feat(plotnine): implement psychrometric-basic (#4895)

## Implementation: `psychrometric-basic` - plotnine

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

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

**Parent Issue:** #4583

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23120046621)*

---------

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/plotnine.py

@@ -0,0 +1,255 @@
+""" pyplots.ai
+psychrometric-basic: Psychrometric Chart for HVAC
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: 87/100 | Created: 2026-03-15
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotate,
+    arrow,
+    coord_cartesian,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_line,
+    geom_point,
+    geom_polygon,
+    geom_ribbon,
+    geom_segment,
+    geom_text,
+    ggplot,
+    labs,
+    scale_alpha_identity,
+    scale_color_identity,
+    scale_linetype_identity,
+    scale_size_identity,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+# Constants
+PATM = 101.325  # kPa, standard atmospheric pressure
+T_MIN, T_MAX = -10, 50
+W_MAX = 30
+
+t_range = np.linspace(T_MIN, T_MAX, 300)
+
+# Inline saturation vapor pressure (Magnus formula) and humidity ratio calculations
+# psat(t) = 0.61078 * exp(17.27 * t / (t + 237.3))
+# w = 622 * pw / (PATM - pw), where pw = rh/100 * psat
+
+# Relative humidity curves (10%-100%)
+rh_frames = []
+for rh in range(10, 110, 10):
+    pw = np.minimum(rh / 100.0 * 0.61078 * np.exp(17.27 * t_range / (t_range + 237.3)), PATM - 0.01)
+    w = 622.0 * pw / (PATM - pw)
+    mask = (w >= 0) & (w <= W_MAX)
+    rh_frames.append(
+        pd.DataFrame(
+            {
+                "t": t_range[mask],
+                "w": w[mask],
+                "group": f"RH {rh}%",
+                "color": "#1B4F72" if rh == 100 else "#7FB3D8",
+                "sz": 2.2 if rh == 100 else 0.55,
+                "alpha": 1.0 if rh == 100 else 0.75,
+            }
+        )
+    )
+df_rh = pd.concat(rh_frames, ignore_index=True)
+
+# RH labels: carefully positioned to avoid overlap with state points and wet-bulb labels
+# Moved 50% label away from point A (35°C), shifted others for even spacing
+rh_label_pos = {10: 48, 20: 44, 30: 40, 40: 36, 50: 17, 60: 32, 70: 13, 80: 9, 90: 4, 100: -5}
+rh_labels = []
+for rh, t_l in rh_label_pos.items():
+    pw_l = rh / 100.0 * 0.61078 * np.exp(17.27 * t_l / (t_l + 237.3))
+    pw_l = min(pw_l, PATM - 0.01)
+    w_l = 622.0 * pw_l / (PATM - pw_l)
+    if 0 < w_l < W_MAX:
+        rh_labels.append({"t": t_l, "w": w_l + 1.2, "label": f"{rh}%"})
+df_rh_labels = pd.DataFrame(rh_labels)
+
+# Wet-bulb temperature lines
+wb_frames = []
+for twb in range(-5, 35, 5):
+    pws_wb = 0.61078 * np.exp(17.27 * twb / (twb + 237.3))
+    ws_wb = 622.0 * pws_wb / (PATM - pws_wb)
+    t_line = np.linspace(twb, min(twb + 30, T_MAX), 150)
+    w_line = ((2501.0 - 2.326 * twb) * ws_wb - 1006.0 * (t_line - twb)) / (2501.0 + 1.86 * t_line - 4.186 * twb)
+    mask = (w_line >= 0) & (w_line <= W_MAX) & (t_line >= T_MIN)
+    if np.sum(mask) > 2:
+        wb_frames.append(pd.DataFrame({"t": t_line[mask], "w": w_line[mask], "group": f"WB {twb}°C"}))
+df_wb = pd.concat(wb_frames, ignore_index=True)
+
+# Wet-bulb labels: placed at right end of lines (lower-right), away from saturation curve
+wb_labels = []
+for twb in range(0, 35, 10):
+    sub = df_wb[df_wb["group"] == f"WB {twb}°C"]
+    if len(sub) > 0:
+        row = sub.loc[sub["t"].idxmax()]
+        wb_labels.append({"t": row["t"] + 1.5, "w": max(row["w"] - 0.3, 0.5), "label": f"{twb}°C"})
+df_wb_labels = pd.DataFrame(wb_labels)
+
+# Enthalpy lines (kJ/kg dry air) - wider spacing to reduce overlap
+enth_frames = []
+for h in range(10, 130, 20):
