egutierrez
46573ccc8e
- Implement dibujar.py for drawing functionality with base64 and PIL image rendering. - Create dibujar_retropaint.py for retro painting features using the Paint widget. - Develop draw_data.py to visualize data with Scatter and Bar widgets, including lazy installation of dependencies. - Add layout configuration for graphical representations in layouts/Graficos_plotly.grid.json. - Enhance shell interaction with mejora_shell_mowidget.py, allowing local library imports and script execution. - Introduce primera_prueba_shell_mowidget.py for testing shell commands and user input handling. - Create prueba_de_embeddings.py for embedding visualizations using Sentence Transformers and dimensionality reduction techniques. - Implement pygwalker_visualizaciones.py for interactive data exploration and visualization using Pygwalker. - Add a sample bash script for user input and ping functionality in scripts/mi_script.sh.
215 lines
7.2 KiB
Python
215 lines
7.2 KiB
Python
import marimo
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__generated_with = "0.15.2"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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return (mo,)
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@app.cell
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def _():
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# import marimo as mo
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import sys
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import subprocess
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import importlib
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import pandas as pd
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import altair as alt
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# Instalación perezosa de drawdata si no está disponible
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def _ensure(pkg: str):
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try:
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importlib.import_module(pkg)
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except ImportError:
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subprocess.check_call([sys.executable, "-m", "pip", "install", pkg, "--quiet"])
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_ensure("drawdata")
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from drawdata import ScatterWidget, BarWidget # API principal
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return BarWidget, ScatterWidget
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@app.cell
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def _(mo):
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mode_selector = mo.ui.radio(
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options=["Scatter", "Barras"],
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value="Scatter",
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label="Modo de dibujo"
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)
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mode_selector
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return (mode_selector,)
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@app.cell
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def _(BarWidget, ScatterWidget, mo):
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# Creamos ambos widgets una sola vez y los reusamos reactivamente
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scatter_widget = ScatterWidget()
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bar_widget = BarWidget(
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collection_names=["Serie A", "Serie B"], # nombres de colecciones (series)
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n_bins=20 # nº de bins para eje X
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)
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mo.md("Widgets inicializados. Usa el selector para mostrar uno u otro.")
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return bar_widget, scatter_widget
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@app.cell
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def _(bar_widget, mode_selector, scatter_widget):
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# Importante en marimo: leer .value en celda separada
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current_widget = scatter_widget if mode_selector.value == "Scatter" else bar_widget
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current_widget
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return
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@app.cell
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def _(bar_widget, mo, mode_selector, scatter_widget):
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# Para ScatterWidget y BarWidget existe .data_as_pandas
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# En scatter: columnas típicas x, y, color
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# En barras: columnas por bin/serie/valor (según widget)
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df_drawn = (scatter_widget.data_as_pandas
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if mode_selector.value == "Scatter"
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else bar_widget.data_as_pandas)
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# Validación básica
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is_empty = (df_drawn is None) or (len(df_drawn) == 0)
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mo.stop(is_empty, mo.md("Dibuja puntos (Scatter) o columnas (Barras) para ver datos."))
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# Vista tabular interactiva
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mo.ui.dataframe(df_drawn)
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return (df_drawn,)
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@app.cell
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def _(df_drawn, mode_selector):
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# Nota: todo está encapsulado en funciones locales para evitar redefinir variables globales.
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def _build_chart_c6(df, mode):
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import altair as alt
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import marimo as mo
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# Si no hay datos, mostramos aviso
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if df is None or len(df) == 0:
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return mo.md("Dibuja datos para visualizar el gráfico.")
