econ-viz 1.8.0

A Python toolkit for producing publication-quality microeconomics diagrams. Define utility functions declaratively, solve for consumer equilibria, and export figures as PNG, PDF, SVG, or pure TikZ — all in a few lines of code.

Installation

pip install econ-viz

Requires Python 3.10 or later.

Quick Start

from econ_viz import Canvas, levels, solve
from econ_viz.models import CobbDouglas

model = CobbDouglas(alpha=0.5, beta=0.5)
eq    = solve(model, px=2.0, py=3.0, income=30.0)
lvls  = levels.around(eq.utility, n=5)

cvs = Canvas(x_max=20, y_max=15, x_label="x", y_label="y",
             title="Cobb-Douglas  $x^{0.5} y^{0.5}$")
cvs.add_utility(model, levels=lvls)
cvs.add_budget(2.0, 3.0, 30.0, fill=True)
cvs.add_equilibrium(eq, show_ray=True)
cvs.save("cobb_douglas.png")

TikZ export writes a standalone LaTeX document with only TikZ drawing commands:

cvs.save("cobb_douglas.tex", tikz_scale=0.0125)

The default TikZ scale maps a 6 inch wide Matplotlib figure to about 7.5 cm.

Notebook

The project ships with an interactive playground notebook:

notebook/econ-viz Playground.ipynb

Download it and open it in Jupyter, VS Code, or Colab. The first code cell upgrades econ-viz from PyPI for fresh runtimes.

Highlights

• Built-in models: Cobb-Douglas, Leontief, Perfect Substitutes, CES, Satiation, Quasi-Linear, Stone-Geary, and Translog
• Solver support for interior, kink, boundary, and corner solutions
• Closed-form demand helpers with solution_tex(...)
• Comparative tools including comparative_statics(...) and slutsky_matrix(...)
• Multi-panel Figure layouts, PricePath / IncomePath, and linked DemandDiagram
• CLI support for plotting and closed-form demand output
• Color-blind-friendly default palette (themes.COLORBLIND_CYCLE_RGB) sourced from thriveth/8560036, with related citation at DOI:10.1080/00220485.1996.10844911

Additional Tools

Axis labels can be placed around their arrowheads, and each axis can use its own arrowhead style and line style (solid, dashed, dotted, or dashdot):

from econ_viz import ArrowStyle, Canvas, LabelPosition, LineStyle

canvas = Canvas(
    x_label_pos=LabelPosition.TOP,
    y_label_pos=LabelPosition.RIGHT,
    x_arrow_style=ArrowStyle.SIMPLE,
    y_arrow_style=ArrowStyle.WEDGE,
    x_line_style=LineStyle.DASHED,
)

Every line can be restyled with a Stroke: width, line style, colour, and an arrowhead at its end. Fields you leave out keep the theme default (see the *_stroke defaults on Theme, such as theme.budget_stroke):

from econ_viz import ArrowStyle, Stroke

canvas = Canvas(axis_stroke=Stroke(width=1.4, arrow=ArrowStyle.SIMPLE))
canvas.add_budget(2, 3, 30, stroke=Stroke(width=3, style="dashed"))
canvas.add_equilibrium(eq, drop_stroke=Stroke(style="dashdot"))
canvas.add_ray(0.5, stroke=Stroke(arrow=ArrowStyle.TRIANGLE))

add_utility, add_path, add_decomposition, DemandDiagram, and EdgeworthBox take one *_stroke argument per kind of line they draw. Stroke is the preferred way to style lines; the separate color, linewidth, and linestyle arguments still work as shorthand and draw the same thing.

Point markers work the same way with Marker (colour, size, and shape); fields you leave out keep the theme default, such as theme.eq_marker:

from econ_viz import Marker

canvas.add_equilibrium(eq, marker=Marker(shape="s", size=8))
canvas.add_point(12, 2, label="A", marker=Marker(color="black", shape="D"))
canvas.add_decomposition(dec, point_marker=Marker(shape="^"))

Point labels take a Label (text, position, offset, colour, size, and visibility) wherever a plain string worked. A label follows its point's Marker colour unless it sets its own:

from econ_viz import Label

canvas.add_equilibrium(eq, label=Label(position="bottom-left", offset=8))
canvas.add_point(12, 2, label=Label(text="A", position="left", fontsize=14))
canvas.add_utility(u, levels=3, ic_label=Label(text="U={:.1f}", position="top"))
canvas.add_decomposition(dec, point_label=Label(visible=False))  # hide A, B, C

Shade the budget set with fill=True, or pass a Fill for a colour and opacity of its own (default theme.budget_fill, coloured like the line):

from econ_viz import Fill

canvas.add_budget(2, 3, 30, color="black", fill=Fill(color="lightgrey", alpha=0.4))

Each axis's label, label position, and stroke fit in one Axis, accepted by Canvas, Figure, DemandDiagram, and EdgeworthBox. x_label, x_label_pos, and x_axis_stroke stay as shorthand; an Axis field wins when both are set:

from econ_viz import Axis, Stroke

canvas = Canvas(
    x_axis=Axis(label="x_1", label_position="bottom", stroke=Stroke(width=1.2)),
    y_axis=Axis(label="x_2"),
)

Set a font for one canvas or a whole multi-panel figure without touching Matplotlib's global settings. Pass a family name, a generic family such as "serif", or a fallback list:

from econ_viz import Figure, Layout

canvas = Canvas(font=["Times New Roman", "serif"], math_font="stix")
figure = Figure(Layout.SIDE_BY_SIDE, font="serif", math_font="stix")

font applies to titles, axis labels, annotations, curve labels, and legends. Math text, including the default axis labels, uses math_font: "stix" (Times-like), "cm" (Computer Modern), "dejavuserif", "dejavusans", or "stixsans". An unavailable font raises InvalidParameterError. TikZ output uses the LaTeX document's fonts, so only generic families are mapped (serif → \rmfamily, monospace → \ttfamily).

Closed-form Marshallian demand in TeX:

from econ_viz import solution_tex
from econ_viz.models import CobbDouglas

tex = solution_tex(CobbDouglas(alpha=0.4, beta=0.6))

Slutsky matrix:

from econ_viz import slutsky_matrix
from econ_viz.models import CobbDouglas

S = slutsky_matrix(CobbDouglas(alpha=0.4, beta=0.6), px=2.0, py=3.0, income=60.0)
# S.s_xx, S.s_xy, S.s_yx, S.s_yy

CLI

econ-viz --version
econ-viz help
econ-viz models
econ-viz solve-tex --model cobb-douglas --symbolic-params

Plotting example:

econ-viz plot --model cobb-douglas --alpha 0.5 --beta 0.5 \
              --px 2 --py 3 --income 30 \
              --fill --show-ray \
              --output cobb_douglas.png

Documentation

Full documentation lives at econ-viz.org.

License

MIT © Anthony Sung