{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-ticks')\n",
    "from matplotlib import transforms\n",
    "import matplotlib.patheffects\n",
    "import numpy as np\n",
    "\n",
    "COLOR_SCHEME = {'G': 'orange', \n",
    "                'A': 'red', \n",
    "                'C': 'blue', \n",
    "                'T': 'darkgreen'}\n",
    "BASES = list(COLOR_SCHEME.keys())\n",
    "\n",
    "ALL_SCORES1 = [[('C', 0.02247014831444764),\n",
    "              ('T', 0.057903843733384308),\n",
    "              ('A', 0.10370837683591219),\n",
    "              ('G', 0.24803586793255664)],\n",
    "             [('T', 0.046608227674354567),\n",
    "              ('G', 0.048827667087419063),\n",
    "              ('A', 0.084338697696451109),\n",
    "              ('C', 0.92994511407402669)],\n",
    "             [('G', 0.0),\n",
    "              ('T', 0.011098351287382456),\n",
    "              ('A', 0.022196702574764911),\n",
    "              ('C', 1.8164301607015951)],\n",
    "             [('C', 0.020803153636453006),\n",
    "              ('T', 0.078011826136698756),\n",
    "              ('G', 0.11268374886412044),\n",
    "              ('A', 0.65529933954826969)],\n",
    "             [('T', 0.017393530660176126),\n",
    "              ('A', 0.030438678655308221),\n",
    "              ('G', 0.22611589858228964),\n",
    "              ('C', 0.45078233627623127)],\n",
    "             [('G', 0.022364103549245576),\n",
    "              ('A', 0.043412671595594352),\n",
    "              ('T', 0.097349627214363091),\n",
    "              ('C', 0.1657574733649966)],\n",
    "             [('C', 0.03264675899941203),\n",
    "              ('T', 0.045203204768416654),\n",
    "              ('G', 0.082872542075430544),\n",
    "              ('A', 1.0949220710572034)],\n",
    "             [('C', 0.0),\n",
    "              ('T', 0.0076232429756614498),\n",
    "              ('A', 0.011434864463492175),\n",
    "              ('G', 1.8867526364762088)],\n",
    "             [('C', 0.0018955903000026028),\n",
    "              ('T', 0.0094779515000130137),\n",
    "              ('A', 0.35637097640048931),\n",
    "              ('G', 0.58005063180079641)],\n",
    "             [('A', 0.01594690817903021),\n",
    "              ('C', 0.017541598996933229),\n",
    "              ('T', 0.2774762023151256),\n",
    "              ('G', 0.48638069946042134)],\n",
    "             [('A', 0.003770051401807444),\n",
    "              ('C', 0.0075401028036148881),\n",
    "              ('T', 0.011310154205422331),\n",
    "              ('G', 1.8624053924928772)],\n",
    "             [('C', 0.036479877757360731),\n",
    "              ('A', 0.041691288865555121),\n",
    "              ('T', 0.072959755514721461),\n",
    "              ('G', 1.1517218549109602)],\n",
    "             [('G', 0.011831087684038642),\n",
    "              ('T', 0.068620308567424126),\n",
    "              ('A', 0.10174735408273231),\n",
    "              ('C', 1.0009100180696691)],\n",
    "             [('C', 0.015871770937774379),\n",
    "              ('T', 0.018757547471915176),\n",
    "              ('A', 0.32176408355669878),\n",
    "              ('G', 0.36505073156881074)],\n",
    "             [('A', 0.022798100897300954),\n",
    "              ('T', 0.024064662058262118),\n",
    "              ('G', 0.24571286522646588),\n",
    "              ('C', 0.34070495229855319)]]\n",
    "\n",
    "ALL_SCORES2 = [[('A', 0.01653482213365913),\n",
    "              ('G', 0.026710097292833978),\n",
    "              ('C', 0.035613463057111966),\n",
    "              ('T', 0.057235922770358522)],\n",
    "             [('C', 0.020055669245080433),\n",
    "              ('G', 0.023816107228533015),\n",
    "              ('A', 0.031336983195438178),\n",
    "              ('T', 0.058913528407423782)],\n",
    "             [('T', 0.018666958185377256),\n",
    "              ('G', 0.084001311834197651),\n",
    "              ('A', 0.093334790926886277),\n",
    "              ('C', 0.30333807051238043)],\n",
    "             [('C', 0.0),\n",
    "              ('G', 0.0),\n",
    "              ('A', 0.32027512306044359),\n",
    "              ('T', 0.82203948252180525)],\n",
    "             [('C', 0.012698627658037786),\n",
    "              ('A', 0.053334236163758708),\n",
    "              ('T', 0.096509570201087178),\n",
    "              ('G', 0.10920819785912497)],\n",
    "             [('C', 0.0),\n",
    "              ('G', 0.089472611853783468),\n",
    "              ('A', 0.1930724782107959),\n",
    "              ('T', 0.22132698721725386)],\n",
    "             [('C', 0.020962390607965918),\n",
    "              ('A', 0.026202988259957396),\n",
    "              ('G', 0.066380903591892068),\n",
    "              ('T', 0.07336836712788071)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.10236420974570831),\n",
    "              ('C', 0.15354631461856247),\n",
    "              ('T', 0.29173799777526871)],\n",
    "             [('G', 0.027681850851852024),\n",
    "              ('C', 0.089966015268519078),\n",
    "              ('A', 0.089966015268519078),\n",
    "              ('T', 0.53287562889815143)],\n",
    "             [('A', 0.034165612000664765),\n",
    "              ('C', 0.06833122400132953),\n",
    "              ('G', 0.072601925501412631),\n",
    "              ('T', 0.28186629900548432)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.037325935579058833),\n",
    "              ('C', 0.23328709736911771),\n",
    "              ('T', 0.72785574379164719)],\n",
