{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Yeast sample data\n\n\nThis example showcases how to load the yeast sample data, plot it and plot the\nlocation of the chromosomes.\n\nThis data set corresponds to the first 5 chromosomes of the budding yeast *S.\ncerevisiae*, from Duan et al.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from iced import datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Start by loading the data.\nThe data is composed of a counts ndarray matrix and a corresponding length\nvector. The size of each much match\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "counts, lengths = datasets.load_sample_yeast()\nassert(counts.shape[0] == lengths.sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We are then going to plot the data. The contact counts are typically very\nenriched close to the diagonal. Thus, we use a Log normalization on the\ncontact count data in order to see all the details of the plot.\nThe data here is very noisy : it corresponds to raw data. We plot the\nchromosome boundaries in white and add a colorbar.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nfrom matplotlib import colors\n\nfig, ax = plt.subplots()\n# Add 0.5 to the contact count matrix to avoid taking the log of 0\nm = ax.matshow(counts+0.5, cmap=\"RdBu_r\", norm=colors.LogNorm(),\n               origin=\"bottom\",\n               extent=(0, counts.shape[0], 0, counts.shape[0]))\nchromosomes = [\"I\", \"II\", \"III\", \"IV\", \"V\", \"VI\"]\n\n# Now plot the chromosomes boundaries and set the title\n[ax.axhline(i, linewidth=1, color=\"#ffffff\") for i in lengths.cumsum()]\n[ax.axvline(i, linewidth=1, color=\"#ffffff\") for i in lengths.cumsum()]\ncb = fig.colorbar(m)\ncb.set_label(\"Contact counts\")\nax.set_title(\"Raw contact counts\", fontweight=\"bold\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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