Add data processing and visualization scripts for filtered section properties

tratar datos/con todos datos.py

@@ -0,0 +1,203 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+
+# ── Leer datos ──────────────────────────────────────────────────────────────
+df = pd.read_csv(
+    'resultados1.txt',
+    sep=r'\s+',
+    decimal=',',
+    header=0
+)
+
+# Variables de interés
+inputs  = ['H', 'b', 'tf', 'tw', 'e', 'L']
+outputs = ['Flecha_Media', 'Peso']
+
+# Convertir a numérico por si acaso
+for col in inputs + outputs:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+df.dropna(subset=inputs + outputs, inplace=True)
+
+print(f"Filas cargadas: {len(df)}")
+print(df[inputs + outputs].describe().to_string())
+
+# ── Paleta de colores ────────────────────────────────────────────────────────
+COLORS = {
+    'H':  '#E63946',
+    'b':  '#457B9D',
+    'tf': '#2A9D8F',
+    'tw': '#E9C46A',
+    'e':  '#F4A261',
+    'L':  '#A8DADC',
+}
+BG   = '#0F1117'
+GRID = '#2A2D3A'
+TEXT = '#E8EAF6'
+
+plt.rcParams.update({
+    'figure.facecolor': BG,
+    'axes.facecolor':   BG,
+    'axes.edgecolor':   GRID,
+    'axes.labelcolor':  TEXT,
+    'xtick.color':      TEXT,
+    'ytick.color':      TEXT,
+    'grid.color':       GRID,
+    'text.color':       TEXT,
+    'font.family':      'monospace',
+})
+
+labels = {
+    'H':  'H (altura)',
+    'b':  'b (ancho)',
+    'tf': 'tf (ala)',
+    'tw': 'tw (alma)',
+    'e':  'e (excentr.)',
+    'L':  'L (longitud)',
+}
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 1 – Scatter: cada entrada vs Flecha_Media y Peso
+# ════════════════════════════════════════════════════════════════════════════
+fig1, axes = plt.subplots(
+    2, 6, figsize=(22, 8),
+    facecolor=BG,
+    gridspec_kw={'hspace': 0.45, 'wspace': 0.35}
+)
+fig1.suptitle('Entradas vs Salidas  ·  Scatter', fontsize=15, fontweight='bold',
+              color=TEXT, y=1.01)
+
+for col_idx, inp in enumerate(inputs):
+    for row_idx, out in enumerate(outputs):
+        ax = axes[row_idx, col_idx]
+        color = COLORS[inp]
+
+        # Muestreo para no saturar
+        sample = df[[inp, out]].dropna().sample(min(2000, len(df)), random_state=42)
+
+        ax.scatter(sample[inp], sample[out],
+                   s=6, alpha=0.35, color=color, linewidths=0)
+
+        # Línea de tendencia
+        z = np.polyfit(sample[inp], sample[out], 1)
+        p = np.poly1d(z)
+        xs = np.linspace(sample[inp].min(), sample[inp].max(), 200)
+        ax.plot(xs, p(xs), color='white', lw=1.2, alpha=0.7)
+
+        if out == 'Flecha_Media':
+            ax.axhline(-25, color='white', lw=1.2, linestyle='--', alpha=0.85, label='-25')
+            ax.legend(fontsize=7, framealpha=0.2, labelcolor=TEXT)
+
+        ax.set_xlabel(labels[inp], fontsize=8)
+        ax.set_ylabel(out, fontsize=8)
+        ax.grid(True, linestyle='--', alpha=0.3)
+        ax.tick_params(labelsize=7)
+
+fig1.tight_layout()
+fig1.savefig('scatter_entradas_vs_salidas.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ scatter_entradas_vs_salidas.png guardado")
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 2 – Distribuciones (histogramas) de entradas + salidas
+# ════════════════════════════════════════════════════════════════════════════
+all_vars = inputs + outputs
+n = len(all_vars)
+fig2, axes2 = plt.subplots(2, 4, figsize=(18, 8), facecolor=BG,
+                            gridspec_kw={'hspace': 0.5, 'wspace': 0.35})
+fig2.suptitle('Distribución de variables', fontsize=15, fontweight='bold',
+              color=TEXT, y=1.01)
