[doc] add measure_density_matrix to all cthyb tutorial using tail_fit

doc/tutorials/images_scripts/Sr2MgOsO6_SOC.py

@@ -16,7 +16,7 @@ SK.calculate_diagonalization_matrix(prop_to_be_diagonal='eal',calc_in_solver_blo
 
 ###########################
 # Now we pick the orbitals:
-# BE CAREFUL: THIS NEEDS TO BE DONE PROPERLY 
+# BE CAREFUL: THIS NEEDS TO BE DONE PROPERLY
 # AND IS DIFFERENT FORM CASE TO CASE!
 SK.block_structure.pick_gf_struct_solver([{'ud_0': [0,1,2],'ud_1': [0,1,2]}])
 ###########################
@@ -49,6 +49,9 @@ p["perform_tail_fit"] = True
 p["fit_max_moment"] = 4
 p["fit_min_w"] = 4.0
 p["fit_max_w"] = 8.0
+# measure impurity density matrix to get self-energy moments for improved tail fit
+p["measure_density_matrix"] = True
+p["use_norm_as_weight"] = True
 
 # double counting correction:
 dc_type = 0  # FLL
@@ -64,7 +67,7 @@ if mpi.is_master_node():
 for iteration_number in range(1,n_loops+1):
 
     mpi.report("Iteration = %s"%iteration_number)
-    
+
     SK.set_Sigma([ S.Sigma_iw ])                    # put Sigma into the SumK class
     chemical_potential = SK.calc_mu( precision = 0.01 )  # find the chemical potential for given density
     S.G_iw << SK.extract_G_loc()[0]
@@ -109,4 +112,4 @@ for iteration_number in range(1,n_loops+1):
 
     # Save stuff into the user_data group of hdf5 archive in case of rerun:
     SK.save(['chemical_potential','dc_imp','dc_energ'])
-    
+

doc/tutorials/images_scripts/Sr2MgOsO6_noSOC.py

@@ -21,7 +21,7 @@ SK.calculate_diagonalization_matrix(prop_to_be_diagonal='eal',calc_in_solver_blo
 
 ###########################
 # Now we pick the orbitals:
-# BE CAREFUL: THIS NEEDS TO BE DONE PROPERLY 
+# BE CAREFUL: THIS NEEDS TO BE DONE PROPERLY
 # AND IS DIFFERENT FORM CASE TO CASE!
 SK.block_structure.pick_gf_struct_solver([{'up_1': [0],'up_2': [0],'up_3': [0],'down_1': [0],'down_2': [0],'down_3': [0]}])
 ###########################
@@ -52,6 +52,9 @@ p["perform_tail_fit"] = True
 p["fit_max_moment"] = 4
 p["fit_min_n"] = 30
 p["fit_max_n"] = 70
+# measure impurity density matrix to get self-energy moments for improved tail fit
+p["measure_density_matrix"] = True
+p["use_norm_as_weight"] = True
 
 # double counting correction:
 dc_type = 0  # FLL
@@ -67,7 +70,7 @@ if mpi.is_master_node():
 for iteration_number in range(1,n_loops+1):
 
     mpi.report("Iteration = %s"%iteration_number)
-    
+
     SK.symm_deg_gf(S.Sigma_iw)                # symmetrizing Sigma
     SK.set_Sigma([ S.Sigma_iw ])                    # put Sigma into the SumK class
     chemical_potential = SK.calc_mu( precision = 0.01 )  # find the chemical potential for given density
@@ -113,5 +116,5 @@ for iteration_number in range(1,n_loops+1):
 
     # Save stuff into the user_data group of hdf5 archive in case of rerun:
     SK.save(['chemical_potential','dc_imp','dc_energ'])
-    
+
 

doc/tutorials/images_scripts/dft_dmft_cthyb.py

@@ -43,6 +43,9 @@ p["perform_tail_fit"] = True
 p["fit_max_moment"] = 4
 p["fit_min_n"] = 30
 p["fit_max_n"] = 60
+# measure impurity density matrix to get self-energy moments for improved tail fit
+p["measure_density_matrix"] = True
+p["use_norm_as_weight"] = True
 
 # If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
 #from triqs_dft_tools.converters.wien2k import *

doc/tutorials/nio_csc_vasp/nio.py

@@ -73,6 +73,9 @@ p["fit_max_moment"] = 4
 p["fit_min_w"] = 20
 p["fit_max_w"] = 30
 p["perform_tail_fit"] = True
+# measure impurity density matrix to get self-energy moments for improved tail fit
+p["measure_density_matrix"] = True
+p["use_norm_as_weight"] = True
 
 # Double Counting: 0 FLL, 1 Held, 2 AMF
 DC_type = 0

doc/tutorials/nio_csc_vasp/nio_csc.py

@@ -79,6 +79,9 @@ def dmft_cycle():
     p["fit_min_w"] = 20
     p["fit_max_w"] = 30
     p["perform_tail_fit"] = True
+    # measure impurity density matrix to get self-energy moments for improved tail fit
+    p["measure_density_matrix"] = True
+    p["use_norm_as_weight"] = True
 
