{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 - Introduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mdf_forge.forge import Forge  # This is the only required import for Forge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authentication\n",
    "Authentication is handled automatically. Just follow the prompt once and let Forge take care of the rest.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can set up Forge with no arguments. Forge will automatically authenticate and connect to MDF.\n",
    "mdf = Forge()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Queries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic full text search\n",
    "Using the `search()` method, you can perform a basic text search of the data in MDF.\n",
    "You will get back a list of matching entries (up to 10,000).\n",
    "\n",
    "Let's say we want to find data on aluminum. We can just search for \"Al\" like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'files': [{'data_type': 'ASCII text, with very long lines, with no line terminators',\n",
       "   'filename': 'nist_xps_41530.json',\n",
       "   'globus': 'globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/nist_xps_db_v1/nist_xps_41530.json',\n",
       "   'length': 1248,\n",
       "   'mime_type': 'text/plain',\n",
       "   'sha512': '69912ca91261bba53dc0df956338baebf81a3f9d1281f4e9108200c3b8473f073ffdff7437a55c8ac3d08d40074a68a5509bbeb1a391426f838427398f3963dd',\n",
       "   'url': 'https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org/MDF/mdf_connect/prod/data/nist_xps_db_v1/nist_xps_41530.json'}],\n",
       " 'material': {'composition': 'Al', 'elements': ['Al']},\n",
       " 'mdf': {'ingest_date': '2018-11-06T16:57:59.847843Z',\n",
       "  'mdf_id': '5be1c83f2ef3883312753d4a',\n",
       "  'parent_id': '5be1c8172ef388331274efdf',\n",
       "  'resource_type': 'record',\n",
       "  'scroll_id': 19819,\n",
       "  'source_id': 'nist_xps_db_v1',\n",
       "  'source_name': 'nist_xps_db',\n",
       "  'version': 1},\n",
       " 'nist_xps_db': {'binding_energy_ev': '72.5',\n",
       "  'energy_uncertainty_ev': '',\n",
       "  'notes': 'Al(111).',\n",
       "  'temperature_k': '300'}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mdf.search(\"Al\")\n",
    "res[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advanced-mode searches\n",
    "You can also query more precisely with the `advanced=True` argument. The basic use is the form `key.subkey:value`. The full documentation for the query syntaz can be found here: http://globus-search-docs.s3-website-us-east-1.amazonaws.com/stable/api/search.html#_query_syntax\n",
    "\n",
    "In this example, we can search for \"Al\" inside the \"mdf.elements\" key.\n",
    "\n",
    "We're also going to limit the number of results to 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cip': {'bv': '79.0',\n",
       "  'energy': '-3.36',\n",
       "  'forcefield': 'Al99.eam.alloy',\n",
       "  'gv': '29.4',\n",
       "  'mpid': 'mp-134',\n",
       "  'totenergy': '-107.52'},\n",
       " 'files': [{'data_type': 'ASCII text, with very long lines, with no line terminators',\n",
       "   'filename': 'classical_interatomic_potentials.json',\n",
       "   'globus': 'globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/cip_v1/classical_interatomic_potentials.json',\n",
       "   'length': 1841203,\n",
       "   'mime_type': 'text/plain',\n",
       "   'sha512': '96635ee0c15d1d0187b18805653a02b1a6dfa5648db82153467045de18adcc08c753e2897d2b48a78a2167a442219e9aeff6b1103732c2158facac8fa4911b33',\n",
       "   'url': 'https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org/MDF/mdf_connect/prod/data/cip_v1/classical_interatomic_potentials.json'}],\n",
       " 'material': {'composition': 'Al32', 'elements': ['Al']},\n",
       " 'mdf': {'ingest_date': '2018-10-29T17:47:57.468388Z',\n",
       "  'mdf_id': '5bd747cf2ef3880b0f213904',\n",
       "  'parent_id': '5bd747cd2ef3880b0f2135d1',\n",
       "  'resource_type': 'record',\n",
       "  'scroll_id': 819,\n",
       "  'source_id': 'cip_v1',\n",
       "  'source_name': 'cip',\n",
       "  'version': 1}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mdf.search(\"material.elements:Al\", advanced=True, limit=10)\n",
    "res[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to search on a value with special characters, such as a colon or space, you must wrap the value in double quotes. Otherwise, you may get unexpected results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': {'endpoint_path': 'globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/ab_initio_solute_database_v1-2/',\n",
       "  'link': 'https://www.globus.org/app/transfer?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=/MDF/mdf_connect/prod/data/ab_initio_solute_database_v1-2/'},\n",
       " 'dc': {'contributors': [{'affiliations': ['University of Wisconsin-Madison'],\n",
       "    'contributorName': 'Morgan, Dane',\n",
       "    'contributorType': 'ContactPerson',\n",
       "    'familyName': 'Morgan',\n",
       "    'givenName': 'Dane'}],\n",
       "  'creators': [{'affiliations': ['University of Wisconsin-Madison'],\n",
       "    'creatorName': 'Morgan, Dane',\n",
       "    'familyName': 'Morgan',\n",
       "    'givenName': 'Dane'},\n",
       "   {'affiliations': ['University of Wisconsin-Madison'],\n",
       "    'creatorName': 'Mayeshiba, Tam',\n",
       "    'familyName': 'Mayeshiba',\n",
       "    'givenName': 'Tam'},\n",
       "   {'affiliations': ['University of Wisconsin-Madison'],\n",
       "    'creatorName': 'Henry, Wu',\n",
       "    'familyName': 'Henry',\n",
       "    'givenName': 'Wu'}],\n",
       "  'dates': [{'date': '2017-08-07T16:07:32.938812Z', 'dateType': 'Collected'}],\n",
       "  'descriptions': [{'description': 'We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity.',\n",
       "    'descriptionType': 'Other'}],\n",
       "  'publicationYear': '2016',\n",
       "  'publisher': 'MDF (placeholder)',\n",
       "  'relatedIdentifiers': [{'relatedIdentifier': 'http://dx.doi.org/10.1038/sdata.2016.54',\n",
       "    'relatedIdentifierType': 'DOI',\n",
       "    'relationType': 'IsPartOf'}],\n",
       "  'resourceType': {'resourceType': 'JSON', 'resourceTypeGeneral': 'Dataset'},\n",
       "  'subjects': [{'subject': 'dilute'},\n",
       "   {'subject': 'solute'},\n",
       "   {'subject': 'DFT'},\n",
       "   {'subject': 'diffusion'},\n",
       "   {'subject': 'dataset'}],\n",
       "  'titles': [{'title': 'High-throughput Ab-initio Dilute Solute Diffusion Database'}]},\n",
       " 'mdf': {'ingest_date': '2018-11-24T08:12:11.852893Z',\n",
       "  'mdf_id': '5bf907db2ef3885ee1191ae0',\n",
       "  'resource_type': 'dataset',\n",
       "  'scroll_id': 0,\n",
       "  'source_id': 'ab_initio_solute_database_v1-2',\n",
       "  'source_name': 'ab_initio_solute_database',\n",
       "  'version': 1},\n",
       " 'services': {'mdf_search': 'This dataset was ingested to MDF Search.'}}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mdf.search('dc.titles.title:\"High-throughput Ab-initio Dilute Solute Diffusion Database\"', advanced=True)\n",
    "res[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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