Skip to content

results 13 #14

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jun 21, 2024
Merged

results 13 #14

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Binary file added DataBase-Outputs/MaxShearX.pdf
View file
Open in desktop
Binary file not shown.
198 changes: 139 additions & 59 deletions Python Scripts/PySeesTKO/pyseestko/plotting.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,18 +2,24 @@
# IMPORT LIBRARIES
# ==================================================================================
# Objects
from matplotlib.ticker import FuncFormatter
from matplotlib import pyplot as plt
from pathlib import Path
from typing import Union, Tuple
from numpy.typing import NDArray
from scipy.signal import savgol_filter # Para suavizado
from pyseestko.errors import PlottingError
from pyseestko.utilities import pwl
from typing import List
from matplotlib.ticker import FuncFormatter
from matplotlib import pyplot as plt
from pathlib import Path
from typing import Union, Tuple
from numpy.typing import NDArray
from scipy.signal import savgol_filter # Para suavizado
from pyseestko.errors import PlottingError
from pyseestko.utilities import pwl
from typing import List
from sklearn.preprocessing import PowerTransformer
from scipy.stats import shapiro, fligner

# Packages
import pandas as pd
import numpy as np
import statsmodels.api as sm
import scipy.stats as stats
import seaborn as sns
import pandas as pd
import numpy as np
# ==================================================================================
# MAIN FUNCTIONS CLASS
# ==================================================================================
Expand Down Expand Up @@ -89,55 +95,129 @@ def plotStatisticByReplicas(df:pd.DataFrame, statistic:str, title:str, ylabel:st
plt.show()
return fig, ax

#NOTE:DEPRECATED
def plotValidation(drifts_df_lst, spectra_df_lst, base_shear_df_lst):
# DRIFTS
mean_drift_x_df, mean_drift_y_df, mean_drift_df = getReplicaCummStatisticDriftDFs(drifts_df_lst, statistic='mean')
std_drift_x_df, std_drift_y_df, std_drift_df = getReplicaCummStatisticDriftDFs(drifts_df_lst, statistic='std')

# Plot the mean drifts
fig1, ax = plotStatisticByReplicas(df=mean_drift_x_df, statistic='mean', ylabel='Drift', title='Number of replicas vs Mean Drift X')
fig2, ax = plotStatisticByReplicas(df=mean_drift_y_df, statistic='mean', ylabel='Drift', title='Number of replicas vs Mean Drift Y')
fig1.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Mean Drift X.png')
fig2.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Mean Drift Y.png')

# Plot the std drifts
fig1, ax = plotStatisticByReplicas(df=std_drift_x_df, statistic='std', ylabel='Acceleration Spectra[m/s/s]', title='Number of replicas vs Std Drift X')
fig2, ax = plotStatisticByReplicas(df=std_drift_y_df, statistic='std', ylabel='Acceleration Spectra[m/s/s]', title='Number of replicas vs Std Drift Y')
fig1.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Std Drift X.png')
fig2.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Std Drift Y.png')

# SPECTRUMSS
mean_spectra_x_df, mean_spectra_y_df, mean_spectra_df = getCummStatisticSpectraDFs(spectra_df_lst, statistic='mean')
std_spectra_x_df, std_spectra_y_df, std_spectra_df = getCummStatisticSpectraDFs(spectra_df_lst, statistic='std')

# Plot the mean spectra
fig1, ax = plotStatisticByReplicas(df=mean_spectra_x_df, statistic='mean', ylabel='Drift', title='Number of replicas vs Mean Spectra X')
fig2, ax = plotStatisticByReplicas(df=mean_spectra_y_df, statistic='mean', ylabel='Drift', title='Number of replicas vs Mean Spectra Y')
fig1.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Mean Spectra X.png')
fig2.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Mean Spectra Y.png')

