def test_get_quadratic_fit(self):
"""get quadratic fit"""
lf = LinCurveFit( self.myds, ['const', 'x', 'x**2'], fit_best_pcent=0)
val = lf.cArr[0]
refVal = 17.116
self.assertAlmostEqual(val, refVal)
val = lf.cArr[1]
refVal = -7.7477857
self.assertAlmostEqual(val, refVal)
val = lf.cArr[2]
refVal = 0.8353571428
self.assertAlmostEqual(val, refVal)
yArr = lf.eval_xrange( array([1.0, 2.0]) )
refVal = 4.9618571428571432
self.assertAlmostEqual(yArr[1], refVal)
def test_get_linear_fit(self):
"""get linear fit"""
lf = LinCurveFit( self.myds, ['const', 'x'], fit_best_pcent=0)
val = lf.cArr[0]
refVal = 9.3193333333333399
self.assertAlmostEqual(val, refVal)
val = lf.cArr[1]
refVal = -1.9002857142857148
self.assertAlmostEqual(val, refVal)
def test_get_fit_for_constant(self):
"""get LinCurveFit for constant"""
lf = LinCurveFit( self.myds, ['const'], fit_best_pcent=0)
val = lf.cArr[0]
refVal = 2.6683333333333348
self.assertAlmostEqual(val, refVal)
from matplotlib import pyplot as plt
got_plt = True
except:
got_plt = False
from xymath.dataset import DataSet
from xymath.linfit import LinCurveFit
concL = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
readingL = [1.54,2.03,3.17,3.67,4.89,6.73,6.74,7.87,8.86,10.35]
DS = DataSet(concL, readingL, xName='Concentration', yName='Instrument Reading')
print('\n\n')
print('='*55)
print(".... First show author's answer ....")
Fit_ref = LinCurveFit(DS, xtranL=['const', 'x'] , ytran='y', cArrInp=[0.199, 9.7926],
fit_best_pcent=0) # 0=fit best total error
print(Fit_ref.get_full_description())
print('='*55)
print('.... Then show XYmath answer ....')
Fit_linear = LinCurveFit(DS, xtranL=['const', 'x'] , ytran='y',
fit_best_pcent=0) # 0=fit best total error
print(Fit_linear.get_full_description())
print('='*55)
print('.... Then show XYmath Quadratic answer ....')
v2_concL = [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.8, 0.9, 1]
v2_readingL = [1.54,2.03,3.17,3.67,4.89,6.74,7.87,8.86,10.35]
DS = DataSet(v2_concL, v2_readingL, xName='Concentration', yName='Instrument Reading')