""" ============================= 1. Exploratory Data Analysis ============================= """ import matplotlib.pyplot as plt import numpy as np import seaborn as sns from dython.nominal import associations from easy_mpl import plot, boxplot from easy_mpl.utils import create_subplots from easy_mpl import pie from utils import SAVE from utils import plot_line, plot_boxplot, set_rcParams from utils import read_data, lineplot, version_info # %% version_info() # %% set_rcParams() # %% # Whole Data # ============ # %% # reading whole data # %% data = read_data() # %% print(data.shape) # %% data.isna().sum() # %% data.head() # %% data.tail() # %% # plotting a line plot of numerical features # %% lineplot(save=SAVE) # %% # plotting a boxplot of numerical features without season # %% wat_chem_cols = ['T (℃)', 'pH', 'TDS (mg/L)', 'DO (mg/L)', 'TN (mg/L)'] fig, axes = create_subplots(len(wat_chem_cols), figsize=(5,4)) colors = ['#FEE1D2', '#FBBEA3', '#FE9473', '#FB6A4B', '#EE3A2C'] for ax, col, color in zip(axes.flat, wat_chem_cols, colors): sns.boxplot(data[col], ax=ax, fliersize=0.6, color=color, orient='h', ) ax.set_xlabel(col) plt.tight_layout() if SAVE: plt.savefig("results/figures/box_num", bbox_inches="tight", dpi=600) plt.show() # %% # histogram of numerical features # %% num_columns = ['T (℃)', 'pH', 'TDS (mg/L)', 'DO (mg/L)', 'TN (mg/L)', 'season'] fig, axes = create_subplots(len(num_columns)) if not isinstance(axes, np.ndarray): axes = np.array([axes]) for ax, col in zip(axes.flat, num_columns): sns.histplot(data[col], ax=ax, ) plt.tight_layout() if SAVE: plt.savefig("results/figures/hist_num", bbox_inches="tight", dpi=600) plt.show() # %% # Season Wise # ------------ SEASONS = { 0: "Fall", 1: "Winter", 2: "Spring", 3: "Summer" } COLUMNS = ['T (℃)', 'pH', 'TDS (mg/L)', 'DO (mg/L)', 'TN (mg/L)'] groups = data.groupby('season') f, axes = create_subplots(5, sharex="all") colors = ['#588F07', '#E79D33', '#34ACAF', '#DD6F6D'] for col, ax in zip(COLUMNS, axes.flatten()): for (label, grp), color in zip(groups, colors): legend = '__nolabel__' if col == 'TN (mg/L)': legend = SEASONS[label] plot(grp[col].values, color=color, label=legend, ax=ax, show=False) ax.set_ylabel(col) if col == 'TN (mg/L)': ax.legend(loc=(1.2, 0.2)) if SAVE: plt.savefig("results/figures/seasonwise_line", bbox_inches="tight", dpi=600) plt.tight_layout() plt.show() # %% f, axes = create_subplots(5, sharex="all") for col, ax in zip(COLUMNS, axes.flatten()): data_ = [grp[col].values for _, grp in groups] labels = [SEASONS[label] for label, _ in groups] boxplot(data_, labels=labels, line_color=colors, #fill_color=colors, patch_artist=True, ax=ax, show=False, flierprops=dict(ms=2.0)) ax.set_ylabel(col) if col in ['TN (mg/L)', 'DO (mg/L)']: ax.set_xticks([1, 2, 3, 4]) ax.set_xticklabels(labels) plt.tight_layout() if SAVE: plt.savefig("results/figures/seasonwise_box.png", bbox_inches="tight", dpi=600) plt.show() # %% # Mean values in each season axes = data[COLUMNS + ['season']].groupby('season').mean().plot(kind="bar", subplots=True) for ax in axes: ax.set_ylabel(ax.get_title()) ax.set_title('') ax.legend().remove() plt.xticks([0, 1, 2, 3], SEASONS.values()) plt.tight_layout() if SAVE: plt.savefig("results/figures/season_mean.png", bbox_inches="tight", dpi=600) plt.show() # %% _ = associations( data.drop(columns=["season", "BIC", "Isolate ID", 'SHV', 'KPC', 'NDM', 'IMP', 'QnrS', 'Qnr A']), nom_nom_assoc="cramer", fmt=".1f", figsize=(8, 8), plot=False, cbar=True ) plt.tight_layout() if SAVE: plt.savefig("results/figures/corr_cat.png", bbox_inches="tight", dpi=600) plt.show() # %% _ = associations( data.drop(columns=["season", "BIC", "Isolate ID"]), nom_nom_assoc="cramer", fmt=".1f", figsize=(8, 8), plot=False, cbar=True, annot=False ) plt.tight_layout() if SAVE: plt.savefig("results/figures/corr_0.png", bbox_inches="tight", dpi=600) plt.show() # %% output = associations( data.drop(columns=["season", "BIC", "Isolate ID", 'SHV', 'KPC', 'NDM', 'IMP', 'QnrS', 'Qnr A']), nom_nom_assoc="theil", fmt=".1f", figsize=(8, 8), plot=False, cbar=True, ) plt.tight_layout() if SAVE: