plot-sourmash-python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a265234f",
   "metadata": {},
   "source": [
    "# Load and plot a sourmash matrix!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae8e03df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sourmash, sourmash.fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c8752c",
   "metadata": {},
   "source": [
    "## Load matrix and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "001888c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat, labels = sourmash.fig.load_matrix_and_labels('compare.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945f5e3b",
   "metadata": {},
   "source": [
    "## Plot using the internal sourmash code\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00bf2d39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 792x576 with 3 Axes>,\n",
       " ['NC_010655.1 Akkermansia muciniphila ATCC BAA-835, complete sequence',\n",
       "  'NC_011663.1 Shewanella baltica OS223, complete sequence',\n",
       "  'NC_009665.1 Shewanella baltica OS185, complete sequence'],\n",
       " array([[1.       , 0.       , 0.       ],\n",
       "        [0.       , 1.       , 0.3206949],\n",
       "        [0.       , 0.3206949, 1.       ]]))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sourmash.fig.plot_composite_matrix(mat, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a2bf9fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38arm",
   "language": "python",
   "name": "py38arm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}