.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/matplotlib/plot_main06_a_heatmap.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download__examples_matplotlib_plot_main06_a_heatmap.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__examples_matplotlib_plot_main06_a_heatmap.py:


06.a ``sns.heatmap`` for CRI ``v1``
-------------------------------------------

Plot rectangular data as a color-encoded matrix.

The generates a heatmap visualization for a dataset related to collateral
sensitivity. It uses the Seaborn library to plot the rectangular data as
a color-encoded matrix. The code loads the data from a CSV file, creates
mappings for categories and colors, and then plots the heatmap using the
loaded data and color maps. It also includes annotations, colorbar axes,
category patches, legend elements, and formatting options to enhance the
visualization.

.. GENERATED FROM PYTHON SOURCE LINES 16-42

.. code-block:: default
   :lineno-start: 16

    # Libraries
    import numpy as np
    import pandas as pd
    import seaborn as sns
    import matplotlib as mpl
    import matplotlib.pyplot as plt

    from pathlib import Path
    from matplotlib.patches import Patch
    from matplotlib.colors import LogNorm
    from matplotlib.patches import Rectangle

    # See https://matplotlib.org/devdocs/users/explain/customizing.html
    mpl.rcParams['axes.titlesize'] = 8
    mpl.rcParams['axes.labelsize'] = 8
    mpl.rcParams['xtick.labelsize'] = 8
    mpl.rcParams['ytick.labelsize'] = 8

    try:
        __file__
        TERMINAL = True
    except:
        TERMINAL = False










.. GENERATED FROM PYTHON SOURCE LINES 43-44

Lets load the data and create color mapping variables

.. GENERATED FROM PYTHON SOURCE LINES 44-64

.. code-block:: default
   :lineno-start: 45


    # Load data
    path = Path('../../datasets/collateral-sensitivity/sample')
    data = pd.read_csv(path / 'matrix.csv', index_col=0)
    abxs = pd.read_csv(path / 'categories.csv', index_col=0)

    # Create dictionary to map category to color
    labels = abxs.category
    palette = sns.color_palette('colorblind', labels.nunique())
    palette = sns.cubehelix_palette(labels.nunique(),
        light=.9, dark=.1, reverse=True, start=1, rot=-2)
    lookup = dict(zip(labels.unique(), palette))

    # Create dictionary to map code to category
    code2cat = dict(zip(abxs.antimicrobial_code, abxs.category))

    # Create colors
    colors = data.columns.to_series().map(code2cat).map(lookup)









.. GENERATED FROM PYTHON SOURCE LINES 65-66

Lets see the data

.. GENERATED FROM PYTHON SOURCE LINES 66-71

.. code-block:: default
   :lineno-start: 66

    if TERMINAL:
        print("\nData:")
        print(data)
    data.iloc[:7,:7]






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>AAMI</th>
          <th>AAMPC</th>
          <th>AAUG</th>
          <th>ACAZ</th>
          <th>ACELX</th>
          <th>ACIP</th>
          <th>ACOL</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>AAMI</th>
          <td>1091.0</td>
          <td>-0.000395</td>
          <td>0.009905</td>
          <td>0.006635</td>
          <td>0.000565</td>
          <td>0.008569</td>
          <td>0.002179</td>
        </tr>
        <tr>
          <th>AAMPC</th>
          <td>5906.0</td>
          <td>600.000000</td>
          <td>-0.255642</td>
          <td>0.070777</td>
          <td>-0.142713</td>
          <td>0.101858</td>
          <td>-0.011118</td>
        </tr>
        <tr>
          <th>AAUG</th>
          <td>11962.0</td>
          <td>5942.000000</td>
          <td>7096.000000</td>
          <td>-0.027724</td>
          <td>0.206276</td>
          <td>0.140313</td>
          <td>0.027190</td>
        </tr>
        <tr>
          <th>ACAZ</th>
          <td>11642.0</td>
          <td>5777.000000</td>
          <td>11681.000000</td>
          <td>1052.000000</td>
          <td>0.455223</td>
          <td>0.204145</td>
          <td>-0.011411</td>
        </tr>
        <tr>
          <th>ACELX</th>
          <td>12005.0</td>
          <td>5979.000000</td>
          <td>93618.000000</td>
          <td>11722.000000</td>
          <td>7103.000000</td>
          <td>0.233739</td>
          <td>-0.013468</td>
        </tr>
        <tr>
          <th>ACIP</th>
          <td>11992.0</td>
          <td>5974.000000</td>
          <td>93507.000000</td>
          <td>11714.000000</td>
          <td>93568.000000</td>
          <td>7115.000000</td>
          <td>-0.011123</td>
        </tr>
        <tr>
          <th>ACOL</th>
          <td>9693.0</td>
          <td>4768.000000</td>
          <td>9726.000000</td>
          <td>9526.000000</td>
          <td>9758.000000</td>
          <td>9745.000000</td>
          <td>854.000000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 72-73