plt.savefig("results/figures/corr_0_nom_nom_theil.png", bbox_inches="tight", dpi=600) plt.show() # %% _ = associations( data.drop(columns=["season", "BIC", "Isolate ID"]), nom_nom_assoc="theil", fmt=".1f", annot=False, figsize=(8, 8), plot=False, cbar=True, ) plt.tight_layout() if SAVE: plt.savefig("results/figures/corr_0_nom_nom_theil0.png", bbox_inches="tight", dpi=600) plt.show() # %% # Antibiotics # ============= # %% # AMP # ------ feature = 'AMP' # The unique values in AMP are listed below. print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% # Following two plots (line and box plot) shows the distribution of # enviormental features of the samples where AMP values is 'R'. plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% # Following two plots (line and box plot) shows the distribution of # enviormental features of the samples where AMP values is 'S'. plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% # CAZ # ------ feature = 'CAZ' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # AMC # ------ feature = 'AMC' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% #hlot_boxplot(feature, 'I') # %% # CTX # ------ feature = 'CTX' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # FEP # ------ feature = 'FEP' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # ATM # ------ feature = 'ATM' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # MEM # ------ feature = 'MEM' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% plot_line(feature, 'S/R', save=SAVE) # %% plot_boxplot(feature, 'S/R', save=SAVE) # %% plot_line(feature, 'RR', save=SAVE) # %% plot_boxplot(feature, 'RR', save=SAVE) # %% # CIP # ------ feature = 'CIP' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # C # ------ feature = 'C' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # NA # ------ feature = 'NA' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% plot_line(feature, 'RS', save=SAVE) # %% plot_boxplot(feature, 'RS', save=SAVE) # %% # CN # ------ feature = 'CN' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # DO # ------ feature = 'DO' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # F # ------ feature = 'F' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # FOT # ------ feature = 'FOT' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # SXT # ------ feature = 'SXT' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, 'R', save=SAVE) # %% plot_boxplot(feature, 'R', save=SAVE) # %% plot_line(feature, 'S', save=SAVE) # %% plot_boxplot(feature, 'S', save=SAVE) # %% plot_line(feature, 'I', save=SAVE) # %% plot_boxplot(feature, 'I', save=SAVE) # %% # Genes # ========= # %% # Qnr A # ------ feature = 'Qnr A' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-') # %% plot_boxplot(feature, '-', save=SAVE) # %% # Qnr B # ------ feature = 'Qnr B' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-') # %% plot_boxplot(feature, '-', save=SAVE) # %% # QnrS # ------ feature = 'QnrS' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # IMP # ------ feature = 'IMP' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # NDM # ------ feature = 'NDM' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-') # %% plot_boxplot(feature, '-', save=SAVE) # %% # BIC # ------ feature = 'BIC' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # KPC # ------ feature = 'KPC' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # OXA48 # ------ feature = 'OXA48' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # TEM # ------ feature = 'TEM' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # SHV # ------ feature = 'SHV' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # CTX-M # ------ feature = 'CTX-M' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE) # %% # MCR-1 # ------ feature = 'MCR-1' print(f'unique values in {feature}: {data[feature].unique()}') # %% _ = pie(data[feature].dropna().values, ax_kws=dict(title=feature), show=False) if SAVE: plt.savefig(f"results/figures/pie_{feature}.png", bbox_inches="tight", dpi=600) plt.show() # %% plot_line(feature, '+', save=SAVE) # %% plot_boxplot(feature, '+', save=SAVE) # %% plot_line(feature, '-', save=SAVE) # %% plot_boxplot(feature, '-', save=SAVE)