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[doc] add measure_density_matrix to all cthyb tutorial using tail_fit
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@ -49,6 +49,9 @@ p["perform_tail_fit"] = True
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_w"] = 4.0
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p["fit_min_w"] = 4.0
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p["fit_max_w"] = 8.0
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p["fit_max_w"] = 8.0
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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# double counting correction:
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# double counting correction:
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dc_type = 0 # FLL
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dc_type = 0 # FLL
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@ -52,6 +52,9 @@ p["perform_tail_fit"] = True
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_min_n"] = 30
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p["fit_max_n"] = 70
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p["fit_max_n"] = 70
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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# double counting correction:
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# double counting correction:
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dc_type = 0 # FLL
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dc_type = 0 # FLL
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@ -43,6 +43,9 @@ p["perform_tail_fit"] = True
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_min_n"] = 30
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p["fit_max_n"] = 60
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p["fit_max_n"] = 60
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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# If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
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# If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
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#from triqs_dft_tools.converters.wien2k import *
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#from triqs_dft_tools.converters.wien2k import *
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@ -73,6 +73,9 @@ p["fit_max_moment"] = 4
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p["fit_min_w"] = 20
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p["fit_min_w"] = 20
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p["fit_max_w"] = 30
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p["fit_max_w"] = 30
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p["perform_tail_fit"] = True
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p["perform_tail_fit"] = True
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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# Double Counting: 0 FLL, 1 Held, 2 AMF
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# Double Counting: 0 FLL, 1 Held, 2 AMF
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DC_type = 0
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DC_type = 0
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@ -79,6 +79,9 @@ def dmft_cycle():
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p["fit_min_w"] = 20
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p["fit_min_w"] = 20
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p["fit_max_w"] = 30
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p["fit_max_w"] = 30
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p["perform_tail_fit"] = True
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p["perform_tail_fit"] = True
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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# Double Counting: 0 FLL, 1 Held, 2 AMF
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# Double Counting: 0 FLL, 1 Held, 2 AMF
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DC_type = 0
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DC_type = 0
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@ -158,6 +158,9 @@ Now we have the interaction Hamiltonian for the solver, which we set up next::
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 40
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p["fit_min_n"] = 40
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p["fit_max_n"] = 100
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p["fit_max_n"] = 100
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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The DMFT loop with automatic basis rotations
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The DMFT loop with automatic basis rotations
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@ -156,6 +156,9 @@ Now we have the interaction Hamiltonian for the solver, which we set up next::
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_w"] = 4.0
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p["fit_min_w"] = 4.0
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p["fit_max_w"] = 8.0
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p["fit_max_w"] = 8.0
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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The DMFT loop with automatic basis rotations
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The DMFT loop with automatic basis rotations
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@ -124,6 +124,9 @@ of parameters for a first guess is::
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_min_n"] = 30
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p["fit_max_n"] = 60
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p["fit_max_n"] = 60
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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Here we use a tail fit to deal with numerical noise of higher Matsubara frequencies.
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Here we use a tail fit to deal with numerical noise of higher Matsubara frequencies.
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For other options and more details on the solver parameters, we refer the user to
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For other options and more details on the solver parameters, we refer the user to
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@ -85,6 +85,9 @@ We also have to specify the `CTHYB solver <https://triqs.github.io/cthyb/latest>
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p["fit_max_moment"] = 4
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_min_n"] = 30
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p["fit_max_n"] = 60
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p["fit_max_n"] = 60
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# measure impurity density matrix to get self-energy moments for improved tail fit
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p["measure_density_matrix"] = True
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p["use_norm_as_weight"] = True
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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>`.
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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>`.
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