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Browse files- Dockerfile +1 -12
- app.py +1091 -324
- assets/custom.css +63 -0
- assets/style.css +437 -0
- requirements.txt +0 -0
Dockerfile
CHANGED
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@@ -1,24 +1,13 @@
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FROM python:3.11-slim
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# 2. Establece el directorio de trabajo dentro del contenedor.
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WORKDIR /app
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# 3. Copia primero el archivo de requisitos.
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# Esto aprovecha el caché de Docker: si no cambias tus requisitos,
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# no se volverán a instalar en cada despliegue, haciéndolo más rápido.
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COPY requirements.txt .
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# 4. Instala las dependencias.
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RUN pip install --no-cache-dir -r requirements.txt
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# 5. Copia el resto de los archivos de tu proyecto al contenedor.
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# Esto incluye app.py, los archivos .csv y la carpeta 'assets'.
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COPY . .
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# 6. Expón el puerto que usará la aplicación. Hugging Face usa 7860 por defecto.
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EXPOSE 7860
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# Usamos Gunicorn para ejecutar el 'server' de tu 'app.py'.
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# El host 0.0.0.0 es crucial para que sea accesible desde fuera del contenedor.
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CMD ["gunicorn", "--bind", "0.0.0.0:7860", "--workers", "1", "--threads", "4", "app:server"]
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FROM python:3.11-slim
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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EXPOSE 7860
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CMD ["gunicorn", "--bind", "0.0.0.0:7860", "--threads", "4", "app:server"]
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app.py
CHANGED
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@@ -7,6 +7,10 @@ import plotly.graph_objects as go
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import pycountry_convert as pc
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import pycountry
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import gunicorn
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iracing_ragions = {
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'US':['US'],
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@@ -31,10 +35,25 @@ iracing_ragions = {
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'Benelux':['NL','BE','LU']
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}
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def load_and_process_data(filename):
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"""Función para cargar y pre-procesar un archivo de disciplina."""
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print(f"Loading and processing {filename}...")
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df = pd.read_csv(filename)
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'''filename_parquet = filename.replace('.csv', '.parquet')
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df = pd.read_parquet(filename_parquet)'''
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df = df[df['IRATING'] > 1]
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@@ -52,6 +71,104 @@ def load_and_process_data(filename):
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print(f"Finished processing {filename}.")
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return df
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def create_irating_trend_line_chart(df):
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"""
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Crea un gráfico de líneas que muestra el promedio de carreras corridas
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))
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fig.update_layout(
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title=dict(
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text='<b>iRating Range By Avg. Races</b>',
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font=dict(color='white', size=14),
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x=0.5,
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xanchor='center'
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),
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template='plotly_dark',
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paper_bgcolor='rgba(11,11,19,1)',
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plot_bgcolor='rgba(11,11,19,1)',
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gridwidth=1,
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gridcolor='rgba(255,255,255,0.1)'
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),
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margin=dict(l=10, r=10, t=
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)
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return fig
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)
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))
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fig.update_layout(
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text='<b>Regions (Avg. iRating, Avg. Races, Qty. Drivers)</b>',
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font=dict(color='white', size=14),
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x=0.5,
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xanchor='center'
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),
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font=GLOBAL_FONT,
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#xaxis_title='Avg. iRating',
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#yaxis_title='Avg. Races',
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template='plotly_dark',
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gridcolor='rgba(255,255,255,0.1)'
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),
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# --- FIN DEL ESTILO DE GRID ---
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margin=dict(l=10, r=10, t=
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)
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return fig
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fig = go.Figure()
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kpis = [
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{'value': total_pilots, 'title': f"Drivers {filter_context}", 'format': ',.0f'},
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{'value': avg_irating, 'title': "
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{'value': avg_starts, 'title': "
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{'value': avg_wins, 'title': "
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]
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for i, kpi in enumerate(kpis):
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fig.add_trace(go.Indicator(
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grid={'rows': 1, 'columns': 4, 'pattern': "independent"},
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template='plotly_dark',
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paper_bgcolor='rgba(0,0,0,0)',
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plot_bgcolor=
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margin=dict(l=20, r=20, t=50, b=10),
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height=60,
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font=GLOBAL_FONT
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plot_bgcolor='rgba(0,0,0,0)',
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xaxis_visible=False,
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yaxis_visible=False,
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height=
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annotations=[
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dict(
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text="<b>Select or search a driver</b>",
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]
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)
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return fig
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# --- FIN DE LA
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# Si SÍ hay información del piloto, procedemos como antes.
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pilot_name = pilot_info.get('DRIVER', 'Piloto')
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kpis_piloto = [
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{'rank': rank_world, 'percentil': percentil_world, 'title': "World Rank"},
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{'rank': rank_region, 'percentil': percentil_region, 'title': "Region Rank
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{'rank': rank_country, 'percentil': percentil_country, 'title': "Country Rank"}
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]
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for i, kpi in enumerate(kpis_piloto):
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fig.add_trace(go.Indicator(
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mode="number",
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value=kpi['rank'],
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number={'prefix': "#", 'font': {'size':
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))
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fig.update_layout(
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title={
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'text': title_text,
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'y':0.
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'font': {'size':
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},
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template='plotly_dark',
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paper_bgcolor='rgba(0,0,0,0)',
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plot_bgcolor='rgba(0,0,0,0)',
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margin=dict(l=20, r=20, t=
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height=
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font=GLOBAL_FONT
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)
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return fig
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max_line_trace = go.Scatter(
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x=x_trend, y=y_trend_max, mode='lines',
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name='
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line=dict(color='red', width=1, dash='dash')
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)
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min_line_trace = go.Scatter(
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x=x_trend, y=y_trend_min, mode='lines',
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name='
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line=dict(color='lime', width=1, dash='dash')
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)
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x=x_trend,
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y=y_trend_mean,
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mode='lines',
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name='
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line=dict(color='black', width=2, dash='solid')
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)
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fig = go.Figure(data=[heatmap_trace, max_line_trace, min_line_trace, mean_line_trace])
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fig.update_layout(
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xaxis_title='iRating',
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yaxis_title='Incidents Per Race',
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template='plotly_dark',
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showframe=False, # <-- Oculta el marco exterior del globo
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showcoastlines=False # <-- Oculta las líneas de la costa
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),
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margin={"r":0,"t":
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coloraxis_showscale=show_scale,
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coloraxis_colorbar=dict(
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title='Drivers',
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def create_histogram_with_percentiles(df, column='IRATING', bin_width=100, highlight_irating=None, highlight_name=None):
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# Crear bins específicos de 100 en 100
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min_val = df[column].min()
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max_val = df[column].max()
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hist, bin_edges = np.histogram(df[column], bins=bin_edges)
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bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
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below = (df[column] < bin_edges[i+1]).sum()
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percentile = below / total * 100
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top_percent = 100 - percentile
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hover_text.append(
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f"Range: {int(bin_edges[i])}-{int(bin_edges[i+1])}<br>"
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f"Drivers: {hist[i]}<br>"
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f"Top: {top_percent:.2f}%"
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)
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# --- FIN DEL BLOQUE NUEVO ---
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fig.update_layout(
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title=dict(
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text='<b>iRating Histogram</b>',
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font=dict(color='white', size=14),
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x=0.5,
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xanchor='center'
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),
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font=GLOBAL_FONT,
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xaxis=dict(
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title_text='iRating', # Texto del título
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template='plotly_dark',
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hovermode='x unified',
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paper_bgcolor='rgba(18,18,26,
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plot_bgcolor='rgba(255,255,255,0)',
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# --- MODIFICACIÓN: Reducir márgenes y tamaño de fuentes ---
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margin=dict(l=10, r=10, t=
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)
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)
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return fig
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def flag_img(code):
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url = f"https://flagcdn.com/16x12/{code.lower()}.png"
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# La función ahora asume que si el código llega aquí, es válido.
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GLOBAL_FONT = {'family': "Lato, sans-serif"}
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DISCIPLINE_DATAFRAMES = {
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'ROAD.csv': load_and_process_data('ROAD.csv'),
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'FORMULA.csv': load_and_process_data('FORMULA.csv'),
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'OVAL.csv': load_and_process_data('OVAL.csv'),
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'DROAD.csv': load_and_process_data('DROAD.csv'),
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'DOVAL.csv': load_and_process_data('DOVAL.csv')
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}
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country_coords = {
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'ES': {'lat': 40.4, 'lon': -3.7}, 'US': {'lat': 39.8, 'lon': -98.5},
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'BR': {'lat': -14.2, 'lon': -51.9}, 'DE': {'lat': 51.1, 'lon': 10.4},
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#df['LOCATION'] = 'a'
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density_heatmap = dcc.Graph(
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id='density-heatmap',
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)
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correlation_heatmap = dcc.Graph(
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id='correlation-heatmap',
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# Usamos las columnas numéricas del dataframe original
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figure=create_correlation_heatmap(df[['IRATING', 'STARTS', 'WINS','TOP25PCNT','AVG_INC','AVG_FINISH_POS']])
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)
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sort_mode="single",
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page_action="custom",
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page_current=0,
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page_size=
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page_count=len(df_table) //
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virtualization=False,
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style_as_list_view=True,
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active_cell={'row': 21,'column':1},
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| 795 |
-
|
| 796 |
-
# --- ELIMINAMOS selected_rows Y AÑADIMOS active_cell ---
|
| 797 |
-
# selected_rows=[], # <-- ELIMINAR ESTA LÍNEA
|
| 798 |
-
|
| 799 |
style_table={
|
| 800 |
-
#'tableLayout': 'fixed', # <-- DESCOMENTA O AÑADE ESTA LÍNEA
|
| 801 |
'overflowX': 'auto',
|
| 802 |
-
'
|
| 803 |
-
'
|
|
|
|
|
|
|
| 804 |
'width': '100%',
|
| 805 |
'borderRadius': '15px',
|
| 806 |
'overflow': 'hidden',
|
| 807 |
'backgroundColor': 'rgba(11,11,19,1)',
|
| 808 |
-
'textOverflow': 'ellipsis',
|
| 809 |
'border': '1px solid #4A4A4A'
|
| 810 |
-
|
| 811 |
},
|
| 812 |
|
| 813 |
style_cell={
|
| 814 |
'textAlign': 'center',
|
| 815 |
-
'padding': '
|
| 816 |
'backgroundColor': 'rgba(11,11,19,1)',
|
| 817 |
'color': 'rgb(255, 255, 255,.8)',
|
| 818 |
-
'border': '1px solid rgba(255, 255, 255, 0)',
|
| 819 |
'overflow': 'hidden',
|
| 820 |
'textOverflow': 'ellipsis',
|
| 821 |
-
'whiteSpace': 'nowrap',
|
| 822 |
-
'maxWidth': 0
|
|
|
|
| 823 |
},
|
|
|
|
| 824 |
style_header={
|
| 825 |
'backgroundColor': 'rgba(30,30,38,1)',
|
| 826 |
'fontWeight': 'bold',
|
| 827 |
'color': 'white',
|
| 828 |
-
'border': '
|
| 829 |
'textAlign': 'center',
|
| 830 |
-
'fontSize':
|
|
|
|
|
|
|
|
|
|
|
|
|
| 831 |
},
|
|
|
|
| 832 |
# --- AÑADIMOS ESTILO PARA LA FILA SELECCIONADA Y LAS CLASES ---
|
| 833 |
style_data_conditional=[
|
| 834 |
{
|
|
@@ -882,7 +1045,7 @@ interactive_table = dash_table.DataTable(
|
|
| 882 |
|
| 883 |
scatter_irating_starts = dcc.Graph(
|
| 884 |
id='scatter-irating',
|
| 885 |
-
style={'height': '
|
| 886 |
# Usamos go.Scattergl en lugar de px.scatter para un rendimiento masivo
|
| 887 |
figure=go.Figure(data=go.Scattergl(
|
| 888 |
x=df['IRATING'],
|
|
@@ -927,191 +1090,700 @@ region_bubble_chart = dcc.Graph(
|
|
| 927 |
app = dash.Dash(__name__)
|
| 928 |
server = app.server # <-- AÑADE ESTA LÍNEA
|
| 929 |
|
| 930 |
-
# Layout principal
|
| 931 |
app.layout = html.Div(
|
| 932 |
-
|
| 933 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 934 |
children=[
|
| 935 |
-
|
| 936 |
-
|
| 937 |
-
# --- CONTENEDOR PRINCIPAL CON 3 COLUMNAS ---
|
| 938 |
html.Div(
|
| 939 |
-
|
| 940 |
-
|
| 941 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 942 |
children=[
|
| 943 |
|
| 944 |
-
#
|
| 945 |
html.Div(
|
| 946 |
-
|
| 947 |
-
|
|
|
|
|
|
|
| 948 |
children=[
|
| 949 |
-
|
| 950 |
-
|
| 951 |
-
style={
|
| 952 |
-
|
| 953 |
-
|
| 954 |
-
|
| 955 |
-
|
| 956 |
-
|
| 957 |
-
|
| 958 |
-
options=[{'label': 'All', 'value': 'ALL'}] +
|
| 959 |
-
[{'label': region, 'value': region} for region in sorted(iracing_ragions.keys())],
|
| 960 |
-
value='ALL',
|
| 961 |
-
className='iracing-dropdown',
|
| 962 |
-
# --- AÑADIMOS ESTILO INICIAL ---
|
| 963 |
-
|
| 964 |
-
)
|
| 965 |
-
], style={'flex': 1, 'marginRight': '10px', 'textAlign': 'center'}),
|
| 966 |
-
|
| 967 |
-
# --- MODIFICACIÓN: Centramos el texto del contenedor del filtro de País ---
|
| 968 |
-
html.Div([
|
| 969 |
-
html.Label("Country:", style={'color': 'white', 'fontSize': 10}),
|
| 970 |
-
dcc.Dropdown(
|
| 971 |
-
id='country-filter',
|
| 972 |
-
options=[{'label': 'All', 'value': 'ALL'}],
|
| 973 |
-
value='ALL',
|
| 974 |
-
className='iracing-dropdown',
|
| 975 |
-
# --- AÑADIMOS ESTILO INICIAL ---
|
| 976 |
-
|
| 977 |
-
)