# Plot the std spectra
fig1, ax = plotStatisticByReplicas(df=std_spectra_x_df, statistic='std', ylabel='Acceleration Spectra[m/s/s]',title='Number of replicas vs Std Spectra X')
fig2, ax = plotStatisticByReplicas(df=std_spectra_y_df, statistic='std', ylabel='Acceleration Spectra[m/s/s]',title='Number of replicas vs Std Spectra Y')
fig1.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Std Spectra X.png')
fig2.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Std Spectra Y.png')

# SHEAR BASE
max_shear_x_df, max_shear_y_df, max_shear_df = getReplicaCummStatisticBaseShearDFs(base_shear_df_lst, statistic='mean')

# Plot the mean base shear
fig1, ax = plotStatisticByReplicas(df=max_shear_x_df, statistic='mean', ylabel='Base Shear [kN]', title='Number of replicas vs Mean Base Shear X')
fig2, ax = plotStatisticByReplicas(df=max_shear_y_df, statistic='mean', ylabel='Base Shear [kN]', title='Number of replicas vs Mean Base Shear Y')
fig1.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Mean Base Shear X')
fig2.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Mean Base Shear Y')

# Plot the std base shear
fig1, ax = plotStatisticByReplicas(df=max_shear_x_df, statistic='std', ylabel='Base Shear [kN]', title='Number of replicas vs Std Base Shear X')
fig2, ax = plotStatisticByReplicas(df=max_shear_y_df, statistic='std', ylabel='Base Shear [kN]', title='Number of replicas vs Std Base Shear Y')
fig1.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Std Base Shear X')
fig2.savefig('C:/Users/oioya/OneDrive - miuandes.cl/Escritorio/Git-Updated/Thesis-Project-Simulation-Data-Analysis/DataBase-Outputs/Analysis Output/Number of replicas vs Std Base Shear Y')

# Función para verificar supuestos y generar gráficos de normalidad
def analyze_manova_assumptions(df, dependent_vars, group_vars, project_path, drift=True, xdir=True):
plt.rcParams.update({
"text.usetex": True,
"font.size": 13,
"font.family": "serif",
"text.latex.preamble": r'\usepackage{amsmath}'
})

# Filtrar solo las columnas de interés
df = df[dependent_vars + group_vars]

# Transformación de Box-Cox
pt = PowerTransformer(method='box-cox', standardize=True)
X_transformed = pt.fit_transform(df[dependent_vars])
df_transformed = pd.DataFrame(X_transformed, columns=dependent_vars)
for group_var in group_vars:
df_transformed[group_var] = df[group_var].values

# Verificación de normalidad individual con Shapiro-Wilk
normality_results = {var: shapiro(X_transformed[:, i]).pvalue for i, var in enumerate(dependent_vars)}

# Verificación de homogeneidad de varianzas con Fligner-Killeen
df_transformed['combined_factors'] = df_transformed[group_vars].astype(str).agg('-'.join, axis=1)
homogeneity_results = {}
for var in dependent_vars:
grouped_data = [group[var].values for name, group in df_transformed.groupby('combined_factors')]
_, p_value = fligner(*grouped_data)
homogeneity_results[var] = p_value

# Generación de gráficos de normalidad en dos filas y cinco columnas
fig, axes = plt.subplots(2, 5, figsize=(9, 5))

# Etiquetas de estaciones
station_labels = [f'Estación {i}' for i in [1, 5, 10, 15, 20]]

for i, var in enumerate(dependent_vars):
# Histograma en la primera fila
sns.histplot(df_transformed[var], kde=True, ax=axes[0, i])
axes[0, i].set_title(f'Histograma de normalidad\n{station_labels[i]}') if i == 2 else axes[0, i].set_title(station_labels[i])
axes[0, i].set_xlabel('')
axes[0, i].set_ylabel('') if i != 0 else axes[0, i].set_ylabel('Frecuencia')

