shalek_2013_and_shalek_2014.ipynb

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     "cell_type": "code",
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     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import flotilla\n",
      "import pandas as pd\n",
      "\n",
      "# Shalek et al 2013 paper (18 single cells, 3 pooled)\n",
      "# ! wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41265/suppl/GSE41265_allGenesTPM.txt.gz\n",
      "\n",
      "expression = pd.read_table(\"GSE41265_allGenesTPM.txt.gz\", compression=\"gzip\", index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expression.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(27723, 21)"
       ]
      }
     ],
     "prompt_number": 3
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expression.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>S1</th>\n",
        "      <th>S2</th>\n",
        "      <th>S3</th>\n",
        "      <th>S4</th>\n",
        "      <th>S5</th>\n",
        "      <th>S6</th>\n",
        "      <th>S7</th>\n",
        "      <th>S8</th>\n",
        "      <th>S9</th>\n",
        "      <th>S10</th>\n",
        "      <th>S11</th>\n",
        "      <th>S12</th>\n",
        "      <th>S13</th>\n",
        "      <th>S14</th>\n",
        "      <th>S15</th>\n",
        "      <th>S16</th>\n",
        "      <th>S17</th>\n",
        "      <th>S18</th>\n",
        "      <th>P1</th>\n",
        "      <th>P2</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GENE</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
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        "      <th></th>\n",
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        "      <th></th>\n",
        "      <th></th>\n",
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        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
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        "      <td> 0.000000</td>\n",
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        "    <tr>\n",
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        "      <td> 0.022996</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPL</th>\n",
        "      <td> 72.008590</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 128.062012</td>\n",
        "      <td> 0.095082</td>\n",
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        "      <td> 0.229197</td>\n",
        "      <td> 0.110582</td>\n",
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        "      <td> 6.727028</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T2</th>\n",
        "      <td>  0.109249</td>\n",
        "      <td> 0.172009</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0.000000</td>\n",
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        "<p>5 rows \u00d7 21 columns</p>\n",
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       "text": [
        "                 S1        S2          S3        S4        S5        S6  \\\n",
        "GENE                                                                      \n",
        "XKR4       0.000000  0.000000    0.000000  0.000000  0.000000  0.000000   \n",
        "AB338584   0.000000  0.000000    0.000000  0.000000  0.000000  0.000000   \n",
        "B3GAT2     0.000000  0.000000    0.023441  0.000000  0.000000  0.029378   \n",
        "NPL       72.008590  0.000000  128.062012  0.095082  0.000000  0.000000   \n",
        "T2         0.109249  0.172009    0.000000  0.000000  0.182703  0.076012   \n",
        "\n",
        "                  S7          S8        S9       S10       S11       S12  \\\n",
        "GENE                                                                       \n",
        "XKR4        0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
        "AB338584    0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
        "B3GAT2      0.000000    0.055452  0.000000  0.029448  0.024137  0.000000   \n",
        "NPL       112.310234  104.329122  0.119230  0.000000  0.000000  0.000000   \n",
        "T2          0.078698    0.000000  0.093698  0.076583  0.000000  0.693459   \n",
        "\n",
        "               S13       S14       S15       S16       S17        S18  \\\n",
        "GENE                                                                    \n",
        "XKR4      0.000000  0.000000  0.000000  0.000000  0.000000   0.000000   \n",
        "AB338584  0.000000  0.000000  0.000000  0.000000  0.000000   0.000000   \n",
        "B3GAT2    0.000000  0.031654  0.000000  0.000000  0.000000  42.150208   \n",
        "NPL       0.116802  0.104200  0.106188  0.229197  0.110582   0.000000   \n",
        "T2        0.010137  0.081936  0.000000  0.000000  0.086879   0.068174   \n",
        "\n",
        "                P1        P2      \n",
        "GENE                              \n",
        "XKR4      0.000000  0.019906 ...  \n",
        "AB338584  0.000000  0.000000 ...  \n",
        "B3GAT2    0.680327  0.022996 ...  \n",
        "NPL       7.109356  6.727028 ...  \n",
        "T2        0.062063  0.000000 ...  \n",
        "\n",
        "[5 rows x 21 columns]"
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      }
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     "prompt_number": 4
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    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These data are formatted with samples on the columns, and genes on the rows. But we want the opposite, with samples on the rows and genes on the columns. This follows [`scikit-learn`](http://scikit-learn.org/stable/tutorial/basic/tutorial.html#loading-an-example-dataset)'s standard of data matrices with size (`n_samples`, `n_features`) as each gene is a feature. So we will simply transpose this."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expression = expression.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expression.head()"
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     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <td> 0</td>\n",
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        "    <tr>\n",
        "      <th>S4</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "GENE  XKR4  AB338584    B3GAT2         NPL        T2  T  PDE10A  \\\n",
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        "S5                0              0       0         0  0.000000   26.273491   \n",
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        "S1                0     0      0         0  2.488120  0.717199         0 ...  \n",
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     "prompt_number": 6
    },
    {
     "cell_type": "code",
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     "input": [
      "expression.index"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "Index([u'S1', u'S2', u'S3', u'S4', u'S5', u'S6', u'S7', u'S8', u'S9', u'S10', u'S11', u'S12', u'S13', u'S14', u'S15', u'S16', u'S17', u'S18', u'P1', u'P2', u'P3'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metadata = pd.DataFrame(index=expression.index)\n",
      "metadata['phenotype'] = 'BDMC'\n",
      "metadata['pooled'] = metadata.index.map(lambda x: x.startswith('P'))\n",
      "\n",
      "metadata"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>phenotype</th>\n",
        "      <th>pooled</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>S1</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S2</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S3</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S4</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S5</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S6</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S7</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S8</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S9</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S10</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S11</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S12</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S13</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S14</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S15</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S16</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S17</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S18</th>\n",
        "      <td> BDMC</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>P1</th>\n",
        "      <td> BDMC</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>P2</th>\n",
        "      <td> BDMC</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>P3</th>\n",
        "      <td> BDMC</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>21 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "    phenotype pooled\n",
        "S1       BDMC  False\n",
        "S2       BDMC  False\n",
        "S3       BDMC  False\n",
        "S4       BDMC  False\n",
        "S5       BDMC  False\n",
        "S6       BDMC  False\n",
        "S7       BDMC  False\n",
        "S8       BDMC  False\n",
        "S9       BDMC  False\n",
        "S10      BDMC  False\n",
        "S11      BDMC  False\n",
        "S12      BDMC  False\n",
        "S13      BDMC  False\n",
        "S14      BDMC  False\n",
        "S15      BDMC  False\n",
        "S16      BDMC  False\n",
        "S17      BDMC  False\n",
        "S18      BDMC  False\n",
        "P1       BDMC   True\n",
        "P2       BDMC   True\n",
        "P3       BDMC   True\n",
        "\n",
        "[21 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shalek2013 = flotilla.Study(metadata, version='0.1.0', expression_data=expression, \n",
      "                            expression_log_base=10, expression_thresh=1, expression_plus_one=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "initializing study\n",
        "predictor ExtraTreesClassifier is of type <class 'sklearn.ensemble.forest.ExtraTreesClassifier'>\n",
        "added ExtraTreesClassifier to default predictors\n",
        "predictor ExtraTreesRegressor is of type <class 'sklearn.ensemble.forest.ExtraTreesRegressor'>\n",
        "added ExtraTreesRegressor to default predictors\n",
        "predictor GradientBoostingClassifier is of type <class 'sklearn.ensemble.gradient_boosting.GradientBoostingClassifier'>\n",
        "added GradientBoostingClassifier to default predictors\n",
        "predictor GradientBoostingRegressor is of type <class 'sklearn.ensemble.gradient_boosting.GradientBoostingRegressor'>\n",
        "added GradientBoostingRegressor to default predictors\n",
        "No phenotype to color mapping was provided, so coming up with reasonable defaults\n",
        "No phenotype to marker (matplotlib plotting symbol) was provided, so each phenotype will be plotted as a circle in the PCA visualizations.\n",
        "loading expression data\n",
        "initializing expression\n",
        "done initializing expression\n",
        "subclasses initialized\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "package validated\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shalek2013.expression.pooled"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>GENE</th>\n",
        "      <th>XKR4</th>\n",
        "      <th>AB338584</th>\n",