|
| 978 |
-
], style={'flex': 1, 'marginRight': '10px', 'textAlign': 'center'}),
|
| 979 |
|
| 980 |
-
|
| 981 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 982 |
),
|
| 983 |
-
|
| 984 |
-
|
| 985 |
-
|
| 986 |
-
|
| 987 |
-
|
| 988 |
-
|
| 989 |
-
|
| 990 |
-
|
| 991 |
-
className='iracing-dropdown',
|
| 992 |
-
searchable=True,
|
| 993 |
-
clearable=True,
|
| 994 |
-
search_value='',
|
| 995 |
-
# Se elimina el estilo de aquí para aplicarlo al contenedor.
|
| 996 |
-
)
|
| 997 |
-
],
|
| 998 |
-
# --- MODIFICACIÓN: Centramos el texto del contenedor de búsqueda ---
|
| 999 |
-
style={'width': '60%', 'marginBottom': '10px', 'margin': '0 auto 10px auto', 'color':'white', 'textAlign': 'center'}
|
| 1000 |
),
|
|
|
|
| 1001 |
|
| 1002 |
html.Div(
|
| 1003 |
-
kpi_pilot,
|
| 1004 |
style={
|
| 1005 |
-
'
|
| 1006 |
-
'
|
| 1007 |
-
|
| 1008 |
-
|
| 1009 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1010 |
]
|
| 1011 |
),
|
| 1012 |
|
| 1013 |
-
#
|
| 1014 |
html.Div(
|
| 1015 |
-
|
| 1016 |
-
|
| 1017 |
-
|
| 1018 |
-
|
| 1019 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1020 |
html.Div(
|
| 1021 |
-
style={
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1022 |
children=[
|
| 1023 |
-
|
| 1024 |
-
html.Div([
|
| 1025 |
-
# <-- AÑADIDO
|
| 1026 |
-
html.Button('Sports Car', id='btn-road', n_clicks=0, className='dashboard-type-button'),
|
| 1027 |
-
html.Button('Formula', id='btn-formula', n_clicks=0, className='dashboard-type-button'),
|
| 1028 |
-
html.Button('Oval', id='btn-oval', n_clicks=0, className='dashboard-type-button'),
|
| 1029 |
-
html.Button('Dirt Road', id='btn-dirt-road', n_clicks=0, className='dashboard-type-button'),
|
| 1030 |
-
html.Button('Dirt Oval', id='btn-dirt-oval', n_clicks=0, className='dashboard-type-button'),
|
| 1031 |
-
], style={'display': 'flex', 'justifyContent': 'center', 'gap': '10px'})
|
| 1032 |
]
|
| 1033 |
),
|
| 1034 |
-
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1035 |
html.Div(
|
| 1036 |
-
kpi_global,
|
| 1037 |
style={
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
| 1038 |
|
| 1039 |
-
|
| 1040 |
-
|
| 1041 |
-
|
| 1042 |
-
|
| 1043 |
-
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
| 1044 |
),
|
|
|
|
|
|
|
| 1045 |
html.Div(
|
| 1046 |
-
continent_map,
|
| 1047 |
style={
|
| 1048 |
-
'flex':
|
| 1049 |
-
'
|
| 1050 |
-
'
|
| 1051 |
-
|
| 1052 |
-
|
| 1053 |
-
|
| 1054 |
-
|
| 1055 |
-
|
| 1056 |
-
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
| 1057 |
style={
|
| 1058 |
-
'
|
| 1059 |
-
'
|
| 1060 |
-
'
|
|
|
|
| 1061 |
}
|
| 1062 |
),
|
| 1063 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1064 |
]
|
| 1065 |
),
|
| 1066 |
|
| 1067 |
-
#
|
| 1068 |
html.Div(
|
| 1069 |
-
|
| 1070 |
-
|
|
|
|
|
|
|
|
|
|
| 1071 |
children=[
|
|
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|
|
|
|
| 1072 |
|
| 1073 |
-
#
|
| 1074 |
-
|
| 1075 |
-
|
| 1076 |
-
|
| 1077 |
-
|
| 1078 |
-
|
| 1079 |
-
|
|
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|
|
|
|
|
| 1080 |
),
|
| 1081 |
|
| 1082 |
-
#
|
| 1083 |
-
|
| 1084 |
-
|
| 1085 |
-
|
| 1086 |
-
|
|
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| 1087 |
)
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| 1088 |
]
|
| 1089 |
)
|
| 1090 |
]
|
| 1091 |
),
|
| 1092 |
|
| 1093 |
-
# Componentes ocultos
|
| 1094 |
-
# --- ELIMINA EL dcc.Store ---
|
| 1095 |
dcc.Store(id='active-discipline-store', data='ROAD.csv'),
|
| 1096 |
dcc.Store(id='shared-data-store', data={}),
|
| 1097 |
dcc.Store(id='shared-data-store_1', data={}),
|
| 1098 |
-
html.Div(id='pilot-info-display', style={'display': 'none'})
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| 1099 |
]
|
| 1100 |
)
|
| 1101 |
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| 1102 |
# --- 4. Callbacks ---
|
| 1103 |
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| 1104 |
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| 1105 |
# --- NUEVO CALLBACK PARA ACTUALIZAR GRÁFICOS DE LA COLUMNA DERECHA ---
|
| 1106 |
@app.callback(
|
| 1107 |
Output('competitiveness-tables-container', 'children'),
|
| 1108 |
Output('region-bubble-chart', 'figure'),
|
| 1109 |
Output('irating-starts-scatter', 'figure'),
|
| 1110 |
-
|
| 1111 |
-
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|
| 1112 |
def update_right_column_graphs(filename):
|
| 1113 |
# 1. Cargar y procesar los datos de la disciplina seleccionada
|
| 1114 |
-
df_discipline =
|
| 1115 |
df_discipline = df_discipline[df_discipline['IRATING'] > 1]
|
| 1116 |
df_discipline = df_discipline[df_discipline['STARTS'] > 1]
|
| 1117 |
df_discipline = df_discipline[df_discipline['CLASS'].str.contains('D|C|B|A|P|R', na=False)]
|
|
@@ -1124,69 +1796,173 @@ def update_right_column_graphs(filename):
|
|
| 1124 |
top_regions.insert(0, '#', range(1, 1 + len(top_regions)))
|
| 1125 |
top_countries.insert(0, '#', range(1, 1 + len(top_countries)))
|
| 1126 |
|
| 1127 |
-
#
|
| 1128 |
def get_country_name(code):
|
| 1129 |
try:
|
| 1130 |
return pycountry.countries.get(alpha_2=code).name
|
| 1131 |
except (LookupError, AttributeError):
|
| 1132 |
-
return code
|
| 1133 |
|
| 1134 |
top_countries['LOCATION'] = top_countries['LOCATION'].apply(get_country_name)
|
| 1135 |
-
# --- FIN DE LA MODIFICACIÓN ---
|
| 1136 |
|
|
|
|
| 1137 |
table_style_base = {
|
| 1138 |
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'style_table': {
|
| 1139 |
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| 1140 |
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| 1142 |
'style_cell_conditional': [
|
| 1143 |
-
{'if': {'column_id': '#'}, 'width': '10%', '
|
| 1144 |
-
{'if': {'column_id': 'REGION'}, 'width': '
|
| 1145 |
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{'if': {'column_id': 'LOCATION'}, 'width': '
|
| 1146 |
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{'if': {'column_id': 'avg_irating'}, 'width': '
|
| 1147 |
]
|
| 1148 |
}
|
| 1149 |
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|
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|
| 1150 |
competitiveness_tables = html.Div(
|
| 1151 |
-
style={
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|
| 1152 |
children=[
|
| 1153 |
-
|
| 1154 |
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| 1155 |
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| 1156 |
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| 1157 |
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| 1160 |
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| 1163 |
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| 1164 |
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| 1165 |
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| 1166 |
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| 1167 |
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| 1168 |
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| 1169 |
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| 1170 |
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| 1171 |
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| 1172 |
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| 1173 |
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| 1174 |
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| 1175 |
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|
| 1177 |
]
|
| 1178 |
)
|
| 1179 |
|
| 1180 |
# 3. Crear los otros gráficos
|
| 1181 |
bubble_chart_fig = create_region_bubble_chart(df_discipline)
|
| 1182 |
line_chart_fig = create_irating_trend_line_chart(df_discipline)
|
| 1183 |
-
|
| 1184 |
-
|
| 1185 |
-
|
| 1186 |
-
|
| 1187 |
-
|
| 1188 |
|
| 1189 |
|
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|
|
|
| 1190 |
|
| 1191 |
# --- ELIMINA EL CALLBACK update_data_source ---
|
| 1192 |
|
|
@@ -1269,67 +2045,86 @@ def update_country_filter_on_map_click(clickData):
|
|
| 1269 |
# Devolvemos el código del país, que actualizará el valor del dropdown 'country-filter'.
|
| 1270 |
return country_code
|
| 1271 |
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|
| 1272 |
@app.callback(
|
| 1273 |
Output('pilot-search-dropdown', 'options'),
|
| 1274 |
-
|
|
|
|
|
|
|
|
|
|
| 1275 |
State('pilot-search-dropdown', 'value'),
|
| 1276 |
State('region-filter', 'value'),
|
| 1277 |
State('country-filter', 'value'),
|
| 1278 |
-
# --- MODIFICACIÓN: Añadimos el State para saber la disciplina activa ---
|
| 1279 |
State('active-discipline-store', 'data'),
|
| 1280 |
-
|
|
|
|
| 1281 |
)
|
| 1282 |
-
def
|
| 1283 |
-
|
| 1284 |
-
|
| 1285 |
-
|
| 1286 |
-
#
|
| 1287 |
-
|
| 1288 |
-
|
| 1289 |
-
|
| 1290 |
-
|
| 1291 |
-
# Asignamos la región para poder filtrar por ella
|
| 1292 |
-
country_to_region_map = {country: region for region, countries in iracing_ragions.items() for country in countries}
|
| 1293 |
-
df_current_discipline['REGION'] = df_current_discipline['LOCATION'].map(country_to_region_map).fillna('International')
|
| 1294 |
-
# --- FIN DE LA MODIFICACIÓN ---
|
| 1295 |
-
|
| 1296 |
-
# Si no hay texto de búsqueda, pero ya hay un piloto seleccionado,
|
| 1297 |
-
# nos aseguramos de que su opción esté disponible para que no desaparezca.
|
| 1298 |
-
if not search_value:
|
| 1299 |
-
if current_selected_pilot:
|
| 1300 |
-
return [{'label': current_selected_pilot, 'value': current_selected_pilot}]
|
| 1301 |
-
return []
|
| 1302 |
-
|
| 1303 |
-
# Mantenemos la optimización de no buscar con texto muy corto
|
| 1304 |
-
if len(search_value) < 2:
|
| 1305 |
-
return []
|
| 1306 |
|
| 1307 |
-
|
| 1308 |
-
|
| 1309 |
-
|
|
|
|
| 1310 |
|
|
|
|
| 1311 |
filtered_df = df_current_discipline
|
| 1312 |
-
if region_filter != 'ALL':
|
| 1313 |
filtered_df = filtered_df[filtered_df['REGION'] == region_filter]
|
| 1314 |
-
if country_filter != 'ALL':
|
| 1315 |
filtered_df = filtered_df[filtered_df['LOCATION'] == country_filter]
|
| 1316 |
|
| 1317 |
-
#
|
| 1318 |
-
|
| 1319 |
-
|
|
|
|
|
|
|
|
|
|
| 1320 |
|
| 1321 |
-
# 3. Creamos las opciones a partir de las coincidencias
|
| 1322 |
options = [{'label': row['DRIVER'], 'value': row['DRIVER']}
|
| 1323 |
for _, row in top_matches.iterrows()]
|
| 1324 |
|
| 1325 |
-
#
|
| 1326 |
-
# (porque borramos el texto, por ejemplo), lo añadimos para que no se borre de la vista.
|
| 1327 |
if current_selected_pilot and not any(opt['value'] == current_selected_pilot for opt in options):
|
| 1328 |
options.insert(0, {'label': current_selected_pilot, 'value': current_selected_pilot})
|
| 1329 |
|
| 1330 |
-
|
| 1331 |
-
|
| 1332 |
-
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
| 1333 |
|
| 1334 |
# --- CALLBACK para limpiar la búsqueda si cambian los filtros ---
|
| 1335 |
@app.callback(
|
|
@@ -1443,8 +2238,8 @@ def update_button_styles(formula_clicks, road_clicks, oval_clicks, dirt_road_cli
|
|
| 1443 |
def update_table_and_search(
|
| 1444 |
region_filter, country_filter, selected_pilot,
|
| 1445 |
page_current, page_size, sort_by, state_active_cell,
|
| 1446 |
-
active_discipline_filename,
|
| 1447 |
-
discipline_change_trigger
|
| 1448 |
):
|
| 1449 |
|
| 1450 |
ctx = dash.callback_context
|
|
@@ -1458,22 +2253,8 @@ def update_table_and_search(
|
|
| 1458 |
# Leemos y procesamos el archivo seleccionado
|
| 1459 |
#df = pd.read_csv(filename)
|
| 1460 |
df = DISCIPLINE_DATAFRAMES[active_discipline_filename]
|
| 1461 |
-
'''df = df[df['IRATING'] > 1]
|
| 1462 |
-
df = df[df['STARTS'] > 1]
|
| 1463 |
-
df = df[df['CLASS'].str.contains('D|C|B|A|P|R', na=False)]
|
| 1464 |
|
| 1465 |
-
country_to_region_map = {country: region for region, countries in iracing_ragions.items() for country in countries}
|
| 1466 |
-
df['REGION'] = df['LOCATION'].map(country_to_region_map).fillna('International')
|
| 1467 |
-
|
| 1468 |
-
df['Rank World'] = df['IRATING'].rank(method='first', ascending=False).fillna(0).astype(int)
|
| 1469 |
-
df['Rank Region'] = df.groupby('REGION')['IRATING'].rank(method='first', ascending=False).fillna(0).astype(int)
|
| 1470 |
-
df['Rank Country'] = df.groupby('LOCATION')['IRATING'].rank(method='first', ascending=False).fillna(0).astype(int)
|
| 1471 |
-
|
| 1472 |
-
df['CLASS'] = df['CLASS'].str[0]'''
|
| 1473 |
df_for_graphs = df.copy() # Copia para gráficos que no deben ser filtrados
|
| 1474 |
-
|
| 1475 |
-
# --- 3. LÓGICA DE FILTRADO Y VISUALIZACIÓN (sin cambios) ---
|
| 1476 |
-
# El resto de la función sigue igual, pero ahora opera sobre el 'df' que acabamos de cargar.
|
| 1477 |
|
| 1478 |
# Lógica de columnas dinámicas
|
| 1479 |
base_cols = ['DRIVER', 'IRATING', 'LOCATION', 'REGION','CLASS', 'STARTS', 'WINS' ]
|
|
@@ -1519,17 +2300,15 @@ def update_table_and_search(
|
|
| 1519 |
elif triggered_id == 'pilot-search-dropdown' and selected_pilot:
|
| 1520 |
match_index = filtered_df.index.get_loc(df[df['DRIVER'] == selected_pilot].index[0])
|
| 1521 |
if match_index is not None:
|
| 1522 |
-
target_page = match_index // page_size
|
| 1523 |
driver_column_index = list(filtered_df.columns).index('DRIVER')
|
| 1524 |
new_active_cell = {
|
| 1525 |
-
'row': match_index %
|
| 1526 |
-
'row_id': match_index %
|
| 1527 |
'column': driver_column_index,
|
| 1528 |
'column_id': 'DRIVER'
|
| 1529 |
}
|
| 1530 |
|
| 1531 |
-
|
| 1532 |
-
|
| 1533 |
# --- 5. GENERACIÓN DE COLUMNAS PARA LA TABLA ---
|
| 1534 |
columns_definition = []
|
| 1535 |
for col_name in filtered_df.columns:
|
|
@@ -1545,8 +2324,8 @@ def update_table_and_search(
|
|
| 1545 |
columns_definition.append({"name": col_name.title(), "id": col_name})
|
| 1546 |
|
| 1547 |
# --- 6. PAGINACIÓN ---
|
|
|
|
| 1548 |
start_idx = target_page * page_size
|
| 1549 |
-
|
| 1550 |
end_idx = start_idx + page_size
|
| 1551 |
|
| 1552 |
# Aplicamos el formato de bandera a los datos de la página actual
|
|
@@ -1555,7 +2334,7 @@ def update_table_and_search(
|
|
| 1555 |
page_data = page_df.to_dict('records')
|
| 1556 |
|
| 1557 |
total_pages = len(filtered_df) // page_size + (1 if len(filtered_df) % page_size > 0 else 0)
|
| 1558 |
-
|
| 1559 |
# --- 7. ACTUALIZACIÓN DE GRÁFICOS ---
|
| 1560 |
graph_indices = filtered_df.index
|
| 1561 |
highlight_irating = None
|
|
@@ -1700,21 +2479,9 @@ def update_active_cell_from_store(active_cell,ds,ds1,a,b):
|
|
| 1700 |
ds1 = ds
|
| 1701 |
return ds.get('active_cell'),ds1
|
| 1702 |
|
| 1703 |
-
|
| 1704 |
-
|
| 1705 |
-
|
| 1706 |
-
'''active_cell = a
|
| 1707 |
-
selected_pilot = shared_data.get('selected_pilot', '')
|
| 1708 |
-
print('..............')
|
| 1709 |
-
print(shared_data)
|
| 1710 |
-
|
| 1711 |
-
print(f"DEBUG: Recuperando active_cell del store: {active_cell}")
|
| 1712 |
-
print(f"DEBUG: Piloto asociado: {selected_pilot}")
|
| 1713 |
-
shared_data['shared_data'] = ''
|
| 1714 |
-
|
| 1715 |
-
|
| 1716 |
-
return active_cell'''
|
| 1717 |
-
|
| 1718 |
|
| 1719 |
if __name__ == "__main__":
|
| 1720 |
-
app.run(debug=True)
|
|
|
|
|
|
|
|
|
|
|
|
| 7 |
import pycountry_convert as pc
|
| 8 |
import pycountry
|
| 9 |
import gunicorn
|
| 10 |
+
from apscheduler.schedulers.background import BackgroundScheduler
|
| 11 |
+
from apscheduler.triggers.interval import IntervalTrigger
|
| 12 |
+
import atexit
|
| 13 |
+
from datetime import datetime, timezone # <-- AÑADE timezone
|
| 14 |
|
| 15 |
iracing_ragions = {
|
| 16 |
'US':['US'],
|
|
|
|
| 35 |
'Benelux':['NL','BE','LU']
|
| 36 |
}
|
| 37 |
|
| 38 |
+
DATA_STATUS = {}
|
| 39 |
+
LAST_SUCCESSFUL_STATUS = {}
|
| 40 |
+
|
| 41 |
def load_and_process_data(filename):
|
| 42 |
"""Función para cargar y pre-procesar un archivo de disciplina."""
|
| 43 |
print(f"Loading and processing {filename}...")
|
| 44 |
df = pd.read_csv(filename)
|
| 45 |
+
|
| 46 |
+
# Nuevas columnas en mayúsculas
|
| 47 |
+
new_columns = [
|
| 48 |
+
'DRIVER', 'CUSTID', 'LOCATION', 'CLUB_NAME', 'STARTS', 'WINS',
|
| 49 |
+
'AVG_START_POS', 'AVG_FINISH_POS', 'AVG_POINTS', 'TOP25PCNT',
|
| 50 |
+
'LAPS', 'LAPSLEAD', 'AVG_INC', 'CLASS', 'IRATING', 'TTRATING',
|
| 51 |
+
'TOT_CLUBPOINTS', 'CHAMPPOINTS'
|
| 52 |
+
]
|
| 53 |
+
|
| 54 |
+
# Reemplazar las columnas (asumiendo que el orden es correcto)
|
| 55 |
+
if len(df.columns) == len(new_columns):
|
| 56 |
+
df.columns = new_columns
|
| 57 |
'''filename_parquet = filename.replace('.csv', '.parquet')
|
| 58 |
df = pd.read_parquet(filename_parquet)'''
|
| 59 |
df = df[df['IRATING'] > 1]
|
|
|
|
| 71 |
print(f"Finished processing {filename}.")
|
| 72 |
return df
|
| 73 |
|
| 74 |
+
def update_all_data():
|
| 75 |
+
"""Actualiza todos los archivos de datos"""
|
| 76 |
+
global DATA_STATUS
|
| 77 |
+
print(f"\n{'='*60}")
|
| 78 |
+
print(f"🔄 SCHEDULED DATA UPDATE STARTED")
|
| 79 |
+
print(f"Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
| 80 |
+
print(f"{'='*60}")
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
files_to_update = {
|
| 84 |
+
'ROAD.csv': 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/ROAD.csv',
|
| 85 |
+
'FORMULA.csv': 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/FORMULA.csv',
|
| 86 |
+
'OVAL.csv': 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/OVAL.csv',
|
| 87 |
+
'DROAD.csv': 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/DROAD.csv',
|
| 88 |
+
'DOVAL.csv': 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/DOVAL.csv'
|
| 89 |
+
}
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
try:
|
| 93 |
+
status_url = 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/branch_actions/update_status.json'
|
| 94 |
+
# Leemos el JSON y lo guardamos en la variable de estado actual
|
| 95 |
+
current_status_data = pd.read_json(status_url, typ='series').to_dict()
|
| 96 |
+
DATA_STATUS = current_status_data
|
| 97 |
+
|
| 98 |
+
# Si el estado actual es exitoso, lo guardamos como el último éxito conocido.
|
| 99 |
+
if DATA_STATUS.get('status') == 'success':
|
| 100 |
+
LAST_SUCCESSFUL_STATUS = DATA_STATUS
|
| 101 |
+
|
| 102 |
+
print(f"✅ Status file loaded successfully: {DATA_STATUS}")
|
| 103 |
+
except Exception as e:
|
| 104 |
+
print(f"❌ Could not load status file: {e}")
|
| 105 |
+
# Si no podemos leer el archivo, el estado actual es desconocido.
|
| 106 |
+
DATA_STATUS = {'status': 'unknown', 'last_update_utc': datetime.now(timezone.utc).isoformat()}
|
| 107 |
+
|
| 108 |
+
print(f"🎉 SCHEDULED DATA UPDATE COMPLETED")
|
| 109 |
+
print(f"{'='*60}\n")
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
for filename in files_to_update:
|
| 114 |
+
try:
|
| 115 |
+
print(f"🔄 Updating {filename}...")
