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https://github.com/triqs/dft_tools
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add calc_mu test and pep8 format
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Alexander Hampel
parent
88094c0f92
commit
45749529ec
@ -1955,22 +1955,23 @@ class SumkDFT(object):
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"""
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def find_bounds(function, x_init, delta_x, max_loops=1000):
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mpi.report("Finding bounds on chemical potential" )
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x= x_init
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mpi.report("Finding bounds on chemical potential")
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x = x_init
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# First find the bounds
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y1 = function(x)
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eps = np.sign(y1)
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x1= x;
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x2= x1;y2 = y1
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nbre_loop=0
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#abort the loop after maxiter is reached or when y1 and y2 have different sign
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while (nbre_loop<= max_loops) and (y2*y1)>0:
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nbre_loop +=1
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x1=x2
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y1=y2
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x1 = x
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x2 = x1
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y2 = y1
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x2 -= eps*delta_x
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nbre_loop = 0
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# abort the loop after maxiter is reached or when y1 and y2 have different sign
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while (nbre_loop <= max_loops) and (y2*y1) > 0:
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nbre_loop += 1
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x1 = x2
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y1 = y2
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x2 -= eps*delta_x
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y2 = function(x2)
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if nbre_loop > (max_loops):
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@ -1978,68 +1979,67 @@ class SumkDFT(object):
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# Make sure that x2 > x1
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if x1 > x2:
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x1,x2 = x2,x1
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y1,y2 = y2,y1
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x1, x2 = x2, x1
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y1, y2 = y2, y1
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mpi.report(f"mu_interval: [ {x1:.4f} ; {x2:.4f} ]")
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mpi.report(f"delta to target density interval: [ {y1:.4f} ; {y2:.4f} ]")
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return x1, x2
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# previous implementation
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def F_bisection(mu): return self.total_density(mu=mu, broadening=broadening).real
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density = self.density_required - self.charge_below
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#using scipy.optimize
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# using scipy.optimize
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def F_optimize(mu):
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mpi.report("Trying out mu = {}".format(str(mu)))
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calc_dens = self.total_density(mu=mu, broadening=broadening).real - density
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mpi.report(f"Target density = {density}; Delta to target = {calc_dens}")
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return calc_dens
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#check for lowercase matching for the method variable
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if method.lower()=="dichotomy":
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mpi.report("\nSUMK calc_mu: Using dichtomy adjustment to find chemical potential\n")
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# check for lowercase matching for the method variable
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if method.lower() == "dichotomy":
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mpi.report("\nsumk calc_mu: Using dichtomy adjustment to find chemical potential\n")
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self.chemical_potential = dichotomy.dichotomy(function=F_bisection,
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x_init=self.chemical_potential, y_value=density,
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precision_on_y=precision, delta_x=delta, max_loops=max_loops,
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x_name="Chemical Potential", y_name="Total Density",
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verbosity=3)[0]
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elif method.lower()=="newton":
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mpi.report("\nSUMK calc_mu: Using Newton method to find chemical potential\n")
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self.chemical_potential = newton(func=F_optimize,
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x0=self.chemical_potential,
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tol=precision, maxiter=max_loops,
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)
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elif method.lower() == "newton":
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mpi.report("\nsumk calc_mu: Using Newton method to find chemical potential\n")
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self.chemical_potential = newton(func=F_optimize,
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x0=self.chemical_potential,
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tol=precision, maxiter=max_loops,
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)
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elif method.lower()=="brent":
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mpi.report("\nSUMK calc_mu: Using Brent method to find chemical potential")
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mpi.report("SUMK calc_mu: Finding bounds \n")
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mu_guess_0, mu_guess_1 = find_bounds(function=F_optimize,
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x_init=self.chemical_potential,
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delta_x=delta, max_loops=max_loops,
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)
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mu_guess_1 += 0.01 #scrambles higher lying interval to avoid getting stuck
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mpi.report("\nSUMK calc_mu: Searching root with Brent method\n")
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elif method.lower() == "brent":
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mpi.report("\nsumk calc_mu: Using Brent method to find chemical potential")
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mpi.report("sumk calc_mu: Finding bounds \n")
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mu_guess_0, mu_guess_1 = find_bounds(function=F_optimize,
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x_init=self.chemical_potential,
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delta_x=delta, max_loops=max_loops,
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)
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mu_guess_1 += 0.01 # scrambles higher lying interval to avoid getting stuck
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mpi.report("\nsumk calc_mu: Searching root with Brent method\n")
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self.chemical_potential = brenth(f=F_optimize,
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a=mu_guess_0,
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b=mu_guess_1,
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xtol=precision, maxiter=max_loops,
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)
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a=mu_guess_0,
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b=mu_guess_1,
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xtol=precision, maxiter=max_loops,
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)
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else:
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raise ValueError(
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f"SUMK calc_mu: The selected method: {method}, is not implemented\n",
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"""
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f"sumk calc_mu: The selected method: {method}, is not implemented\n",
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"""
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Please check for typos or select one of the following:
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* dichotomy: usual bisection algorithm from the TRIQS library
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* newton: newton method, fastest convergence but more unstable
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* brent: finds bounds and proceeds with hyperbolic brent method, a compromise between speed and ensuring convergence
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"""
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)
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)
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return self.chemical_potential
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56
test/python/calc_mu.py
Normal file
56
test/python/calc_mu.py
Normal file
@ -0,0 +1,56 @@
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##########################################################################
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#
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# TRIQS: a Toolbox for Research in Interacting Quantum Systems
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#
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# Copyright (C) 2023 by A. Hampel
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#
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# TRIQS is free software: you can redistribute it and/or modify it under the
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# terms of the GNU General Public License as published by the Free Software
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# Foundation, either version 3 of the License, or (at your option) any later
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# version.
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#
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# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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# details.
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#
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# You should have received a copy of the GNU General Public License along with
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# TRIQS. If not, see <http://www.gnu.org/licenses/>.
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#
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##########################################################################
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import unittest
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import numpy as np
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from h5 import *
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from triqs.gf import MeshImFreq
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from triqs_dft_tools.sumk_dft import *
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class test_solver(unittest.TestCase):
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def setUp(self):
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self.iw_mesh = MeshImFreq(beta=40, S='Fermion', n_iw=300)
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# magic reference value for the Wien2k SVO t2g example
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self.ref_mu = 0.281
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def test_dichotomy(self):
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sumk = SumkDFT('SrVO3.ref.h5', mesh=self.iw_mesh)
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mu = sumk.calc_mu(method='dichotomy', precision=0.001, delta=0.1)
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self.assertTrue(abs(self.ref_mu - mu) < 0.01)
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def test_brent(self):
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sumk = SumkDFT('SrVO3.ref.h5', mesh=self.iw_mesh)
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mu = sumk.calc_mu(method='brent', precision=0.001, delta=0.1)
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self.assertTrue(abs(self.ref_mu - mu) < 0.01)
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# Newton seems to be quite unstable here. Not tested right now!
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# def test_newton(self):
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# sumk = SumkDFT('SrVO3.ref.h5', mesh = self.iw_mesh)
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# mu = sumk.calc_mu(method='newton', precision= 0.1, delta=0.01)
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# self.assertTrue(abs(self.ref_mu - mu) < 0.01)
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if __name__ == '__main__':
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unittest.main()
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