+    t_line = np.linspace(T_MIN, T_MAX, 200)
+    w_line = (h - 1.006 * t_line) / (2.501 + 0.00186 * t_line)
+    mask = (w_line >= 0) & (w_line <= W_MAX) & (t_line >= T_MIN) & (t_line <= T_MAX)
+    if np.sum(mask) > 2:
+        enth_frames.append(pd.DataFrame({"t": t_line[mask], "w": w_line[mask], "group": f"h={h}"}))
+df_enth = pd.concat(enth_frames, ignore_index=True) if enth_frames else pd.DataFrame(columns=["t", "w", "group"])
+
+# Enthalpy labels at the right/bottom end of lines (away from saturation curve)
+enth_labels = []
+for h in range(10, 130, 20):
+    sub = df_enth[df_enth["group"] == f"h={h}"]
+    if len(sub) > 0:
+        row = sub.loc[sub["t"].idxmax()]
+        if row["w"] > 0.5:
+            enth_labels.append({"t": row["t"] + 1.0, "w": max(row["w"] - 0.5, 0.5), "label": f"{h} kJ/kg"})
+df_enth_labels = pd.DataFrame(enth_labels)
+
+# Specific volume lines (m³/kg dry air) - wider step for clarity
+sv_frames = []
+for v_10 in range(80, 94, 2):
+    v = v_10 / 100.0
+    t_line = np.linspace(T_MIN, T_MAX, 200)
+    w_line = (v * PATM / (0.287055 * (t_line + 273.15)) - 1.0) / 1.6078 * 1000.0
+    mask = (w_line >= 0) & (w_line <= W_MAX) & (t_line >= T_MIN) & (t_line <= T_MAX)
+    if np.sum(mask) > 2:
+        sv_frames.append(pd.DataFrame({"t": t_line[mask], "w": w_line[mask], "group": f"v={v:.2f}"}))
+df_sv = pd.concat(sv_frames, ignore_index=True) if sv_frames else pd.DataFrame(columns=["t", "w", "group"])
+
+# Specific volume labels at bottom-right end (well separated from enthalpy labels)
+sv_labels = []
+for v_10 in range(80, 94, 4):
+    v = v_10 / 100.0
+    sub = df_sv[df_sv["group"] == f"v={v:.2f}"]
+    if len(sub) > 0:
+        row = sub.loc[sub["t"].idxmax()]
+        sv_labels.append(
+            {"t": min(row["t"] + 1.0, T_MAX - 1), "w": max(row["w"] - 0.8, 0.5), "label": f"{v:.2f} m³/kg"}
+        )
+df_sv_labels = pd.DataFrame(sv_labels)
+
+# Comfort zone polygon (20-26°C, 30-60% RH)
+pw_cz_lo = np.minimum(
+    0.30 * 0.61078 * np.exp(17.27 * np.array([20.0, 26.0]) / (np.array([20.0, 26.0]) + 237.3)), PATM - 0.01
+)
+c_w_lo = 622.0 * pw_cz_lo / (PATM - pw_cz_lo)
+pw_cz_hi = np.minimum(
+    0.60 * 0.61078 * np.exp(17.27 * np.array([20.0, 26.0]) / (np.array([20.0, 26.0]) + 237.3)), PATM - 0.01
+)
+c_w_hi = 622.0 * pw_cz_hi / (PATM - pw_cz_hi)
+df_comfort = pd.DataFrame({"t": [20, 26, 26, 20, 20], "w": [c_w_lo[0], c_w_lo[1], c_w_hi[1], c_w_hi[0], c_w_lo[0]]})
+
+# Comfort zone ribbon for shading between RH 30% and 60% within 20-26°C
+t_comfort = np.linspace(20, 26, 50)
+pw_crib_lo = np.minimum(0.30 * 0.61078 * np.exp(17.27 * t_comfort / (t_comfort + 237.3)), PATM - 0.01)
+pw_crib_hi = np.minimum(0.60 * 0.61078 * np.exp(17.27 * t_comfort / (t_comfort + 237.3)), PATM - 0.01)
+df_comfort_ribbon = pd.DataFrame(
+    {"t": t_comfort, "w_lo": 622.0 * pw_crib_lo / (PATM - pw_crib_lo), "w_hi": 622.0 * pw_crib_hi / (PATM - pw_crib_hi)}
+)
+
+# HVAC process: cooling and dehumidification
+pw_a = min(0.50 * 0.61078 * np.exp(17.27 * 35 / (35 + 237.3)), PATM - 0.01)
+w_a = 622.0 * pw_a / (PATM - pw_a)
+pw_b = min(0.50 * 0.61078 * np.exp(17.27 * 24 / (24 + 237.3)), PATM - 0.01)
+w_b = 622.0 * pw_b / (PATM - pw_b)
+df_states = pd.DataFrame({"t": [35, 24], "w": [w_a, w_b], "label": ["A", "B"]})
+df_arrow = pd.DataFrame({"x": [35], "y": [w_a], "xend": [24], "yend": [w_b]})
+
+# Add linetype columns for identity scale
+df_enth["lt"] = "dashed"
+df_sv["lt"] = "dotted"
+
+# Build plot using plotnine grammar of graphics with layered composition
+plot = (
+    ggplot()
+    # Comfort zone ribbon (plotnine-distinctive: geom_ribbon with ymin/ymax mapping)
+    + geom_ribbon(aes(x="t", ymin="w_lo", ymax="w_hi"), data=df_comfort_ribbon, fill="#306998", alpha=0.12)
