From 8beb46d5b4d5666ec9b7465628a7f49e42408783 Mon Sep 17 00:00:00 2001 From: merkelm <57808209+merkelm@users.noreply.github.com> Date: Tue, 27 Feb 2024 15:09:06 -0500 Subject: [PATCH] multiple solvers and toml input parser (#74) This PR changes the config file of solid_dmft to the toml format and allows to use multiple solvers within a single calculation. Requires dft_tools from commit b355173 on the unstable branch or later. - The following input parameters can now be a list per impurity: - `general_params`: U, J, U_prime, ratio_F4_F2, h_int_type, enforce_off_diag, dc_type - `advanced_params`: dc_U, dc_J, dc_fixed_occ, map_solver_struct, pick_solver_struct, mapped_solver_struct_degeneracies - Multiple solvers can be used, which only solve the impurity problems specified in `idx_impurities` - general parameter `solver_type` moved to solver section and renamed to `type` - general parameter `n_l` moved to solver section - general parameter `measure_chi` moved to solver section - general parameter `delta_interface` moved to solver section - All possible input parameters are defined in the `python/solid_dmft/io_tools/default.toml` - according to toml format the config file is now called .toml (instead of .ini), and boolean are not capitalized, strings are given with quotes and lists are given with brackets. - Documentation of the input is now generated from `python/solid_dmft/io_tools/documentation.txt` - For an example, refer to the new integration test (see below) - Updated interface to python scripts wrapping solid_dmft: new routine `main.run_dmft` that expects the params as python dictionaries, which are then supplemented with the defaults etc equivalent to what happens when reading in a toml file - the existence of the parameter `general_params['beta']` now determines if a imaginary- or real-frequency grid is used within solid_dmft - Bug fix: Slater interaction for p orbitals can now be constructed - Renaming of solver parameters for the different solvers is now moved to `solver.py`. The idea is that every other part of solid_dmft should care as little as possible what solvers are used, with the details abstracted by the SolverStructure class - In `solver.py`, all solver parameters that are passed to the triqs solver are transferred to a dict `triqs_solver_params`. When adding new triqs solver parameter to solid_dmft in the future, they also need to be added within solver.py. - In the determination of the block structure, the largely unused parameter `general_params['block_suppress_orbital_symm']` removed. Its behavior can be replaced by using `advanced_params['mapped_solver_struct_degeneracies']` - Integration tests: previously existing tests updated, new tests added. One with ftps solver (requires installation of ftps, otherwise just passes without doing anything) and one with a combination of CT-HYB and Hartree solver - Unit tests: added test for toml-related functionality - `read_config.py` removed and the functionality for dealing with the dicts from reading a toml file moved to `postproc_toml_dict.py` - `io_tools/verify_input_params.py` contains all checks of the input params that the code performs before starting the DMFT calculations - Updated the documentation of the input parameters --------- Co-authored-by: Alexander Hampel --- doc/documentation.rst | 10 +- .../DMFT_input/generate_doc_from_comments.sh | 37 +- doc/tutorials/Ce2O3_csc_w90/dmft_config.ini | 44 - doc/tutorials/Ce2O3_csc_w90/dmft_config.toml | 44 + doc/tutorials/Ce2O3_csc_w90/tutorial.ipynb | 45 +- .../{config.ini => config.toml} | 28 +- doc/tutorials/NNO_os_plo_mag/ref/config.ini | 47 - doc/tutorials/NNO_os_plo_mag/tutorial.ipynb | 37 +- .../{dmft_config.ini => dmft_config.toml} | 37 +- .../PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb | 28 +- doc/tutorials/SVO_os_qe/dmft_config.ini | 38 - doc/tutorials/SVO_os_qe/dmft_config.toml | 37 + doc/tutorials/SVO_os_qe/run_MIT_coarse.sh | 41 +- doc/tutorials/SVO_os_qe/run_MIT_fine.sh | 32 +- python/solid_dmft/CMakeLists.txt | 6 +- python/solid_dmft/csc_flow.py | 10 +- python/solid_dmft/dmft_cycle.py | 405 +++--- python/solid_dmft/dmft_tools/afm_mapping.py | 10 +- python/solid_dmft/dmft_tools/convergence.py | 3 +- python/solid_dmft/dmft_tools/formatter.py | 8 +- .../dmft_tools/greens_functions_mixer.py | 22 - .../dmft_tools/initial_self_energies.py | 90 +- .../dmft_tools/interaction_hamiltonian.py | 95 +- .../manipulate_chemical_potential.py | 41 +- python/solid_dmft/dmft_tools/observables.py | 57 +- .../dmft_tools/results_to_archive.py | 40 +- python/solid_dmft/dmft_tools/solver.py | 232 ++- python/solid_dmft/io_tools/default.toml | 184 +++ python/solid_dmft/io_tools/dict_to_h5.py | 25 + python/solid_dmft/io_tools/documentation.txt | 481 ++++++ .../solid_dmft/io_tools/postproc_toml_dict.py | 188 +++ .../io_tools/verify_input_params.py | 111 ++ python/solid_dmft/main.py | 107 +- python/solid_dmft/read_config.py | 1294 ----------------- python/solid_dmft/util/update_dmft_config.py | 149 -- python/solid_dmft/util/update_results_h5.py | 92 -- test/python/CMakeLists.txt | 31 +- test/python/lco_ftps/dmft_config.toml | 51 + test/python/lco_ftps/inp.h5 | Bin 0 -> 175231 bytes test/python/lco_ftps/ref.h5 | Bin 0 -> 1623101 bytes test/python/lco_ftps/test.py | 35 + test/python/lno_hubbardI_mag/dmft_config.ini | 31 - test/python/lno_hubbardI_mag/dmft_config.toml | 33 + test/python/lno_hubbardI_mag/test.py | 3 +- .../python/nio_cthyb_hartree/dmft_config.toml | 46 + test/python/nio_cthyb_hartree/inp.h5 | Bin 0 -> 133750 bytes test/python/nio_cthyb_hartree/ref.h5 | Bin 0 -> 50520300 bytes test/python/nio_cthyb_hartree/test.py | 24 + .../{dmft_config.ini => dmft_config.toml} | 30 +- test/python/svo_cthyb_basic_gl/test.py | 2 +- .../{dmft_config.ini => dmft_config.toml} | 34 +- test/python/svo_cthyb_basic_tf/test.py | 4 +- test/python/svo_hartree/dmft_config.ini | 37 - test/python/svo_hartree/dmft_config.toml | 38 + test/python/svo_hartree/test.py | 3 +- .../python/svo_hubbardI_basic/dmft_config.ini | 33 - .../svo_hubbardI_basic/dmft_config.toml | 33 + test/python/svo_hubbardI_basic/ref_restart.h5 | Bin 0 -> 2110954 bytes test/python/svo_hubbardI_basic/test.py | 2 +- .../python/svo_hubbardI_basic/test_restart.py | 21 + test/python/test_dict_to_h5.py | 13 + test/python/test_observables.py | 13 +- test/python/test_postproc_toml_dict.py | 165 +++ .../test_postproc_toml_dict_integration.py | 106 ++ test/python/test_read_config.py | 243 ---- test/python/test_update_dmft_config.py | 74 - test/python/test_update_results_h5.py | 43 - 67 files changed, 2429 insertions(+), 2874 deletions(-) delete mode 100644 doc/tutorials/Ce2O3_csc_w90/dmft_config.ini create mode 100644 doc/tutorials/Ce2O3_csc_w90/dmft_config.toml rename doc/tutorials/NNO_os_plo_mag/{config.ini => config.toml} (52%) delete mode 100644 doc/tutorials/NNO_os_plo_mag/ref/config.ini rename doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/{dmft_config.ini => dmft_config.toml} (53%) delete mode 100644 doc/tutorials/SVO_os_qe/dmft_config.ini create mode 100644 doc/tutorials/SVO_os_qe/dmft_config.toml create mode 100644 python/solid_dmft/io_tools/default.toml create mode 100644 python/solid_dmft/io_tools/dict_to_h5.py create mode 100644 python/solid_dmft/io_tools/documentation.txt create mode 100644 python/solid_dmft/io_tools/postproc_toml_dict.py create mode 100644 python/solid_dmft/io_tools/verify_input_params.py delete mode 100755 python/solid_dmft/read_config.py delete mode 100644 python/solid_dmft/util/update_dmft_config.py delete mode 100644 python/solid_dmft/util/update_results_h5.py create mode 100644 test/python/lco_ftps/dmft_config.toml create mode 100644 test/python/lco_ftps/inp.h5 create mode 100644 test/python/lco_ftps/ref.h5 create mode 100644 test/python/lco_ftps/test.py delete mode 100644 test/python/lno_hubbardI_mag/dmft_config.ini create mode 100644 test/python/lno_hubbardI_mag/dmft_config.toml create mode 100644 test/python/nio_cthyb_hartree/dmft_config.toml create mode 100644 test/python/nio_cthyb_hartree/inp.h5 create mode 100644 test/python/nio_cthyb_hartree/ref.h5 create mode 100644 test/python/nio_cthyb_hartree/test.py rename test/python/svo_cthyb_basic_gl/{dmft_config.ini => dmft_config.toml} (53%) rename test/python/svo_cthyb_basic_tf/{dmft_config.ini => dmft_config.toml} (52%) delete mode 100644 test/python/svo_hartree/dmft_config.ini create mode 100644 test/python/svo_hartree/dmft_config.toml delete mode 100644 test/python/svo_hubbardI_basic/dmft_config.ini create mode 100644 test/python/svo_hubbardI_basic/dmft_config.toml create mode 100644 test/python/svo_hubbardI_basic/ref_restart.h5 create mode 100644 test/python/svo_hubbardI_basic/test_restart.py create mode 100644 test/python/test_dict_to_h5.py create mode 100644 test/python/test_postproc_toml_dict.py create mode 100644 test/python/test_postproc_toml_dict_integration.py delete mode 100755 test/python/test_read_config.py delete mode 100755 test/python/test_update_dmft_config.py delete mode 100644 test/python/test_update_results_h5.py diff --git a/doc/documentation.rst b/doc/documentation.rst index d53d95f2..4502a333 100644 --- a/doc/documentation.rst +++ b/doc/documentation.rst @@ -37,10 +37,10 @@ Input/Output Further details for running =========================== - + .. toctree:: :maxdepth: 1 - + md_notes/docker.md md_notes/run_locally.md md_notes/run_cluster.md @@ -57,10 +57,10 @@ Module reference manual dft_managers dmft_cycle dmft_tools + io_tools postprocessing - read_config util - - + + diff --git a/doc/input_output/DMFT_input/generate_doc_from_comments.sh b/doc/input_output/DMFT_input/generate_doc_from_comments.sh index 1a8a39f4..3ee3f120 100755 --- a/doc/input_output/DMFT_input/generate_doc_from_comments.sh +++ b/doc/input_output/DMFT_input/generate_doc_from_comments.sh @@ -7,25 +7,23 @@ green='\033[1;32m' wipe="\033[1m\033[0m" -echo -e "${green}Extracting the comments from read_config.py file${wipe}" +echo -e "${green}Extracting the comments from io_tools/documentation.txt file${wipe}" buildfolder="../../.." -docfile="$buildfolder/python/solid_dmft/read_config.py" +docfile="$buildfolder/python/solid_dmft/io_tools/documentation.txt" -awk '/---XXX---start/{flag=1; c=0} flag; /---XXX/&& ++c==2{flag=0}' $docfile | tail -n +2 | head -n -2 > python_comments.txt - -#from the python comments +#from the documentation file echo "Generating .rst syntax from the python syntax" -sed 's/\(.