#!/usr/bin/env python

import sys
from scipy_test.testing import *
set_package_path()
from optimize import zeros as cc
del sys.path[0]

from math import sin,sqrt,log
from random import random

def f1(x) :
    return x*(x-1.)

def f2(x) :
    return x**2 - 1

def f3(x) :
    return x*(x-1.)*(x-2.)*(x-3.)

def f4(x) :
    if x > 1 : return  1.0 + .1*x
    if x < 1 : return -1.0 + .1*x
    return 0

def f5(x) :
    if x != 1 : return 1.0/(1. - x)
    return 0

def f6(x) :
    if   x > 1 : return random()
    elif x < 1 : return -random()
    else : return 0

description =\
"""
f2 is a symmetric parabola, x**2 - 1
f3 is a quartic polynomial with large hump in interval
f4 is step function with a discontinuity at 1
f5 is a hyperbola with vertical asymptote at 1
f6 has random values positive to left of 1 , negative to right

of course these are not real problems. They just test how the
'good' solvers behave in bad circumstances where bisection is
really the best. A good solver should not be much worse than
bisection in such circumstance, while being faster for smooth
monotone sorts of functions.
"""

methods = [cc.bisect,cc.ridder,cc.brenth,cc.brentq]
mstrings = ['cc.bisect','cc.ridder','cc.brenth','cc.brentq']
functions = [f2,f3,f4,f5,f6]
fstrings = ['f2','f3','f4','f5','f6']

from scipy_test.testing import ScipyTestCase
import unittest


class test_basic(ScipyTestCase) :

    def check_run(self) :
        a = .5
        b = sqrt(3)
        repeat = 2000

        print 'TESTING CONVERGENCE\n'
        print 'zero should be 1\n'
        for i in range(len(functions)) :
            print 'function %s\n'%fstrings[i]
            for j in range(len(methods)) :
                try:
                    zero = methods[j](functions[i],a,b)
                except:
                    print '%s : failed'%mstrings[j]
                else:
                    print '%s : %21.16f'%(mstrings[j],zero)
            print '\n\n'

    def bench_run(self,level=5):
        a = .5
        b = sqrt(3)
        repeat = 2000

        print '%s\n',description

        print 'TESTING SPEED\n'
        print 'times in seconds for %d iterations \n'%repeat
        for i in range(len(functions)) :
            print 'function %s\n'%fstrings[i]
            func = functions[i]
            for j in range(len(methods)) :
                meth = methods[j]
                try:
                    t = self.measure("meth(func,a,b)",repeat)
                except:
                    print '%s : failed'%mstrings[j]
                else:
                    print '%s : %5.3f'%(mstrings[j],t)
            print '\n\n'

if __name__ == '__main__' :
    ScipyTest('optimize.zeros').run()