feat(letsplot): implement psychrometric-basic (#4889)

## Implementation: `psychrometric-basic` - letsplot

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

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

**Parent Issue:** #4583

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23120046652)*

---------

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/psychrometric-basic/implementations/letsplot.py

@@ -0,0 +1,359 @@
+""" pyplots.ai
+psychrometric-basic: Psychrometric Chart for HVAC
+Library: letsplot 4.9.0 | Python 3.14.3
+Quality: 81/100 | Created: 2026-03-15
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import (
+    LetsPlot,
+    aes,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_line,
+    geom_path,
+    geom_point,
+    geom_polygon,
+    geom_segment,
+    geom_text,
+    ggplot,
+    ggsize,
+    labs,
+    scale_color_identity,
+    scale_fill_identity,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+from lets_plot.export import ggsave
+
+
+LetsPlot.setup_html()
+
+# Constants
+P_ATM = 101325.0  # Pa, standard atmospheric pressure
+
+# Colorblind-safe palette (no red-green)
+COLOR_SATURATION = "#306998"  # Python blue for 100% RH
+COLOR_WET_BULB = "#0072B2"  # Blue (colorblind-safe)
+COLOR_ENTHALPY = "#D55E00"  # Vermillion (colorblind-safe)
+COLOR_VOLUME = "#9467bd"  # Purple
+COLOR_COMFORT = "#306998"  # Python blue
+COLOR_PROCESS = "#e67e22"  # Orange
+
+
+# Psychrometric equations (ASHRAE-based)
+def saturation_pressure(t):
+    t_k = t + 273.15
+    if t >= 0:
+        ln_ps = (
+            -5.8002206e3 / t_k
+            + 1.3914993
+            - 4.8640239e-2 * t_k
+            + 4.1764768e-5 * t_k**2
+            - 1.4452093e-8 * t_k**3
+            + 6.5459673 * np.log(t_k)
+        )
+    else:
+        ln_ps = (
+            -5.6745359e3 / t_k
+            + 6.3925247
+            - 9.677843e-3 * t_k
+            + 6.2215701e-7 * t_k**2
+            + 2.0747825e-9 * t_k**3
+            - 9.484024e-13 * t_k**4
+            + 4.1635019 * np.log(t_k)
+        )
+    return np.exp(ln_ps)
+
+
+def humidity_ratio(t_db, rh):
+    p_s = saturation_pressure(t_db)
+    p_w = rh * p_s
+    return 0.621945 * p_w / (P_ATM - p_w)
+
+
+def wet_bulb_line(t_wb, t_db_range):
+    w_sat = humidity_ratio(t_wb, 1.0)
+    h_fg = 2501.0
+    cp_a = 1.006
+    cp_w = 1.86
+    w_values = []
+    for t_db in t_db_range:
+        w = (h_fg * w_sat - cp_a * (t_db - t_wb)) / (h_fg + cp_w * t_db - cp_a * t_wb + (cp_w - cp_a) * t_wb)
+        w_values.append(max(w, 0))
+    return np.array(w_values)
+
+
+# Data - Generate psychrometric curves
+t_db_fine = np.linspace(-10, 50, 300)
+
+all_frames = []
+
+# Relative humidity curves (10% to 100%)
+rh_colors = {
+    0.1: "#b0b0b0",
+    0.2: "#a0a0a0",
+    0.3: "#909090",
+    0.4: "#808080",
+    0.5: "#707070",
+    0.6: "#606060",
+    0.7: "#505050",
+    0.8: "#404040",
+    0.9: "#303030",
+    1.0: "#306998",
+}
+
+for rh_val in np.arange(0.1, 1.05, 0.1):
+    rh_val = round(rh_val, 1)
+    w_values = []
+    t_valid = []
+    for t in t_db_fine:
+        w = humidity_ratio(t, rh_val) * 1000  # convert to g/kg
+        if 0 <= w <= 30:
+            w_values.append(w)
+            t_valid.append(t)
+    df_rh = pd.DataFrame(
+        {
+            "t_db": t_valid,
+            "w": w_values,
+            "group": f"RH {int(rh_val * 100)}%",
+            "color": rh_colors[rh_val],
+            "line_type": "rh",
+        }
+    )
+    all_frames.append(df_rh)
+
+df_rh_all = pd.concat(all_frames, ignore_index=True)
+
+# Wet-bulb temperature lines
+wb_frames = []
+wb_temps = [0, 5, 10, 15, 20, 25, 30, 35]
+
+for t_wb in wb_temps:
+    t_range = np.linspace(t_wb, min(t_wb + 30, 50), 100)
+    w_vals = wet_bulb_line(t_wb, t_range) * 1000  # g/kg
+    mask = (w_vals >= 0) & (w_vals <= 30)
+    df_wb = pd.DataFrame(
+        {"t_db": t_range[mask], "w": w_vals[mask], "group": f"WB {t_wb}°C", "color": COLOR_WET_BULB, "line_type": "wb"}