+    # Comfort zone border
+    + geom_polygon(
+        aes(x="t", y="w"), data=df_comfort, fill="none", color="#306998", size=0.8, linetype="solid", alpha=0.6
+    )
+    # RH curves with identity aesthetics for per-group color/size/alpha
+    + geom_line(aes(x="t", y="w", group="group", color="color", size="sz", alpha="alpha"), data=df_rh)
+    + scale_color_identity()
+    + scale_size_identity()
+    + scale_alpha_identity()
+    # Wet-bulb lines - warm orange, distinct from RH blues
+    + geom_line(aes(x="t", y="w", group="group"), data=df_wb, color="#D4760A", size=0.55, alpha=0.65)
+    # Enthalpy lines - olive green, dashed, increased visibility
+    + geom_line(aes(x="t", y="w", group="group", linetype="lt"), data=df_enth, color="#5B8C3E", size=0.6, alpha=0.75)
+    # Specific volume lines - muted purple, dotted, distinct from enthalpy
+    + geom_line(aes(x="t", y="w", group="group", linetype="lt"), data=df_sv, color="#8E5EA2", size=0.5, alpha=0.6)
+    + scale_linetype_identity()
+    # HVAC process arrow
+    + geom_segment(
+        aes(x="x", y="y", xend="xend", yend="yend"),
+        data=df_arrow,
+        color="#C0392B",
+        size=2.0,
+        arrow=arrow(length=0.18, type="closed"),
+    )
+    # State points with fill identity
+    + geom_point(aes(x="t", y="w"), data=df_states, color="#C0392B", fill="#E74C3C", size=5, shape="o")
+    # RH labels - larger, bold, positioned away from state points
+    + geom_text(aes(x="t", y="w", label="label"), data=df_rh_labels, size=9.5, color="#2471A3", fontweight="bold")
+    # Wet-bulb labels - at right end of lines, larger
+    + geom_text(aes(x="t", y="w", label="label"), data=df_wb_labels, size=9, color="#D4760A", fontstyle="italic")
+    # Enthalpy labels at right/bottom end of lines (away from saturation curve)
+    + geom_text(aes(x="t", y="w", label="label"), data=df_enth_labels, size=8.5, color="#5B8C3E", ha="left")
+    # Specific volume labels at bottom-right of lines
+    + geom_text(aes(x="t", y="w", label="label"), data=df_sv_labels, size=8.5, color="#8E5EA2", ha="left")
+    # State point annotations - repositioned to avoid comfort zone overlap
+    + annotate("text", x=37, y=w_a + 1.5, label="A (35°C)", size=10, color="#C0392B", ha="left", fontweight="bold")
+    + annotate("text", x=29, y=w_b + 1.8, label="B (24°C)", size=9.5, color="#C0392B", ha="left", fontweight="bold")
+    # Comfort zone label - centered in the zone
+    + annotate(
+        "text",
+        x=23,
+        y=(c_w_lo.mean() + c_w_hi.mean()) / 2,
+        label="Comfort\nZone",
+        size=9,
+        color="#306998",
+        alpha=0.7,
+        fontweight="bold",
+        ha="center",
+        va="center",
+    )
+    # Axes
+    + labs(
+        x="Dry-Bulb Temperature (°C)",
+        y="Humidity Ratio (g/kg dry air)",
+        title="psychrometric-basic · plotnine · pyplots.ai",
+    )
+    + scale_x_continuous(breaks=range(T_MIN, T_MAX + 1, 5))
+    + scale_y_continuous(breaks=range(0, W_MAX + 1, 5))
+    + coord_cartesian(xlim=(T_MIN - 1, T_MAX + 5), ylim=(0, W_MAX))
+    # Theme: polished publication style
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, weight="bold", color="#1B2631"),
+        axis_title_x=element_text(size=20, color="#2C3E50"),
+        axis_title_y=element_text(size=20, color="#2C3E50"),
+        axis_text=element_text(size=16, color="#566573"),
+        panel_grid_major=element_line(color="#E8E8E8", size=0.25),
+        panel_grid_minor=element_blank(),
+        legend_position="none",
+        plot_background=element_rect(fill="#FAFBFC", color="none"),
+        panel_background=element_rect(fill="#FAFBFC", color="none"),
+    )
+)
+
+plot.save("plot.png", dpi=300)