Merge branch 'main' into implementation/heatmap-basic/seaborn

Author: github-actions[bot]

plots/campbell-basic/implementations/altair.py

@@ -0,0 +1,198 @@
+""" pyplots.ai
+campbell-basic: Campbell Diagram
+Library: altair 6.0.0 | Python 3.14.3
+Quality: 85/100 | Created: 2026-02-15
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data
+np.random.seed(42)
+speeds = np.linspace(0, 6000, 80)
+
+mode_labels = ["1st Bending", "2nd Bending", "1st Torsional", "Axial", "3rd Bending"]
+base_freqs = [45, 95, 130, 175, 220]
+slopes = [0.004, -0.003, 0.005, 0.001, -0.004]
+curvatures = [5e-7, -4e-7, 2e-7, 1e-7, -3e-7]
+
+mode_rows = []
+for label, base, slope, curv in zip(mode_labels, base_freqs, slopes, curvatures, strict=True):
+    freqs = base + slope * speeds + curv * speeds**2
+    for s, f in zip(speeds, freqs, strict=True):
+        mode_rows.append({"RPM": s, "Hz": f, "Mode": label})
+
+engine_orders = [1, 2, 3]
+eo_rows = []
+for order in engine_orders:
+    for s in speeds:
+        eo_rows.append({"RPM": s, "Hz": order * s / 60, "EO": f"{order}x"})
+
+df_modes = pd.DataFrame(mode_rows)
+df_eo = pd.DataFrame(eo_rows)
+
+# Find critical speed intersections
+critical_rows = []
+dense_speeds = np.linspace(0, 6000, 5000)
+for label, base, slope, curv in zip(mode_labels, base_freqs, slopes, curvatures, strict=True):
+    mode_freq = base + slope * dense_speeds + curv * dense_speeds**2
+    for order in engine_orders:
+        eo_freq = order * dense_speeds / 60
+        diff = mode_freq - eo_freq
+        sign_changes = np.where(np.diff(np.sign(diff)))[0]
+        for idx in sign_changes:
+            s_crit = dense_speeds[idx]
+            f_crit = eo_freq[idx]
+            if 100 < s_crit < 5900 and 5 < f_crit < 295:
+                in_op_range = 3000 <= s_crit <= 5000
+                critical_rows.append(
+                    {
+                        "RPM": round(s_crit),
+                        "Hz": round(f_crit, 1),
+                        "Label": f"{label} / {order}x",
+                        "InOpRange": in_op_range,
+                    }
+                )
+
+df_critical = pd.DataFrame(critical_rows)
+
+# Select well-spaced key critical speeds for annotation
+key_in_range = df_critical[df_critical["InOpRange"]]
+key_outside = df_critical[~df_critical["InOpRange"]].sort_values("Hz")
+# Pick annotations that are well separated: one low outside, one mid in-range, one high in-range
+df_annot = pd.DataFrame(
+    [
+        {**key_outside.iloc[0].to_dict(), "dx": 12, "dy": -18},  # 1st Bending / 3x (~988 RPM, ~49 Hz)
+        {**key_in_range.iloc[0].to_dict(), "dx": -130, "dy": -20},  # 1st Bending / 1x (~4273 RPM, ~71 Hz) — left
+        {**key_in_range.iloc[2].to_dict(), "dx": 14, "dy": -18},  # 1st Torsional / 2x (~4747 RPM, ~158 Hz)
+    ]
+)
+
+# Operating range
+op_min, op_max = 3000, 5000
+
+# Color palette — colorblind-safe, high contrast between all modes
+mode_palette = ["#306998", "#E8833A", "#55A868", "#BA6BC9", "#C44E52"]
+
+x_scale = alt.Scale(domain=[0, 6200], nice=False)
+y_scale = alt.Scale(domain=[0, 310])
+
+# Operating range shaded band
+op_band = (
+    alt.Chart(pd.DataFrame({"x": [op_min], "x2": [op_max]}))
+    .mark_rect(opacity=0.08, color="#306998")
+    .encode(x=alt.X("x:Q", scale=x_scale), x2="x2:Q")
+)
+
+# Operating range label
+op_label = (
+    alt.Chart(pd.DataFrame({"RPM": [(op_min + op_max) / 2], "Hz": [8], "label": ["Operating Range"]}))
+    .mark_text(fontSize=15, fontStyle="italic", color="#306998", fontWeight="bold")
+    .encode(x=alt.X("RPM:Q", scale=x_scale), y=alt.Y("Hz:Q", scale=y_scale), text="label:N")
+)
+
+# Engine order lines — no legend (direct labels at right edge are cleaner)
+eo_chart = (
