from numpy.testing import *
import numpy as N
import copy

from hyperspy.misc.mpfit.mpfit import mpfit


def Flin(x,p):
    y =  p[0] -p[1]*x 
    return y


def myfunctlin(p, fjac=None, x=None, y=None, err=None):
    # Parameter values are passed in "p"
    # If fjac==None then partial derivatives should not be
    # computed.  It will always be None if MPFIT is called with default
    # flag.
    model = Flin(x, p)
    # Non-negative status value means MPFIT should continue, negative means
    # stop the calculation.
    status = 0
    return [status, (y-model)/err]



def test_linfit():
    x=N.array([-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
		-2.8394297E-01,1.3416969E+00,1.3757038E+00,
		-1.3703436E+00,4.2581975E-02,-1.4970151E-01,
		8.2065094E-01])
    y=N.array([1.9000429E-01,6.5807428E+00,1.4582725E+00,
		2.7270851E+00,5.5969253E+00,5.6249280E+00,
		0.787615,3.2599759E+00,2.9771762E+00,
		4.5936475E+00])
    ey=0.07*N.ones(y.shape,dtype='float64')
    p0=N.array([1.0,1.0],dtype='float64')  #initial conditions
    pactual=N.array([3.2,1.78]) #actual values used to make data
    parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
    parinfo=[]
    for i in range(len(pactual)):
        parinfo.append(copy.deepcopy(parbase))
    for i in range(len(pactual)): 
        parinfo[i]['value']=p0[i]
    fa = {'x':x, 'y':y, 'err':ey}
    m = mpfit(myfunctlin, p0, parinfo=parinfo,functkw=fa)
    if (m.status <= 0): 
        print 'error message = ', m.errmsg
    assert N.allclose(m.params,N.array([ 3.20996572, -1.7709542 ],dtype='float64'))
    assert N.allclose(m.perror,N.array([ 0.02221018,  0.01893756],dtype='float64'))
    chisq=(myfunctlin(m.params, x=x, y=y, err=ey)[1]**2).sum()
    
    assert N.allclose(N.array([chisq],dtype='float64'),N.array([2.756284983],dtype='float64'))
    assert m.dof==8
    return

def myfunctrosenbrock(p, fjac=None):
    # rosenbrock function 
    res = N.array([1-p[0],-(1-p[0]),10*(p[1]-p[0]**2),-10*(p[1]-p[0]**2)])
    status = 0
    return [status, res]



def test_rosenbrock():
    p0=N.array([-1,1.],dtype='float64')  #initial conditions
    pactual=N.array([1.,1.]) #actual minimum of the rosenbrock function
    m = mpfit(myfunctrosenbrock, p0)
    if (m.status <= 0): 
        print 'error message = ', m.errmsg
    assert m.status > 0
    assert N.allclose(m.params,pactual)
    assert N.allclose(m.fnorm,0)
    return

if __name__ == "__main__":
    run_module_suite()
