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Pyslvs Libraries

A no-GUI module of mechanism synthesis system and a 2D geometric constraint solver.

The submodule is located at pyslvs.

Pyslvs is now available on PyPI, you can install it by pip individually:

pip install pyslvs

Warning

Pyslvs-UI is using global pyslvs module as kernel. Please make sure the version is same with it.

Build and Test

Execute the unit test script after the kernel compiled.

pip install -e .
python test

Module parts

Pyslvs libraries is divided into two following sections:

  • Solver:

    Geometric solver and verification functions.

  • Synthesis:

    Mechanism synthesis system that including several random algorithm and enumeration algorithm, dependent with geometric solver.

Most of classes and functions can be work with a generic Python format (just like a list of coordinates or string-like expression), and you also can cut in at any step.

Solver

  • parser module:

    Analysis expression from strings, turn into symbols object.

  • expression library:

    Including PMKS expression object classes.

  • bfgs library:

    preload: expression

    Python wrapper of Sketch Solve. A simple and fast constraint solver with BFGS algorithm.

  • tinycadlib library:

    preload: expression, bfgs

    Particular solution takes more faster then constraint solving.

  • topo_config library:

    preload: expression, tinycadlib

    Autometic configuration algorithm for particular solution function in “tinycadlib”.

Kinematic Graph Synthesis

Submodule pyslvs.graph.

  • graph library:

    Graph expression came from NetworkX. Also contains graph verification functions.

  • planar library:

    preload: graph

    Planar graph checking came from NetworkX.

  • structural library:

    preload: graph, planar

    Structural synthesis algorithm.

  • layout library:

    preload: graph

    Layout algorithm for graphs.

Meta-heuristics Algorithm

Submodule pyslvs.metaheuristics.

metaheuristics module: Cython algorithms libraries provide evolution designing.

  • utility library:

    Provide base fitness function class for algorithm.

  • rga library:

    preload: utility

    Real-coded genetic algorithm for dimensional synthesis.

  • firefly library:

    preload: utility

    Firefly algorithm for dimensional synthesis.

  • de library:

    preload: utility

    Differential Evolution for dimensional synthesis.

Dimensional Synthesis

Submodule pyslvs.optimization.

  • f_planar library:

    preload: expression, topo_config, bfgs, tinycadlib, utility

    Dimensional synthesis for multi-link mechanisms. (defect allowable)

  • n_planar library:

preload: expression, tinycadlib, utility

Dimensional synthesis for normalized four-bar linkages. (defect free)

This function is not implemented yet.

Meta-heuristic Algorithms

\min_x F(x) = E + \sum_i g_i(x)

Pyslvs provides some search algorithms for optimization problems. The algorithm requires an objective function F(x), which accept variables x, and returns a fitness value E to minimize. The objective function is subject to several constraints g_i(x) that provides a sum of penalty values on the fitness value.

As shown in the example, objective function is created through inheriting a base class ObjFunc. The upper bound and lower bound are set by initialization method __cinit__, and the fitness value is defined by fitness method. The final result is defined by result method, which usually is your variables.

cimport cython
from numpy import array, float64 as f64
from pyslvs.metaheuristic cimport ObjFunc


@cython.final
cdef class TestObj(ObjFunc):
    """
    f(x) = x1^2 + 8*x2
    """

    def __cinit__(self):
        self.ub = array([100, 100], dtype=f64)
        self.lb = array([0, 0], dtype=f64)

    cdef double fitness(self, double[:] v) nogil:
        return v[0] * v[0] + 8 * v[1]

    cpdef object result(self, double[:] v):
        """x = (0, 0)"""
        return tuple(v)

The function object can be created with specific optionals by designing initialization function, but there are no options in this example.

We choose a algorithm called “Real-coded Genetic Algorithm” (RGA) to solve this problem.

This example is recorded in pyslvs.metaheuristics.test.

from numpy import array
from matplotlib.pyplot import plot, show
from pyslvs.metaheuristics import AlgorithmType, algorithm, default
from pyslvs.metaheuristics.test import TestObj

t = AlgorithmType.RGA
a = algorithm(t)(TestObj(), default(t))
# Show answer
ans = a.run()
# Plot convergance graph
h = array(a.history())
plot(h[:, 0], h[:, 1], 'ro-')
show()

Last update: February 25, 2021