{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Profile plots\n",
    "=============\n",
    "\n",
    "Profile plots are a useful way to compare multiple sets of per-nucleotide data,\n",
    "similar to [skyline plots](skyline-plots.md). Unlike skyline plots, values are\n",
    "displayed using a color-coded bar graph, like ShapeMapper plots, with sequences\n",
    "and sequence annotations along the x-axis. Unlike ShapeMapper plots, this are\n",
    "more flexible, and color-coding can be defined in the data class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1645x996.8 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 541.935x176.159 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rnavigate as rnav\n",
    "from rnavigate.examples import rnasep_1, rnasep_2\n",
    "\n",
    "plot = rnav.plot_profile(\n",
    "    samples=[rnasep_1, rnasep_2],\n",
    "    profile=\"shapemap\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot_profile in module rnavigate.plotting_functions:\n",
      "\n",
      "plot_profile(samples, profile, sequence=None, annotations=None, domains=None, labels=None, nt_ticks=(20, 5), column=None, plot_error=True, annotations_mode='track', seqbar=True, region='all', colorbars=True, plot_kwargs=None)\n",
      "    Aligns reactivity profiles by sequence and plots them on seperate axes.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    samples : list of rnavigate Samples\n",
      "        samples used to retrieve data\n",
      "    profile : data keyword string or data object\n",
      "        Profile from which values will be plotted\n",
      "    sequence : data keyword str, data obj, or sequence str, defaults to `profile`\n",
      "        All data are mapped to this sequence before plotting\n",
      "        If a data keyword, data from the first sample will be used\n",
      "    annotations : list of data keyword strings or data objects, defaults to []\n",
      "        Annotations used to highlight regions or sites of interest\n",
      "    domains : data keyword string or data object, defaults to None\n",
      "        domains to label along x-axis\n",
      "    labels : list of strings, defaults to sample.sample for each sample\n",
      "        list containing Labels to be used in plot legends\n",
      "    nt_ticks : tuple of two integers, defaults to (20, 5)\n",
      "        first integer is the gap between major tick marks\n",
      "        second integer is the gap between minor tick marks\n",
      "    column : string, defaults to profile.metric\n",
      "        column name of values from profile to plot\n",
      "    plot_error : bool, defaults to True\n",
      "        Whether to plot error bars, values are determined by profile.metric\n",
      "    annotations_mode : \"track\" or \"bars\", defaults to \"track\"\n",
      "        \"track\" will highlight annotations along the x-axis\n",
      "        \"bars\" will use a vertical transparent bar over the plot\n",
      "    seqbar : bool, defaults to ``True``\n",
      "        whether to display the sequence along the x-axis\n",
      "    region : list of 2 integers, defaults to [1, length of sequence]\n",
      "        start and end positions to plot. 1-indexed, inclusive.\n",
      "    colorbars : bool, defaults to True\n",
      "        Whether to plot color scales for per-nucleotide data\n",
      "    plot_kwargs : dictionary, defaults to {}\n",
      "        Keyword-arguments passed to matplotlib.pyplot.subplots\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    rnavigate.plots.Profile\n",
      "        the Profile plot object\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(rnav.plot_profile)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "RNAvigate",
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}