|
| 116 |
+
new_data = load_and_process_data(files_to_update[filename])
|
| 117 |
+
DISCIPLINE_DATAFRAMES[filename] = new_data
|
| 118 |
+
print(f"✅ {filename} updated successfully! ({len(new_data)} records)")
|
| 119 |
+
except Exception as e:
|
| 120 |
+
print(f"❌ Error updating {filename}: {str(e)}")
|
| 121 |
+
|
| 122 |
+
print(f"🎉 SCHEDULED DATA UPDATE COMPLETED")
|
| 123 |
+
print(f"{'='*60}\n")
|
| 124 |
+
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
try:
|
| 128 |
+
status_url = 'https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/branch_actions/update_status.json'
|
| 129 |
+
DATA_STATUS = pd.read_json(status_url, typ='series').to_dict()
|
| 130 |
+
|
| 131 |
+
# Si la carga inicial es exitosa, la guardamos como el último éxito.
|
| 132 |
+
if DATA_STATUS.get('status') == 'success':
|
| 133 |
+
LAST_SUCCESSFUL_STATUS = DATA_STATUS
|
| 134 |
+
|
| 135 |
+
print(f"✅ Initial status loaded: {DATA_STATUS}")
|
| 136 |
+
except Exception as e:
|
| 137 |
+
print(f"❌ Initial status load failed: {e}")
|
| 138 |
+
DATA_STATUS = {'status': 'unknown', 'last_update_utc': datetime.now(timezone.utc).isoformat()}
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
|
| 142 |
+
# CARGA INICIAL
|
| 143 |
+
DISCIPLINE_DATAFRAMES = {
|
| 144 |
+
'ROAD.csv': load_and_process_data('https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/ROAD.csv'),
|
| 145 |
+
'FORMULA.csv': load_and_process_data('https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/FORMULA.csv'),
|
| 146 |
+
'OVAL.csv': load_and_process_data('https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/OVAL.csv'),
|
| 147 |
+
'DROAD.csv': load_and_process_data('https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/DROAD.csv'),
|
| 148 |
+
'DOVAL.csv': load_and_process_data('https://raw.githubusercontent.com/danielsaed/iRacing-dashboard/refs/heads/branch_actions/data/DOVAL.csv')
|
| 149 |
+
}
|
| 150 |
+
|
| 151 |
+
|
| 152 |
+
# CONFIGURAR EL SCHEDULER
|
| 153 |
+
scheduler = BackgroundScheduler()
|
| 154 |
+
scheduler.add_job(
|
| 155 |
+
func=update_all_data,
|
| 156 |
+
trigger=IntervalTrigger(hours=12), # Ejecutar cada 2 horas
|
| 157 |
+
id='data_update_job',
|
| 158 |
+
name='Update iRacing Data',
|
| 159 |
+
replace_existing=True
|
| 160 |
+
)
|
| 161 |
+
|
| 162 |
+
# INICIAR EL SCHEDULER
|
| 163 |
+
scheduler.start()
|
| 164 |
+
print("🚀 Automatic data updater started with APScheduler!")
|
| 165 |
+
print("📅 Updates scheduled every 2 hours")
|
| 166 |
+
|
| 167 |
+
# ASEGURAR QUE EL SCHEDULER SE CIERRE AL CERRAR LA APP
|
| 168 |
+
atexit.register(lambda: scheduler.shutdown())
|
| 169 |
+
|
| 170 |
+
|
| 171 |
+
|
| 172 |
def create_irating_trend_line_chart(df):
|
| 173 |
"""
|
| 174 |
Crea un gráfico de líneas que muestra el promedio de carreras corridas
|
|
|
|
| 218 |
))
|
| 219 |
|
| 220 |
fig.update_layout(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 221 |
template='plotly_dark',
|
| 222 |
paper_bgcolor='rgba(11,11,19,1)',
|
| 223 |
plot_bgcolor='rgba(11,11,19,1)',
|
|
|
|
| 236 |
gridwidth=1,
|
| 237 |
gridcolor='rgba(255,255,255,0.1)'
|
| 238 |
),
|
| 239 |
+
margin=dict(l=10, r=10, t=0, b=10),
|
| 240 |
)
|
| 241 |
return fig
|
| 242 |
|
|
|
|
| 337 |
)
|
| 338 |
))
|
| 339 |
|
| 340 |
+
|
| 341 |
+
"""title=dict(
|
| 342 |
+
text='Regions (Avg. iRating, Avg. Races, Qty. Drivers)',
|
| 343 |
+
font=dict(color='white', size=14),
|
| 344 |
+
x=0.5,
|
| 345 |
+
xanchor='center'
|
| 346 |
+
),
|
| 347 |
+
font=GLOBAL_FONT,"""
|
| 348 |
fig.update_layout(
|
| 349 |
+
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 350 |
#xaxis_title='Avg. iRating',
|
| 351 |
#yaxis_title='Avg. Races',
|
| 352 |
template='plotly_dark',
|
|
|
|
| 370 |
gridcolor='rgba(255,255,255,0.1)'
|
| 371 |
),
|
| 372 |
# --- FIN DEL ESTILO DE GRID ---
|
| 373 |
+
margin=dict(l=10, r=10, t=0, b=10),
|
| 374 |
)
|
| 375 |
return fig
|
| 376 |
|
|
|
|
| 383 |
fig = go.Figure()
|
| 384 |
kpis = [
|
| 385 |
{'value': total_pilots, 'title': f"Drivers {filter_context}", 'format': ',.0f'},
|
| 386 |
+
{'value': avg_irating, 'title': "Avg iRating", 'format': ',.0f'},
|
| 387 |
+
{'value': avg_starts, 'title': "Avg Starts", 'format': '.1f'},
|
| 388 |
+
{'value': avg_wins, 'title': "Avg Wins", 'format': '.2f'}
|
| 389 |
]
|
| 390 |
for i, kpi in enumerate(kpis):
|
| 391 |
fig.add_trace(go.Indicator(
|
|
|
|
| 400 |
grid={'rows': 1, 'columns': 4, 'pattern': "independent"},
|
| 401 |
template='plotly_dark',
|
| 402 |
paper_bgcolor='rgba(0,0,0,0)',
|
| 403 |
+
plot_bgcolor="#BD1818",
|
| 404 |
margin=dict(l=20, r=20, t=50, b=10),
|
| 405 |
height=60,
|
| 406 |
font=GLOBAL_FONT
|
|
|
|
| 419 |
plot_bgcolor='rgba(0,0,0,0)',
|
| 420 |
xaxis_visible=False,
|
| 421 |
yaxis_visible=False,
|
| 422 |
+
height=240, # Aumentamos la altura considerablemente
|
| 423 |
annotations=[
|
| 424 |
dict(
|
| 425 |
text="<b>Select or search a driver</b>",
|
|
|
|
| 436 |
]
|
| 437 |
)
|
| 438 |
return fig
|
|
|
|
| 439 |
|
| 440 |
# Si SÍ hay información del piloto, procedemos como antes.
|
| 441 |
pilot_name = pilot_info.get('DRIVER', 'Piloto')
|
|
|
|
| 452 |
|
| 453 |
kpis_piloto = [
|
| 454 |
{'rank': rank_world, 'percentil': percentil_world, 'title': "World Rank"},
|
| 455 |
+
{'rank': rank_region, 'percentil': percentil_region, 'title': "Region Rank"},
|
| 456 |
{'rank': rank_country, 'percentil': percentil_country, 'title': "Country Rank"}
|
| 457 |
]
|
| 458 |
|
| 459 |
+
# Layout vertical con más espacio
|
| 460 |
for i, kpi in enumerate(kpis_piloto):
|
| 461 |
fig.add_trace(go.Indicator(
|
| 462 |
mode="number",
|
| 463 |
value=kpi['rank'],
|
| 464 |
+
number={'prefix': "#", 'font': {'size': 18}}, # Número más grande
|
| 465 |
+
title={
|
| 466 |
+
"text": f"<b>{kpi['title']}</b><span style='font-size:11px;color:gray'>(Top {100-kpi['percentil']:.1f}%)</span>",
|
| 467 |
+
'font': {'size': 12} # Título más grande
|
| 468 |
+
},
|
| 469 |
+
domain={
|
| 470 |
+
'row': i,
|
| 471 |
+
'column': 0,
|
| 472 |
+
# AGREGAMOS ESPACIADO ESPECÍFICO PARA CADA KPI
|
| 473 |
+
'y': [0.75 - i*0.32, 0.95 - i*0.32] # Da más espacio vertical a cada KPI
|
| 474 |
+
}
|
| 475 |
))
|
| 476 |
+
|
| 477 |
fig.update_layout(
|
| 478 |
title={
|
| 479 |
'text': title_text,
|
| 480 |
+
'y': 0.98, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top',
|
| 481 |
+
'font': {'size': 28}
|
| 482 |
},
|
| 483 |
+
# CAMBIO: Eliminamos el grid automático y usamos dominios manuales
|
| 484 |
template='plotly_dark',
|
| 485 |
paper_bgcolor='rgba(0,0,0,0)',
|
| 486 |
plot_bgcolor='rgba(0,0,0,0)',
|
| 487 |
+
margin=dict(l=20, r=20, t=35, b=15), # Más margen arriba y abajo
|
| 488 |
+
height=240, # Altura aumentada considerablemente
|
| 489 |
+
font=GLOBAL_FONT,
|
| 490 |
+
showlegend=False
|
| 491 |
)
|
| 492 |
return fig
|
| 493 |
|
|
|
|
| 529 |
|
| 530 |
max_line_trace = go.Scatter(
|
| 531 |
x=x_trend, y=y_trend_max, mode='lines',
|
| 532 |
+
name='Tendency Max AVG_INC',
|
| 533 |
line=dict(color='red', width=1, dash='dash')
|
| 534 |
)
|
| 535 |
|
| 536 |
min_line_trace = go.Scatter(
|
| 537 |
x=x_trend, y=y_trend_min, mode='lines',
|
| 538 |
+
name='Tendency Min AVG_INC',
|
| 539 |
line=dict(color='lime', width=1, dash='dash')
|
| 540 |
)
|
| 541 |
|
|
|
|
| 544 |
x=x_trend,
|
| 545 |
y=y_trend_mean,
|
| 546 |
mode='lines',
|
| 547 |
+
name='Tendency Average Incidents',
|
| 548 |
line=dict(color='black', width=2, dash='solid')
|
| 549 |
)
|
| 550 |
|
|
|
|
| 553 |
fig = go.Figure(data=[heatmap_trace, max_line_trace, min_line_trace, mean_line_trace])
|
| 554 |
|
| 555 |
fig.update_layout(
|
| 556 |
+
|
| 557 |
+
font=GLOBAL_FONT,
|
| 558 |
xaxis_title='iRating',
|
| 559 |
yaxis_title='Incidents Per Race',
|
| 560 |
template='plotly_dark',
|
|
|
|
| 656 |
showframe=False, # <-- Oculta el marco exterior del globo
|
| 657 |
showcoastlines=False # <-- Oculta las líneas de la costa
|
| 658 |
),
|
| 659 |
+
margin={"r":0,"t":0,"l":0,"b":0},
|
| 660 |
coloraxis_showscale=show_scale,
|
| 661 |
coloraxis_colorbar=dict(
|
| 662 |
title='Drivers',
|
|
|
|
| 673 |
|
| 674 |
def create_histogram_with_percentiles(df, column='IRATING', bin_width=100, highlight_irating=None, highlight_name=None):
|
| 675 |
# Crear bins específicos de 100 en 100
|
|
|
|
| 676 |
max_val = df[column].max()
|
| 677 |
+
# Por ejemplo, si max_val es 11250, (ceil(11250 / 100)) = 113, * 100 = 11300.
|
| 678 |
+
upper_limit = (np.ceil(max_val / bin_width)) * bin_width
|
| 679 |
+
|
| 680 |
+
# Creamos los bordes de los bins desde 0 hasta el límite superior, en pasos de 100.
|
| 681 |
+
# Esto generará [0, 100, 200, 300, ... , 11300]
|
| 682 |
+
bin_edges = np.arange(0, upper_limit + bin_width, bin_width)
|
| 683 |
+
# --- FIN DE LA CORRECCIÓN ---
|
| 684 |
|
| 685 |
hist, bin_edges = np.histogram(df[column], bins=bin_edges)
|
| 686 |
bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
|
|
|
|
| 695 |
below = (df[column] < bin_edges[i+1]).sum()
|
| 696 |
percentile = below / total * 100
|
| 697 |
top_percent = 100 - percentile
|
| 698 |
+
|
| 699 |
+
# --- CORRECCIÓN: Mostrar el rango correctamente (ej. 0-99) ---
|
| 700 |
hover_text.append(
|
| 701 |
+
f"Range: {int(bin_edges[i])}-{int(bin_edges[i+1]-1)}<br>"
|
| 702 |
f"Drivers: {hist[i]}<br>"
|
| 703 |
f"Top: {top_percent:.2f}%"
|
| 704 |
)
|
|
|
|
| 739 |
# --- FIN DEL BLOQUE NUEVO ---
|
| 740 |
|
| 741 |
fig.update_layout(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 742 |
font=GLOBAL_FONT,
|
| 743 |
xaxis=dict(
|
| 744 |
title_text='iRating', # Texto del título
|
|
|
|
| 756 |
),
|
| 757 |
template='plotly_dark',
|
| 758 |
hovermode='x unified',
|
| 759 |
+
paper_bgcolor='rgba(18,18,26,0)',
|
| 760 |
plot_bgcolor='rgba(255,255,255,0)',
|
| 761 |
|
| 762 |
|
| 763 |
# --- MODIFICACIÓN: Reducir márgenes y tamaño de fuentes ---
|
| 764 |
+
margin=dict(l=10, r=10, t=0, b=10) # Reduce los márgenes (izquierda, derecha, arriba, abajo) # Reduce el tamaño del título principal
|
| 765 |
|
| 766 |
)
|
| 767 |
|
|
|
|
| 790 |
)
|
| 791 |
return fig
|
| 792 |
|
| 793 |
+
def create_starts_vs_irating_scatter(df):
|
| 794 |
+
"""Crea un gráfico de dispersión no interactivo para Carreras vs. iRating."""
|
| 795 |
+
|
| 796 |
+
# Para un rendimiento óptimo, si hay demasiados datos, tomamos una muestra aleatoria.
|
| 797 |
+
# Esto evita sobrecargar el navegador del cliente sin perder la forma general de la distribución.
|
| 798 |
+
if len(df) > 50000:
|
| 799 |
+
df_sample = df.sample(n=50000, random_state=42)
|
| 800 |
+
else:
|
| 801 |
+
df_sample = df
|
| 802 |
+
|
| 803 |
+
# Usamos go.Scattergl que está optimizado para grandes datasets.
|
| 804 |
+
fig = go.Figure(data=go.Scattergl(
|
| 805 |
+
x=df_sample['IRATING'],
|
| 806 |
+
y=df_sample['STARTS'],
|
| 807 |
+
mode='markers',
|
| 808 |
+
marker=dict(
|
| 809 |
+
color='rgba(0, 111, 255, 0.3)', # Color azul semitransparente
|
| 810 |
+
# --- CORRECCIÓN: Puntos 50% más grandes (de 4 a 6) ---
|
| 811 |
+
size=6,
|
| 812 |
+
line=dict(width=0)
|
| 813 |
+
),
|
| 814 |
+
# Desactivamos el hover para máxima velocidad ya que es estático.
|
| 815 |
+
hoverinfo='none'
|
| 816 |
+
))
|
| 817 |
+
|
| 818 |
+
fig.update_layout(
|
| 819 |
+
|
| 820 |
+
font=GLOBAL_FONT,
|
| 821 |
+
xaxis_title='iRating',
|
| 822 |
+
yaxis_title='Races (Starts)',
|
| 823 |
+
template='plotly_dark',
|
| 824 |
+
paper_bgcolor='rgba(0,0,0,0)',
|
| 825 |
+
plot_bgcolor='rgba(0,0,0,0)',
|
| 826 |
+
xaxis=dict(range=[0, 12000], showgrid=True, gridwidth=1, gridcolor='rgba(255,255,255,0.1)'),
|
| 827 |
+
# --- CORRECCIÓN: Altura máxima de 1500 en el eje Y ---
|
| 828 |
+
yaxis=dict(range=[0, 1500], showgrid=True, gridwidth=1, gridcolor='rgba(255,255,255,0.1)'),
|
| 829 |
+
legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99)
|
| 830 |
+
)
|
| 831 |
+
return fig
|
| 832 |
+
|
| 833 |
+
|
| 834 |
def flag_img(code):
|
| 835 |
url = f"https://flagcdn.com/16x12/{code.lower()}.png"
