figures/simulation_figure.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from deepom.figures import *\n",
    "from matplotlib_scalebar.scalebar import ScaleBar\n",
    "\n",
    "data = Images_FigureData.load_figure_data()\n",
    "\n",
    "data_item = data.data_item\n",
    "item = data.item\n",
    "refs = data.refs\n",
    "inference_item = data.inference_item\n",
    "\n",
    "scalebar_scale = 130e-9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "deepom.bionano_compare.AlignmentItem"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(item)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "_, axs = pyplot.subplots(figsize=(7, 4), dpi=200, nrows=1)\n",
    "\n",
    "image = data_item.image[0]\n",
    "labeled_pos = data_item.labeled_coords.T[1]\n",
    "\n",
    "pyplot.imshow(image, cmap=\"gray\")\n",
    "objs = pyplot.eventplot([inference_item.loc_pred - .5, labeled_pos - .5], lineoffsets=[-3.5, -1.5], colors=[\"g\", \"b\"],\n",
    "                        linewidths=.5, linelengths=2)\n",
    "pyplot.legend(objs, [\"Simulated ground-truth positions\", \"Neural net predicted localizations\"],\n",
    "              ncol=3, loc=\"upper center\",\n",
    "              fontsize=6,\n",
    "              bbox_to_anchor=(0.5, 1.5)\n",
    "              )\n",
    "set_only_x_visible()\n",
    "set_formatter(lambda x, _: int((x * data_item.scale) / 1000))\n",
    "pyplot.gca().invert_yaxis()\n",
    "pyplot.gca().add_artist(\n",
    "    ScaleBar(scalebar_scale, color=\"w\", box_alpha=0, scale_loc=\"top\", location=\"upper right\", width_fraction=.02,\n",
    "             sep=0))\n",
    "pyplot.xlabel(\"sequence position (kb)\")\n",
    "pyplot.xlim(None, 100)\n",
    "pyplot.ylim(-4.5, None)\n",
    "\n",
    "for ext in [\".pdf\", \".jpg\"]:\n",
    "    pyplot.savefig(Path(\"../../DeepOM-Paper/figures\") / (\"simulation_figure\" + ext), bbox_inches='tight')"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}