improv: Add more relevant snippets to the snippets panel (2/2)

marimo/_snippets/data/bokeh-0.py

@@ -0,0 +1,62 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Bokeh: Interactive Line Plot
+
+        Create an interactive line plot with pan, zoom, and hover tools.
+        Common usage: `figure(tools='pan,box_zoom,reset,save')`.
+        Commonly used in: time series analysis, trend visualization.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource
+    import numpy as np
+
+    # Generate sample data
+    x = np.linspace(0, 10, 100)
+    y = np.sin(x) + np.random.normal(0, 0.1, 100)
+
+    # Create data source
+    source = ColumnDataSource(data=dict(x=x, y=y))
+
+    # Create figure with tools
+    p = figure(
+        height=400,
+        width=700,
+        tools='pan,box_zoom,wheel_zoom,reset,save,hover',
+        title='Interactive Line Plot'
+    )
+
+    # Add line with hover tooltips
+    p.line('x', 'y', line_width=2, source=source)
+    p.hover.tooltips = [
+        ('x', '@x{0.00}'),
+        ('y', '@y{0.00}')
+    ]
+
+    p
+    return ColumnDataSource, figure, np, p, source, x, y
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

marimo/_snippets/data/bokeh-1.py

@@ -0,0 +1,87 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Interactive Scatter Plot
+
+        Create a scatter plot with interactive tools and color mapping.
+        Common usage: `figure(tools='box_select,lasso_select,reset')`.
+        Commonly used in: data exploration, cluster analysis, outlier detection.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, ColorBar
+    from bokeh.transform import linear_cmap
+    import numpy as np
+
+    # Generate sample clustered data
+    np.random.seed(42)
+    N = 500
+    x = np.random.normal(size=N)
+    y = np.random.normal(size=N)
+    color_value = np.random.uniform(size=N)  # Value for color mapping
+
+    # Create data source
+    source = ColumnDataSource(data=dict(
+        x=x,
+        y=y,
+        color=color_value,
+    ))
+
+    # Create figure with selection tools
+    p = figure(
+        height=400,
+        width=700,
+        tools='box_select,lasso_select,wheel_zoom,pan,reset,save',
+        title='Interactive Scatter Plot'
+    )
+
+    # Add points with color mapping
+    mapper = linear_cmap('color', 'Viridis256', 0, 1)
+    scatter = p.scatter('x', 'y', 
+                       size=8,
+                       source=source,
+                       fill_color=mapper,
+                       line_color='white',
+                       alpha=0.6,
+                       selection_color='red',
+                       nonselection_alpha=0.1)
+
+    # Add colorbar
+    color_bar = ColorBar(color_mapper=mapper['transform'], 
+                        width=8,
+                        location=(0,0))
+    p.add_layout(color_bar, 'right')
+
+    # Add hover tooltips
+    p.hover.tooltips = [
+        ('x', '@x{0.00}'),
+        ('y', '@y{0.00}'),
+        ('value', '@color{0.00}')
+    ]
+
+    p
+    return (ColorBar, ColumnDataSource, color_bar, color_value, figure, linear_cmap,
+            mapper, np, p, scatter, source, x, y)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