|
| 836 |
# La función ahora asume que si el código llega aquí, es válido.
|
|
|
|
| 839 |
|
| 840 |
GLOBAL_FONT = {'family': "Lato, sans-serif"}
|
| 841 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 842 |
country_coords = {
|
| 843 |
'ES': {'lat': 40.4, 'lon': -3.7}, 'US': {'lat': 39.8, 'lon': -98.5},
|
| 844 |
'BR': {'lat': -14.2, 'lon': -51.9}, 'DE': {'lat': 51.1, 'lon': 10.4},
|
|
|
|
| 906 |
#df['LOCATION'] = 'a'
|
| 907 |
density_heatmap = dcc.Graph(
|
| 908 |
id='density-heatmap',
|
| 909 |
+
# --- CORRECCIÓN: Ajustamos la altura para que sea más visible ---
|
| 910 |
+
style={'height': '600px', 'borderRadius': '15px', 'overflow': 'hidden'},
|
| 911 |
+
figure=create_density_heatmap(df_for_graphs),
|
| 912 |
+
config={'displayModeBar': False} # <-- AÑADIMOS ESTO
|
| 913 |
)
|
| 914 |
correlation_heatmap = dcc.Graph(
|
| 915 |
id='correlation-heatmap',
|
| 916 |
+
# --- CORRECCIÓN: Ajustamos la altura a un valor más estándar ---
|
| 917 |
+
style={'height': '500px'},
|
| 918 |
# Usamos las columnas numéricas del dataframe original
|
| 919 |
+
figure=create_correlation_heatmap(df[['IRATING', 'STARTS', 'WINS','TOP25PCNT','AVG_INC','AVG_FINISH_POS']]),
|
| 920 |
+
config={'displayModeBar': False} # <-- AÑADIMOS ESTO
|
| 921 |
)
|
| 922 |
|
| 923 |
|
|
|
|
| 947 |
sort_mode="single",
|
| 948 |
page_action="custom",
|
| 949 |
page_current=0,
|
| 950 |
+
page_size=100, # CAMBIO: De 20 a 100 elementos por página
|
| 951 |
+
page_count=len(df_table) // 100 + (1 if len(df_table) % 100 > 0 else 0), # CAMBIO: Actualizar cálculo
|
| 952 |
virtualization=False,
|
| 953 |
style_as_list_view=True,
|
| 954 |
active_cell={'row': 21,'column':1},
|
| 955 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 956 |
style_table={
|
|
|
|
| 957 |
'overflowX': 'auto',
|
| 958 |
+
'overflowY': 'auto', # SCROLL VERTICAL habilitado
|
| 959 |
+
'height': '100%', # CAMBIO: Usar 100% del contenedor padre
|
| 960 |
+
'maxHeight': '100%', # CAMBIO: Usar 100% del contenedor padre
|
| 961 |
+
'minHeight': '700px', # CAMBIO: Altura mínima aumentada
|
| 962 |
'width': '100%',
|
| 963 |
'borderRadius': '15px',
|
| 964 |
'overflow': 'hidden',
|
| 965 |
'backgroundColor': 'rgba(11,11,19,1)',
|
|
|
|
| 966 |
'border': '1px solid #4A4A4A'
|
|
|
|
| 967 |
},
|
| 968 |
|
| 969 |
style_cell={
|
| 970 |
'textAlign': 'center',
|
| 971 |
+
'padding': '6px 3px', # CAMBIO: Padding más compacto para aprovechar espacio
|
| 972 |
'backgroundColor': 'rgba(11,11,19,1)',
|
| 973 |
'color': 'rgb(255, 255, 255,.8)',
|
| 974 |
+
'border': '1px solid rgba(255, 255, 255, 0.1)',
|
| 975 |
'overflow': 'hidden',
|
| 976 |
'textOverflow': 'ellipsis',
|
| 977 |
+
'whiteSpace': 'nowrap',
|
| 978 |
+
'maxWidth': 0,
|
| 979 |
+
'fontSize': '11px' # CAMBIO: Fuente ligeramente más pequeña para más filas
|
| 980 |
},
|
| 981 |
+
|
| 982 |
style_header={
|
| 983 |
'backgroundColor': 'rgba(30,30,38,1)',
|
| 984 |
'fontWeight': 'bold',
|
| 985 |
'color': 'white',
|
| 986 |
+
'border': '1px solid rgba(255, 255, 255, 0.2)',
|
| 987 |
'textAlign': 'center',
|
| 988 |
+
'fontSize': '12px',
|
| 989 |
+
'position': 'sticky', # Header pegajoso
|
| 990 |
+
'top': 0, # Se queda arriba al hacer scroll
|
| 991 |
+
'zIndex': 10, # Prioridad visual
|
| 992 |
+
'padding': '6px 3px' # CAMBIO: Padding más compacto
|
| 993 |
},
|
| 994 |
+
|
| 995 |
# --- AÑADIMOS ESTILO PARA LA FILA SELECCIONADA Y LAS CLASES ---
|
| 996 |
style_data_conditional=[
|
| 997 |
{
|
|
|
|
| 1045 |
|
| 1046 |
scatter_irating_starts = dcc.Graph(
|
| 1047 |
id='scatter-irating',
|
| 1048 |
+
style={'height': '30vh','borderRadius': '15px','overflow': 'hidden'},
|
| 1049 |
# Usamos go.Scattergl en lugar de px.scatter para un rendimiento masivo
|
| 1050 |
figure=go.Figure(data=go.Scattergl(
|
| 1051 |
x=df['IRATING'],
|
|
|
|
| 1090 |
app = dash.Dash(__name__)
|
| 1091 |
server = app.server # <-- AÑADE ESTA LÍNEA
|
| 1092 |
|
| 1093 |
+
# Layout principal MODIFICADO
|
| 1094 |
app.layout = html.Div(
|
| 1095 |
+
style={
|
| 1096 |
+
'margin': '0',
|
| 1097 |
+
'padding': '0',
|
| 1098 |
+
'backgroundColor': 'rgba(5,5,15,255)',
|
| 1099 |
+
'color': '#ffffff',
|
| 1100 |
+
'fontFamily': 'Lato, sans-serif',
|
| 1101 |
+
'minHeight': '100vh'
|
| 1102 |
+
},
|
| 1103 |
children=[
|
| 1104 |
+
# CONTENEDOR PRINCIPAL CENTRADO
|
|
|
|
|
|
|
| 1105 |
html.Div(
|
| 1106 |
+
style={
|
| 1107 |
+
'maxWidth': '1400px',
|
| 1108 |
+
'margin': '0 auto',
|
| 1109 |
+
'padding': '20px 5vw',
|
| 1110 |
+
'minHeight': '100vh',
|
| 1111 |
+
'display': 'flex',
|
| 1112 |
+
'flexDirection': 'column',
|
| 1113 |
+
'gap': '30px'
|
| 1114 |
+
},
|
| 1115 |
children=[
|
| 1116 |
|
| 1117 |
+
# 1. SECCIÓN HEADER - Título y Botones
|
| 1118 |
html.Div(
|
| 1119 |
+
style={
|
| 1120 |
+
'textAlign': 'center',
|
| 1121 |
+
'marginBottom': '20px'
|
| 1122 |
+
},
|
| 1123 |
children=[
|
| 1124 |
+
html.H1(
|
| 1125 |
+
"🏁 Top iRating",
|
| 1126 |
+
style={
|
| 1127 |
+
'fontSize': 'clamp(36px, 5vw, 48px)',
|
| 1128 |
+
'color': 'white',
|
| 1129 |
+
'margin': '0 0 20px 0',
|
| 1130 |
+
'fontWeight': '900'
|
| 1131 |
+
}
|
| 1132 |
+
),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1133 |
|
| 1134 |
+
# --- AÑADE ESTE COMPONENTE AQUÍ ---
|
| 1135 |
+
html.P(
|
| 1136 |
+
id='last-update-display',
|
| 1137 |
+
style={
|
| 1138 |
+
'color': '#A0A0A0',
|
| 1139 |
+
'fontSize': '12px',
|
| 1140 |
+
'margin': '-15px 0 20px 0', # Margen para acercarlo al título
|
| 1141 |
+
'fontStyle': 'italic'
|
| 1142 |
+
}
|
| 1143 |
),
|
| 1144 |
+
html.P(
|
| 1145 |
+
"Only drivers with 1 < race start and 1 < irating are consider",
|
| 1146 |
+
style={
|
| 1147 |
+
'color': '#A0A0A0',
|
| 1148 |
+
'fontSize': '12px',
|
| 1149 |
+
'margin': '-15px 0 20px 0', # Margen para acercarlo al título
|
| 1150 |
+
'fontStyle': 'italic'
|
| 1151 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1152 |
),
|
| 1153 |
+
# --- FIN DEL BLOQUE A AÑADIR ---
|
| 1154 |
|
| 1155 |
html.Div(
|
|
|
|
| 1156 |
style={
|
| 1157 |
+
'display': 'flex',
|
| 1158 |
+
'justifyContent': 'center',
|
| 1159 |
+
'gap': '10px',
|
| 1160 |
+
'flexWrap': 'wrap'
|
| 1161 |
+
},
|
| 1162 |
+
children=[
|
| 1163 |
+
html.Button('Sports Car', id='btn-road', n_clicks=0, className='dashboard-type-button'),
|
| 1164 |
+
html.Button('Formula', id='btn-formula', n_clicks=0, className='dashboard-type-button'),
|
| 1165 |
+
html.Button('Oval', id='btn-oval', n_clicks=0, className='dashboard-type-button'),
|
| 1166 |
+
html.Button('Dirt Road', id='btn-dirt-road', n_clicks=0, className='dashboard-type-button'),
|
| 1167 |
+
html.Button('Dirt Oval', id='btn-dirt-oval', n_clicks=0, className='dashboard-type-button'),
|
| 1168 |
+
]
|
| 1169 |
+
)
|
| 1170 |
]
|
| 1171 |
),
|
| 1172 |
|
| 1173 |
+
# 2. SECCIÓN MAPA Y KPIs GLOBALES - Solo KPIs globales superpuestos
|
| 1174 |
html.Div(
|
| 1175 |
+
style={
|
| 1176 |
+
'position': 'relative',
|
| 1177 |
+
'top': '0px',
|
| 1178 |
+
'backgroundColor': 'transparent', # Fondo transparente
|
| 1179 |
+
'borderRadius': '0px', # Sin bordes redondeados
|
| 1180 |
+
'border': '0px solid #4A4A4A',
|
| 1181 |
+
'padding': '20px',
|
| 1182 |
+
'overflow': 'hidden'
|
| 1183 |
+
},
|
| 1184 |
+
children=[
|
| 1185 |
+
# Solo KPIs globales superpuestos
|
| 1186 |
html.Div(
|
| 1187 |
+
style={
|
| 1188 |
+
'position': 'absolute',
|
| 1189 |
+
'top': '0px',
|
| 1190 |
+
'left': '50%',
|
| 1191 |
+
'transform': 'translateX(-50%)',
|
| 1192 |
+
'width': '80%',
|
| 1193 |
+
'maxWidth': '800px',
|
| 1194 |
+
'zIndex': '10',
|
| 1195 |
+
'backgroundColor': 'rgba(11,11,19,0)',
|
| 1196 |
+
'borderRadius': '10px',
|
| 1197 |
+
'border': '0px solid #4A4A4A',
|
| 1198 |
+
'padding': '10px'
|
| 1199 |
+
},
|
| 1200 |
children=[
|
| 1201 |
+
dcc.Graph(id='kpi-global', style={'height': '50px', 'margin': '0'})
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1202 |
]
|
| 1203 |
),
|
| 1204 |
+
# Mapa de fondo
|
| 1205 |
+
dcc.Graph(
|
| 1206 |
+
id='continent-map',
|
| 1207 |
+
|
| 1208 |
+
style={'height': '650px',
|
| 1209 |
+
'backgroundColor': 'transparent',
|
| 1210 |
+
'margin': '0'},
|
| 1211 |
+
config={'displayModeBar': False}
|
| 1212 |
+
)
|
| 1213 |
+
]
|
| 1214 |
+
),
|
| 1215 |
+
|
| 1216 |
+
# 3. SECCIÓN FILTROS Y TABLA - Lado a lado CON KPIs DEL PILOTO
|
| 1217 |
+
html.Div(
|
| 1218 |
+
style={
|
| 1219 |
+
'display': 'flex',
|
| 1220 |
+
'gap': '20px',
|
| 1221 |
+
'flexWrap': 'wrap'
|
| 1222 |
+
|
| 1223 |
+
},
|
| 1224 |
+
children=[
|
| 1225 |
+
# Contenedor de Filtros CON KPIs DEL PILOTO
|
| 1226 |
html.Div(
|
|
|
|
| 1227 |
style={
|
| 1228 |
+
'flex': '1',
|
| 1229 |
+
'minWidth': '300px',
|
| 1230 |
+
'maxWidth': '600px',
|
| 1231 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1232 |
+
'borderRadius': '15px',
|
| 1233 |
+
'border': '1px solid #4A4A4A',
|
| 1234 |
+
'padding': '20px'
|
| 1235 |
+
},
|
| 1236 |
+
children=[
|
| 1237 |
+
html.H3(
|
| 1238 |
+
"Filters",
|
| 1239 |
+
style={
|
| 1240 |
+
'color': 'white',
|
| 1241 |
+
'textAlign': 'center',
|
| 1242 |
+
'marginBottom': '20px',
|
| 1243 |
+
'fontWeight': '700'
|
| 1244 |
+
}
|
| 1245 |
+
),
|
| 1246 |
|
| 1247 |
+
# Filtros de región y país
|
| 1248 |
+
html.Div(
|
| 1249 |
+
style={
|
| 1250 |
+
'display': 'flex',
|
| 1251 |
+
'gap': '15px',
|
| 1252 |
+
'marginBottom': '20px',
|
| 1253 |
+
'flexDirection': 'column'
|
| 1254 |
+
},
|
| 1255 |
+
children=[
|
| 1256 |
+
html.Div([
|
| 1257 |
+
|
| 1258 |
+
html.Label(
|
| 1259 |
+
"Region:",
|
| 1260 |
+
style={
|
| 1261 |
+
'color': 'white',
|
| 1262 |
+
'fontSize': '14px',
|
| 1263 |
+
'marginBottom': '5px',
|
| 1264 |
+
'display': 'block',
|
| 1265 |
+
'textAlign': 'center'
|
| 1266 |
+
}
|
| 1267 |
+
),
|
| 1268 |
+
dcc.Dropdown(
|
| 1269 |
+
id='region-filter',
|
| 1270 |
+
options=[{'label': 'All', 'value': 'ALL'}] +
|
| 1271 |
+
[{'label': region, 'value': region} for region in sorted(iracing_ragions.keys())],
|
| 1272 |
+
value='ALL',
|
| 1273 |
+
className='iracing-dropdown'
|
| 1274 |
+
)
|
| 1275 |
+
]),
|
| 1276 |
+
|
| 1277 |
+
html.Div([
|
| 1278 |
+
html.Label(
|
| 1279 |
+
"Country:",
|
| 1280 |
+
style={
|
| 1281 |
+
'color': 'white',
|
| 1282 |
+
'fontSize': '14px',
|
| 1283 |
+
'marginBottom': '5px',
|
| 1284 |
+
'display': 'block',
|
| 1285 |
+
'textAlign': 'center'
|
| 1286 |
+
}
|
| 1287 |
+
),
|
| 1288 |
+
dcc.Dropdown(
|
| 1289 |
+
id='country-filter',
|
| 1290 |
+
options=[{'label': 'All', 'value': 'ALL'}],
|
| 1291 |
+
value='ALL',
|
| 1292 |
+
className='iracing-dropdown'
|
| 1293 |
+
)
|
| 1294 |
+
])
|
| 1295 |
+
]
|
| 1296 |
+
),
|
| 1297 |
+
|
| 1298 |
+
# Búsqueda de piloto
|
| 1299 |
+
html.Div([
|
| 1300 |
+
html.Label(
|
| 1301 |
+
"Search Driver:",
|
| 1302 |
+
style={
|
| 1303 |
+
'color': 'white',
|
| 1304 |
+
'fontSize': '14px',
|
| 1305 |
+
'marginBottom': '5px',
|
| 1306 |
+
'display': 'block',
|
| 1307 |
+
'textAlign': 'center'
|
| 1308 |
+
}
|
| 1309 |
+
),
|
| 1310 |
+
dcc.Dropdown(
|
| 1311 |
+
id='pilot-search-dropdown',
|
| 1312 |
+
options=[],
|
| 1313 |
+
placeholder='Search Driver...',
|
| 1314 |
+
className='iracing-dropdown',
|
| 1315 |
+
searchable=True,
|
| 1316 |
+
clearable=True,
|
| 1317 |
+
search_value=''
|
| 1318 |
+
),
|
| 1319 |
+
# --- NUEVOS COMPONENTES PARA DEBOUNCING ---
|
| 1320 |
+
dcc.Interval(
|
| 1321 |
+
id='search-debounce-interval',
|
| 1322 |
+
interval=400, # 400ms de delay
|
| 1323 |
+
n_intervals=0,
|
| 1324 |
+
disabled=True # Inicialmente deshabilitado
|
| 1325 |
+
),
|
| 1326 |
+
dcc.Store(id='last-search-store', data='')
|
| 1327 |
+
]),
|
| 1328 |
+
|
| 1329 |
+
# NUEVA SECCIÓN: KPIs del piloto debajo de los filtros
|
| 1330 |
+
html.Div(
|
| 1331 |
+
style={
|
| 1332 |
+
'marginTop': '30px',
|
| 1333 |
+
'padding': '15px',
|
| 1334 |
+
'backgroundColor': 'rgba(11,11,19,0.8)',
|
| 1335 |
+
'borderRadius': '10px',
|
| 1336 |
+
'border': '1px solid #4A4A4A'
|
| 1337 |
+
},
|
| 1338 |
+
children=[
|
| 1339 |
+
html.H4(
|
| 1340 |
+
"Selected Driver",
|
| 1341 |
+
style={
|
| 1342 |
+
'color': 'white',
|
| 1343 |
+
'textAlign': 'center',
|
| 1344 |
+
'marginBottom': '15px',
|
| 1345 |
+
'fontSize': '16px',
|
| 1346 |
+
'fontWeight': '700'
|
| 1347 |
+
}
|
| 1348 |
+
),
|
| 1349 |
+
dcc.Graph(
|
| 1350 |
+
id='kpi-pilot',
|
| 1351 |
+
style={
|
| 1352 |
+
'height': '260px', # Aumentamos altura del contenedor
|
| 1353 |
+
'margin': '0'
|
| 1354 |
+
}
|
| 1355 |
+
)
|
| 1356 |
+
]
|
| 1357 |
+
)
|
| 1358 |
+
]
|
| 1359 |
),
|
| 1360 |
+
|
| 1361 |
+
# Contenedor de Tabla MODIFICADO
|
| 1362 |
html.Div(
|
|
|
|
| 1363 |
style={
|
| 1364 |
+
'flex': '2',
|
| 1365 |
+
'minWidth': '300px',
|
| 1366 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1367 |
+
'borderRadius': '15px',
|
| 1368 |
+
'border': '1px solid #4A4A4A',
|
| 1369 |
+
'padding': '20px',
|
| 1370 |
+
'display': 'flex', # CAMBIO: Usar flexbox
|
| 1371 |
+
'flexDirection': 'column', # CAMBIO: Dirección vertical
|
| 1372 |
+
'height': '880px' # CAMBIO: Altura fija del contenedor
|
| 1373 |
+
},
|
| 1374 |
+
children=[
|
| 1375 |
+
html.H3(
|
| 1376 |
+
"Rankings",
|
| 1377 |
+
style={
|
| 1378 |
+
'color': 'white',
|
| 1379 |
+
'textAlign': 'center',
|
| 1380 |
+
'marginBottom': '20px',
|
| 1381 |
+
'fontWeight': '700',
|
| 1382 |
+
'flexShrink': 0 # CAMBIO: No se encoge
|
| 1383 |
+
}
|
| 1384 |
+
),
|
| 1385 |
+
