def __init__(self, efloat_mode="float"):
self.efloat_mode = efloat_mode
self.efloat = EFloat(efloat_mode)
self.alphas = []
self.betas = []
self.unique_id_string = ""
def _tanhsinh(s, h):
N = s
k = numpy.array(range(-N//2, N//2+1))
if floatmode == "float":
x = numpy.tanh(0.5*numpy.pi*numpy.sinh(k*h))
w = 0.5*h*numpy.pi*numpy.cosh(k*h)/numpy.power(numpy.cosh(0.5*numpy.pi*numpy.sinh(k*h)), 2.0)
# To interval [0;1]
x = (x+1.0)*0.5
w *= 0.5
elif floatmode == "mpfloat":
efloat_mode="mpfloat"
efloat = ef.EFloat(efloat_mode)
import mpmath as mp
x = efloat.fromArray(numpy.zeros([N]))
w = efloat.fromArray(numpy.zeros([N]))
for i in range(N):
x[i,0] = mp.mp.tanh(0.5*mp.mp.pi*mp.mp.sinh(k[i]*h))
w[i,0] = 0.5*h*mp.mp.pi*mp.mp.cosh(k[i]*h)/mp.mp.power(mp.mp.cosh(0.5*mp.mp.pi*mp.mp.sinh(k[i]*h)), 2.0)
# To interval [0;1]
x = (x+1.0)*0.5
w *= 0.5
x = numpy.array(x.tolist(), dtype=float)[:,0]
w = numpy.array(w.tolist(), dtype=float)[:,0]
return x, w
def toFloat(self):
c = REXICoefficients()
c.efloat = EFloat("float")
c.function_name = self.function_name
if self.efloat != None:
if self.efloat.abs(self.efloat.im(self.gamma)) > 1e-10:
print("WARNING")
print("WARNING")
print("WARNING")
print("WARNING: Imaginary value " +
str(self.efloat.im(self.gamma)) +
" should be close to zero")
print("WARNING")
print("WARNING")
print("WARNING")
c.gamma = float(self.efloat.re(self.gamma))
else:
c.gamma = float(self.gamma.real)
c.alphas = [complex(a) for a in self.alphas]
c.betas = [complex(b) for b in self.betas]
c.unique_id_string = self.unique_id_string
return c
def __init__(self, efloat_mode="float"):
self.efloat_mode = efloat_mode
self.efloat = EFloat(efloat_mode)
self.unique_id_string = ""
self.section_factory = SectionFactory()
self.section_list = []
def main():
# double precision
floatmode = 'float'
# multiprecision floating point numbers
#floatmode = 'mpfloat'
# load float handling library
efloat = ef.EFloat(floatmode)
h = efloat.to(0.2)
M = 64
print("M: " + str(M))
print("h: " + str(h))
print("")
for half in [False, True]:
print("*" * 80)
print("* Original REXI coefficients")
rexi = TREXI(
N=M,
h=h,
function_name="phi0",
reduce_to_half=half,
floatmode=floatmode,
)
print("")
print("*" * 80)
print("Running tests...")
rexi.runTests()
print("*" * 80)
print("* REXI coefficients based on original REXI mu")
print("*" * 80)
rexi = TREXI(
N=M,
h=h,
ratgauss_basis_function_spacing=1.0,
ratgauss_basis_function_rat_shift=efloat.to(
'-4.31532151087502402475593044073320925235748291015625'),
reduce_to_half=half,
floatmode=floatmode,
)
print("*" * 80)
print("Rational approx. of Gaussian data:")
#rexi.ratgauss_coeffs.print_coefficients()
print("")
print("*" * 80)
print("Running tests...")
