A Walter-Lieth climate diagram (climograph) is the canonical visualization of a location's annual climate, plotting monthly mean temperature and monthly precipitation over the twelve months on a single panel. It uses the classic 1:2 axis-scaling convention where 10 °C on the temperature axis aligns with 20 mm on the precipitation axis, so the relationship between the two curves directly reveals water availability: where precipitation exceeds the temperature curve the period is humid (filled blue/hatched), and where the temperature curve rises above precipitation the period is arid (filled red/dotted). A header carries station metadata and annual means, while frost indicators along the baseline mark cold months.
""" anyplot.ai
climograph-walter-lieth: Walter-Lieth Climate Diagram
Library: plotly 6.8.0 | Python 3.13.13
Quality: 88/100 | Created: 2026-06-15
"""
import os
import sys
# Prevent the local plotly.py from shadowing the installed plotly package.
_here = os.path.dirname(os.path.abspath(__file__))
sys.path = [p for p in sys.path if p != _here]
import numpy as np
import plotly.graph_objects as go
from scipy.interpolate import PchipInterpolator
# Theme tokens — Imprint style guide
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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
# Imprint palette — semantic roles for this diagram
TEMP_COLOR = "#AE3030" # matte red — temperature / heat (Imprint pos 5)
PRECIP_COLOR = "#4467A3" # blue — water / precipitation (Imprint pos 3)
# Station: Naples, Italy — 1991–2020 climate normals (Mediterranean Cfsa)
STATION = "Naples, Italy"
ELEVATION = 17 # m a.s.l.
TEMP_MEAN = 17.4 # °C annual mean
PRECIP_TOT = 902 # mm annual total
MONTHS = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
TEMP = [9.2, 9.7, 11.5, 14.7, 19.2, 23.6, 26.8, 27.1, 23.8, 18.5, 14.1, 10.4]
PRECIP = [97, 80, 72, 63, 38, 22, 14, 26, 80, 142, 148, 120]
def p_to_y(mm):
"""Walter-Lieth transform: precipitation mm → temperature-axis units.
2:1 scale up to 100 mm (10°C ↔ 20 mm); compressed 10:1 above 100 mm."""
return mm / 2.0 if mm <= 100 else 50.0 + (mm - 100) / 10.0
# Smooth curves via PCHIP interpolation in original units, then transform
xi = np.arange(12, dtype=float)
xf = np.linspace(0, 11, 1200)
t_fine = PchipInterpolator(xi, TEMP)(xf)
p_fine_mm = np.clip(PchipInterpolator(xi, PRECIP)(xf), 0, None)
p_fine = np.array([p_to_y(v) for v in p_fine_mm])
P100 = 50.0 # 100 mm line in y-axis (temperature) units
Y_MIN, Y_MAX = -10.0, 62.0
def extract_segs(x, lo, hi, mask):
"""Split arrays into contiguous segments where mask is True."""
segs, n, i = [], len(mask), 0
while i < n:
if mask[i]:
j = i
while j < n and mask[j]:
j += 1
segs.append((x[i:j], lo[i:j], hi[i:j]))
i = j
else:
i += 1
return segs
# Classify each fine-grid point
humid_mask = p_fine > t_fine
perhumid_mask = p_fine > P100
humid_segs = extract_segs(xf, t_fine, p_fine, humid_mask)
arid_segs = extract_segs(xf, p_fine, t_fine, ~humid_mask)
perhumid_segs = extract_segs(xf, np.full_like(p_fine, P100), p_fine, perhumid_mask)
fig = go.Figure()
# Humid fill — blue, between temp curve and precipitation curve (below 100 mm)
for sx, sl, sh in humid_segs:
sh_clip = np.minimum(sh, P100)
if np.any(sh_clip > sl + 0.05):
px = np.concatenate([sx, sx[::-1]]).tolist()
py = np.concatenate([sh_clip, sl[::-1]]).tolist()
fig.add_trace(
go.Scatter(
x=px,
y=py,
fill="toself",
fillcolor="rgba(68,103,163,0.28)",
line={"width": 0},
showlegend=False,
hoverinfo="skip",
)
)
# Perhumid fill — solid blue above the 100 mm threshold line
for sx, sl, sh in perhumid_segs:
px = np.concatenate([sx, sx[::-1]]).tolist()
py = np.concatenate([sh, sl[::-1]]).tolist()
fig.add_trace(
go.Scatter(
x=px,
y=py,
fill="toself",
fillcolor="rgba(68,103,163,0.78)",
line={"width": 0},
showlegend=False,
hoverinfo="skip",
)
)
# Arid fill — light red, between precipitation curve and temperature curve
for sx, sl, sh in arid_segs:
px = np.concatenate([sx, sx[::-1]]).tolist()
py = np.concatenate([sh, sl[::-1]]).tolist()
fig.add_trace(
go.Scatter(
x=px,
y=py,
fill="toself",
fillcolor="rgba(174,48,48,0.22)",
line={"width": 0},
showlegend=False,
hoverinfo="skip",
)
)
# Legend swatches for fill regions
fig.add_trace(
go.Scatter(
x=[None],