+axes2_flat = axes2.flatten()
+
+for i, var in enumerate(all_vars):
+    ax = axes2_flat[i]
+    clr = COLORS.get(var, '#90CAF9')
+    data = df[var].dropna()
+    ax.hist(data, bins=40, color=clr, alpha=0.85, edgecolor='none')
+    ax.axvline(data.mean(), color='white', lw=1.4, linestyle='--', label=f'μ={data.mean():.3g}')
+    ax.set_title(var, fontsize=10, fontweight='bold', color=clr)
+    ax.set_ylabel('Frecuencia', fontsize=8)
+    ax.grid(True, linestyle='--', alpha=0.3)
+    ax.tick_params(labelsize=7)
+    ax.legend(fontsize=7, framealpha=0.2, labelcolor=TEXT)
+
+# Ocultar último subplot vacío si n < 8
+for j in range(n, len(axes2_flat)):
+    axes2_flat[j].set_visible(False)
+
+fig2.tight_layout()
+fig2.savefig('distribuciones.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ distribuciones.png guardado")
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 3 – Correlación: heatmap
+# ════════════════════════════════════════════════════════════════════════════
+corr = df[inputs + outputs].corr()
+
+fig3, ax3 = plt.subplots(figsize=(9, 7), facecolor=BG)
+fig3.suptitle('Mapa de correlación', fontsize=14, fontweight='bold', color=TEXT)
+
+cmap = plt.cm.RdYlGn
+im = ax3.imshow(corr.values, cmap=cmap, vmin=-1, vmax=1, aspect='auto')
+plt.colorbar(im, ax=ax3, fraction=0.046, pad=0.04).ax.tick_params(colors=TEXT)
+
+ticks = list(range(len(corr.columns)))
+ax3.set_xticks(ticks); ax3.set_yticks(ticks)
+ax3.set_xticklabels(corr.columns, rotation=45, ha='right', fontsize=9)
+ax3.set_yticklabels(corr.columns, fontsize=9)
+
+for i in range(len(corr)):
+    for j in range(len(corr)):
+        val = corr.values[i, j]
+        color_txt = 'black' if abs(val) > 0.5 else TEXT
+        ax3.text(j, i, f'{val:.2f}', ha='center', va='center',
+                 fontsize=8, color=color_txt, fontweight='bold')
+
+ax3.grid(False)
+fig3.tight_layout()
+fig3.savefig('correlacion_heatmap.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ correlacion_heatmap.png guardado")
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 4 – Boxplots de salidas por cuantiles de L
+# ════════════════════════════════════════════════════════════════════════════
+df['L_cat'] = pd.qcut(df['L'], q=5, labels=['L_Q1','L_Q2','L_Q3','L_Q4','L_Q5'])
+
+fig4, axes4 = plt.subplots(1, 2, figsize=(14, 6), facecolor=BG)
+fig4.suptitle('Distribución de salidas por cuantiles de L', fontsize=14,
+              fontweight='bold', color=TEXT)
+
+palette = ['#E63946','#F4A261','#E9C46A','#2A9D8F','#457B9D']
+
+for ax_idx, out in enumerate(outputs):
+    ax = axes4[ax_idx]
+    groups = [df.loc[df['L_cat'] == cat, out].dropna().values
+              for cat in df['L_cat'].cat.categories]
+
+    bp = ax.boxplot(groups, patch_artist=True, notch=False,
+                    medianprops=dict(color='white', lw=2),
+                    whiskerprops=dict(color=TEXT),
+                    capprops=dict(color=TEXT),
+                    flierprops=dict(marker='o', color=TEXT, alpha=0.2, markersize=2))
+
+    for patch, clr in zip(bp['boxes'], palette):
+        patch.set_facecolor(clr)
+        patch.set_alpha(0.75)
+
+    ax.set_xticklabels(df['L_cat'].cat.categories, rotation=20, fontsize=8)
+    ax.set_title(out, fontsize=11, fontweight='bold', color=TEXT)
+    ax.set_ylabel(out, fontsize=9)
+    ax.grid(True, axis='y', linestyle='--', alpha=0.3)
+
+fig4.tight_layout()
+fig4.savefig('boxplot_salidas_por_L.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ boxplot_salidas_por_L.png guardado")
+
+print("\n✅ Todos los gráficos generados correctamente.")

