.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples\indexes\plot_sari_c_relmap.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download__examples_indexes_plot_sari_c_relmap.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr__examples_indexes_plot_sari_c_relmap.py:
``SARI`` - By specimen w/ ``sns.relmap``
----------------------------------------
.. todo:: Explain...
.. GENERATED FROM PYTHON SOURCE LINES 8-168
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_001.png
:alt: BLDCUL, Antibiogram (with frequency)
:srcset: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_001.png
:class: sphx-glr-multi-img
*
.. image-sg:: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_002.png
:alt: SPTCUL, Antibiogram (with frequency)
:srcset: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_002.png
:class: sphx-glr-multi-img
*
.. image-sg:: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_003.png
:alt: URICUL, Antibiogram (with frequency)
:srcset: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_003.png
:class: sphx-glr-multi-img
*
.. image-sg:: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_004.png
:alt: WOUCUL, Antibiogram (with frequency)
:srcset: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_004.png
:class: sphx-glr-multi-img
*
.. image-sg:: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_005.png
:alt: XINCUL, Antibiogram (with frequency)
:srcset: /_examples/indexes/images/sphx_glr_plot_sari_c_relmap_005.png
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
.. code-block:: none
Data:
date_received date_outcome patient_id laboratory_number specimen_code specimen_name specimen_description ... microorganism_name antimicrobial_code antimicrobial_name sensitivity_method sensitivity mic reported
0 2009-01-03 NaN 20091 X428501 BLDCUL NaN blood ... klebsiella AAMI amikacin NaN sensitive NaN NaN
1 2009-01-03 NaN 20091 X428501 BLDCUL NaN blood ... klebsiella AAMO amoxycillin NaN resistant NaN NaN
2 2009-01-03 NaN 20091 X428501 BLDCUL NaN blood ... klebsiella AAUG augmentin NaN sensitive NaN NaN
3 2009-01-03 NaN 20091 X428501 BLDCUL NaN blood ... klebsiella AAZT aztreonam NaN sensitive NaN NaN
4 2009-01-03 NaN 20091 X428501 BLDCUL NaN blood ... klebsiella ACAZ ceftazidime NaN sensitive NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... .. ...
319117 2009-12-31 NaN 24645 H2012337 BLDCUL NaN blood ... enterococcus AAMO amoxycillin NaN sensitive NaN NaN
319118 2009-12-31 NaN 24645 H2012337 BLDCUL NaN blood ... enterococcus ALIN linezolid NaN sensitive NaN NaN
319119 2009-12-31 NaN 24645 H2012337 BLDCUL NaN blood ... enterococcus ASYN synercid NaN resistant NaN NaN
319120 2009-12-31 NaN 24645 H2012337 BLDCUL NaN blood ... enterococcus ATEI teicoplanin NaN sensitive NaN NaN
319121 2009-12-31 NaN 24645 H2012337 BLDCUL NaN blood ... enterococcus AVAN vancomycin NaN sensitive NaN NaN
[319122 rows x 15 columns]
Columns:
Index(['date_received', 'date_outcome', 'patient_id', 'laboratory_number', 'specimen_code', 'specimen_name', 'specimen_description', 'microorganism_code', 'microorganism_name', 'antimicrobial_code', 'antimicrobial_name', 'sensitivity_method', 'sensitivity', 'mic', 'reported'], dtype='object')
SARI (overall):
intermediate resistant sensitive freq sari
specimen_code microorganism_code antimicrobial_code
BFLCUL AHS ACHL 0.0 0.0 1.0 1.0 0.0000
ACLI 0.0 0.0 2.0 2.0 0.0000
ACTX 0.0 0.0 1.0 1.0 0.0000
AERY 0.0 1.0 1.0 2.0 0.5000
APEN 0.0 0.0 2.0 2.0 0.0000
... ... ... ... ... ...
XINCUL SAUR ATEI 0.0 0.0 1365.0 1365.0 0.0000
ATET 0.0 67.0 1288.0 1355.0 0.0494
ATRI 0.0 145.0 1213.0 1358.0 0.1068
AVAN 0.0 0.0 1364.0 1364.0 0.0000
SMAL ACOT 0.0 0.0 8.0 8.0 0.0000
[4491 rows x 5 columns]
Cultures:
specimen_code
URICUL 116627.0
WOUCUL 94918.0
XINCUL 21427.0
SPTCUL 21113.0
BLDCUL 20333.0
ENTCUL 13110.0
T&FCUL 8150.0
MRSCUL 7865.0
VAGCUL 7425.0
EYECUL 2839.0
GUMCUL 1634.0
FAECUL 1317.0
URECUL 802.0
TISCUL 474.0
BFLCUL 450.0
SEMCUL 290.0
NEOCUL 213.0
PDFCUL 68.0
CSFCUL 32.0
RGNS 20.0
FUNSTC 14.0
TBCUL 1.0
Name: freq, dtype: float64
C:\Users\kelda\Desktop\repositories\github\pyAMR\main\examples\indexes\plot_sari_c_relmap.py:154: MatplotlibDeprecationWarning:
The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
C:\Users\kelda\Desktop\repositories\github\pyAMR\main\examples\indexes\plot_sari_c_relmap.py:154: MatplotlibDeprecationWarning:
The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
C:\Users\kelda\Desktop\repositories\github\pyAMR\main\examples\indexes\plot_sari_c_relmap.py:154: MatplotlibDeprecationWarning:
The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
C:\Users\kelda\Desktop\repositories\github\pyAMR\main\examples\indexes\plot_sari_c_relmap.py:154: MatplotlibDeprecationWarning:
The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
C:\Users\kelda\Desktop\repositories\github\pyAMR\main\examples\indexes\plot_sari_c_relmap.py:154: MatplotlibDeprecationWarning:
The legendHandles attribute was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use legend_handles instead.