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# Mapeo robusto (lower -> original) DENTRO de la función
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orig_cols = list(df.columns)
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lower_map = {str(c).lower(): c for c in orig_cols}
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if mode == "Scatter":
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x_col = lower_map.get("x", orig_cols[0])
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y_col = lower_map.get("y", orig_cols[1 if len(orig_cols) > 1 else 0])
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color_col = lower_map.get("color", None)
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chart_scatter = (
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alt.Chart(df)
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.mark_point(filled=True, opacity=0.8, size=80)
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.encode(
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x=alt.X(x_col, title=str(x_col)),
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y=alt.Y(y_col, title=str(y_col)),
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color=alt.Color(color_col, title=str(color_col)) if color_col else alt.value("steelblue"),
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tooltip=orig_cols
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)
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.properties(title="Dispersión dibujada (reactivo)")
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.interactive()
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)
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return chart_scatter
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else:
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# Barras: detectar bin/x, value/y y collection/serie si existe
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bin_col = lower_map.get("bin") or lower_map.get("x") or orig_cols[0]
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val_col = lower_map.get("value") or lower_map.get("y") or (orig_cols[1] if len(orig_cols) > 1 else orig_cols[0])
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serie_col = (
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lower_map.get("collection")
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or lower_map.get("serie")
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or lower_map.get("series")
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or lower_map.get("group")
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or None
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)
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base = alt.Chart(df).encode(
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x=alt.X(bin_col, title=str(bin_col)),
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y=alt.Y(val_col, title=str(val_col)),
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tooltip=orig_cols
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)
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chart_bars = (
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base.mark_bar()
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.encode(color=alt.Color(serie_col, title=str(serie_col)) if serie_col else alt.value("steelblue"))
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.properties(title="Barras dibujadas (reactivo)")
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.interactive()
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)
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return chart_bars
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# Construimos el gráfico en una variable y la devolvemos como última expresión
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_chart_c6_output = _build_chart_c6(df_drawn, mode_selector.value)
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_chart_c6_output
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return
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@app.cell
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def _(df_drawn, mode_selector, scatter_widget):
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# También encapsulado para no chocar con nombres de otras celdas.
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def _prepare_ml_or_matrix_c7(df, mode):
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import pandas as pd
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import marimo as mo
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if df is None or len(df) == 0:
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return mo.md("No hay datos para preparar. Dibuja primero en el lienzo.")
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if mode == "Scatter":
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xy = getattr(scatter_widget, "data_as_X_y", None)
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if (xy is None) or (xy[0] is None) or (xy[1] is None):
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return mo.md("Añade puntos en el scatter para obtener X, y.")
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X, y = xy
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n_feats = X.shape[1] if hasattr(X, "shape") and len(X.shape) > 1 else 1
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if n_feats == 1:
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x_cols = ["x"]
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elif n_feats == 2:
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x_cols = ["x", "y"]
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else:
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x_cols = [f"x{i+1}" for i in range(n_feats)]
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preview = pd.DataFrame(X, columns=x_cols)
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preview["target"] = y
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return mo.vstack([
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mo.md(f"Listo para ML — **X**: {getattr(X, 'shape', '')}, **y**: {getattr(y, 'shape', '')}"),
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mo.ui.dataframe(preview.head(20))
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])
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else:
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# Barras → matriz (filas=bins, columnas=series) o columna única si no hay series
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orig_cols = list(df.columns)
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lower_map = {str(c).lower(): c for c in orig_cols}
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bin_col = lower_map.get("bin") or lower_map.get("x") or orig_cols[0]
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val_col = lower_map.get("value") or lower_map.get("y") or (orig_cols[-1] if len(orig_cols) > 1 else orig_cols[0])
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serie_col = (
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lower_map.get("collection")
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or lower_map.get("serie")
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or lower_map.get("series")
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or lower_map.get("group")
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or None
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)
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if serie_col:
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mat_df = df.pivot_table(index=bin_col, columns=serie_col, values=val_col, fill_value=0, aggfunc="sum").sort_index()
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else:
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mat_df = df[[bin_col, val_col]].set_index(bin_col).sort_index()
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return mo.vstack([
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mo.md(f"Matriz lista para modelar/featurear — forma: **{mat_df.shape}**"),
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mo.ui.dataframe(mat_df.reset_index().head(50))
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])
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_ml_matrix_c7_output = _prepare_ml_or_matrix_c7(df_drawn, mode_selector.value)
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_ml_matrix_c7_output
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return
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@app.cell
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def _():
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return
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if __name__ == "__main__":
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app.run()
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