feat(letsplot): implement campbell-basic (#4248)

## Implementation: `campbell-basic` - letsplot

Implements the **letsplot** version of `campbell-basic`.

**File:** `plots/campbell-basic/implementations/letsplot.py`

**Parent Issue:** #4241

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/22043026984)*

---------

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>

Author: github-actions[bot]

plots/campbell-basic/implementations/letsplot.py

@@ -0,0 +1,189 @@
+""" pyplots.ai
+campbell-basic: Campbell Diagram
+Library: letsplot 4.8.2 | Python 3.14.3
+Quality: 88/100 | Created: 2026-02-15
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data
+np.random.seed(42)
+speed_rpm = np.linspace(0, 6000, 80)
+max_freq = 120
+
+# Natural frequency modes (Hz) with realistic speed-dependent variation
+# Gyroscopic effects: forward whirl modes increase, backward whirl modes decrease
+modes = {
+    "1st Bending": 25 + 0.0008 * speed_rpm + np.random.normal(0, 0.15, 80),
+    "1st Torsional": 48 - 0.0005 * speed_rpm + np.random.normal(0, 0.12, 80),
+    "2nd Bending": 72 + 0.0025 * speed_rpm + np.random.normal(0, 0.18, 80),
+    "2nd Torsional": 88 + 0.0006 * speed_rpm + np.random.normal(0, 0.14, 80),
+    "Axial": 105 - 0.0004 * speed_rpm + np.random.normal(0, 0.10, 80),
+}
+
+# Build long-format DataFrame for natural frequency modes
+modes_df = pd.concat(
+    [pd.DataFrame({"Speed": speed_rpm, "Frequency": freq, "Mode": name}) for name, freq in modes.items()],
+    ignore_index=True,
+)
+
+# Engine order lines: frequency = order * speed_rpm / 60
+orders = [1, 2, 3]
+order_labels = ["1\u00d7", "2\u00d7", "3\u00d7"]
+
+eo_df = pd.concat(
+    [
+        pd.DataFrame(
+            {
+                "Speed": np.linspace(0, min(6000, max_freq * 60 / o), 80),
+                "Frequency": o * np.linspace(0, min(6000, max_freq * 60 / o), 80) / 60,
+                "Order": lbl,
+            }
+        )
+        for o, lbl in zip(orders, order_labels)
+    ],
+    ignore_index=True,
+)
+
+# Find critical speed intersections (EO lines crossing mode curves)
+critical_rows = []
+for order, olabel in zip(orders, order_labels):
+    eo_at = order * speed_rpm / 60
+    for mname, mfreq in modes.items():
+        diff = eo_at - mfreq
+        for idx in np.where(np.diff(np.sign(diff)))[0]:
+            t = abs(diff[idx]) / (abs(diff[idx]) + abs(diff[idx + 1]))
+            cs = speed_rpm[idx] + t * (speed_rpm[idx + 1] - speed_rpm[idx])
+            cf = order * cs / 60
+            if cf <= max_freq:
+                critical_rows.append({"Speed": cs, "Frequency": cf, "Intersection": f"{mname} \u00d7 {olabel}"})
+
+crit_df = pd.DataFrame(critical_rows)
+
+# Highlight zones around critical speeds
+zone_df = pd.DataFrame(
+    [
+        {"xmin": r["Speed"] - 150, "xmax": r["Speed"] + 150, "ymin": r["Frequency"] - 3.5, "ymax": r["Frequency"] + 3.5}
+        for _, r in crit_df.iterrows()
+    ]
+)
+
+# EO label positions along each line
+eo_label_df = pd.DataFrame(
+    [
+        {"Speed": spd, "Frequency": eo * spd / 60 + 2.5, "Label": lbl}
+        for eo, lbl, spd in zip(orders, order_labels, [5200, 3400, 2200])
+    ]
+)
+
+# Colorblind-safe palette (blue-teal-amber-purple-gray) — avoids red-green confusion
+mode_colors = ["#306998", "#17A589", "#D4A017", "#8E44AD", "#5D6D7E"]
+mode_order = list(modes.keys())
+eo_color = "#AAAAAA"
+crit_color = "#C0392B"
+
+# Annotate the most dangerous critical speed (highest frequency intersection)
