def interpolate(self,
inputvars,
locs,
datavec,
interpval=0.27,
interpmethod='linear'):
locations = range(len(inputvars) + 2)
jj = 2
# search for the column locations of the desired variables
for inputvar in inputvars:
ii = 0
flag = 0
for title in self.gettitles():
if title == inputvar:
locations[jj] = ii
flag = 1
ii = ii + 1
if flag == 0:
raise IOError(
"user specified column header %s not found in specified data file %s"
% (inputvar, self._sfile))
jj = jj + 1
locs[:] = locations[2:]
# generate data matrix
dataPy = np.genfromtxt(self._sfile,
unpack=False,
skip_header=2,
delimiter="\t",
usecols=locations)
norows = dataPy.shape[0]
nocols = dataPy.shape[1]
datavec1 = np.zeros((nocols))
data = matrix.Matrix(norows, nocols)
for i in range(norows):
for j in range(nocols):
data.SetVal(i, j, dataPy[i, j])
if interpmethod == 'linear':
interp = interpolator.LinInterp()
elif interpmethod == 'hermite':
interp = interpolator.HermiteInterp()
elif interpmethod == 'cubic':
interp = interpolator.CubicInterp()
else:
raise IOError(
"Specified interpolation method: %s not supported, use <linear>"
% interpmethod)
flag = 0
flag = interp.Interp(data, 0, interpval, datavec1)
if flag == 1:
raise RuntimeError(
"Interpolation failed: interpolation value out of bounds")
datavec[:] = datavec1[1:]
return 0
def testCubicExtrap(self):
# Test if cubic interpolator returns correct flag when attempting extrapolation
data = matrix.Matrix(2, 2)
for i in range(2):
data.SetVal(i, 0, i)
data.SetVal(i, 1, 3 * i)
interp = interpolator.CubicInterp()
icol = 0
cols = 2
vecout = np.zeros((cols))
self.assertEqual(interp.Interp(data, icol, -1.0, vecout),
1) # x < xmin
self.assertEqual(interp.Interp(data, icol, 2.0, vecout), 1) # x > xmax
def testCubicNode(self):
# Test if cubic interpolator gives node value when interpolating at node
data = matrix.Matrix(10, 2)
for i in range(10):
data.SetVal(i, 0, i)
data.SetVal(i, 1, np.cos(i))
interp = interpolator.CubicInterp()
icol = 0
cols = 2
vecout = np.zeros((cols))
for i in range(10):
flag = interp.Interp(data, icol, data.GetVal(i, 0), vecout)
self.assertEqual(flag, 0)
self.assertAlmostEqual(vecout[1], data.GetVal(i, 1))
def testCubicLinearfunc(self):
# Test if cubic interpolator is exact for data generated by linear function
def f(x):
return 4.0 * x - 1.1
data = matrix.Matrix(10, 2)
for i in range(10):
data.SetVal(i, 0, i)
data.SetVal(i, 1, f(i))
interp = interpolator.CubicInterp()
icol = 0
cols = 2
vecout = np.zeros((cols))
for i in range(9):
x = i + 0.2
flag = interp.Interp(data, icol, x, vecout)
self.assertEqual(flag, 0)
self.assertAlmostEqual(vecout[1], f(x))
def testCase(self):
# Test if the interpolators return the correct answer for a specific case
data = matrix.Matrix(4, 3)
for i in range(4):
data.SetVal(i, 0, i)
data.SetVal(i, 1, i * i * i - 1)
data.SetVal(i, 2, i * i + 1)
interps = [
interpolator.LinInterp(),
interpolator.CubicInterp(),
interpolator.HermiteInterp()
]
icol = 0
cols = 3
vecout = np.zeros((cols))
x = 1.5
yt = [[x, 3.5, 3.5], [x, 2.15, 3.2], [x, 2.375, 3.25]]
for i in range(3):
flag = interps[i].Interp(data, icol, x, vecout)
self.assertEqual(flag, 0)
np.testing.assert_array_almost_equal(vecout, yt[i])
# Run the fit to grid function to generate final data
# Setup
dim1 = 2 # w~ and c~
dim2 = ZmeanPoints # dimension of z~
dim3 = ZvarPoints # dimension of z_v
dim4 = nofiles # number of files
lcgrid = int(options["LCgrid"][0])
# length of cgrid
cgrid = np.linspace(0.0, maxC * 1.5, lcgrid)
interpmethod = options["InterpMethod"][0]
if interpmethod == 'linear': # add more interpolation options later
interp = interpolator.LinInterp()
elif interpmethod == 'hermite':
interp = interpolator.HermiteInterp()
elif interpmethod == 'cubic':
interp = interpolator.CubicInterp()
else:
raise IOError(
"Specified interpolation method: %s not supported, use <linear>" %
interpmethod)
datain = matrix4d.Matrix4D(dim1, dim2, dim3, dim4)
dataout = matrix3d.Matrix3D(dim2, dim3, lcgrid)
print "Fitting final data to grid using %s interpolation" % interpmethod
for i in range(2):
for l in range(nofiles):
for j in range(ZmeanPoints):
for k in range(ZvarPoints):
if i == 0:
datain.SetVal(i, j, k, l, convolutedST[l].GetVal(k, j))
if i == 1:
datain.SetVal(i, j, k, l, convolutedC[l].GetVal(k, j))