feat(letsplot): implement skewt-logp-atmospheric (#3931)

## Implementation: `skewt-logp-atmospheric` - letsplot

Implements the **letsplot** version of `skewt-logp-atmospheric`.

**File:** `plots/skewt-logp-atmospheric/implementations/letsplot.py`

**Parent Issue:** #3802

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21092078733)*

---------

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/skewt-logp-atmospheric/implementations/letsplot.py

@@ -0,0 +1,214 @@
+""" pyplots.ai
+skewt-logp-atmospheric: Skew-T Log-P Atmospheric Diagram
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 90/100 | Created: 2026-01-17
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import (
+    LetsPlot,
+    aes,
+    element_blank,
+    element_text,
+    geom_path,
+    geom_point,
+    geom_segment,
+    geom_text,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    scale_y_log10,
+    scale_y_reverse,
+    theme,
+)
+
+
+LetsPlot.setup_html()
+
+# Atmospheric sounding data (synthetic radiosonde profile)
+np.random.seed(42)
+
+# Pressure levels from surface to upper troposphere (hPa)
+pressure = np.array([1000, 950, 900, 850, 800, 750, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, 100])
+
+# Temperature profile (°C) - typical mid-latitude sounding
+# Decreasing with height, with a tropopause around 200 hPa
+temperature = np.array([25, 22, 18, 14, 10, 6, 2, -3, -8, -14, -21, -29, -38, -47, -55, -58, -56, -55, -56])
+
+# Dewpoint profile (°C) - typically lower than temperature
+# Shows moisture decreasing with height
+dewpoint = np.array([18, 16, 12, 8, 4, 0, -5, -12, -20, -28, -35, -42, -50, -58, -65, -70, -72, -75, -78])
+
+# For Skew-T: we need to skew the temperature by adding an offset based on pressure
+# The skew factor converts the diagram coordinates
+# In a Skew-T, x_plot = T + skew_factor * log(p0/p)
+p0 = 1000  # Reference pressure
+skew_factor = 40  # Degrees of skew per pressure decade
+
+# Calculate skewed coordinates for plotting
+log_pressure = np.log10(p0 / pressure)
+temp_skewed = temperature + skew_factor * log_pressure
+dewpoint_skewed = dewpoint + skew_factor * log_pressure
+
+# Create DataFrame for main profiles with type for legend
+df_temp = pd.DataFrame({"pressure": pressure, "value": temp_skewed, "type": "Temperature"})
+
+df_dewpoint = pd.DataFrame({"pressure": pressure, "value": dewpoint_skewed, "type": "Dewpoint"})
+
+# Generate isotherms (temperature reference lines, skewed 45 degrees)
+isotherm_temps = np.arange(-80, 50, 10)  # °C
+isotherm_data = []
+p_range = np.array([1000, 100])
+for t in isotherm_temps:
+    log_p_vals = np.log10(p0 / p_range)
+    t_skewed = t + skew_factor * log_p_vals
+    isotherm_data.append(
+        {"x_start": t_skewed[0], "x_end": t_skewed[1], "y_start": p_range[0], "y_end": p_range[1], "temp": t}
+    )
+df_isotherms = pd.DataFrame(isotherm_data)
+
+# Generate dry adiabats (lines of constant potential temperature)
+# theta = T * (p0/p)^(R/cp) where R/cp ≈ 0.286
+dry_adiabat_thetas = np.arange(250, 450, 20)  # K
+dry_adiabat_data = []
+p_levels = np.linspace(1000, 100, 50)
+for theta in dry_adiabat_thetas:
+    temps = (theta * (p_levels / p0) ** 0.286) - 273.15  # Convert to Celsius
+    log_p_vals = np.log10(p0 / p_levels)
+    temps_skewed = temps + skew_factor * log_p_vals
+    for i in range(len(p_levels)):
+        dry_adiabat_data.append(
+            {"pressure": p_levels[i], "temp_skewed": temps_skewed[i], "theta": theta, "line_type": "Dry Adiabat"}
+        )
+df_dry_adiabats = pd.DataFrame(dry_adiabat_data)
+
+# Generate moist adiabats (equivalent potential temperature lines)
+# Moist adiabats follow a different curve - steeper at low levels, flatter aloft
+# theta_e is conserved along moist adiabats
+moist_adiabat_theta_e = np.arange(280, 360, 10)  # K
+moist_adiabat_data = []
+for theta_e in moist_adiabat_theta_e:
+    # Approximate moist adiabat using simplified formula
+    # At surface, T ≈ theta_e - 273.15, then follows moist lapse rate (~6.5°C/km)
+    t_surface = theta_e - 273.15
+    for p in p_levels:
+        # Moist adiabatic lapse rate is variable, roughly 6.5 C/km
+        # Use pseudo-adiabatic approximation
+        height_factor = np.log(p0 / p) * 2.5  # Scale factor for height
+        t_moist = t_surface - 6.5 * height_factor * 0.8  # Moist lapse rate effect
+        log_p_val = np.log10(p0 / p)
+        t_skewed = t_moist + skew_factor * log_p_val