+danger_idx = crit_df["Frequency"].idxmax()
+danger_row = crit_df.loc[danger_idx]
+annot_df = pd.DataFrame(
+    [
+        {
+            "Speed": danger_row["Speed"] + 250,
+            "Frequency": danger_row["Frequency"] + 5,
+            "Label": f"\u2190 {danger_row['Intersection']}\n    ({int(danger_row['Speed'])} RPM)",
+        }
+    ]
+)
+
+# Plot
+plot = (
+    ggplot()
+    + geom_rect(
+        data=zone_df,
+        mapping=aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"),
+        fill=crit_color,
+        alpha=0.08,
+        color="transparent",
+    )
+    + geom_line(data=eo_df, mapping=aes(x="Speed", y="Frequency", linetype="Order"), color=eo_color, size=0.9)
+    + geom_line(
+        data=modes_df,
+        mapping=aes(x="Speed", y="Frequency", color="Mode"),
+        size=2.5,
+        tooltips=layer_tooltips()
+        .line("@{Mode}")
+        .line("Speed|@{Speed} RPM")
+        .line("Freq|@{Frequency} Hz")
+        .format("Speed", ",.0f")
+        .format("Frequency", ".1f"),
+    )
+    + geom_point(
+        data=crit_df,
+        mapping=aes(x="Speed", y="Frequency"),
+        color=crit_color,
+        fill=crit_color,
+        size=8,
+        shape=18,
+        tooltips=layer_tooltips()
+        .title("Critical Speed")
+        .line("@{Intersection}")
+        .line("Speed|@{Speed} RPM")
+        .line("Freq|@{Frequency} Hz")
+        .format("Speed", ",.0f")
+        .format("Frequency", ".1f"),
+    )
+    + geom_text(
+        data=eo_label_df,
+        mapping=aes(x="Speed", y="Frequency", label="Label"),
+        color="#777777",
+        size=14,
+        fontface="bold",
+    )
+    + geom_text(
+        data=annot_df,
+        mapping=aes(x="Speed", y="Frequency", label="Label"),
+        color=crit_color,
+        size=12,
+        fontface="italic",
+        hjust=0,
+    )
+    + scale_color_manual(name="Natural Frequency", values=mode_colors, limits=mode_order)
+    + scale_linetype_manual(name="Engine Order", values=["dashed", "dashed", "dashed"])
+    + scale_y_continuous(limits=[0, max_freq], expand=[0, 2])
+    + scale_x_continuous(limits=[0, 6200], format=",d")
+    + guides(linetype=guide_legend(override_aes={"color": eo_color}))
+    + labs(
+        title="campbell-basic \u00b7 letsplot \u00b7 pyplots.ai",
+        subtitle="Red diamonds mark critical speeds where engine order excitations cross natural frequency modes",
+        x="Rotational Speed (RPM)",
+        y="Frequency (Hz)",
+        caption="Data: synthetic rotordynamic model | Highlight zones indicate resonance risk regions",
+    )
+    + flavor_high_contrast_light()
+    + theme(
+        plot_title=element_text(size=28, hjust=0.5, face="bold"),
+        plot_subtitle=element_text(size=16, hjust=0.5, color="#555555"),
+        plot_caption=element_text(size=13, color="#888888", face="italic"),
+        axis_title=element_text(size=22),
+        axis_text=element_text(size=18),
+        legend_title=element_text(size=18, face="bold"),
+        legend_text=element_text(size=16),
+        legend_position=[0.02, 0.98],
+        legend_justification=[0, 1],
+        legend_background=element_rect(fill="white", color="#CCCCCC", size=0.5),
+        panel_grid_major=element_line(color="#E5E5E5", size=0.3),
+        panel_grid_minor=element_blank(),
+        axis_line=element_line(color="#BBBBBB", size=0.5),
+        plot_margin=[40, 20, 20, 20],
+    )
+    + ggsize(1600, 900)
+)
+
+# Save
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")