marimo/_snippets/data/bokeh-2.py

@@ -0,0 +1,111 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Linked Brushing and Selection
+
+        Create linked plots with synchronized selection.
+        Common usage: `layout([p1, p2])` with shared `ColumnDataSource`.
+        Commonly used in: exploratory data analysis, correlation analysis, outlier detection.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource
+    from bokeh.layouts import row
+    import numpy as np
+
+    # Generate sample data
+    np.random.seed(42)
+    N = 1000
+    x = np.random.normal(0, 1, N)
+    y = x * 0.5 + np.random.normal(0, 0.5, N)  # Positive correlation with x
+    z = x * -0.2 + np.random.normal(0, 0.2, N)  # Negative correlation with x
+
+    # Create data source
+    source = ColumnDataSource(data=dict(x=x, y=y, z=z))
+
+    # Common figure properties
+    plot_config = dict(
+        height=400,
+        width=400,
+        tools='box_select,lasso_select,wheel_zoom,pan,reset,hover',
+        background_fill_color='#f5f5f5'
+    )
+
+    # Create first plot
+    p1 = figure(
+        title='Y vs X (Positive Correlation)',
+        **plot_config
+    )
+    p1.circle('x', 'y', 
+              source=source,
+              size=8,
+              fill_color='navy',
+              fill_alpha=0.5,
+              line_color='white',
+              selection_color='red',
+              nonselection_alpha=0.1)
+
+    # Create second plot
+    p2 = figure(
+        title='Z vs X (Negative Correlation)',
+        **plot_config
+    )
+    p2.circle('x', 'z', 
+              source=source,
+              size=8,
+              fill_color='navy',
+              fill_alpha=0.5,
+              line_color='white',
+              selection_color='red',
+              nonselection_alpha=0.1)
+
+    # Add hover tooltips
+    tooltip_config = [
+        ('x', '@x{0.00}'),
+        ('y', '@y{0.00}'),
+        ('z', '@z{0.00}')
+    ]
+    p1.hover.tooltips = tooltip_config
+    p2.hover.tooltips = tooltip_config
+
+    # Add grid styling
+    for p in [p1, p2]:
+        p.grid.grid_line_color = 'white'
+        p.grid.grid_line_width = 2
+        p.axis.axis_label_text_font_size = '12pt'
+        p.axis.axis_label_text_font_style = 'bold'
+
+    # Add axis labels
+    p1.xaxis.axis_label = 'X Value'
+    p1.yaxis.axis_label = 'Y Value'
+    p2.xaxis.axis_label = 'X Value'
+    p2.yaxis.axis_label = 'Z Value'
+
+    # Layout plots side by side
+    layout = row(p1, p2)
+
+    layout
+    return (ColumnDataSource, figure, layout, np, p1, p2, row, source, x, y, z)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

marimo/_snippets/data/bokeh-3.py

@@ -0,0 +1,105 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Real-time Data Streaming
+
+        Create a real-time streaming visualization with rolling window.
+        Common usage: `ColumnDataSource` with periodic updates.
+        Commonly used in: system monitoring, IoT dashboards, live analytics.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, DatetimeTickFormatter
+    import numpy as np
+    from datetime import datetime, timedelta
+    import pandas as pd
+
+    # Initialize data with rolling window
+    window_size = 100
+    now = datetime.now()
+    times = [(now + timedelta(seconds=i)) for i in range(-window_size, 0)]
+
+    # Simulate metric data (e.g., system metrics)
+    values = np.random.normal(100, 10, window_size)
+    values_smooth = pd.Series(values).rolling(window=5).mean()
+
+    # Create data source
+    source = ColumnDataSource(data=dict(
+        time=times,
+        value=values,
+        value_smooth=values_smooth
+    ))
+
+    # Create figure
+    p = figure(
+        height=400,
+        width=800,
+        title='Real-time Metric Monitoring',
+        x_axis_type='datetime',
+        tools='pan,box_zoom,wheel_zoom,reset,save'
+    )
+
+    # Add raw data line
+    p.line('time', 'value', 
+           line_color='lightgray',
+           line_alpha=0.5,
+           line_width=1,
+           legend_label='Raw Data',
+           source=source)
+
+    # Add smoothed line
+    p.line('time', 'value_smooth',
+           line_color='navy',
+           line_width=2,
+           legend_label='Smoothed (5-point MA)',
+           source=source)
+
+    # Style the plot
+    p.grid.grid_line_alpha = 0.3
+    p.xaxis.formatter = DatetimeTickFormatter(
+        seconds="%Y-%m-%d %H:%M:%S",
+        minsec="%Y-%m-%d %H:%M:%S",
+        minutes="%Y-%m-%d %H:%M:%S",
+        hourmin="%Y-%m-%d %H:%M"
+    )
+    p.xaxis.axis_label = 'Time'
+    p.yaxis.axis_label = 'Value'
+
+    # Add hover tool
+    p.hover.tooltips = [
+        ('Time', '@time{%Y-%m-%d %H:%M:%S}'),
+        ('Value', '@value{0.00}'),
+        ('Smoothed', '@value_smooth{0.00}')
+    ]
+    p.hover.formatters = {'@time': 'datetime'}
+
+    # Configure legend
+    p.legend.location = 'top_left'
+    p.legend.click_policy = 'hide'
+
+    p
+    return (ColumnDataSource, DatetimeTickFormatter, datetime, figure, now, np, p,
+            pd, source, timedelta, times, values, values_smooth, window_size)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()