*\) :\s*\(.*\)/\n.. admonition:: \1 \n :class: intag \n \n \*\*type=\*\* \2\n/g' python_comments.txt > matches_comments.txt +sed 's/\(.*\) :\s*\(.*\)/\n.. admonition:: \1 \n :class: intag \n \n \*\*type\*\* = \2\n/g' $docfile > matches_comments.txt #add blank line after type #sed -i 's/\(\*type=.*\),/\1 \n\n /g' matches_comments.txt #sed -i 's/\(\*type=.*\),/\1 \n\n /g' matches_comments.txt -# make 'optional' and 'default' bold -sed -i 's/,.*\(\optional\)/; \*\*\1\*\*/g' matches_comments.txt -sed -i 's/,.*\(\default=\)/; \*\*\1\*\* /g' matches_comments.txt +# make 'mandatory' and 'default' bold +sed -i 's/,.*\(\mandatory\)/; \*\*\1\*\*/g' matches_comments.txt +sed -i 's/,.*\(\default\)/; \*\*\1\*\* /g' matches_comments.txt -# grep all admonitions and store them in a file +# grep all admonitions and store them in a file echo "Generating input page" cat > input.rst << EOF @@ -51,7 +49,7 @@ EOF # # awk '/\[ SECTION \]/{flag=1; c=0} flag; /\[ /&& ++c==2{flag=0}' # matches from the pattern '[ SECTION ]'(note, two spaces are important) up to either the next occurence of -# '[ ', which is the next group, or the end of the file +# '[ ', which is the next group, or the end of the file # # # the second part takes the divided section and extracts a list out of it: @@ -99,7 +97,7 @@ cat > general.rst << EOF [general]: General parameters ----------------------------- -Includes the majority of the parameters +Frequently used parameters that apply to the whole simulation. EOF @@ -113,10 +111,9 @@ cat > solver.rst << EOF [solver]: solver specific parameters ------------------------------------ -Here are the parameters that are uniquely dependent on the solver chosen. Below a list of the supported solvers: - - - +Here are the parameters that are uniquely dependent on the solver chosen. Some parameters are used within solid_dmft and some are passed directly into the triqs solver. +To see which parameters were passed to the solver for a given calculation, look at the triqs_solver_params in DMFT_input/solver in the h5 archive. +Solver-specific parameters are listed in the respective sections. EOF @@ -125,16 +122,14 @@ echo -e "\n" >> solver.rst cat solver.tmp >> solver.rst ############## - - ############### cat > dft.rst << EOF [dft]: DFT related inputs ------------------------- -List of parameters that relate to the DFT calculation, useful mostly when doing CSC. - +List of parameters for the DFT calculation in charge-self-consistent calculations. +The parameters are ignored in one-shot calculations. EOF @@ -149,7 +144,7 @@ cat > advanced.rst << EOF [advanced]: Advanced inputs --------------------------- -Advanced parameters, do not modify default value unless you know what you are doing +Advanced parameters, do not modify the default value unless you know what you are doing. EOF diff --git a/doc/tutorials/Ce2O3_csc_w90/dmft_config.ini b/doc/tutorials/Ce2O3_csc_w90/dmft_config.ini deleted file mode 100644 index 7bb8c2c8..00000000 --- a/doc/tutorials/Ce2O3_csc_w90/dmft_config.ini +++ /dev/null @@ -1,44 +0,0 @@ -[general] -seedname = ce2o3 -jobname = b10-U6.46-J0.46 -csc = True -#dft_mu = 0. -solver_type = hubbardI -n_l = 15 -eta = 0.5 -n_iw = 100 -n_tau = 5001 - -n_iter_dmft_first = 2 -n_iter_dmft_per = 1 -n_iter_dmft = 5 - -block_threshold = 1e-03 - -h_int_type = density_density -U = 6.46 -J = 0.46 -beta = 10 -prec_mu = 0.1 - -sigma_mix = 1.0 -g0_mix = 1.0 -dc_type = 0 -dc = True -dc_dmft = True -calc_energies = True - -h5_save_freq = 1 - -[solver] -store_solver = False -measure_G_l = False -measure_density_matrix = True - -[dft] -dft_code = qe -n_cores = 10 -mpi_env = default -projector_type = w90 -dft_exec = pw.x -w90_tolerance = 1.e-1 diff --git a/doc/tutorials/Ce2O3_csc_w90/dmft_config.toml b/doc/tutorials/Ce2O3_csc_w90/dmft_config.toml new file mode 100644 index 00000000..23021830 --- /dev/null +++ b/doc/tutorials/Ce2O3_csc_w90/dmft_config.toml @@ -0,0 +1,44 @@ +[general] +seedname = "ce2o3" +jobname = "b10-U6.46-J0.46" +csc = true + +eta = 0.5 +n_iw = 100 +n_tau = 5001 + +n_iter_dmft_first = 2 +n_iter_dmft_per = 1 +n_iter_dmft = 5 + +block_threshold = 1e-03 + +h_int_type = "density_density" +U = 6.46 +J = 0.46 +beta = 10 +prec_mu = 0.1 + +sigma_mix = 1.0 +g0_mix = 1.0 +dc_type = 0 +dc = true +dc_dmft = true +calc_energies = true + +h5_save_freq = 1 + +[solver] +type = "hubbardI" +n_l = 15 +store_solver = false +measure_G_l = false +measure_density_matrix = true + +[dft] +dft_code = "qe" +n_cores = 10 +mpi_env = "default" +projector_type = "w90" +dft_exec = "pw.x" +w90_tolerance = 1e-1 diff --git a/doc/tutorials/Ce2O3_csc_w90/tutorial.ipynb b/doc/tutorials/Ce2O3_csc_w90/tutorial.ipynb index 65a50578..6e154533 100644 --- a/doc/tutorials/Ce2O3_csc_w90/tutorial.ipynb +++ b/doc/tutorials/Ce2O3_csc_w90/tutorial.ipynb @@ -117,14 +117,14 @@ "### DMFT\n", "\n", "1. Wannier90Converter: [ce2o3.inp](./dft_input/ce2o3.inp)\n", - "2. solid_dmft: [dmft_config.ini](./dmft_config.ini)\n", + "2. solid_dmft: [dmft_config.toml](./dmft_config.toml)\n", "\n", "Here we'll discuss the most important input flags for solid_dmft:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "165c087b", "metadata": {}, "outputs": [ @@ -133,12 +133,10 @@ "output_type": "stream", "text": [ "[general]\n", - "seedname = ce2o3\n", - "jobname = b10-U6.46-J0.46\n", - "csc = True\n", - "#dft_mu = 0.\n", - "solver_type = hubbardI\n", - "n_l = 15\n", + "seedname = \"ce2o3\"\n", + "jobname = \"b10-U6.46-J0.46\"\n", + "csc = true\n", + "\n", "eta = 0.5\n", "n_iw = 100\n", "n_tau = 5001\n", @@ -149,7 +147,7 @@ "\n", "block_threshold = 1e-03\n", "\n", - "h_int_type = density_density\n", + "h_int_type = \"density_density\"\n", "U = 6.46\n", "J = 0.46\n", "beta = 10\n", @@ -158,30 +156,31 @@ "sigma_mix = 1.0\n", "g0_mix = 1.0\n", "dc_type = 0\n", - "dc = True\n", - "dc_dmft = True\n", - "calc_energies = True\n", + "dc = true\n", + "dc_dmft = true\n", + "calc_energies = true\n", "\n", "h5_save_freq = 1\n", "\n", "[solver]\n", - "store_solver = False\n", - "measure_G_l = False\n", - "measure_density_matrix = True\n", + "type = \"hubbardI\"\n", + "n_l = 15\n", + "store_solver = false\n", + "measure_G_l = false\n", + "measure_density_matrix = true\n", "\n", "[dft]\n", - "dft_code = qe\n", + "dft_code = \"qe\"\n", "n_cores = 10\n", - "mpi_env = default\n", - "projector_type = w90\n", - "dft_exec = \n", - "w90_exec = wannier90.x\n", + "mpi_env = \"default\"\n", + "projector_type = \"w90\"\n", + "dft_exec = \"pw.x\"\n", "w90_tolerance = 1.e-1\n" ] } ], "source": [ - "!cat ./dmft_config.ini" + "!cat ./dmft_config.toml" ] }, { @@ -509,7 +508,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -523,7 +522,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/tutorials/NNO_os_plo_mag/config.ini b/doc/tutorials/NNO_os_plo_mag/config.toml similarity index 52% rename from doc/tutorials/NNO_os_plo_mag/config.ini rename to doc/tutorials/NNO_os_plo_mag/config.toml index 26d4237d..e2c9f594 100644 --- a/doc/tutorials/NNO_os_plo_mag/config.ini +++ b/doc/tutorials/NNO_os_plo_mag/config.toml @@ -1,47 +1,47 @@ [general] -seedname = nno -jobname = NNO_lowT +seedname = "nno" +jobname = "NNO_lowT" -enforce_off_diag = False +enforce_off_diag = false block_threshold = 0.001 -solver_type = cthyb n_iw = 2001 n_tau = 20001 prec_mu = 0.001 -h_int_type = density_density +h_int_type = "density_density" U = 8.0 J = 1.0 # temperature ~290 K beta = 40 -magnetic = True -magmom = -0.3, 0.3 -afm_order = True +magnetic = true +magmom = [-0.3, 0.3] +afm_order = true n_iter_dmft = 14 g0_mix = 0.9 dc_type = 0 -dc = True -dc_dmft = False +dc = true +dc_dmft = false -load_sigma = False -path_to_sigma = pre_AFM.h5 +load_sigma = false +path_to_sigma = "pre_AFM.h5" [solver] +type = "cthyb" length_cycle = 2000 n_warmup_cycles = 5e+3 n_cycles_tot = 1e+7 imag_threshold = 1e-5 -perform_tail_fit = True +perform_tail_fit = true fit_max_moment = 6 fit_min_w = 10 fit_max_w = 16 -measure_density_matrix = True +measure_density_matrix = true diff --git a/doc/tutorials/NNO_os_plo_mag/ref/config.ini b/doc/tutorials/NNO_os_plo_mag/ref/config.ini deleted file mode 100644 index 26d4237d..00000000 --- a/doc/tutorials/NNO_os_plo_mag/ref/config.ini +++ /dev/null @@ -1,47 +0,0 @@ -[general] -seedname = nno -jobname = NNO_lowT - -enforce_off_diag = False -block_threshold = 0.001 - -solver_type = cthyb -n_iw = 2001 -n_tau = 20001 - -prec_mu = 0.001 - -h_int_type = density_density -U = 8.0 -J = 1.0 - -# temperature ~290 K -beta = 40 - -magnetic = True -magmom = -0.3, 0.3 -afm_order = True - -n_iter_dmft = 14 - -g0_mix = 0.9 - -dc_type = 0 -dc = True -dc_dmft = False - -load_sigma = False -path_to_sigma = pre_AFM.h5 - -[solver] -length_cycle = 2000 -n_warmup_cycles = 5e+3 -n_cycles_tot = 1e+7 -imag_threshold = 1e-5 - -perform_tail_fit = True -fit_max_moment = 6 -fit_min_w = 10 -fit_max_w = 16 -measure_density_matrix = True - diff --git a/doc/tutorials/NNO_os_plo_mag/tutorial.ipynb b/doc/tutorials/NNO_os_plo_mag/tutorial.ipynb index 4b938604..1d3cd1f9 100644 --- a/doc/tutorials/NNO_os_plo_mag/tutorial.ipynb +++ b/doc/tutorials/NNO_os_plo_mag/tutorial.ipynb @@ -395,51 +395,52 @@ "source": [ "## 3. Running the AFM calculation\n", "\n", - "now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file [config.ini](config.ini) for solid_dmft looks like this: \n", + "now we run the calculation at around 290 K, which should be below the ordering temperature of NdNiO2 in DMFT. The config file [config.toml](config.toml) for solid_dmft looks like this: \n", "\n", " [general]\n", - " seedname = nno\n", - " jobname = NNO_lowT\n", + " seedname = \"nno\"\n", + " jobname = \"NNO_lowT\"\n", "\n", - " enforce_off_diag = False\n", + " enforce_off_diag = false\n", " block_threshold = 0.001\n", "\n", - " solver_type = cthyb\n", + " \n", " n_iw = 2001\n", " n_tau = 20001\n", "\n", " prec_mu = 0.001\n", "\n", - " h_int_type = density_density\n", + " h_int_type = \"density_density\"\n", " U = 8.0\n", " J = 1.0\n", "\n", " # temperature ~290 K\n", " beta = 40\n", "\n", - " magnetic = True\n", + " magnetic = true\n", " magmom = -0.3, 0.3\n", - " afm_order = True\n", + " afm_order = true\n", "\n", " n_iter_dmft = 14\n", "\n", " g0_mix = 0.9\n", "\n", " dc_type = 0\n", - " dc = True\n", - " dc_dmft = False\n", + " dc = true\n", + " dc_dmft = false\n", "\n", " [solver]\n", + " type = \"cthyb\"\n", " length_cycle = 2000\n", " n_warmup_cycles = 5e+3\n", " n_cycles_tot = 1e+7\n", " imag_threshold = 1e-5\n", "\n", - " perform_tail_fit = True\n", + " perform_tail_fit = true\n", " fit_max_moment = 6\n", " fit_min_w = 10\n", " fit_max_w = 16\n", - " measure_density_matrix = True" + " measure_density_matrix = true" ] }, { @@ -452,14 +453,14 @@ "* we changed `n_iw=2000` because the large energy window of the calculation requires more Matsubara frequencies\n", "* `h_int_type` is set to `density_density` to reduce complexity of the problem\n", "* `beta=40` here we set the temperature to ~290K\n", - "* `magnetic=True` lift spin degeneracy\n", + "* `magnetic=true` lift spin degeneracy\n", "* `magmom` here we specify the magnetic order. Here, we say that both Ni sites have the same spin, which should average to 0 at this high temperature. The magnetic moment is specified as an potential in eV splitting up / down channel of the initial self-energy\n", - "* `afm_order=True` tells solid_dmft to not solve impurities with the same `magmom` but rather copy the self-energy and if necessary flip the spin accordingly\n", + "* `afm_order=true` tells solid_dmft to not solve impurities with the same `magmom` but rather copy the self-energy and if necessary flip the spin accordingly\n", "* `length_cycle=2000` is the length between two Green's function measurements in cthyb. This number has to be choosen carefully to give an autocorrelation time ~1 for all orbitals\n", - "* `perform_tail_fit=True` : here we use tail fitting to get good high frequency self-energy behavior\n", - "* `measure_density_matrix = True ` measures the impurity many-body density matrix in the Fock basis for a multiplet analysis\n", + "* `perform_tail_fit=true` : here we use tail fitting to get good high frequency self-energy behavior\n", + "* `measure_density_matrix = true ` measures the impurity many-body density matrix in the Fock basis for a multiplet analysis\n", "\n", - "By setting the flag magmom to a small value with a flipped sign on both sites we tell solid_dmft that both sites are related by flipping the down and up channel. Now we run solid_dmft simply by executing `mpirun solid_dmft config.ini`. \n", + "By setting the flag magmom to a small value with a flipped sign on both sites we tell solid_dmft that both sites are related by flipping the down and up channel. Now we run solid_dmft simply by executing `mpirun solid_dmft config.toml`. \n", "\n", "Caution: this is a very heavy job, which should be submitted on a cluster. \n", "\n", @@ -630,7 +631,7 @@ "id": "fe674d6b-dae6-4497-82f5-6b8004afb275", "metadata": {}, "source": [ - "first we have to load the measured density matrix and the local Hamiltonian of the impurity problem from the h5 archive, which we stored by setting `measure_density_matrix=True` in the config file: " + "first we have to load the measured density matrix and the local Hamiltonian of the impurity problem from the h5 archive, which we stored by setting `measure_density_matrix=true` in the config file: " ] }, { diff --git a/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.ini b/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.toml similarity index 53% rename from doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.ini rename to doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.toml index e01b4763..42227590 100755 --- a/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.ini +++ b/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/2_dmft_csc/dmft_config.toml @@ -1,17 +1,13 @@ [general] -seedname = vasp -enforce_off_diag = True -set_rot = hloc +seedname = "vasp" +set_rot = "hloc" -csc = True -plo_cfg = plo.cfg +csc = true -solver_type = cthyb -n_l = 33 prec_mu = 0.001 -h_int_type = kanamori +h_int_type = "kanamori" U = 2.50 J = 0.50 beta = 40 @@ -22,28 +18,31 @@ n_iter_dmft_per = 2 n_iter_dmft = 26 h5_save_freq = 5 -dc = True +dc = true dc_type = 1 -dc_dmft = True +dc_dmft = true -calc_energies = True +calc_energies = true [solver] +type = "cthyb" +n_l = 33 length_cycle = 1000 n_warmup_cycles = 10000 n_cycles_tot = 2e+6 imag_threshold = 1e-5 -legendre_fit = True -measure_density_matrix = True -measure_pert_order = True +legendre_fit = true +measure_density_matrix = true +measure_pert_order = true [dft] n_iter = 4 # as of openmpi ver 4.0.7 there is a problem running with more than one core # use OMP_NUM_THREADS instead n_cores = 1 -dft_code = vasp -dft_exec = vasp_std -mpi_env = default -projector_type = plo -store_eigenvals = True +dft_code = "vasp" +dft_exec = "vasp_std" +mpi_env = "default" +plo_cfg = "plo.cfg" +projector_type = "plo" +store_eigenvals = true diff --git a/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb b/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb index 678f59fe..f53ec5dc 100644 --- a/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb +++ b/doc/tutorials/PrNiO3_csc_vasp_plo_cthyb/tutorial.ipynb @@ -158,27 +158,27 @@ "\n", "### Input files for CSC DMFT calculations\n", "\n", - "We first take a look into the input file [dmft_config.ini](2_dmft_csc/dmft_config.ini) and discuss some parameters. Please make sure you understand the role of the other parameters as well, as documented in the [reference manual of the read_config.py](https://triqs.github.io/solid_dmft/_ref/read_config.html) on the solid_dmft website. This is a selection of parameters from the dmft_config.ini:\n", + "We first take a look into the input file [dmft_config.toml](2_dmft_csc/dmft_config.toml) and discuss some parameters. Please make sure you understand the role of the other parameters as well, as documented in the [reference manual of the read_config.py](https://triqs.github.io/solid_dmft/_ref/read_config.html) on the solid_dmft website. This is a selection of parameters from the dmft_config.toml:\n", "\n", "Group [general]:\n", "\n", - "* `set_rot = hloc`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n", - "* `plo_cfg = plo.cfg`: the name of the config file for constructing the PLOs (see below)\n", + "* `set_rot = \"hloc\"`: rotates the local impurity problem into a basis where the local Hamiltonian is diagonal\n", "* `n_l = 35`: the number of Legendre coefficients to measure the imaginary-time Green's function in. Too few resulting in a \"bumpy\" Matsubara self-energy, too many include simulation noise. See also https://doi.org/10.1103/PhysRevB.84.075145.