rexi.runTests()
def __init__(
self,
gaussphi0_N, # required argument
gaussphi0_basis_function_spacing, # required argument
floatmode = None
):
self.efloat = ef.EFloat(floatmode)
self.h = self.efloat.to(gaussphi0_basis_function_spacing)
self.M = int(gaussphi0_N)
self.b = [self.efloat.exp(self.h*self.h)*self.efloat.exp(-1j*(float(m)*self.h)) for m in range(-self.M, self.M+1)]
# Generate dummy Gaussian function
fafcoeffs = TREXI_GaussianCoefficients()
fafcoeffs.function_name = 'gaussianorig'
fafcoeffs.function_scaling = self.efloat.to(1.0)
def __init__(
self,
function_name = "phi0",
efloat_mode = None
):
self.efloat = ef.EFloat(efloat_mode)
self.function_name = function_name
self.function_complex = True
if self.efloat.floatmode == 'mpfloat':
import mpmath as mp
# Set numerical threshold to half of precision
self.epsthreshold = 1e-15
else:
self.epsthreshold = 1e-10
# Exponential integrator: phi0
if self.function_name[0:3] == 'phi':
N = int(self.function_name[3:])
def fun(x):
return self.phiN(N, x)
self.eval = fun
if self.function_complex:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = True
else:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = False
elif self.function_name[0:3] == 'ups':
N = int(self.function_name[3:])
if self.efloat.floatmode == 'mpfloat':
import mpmath as mp
# Set numerical threshold to half of precision
self.epsthreshold = 1e-10
else:
self.epsthreshold = 1e-10
if N == 1:
#
# Setup \upsilon_1 for EDTRK4
# See document notes_on_time_splitting_methods.lyx
#
def fun(x):
K = x
if abs(x) < self.epsthreshold:
return self.efloat.to(1.0)/self.efloat.to(2.0*3.0)
else:
return (-self.efloat.to(4.0)-K+self.efloat.exp(K)*(self.efloat.to(4.0)-self.efloat.to(3.0)*K+K*K))/(K*K*K)
self.eval = fun
if self.function_complex:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = True
else:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = False
elif N == 2:
#
# Setup \upsilon_2 for EDTRK4
# See document notes_on_time_splitting_methods.lyx
#
def fun(x):
K = x
if abs(x) < self.epsthreshold:
return self.efloat.to(1.0)/self.efloat.to(2.0*3.0)
else:
return (self.efloat.to(2.0)+1.0*K+self.efloat.exp(K)*(self.efloat.to(-2.0)+K))/(K*K*K)
self.eval = fun
if self.function_complex:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = True
else:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = False
elif N == 3:
#
# Setup \upsilon_3 for EDTRK4
# See document notes_on_time_splitting_methods.lyx
#
def fun(x):
K = x
if abs(x) < self.epsthreshold:
return self.efloat.to(1.0)/self.efloat.to(2.0*3.0)
else:
return (-self.efloat.to(4.0) - 3.0*K - K*K + self.efloat.exp(K)*(self.efloat.to(4.0)-K))/(K*K*K)
self.eval = fun
if self.function_complex:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = True
else:
self.is_real_symmetric = True
self.is_complex_conjugate_symmetric = False
else:
print("Unknown ups function "+str(N))
sys.exit(1)
else:
print("Unknown basis function "+str(self.function_name))
sys.exit(1)
def __init__(
self,
efloat_mode = None,
):
self.efloat = ef.EFloat(efloat_mode)
def _golubwelsch(n, lo=-1, hi=1, radau=False):
if floatmode == "float":
beta = .5 / numpy.sqrt(1-(2*(numpy.arange(1,n)))**(-2.)) # 3-term recurrence coeffs
T = numpy.diag(beta,1) + numpy.diag(beta,-1); # Jacobi matrix
if radau:
# Eq. 2.4 of Gautschi, Gauss–Radau formulae for Jacobi and Laguerre weight functions, 2000
if n == 1:
T[-1,-1] = 1
else:
T[-1,-1] = 1 - 2*(n-1)**2 / (2*(n-1)*(2*(n-1)+1))
D, V = numpy.linalg.eigh(T) # Eigenvalue decomposition
i = numpy.argsort(D) # Legendre points
x = D[i]
w = 2*V[0,i]**2 # Quadrature weights
x = 0.5*((hi+lo) + (hi-lo)*x)
w = 0.5*(hi-lo)*w
elif floatmode == "mpfloat":
efloat_mode="mpfloat"
efloat = ef.EFloat(efloat_mode)
T = efloat.fromArray(numpy.zeros([n,n]))
for i in range(1,n):
beta = .5 / efloat.sqrt(1.-(2*(i))**(-2.)) # 3-term recurrence coeffs
# upper diagonal
T[i,i-1] = beta
# lower diagonal
T[i-1,i] = beta
if radau:
# Eq. 2.4 of Gautschi, Gauss–Radau formulae for Jacobi and Laguerre weight functions, 2000
if n == 1:
T[-1,-1] = 1
else:
T[-1,-1] = 1 - 2*(n-1)**2 / (2*(n-1)*(2*(n-1)+1))
D, V = efloat.eigh(T) # Eigenvalue decomposition
N = len(D)
i = numpy.argsort(D) # Legendre points
x = efloat.fromArray(numpy.zeros(N))
w = efloat.fromArray(numpy.zeros(N))
for i in range(len(D)):
x[i] = D[i]
w[i] = 2*V[0,i]**2
x[i] = 0.5*((hi+lo) + (hi-lo)*x[i])
w[i] = 0.5*(hi-lo)*w[i]
x = numpy.array(x.tolist(), dtype=float)[:,0]
w = numpy.array(w.tolist(), dtype=float)[:,0]
else:
raise Exception("Unsupported floatmode")
return x, w
class ELREXI:
#
# Constructor
# See setup(...) for documentation on parameters
#
def __init__(self, efloat_mode="float"):
self.efloat_mode = efloat_mode
self.efloat = EFloat(efloat_mode)
self.alphas = []
self.betas = []
self.unique_id_string = ""
def setup(self,
function_name,
N: int,
lambda_max_real: float = None,
lambda_max_imag: float = None):
"""
Setup coefficients for Cauchy contour integral coefficients
using an ellipse with height given by lambda_max_imag
and width given by lambda_max_real
See doc/rexi/rexi_wit_laplace for details
Args:
N (int):
Number of points on Cauchy contour.
"""
self.alphas = []
self.betas = []
self.function_name = function_name
self.N = N
gamma = lambda_max_imag
delta = lambda_max_real
r = np.sqrt((1 - delta / gamma) / (1 + delta / gamma))
d = r + (1.0 / r)
self.fun = Functions(function_name, efloat_mode=self.efloat_mode)
#print("Setting up ellipse:")
#print("gamma: ", gamma)
#print("delta: ", delta)
#print("r: ", r)
if lambda_max_real == None or lambda_max_imag == None:
raise Exception(
"ELREXI: please provide max imag and real values of contour")
c1 = (gamma * self.efloat.i / d)
c2 = -(gamma / d)
for j in range(N):
theta_j = self.efloat.pi2 * (j + self.efloat.to(0.5)) / N
# sampling position of support point
sn = c1 * (r * self.efloat.exp(self.efloat.i * theta_j) +
(1.0 / r) * self.efloat.exp(-self.efloat.i * theta_j))
#metric term
sn_prime = c2 * (
r * self.efloat.exp(self.efloat.i * theta_j) -
(1.0 / r) * self.efloat.exp(-self.efloat.i * theta_j))
self.betas.append(-self.efloat.i * self.fun.eval(sn) * sn_prime /
N)
self.alphas.append(sn)
coeffs = REXICoefficients()
coeffs.alphas = self.alphas[:]
coeffs.betas = self.betas[:]
coeffs.gamma = 0
coeffs.function_name = self.function_name
self.unique_id_string = ""
self.unique_id_string += self.function_name
self.unique_id_string += "_N" + str(self.N)
self.unique_id_string += "_im" + str(self.efloat.re(lambda_max_imag))
self.unique_id_string += "_re" + str(self.efloat.re(lambda_max_real))
coeffs.unique_id_string = self.getUniqueId()
return coeffs
def getUniqueId(self):
return "ELREXI_" + self.unique_id_string
class CIREXI:
#
# Constructor
# See setup(...) for documentation on parameters
#
def __init__(self, efloat_mode="float"):
self.efloat_mode = efloat_mode
self.efloat = EFloat(efloat_mode)
self.alphas = []
self.betas = []
self.unique_id_string = ""
def setup(self,
function_name,
N: int,
R: float = None,
lambda_shift: complex = None,
lambda_max_real: float = None,
lambda_include_imag: float = None):
self.alphas = []
self.betas = []
self.function_name = ""
if lambda_shift != None:
self.setup_shifted_circle(function_name,
N=N,
R=R,
lambda_shift=lambda_shift)
elif lambda_max_real != None:
self.setup_limited_circle(function_name,
N=N,
lambda_max_real=lambda_max_real,
lambda_include_imag=lambda_include_imag)
else:
self.setup_circle(function_name, N=N, R=R)
coeffs = REXICoefficients()
coeffs.alphas = self.alphas[:]
coeffs.betas = self.betas[:]
coeffs.gamma = 0
coeffs.function_name = self.function_name
coeffs.unique_id_string = self.getUniqueId()
return coeffs
def setup_shifted_circle(self, function_name, N: int, R: float,
lambda_shift: complex):
"""
Setup coefficients for Cauchy contour integral coefficients
using a shifted circle with the shift given by lambda_shift
which fulfills the constraints given by lambda_*
Args:
N (int):
Number of points on Cauchy contour.