Lets see the antimicrobials

.. GENERATED FROM PYTHON SOURCE LINES 73-79

.. code-block:: default
   :lineno-start: 73

    if TERMINAL:
        print("\nAntimicrobials:")
        print(abxs)
    abxs







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>name</th>
          <th>category</th>
          <th>acronym</th>
          <th>exists_in_registry</th>
          <th>antimicrobial_code</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Amikacin</td>
          <td>Aminoglycosides</td>
          <td>AMIK</td>
          <td>True</td>
          <td>AAMI</td>
        </tr>
        <tr>
          <th>2</th>
          <td>Amp c markers</td>
          <td>NaN</td>
          <td>AMP_C</td>
          <td>True</td>
          <td>AAMPC</td>
        </tr>
        <tr>
          <th>5</th>
          <td>Augmentin</td>
          <td>NaN</td>
          <td>AUGM</td>
          <td>True</td>
          <td>AAUG</td>
        </tr>
        <tr>
          <th>13</th>
          <td>Cefotaxime</td>
          <td>Cephalosporins</td>
          <td>CEFO</td>
          <td>True</td>
          <td>ACTX</td>
        </tr>
        <tr>
          <th>14</th>
          <td>Cefoxitin</td>
          <td>Cephalosporins</td>
          <td>CEFOX</td>
          <td>True</td>
          <td>ACXT</td>
        </tr>
        <tr>
          <th>16</th>
          <td>Ceftazidime</td>
          <td>Cephalosporins</td>
          <td>CEFT</td>
          <td>True</td>
          <td>ACAZ</td>
        </tr>
        <tr>
          <th>20</th>
          <td>Cefuroxime</td>
          <td>Cephalosporins</td>
          <td>CEFU</td>
          <td>True</td>
          <td>ACXM</td>
        </tr>
        <tr>
          <th>21</th>
          <td>Cephalexin</td>
          <td>Cephalosporins</td>
          <td>CEPH</td>
          <td>True</td>
          <td>ACELX</td>
        </tr>
        <tr>
          <th>24</th>
          <td>Ciprofloxacin</td>
          <td>Fluoroquinolones</td>
          <td>CIPR</td>
          <td>True</td>
          <td>ACIP</td>
        </tr>
        <tr>
          <th>29</th>
          <td>Colistin sulphate</td>
          <td>Polypeptides</td>
          <td>COLI_SULP</td>
          <td>True</td>
          <td>ACOL</td>
        </tr>
        <tr>
          <th>35</th>
          <td>Ertapenem</td>
          <td>Meropenems</td>
          <td>ERTA</td>
          <td>True</td>
          <td>AERT</td>
        </tr>
        <tr>
          <th>37</th>
          <td>Esbl markers</td>
          <td>NaN</td>
          <td>ESBL_MARK</td>
          <td>True</td>
          <td>AESBL</td>
        </tr>
        <tr>
          <th>44</th>
          <td>Gentamicin</td>
          <td>Aminoglycosides</td>
          <td>GENT</td>
          <td>True</td>
          <td>AGEN</td>
        </tr>
        <tr>
          <th>45</th>
          <td>Imipenem</td>
          <td>Meropenems</td>
          <td>IMIP</td>
          <td>True</td>
          <td>AIMP</td>
        </tr>
        <tr>
          <th>52</th>
          <td>Mecillinam</td>
          <td>Penicillins</td>
          <td>MECI</td>
          <td>True</td>
          <td>AMEC</td>
        </tr>
        <tr>
          <th>53</th>
          <td>Meropenem</td>
          <td>Meropenems</td>
          <td>MERO</td>
          <td>True</td>
          <td>AMER</td>
        </tr>
        <tr>
          <th>62</th>
          <td>Nitrofurantoin</td>
          <td>NaN</td>
          <td>NITR</td>
          <td>True</td>
          <td>ANIT</td>
        </tr>
        <tr>
          <th>81</th>
          <td>Tazocin</td>
          <td>NaN</td>
          <td>TAZO</td>
          <td>True</td>
          <td>ATAZ</td>
        </tr>
        <tr>
          <th>83</th>
          <td>Temocillin</td>
          <td>Penicillins</td>
          <td>TEMO</td>
          <td>True</td>
          <td>ATEM</td>
        </tr>
        <tr>
          <th>88</th>
          <td>Tobramycin</td>
          <td>Aminoglycosides</td>
          <td>TOBR</td>
          <td>True</td>
          <td>ATOB</td>
        </tr>
        <tr>
          <th>89</th>
          <td>Trimethoprim</td>
          <td>NaN</td>
          <td>TRIM</td>
          <td>True</td>
          <td>ATRI</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 80-81

