[feat] add std tail fitting in ctseg

python/solid_dmft/dmft_tools/solver.py

@@ -140,7 +140,7 @@ def _fit_tail_window(
     tail_barr : dict of arr
                 fitted tail of Sigma_iw
     """
-    from triqs.gf import fit_hermitian_tail_on_window
+    from triqs.gf.gf_fnt import fit_hermitian_tail_on_window, replace_by_tail
 
     # Define default tail quantities
     if fit_min_w is not None:
@@ -162,7 +162,8 @@ def _fit_tail_window(
 
     # Now fit the tails of Sigma_iw and replace the high frequency part with the tail expansion
     tail_barr = {}
-    for name, sig in Sigma_iw:
+    Sigma_fit = Sigma_iw.copy()
+    for name, sig in Sigma_fit:
 
         tail, err = fit_hermitian_tail_on_window(
             sig,
@@ -174,8 +175,9 @@ def _fit_tail_window(
             expansion_order = fit_max_moment
             )
         tail_barr[name] = tail
+        replace_by_tail(sig, tail, n_min=fit_min_n)
 
-    return tail_barr
+    return Sigma_fit, tail_barr
 
 class SolverStructure:
 
@@ -1427,6 +1429,19 @@ def set_Gs_from_G_l():
             self.G_l << legendre_filter.apply(self.G_time, self.solver_params['n_l'])
             # get G_time, G_freq, Sigma_freq from G_l
             set_Gs_from_G_l()
+        elif self.solver_params['perform_tail_fit']:
+            self.Sigma_freq = inverse(self.G0_freq) - inverse(self.G_freq)
+            # without any degenerate shells we run the minimization for all blocks
+            self.Sigma_freq, tail = _fit_tail_window(self.Sigma_freq,
+                                                fit_min_n=self.solver_params['fit_min_n'],
+                                                fit_max_n=self.solver_params['fit_max_n'],
+                                                fit_min_w=self.solver_params['fit_min_w'],
+                                                fit_max_w=self.solver_params['fit_max_w'],
+                                                fit_max_moment=self.solver_params['fit_max_moment'],)
+            self.Sigma_Hartree = {}
+            for block in tail.keys():
+                self.Sigma_Hartree[block] = tail[block][0]
+
         # if improved estimators are turned on calc Sigma from F_tau, otherwise:
         elif self.solver_params['improved_estimator']:
             self.F_freq = self.G_freq.copy()
@@ -1484,7 +1499,7 @@ def set_Gs_from_G_l():
                 # minimize dyson for the first entry of each deg shell
                 self.Sigma_dlr = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, mesh=mesh_dlr_iw, space='solver')
                 # without any degenerate shells we run the minimization for all blocks
-                tail = _fit_tail_window(Sigma_iw,
+                _, tail = _fit_tail_window(Sigma_iw,
                                         fit_min_n=self.solver_params['fit_min_n'],
                                         fit_max_n=self.solver_params['fit_max_n'],
                                         fit_min_w=self.solver_params['fit_min_w'],

python/solid_dmft/gw_embedding/gw_flow.py

@@ -32,9 +32,9 @@
 
 from h5 import HDFArchive
 from triqs.utility import mpi
-from triqs.gf.tools import inverse
 from triqs.gf import (
     iOmega_n,
+    inverse,
     fit_hermitian_tail,
     Gf,
     BlockGf,

python/solid_dmft/io_tools/default.toml

@@ -131,6 +131,7 @@ n_l = "<none>"
 n_tau_k = 10001
 n_warmup_cycles = "<no default>"
 off_diag_threshold = 0.0
+perform_tail_fit = true
 random_seed = "<none>"
 
 [[solver]]

python/solid_dmft/io_tools/documentation.txt

@@ -372,6 +372,8 @@ n_warmup_cycles : int, mandatory
         number of warmup cycles before real measurement sets in
 off_diag_threshold : float, default = 0.0
         threshold for off-diag elements in Hloc0
+perform_tail_fit : bool, default = False
+        tail fitting if legendre is off?
 random_seed : str, default = None
         if None, uses default seed by triqs.
         If specified the int will be used for random seeds. Careful, this will give the same random

test/python/svo_gw_emb_stat/dmft_config.toml

@@ -6,8 +6,8 @@ enforce_off_diag = false
 
 n_iw = 20000
 n_tau = 200001
-dlr_wmax = 0.5
-dlr_eps = 1e-8
+dlr_wmax = 20
+dlr_eps = 1e-10
 
 gw_embedding = true
 h_int_type = "crpa_density_density"
@@ -20,17 +20,13 @@ it_1e = 1
 it_2e = 1
 
 [solver]
-type = "cthyb"
+type = "ctseg"
 length_cycle = 120
 n_warmup_cycles = 1e+3
 n_cycles_tot = 5e+5
 off_diag_threshold = 1e-5
-imag_threshold = 1e-4
-delta_interface = false
 diag_delta = false
-measure_density_matrix = true
-crm_dyson_solver = true
-# perform_tail_fit = true
-# fit_max_moment = 4
-# fit_min_w = 0.2
-# fit_max_w = 0.8
+perform_tail_fit = true
+fit_max_moment = 4
+fit_min_w = 0.2
+fit_max_w = 0.8

test/python/svo_gw_emb_stat/test.py

@@ -1,17 +1,15 @@
 import sys
 import shutil
+import importlib.util
 
-from triqs.gf import *
-from triqs.utility.comparison_tests import assert_block_gfs_are_close, assert_arrays_are_close
-from h5 import HDFArchive
 import triqs.utility.mpi as mpi
 
 import solid_dmft.main as solid
 
-try:
-    from triqs_cthyb import Solver
-except ImportError:
-    print('ctseg solver not installed skipping')
+# try triqs_ctseg import
+ctseg = importlib.util.find_spec("triqs_ctseg") is not None
+if not ctseg:
+    mpi.report('ImportWarning: ctseg needs to be installed to run this test')
     sys.exit()
 
 if mpi.is_master_node():