R (float):
Radius of circle for Cauchy contour
lambda_shift (complex):
` Shift of center of circle on complex plane
"""
self.function_name = function_name
self.lambda_shift = lambda_shift
self.fun = Functions(function_name, efloat_mode=self.efloat_mode)
for j in range(N):
theta_j = self.efloat.pi2 * (j + self.efloat.to(0.5)) / N
# sampling position of support point
pos = R * self.efloat.exp(self.efloat.i * theta_j)
# shifted position
gamma_j = pos + lambda_shift
self.betas.append(-self.fun.eval(gamma_j) * pos / N)
self.alphas.append(-gamma_j)
self.unique_id_string = "shic"
self.unique_id_string += "_" + function_name
self.unique_id_string += "_" + str(N)
self.unique_id_string += "_" + str(self.efloat.re(lambda_shift))
self.unique_id_string += "_" + str(self.efloat.im(lambda_shift))
def setup_circle(self, function_name, N: int, R: float):
"""
Setup circle contour integral centered at origin
Args:
N (int):
Number of quadrature poles
R (float):
Radius of circle
"""
self.setup_shifted_circle(function_name, N, R, 0.0)
self.unique_id_string = "circ"
self.unique_id_string += "_" + function_name
self.unique_id_string += "_" + str(N)
self.unique_id_string += "_" + str(lambda_max_real)
self.unique_id_string += "_" + str(lambda_include_imag)
def setup_limited_circle(self, function_name: str, N: int,
lambda_max_real: float,
lambda_include_imag: float):
"""
Setup coefficients for Cauchy contour integral coefficients circle
which fulfills the constraints given by lambda_*
Args:
N (int):
Number of points on Cauchy contour.
lambda_max_real (float):
Maximum allowed real number on contour.
lambda_include_imag (float):
Include at least this imaginary value as part of the contour.
Even, if the contour has to be enlarged
"""
# If the maximal real number is larger than the max to be included imaginary number, set a lower value for the max real number
if lambda_max_real > lambda_include_imag:
lambda_max_real = lambda_include_imag
x0 = lambda_max_real
xm = lambda_include_imag
r = (x0 * x0 + xm * xm) / (2.0 * x0)
center = lambda_max_real - r
self.setup_shifted_circle(function_name, N, r, center)
self.unique_id_string = "limc"
self.unique_id_string += "_" + str(N)
self.unique_id_string += "_" + function_name
self.unique_id_string += "_" + str(lambda_max_real)
self.unique_id_string += "_" + str(lambda_include_imag)
def getUniqueId(self):
return "CIREXI_" + self.unique_id_string
def setupFloatmode(self):
self.efloat = ef.EFloat(self.floatmode, self.mpfloat_digits)
self.floatmode = self.efloat.floatmode
new_alphas = []
new_betas = []
self.symmetric_reduction_applied = True
if not '_betafilter' in self.unique_id_string:
self.unique_id_string += '_betafilter'
return
def eval(self, x):
retval = self.gamma
for i in range(len(self.alphas)):
retval += self.betas[i] / (x + self.alphas[i])
return retval
if __name__ == "__main__":
r = REXICoefficients()
r.efloat = EFloat()
r.gamma = 1e-12
r.alphas.append(1j + 2.0)
r.betas.append(3j + 4.0)
r.write_file("/tmp/test.txt")
r2 = r.toFloat()