        "      <th>B3GAT2</th>\n",
        "      <th>NPL</th>\n",
        "      <th>T2</th>\n",
        "      <th>T</th>\n",
        "      <th>PDE10A</th>\n",
        "      <th>1700010I14RIK</th>\n",
        "      <th>6530411M01RIK</th>\n",
        "      <th>PABPC6</th>\n",
        "      <th>AK019626</th>\n",
        "      <th>AK020722</th>\n",
        "      <th>QK</th>\n",
        "      <th>B930003M22RIK</th>\n",
        "      <th>RGS8</th>\n",
        "      <th>PACRG</th>\n",
        "      <th>AK038428</th>\n",
        "      <th>AK163153</th>\n",
        "      <th>PARK2</th>\n",
        "      <th>AK080902</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>P1</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0.908986</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1.680330</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0.354055</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>P2</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0.888012</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1.602973</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>P3</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1.191044</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1.594659</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>      NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 27723 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "GENE  XKR4  AB338584  B3GAT2       NPL  T2   T  PDE10A  1700010I14RIK  \\\n",
        "P1     NaN       NaN     NaN  0.908986 NaN NaN     NaN            NaN   \n",
        "P2     NaN       NaN     NaN  0.888012 NaN NaN     NaN            NaN   \n",
        "P3     NaN       NaN     NaN  1.191044 NaN NaN     NaN            NaN   \n",
        "\n",
        "GENE  6530411M01RIK  PABPC6  AK019626  AK020722        QK  B930003M22RIK  \\\n",
        "P1              NaN     NaN       NaN       NaN  1.680330            NaN   \n",
        "P2              NaN     NaN       NaN       NaN  1.602973            NaN   \n",
        "P3              NaN     NaN       NaN       NaN  1.594659            NaN   \n",
        "\n",
        "GENE  RGS8  PACRG  AK038428  AK163153     PARK2  AK080902      \n",
        "P1     NaN    NaN       NaN       NaN  0.354055       NaN ...  \n",
        "P2     NaN    NaN       NaN       NaN       NaN       NaN ...  \n",
        "P3     NaN    NaN       NaN       NaN       NaN       NaN ...  \n",
        "\n",
        "[3 rows x 27723 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shalek2013.pooled"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "Index([u'P1', u'P2', u'P3'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pdb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Automatic pdb calling has been turned ON\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# fig, ax = plt.subplots()\n",
      "\n",
      "shalek2013.expression.twoway('P1', 'P2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['Helvetica'] not found. Falling back to Bitstream Vera Sans\n",
        "  (prop.get_family(), self.defaultFamily[fontext]))\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<seaborn.axisgrid.JointGrid at 0x108850210>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/site-packages/matplotlib/figure.py:1595: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
        "  warnings.warn(\"This figure includes Axes that are not \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3D/3790dkZKSUpRBJ6se9l7Fhxzlk5DU/nIL8vPDsvQlOrYtc2++nsuzfp+5I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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108850250>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shalek2013.expression.twoway('S1', 'S2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<seaborn.axisgrid.JointGrid at 0x102655790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+O1Xp3WdEKmBApHtGlc3mYPPefLcSRQBqFSyeMfhcD1GIXk8ClOiRrDZHg6/t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\ncHlp7bJx++p8emwupuUl8OTL+0K+j9M1sr5NZxsnkhHocnv5clctP7p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BYndTXtfNo3/d\nhkKhIEKrIiU+gml5iRSWt9PYZiEqUsPaS8b5RwDBsVE91vAmDkEQSEvoL9x99t1CjvbWibV12fn7\n+4U89dCSkX5VGRmZU8gFI1AAapWS7NRoth6s56OtFdQ290jqZ6obu9lzpJH3NpXTYbITqVOHLN7r\ndSpuWz2e1k4L//XsDsl7K2ZlhkydaNVKrl2WgyiKPP/BYQ6VttIaps1GIME1PecaWw42IOCrxWrq\nsA669hYOX62S7xqMionkrisn4fGKdPc4MOrVkhq09EQDFQHdktNGDZ29B6FrkqaewR2eMjJnI6cz\n6ig4umgwjifWKBwXlEAB1LaYQwJX+4iK1PL+pgpKanzt39uwo1Ur/MYFpcIXQJscH4kAJMRE0Nq7\n3hMXreOmiwcO/fxsexWfba8Me7PWqBWSOJ/zARGoa7X0NnSUfme9TkWsUUt9q4VRMTo6TXYGaoNk\n7hUSpUIgLkoX8v5dV07kYEmL320owrAMAGkJBl8Dyl6ShylsMjJnE6cr6mig6KLBGGmsUTguOIEq\nLGsLESe1SkGMUctVi8fw1pclkvf6pvcEwZfvdl1vwOhnO6r84gTQ0W1n4/56ibsskKpGU4g4jcuM\nYWxGDN09DrYdCl3TOh8I14BvTEoUj/7HPBrbekiI1XPf/32FxR5+PrOl00qX2U6MMVScAN77pkxi\nhd9ztInD5e1Mzhk8YuXBNVNRKQQa2yzEGLU8cP2UEXwrGZmzg9MVdXSyootGygUnUHmZsRj0av9a\nhUopcMfq8Vw6fzQdJjv//OiIZPs+Y4EoQk1zD4fL25mSm0BEUDSMgC8pIpD1O6ooru4kOV7P2PRo\niYsuLkrHT+6YTWKsnnc3lg4qUGqVAr1WSbdl6KH81Jw4NBo1JdWddIdxCw6HkxVfFA6dRsnMCUno\ndSqy02J48YNCbAMkRgC0d9v5878P8PP/mB/2/WAB9HgZdH99aNRKHrrp9ERtycjIHB8XnEDlpMdw\n44o8Pt9ZBUBeRiwGvabXpOAc1Fzg9nipbjYzLisWY4Sa5Hg9Te1WBAFmj09iyYx+d96bXx7j7a9K\ncLq9CMCS6Wkkj4qkqc2CQiFw8ZxMDhxr5XB5Gwa9mslj4yksDx8W63Z7uffmaby3sZzGVgtRkWo8\nXjFske3Nq8YzJi2a7/xmw3Ffo3DipABOdBJSr1Vhdbh59bNiqntjnTburx/iU7D/WCvvbCiV9IHq\nY/WC0ewrbqG5d10vPyuWaQEZezIyMucuF5RAVdR3sa2gge0FjZgsDpQKBTsON7DxQB2JsRHcd91k\n/00UfCMJjUrA4fLdsRUCvPrpUV4OcP4lxkZw95UTmT85VZKevetwY//oC19KeqB77/OdVdgdHhy9\n2UXT8hLISjJS3SwtSgUYnRrFvqIWEEGrUdJhduAZwHjQ1xBxoBy742U44qRUCCERRH3ER2tp7+5r\nqiiy5VAD+jAW+XC4PSLrNpQwe0IymclGyXujU6L5+d1zWb+rCo1ayY0r89ColQPsSUZG5lzighGo\nj7aU8/r6YwN2s23ptPHGFyUSERFFEFHQ5yjzivjTsAM/V1TZwcKpaf7Xusx2aoOExu2Vfs5skY7W\njla2hzVKpMTr6TTZqWwY2hUTa9QwobeP0eiUKL+NejjotUq0GhWd5uNzs/mSGgYefkZo1UD/vj0e\nEat9+O6jHpubmmZTiEABZKVE8e1r5DUkGZnzjQumUPejLRWDtloHQBQJ9n45h5HOGnxf3nqoISQ9\nPcYgXeQPrJcCQo4LkJFoID3RGNKmfrDzUCoElAqBn989l4vnZLJwSgoR2sFHFAqFgNXhodPsGLBZ\noTFy4HYMeZkxCGG/QT8tndJiY6VCCFtk3IeAL3Ovj6Q4PZPGnJ89mmRkZMJzQQjUa58Xh3S6DUav\nU7FidiZG/cj64qQnGrhmqdS5Fx/ty34L5JK5Wayam0V2ahRJcXrmTkwkK9mIIPhy4fIypZbMOKOW\nJx5YNKLpqsDmiAa9hgfWTOVnd84Z1HKtVSskjf7EXpEDUAowfnQsP147k/9391xUyvD7ae204h5k\n9KRVK3G6pO97vCJJcfoBPgEz8xO5/fIJTMtLYGZ+Ig/fMv2Czc+TkblQOe+n+Kx2F1/urib49pme\naGDSmHiS4vS0dduZmjuKrJQoXvpY6uIbFa2j0xy+TmfGuAQevHF6SLuCeZNSWDwtnR2FDXi8IlNz\nE7huRS4Wm5NHn9tBc4eV5g4r47PiePLBRSTFRWKM1PDMukNU1Hej16m487LxxBi1XLEom4PHWrCE\nSZ8IRqtWsq2gnqY2C9sLG+kw2YkxaBEHseSF65M1OSeexdPSGZsWzdiAPkG/fnAxf3+vgKoGaXfa\nzgHSLMAn1qNidByr7pK8PipGxw/XzuSN9cfo6nEQH6XD4/HS0mVjxrhEfzJ5X2beQDS1W/hsRxV6\nrYprl+XI608yMucR571AOV3ekLWdnIxofv+DZSHbdprt6DQqHK7+G+7knFFMzRvFX946KGkQGGPU\n8sitM8O2KhAEgUdunUFD2ziOVXfw8ZYKvvObrxFFUZJKXlTdwW9e3ssVi8Zw/Ypcbl01jqKqdoqr\nOln3TRmxhjqWzUrHPlTmUS/6CBW/fmmv5LW2ME4/tVLAFWCyCDY3NLRaWDgllaOV7fz56Y1Y7C6y\nU6P54dqZ/O77S6lpMvFfz24L6Y0VDrPFiS1orUmvVfHgDdPITIrip3fMBuC1z4s4UNKKWqUkLyjs\ndSDqWnr4xQs7/RmKh0rbePy++b3FwTIyMuc6571ARRs05I+OZc9RX3fZyAg1Vy4M/1Qea9Sxam4W\nn26vxGJ3MyY1mtsvm0BCTAS1zT18tasGm9PNqOgIfnHvvEH76AiCQOqoSH77yl7K67sH3K6t286/\nvy7B6fLw6fYquoIidw5XtA2YshCIWiXQ2jl4b6c+NGolLk+/6AULVHu3nca2Hl788Ig/Sb2p3crz\n7xditjipajINO/nC6fYSaCiMjFDx33fNYfLYfiv45zuqeOebMly9TwAtnVZyMmKIDVOc+4+PDvPV\n7hq8IsQYNJKA38LyNgrL2pg+LjHkczIyMuce571ACYLAf945h7e+OkaX2cGs8Ukhze4CuePyCSyY\nkkphWRuzJiT6p+++dflEbl2Vj9crDrq432Nz8ey6Q3Sa7RgjNdS1SN18fUGwgVjtbj7fGSpOvv05\nUauEATve9hFj1NEaphNtODKSjCTG6mnusFLf1hMSsJoyKtLn6DNJBe/AsZaw3W5HgsXm5g9vHOBn\nd8z2t8Uore30ixNAa6eNstouZk+QVsh/s7eG9zeW+6drLUGmF0FA0jJFRuZ851Rk8YXL3DtZ2Xoj\n5bwXKPDdtG4boNtqMEVV7fz+jQM0tllYt7GUWy4exxW96yCB6xsut4f6VgvRkRpiAzLinnp1L/uK\nWwbc//wpKSgEgS0H6v3rOFF6Na4B3IJOl5dbV+Xz0dZKrDYngiDgcHnxBq0rtXcPc/SkUpCZbGT2\nhGRUCoFfvLhL8r4A3HRRHlGRGiK0SontvvskBaq2dNr4aEsFj6ydCfhy8RRCvxsyxqhldEpoS/b9\nx1pC1hLVKgWu3mLoOeOTmJA9/KwwGZlznZOdxTdY5t7JyNYbKReEQAUiiiJ2pwedRhnW3fbmFyU0\n9vZfMvU4+WhLBZcvzPZv6/F4+fPbB9l2qAG704NRr2bNilyuW+5LORiodxPApDFxrL0kn8RYPWNS\no9l8sB6Px0uEVuWbSrOFrjVZ7W4+2V7Jw7dMZ1qeb+qqrsXMr1/eQ3Vj/+jMO8z+Ghq1gi921fDF\nrhpy0qNDOs2KwO9e3x+26DbYUCHQHwMb3AwxcNSnUStwubwScdl3rIXdR5qYMzGZa5bm0NhmobC8\nHbVKwVWLx5AQKzWeAOSPjg9JnpiWO4rp45Iw6NUsmZ4uKZaWkTnfOdlZfGcqc28gLiiBOlrRznPv\nFdBldpAQq+eRW2dIegRBaLaby+3F4xX9FuuXPj3K13v6u+SarS4+3FzOJfNGExmhJipS4xe4YCob\nTPzg6Y1Mzkngp3fM5qI5mfzsma0DRhz10dZl57ev7uOBNVNZOCWV9EQjVy4aw1/ePjTia9ATIIJl\ndd2kjIoMe74DFd2mjIrEqFdT1+LrMtyHy+2VjIIkU5KiSFyUjvaAKUOTxclf3znEmLRoRsVE8N0b\npoUcq7qxmwMlrYzLjGV8djyXL8xm26F6jlR0IIoiyfF6fnTbLPS6kZUGyMjInBtcUAL14keH/YkM\nnWYHz79fyGP3SkNIZ+Yncqy60x9BlD86VuIKq2kKjSKyOdxY7W4iI9Tcc/Uknnu3gI5uOxa7y28m\nEAT8id07Cxt5f2MZsVHaYSVEgO+G/ud/H6Cty8bVS8ZyqKR1yM9kJhlRCFAV5pz7MI9w2m7p9DTW\nXjqe7z21gaqAEZyvzUX4zzjdokSc+mjvtvPDP27ivmunsGBKquS9Tfvr+Pv7hZgsTpQKgUvnj+b+\n66bwxAOL6O5xYLG7SIrVD9muvaC0ldfXF2NzesjNiOE710/113nJyMic3VxQAtXWJV3gN4dJlrhu\neS7RBi1HKtpJiI3gxpXSHk/hnHvZadHER/vWofKz4vjDw8twuT04XF42H6ijrLaTL3f3j7pEoMNk\nD2uHDp5yC8Ric/PmF8fITDKyPUzL+UAyk408fu98PtxSMahA9djdKBTCsKcINx+oY+XszJM2aukw\nOfjNK3tZPjOd79803T+V+sm2Cky9aewer8hnO6q4eE4mY9NjiDZoB3VQ9mF3uPnruwXUt/iciJX1\n3cQYtNy2enjrkTIyMmeWC0agDpW2+m94fQzUfXXl7ExWzs4M+959106my+ygtLYLr1cUT/mWAAAg\nAElEQVRkfHYsP1o7E49XlKx/qFVK1Colly3IpqM7mcMVHf6ptFijloVTUjlcETq1N5SlvMfm4uOt\nFQOGxQoC3HnZBGKjtLzyeRFmi1PSdDEcgxXyBtPQZuWDTeV0HWdmnyD4WpwETgF6vSIb99UxPS+B\npTMyAEJqrLxekU+2VY6oRUZrl42WgO7FviaKA4u1jIzM2cUFI1BbDzaENAycP3lgu/lA6HVqfvHt\n+Xy1u5ptBQ20dNr43u824vWKZKf6Ck91QTl7cdER/OyO2bz9dQker8hFszOZMCYej9eLXqfyr+UM\ntw/TYG46UYTCslaKa7pCbNjhMESosDvduANMhDFGDVcuGsOWA/VUN5lDnHMKhSCxhQ9GoJGi7/yS\n4yKp7R3V9OHxipK1sGl5CSGGk6IRhN8CjIqJICEmQrKflHjDIJ+QkZE5m7hgBEqvk0bgaNQKEmMH\nzoLr42BpKweKW8jJiGHxNF9i+Y7CBp7/4LDEJAC+NZWXPjnKfddKk7U7um1UNXZz3fIccjP6UxIm\n5yRww4pcvtlfh+gV0WiUlNcNXNTbx1Chtw1tlmGJE/ja3OePjmdvka+QWaUUGJsWTXOHNezUYKxR\ny5oVubR32yUdhaHf8h2ITqvEFpQAn5oQybisWDYHWO0TYyNYPK2/n9ZdV05k/a4qPBLhHFkWX4RW\nxbevncwb649hc7rJTY/htkvzR7QPGRmZM8cFI1C3rMqnpLaLoqoONColK2ZlSHLmwvHZ9kpe/rSI\nHpsLrVpJZUM3d1w2gX1FLSHi1EdwcevRynaefn0/zR1WIrRKrlmaw62X9N8k16zMY03vOtef/n1g\nWAJltg5c9yAI0G4a/vRbQ5uFbovDbyt3e0T2FbcOaCTo6nHwr0+O8qPbZqJ8U2BnQSMikBAbwWUL\nsnjpk2K/SKmVCr51xQTe+OIYXb15fQK+Hk5zJiYTY9BS3WRGo1Fw1eKxpCX2j250GhX5mXEcCRg1\nZaeF1kYNxcz8JGbmJ434czIyMmeeC0agdFoVv7xvAbXNZnRaFcnx4defAtm4v84/WnG4PGwvaOCO\nyyYQExX+SV6pEPzpCH28s6HU3+3V5vDw1e4abliZF5J40N3jYOvB0O6ywVNkAKZBWr+L4vBahARi\nCVN/NZDNXBRhe0EDSXERVNZ1+0dADa0W3lhfIhlBuTxeIrRq/uc/5vHSJ0exOdzkZMTiFUX++9lt\n2J0ekuP1PHzLjLAFtj+9YzZ/e68Qs9VJVkoU/3HlxBF9LxkZmXOb816gthys44tdNQjAqrlZLJqW\nNuRn+ggeQ/Q5zG66aBzbDjVQF7COYohQc/nCbK5bloPL7UWlFBAEIaRxocfrxePxhghUZaMpZCpM\no1ISqVfSaTr+SnG1EkaoV0Nid3p444uSkN5RljCjSgHYU9SMVwRjpJaVszJ44l97/HFPTe1W3tlQ\nyoT/CBWo2CgdP7tz9sk9+ZPM5gN1bDlYj0qp4JZV48hMjjrTpyQjc95wXgtUSXUnf3+/0D+9VNVo\nIileL1kHGoxV80ZT19JDt8VJhFbJ0hm+NRK1SsFff7KCdRtKKavtIi5ax52XT8Dp8vDoc9upb+3B\nqNcQF6WlMagP1YTs+LBZfmqlgoig9ZrYaC3NQ/SxAt/ITSGIYYXoRMQp3OgtkKEMHRlJBjrNdt7+\nutQ/smrusOD2SE/KMwIX4dnE3qImnnu3AHNvlmFVo4knv7cYo15zhs9M5kLhZGbx6fV6rJazy+V6\nXgvUrqONfnECX3Hu7iNNwxaoFbMyyEgysL+4hfzRcUzN7U/gFgSBG4JqpP7y74MUlLUBPsNEdZhS\npRsuygt5rbiqg6df3+cXJ4UgoFErhl1E6/GKeAClErRqFW63VxI7dLyMRDb0WiUqldJv5Y+O1PC/\n9y3gHx8ekUz7NbT2MC0vkf3Fvlw9o17Nkqlp2J1unll3iOZ2KzEGLd+9YQrRhtA087OJ3Uea/eIE\nvvYfhWVtIUXHMjKnipOVxdefwZd1RjL3BuK8FqjslGiJs0yjUpCdOrKF9tyM2CEFzeX2YHd66A6q\nswq+wQsCRIV5uv5gc7kkJdzbmxcYTFJsBM2DpIl7PJCeZuC21eP53xd2+ns+qZQCo1OiSIzVs+do\nM67h9O8YgIGs8AmxetZeks/G/XUolQI3XzyO+OgI4qKkIhNr1PH9m6fzxa5q9he1YLY5+WxnFR9t\nq6Cstt8gYne5efzbC477PE8HMUbp71KnUZIcP7QzVEbmZHGysvjOtgy+Ps5rgVo0LY3i6g52FDaC\nAAsnpw7r6VYURdbvqqa53cri6WmMGUTUPtxczoebK7C53OiG6OaaNiqSUTGhIahltV1htu5HqfCt\nn62clcmP/rxl0G3L6rp464tjkhbscVE6nv7BUgRB4Du/+VqydjYSkuL05GfFsulAqJkjKzmK+VNS\nmR90fW+/bDyN7RbK6rrQaVSsWZHL13tq+XhrhWR0G2wabBrG1GY4PF6RFz4opLS2C71OxV1XTBzx\nQ8lwuWHlOMrquimq7ECtUrBydiZj0gZ3hsrIyAyf81qgAO65ejJ3XTHRl2Lu8uINSHwQRZEPt1RQ\n19JDfmYsK+f40iOefmM/m/fX4RXhm321/ODm/iTxQDrNdt7eUOpPVTAB2alRGCLUmCwuqpv6c/Y0\nKoGf3BF+wV+hHCobTuCWVfn89Z2hw2G9XiTWbPC1ve80O4iL0mHUH39E0cTsOG5aNY6WTivHajpR\nCALx0RHMnpDE3QM47DRqJY/ePddvHGnttPH9p7+RhNZCf8hsH7HDiDIKxwvvF/Dxtir/zx2mffzh\n4WWnpMuuWqXg53fPpcvsQKNWEhkhh9bKyJxMznuBAmjusPK71/fT3GEhxqjl3qsnMzU3gefeLWD9\nzio8Xl8zvJYuK6vnZ7OvqMV/w2zvtvPZ9qrwAmWyYwpaJ8pMNvKjtbPwekX+8vZBDpW2olIqWDU3\ni8wkI8+9e4jKBhMalYL0RCPRRi0xBi0Nrf1pB8HmhAitCpfHO2ifqcHQalToNL7RXbipw4EITCeP\n0CgpqurgR3/cjFIpMDs/idtWj2f0MEcnfa7F1i5biDgBaNVKEuMicLq8xBq1fPu6ycM+z0CCR3d1\nzT10mOzDKso+HgRBkPQDk5GROXlcEAL1/PuHKanpBHwZb//6+Ai/f3gZh0r726k7XD4BuGxBdoh9\nOvQFH2mJRjKTo6hq9I2U1EqB8aN9dmmFQuChm6b7MvoE343shQ8K+STg6f5gqc9QkZVsJCPJQFOb\nBbVKybzJKewrbsZicxGhVXHftVPQqpVha5NmjU+kuLKDnl6Ld3DYrEohsHr+aH+4q+r/s3fe8VXV\n9/9/nrvvTXJvcrN3CIGEPcIWREBcVVDcq2od2IraXatVq9VWq1XrT/u11lX3VlQqKiACguwZCAkJ\nZO91783d957fH5dcclcShiSE83w8qM2955z7uZdwXvfzHq+3rO87iXmTM/F4weXyUNtsoazmyI5w\n494GGtts/OX26UdVzKBWhjPIFbh49lCuO38EoiiGndPVF9pM9pBSd7lMIPYYd2MSEhL9y2kxHzvY\nGshsdSGKR2Y8daGQyzBEq5k2OsX/XFKclgWzhoS9rlop5483TibZqEUQwOURWbm5MsDpQS4T/Dfc\nyobwJZwV9WYsVhcuj4jV4ebbLVVoVArG5SXyyO0zmD0xA0O0muigEFJaQhQP3jKdP9wwmflTs7ho\nZi5XzMtHH+VL3qcYdTy8eDpXzs+nqc3K/f/+nvKanvNd3XG5RW5ZOBoEgZqm0JlRB+tMfPTtAdrN\nDirrTbjcve/O3l9ZGvLY8KxYrjt/BDaHm3++u50/vfA9//poR5+u14Xd6ebhl38IcWXPzTAETEKW\nkJA4dTgtdlDZqTEUVxzJy2QmRSMIAhfOzOWN5fswWZwkGDRcPHsoAEsuH8/4YUnUNluYNT6dtMTI\nBqNOl5c2s8Nf2VZa1c5by/dx+6JxIcf2lAZp6+YOLuILSza0Wmlqt/Hk3WdS22wJKCRQyGHGmFTe\nWr6PsXkJ3LJgNI+8son9la243F60ahl5mQayU/U89NIGtu9viugOEYnV26opqWzrcUpwaWU7d/3j\nW8xWJzmpBu69aQqJQYUgLreXdrOD2Bh1WJPZrrf15Jtb2LTX5wm4s7SZfQfb+H+/nRPxtasaTLz0\nWRE2uxt9lJIDQTZRRr2aP900pY/vVkJCYqBxWgjU7YvGolLKqW4wExejZvEin5nredNzGJuXwMG6\nDvKz4kiI9eUpBEFgTF48K7ZUsHpbNbExapZcNo70pJiQa1usR4YSdhH8M/iGGta3hC8RVynlEe2J\nKhvM/PPdbZRUttMeMIJCYNn6g9gcHj5fd5Bko47ymiM3aJvDy7qddazfXYf3OFqi2syhgwa70KoV\n1DZ3+sX1QHU7r31RxO+um4Td6ebNL4upajAd3iE6EQSB6DBFGl0u811h2C4q6kzsKm1ibLf+sy48\nHi9PvrUt4D3LZAS811njM3oMP9Yfri7Mz4oj8XCOqqLOxGtfFGF3esjPjuOGn4w85pCjhITE8XFa\nCJRCLuO2i8Mn3Usq21m3s4Y122t8VjXJet5avo+vN1b4BaG60cIjr27i2d/MCbEoys+OJT87jv0V\nvpurUa/mrMJM//OiKLJ6WzW7SpuoaQpf3t1bgdn3u0I7fj0e0e/I0GlzUdMYPnzYV3GKN2ho6QgV\no2D7pe6kJUbR0BK4u3K4PIiiyKOvbmJHmKm/4Yo0jIeHPQYLgQiU13aEFah2i4OG1sDXjtdrMHU6\ncbq95GfHcc25+RHXvnJzJa8t20u72YFRr+bWi8cwdVQKT7611Z9T3HeoBY1awVXzI19HQkLix+O0\nEKhg6ls6ee6DHdQ1d9LSYfeHvirrzYzNS+DLDYdCmlGrGy08+OJ6/nzr9ICchlIh56Fbp/PWV8U4\nnG7mFGYyeuiRZrf/fLqH5T8c6nF+ksfjJSlOR2Nb33t/tN3mSPnW0fNQwu4o5QJur+h/jyOHGLHY\nXLSa7P7H+jKbqrnNhtDNsVCtlDFheCKtJjulVW09nOkTRJ1aQZJRx5LLxwNw6dxhvPLZHn/loCFa\nFdGJXB+lIi5aQ6ftiOhPLEhmwaxcOu1u8jIMKBWRc09frC33twe0mhws/a6c3HQDdc1Hrufx+qbw\nSkgMVE6U1VGnxUxLS6CBttFoRHYURVU/BqelQD373nZ2l4VOs61utOB2eyPemHeXtfDFunIWzRkW\n8HiUVhl2h+bxeFm5pbLX4X5ur8icwgzeW1HSp/XnZ8cxdVQKH6wsxeZw+8rBvb4ZV+HCi11o1Qrm\nT83i263VmDuPjL9oNdlDGmMVMsHvRBGJLucMuUxARESpkOH1imjVCtQqRViX9C48Xi8zx6ex92Ar\nD7/8A0MzYll8yVi0ajlL15TRaXMzMic+ouu8UiHnZwtG8dbyYjptLrJT9dyycHTIsMhIBLtpuD1e\n4mI0xOk1AZ9F1+5OQmIgcqKsjjQaNdsOmJGV+6ISnRYTC+aM7ndnidNSoJrDhLLAd7Oub+15F2N3\nevB6RVZtqaSlw85ZEzPQapS0meykJESh7ra7Wr7xUMS5Ud3xeuGT78r6vH5DlJrL5w1naLqBFz/d\nTU1TJ1ZHz69jiFbx0r3z2VhUz2dryv2Pi/jyaMH0Jk7d6dqBWmxuPlldxtxJWVwwI4el35UFeNV1\np93s5P0Vpf5zSyrbaemwMXtCOm0mB2ari7U7a7A73dx/89SweaDJI1OYPDIFURT5dksVj7++GZkg\ncNm8Yf5y/0iMG5ZITZMFt0dEqZQxIT8RrVrBDReM5N1v9mNzuBmSZuDGC6URHxIDlxNldTRQOS0F\nKsGgCRgvLpf5AlXuXqrcspJjOH96Nk+8uYX1u2rxivDZ2jJkgNnuJjMpht9fX0hmsm/kwvodtX1e\nU08znHRqOdbDuSBBgNx03/UnFiRj1JeGLQEPJt6gRaNWMCLHSIJB4xdpjUpOakIUpRHslro36/YF\ni82FxebiyrPzmVOYyaOvbKS81hT22OCqws17G6isMweI2r5DLX4XjEhsLW7gpc/2+M/btr+R/Kw4\nbr90LDmp4RuJb1k4mtSEKCrqzORlGjh3Wg7gs8eaOT4dj1eMOLRRQkLi5HBa9EEFc9eVExg/PJGc\nVD1TRibz3G/nBExzDSY7JYaFZw7ll1dP4LHXt7BuZ63/pm3qdNHe6cLjETlUZ+K/y/YC8NqyIooO\nhoYRj4WEWB1D0vQU5MRxyew8rj7nyETevswfEgTfvCpRFEky6rh90ThGD41nRI7R58geIaYpAD+7\naCR3XTGey+YMDXvdeIMmoJ8sNV5HwuGwWFKcjuHZkY12g/vQRBFag6oGRVHgw5Ul7CoNLbjoYmtx\nY4CouT0iRQdbeebd7SF9UUfW7mszuOPycX5x6o4kThIS/c9pKVAp8VH8ZfEM/t9v53D/zdPISI5h\nRI4xwDBCo5YTo1Mxemg8jy+ZhUop457n1rE3yOcuGIfTy6HaDj5bU85xmIYHUNlg5mCticp6M9NG\np/i9BAH0UUpUip7/GkURispb+HL9IQCmjk7hnp9OIiFWy/pdNZRGGDMvAu9+U8L8qdnMnZwV8nxS\nnI7nfzfX3xgMvibohm7FHrddPBZDdKDrt1YtZ9KIJG5ZOBqdOrCQIUqjID3Rl3eSywTsDhefrzvI\nY69v5ssNB8OuMyFWGzJcEqCpzRbQNC0hIXFqcVqG+MLx80vHYTRoqG6wkJEczSWz83C4POijVFTU\nm/liXXnYGUvdq93kcgGz1ckfnl/Xa2FEd4x6FTaHp8eSbgCr3c3/fbSTZ387F4BvNlbw4aoDfXot\nj1ekuulIKfpj/93CnvLed3gWm5sVmyqobeoMeK8KOfz6moms2V5Nq+lIk3FTu43lGw7xs4tGA77q\nwt9cU8gLH++isc1KUpyO2y4Z46/OM8Zo+L+Pd9FmdqBSyJg9MYMrz85nd1kz//l0N03tvh2V2epi\n9dZqzp8e6upx8ew8yqvb+WFPfcDfUZxeTbQ0PFBC4pTltBUot8fL6m3V2B2+0vAordIfOvty/UH+\n8fZWNCo5N/5kFJ+uLo0oHqIIRoOaUUMSqGm0UBamLFk4/D+RqgNnjktn54FmKuvMvQ4J7O41V1zR\n2mch1KrljMvz9RNZ7a6IPVnheGnpHrzdytIBRuTEk5Ws59FXN4Ucrw6qpJuQn8TTv5pNU7uNxFit\n3xcQYPrYNJLidWzcU8+QND3Tx/jGdUwfk8bry/YFXjhC1E0uE/jd9ZPZX9HCQy9txGp3o1LKuGLe\nMClUJyFxCnNaCpTH4+UvL//Atv2+vMY3myr4y+IZ6KPUfP1DBa98XuRvKD1Q3RHQGxMOpVzO764r\n5K5/fBv+eYWsxwm3X3x/KGKuJBiv18uSJ1YRE6VCLgi99isZY9TYXW7kMhn/99EuXvhkF16v2GvV\nX3eCDVjB1/x8z/Nr/RN0u4g3aLh0Tl7I8TqNkuyU8OMohqbHMjTMHKXZhRl8vKoUm9ODIVrFuVOy\ne1znG18W+3NRNoeHbzZVceaEzB7PkZCQGLiclgK1sajeL04A5TUmXvm8iF9eNZEdpY0Bbgc1TZYQ\nAdCo5AHHxMWoEQQhxJRWJhPISIyisqFngeurOAE0dzho7vCF1BQyyMuI5WBth98uKJhWv8efBwha\nHyDIBNQqeZ/K4btQK2VMyE/itWVFIc9NHZ3a516k3rhqfj4F2XEUV7QxMT+J4Vk9TzbuCBp90t6D\nTZOEhMTA57QsknCHqV5Yt6OWdrM9RCxUYdwIRuQYmZifSHpiNCOHGLnjcp8xbIw2MN9hiFIyMrfn\nfpzjwe2F1Pgo8jJ6vnFHwosvN3U04gQwLCOWdTtrQmyUNEoZF54R3vn9WBk/PImr5uczLDOW8up2\nSqvaIwp68MynJGP4Jl8JCYlTg9NyBzVtdCpxejVt3ZL7DpeH5z/YSbMp8Ft3YpyGDovTHzpSKWUs\nPHMohSOOWPBYbC72lLWg1QR+nG1mJ8s3VAQ8NjYvnsZWW8SG4OBhhb1h1GuYPjaVxk+ttJrsR31+\nTygVAh6PiFwu8+e6orVK9oSpZBSA31xXSGZyqKHu8eL1ijz2+mY2761HFH05rT/dNAV5kInhL6+a\nwHMf7KS5w0aCQcuSy0Md5SUkJE4dTqpALVu2jLfeeov9+/djt9spKgoNEZ0MVEo5503L4Z2v9wc8\nvrGontiYwOF2MToVtywcw9I1ZXi9ImdOyAgQp5KKNp56Zxs1TRa0ajkKuRA23KaQC/zq6onMGJvG\nZ2vKePWLvWHXpuzB2bw7apWcMUPjmTs5k4ykaNLidTz1znaa2m1HvSOKhMvtex9et5dko5Zpo1NZ\n2s2FojvpSdFMHpmK1ytS1WBCoZCTlhB1QpzAV22p5Ic9df5Q65Z9DXyxrpyFswNzXTFRav54ozRe\nQ+L04UR58XVHp9Nh7QxvPn2yOakCZTAYuO6667DZbDzwwAMn86VDuOLs4Sz9riygWEAkNI9R29xJ\nfraRh29LpsNi57/L9rGrtIlpY1KZOS6dt7/e76+I66lM3O0RUchl/PW1TTS2WiMWNxiiVSCK/vLq\ncGQnRzN9TCrfbKrkt8+uIT0xmrgYNRX1R36p1Eo5wzJjqazvwGT1vUcBmFOYwdbiRr+PngDERKlC\nih2C0agUFAwxhgiURiUnNT6K6y/wTcP9yysb2VnajEIG08am8eurJyIIAjaHm2ff205dSyeGKDU/\nv3RsRJ+9YNq7zdvqwhTBQikSFpuLj78txesVWXDm0B6dKSQkThVOlBdfF9ZOC2eMSSI+Phuj0XjC\nrnusnFSBmjlzJgAbN248mS8bFoVcxvXnF/DvT/cEPO4VA3ubOixOXl66m2itkq82VmI7LGgbi+qo\najDjcvV9t/K3/24O+3hXWC5Gp+TiM4cyIT+Rx1/fEiA43alp7uTdFUcm0x6sNdEcNGcpRqfk4cUz\n6LQ5efHT3VTWm2jtcLDzQLNfnDj8uqnxul4FKiFWy+tBu75orYIZY9O59tx8jAYtH6wsYWtxIwBu\nD6zdXsMZY9OYNjqV5z/cwbqdR6yfnnl3G4/dMavH1+xi7qRMVmyu9Fs6pRh1nD2579V5VruLP/3f\n9/4WgM37Gnj09hnExkgiJXFqc6K9+DraW4mPj+93k9guTsscVBcXzhrK21+XhLgNBH9b33eoLaRv\nyO708vZX+1Erj73ORBBg2ugUpoxMOVyGHUN6UgyiKPYY5gsXQgyeU9Vpd7NycwVfb6zEbHXQ0Grz\nvS9b6M7DZHWRlhAVMjlXIZcxKtdIZnIM7WYHdUGO5xabm683VvDdtioS4rS4gpzUPV6R5nbfkMbG\n1sBhjU1tNkRR7FMI0GjQ8sDN0/hwVSmiCAvPzCU1IbI1VTArNlUG9KdV1pv5fG05118wss/XkJCQ\nOPkMeoHaW97CO1/vx+n2MGZoAteeVxBwU7z/pik89PIPvrxNmLCb78Yfuewg3AwmuUxALhN67H0C\nX9jsV1cXolUH/jWU1bSHuICrlDKykmNCxpqDz9B13uQsvt5YQcfhIYs2h5sXPtmNpw+u5MOzYpk/\nJZsn39pCu/mIWBfkxPHwbTOQyQQefyP87g98n0FNY6hhbUZSNDPH+Rpvg0NqsYdL8/tKlFbJhTNz\nSUuIQqM+ul/b7vO7uggWdAkJiYHHoBYoi83Fs+9v94eGSivbMESruWhWrv+YEbnxvPPIBbR02Hn5\nsyLW7azxPycIcPHsoew+0Az07hjehccrMmt8Gut21kbsTwLfDiXYMLW0so2//XdzQE9VslHLXVdO\nJCVex51PfhtQBKFSCFx5TgGXzx3G2h01foEC+iROguDLS323rSpAnADMnU4aWjvRqBTE6zXIZfTJ\nX1CjkjEyN57kOB0vfbaH9MRo5hSmsWlvvb8aUK2U93kH9fUPh3j76/20mexkJMdw9xXjkctlxOk1\nfcolzZucydodNew60Az45mkFF1hISEgMPAa1QFXVmwJGUbg8YtixEoIgkBCr5cqzh1FW005dcycy\nASbkJzJuWAKbikJHrvfG9v1NPHjrdD5cVUKH2YHT5Q0JoWk1cmRBN+ivNlbQ1B4YDrt5wWjG5vli\nwtedV8AHK0ux2t3E6dU8eecsDDEaSivbQgo8+oIoQqfNzZowo0Eq6s08/8FOmtpt1B7+TPqC3ell\nW3Gg+3i8QRNgy1Rc0UZdSydpvYTqvF6Rj1cf8I+jr6w386d/b8DhdKOPUnPp3Dwu7kVslAo5f751\nOmu2V+PxiJw5Mf2ENRNLSEj8eJzUf6VerxeXy4XL5dsdOJ1ORFFErVb3cuaxkZIQRbxB47+5AZRW\ntfHl+oOcPyOwoVQURXLSDDyyeDrfba+h+FAbuw40sbV4A/Iw0aBonQKLNXKBREenk6fe2sKjPz8D\nlVLB3UE2SHKZwFkTM0J6eRQhPwskGLT+ny+aNZSfnJGLxysGhKle+bwoYhWhQi74R8oH7+iSjVpG\n58bjipDzqmww03bYjSK4gORoaA3qL3O5vWzcU88lZ/UsLh6vN8C1A/AXqrRbHHy2tpzzpueglMto\nNTkwRKsihvTmhXFkl5CQGLic1ED8p59+yrhx47jlllvwer2MHTuW8ePHU1vb98F+R0NcjIbrzx9B\nVnIMSoXv6391o4WXPy/iy/W+0Q0er8jT72xl8d9WsOSJVewsbWb+lCy2lxyxPAoOa8kEOHtSFpnJ\nPX/7bzM7efur/XTanNidgWI2Ji+B68/3Jek7LA5WbalkT1kzV5+Tz9B035A9uUxg5rg08jIDfepk\nMiG0KCKo+KH7ZsftEalt7gwRJ7VSxt1XTsRo0CKPkJOx2gOvKz/GvqZworbrQOQZT10oFXLyMo68\n/+CXtzs8lFe38+t/rmHJk6u48x/fsnHP0e94BxNWu4uKelPI352ExKnGSd1BLS+ed1YAACAASURB\nVFq0iEWLFp3Ml2Te5CxG5caz5IlVdBU7OJwevlhXTnpiNHsPtrJqS7X/+H99tIurTMPCuoTL5T5n\nBa8In68rJ8moDTkmmJomC5nJeoZl+jzlAPRRKi48w5cHq2ow8eirm6hp6kSlkHHOtGz+dsdMtu9v\nJEqrZGxeQp/yNNmpeg7WHZlcm5cZS1WDOWT30YUAzJ2UxZjDocMhKTGUVIUWYAQXgchkAsZoVcCI\nDfBNG85MiaG6wUxlfagre9idVx93Yr+/fhJvfLmPVpMdj0dka3GD/30NTTfw/ooSyg9X6Vntbt76\nqpipo1P7dvFBxtZ9Dbz46W4a26wkG3X8/NJxjBuW2N/LkpA4Jk6LQHy0VolKIQ+42VY2WHjk1U2k\nxAf6t7k9XtbsqAm+hI9uN1SPF+qabeGP60Z6YjRKhYw/3zqdN77ch93p5oyxaUwe6etdePebEn+e\nzOn2smzdQTosDn5zTWFI+K8n7rpyPFFaJfUtnSQbdZw3LZtfPfNdxONFYMOeWqK1Sq45r4CFs/N4\n+p1tPRZ1dK3R4xEDxtADjMyN547LxuHxinywcj81jZ0cqG6nud2KSun77B1BYjl/as/u5F2olHJu\nXjDa//M3myrYVdpEtE7FDReM5MH/bAg43mp397kAY7Dxzjf7/bnOmqZO3v6qWBIoiVOW00Kgtuxr\nxOkO3UnYHG5aO0IdG+QygbgYFW1BVW2eo3Ad72JveTNF5S2Myo3n9kVjQ54PNj4VgbU7atm6r4Hs\nVAPXXzCCMUN7b5pTKuT+63u9In955YdeK+7azU4+XFXKsvUHkQm+HWJvAgVg6nQybngCO0uaEfF9\nXplJvnCnXCZw1XzfXC1RFOmwOPnH21vZURIYzstJ1TNl1LE1GM6fks38bqM38jJi2Xeo1b9Dy06J\nOS3FCcAeNEYl0g5aQuJU4LQQqPW7a8P2K4FvdyXIBNrNR0JWh2p7HxzYV1rNTp58cwtTRqUwa3w6\no4PEZu6kTDYV1Yf0TFkdHvYdauXpd7Zx342TGZoRx8rNvjxVfKyWq+bnhxRUdNHQamVPWe/TcsEn\niEfr3ZeRHIPZ6vJ/Rr5dUylVjRZmjkvzf2MXBIHYGHXI2AutWs59N02JuH6LzUV9SycpRl2fJuLe\nvGA0WrWCQ3Um4vQabl4wKuxxdqeb8poO4mI0pCYMTqfzYZlx/hCrAAzLDJ2zJTF4ONFefJ0WMy0t\nR/5tGI1GZLL+6xk8LQTKFWb3BL55Spkp0Vx9zgheW7aXDouDg7Udx1Sl1hPNHXb+t/4Q322r8huw\n5mXE8tc7ZjJ5ZAqzJ6bzzaaqsOc2tdn4/XPryMuIpaymwx8mq2qw8McbJod/X3IZcvmJ20Eo5AJZ\nKXp0GgValYLrzh/Bs+9vDzim3eJg+YZD/LCnlruumMCkEcm8+WUxRQdbQnapBTnxEX34thY38O+P\nd1HfaiU5TsetF4/pdaclkwlcd/6IHo9pbrfx0Es/cKjOhEwGWSkxPHHnmYOu3HzJFeOJN2iobbKQ\nkRTNlYenREsMTk60F59Go2bbATOy8k46LSYWzBndr7ZHg+tfZwQyU2LYsq8x4DEB3zyljUUNCIKM\nh26dRkWdiTv/sfqYX0cQoCA7jvqWzpDwIECn/YhQ7j3UyoMvrueR28/gjsvGU99ipai8hXBRRKfL\nGxDCAiipaMXU6aCovIVonYrRufH+sFa0Tsn00Wms3lbVp5BdJIwGNYgQpVGSbNQxpzDDP5J96qgU\nahotISGkdrOT77ZVU91o4ePVpf7XV6vkJBg0JMbpuPOK8RFf871vSvyWSvWtVt5fUXLMocDuvPHl\nPg4dLiLxen275H+8tZX7bpp63NceSMj7INYSg4cT7cU30DgtBGr0kHg++bYs4LGu27YowqaievZX\ntDEsMxa5TOhTrmlEThwlle0Bx4qib2dz1TnDeO2Lfb2KQ0mlr6pPLpfx11/MpKXdRnWT5fBOpC7g\n/OBdnVwm48EXN3CgusNfjn73VRN44o0tFFe0oZTLOGtiBtE6JSs3V2LuoWcrHHExah5ZPIM/PLeO\nqkYLVY0WNu+t55yp2fz80nFcfU4BaQlR7ChpYs2OGpzdQqgKhYzSqvaA9bs9Xv54wxSyU/U9vq4j\nqB/LcRRmvD0Rbhdd29zzpGMJCYn+5bQwJJsyKpXE2MiWOB6viNvjRS6XMX1M799GMpKiOXdadlgh\ns9hcvLx0b592LsEuEvGxWsYNS+QPP51MRlLo4L/uRzvdHr8vn8crsm5XLc99uJMNe+ppMztobLex\nsaien5yRy++vn8zcwgzG5yUiD7KD6PpJJvj+qBQyUow6Hv35Gfzl5Y0BnoBuj8j/1h/imvuXsamo\nntkTM7n7qomcMzUb1WHT3JxUPdeeO4J4Q+DnbdRrMBp6tyUqyI7z9zoJAgzPOjGW/9PHpBIcSjdK\nbuYSEgOa02IHBbBg1lBe/3IvLrfPgaF7n5NMJtBu8eVJfn/9ZByODWwujtxE2tDayb8+3BXx+b4G\n1YZmGCI/l27wh6TAJx7d9bDNHNiH5PGItAXlesxWF+W17cwYk86o3HjufPLbAFHt3pvkFWH2hHR+\ndU0hMgEefHFDiHv5keu6eebdbTz3u7kY9RpuunAUSoUMc6eTa88rICFWy08vGEFtk4Xiila0agXX\nnjeCmD4UPCy+ZCxGvYaqRjPpidFceXZ+r+cE09Rmo6rRxND0WAzRPpeSWeMzaG6z88GqUpxuD9nJ\nMdx+qTRxV0JiIDOoBcrj8fL/PtjBgao2aput/gKF4CZcr1dkR0kzM8dlIAgCv71+Mn98fh3ltaZw\nlz18neOvpJDLAi15RFHk7a98hQVKhZxxwxKoqDfjdHoCBit2YYhS+Wc7RWuV1LWEGtqu2lzFjDHp\nmDqdtAd59QkIiN3eR0uHjb3lLVhsTnb24vJgtrooqWxjYn4SD764gT3lvqrBg7UmHv35Gdgdbpra\nbZg6XdidHkoq25hT2PsMJ5lM4Mr5Ry9KXSzfcIg3l++jw+IkJV7HXVdO8JfpXzInj0vm5OFyeyU3\ncwmJU4BBLVBvfLmPlZvDV8cF090VW6dR8vDi6Tz3wU5qmy3UNVvDOkuAr5BAIZPR2BbYtNs1hLAn\nJo9M8v9/u9PN68v28r/1B/39S1FaJTa7K2zhhDFGzS0Xj+a1ZXtp7bBjsbkCHNC7aDU58Hi86KPU\npBij/HORBMFX7t3ZrcR8T3kr973wPemJ0Xh76aHSqhXkphv4dmu1X5wAymo6+Hj1AdpMdg4eFnin\ny8vKzVUsPHNon6foHguiKPLZ2jK/o3t9i5X3vykJ6SOTxElC4tRgUAtUXXPfRmQIAqzYeIjmdhuL\nF41FrZSzZkctu8taQjzugknQa0Pcx2N0ShxOT6/zoDbtreeiWUNpbLPy6CubKK8NtBqK5K9nNGh4\n/I4zeOz1rSGDAINpM9m59W8rUB7Orxn1GmwONzFRKrbtqw85XhR9foXBYdDuyAS4ZcEokuJ0eMXQ\nY5rbQ01pnW5PSBPpiUYUCSjWAHD1ZT6IhITEgGRQf5VMS+zb1FVRhKYOB99squSpt7bicnv5YMX+\nXsVJqZBh6nSETOTNz47rVZzAV+oM8PqyvSHiBKHGqPpoFc/+5ixe/tM5yOVy6iJUoel1KjKTokkw\naGjusNPU5huXsfyHCm66aBSP/vwMdGo5DnfkPV4kcQKI1qowWV14PF7mFGaSnx0XsOZVW6opq27D\nEH0k5zQixzeZ98dEJhMYOcToF3KVQsaE4ZLNj4TEqcqg3kFdd14BHRYHB6ra6bS7fGPGezmntKqd\nf763PWwfUzBqpYz6bjsYlVLG1FGpRGtDP1aNSobdGXjT10cpAcKKmUYlD+kxMnc6qW60kJNmICZK\nhT5aHRCiUytlFOQY+c01hWza28ALH+8MOL/T5qa0so1Xvyhi896GXt9fJExWJ/9dtpeSyjb+eMNk\nHlk8g9e/3Mf/vj/oL8KoabIyZWQyRr0WnUbO1ecUHJW34LFy91UTSU+Mpr7VSkG2kXOn9c3vT0JC\nYuAxqAVKLpdx15UTAF8hxPMf7mD97jos1sg7I61azr5DfbMJCrYIcrq8tFvs7D0YurMJFieACQW+\nHNSUkcnsLG3yX29kjpHqplAncq/oc1rYVtKITq3g7ClZvPm/fX7RNeo13PPTyUTrVKzaUhm21H39\nrtrjEqfubCyq484nv2XEkHgOVLWGlN1rVArazHZ2l5vZVtzIzQtGMT4/+YS8diTkx1lkISFxKnGi\nrY660Ol0WDvNJ/y6R8ugFqjumK0OhmXGMXVUKk+/sy1sQYFCLpCRFMO2/Y1hrhBKuOKFPWUtIf1N\nkZg43CdQZ0/JRiGXs21/I9E6JWdPyeQ3z6wJOV6tkvPDnjosNp+QqZXygB1hXYuVb7dWMacwk9qm\n8OE/TwQfp+4l530p8ACfI0NFvZmK+tBf5GitglaTjT3lrf7H7n/xBzISo3lsyUx/+Xcwtc0WrDY3\nOWn6iF59gwWX20tJZRsalZzcdMNpa3ArceycaKsjAGunhTPGJBEfn43ReGL6EI+V00KgahotPPLq\nRqobLSjkAlFaZcDzibEalAoZtc1W1u8OP+zu7MmZFFe0Ut0YWHih0ygCdlKiGFkEujMyJ47lGyp4\nY3kxcTFqllw+nrMKMwB49NWNYXc/SrngFycIdV3wLQDu+PtK2i2hv7R56THsPdgaeg6BThVqlZy0\nhKiwZfY6tSJsyXswF83KZWdpc8jj1U0W/v3JLn5/faiP4P99tJNvt1bhdHkoyDby4K3T0aoH56+o\n3eHmzy/9wN7yFhQKGWeMS+PXV0+UREriqPgxrI462luJj4/vVw++Lgb3V9TDvL9yP9WNvh2F2yNi\nd7hJMGiQCZASr2Pm+HRqm8M3pXZxwRlDuPvKCRj1R775pyZE8edbp5ETwb5HrQz/8Y4cYkQfrWbD\nnjoOVLWzeW8Dz7yzzf988Hh0//WCjE2D/WB1ajlfbaygNUz+TCZAea25T87ldqeHS+cOC3HC1mnk\nPPe7OZwxNnQYYPelZKfEcPHsPBJjww907AhqMgYoq25nxeZKbA4PHi8UHWzlna+Ke13rqcr7K0so\nKm9BxLeTWrejhp2lvU8YlpA4nRicX0+DCO7pcbi8OA67LijkMnaW9HxjkMngoZd/ID0hmlsWjOb7\nXbUIgsClc/NQK+VY7S5UCllAsYNSIbD40nF0mOy83i1PBDCpIJn1uwPH3Hfvo0o26iipbA94Pi1B\nR2ZSDDaHG6vdjVop4+wpWTS32SitaUerlHPp3Dye/3B3+M/gKPqKE2I1jBxipLymg/Kadn9f1rhh\nSRj1Gn8vVXdEfL1Rk0YkccMFI9FplIzJS2DN9pqQcGFWGEFvNztCSsT7slM7VbEFvTe3R8TUeWJD\nNRISpzqnhUCdOy2b3WXNtIQZTljdaEGt6Dms4vVCh9lJh7mVlg47/7n3bARBwO32cM0DX2LrNlnW\nlzYRcLlFXlm6m8vnDWfamFQ2FdXh9cK4YQlcfNZQ9h1qBY7c6OO67cyWXD4erwgNLZ3oNErOm5bF\n15ur2NituGHKqBTG5iXyxbpynE4vdoeHt78qQTzOWSFpCVHcsnA0CbE6bvjJSKK0SsprOog3aLjh\nJyNxur0Rh+A5XW4unzec5MPNuN9urQoQJ0GAeZMzA6bjdjEqN57cdD3lNb6wYlyMmtkTM47rvQxk\n5k3O5Ic9dTS3+34nh6TpmTTixy0gkZA41TgtBGr00ATuu3EKT761JWwor6d+oGAaWq38+aUfuGXB\nKJxuMUCcwGcf5D68XbHY3Hy1sYJ//W7uYXdvLyNyjMjlMpZcPo6n391OU5vNn4PqQqdRcs9PfTma\nrfsa+M9ne6hpDCx6qKg3s6dsV4Ann9Xu5mgzGFkpMZg7nYhAYUESd185wZ8HEQSBy+cNDzheqfCJ\nWHuYMF28QUvC4bDeO18Xs+9QW9DzGm5fNC5s8YNGreDBW6bz1vJiPB4vZxVm9GmS8I9BVYOZbcUN\n5GXGMSo3/kd5jbyMOO756WS+3liBQi7jyvn56DTK3k+UkDiNGPQC9dmaMrYUNyCKoWGVY2VbcSMP\nNVq4eHZuyHPuoFia0+kBQaAgJ7AaxmjQ8pfFM3p9rTeX7wsRJ4CWDnvYRuJwUjs8KxadRolWpeDC\nWUNYtu4gdqeb7FQ9N1wwEofLgygSUjzSnQ6Lg6fe3kp9qxWdWoFC5pun1UWMTslPLxhJjE6F1e5i\n+YaKgHH2MgHmTspCrZSHuboPo17T46yoYBwuDys2VSKKImdPyTohwwc37K7lhY930WpyIJf5vixk\nJEVzy8IxDM+K6/0CfaC0so01O2qIi1Hzi0vHnZT+MAmJU5FBLVDLNxzitWV7e3RFOFYaWq28+Mme\nXo9TKmXIZQI2h5u3lhdjtbs4Y1wahQV9C+dEysNYba4+z67qsDhp7bCTEKulqdXGH2+cwppt1Xzy\nXRl3PbWaUbnx/HzR2B6v8fwHO9m2/0iuLjFOi8fjxebwkGzUYohW8/XGClpNdmZPzMAZVGE4coiR\n63sZpOfxeGm3ONBHqVAqIgsZ+LwLH/j3hsOhUli9rZpHFs9Ac5xVf1+sO0irybc79Hh9prj7DrXx\nr4928vQvZx93ld3OkkaefncbLR2+19h3qJV7b5wiVe9JSIRhUAvU7gPNP4o4ddGXwODQ9Fg8Hi8P\nvfQDRYdNVTcV1XPXlRN6nBRb22zhv1/sxRrBbknEZ7XkiZAP6k5Dqy+s2dxh50B1GxUNJtZsr/Hn\n5KobzKQYo1g0Jw/w3fzX76pFrZIzbXQacplAmyUwfycTBP752zk0tNl4/PXNHDpcUr6/0mdxlJ8d\nx9ZiXz9ZlEbJ2VN6dnSoajDxj7e2Ud/aSWy0hpsXjGLyyMifz/INh/ziBLC/oo3P15WHhCSPlkg5\nvDaTHYfLw46SJrYVN5IQq+XSOXlHvfv5amOFX5wAdpY209hmI9moO651S0gMRga1QMVE9W9MP1qr\nZMbYVKoPz0XqoqPTydodNREFyuny8Nh/N/vdwCPhcHpCqgd7w+3xCWT3ghGv6BMIAKvdxf3/Xk9J\nZTsCUDgiiT/9bBrJcTqKu+WUkuJ0xESpKa5oo77b3CiH00NReQv33jiFt78qxmR1MqkgmRlj03pc\n1yufFfmrAzttFt5cXtyjQHnC9IkVlbdw5gTrcd3sZ41Pp6SyDUdQRWGcXsva7TW88nkRFpsLAThY\n08Efbgjt5+qJ4J2SXOZrEJeQkAhlUAvUjT8ZxYY9dbR2hCb0IyETIF6vITk+ipT4KL7fWYOth12K\nIECMVsWInFg8oi+cZrW7GZphwOX28PZX+/F4RWSCgKfbnkupkOH2eHE4PSG5n7rmTirDuDMEIwI5\nafqQkvTe0KoVqJWygJtw7OEqwg9XlfqvJwJb9jWyflcNdxyuLKxr7sTmcKGPUvLZ2jImj0gmRqfy\nG+YK+MRLpZRz44WjIq5hU1E9m/c2EBuj4oqz8wM8BcHn5C6KIoIg4HJ7aTf7ckIvfbaHNrODxFgt\nuWmGAJPdrcWN3Pd/3/PbawtDcn595fwZQ/h+Zy07DwQ2GV8yO5dvt1b7HUhEoOhgCzaH+6iaiS+f\nO4zSqnbqmjtRyAVmjE0j3hC+X0xC4nRnUAuURq0gxRh1VAI1PCuWJ+6a7f/51otHc++/1lFWE7qb\nUSkEbl80lmlj0kKmxa7eWsUz727354jkcsE/wiI3XU9mUhS/eHwVNofP1ufeG6f4b3SxMWoM0Sp/\nLqQnDobpSeoNETGk56jN5MDt8YZ1SH/ti700tNr4/fWTeP6DHXy9sYKapk42FtXT0m7nqvnDWfb9\nQV+V4hAjl/USZvt2axX/+XS3f5x8WU0HQzMMFB9q9Ut4dooeQRDYd6iF5z7YSXO7Da9XDChxnzU+\njfH5iSxbd9DvqtHQauXBlzYwItvIPTdMQaPqOZcVjmFZcQECFRutZvTQBNbuCOxdU8hlR23HlJNm\n4G8/P4P1u+tIjtcxOai0XBRF9le0YXO4GZUbj6qHohIJieLi/cTUndgGb6vVgl7eQWxsHAZ9FCML\nhp3Q6x8Ng1qggKNufpwwPCng5+JDrVQ1hPe1c7pFSirbmT81x/+Y1yvy5pf7WLqmLKCAweMRmTkh\nlamjUxkxJJ7fP7vWP0dqR0kTr35exC8u840gN0SrueLsfD5ZfQC70016YjStJntAKK0LV5hQVzAK\nuRBgnVTfbA3Jn9U1W7jj76uoDTNDq7HNxvsr9pOeEMXeg63+pl+3R2R3WTNP/XI2F83KxesV+5ST\n+X5nrV+cwPcZ33HZODQqBQeq21Gr5PziUl/Rxmtf7I24m2xotbL4krGs2FQZYPtktbnZWtzIrY9+\nzWsPnHvUeaJrzi2gutFMaVU7KqWcBbNyiTdouWLecCobzNQ1d6LTKDhnatYxDT+Mj9Vy0azQClBR\nFPnHW1tZt6sWj0ekICeOh26dLpWfS0QkPS2FuPgTP1JGjIqlwyOjtbpeEqgfE626799Ao7QKhmYa\nqGu2kJoQzea99bz6RVGPOZ7ggXivLSvik9VlIccJwLpddWzb38zMcWl0dAbujkzWIz8frO2grtnC\nnMJMLpiRQ5xeQ31LJ899sCOsv11vqJVy3J4jIbRw72fvwbaQx7pjc3jYe6g15Ibc9bMgCOwsbeSj\nVaWoVHLGDE3gkrPywlanBe86lAo5WrWC5HgdKzdX0mZ2UNNo4ddXT8TWgzWTSiknSqNg3LAE1u2s\nJbi+od3iZE95C+OGHd0/YKVCxn03TcXh8qCQ+6owAYZnx/HEnbPYfaCZjKRoctIMR3Xd3th1oJl1\nO2v9X2yKD7Xx3ooSbuohVCpxehOjN2CI7V9D1x+TQS9QamXf3qJOo8Dp8vDIK5uJ0iiYNzmTdTvr\nIvriARj1auYEuR2UVoXPB4n4dlFmq5M126tJMUZR2eDbGSgUMkbk+BpCSyraeOyNzTQdtj7ad6iF\nh26dzoZdtUctThqVnMKCJM6Zms3rX+6j3ewgOU5LdZPlqKsbBQGGZ8UxLCOWV74ooqXDTlKclsvm\n+r5dPfveNlZsOuIc0eUrt2hO6Levq84ZTkW9iepGC9rDY0O0agWfri7zNx5XN1p466tictMNHKw7\nEl7VqeXYXR68Xigqa+Hup1bz0OIZFOQYeevL4pCyfFUEP8S+oFbK8XhFmtpsxOiUaNQKDNFqZo5P\nP+Zr9oTF5gxpG3D0oUpTQmKwMugFamJBEqVVoVVZwXQ3Ue20u1mxqRJrkEtEWoKOghwj24obEUUY\nm5fI2KBv57o+JMxtDjfXnZfPt1trcLg8jB4az4LDIZ8v1pf7xQl8pfJlNR18tPpAr9cNJj05mgn5\nyaTER/HMr87C6fKwqaie5z7YcdTXSjXqmHX4xjwmL4HKBhOGKDWxMRoq6kys3lYdEDZ0uUX2Hmxl\n0ZzQa+WkGnjg5ql8/O0BUhOiuHh2Hh6vGNI75XR5WHLFeKJ1SuparKQYdVQ1mNl+2DtRBCobfOX4\nv7m2kKRYHY+9vtl/k09LiPIL/7HQ2mHjr69toqrRQoxOxZVnD2f+1B9vAGJhfjJ5GQYOVPvyiomx\nWuZPyfrRXk9CYqAz6AWqqy/mva/344iwawg3/yhYnACmjUll7fZa/yiL73fVMjLXyPnTh/iPyc0w\nsLusGYfTE9GgNVqn5D9L9yCXy5g/Jcu/RofLw/biwISnTCb4Ssl7EVjwDetTq+RY7W6itQpqGi08\n98EOjHo1t108hnU7a1m/q7ZPxrEy4YjBbJRWQbROxSOvbGTW+HRmjk9n+ScV7ChtRCaTMSInDlcY\nuyidJvyvV1O7jUdf3URFvRmZAPsr27nnp5MYlhXrz8uBb76V0+XhloVj/I/d/dTqkOt1OYRMG5PK\nU788kw9XlpISr0OrVvDm8n1cMD0H4zFUyr38eRH7D1c0Wu1u3l9ZwlmFmb3mnbYWN7BicyUyQeCK\ns4eRndK3UKBGreDPt07nvW9KcHk8nDM1m6EZsb2fKCExSBn0AmVzuPluW3VEcYK+NdwCfL2hAku3\nnZbL7eVAVTtM9/28dkc1S1eX+cvSu9/kAYak6lGq5JRWtGHCVyTw0bcHmJifRF5mHMs3HKTdEpib\nykiKJjtVT1ZKDPsres4TDUmN4Y83TeVQnYlXPttDTZOv4KHV5ODDVaUcqjP32dXcK8LoXCOZKXo2\n7qmj5HDocu/BFnYdaGTdriMVbVuKG8lMiqaqmyWTUi6jsc3Kht01rNpSjdcLZ01MZ9aEDN79er9/\nyKFXhA27avl/721nTmE6G4vq/T1Oze023vhyH4svOeJykZUcTXm3ykVB8O3oushNj+VX1xTy4Isb\n2F3mC4l+v7OWh26bTlJcz/1RXq/I/9YfpKnNxsxxaVjtgU3SVrsbq90Vcdgi+EKyz7633V+BWVbd\nzt9+MZM4vabH1+7CEK3mtkvG9H6ghMRpwKAXqIO1HWEnvh4LFrsbpULw7xbkMshIivE/v7O0OaBn\nyiv6hgy6vSJqpZyzp2RS12ylpJvQWO1uDtaZyMuMwx1mFzJjbBqCIPDrqyfy9Lvb2H+oLaKg1rVY\nae2wM2VkCq98VhTwXHO7Hbfn6PJOdc1WymtMAXkds9XFuqBya49HZM6kTMydTr7fWUNjux2Xx0tR\neSulle3+ooziilb0USp2lwXuEkXgm8Nu7cENuJX1geX9o3LjKalsp81sR6dWcMHMISw8c2jAMduK\nG/ziBL581qery3q88YuiyD/e3sraHTWIos86acLwRJRywV8pmZkUjT5KFfEaAOt21Aa0B3SV4583\nPcf/2P6KNlZsqkCplHPNOflE63q+poTE6cqgd6k0RvjmquxlxEYkYqM15KYbyEyK5pypOVw8+8jN\nMTFWG+AmrpDLcHlERNE3BPCj1WXkZxuJ0ioCzhmX58tjnT8jh7yMI+GgQzPRwgAAD5dJREFUIWl6\nLprpy02lJUbz+B2zSE+Kjri2Trvbb6ekD3LRMHX2vResixaTPawXYLCDu0Yt56yJmfxswWgMMYGf\nd/eKQVOnkx+K6hEieK6bOp0hz3TPx7355T7+8+keaps7cXtFzirM5Ip5+SHXEWSh1+/N6q7d4mD7\n/kZ/JWCryU6n3cW1549g6qgU5k3K5E8/m9qrZ15sTODuSqmQBThb7K9o47H/bmL5DxV8vracB17c\ngN05eOdeSUgcD4N+B1VaGRoWE4D8rDgsNheV9X0Pe4GvMu6xO2aGdQ+4bN5wKuvN7ClvRgBsTjfu\nbvdyi9XJsKxYbrhgJOt21SKXCVwyO4+kwzcwnUbJI7efwadryhBFkYvPHBrw7VomE7j2vALe/HIf\nbWYHMgFsdjddmw59lIpxwxNpN9spqwmsJgz3HoNDkH1BwHez717SPWZoAolxvhxPvEFDaVW347sd\nK+AT5OgeXNODlyOTHfkOtWVfg1/wXC4v24obufHCUbjcHtwe0f93UpifxIT8RLYfNrfNTonh0rk9\n93LIBAFZkPjIZAKXzhkGYQo9InHJWXnsr2hj14Em5HIZZ45PZ0L+kd66FZsraO5mM1Va1c7uA809\n2jpJSJyuDHqB2lhUH/KYCOwpb0Uug7MKM1m3o6bPfnZ1LZ0s/tsKRufG85vrJvl7ZMBXpJCbbmDX\ngSZMnc6Qm39irJZko47zZwzh/BlDCEeUVsm15xZEfP2Z49LJz4rj/n9voKbJl/NRK+VkJEdz/vQc\nhqQZuPXRr3G6Al+8uxhpVHKykmOoqDdFrG5UyAkQ1y5EICU+ivrmTkR8lXI3XTjS//ydV4zH491O\nY6uVOL2GFKOOTXsb8Hi9jB2agMnipKbJ4hM6GWjVyrBjQ7oY2m1HKQ/yrJPLBd5cvo9VW6rweLyM\nHprAr68pRC6X8cDN0/hmUwVWm5v5U7PQR0XOG4Ev9zN9TCortlThdntJNmq5ZHZej+eEQyGXcd9N\nU2hss6FUyEJ28MFtDwq5EOJCIiEh4WPQC1ROmoHvtteEfc7jhZYOG0pl3w1X3R6RNrODdTtryc04\n4O8DAjBbnXy2tsxf5RfM/ClZR22NE47lGw75xQl81X9zCzM5d1oO1Y1m/5TW7kwamUJrhx1B8K3j\n0+/Keiy9H5JqICfNwLdbqwJcKKK1ShZfMoamNhvtZjtzJ2WSZIzyP6+PUvPAzdMCrnWzw43bK9La\nYeO3z67xhwhFL8yfnMmusmb/JF3wCej44YmkJkTx0wuOiN+FZ+TySlsR7WYHhmgVhQXJLP2uzG9/\ntHZHDUPTDSyaMwyFXBZQXdkXfnHZOApHJFPX3Mn0MamkxEf1flIYBEGIaFh79Tn5FB9qZX9lGwq5\nwJkT0snPPjFzpiQkBhuDXqAunj2UXaVN7ChpAgFUCnmALY5WrWR4lpHt+xtDzu2at6RUyEiI1VLX\nzQZIBJraAq2HLFZXxPEYyUYtsyacmBHmijBlzl2ebeGcsaO1Su7/2VT/zx6vyIerSnt8DZ1WyV1X\nTuC680ewbkct63fVIAoCZ01I7/Msqy66ZjSVVLaG5K+sDjc/WzCaf76znaZ2365j3uQsbg8zn2rO\npEyGZ8dRfKiV/MP/7e7NJ4qBOaujRRAEpo1OPebz+0KUVslff3EGReUtRGkUDMuKk2ZBSRwzOqGD\nKOHH24HrU/q3zWHQC5RCLuPBW6dTXtOBXICqRjNvLi/G1OkkLSGamxeMQquW8+x7O6hsMNPaYcfp\n9hKjU7LwzFyyUvTkpOpRq+T84bl1fj+8KK0y5EadFKdlSLrBP+pcKRdIS4om2ahj0Vl5vZY595WL\nZ+extbjRX3Y+Ni+BeZN9DZ3JxijSEqP8JeYCcOmcwCo3uUxg9NAEvt1ahSj6ihzGD0tk78FWTJ1O\nko1aLj+8MzTqNSw4M5cFZ4Z6xx0tBdlGslNjqKjzVVXG6JRMG53KuLxE/nL7DDbsriMzOZqpoyKL\nRHpiNOmJvkIRjUpBslHnn3cVo1NSOCIp4rkDBZVSHpCXkpA4ViZPGEVGxon54jsQEcRIE9oGENXV\n1cybN4+VK1eekL8Mu9ONqdNJvF4TYiTa3G5l78FWhqQZyEyOCXiu+FArH6wsweMVmTkunbPDdPmb\nrU5e+2IvNruLCQVJzO9lUN/xvIe1O2qQywRmjc8IaB61O928/NkeWk0OpoxI5txuJc5deL0iH68+\nQEOLldFD45k9MYO6ZguV9Wbys40h1WgnivqWTt5aXozb42XmuDTOGHd8tkF7y1v46NtSPF6RWePT\n/UItITGYOdH3xIHKaSlQEhISEqcyp8s9cdD3QUlISEhInJpIAiUhISEhMSCRBEpCQkJCYkAiCZSE\nhISExIBEEigJCQkJiQGJJFASEhISEgMSSaAkJCQkJAYkkkBJSEhISAxIJIGSkJCQkBiQSAIlISEh\nITEgOekC5fF4ePzxx5k+fToTJ07krrvuoq0tdKighISEhMTpzUkXqBdffJFVq1bxwQcfsGbNGgB+\n//vfn+xlSEhISEgMcE66QL3//vvcdtttZGRkEB0dze9+9zvWrl1LXV3dyV6KhISEhMQA5qQKlMlk\noq6ujlGjRvkfy8zMJDo6muLi4pO5FAkJCQmJAc5JHVjY2ekbohcTEzhnSa/XY7FYwp0SQH19/Y+y\nLgkJCYn+Qq/Xo9fr+3sZA5KTKlBRUVEAmM3mgMdNJhPR0dERz9Pr9UyePJlrr732R12fhISExMlm\nyZIl3HnnnUd1jl6vZ8mSJYNe2E6qQOn1etLS0igqKqKgoACAyspKLBYL+fn5PZ73r3/9C5PJdLKW\nKiEhIXFSOBaR0ev1Ry1qpyInVaAArrjiCv7zn/8wdepUDAYDTzzxBLNmzSItLa3H86RtsISEhMTp\nxUkXqNtuu42Ojg4uu+wynE4nM2fO5IknnjjZy5CQkJCQGOAIoiiK/b0ICQkJCQmJYCSrIwkJCQmJ\nAYkkUBISEhISAxJJoCQkJCQkBiSSQElISEhIDEgkgZKQkJCQGJBIAiUhISEhMSAZ8AI1mOZHLVu2\njGuuuYbCwsIAw9xTlSeeeIILL7yQwsJCZs2axf33309HR0d/L+uYefrpp5k3bx6FhYVMnTqVm2++\nmX379vX3so4br9fLVVddRUFBAQ0NDf29nGPinnvuYfTo0UyYMMH/55133unvZR0X69ev54orrmDC\nhAlMmzaNhx56qL+XNOAY8AI1mOZHGQwGrrvuOu69997+XsoJQaFQ8OSTT7Jp0yaWLl1KfX0999xz\nT38v65hZuHAhS5cuZevWraxZs4Zhw4axZMmS/l7WcfPaa6+h1WoRBKG/l3LMCILAJZdcwvbt2/1/\nrr766v5e1jGzceNG7r77bm655RY2bdrEmjVruPzyy/t7WQOOAS9Qg2l+1MyZM7ngggvIyMjo76Wc\nEH71q19RUFCAXC7HaDRy/fXXs2nTpv5e1jGTm5vrNy32eDwIgkBycnI/r+r4OHjwIO+88w5/+MMf\nOJV78kVRPKXXH8xTTz3F1VdfzTnnnINSqUSlUjFy5Mj+XtaAY0ALlDQ/6tRiw4YNjBgxor+XcVx8\n/vnnTJo0iYkTJ7Ju3TqeeeaZ/l7SMeP1ern33nv5wx/+0OO0gFMBQRD4+uuvmTp1Kueeey5///vf\nsVqt/b2sY8JqtbJ7925cLheLFi1i2rRpXH/99ezZs6e/lzbgGNACdbzzoyROHl999RXvvfce9913\nX38v5bi46KKL2LJlC+vWrSMvL++Udox+/fXXSUpK4uyzz+7vpRw31113HcuXL2fjxo08//zzbN68\nmfvvv7+/l3VMmEwmvF4v//vf/3jsscdYu3YtM2fO5LbbbgsZRXS6M6AF6ljnR0mcXL788kseeOAB\nXnjhhVN+B9VFQkIC999/Pzt37uTAgQP9vZyjpqKigldfffWUvYkHM2rUKIxGIwB5eXnce++9fPXV\nV7hcrn5e2dHTdV9btGgRw4cPR6lUsnjxYtxuN9u3b+/n1Q0sTrqb+dFwrPOjJE4eH330EX//+995\n4YUXmDBhQn8v54TSdfPruqGcSmzdupXW1lYuvPBCAH/+ZsGCBfzyl788pQsMunMq5qViYmJIT08P\neEwURQRBOKULWX4MBvQOCo7Mj6qursZsNvd5ftRAxOv14nA4/Dc+p9OJw+Ho51UdO6+//jp///vf\nefnll095cRJFkTfffJPW1lYA6uvrefjhhyksLCQ1NbWfV3f0XHDBBaxYsYKlS5eydOlSXnzxRQBe\neeUVFi5c2M+rO3qWLVvmj6QcOnSIxx9/nLlz56JSqfp5ZcfGNddcw8cff0xZWRlut5uXXnoJlUp1\nyv87OtEM+HEbXq+XJ554gk8++cQ/P+rhhx8mNja2v5d21Hz88cf+EnNBEPzfmlauXHlKCm5BQQEK\nhQKlUul/TBAEtm3b1o+rOjZEUWTx4sXs2bMHm81GXFwcs2fP5s477/SHlk5lqqurmT9/PqtXrz4l\nKxOvv/56SkpKcDqdGI1GzjnnHJYsWXJK7m67ePbZZ3n//fdxOByMHDmSP/7xj/5IkYSPAS9QEhIS\nEhKnJwM+xCchISEhcXoiCZSEhISExIBEEigJCQkJiQGJJFASEhISEgMSSaAkJCQkJAYkkkBJSEhI\nSAxIJIGSkJCQkBiQSAIlISEhITEgkQRK4rTDbrfzzDPPcO655zJu3DimTp3KZZddxhtvvOE/prS0\nlDvvvJO5c+dSUFDgtwqSkJA4eQxos1gJiR+DP//5z2zatIn77ruPgoICLBYLe/fuDRiCabfbycrK\n4vzzz+evf/2rZOIpIdEPSAIlcdqxcuVKfvvb3zJv3jz/Y8Hu+GPGjGHMmDEAPPnkkyd1fRISEj6k\nEJ/EaUdiYiLfffcdJpOpv5ciISHRA5JASZx2PPLIIxQXFzN9+nQWLlzIAw88wMqVK/t7WRISEkFI\nAiVx2jFx4kS++eYb3nzzTS6++GKam5u56667uP322/t7aRISEt2QBEritEQulzNhwgRuuukm/vWv\nf/G3v/2N1atXs3nz5v5emoSExGEkgZKQAHJzc/n/7d2hjYVAGEXhY9AkIJHg0IQGkJSApxsKoBuq\nwJCQINEoMCtedkvY9yecr4IxkyNmkgv8LepK+j5/8el1hmGg73vquibLMo7jYJom0jSlbVsAnudh\n2zYA7vvmPE/WdSVJEqqq+ubxpddwUVevM88zy7Kw7zvXdZHnOU3TMI4jZVkCn4n0ruuAz4z97zUp\nisIPFdI/MVCSpJB8g5IkhWSgJEkhGShJUkgGSpIUkoGSJIVkoCRJIRkoSVJIBkqSFNIPCY0lTfpZ\n5OwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x102655710>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "shalek2013.expression.twoway('P1', 'S1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<seaborn.axisgrid.JointGrid at 0x108897c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mzn1uqc7kffsPh0OhtNKIyWzzevx8BDVKygjy16HVyNfUpUp6UEIID5Fh/iTE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1093e6ed0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "shalek2013.plot_pca()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'shalek2013' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-11-1fecc416cb6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshalek2013\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_pca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'shalek2013' is not defined"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "! wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48968/suppl/GSE48968_allgenesTPM_GSM1189042_GSM1190902.txt.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2014-10-07 10:58:57--  ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48968/suppl/GSE48968_allgenesTPM_GSM1189042_GSM1190902.txt.gz\r\n",
        "           => 'GSE48968_allgenesTPM_GSM1189042_GSM1190902.txt.gz'\r\n",
        "Resolving ftp.ncbi.nlm.nih.gov... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "130.14.250.12, 2607:f220:41e:250::12\r\n",
        "Connecting to ftp.ncbi.nlm.nih.gov|130.14.250.12|:21... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "connected.\r\n",
        "Logging in as anonymous ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Logged in!\r\n",
        "==> SYST ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.    ==> PWD ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "==> TYPE I ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.  ==> CWD (1) /geo/series/GSE48nnn/GSE48968/suppl ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "==> SIZE GSE48968_allgenesTPM_GSM1189042_GSM1190902.txt.gz ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "69309954\r\n",
        "==> PASV ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.    ==> RETR GSE48968_allgenesTPM_GSM1189042_GSM1190902.txt.gz ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "Length: 69309954 (66M) (unauthoritative)\r\n",
        "\r\n",
        "\r",
        " 0% [                                       ] 0           --.-K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 0% [                                       ] 68,056       255KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 0% [                                       ] 266,432      531KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 0% [                                       ] 676,216      922KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 1% [                                       ] 1,195,552   1.21MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 2% [>                                      ] 1,799,864   1.43MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 3% [>                                      ] 2,523,864   1.68MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 4% [>                                      ] 3,337,640   1.92MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 6% [=>                                     ] 4,271,600   2.16MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 7% [=>                                     ] 5,301,128   2.39MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 9% [==>                                    ] 6,427,672   2.65MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "10% [===>                                   ] 7,416,656   2.81MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "12% [===>                                   ] 8,562,976   2.99MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "14% [====>                                  ] 9,866,672   3.21MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "16% [=====>                                 ] 11,220,552  3.42MB/s  eta 16s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "18% [======>                                ] 12,542,576  3.78MB/s  eta 16s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "19% [======>                                ] 13,838,536  4.08MB/s  eta 16s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "21% [=======>                               ] 15,161,274  4.48MB/s  eta 16s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "24% [========>                              ] 16,688,914  4.77MB/s  eta 16s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "25% [=========>                             ] 17,847,314  4.94MB/s  eta 12s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "27% [=========>                             ] 19,290,354  5.31MB/s  eta 12s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "29% [==========>                            ] 20,697,810  5.51MB/s  eta 12s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "31% [===========>                           ] 22,018,052  5.77MB/s  eta 12s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "33% [============>                          ] 23,303,876  5.87MB/s  eta 12s    "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "35% [============>                          ] 24,733,052  6.01MB/s  eta 9s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "37% [=============>                         ] 26,001,500  6.16MB/s  eta 9s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "39% [==============>                        ] 27,420,540  6.22MB/s  eta 9s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "41% [===============>                       ] 28,883,020  6.31MB/s  eta 9s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "43% [===============>                       ] 30,150,972  6.33MB/s  eta 9s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "45% [================>                      ] 31,534,308  6.33MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "47% [=================>                     ] 32,930,180  6.35MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "49% [==================>                    ] 34,234,828  6.32MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "51% [===================>                   ] 35,645,180  6.35MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "53% [===================>                   ] 36,955,620  6.34MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "55% [====================>                  ] 38,319,636  6.37MB/s  eta 6s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "57% [=====================>                 ] 39,605,460  6.35MB/s  eta 6s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "59% [======================>                ] 40,970,924  6.35MB/s  eta 6s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "61% [======================>                ] 42,356,164  6.37MB/s  eta 6s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "63% [=======================>               ] 43,680,132  6.36MB/s  eta 6s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "65% [========================>              ] 45,052,836  6.36MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "66% [=========================>             ] 46,328,524  6.36MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "68% [=========================>             ] 47,789,556  6.38MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "70% [==========================>            ] 49,034,836  6.35MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "72% [===========================>           ] 50,398,852  6.36MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "74% [============================>          ] 51,797,620  6.38MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "76% [============================>          ] 53,102,268  6.37MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "78% [=============================>         ] 54,185,372  6.28MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "79% [==============================>        ] 55,411,828  6.23MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "82% [===============================>       ] 56,939,468  6.28MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "83% [===============================>       ] 58,096,420  6.22MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "85% [================================>      ] 59,534,284  6.26MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "87% [=================================>     ] 60,869,340  6.28MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "89% [=================================>     ] 62,188,468  6.25MB/s  eta 2s     "
