.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/matplotlib/plot_main06_b_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_b_heatmap.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr__examples_matplotlib_plot_main06_b_heatmap.py:
06.b ``sns.heatmap`` for CRI ``v2``
-------------------------------------------
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 16-40
.. 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 41-42
Lets load the data and create color mapping variables
.. GENERATED FROM PYTHON SOURCE LINES 42-60
.. code-block:: default
:lineno-start: 43
# 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())
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 61-62
Lets see the data
.. GENERATED FROM PYTHON SOURCE LINES 62-67
.. code-block:: default
:lineno-start: 62
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 68-69
Lets see the antimicrobials
.. GENERATED FROM PYTHON SOURCE LINES 69-75
.. code-block:: default
:lineno-start: 69
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 76-77
Lets create some variables.
.. GENERATED FROM PYTHON SOURCE LINES 77-87
.. code-block:: default
:lineno-start: 78
# 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 88-90
Let's display a heatmap
.. GENERATED FROM PYTHON SOURCE LINES 90-147
.. code-block:: default
:lineno-start: 91
# Draw (heatmap)
fig, axs = plt.subplots(nrows=1, ncols=1,
sharey=False, sharex=False, figsize=(8, 5)
)
# 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_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_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='upper center',
ncol=3, bbox_to_anchor=(0.5, -0.25), fontsize=8,
fancybox=False, shadow=False)
# Format
plt.suptitle('URINE - Escherichia Coli')
#plt.subplots_adjust(right=0.2)
plt.tight_layout()
# Show
plt.show()
.. image-sg:: /_examples/matplotlib/images/sphx_glr_plot_main06_b_heatmap_001.png
:alt: URINE - Escherichia Coli
:srcset: /_examples/matplotlib/images/sphx_glr_plot_main06_b_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
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.529 seconds)
.. _sphx_glr_download__examples_matplotlib_plot_main06_b_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_b_heatmap.py <plot_main06_b_heatmap.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_main06_b_heatmap.ipynb <plot_main06_b_heatmap.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_