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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from bcbio import isatab\n",
      "import synapseclient\n",
      "import tempfile"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#login to synapse\n",
      "syn=synapseclient.Synapse()\n",
      "syn.login()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Welcome, Abhishek Pratap!\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create a project\n",
      "#project = synapseclient.Project('test_isa_to_synpase')\n",
      "project = synapseclient.Project('test_NSD_isa_to_synpase')\n",
      "project = syn.store(project)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#parse a ISA tab file \n",
      "#rec = isatab.parse('/Users/abhishek/projects/isa_tab/data/BII-S-3')\n",
      "rec= isatab.parse('/Users/abhishek/projects/isa_tab/data/ISA-tab_Munoz-Heck_July26')\n",
      "study = rec.studies[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#start building the study level markdown\n",
      "study_markdown_text = '###Title: %s  \\n' % study.metadata['Study Title']\n",
      "study_markdown_text += '---\\n'\n",
      "\n",
      "#study organism\n",
      "study_markdown_text += '#####Organism(s): \\n %s \\n\\n' % 'TBD'\n",
      "\n",
      "#study description\n",
      "study_markdown_text += '>**Description:** %s  \\n\\n' % study.metadata['Study Description']\n",
      "\n",
      "\n",
      "#Study Design Type \n",
      "study_markdown_text += '#####Design(s): \\n %s\\n\\n' % ' '.join([ descp['Study Design Type'] for descp in study.design_descriptors ])\n",
      "\n",
      "#Publications\n",
      "publications_line = ''\n",
      "for publication in study.publications:\n",
      "    publications_line += publication['Study Publication Author List'] + '\\n'\n",
      "    publications_line += publication['Study Publication Title']\n",
      "    publications_line += '[Pubmed](http://www.ncbi.nlm.nih.gov/pubmed/%s) \\n\\n' % publication.get('Study PubMed ID','NA')\n",
      "study_markdown_text += '#####Publication(s):\\n %s \\n ' % publications_line\n",
      "\n",
      "\n",
      "#get the contact names\n",
      "all_full_names = []\n",
      "for contact in study.contacts:\n",
      "    full_name = '%s %s %s' % ( contact.get('Study Person First Name','NA'),\n",
      "                               contact.get('Study Person Mid Initials','NA'),\n",
      "                               contact.get('Study Person Last Name'))\n",
      "    all_full_names.append(full_name)\n",
      "\n",
      "study_markdown_text += '**Contact(s):** \\n  %s  \\n\\n' % ', '.join(all_full_names)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#create a project wiki and add the markdown text\n",
      "project_wiki = synapseclient.Wiki(owner = project,\n",
      "                                  markdown = study_markdown_text )\n",
      "project_wiki = syn.store(project_wiki)\n",
      "\n",
      "#workaround\n",
      "#next two lines mainly to make sure if new markdown content is there, the wiki content can be updated\n",
      "project_wiki = syn.getWiki(project)\n",
      "project_wiki.markdown = study_markdown_text\n",
      "project_wiki = syn.store(project_wiki)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#adding the annotations\n",
      "project['Data_Submission_Date'] = study.metadata['Study Submission Date']\n",
      "project['Data_Release_Date']    = study.metadata['Study Public Release Date']\n",
      "project = syn.store(project)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#store protocols unders a separate folder\n",
      "protocols_folder = synapseclient.Folder('Protocols',parent=project)\n",
      "protocols_folder = syn.store(protocols_folder)\n",
      "\n",
      "#store protocols name and corresponding synapse File entities object \n",
      "#this wud be needed for creating provenance records\n",
      "protocols_synapse_files = {}\n",
      "\n",
      "for count,protocol in enumerate(study.protocols):\n",
      "    temp_file_name = protocol.get('Study Protocol Name','NA').replace(' ','_') + '.txt'\n",
      "    temp_file = open(temp_file_name,'w')\n",
      "    protocols_markdown_text  = '%s \\n' % (unicode(protocol.get('Study Protocol Name','NA')) )\n",
      "    protocols_markdown_text += '%r \\n\\n' %   (protocol.get('Study Protocol Description', 'NA'))\n",
      "    temp_file.write(protocols_markdown_text)\n",
      "    temp_file.close()\n",
      "    syn_file = synapseclient.File(path=temp_file_name,parent=protocols_folder)\n",
      "    syn_file = syn.store(syn_file)\n",
      "    protocols_synapse_files[protocol.get('Study Protocol Name','NA')] = syn_file"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
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       "text": [
        "."
