+ "cell_type": "code",+ "collapsed": false,+ "input": [],+ "language": "python",+ "metadata": {},+ "outputs": []+ }+ ],+ "metadata": {}+ }+ ]+}
examples/runexamples.hs view
@@ -1,4 +1,4 @@--- same test runner as in HList+-- similar to the test runner as in HList module Main where import Control.Exception@@ -16,18 +16,19 @@ es <- getDirectoryContents "examples" + putStrLn "Candidate tests" print es -- very dumb es <- filterM (\e -> allM [return (takeExtension e == ".hs"),- doesFileExist (dropExtension ("examples"</>e) ++ ".ref") ]) es+ doesFileExist (dropExtension ("examples"</>e) ++ ".ref")]) es + putStrLn "\nSelected tests" print es hspec $ do mapM_ runghcwith es - runghcwith f = describe f $ it "ok" $ checkResult $ do let ex = ("examples" </>)@@ -37,7 +38,9 @@ (ec, stdout, stderr) <- readProcessWithExitCode "cabal" ["repl","-v0", "--ghc-options", "-w -fcontext-stack=50 -iexamples -v0 "]- (":set -package lens\n:load " ++ inFile ++ "\nmain")+ (":set -package lens\n\+ \:load " ++ inFile +++ "\nmain\n") writeFile outFile stdout
examples/test.png view
binary file changed (8518 → 8085 bytes)
− examples/test2.hs
@@ -1,25 +0,0 @@-{-# LANGUAGE NoMonomorphismRestriction #-}-{-# LANGUAGE TypeOperators #-}-{-# LANGUAGE DataKinds #-}-{-# LANGUAGE QuasiQuotes #-}-module Main where-import RlangQQ-import Data.HList---x = [0 .. 10 :: Double]-y = map (sin . (*pi) . (/10)) x---test = [r| print('hi'); hs_z <- hs_x + hs_y |]--test2 = fmap (.!. (Label :: Label "z")) test--test3 = print =<< (test2 :: IO [Double])---main = do- test3- let hs = zipWith (+) x y- let sse x y = sum $ zipWith (\a b -> (a-b)^2) x y- fmap (sse hs) test2
− examples/test2.ref
@@ -1,2 +0,0 @@-[0.0,1.3090169943749475,2.5877852522924734,3.8090169943749475,4.951056516295154,6.0,6.951056516295154,7.8090169943749475,8.587785252292473,9.309016994374948,10.0]-0.0
− examples/test3.hs
@@ -1,22 +0,0 @@-{-# LANGUAGE ViewPatterns #-} -- for pun-{-# LANGUAGE DataKinds #-}-{-# LANGUAGE TemplateHaskell, QuasiQuotes #-}-module Main where-import RlangQQ-import GHC.TypeLits--makeLabels6 (words "x y z")--inRec = x .=. "xlabel" .*. y .=. [1,2,3::Double] .*. z .=. [3,2,1 :: Double] .*. emptyRecord---- fixity is bad for .!. see test3_lens.hs for something better?-main = do- o <- [r|- print(hs_inRec)- hs_inRec <- within(hs_inRec, y <- y + z) |]- print ((o .!. (Label :: Label "inRec")) .!. y)--- -- alternative to the above (shadows the label y)- let [pun| inRec{ y } |] = o- print y
− examples/test3.ref
@@ -1,2 +0,0 @@-[4.0,4.0,4.0]-[4.0,4.0,4.0]
− examples/test3_lens.hs
@@ -1,18 +0,0 @@-{-# LANGUAGE FlexibleContexts #-}-{-# LANGUAGE DataKinds #-}-{-# LANGUAGE TemplateHaskell, QuasiQuotes #-}-module Main where-import RlangQQ-import GHC.TypeLits-import Control.Lens--makeLabelable "x y z"--inRec = x .==. "xlabel" .*. y .==. [1,2,3::Double] .*. z .==. [3,2,1 :: Double] .*. emptyRecord--main = do- o <- [r|- print(hs_inRec)- hs_inRec <- within(hs_inRec, y <- y + z) |]- let inR = hLens' (Label :: Label "inRec")- print (o ^.inR.y)
− examples/test3_lens.ref
@@ -1,1 +0,0 @@-[4.0,4.0,4.0]
− examples/test4.hs
@@ -1,52 +0,0 @@-{-# LANGUAGE DataKinds, TemplateHaskell, QuasiQuotes #-}-module Main where-import RlangQQ-import GHC.TypeLits -- ghc7.6 workaround-import Control.Monad--makeLabels6 (words "x y z")--inRec = x .=. "xlabel" .*. y .=. [1,2,3::Double] .*. z .=. [3,2,1 :: Double] .*. emptyRecord--inRecL = Label :: Label "inRec"--{- $test--note that the same R-session is run for all calls to f1,-that is why the results differ between f1 and f2:-->>> f1-[5.0,5.0,5.0]-[10.0,9.0,8.0]-[16.0,14.0,12.0]-[23.0,20.0,17.0]--->>> f2-[5.0,5.0,5.0]-[6.0,6.0,6.0]-[7.0,7.0,7.0]-[8.0,8.0,8.0]---}-f1 = do-- w <- newRChan-- [r|- print(hs_inRec) # this shouldn't be necessary to force INOUT- hs_inRec <- within(hs_inRec, y <- y + z + ch_w) |]--- forM_ [1,2,3,4 :: Double] $ \n -> do- o' <- sendRcv w n- print ((o' .!. inRecL) .!. y)--f2 = forM_ [1,2,3,4 :: Double] $ \n -> do- o' <- [r|- print(hs_inRec) # this shouldn't be necessary to force INOUT- hs_inRec <- within(hs_inRec, y <- y + z + hs_n) |]- print ((o' .!. inRecL) .!. y)---main = do f1;f2
− examples/test4.ref
@@ -1,8 +0,0 @@-[5.0,5.0,5.0]-[10.0,9.0,8.0]-[16.0,14.0,12.0]-[23.0,20.0,17.0]-[5.0,5.0,5.0]-[6.0,6.0,6.0]-[7.0,7.0,7.0]-[8.0,8.0,8.0]
− examples/test5.hs
@@ -1,31 +0,0 @@-{-# LANGUAGE DataKinds, TemplateHaskell, QuasiQuotes #-}-module Main where-import RlangQQ-import GHC.TypeLits -- ghc7.6 workaround-import Control.Monad--makeLabels6 (words "x y z")--inRec = x .=. "xlabel" .*. y .=. [1,2,3::Double] .*. z .=. [3,2,1 :: Double] .*. emptyRecord--inRecL = Label :: Label "inRec"--f2 = forM_ [1,2,3,4 :: Double] $ \n -> do- o' <- [r| # this shouldn't be necessary to force INOUT- print(hs_inRec)- hs_inRec <- within(hs_inRec, y <- y + z + $(n+1) ) |]- print ((o' .!. inRecL) .!. y)---{- $output--->>> f2-[6.0,6.0,6.0]-[7.0,7.0,7.0]-[8.0,8.0,8.0]-[9.0,9.0,9.0]---}--main = f2
− examples/test5.ref
@@ -1,4 +0,0 @@-[6.0,6.0,6.0]-[7.0,7.0,7.0]-[8.0,8.0,8.0]-[9.0,9.0,9.0]
