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: bokeh 3.9.1 | Python 3.13.13
Quality: 92/100 | Updated: 2026-06-16
"""
import base64
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import Arrow, Band, ColumnDataSource, Label, Title, VeeHead
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# 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 — each psychrometric property family gets one hue.
RH_COLOR = "#009E73" # brand green — relative humidity (first series, always #009E73)
WB_COLOR = "#4467A3" # blue — wet-bulb temperature (cool/temperature cue)
ENTH_COLOR = "#C475FD" # lavender — enthalpy
SV_COLOR = "#BD8233" # ochre — specific volume
COMFORT_COLOR = "#2ABCCD" # cyan — comfort-zone region
PROCESS_COLOR = "#AE3030" # matte red — highlighted HVAC process path
# Constants — standard sea-level atmosphere
P_ATM = 101325.0 # Pa
# ASHRAE saturation-pressure coefficients (over liquid water, t >= 0 °C)
C8, C9, C10, C11, C12, C13 = (-5.8002206e3, 1.3914993, -4.8640239e-2, 4.1764768e-5, -1.4452093e-8, 6.5459673)
# Over ice (t < 0 °C)
C1, C2, C3, C4, C5, C6, C7 = (
-5.6745359e3,
6.3925247,
-9.677843e-3,
6.2215701e-7,
2.0747825e-9,
-9.484024e-13,
4.1635019,
)
# Data — saturation vapor pressure across the dry-bulb range
t_db = np.linspace(-10, 45, 400)
tk = t_db + 273.15
psat = np.where(
tk >= 273.15,
np.exp(C8 / tk + C9 + C10 * tk + C11 * tk**2 + C12 * tk**3 + C13 * np.log(tk)),
np.exp(C1 / tk + C2 + C3 * tk + C4 * tk**2 + C5 * tk**3 + C6 * tk**4 + C7 * np.log(tk)),
)
# Plot
title = "psychrometric-basic · python · bokeh · anyplot.ai"
p = figure(
width=3200,
height=1800,
title=title,
x_axis_label="Dry-Bulb Temperature (°C)",
y_axis_label="Humidity Ratio (g water / kg dry air)",
toolbar_location=None,
x_range=(-10, 45),
y_range=(0, 30),
min_border_bottom=160,
min_border_left=180,
min_border_top=130,
min_border_right=70,
)
# --- Relative-humidity curves (10 % to 100 %) ---
rh_label_x = {10: 44, 20: 42, 30: 40, 40: 38, 50: 36, 60: 34, 70: 31, 80: 28, 90: 25, 100: 21}
for rh_pct in range(10, 101, 10):
pw = (rh_pct / 100.0) * psat
w = 0.621945 * pw / (P_ATM - pw) * 1000.0
mask = (w >= 0) & (w <= 30)
t_plot, w_plot = t_db[mask], w[mask]
is_sat = rh_pct == 100
source = ColumnDataSource(data={"t": t_plot, "w": w_plot})
p.line(
"t",
"w",
source=source,
line_color=RH_COLOR,
line_width=6.0 if is_sat else 2.6,
line_alpha=1.0 if is_sat else 0.55,
)
target = rh_label_x[rh_pct]
idx = int(np.argmin(np.abs(t_plot - target)))
if w_plot[idx] > 28.5:
idx = int(np.argmin(np.abs(w_plot - 27.5)))
p.add_layout(
Label(
x=t_plot[idx],
y=w_plot[idx],
text=f"{rh_pct}%",
text_font_size="26pt" if is_sat else "22pt",
text_color=RH_COLOR,
text_font_style="bold" if is_sat else "normal",
x_offset=8,
y_offset=-2,
)
)
# --- Wet-bulb temperature lines (diagonal, upper-left to lower-right) ---
for twb in [5, 10, 15, 20, 25, 30]:
tk_wb = twb + 273.15
ps_wb = np.exp(C8 / tk_wb + C9 + C10 * tk_wb + C11 * tk_wb**2 + C12 * tk_wb**3 + C13 * np.log(tk_wb))
ws_wb = 0.621945 * ps_wb / (P_ATM - ps_wb) * 1000.0
t_range = np.linspace(twb, 45, 200)
w = ws_wb - 1.006 * (t_range - twb) / (2501.0 - 2.326 * twb) * 1000.0
