A psychrometric chart plots dry-bulb temperature against humidity ratio, overlaid with curves for relative humidity, wet-bulb temperature, enthalpy, and specific volume. It is the fundamental tool for HVAC system design and air conditioning process analysis. The chart reveals the thermodynamic properties of moist air at a glance, enabling engineers to trace heating, cooling, humidification, and dehumidification processes as paths on the diagram.
""" anyplot.ai
psychrometric-basic: Psychrometric Chart for HVAC
Library: seaborn 0.13.2 | Python 3.13.14
Quality: 90/100 | Updated: 2026-06-16
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.lines import Line2D
# Theme-adaptive chrome (see prompts/default-style-guide.md "Theme-adaptive Chrome")
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Imprint palette — first family is brand green, then canonical order; line styles
# reinforce colour for colourblind safety.
RH_COLOR = "#009E73" # brand green — relative-humidity curves (most prominent family)
WB_COLOR = "#4467A3" # blue — wet-bulb isotherms
ENTH_COLOR = "#AE3030" # matte red — constant enthalpy (energy)
SV_COLOR = "#BD8233" # ochre — constant specific volume
COMFORT_COLOR = "#2ABCCD" # cyan — comfort-zone region
PROCESS_COLOR = "#C475FD" # lavender — HVAC process path
# Data — moist-air properties at standard sea-level pressure (101.325 kPa)
np.random.seed(42)
P_ATM = 101325 # Pa
T_GRID = np.linspace(-10, 50, 600)
# Saturation vapour pressure (Pa) and saturation humidity ratio (g/kg) over water
# (ASHRAE) — a single continuous relation across the whole range keeps the curves
# smooth (no kink at 0 °C).
P_SAT = np.exp(23.196 - 3816.44 / (T_GRID + 227.02))
W_SAT = 0.62198 * P_SAT / (P_ATM - P_SAT) * 1000
# Constant relative-humidity curves (10% – 100%)
rh_rows = []
for rh in np.arange(10, 101, 10):
p_v = (rh / 100) * P_SAT
w = 0.62198 * p_v / (P_ATM - p_v) * 1000
keep = (w >= 0) & (w <= 30)
for t, wv in zip(T_GRID[keep], w[keep], strict=True):
rh_rows.append({"t": t, "w": wv, "rh": f"{rh}%"})
rh_df = pd.DataFrame(rh_rows)
# Constant wet-bulb isotherms (line slope ≈ -0.402 g/kg per °C)
wb_rows = []
for t_wb in np.arange(2, 31, 4):
w0 = float(np.interp(t_wb, T_GRID, W_SAT))
t_line = np.linspace(t_wb, 50, 160)
w_line = w0 - 0.402 * (t_line - t_wb)
cap = np.minimum(np.interp(t_line, T_GRID, W_SAT), 30)
keep = (w_line >= 0) & (w_line <= cap)
for t, wv in zip(t_line[keep], w_line[keep], strict=True):
wb_rows.append({"t": t, "w": wv, "twb": f"{t_wb}"})
wb_df = pd.DataFrame(wb_rows)
# Constant enthalpy lines (kJ/kg dry air)
enth_rows = []
for h in np.arange(20, 101, 20):
w_line = (h - 1.006 * T_GRID) / (2.501 + 0.00186 * T_GRID)
keep = (w_line >= 0) & (w_line <= np.minimum(W_SAT, 30))
for t, wv in zip(T_GRID[keep], w_line[keep], strict=True):
enth_rows.append({"t": t, "w": wv, "h": f"{h}"})
enth_df = pd.DataFrame(enth_rows)