+ examples/test6.html view
@@ -0,0 +1,48 @@+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">+<html xmlns="http://www.w3.org/1999/xhtml">+<head>+ <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />+ <meta http-equiv="Content-Style-Type" content="text/css" />+ <meta name="generator" content="pandoc" />+ <title></title>+ <style type="text/css">+table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {+ margin: 0; padding: 0; vertical-align: baseline; border: none; }+table.sourceCode { width: 100%; }+td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }+td.sourceCode { padding-left: 5px; }+code > span.kw { color: #007020; font-weight: bold; }+code > span.dt { color: #902000; }+code > span.dv { color: #40a070; }+code > span.bn { color: #40a070; }+code > span.fl { color: #40a070; }+code > span.ch { color: #4070a0; }+code > span.st { color: #4070a0; }+code > span.co { color: #60a0b0; font-style: italic; }+code > span.ot { color: #007020; }+code > span.al { color: #ff0000; font-weight: bold; }+code > span.fu { color: #06287e; }+code > span.er { color: #ff0000; font-weight: bold; }+ </style>+</head>+<body>+<h1 id="test-6">test 6</h1>+<p>This file can be loaded into whatever editor you like to use for knitr. ghc is called for the second code block.</p>+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(knitr)+opts_chunk$<span class="kw">set</span>(<span class="dt">engine =</span> <span class="st">"haskell"</span>, <span class="dt">engine.path =</span> <span class="st">"ghc"</span>)</code></pre>+<pre class="sourceCode haskell"><code class="sourceCode haskell"><span class="fu">:</span>set <span class="fu">-</span><span class="dt">XQuasiQuotes</span>+<span class="fu">:</span><span class="kw">module</span> <span class="fu">+</span><span class="dt">RlangQQ</span>+<span class="kw">let</span> fibs <span class="fu">=</span> <span class="fl">0.0</span><span class="fu">:</span><span class="dv">1</span><span class="fu">:</span> <span class="fu">zipWith</span> (<span class="fu">+</span>) fibs (<span class="fu">drop</span> <span class="dv">1</span> fibs)+[r<span class="fu">|</span> png(<span class="ch">'test6_fig1.png'</span>); plot(<span class="dv">1</span><span class="fu">:</span><span class="dv">20</span>, <span class="fu">$</span>(<span class="fu">take</span> <span class="dv">20</span> fibs)); dev<span class="fu">.</span>off() <span class="fu">|</span>]</code></pre>+<div class="figure">+<img src="test6_fig1.png" alt="test6 caption here" /><p class="caption">test6 caption here</p>+</div>+<h2 id="limitations">limitations</h2>+<ul>+<li>This simple example works but unfortunately the haskell knitr engine doesn't allow newlines instead of semicolons in the quasiquote (<code>[r| |]</code>)</li>+<li><p>Also note a comment from here <a href="http://yihui.name/knitr/demo/engines/"><code class="url">http://yihui.name/knitr/demo/engines/</code></a>:</p>+<pre><code>Except engine='R' (default), all chunks are executed in separate sessions, so the variables cannot be directly shared. If we want to make use of objects created in previous chunks, we usually have to write them to files (as side effects). For the bash engine, we can use Sys.setenv() to export variables from R to bash (example).</code></pre></li>+<li><p>Graphical output from haskell chunks has to be manually included.</p></li>+</ul>+</body>+</html>
+ examples/test7.html view
@@ -0,0 +1,58 @@+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">+<html xmlns="http://www.w3.org/1999/xhtml">+<head>+ <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />+ <meta http-equiv="Content-Style-Type" content="text/css" />+ <meta name="generator" content="pandoc" />+ <title></title>+ <style type="text/css">+table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {+ margin: 0; padding: 0; vertical-align: baseline; border: none; }+table.sourceCode { width: 100%; }+td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }+td.sourceCode { padding-left: 5px; }+code > span.kw { color: #007020; font-weight: bold; }+code > span.dt { color: #902000; }+code > span.dv { color: #40a070; }+code > span.bn { color: #40a070; }+code > span.fl { color: #40a070; }+code > span.ch { color: #4070a0; }+code > span.st { color: #4070a0; }+code > span.co { color: #60a0b0; font-style: italic; }+code > span.ot { color: #007020; }+code > span.al { color: #ff0000; font-weight: bold; }+code > span.fu { color: #06287e; }+code > span.er { color: #ff0000; font-weight: bold; }+ </style>+</head>+<body>+<h1 id="test-7">test 7</h1>+<p>Same as test6, except needs a change to knitr as of October 2013 (ie. 1.5 is not good enough). <a href="https://github.com/yihui/knitr/pull/633">This commit is needed</a>.</p>+<p>This file can be loaded into whatever editor you like to use for knitr. I use <a href="http://www.vim.org/scripts/script.php?script_id=2628">vim-r-plugin</a>, but perhaps ESS or Rstudio can also work. After some set-up in the first block. ghc is called for the second code block.