Welcome to CPpy
CPpy is a numpy-based light-weight Python library for conveniently modeling constraint problems in Python. It aims to connect to common constraint solving systems that have a Python API, such as MiniZinc (with solvers gecode, chuffed, ortools, picatsat, etc), or-tools through its Python API and more.
It is inspired by CVXpy, SciPy and Numberjack, and as most modern scientific Python tools, it uses numpy arrays as basic data structure.
The software is in ALPHA state, and more of a proof-of-concept really. Do send suggestions, additions, API changes, or even reuse some of these ideas in your own project!
Some TODOs:
- auto translate to or-tools
- auto translate to numberjack, which is Python-based but not numpy-based
- add more models (see Hakan K's page(s))
- publish on pypi, with proper docs
The following examples show the elegance of building on Python/Numpy:
from cppy import *
import numpy as np
# Construct the model
s,e,n,d,m,o,r,y = IntVar(0,9, 8)
constraint = []
constraint += [ alldifferent([s,e,n,d,m,o,r,y]) ]
constraint += [ sum( [s,e,n,d] * np.flip(10**np.arange(4)) )
+ sum( [m,o,r,e] * np.flip(10**np.arange(4)) )
== sum( [m,o,n,e,y] * np.flip(10**np.arange(5)) ) ]
constraint += [ s > 0, m > 0 ]
model = Model(constraint)
print(model)
stats = model.solve()
print(" S,E,N,D = ", [x.value() for x in [s,e,n,d]])
print(" M,O,R,E = ", [x.value() for x in [m,o,r,e]])
print("M,O,N,E,Y =", [x.value() for x in [m,o,n,e,y]])sudoku and others need matrix indexing, which numpy supports extensively:
from cppy import *
import numpy
x = 0 # cells whose value we seek
n = 9 # matrix size
given = numpy.array([
[x, x, x, 2, x, 5, x, x, x],
[x, 9, x, x, x, x, 7, 3, x],
[x, x, 2, x, x, 9, x, 6, x],
[2, x, x, x, x, x, 4, x, 9],
[x, x, x, x, 7, x, x, x, x],
[6, x, 9, x, x, x, x, x, 1],
[x, 8, x, 4, x, x, 1, x, x],
[x, 6, 3, x, x, x, x, 8, x],
[x, x, x, 6, x, 8, x, x, x]])
# Variables
puzzle = IntVar(1, n, shape=given.shape)
constraint = []
# constraints on rows and columns
constraint += [ alldifferent(row) for row in puzzle ]
constraint += [ alldifferent(col) for col in puzzle.T ]
# constraint on blocks
for i in range(0,n,3):
for j in range(0,n,3):
constraint += [ alldifferent(puzzle[i:i+3, j:j+3]) ]
# constraints on values
constraint += [ puzzle[given>0] == given[given>0] ]
model = Model(constraint)
stats = model.solve()and an OR problem for good faith:
from cppy import *
import numpy
# data
demands = [8, 10, 7, 12, 4, 4]
slots = len(demands)
# variables
x = IntVar(0,sum(demands), slots)
constraint = [x[i] + x[i+1] >= demands[i] for i in range(0,slots-1)]
constraint += [x[-1] + x[0] == demands[-1]] # 'around the clock' constraint
objective = sum(x) # number of buses
model = Model(constraint, minimize=objective)
stats = model.solve()