A calibration curve plotting absorbance versus concentration following Beer-Lambert law (A = εlc). Measured calibration standards are shown as scatter points with a linear regression fit line. The regression equation (y = mx + b) and R² value are displayed on the plot. An example unknown sample is marked with dashed lines extending to both axes, demonstrating how the curve is used to determine concentration from a measured absorbance. This plot is fundamental in analytical chemistry for quantitative spectrophotometric analysis.
""" anyplot.ai
calibration-beer-lambert: Beer-Lambert Calibration Curve
Library: plotnine 0.15.5 | Python 3.13.13
Quality: 89/100 | Updated: 2026-06-03
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_label,
geom_point,
geom_segment,
ggplot,
labs,
scale_color_manual,
scale_fill_manual,
scale_shape_manual,
scale_x_continuous,
scale_y_continuous,
stat_smooth,
theme,
theme_minimal,
)
from scipy import stats
# Theme tokens — Imprint palette, 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 — positions 1 and 5 (green = calibration standards, red = unknown)
BRAND = "#009E73" # position 1 — always first series
UNKNOWN_COLOR = "#AE3030" # position 5 — semantic red for "unknown" highlight
# Data
np.random.seed(42)
concentrations = np.array([0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0])
molar_absorptivity = 0.045
absorbances = molar_absorptivity * concentrations + np.random.normal(0, 0.008, len(concentrations))
absorbances = np.clip(absorbances, 0, None)
# Linear regression
slope, intercept, r_value, p_value, std_err = stats.linregress(concentrations, absorbances)
r_squared = r_value**2
# Unknown sample — ~10.5 mg/L (differentiated from Letsplot sibling at 7.5 mg/L)
unknown_absorbance = 0.47
unknown_concentration = (unknown_absorbance - intercept) / slope
df_standards = pd.DataFrame(
{"concentration": concentrations, "absorbance": absorbances, "series": "Calibration Standards"}
)
df_unknown = pd.DataFrame(
{"concentration": [unknown_concentration], "absorbance": [unknown_absorbance], "series": "Unknown Sample"}
)
df_all = pd.concat([df_standards, df_unknown], ignore_index=True)
# Dashed projection lines to both axes
df_seg_h = pd.DataFrame(
{"x": [0.0], "xend": [unknown_concentration], "y": [unknown_absorbance], "yend": [unknown_absorbance]}
)
df_seg_v = pd.DataFrame(
{"x": [unknown_concentration], "xend": [unknown_concentration], "y": [0.0], "yend": [unknown_absorbance]}
)
# Annotation text
eq_text = f"y = {slope:.4f}x + {intercept:.4f}"
r2_text = f"R² = {r_squared:.5f}"
df_eq = pd.DataFrame({"x": [0.5], "y": [0.52], "label": [eq_text]})
df_r2 = pd.DataFrame({"x": [0.5], "y": [0.42], "label": [r2_text]})
# Plot
plot = (
ggplot(df_standards, aes(x="concentration", y="absorbance"))
+ stat_smooth(method="lm", color=BRAND, fill=BRAND, alpha=0.15, size=1.2, fullrange=True)
+ geom_segment(
df_seg_h,
aes(x="x", xend="xend", y="y", yend="yend"),
linetype="dashed",
color=INK_SOFT,
size=0.6,
inherit_aes=False,
)
+ geom_segment(
df_seg_v,
aes(x="x", xend="xend", y="y", yend="yend"),
linetype="dashed",
color=INK_SOFT,
size=0.6,
inherit_aes=False,
)
+ geom_point(
df_all,
aes(x="concentration", y="absorbance", color="series", shape="series", fill="series"),
size=4,
stroke=1.0,
inherit_aes=False,
)
+ scale_color_manual(values={"Calibration Standards": BRAND, "Unknown Sample": UNKNOWN_COLOR}, name=" ")
+ scale_fill_manual(values={"Calibration Standards": BRAND, "Unknown Sample": UNKNOWN_COLOR}, name=" ")
+ scale_shape_manual(values={"Calibration Standards": "o", "Unknown Sample": "D"}, name=" ")
+ geom_label(
df_eq,
aes(x="x", y="y", label="label"),
ha="left",
size=3.5,
color=INK,
fill=ELEVATED_BG,
label_size=0.25,
label_r=0.03,
inherit_aes=False,
show_legend=False,
)
+ geom_label(
df_r2,
aes(x="x", y="y", label="label"),
ha="left",
size=3.5,
color=INK,
fill=ELEVATED_BG,
label_size=0.25,
label_r=0.03,
inherit_aes=False,
show_legend=False,
)
+ scale_x_continuous(breaks=np.arange(0, 14, 2), limits=(-0.5, 13.5), expand=(0, 0.3))
+ scale_y_continuous(breaks=np.arange(0, 0.65, 0.1), limits=(-0.02, 0.62), expand=(0, 0.01))
+ labs(x="Concentration (mg/L)", y="Absorbance", title="calibration-beer-lambert · python · plotnine · anyplot.ai")
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
text=element_text(size=7, family="sans-serif"),
axis_title=element_text(size=10, weight="bold", color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
plot_title=element_text(size=10, weight="bold", color=INK),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_y=element_line(color=INK, size=0.2, alpha=0.15),
axis_line_x=element_line(color=INK_SOFT, size=0.4),
axis_line_y=element_line(color=INK_SOFT, size=0.4),
legend_position="bottom",
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_key=element_rect(fill=PAGE_BG, color=PAGE_BG),
plot_margin=0.04,
)
)
# Save — canonical 3200×1800 px (8 in × 4.5 in @ dpi=400)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Beer-Lambert Calibration Curve on anyplot.ai.