       ]
      },
      {
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       "text": [
        ","
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 3 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
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        ","
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      },
      {
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       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 2 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        "."
       ]
      },
      {
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        ","
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      },
      {
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       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 2 seconds.\n",
        "."
       ]
      },
      {
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       "text": [
        "."
       ]
      },
      {
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        ","
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      },
      {
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       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 2 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        "."
       ]
      },
      {
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        ","
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      },
      {
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       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 2 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
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       "text": [
        ","
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 2 seconds.\n",
        "."
       ]
      },
      {
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       "text": [
        "."
       ]
      },
      {
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       "text": [
        ","
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 3 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
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       "text": [
        ","
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 3 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ","
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 3 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
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       "stream": "stdout",
       "text": [
        ","
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 3 seconds.\n",
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ","
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "!\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Upload completed in 3 seconds.\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#store assays under separate folder\n",
      "assays_folder = synapseclient.Folder('Assays',parent=project)\n",
      "assays_folder = syn.store(assays_folder)\n",
      "    \n",
      "for assay in study.assays:\n",
      "    assay_name = assay.metadata.get('Study Assay Measurement Type') + ' using ' + assay.metadata.get('Study Assay Technology Type')\n",
      "    print 'Assay Name', assay_name\n",
      "    \n",
      "    #create a new folder for each assay type\n",
      "    individual_assay_folder = synapseclient.Folder(assay_name, parent=assays_folder)\n",
      "    \n",
      "    #inserting assay level metadata\n",
      "    for key,val in assay.metadata.iteritems():\n",
      "        if val == '':\n",
      "            val = 'NA'\n",
      "        #replacing the spaces as currently synapse doesnt allow spaces in annotation keys\n",
      "        key = key.replace(' ','_')\n",
      "        individual_assay_folder[key] = val\n",
      "    individual_assay_folder = syn.store(individual_assay_folder)\n",
      "    \n",
      "    \n",
      "    #uploading files under each assay as links to synapse\n",
      "    for node in assay.nodes:\n",
      "        print 'Node name', node\n",
      "        assay_file = synapseclient.File(node.replace('*',''), parent = individual_assay_folder,synapseStore = False)\n",
      "        assay_file['Sample_Name'] = assay.nodes[node].metadata['Sample Name']\n",
      "        #assay_file['Derived_Data_File'] = assay.nodes[node].metadata['Derived Data File']\n",
      "        assay_file['Raw_Data_File'] = assay.nodes[node].metadata['Raw Data File']\n",
      "        \n",
      "        \n",
      "        \n",
      "        if assay.nodes[node].metadata.get('Material Type') is not None:\n",
      "            assay_file['Material_Type'] = '%s : %s : %s' % ( assay.nodes[node].metadata['Material Type'][0].Material_Type,\n",
      "                                                             assay.nodes[node].metadata['Material Type'][0].Term_Source_REF,\n",
      "                                                             assay.nodes[node].metadata['Material Type'][0].Term_Accession_Number)\n",
      "        else:\n",
      "            assay_file['Material_Type'] = 'NA : NA : NA'\n",
      "        \n",
      "        \n",
      "        \n",
      "        #create the provenance for how these files were generated\n",
      "        used_protocols_synapse_files = []\n",
      "        for used_protocol in assay.nodes[node].metadata['Protocol REF']:\n",
      "            if protocols_synapse_files.get(used_protocol.Protocol_REF,None) is None:\n",
      "                print 'Error: protocol %s not found in the study protocols listed' % used_protocol\n",
      "            else:\n",
      "                used_protocols_synapse_files.append(protocols_synapse_files.get(used_protocol.Protocol_REF,None))\n",
      "                \n",
      "        \n",
      "        #generate the provenance\n",
      "        assay_file_provenance_activity = synapseclient.Activity(used = used_protocols_synapse_files)\n",
      "        assay_file = syn.store(assay_file, activity = assay_file_provenance_activity )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Assay Name protein expression profiling using mass spectrometry\n",
        "Node name"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ftp://ftp.pride.ebi.ac.uk/2013/02/PXD000134/OR1_091015_Javier_iPS_Singapore_Mix1_BigSCX*.raw\n",