    "             [('A', 0.017470244196759552),\n",
    "              ('C', 0.062892879108334396),\n",
    "              ('G', 0.094339318662501587),\n",
    "              ('T', 0.19916078384305891)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.096447131567581681),\n",
    "              ('C', 0.15844885900388422),\n",
    "              ('T', 0.48223565783790845)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.069291952024925829),\n",
    "              ('C', 0.20787585607477749),\n",
    "              ('T', 0.46425607856700307)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.0),\n",
    "              ('C', 0.21713201856318373),\n",
    "              ('T', 1.1495224512168551)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.048934292002649343),\n",
    "              ('T', 0.27263391258618919),\n",
    "              ('C', 0.42642740173737281)],\n",
    "             [('A', 0.0),\n",
    "              ('G', 0.053607190685875404),\n",
    "              ('C', 0.2054942309625224),\n",
    "              ('T', 0.69689347891638032)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.0),\n",
    "              ('C', 0.31312908494534769),\n",
    "              ('T', 0.84220926295645249)],\n",
    "             [('G', 0.0),\n",
    "              ('C', 0.068079835765814778),\n",
    "              ('A', 0.068079835765814778),\n",
    "              ('T', 1.3207488138568066)],\n",
    "             [('G', 0.020257705570431345),\n",
    "              ('A', 0.020257705570431345),\n",
    "              ('C', 0.048618493369035232),\n",
    "              ('T', 0.055371061892512348)],\n",
    "             [('G', 0.0),\n",
    "              ('A', 0.076286510680262556),\n",
    "              ('C', 0.20538675952378382),\n",
    "              ('T', 0.34622339462580698)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Scale(matplotlib.patheffects.RendererBase):\n",
    "    def __init__(self, sx, sy=None):\n",
    "        self._sx = sx\n",
    "        self._sy = sy\n",
    "\n",
    "    def draw_path(self, renderer, gc, tpath, affine, rgbFace):\n",
    "        affine = affine.identity().scale(self._sx, self._sy)+affine\n",
    "        renderer.draw_path(gc, tpath, affine, rgbFace)\n",
    "\n",
    "def draw_logo(all_scores):\n",
    "    fig = plt.figure()\n",
    "    fig.set_size_inches(len(all_scores),2.5)\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.set_xticks(range(len(all_scores)))\n",
    "\n",
    "    xshift = 0\n",
    "    trans_offset = transforms.offset_copy(ax.transAxes, \n",
    "                                      fig=fig, \n",
    "                                      x=0, \n",
    "                                      y=0, \n",
    "                                      units='points')\n",
    "\n",
    "   \n",
    "    for scores in all_scores:\n",
    "        yshift = 0\n",
    "        for base, score in scores:\n",
    "            txt = ax.text(0, \n",
    "                          0, \n",
    "                          base, \n",
    "                          transform=trans_offset,\n",
    "                          fontsize=80, \n",
    "                          color=COLOR_SCHEME[base],\n",
    "                          weight='bold',\n",
    "                          ha='center',\n",
    "                          family='sans-serif'\n",
    "                          )\n",
    "            txt.set_clip_on(False) \n",
    "            txt.set_path_effects([Scale(1.0, score)])\n",
    "            fig.canvas.draw()\n",
    "            window_ext = txt.get_window_extent(txt._renderer)\n",
    "            yshift = window_ext.height*score\n",
    "            trans_offset = transforms.offset_copy(txt._transform, fig=fig, y=yshift, units='points')\n",
    "        xshift += window_ext.width\n",
    "        trans_offset = transforms.offset_copy(ax.transAxes, fig=fig, x=xshift, units='points')\n",
    "\n",
    "\n",
    "    ax.set_yticks(range(0,3))\n",
    "\n",
    "\n",
    "    seaborn.despine(ax=ax, offset=30, trim=True)\n",
    "    ax.set_xticklabels(range(1,len(all_scores)+1), rotation=90)\n",
    "    ax.set_yticklabels(np.arange(0,3,1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Okayish logo\n",
    "The ticks are off by a bit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mm9xPXA63vDw47DAzl+300/3ORsRfmmIiTv30E/ztb9CxIwwfDm+8EZ4CYk2+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\nX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GVwYBHRDy+//DIOHjwIwH+UnCAI2Lt3b6iGFXFYLBYkJSWd+1kQBDz11FPQ6XR44okn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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f568db2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_logo(ALL_SCORES1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Poorly aligned ticks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GvZZtarJzfl8F7ffaS/rJT2wKhVyMn+T7FXyOY31zv/ud9Ic/SCtX+tveuHFW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\no+wQ4JY2Se3oKypKXI8kX5u46+5AnuTW0WGdDW7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f567c8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_logo(ALL_SCORES2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How they should ideally look like:\n",
    "Plotted using [meme-suite](http://meme-suite.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](./meme_out/logo1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](./meme_out/logo2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:clipseq]",
   "language": "python",
   "name": "conda-env-clipseq-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}