</p>+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(knitr)+opts_chunk$<span class="kw">set</span>(<span class="dt">engine =</span> <span class="st">"haskell"</span>, <span class="dt">engine.path =</span> <span class="st">"ghc"</span>, <span class="dt">engine.opts =</span> <span class="st">"-e </span><span class="ch">\"</span><span class="st">:set -XQuasiQuotes</span><span class="ch">\"</span><span class="st"> -e </span><span class="ch">\"</span><span class="st">import RlangQQ</span><span class="ch">\"</span><span class="st">"</span>)</code></pre>+<pre class="sourceCode haskell"><code class="sourceCode haskell">+<span class="kw">let</span> fibs <span class="fu">=</span> <span class="fl">0.0</span><span class="fu">:</span><span class="dv">1</span><span class="fu">:</span> <span class="fu">zipWith</span> (<span class="fu">+</span>) fibs (<span class="fu">drop</span> <span class="dv">1</span> fibs)++[r<span class="fu">|</span> png(<span class="ch">'test6_fig1.png'</span>)+ plot(<span class="dv">1</span><span class="fu">:</span><span class="dv">20</span>, <span class="fu">$</span>(<span class="fu">take</span> <span class="dv">20</span> fibs))+ dev<span class="fu">.</span>off() <span class="fu">|</span>]</code></pre>+<pre class="sourceCode haskell"><code class="sourceCode haskell">+<span class="kw">do</span> <span class="kw">let</span> x <span class="fu">=</span> <span class="dv">1</span>+ <span class="fu">print</span>(<span class="dv">1</span><span class="fu">+</span>x)+ <span class="fu">print</span>(<span class="dv">2</span><span class="fu">+</span>x)</code></pre>+<pre><code>## 2+## 3</code></pre>+<div class="figure">+<img src="test6_fig1.png" alt="test6 caption here" /><p class="caption">test6 caption here</p>+</div>+<h2 id="limitations">limitations</h2>+<ul>+<li><p>each block is a separate session. You would have to manually save/load variables defined in the previous chunk to make things work as they do for R.</p></li>+<li><p>code is interpreted, not compiled</p></li>+<li><p>graphical output from haskell chunks has to be manually included</p></li>+</ul>+<h2 id="raw-code">raw code</h2>+<p>See <a href="test7.Rmd.html">here</a></p>+</body>+</html>
+ examples/testplot.png view
binary file changed (absent → 8085 bytes)
+ examples/tests.hs view
@@ -0,0 +1,87 @@+{-# LANGUAGE ViewPatterns #-}+{-# LANGUAGE NoMonomorphismRestriction #-}+{-# LANGUAGE DataKinds, PolyKinds, FlexibleContexts, TemplateHaskell, QuasiQuotes #-}+module Main where+import Test.Hspec++import System.Directory+import RlangQQ+import GHC.TypeLits+import Control.Lens++import Control.Monad+import qualified Data.Vector as V++makeLabelable "x y z"++inRecL = hLens' (Label :: Label "inRec")++inRec = [pun| x y z |] where+ x = "xlabel"+ y,z :: [Double]+ y = [1,2,3]+ z = [3,2,1]+++main = hspec $ describe "rlangqq" $ do+ it "can read a tsv file" $ do+ [pun| x@(a b) s |] <- [r|+ # find the file whether we runghc tests.hs+ # runghc examples/tests.hs+ fname <- c(Sys.glob('*.tsv'), Sys.glob('examples/*.tsv'))[1]+ hs_x <- as.list(read.delim(fname, sep=' '))+ hs_s <- as.numeric(with(hs_x, t(a) %*% b))+ |]+ (V.sum (V.zipWith (*) a b) :: Double, s :: Double)+ `shouldBe` (93.783, 93.783)++ it "does the same as zipWith" $ do++ o <- [r| print(hs_inRec) # should be unnecessary+ hs_inRec <- within(hs_inRec, y <- y + z) |]+ let inR = hLens' (Label :: Label "inRec")+ (o ^.inR.y) `shouldBe` zipWith (+) (inRec^.y) (inRec^.z)+ + it "accumulates when variables are ch_" $ do+ w <- newRChan+ [r|+ print(hs_inRec) # this shouldn't be necessary to force INOUT+ hs_inRec <- within(hs_inRec, y <- y + z + ch_w) |]++ out <- forM [1,2,3,4 :: Double] $ \n -> do+ sendRcv w n <&> view (inRecL . y)+ out `shouldBe`+ tail (scanl+ (\ accum n -> map (+n) $ zipWith (+) (inRec^.z) accum)+ (inRec^.y)+ [1 .. 4])++ it "repeats when variables are not ch_" $ do+ out <- forM [1,2,3,4 :: Double] $ \n -> do+ [r| print(hs_inRec) # this shouldn't be necessary to force INOUT+ hs_inRec <- within(hs_inRec, y <- y + z + hs_n) |]+ <&> view (inRecL . y)+ out `shouldBe`+ map (\n -> map (+n) (zipWith (+) (inRec^.y) (inRec^.z))) [1 .. 4]+ + it "interpolates haskell expressions" $ do+ ys <- forM [1,2,3,4 :: Double] $ \n -> do+ [pun| inRec{y} |] <- [r| # this shouldn't be necessary to force INOUT+ print(hs_inRec)+ hs_inRec <- within(hs_inRec, y <- y + z + $(n+1) ) |]+ return y+ ys `shouldBe` map (\n -> map (+(1+n)) (zipWith (+) (inRec^.y) (inRec^.z))) [1 .. 4]++ it "can make a plot where you ask it to" $ do+ let x = [0 .. 10 :: Double]+ y = map (sin . (*pi) . (/10)) x+ b <- doesFileExist "testplot.png"+ when b (removeFile "testplot.png")+ -- normally you would just call qplot, and find a plot in Rtmp/figN/...+ [r| library(ggplot2)+ print('blahh')+ png(file='testplot.png')+ plot(qplot( hs_x, hs_y ))+ dev.off()+ |]+ doesFileExist "testplot.png" `shouldReturn` True
src/HListExtras.hs view
