Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# %load /Users/hwayment/ipynb_defaults.py\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%pylab inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('ticks')\n",
    "sns.set_context('paper')\n",
    "\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "from glob import glob\n",
    "import os, sys, pickle, requests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from RiboGraphViz import RGV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1a243b4fd0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from arnie.mfe import mfe\n",
    "\n",
    "# rg_objs = []\n",
    "# for i in range(3):\n",
    "#     rand_rna = ''.join(np.random.choice(['A','C','G','U'], size=30))\n",
    "#     new_struct = mfe(rand_rna)\n",
    "    \n",
    "#     rg_objs.append(RGV(new_struct))\n",
    "\n",
    "\n",
    "figure(figsize=(20,20))\n",
    "\n",
    "xptr,yptr=0,0\n",
    "\n",
    "\n",
    "\n",
    "for i, obj in enumerate(rg_objs):\n",
    "    obj.draw(align=True, x0=xptr, y0=yptr)\n",
    "    text(xptr,yptr-5,\"HI\",fontsize=20)\n",
    "        \n",
    "    ax = gca()\n",
    "    \n",
    "    xmin, xmax = ax.get_xlim()\n",
    "    \n",
    "    xptr = xmax\n",
    "    \n",
    "ax.set_xlim([-5,xmax])\n",
    "axhline(0)\n",
    "\n",
    "    #ax.set_anchor('NW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_anchor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manchor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshare\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Define the anchor location.\n",
       "\n",
       "The actual drawing area (active position) of the Axes may be smaller\n",
       "than the Bbox (original position) when a fixed aspect is required. The\n",
       "anchor defines where the drawing area will be located within the\n",
       "available space.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "anchor : 2-tuple of floats or {'C', 'SW', 'S', 'SE', ...}\n",
       "    The anchor position may be either:\n",
       "\n",
       "    - a sequence (*cx*, *cy*). *cx* and *cy* may range from 0\n",
       "      to 1, where 0 is left or bottom and 1 is right or top.\n",
       "\n",
       "    - a string using cardinal directions as abbreviation:\n",
       "\n",
       "      - 'C' for centered\n",
       "      - 'S' (south) for bottom-center\n",
       "      - 'SW' (south west) for bottom-left\n",
       "      - etc.\n",
       "\n",
       "      Here is an overview of the possible positions:\n",
       "\n",
       "      +------+------+------+\n",
       "      | 'NW' | 'N'  | 'NE' |\n",
       "      +------+------+------+\n",
       "      | 'W'  | 'C'  | 'E'  |\n",
       "      +------+------+------+\n",
       "      | 'SW' | 'S'  | 'SE' |\n",
       "      +------+------+------+\n",
       "\n",
       "share : bool, optional\n",
       "    If ``True``, apply the settings to all shared Axes.\n",
       "    Default is ``False``.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "matplotlib.axes.Axes.set_aspect\n",
       "    for a description of aspect handling.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/miniconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax.set_anchor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2476e8d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg_objs[0].draw(align=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}