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: plotnine 0.15.7 | Python 3.13.13
Quality: 86/100 | Created: 2026-06-15
"""
import os
import sys
# Remove this script's own directory from sys.path so that
# `from plotnine import ...` resolves to the installed package, not this file.
_script_dir = os.path.dirname(os.path.abspath(__file__))
sys.path = [
p for p in sys.path if os.path.normpath(os.path.abspath(p) if p else os.getcwd()) != os.path.normpath(_script_dir)
]
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
element_blank,
element_line,
element_rect,
element_text,
geom_hline,
geom_line,
geom_ribbon,
geom_segment,
geom_text,
ggplot,
labs,
scale_color_manual,
scale_x_continuous,
scale_y_continuous,
theme,
)
# Theme tokens
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"
# Imprint palette — semantic override: temperature=red (heat), precipitation=blue (water)
TEMP_COLOR = "#AE3030"
PRECIP_COLOR = "#4467A3"
# Fill alpha — higher in dark mode to keep fills visible on near-black background
FILL_ALPHA = 0.35 if THEME == "light" else 0.60
# Station: Athens, Greece — Mediterranean 1991–2020 climate normals
months_labels = ["J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D"]
month_num = np.arange(1, 13)
temperature = np.array([9.9, 10.9, 13.2, 17.2, 22.3, 27.0, 29.5, 29.4, 24.7, 19.7, 15.0, 11.6])
precipitation = np.array([57, 37, 38, 24, 17, 8, 6, 7, 15, 52, 58, 71])
temp_annual = round(float(np.mean(temperature)), 1)
precip_annual = int(np.sum(precipitation))
# Walter-Lieth scaling: 10 °C ≡ 20 mm → divide precip by 2 to plot on temperature axis
precip_scaled = precipitation / 2.0
# Humid ribbon (blue): regions where precip_scaled ≥ temperature
humid_ymin = temperature.astype(float).copy()
humid_ymax = np.maximum(temperature, precip_scaled)
mask_arid = temperature > precip_scaled
humid_ymin[mask_arid] = temperature[mask_arid]
humid_ymax[mask_arid] = temperature[mask_arid]
# Arid ribbon (red): regions where temperature > precip_scaled
arid_ymin = np.minimum(temperature, precip_scaled).astype(float)
arid_ymax = temperature.astype(float).copy()
mask_humid = precip_scaled >= temperature
arid_ymin[mask_humid] = temperature[mask_humid]
arid_ymax[mask_humid] = temperature[mask_humid]
df = pd.DataFrame(
{
"month": month_num,
"temperature": temperature,
"precip_sc": precip_scaled,
"hum_lo": humid_ymin,
"hum_hi": humid_ymax,
"ari_lo": arid_ymin,
"ari_hi": arid_ymax,
}
)
# Long-format dataframe for the two curves — enables a proper legend via color aesthetic
TEMP_LABEL = "Temperature (°C)"
PRECIP_LABEL = "Precipitation (mm)"
df_lines = pd.DataFrame(
{
"month": np.tile(month_num, 2),
"value": np.concatenate([temperature, precip_scaled]),
"variable": [TEMP_LABEL] * 12 + [PRECIP_LABEL] * 12,
}
)
# Y and X axis limits — extend Y_MAX to 50 to accommodate the 100 mm right-axis tick
# (Walter-Lieth convention: 100 mm ≡ 50 °C on the temperature scale)
Y_MIN, Y_MAX = -5, 50
X_MAX_EXTENDED = 14.5
# Right-side precipitation axis: spec-mandated ticks 0/20/40/60/100 mm
# In temperature units: 0/10/20/30/50 °C
p_y_ticks = [0, 10, 20, 30, 50]
p_mm_labels = ["0", "20", "40", "60", "100"]
prec_ticks_df = pd.DataFrame(
{"x": 12.65, "xend": 12.95, "y": p_y_ticks, "yend": p_y_ticks, "lx": 13.15, "label": p_mm_labels}
)
prec_axis_line_df = pd.DataFrame({"x": [12.65], "xend": [12.65], "y": [0], "yend": [50]})
# Title — 67 chars → default size 12
plot_title = "Athens · climograph-walter-lieth · python · plotnine · anyplot.ai"
title_len = len(plot_title)
title_size = max(8, round(12 * 67 / title_len))
subtitle = f"Athens, Greece · 107 m a.s.l. · T̅ = {temp_annual} °C · ΣP = {precip_annual} mm · 1991–2020"
anyplot_theme = theme(
figure_size=(8, 4.5),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.3, alpha=0.18),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT, size=0.5),
axis_ticks=element_line(color=INK_SOFT, size=0.3),
plot_title=element_text(size=title_size, color=INK, face="bold"),
plot_subtitle=element_text(size=8, color=INK_SOFT),
legend_position="bottom",
legend_background=element_rect(fill=PAGE_BG, color="none"),
legend_key=element_rect(fill=PAGE_BG),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_blank(),
)
plot = (
ggplot(df, aes(x="month"))
# Humid fill (blue — wet periods where precip > temp curve)
+ geom_ribbon(aes(ymin="hum_lo", ymax="hum_hi"), fill=PRECIP_COLOR, alpha=FILL_ALPHA)
# Arid fill (red — dry periods where temp > precip curve)
+ geom_ribbon(aes(ymin="ari_lo", ymax="ari_hi"), fill=TEMP_COLOR, alpha=FILL_ALPHA)
# 0 °C frost reference line
+ geom_hline(yintercept=0, color=INK_SOFT, size=0.5, linetype="dashed", alpha=0.55)
# Temperature and precipitation curves — color mapped to variable for legend
+ geom_line(data=df_lines, mapping=aes(x="month", y="value", group="variable", color="variable"), size=1.1)
+ scale_color_manual(values={TEMP_LABEL: TEMP_COLOR, PRECIP_LABEL: PRECIP_COLOR}, name="")
# Right-side precipitation axis: axis line
+ geom_segment(
data=prec_axis_line_df, mapping=aes(x="x", xend="xend", y="y", yend="yend"), color=INK_SOFT, size=0.5
)
# Right-side precipitation axis: tick marks
+ geom_segment(data=prec_ticks_df, mapping=aes(x="x", xend="xend", y="y", yend="yend"), color=INK_SOFT, size=0.3)
# Right-side precipitation axis: tick labels (mm)
+ geom_text(
data=prec_ticks_df, mapping=aes(x="lx", y="y", label="label"), size=3.2, color=INK_SOFT, ha="left", va="center"
)
# Right-side precipitation axis: axis title (rotated)
+ annotate("text", x=14.1, y=25, label="Precipitation (mm)", angle=90, size=4, color=INK, ha="center", va="bottom")
+ scale_x_continuous(breaks=month_num.tolist(), labels=months_labels, limits=(0.5, X_MAX_EXTENDED), expand=(0, 0))
+ scale_y_continuous(name="Temperature (°C)", breaks=[0, 10, 20, 30, 40], limits=(Y_MIN, Y_MAX))
+ labs(title=plot_title, subtitle=subtitle, x="Month")
+ anyplot_theme
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in")
Part of Walter-Lieth Climate Diagram on anyplot.ai.