+        moist_adiabat_data.append(
+            {"pressure": p, "temp_skewed": t_skewed, "theta_e": theta_e, "line_type": "Moist Adiabat"}
+        )
+df_moist_adiabats = pd.DataFrame(moist_adiabat_data)
+
+# Generate mixing ratio lines (lines of constant water vapor mixing ratio)
+# Simplified: ws = 622 * es(T) / (p - es(T)) where es is saturation vapor pressure
+mixing_ratios = [1, 2, 4, 7, 10, 15, 20]  # g/kg
+mixing_data = []
+for ws in mixing_ratios:
+    for p in p_levels[::5]:  # Sample every 5th pressure level for smooth lines
+        # Approximate temperature for given mixing ratio and pressure
+        # Using simplified formula: T ≈ 35 * log(ws * p / 622) - 20
+        t = 35 * np.log10(ws * p / 622) - 20
+        log_p_val = np.log10(p0 / p)
+        t_skewed = t + skew_factor * log_p_val
+        mixing_data.append({"pressure": p, "temp_skewed": t_skewed, "ws": ws, "line_type": "Mixing Ratio"})
+df_mixing = pd.DataFrame(mixing_data)
+
+# Build the plot using lets-plot
+plot = (
+    ggplot()
+    # Isotherms (skewed temperature lines) - light gray
+    + geom_segment(
+        aes(x="x_start", xend="x_end", y="y_start", yend="y_end"),
+        data=df_isotherms,
+        color="#999999",
+        size=0.6,
+        alpha=0.8,
+    )
+    # Dry adiabats - orange dashed (increased visibility)
+    + geom_path(
+        aes(x="temp_skewed", y="pressure", group="theta"),
+        data=df_dry_adiabats,
+        color="#D97706",
+        size=0.8,
+        alpha=0.7,
+        linetype="dashed",
+    )
+    # Moist adiabats - purple dash-dot
+    + geom_path(
+        aes(x="temp_skewed", y="pressure", group="theta_e"),
+        data=df_moist_adiabats,
+        color="#7C3AED",
+        size=0.8,
+        alpha=0.7,
+        linetype="dotdash",
+    )
+    # Mixing ratio lines - green dotted (increased visibility)
+    + geom_path(
+        aes(x="temp_skewed", y="pressure", group="ws"),
+        data=df_mixing,
+        color="#059669",
+        size=0.8,
+        alpha=0.7,
+        linetype="dotted",
+    )
+    # Temperature profile - solid red line
+    + geom_path(aes(x="value", y="pressure"), data=df_temp, color="#DC2626", size=2.5)
+    # Dewpoint profile - dashed blue line
+    + geom_path(aes(x="value", y="pressure"), data=df_dewpoint, color="#2563EB", size=2.5, linetype="dashed")
+    # Points on profiles for clarity
+    + geom_point(aes(x="value", y="pressure"), data=df_temp, color="#DC2626", size=4)
+    + geom_point(aes(x="value", y="pressure"), data=df_dewpoint, color="#2563EB", size=4)
+    # Reverse y-axis (pressure decreases upward) and log scale
+    + scale_y_log10()
+    + scale_y_reverse(limits=[1000, 100])
+    # Labels and title
+    + labs(x="Temperature (°C) - Skewed", y="Pressure (hPa)", title="skewt-logp-atmospheric · letsplot · pyplots.ai")
+    + ggsize(1600, 900)
+    + theme(
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24),
+        panel_grid_major=element_blank(),
+        panel_grid_minor=element_blank(),
+        panel_background=element_blank(),
+        axis_line=element_blank(),
+    )
+)
+
+# Create legend using actual line segments with matching styles
+# Position legend in upper right area
+legend_x_start = 95
+legend_x_end = 115
+legend_text_x = 118
+
+# Legend items with their properties matching the actual plot lines
+legend_entries = [
+    {"label": "Temp", "color": "#DC2626", "linetype": "solid", "size": 2.5, "y": 125},
+    {"label": "Dewpt", "color": "#2563EB", "linetype": "dashed", "size": 2.5, "y": 140},
+    {"label": "Dry Ad.", "color": "#D97706", "linetype": "dashed", "size": 0.8, "y": 158},
+    {"label": "Moist Ad.", "color": "#7C3AED", "linetype": "dotdash", "size": 0.8, "y": 178},
+    {"label": "Mix. Ratio", "color": "#059669", "linetype": "dotted", "size": 0.8, "y": 200},
+]
+
+# Add legend line segments with actual linetypes
+for entry in legend_entries:
+    plot = plot + geom_segment(
+        aes(x="x_start", xend="x_end", y="y", yend="y"),
+        data=pd.DataFrame([{"x_start": legend_x_start, "x_end": legend_x_end, "y": entry["y"]}]),
+        color=entry["color"],
+        size=entry["size"],
+        linetype=entry["linetype"],
+    )
+    plot = plot + geom_text(x=legend_text_x, y=entry["y"], label=entry["label"], color="#333333", size=12, hjust=0)
+
+# Save the plot
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save HTML for interactive version
+ggsave(plot, "plot.html", path=".")