Doc/login_and_searching.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BacDivePy\n",
    "from  bacdive.DSMZClient import DSMZ_login\n",
    "from  bacdive.DSMZClient import retrieve\n",
    "#from  bacdive.utils import pullopt\n",
    "\n",
    "#utils\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#plot style\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "#test style\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"<style>div.cell { /* Tunes the space between cells */margin-top:1em;margin-bottom:1em;}div.text_cell_render h1 { /* Main titles bigger, centered */\n",
    "font-size: 3em;line-height:1.4em;text-align:center;}\n",
    "div.text_cell_render h2 { /*  Parts names nearer from text */margin-bottom: -0.4em;}\n",
    "div.text_cell_render { /* Customize text cells */font-family: 'Times New Roman';font-size:1.2em;line-height:1.4em;\n",
    "padding-left:3em;padding-right:3em;}</style>\"\"\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This python package is an interface for the [Bacterial\n",
    "Diversity Metadatabase][BD] ([Söhngen et al. 2014 & 2016\n",
    "](#references)) of the [DSMZ (German Collection of Microorganisms and Cell Cultures)][DMSZ].\n",
    "\n",
    "[BD]: https://bacdive.dsmz.de/\n",
    "[DMSZ]: https://www.dsmz.de/about-us.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Login\n",
    "\n",
    "To login, you must be [registered][reg] for the BacDive API. Once you have your login credentials you may use them below. \n",
    "\n",
    "\n",
    "[reg]: https://bacdive.dsmz.de/api/bacdive/registration/register/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "DSMZ_login('username@email.com',password='friend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## There are four methods to search the BacDive Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Species-Level Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: Meiothermus hypogaeus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: Megasphaera elsdenii\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.16it/s]\n"
     ]
    }
   ],
   "source": [
    "species_search_data = retrieve(['Meiothermus hypogaeus',\n",
    "                                'Megasphaera elsdenii'],\n",
    "                               search_type='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genus-Level Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: Meiothermus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18/18 [00:16<00:00,  1.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: Megasphaera\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11/11 [00:09<00:00,  1.15it/s]\n"
     ]
    }
   ],
   "source": [
    "genus_search_data = retrieve(['Meiothermus',\n",
    "                              'Megasphaera'],\n",
    "                             search_type='genus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DSMZ Culture Number Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: DSM 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.80s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: DSM 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.15it/s]\n"
     ]
    }
   ],
   "source": [
    "DSMZnum_search_data = retrieve(['DSM 1',\n",
    "                                'DSM 2'],\n",
    "                               search_type='culture no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence Accession Number Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: KT935587\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:00<00:00,  1.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: ALAS01000001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.52s/it]\n"
     ]
    }
   ],
   "source": [
    "Acc_search_data = retrieve(['KT935587',\n",
    "                            'ALAS01000001'],\n",
    "                            search_type='sequence accession number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data format\n",
    "\n",
    "All searches output a multilevel indexed pandas dataframe with the DSMZ culture IDs associated with the search in the columns. The levels of the index are the:\n",
    "\n",
    "Sections ( there are 8 sections ) \n",
    "- application_interaction\n",
    "- culture_growth_condition\n",
    "- environment_sampling_isolation_source\n",
    "- molecular_biology\n",
    "- morphology_physiology\n",
    "- references\n",
    "- strain_availability\n",
    "- taxonomy_name\n",
    "\n",
    "Within that subsection there are several subsections and within those subsections, there are various fields.\n",
    "\n",
    "To view the subsections:\n",
    "- set(df.index.get_level_values('Subsection'))\n",
    "\n",
    "To view the Fields:\n",
    "- set(df.index.get_level_values('Field'))\n",
    "\n",
    "If a certain field needs to be indexed it can be done by calling:\n",
    "\n",
    "- df.loc([Section_value,Subsection_value,Field_value])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: Bacillus halodurans\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:19<00:00,  1.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving information on: Aneurinibacillus thermoaerophilus\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:13<00:00,  1.16it/s]\n"
     ]
    }
   ],
   "source": [
    "species_search_data = retrieve(['Bacillus halodurans',\n",
    "                                'Aneurinibacillus thermoaerophilus'],\n",
    "                                 search_type='species')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values of the dataframe will be lists. This is because there can be multiple entries for many of the values. However, it is fairly trivial to subset and then flatten this data. For example, let's explore the recorded culture temperatures at the genus level from the data we retrieved above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Opt. Culture Temp. $C^{o}$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DSMZ_1117</td>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSMZ_1114</td>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSMZ_1118</td>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSMZ_1119</td>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DSMZ_1116</td>\n",
       "      <td>Bacillus halodurans</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Species  Opt. Culture Temp. $C^{o}$\n",
       "DSMZ_1117  Bacillus halodurans                          30\n",
       "DSMZ_1114  Bacillus halodurans                          30\n",
       "DSMZ_1118  Bacillus halodurans                          30\n",
       "DSMZ_1119  Bacillus halodurans                          30\n",
       "DSMZ_1116  Bacillus halodurans                          30"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Species level taxonomy\n",
    "gtax = pd.DataFrame(dict(species_search_data.loc['taxonomy_name','strains','species']))\n",
    "#temp\n",
    "temps = species_search_data.loc[[('taxonomy_name','strains','species'),\n",
    "                                 ('culture_growth_condition','culture_temp','temp')],:]\n",
    "temps = temps.reset_index(drop=True).T.dropna()\n",
    "temps = temps.apply(lambda s: list(itertools.chain(*s.values)), axis=0)\n",
    "temps.columns = ['Species','Opt. Culture Temp. $C^{o}$']\n",
    "temps.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"axes.titlesize\"] = 40\n",
    "plt.rcParams[\"axes.labelsize\"] = 18\n",
    "plt.rcParams['xtick.labelsize'] = 16\n",
    "plt.rcParams['ytick.labelsize'] = 16\n",
    "\n",
    "#now we can plot the data\n",
    "fig,ax=plt.subplots(1,1,figsize=(10,4))\n",
    "sns.boxplot(y='Opt. Culture Temp. $C^{o}$',x='Species',data=temps,ax=ax)\n",
    "sns.swarmplot(y='Opt. Culture Temp. $C^{o}$',x='Species',data=temps,ax=ax)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}