\n", - "* `dc_dmft = True`: using the DMFT occupations for the double counting is mandatory in CSC calculations. The DFT occupations are not well defined after the first density correction anymore\n", + "* `dc_dmft = true`: using the DMFT occupations for the double counting is mandatory in CSC calculations. The DFT occupations are not well defined after the first density correction anymore\n", "\n", "Group [solver]:\n", "\n", - "* `legendre_fit = True`: turns on measuring the Green's function in Legendre coefficients\n", + "* `legendre_fit = true`: turns on measuring the Green's function in Legendre coefficients\n", "\n", "Group [dft]:\n", "\n", "* `n_iter = 4`: number of DFT iterations between the DMFT occupations. Should be large enough for the density correction to be fully mixed into the DFT calculation\n", "* `n_cores = 32`: number of cores that DFT is run on. Check how many cores achieve the optimal DFT performance\n", - "* `dft_code = vasp`: we are running VASP\n", - "* `dft_exec = vasp_std`: the executable is vasp_std and its path is in the ROOT variable in our docker setup \n", - "* `mpi_env = default`: sets the mpi environment\n", - "* `projector_type = plo`: chooses PLO projectors\n", + "* `dft_code = \"vasp\"`: we are running VASP\n", + "* `dft_exec = \"vasp_std\"`: the executable is vasp_std and its path is in the ROOT variable in our docker setup \n", + "* `mpi_env = \"default\"`: sets the mpi environment\n", + "* `plo_cfg = \"plo.cfg\"`: the name of the config file for constructing the PLOs\n", + "* `projector_type = \"plo\"`: chooses PLO projectors\n", "\n", "The [plo.cfg](2_dmft_csc/plo.cfg) file is described [here](https://triqs.github.io/dft_tools/latest/guide/conv_vasp.html). The [rotations.dat](2_dmft_csc/rotations.dat) file is generated by diagonalizing the local d-shell density matrix and identifying the least occupied eigenstates as eg states. This we have limited k-point resolution wie also specify the band indices that describe our target space (isolated set of correlated states).\n", "\n", @@ -432,11 +432,19 @@ "\n", "* change U and J and try to see if you can reach a metallic state. What does the occupation look like?\n", "* try for better convergence: change `n_cycles_tot`, `n_iter_dmft` and `n_l`\n", - "* play around with the other parameters in the dmft_config.ini\n", + "* play around with the other parameters in the dmft_config.toml\n", "* analyze other quantities or have a look at the spectral functions from analytical continuation\n", "* try other ways to construct the correlated orbitals in CSC, e.g., with Wannier90\n", "* apply this to the material of your choice!" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "550fa534-c187-49d7-96e4-0848f53dd854", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/doc/tutorials/SVO_os_qe/dmft_config.ini b/doc/tutorials/SVO_os_qe/dmft_config.ini deleted file mode 100644 index 72d8540e..00000000 --- a/doc/tutorials/SVO_os_qe/dmft_config.ini +++ /dev/null @@ -1,38 +0,0 @@ -[general] -seedname = svo -jobname = out -enforce_off_diag = True -block_threshold= 0.001 - -solver_type = cthyb -n_l = 35 - -prec_mu = 0.001 - -h_int_type = kanamori -U = 2.0 -J = 0.65 - -beta = 40 - -n_iter_dmft = 8 - -dc_type = 1 -dc = True -dc_dmft = False - -calc_energies = False -sigma_mix = 1.0 - -h5_save_freq = 2 - -[solver] -length_cycle = 120 -n_warmup_cycles = 8000 -n_cycles_tot = 10e+6 -imag_threshold = 1e-5 -measure_g_l = True -measure_g_tau =True -measure_density_matrix = True - -perform_tail_fit = False diff --git a/doc/tutorials/SVO_os_qe/dmft_config.toml b/doc/tutorials/SVO_os_qe/dmft_config.toml new file mode 100644 index 00000000..a196fe61 --- /dev/null +++ b/doc/tutorials/SVO_os_qe/dmft_config.toml @@ -0,0 +1,37 @@ +[general] +seedname = "svo" +jobname = "out" +enforce_off_diag = true +block_threshold = 0.001 + +prec_mu = 0.001 + +h_int_type = "kanamori" +U = 2.0 +J = 0.65 + +mu_initial_guess = 12.297745 + +beta = 40 + +n_iter_dmft = 8 + +dc_type = 1 +dc = true +dc_dmft = false + +calc_energies = false +sigma_mix = 1.0 + +h5_save_freq = 2 + +[solver] +type = "cthyb" +n_l = 35 +length_cycle = 120 +n_warmup_cycles = 8000 +n_cycles_tot = 10e+6 +imag_threshold = 1e-5 +measure_G_l = true +perform_tail_fit = false + diff --git a/doc/tutorials/SVO_os_qe/run_MIT_coarse.sh b/doc/tutorials/SVO_os_qe/run_MIT_coarse.sh index abe56273..dd3ef507 100755 --- a/doc/tutorials/SVO_os_qe/run_MIT_coarse.sh +++ b/doc/tutorials/SVO_os_qe/run_MIT_coarse.sh @@ -5,59 +5,56 @@ for J in 0.0 0.25 0.5 0.75 1.0 echo "Creating directory for J=$J eV" for U in {2..10..2} #for U in 2 7 - do + do echo "Creating directory for U=$U eV" mkdir -p "./J$J/U$U/" cp ./svo.h5 "./J$J/U$U/svo.h5" - cat > "./J$J/U$U/dmft_config.ini" << EOF + cat > "./J$J/U$U/dmft_config.toml" << EOF [general] -seedname = svo -jobname = out -enforce_off_diag = True -block_threshold= 0.001 - -solver_type = cthyb -n_l = 35 +seedname = "svo" +jobname = "out" +enforce_off_diag = true +block_threshold = 0.001 prec_mu = 0.001 -h_int_type = kanamori -U = $U.0 +h_int_type = "kanamori" +U = $U J = $J +mu_initial_guess = 12.297745 + beta = 40 n_iter_dmft = 8 dc_type = 1 -dc = True -dc_dmft = False +dc = true +dc_dmft = false -calc_energies = False +calc_energies = false sigma_mix = 1.0 h5_save_freq = 2 -load_sigma = False [solver] +type = "cthyb" +n_l = 35 length_cycle = 120 n_warmup_cycles = 8000 n_cycles_tot = 10e+6 imag_threshold = 1e-5 -measure_g_l = True -measure_g_tau =True -measure_density_matrix = True - -perform_tail_fit = False +measure_G_l = true +perform_tail_fit = false EOF cd "./J$J/U$U/" echo "I am in folder:" pwd echo "Running the DMFT cycle for parameters: U=$U eV, J=$J eV" - + mpirun -n 72 solid_dmft - + cd .. cd .. done diff --git a/doc/tutorials/SVO_os_qe/run_MIT_fine.sh b/doc/tutorials/SVO_os_qe/run_MIT_fine.sh index e35d42ea..d830441c 100755 --- a/doc/tutorials/SVO_os_qe/run_MIT_fine.sh +++ b/doc/tutorials/SVO_os_qe/run_MIT_fine.sh @@ -8,45 +8,43 @@ for J in 0.0 0.25 0.5 0.75 1.0 echo "Creating directory for U=$U eV" mkdir -p "./J$J/U$U/" cp ./svo.h5 "./J$J/U$U/svo.h5" - cat > "./J$J/U$U/dmft_config.ini" << EOF + cat > "./J$J/U$U/dmft_config.toml" << EOF [general] -seedname = svo -jobname = out -enforce_off_diag = True -block_threshold= 0.001 - -solver_type = cthyb -n_l = 35 +seedname = "svo" +jobname = "out" +enforce_off_diag = true +block_threshold = 0.001 prec_mu = 0.001 -h_int_type = kanamori +h_int_type = "kanamori" U = $U J = $J +mu_initial_guess = 12.297745 + beta = 40 n_iter_dmft = 8 dc_type = 1 -dc = True -dc_dmft = False +dc = true +dc_dmft = false -calc_energies = False +calc_energies = false sigma_mix = 1.0 h5_save_freq = 2 [solver] +type = "cthyb" +n_l = 35 length_cycle = 120 n_warmup_cycles = 8000 n_cycles_tot = 10e+6 imag_threshold = 1e-5 -measure_g_l = True -measure_g_tau =True -measure_density_matrix = True - -perform_tail_fit = False +measure_G_l = true +perform_tail_fit = false EOF cd "./J$J/U$U/" diff --git a/python/solid_dmft/CMakeLists.txt b/python/solid_dmft/CMakeLists.txt index 72fbf5ad..d97562c5 100644 --- a/python/solid_dmft/CMakeLists.txt +++ b/python/solid_dmft/CMakeLists.txt @@ -1,8 +1,8 @@ # Configure the version configure_file(version.py.in version.py) -# All Python files. Copy them in the build dir to have a complete package for the tests. -file(GLOB_RECURSE python_sources RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.py) +# All Python and toml files. Copy them in the build dir to have a complete package for the tests. +file(GLOB_RECURSE python_sources RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} *.py *.toml) foreach(file ${python_sources}) configure_file(${file} ${file} COPYONLY) endforeach() @@ -10,4 +10,4 @@ endforeach() # Install python files to proper location set(PYTHON_LIB_DEST ${TRIQS_PYTHON_LIB_DEST_ROOT}/${PROJECT_NAME}) install(FILES ${CMAKE_CURRENT_BINARY_DIR}/version.py DESTINATION ${PYTHON_LIB_DEST}) -install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} DESTINATION ${TRIQS_PYTHON_LIB_DEST_ROOT} FILES_MATCHING PATTERN "*.py" PATTERN "*_desc.py" EXCLUDE) +install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} DESTINATION ${TRIQS_PYTHON_LIB_DEST_ROOT} FILES_MATCHING PATTERN "*.py" PATTERN "*.toml" PATTERN "*_desc.py" EXCLUDE) diff --git a/python/solid_dmft/csc_flow.py b/python/solid_dmft/csc_flow.py index 507b7090..f410134b 100644 --- a/python/solid_dmft/csc_flow.py +++ b/python/solid_dmft/csc_flow.py @@ -44,18 +44,18 @@ from solid_dmft.dft_managers import vasp_manager as vasp from solid_dmft.dft_managers import qe_manager as qe -def _run_plo_converter(general_params): +def _run_plo_converter(general_params, dft_params): if not mpi.is_master_node(): return # Checks for plo file for projectors - if not os.path.exists(general_params['plo_cfg']): + if not os.path.exists(dft_params['plo_cfg']): print('*** Input PLO config file not found! ' - + 'I was looking for {} ***'.format(general_params['plo_cfg'])) + + 'I was looking for {} ***'.format(dft_params['plo_cfg'])) mpi.MPI.COMM_WORLD.Abort(1) # Runs plo converter - plo_converter.generate_and_output_as_text(general_params['plo_cfg'], vasp_dir='./') + plo_converter.generate_and_output_as_text(dft_params['plo_cfg'], vasp_dir='./') # Writes new H(k) to h5 archive converter = VaspConverter(filename=general_params['seedname']) converter.convert_dft_input() @@ -190,7 +190,7 @@ def _full_vasp_run(general_params, dft_params, initial_run, n_iter_dft=1, sum_k= vasp.run_charge_update() if dft_params['projector_type'] == 'plo': - _run_plo_converter(general_params) + _run_plo_converter(general_params, dft_params) irred_indices = None elif dft_params['projector_type'] == 'w90': _run_wannier90(general_params, dft_params) diff --git a/python/solid_dmft/dmft_cycle.py b/python/solid_dmft/dmft_cycle.py index f8c11ad0..127f6da0 100755 --- a/python/solid_dmft/dmft_cycle.py +++ b/python/solid_dmft/dmft_cycle.py @@ -40,6 +40,7 @@ import triqs.utility.mpi as mpi from triqs.gf import Gf, make_hermitian, MeshReFreq, MeshImFreq from triqs.gf.tools import inverse +from triqs_dft_tools import BlockStructure from triqs_dft_tools.sumk_dft import SumkDFT # own modules @@ -56,9 +57,52 @@ from solid_dmft.dmft_tools import manipulate_chemical_potential as manipulate_mu from solid_dmft.dmft_tools import initial_self_energies as initial_sigma from solid_dmft.dmft_tools import greens_functions_mixer as gf_mixer +from solid_dmft.io_tools import verify_input_params, dict_to_h5 +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 _determine_block_structure(sum_k, general_params, advanced_params): + """ + + + def formatter(value): + if isinstance(value, float): + return '{:.2f}'.format(value) + return str(value) + + param = general_params[param_name] + if not isinstance(param, list): + mpi.report('Assuming {} = '.format(param_name) + + '{} for all correlated shells'.format(formatter(param))) + general_params[param_name] = [param] * n_inequiv_shells + elif len(param) == n_inequiv_shells: + mpi.report(f'{param_name} list for correlated shells: ' + + ', '.join([formatter(p) for p in param])) + else: + raise IndexError(f'{param_name} must be list of length ' + f'n_inequiv_shells={n_inequiv_shells} ' + 'or single value but is ' + str(general_params[param_name])) + + return general_params + +def _determine_block_structure(sum_k, general_params, advanced_params, solver_type_per_imp, dens_mat): """ Determines block structrure and degenerate deg_shells computes first DFT density matrix to determine block structure and changes @@ -74,66 +118,44 @@ def _determine_block_structure(sum_k, general_params, advanced_params): sum_k : SumK Object instances updated sum_k Object """ - mpi.report('\n *** determination of block structure ***') - - # this returns a list of dicts (one entry for each corr shell) - # the dict contains one entry for up and one for down - # each entry is a square complex numpy matrix with dim=corr_shell['dim'] - zero_Sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] - sum_k.put_Sigma(zero_Sigma) - if general_params['solver_type'] in ['ftps']: - G_loc_all = sum_k.extract_G_loc(broadening=general_params['eta'], transform_to_solver_blocks=False) - dens_mat = [G_loc_all[iineq].density() for iineq in range(sum_k.n_inequiv_shells)] + mpi.report('\n *** Determination of block structure ***') + + # Adds spin degeneracies if not spin-orbit coupled + if sum_k.SO == 0: + sum_k.block_structure.deg_shells = [[['up_0', 'down_0']] for _ in range(sum_k.n_inequiv_shells)] + + # Evaluates degeneracies for ftps + imp_ftps = [i for i, s in enumerate(solver_type_per_imp) if s == 'ftps'] + if imp_ftps: + mock_sumk = deepcopy(sum_k) + mock_sumk.analyse_block_structure(dm=dens_mat, threshold=general_params['block_threshold'], include_shells=imp_ftps) + deg_orbs_ftps = [mock_sumk.deg_shells[icrsh] if icrsh in imp_ftps else None + for icrsh in range(sum_k.n_inequiv_shells)] + mpi.report('Block structure written to "deg_orbs_ftps":') + mpi.report(deg_orbs_ftps) else: - dens_mat = sum_k.density_matrix(method='using_gf') - - original_dens_mat = deepcopy(dens_mat) - - # for certain systems it is needed to keep off diag elements - # this enforces to use the full corr subspace matrix - solver_struct_ftps = None - if general_params['enforce_off_diag'] or general_params['solver_type'] in ['ftps', 'hartree']: - if general_params['solver_type'] in ['ftps']: - # first round to determine real blockstructure - mock_sumk = deepcopy(sum_k) - mock_sumk.analyse_block_structure(dm=dens_mat, threshold=general_params['block_threshold']) - solver_struct_ftps = [None] * sum_k.n_inequiv_shells - for icrsh in range(sum_k.n_inequiv_shells): - solver_struct_ftps[icrsh] = mock_sumk.deg_shells[icrsh] - mpi.report('Block structure written to "solver_struct_ftps":') - mpi.report(solver_struct_ftps) - - mpi.report('enforcing off-diagonal elements in block structure finder') - for dens_mat_per_imp in dens_mat: - for dens_mat_per_block in dens_mat_per_imp.values(): - dens_mat_per_block += 2 * general_params['block_threshold'] - - if not general_params['enforce_off_diag'] and general_params['block_suppress_orbital_symm']: - mpi.report('removing orbital symmetries in block structure finder') - for dens_mat_per_imp in dens_mat: - for dens_mat_per_block in dens_mat_per_imp.values(): - dens_mat_per_block += 2*np.diag(np.arange(dens_mat_per_block.shape[0])) - - mpi.report('using 1-particle density matrix and Hloc (atomic levels) to ' - 'determine the block structure') - sum_k.analyse_block_structure(dm=dens_mat, threshold=general_params['block_threshold']) - - if advanced_params['pick_solver_struct'] != 'none': + deg_orbs_ftps = None + + # Only removes the off-diagonal terms for the selected impurities + imp_to_analyze = [i for i, offdiag in enumerate(general_params['enforce_off_diag']) if not offdiag] + mpi.report('using 1-particle density matrix and Hloc (atomic levels) to determine the block structure') + sum_k.analyse_block_structure(dm=dens_mat, threshold=general_params['block_threshold'], include_shells=imp_to_analyze) + + # Applies manual selection of the solver struct + if any(s is not None for s in advanced_params['pick_solver_struct']): mpi.report('selecting subset of orbital space for gf_struct_solver from input:') - mpi.report(advanced_params['pick_solver_struct']) + mpi.report(advanced_params['pick_solver_struct'][icrsh]) sum_k.block_structure.pick_gf_struct_solver(advanced_params['pick_solver_struct']) # Applies the manual mapping to each inequivalent shell - if advanced_params['map_solver_struct'] != 'none': + if any(s is not None for s in advanced_params['map_solver_struct']): sum_k.block_structure.map_gf_struct_solver(advanced_params['map_solver_struct']) - if advanced_params['mapped_solver_struct_degeneracies'] != 'none': + if any(s is not None for s in advanced_params['mapped_solver_struct_degeneracies']): sum_k.block_structure.deg_shells = advanced_params['mapped_solver_struct_degeneracies'] # if we want to do a magnetic calculation we need to lift up/down degeneracy if general_params['magnetic'] and sum_k.SO == 0: - mpi.report('magnetic calculation: removing the spin degeneracy from the block structure') - + mpi.report('Magnetic calculation: removing the spin degeneracy from the block structure') for icrsh, deg_shells_site in enumerate(sum_k.block_structure.deg_shells): deg_shell_mag = [] # find degenerate orbitals that do not simply connect different spin channels @@ -145,12 +167,11 @@ def _determine_block_structure(sum_k, general_params, advanced_params): if len(deg) > 1: deg_shell_mag.append(deg) sum_k.block_structure.deg_shells[icrsh] = deg_shell_mag - # for SOC we remove all degeneracies - elif general_params['magnetic'] and sum_k.SO == 1: - sum_k.block_structure.deg_shells = [[] for icrsh in range(len(sum_k.block_structure.deg_shells))] + elif sum_k.SO == 1: + sum_k.block_structure.deg_shells = [[] for _ in range(sum_k.n_inequiv_shells)] - return sum_k, original_dens_mat, solver_struct_ftps + return sum_k, deg_orbs_ftps def _calculate_rotation_matrix(general_params, sum_k): @@ -161,6 +182,18 @@ def _calculate_rotation_matrix(general_params, sum_k): diagonalizing the local Hamiltonian has. Note that the interaction Hamiltonian has to be rotated if it is not fully orbital-gauge invariant (only the Kanamori fulfills that). + + Parameters + ---------- + general_params : dict + general parameters as a dict + sum_k : SumkDFT object + Sumk Object + + Returns + ------- + sum_k : SumkDFT Object + updated Sumk object with the new rotation matrices """ # Extracts new rotation matrices from density_mat or local Hamiltonian @@ -196,47 +229,40 @@ def _calculate_rotation_matrix(general_params, sum_k): return sum_k -def _chi_setup(sum_k, general_params, solver_params): +def _chi_setup(sum_k, solver_params, map_imp_solver): """ Parameters ---------- sum_k : SumkDFT object Sumk object with the information about the correct block structure - general_paramters: general params dict solver_params: solver params dict Returns ------- - solver_params : dict - solver_paramters for the QMC solver - Op_list : list of one-particle operators to measure per impurity + ops_chi_measurement : list of one-particle operators to measure per impurity """ - if general_params['measure_chi'] == 'SzSz': - mpi.report('\nSetting up Chi(S_z(tau),S_z(0)) measurement') - elif general_params['measure_chi'] == 'NN': - mpi.report('\nSetting up Chi(n(tau),n(0)) measurement') - - Op_list = [None] * sum_k.n_inequiv_shells + ops_chi_measure = [None] * sum_k.n_inequiv_shells for icrsh in range(sum_k.n_inequiv_shells): + isolv = map_imp_solver[icrsh] + if 'measure_chi' not in solver_params[isolv] or solver_params[isolv]['measure_chi'] is None: + continue + n_orb = sum_k.corr_shells[icrsh]['dim'] - orb_names = list(range(n_orb)) + if solver_params[isolv]['measure_chi'] == 'SzSz': + mpi.report(f'\nImp {icrsh} with solver #{isolv}: Setting up Chi(S_z(tau),S_z(0)) measurement') - if general_params['measure_chi'] == 'SzSz': - Op_list[icrsh] = S_op('z', - spin_names=sum_k.spin_block_names[sum_k.SO], - n_orb=n_orb, - map_operator_structure=sum_k.sumk_to_solver[icrsh]) - elif general_params['measure_chi'] == 'NN': - Op_list[icrsh] = N_op(spin_names=sum_k.spin_block_names[sum_k.SO], - n_orb=n_orb, - map_operator_structure=sum_k.sumk_to_solver[icrsh]) + ops_chi_measure[icrsh] = S_op('z', spin_names=sum_k.spin_block_names[sum_k.SO], + n_orb=n_orb, map_operator_structure=sum_k.sumk_to_solver[icrsh]) + elif solver_params[isolv]['measure_chi'] == 'NN': + mpi.report(f'\nImp {icrsh} with solver #{isolv}: Setting up Chi(n(tau),n(0)) measurement') - solver_params['measure_O_tau_min_ins'] = general_params['measure_chi_insertions'] + ops_chi_measure[icrsh] = N_op(spin_names=sum_k.spin_block_names[sum_k.SO], + n_orb=n_orb, map_operator_structure=sum_k.sumk_to_solver[icrsh]) - return solver_params, Op_list + return ops_chi_measure def dmft_cycle(general_params, solver_params, advanced_params, dft_params, @@ -266,18 +292,20 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, updated observable array for calculation """ - # create Sumk object - # TODO: use_dft_blocks=True yields inconsistent number of blocks! - - # first we have to determine the mesh - if general_params['solver_type'] in ['ftps']: - sumk_mesh = MeshReFreq(window=general_params['w_range'], - n_w=general_params['n_w']) - else: + # Creates real- or imaginary-frequency mesh to store Green functions on + if general_params['beta'] is not None: + mpi.report('Running solid_dmft on imag-freq grid because "general.beta" specified') sumk_mesh = MeshImFreq(beta=general_params['beta'], S='Fermion', n_iw=general_params['n_iw']) + broadening = None + else: + mpi.report('Running solid_dmft on real-freq grid because "general.beta" not specified') + sumk_mesh = MeshReFreq(window=general_params['w_range'], + n_w=general_params['n_w']) + broadening = general_params['eta'] + # Creates SumkDFT object sum_k = SumkDFT(hdf_file=general_params['jobname']+'/'+general_params['seedname']+'.h5', mesh=sumk_mesh, use_dft_blocks=False, h_field=general_params['h_field']) @@ -313,27 +341,43 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, iteration_offset = mpi.bcast(iteration_offset) sum_k.chemical_potential = mpi.bcast(sum_k.chemical_potential) - # Incompatabilities for SO coupling - if sum_k.SO == 1: - if not general_params['csc'] and general_params['magnetic'] and general_params['afm_order']: - raise ValueError('AFM order not supported with SO coupling') - - # need to set sigma immediately here, otherwise mesh in unclear for sumK - # Initializes empty Sigma for calculation of DFT density even if block structure changes later - zero_Sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] - sum_k.put_Sigma(zero_Sigma) + # Checks the general parameters that are impurity-dependent and ensures they are a list + mpi.report('Checking parameters that are impurity-dependent') + for key in ['U', 'J', 'U_prime', 'ratio_F4_F2', 'h_int_type', 'enforce_off_diag', 'dc_type']: + general_params = _extract_quantity_per_inequiv(key, sum_k.n_inequiv_shells, general_params) + # Same for advanced params + for key in ['dc_U', 'dc_J', 'dc_fixed_occ', 'map_solver_struct', 'pick_solver_struct', 'mapped_solver_struct_degeneracies']: + advanced_params = _extract_quantity_per_inequiv(key, sum_k.n_inequiv_shells, advanced_params) + + # Checks that all impurities have an associated solver + # and creates a map between impurity index and solver index + if len(solver_params) == 1 and solver_params[0]['idx_impurities'] is None: + map_imp_solver = [0] * sum_k.n_inequiv_shells + else: + all_idx_imp = [i for entry in solver_params for i in entry['idx_impurities']] + if sorted(all_idx_imp) != list(range(sum_k.n_inequiv_shells)): + raise ValueError('All impurities must be listed exactly once in solver.idx_impurities' + f'but instead got {all_idx_imp}') + + map_imp_solver = [] + for iineq in range(sum_k.n_inequiv_shells): + for isolver, entry in enumerate(solver_params): + if iineq in entry['idx_impurities']: + 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'\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) # Initializes chemical potential with mu_initial_guess if this is the first iteration - if general_params['mu_initial_guess'] != 'none' and iteration_offset == 0: - sum_k.chemical_potential = general_params['mu_initial_guess'] - mpi.report('\ninitial chemical potential set to {:.3f} eV\n'.format(sum_k.chemical_potential)) + if general_params['mu_initial_guess'] is not None and iteration_offset == 0: + sum_k.chemical_potential = general_params['mu_initial_guess'] + mpi.report('\nInitial chemical potential set to {:.3f} eV\n'.format(sum_k.chemical_potential)) - if general_params['solver_type'] in ['ftps']: - dft_mu = sum_k.calc_mu(precision=general_params['prec_mu'], - broadening=general_params['eta']) - else: - dft_mu = sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + dft_mu = sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method'], + broadening=broadening) # calculate E_kin_dft for one shot calculations if not general_params['csc'] and general_params['calc_energies']: @@ -348,7 +392,7 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, for _ in range(sum_k.n_inequiv_shells)] # Generates a rotation matrix to change the basis - if general_params['set_rot'] != 'none': + if general_params['set_rot'] is not None: # calculate new rotation matrices sum_k = _calculate_rotation_matrix(general_params, sum_k) # Saves rotation matrix to h5 archive: @@ -356,6 +400,14 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, archive['DMFT_input']['rot_mat'] = sum_k.rot_mat mpi.barrier() + # Calculates density in default block structure + local_gf_dft_corr = sum_k.extract_G_loc(broadening=broadening, transform_to_solver_blocks=False, with_Sigma=False) + dens_mat_dft = [gf.density() for gf in local_gf_dft_corr] + + for iineq, gf in enumerate(local_gf_dft_corr): + mpi.report(f'Total density for imp {iineq} from DFT: ' + '{:10.6f}'.format(np.real(gf.total_density()))) + # determine block structure for solver det_blocks = None # load previous block_structure if possible @@ -365,10 +417,10 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, # Previous rot_mat only not None if the rot_mat changed from load_sigma or previous run previous_rot_mat = None - solver_struct_ftps = None + deg_orbs_ftps = None # determine block structure for GF and Hyb function if det_blocks and not general_params['load_sigma']: - sum_k, dm, solver_struct_ftps = _determine_block_structure(sum_k, general_params, advanced_params) + sum_k, deg_orbs_ftps = _determine_block_structure(sum_k, general_params, advanced_params, solver_type_per_imp, dens_mat_dft) # if load sigma we need to load everything from this h5 archive elif general_params['load_sigma']: #loading block_struc and rot_mat and deg_shells @@ -377,8 +429,8 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, sum_k.block_structure = old_calc['DMFT_input/block_structure'] sum_k.deg_shells = old_calc['DMFT_input/deg_shells'] previous_rot_mat = old_calc['DMFT_input/rot_mat'] - if general_params['solver_type'] in ['ftps']: - solver_struct_ftps = old_calc['DMFT_input/solver_struct_ftps'] + if deg_orbs_ftps is not None: + deg_orbs_ftps = old_calc['DMFT_input/solver_struct_ftps'] if not all(np.allclose(x, y) for x, y in zip(sum_k.rot_mat, previous_rot_mat)): print('WARNING: rot_mat in current run is different from loaded_sigma run.') @@ -388,12 +440,11 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, sum_k.block_structure = mpi.bcast(sum_k.block_structure) sum_k.deg_shells = mpi.bcast(sum_k.deg_shells) previous_rot_mat = mpi.bcast(previous_rot_mat) - solver_struct_ftps = mpi.bcast(solver_struct_ftps) + deg_orbs_ftps = mpi.bcast(deg_orbs_ftps) # In a magnetic calculation, no shells are degenerate if general_params['magnetic'] and sum_k.SO == 0: sum_k.deg_shells = [[] for _ in range(sum_k.n_inequiv_shells)] - dm = None else: # Master node checks if rot_mat stayed the same if mpi.is_master_node(): @@ -405,14 +456,13 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, archive['DMFT_input']['rot_mat'] = sum_k.rot_mat else: previous_rot_mat = None - if general_params['solver_type'] in ['ftps']: - solver_struct_ftps = archive['DMFT_input/solver_struct_ftps'] + if 'solver_struct_ftps' in archive['DMFT_input']: + deg_orbs_ftps = archive['DMFT_input/solver_struct_ftps'] sum_k.block_structure = mpi.bcast(sum_k.block_structure) sum_k.deg_shells = mpi.bcast(sum_k.deg_shells) previous_rot_mat = mpi.bcast(previous_rot_mat) - solver_struct_ftps = mpi.bcast(solver_struct_ftps) - dm = None + deg_orbs_ftps = mpi.bcast(deg_orbs_ftps) # Compatibility with h5 archives from the triqs2 version # Sumk doesn't hold corr_to_inequiv anymore, which is in block_structure now @@ -424,76 +474,66 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, # Determination of shell_multiplicity shell_multiplicity = [sum_k.corr_to_inequiv.count(icrsh) for icrsh in range(sum_k.n_inequiv_shells)] - # Initializes new empty Sigma with new blockstructure for calculation of DFT density - zero_Sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] - sum_k.put_Sigma(zero_Sigma) - # print block structure and DFT input quantitites! - formatter.print_block_sym(sum_k, dm, general_params) - - # extract free lattice greens function - if general_params['solver_type'] in ['ftps']: - G_loc_all_dft = sum_k.extract_G_loc(broadening=general_params['eta'], with_Sigma=False, mu=dft_mu) - else: - G_loc_all_dft = sum_k.extract_G_loc(with_Sigma=False, mu=dft_mu) - density_mat_dft = [G_loc_all_dft[iineq].density() for iineq in range(sum_k.n_inequiv_shells)] - - for iineq in range(sum_k.n_inequiv_shells): - density_shell_dft = G_loc_all_dft[iineq].total_density() - mpi.report('total density for imp {} from DFT: {:10.6f}'.format(iineq, np.real(density_shell_dft))) + formatter.print_block_sym(sum_k, dens_mat_dft, general_params) if general_params['magnetic']: sum_k.SP = 1 - if general_params['afm_order']: general_params = afm_mapping.determine(general_params, archive, sum_k.n_inequiv_shells) # Constructs interaction Hamiltonian and writes it to the h5 archive - h_int = interaction_hamiltonian.construct(sum_k, general_params, advanced_params) + h_int = interaction_hamiltonian.construct(sum_k, general_params, solver_type_per_imp) if mpi.is_master_node(): archive['DMFT_input']['h_int'] = h_int # If new calculation, writes input parameters and sum_k <-> solver mapping to archive if iteration_offset == 0: if mpi.is_master_node(): - archive['DMFT_input']['general_params'] = general_params - archive['DMFT_input']['solver_params'] = solver_params - archive['DMFT_input']['advanced_params'] = advanced_params + archive['DMFT_input']['general_params'] = dict_to_h5.prep_params_for_h5(general_params) + archive['DMFT_input']['solver_params'] = dict_to_h5.prep_params_for_h5(solver_params) + archive['DMFT_input']['dft_params'] = dict_to_h5.prep_params_for_h5(dft_params) + archive['DMFT_input']['advanced_params'] = dict_to_h5.prep_params_for_h5(advanced_params) archive['DMFT_input']['block_structure'] = sum_k.block_structure archive['DMFT_input']['deg_shells'] = sum_k.deg_shells archive['DMFT_input']['shell_multiplicity'] = shell_multiplicity - if general_params['solver_type'] in ['ftps']: - archive['DMFT_input']['solver_struct_ftps'] = solver_struct_ftps + if deg_orbs_ftps is not None: + archive['DMFT_input']['solver_struct_ftps'] = deg_orbs_ftps + mpi.barrier() solvers = [None] * sum_k.n_inequiv_shells for icrsh in range(sum_k.n_inequiv_shells): # Construct the Solver instances - solvers[icrsh] = SolverStructure(general_params, solver_params, advanced_params, - sum_k, icrsh, h_int[icrsh], - iteration_offset, solver_struct_ftps) + solvers[icrsh] = SolverStructure(general_params, solver_params[map_imp_solver[icrsh]], + advanced_params, sum_k, icrsh, h_int[icrsh], + iteration_offset, deg_orbs_ftps) # store solver hash to archive if mpi.is_master_node(): if 'version' not in archive['DMFT_input']: archive['DMFT_input'].create_group('version') - archive['DMFT_input']['version']['solver_name'] = general_params['solver_type'] - archive['DMFT_input']['version']['solver_hash'] = solvers[0].git_hash - archive['DMFT_input']['version']['solver_version'] = solvers[0].version + for iineq, solver in enumerate(solvers): + if f'solver {iineq}' not in archive['DMFT_input']['version']: + archive['DMFT_input']['version'].create_group(f'solver {iineq}') + archive['DMFT_input']['version'][f'solver {iineq}']['name'] = solver.solver_params['type'] + archive['DMFT_input']['version'][f'solver {iineq}']['hash'] = solver.git_hash + archive['DMFT_input']['version'][f'solver {iineq}']['version'] = solver.version + + # Extracts local GF per *inequivalent* shell + local_gf_dft = sum_k.extract_G_loc(broadening=broadening, with_Sigma=False, mu=dft_mu) + density_mat_dft = [gf.density() for gf in local_gf_dft] # Determines initial Sigma and DC sum_k, solvers = initial_sigma.determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, - archive, iteration_offset, density_mat_dft, solvers) + archive, iteration_offset, density_mat_dft, solvers, + solver_type_per_imp) - sum_k = manipulate_mu.set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multiplicity) + sum_k = manipulate_mu.set_initial_mu(general_params, sum_k, iteration_offset, archive, broadening) # setup of measurement of chi(SzSz(tau) if requested - if general_params['measure_chi'] != 'none': - solver_params, Op_list = _chi_setup(sum_k, general_params, solver_params) - else: - Op_list = None + ops_chi_measure = _chi_setup(sum_k, solver_params, map_imp_solver) mpi.report('\n {} DMFT cycles requested. Starting with iteration {}.\n'.format(n_iter, iteration_offset+1)) @@ -502,14 +542,14 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, conv_obs = None if mpi.is_master_node(): observables = prep_observables(archive, sum_k) - conv_obs = convergence.prep_conv_obs(archive, sum_k) + conv_obs = convergence.prep_conv_obs(archive) observables = mpi.bcast(observables) conv_obs = mpi.bcast(conv_obs) if mpi.is_master_node() and iteration_offset == 0: write_header_to_file(general_params, sum_k) - observables = add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_energy, sum_k, - G_loc_all_dft, density_mat_dft, shell_multiplicity) + observables = add_dft_values_as_zeroth_iteration(observables, general_params, solver_type_per_imp, dft_mu, dft_energy, sum_k, + local_gf_dft, density_mat_dft, shell_multiplicity) write_obs(observables, sum_k, general_params) # write convergence file convergence.prep_conv_file(general_params, sum_k) @@ -529,9 +569,9 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, mpi.report('Running iteration: {} / {}'.format(it, iteration_offset + n_iter)) (sum_k, solvers, observables, is_converged) = _dmft_step(sum_k, solvers, it, general_params, - solver_params, advanced_params, dft_params, + solver_params, advanced_params, dft_params, map_imp_solver, solver_type_per_imp, h_int, archive, shell_multiplicity, E_kin_dft, - observables, conv_obs, Op_list, dft_irred_kpt_indices, dft_energy, + observables, conv_obs, ops_chi_measure, dft_irred_kpt_indices, dft_energy, broadening, is_converged, is_sampling=False) if is_converged: @@ -553,9 +593,9 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, mpi.report('#'*80) mpi.report('Running iteration: {} / {}'.format(it, iteration_offset+general_params['sampling_iterations'])) sum_k, solvers, observables, _ = _dmft_step(sum_k, solvers, it, general_params, - solver_params, advanced_params, dft_params, + solver_params, advanced_params, dft_params, map_imp_solver, solver_type_per_imp, h_int, archive, shell_multiplicity, E_kin_dft, - observables, conv_obs, Op_list, dft_irred_kpt_indices, dft_energy, + observables, conv_obs, ops_chi_measure, dft_irred_kpt_indices, dft_energy, broadening, is_converged=True, is_sampling=True) mpi.report('** Sampling finished ***') @@ -571,9 +611,9 @@ def dmft_cycle(general_params, solver_params, advanced_params, dft_params, def _dmft_step(sum_k, solvers, it, general_params, - solver_params, advanced_params, dft_params, + solver_params, advanced_params, dft_params, map_imp_solver, solver_type_per_imp, h_int, archive, shell_multiplicity, E_kin_dft, - observables, conv_obs, Op_list, dft_irred_kpt_indices, dft_energy, + observables, conv_obs, ops_chi_measure, dft_irred_kpt_indices, dft_energy, broadening, is_converged, is_sampling): """ Contains the actual dmft steps when all the preparation is done @@ -595,16 +635,11 @@ def _dmft_step(sum_k, solvers, it, general_params, printed = ((np.real, 'real'), ) # Extracts G local - if general_params['solver_type'] in ['ftps']: - G_loc_all = sum_k.extract_G_loc(broadening=general_params['eta']) - else: - G_loc_all = sum_k.extract_G_loc() + G_loc_all = sum_k.extract_G_loc(broadening=broadening) # Copies Sigma and G0 before Solver run for mixing later Sigma_freq_previous = [solvers[iineq].Sigma_freq.copy() for iineq in range(sum_k.n_inequiv_shells)] G0_freq_previous = [solvers[iineq].G0_freq.copy() for iineq in range(sum_k.n_inequiv_shells)] - if general_params['dc'] and general_params['dc_type'] == 4: - cpa_G_loc = gf_mixer.init_cpa(sum_k, solvers, general_params) # looping over inequiv shells and solving for each site seperately for icrsh in range(sum_k.n_inequiv_shells): @@ -625,16 +660,12 @@ def _dmft_step(sum_k, solvers, it, general_params, # dyson equation to extract G0_freq, using Hermitian symmetry solvers[icrsh].G0_freq << inverse(solvers[icrsh].Sigma_freq + inverse(solvers[icrsh].G_freq)) - # dyson equation to extract cpa_G0_freq - if general_params['dc'] and general_params['dc_type'] == 4: - cpa_G0_freq[icrsh] << inverse(solvers[icrsh].Sigma_freq + inverse(cpa_G_loc[icrsh])) - # mixing of G0 if wanted from the second iteration on if it > 1: solvers[icrsh] = gf_mixer.mix_g0(solvers[icrsh], general_params, icrsh, archive, G0_freq_previous[icrsh], it, sum_k.deg_shells[icrsh]) - if general_params['solver_type'] in ['cthyb', 'ctint', 'hubbardI', 'inchworm']: + if isinstance(sum_k.mesh, MeshImFreq): solvers[icrsh].G0_freq << make_hermitian(solvers[icrsh].G0_freq) sum_k.symm_deg_gf(solvers[icrsh].G0_freq, ish=icrsh) @@ -648,13 +679,14 @@ def _dmft_step(sum_k, solvers, it, general_params, archive['DMFT_results/last_iter']['G0_freq_{}'.format(icrsh)] = solvers[icrsh].G0_freq # setup of measurement of chi(SzSz(tau) if requested - if general_params['measure_chi'] != 'none': - solvers[icrsh].solver_params['measure_O_tau'] = (Op_list[icrsh], Op_list[icrsh]) + # TODO: move this into solver class? + if ops_chi_measure[icrsh] is not None: + solvers[icrsh].solver_params['measure_O_tau'] = (ops_chi_measure[icrsh], ops_chi_measure[icrsh]) if (general_params['magnetic'] and general_params['afm_order'] and general_params['afm_mapping'][icrsh][0]): # If we do a AFM calculation we can use the init magnetic moments to # copy the self energy instead of solving it explicitly - solvers = afm_mapping.apply(general_params, solver_params, icrsh, sum_k.gf_struct_solver[icrsh], solvers) + solvers = afm_mapping.apply(general_params, icrsh, sum_k.gf_struct_solver[icrsh], solvers) else: # Solve the impurity problem for this shell mpi.report('\nSolving the impurity problem for shell {} ...'