# Contenedor específico para la tabla
|
| 1386 |
+
html.Div(
|
| 1387 |
+
style={
|
| 1388 |
+
'flex': '1', # CAMBIO: Ocupa todo el espacio restante
|
| 1389 |
+
'display': 'flex',
|
| 1390 |
+
'flexDirection': 'column',
|
| 1391 |
+
'minHeight': 0 # CAMBIO: Permite que se encoja si es necesario
|
| 1392 |
+
},
|
| 1393 |
+
children=[
|
| 1394 |
+
dash_table.DataTable(
|
| 1395 |
+
id='datatable-interactiva',
|
| 1396 |
+
data=[],
|
| 1397 |
+
sort_action="custom",
|
| 1398 |
+
sort_mode="single",
|
| 1399 |
+
page_action="custom",
|
| 1400 |
+
page_current=0,
|
| 1401 |
+
page_size=100, # CAMBIO: 100 elementos por página
|
| 1402 |
+
page_count=len(df_table) // 100 + (1 if len(df_table) % 100 > 0 else 0),
|
| 1403 |
+
virtualization=False,
|
| 1404 |
+
style_as_list_view=True,
|
| 1405 |
+
active_cell={'row': 101,'column':1},
|
| 1406 |
+
|
| 1407 |
+
style_table={
|
| 1408 |
+
'overflowX': 'auto',
|
| 1409 |
+
'overflowY': 'auto',
|
| 1410 |
+
'height': '100%', # CAMBIO: Usa todo el espacio del contenedor padre
|
| 1411 |
+
'maxHeight': '100%', # CAMBIO: No limitar altura
|
| 1412 |
+
'minHeight': '820px',
|
| 1413 |
+
'width': '100%',
|
| 1414 |
+
'borderRadius': '15px',
|
| 1415 |
+
'overflow': 'hidden',
|
| 1416 |
+
'backgroundColor': 'rgba(11,11,19,1)',
|
| 1417 |
+
'border': '1px solid #4A4A4A'
|
| 1418 |
+
},
|
| 1419 |
+
|
| 1420 |
+
style_cell={
|
| 1421 |
+
'textAlign': 'center',
|
| 1422 |
+
'padding': '3px 1px', # Padding más compacto
|
| 1423 |
+
'backgroundColor': 'rgba(11,11,19,1)',
|
| 1424 |
+
'height': '15px',
|
| 1425 |
+
'color': 'rgb(255, 255, 255,.8)',
|
| 1426 |
+
'border': '1px solid rgba(255, 255, 255, 0.1)',
|
| 1427 |
+
'overflow': 'hidden',
|
| 1428 |
+
'textOverflow': 'ellipsis',
|
| 1429 |
+
'whiteSpace': 'nowrap',
|
| 1430 |
+
'maxWidth': 0,
|
| 1431 |
+
'fontSize': '11px' # Fuente más compacta
|
| 1432 |
+
},
|
| 1433 |
+
|
| 1434 |
+
style_header={
|
| 1435 |
+
'backgroundColor': 'rgba(30,30,38,1)',
|
| 1436 |
+
'fontWeight': 'bold',
|
| 1437 |
+
'color': 'white',
|
| 1438 |
+
'border': '1px solid rgba(255, 255, 255, 0.2)',
|
| 1439 |
+
'textAlign': 'center',
|
| 1440 |
+
'fontSize': '12px',
|
| 1441 |
+
'position': 'sticky',
|
| 1442 |
+
'top': 0,
|
| 1443 |
+
'zIndex': 10,
|
| 1444 |
+
'padding': '6px 3px'
|
| 1445 |
+
},
|
| 1446 |
+
|
| 1447 |
+
# Mantén todos tus estilos existentes
|
| 1448 |
+
style_data_conditional=[
|
| 1449 |
+
{
|
| 1450 |
+
'if': {'state': 'active'},
|
| 1451 |
+
'backgroundColor': 'rgba(0, 111, 255, 0.3)',
|
| 1452 |
+
'border': '1px solid rgba(0, 111, 255)'
|
| 1453 |
+
},
|
| 1454 |
+
{
|
| 1455 |
+
'if': {'state': 'selected'},
|
| 1456 |
+
'backgroundColor': 'rgba(0, 111, 255, 0)',
|
| 1457 |
+
'border': '1px solid rgba(0, 111, 255,0)'
|
| 1458 |
+
},
|
| 1459 |
+
# --- REGLAS MEJORADAS CON BORDES REDONDEADOS ---
|
| 1460 |
+
{'if': {'filter_query': '{CLASS} contains "P"','column_id': 'CLASS'},
|
| 1461 |
+
'backgroundColor': 'rgba(54,54,62,255)', 'color': 'rgba(166,167,171,255)', 'fontWeight': 'bold','border': '1px solid rgba(134,134,142,255)'},
|
| 1462 |
+
|
| 1463 |
+
{'if': {'filter_query': '{CLASS} contains "A"','column_id': 'CLASS'},
|
| 1464 |
+
'backgroundColor': 'rgba(0,42,102,255)', 'color': 'rgba(107,163,238,255)', 'fontWeight': 'bold','border': '1px solid rgba(35,104,195,255)'},
|
| 1465 |
+
|
| 1466 |
+
{'if': {'filter_query': '{CLASS} contains "B"','column_id': 'CLASS'},
|
| 1467 |
+
'backgroundColor': 'rgba(24,84,14,255)', 'color': 'rgba(139,224,105,255)', 'fontWeight': 'bold','border': '1px solid rgba(126,228,103,255)'},
|
| 1468 |
+
|
| 1469 |
+
{'if': {'filter_query': '{CLASS} contains "C"','column_id': 'CLASS'},
|
| 1470 |
+
'backgroundColor': 'rgba(81,67,6,255)', 'color': 'rgba(224,204,109,255)', 'fontWeight': 'bold','border': '1px solid rgba(220,193,76,255)'},
|
| 1471 |
+
|
| 1472 |
+
{'if': {'filter_query': '{CLASS} contains "D"','column_id': 'CLASS'},
|
| 1473 |
+
'backgroundColor': 'rgba(102,40,3,255)', 'color': 'rgba(255,165,105,255)', 'fontWeight': 'bold','border': '1px solid rgba(208,113,55,255)'},
|
| 1474 |
+
|
| 1475 |
+
{'if': {'filter_query': '{CLASS} contains "R"','column_id': 'CLASS'},
|
| 1476 |
+
'backgroundColor': 'rgba(91,19,20,255)', 'color': 'rgba(225,125,123,255)', 'fontWeight': 'bold','border': '1px solid rgba(172,62,61,255)'},
|
| 1477 |
+
],
|
| 1478 |
+
|
| 1479 |
+
style_cell_conditional=[
|
| 1480 |
+
{'if': {'column_id': 'CLASS'}, 'width': '5%', 'minWidth': '5%', 'maxWidth': '5%'},
|
| 1481 |
+
{'if': {'column_id': 'Rank World'}, 'width': '10%', 'minWidth': '10%', 'maxWidth': '10%'},
|
| 1482 |
+
{'if': {'column_id': 'Rank Region'}, 'width': '10%', 'minWidth': '10%', 'maxWidth': '10%'},
|
| 1483 |
+
{'if': {'column_id': 'Rank Country'}, 'width': '10%', 'minWidth': '10%', 'maxWidth': '10%'},
|
| 1484 |
+
{'if': {'column_id': 'DRIVER'}, 'width': '30%', 'minWidth': '30%', 'maxWidth': '30%', 'textAlign': 'center'},
|
| 1485 |
+
{'if': {'column_id': 'IRATING'}, 'width': '10%', 'minWidth': '10%', 'maxWidth': '10%'},
|
| 1486 |
+
{'if': {'column_id': 'LOCATION'}, 'width': '5%', 'minWidth': '5%', 'maxWidth': '5%', 'justify-content': 'center', 'align-items': 'center'},
|
| 1487 |
+
{'if': {'column_id': 'WINS'}, 'width': '5%', 'minWidth': '5%', 'maxWidth': '5%'},
|
| 1488 |
+
{'if': {'column_id': 'STARTS'}, 'width': '5%', 'minWidth': '5%', 'maxWidth': '5%'},
|
| 1489 |
+
{'if': {'column_id': 'REGION'}, 'width': '20%', 'minWidth': '20%', 'maxWidth': '20%'},
|
| 1490 |
+
]
|
| 1491 |
+
)
|
| 1492 |
+
]
|
| 1493 |
+
)
|
| 1494 |
+
]
|
| 1495 |
+
)
|
| 1496 |
+
]
|
| 1497 |
+
),
|
| 1498 |
+
|
| 1499 |
+
# 4. SECCIÓN HISTOGRAMA - Ancho completo (sin cambios)
|
| 1500 |
+
html.Div(
|
| 1501 |
+
style={
|
| 1502 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1503 |
+
'borderRadius': '15px',
|
| 1504 |
+
'border': '1px solid #4A4A4A',
|
| 1505 |
+
'padding': '20px'
|
| 1506 |
+
},
|
| 1507 |
+
children=[
|
| 1508 |
+
html.H3(
|
| 1509 |
+
"iRating Distribution",
|
| 1510 |
style={
|
| 1511 |
+
'color': 'white',
|
| 1512 |
+
'textAlign': 'center',
|
| 1513 |
+
'marginBottom': '20px',
|
| 1514 |
+
'fontWeight': '700'
|
| 1515 |
}
|
| 1516 |
),
|
| 1517 |
+
dcc.Graph(
|
| 1518 |
+
id='histogram-plot',
|
| 1519 |
+
style={'height': '350px'},
|
| 1520 |
+
config={'displayModeBar': False}
|
| 1521 |
+
)
|
| 1522 |
]
|
| 1523 |
),
|
| 1524 |
|
| 1525 |
+
# 5. SECCIÓN GRÁFICOS ADICIONALES (sin cambios)
|
| 1526 |
html.Div(
|
| 1527 |
+
style={
|
| 1528 |
+
'display': 'flex',
|
| 1529 |
+
'flexDirection': 'column',
|
| 1530 |
+
'gap': '20px'
|
| 1531 |
+
},
|
| 1532 |
children=[
|
| 1533 |
+
# Primera fila: Tabla de competitividad (sin cambios)
|
| 1534 |
+
html.Div(
|
| 1535 |
+
style={
|
| 1536 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1537 |
+
'borderRadius': '15px',
|
| 1538 |
+
'border': '1px solid #4A4A4A',
|
| 1539 |
+
'padding': '20px'
|
| 1540 |
+
},
|
| 1541 |
+
children=[
|
| 1542 |
+
html.H3(
|
| 1543 |
+
"Top Competitive Regions & Countries",
|
| 1544 |
+
style={
|
| 1545 |
+
'color': 'white',
|
| 1546 |
+
'textAlign': 'center',
|
| 1547 |
+
'marginBottom': '10px',
|
| 1548 |
+
'fontWeight': '700'
|
| 1549 |
+
}
|
| 1550 |
+
),
|
| 1551 |
+
html.P(
|
| 1552 |
+
"Based on average iRating of top 100 drivers per region/country (minimum 100 drivers required)",
|
| 1553 |
+
style={
|
| 1554 |
+
'color': '#CCCCCC',
|
| 1555 |
+
'textAlign': 'center',
|
| 1556 |
+
'marginBottom': '20px',
|
| 1557 |
+
'fontSize': '12px',
|
| 1558 |
+
'fontStyle': 'italic'
|
| 1559 |
+
}
|
| 1560 |
+
),
|
| 1561 |
+
html.Div(id='competitiveness-tables-container')
|
| 1562 |
+
]
|
| 1563 |
+
),
|
| 1564 |
|
| 1565 |
+
# CAMBIO: Ahora los gráficos están en columna vertical
|
| 1566 |
+
# Primer gráfico: Regional Analysis
|
| 1567 |
+
html.Div(
|
| 1568 |
+
style={
|
| 1569 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1570 |
+
'borderRadius': '15px',
|
| 1571 |
+
'border': '1px solid #4A4A4A',
|
| 1572 |
+
'padding': '20px'
|
| 1573 |
+
},
|
| 1574 |
+
children=[
|
| 1575 |
+
html.H3(
|
| 1576 |
+
"Regional Analysis",
|
| 1577 |
+
style={
|
| 1578 |
+
'color': 'white',
|
| 1579 |
+
'textAlign': 'center',
|
| 1580 |
+
'marginBottom': '5px',
|
| 1581 |
+
'fontWeight': '700'
|
| 1582 |
+
}
|
| 1583 |
+
),
|
| 1584 |
+
html.P(
|
| 1585 |
+
"Average races with average iRating relation, bubble size represents quantity drivers in region.",
|
| 1586 |
+
style={
|
| 1587 |
+
'color': '#CCCCCC',
|
| 1588 |
+
'textAlign': 'center',
|
| 1589 |
+
'marginBottom': '20px',
|
| 1590 |
+
'fontSize': '12px',
|
| 1591 |
+
'fontStyle': 'italic'
|
| 1592 |
+
}
|
| 1593 |
+
),
|
| 1594 |
+
dcc.Graph(
|
| 1595 |
+
id='region-bubble-chart',
|
| 1596 |
+
style={'height': '400px'}, # Aumentamos altura ya que ahora ocupa todo el ancho
|
| 1597 |
+
config={'displayModeBar': False}
|
| 1598 |
+
)
|
| 1599 |
+
]
|
| 1600 |
),
|
| 1601 |
|
| 1602 |
+
# Segundo gráfico: Experience vs Performance
|
| 1603 |
+
html.Div(
|
| 1604 |
+
style={
|
| 1605 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1606 |
+
'borderRadius': '15px',
|
| 1607 |
+
'border': '1px solid #4A4A4A',
|
| 1608 |
+
'padding': '20px'
|
| 1609 |
+
},
|
| 1610 |
+
children=[
|
| 1611 |
+
html.H3(
|
| 1612 |
+
"Average Races vs iRating",
|
| 1613 |
+
style={
|
| 1614 |
+
'color': 'white',
|
| 1615 |
+
'textAlign': 'center',
|
| 1616 |
+
'marginBottom': '5px',
|
| 1617 |
+
'fontWeight': '700'
|
| 1618 |
+
}
|
| 1619 |
+
),
|
| 1620 |
+
html.P(
|
| 1621 |
+
"Average races for iRating ranges",
|
| 1622 |
+
style={
|
| 1623 |
+
'color': '#CCCCCC',
|
| 1624 |
+
'textAlign': 'center',
|
| 1625 |
+
'marginBottom': '20px',
|
| 1626 |
+
'fontSize': '12px',
|
| 1627 |
+
'fontStyle': 'italic'
|
| 1628 |
+
}
|
| 1629 |
+
),
|
| 1630 |
+
dcc.Graph(
|
| 1631 |
+
id='irating-starts-scatter',
|
| 1632 |
+
style={'height': '400px'}, # Aumentamos altura ya que ahora ocupa todo el ancho
|
| 1633 |
+
config={'displayModeBar': False}
|
| 1634 |
+
)
|
| 1635 |
+
]
|
| 1636 |
+
),
|
| 1637 |
+
|
| 1638 |
+
# --- INICIO DE GRÁFICOS AÑADIDOS ---
|
| 1639 |
+
|
| 1640 |
+
# Tercer gráfico: Density Heatmap (iRating vs Incidents)
|
| 1641 |
+
html.Div(
|
| 1642 |
+
style={
|
| 1643 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1644 |
+
'borderRadius': '15px',
|
| 1645 |
+
'border': '1px solid #4A4A4A',
|
| 1646 |
+
'padding': '10px'
|
| 1647 |
+
},
|
| 1648 |
+
children=[
|
| 1649 |
+
html.H3(
|
| 1650 |
+
"Incidents vs iRating",
|
| 1651 |
+
style={
|
| 1652 |
+
'color': 'white',
|
| 1653 |
+
'textAlign': 'center',
|
| 1654 |
+
'marginBottom': '5px',
|
| 1655 |
+
'fontWeight': '700'
|
| 1656 |
+
}
|
| 1657 |
+
),
|
| 1658 |
+
html.P(
|
| 1659 |
+
"Correlation between incidents and iRating",
|
| 1660 |
+
style={
|
| 1661 |
+
'color': '#CCCCCC',
|
| 1662 |
+
'textAlign': 'center',
|
| 1663 |
+
'marginBottom': '0px',
|
| 1664 |
+
'fontSize': '12px',
|
| 1665 |
+
'fontStyle': 'italic'
|
| 1666 |
+
}
|
| 1667 |
+
),
|
| 1668 |
+
# Usamos la variable que ya definimos
|
| 1669 |
+
density_heatmap
|
| 1670 |
+
]
|
| 1671 |
+
),
|
| 1672 |
+
html.Div(
|
| 1673 |
+
style={
|
| 1674 |
+
'backgroundColor': 'rgba(18,18,26,.5)',
|
| 1675 |
+
'borderRadius': '15px',
|
| 1676 |
+
'border': '1px solid #4A4A4A',
|
| 1677 |
+
'padding': '10px'
|
| 1678 |
+
},
|
| 1679 |
+
children=[
|
| 1680 |
+
html.H3(
|
| 1681 |
+
"Races vs iRating", # <-- Título actualizado
|
| 1682 |
+
style={
|
| 1683 |
+
'color': 'white',
|
| 1684 |
+
'textAlign': 'center',
|
| 1685 |
+
'marginBottom': '5px',
|
| 1686 |
+
'fontWeight': '700'
|
| 1687 |
+
}
|
| 1688 |
+
),
|
| 1689 |
+
html.P(
|
| 1690 |
+
"iRating - races scatter",
|
| 1691 |
+
style={
|
| 1692 |
+
'color': '#CCCCCC',
|
| 1693 |
+
'textAlign': 'center',
|
| 1694 |
+
'marginBottom': '0px',
|
| 1695 |
+
'fontSize': '12px',
|
| 1696 |
+
'fontStyle': 'italic'
|
| 1697 |
+
}
|
| 1698 |
+
),
|
| 1699 |
+
# Añadimos el nuevo gráfico aquí
|
| 1700 |
+
dcc.Graph(
|
| 1701 |
+
id='starts-vs-irating-heatmap',
|
| 1702 |
+
style={'height': '600px'},
|
| 1703 |
+
config={'staticPlot': True} # <-- CLAVE: Hace el gráfico no interactivo
|
| 1704 |
+
)
|
| 1705 |
+
]
|
| 1706 |
)
|
| 1707 |
+
# --- FIN DE GRÁFICOS AÑADIDOS ---
|
| 1708 |
]
|
| 1709 |
)
|
| 1710 |
]
|
| 1711 |
),
|
| 1712 |
|
| 1713 |
+
# Componentes ocultos (sin cambios)
|
|
|
|
| 1714 |
dcc.Store(id='active-discipline-store', data='ROAD.csv'),
|
| 1715 |
dcc.Store(id='shared-data-store', data={}),
|
| 1716 |
dcc.Store(id='shared-data-store_1', data={}),
|
| 1717 |
+
html.Div(id='pilot-info-display', style={'display': 'none'}),
|
| 1718 |
+
# --- AÑADE ESTE COMPONENTE AQUÍ ---
|
| 1719 |
+
dcc.Interval(
|
| 1720 |
+
id='update-timestamp-interval',
|
| 1721 |
+
interval=60 * 1000, # Cada minuto (en milisegundos)
|
| 1722 |
+
n_intervals=0
|
| 1723 |
+
)
|
| 1724 |
+
# --- FIN DEL BLOQUE A AÑADIR ---
|
| 1725 |
]
|
| 1726 |
)
|
| 1727 |
|
| 1728 |
# --- 4. Callbacks ---
|
| 1729 |
|
| 1730 |
+
# --- CALLBACK MODIFICADO PARA MOSTRAR LA FECHA CON ESTADO Y COLORES ---
|
| 1731 |
+
@app.callback(
|
| 1732 |
+
Output('last-update-display', 'children'),
|
| 1733 |
+
Input('update-timestamp-interval', 'n_intervals')
|
| 1734 |
+
)
|
| 1735 |
+
def update_timestamp_display(n):
|
| 1736 |
+
if not DATA_STATUS:
|
| 1737 |
+
return "Checking data status..."