@@ -1,18 +1,7 @@-{-# LANGUAGE FunctionalDependencies #-}-{-# LANGUAGE TypeFamilies #-} {-# LANGUAGE NoMonomorphismRestriction #-}-{-# LANGUAGE ConstraintKinds #-}-{-# LANGUAGE UndecidableInstances #-}-{-# LANGUAGE MultiParamTypeClasses #-}-{-# LANGUAGE TemplateHaskell #-} {-# LANGUAGE PolyKinds #-}-{-# LANGUAGE KindSignatures #-}-{-# LANGUAGE TypeOperators #-}-{-# LANGUAGE GADTs #-}-{-# LANGUAGE ScopedTypeVariables #-}-{-# LANGUAGE FlexibleContexts #-}-{-# LANGUAGE DataKinds #-}-{-# LANGUAGE FlexibleInstances #-}+{-# LANGUAGE TemplateHaskell #-}+{-# LANGUAGE UndecidableInstances #-} module HListExtras where import Data.HList.CommonMain@@ -22,65 +11,4 @@ instance (ApplyAB f a b, ab ~ (a -> b)) => ApplyAB FApply f ab where applyAB _ = applyAB -class HReplicate2 (n ::HNat) e l | n e -> l, l -> n- where hReplicate2 :: Proxy n -> e -> HList l-instance HReplicate2 HZero e '[] where- hReplicate2 _ _ = HNil-instance (HReplicate2 n e es') => HReplicate2 (HSucc n) e (e ': es') where- hReplicate2 n e = e `HCons` hReplicate2 (hPred n) e---- hReplicateApp :: HReplicate2 (HLength r) e r' => e -> HList r-hReplicateApp e = let r = hMap FApply (hReplicate2 (hLength r) e) in r---- | splitAt-hSplitAt n xs = hSplitAtAux n xs HNil--class HSplitAtAux (n :: HNat) l a0 a b | n -> l a0 a b where- hSplitAtAux :: Proxy n -> HList l -> HList a0 -> (HList a, HList b)--instance (l ~ b, a ~ HRevApp a0 '[]) => HSplitAtAux HZero l a0 a b where- hSplitAtAux _ l acc = (hReverse acc, l)--instance ( (x ': xs) ~ ls, acc' ~ (x ': acc),- HSplitAtAux n xs acc' a b) => HSplitAtAux (HSucc n) ls acc a b where- hSplitAtAux n (HCons x xs) acc = hSplitAtAux (hPred n) xs (HCons x acc)---hEnumFromTo :: Proxy a -> Proxy b -> Proxy (HEnumFromTo a b)-hEnumFromTo a b = proxy---- | [n1 .. n1 + n2]--type family HEnumFromTo (from::HNat) (to::HNat) :: [HNat]-type instance HEnumFromTo a HZero = '[a]-type instance HEnumFromTo a (HSucc b) = a ': HEnumFromTo (HSucc a) b----- this doesn't work any better... want ghc to be able to simplify--- that (hReverse (hReverse x) `asTypeOf` x)-class (SameLength a b, SameLength b a) => HReverse2 a b | a -> b, b -> a where- hReverse2 :: HList a -> HList b--instance (SameLength a b, SameLength b a, HRevApp a '[] ~ b, HRevApp b '[] ~ a) => HReverse2 a b where- hReverse2 = hReverse---class HMap1 f a b where hMap1 :: f -> HList a -> HList b--instance (a ~ '[]) => HMap1 f a '[] where- hMap1 _ _ = HNil--instance (ApplyAB f a b, (a ': as) ~ aas, HMap1 f as bs) =>- HMap1 f aas (b ': bs) where- hMap1 f (HCons a as) = HCons (applyAB f a) (hMap1 f as)---class HMap2 f a b where hMap2 :: f -> HList a -> HList b--instance (a ~ '[]) => HMap2 f '[] a where- hMap2 _ _ = HNil--instance (ApplyAB f a b, (b ': bs) ~ bbs, HMap2 f as bs) =>- HMap2 f (a ': as) bbs where- hMap2 f (HCons a as) = HCons (applyAB f a) (hMap2 f as)
src/RlangQQ.hs view
@@ -36,15 +36,10 @@ import Language.Haskell.TH.Quote import Data.HList.CommonMain- import GHC.TypeLits-- import Control.Concurrent -- {- | Calls R with the supplied string. Variables in R prefixed hs_ cause the corresponding (un-prefixed) variable to be converted. The variable(s) must be in at least one class 'FromRDS' or 'ToRDS'. Currently the relation between@@ -74,24 +69,22 @@ While it is only somewhat usable, you can have Rnw/Rmd documents (knitr) that include haskell code. One example is given-<http://code.haskell.org/~aavogt/Rlang-QQ/examples/test7.html here>+<http://code.haskell.org/~aavogt/Rlang-QQ/examples/test7.Rmd here>. A second+option is to use <http://hackage.haskell.org/package/ihaskell IHaskell>, for+which there are two example notebooks in the same <http://code.haskell.org/~aavogt/Rlang-QQ/examples> -} r = QuasiQuoter { quoteExp = \s -> do n <- getRlangQQ_n- quoteRExpression2 n False ("print('');"++ s)- -- the first expression gets dropped somewhere else- -- this hack is easiest+ quoteRExpression2 n False s } --- | same as @[rChan| |]@ does the same as [r| |], except the+-- | @[rChan| |]@ does the same as [r| |], except the -- return value will be a @Chan (Record a)@. rChan = QuasiQuoter { quoteExp = \s -> do n <- getRlangQQ_n- quoteRExpression2 n True ("print('');"++ s)- -- the first expression gets dropped somewhere else- -- this hack is easiest+ quoteRExpression2 n True s } {- $records@@ -149,6 +142,8 @@ -- | @y <- sendRcv c x@ sends the value @x@ using the chan @c@. -- Provided that an @[r| |]@ quasiquote above refers to a @ch_c@, -- the call to 'sendRcv' will eventually produce a 'Record' @y@+-- which provides a reference to all the output variables that+-- R calculated with the given @x@. sendRcv :: Chan (t, b -> IO ()) -> t -> IO b sendRcv ch e = do v <- newEmptyMVar@@ -164,7 +159,7 @@ also, return R's stdout / stderr / exitcode in the HList. This won't be practical for the Chan option since the stdout is getting consumed? -[@antiquote@] doesn't do escapes, so a string '$( blah )' might end badly+[@antiquote@] doesn't do escapes, so a string \'$( blah )\' might end badly [@more examples@] conversion both ways etc. @@ -173,7 +168,7 @@ [@more datatypes@] support things like ??, ... [@call R functions as if they were defined in haskell@]-This can be achievede already by doing something like+This can be achieved already by doing something like > x <- newRChan > [r| hs_f <- ch_x + 1 |]@@ -188,8 +183,10 @@ Can this be made to work without looking at whether there is a function() after the <-? [@call hs functions as if they were defined in R@]-we might like to be able to have values like @f x = x + 1@-be callable by.