+    )
+    wb_frames.append(df_wb)
+
+df_wb_all = pd.concat(wb_frames, ignore_index=True)
+
+# Enthalpy lines
+enth_frames = []
+enthalpy_values_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110]
+
+for h_target in enthalpy_values_list:
+    t_points = []
+    w_points = []
+    for t in np.linspace(-10, 50, 200):
+        w = (h_target - 1.006 * t) / (2501.0 + 1.86 * t)
+        w_gkg = w * 1000
+        if 0 <= w_gkg <= 30 and -10 <= t <= 50:
+            t_points.append(t)
+            w_points.append(w_gkg)
+    if len(t_points) > 1:
+        df_h = pd.DataFrame(
+            {
+                "t_db": t_points,
+                "w": w_points,
+                "group": f"h={h_target}",
+                "color": COLOR_ENTHALPY,
+                "line_type": "enthalpy",
+            }
+        )
+        enth_frames.append(df_h)
+
+df_enth_all = pd.concat(enth_frames, ignore_index=True)
+
+# Specific volume lines
+vol_frames = []
+vol_values = [0.78, 0.80, 0.82, 0.84, 0.86, 0.88, 0.90, 0.92, 0.94]
+
+for v_target in vol_values:
+    t_points = []
+    w_points = []
+    for t in np.linspace(-10, 50, 200):
+        w = (v_target * (P_ATM / 1000) / (0.287042 * (t + 273.15)) - 1) / 1.6078
+        w_gkg = w * 1000
+        if 0 <= w_gkg <= 30 and -10 <= t <= 50:
+            t_points.append(t)
+            w_points.append(w_gkg)
+    if len(t_points) > 1:
+        df_v = pd.DataFrame(
+            {"t_db": t_points, "w": w_points, "group": f"v={v_target}", "color": COLOR_VOLUME, "line_type": "volume"}
+        )
+        vol_frames.append(df_v)
+
+df_vol_all = pd.concat(vol_frames, ignore_index=True)
+
+# Comfort zone polygon (20-26°C, humidity ratio at 30-60% RH)
+comfort_t = [20, 26, 26, 20, 20]
+comfort_w = [
+    humidity_ratio(20, 0.3) * 1000,
+    humidity_ratio(26, 0.3) * 1000,
+    humidity_ratio(26, 0.6) * 1000,
+    humidity_ratio(20, 0.6) * 1000,
+    humidity_ratio(20, 0.3) * 1000,
+]
+df_comfort = pd.DataFrame({"t_db": comfort_t, "w": comfort_w})
+
+# HVAC process path: cooling and dehumidification (32°C, 50% RH → 24°C, 50% RH)
+state1_t, state1_rh = 32, 0.50
+state2_t, state2_rh = 24, 0.50
+state1_w = humidity_ratio(state1_t, state1_rh) * 1000
+state2_w = humidity_ratio(state2_t, state2_rh) * 1000
+
+df_process = pd.DataFrame({"t_db": [state1_t, state2_t], "w": [state1_w, state2_w]})
+
+# RH label positions (staggered to avoid overlap)
+rh_label_frames = []
+for rh_val in np.arange(0.1, 1.05, 0.1):
+    rh_val = round(rh_val, 1)
+    t_label = (
+        44
+        if rh_val <= 0.2
+        else (
+            40
+            if rh_val == 0.3
+            else (
+                34
+                if rh_val == 0.4
+                else (
+                    30
+                    if rh_val <= 0.6
+                    else (24 if rh_val == 0.7 else (22 if rh_val == 0.8 else (17 if rh_val == 0.9 else 14)))
+                )
+            )
+        )
+    )
+    w_label = humidity_ratio(t_label, rh_val) * 1000
+    if w_label <= 28:
+        rh_label_frames.append({"t_db": t_label, "w": w_label, "label": f"{int(rh_val * 100)}%"})
+
+df_rh_labels = pd.DataFrame(rh_label_frames)
+
+# Wet-bulb labels (offset from saturation curve to avoid RH label collision)
+wb_label_frames = []
+for t_wb in wb_temps:
+    w_at_sat = humidity_ratio(t_wb, 1.0) * 1000
+    if w_at_sat <= 28:
+        wb_label_frames.append({"t_db": t_wb + 2.5, "w": w_at_sat + 1.0, "label": f"{t_wb}°C"})
+
+df_wb_labels = pd.DataFrame(wb_label_frames)
+
+# Enthalpy labels (with units)
+enth_label_frames = []
+for h_target in enthalpy_values_list:
+    w_at_zero = (h_target - 1.006 * (-5)) / (2501.0 + 1.86 * (-5))
+    w_gkg = w_at_zero * 1000
+    if 0 < w_gkg <= 28:
+        enth_label_frames.append({"t_db": -8, "w": w_gkg, "label": f"{h_target} kJ/kg"})
+
+df_enth_labels = pd.DataFrame(enth_label_frames)
+
