class Folder:
"""Fold a function with normalised Gaussians or Lorentzians"""
def __init__(self, width,
folding='Gauss'):
self.width = width
if folding == 'Gauss':
self.func = Gauss(width)
elif folding == 'Lorentz':
self.func = Lorentz(width)
elif folding == None:
self.func = None
else:
raise RuntimeError('unknown folding "' + folding + '"')
def fold(self, x, y, dx=None, min=None, max=None):
X = np.array(x)
assert len(X.shape) == 1
Y = np.array(y)
assert X.shape[0] == Y.shape[0]
if self.func is None:
xl = X
yl = Y
else:
if min is None:
min = np.min(X) - 4 * self.width
if max is None:
max = np.max(X) + 4 * self.width
if dx is None:
dx = self.width / 4.
xl = np.arange(min, max + 0.5 * dx, dx)
# weight matrix
weightm = np.empty((xl.shape[0], X.shape[0]))
for i, x in enumerate(X):
weightm[:, i] = self.func.get(xl - x)
yl = np.dot(weightm, Y)
return xl, yl
class Folder:
"""Fold a function with normalised Gaussians or Lorentzians"""
def __init__(self, width, folding='Gauss'):
self.width = width
if folding == 'Gauss':
self.func = Gauss(width)
elif folding == 'Lorentz':
self.func = Lorentz(width)
elif folding == None:
self.func = None
else:
raise RuntimeError('unknown folding "' + folding + '"')
def fold(self, x, y, dx=None, min=None, max=None):
X = np.array(x)
assert len(X.shape) == 1
Y = np.array(y)
assert X.shape[0] == Y.shape[0]
if self.func is None:
xl = X
yl = Y
else:
if min is None:
min = np.min(X) - 4 * self.width
if max is None:
max = np.max(X) + 4 * self.width
if dx is None:
dx = self.width / 4.
xl = np.arange(min, max + 0.5 * dx, dx)
# weight matrix
weightm = np.empty((xl.shape[0], X.shape[0]))
for i, x in enumerate(X):
weightm[:, i] = self.func.get(xl - x)
yl = np.dot(weightm, Y)
return xl, yl
# folder function
for name in ['Gauss', 'Lorentz']:
folder = Folder(width, name)
x = [0, 2]
y = [[2, 0, 1], [1, 1, 1]]
xl, yl = folder.fold(x, y, dx=.7)
# check first value
if name == 'Lorentz':
func = Lorentz(width)
else:
func = Gauss(width)
yy = np.dot(np.array(y)[:, 0], func.get(xl[0] - np.array(x)))
equal(yl[0, 0], yy, 1.e-15)
# write spectrum
from gpaw.lrtddft import LrTDDFT
from gpaw.lrtddft.spectrum import spectrum
fname = 'lr.dat.gz'
if os.path.exists(fname):
lr = LrTDDFT(fname)
lr.diagonalize()
spectrum(lr, 'spectrum.dat')