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "! wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48968/suppl/GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2014-09-10 11:24:35--  ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48968/suppl/GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz\r\n",
        "           => 'GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz.1'\r\n",
        "Resolving ftp.ncbi.nlm.nih.gov... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "130.14.250.13, 2607:f220:41e:250::10\r\n",
        "Connecting to ftp.ncbi.nlm.nih.gov|130.14.250.13|:21... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "connected.\r\n",
        "Logging in as anonymous ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Logged in!\r\n",
        "==> SYST ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.    ==> PWD ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "==> TYPE I ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.  ==> CWD (1) /geo/series/GSE48nnn/GSE48968/suppl ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "==> SIZE GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "18671847\r\n",
        "==> PASV ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.    ==> RETR GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "Length: 18671847 (18M) (unauthoritative)\r\n",
        "\r\n",
        "\r",
        " 0% [                                       ] 0           --.-K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 0% [                                       ] 23,168      86.9KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 0% [                                       ] 65,160       132KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 0% [                                       ] 152,040      210KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 1% [                                       ] 317,112      333KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 3% [>                                      ] 599,472      508KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 4% [>                                      ] 920,928      653KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 6% [=>                                     ] 1,284,376    783KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        " 9% [==>                                    ] 1,686,920    898KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "11% [===>                                   ] 2,137,248   1012KB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "14% [====>                                  ] 2,636,808   1.10MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "17% [=====>                                 ] 3,184,152   1.20MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "20% [======>                                ] 3,782,176   1.31MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "23% [========>                              ] 4,419,296   1.41MB/s             "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "27% [=========>                             ] 5,114,336   1.52MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "29% [==========>                            ] 5,431,448   1.51MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "31% [===========>                           ] 5,954,176   1.53MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "33% [============>                          ] 6,301,696   1.53MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "34% [============>                          ] 6,427,672   1.54MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "35% [============>                          ] 6,586,952   1.58MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "36% [=============>                         ] 6,769,400   1.67MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "37% [=============>                         ] 6,960,536   1.66MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "38% [=============>                         ] 7,153,120   1.63MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "39% [==============>                        ] 7,348,600   1.56MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "40% [==============>                        ] 7,552,768   1.54MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "41% [===============>                       ] 7,791,688   1.47MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "43% [===============>                       ] 8,037,848   1.37MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "44% [================>                      ] 8,324,552   1.36MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "46% [=================>                     ] 8,643,112   1.29MB/s  eta 8s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "48% [=================>                     ] 9,012,352   1.16MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "50% [==================>                    ] 9,414,896   1.08MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "52% [===================>                   ] 9,875,360   1.18MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "55% [====================>                  ] 10,387,952  1.17MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "57% [=====================>                 ] 10,781,808  1.20MB/s  eta 7s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "60% [======================>                ] 11,352,320  1.33MB/s  eta 5s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "62% [=======================>               ] 11,641,920  1.37MB/s  eta 5s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "64% [=======================>               ] 11,957,584  1.39MB/s  eta 5s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "65% [========================>              ] 12,263,112  1.44MB/s  eta 5s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "67% [=========================>             ] 12,582,624  1.48MB/s  eta 5s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "69% [=========================>             ] 12,911,816  1.54MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "70% [==========================>            ] 13,243,408  1.54MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "72% [===========================>           ] 13,593,328  1.58MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "74% [============================>          ] 13,967,408  1.62MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "76% [============================>          ] 14,309,136  1.62MB/s  eta 4s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "78% [=============================>         ] 14,659,056  1.63MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "80% [==============================>        ] 15,064,992  1.62MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "82% [===============================>       ] 15,415,408  1.57MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "84% [================================>      ] 15,868,632  1.58MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "86% [================================>      ] 16,142,304  1.52MB/s  eta 3s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "87% [=================================>     ] 16,423,216  1.46MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "89% [=================================>     ] 16,703,632  1.46MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "90% [==================================>    ] 16,987,440  1.45MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "92% [===================================>   ] 17,273,192  1.44MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "94% [===================================>   ] 17,564,240  1.44MB/s  eta 2s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "95% [====================================>  ] 17,879,904  1.43MB/s  eta 1s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "97% [=====================================> ] 18,221,632  1.43MB/s  eta 1s     "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "99% [=====================================> ] 18,601,008  1.44MB/s  eta 1s     \r",
        "100%[======================================>] 18,671,847  1.45MB/s   in 13s    \r\n",
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-09-10 11:24:50 (1.41 MB/s) - 'GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz.1' saved [18671847]\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "! wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57872/matrix/GSE57872_series_matrix.txt.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "--2014-10-01 17:09:11--  ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57872/matrix/GSE57872_series_matrix.txt.gz\r\n",
        "           => 'GSE57872_series_matrix.txt.gz'\r\n",
        "Resolving ftp.ncbi.nlm.nih.gov... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "130.14.250.10, 2607:f220:41e:250::11\r\n",
        "Connecting to ftp.ncbi.nlm.nih.gov|130.14.250.10|:21... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "connected.\r\n",
        "Logging in as anonymous ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Logged in!\r\n",
        "==> SYST ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.    ==> PWD ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "==> TYPE I ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.  ==> CWD (1) /geo/series/GSE57nnn/GSE57872/matrix ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "==> SIZE GSE57872_series_matrix.txt.gz ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "32577\r\n",
        "==> PASV ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.    ==> RETR GSE57872_series_matrix.txt.gz ... "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done.\r\n",
        "Length: 32577 (32K) (unauthoritative)\r\n",
        "\r\n",
        "\r",
        " 0% [                                       ] 0           --.-K/s              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "100%[======================================>] 32,577      --.-K/s   in 0.1s    \r\n",
        "\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-10-01 17:09:12 (307 KB/s) - 'GSE57872_series_matrix.txt.gz' saved [32577]\r\n",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dendritic_cells1 = pd.read_table('GSE48968_allgenesTPM_GSM1189042_GSM1190902.txt.gz', compression='gzip', index_col=0)\n",