Lets create some variables.

.. GENERATED FROM PYTHON SOURCE LINES 81-91

.. code-block:: default
   :lineno-start: 82


    # Create color maps
    cmapu = sns.color_palette("YlGn", as_cmap=True)
    cmapl = sns.diverging_palette(220, 20, as_cmap=True)

    # Create triangular matrices
    masku = np.triu(np.ones_like(data))
    maskl = np.tril(np.ones_like(data))









.. GENERATED FROM PYTHON SOURCE LINES 92-94

Let's display a heatmap


.. GENERATED FROM PYTHON SOURCE LINES 94-165

.. code-block:: default
   :lineno-start: 95


    # Draw (heatmap)
    fig, axs = plt.subplots(nrows=1, ncols=1,
        sharey=False, sharex=False, figsize=(8, 5)
    )

    # .. note: This is used to create the colorbar axes. If we want
    #          the default display in which the use the whole fig
    #          height just pass cbar_ax=None.

    # Create own colorbar axes
    # Params are [left, bottom, width, height]
    cbar_ax1 = fig.add_axes([0.76, 0.5, 0.03, 0.38])
    cbar_ax2 = fig.add_axes([0.90, 0.5, 0.03, 0.38])

    # Display
    r1 = sns.heatmap(data=data, cmap=cmapu, mask=masku, ax=axs,
                annot=False, linewidth=0.5, norm=LogNorm(),
                annot_kws={"size": 8}, square=True, vmin=0,
                cbar_ax=cbar_ax2,
                cbar_kws={'label': 'Number of isolates'})

    r2 = sns.heatmap(data=data, cmap=cmapl, mask=maskl, ax=axs,
                annot=False, linewidth=0.5, vmin=-0.7, vmax=0.7,
                center=0, annot_kws={"size": 8}, square=True,
                xticklabels=True, yticklabels=True,
                cbar_ax=cbar_ax1,
                cbar_kws={'label': 'Collateral Resistance Index'})