|
| 1738 |
+
|
| 1739 |
+
status = DATA_STATUS.get('status', 'unknown')
|
| 1740 |
+
|
| 1741 |
+
try:
|
| 1742 |
+
# Convertimos la fecha UTC del archivo a un objeto datetime
|
| 1743 |
+
last_update_utc = datetime.fromisoformat(DATA_STATUS['last_update_utc'].replace('Z', '+00:00'))
|
| 1744 |
+
# Calculamos cuánto tiempo ha pasado
|
| 1745 |
+
age = datetime.now(timezone.utc) - last_update_utc
|
| 1746 |
+
|
| 1747 |
+
# Convertimos a la hora local para mostrarla
|
| 1748 |
+
last_update_local = last_update_utc.astimezone(tz=None)
|
| 1749 |
+
formatted_time = last_update_local.strftime('%B %d, %Y')
|
| 1750 |
+
|
| 1751 |
+
# Lógica de colores y mensajes
|
| 1752 |
+
if status.lower() != 'success':
|
| 1753 |
+
color = '#FF5A5A' # Rojo para fallos
|
| 1754 |
+
message = f"⚠️ Last update attempt failed on: {formatted_time}"
|
| 1755 |
+
elif age.total_seconds() > (13 * 3600): # Si tiene más de 13 horas (damos 1h de margen)
|
| 1756 |
+
color = '#FFA500' # Naranja para datos viejos
|
| 1757 |
+
message = f"Warning: Data is over 12 hours old. Last updated: {formatted_time}"
|
| 1758 |
+
else:
|
| 1759 |
+
color = '#50C878' # Verde para éxito
|
| 1760 |
+
message = f"Data last updated on: {formatted_time}"
|
| 1761 |
+
|
| 1762 |
+
except Exception as e:
|
| 1763 |
+
color = '#FF5A5A'
|
| 1764 |
+
message = f"Error reading update status: {e}"
|
| 1765 |
+
|
| 1766 |
+
# Devolvemos un componente html.P para poder aplicarle el estilo de color
|
| 1767 |
+
return html.P(message, style={'color': color, 'margin': 0, 'padding': 0})
|
| 1768 |
+
|
| 1769 |
+
# --- FIN DEL CALLBACK MODIFICADO ---
|
| 1770 |
+
|
| 1771 |
+
|
| 1772 |
+
|
| 1773 |
|
| 1774 |
# --- NUEVO CALLBACK PARA ACTUALIZAR GRÁFICOS DE LA COLUMNA DERECHA ---
|
| 1775 |
@app.callback(
|
| 1776 |
Output('competitiveness-tables-container', 'children'),
|
| 1777 |
Output('region-bubble-chart', 'figure'),
|
| 1778 |
Output('irating-starts-scatter', 'figure'),
|
| 1779 |
+
Output('density-heatmap', 'figure'),
|
| 1780 |
+
# --- CAMBIO: Reemplazamos la salida del gráfico de correlación por el nuevo ---
|
| 1781 |
+
Output('starts-vs-irating-heatmap', 'figure'),
|
| 1782 |
+
Input('active-discipline-store', 'data'))
|
| 1783 |
+
|
| 1784 |
def update_right_column_graphs(filename):
|
| 1785 |
# 1. Cargar y procesar los datos de la disciplina seleccionada
|
| 1786 |
+
df_discipline = DISCIPLINE_DATAFRAMES[filename]
|
| 1787 |
df_discipline = df_discipline[df_discipline['IRATING'] > 1]
|
| 1788 |
df_discipline = df_discipline[df_discipline['STARTS'] > 1]
|
| 1789 |
df_discipline = df_discipline[df_discipline['CLASS'].str.contains('D|C|B|A|P|R', na=False)]
|
|
|
|
| 1796 |
top_regions.insert(0, '#', range(1, 1 + len(top_regions)))
|
| 1797 |
top_countries.insert(0, '#', range(1, 1 + len(top_countries)))
|
| 1798 |
|
| 1799 |
+
# Traducir códigos de país a nombres completos
|
| 1800 |
def get_country_name(code):
|
| 1801 |
try:
|
| 1802 |
return pycountry.countries.get(alpha_2=code).name
|
| 1803 |
except (LookupError, AttributeError):
|
| 1804 |
+
return code
|
| 1805 |
|
| 1806 |
top_countries['LOCATION'] = top_countries['LOCATION'].apply(get_country_name)
|
|
|
|
| 1807 |
|
| 1808 |
+
# ESTILOS CORREGIDOS - Control estricto de ancho
|
| 1809 |
table_style_base = {
|
| 1810 |
+
'style_table': {
|
| 1811 |
+
'borderRadius': '10px',
|
| 1812 |
+
'overflow': 'hidden',
|
| 1813 |
+
'border': '1px solid #4A4A4A',
|
| 1814 |
+
'backgroundColor': 'rgba(11,11,19,1)',
|
| 1815 |
+
'height': '350px',
|
| 1816 |
+
'overflowY': 'auto',
|
| 1817 |
+
'overflowX': 'hidden', # CLAVE: Prevenir scroll horizontal
|
| 1818 |
+
'width': '100%',
|
| 1819 |
+
'maxWidth': '100%' # CLAVE: Forzar límite de ancho
|
| 1820 |
+
},
|
| 1821 |
+
'style_cell': {
|
| 1822 |
+
'textAlign': 'center',
|
| 1823 |
+
'padding': '6px 4px', # Padding más compacto
|
| 1824 |
+
'backgroundColor': 'rgba(11,11,19,1)',
|
| 1825 |
+
'color': 'rgb(255, 255, 255,.8)',
|
| 1826 |
+
'border': 'none',
|
| 1827 |
+
'fontSize': '11px', # Fuente más pequeña
|
| 1828 |
+
'textOverflow': 'ellipsis',
|
| 1829 |
+
'whiteSpace': 'nowrap',
|
| 1830 |
+
'overflow': 'hidden',
|
| 1831 |
+
'maxWidth': '0' # CLAVE: Forzar truncado de texto
|
| 1832 |
+
},
|
| 1833 |
+
'style_header': {
|
| 1834 |
+
'backgroundColor': 'rgba(30,30,38,1)',
|
| 1835 |
+
'fontWeight': 'bold',
|
| 1836 |
+
'color': 'white',
|
| 1837 |
+
'border': 'none',
|
| 1838 |
+
'textAlign': 'center',
|
| 1839 |
+
'fontSize': '12px',
|
| 1840 |
+
'padding': '6px 4px',
|
| 1841 |
+
'overflow': 'hidden',
|
| 1842 |
+
'textOverflow': 'ellipsis'
|
| 1843 |
+
},
|
| 1844 |
'style_cell_conditional': [
|
| 1845 |
+
{'if': {'column_id': '#'}, 'width': '10%', 'minWidth': '10%', 'maxWidth': '10%'},
|
| 1846 |
+
{'if': {'column_id': 'REGION'}, 'width': '60%', 'minWidth': '60%', 'maxWidth': '60%', 'textAlign': 'left'},
|
| 1847 |
+
{'if': {'column_id': 'LOCATION'}, 'width': '60%', 'minWidth': '60%', 'maxWidth': '60%', 'textAlign': 'left'},
|
| 1848 |
+
{'if': {'column_id': 'avg_irating'}, 'width': '30%', 'minWidth': '30%', 'maxWidth': '30%'},
|
| 1849 |
]
|
| 1850 |
}
|
| 1851 |
|
| 1852 |
+
# CONTENEDOR CON CONTROL ESTRICTO DE ANCHO
|
| 1853 |
competitiveness_tables = html.Div(
|
| 1854 |
+
style={
|
| 1855 |
+
'display': 'grid',
|
| 1856 |
+
'gridTemplateColumns': '1fr 1fr',
|
| 1857 |
+
'gap': '15px', # Gap más pequeño
|
| 1858 |
+
'width': '100%',
|
| 1859 |
+
'maxWidth': '100%',
|
| 1860 |
+
'overflow': 'hidden',
|
| 1861 |
+
'boxSizing': 'border-box' # CLAVE: Incluir padding en el ancho
|
| 1862 |
+
},
|
| 1863 |
children=[
|
| 1864 |
+
# Tabla de Regiones
|
| 1865 |
+
html.Div(
|
| 1866 |
+
style={
|
| 1867 |
+
'width': '100%',
|
| 1868 |
+
'maxWidth': '100%',
|
| 1869 |
+
'overflow': 'hidden',
|
| 1870 |
+
'boxSizing': 'border-box' # CLAVE: Control de ancho
|
| 1871 |
+
},
|
| 1872 |
+
children=[
|
| 1873 |
+
html.H4(
|
| 1874 |
+
"Top Regions",
|
| 1875 |
+
style={
|
| 1876 |
+
'color': 'white',
|
| 1877 |
+
'textAlign': 'center',
|
| 1878 |
+
'marginBottom': '10px',
|
| 1879 |
+
'fontSize': '14px',
|
| 1880 |
+
'margin': '0 0 10px 0'
|
| 1881 |
+
}
|
| 1882 |
+
),
|
| 1883 |
+
html.Div(
|
| 1884 |
+
style={
|
| 1885 |
+
'width': '100%',
|
| 1886 |
+
'maxWidth': '100%',
|
| 1887 |
+
'overflow': 'hidden'
|
| 1888 |
+
},
|
| 1889 |
+
children=[
|
| 1890 |
+
dash_table.DataTable(
|
| 1891 |
+
columns=[
|
| 1892 |
+
{'name': '#', 'id': '#'},
|
| 1893 |
+
{'name': 'Region', 'id': 'REGION'},
|
| 1894 |
+
{'name': 'Avg iRating', 'id': 'avg_irating', 'type': 'numeric', 'format': {'specifier': '.0f'}}
|
| 1895 |
+
],
|
| 1896 |
+
data=top_regions.to_dict('records'),
|
| 1897 |
+
page_action='none',
|
| 1898 |
+
style_table=table_style_base['style_table'],
|
| 1899 |
+
style_cell=table_style_base['style_cell'],
|
| 1900 |
+
style_header=table_style_base['style_header'],
|
| 1901 |
+
style_cell_conditional=table_style_base['style_cell_conditional']
|
| 1902 |
+
)
|
| 1903 |
+
]
|
| 1904 |
+
)
|
| 1905 |
+
]
|
| 1906 |
+
),
|
| 1907 |
+
|
| 1908 |
+
# Tabla de Países
|
| 1909 |
+
html.Div(
|
| 1910 |
+
style={
|
| 1911 |
+
'width': '100%',
|
| 1912 |
+
'maxWidth': '100%',
|
| 1913 |
+
'overflow': 'hidden',
|
| 1914 |
+
'boxSizing': 'border-box' # CLAVE: Control de ancho
|
| 1915 |
+
},
|
| 1916 |
+
children=[
|
| 1917 |
+
html.H4(
|
| 1918 |
+
"Top Countries",
|
| 1919 |
+
style={
|
| 1920 |
+
'color': 'white',
|
| 1921 |
+
'textAlign': 'center',
|
| 1922 |
+
'marginBottom': '10px',
|
| 1923 |
+
'fontSize': '14px',
|
| 1924 |
+
'margin': '0 0 10px 0'
|
| 1925 |
+
}
|
| 1926 |
+
),
|
| 1927 |
+
html.Div(
|
| 1928 |
+
style={
|
| 1929 |
+
'width': '100%',
|
| 1930 |
+
'maxWidth': '100%',
|
| 1931 |
+
'overflow': 'hidden'
|
| 1932 |
+
},
|
| 1933 |
+
children=[
|
| 1934 |
+
dash_table.DataTable(
|
| 1935 |
+
columns=[
|
| 1936 |
+
{'name': '#', 'id': '#'},
|
| 1937 |
+
{'name': 'Country', 'id': 'LOCATION'},
|
| 1938 |
+
{'name': 'Avg iRating', 'id': 'avg_irating', 'type': 'numeric', 'format': {'specifier': '.0f'}}
|
| 1939 |
+
],
|
| 1940 |
+
data=top_countries.to_dict('records'),
|
| 1941 |
+
page_action='none',
|
| 1942 |
+
style_table=table_style_base['style_table'],
|
| 1943 |
+
style_cell=table_style_base['style_cell'],
|
| 1944 |
+
style_header=table_style_base['style_header'],
|
| 1945 |
+
style_cell_conditional=table_style_base['style_cell_conditional']
|
| 1946 |
+
)
|
| 1947 |
+
]
|
| 1948 |
+
)
|
| 1949 |
+
]
|
| 1950 |
+
)
|
| 1951 |
]
|
| 1952 |
)
|
| 1953 |
|
| 1954 |
# 3. Crear los otros gráficos
|
| 1955 |
bubble_chart_fig = create_region_bubble_chart(df_discipline)
|
| 1956 |
line_chart_fig = create_irating_trend_line_chart(df_discipline)
|
| 1957 |
+
# --- AÑADIMOS LA CREACIÓN DE LOS NUEVOS GRÁFICOS ---
|
| 1958 |
+
density_fig = create_density_heatmap(df_discipline)
|
| 1959 |
+
# --- CAMBIO: Creamos la figura para el nuevo gráfico ---
|
| 1960 |
+
starts_scatter_fig = create_starts_vs_irating_scatter(df_discipline)
|
|
|
|
| 1961 |
|
| 1962 |
|
| 1963 |
+
# 4. Devolver todos los componentes actualizados
|
| 1964 |
+
# --- CAMBIO: Devolvemos la nueva figura en lugar de la antigua ---
|
| 1965 |
+
return competitiveness_tables, bubble_chart_fig, line_chart_fig, density_fig, starts_scatter_fig
