{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intermine-Python: Tutorial 9: Creating Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will talk about how you can create and save lists in your account. Like the previous tutorial, you will need to provide your login information while creating a service object. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can either provide a list of identifiers (for example, gene symbols) and the Intermine Server will try to resolve them into objects in the database or you provide a query that specifies exactly what you want. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from intermine.webservice import Service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter your login information, uncomment the line of code and then run it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#service=Service(\"www.flymine.org/flymine/service\",username=\"Enter username\",password=\"Enter password\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will look at three methods in this tutorial. Let's say you want to upload a list of strings (Gene Symbols). We begin by declaring a list, which I've called symbols. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "symbols=[\"eve\",\"zen\",\"rudimentary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can use their secondary identifiers or DB identifiers instead if you're more comfortable with that. So if you changed symbols to [\"CG2328\",\"zen\",\"rudimentary\"] or [\"eve\",\"FBgn0004053\",\"rudimentary] it would not be an issue as Intermine would be able to resolve it for you. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a list, you need to declare a list manager object. We then use the create_list method as shown below. Remember to define a name for the list, otherwise the list will be lost once the session will be terminated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lm=service.list_manager()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<intermine.lists.list.List at 0x7fec5547f4e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.create_list(content=symbols,list_type=\"Gene\",name=\"my list\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now look at how you can save a list from a query. Suppose that you want to extract only the information regarding the gene symbol \"eve\" stored in the previous list that we uploaded. You can do it as shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "query=service.new_query(\"Gene\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ListConstraint: Gene IN my list>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query.add_constraint(\"Gene\",\"IN\",\"my list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BinaryConstraint: Gene.symbol = eve>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query.add_constraint(\"symbol\",\"=\",\"eve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<intermine.lists.list.List at 0x7fec554874a8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.create_list(query,name=\"my list 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's say that you have a file with a list of gene identifiers stored on your machine, which you want to upload as a list. Change the string stored to match the path location of your file. Uncomment the lines of code and run it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#file=\"Enter/File/Path.tsv\"\n",
    "#lm.create_list(file,list_type=\"Gene\",name=\"my list 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to view the names of all the lists available, use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['PL FlyAtlas_brain_top', 'second list', 'PL FlyTF_putativeTFs', 'H. sapiens orthologues of FlyTF_site_specific_1', 'esyN demo list', 'my list', 'first list', 'PL FlyAtlas_maleglands_top', 'my_list_1', 'PL FlyAtlas_hindgut_top', 'my list 2', 'PL FlyTF_site_specific_TFs'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.get_all_list_names()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}