mask = (w >= 0) & (w <= 30)
t_plot, w_plot = t_range[mask], w[mask]
if t_plot.size < 2:
continue
source = ColumnDataSource(data={"t": t_plot, "w": w_plot})
p.line("t", "w", source=source, line_color=WB_COLOR, line_width=2.2, line_alpha=0.7, line_dash="dashed")
# Label at the saturation-curve end (spreads naturally along the boundary)
p.add_layout(
Label(
x=t_plot[0],
y=w_plot[0],
text=f"{twb}°C wb",
text_font_size="18pt",
text_color=WB_COLOR,
text_alpha=0.9,
x_offset=-2,
y_offset=12,
)
)
# --- Enthalpy lines (oblique, kJ/kg dry air) ---
for h in [20, 40, 60, 80]:
t_range = np.linspace(-10, 45, 200)
w = (h - 1.006 * t_range) / (2501.0 + 1.86 * t_range) * 1000.0
mask = (w >= 0) & (w <= 30)
t_plot, w_plot = t_range[mask], w[mask]
if t_plot.size < 2:
continue
source = ColumnDataSource(data={"t": t_plot, "w": w_plot})
p.line("t", "w", source=source, line_color=ENTH_COLOR, line_width=2.2, line_alpha=0.7, line_dash="dotted")
# Label at the lower-right (low-humidity) end — keeps enthalpy text out of the
# crowded saturation corner where wet-bulb labels already sit.
p.add_layout(
Label(
x=t_plot[-1],
y=w_plot[-1],
text=f"{h} kJ/kg",
text_font_size="18pt",
text_color=ENTH_COLOR,
text_alpha=0.95,
x_offset=10,
y_offset=6,
)
)
# --- Specific-volume lines (m³/kg dry air) ---
for v in [0.80, 0.84, 0.88, 0.92]:
t_range = np.linspace(-10, 45, 200)
w = (v * 101.325 / (0.287055 * (t_range + 273.15)) - 1.0) / 1.6078 * 1000.0
mask = (w >= 0) & (w <= 30)
t_plot, w_plot = t_range[mask], w[mask]
if t_plot.size < 2:
continue
source = ColumnDataSource(data={"t": t_plot, "w": w_plot})
p.line("t", "w", source=source, line_color=SV_COLOR, line_width=2.2, line_alpha=0.7, line_dash="dashdot")
# Label at the high-humidity (top) end — the four lines fan out across the top.
idx = int(np.argmax(w_plot))
p.add_layout(
Label(
x=t_plot[idx],
y=w_plot[idx],
text=f"{v:.2f} m³/kg",
text_font_size="17pt",
text_color=SV_COLOR,
text_alpha=0.95,
x_offset=8,
y_offset=-6,
)
)
# --- Comfort zone (20-26 °C, 30-60 % RH) as a filled Band ---
comfort_t = np.linspace(20, 26, 40)
comfort_tk = comfort_t + 273.15
comfort_psat = np.exp(
C8 / comfort_tk + C9 + C10 * comfort_tk + C11 * comfort_tk**2 + C12 * comfort_tk**3 + C13 * np.log(comfort_tk)
)
pw_lo, pw_hi = 0.30 * comfort_psat, 0.60 * comfort_psat
w_lo = 0.621945 * pw_lo / (P_ATM - pw_lo) * 1000.0
w_hi = 0.621945 * pw_hi / (P_ATM - pw_hi) * 1000.0
comfort_source = ColumnDataSource(data={"t": comfort_t, "w_lo": w_lo, "w_hi": w_hi})
p.add_layout(
Band(
base="t",
lower="w_lo",
upper="w_hi",
source=comfort_source,
fill_color=COMFORT_COLOR,
fill_alpha=0.18,
line_color=COMFORT_COLOR,
line_width=2.5,
line_alpha=0.7,
)
)
p.add_layout(
Label(
x=23,
y=(w_lo[20] + w_hi[20]) / 2,
text="Comfort Zone",
text_font_size="22pt",
text_color=COMFORT_COLOR,
text_font_style="bold",
text_align="center",
)
)
# --- HVAC process path: cooling & dehumidification (state 1 -> state 2) ---
state1_t, state1_rh = 33, 0.55
state2_t, state2_rh = 14, 0.90
tk1 = state1_t + 273.15
ps1 = np.exp(C8 / tk1 + C9 + C10 * tk1 + C11 * tk1**2 + C12 * tk1**3 + C13 * np.log(tk1))