+# Volume labels (with units)
+vol_label_frames = []
+for v_target in vol_values:
+    w_at_45 = (v_target * (P_ATM / 1000) / (0.287042 * (45 + 273.15)) - 1) / 1.6078
+    w_gkg = w_at_45 * 1000
+    if 0 < w_gkg <= 28:
+        vol_label_frames.append({"t_db": 46, "w": w_gkg, "label": f"{v_target} m³/kg"})
+
+df_vol_labels = pd.DataFrame(vol_label_frames)
+
+# Plot
+plot = (
+    ggplot()
+    # Comfort zone
+    + geom_polygon(
+        data=df_comfort,
+        mapping=aes(x="t_db", y="w"),
+        fill=COLOR_COMFORT,
+        alpha=0.10,
+        color=COLOR_COMFORT,
+        size=1.0,
+        linetype="dashed",
+    )
+    # RH curves
+    + geom_line(data=df_rh_all, mapping=aes(x="t_db", y="w", group="group", color="color"), size=1.0)
+    # Wet-bulb lines (colorblind-safe blue)
+    + geom_line(data=df_wb_all, mapping=aes(x="t_db", y="w", group="group"), color=COLOR_WET_BULB, size=0.6, alpha=0.55)
+    # Enthalpy lines (colorblind-safe vermillion)
+    + geom_line(
+        data=df_enth_all, mapping=aes(x="t_db", y="w", group="group"), color=COLOR_ENTHALPY, size=0.5, alpha=0.45
+    )
+    # Specific volume lines
+    + geom_line(data=df_vol_all, mapping=aes(x="t_db", y="w", group="group"), color=COLOR_VOLUME, size=0.5, alpha=0.45)
+    # HVAC process path with arrow
+    + geom_path(
+        data=df_process,
+        mapping=aes(x="t_db", y="w"),
+        color=COLOR_PROCESS,
+        size=2.5,
+        arrow={"type": "closed", "length": 12},
+    )
+    # State points
+    + geom_point(data=df_process, mapping=aes(x="t_db", y="w"), color=COLOR_PROCESS, size=5, shape=16)
+    # RH labels
+    + geom_text(data=df_rh_labels, mapping=aes(x="t_db", y="w", label="label"), size=8, color="#505050")
+    # Wet-bulb labels
+    + geom_text(data=df_wb_labels, mapping=aes(x="t_db", y="w", label="label"), size=7, color=COLOR_WET_BULB)
+    # Enthalpy labels (with units, on left edge)
+    + geom_text(data=df_enth_labels, mapping=aes(x="t_db", y="w", label="label"), size=7, color=COLOR_ENTHALPY, hjust=1)
+    # Volume labels (with units, on right edge)
+    + geom_text(data=df_vol_labels, mapping=aes(x="t_db", y="w", label="label"), size=7, color=COLOR_VOLUME, hjust=0)
+    # Comfort zone label
+    + geom_text(
+        data=pd.DataFrame({"t_db": [23], "w": [7.5], "label": ["Comfort\nZone"]}),
+        mapping=aes(x="t_db", y="w", label="label"),
+        size=10,
+        color=COLOR_COMFORT,
+        fontface="bold",
+    )
+    # Process label (positioned clear of RH labels)
+    + geom_text(
+        data=pd.DataFrame({"t_db": [40], "w": [16.0], "label": ["Cooling &\nDehumidification"]}),
+        mapping=aes(x="t_db", y="w", label="label"),
+        size=9,
+        color=COLOR_PROCESS,
+        fontface="bold",
+    )
+    # Arrow annotation connecting label to process path
+    + geom_segment(
+        data=pd.DataFrame({"x": [38], "xend": [33], "y": [15.5], "yend": [14.0]}),
+        mapping=aes(x="x", y="y", xend="xend", yend="yend"),
+        color=COLOR_PROCESS,
+        size=0.5,
+        alpha=0.6,
+    )
+    + scale_color_identity()
+    + scale_fill_identity()
+    + scale_x_continuous(limits=[-10, 50], breaks=list(range(-10, 55, 5)))
+    + scale_y_continuous(limits=[0, 30], breaks=list(range(0, 35, 5)))
+    + labs(
+        x="Dry-Bulb Temperature (°C)", y="Humidity Ratio (g/kg)", title="psychrometric-basic · letsplot · pyplots.ai"
+    )
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold", color="#1a1a1a"),
+        axis_title=element_text(size=20, color="#333333"),
+        axis_text=element_text(size=16, color="#555555"),
+        legend_position="none",
+        panel_grid_major=element_line(color="#e8e8e8", size=0.3),
+        panel_grid_minor=element_blank(),
+        plot_background=element_rect(fill="white", color="white"),
+        panel_background=element_rect(fill="#fafafa"),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")