      "dendritic_cells2 = pd.read_table('GSE48968_allgenesTPM_GSM1406531_GSM1407094.txt.gz', compression='gzip', index_col=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dendritic_cells1.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S10</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S11</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S12</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S13</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S15</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S16</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S17</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S18</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S19</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S1</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S20</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S21</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S22</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S23</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S24</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S25</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S26</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S27</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S28</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S29</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>XKR4</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AB338584</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B3GAT2</th>\n",
        "      <td> 0.626418</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 10.30303</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPL</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15.230438</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T2</th>\n",
        "      <td> 1.639493</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 7.157425</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.504472</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1861 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "          On_Chip_Stimulation_LPS_4h_S10  On_Chip_Stimulation_LPS_4h_S11  \\\n",
        "XKR4                            0.000000                               0   \n",
        "AB338584                        0.000000                               0   \n",
        "B3GAT2                          0.626418                               0   \n",
        "NPL                             0.000000                               0   \n",
        "T2                              1.639493                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S12  On_Chip_Stimulation_LPS_4h_S13  \\\n",
        "XKR4                                   0                               0   \n",
        "AB338584                               0                               0   \n",
        "B3GAT2                                 0                               0   \n",
        "NPL                                    0                               0   \n",
        "T2                                     0                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S15  On_Chip_Stimulation_LPS_4h_S16  \\\n",
        "XKR4                                   0                               0   \n",
        "AB338584                               0                               0   \n",
        "B3GAT2                                 0                               0   \n",
        "NPL                                    0                               0   \n",
        "T2                                     0                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S17  On_Chip_Stimulation_LPS_4h_S18  \\\n",
        "XKR4                                   0                               0   \n",
        "AB338584                               0                               0   \n",
        "B3GAT2                                 0                               0   \n",
        "NPL                                    0                               0   \n",
        "T2                                     0                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S19  On_Chip_Stimulation_LPS_4h_S1  \\\n",
        "XKR4                                   0                              0   \n",
        "AB338584                               0                              0   \n",
        "B3GAT2                                 0                              0   \n",
        "NPL                                    0                              0   \n",
        "T2                                     0                              0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S20  On_Chip_Stimulation_LPS_4h_S21  \\\n",
        "XKR4                            0.000000                               0   \n",
        "AB338584                        0.000000                               0   \n",
        "B3GAT2                          0.000000                               0   \n",
        "NPL                             0.000000                               0   \n",
        "T2                              7.157425                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S22  On_Chip_Stimulation_LPS_4h_S23  \\\n",
        "XKR4                                   0                        0.000000   \n",
        "AB338584                               0                        0.000000   \n",
        "B3GAT2                                 0                        0.000000   \n",
        "NPL                                    0                        0.000000   \n",
        "T2                                     0                        0.504472   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S24  On_Chip_Stimulation_LPS_4h_S25  \\\n",
        "XKR4                                   0                        0.000000   \n",
        "AB338584                               0                        0.000000   \n",
        "B3GAT2                                 0                        0.000000   \n",
        "NPL                                    0                       15.230438   \n",
        "T2                                     0                        0.000000   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S26  On_Chip_Stimulation_LPS_4h_S27  \\\n",
        "XKR4                                   0                         0.00000   \n",
        "AB338584                               0                         0.00000   \n",
        "B3GAT2                                 0                        10.30303   \n",
        "NPL                                    0                         0.00000   \n",
        "T2                                     0                         0.00000   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S28  On_Chip_Stimulation_LPS_4h_S29      \n",
        "XKR4                                   0                               0 ...  \n",
        "AB338584                               0                               0 ...  \n",
        "B3GAT2                                 0                               0 ...  \n",
        "NPL                                    0                               0 ...  \n",
        "T2                                     0                               0 ...  \n",
        "\n",
        "[5 rows x 1861 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dendritic_cells2.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>gpc_t4_S10_rsem</th>\n",
        "      <th>gpc_t4_S13_rsem</th>\n",
        "      <th>gpc_t4_S14_rsem</th>\n",
        "      <th>gpc_t4_S16_rsem</th>\n",
        "      <th>gpc_t4_S17_rsem</th>\n",
        "      <th>gpc_t4_S18_rsem</th>\n",
        "      <th>gpc_t4_S19_rsem</th>\n",
        "      <th>gpc_t4_S20_rsem</th>\n",
        "      <th>gpc_t4_S25_rsem</th>\n",
        "      <th>gpc_t4_S27_rsem</th>\n",
        "      <th>gpc_t4_S28_rsem</th>\n",
        "      <th>gpc_t4_S29_rsem</th>\n",
        "      <th>gpc_t4_S32_rsem</th>\n",
        "      <th>gpc_t4_S36_rsem</th>\n",
        "      <th>gpc_t4_S37_rsem</th>\n",
        "      <th>gpc_t4_S38_rsem</th>\n",
        "      <th>gpc_t4_S40_rsem</th>\n",
        "      <th>gpc_t4_S42_rsem</th>\n",
        "      <th>gpc_t4_S43_rsem</th>\n",
        "      <th>gpc_t4_S46_rsem</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>B3GAT2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.292603</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0.419727</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPL</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
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        "    <tr>\n",
        "      <th>T2</th>\n",
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        "      <td> 0.70795</td>\n",
        "      <td>  1.099699</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 1.141366</td>\n",
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        "      <td>  0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
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        "    <tr>\n",
        "      <th>AK020722</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
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        "    <tr>\n",
        "      <th>QK</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.00000</td>\n",
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        "      <td> 0</td>\n",
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        "      <td> 38.938209</td>\n",
        "      <td> 152.308965</td>\n",
        "      <td> 0</td>\n",
        "      <td> 319.252123</td>\n",
        "      <td> 105.636681</td>\n",
        "      <td> 41.748884</td>\n",
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        "      <td> 0.000000</td>\n",
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        "      <td> 0.204947</td>\n",
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        "      <td> 5.543801</td>\n",
        "      <td> 263.242154</td>\n",
        "      <td>...</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 564 columns</p>\n",
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        "          gpc_t4_S10_rsem  gpc_t4_S13_rsem  gpc_t4_S14_rsem  gpc_t4_S16_rsem  \\\n",
        "B3GAT2                  0          0.00000         0.000000                0   \n",
        "NPL                     0          0.00000         0.000000                0   \n",
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        "AK020722                0          0.00000         0.000000                0   \n",
        "QK                      0          0.00000        13.747541                0   \n",
        "\n",
        "          gpc_t4_S17_rsem  gpc_t4_S18_rsem  gpc_t4_S19_rsem  gpc_t4_S20_rsem  \\\n",
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        "\n",
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        "AK020722         0.000000         0.000000         0.000000         0.000000   \n",
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        "\n",
        "              \n",
        "B3GAT2   ...  \n",
        "NPL      ...  \n",
        "T2       ...  \n",
        "AK020722 ...  \n",
        "QK       ...  \n",
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        "       'tr2_t4', 'tr3_t2', 'tr4_t2', 'wps_t4'], dtype=object)"
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     ],
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      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Accession</th>\n",
        "      <th>Title</th>\n",
        "      <th>Sample Type</th>\n",
        "      <th>Taxonomy</th>\n",