# Constant specific-volume lines (m³/kg dry air)
sv_rows = []
for v in np.arange(0.80, 0.93, 0.04):
w_line = ((v * P_ATM / 1000) / (0.287042 * (T_GRID + 273.15)) - 1) / 1.6078 * 1000
keep = (w_line >= 0) & (w_line <= np.minimum(W_SAT, 30))
for t, wv in zip(T_GRID[keep], w_line[keep], strict=True):
sv_rows.append({"t": t, "w": wv, "v": f"{v:.2f}"})
sv_df = pd.DataFrame(sv_rows)
# Comfort zone (≈20–26 °C, 30–60% RH) and a cooling + dehumidification process path
comfort_t = np.array([20, 26, 26, 20])
comfort_w = np.array(
[
0.62198 * (0.30 * np.interp(20, T_GRID, P_SAT)) / (P_ATM - 0.30 * np.interp(20, T_GRID, P_SAT)) * 1000,
0.62198 * (0.30 * np.interp(26, T_GRID, P_SAT)) / (P_ATM - 0.30 * np.interp(26, T_GRID, P_SAT)) * 1000,
0.62198 * (0.60 * np.interp(26, T_GRID, P_SAT)) / (P_ATM - 0.60 * np.interp(26, T_GRID, P_SAT)) * 1000,
0.62198 * (0.60 * np.interp(20, T_GRID, P_SAT)) / (P_ATM - 0.60 * np.interp(20, T_GRID, P_SAT)) * 1000,
]
)
state_points = pd.DataFrame(
{
"t": [34.0, 13.0, 24.0],
"w": [
0.62198 * (0.45 * np.interp(34, T_GRID, P_SAT)) / (P_ATM - 0.45 * np.interp(34, T_GRID, P_SAT)) * 1000,
float(np.interp(13, T_GRID, W_SAT)),
0.62198 * (0.50 * np.interp(24, T_GRID, P_SAT)) / (P_ATM - 0.50 * np.interp(24, T_GRID, P_SAT)) * 1000,
],
"label": ["A · supply 34°C/45%", "B · cooled 13°C/100%", "C · room 24°C/50%"],
}
)
# Plot — 16:9 canvas (8 × 4.5 in @ dpi=400 → 3200 × 1800 px)
sns.set_theme(
style="whitegrid",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.12,
"grid.linewidth": 0.6,
"font.family": "sans-serif",
},
)
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)
# Comfort zone sits behind the property lines
ax.fill(comfort_t, comfort_w, color=COMFORT_COLOR, alpha=0.16, zorder=1)
ax.plot(
np.append(comfort_t, comfort_t[0]),
np.append(comfort_w, comfort_w[0]),
color=COMFORT_COLOR,
linewidth=1.2,
alpha=0.7,
zorder=1,
)
# Property-line families, each drawn as one seaborn lineplot (units → one line per level)
sns.lineplot(
data=sv_df,
x="t",
y="w",
units="v",
estimator=None,
color=SV_COLOR,
linewidth=0.8,
linestyle=":",
alpha=0.7,
ax=ax,
legend=False,
)
sns.lineplot(
data=enth_df,
x="t",
y="w",
units="h",
estimator=None,
color=ENTH_COLOR,
linewidth=0.9,
linestyle="-.",
alpha=0.65,
ax=ax,
legend=False,
)
sns.lineplot(
data=wb_df,
x="t",
y="w",
units="twb",
estimator=None,
color=WB_COLOR,
linewidth=0.9,
linestyle="--",
alpha=0.7,
ax=ax,
legend=False,
)
sns.lineplot(
data=rh_df, x="t", y="w", units="rh", estimator=None, color=RH_COLOR, linewidth=1.2, alpha=0.85, ax=ax, legend=False
)
# Saturation curve (100% RH) — prominent upper boundary
sat = rh_df[rh_df["rh"] == "100%"]
ax.plot(sat["t"], sat["w"], color=RH_COLOR, linewidth=2.6, zorder=4)
# HVAC process path: cooling + dehumidification (A→B), then sensible reheat (B→C)
for i in range(2):
ax.annotate(
"",
xy=(state_points["t"][i + 1], state_points["w"][i + 1]),