# Q-Q plot en la segunda fila
sm.qqplot(df_transformed[var], line='s', ax=axes[1, i])
#axes[0, i].set_title(station_labels[i])
axes[1, i].set_title(f'Q-Q Plot de normalidad\n{station_labels[i]}') if i == 2 else axes[1, i].set_title(station_labels[i])
axes[1, i].set_ylabel('') if i != 0 else axes[1, i].set_ylabel('Cuantiles de la muestra')
axes[1, i].set_xlabel('Cuantiles teóricos') if i==2 else axes[1, i].set_xlabel('')

plt.tight_layout()
plt.show()
file_name = 'drift' if drift else 'spectra'
xdir = 'x' if xdir else 'y'
fig.savefig(project_path / f'manova_supps_{file_name}_{xdir}.pdf', dpi=100)
# Resultados en un DataFrame
results_df = pd.DataFrame({
'Variable': dependent_vars,
'Normalidad (p-valor)': list(normality_results.values()),
'Homogeneidad (p-valor)': list(homogeneity_results.values())
})

return results_df


def analyze_anova_assumptions(df, analysis_columns, project_path):
"""
Realiza un análisis de los supuestos para ANOVA en las columnas especificadas y guarda los resultados en archivos de imagen.

Parameters:
df (pd.DataFrame): DataFrame con las columnas 'Sim_Type', 'Nsubs', 'Station', 'Iteration' y columnas de análisis.
analysis_columns (list): Lista de las columnas para las cuales se desea realizar el análisis.
output_file_prefix (str): Prefijo del nombre del archivo para guardar las imágenes de los resultados.
"""
plt.rcParams.update({
"text.usetex": True,
"font.size": 13,
"font.family": "serif",
"text.latex.preamble": r'\usepackage{amsmath}'
})

for col in analysis_columns:
# Agrupar por Sim_Type, Nsubs y Station, y calcular la media de la columna especificada
df_grouped = df.groupby(['Sim_Type', 'Nsubs', 'Station']).agg({col: 'mean'}).reset_index()

# Aplicar la transformación Box-Cox
df_grouped[f'{col}_boxcox'], fitted_lambda = stats.boxcox(df_grouped[col])

# Verificar normalidad usando la prueba de Shapiro-Wilk
shapiro_test = stats.shapiro(df_grouped[f'{col}_boxcox'])

# Verificar homocedasticidad usando la prueba de Levene
grouped_data = [df_grouped[df_grouped['Sim_Type'] == g][f'{col}_boxcox'].values for g in df_grouped['Sim_Type'].unique()]
levene_test = stats.levene(*grouped_data)

# Crear la figura y los ejes
fig, ax = plt.subplots(1, 2, figsize=(8, 5))

# Histograma para los datos transformados con Box-Cox
sns.histplot(df_grouped[f'{col}_boxcox'], kde=True, ax=ax[0])
ax[0].set_title(f'Histograma de {col}')

# Gráfico Q-Q para verificar normalidad
sm.qqplot(df_grouped[f'{col}_boxcox'], line='s', ax=ax[1])
ax[1].set_title(f'Q-Q Plot de {col}')

# Ajustar la disposición de la figura
plt.tight_layout()

# Guardar la figura como imagen
save_path = project_path / f'{col}.pdf'
save_path.parent.mkdir(parents=True, exist_ok=True) # Crear directorio si no existe
fig.savefig(save_path, dpi=100)
plt.close(fig)

# Mostrar resultados de las pruebas en consola
print(f'fitted_lambda: {fitted_lambda:.3f}')
print(f"Resultados de la Prueba de Shapiro-Wilk para {col} (Normalidad)")
print(f"Estadístico: {shapiro_test.statistic:.3f}, Valor p: {shapiro_test.pvalue:.3f}\n")

print(f"Resultados de la Prueba de Levene para {col} (Homocedasticidad)")
print(f"Estadístico: {levene_test.statistic:.3f}, Valor p: {levene_test.pvalue:.3f}\n")


# ==================================================================================
Expand Down
Loading