feat(bokeh): implement polar-line

plots/polar-line/implementations/bokeh.py

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+""" pyplots.ai
+polar-line: Polar Line Plot
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure, output_file, save
+
+
+# Data: Wind speed pattern over 24 hours (cyclical)
+np.random.seed(42)
+
+# Hours of day (cyclical, 0-24)
+hours = np.linspace(0, 24, 25)
+theta = hours * (2 * np.pi / 24)  # Convert to radians
+
+# Wind speed pattern: lower at night, higher during afternoon
+base_pattern = 5 + 3 * np.sin(theta - np.pi / 2) + 2 * np.cos(2 * theta)
+wind_speed_day1 = base_pattern + np.random.normal(0, 0.5, len(theta))
+wind_speed_day2 = base_pattern * 0.8 + np.random.normal(0, 0.4, len(theta))
+
+# Ensure positive values
+wind_speed_day1 = np.maximum(wind_speed_day1, 0.5)
+wind_speed_day2 = np.maximum(wind_speed_day2, 0.5)
+
+# Close the loop by connecting last point to first
+theta = np.append(theta, theta[0])
+wind_speed_day1 = np.append(wind_speed_day1, wind_speed_day1[0])
+wind_speed_day2 = np.append(wind_speed_day2, wind_speed_day2[0])
+
+# Convert polar to Cartesian for Bokeh (which doesn't have native polar support)
+x1 = wind_speed_day1 * np.cos(theta)
+y1 = wind_speed_day1 * np.sin(theta)
+x2 = wind_speed_day2 * np.cos(theta)
+y2 = wind_speed_day2 * np.sin(theta)
+
+# Create figure (square for polar plot)
+p = figure(
+    width=3600,
+    height=3600,
+    title="polar-line · bokeh · pyplots.ai",
+    x_axis_label="Wind Speed (m/s)",
+    y_axis_label="Wind Speed (m/s)",
+    x_range=(-12, 12),
+    y_range=(-12, 12),
+    tools="",
+    toolbar_location=None,
+)
+
+# Draw concentric circles for radial grid
+for r in [2, 4, 6, 8, 10]:
+    circle_theta = np.linspace(0, 2 * np.pi, 100)
+    cx = r * np.cos(circle_theta)
+    cy = r * np.sin(circle_theta)
+    p.line(cx, cy, line_color="#cccccc", line_width=1.5, line_alpha=0.5)
+
+# Draw radial lines for angular grid (every 30 degrees = 2 hours)
+for angle in np.linspace(0, 2 * np.pi, 12, endpoint=False):
+    p.line([0, 11 * np.cos(angle)], [0, 11 * np.sin(angle)], line_color="#cccccc", line_width=1.5, line_alpha=0.5)
+
+# Add hour labels around the circle
+hour_labels = ["0h", "2h", "4h", "6h", "8h", "10h", "12h", "14h", "16h", "18h", "20h", "22h"]
+label_radius = 11.5
+for i, label in enumerate(hour_labels):
+    angle = i * (2 * np.pi / 12)
+    lx = label_radius * np.cos(angle)
+    ly = label_radius * np.sin(angle)
+    p.text(
+        [lx],
+        [ly],
+        text=[label],
+        text_align="center",
+        text_baseline="middle",
+        text_font_size="18pt",
+        text_color="#444444",
+    )
+
+# Add radius labels
+for r in [2, 4, 6, 8, 10]:
+    p.text([r + 0.3], [0.5], text=[f"{r}"], text_font_size="14pt", text_color="#666666")
+
+# Create data sources
+source1 = ColumnDataSource(data={"x": x1, "y": y1})
+source2 = ColumnDataSource(data={"x": x2, "y": y2})
+
+# Plot the polar lines
+p.line("x", "y", source=source1, line_color="#306998", line_width=4, legend_label="Day 1", line_alpha=0.9)
+p.scatter("x", "y", source=source1, color="#306998", size=12, alpha=0.9)
+
+p.line("x", "y", source=source2, line_color="#FFD43B", line_width=4, legend_label="Day 2", line_alpha=0.9)
+p.scatter("x", "y", source=source2, color="#FFD43B", size=12, alpha=0.9)
+
+# Style the plot
+p.title.text_font_size = "32pt"
+p.title.align = "center"
+
+p.xaxis.axis_label_text_font_size = "22pt"
+p.yaxis.axis_label_text_font_size = "22pt"
+p.xaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_font_size = "18pt"
+
+# Hide the default axes for cleaner polar appearance
+p.xaxis.visible = False
+p.yaxis.visible = False
+p.xgrid.visible = False
+p.ygrid.visible = False
+
+# Style legend
+p.legend.location = "top_right"
+p.legend.label_text_font_size = "18pt"
+p.legend.background_fill_alpha = 0.8
+p.legend.border_line_color = "#cccccc"
+p.legend.padding = 15
+p.legend.spacing = 10
+
+# Background
+p.background_fill_color = "#fafafa"
+p.border_fill_color = "#ffffff"
+p.outline_line_color = "#dddddd"
+
+# Save PNG and HTML
+export_png(p, filename="plot.png")
+output_file("plot.html", title="polar-line · bokeh · pyplots.ai")
+save(p)

plots/polar-line/metadata/bokeh.yaml

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+library: bokeh
+specification_id: polar-line
+created: '2025-12-30T16:30:25Z'
+updated: '2025-12-30T16:37:54Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601057155
+issue: 0
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/polar-line/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/polar-line/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/polar-line/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent simulation of polar coordinates in Bokeh (which lacks native polar support)
+    using manual Cartesian conversion
+  - Clean, readable hour labels around the perimeter with good spacing
+  - Appropriate data scenario (wind speed pattern) that naturally fits polar/cyclical
+    visualization
+  - Good color contrast between the two series (blue/yellow)
+  - Properly closed loops connecting back to starting point
+  weaknesses:
+  - Missing axis label for radial dimension (could add Wind Speed m/s text annotation)
+  - Radius labels (2, 4, 6, 8, 10) positioned only on positive X-axis could be more
+    prominent