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feat(letsplot): implement line-realtime (#3135)
## Implementation: `line-realtime` - letsplot Implements the **letsplot** version of `line-realtime`. **File:** `plots/line-realtime/implementations/letsplot.py` **Parent Issue:** #3073 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620617983)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/line-realtime/implementations/letsplot.py‎

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""" pyplots.ai
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line-realtime: Real-Time Updating Line Chart
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Library: letsplot 4.8.2 | Python 3.13.11
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Quality: 91/100 | Created: 2025-12-31
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"""
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import numpy as np
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import pandas as pd
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from lets_plot import (
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LetsPlot,
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aes,
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element_blank,
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element_line,
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element_text,
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geom_area,
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geom_hline,
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geom_line,
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geom_point,
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geom_text,
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ggplot,
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ggsize,
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labs,
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scale_alpha_identity,
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scale_y_continuous,
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theme,
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theme_minimal,
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)
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from lets_plot.export import ggsave
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LetsPlot.setup_html()
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# Data - Simulated CPU usage with real-time streaming effect
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np.random.seed(42)
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n_points = 100
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# Generate realistic CPU usage with some noise and occasional spikes
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base_usage = 35 + 15 * np.sin(np.linspace(0, 4 * np.pi, n_points)) # Oscillating base
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noise = np.random.normal(0, 5, n_points)
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spikes = np.zeros(n_points)
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spike_indices = [25, 48, 72, 88]
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for idx in spike_indices:
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spikes[idx : idx + 3] = np.array([15, 25, 10])[: min(3, n_points - idx)]
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cpu_usage = np.clip(base_usage + noise + spikes, 0, 100)
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# Create alpha gradient for fade effect (older points fade out)
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alpha_values = np.linspace(0.2, 1.0, n_points)
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# Use numeric index for x-axis
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df = pd.DataFrame({"x_idx": range(n_points), "cpu_usage": cpu_usage, "alpha": alpha_values})
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# Get latest value for annotation
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latest_value = cpu_usage[-1]
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# Create annotation dataframes
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annotation_current = pd.DataFrame(
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{"x_idx": [n_points - 1], "cpu_usage": [latest_value + 8], "label": [f"Current: {latest_value:.1f}%"]}
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)
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annotation_fade = pd.DataFrame({"x_idx": [5], "cpu_usage": [10], "label": ["← older data fades"]})
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annotation_threshold = pd.DataFrame({"x_idx": [n_points - 1], "cpu_usage": [83], "label": ["Warning threshold"]})
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# Create the plot with streaming visualization effect
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plot = (
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ggplot(df, aes(x="x_idx", y="cpu_usage"))
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# Main line
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+ geom_line(color="#306998", size=2.5, alpha=0.9)
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# Add area fill for visual depth
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+ geom_area(fill="#306998", alpha=0.15)
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# Add points with size/alpha gradient to show streaming direction
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+ geom_point(aes(alpha="alpha"), color="#306998", size=3, show_legend=False)
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# Highlight the latest point prominently
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+ geom_point(
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data=df.tail(1),
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mapping=aes(x="x_idx", y="cpu_usage"),
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color="#FFD43B",
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size=8,
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shape=21,
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fill="#FFD43B",
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stroke=2,
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)
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# Add horizontal reference lines for CPU thresholds
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+ geom_hline(yintercept=80, linetype="dashed", color="#DC2626", alpha=0.6, size=1)
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+ geom_hline(yintercept=50, linetype="dotted", color="#888888", alpha=0.4, size=0.8)
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# Add text annotations using geom_text
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+ geom_text(
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data=annotation_current,
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mapping=aes(x="x_idx", y="cpu_usage", label="label"),
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color="#FFD43B",
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size=16,
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fontface="bold",
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hjust=1,
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)
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+ geom_text(
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data=annotation_fade, mapping=aes(x="x_idx", y="cpu_usage", label="label"), color="#888888", size=12, hjust=0
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)
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+ geom_text(
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data=annotation_threshold,
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mapping=aes(x="x_idx", y="cpu_usage", label="label"),
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color="#DC2626",
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size=11,
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hjust=1,
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)
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# Labels and title
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+ labs(title="line-realtime · letsplot · pyplots.ai", x="Time (samples at 100ms interval)", y="CPU Usage (%)")
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# Scale configuration
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+ scale_y_continuous(limits=[0, 100], breaks=[0, 25, 50, 75, 100])
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+ scale_alpha_identity()
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# Theme for large canvas
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+ theme_minimal()
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+ theme(
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plot_title=element_text(size=28, face="bold"),
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axis_title=element_text(size=22),
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axis_text=element_text(size=16),
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panel_grid_major=element_line(color="#cccccc", size=0.5),
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panel_grid_minor=element_blank(),
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)
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# Set figure size (will be scaled 3x to 4800x2700)
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+ ggsize(1600, 900)
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)
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# Save PNG (scale=3 gives 4800x2700)
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ggsave(plot, "plot.png", path=".", scale=3)
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# Save HTML for interactivity
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ggsave(plot, "plot.html", path=".")

‎plots/line-realtime/metadata/letsplot.yaml‎

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library: letsplot
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specification_id: line-realtime
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created: '2025-12-31T14:14:33Z'
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updated: '2025-12-31T14:49:35Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20620617983
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issue: 3073
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python_version: 3.13.11
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library_version: 4.8.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-realtime/letsplot/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-realtime/letsplot/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/line-realtime/letsplot/plot.html
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quality_score: 91
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review:
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strengths:
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- Excellent visual representation of real-time monitoring concept with clear current
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value annotation
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- Well-implemented fade effect indication with text annotation and threshold reference
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lines
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- Clean code structure following KISS principles with proper reproducibility
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- Good use of lets-plot ggplot2 grammar including multiple geom layers
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- Professional color scheme with accessible colors (blue line, yellow highlight,
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red threshold)
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- Proper figure sizing with scale=3 for high-resolution output
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weaknesses:
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- Title uses letsplot instead of lets-plot - should match the official library name
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format
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- The point alpha gradient (fade effect) is implemented but barely visible in the
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output; the text annotation helps but actual visual fade would be stronger
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- Grid alpha at 0.5 could be more subtle (0.2-0.3 recommended)

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