        "      <th>Channels</th>\n",
        "      <th>Platform</th>\n",
        "      <th>Series</th>\n",
        "      <th>Supplementary Types</th>\n",
        "      <th>Supplementary Links</th>\n",
        "      <th>SRA Accession</th>\n",
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        "      <th>Release Date</th>\n",
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        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> GSM1189042</td>\n",
        "      <td> On_Chip_Stimulation_LPS_4h_S1</td>\n",
        "      <td> SRA</td>\n",
        "      <td> Mus musculus</td>\n",
        "      <td> 1</td>\n",
        "      <td> GPL17021</td>\n",
        "      <td> GSE48968</td>\n",
        "      <td> SRA Experiment</td>\n",
        "      <td> ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...</td>\n",
        "      <td> SRX323819</td>\n",
        "      <td> Rahul Satija</td>\n",
        "      <td> Jun 11, 2014</td>\n",
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        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> GSM1189043</td>\n",
        "      <td> On_Chip_Stimulation_LPS_4h_S2</td>\n",
        "      <td> SRA</td>\n",
        "      <td> Mus musculus</td>\n",
        "      <td> 1</td>\n",
        "      <td> GPL17021</td>\n",
        "      <td> GSE48968</td>\n",
        "      <td> SRA Experiment</td>\n",
        "      <td> ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...</td>\n",
        "      <td> SRX323820</td>\n",
        "      <td> Rahul Satija</td>\n",
        "      <td> Jun 11, 2014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> GSM1189044</td>\n",
        "      <td> On_Chip_Stimulation_LPS_4h_S3</td>\n",
        "      <td> SRA</td>\n",
        "      <td> Mus musculus</td>\n",
        "      <td> 1</td>\n",
        "      <td> GPL17021</td>\n",
        "      <td> GSE48968</td>\n",
        "      <td> SRA Experiment</td>\n",
        "      <td> ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...</td>\n",
        "      <td> SRX323821</td>\n",
        "      <td> Rahul Satija</td>\n",
        "      <td> Jun 11, 2014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> GSM1189045</td>\n",
        "      <td> On_Chip_Stimulation_LPS_4h_S4</td>\n",
        "      <td> SRA</td>\n",
        "      <td> Mus musculus</td>\n",
        "      <td> 1</td>\n",
        "      <td> GPL17021</td>\n",
        "      <td> GSE48968</td>\n",
        "      <td> SRA Experiment</td>\n",
        "      <td> ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...</td>\n",
        "      <td> SRX323822</td>\n",
        "      <td> Rahul Satija</td>\n",
        "      <td> Jun 11, 2014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> GSM1189046</td>\n",
        "      <td> On_Chip_Stimulation_LPS_4h_S5</td>\n",
        "      <td> SRA</td>\n",
        "      <td> Mus musculus</td>\n",
        "      <td> 1</td>\n",
        "      <td> GPL17021</td>\n",
        "      <td> GSE48968</td>\n",
        "      <td> SRA Experiment</td>\n",
        "      <td> ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...</td>\n",
        "      <td> SRX323823</td>\n",
        "      <td> Rahul Satija</td>\n",
        "      <td> Jun 11, 2014</td>\n",
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        "    Accession                          Title Sample Type      Taxonomy  \\\n",
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        "1  GSM1189043  On_Chip_Stimulation_LPS_4h_S2         SRA  Mus musculus   \n",
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        "4  GSM1189046  On_Chip_Stimulation_LPS_4h_S5         SRA  Mus musculus   \n",
        "\n",
        "   Channels  Platform    Series Supplementary Types  \\\n",
        "0         1  GPL17021  GSE48968      SRA Experiment   \n",
        "1         1  GPL17021  GSE48968      SRA Experiment   \n",
        "2         1  GPL17021  GSE48968      SRA Experiment   \n",
        "3         1  GPL17021  GSE48968      SRA Experiment   \n",
        "4         1  GPL17021  GSE48968      SRA Experiment   \n",
        "\n",
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        "3  ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...     SRX323822   \n",
        "4  ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-insta...     SRX323823   \n",
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        "        Contact  Release Date  \n",
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        "1  Rahul Satija  Jun 11, 2014  \n",
        "2  Rahul Satija  Jun 11, 2014  \n",
        "3  Rahul Satija  Jun 11, 2014  \n",
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        "      <th>On_Chip_Stimulation_LPS_4h_S26</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S27</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S28</th>\n",
        "      <th>On_Chip_Stimulation_LPS_4h_S29</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>XKR4</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AB338584</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B3GAT2</th>\n",
        "      <td> 0.626418</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 10.30303</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPL</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 15.230438</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>T2</th>\n",
        "      <td> 1.639493</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 7.157425</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.504472</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0.00000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1861 columns</p>\n",
        "</div>"
       ],
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       "prompt_number": 33,
       "text": [
        "          On_Chip_Stimulation_LPS_4h_S10  On_Chip_Stimulation_LPS_4h_S11  \\\n",
        "XKR4                            0.000000                               0   \n",
        "AB338584                        0.000000                               0   \n",
        "B3GAT2                          0.626418                               0   \n",
        "NPL                             0.000000                               0   \n",
        "T2                              1.639493                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S12  On_Chip_Stimulation_LPS_4h_S13  \\\n",
        "XKR4                                   0                               0   \n",
        "AB338584                               0                               0   \n",
        "B3GAT2                                 0                               0   \n",
        "NPL                                    0                               0   \n",
        "T2                                     0                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S15  On_Chip_Stimulation_LPS_4h_S16  \\\n",
        "XKR4                                   0                               0   \n",
        "AB338584                               0                               0   \n",
        "B3GAT2                                 0                               0   \n",
        "NPL                                    0                               0   \n",
        "T2                                     0                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S17  On_Chip_Stimulation_LPS_4h_S18  \\\n",
        "XKR4                                   0                               0   \n",
        "AB338584                               0                               0   \n",
        "B3GAT2                                 0                               0   \n",
        "NPL                                    0                               0   \n",
        "T2                                     0                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S19  On_Chip_Stimulation_LPS_4h_S1  \\\n",
        "XKR4                                   0                              0   \n",
        "AB338584                               0                              0   \n",
        "B3GAT2                                 0                              0   \n",
        "NPL                                    0                              0   \n",
        "T2                                     0                              0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S20  On_Chip_Stimulation_LPS_4h_S21  \\\n",
        "XKR4                            0.000000                               0   \n",
        "AB338584                        0.000000                               0   \n",
        "B3GAT2                          0.000000                               0   \n",
        "NPL                             0.000000                               0   \n",
        "T2                              7.157425                               0   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S22  On_Chip_Stimulation_LPS_4h_S23  \\\n",
        "XKR4                                   0                        0.000000   \n",
        "AB338584                               0                        0.000000   \n",
        "B3GAT2                                 0                        0.000000   \n",
        "NPL                                    0                        0.000000   \n",
        "T2                                     0                        0.504472   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S24  On_Chip_Stimulation_LPS_4h_S25  \\\n",
        "XKR4                                   0                        0.000000   \n",
        "AB338584                               0                        0.000000   \n",
        "B3GAT2                                 0                        0.000000   \n",
        "NPL                                    0                       15.230438   \n",
        "T2                                     0                        0.000000   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S26  On_Chip_Stimulation_LPS_4h_S27  \\\n",
        "XKR4                                   0                         0.00000   \n",
        "AB338584                               0                         0.00000   \n",
        "B3GAT2                                 0                        10.30303   \n",
        "NPL                                    0                         0.00000   \n",
        "T2                                     0                         0.00000   \n",
        "\n",
        "          On_Chip_Stimulation_LPS_4h_S28  On_Chip_Stimulation_LPS_4h_S29      \n",
        "XKR4                                   0                               0 ...  \n",
        "AB338584                               0                               0 ...  \n",
        "B3GAT2                                 0                               0 ...  \n",
        "NPL                                    0                               0 ...  \n",
        "T2                                     0                               0 ...  \n",
        "\n",
        "[5 rows x 1861 columns]"
       ]
      }
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     "prompt_number": 33
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