        "Error: protocol  not found in the study protocols listed\n",
        "Node name"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ftp://ftp.pride.ebi.ac.uk/2013/02/PXD000134/4Skin_Mix2_SCX*.raw\n",
        "Error: protocol  not found in the study protocols listed\n",
        "Node name"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ftp://ftp.pride.ebi.ac.uk/2013/02/PXD000134/4Skin_Mix1_SCX*.raw\n",
        "Error: protocol  not found in the study protocols listed\n",
        "Node name"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ftp://ftp.pride.ebi.ac.uk/2013/02/PXD000134/OR1_091015_Javier_iPS_Singapore_Mix2_BigSCX*.raw\n",
        "Error: protocol  not found in the study protocols listed\n",
        "Assay Name"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " transcription profiling using DNA microarray\n",
        "Node name"
       ]
      },
      {
       "ename": "SynapseHTTPError",
       "evalue": "400 Client Error: Bad Request\nInvalid Entity name: '?acc=GSM649323&format=file&file=GSM649323%2ECEL%2Egz'. Entity names may only contain: letters, numbers, spaces, underscores, hypens, periods, plus signs, and parentheses",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mSynapseHTTPError\u001b[0m                          Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-26-f03a3a864d98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m#generate the provenance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0massay_file_provenance_activity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msynapseclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mActivity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mused_protocols_synapse_files\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m         \u001b[0massay_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msyn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massay_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massay_file_provenance_activity\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/abhishek/anaconda/lib/python2.7/site-packages/synapseclient/client.pyc\u001b[0m in \u001b[0;36mstore\u001b[0;34m(self, obj, **kwargs)\u001b[0m\n\u001b[1;32m    671\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m                 \u001b[0mproperties\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_createEntity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproperties\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mSynapseHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mcreateOrUpdate\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m409\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/abhishek/anaconda/lib/python2.7/site-packages/synapseclient/client.pyc\u001b[0m in \u001b[0;36m_createEntity\u001b[0;34m(self, entity)\u001b[0m\n\u001b[1;32m   1975\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1976\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"\\n\\n~~~ creating ~~~\\n\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_properties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1977\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestPOST\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muri\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/entity'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_properties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/abhishek/anaconda/lib/python2.7/site-packages/synapseclient/retry.pyc\u001b[0m in \u001b[0;36mwith_retry\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0;31m# Try making the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m                     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m                     \u001b[0mexc_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/abhishek/anaconda/lib/python2.7/site-packages/synapseclient/client.pyc\u001b[0m in \u001b[0;36mrestPOST\u001b[0;34m(self, uri, body, endpoint, headers, **kwargs)\u001b[0m\n\u001b[1;32m   2099\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m             \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2101\u001b[0;31m         \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2103\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'content-type'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'application/json'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/abhishek/anaconda/lib/python2.7/site-packages/synapseclient/exceptions.pyc\u001b[0m in \u001b[0;36m_raise_for_status\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0;31m## TODO: Might as well append more information to the exception message\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mSynapseHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mSynapseHTTPError\u001b[0m: 400 Client Error: Bad Request\nInvalid Entity name: '?acc=GSM649323&format=file&file=GSM649323%2ECEL%2Egz'. Entity names may only contain: letters, numbers, spaces, underscores, hypens, periods, plus signs, and parentheses"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " http://www.ncbi.nlm.nih.gov/geo/download/?acc=GSM649323&format=file&file=GSM649323%2ECEL%2Egz\n",
        "Error: protocol  not found in the study protocols listed\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "assay.nodes[node].metadata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "{'Data Record Accession': [Attrs(Data_Record_Accession='GSE26451'),\n",
        "  Attrs(Data_Record_Accession='')],\n",
        " 'Data Repository': [Attrs(Data_Repository=''),\n",
        "  Attrs(Data_Repository='Gene Expression Omnibus')],\n",
        " 'Derived Data File': [''],\n",
        " 'Label': [Attrs(Label='')],\n",
        " 'Labeled Extract Name': [Attrs(Labeled_Extract_Name='')],\n",
        " 'Protocol REF': [Attrs(Protocol_REF='Microarray data analysis'),\n",
        "  Attrs(Protocol_REF=''),\n",
        "  Attrs(Protocol_REF='Transcriptome analysis'),\n",
        "  Attrs(Protocol_REF='RNA isolation')],\n",
        " 'Raw Data File': ['http://www.ncbi.nlm.nih.gov/geo/download/?acc=GSM649323&format=file&file=GSM649323%2ECEL%2Egz'],\n",
        " 'Sample Name': ['4Skin_Fibro_1']}"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print node"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "EVUSNDQ02.sff\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}