tratar datos/solo25

@@ -0,0 +1,203 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+
+# ── Leer datos ──────────────────────────────────────────────────────────────
+df_raw = pd.read_csv(
+    'resultados1.txt',
+    sep=r'\s+',
+    decimal=',',
+    header=0
+)
+
+inputs  = ['H', 'b', 'tf', 'tw', 'e', 'L']
+outputs = ['Flecha_Media', 'Peso']
+
+for col in inputs + outputs:
+    df_raw[col] = pd.to_numeric(df_raw[col], errors='coerce')
+df_raw.dropna(subset=inputs + outputs, inplace=True)
+
+# ── FILTRO: Flecha_Media > -25 ───────────────────────────────────────────────
+df = df_raw[df_raw['Flecha_Media'] > -25].copy()
+
+print(f"Filas totales  : {len(df_raw)}")
+print(f"Filas filtradas: {len(df)}  (Flecha_Media > -25)")
+print(df[inputs + outputs].describe().to_string())
+
+# ── Paleta de colores ────────────────────────────────────────────────────────
+COLORS = {
+    'H':  '#E63946',
+    'b':  '#457B9D',
+    'tf': '#2A9D8F',
+    'tw': '#E9C46A',
+    'e':  '#F4A261',
+    'L':  '#A8DADC',
+}
+BG   = '#0F1117'
+GRID = '#2A2D3A'
+TEXT = '#E8EAF6'
+
+plt.rcParams.update({
+    'figure.facecolor': BG,
+    'axes.facecolor':   BG,
+    'axes.edgecolor':   GRID,
+    'axes.labelcolor':  TEXT,
+    'xtick.color':      TEXT,
+    'ytick.color':      TEXT,
+    'grid.color':       GRID,
+    'text.color':       TEXT,
+    'font.family':      'monospace',
+})
+
+labels = {
+    'H':  'H (altura)',
+    'b':  'b (ancho)',
+    'tf': 'tf (ala)',
+    'tw': 'tw (alma)',
+    'e':  'e (excentr.)',
+    'L':  'L (longitud)',
+}
+
+SUBTITLE = f'Filtro: Flecha_Media > -25  ·  {len(df)} filas de {len(df_raw)}'
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 1 – Scatter: cada entrada vs Flecha_Media y Peso
+# ════════════════════════════════════════════════════════════════════════════
+fig1, axes = plt.subplots(
+    2, 6, figsize=(22, 8),
+    facecolor=BG,
+    gridspec_kw={'hspace': 0.45, 'wspace': 0.35}
+)
+fig1.suptitle(f'Entradas vs Salidas  ·  Scatter\n{SUBTITLE}',
+              fontsize=13, fontweight='bold', color=TEXT, y=1.02)
+
+for col_idx, inp in enumerate(inputs):
+    for row_idx, out in enumerate(outputs):
+        ax = axes[row_idx, col_idx]
+        color = COLORS[inp]
+
+        sample = df[[inp, out]].dropna().sample(min(2000, len(df)), random_state=42)
+
+        ax.scatter(sample[inp], sample[out],
+                   s=6, alpha=0.35, color=color, linewidths=0)
+
+        z = np.polyfit(sample[inp], sample[out], 1)
+        p = np.poly1d(z)
+        xs = np.linspace(sample[inp].min(), sample[inp].max(), 200)
+        ax.plot(xs, p(xs), color='white', lw=1.2, alpha=0.7)
+
+        if out == 'Flecha_Media':
+            ax.axhline(-25, color='white', lw=1.2, linestyle='--', alpha=0.85, label='-25')
+            ax.legend(fontsize=7, framealpha=0.2, labelcolor=TEXT)
+
+        ax.set_xlabel(labels[inp], fontsize=8)
+        ax.set_ylabel(out, fontsize=8)
+        ax.grid(True, linestyle='--', alpha=0.3)
+        ax.tick_params(labelsize=7)
+
+fig1.tight_layout()
+fig1.savefig('filtrado_scatter.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ filtrado_scatter.png guardado")
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 2 – Distribuciones
+# ════════════════════════════════════════════════════════════════════════════
+all_vars = inputs + outputs
+n = len(all_vars)
+fig2, axes2 = plt.subplots(2, 4, figsize=(18, 8), facecolor=BG,
+                            gridspec_kw={'hspace': 0.5, 'wspace': 0.35})
+fig2.suptitle(f'Distribución de variables\n{SUBTITLE}',
+              fontsize=13, fontweight='bold', color=TEXT, y=1.02)
+axes2_flat = axes2.flatten()