    # Create patches for categories
    category_patches = []
    for i in axs.get_xticklabels():
        try:
            x, y = i.get_position()
            c = colors.to_dict().get(i.get_text(), 'k')
            #i.set_color(c) # for testing

            # Add patch.
            category_patches.append(
                Rectangle((x-0.35, y-0.5), 0.8, 0.3, edgecolor='k',
                    facecolor=c, fill=True, lw=0.25, alpha=0.5, zorder=1000,
                    transform=axs.transData
                )
            )
        except Exception as e:
            print(i.get_text(), e)

    # Add category rectangles
    fig.patches.extend(category_patches)

    # Create legend elements
    legend_elements = [
        Patch(facecolor=v, edgecolor='k',
            fill=True, lw=0.25, alpha=0.5, label=k)
                for k, v in lookup.items()
    ]

    # Add legend
    axs.legend(handles=legend_elements, loc='lower left',
        ncol=1, bbox_to_anchor=(1.1, 0.1), fontsize=8,
        fancybox=False, shadow=False)

    # Format
    plt.suptitle('URINE - Escherichia Coli')
    #plt.subplots_adjust(left=0.8)
    plt.tight_layout()

    # Show
    plt.show()





.. image-sg:: /_examples/matplotlib/images/sphx_glr_plot_main06_a_heatmap_001.png
   :alt: URINE - Escherichia Coli
   :srcset: /_examples/matplotlib/images/sphx_glr_plot_main06_a_heatmap_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    AMEC Invalid RGBA argument: nan
    ATEM Invalid RGBA argument: nan
    /Users/cbit/Desktop/repositories/github/python-spare-code/main/examples/matplotlib/plot_main06_a_heatmap.py:159: UserWarning:

    This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.





.. GENERATED FROM PYTHON SOURCE LINES 166-167

Let's draw a clustermap

.. GENERATED FROM PYTHON SOURCE LINES 167-185

.. code-block:: default
   :lineno-start: 168


    """
    # Display
    result = sns.clustermap(data=data, figsize=(6,6),
        cmap=cmapu, mask=masku, annot=False, linewidth=0.5,
        norm=LogNorm(), annot_kws={"size": 8}, square=True, vmin=0,
        row_cluster=False, col_cluster=False,
        col_colors=colors,
        xticklabels=True, yticklabels=True)

    #sns.heatmap(data=data, cmap=cmapl, mask=maskl, ax=result.ax_heatmap,
    #            annot=False, linewidth=0.5, vmin=-0.7, vmax=0.7,
    #            center=0, annot_kws={"size": 8}, square=True,
    #            xticklabels=False, yticklabels=False)

    # Move the colorbar to the empty space.
    #result.ax_col_dendrogram.legend(loc="center", ncol=6)
    #result.cax.set_position([.15, .2, .03, .45])
    """



.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    '\n# Display\nresult = sns.clustermap(data=data, figsize=(6,6),\n    cmap=cmapu, mask=masku, annot=False, linewidth=0.5,\n    norm=LogNorm(), annot_kws={"size": 8}, square=True, vmin=0,\n    row_cluster=False, col_cluster=False,\n    col_colors=colors,\n    xticklabels=True, yticklabels=True)\n\n#sns.heatmap(data=data, cmap=cmapl, mask=maskl, ax=result.ax_heatmap,\n#            annot=False, linewidth=0.5, vmin=-0.7, vmax=0.7,\n#            center=0, annot_kws={"size": 8}, square=True,\n#            xticklabels=False, yticklabels=False)\n\n# Move the colorbar to the empty space.\n#result.ax_col_dendrogram.legend(loc="center", ncol=6)\n#result.cax.set_position([.15, .2, .03, .45])\n'




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.539 seconds)


.. _sphx_glr_download__examples_matplotlib_plot_main06_a_heatmap.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_main06_a_heatmap.py <plot_main06_a_heatmap.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_main06_a_heatmap.ipynb <plot_main06_a_heatmap.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_