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line_width = 1.5
fig, axs = plt.subplots(figsize=(12,8))
text_size = 10
plt.rc('font', size=text_size) # controls default text sizes
plt.rc('axes', titlesize=text_size) # fontsize of the axes title
plt.rc('axes', labelsize=text_size) # fontsize of the x and y labels
plt.rc('xtick', labelsize=text_size) # fontsize of the tick labels
plt.rc('ytick', labelsize=text_size) # fontsize of the tick labels
plt.rc('legend', fontsize=text_size) # legend fontsize
plt.rc('figure', titlesize=text_size) # fontsize of the figure title
plt.xlabel('xlabel', fontsize=text_size)
plt.ylabel('ylabel', fontsize=text_size)
sns.lineplot(data=df_filtered['U120_H2S_Content'],
linewidth = line_width, ax=axs, label="H2S content total"
, color="green"
)
sns.lineplot(data=df_filtered['110FI2001.PV -(SWEET FG TO BL)_6h_pct_change'],
linewidth = line_width, ax=axs, label="110FI"
, color="orange"
)
sns.lineplot(data=df_filtered['delta_lean_rich_amin_6h_pct_change'],
linewidth = line_width, ax=axs, label="delta lean rich amin"
, color="black"
)
'''
sns.lineplot(data=df1['U150 Feed N2 (150SCI0004_AZOT_D5762)_1'],
linewidth = line_width, ax=axs, label="Target Variable Lag1 - 150SCI0004_AZOT_D5762)_1"
, color="blue"
)
'''
axs.set_title(label = "Top Correlated Features vs Target Variable",
fontweight="bold")
axs.set(xlabel="Date", ylabel="Distribution of Features")
# axs.legend(loc = "upper left")
# axs.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))
axs.legend(loc='upper center', bbox_to_anchor=(0.5, -0.08),
fancybox=True, shadow=True, ncol=6)
plt.savefig('Distribution_of_top_correlated_features.png')
plt.show()
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