pybis playground localhost.ipynb

       "  'properties': {},\n",
       "  'registrationDate': 1467670542904,\n",
       "  'registrator': None,\n",
       "  'sample': None,\n",
       "  'tags': None,\n",
       "  'type': None},\n",
       " {'@id': 32,\n",
       "  '@type': 'as.dto.dataset.DataSet',\n",
       "  'accessDate': 1467672114927,\n",
       "  'children': None,\n",
       "  'code': '20160705000958412-193',\n",
       "  'components': None,\n",
       "  'containers': None,\n",
       "  'dataProducer': None,\n",
       "  'dataProductionDate': None,\n",
       "  'dataStore': None,\n",
       "  'experiment': None,\n",
       "  'fetchOptions': {'@id': 33,\n",
       "   '@type': 'as.dto.dataset.fetchoptions.DataSetFetchOptions',\n",
       "   'cacheMode': 'NO_CACHE',\n",
       "   'children': None,\n",
       "   'components': None,\n",
       "   'containers': None,\n",
       "   'count': None,\n",
       "   'dataStore': None,\n",
       "   'experiment': None,\n",
       "   'from': None,\n",
       "   'history': None,\n",
       "   'linkedData': None,\n",
       "   'materialProperties': None,\n",
       "   'modifier': None,\n",
       "   'parents': None,\n",
       "   'physicalData': None,\n",
       "   'properties': 10,\n",
       "   'registrator': None,\n",
       "   'sample': None,\n",
       "   'sort': None,\n",
       "   'sortBy': None,\n",
       "   'tags': None,\n",
       "   'type': None},\n",
       "  'history': None,\n",
       "  'linkedData': None,\n",
       "  'materialProperties': None,\n",
       "  'measured': True,\n",
       "  'modificationDate': 1467670202154,\n",
       "  'modifier': None,\n",
       "  'parents': None,\n",
       "  'permId': {'@id': 34,\n",
       "   '@type': 'as.dto.dataset.id.DataSetPermId',\n",
       "   'permId': '20160705000958412-193'},\n",
       "  'physicalData': None,\n",
       "  'postRegistered': True,\n",
       "  'properties': {},\n",
       "  'registrationDate': 1467670202154,\n",
       "  'registrator': None,\n",
       "  'sample': None,\n",
       "  'tags': None,\n",
       "  'type': None}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.dataSets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## working with datasets and files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>properties</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20160705105115020-196</td>\n",
       "      <td>{'DESCRIPTION': 'This is a new description of ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20160705105113015-195</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20160705171928021-202</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20160705175418011-204</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20160705001541496-194</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20160705000958412-193</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    code                                         properties\n",
       "0  20160705105115020-196  {'DESCRIPTION': 'This is a new description of ...\n",
       "1  20160705105113015-195                                                 {}\n",
       "2  20160705171928021-202                                                 {}\n",
       "3  20160705175418011-204                                                 {}\n",
       "4  20160705001541496-194                                                 {}\n",
       "5  20160705000958412-193                                                 {}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.get_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1 = o.get_dataset('20160705105115020-196')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['original/1468931666.06/Untitled.ipynb',\n",
       " 'original/1468931666.53/Untitled.ipynb']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1.file_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds1.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Untitled.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "ls localhost/20160705105115020-196/original/1468931666.06/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis ##\n",
    "An analysis has a name, description and parent dataset. Anytime it is saved, a new version of it is saved. On openBIS, an analysis is a container object which contains the result datafiles and the jupyter notebook. If an analysis is saved a second time, two new datasets are generated, both having parents of their previous datasets set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20160630105337180-82'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = o.get_sample('/VERMEUL/HOW_WONDERFUL2')\n",
    "s1.permid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'@id': 1,\n",
       " '@type': 'QueryTableModel',\n",
       " 'columns': [{'@id': 2,\n",
       "   '@type': 'QueryTableColumn',\n",
       "   'dataType': 'STRING',\n",
       "   'title': 'STATUS'},\n",
       "  {'@id': 3,\n",
       "   '@type': 'QueryTableColumn',\n",
       "   'dataType': 'STRING',\n",
       "   'title': 'MESSAGE'},\n",
       "  {'@id': 4,\n",
       "   '@type': 'QueryTableColumn',\n",
       "   'dataType': 'STRING',\n",
       "   'title': 'RESULT'}],\n",
       " 'rows': [[{'@id': 5, 'type': 'STRING', 'value': 'OK'},\n",
       "   {'@id': 6, 'type': 'STRING', 'value': 'Dataset registration successful'},\n",
       "   {'@id': 7, 'type': 'STRING', 'value': ''}]]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o.new_analysis(\n",
    "    name='my_delightful_analysis', \n",
    "    description='ave, some!', \n",
    "    sample = s1,\n",
    "    notebook_files=['pybis playground localhost.ipynb'],\n",
    "    result_files=['testfile.txt']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Semantic Annotation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create semantic annotation for sample type\n",
    "o.new_semantic_annotation(entityType = 'UNKNOWN')\n",
    "\n",
    "# create semantic annotation for property type\n",
    "o.new_semantic_annotation(propertyType = 'DESCRIPTION')\n",
    "\n",
    "# create semantic annotation for sample property assignment\n",
    "o.new_semantic_annotation(entityType = 'UNKNOWN', propertyType = 'DESCRIPTION')\n",
    "\n",
    "# create semantic annotation with additional fields\n",
    "o.new_semantic_annotation(entityType = 'UNKNOWN', \n",
    "                          predicateOntologyId = 'po_id', \n",
    "                          predicateOntologyVersion = 'po_version', \n",
    "                          predicateAccessionId = 'pa_id',\n",
    "                          descriptorOntologyId = 'do_id',\n",
    "                          descriptorOntologyVersion = 'do_version',\n",
    "                          descriptorAccessionId = 'da_id')\n",
    "\n",
    "# get all semantic annotations\n",
    "o.get_semantic_annotations()\n",
    "\n",
    "# get semantic annotation by perm id\n",
    "sa = o.get_semantic_annotation(\"20171015135637955-30\")\n",
    "\n",
    "# update semantic annotation\n",
    "sa.predicateOntologyId = 'new_po_id'\n",
    "sa.descriptorOntologyId = 'new_do_id'\n",
    "sa.save()\n",
    "                          \n",
    "# delete semantic annotation\n",
    "sa.delete('reason')"
   ]
  }
 ],
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