+    alt.Chart(df_eo)
+    .mark_line(strokeWidth=1.5, strokeDash=[8, 6], color="#999999", opacity=0.55)
+    .encode(x=alt.X("RPM:Q", scale=x_scale), y=alt.Y("Hz:Q", scale=y_scale), detail="EO:N")
+)
+
+# Engine order text labels near right edge
+eo_label_rows = []
+for order in engine_orders:
+    max_freq = order * 6000 / 60
+    if max_freq <= 295:
+        eo_label_rows.append({"RPM": 6080, "Hz": order * 6080 / 60, "label": f"{order}x"})
+    else:
+        target_speed = 290 * 60 / order
+        eo_label_rows.append({"RPM": target_speed + 80, "Hz": 295, "label": f"{order}x"})
+
+eo_label_chart = (
+    alt.Chart(pd.DataFrame(eo_label_rows))
+    .mark_text(fontSize=15, fontWeight="bold", color="#777777", align="left", dy=-8)
+    .encode(x=alt.X("RPM:Q", scale=x_scale), y=alt.Y("Hz:Q", scale=y_scale), text="label:N")
+)
+
+# Natural frequency mode lines
+modes_chart = (
+    alt.Chart(df_modes)
+    .mark_line(strokeWidth=3)
+    .encode(
+        x=alt.X("RPM:Q", title="Rotational Speed (RPM)", scale=x_scale),
+        y=alt.Y("Hz:Q", title="Frequency (Hz)", scale=y_scale),
+        color=alt.Color(
+            "Mode:N",
+            scale=alt.Scale(domain=mode_labels, range=mode_palette),
+            legend=alt.Legend(
+                title="Natural Frequencies", titleFontSize=14, labelFontSize=13, symbolStrokeWidth=3, symbolSize=150
+            ),
+        ),
+    )
+)
+
+# Critical speed markers — size-differentiated for operating range emphasis
+crit_outside_chart = (
+    alt.Chart(df_critical[~df_critical["InOpRange"]])
+    .mark_point(size=200, shape="diamond", filled=True, color="#D62728", stroke="white", strokeWidth=1.5)
+    .encode(x=alt.X("RPM:Q", scale=x_scale), y=alt.Y("Hz:Q", scale=y_scale), tooltip=["Label:N", "RPM:Q", "Hz:Q"])
+)
+
+crit_inside_chart = (
+    alt.Chart(df_critical[df_critical["InOpRange"]])
+    .mark_point(size=380, shape="diamond", filled=True, color="#D62728", stroke="white", strokeWidth=2.5)
+    .encode(x=alt.X("RPM:Q", scale=x_scale), y=alt.Y("Hz:Q", scale=y_scale), tooltip=["Label:N", "RPM:Q", "Hz:Q"])
+)
+
+# Annotations for key critical speeds — single consolidated layer per offset group
+annot_layers = []
+for _, row in df_annot.iterrows():
+    annot_layers.append(
+        alt.Chart(pd.DataFrame([row]))
+        .mark_text(fontSize=13, color="#8B0000", fontWeight="bold", align="left", dx=row["dx"], dy=row["dy"])
+        .encode(x=alt.X("RPM:Q", scale=x_scale), y=alt.Y("Hz:Q", scale=y_scale), text="Label:N")
+    )
+
+# Compose chart
+combined = op_band + eo_chart + modes_chart + crit_outside_chart + crit_inside_chart + eo_label_chart + op_label
+for layer in annot_layers:
+    combined = combined + layer
+
+chart = (
+    combined.properties(
+        width=1600,
+        height=900,
+        title=alt.Title(
+            "campbell-basic · altair · pyplots.ai",
+            fontSize=28,
+            fontWeight=500,
+            anchor="start",
+            subtitle="Natural Frequency Modes vs Engine Order Excitations",
+            subtitleFontSize=16,
+            subtitleColor="#666666",
+        ),
+    )
+    .configure_axis(
+        labelFontSize=18,
+        titleFontSize=22,
+        titleColor="#333333",
+        labelColor="#444444",
+        grid=True,
+        gridOpacity=0.10,
+        gridWidth=0.5,
+        gridColor="#cccccc",
+        domainColor="#aaaaaa",
+        domainWidth=0.8,
+        tickColor="#aaaaaa",
+        tickSize=6,
+    )
+    .configure_view(strokeWidth=0)
+    .configure_legend(orient="right", padding=6, offset=2, titlePadding=4, rowPadding=2)
+    .configure_title(anchor="start", offset=10)
+)
+
+# Save
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")