     # Double Counting: 0 FLL, 1 Held, 2 AMF
     DC_type = 0

doc/tutorials/sr2mgoso6_nosoc.rst

@@ -158,6 +158,9 @@ Now we have the interaction Hamiltonian for the solver, which we set up next::
     p["fit_max_moment"] = 4
     p["fit_min_n"] = 40
     p["fit_max_n"] = 100
+    # measure impurity density matrix to get self-energy moments for improved tail fit
+    p["measure_density_matrix"] = True
+    p["use_norm_as_weight"] = True
 
 
 The DMFT loop with automatic basis rotations
@@ -210,4 +213,4 @@ The only difference to the other example is in the initialisation of the real pa
 
 The two :math:`d_{xz}` and :math:`d_{yz}` orbitals are degenerate (blocks *up_2* and *up_3*), whereas the :math:`d_{xy}`-like orbital is different.
 
-A complete python script for this tutorial, including some more input/output, is available (:download:`Sr2MgOsO6_noSOC.py <images_scripts/Sr2MgOsO6_noSOC.py>`). When running the script, you will encounter warnings during the transformation from the *sumk* to the *solver* basis. These warnings just reflect that the off-diagonal elements of the full Greens function are not zero at all frequencies, although the local Hamiltonian is. In that sense, we still do an approximation when restricting ourselves to the :math:`t_{2g}`-like orbitals.
+A complete python script for this tutorial, including some more input/output, is available (:download:`Sr2MgOsO6_noSOC.py <images_scripts/Sr2MgOsO6_noSOC.py>`). When running the script, you will encounter warnings during the transformation from the *sumk* to the *solver* basis. These warnings just reflect that the off-diagonal elements of the full Greens function are not zero at all frequencies, although the local Hamiltonian is. In that sense, we still do an approximation when restricting ourselves to the :math:`t_{2g}`-like orbitals.

doc/tutorials/sr2mgoso6_soc.rst

@@ -156,6 +156,9 @@ Now we have the interaction Hamiltonian for the solver, which we set up next::
     p["fit_max_moment"] = 4
     p["fit_min_w"] = 4.0
     p["fit_max_w"] = 8.0
+    # measure impurity density matrix to get self-energy moments for improved tail fit
+    p["measure_density_matrix"] = True
+    p["use_norm_as_weight"] = True
 
 
 The DMFT loop with automatic basis rotations
@@ -224,4 +227,4 @@ The imaginary part of the self energy matrix of the *ud_0* block looks like this
 
 Plotted on the same scale, the off-diagonal elements are very small, only the *(1,2)* and *(2,1)* elements are visibly different from zero.
 
-A complete python script for this tutorial, including some more input/output, is available (:download:`Sr2MgOsO6_SOC.py <images_scripts/Sr2MgOsO6_SOC.py>`). When running the script, you will encounter warnings during the transformation from the *sumk* to the *solver* basis. These warnings just reflect that the off-diagonal elements of the full Greens function are not zero at all frequencies, although the local Hamiltonian is. In that sense, we still do an approximation when restricting ourselves to the low-energy subset.
+A complete python script for this tutorial, including some more input/output, is available (:download:`Sr2MgOsO6_SOC.py <images_scripts/Sr2MgOsO6_SOC.py>`). When running the script, you will encounter warnings during the transformation from the *sumk* to the *solver* basis. These warnings just reflect that the off-diagonal elements of the full Greens function are not zero at all frequencies, although the local Hamiltonian is. In that sense, we still do an approximation when restricting ourselves to the low-energy subset.

doc/tutorials/srvo3.rst

@@ -124,6 +124,9 @@ of parameters for a first guess is::
   p["fit_max_moment"] = 4
   p["fit_min_n"] = 30
   p["fit_max_n"] = 60
+  # measure impurity density matrix to get self-energy moments for improved tail fit
+  p["measure_density_matrix"] = True
+  p["use_norm_as_weight"] = True
 
 Here we use a tail fit to deal with numerical noise of higher Matsubara frequencies.
 For other options and more details on the solver parameters, we refer the user to

doc/tutorials/svo_elk/srvo3.rst

@@ -85,6 +85,9 @@ We also have to specify the `CTHYB solver <https://triqs.github.io/cthyb/latest>
   p["fit_max_moment"] = 4
   p["fit_min_n"] = 30
   p["fit_max_n"] = 60
+  # measure impurity density matrix to get self-energy moments for improved tail fit
+  p["measure_density_matrix"] = True
+  p["use_norm_as_weight"] = True
 
 Here we use a tail fit to deal with numerical noise of higher Matsubara frequencies. For other options and more details on the solver parameters, we refer to the `CTHYB solver <https://triqs.github.io/cthyb/latest/reference/constr_parameters.html>`_ documentation. It is important to note that the solver parameters have to be adjusted for each material individually. A guide on how to set the tail fit parameters is given :ref:`below <tailfit>`.