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compound_plot.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 3 20:58:51 2021
@author: morenodu
"""
df_features_ec_season, df_features_ec_season_2C = df_features_ec_season_us, df_features_ec_season_2C_us
y_pred = brf_model_us.predict(df_features_ec_season)
y_pred_2C = brf_model_us.predict(df_features_ec_season_2C)
# Plot graphs comparing the difference between 2C and PD
for (df_features_ec_season_1,df_features_ec_2C_season_1) in zip([df_features_ec_season],
[df_features_ec_season_2C]):
df_features_ec_season_fail_PD =pd.concat([
df_features_ec_season_1, pd.DataFrame(
np.array([y_pred < -1]).T,
index=df_features_ec_season_1.index, columns=['Scenario'])],axis=1)
df_features_ec_season_fail_PD['Scenario'] = 'PD'
df_features_ec_season_fail_PD_t = df_features_ec_season_fail_PD[y_pred == 1]
df_features_ec_season_fail_PD_t['Scenario'] = 'Failure PD'
df_features_ec_season_fail_2C =pd.concat([
df_features_ec_2C_season_1, pd.DataFrame(
np.array([y_pred_2C > -1 ]).T,
index=df_features_ec_2C_season_1.index,columns=['Scenario'])],axis=1)
df_features_ec_season_fail_2C['Scenario'] = '2C'
df_features_ec_season_fail_2C_t = df_features_ec_season_fail_2C[y_pred_2C == 1]
df_features_ec_season_fail_2C_t['Scenario'] = 'Failure 2C'
df_features_ec_season_scenarios = pd.concat([
df_features_ec_season_fail_PD, df_features_ec_season_fail_2C], axis= 0)
df_features_ec_season_scenarios_t = pd.concat([
df_features_ec_season_fail_PD_t, df_features_ec_season_fail_2C_t], axis= 0)
for (y_axis, x_axis) in zip([1,0,0],[2,2,1]):
# sns.lmplot(data=df_features_ec_season_scenarios, y=df_features_ec_season.columns[y_axis],
# x=df_features_ec_season.columns[x_axis],fit_reg=True,
# scatter_kws={"s": 10}, hue='Scenario',legend_out=False)
# plt.title("Climatic variables for each scenario")
plt.figure(figsize=(10,10))
g = sns.JointGrid()
g1 = sns.kdeplot(data=df_features_ec_season_scenarios, y=df_features_ec_season.columns[y_axis],
x=df_features_ec_season.columns[x_axis],hue='Scenario',fill=True, alpha= 0.7, ax=g.ax_joint)
g2 = sns.kdeplot(data=df_features_ec_season_scenarios_t, y=df_features_ec_season_scenarios_t.columns[y_axis],
x=df_features_ec_season_scenarios_t.columns[x_axis],hue='Scenario',fill=True, alpha= 0.7, ax=g.ax_joint)
g3 = sns.kdeplot(data=df_features_ec_season_scenarios, y=df_features_ec_season.columns[y_axis],hue='Scenario',fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_y)
g4 = sns.kdeplot(data=df_features_ec_season_scenarios, x=df_features_ec_season.columns[x_axis],hue='Scenario',fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_x)
g5 = sns.kdeplot(data=df_features_ec_season_scenarios_t, y=df_features_ec_season.columns[y_axis],hue='Scenario',fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_y)
g6 = sns.kdeplot(data=df_features_ec_season_scenarios_t, x=df_features_ec_season.columns[x_axis],hue='Scenario',fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_x)
plt.show()
# plt.title("Climatic variables for each scenario")
plt.figure(figsize=(10,10))
g = sns.JointGrid()
g1 = sns.kdeplot(data=df_features_ec_season_scenarios, y=df_features_ec_season.columns[y_axis], palette = ["#92C6FF", "#fabbff"],
x=df_features_ec_season.columns[x_axis],hue='Scenario',fill=True, alpha= 0.7, ax=g.ax_joint)
# g2 = sns.kdeplot(data=df_features_ec_season_scenarios_t, y=df_features_ec_season_scenarios_t.columns[y_axis],palette = ["#97F0AA","#FF9F9A"],
# x=df_features_ec_season_scenarios_t.columns[x_axis],hue='Scenario',fill=True, alpha= 0.7, ax=g.ax_joint)
g3 = sns.kdeplot(data=df_features_ec_season_scenarios, y=df_features_ec_season.columns[y_axis],hue='Scenario',palette = ["#92C6FF", "#fabbff"],fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_y)
g4 = sns.kdeplot(data=df_features_ec_season_scenarios, x=df_features_ec_season.columns[x_axis],hue='Scenario',palette = ["#92C6FF", "#fabbff"],fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_x)
g5 = sns.kdeplot(data=df_features_ec_season_scenarios_t, y=df_features_ec_season.columns[y_axis],hue='Scenario',palette = ["#97F0AA","#FF9F9A"],fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_y)
g6 = sns.kdeplot(data=df_features_ec_season_scenarios_t, x=df_features_ec_season.columns[x_axis],hue='Scenario',palette = ["#97F0AA","#FF9F9A"],fill=True, legend=False, alpha= 0.7, ax=g.ax_marg_x)
plt.show()
g5 = sns.kdeplot(data=df_features_ec_season_scenarios, x=df_features_ec_season.columns[x_axis],hue='Scenario',fill=True, legend=False, alpha= 0.7)
g6 = sns.kdeplot(data=df_features_ec_season_scenarios_t, x=df_features_ec_season.columns[x_axis],hue='Scenario',fill=True, legend=False, alpha= 0.7)