[fix] fix small bugs and improve NiO test

python/solid_dmft/dmft_cycle.py

@@ -64,8 +64,24 @@ def _extract_quantity_per_inequiv(param_name, n_inequiv_shells, general_params):
     For quantities that can be different for each inequivalent shell, this
     function checks if the quantity is a single value or a list of the correct
     length. If just a single value is given, this value is applied to each shell.
+
+    Parameters
+    ----------
+    param_name : str
+        name of the parameter to be checked
+    n_inequiv_shells : int
+        number of inequivalent shells
+    general_params : dict
+        general parameters as a dict
+
+    Returns
+    -------
+    general_params : dict
+        updated general parameters as a dict
+
     """
 
+
     def formatter(value):
         if isinstance(value, float):
             return '{:.2f}'.format(value)
@@ -338,7 +354,7 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params,
                     map_imp_solver.append(isolver)
                     break
     solver_type_per_imp = [solver_params[map_imp_solver[iineq]]['type'] for iineq in range(sum_k.n_inequiv_shells)]
-    mpi.report(f'Solver type per impurity: {solver_type_per_imp}')
+    mpi.report(f'\nSolver type per impurity: {solver_type_per_imp}')
 
     # Checks all parameters that need to be checked against info in SumkDFT object
     verify_input_params.verify_h5_dependent(sum_k, solver_type_per_imp, general_params)

python/solid_dmft/dmft_tools/results_to_archive.py

@@ -71,7 +71,7 @@ def _compile_information(sum_k, general_params, solver_params, solvers, map_imp_
 
             # if legendre was set, that we have both now!
             if (solver_params[isolvsec]['measure_G_l']
-                or not solver_params[isolvsec]['perform_tail_fit'] and general_params['legendre_fit']):
+                or not solver_params[isolvsec]['perform_tail_fit'] and solver_params[isolvsec]['legendre_fit']):
                 write_to_h5['G_time_orig_{}'.format(icrsh)] = solvers[icrsh].G_time_orig
                 write_to_h5['Gimp_l_{}'.format(icrsh)] = solvers[icrsh].G_l
 
@@ -91,7 +91,7 @@ def _compile_information(sum_k, general_params, solver_params, solvers, map_imp_
 
         if solver_type_per_imp[icrsh] == 'ctseg':
             # if legendre was set, that we have both now!
-            if (solver_params[isolvsec]['measure_gl'] or general_params['legendre_fit']):
+            if (solver_params[isolvsec]['measure_gl'] or solver_params[isolvsec]['legendre_fit']):
                 write_to_h5['G_time_orig_{}'.format(icrsh)] = solvers[icrsh].G_time_orig
                 write_to_h5['Gimp_l_{}'.format(icrsh)] = solvers[icrsh].G_l
             if solver_params[isolvsec]['measure_ft']:

python/solid_dmft/dmft_tools/solver.py

@@ -623,12 +623,14 @@ def _create_cthyb_solver(self):
         self.triqs_solver_params = {}
         keys_to_pass = ('imag_threshold', 'length_cycle', 'max_time', 'measure_density_matrix',
                         'measure_G_l', 'measure_pert_order', 'move_double', 'move_shift',
-                        'n_warmup_cycles', 'loc_n_max', 'loc_n_min', 'off_diag_threshold', 'perform_tail_fit')
+                        'off_diag_threshold', 'perform_tail_fit')
         for key in keys_to_pass:
             self.triqs_solver_params[key] = self.solver_params[key]
 
         # Calculates number of sweeps per rank
         self.triqs_solver_params['n_cycles'] = int(self.solver_params['n_cycles_tot'] / mpi.size)
+        # cast warmup cycles to int in case given in scientific notation
+        self.triqs_solver_params['n_warmup_cycles'] = int(self.solver_params['n_warmup_cycles'])
 
         # Renames measure chi param
         self.triqs_solver_params['measure_O_tau_min_ins'] = self.solver_params['measure_chi_insertions']
@@ -640,11 +642,11 @@ def _create_cthyb_solver(self):
             for key in ('fit_max_moment', 'fit_max_n', 'fit_max_w', 'fit_min_n', 'fit_min_w'):
                 self.triqs_solver_params[key] = self.solver_params[key]
 
-        # Resolve defaults for n_loc_min and n_loc_max
-        if self.triqs_solver_params['loc_n_min'] is None:
-            self.triqs_solver_params['loc_n_min'] = 0
-        if self.triqs_solver_params['loc_n_max'] is None:
-            self.triqs_solver_params['loc_n_max'] = 100000 # large number
+        # set loc_n_min and loc_n_max
+        if self.solver_params['loc_n_min'] is not None:
+            self.triqs_solver_params['loc_n_min'] = self.solver_params['loc_n_min']
+        if self.solver_params['loc_n_max'] is not None:
+            self.triqs_solver_params['loc_n_max'] = self.solver_params['loc_n_max']
 
         gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh]
         # Construct the triqs_solver instances

python/solid_dmft/io_tools/default.toml

@@ -43,7 +43,7 @@ noise_level_initial_sigma = 0.0
 occ_conv_crit = -1.0
 path_to_sigma = "<none>"
 plo_cf = "plo.cfg"
-prec_mu = "<no default>"
+prec_mu = 1e-4
 ratio_F4_F2 = "<none>"
 sampling_h5_save_freq = 5
 sampling_iterations = 0

test/python/nio_cthyb_hartree/dmft_config.toml

@@ -2,21 +2,22 @@
 seedname = "inp"
 jobname = "out"
 
-mu_initial_guess = 13.724188
+mu_initial_guess = 13.751
+enforce_off_diag = [false, true]
 
 n_iw = 501
-beta = 40
+beta = 20
 w_range = [-10, 10]
 eta = 0.05
 n_w = 501
 
-prec_mu = 0.001
 calc_mu_method = "brent"
 
 h_int_type = ["density_density", "kanamori"]
-U = [10, 15]
+U = [8.32, 6.8]
 U_prime = ["<none>", 0]
-J = [1, 0]
+J = [1.19, 0.8]
+ratio_F4_F2 = [0.68, "<none>"]
 
 calc_energies = true
 
@@ -29,12 +30,12 @@ dc_dmft = false
 [[solver]]
 type = "cthyb"
 idx_impurities = [0]
-n_l = 35
-length_cycle = 120
-n_warmup_cycles = 8000
+n_l = 25
+length_cycle = 1000
+n_warmup_cycles = 1e+3
 n_cycles_tot = 1e+4
 imag_threshold = 1e-5
-measure_G_l = true
+legendre_fit = true
 measure_density_matrix = true
 
 [[solver]]