|
| 1966 |
|
| 1967 |
# --- ELIMINA EL CALLBACK update_data_source ---
|
| 1968 |
|
|
|
|
| 2045 |
# Devolvemos el código del país, que actualizará el valor del dropdown 'country-filter'.
|
| 2046 |
return country_code
|
| 2047 |
|
| 2048 |
+
# CALLBACK 1: Inicia el temporizador de debouncing cuando el usuario escribe.
|
| 2049 |
+
@app.callback(
|
| 2050 |
+
Output('search-debounce-interval', 'disabled'),
|
| 2051 |
+
Input('pilot-search-dropdown', 'search_value')
|
| 2052 |
+
)
|
| 2053 |
+
def start_search_debounce(search_value):
|
| 2054 |
+
# Si el texto de búsqueda es muy corto, deshabilita el temporizador.
|
| 2055 |
+
if not search_value or len(search_value) < 3:
|
| 2056 |
+
return True # Deshabilitado
|
| 2057 |
+
# Si hay texto válido, habilita el temporizador (se disparará en 400ms).
|
| 2058 |
+
return False # Habilitado
|
| 2059 |
+
|
| 2060 |
+
# CALLBACK 2: Ejecuta la búsqueda real cuando el temporizador se dispara.
|
| 2061 |
@app.callback(
|
| 2062 |
Output('pilot-search-dropdown', 'options'),
|
| 2063 |
+
Output('last-search-store', 'data'),
|
| 2064 |
+
Output('search-debounce-interval', 'disabled', allow_duplicate=True), # Deshabilita el timer después de usarlo
|
| 2065 |
+
Input('search-debounce-interval', 'n_intervals'), # Se activa por el temporizador
|
| 2066 |
+
State('pilot-search-dropdown', 'search_value'),
|
| 2067 |
State('pilot-search-dropdown', 'value'),
|
| 2068 |
State('region-filter', 'value'),
|
| 2069 |
State('country-filter', 'value'),
|
|
|
|
| 2070 |
State('active-discipline-store', 'data'),
|
| 2071 |
+
State('last-search-store', 'data'),
|
| 2072 |
+
prevent_initial_call=True
|
| 2073 |
)
|
| 2074 |
+
def update_pilot_search_options_debounced(n, search_value, current_selected_pilot,
|
| 2075 |
+
region_filter, country_filter, active_discipline_filename,
|
| 2076 |
+
last_search):
|
| 2077 |
+
|
| 2078 |
+
# --- OPTIMIZACIONES CLAVE ---
|
| 2079 |
+
# 1. Si la búsqueda es inválida o es la misma que la anterior, no hacer nada.
|
| 2080 |
+
if not search_value or len(search_value) < 3 or search_value == last_search:
|
| 2081 |
+
# Devolvemos dash.no_update para las opciones y el store, y deshabilitamos el timer.
|
| 2082 |
+
return dash.no_update, dash.no_update, True
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2083 |
|
| 2084 |
+
print(f"🚀 EXECUTING OPTIMIZED SEARCH for: '{search_value}'")
|
| 2085 |
+
|
| 2086 |
+
# --- LÓGICA DE BÚSQUEDA (sin cambios, pero ahora se ejecuta mucho menos) ---
|
| 2087 |
+
df_current_discipline = DISCIPLINE_DATAFRAMES[active_discipline_filename]
|
| 2088 |
|
| 2089 |
+
# Filtramos el DataFrame una sola vez
|
| 2090 |
filtered_df = df_current_discipline
|
| 2091 |
+
if region_filter and region_filter != 'ALL':
|
| 2092 |
filtered_df = filtered_df[filtered_df['REGION'] == region_filter]
|
| 2093 |
+
if country_filter and country_filter != 'ALL':
|
| 2094 |
filtered_df = filtered_df[filtered_df['LOCATION'] == country_filter]
|
| 2095 |
|
| 2096 |
+
# Búsqueda de coincidencias (más eficiente en un DF ya filtrado)
|
| 2097 |
+
# Usamos `na=False` para evitar errores con valores nulos
|
| 2098 |
+
matches = filtered_df[filtered_df['DRIVER'].str.contains(search_value, case=False, na=False)]
|
| 2099 |
+
|
| 2100 |
+
# OPTIMIZACIÓN: Devolvemos menos resultados para que la respuesta sea más ligera.
|
| 2101 |
+
top_matches = matches.nlargest(15, 'IRATING')
|
| 2102 |
|
|
|
|
| 2103 |
options = [{'label': row['DRIVER'], 'value': row['DRIVER']}
|
| 2104 |
for _, row in top_matches.iterrows()]
|
| 2105 |
|
| 2106 |
+
# Asegurarse de que el piloto seleccionado no desaparezca de las opciones
|
|
|
|
| 2107 |
if current_selected_pilot and not any(opt['value'] == current_selected_pilot for opt in options):
|
| 2108 |
options.insert(0, {'label': current_selected_pilot, 'value': current_selected_pilot})
|
| 2109 |
|
| 2110 |
+
# Devolvemos las nuevas opciones, actualizamos el 'last_search' y deshabilitamos el timer.
|
| 2111 |
+
return options, search_value, True
|
| 2112 |
+
|
| 2113 |
+
# CALLBACK 3: Limpia las opciones si el usuario borra el texto.
|
| 2114 |
+
@app.callback(
|
| 2115 |
+
Output('pilot-search-dropdown', 'options', allow_duplicate=True),
|
| 2116 |
+
Input('pilot-search-dropdown', 'search_value'),
|
| 2117 |
+
State('pilot-search-dropdown', 'value'),
|
| 2118 |
+
prevent_initial_call=True
|
| 2119 |
+
)
|
| 2120 |
+
def clear_options_on_empty_search(search_value, current_selected_pilot):
|
| 2121 |
+
if not search_value:
|
| 2122 |
+
# Si no hay texto, solo muestra la opción del piloto seleccionado (si existe).
|
| 2123 |
+
if current_selected_pilot:
|
| 2124 |
+
return [{'label': current_selected_pilot, 'value': current_selected_pilot}]
|
| 2125 |
+
return []
|
| 2126 |
+
return dash.no_update
|
| 2127 |
+
|
| 2128 |
|
| 2129 |
# --- CALLBACK para limpiar la búsqueda si cambian los filtros ---
|
| 2130 |
@app.callback(
|
|
|
|
| 2238 |
def update_table_and_search(
|
| 2239 |
region_filter, country_filter, selected_pilot,
|
| 2240 |
page_current, page_size, sort_by, state_active_cell,
|
| 2241 |
+
active_discipline_filename,
|
| 2242 |
+
discipline_change_trigger
|
| 2243 |
):
|
| 2244 |
|
| 2245 |
ctx = dash.callback_context
|
|
|
|
| 2253 |
# Leemos y procesamos el archivo seleccionado
|
| 2254 |
#df = pd.read_csv(filename)
|
| 2255 |
df = DISCIPLINE_DATAFRAMES[active_discipline_filename]
|
|
|
|
|
|
|
|
|
|
| 2256 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2257 |
df_for_graphs = df.copy() # Copia para gráficos que no deben ser filtrados
|
|
|
|
|
|
|
|
|
|
| 2258 |
|
| 2259 |
# Lógica de columnas dinámicas
|
| 2260 |
base_cols = ['DRIVER', 'IRATING', 'LOCATION', 'REGION','CLASS', 'STARTS', 'WINS' ]
|
|
|
|
| 2300 |
elif triggered_id == 'pilot-search-dropdown' and selected_pilot:
|
| 2301 |
match_index = filtered_df.index.get_loc(df[df['DRIVER'] == selected_pilot].index[0])
|
| 2302 |
if match_index is not None:
|
| 2303 |
+
target_page = match_index // 100 # CAMBIO: Usar 100 en lugar de page_size
|
| 2304 |
driver_column_index = list(filtered_df.columns).index('DRIVER')
|
| 2305 |
new_active_cell = {
|
| 2306 |
+
'row': match_index % 100, # CAMBIO: Usar 100 en lugar de page_size
|
| 2307 |
+
'row_id': match_index % 100, # CAMBIO: Usar 100
|
| 2308 |
'column': driver_column_index,
|
| 2309 |
'column_id': 'DRIVER'
|
| 2310 |
}
|
| 2311 |
|
|
|
|
|
|
|
| 2312 |
# --- 5. GENERACIÓN DE COLUMNAS PARA LA TABLA ---
|
| 2313 |
columns_definition = []
|
| 2314 |
for col_name in filtered_df.columns:
|
|
|
|
| 2324 |
columns_definition.append({"name": col_name.title(), "id": col_name})
|
| 2325 |
|
| 2326 |
# --- 6. PAGINACIÓN ---
|
| 2327 |
+
page_size = 100 # FORZAR: Siempre usar 100 elementos por página
|
| 2328 |
start_idx = target_page * page_size
|
|
|
|
| 2329 |
end_idx = start_idx + page_size
|
| 2330 |
|
| 2331 |
# Aplicamos el formato de bandera a los datos de la página actual
|
|
|
|
| 2334 |
page_data = page_df.to_dict('records')
|
| 2335 |
|
| 2336 |
total_pages = len(filtered_df) // page_size + (1 if len(filtered_df) % page_size > 0 else 0)
|
| 2337 |
+
|
| 2338 |
# --- 7. ACTUALIZACIÓN DE GRÁFICOS ---
|
| 2339 |
graph_indices = filtered_df.index
|
| 2340 |
highlight_irating = None
|
|
|
|
| 2479 |
ds1 = ds
|
| 2480 |
return ds.get('active_cell'),ds1
|
| 2481 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2482 |
|
| 2483 |
if __name__ == "__main__":
|
| 2484 |
+
app.run(debug=True)
|
| 2485 |
+
|
| 2486 |
+
|
| 2487 |
+
|
assets/custom.css
ADDED
|
@@ -0,0 +1,63 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
/* assets/custom.css */
|
| 2 |
+
.dash-table-container .dash-spreadsheet-container .dash-cell {
|
| 3 |
+
height: calc(70vh / 21.5); /* 70vh dividido por el número de filas por página */
|
| 4 |
+
min-height: 1px;
|
| 5 |
+
max-height: 5px;
|
| 6 |
+
}
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
@font-face {
|
| 10 |
+
font-family: 'DashboardFont'; /* Dale un nombre para usarla en tu app */
|
| 11 |
+
src: url('ADDCN___.ttf') format('truetype'); /* Apunta al archivo de tu fuente */
|
| 12 |
+
}
|
| 13 |
+
/* 2. Aplica la fuente a toda la aplicación */
|
| 14 |
+
/* body {
|
| 15 |
+
font-family: 'DashboardFont', sans-serif; Usa tu fuente. 'sans-serif' es un respaldo
|
| 16 |
+
} */
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
/* Opcional: Asegúrate de que los títulos también la usen */
|
| 20 |
+
h1, h2, h3, h4, h5, h6 {
|
| 21 |
+
font-family: 'DashboardFont', 'Lato', sans-serif;
|
| 22 |
+
}
|
| 23 |
+
|
| 24 |
+
.iracing-dropdown .Select-control {
|
| 25 |
+
background-color: rgba(11, 11, 19, 1) !important; /* Fondo gris oscuro */
|
| 26 |
+
border: 1px solid #4A4A4A !important; /* Borde sutil un poco más claro */
|
| 27 |
+
border-radius: 4px !important;
|
| 28 |
+
box-shadow: none !important; /* Sin sombras */
|
| 29 |
+
}
|
| 30 |
+
|
| 31 |
+
/* Texto de la opción seleccionada */
|
| 32 |
+
.iracing-dropdown .Select-value-label {
|
| 33 |
+
color: #E0E0E0 !important; /* Color de texto gris claro */
|
| 34 |
+
}
|
| 35 |
+
|
| 36 |
+
/* Flecha del dropdown */
|
| 37 |
+
.iracing-dropdown .Select-arrow {
|
| 38 |
+
border-color: #E0E0E0 transparent transparent !important; /* Hace la flecha del color del texto */
|
| 39 |
+
}
|
| 40 |
+
|
| 41 |
+
/* Menú que se despliega */
|
| 42 |
+
.iracing-dropdown .Select-menu-outer {
|
| 43 |
+
background-color: #323232 !important; /* Mismo fondo que el control */
|
| 44 |
+
border: 1px solid #4A4A4A !important; /* Mismo borde */
|
| 45 |
+
border-radius: 4px !important;
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
/* Estilo de cada opción en el menú */
|
| 49 |
+
.iracing-dropdown .Select-option {
|
| 50 |
+
background-color: #323232 !important; /* Fondo de la opción */
|
| 51 |
+
color: #E0E0E0 !important; /* Color del texto de la opción */
|
| 52 |
+
}
|
| 53 |
+
|
| 54 |
+
/* Estilo de la opción cuando el ratón está encima (hover) */
|
| 55 |
+
.iracing-dropdown .Select-option:hover {
|
| 56 |
+
background-color: #4A4A4A !important; /* Un gris un poco más claro para el hover */
|
| 57 |
+
}
|
| 58 |
+
|
| 59 |
+
/* Estilo de la opción que está seleccionada/enfocada */
|
| 60 |
+
.iracing-dropdown .Select-option.is-focused {
|
| 61 |
+
background-color: #4A4A4A !important;
|
| 62 |
+
}
|
| 63 |
+
|
assets/style.css
ADDED
|
@@ -0,0 +1,437 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
|
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|
|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
|
|
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|
|
|
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|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
|
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|
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|
|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
|
|
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|
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|
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|
|
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|
|
|
|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
@import url('https://fonts.googleapis.com/css2?family=Lato:wght@400;700;900&display=swap');
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
body {
|
| 5 |
+
margin: 0;
|
| 6 |
+
padding: 0;
|
| 7 |
+
background-color: rgba(5,5,15,255);
|
| 8 |
+
color: #ffffff;
|
| 9 |
+
font-family: 'Lato', sans-serif;
|
| 10 |
+
font-weight: 500;
|
| 11 |
+
animation: fadeIn 0.8s ease-in-out;
|
| 12 |
+
}
|
| 13 |
+
|
| 14 |
+
@keyframes fadeIn {
|
| 15 |
+
from { opacity: 0; }
|
| 16 |
+
to { opacity: 1; }
|
| 17 |
+
}
|
| 18 |
+
|
| 19 |
+
/* RESPONSIVE DESIGN PARA EL NUEVO LAYOUT */
|
| 20 |
+
|
| 21 |
+
/* Diseño responsive para la sección de filtros y tabla */
|
| 22 |
+
@media (max-width: 1024px) {
|
| 23 |
+
/* Filtros y tabla se vuelven verticales en tablets */
|
| 24 |
+
.filters-table-container {
|
| 25 |
+
flex-direction: column !important;
|
| 26 |
+
}
|
| 27 |
+
|
| 28 |
+
.filters-table-container > div {
|
| 29 |
+
min-width: 100% !important;
|
| 30 |
+
max-width: 100% !important;
|
| 31 |
+
}
|
| 32 |
+
}
|
| 33 |
+
|
| 34 |
+
@media (max-width: 768px) {
|
| 35 |
+
/* Contenedor principal con menos padding en móvil */
|
| 36 |
+
.main-container {
|
| 37 |
+
padding: 10px 3vw !important;
|
| 38 |
+
}
|
| 39 |
+
|
| 40 |
+
/* Grid de gráficos adicionales en una sola columna */
|
| 41 |
+
.additional-charts-grid {
|
| 42 |
+
grid-template-columns: 1fr !important;
|
| 43 |
+
}
|
| 44 |
+
|
| 45 |
+
/* Ajustar títulos para móvil */
|
| 46 |
+
h1 {
|
| 47 |
+
font-size: clamp(28px, 8vw, 36px) !important;
|
| 48 |
+
}
|
| 49 |
+
|
| 50 |
+
h3 {
|
| 51 |
+
font-size: clamp(16px, 4vw, 20px) !important;
|
| 52 |
+
}
|
| 53 |
+
|
| 54 |
+
/* Botones más pequeños en móvil */
|
| 55 |
+
.dashboard-type-button {
|
| 56 |
+
width: 70px !important;
|
| 57 |
+
font-size: 10px !important;
|
| 58 |
+
padding: 6px 4px !important;
|
| 59 |
+
}
|
| 60 |
+
|
| 61 |
+
/* KPIs overlay más pequeño en móvil */
|
| 62 |
+
.kpi-overlay {
|
| 63 |
+