+
+for i, var in enumerate(all_vars):
+    ax = axes2_flat[i]
+    clr = COLORS.get(var, '#90CAF9')
+    data = df[var].dropna()
+    ax.hist(data, bins=40, color=clr, alpha=0.85, edgecolor='none')
+    ax.axvline(data.mean(), color='white', lw=1.4, linestyle='--', label=f'μ={data.mean():.3g}')
+    ax.set_title(var, fontsize=10, fontweight='bold', color=clr)
+    ax.set_ylabel('Frecuencia', fontsize=8)
+    ax.grid(True, linestyle='--', alpha=0.3)
+    ax.tick_params(labelsize=7)
+    ax.legend(fontsize=7, framealpha=0.2, labelcolor=TEXT)
+
+for j in range(n, len(axes2_flat)):
+    axes2_flat[j].set_visible(False)
+
+fig2.tight_layout()
+fig2.savefig('filtrado_distribuciones.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ filtrado_distribuciones.png guardado")
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 3 – Heatmap correlación
+# ════════════════════════════════════════════════════════════════════════════
+corr = df[inputs + outputs].corr()
+
+fig3, ax3 = plt.subplots(figsize=(9, 7), facecolor=BG)
+fig3.suptitle(f'Mapa de correlación\n{SUBTITLE}', fontsize=13, fontweight='bold', color=TEXT)
+
+cmap = plt.cm.RdYlGn
+im = ax3.imshow(corr.values, cmap=cmap, vmin=-1, vmax=1, aspect='auto')
+plt.colorbar(im, ax=ax3, fraction=0.046, pad=0.04).ax.tick_params(colors=TEXT)
+
+ticks = list(range(len(corr.columns)))
+ax3.set_xticks(ticks); ax3.set_yticks(ticks)
+ax3.set_xticklabels(corr.columns, rotation=45, ha='right', fontsize=9)
+ax3.set_yticklabels(corr.columns, fontsize=9)
+
+for i in range(len(corr)):
+    for j in range(len(corr)):
+        val = corr.values[i, j]
+        color_txt = 'black' if abs(val) > 0.5 else TEXT
+        ax3.text(j, i, f'{val:.2f}', ha='center', va='center',
+                 fontsize=8, color=color_txt, fontweight='bold')
+
+
+ax3.grid(False)
+fig3.tight_layout()
+fig3.savefig('filtrado_correlacion_heatmap.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ filtrado_correlacion_heatmap.png guardado")
+
+# ════════════════════════════════════════════════════════════════════════════
+# FIGURA 4 – Boxplots de salidas por cuantiles de L
+# ════════════════════════════════════════════════════════════════════════════
+df['L_cat'] = pd.qcut(df['L'], q=5, labels=['L_Q1','L_Q2','L_Q3','L_Q4','L_Q5'])
+
+fig4, axes4 = plt.subplots(1, 2, figsize=(14, 6), facecolor=BG)
+fig4.suptitle(f'Distribución de salidas por cuantiles de L\n{SUBTITLE}',
+              fontsize=13, fontweight='bold', color=TEXT)
+
+palette = ['#E63946','#F4A261','#E9C46A','#2A9D8F','#457B9D']
+
+for ax_idx, out in enumerate(outputs):
+    ax = axes4[ax_idx]
+    groups = [df.loc[df['L_cat'] == cat, out].dropna().values
+              for cat in df['L_cat'].cat.categories]
+
+    bp = ax.boxplot(groups, patch_artist=True, notch=False,
+                    medianprops=dict(color='white', lw=2),
+                    whiskerprops=dict(color=TEXT),
+                    capprops=dict(color=TEXT),
+                    flierprops=dict(marker='o', color=TEXT, alpha=0.2, markersize=2))
+
+    for patch, clr in zip(bp['boxes'], palette):
+        patch.set_facecolor(clr)
+        patch.set_alpha(0.75)
+
+    ax.set_xticklabels(df['L_cat'].cat.categories, rotation=20, fontsize=8)
+    ax.set_title(out, fontsize=11, fontweight='bold', color=TEXT)
+    ax.set_ylabel(out, fontsize=9)
+    ax.grid(True, axis='y', linestyle='--', alpha=0.3)
+
+fig4.tight_layout()
+fig4.savefig('filtrado_boxplot_salidas_por_L.png',
+             dpi=150, bbox_inches='tight', facecolor=BG)
+print("✓ filtrado_boxplot_salidas_por_L.png guardado")
+
+print("\n✅ Todos los gráficos filtrados generados correctamente.")