y=[None],
mode="markers",
name="Humid period",
marker={"symbol": "square", "size": 13, "color": "rgba(68,103,163,0.45)"},
showlegend=True,
)
)
fig.add_trace(
go.Scatter(
x=[None],
y=[None],
mode="markers",
name="Perhumid (> 100 mm)",
marker={"symbol": "square", "size": 13, "color": "rgba(68,103,163,0.85)"},
showlegend=True,
)
)
fig.add_trace(
go.Scatter(
x=[None],
y=[None],
mode="markers",
name="Arid period",
marker={"symbol": "square", "size": 13, "color": "rgba(174,48,48,0.42)"},
showlegend=True,
)
)
# Precipitation curve (values transformed to temperature-axis units)
fig.add_trace(
go.Scatter(
x=list(range(12)),
y=[p_to_y(p) for p in PRECIP],
mode="lines+markers",
name="Precipitation",
line={"color": PRECIP_COLOR, "width": 3},
marker={"color": PRECIP_COLOR, "size": 7, "line": {"color": PAGE_BG, "width": 1.5}},
customdata=PRECIP,
hovertemplate="%{customdata} mm<extra>Precipitation</extra>",
showlegend=True,
)
)
# Temperature curve
fig.add_trace(
go.Scatter(
x=list(range(12)),
y=TEMP,
mode="lines+markers",
name="Temperature",
line={"color": TEMP_COLOR, "width": 3},
marker={"color": TEMP_COLOR, "size": 7, "line": {"color": PAGE_BG, "width": 1.5}},
hovertemplate="%{y:.1f} °C<extra>Temperature</extra>",
showlegend=True,
)
)
# Ghost trace to anchor the right (precipitation) y-axis
fig.add_trace(
go.Scatter(
x=[0, 11],
y=[Y_MIN, Y_MAX],
yaxis="y2",
mode="markers",
marker={"color": "rgba(0,0,0,0.01)", "size": 1},
showlegend=False,
hoverinfo="skip",
)
)
# Reference lines
fig.add_shape(type="line", x0=-0.5, x1=11.5, y0=P100, y1=P100, line={"color": INK_MUTED, "width": 1, "dash": "dot"})
fig.add_shape(type="line", x0=-0.5, x1=11.5, y0=0, y1=0, line={"color": INK_SOFT, "width": 1})
# Frost bands — months with mean temperature below 0°C (Naples: none)
for i, t in enumerate(TEMP):
if t < 0:
fig.add_shape(
type="rect",
x0=i - 0.4,
x1=i + 0.4,
y0=Y_MIN,
y1=Y_MIN + 2,
fillcolor=PRECIP_COLOR,
line={"width": 0},
opacity=0.85,
)
# Title — scaled for length per style-guide formula
title_str = "Naples, Italy · climograph-walter-lieth · python · plotly · anyplot.ai"
n = len(title_str)
title_fs = max(round(16 * 67 / n), 11) if n > 67 else 16
# Right-axis tick positions (precipitation mm mapped to y-axis units)
raxis_mm = [0, 20, 40, 60, 80, 100, 150]
raxis_y = [p_to_y(m) for m in raxis_mm]
fig.update_layout(
autosize=False,
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK, "family": "Arial, sans-serif"},
title={"text": title_str, "font": {"size": title_fs, "color": INK}, "x": 0.5, "xanchor": "center"},
margin={"l": 80, "r": 85, "t": 75, "b": 55},
xaxis={
"tickvals": list(range(12)),
"ticktext": MONTHS,
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"linecolor": INK_SOFT,
"showgrid": False,
"range": [-0.5, 11.5],
"title": {"text": "Month", "font": {"size": 12, "color": INK}},
},
yaxis={
"title": {"text": "Temperature (°C)", "font": {"size": 12, "color": INK}},
"tickvals": [-10, 0, 10, 20, 30],
"ticktext": ["-10", "0", "10", "20", "30"],
"tickfont": {"size": 10, "color": INK_SOFT},
"gridcolor": GRID,
"linecolor": INK_SOFT,
"zerolinecolor": INK_SOFT,
"zerolinewidth": 1.5,
"range": [Y_MIN, Y_MAX],
"showgrid": True,
},
yaxis2={
"overlaying": "y",
"side": "right",
"range": [Y_MIN, Y_MAX],
"tickvals": raxis_y,
"ticktext": [f"{m}" for m in raxis_mm],
"title": {"text": "Precipitation (mm)", "font": {"size": 12, "color": INK}},
"tickfont": {"size": 10, "color": INK_SOFT},
"linecolor": INK_SOFT,
"showgrid": False,
},
legend={
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
"font": {"color": INK_SOFT, "size": 10},
"x": 0.99,
"y": 0.99,
"xanchor": "right",
"yanchor": "top",
"tracegroupgap": 2,
},
)
# Station metadata annotation — top-left inside the plot area
meta_text = f"<b>{STATION}</b><br>{ELEVATION} m a.s.l.<br>T = {TEMP_MEAN}°C · P = {PRECIP_TOT} mm"
fig.add_annotation(
xref="x",
yref="y",
x=0.2,
y=59.5,
text=meta_text,
showarrow=False,
align="left",
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
font={"size": 10, "color": INK},
xanchor="left",
yanchor="top",
opacity=0.95,
)
# Save
fig.write_image(f"plot-{THEME}.png", width=800, height=450, scale=4)
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Part of Walter-Lieth Climate Diagram on anyplot.ai.