width: 95% !important;
|
| 64 |
+
top: 10px !important;
|
| 65 |
+
}
|
| 66 |
+
}
|
| 67 |
+
|
| 68 |
+
@media (max-width: 480px) {
|
| 69 |
+
/* Contenedor muy compacto para móviles pequeños */
|
| 70 |
+
.main-container {
|
| 71 |
+
padding: 5px 2vw !important;
|
| 72 |
+
gap: 15px !important;
|
| 73 |
+
}
|
| 74 |
+
|
| 75 |
+
/* Secciones con menos padding */
|
| 76 |
+
.section-container {
|
| 77 |
+
padding: 10px !important;
|
| 78 |
+
}
|
| 79 |
+
|
| 80 |
+
/* Filtros en columna única */
|
| 81 |
+
.filters-row {
|
| 82 |
+
flex-direction: column !important;
|
| 83 |
+
gap: 10px !important;
|
| 84 |
+
}
|
| 85 |
+
}
|
| 86 |
+
|
| 87 |
+
/* ESTILOS PARA LOS BOTONES DEL TIPO DE TABLERO */
|
| 88 |
+
.dashboard-type-button {
|
| 89 |
+
background-color: rgba(18,18,26,.5);
|
| 90 |
+
color: white;
|
| 91 |
+
border: 1px solid #4A4A4A;
|
| 92 |
+
padding: 8px 12px;
|
| 93 |
+
border-radius: 5px;
|
| 94 |
+
cursor: pointer;
|
| 95 |
+
font-weight: bold;
|
| 96 |
+
width: 100px;
|
| 97 |
+
text-align: center;
|
| 98 |
+
transition: background-color 0.2s, border-color 0.2s;
|
| 99 |
+
}
|
| 100 |
+
|
| 101 |
+
.dashboard-type-button:hover {
|
| 102 |
+
background-color: rgba(0, 111, 255, 0.3);
|
| 103 |
+
border-color: rgb(0, 111, 255);
|
| 104 |
+
}
|
| 105 |
+
|
| 106 |
+
/* ESTILOS PARA DROPDOWNS ACTIVOS */
|
| 107 |
+
.iracing-dropdown.active-filter .Select-control {
|
| 108 |
+
background-color: rgba(0, 111, 255, 0.25) !important;
|
| 109 |
+
border: 1px solid rgba(0, 111, 255, 0.7) !important;
|
| 110 |
+
box-shadow: 0 0 10px rgba(0, 111, 255, 0.3) !important;
|
| 111 |
+
}
|
| 112 |
+
|
| 113 |
+
.iracing-dropdown.active-filter .Select-placeholder,
|
| 114 |
+
.iracing-dropdown.active-filter .Select-value-label {
|
| 115 |
+
color: #FFFFFF !important;
|
| 116 |
+
}
|
| 117 |
+
|
| 118 |
+
.iracing-dropdown.active-filter .Select-arrow {
|
| 119 |
+
border-top-color: #FFFFFF !important;
|
| 120 |
+
}
|
| 121 |
+
|
| 122 |
+
/* ESTILOS PARA LA TABLA INTERACTIVA */
|
| 123 |
+
#datatable-interactiva .dash-cell {
|
| 124 |
+
font-size: clamp(8px, 0.6vw, 12px) !important;
|
| 125 |
+
padding: 1px 1px !important;
|
| 126 |
+
}
|
| 127 |
+
|
| 128 |
+
#datatable-interactiva .dash-header {
|
| 129 |
+
font-size: clamp(9px, 0.7vw, 10px) !important;
|
| 130 |
+
padding: 2px 1px !important;
|
| 131 |
+
}
|
| 132 |
+
|
| 133 |
+
/* ESTILOS ESPECÍFICOS POR COLUMNA */
|
| 134 |
+
#datatable-interactiva .dash-cell[data-dash-column="REGION"] {
|
| 135 |
+
font-size: 10px !important;
|
| 136 |
+
}
|
| 137 |
+
|
| 138 |
+
#datatable-interactiva .dash-cell[data-dash-column="DRIVER"] {
|
| 139 |
+
font-size: 10px !important;
|
| 140 |
+
}
|
| 141 |
+
|
| 142 |
+
#datatable-interactiva .dash-cell[data-dash-column="CLASS"] {
|
| 143 |
+
font-size: 12px !important;
|
| 144 |
+
}
|
| 145 |
+
|
| 146 |
+
#datatable-interactiva .dash-cell[data-dash-column="IRATING"] {
|
| 147 |
+
font-size: 10px !important;
|
| 148 |
+
}
|
| 149 |
+
|
| 150 |
+
#datatable-interactiva .dash-cell[data-dash-column="STARTS"] {
|
| 151 |
+
font-size: 10px !important;
|
| 152 |
+
}
|
| 153 |
+
|
| 154 |
+
#datatable-interactiva .dash-cell[data-dash-column="WINS"] {
|
| 155 |
+
font-size: 10px !important;
|
| 156 |
+
}
|
| 157 |
+
|
| 158 |
+
/* ESTILOS PARA DROPDOWNS Y LABELS */
|
| 159 |
+
.iracing-dropdown,
|
| 160 |
+
.iracing-dropdown .Select-value-label,
|
| 161 |
+
.iracing-dropdown .Select-placeholder {
|
| 162 |
+
font-size: clamp(10px, 0.8vw, 14px) !important;
|
| 163 |
+
}
|
| 164 |
+
|
| 165 |
+
label {
|
| 166 |
+
font-size: clamp(11px, 0.8vw, 14px) !important;
|
| 167 |
+
}
|
| 168 |
+
|
| 169 |
+
/* ESTILOS PARA LAS TABLAS DE COMPETITIVIDAD */
|
| 170 |
+
#competitiveness-tables-container .pagination {
|
| 171 |
+
justify-content: center;
|
| 172 |
+
margin-top: 5px;
|
| 173 |
+
margin-bottom: 0;
|
| 174 |
+
}
|
| 175 |
+
|
| 176 |
+
#competitiveness-tables-container .page-item .page-link {
|
| 177 |
+
background-color: rgba(11,11,19,1);
|
| 178 |
+
color: rgb(255, 255, 255, .8);
|
| 179 |
+
border: 1px solid #4A4A4A;
|
| 180 |
+
font-size: 12px;
|
| 181 |
+
padding: 4px 10px;
|
| 182 |
+
margin: 0 2px;
|
| 183 |
+
border-radius: 4px;
|
| 184 |
+
}
|
| 185 |
+
|
| 186 |
+
#competitiveness-tables-container .page-item:not(.active) .page-link:hover {
|
| 187 |
+
background-color: #323232;
|
| 188 |
+
color: white;
|
| 189 |
+
}
|
| 190 |
+
|
| 191 |
+
#competitiveness-tables-container .page-item.active .page-link {
|
| 192 |
+
background-color: rgba(0, 111, 255, 0.5);
|
| 193 |
+
border-color: rgb(0, 111, 255);
|
| 194 |
+
color: white;
|
| 195 |
+
font-weight: bold;
|
| 196 |
+
}
|
| 197 |
+
|
| 198 |
+
#competitiveness-tables-container .page-item.disabled .page-link {
|
| 199 |
+
background-color: rgba(11,11,19,1);
|
| 200 |
+
color: #4A4A4A;
|
| 201 |
+
border-color: #323232;
|
| 202 |
+
}
|
| 203 |
+
|
| 204 |
+
/* ESTILOS ESPECÍFICOS PARA LAS TABLAS DE COMPETITIVIDAD */
|
| 205 |
+
#competitiveness-tables-container .dash-table-container {
|
| 206 |
+
border-radius: 10px !important;
|
| 207 |
+
overflow: hidden !important;
|
| 208 |
+
max-width: 100% !important;
|
| 209 |
+
}
|
| 210 |
+
|
| 211 |
+
#competitiveness-tables-container .dash-spreadsheet-container {
|
| 212 |
+
max-height: 350px !important;
|
| 213 |
+
overflow-y: auto !important;
|
| 214 |
+
overflow-x: hidden !important;
|
| 215 |
+
}
|
| 216 |
+
|
| 217 |
+
#competitiveness-tables-container .dash-cell {
|
| 218 |
+
font-size: 12px !important;
|
| 219 |
+
padding: 6px 8px !important;
|
| 220 |
+
white-space: nowrap !important;
|
| 221 |
+
text-overflow: ellipsis !important;
|
| 222 |
+
overflow: hidden !important;
|
| 223 |
+
}
|
| 224 |
+
|
| 225 |
+
#competitiveness-tables-container .dash-header {
|
| 226 |
+
font-size: 13px !important;
|
| 227 |
+
font-weight: bold !important;
|
| 228 |
+
padding: 8px 6px !important;
|
| 229 |
+
position: sticky !important;
|
| 230 |
+
top: 0 !important;
|
| 231 |
+
z-index: 10 !important;
|
| 232 |
+
}
|
| 233 |
+
|
| 234 |
+
/* SCROLL PERSONALIZADO PARA LAS TABLAS DE COMPETITIVIDAD */
|
| 235 |
+
#competitiveness-tables-container .dash-spreadsheet-container::-webkit-scrollbar {
|
| 236 |
+
width: 6px;
|
| 237 |
+
}
|
| 238 |
+
|
| 239 |
+
#competitiveness-tables-container .dash-spreadsheet-container::-webkit-scrollbar-track {
|
| 240 |
+
background: rgba(30,30,38,1);
|
| 241 |
+
border-radius: 3px;
|
| 242 |
+
}
|
| 243 |
+
|
| 244 |
+
#competitiveness-tables-container .dash-spreadsheet-container::-webkit-scrollbar-thumb {
|
| 245 |
+
background: #4A4A4A;
|
| 246 |
+
border-radius: 3px;
|
| 247 |
+
}
|
| 248 |
+
|
| 249 |
+
#competitiveness-tables-container .dash-spreadsheet-container::-webkit-scrollbar-thumb:hover {
|
| 250 |
+
background: #6c6c6c;
|
| 251 |
+
}
|
| 252 |
+
|
| 253 |
+
/* RESPONSIVE: En móvil las tablas se vuelven verticales */
|
| 254 |
+
@media (max-width: 768px) {
|
| 255 |
+
#competitiveness-tables-container > div {
|
| 256 |
+
grid-template-columns: 1fr !important;
|
| 257 |
+
gap: 15px !important;
|
| 258 |
+
}
|
| 259 |
+
|
| 260 |
+
#competitiveness-tables-container .dash-spreadsheet-container {
|
| 261 |
+
max-height: 300px !important;
|
| 262 |
+
}
|
| 263 |
+
}
|
| 264 |
+
|
| 265 |
+
/* ASEGURAR QUE LOS CONTENEDORES NO SE SALGAN */
|
| 266 |
+
.chart-card {
|
| 267 |
+
box-sizing: border-box !important;
|
| 268 |
+
overflow: hidden !important;
|
| 269 |
+
}
|
| 270 |
+
|
| 271 |
+
.chart-card > div {
|
| 272 |
+
max-width: 100% !important;
|
| 273 |
+
overflow: hidden !important;
|
| 274 |
+
}
|
| 275 |
+
|
| 276 |
+
/* UTILIDADES RESPONSIVE */
|
| 277 |
+
.container-fluid {
|
| 278 |
+
max-width: 100%;
|
| 279 |
+
padding: 0;
|
| 280 |
+
}
|
| 281 |
+
|
| 282 |
+
.no-gutters {
|
| 283 |
+
margin: 0;
|
| 284 |
+
padding: 0;
|
| 285 |
+
}
|
| 286 |
+
|
| 287 |
+
/* SCROLLBAR PERSONALIZADO */
|
| 288 |
+
::-webkit-scrollbar {
|
| 289 |
+
width: 8px;
|
| 290 |
+
}
|
| 291 |
+
|
| 292 |
+
::-webkit-scrollbar-track {
|
| 293 |
+
background: rgba(18,18,26,.5);
|
| 294 |
+
border-radius: 4px;
|
| 295 |
+
}
|
| 296 |
+
|
| 297 |
+
::-webkit-scrollbar-thumb {
|
| 298 |
+
background: #4A4A4A;
|
| 299 |
+
border-radius: 4px;
|
| 300 |
+
}
|
| 301 |
+
|
| 302 |
+
::-webkit-scrollbar-thumb:hover {
|
| 303 |
+
background: #6c6c6c;
|
| 304 |
+
}
|
| 305 |
+
|
| 306 |
+
/* ESTILOS MEJORADOS PARA LA TABLA CON SCROLL */
|
| 307 |
+
#datatable-interactiva .dash-table-container {
|
| 308 |
+
border-radius: 15px !important;
|
| 309 |
+
overflow: hidden !important;
|
| 310 |
+
max-height: 450px !important;
|
| 311 |
+
}
|
| 312 |
+
|
| 313 |
+
#datatable-interactiva .dash-spreadsheet-container {
|
| 314 |
+
max-height: 450px !important;
|
| 315 |
+
overflow-y: auto !important;
|
| 316 |
+
overflow-x: auto !important;
|
| 317 |
+
}
|
| 318 |
+
|
| 319 |
+
/* SCROLL PERSONALIZADO PARA LA TABLA PRINCIPAL */
|
| 320 |
+
#datatable-interactiva .dash-spreadsheet-container::-webkit-scrollbar {
|
| 321 |
+
width: 8px;
|
| 322 |
+
height: 8px;
|
| 323 |
+
}
|
| 324 |
+
|
| 325 |
+
#datatable-interactiva .dash-spreadsheet-container::-webkit-scrollbar-track {
|
| 326 |
+
background: rgba(30,30,38,1);
|
| 327 |
+
border-radius: 4px;
|
| 328 |
+
}
|
| 329 |
+
|
| 330 |
+
#datatable-interactiva .dash-spreadsheet-container::-webkit-scrollbar-thumb {
|
| 331 |
+
background: #4A4A4A;
|
| 332 |
+
border-radius: 4px;
|
| 333 |
+
}
|
| 334 |
+
|
| 335 |
+
#datatable-interactiva .dash-spreadsheet-container::-webkit-scrollbar-thumb:hover {
|
| 336 |
+
background: #6c6c6c;
|
| 337 |
+
}
|
| 338 |
+
|
| 339 |
+
#datatable-interactiva .dash-spreadsheet-container::-webkit-scrollbar-corner {
|
| 340 |
+
background: rgba(30,30,38,1);
|
| 341 |
+
}
|
| 342 |
+
|
| 343 |
+
/* ASEGURAR QUE EL HEADER PERMANEZCA VISIBLE */
|
| 344 |
+
#datatable-interactiva .dash-header {
|
| 345 |
+
position: sticky !important;
|
| 346 |
+
top: 0 !important;
|
| 347 |
+
z-index: 10 !important;
|
| 348 |
+
background-color: rgba(30,30,38,1) !important;
|
| 349 |
+
}
|
| 350 |
+
|
| 351 |
+
/* ESTILOS MEJORADOS PARA LOS KPIs VERTICALES DEL PILOTO */
|
| 352 |
+
#kpi-pilot .main-svg {
|
| 353 |
+
background: rgba(0,0,0,0) !important;
|
| 354 |
+
}
|
| 355 |
+
|
| 356 |
+
#kpi-pilot .indicator {
|
| 357 |
+
text-align: center !important;
|
| 358 |
+
}
|
| 359 |
+
|
| 360 |
+
/* RESPONSIVE: Ajustar en móvil */
|
| 361 |
+
@media (max-width: 768px) {
|
| 362 |
+
#datatable-interactiva .dash-spreadsheet-container {
|
| 363 |
+
max-height: 400px !important;
|
| 364 |
+
}
|
| 365 |
+
|
| 366 |
+
/* KPIs del piloto más compactos en móvil */
|
| 367 |
+
#kpi-pilot {
|
| 368 |
+
height: 220px !important;
|
| 369 |
+
}
|
| 370 |
+
}
|
| 371 |
+
|
| 372 |
+
/* ESTILOS PARA LA TABLA KPI DEL PILOTO */
|
| 373 |
+
#kpi-pilot .dash-table-container {
|
| 374 |
+
background: transparent !important;
|
| 375 |
+
border: none !important;
|
| 376 |
+
}
|
| 377 |
+
|
| 378 |
+
#kpi-pilot .dash-spreadsheet-container {
|
| 379 |
+
background: transparent !important;
|
| 380 |
+
border: none !important;
|
| 381 |
+
}
|
| 382 |
+
|
| 383 |
+
#kpi-pilot .dash-cell {
|
| 384 |
+
background: transparent !important;
|
| 385 |
+
border: none !important;
|
| 386 |
+
font-family: 'Lato, sans-serif' !important;
|
| 387 |
+
}
|
| 388 |
+
|
| 389 |
+
#kpi-pilot .dash-header {
|
| 390 |
+
display: none !important;
|
| 391 |
+
}
|
| 392 |
+
|
| 393 |
+
/* Hover effect sutil para las filas de KPI */
|
| 394 |
+
#kpi-pilot tr:hover {
|
| 395 |
+
background-color: rgba(255, 255, 255, 0.05) !important;
|
| 396 |
+
border-radius: 8px;
|
| 397 |
+
transition: background-color 0.2s ease;
|
| 398 |
+
}
|
| 399 |
+
|
| 400 |
+
/* Responsive para la tabla KPI */
|
| 401 |
+
@media (max-width: 768px) {
|
| 402 |
+
#kpi-pilot {
|
| 403 |
+
height: 220px !important;
|
| 404 |
+
}
|
| 405 |
+
|
| 406 |
+
#kpi-pilot .dash-cell {
|
| 407 |
+
padding: 8px 4px !important;
|
| 408 |
+
font-size: 12px !important;
|
| 409 |
+
}
|
| 410 |
+
|
| 411 |
+
#kpi-pilot .dash-cell[data-dash-column="value"] {
|
| 412 |
+
font-size: 18px !important;
|
| 413 |
+
}
|
| 414 |
+
}
|
| 415 |
+
#pilot-search-dropdown .Select-input > input,
|
| 416 |
+
#pilot-search-dropdown .Select-input input {
|
| 417 |
+
color: #FFFFFF !important;
|
| 418 |
+
background-color: transparent !important;
|
| 419 |
+
border: none !important;
|
| 420 |
+
outline: none !important;
|
| 421 |
+
font-family: 'Lato', sans-serif !important;
|
| 422 |
+
caret-color: #FFFFFF !important; /* Color del cursor de texto */
|
| 423 |
+
}
|
| 424 |
+
|
| 425 |
+
#pilot-search-dropdown .Select-input > input::placeholder {
|
| 426 |
+
color: #A0A0A0 !important;
|
| 427 |
+
opacity: 1 !important;
|
| 428 |
+
}
|
| 429 |
+
|
| 430 |
+
#pilot-search-dropdown .Select-placeholder {
|
| 431 |
+
color: #A0A0A0 !important;
|
| 432 |
+
}
|
| 433 |
+
|
| 434 |
+
#pilot-search-dropdown .Select-input > input:focus {
|
| 435 |
+
color: #FFFFFF !important;
|
| 436 |
+
background-color: rgba(11,11,19,1) !important;
|
| 437 |
+
}
|
requirements.txt
CHANGED
|
Binary files a/requirements.txt and b/requirements.txt differ
|
|
|