|
.. code-block:: default
:lineno-start: 9
# Libraries
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
# Import own libraries
from pyamr.core.sari import SARI
from pyamr.core.freq import Frequency
from pyamr.datasets.load import make_susceptibility
# -------------------------
# Configuration
# -------------------------
# Configure seaborn style (context=talk)
sns.set(style="white")
# Set matplotlib
mpl.rcParams['xtick.labelsize'] = 9
mpl.rcParams['ytick.labelsize'] = 9
mpl.rcParams['axes.titlesize'] = 11
mpl.rcParams['legend.fontsize'] = 9
# Pandas configuration
pd.set_option('display.max_colwidth', 40)
pd.set_option('display.width', 300)
pd.set_option('display.precision', 4)
# Numpy configuration
np.set_printoptions(precision=2)
# -------------------------------------------
# Methods
# -------------------------------------------
def create_mapper(dataframe, column_key, column_value):
"""This method constructs a mapper
Parameters
----------
dataframe: dataframe-like
The dataframe from which the columns are extracted
column_key: string-like
The name of the column with the values for the keys of the mapper
column_value: string-like
The name of the column with the values for the values of the mapper
Returns
-------
dictionary
"""
dataframe = dataframe[[column_key, column_value]]
dataframe = dataframe.drop_duplicates()
return dict(zip(dataframe[column_key], dataframe[column_value]))
# -------------------------------------------
# Load data
# -------------------------------------------
# Load data
data = make_susceptibility()
# Show
print("\nData:")
print(data)
print("\nColumns:")
print(data.columns)
# -------------------------------------------
# Compute SARI
# -------------------------------------------
# Libraries
from pyamr.core.sari import SARI
# Create sari instance
sari = SARI(groupby=['specimen_code',
'microorganism_code',
'antimicrobial_code',
'sensitivity'])
# Compute SARI overall
sari_overall = sari.compute(data,
return_frequencies=True)
# Show
print("SARI (overall):")
print(sari_overall)
# -------------------------------------------
# Plot
# -------------------------------------------
# Reset
sari_overall = sari_overall.reset_index()
# Count records per specimen
specimen_count = sari_overall \
.groupby('specimen_code').freq.sum() \
.sort_values(ascending=False)
# Show
print("Cultures:")
print(specimen_count)
# Filter
sari_overall = sari_overall[sari_overall \
.specimen_code.isin( \
specimen_count.index.values[:5])]
# Loop
for specimen, df in sari_overall.groupby(by='specimen_code'):
# ------------
# Plot Heatmap
# ------------
# Create colormap
cmap = sns.color_palette("Reds", desat=0.5, n_colors=10)
# Configura
sizes = (df.freq.min(), df.freq.max())
# Plot
g = sns.relplot(data=df.reset_index(), x='microorganism_code',
y='antimicrobial_code', hue="sari", size="freq",
palette='Reds', hue_norm=(0, 1), edgecolor="gray",
linewidth=0.5, sizes=sizes, # size_norm=sizes,
dashes=True, legend='brief', height=10)
# Configure plot
g.set(xlabel="Antimicrobial",
ylabel="Microorganism",
title='Antibiogram (with frequency)',
#aspect='equal'
)
g.despine(left=True, bottom=True)
g.ax.margins(.1)
# Configure xticks
for label in g.ax.get_xticklabels():
label.set_rotation(90)
# Configure legend
for artist in g.legend.legendHandles:
artist.set_edgecolor("k")
artist.set_linewidth(0.5)
# Superior title
plt.suptitle(specimen)
# Add grid lines.
# plt.grid(linestyle='-', linewidth=0.5, color='.7')
# Adjust
plt.tight_layout()
# Show
plt.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 5.216 seconds)
.. _sphx_glr_download__examples_indexes_plot_sari_c_relmap.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_sari_c_relmap.py <plot_sari_c_relmap.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_sari_c_relmap.ipynb <plot_sari_c_relmap.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_