xytext=(state_points["t"][i], state_points["w"][i]),
arrowprops={"arrowstyle": "-|>", "color": PROCESS_COLOR, "lw": 2.6},
zorder=5,
)
sns.scatterplot(
data=state_points,
x="t",
y="w",
color=PROCESS_COLOR,
s=130,
edgecolor=PAGE_BG,
linewidth=1.6,
zorder=6,
legend=False,
ax=ax,
)
# Direct labels (spec: label property lines on the chart) — spread to edges to avoid crowding
for rh in rh_df["rh"].unique():
seg = rh_df[rh_df["rh"] == rh]
ax.text(
float(seg["t"].iloc[-1]) + 0.3,
float(seg["w"].iloc[-1]),
rh,
fontsize=7,
color=RH_COLOR,
ha="left",
va="center",
fontweight="bold",
)
for twb in wb_df["twb"].unique():
seg = wb_df[wb_df["twb"] == twb]
ax.text(
float(seg["t"].iloc[-1]) + 0.3,
float(seg["w"].iloc[-1]) - 0.2,
f"{twb}°",
fontsize=6.5,
color=WB_COLOR,
ha="left",
va="top",
)
for h in enth_df["h"].unique():
seg = enth_df[enth_df["h"] == h]
ax.text(
float(seg["t"].iloc[0]) - 0.3,
float(seg["w"].iloc[0]) + 0.2,
h,
fontsize=6.5,
color=ENTH_COLOR,
ha="right",
va="bottom",
rotation=-38,
)
for v in sv_df["v"].unique():
seg = sv_df[sv_df["v"] == v]
ax.text(
float(seg["t"].iloc[0]) + 0.2,
float(seg["w"].iloc[0]) - 0.2,
v,
fontsize=6.5,
color=SV_COLOR,
ha="left",
va="top",
)
ax.text(
23,
float(comfort_w.mean()),
"Comfort\nzone",
fontsize=8,
color=COMFORT_COLOR,
ha="center",
va="center",
fontweight="bold",
)
for _, row in state_points.iterrows():
dx = -0.8 if row["label"].startswith("B") else 0.8
ha = "right" if row["label"].startswith("B") else "left"
ax.text(
row["t"] + dx,
row["w"] + 0.7,
row["label"],
fontsize=7.5,
color=PROCESS_COLOR,
ha=ha,
va="bottom",
fontweight="bold",
)
# Legend — sits in the empty upper-left zone (cold + humid is physically unreachable)
legend_handles = [
Line2D([0], [0], color=RH_COLOR, lw=2.2, label="Relative humidity"),
Line2D([0], [0], color=WB_COLOR, lw=1.4, ls="--", label="Wet-bulb temp (°C)"),
Line2D([0], [0], color=ENTH_COLOR, lw=1.4, ls="-.", label="Enthalpy (kJ/kg)"),
Line2D([0], [0], color=SV_COLOR, lw=1.4, ls=":", label="Specific volume (m³/kg)"),
Line2D(
[0],
[0],
color=PROCESS_COLOR,
lw=2.4,
marker="o",
markersize=6,
markeredgecolor=PAGE_BG,
label="HVAC process path",
),
]
legend = ax.legend(
handles=legend_handles, loc="upper left", fontsize=8, framealpha=0.95, facecolor=ELEVATED_BG, edgecolor=INK_SOFT
)
legend.get_title().set_color(INK)
for text in legend.get_texts():
text.set_color(INK_SOFT)
# Style
ax.set_xlim(-10, 50)
ax.set_ylim(0, 30)
ax.set_xlabel("Dry-bulb temperature (°C)", fontsize=11, color=INK)
ax.set_ylabel("Humidity ratio (g/kg dry air)", fontsize=11, color=INK)
ax.set_title("psychrometric-basic · python · seaborn · anyplot.ai", fontsize=13, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=9, colors=INK_SOFT)
sns.despine(ax=ax)
# Save
fig.subplots_adjust(left=0.07, right=0.97, top=0.93, bottom=0.1)
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Part of Psychrometric Chart for HVAC on anyplot.ai.