def test_circle_fit_paper():
"""
This test was extracted from the paper: "Least-Squares Circle" Fit by Randy Bullock.
It test correctness of the fit.
"""
x = numpy.array([0.000, 0.500, 1.000, 1.500, 2.000, 2.500, 3.000])
y = numpy.array([0.000, 0.250, 1.000, 2.250, 4.000, 6.250, 9.000])
xc, yc, r = circle_fit(x,y)
assert(xc + 11.839 < 0.001)
assert(yc - 8.4464 < 0.0001)
assert(r - 14.686 < 0.001)
# This is to check if it works also with uncertainties values
X = unarray(x, [0.001]*len(x))
Y = unarray(y, [0.001]*len(y))
Xc, Yc, R = circle_fit(X,Y)
# test the chi squared quantity
assert(relative_difference_circle_fit(x,y,xc,yc,r)==relative_difference_circle_fit(X,Y,Xc,Yc,R))
def test_siunitx_num():
tc = TabularContent()
tc.add_column(a_v, siunitx_num = False)
try:
tc.save('test.tmp')
except NotImplementedError:
pass
tc = TabularContent()
tc.add_column(unarray(a_v, a_u), siunitx_num = False)
try:
tc.save('test.tmp')
except NotImplementedError:
pass
def test_column():
tc = TabularContent()
c1 = TabularColumn(a_v, global_unc = 6.0)
c2 = TabularColumn(unarray(b_v, b_u), show_unc = False, unc_digits = 2)
c3 = TabularColumn(a_v, value_digits = 1)
c4 = TabularColumn(unarray(a_v, a_u), unc_digits = 2)
c5 = TabularColumn(unarray(b_v, b_u), unc_digits=3)
tc.add_column(c1)
tc.add_column(c2)
tc.add_column(c3)
tc.add_column(c4)
tc.add_column(c5)
tc.save('test.tmp')
assert(filecmp.cmp('test/outputs/test_column.tex', 'test.tmp'))
tc = TabularContent()
tc.add_column(b_v, global_unc = 0.00044528, unc_digits = 4)
try:
tc.add_column('error')
except TypeError:
pass
tc.save('test.tmp')
assert(filecmp.cmp('test/outputs/test_column2.tex', 'test.tmp'))
def umodel(x, a, b):
return unumpy.sqrt(a * x + b)
def dumodel(x, a, b):
return a / (2 * unumpy.sqrt(a * x + b))
x = numpy.array([3., 41., 100.])
y = numpy.array([5., 15., 25.])
ux = x / 100.
uy = y / 100.
X = unarray(x, ux)
Y = unarray(y, uy)
p0 = ucurve_fit(umodel, X, Y)
def test_ucurve_fit_with_chi2():
print(chi2(umodel, p0, X, Y))
def test_iteraed_fit_with_chi2():
p = iterated_fit(umodel, X, Y, df=dumodel)
assert ((p[0] - p0[0]).n < 0.01)
assert ((p[1] - p0[1]).n < 0.01)
print(chi2iterated(umodel, p, X, Y, df=dumodel))