+Likewise, we might like values like @f x = x + 1@ to be callable from+R (for example by defining a foreign export ccall f :: Int32 -> Int32), and+dyn.load('something'); .C('f', x=1L). Alternatively the R interpreter might+have a way to communicate some other way. [@use libR.so@] there is a <http://hackage.haskell.org/package/hR-0.1.1 hR package> which might@@ -218,5 +215,7 @@ or run the code first with some dummy inputs (say vectors of length 10 or so), and assume those types will continue to be the same.++<http://cran.r-project.org/web/packages/lambda.r/index.html> -}
src/RlangQQ/Binary.hs view
@@ -1,19 +1,7 @@-{-# LANGUAGE FunctionalDependencies #-}-{-# LANGUAGE TypeFamilies #-}+{-# LANGUAGE PolyKinds #-} {-# LANGUAGE NoMonomorphismRestriction #-}-{-# LANGUAGE ConstraintKinds #-}-{-# LANGUAGE UndecidableInstances #-}-{-# LANGUAGE MultiParamTypeClasses #-} {-# LANGUAGE TemplateHaskell #-}-{-# LANGUAGE PolyKinds #-}-{-# LANGUAGE KindSignatures #-}-{-# LANGUAGE TypeOperators #-}-{-# LANGUAGE GADTs #-}-{-# LANGUAGE ScopedTypeVariables #-}-{-# LANGUAGE FlexibleContexts #-}-{-# LANGUAGE DataKinds #-}-{-# LANGUAGE FlexibleInstances #-}-+{-# LANGUAGE UndecidableInstances #-} {- | Conversions between R's RDS/RDA format and haskell data types. tested with R 3.0.1@@ -53,6 +41,8 @@ import Data.Int import Data.HList.CommonMain import qualified Data.Vector as V+import qualified Data.Vector.Storable as VS+import qualified Data.Vector.Generic as VG import qualified Data.Map as M import Data.Binary@@ -77,12 +67,12 @@ import HListExtras data FLVPair = FLVPair-instance (LVPair l a ~ b) => ApplyAB FLVPair a b where- applyAB FLVPair x = LVPair x+instance (Tagged l a ~ b) => ApplyAB FLVPair a b where+ applyAB FLVPair x = Tagged x -- this should not be necessary type family UnHMapFLVPair (a :: [*]) :: [*]-type instance UnHMapFLVPair (LVPair l a ': as) = a ': UnHMapFLVPair as+type instance UnHMapFLVPair (Tagged l a ': as) = a ': UnHMapFLVPair as type instance UnHMapFLVPair '[] = '[] -- | this seems to be a magic number. Corresponds to R 3.0.1@@ -103,30 +93,42 @@ putDouble = put . (unsafeCoerce :: Double -> Int64) getDouble = fmap (unsafeCoerce :: Int64 -> Double) get +getVectorDouble = do+ 14 <- get :: Get Int32+ len <- get :: Get Int32+ VG.replicateM (fromIntegral len) getDouble++putVectorDouble x = do+ put (14 :: Int32)+ put (fromIntegral $ VG.length x :: Int32)+ VG.mapM_ putDouble x++getVectorInt = do+ 13 <- get :: Get Int32+ len <- get :: Get Int32+ VG.replicateM (fromIntegral len) get++putVectorInt x = do+ put (13 :: Int32)+ put (fromIntegral $ VG.length x :: Int32)+ VG.mapM_ put x+ -- | becomes a numeric vector-instance ToRDS (V.Vector Double) where- toRDS x = do- put (14 :: Int32)- put (fromIntegral $ V.length x :: Int32)- V.mapM_ putDouble x-instance FromRDS (V.Vector Double) where- fromRDS = do- 14 <- get :: Get Int32- len <- get :: Get Int32- V.mapM (const getDouble) $ V.replicate (fromIntegral len) ()+instance ToRDS (V.Vector Double) where toRDS = putVectorDouble +-- | becomes a numeric vector+instance ToRDS (VS.Vector Double) where toRDS = putVectorDouble++instance FromRDS (V.Vector Double) where fromRDS = getVectorDouble+instance FromRDS (VS.Vector Double) where fromRDS = getVectorDouble+ -- | integer vector-instance ToRDS (V.Vector Int32) where- toRDS x = do- put (13 :: Int32)- put (fromIntegral $ V.length x :: Int32)- V.mapM_ put x-instance FromRDS (V.Vector Int32) where- fromRDS = do- 13 <- get :: Get Int32- len <- get- V.mapM (const get) $ V.replicate len ()+instance ToRDS (V.Vector Int32) where toRDS = putVectorInt+instance FromRDS (V.Vector Int32) where fromRDS = getVectorInt +instance ToRDS (VS.Vector Int32) where toRDS = putVectorInt+instance FromRDS (VS.Vector Int32) where fromRDS = getVectorInt+ -- | character vector @c(\'ab\',\'cd\')@ instance ToRDS (V.Vector T.Text) where toRDS x = do@@ -183,7 +185,7 @@ instance (RDSHLIST xs' xs) => ToRDS (LST xs) where toRDS (LST xs) = do put (531::Int32)- let [(len,"")] = reads (drop 1 $ show (proxy :: Proxy (HLength xs)))+ let len = hNat2Integral (Proxy :: Proxy (HLength xs)) put (fromIntegral (len - 2 :: Int) :: Int32) hFoldr (HSeq FToRDS) (return () :: Put) xs :: Put put (254::Int32)@@ -198,7 +200,7 @@ instance (RDSHLIST2 ___ __ l) => FromRDS (LST l) where fromRDS = withSelf $ \(self) -> do 531 <- get :: Get Int32- let [(len,"")] = reads (drop 1 $ show (hLength self))+ let len = hNat2Integral (hLength self) len2 <- get :: Get Int32 when (len /= len2) $ error $ "fromRDS expected length: " ++ show len ++ " rds file has length: " ++ show len2 r <- hSequence (hSequence (hMap FApply $ hReplicate (hLength self) FFromRDS) ())@@ -223,9 +225,9 @@ type ToRDSRecord __ ___ xs = (RDSHLIST __ ___, ToRDS (LST ___), RecordValues xs,- HList ___ ~ (HList (RecordValuesR xs) `HAppendR` HList '[ListStart, LVPair "names" [String]]),+ HList ___ ~ (HList (RecordValuesR xs) `HAppendR` HList '[ListStart, Tagged "names" [String]]), RecordLabelsString (RecordLabels xs),- HAppend (HList (RecordValuesR xs)) (HList '[ ListStart, LVPair "names" [String]]))+ HAppend (HList (RecordValuesR xs)) (HList '[ ListStart, Tagged "names" [String]])) @@ -233,7 +235,6 @@ -- functions called type FromRDSRec a b as' as'2 bs' = (HSequence Get b as', HSequence ((->) ()) a b,- -- HMap1 FLVPair as' bs', HMapAux FLVPair as' bs', SameLength as' bs', SameLength bs' as',@@ -245,19 +246,32 @@ HMapOut (HComp FShowLabel FLabelLVPair) bs' String, RecordLabelsString (RecordLabels bs'), HNat2Integral (HLength bs'),- HReplicate2 (HLength bs') FFromRDS as'2)+ HReplicate (HLength bs') FFromRDS,+ HReplicateR (HLength bs')FFromRDS ~ as'2) +{- | R lists become HList records. @list(x=1,y='b')@ parses to something+like++> let x = Label :: Label "x"+> y = Label :: Label "y"+> in x .=. 1+> .*. y .=. "b"+> .*. emptyRecord++You have to get the result type right+(ie. @Record [Tagged \"x\" Double, Tagged \"y\" String]@) for things to parse+-} instance FromRDSRec a b as' as'2 bs' => FromRDS (Record bs') where fromRDS = do 531 <- get :: Get Int32- let len = hNat2Integral (proxy :: Proxy (HLength bs'))+ let len = hNat2Integral (Proxy :: Proxy (HLength bs')) len2 <- get :: Get Int32 when (len /= len2) $ error $ "fromRDS expected length: " ++ show len ++ " rds file has length: " ++ show len2 r <- hSequence (hSequence (hMap FApply- (hReplicate2 (proxy :: Proxy (HLength bs')) FFromRDS) )+ (hReplicate (Proxy :: Proxy (HLength bs')) FFromRDS) ) ()) getListHdr "names" <- getString@@ -269,7 +283,7 @@ ++ " rds file has names attribute : " ++ show names 254 <- get :: Get Int32- return (Record (hMap FLVPair (r :: HList as')))+ return (Record (hMap FLVPair (r :: HList as') :: HList bs')) -- this hMap1 can't be replaced by hMap? class RecordLabelsString (a :: [Symbol]) where@@ -280,14 +294,14 @@ instance (ShowLabel x, RecordLabelsString xs) => RecordLabelsString (x ': xs) where- recordLabelsString _ = showLabel (Label :: Label x) : recordLabelsString (proxy :: Proxy xs)+ recordLabelsString _ = showLabel (Label :: Label x) : recordLabelsString (Proxy :: Proxy xs) -- same as recordLabelsString, but less lazy recLabs (Record xs) = hMapOut (HComp FShowLabel FLabelLVPair) xs data FLabelLVPair = FLabelLVPair-instance(LVPair l a ~ x, y ~ Label l) => ApplyAB FLabelLVPair x y where- applyAB FLabelLVPair = labelLVPair+instance(Tagged l a ~ x, y ~ Label l) => ApplyAB FLabelLVPair x y where+ applyAB FLabelLVPair _ = (Label :: Label l) data FShowLabel = FShowLabel instance (string ~ String, ShowLabel l, ll ~ Label l) => ApplyAB FShowLabel ll string@@ -302,6 +316,8 @@ instance ToRDS Double where toRDS = toRDS . (:[]) instance ToRDS Int32 where toRDS = toRDS . (:[])+instance ToRDS Integer where toRDS = toRDS . (fromIntegral :: Integer -> Int32)+instance ToRDS Int where toRDS = toRDS . (fromIntegral :: Int -> Int32) instance FromRDS Int32 where fromRDS = do@@ -313,13 +329,30 @@ [x] <- fromRDS return x +instance FromRDS Int where+ fromRDS = fromIntegral <$> (fromRDS :: Get Int32) +instance FromRDS Integer where+ fromRDS = fromIntegral <$> (fromRDS :: Get Int32)++ instance ToRDS [Int32] where toRDS = toRDS . V.fromList instance FromRDS [Int32] where fromRDS = fmap V.toList $ fromRDS +-- | converts to an 'Int32', which is bad on 64 bit systems where+-- @maxBound :: Int@ is a bigger number than @maxBound :: Int32@+instance ToRDS [Int] where+ toRDS = toRDS . V.fromList . map (fromIntegral :: Int -> Int32)+instance FromRDS [Int] where+ fromRDS = map (fromIntegral :: Int32 -> Int) . V.toList <$> fromRDS +-- | converts to an 'Int32' first+instance ToRDS [Integer] where+ toRDS = toRDS . V.fromList . map (fromIntegral :: Integer -> Int32)+instance FromRDS [Integer] where+ fromRDS = map (fromIntegral :: Int32 -> Integer) . V.toList <$> fromRDS putString s = do@@ -355,12 +388,12 @@ -- | probably internal-instance forall t l. (ToRDS t, ShowLabel l) => ToRDS (LVPair l t) where- toRDS (LVPair x) = do+instance forall t l. (ToRDS t, ShowLabel l) => ToRDS (Tagged l t) where+ toRDS (Tagged x) = do putString (showLabel (undefined :: Label l)) toRDS x -- | probably internal-instance forall t l. (FromRDS t, ShowLabel l) => FromRDS (LVPair l t) where+instance forall t l. (FromRDS t, ShowLabel l) => FromRDS (Tagged l t) where fromRDS = do varName <- getString @@ -368,24 +401,24 @@ unless (varName == s) $ fail $ unwords ["FromRDS: expecting label `", s , "', but got: `" , varName , "'"] x <- fromRDS- return (LVPair x)+ return (Tagged x) -- | labels are stored with the variables here. compare with the instance for 'Record' / 'LST' which collects -- the labels and saves them as an attribute called \"names\" newtype RDA a = RDA (HList a) -- | internal-instance forall rs l2 t. (ToRDS t, ToRDS (RDA rs), ShowLabel l2) => ToRDS (RDA (LVPair l2 t ': rs)) where+instance forall rs l2 t. (ToRDS t, ToRDS (RDA rs), ShowLabel l2) => ToRDS (RDA (Tagged l2 t ': rs)) where toRDS (RDA (x `HCons` xs)) = do putListHdr toRDS x toRDS (RDA xs) -- | internal-instance forall rs l2 t. (FromRDS t, FromRDS (RDA rs), ShowLabel l2) => FromRDS (RDA (LVPair l2 t ': rs)) where+instance forall rs l2 t. (FromRDS t, FromRDS (RDA rs), ShowLabel l2) => FromRDS (RDA (Tagged l2 t ': rs)) where fromRDS = do getListHdr- x <- fromRDS :: Get (LVPair l2 t)+ x <- fromRDS :: Get (Tagged l2 t) RDA xs <- fromRDS :: Get (RDA rs) return (RDA (x `HCons` xs)) @@ -592,7 +625,7 @@ A typical type would be -> Record '[LVPair l1 (LVPair m1 x), LVPair l2 (LVPair m2 x)]+> Record '[Tagged l1 (Tagged m1 x), Tagged l2 (Tagged m2 x)] The outer labels (@l1@, @l2@) are the haskell-side. The inner labels are the @m1@ @m2@ which are the names of the variables on the R side.@@ -608,9 +641,9 @@ hdr' <- fmap (BS.toString) $ getByteString (BS.length (BS.fromString hdr)) unless (hdr == hdr') $ fail "wrong header" [{- 2 , 196609, 131840 -} _, _, _ :: Int32 ] <- replicateM 3 get- fmap (\(RDA a) -> Record (hMap1 FLVPair (a::HList __) )) fromRDS+ fmap (\(RDA a) -> Record (hMap FLVPair (a::HList __) )) fromRDS -type FromRDA a r = (HMap1 FLVPair a r, FromRDS (RDA a))+type FromRDA a r = (HMapCxt FLVPair (HList a) (HList r) a r, FromRDS (RDA a)) -- * tests
src/RlangQQ/FN.hs view
@@ -4,7 +4,7 @@ {-# LANGUAGE FlexibleInstances #-} {-# LANGUAGE TypeFamilies #-} {-# LANGUAGE MultiParamTypeClasses #-}--- | 'hMap' 'FN' drops the inner LVPair, which was+-- | 'hMap' 'FN' drops the inner Tagged, which was -- just used to keep track of the variable name on the -- R side. --@@ -15,10 +15,10 @@ import GHC.TypeLits data FN = FN -instance (a ~ (LVPair (t :: k) (LVPair (t2::k) x)), b ~LVPair t x) => ApplyAB FN a b where- applyAB _ (LVPair (LVPair x)) = LVPair x+instance (a ~ (Tagged (t :: k) (Tagged (t2::k) x)), b ~Tagged t x) => ApplyAB FN a b where+ applyAB _ (Tagged (Tagged x)) = Tagged x data NoLabel = NoLabel-instance (la ~ LVPair "" a) => ApplyAB NoLabel a la where- applyAB _ x = LVPair x+instance (la ~ Tagged "" a) => ApplyAB NoLabel a la where+ applyAB _ x = Tagged x
src/RlangQQ/Internal.hs view
@@ -1,7 +1,3 @@-{-# LANGUAGE ViewPatterns #-}-{-# LANGUAGE PatternGuards #-}-{-# LANGUAGE TypeOperators #-}-{-# LANGUAGE FlexibleInstances #-} {-# LANGUAGE TemplateHaskell #-} module RlangQQ.Internal where @@ -23,6 +19,7 @@ import qualified Data.ByteString.Lazy.UTF8 as B import qualified Data.ByteString.Lazy as B+import qualified Data.ByteString.Lazy import qualified Data.ByteString.Lazy.Char8 as B8 import Control.Monad.State@@ -32,12 +29,14 @@ import qualified Data.Map as M import Data.Foldable (foldMap) +import System.FilePath import Paths_Rlang_QQ import RlangQQ.Binary import RlangQQ.Antiquote import RlangQQ.FN+-- import RlangQQ.ParseKnitted import Control.Concurrent.Chan import Control.Concurrent.MVar@@ -51,21 +50,26 @@ -- | going via binary serialization (classes from "RlangQQ.Binary"). This is used in 'RlangQQ.r' quoteRExpression2 i returnChan str0 = do let rawFile, mdFile, rmdFile, inputFile, inputFile2, outputFile, figPath :: String- rawFile = printf "Rtmp/raw%d.R" (i :: Int)- rmdFile = printf "Rtmp/raw%d.Rmd" i- mdFile = printf "Rtmp/raw%d.md" i- figPath = printf "Rtmp/fig%d/" i- inputFile = printf "Rtmp/%d_hs.rdx" i+ rawFile = printf "Rtmp/raw%d.R" (i :: Int)+ rmdFile = printf "Rtmp/raw%d.Rmd" i+ rmdFile2 = printf "Rtmp/raw%d_.Rmd" i+ mdFile = printf "Rtmp/raw%d.md" i+ figPath = printf "Rtmp/fig%d/" i+ inputFile = printf "Rtmp/%d_hs.rdx" i inputFile2 = printf "Rtmp/%d_hs2.rdx" i outputFile = printf "Rtmp/%d_r.rdx" i - (knitrHdr, (str, addAntiquotes)) = fmap withRawFile (splitKnitrHdr str0)+ -- the print('') is to fix a bug that somehow drops the first+ -- expression sent to R+ (knitrHdr, withRawFile -> (("setwd('../');"++) -> str, addAntiquotes))+ = splitKnitrHdr str0+ let inRmdChunk s = unlines ["```{r " ++ knitrHdr ++ "}", s, "```"] variables <- runIO $ do t <- getDataFileName "Tree.R" createDirectoryIfMissing False "Rtmp" writeFile rawFile str- writeFile rmdFile (unlines ["```{r " ++ knitrHdr ++ "}", str, "```"])+ writeFile rmdFile (inRmdChunk str) rt <- readProcess "R" ["--no-save","--quiet"] $ printf "source('%s'); rlangQQ_toTree(parse('%s'))" t rawFile case parseString parseTree mempty rt of@@ -132,26 +136,33 @@ let mapSnd (Record x) = Record (hMap FN x) fixTy x = x `asTypeOf` Record $sampOutput fixTy2 x = x `asTypeOf` Record $sampOutput2- in fixTy2 . mapSnd . fixTy . fromRDA <$> B.readFile outputFile |]+ in do+ cts <- B.readFile outputFile+ -- lazy IO makes for confusings: possibly better to switch+ -- over to strict BS instead of this+ Data.ByteString.Lazy.length cts `seq` return ()+ return . fixTy2 . mapSnd . fixTy $ fromRDA cts |] outVars :: [String] outVars = [ s | (s, intent) <- M.toList variables ++ M.toList chanVars {- necessary? -}, notIn intent ] -- writeOutputFile | [] <- outVars = [| "" |]- | otherwise = stringE (printf "save(%s, file='%s', compress='gzip');"+ saveRVars :: String+ saveRVars+ | [] <- outVars = ""+ | otherwise = printf "save(%s, file='%s', compress='gzip');" (intercalate "," $ reverse outVars)- outputFile)+ (takeFileName outputFile)+ rmd2Content :: String+ rmd2Content = inRmdChunk str ++ inRmdChunk saveRVars runRNoChan :: ExpQ runRNoChan = do [| readProcess "R" ["--no-save", "--quiet"] $ concat [ $(stringE (printf "library(knitr);\- \opts_chunk$set(fig.path='%s');\+ \opts_chunk$set(fig.path='%s',tidy=F);\ \load('%s');" figPath inputFile)),- $(stringE (printf "knit('%s', output='%s', quiet=TRUE);" rmdFile mdFile)),- $writeOutputFile ]+ $(stringE (printf "knit('%s', output='%s', quiet=TRUE);" rmdFile2 mdFile))] |] @@ -178,8 +189,8 @@ \ string that will never happen by chance\"" forkIO $ forever $ do withOutputFn <- $writeChanInput- hPutStrLn i $ printf "load('%s'); knit('%s', output='%s', quiet=TRUE); %s %s"- inputFile2 rmdFile mdFile $writeOutputFile+ hPutStrLn i $ printf "load('%s'); knit('%s', output='%s', quiet=TRUE); %s"+ inputFile2 rmdFile2 mdFile uniqueDoneString hFlush i $(whenOutVars [| putMVar outVar withOutputFn |] )@@ -200,17 +211,18 @@ -- but that doesn't typecheck here... ) |])- return chOut |] + runIO $ writeFile rmdFile2 rmd2Content+ addAntiquotes [| do {- already done, but Rtmp could have moved since compile time -} createDirectoryIfMissing False "Rtmp" do- f <- doesFileExist rawFile- unless f $ writeFile rawFile str+ f <- doesFileExist rmdFile2+ unless f $ writeFile rmdFile2 rmd2Content $writeInputFile $(if M.size chanVars == 0 then [| do
src/RlangQQ/MakeRecord.hs view
@@ -1,4 +1,3 @@-{-# LANGUAGE ConstraintKinds, DataKinds,GADTs,FlexibleContexts, FlexibleInstances,KindSignatures,MultiParamTypeClasses #-} -- | functions to help making lists for consumption on the R side module RlangQQ.MakeRecord where @@ -11,7 +10,7 @@ -- | convert a haskell list into a record with labels all of type \"\". The length -- of the list is decided by the (type of the) first argument which is a 'HNat' listToRecN :: ListToRecN __ (n :: HNat) x r => Proxy n -> [x] -> Record r-listToRecN n xs = Record $ hMap2 NoLabel $ flip evalState xs $ hSequence $ hReplicate n comp+listToRecN n xs = Record $ hMap NoLabel $ flip evalState xs $ hSequence $ hReplicate n comp where comp = do x : xs' <- get return () :: State [x] ()@@ -20,11 +19,11 @@ type ListToRecN __ (n :: HNat) x r = (HReplicate n (StateT [x] Identity x), HSequence (StateT [x] Identity) (HReplicateR n (StateT [x] Identity x)) __,- HMap2 NoLabel __ r)+ HMapCxt NoLabel (HList __) (HList r) __ r) data NoLabel = NoLabel-instance (LVPair "" a ~ la) => ApplyAB NoLabel a la where- applyAB _ x = LVPair x+instance (Tagged "" a ~ la) => ApplyAB NoLabel a la where+ applyAB _ x = Tagged x
+ src/RlangQQ/ParseKnitted.hs view
@@ -0,0 +1,64 @@+{-# LANGUAGE ViewPatterns #-}+module RlangQQ.ParseKnitted+ (parseKnitted,+ KnitInteraction(..),+ KnitOutput(..)) where+import Data.List.Split hiding (chunk)+import Data.List+import Data.Maybe+import Data.Char++-- | splits up the contents of the .md file generated by @knitr::knit@+parseKnitted file = grp . chunk . lines $ file++data KnitInteraction = KnitInteraction String [KnitOutput]+ deriving (Show)++data KnitOutput+ = KnitAsIs String -- ^ has markup to be interpreted+ | KnitPrint String+ | KnitWarning String+ | KnitError String+ | KnitImage String FilePath -- ^ @@+ deriving (Show)+++chunk ( (stripPrefix "```" -> Just x) : xs) = + let (a,b) = break (isPrefixOf "```") xs+ in (x,a) : chunk b+chunk ("" : xs) = chunk xs+chunk [] = []++-- | depends on knitr's default opts_chunk$get(comment) = "##"+classify :: [String] -> [KnitOutput]+classify (x:xs) | Just r <- stripPrefix "## Warning: " x =+ [KnitWarning (unlines $ r : map (tryStripPrefix "## ") xs)]+classify (x:xs) | Just r <- stripPrefix "## Error: " x =+ [KnitError (unlines $ r : map (tryStripPrefix "## ") xs)]+classify xs = splitImg xs++splitImg (x:xs) | Just r <- stripPrefix "![" x =+ let end s = case splitOn "](" s of+ [a,b] -> Just (KnitImage a b)+ _ -> Nothing+ -- plot(lm(y ~ x)) comes out as  + getCaps [] = []+ getCaps s = mapMaybe end $ splitOn ") ![" (r ++ "![")+ in getCaps r ++ splitImg xs+splitImg [] = []+splitImg xs @ (x : _) | "## " `isPrefixOf` x =+ let (a,b) = span (isPrefixOf "## ") xs+ in KnitPrint (unlines $ map (tryStripPrefix "## ") a) : splitImg b+splitImg xs =+ let (a,b) = break (\x -> any (`isPrefixOf` x) ["![","## "]) xs+ in KnitAsIs (unlines a) : splitImg b++tryStripPrefix p x = fromMaybe x $ stripPrefix p x ++grp (((/= "") -> True, a) : xs) =+ let (b,c) = span ((== "") . fst) xs+ in KnitInteraction (unlines a) (filter notBlank $ concatMap (classify . snd) b) : grp c+grp [] = []++notBlank (KnitPrint x) = not (all isSpace x)+notBlank _ = True