econ-viz 0.1.2

A Python toolkit for producing publication-quality microeconomics diagrams.

econ-viz

A Python toolkit for producing publication-quality microeconomics diagrams. Define utility functions declaratively, solve for consumer equilibria, and export figures as raster images or LaTeX/TikZ source — all in a few lines of code.

Installation

pip install econ-viz

Requires Python 3.12 or later.

Quick Start

import numpy as np
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")

Utility Models

Cobb-Douglas

from econ_viz.models import CobbDouglas

model = CobbDouglas(alpha=0.3, beta=0.7)

Leontief (Perfect Complements)

from econ_viz.models import Leontief

model = Leontief(a=1.0, b=1.0)   # U = min(ax, by)

Perfect Substitutes

from econ_viz.models import PerfectSubstitutes

model = PerfectSubstitutes(a=1.0, b=2.0)   # U = ax + by

CES

from econ_viz.models import CES

model = CES(rho=-0.5, alpha=0.5)   # elasticity of substitution = 1/(1+rho)

Satiation (Bliss Point)

from econ_viz.models import Satiation

model = Satiation(bliss_x=6.0, bliss_y=4.0, a=1.0, b=1.0)

Quasi-Linear

import numpy as np
from econ_viz.models import QuasiLinear

model = QuasiLinear(v_func=np.log, linear_in="y")   # U = log(x) + y

LaTeX Input

Parse standard LaTeX math expressions directly into model instances:

from econ_viz import parse_latex

cd  = parse_latex(r"x^{0.4} y^{0.6}")
leo = parse_latex(r"\min(2x, 3y)")
ps  = parse_latex(r"2x + 3y")

The parser accepts common preambles such as U(x,y) =, U =, and bare expressions. Unrecognised patterns raise ParseError.

Advanced Models

Custom Utility

Wrap any vectorised Python callable as a first-class model. The callable is validated at construction time against a random NumPy mesh-grid.

import numpy as np
from econ_viz.models import CustomUtility

model = CustomUtility(func=lambda x, y: np.log(x) + np.log(y), name="log+log")

Multi-Good Cobb-Douglas

Model preferences over N goods and project to a 2-D canvas via freeze():

from econ_viz.models import MultiGoodCD

m3   = MultiGoodCD({'x': 0.3, 'y': 0.3, 'z': 0.4})
flat = m3.freeze(z=10.0)   # returns a CustomUtility ready for Canvas

from econ_viz import Canvas, levels, solve

eq   = solve(flat, px=2.0, py=3.0, income=30.0)
lvls = levels.around(eq.utility, n=5)

cvs = Canvas(x_max=20, y_max=15, title=r"MultiGoodCD  $z=10$")
cvs.add_utility(flat, levels=lvls)
cvs.add_budget(2.0, 3.0, 30.0, fill=True)
cvs.add_equilibrium(eq)
cvs.save("multigood.png")

Solving for Equilibrium

solve() returns an Equilibrium named tuple with fields x, y, and utility:

from econ_viz import solve
from econ_viz.models import CobbDouglas

eq = solve(CobbDouglas(), px=2.0, py=3.0, income=30.0)
print(eq.x, eq.y, eq.utility)

Themes

from econ_viz import Canvas, themes

cvs = Canvas(x_max=20, y_max=15)                    # default theme
cvs = Canvas(x_max=20, y_max=15, theme=themes.nord) # nord theme

Default	Nord

Export

cvs.save("figure.png")    # raster (DPI controlled by Canvas(dpi=300))
cvs.save("figure.tikz")   # TikZ/PGFPlots source for LaTeX

License

MIT © Anthony Sung