state1_w = 0.621945 * (state1_rh * ps1) / (P_ATM - state1_rh * ps1) * 1000.0
tk2 = state2_t + 273.15
ps2 = np.exp(C8 / tk2 + C9 + C10 * tk2 + C11 * tk2**2 + C12 * tk2**3 + C13 * np.log(tk2))
state2_w = 0.621945 * (state2_rh * ps2) / (P_ATM - state2_rh * ps2) * 1000.0
h1 = 1.006 * state1_t + (state1_w / 1000.0) * (2501.0 + 1.86 * state1_t)
h2 = 1.006 * state2_t + (state2_w / 1000.0) * (2501.0 + 1.86 * state2_t)
delta_h = h1 - h2
p.add_layout(
Arrow(
end=VeeHead(size=45, fill_color=PROCESS_COLOR, line_color=PROCESS_COLOR),
x_start=state1_t,
y_start=state1_w,
x_end=state2_t,
y_end=state2_w,
line_color=PROCESS_COLOR,
line_width=6.0,
)
)
state_source = ColumnDataSource(data={"t": [state1_t, state2_t], "w": [state1_w, state2_w]})
p.scatter("t", "w", source=state_source, size=22, fill_color=PROCESS_COLOR, line_color=PAGE_BG, line_width=4)
p.add_layout(
Label(
x=state1_t,
y=state1_w,
text=f"Outdoor Air ({state1_t}°C, {int(state1_rh * 100)}% RH)",
text_font_size="19pt",
text_color=PROCESS_COLOR,
text_font_style="bold",
x_offset=14,
y_offset=8,
)
)
p.add_layout(
Label(
x=state2_t,
y=state2_w,
text=f"Supply Air ({state2_t}°C, {int(state2_rh * 100)}% RH)",
text_font_size="19pt",
text_color=PROCESS_COLOR,
text_font_style="bold",
x_offset=-380,
y_offset=-6,
)
)
p.add_layout(
Label(
x=(state1_t + state2_t) / 2,
y=(state1_w + state2_w) / 2,
text=f"Cooling & dehumidification Δh = {delta_h:.1f} kJ/kg",
text_font_size="18pt",
text_color=PROCESS_COLOR,
text_font_style="italic",
x_offset=-90,
y_offset=22,
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.85,
)
)
# --- Style (theme-adaptive chrome + native-pixel sizing) ---
p.title.text_font_size = "50pt"
p.title.text_color = INK
p.title.text_font_style = "bold"
p.title.offset = 6
p.add_layout(
Title(
text="Standard atmosphere (101.325 kPa) · ASHRAE psychrometric properties",
text_font_size="26pt",
text_color=INK_SOFT,
text_font_style="italic",
),
"above",
)
p.xaxis.axis_label_text_font_size = "42pt"
p.yaxis.axis_label_text_font_size = "42pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "34pt"
p.yaxis.major_label_text_font_size = "34pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
p.xaxis.minor_tick_line_color = None
p.yaxis.minor_tick_line_color = None
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.12
p.ygrid.grid_line_alpha = 0.12
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Save — HTML artifact + headless-Chrome screenshot at the exact canvas size
output_file(f"plot-{THEME}.html", title=title)
save(p)
W, H = 3200, 1800
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
# CDP screenshot with an explicit clip: `driver.save_screenshot` only captures the
# visible viewport, which is ~140px shorter than the window in headless Chrome and
# would crop the canvas to 3200x1657. `captureBeyondViewport` grabs the full clip.
shot = driver.execute_cdp_cmd(
"Page.captureScreenshot",
{"clip": {"x": 0, "y": 0, "width": W, "height": H, "scale": 1}, "captureBeyondViewport": True},
)
Path(f"plot-{THEME}.png").write_bytes(base64.b64decode(shot["data"]))
driver.quit()
Part of Psychrometric Chart for HVAC on anyplot.ai.