06.d Collateral Sensitivity Index (CRI)

Since the computation of the Collateral Sensitivity Index is quite computationally expensive, the results are saved into a .csv file so that can be easily loaded and displayed. This script shows a very basic graph of such information.

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.

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

# Specific libraries
from pathlib import Path
from matplotlib.patches import Patch
from matplotlib.patches import Rectangle
from matplotlib.colors import LogNorm, Normalize

# 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


# --------------------------------
# Methods
# --------------------------------
def _check_ax_ay_equal(ax, ay):
    return ax==ay

def _check_ax_ay_greater(ax, ay):
    return  ax>ay

# --------------------------------
# Constants
# --------------------------------
# Figure size
figsize = (10, 5)

Let’s load the data

# Load data
path = Path('../../datasets/collateral-sensitivity/20230525-135511')
data = pd.read_csv(path / 'contingency.csv')
abxs = pd.read_csv(path / 'categories.csv')

# Format data
data = data.set_index(['specimen', 'o', 'ax', 'ay'])
data.RR = data.RR.fillna(0).astype(int)
data.RS = data.RS.fillna(0).astype(int)
data.SR = data.SR.fillna(0).astype(int)
data.SS = data.SS.fillna(0).astype(int)
data['samples'] = data.RR + data.RS + data.SR + data.SS

#data['samples'] = data.iloc[:, :4].sum(axis=1)

def filter_top_pairs(df, n=5):
    """Filter top n (Specimen, Organism) pairs."""
    # Find top
    top = df.groupby(level=[0, 1]) \
        .samples.sum() \
        .sort_values(ascending=False) \
        .head(n)

    # Filter
    idx = pd.IndexSlice
    a = top.index.get_level_values(0).unique()
    b = top.index.get_level_values(1).unique()

    # Return
    return df.loc[idx[a, b, :, :]]

# Filter
data = filter_top_pairs(data, n=2)
data = data[data.samples > 500]

Lets see the data

if TERMINAL:
    print("\n")
    print("Number of samples: %s" % data.samples.sum())
    print("Number of pairs: %s" % data.shape[0])
    print("Data:")
    print(data)
data.iloc[:7,:].dropna(axis=1, how='all')

				RR	RS	SI	SR	SS	MIS	samples
specimen	o	ax	ay
WOUCUL	SAUR	ACHL	ACLI	2	10	3.0	165	585	-0.003227	762
			AERY	2	10	4.0	213	536	-0.007012	761
			AFUS	2	10	NaN	86	667	0.003373	765
			ALIN	0	10	NaN	0	523	0.000000	533
			AMET	3	9	NaN	71	679	0.010702	762
			AMUP	0	10	6.0	3	690	-0.000183	703
			ANEO	1	9	NaN	27	622	0.003881	659

Lets load the antimicrobial data and create color mapping variables

# 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))

Let’s display the information

# Loop
for i, df in data.groupby(level=[0, 1]):

    # Drop level
    df = df.droplevel(level=[0, 1])

    # Check possible issues.
    ax = df.index.get_level_values(0)
    ay = df.index.get_level_values(1)
    idx1 = _check_ax_ay_equal(ax, ay)
    idx2 = _check_ax_ay_greater(ax, ay)

    # Show
    print("%25s. ax==ay => %5s | ax>ay => %5s" % \
          (i, idx1.sum(), idx2.sum()))

    # Re-index to have square matrix
    abxs = set(ax) | set(ay)
    index = pd.MultiIndex.from_product([abxs, abxs])

    # Reformat MIS
    mis = df['MIS'] \
        .reindex(index, fill_value=np.nan) \
        .unstack()

    # Reformat samples
    freq = df['samples'] \
        .reindex(index, fill_value=0) \
        .unstack()

    # Combine in square matrix
    m1 = mis.copy(deep=True).to_numpy()
    m2 = freq.to_numpy()
    il1 = np.tril_indices(mis.shape[1])
    m1[il1] = m2.T[il1]
    m = pd.DataFrame(m1,
        index=mis.index, columns=mis.columns)

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

    # Masks
    masku = np.triu(np.ones_like(m))
    maskl = np.tril(np.ones_like(m))

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

    # Create own colorbar axes
    # Params are [left, bottom, width, height]
    cbar_ax1 = fig.add_axes([0.66, 0.5, 0.03, 0.38])
    cbar_ax2 = fig.add_axes([0.76, 0.5, 0.03, 0.38])

    # Display
    r1 = sns.heatmap(data=m, 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=m, 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'})


    # ------------------------------------------
    # Add category rectangular patches
    # ------------------------------------------
    # Create colors
    colors = m.columns.to_series().map(code2cat).map(lookup)

    # Create patches for categories
    category_patches = []
    for lbl in axs.get_xticklabels():
        try:
            x, y = lbl.get_position()
            c = colors.to_dict().get(lbl.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(lbl.get_text(), e)

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


    # ------------------------------------------
    # Add category legend
    # ------------------------------------------
    # Unique categories
    unique_categories = m.columns \
        .to_series().map(code2cat).unique()

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

    # Add legend
    axs.legend(handles=legend_elements, loc='lower left',
               ncol=1, bbox_to_anchor=(1.1, 0.00), fontsize=8,
               fancybox=False, shadow=False)

    # Configure plot
    plt.suptitle('%s - %s' % (i[0], i[1]))
    plt.tight_layout()
    plt.subplots_adjust(left=-0.1, wspace=0.1)


# Show
plt.show()

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Out:

       ('URICUL', 'ECOL'). ax==ay =>    18 | ax>ay =>     0
/Users/cbit/Desktop/repositories/github/python-spare-code/main/examples/matplotlib/plot_main06_d_collateral_sensitivity.py:246: UserWarning:

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

       ('URICUL', 'SAUR'). ax==ay =>     0 | ax>ay =>     0
ATET Invalid RGBA argument: nan
/Users/cbit/Desktop/repositories/github/python-spare-code/main/examples/matplotlib/plot_main06_d_collateral_sensitivity.py:246: UserWarning:

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

       ('WOUCUL', 'ECOL'). ax==ay =>     0 | ax>ay =>     0
ATIG Invalid RGBA argument: nan
/Users/cbit/Desktop/repositories/github/python-spare-code/main/examples/matplotlib/plot_main06_d_collateral_sensitivity.py:246: UserWarning:

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

       ('WOUCUL', 'SAUR'). ax==ay =>    13 | ax>ay =>     0
ATET Invalid RGBA argument: nan
/Users/cbit/Desktop/repositories/github/python-spare-code/main/examples/matplotlib/plot_main06_d_collateral_sensitivity.py:246: UserWarning:

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