.format(icrsh)) @@ -676,10 +708,6 @@ def _dmft_step(sum_k, solvers, it, general_params, if general_params['store_solver'] and mpi.is_master_node(): archive['DMFT_input/solver/it_'+str(it)]['S_'+str(icrsh)] = solvers[icrsh].triqs_solver - # add to cpa_G_time - if general_params['dc'] and general_params['dc_type'] == 4: - cpa_G_time << cpa_G_time + general_params['cpa_x'][icrsh] * solvers[icrsh].G_time - # Done with loop over impurities if mpi.is_master_node(): @@ -687,28 +715,27 @@ def _dmft_step(sum_k, solvers, it, general_params, print('\n *** Post-processing the solver output ***') print('Total charge of all correlated shells : {:.6f}\n'.format(density_tot)) - # if CPA average Sigma over impurities before mixing - if general_params['dc'] and general_params['dc_type'] == 4: - solvers = gf_mixer.mix_cpa(cpa_G0_freq, sum_k.n_inequiv_shells, solvers) solvers = gf_mixer.mix_sigma(general_params, sum_k.n_inequiv_shells, solvers, Sigma_freq_previous) # calculate new DC # for the hartree solver the DC potential will be formally set to zero as it is already present in the Sigma if general_params['dc'] and general_params['dc_dmft']: sum_k = initial_sigma.calculate_double_counting(sum_k, density_mat, - general_params, advanced_params) - + general_params, advanced_params, + solver_type_per_imp) + #The hartree solver computes the DC energy internally, set it in sum_k - if general_params['solver_type'] == 'hartree': - for icrsh in range(sum_k.n_inequiv_shells): - sum_k.dc_energ[icrsh] = solvers[icrsh].DC_energy + for icrsh in range(sum_k.n_corr_shells): + iineq = sum_k.corr_to_inequiv[icrsh] + if solver_type_per_imp[iineq] == 'hartree': + sum_k.dc_energ[icrsh] = solvers[iineq].DC_energy # doing the dmft loop and set new sigma into sumk sum_k.put_Sigma([solvers[icrsh].Sigma_freq for icrsh in range(sum_k.n_inequiv_shells)]) # saving previous mu for writing to observables file previous_mu = sum_k.chemical_potential - sum_k = manipulate_mu.update_mu(general_params, sum_k, it, archive) + sum_k = manipulate_mu.update_mu(general_params, sum_k, it, archive, broadening) # if we do a CSC calculation we need always an updated GAMMA file E_bandcorr = 0.0 @@ -724,7 +751,7 @@ def _dmft_step(sum_k, solvers, it, general_params, E_bandcorr = calc_bandcorr_man(general_params, sum_k, E_kin_dft) # Writes results to h5 archive - results_to_archive.write(archive, sum_k, general_params, solver_params, solvers, it, + results_to_archive.write(archive, sum_k, general_params, solver_params, solvers, map_imp_solver, solver_type_per_imp, it, is_sampling, previous_mu, density_mat_pre, density_mat, deltaN, dens) mpi.barrier() @@ -735,6 +762,8 @@ def _dmft_step(sum_k, solvers, it, general_params, observables = add_dmft_observables(observables, general_params, solver_params, + map_imp_solver, + solver_type_per_imp, dft_energy, it, solvers, diff --git a/python/solid_dmft/dmft_tools/afm_mapping.py b/python/solid_dmft/dmft_tools/afm_mapping.py index 2d877d11..5aa93bff 100644 --- a/python/solid_dmft/dmft_tools/afm_mapping.py +++ b/python/solid_dmft/dmft_tools/afm_mapping.py @@ -80,7 +80,7 @@ def determine(general_params, archive, n_inequiv_shells): return general_params -def apply(general_params, solver_params, icrsh, gf_struct_solver, solvers): +def apply(general_params, icrsh, gf_struct_solver, solvers): imp_source = general_params['afm_mapping'][icrsh][1] invert_spin = general_params['afm_mapping'][icrsh][2] mpi.report('\ncopying the self-energy for shell {} from shell {}'.format(icrsh, imp_source)) @@ -99,7 +99,7 @@ def apply(general_params, solver_params, icrsh, gf_struct_solver, solvers): solvers[icrsh].G0_freq[spin_channel] << solvers[imp_source].G0_freq[target_channel] solvers[icrsh].G_time[spin_channel] << solvers[imp_source].G_time[target_channel] - if solver_params['measure_pert_order']: + if solvers[icrsh].solver_params['measure_pert_order']: if not hasattr(solvers[icrsh], 'perturbation_order'): solvers[icrsh].perturbation_order = {} solvers[icrsh].perturbation_order[spin_channel] = solvers[imp_source].perturbation_order[target_channel] @@ -112,15 +112,15 @@ def apply(general_params, solver_params, icrsh, gf_struct_solver, solvers): solvers[icrsh].G0_freq << solvers[imp_source].G0_freq solvers[icrsh].G_time << solvers[imp_source].G_time - if solver_params['measure_pert_order']: + if solvers[icrsh].solver_params['measure_pert_order']: solvers[icrsh].perturbation_order = solvers[imp_source].perturbation_order solvers[icrsh].perturbation_order_total = solvers[imp_source].perturbation_order_total - if solver_params['measure_density_matrix']: + if solvers[icrsh].solver_params['measure_density_matrix']: solvers[icrsh].density_matrix = solvers[imp_source].density_matrix solvers[icrsh].h_loc_diagonalization = solvers[imp_source].h_loc_diagonalization - if general_params['measure_chi'] != 'none': + if 'measure_chi' in solvers[icrsh].solver_params and solvers[icrsh].solver_params['measure_chi'] is not None: solvers[icrsh].O_time = solvers[imp_source].O_time return solvers diff --git a/python/solid_dmft/dmft_tools/convergence.py b/python/solid_dmft/dmft_tools/convergence.py index 6f425a9e..bfe3652f 100644 --- a/python/solid_dmft/dmft_tools/convergence.py +++ b/python/solid_dmft/dmft_tools/convergence.py @@ -169,7 +169,7 @@ def max_G_diff(G1, G2, norm_temp = True): return norm -def prep_conv_obs(h5_archive, sum_k): +def prep_conv_obs(h5_archive): """ prepares the conv arrays and files for the DMFT calculation @@ -177,7 +177,6 @@ def prep_conv_obs(h5_archive, sum_k): ---------- h5_archive: hdf archive instance hdf archive for calculation - sum_k : SumK Object instances __Returns:__ conv_obs : dict diff --git a/python/solid_dmft/dmft_tools/formatter.py b/python/solid_dmft/dmft_tools/formatter.py index 0c3dc46c..4fb33aac 100644 --- a/python/solid_dmft/dmft_tools/formatter.py +++ b/python/solid_dmft/dmft_tools/formatter.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- ################################################################################ # # solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations @@ -90,13 +89,10 @@ def print_block_sym(sum_k, dm, general_params): if dm: # Prints dft density matrix print('\nDFT density matrix') + # Double loop to sort by impurities for icrsh in range(sum_k.n_inequiv_shells): spins = sum_k.spin_block_names[sum_k.SO] - # ftps dens_mat only knows inequiv_shells - if general_params['solver_type'] in ['ftps']: - shlst = [icrsh] - else: - shlst = [ish for ish, ineq_shell in enumerate(sum_k.corr_to_inequiv) if ineq_shell == icrsh] + shlst = [ish for ish, ineq_shell in enumerate(sum_k.corr_to_inequiv) if ineq_shell == icrsh] for sh in shlst: n_orb = sum_k.corr_shells[sh]['dim'] if sum_k.SO == 0: diff --git a/python/solid_dmft/dmft_tools/greens_functions_mixer.py b/python/solid_dmft/dmft_tools/greens_functions_mixer.py index 2ac2ca25..2e5cf47d 100644 --- a/python/solid_dmft/dmft_tools/greens_functions_mixer.py +++ b/python/solid_dmft/dmft_tools/greens_functions_mixer.py @@ -219,25 +219,3 @@ def mix_sigma(general_params, n_inequiv_shells, solvers, Sigma_freq_previous): solvers[icrsh].Sigma_freq << mpi.bcast(solvers[icrsh].Sigma_freq) return solvers - - -def init_cpa(sum_k, solvers, general_params): - # initialize cpa_G_time, cpa_G0_freq; extract cpa_G_loc - cpa_G_time = sum_k.block_structure.create_gf(ish=0, gf_function=Gf, space='solver', - mesh=MeshImTime(beta=general_params['beta'], - S='Fermion', n_tau=general_params['n_tau']) - ) - cpa_G0_freq = [solvers[iineq].G0_freq.copy() for iineq in range(sum_k.n_inequiv_shells)] - cpa_G_loc = sum_k.extract_G_loc(with_Sigma=True, with_dc=False) - - return cpa_G_loc - - -def mix_cpa(cpa_G0_freq, n_inequiv_shells, solvers): - cpa_G_freq = solvers[0].G_freq.copy() - cpa_G_freq << Fourier(cpa_G_time) - # replace solver Sigma with cpa Sigma - for icrsh in range(n_inequiv_shells): - solvers[icrsh].Sigma_freq << inverse(cpa_G0_freq[icrsh]) - inverse(cpa_G_freq) - - return solvers diff --git a/python/solid_dmft/dmft_tools/initial_self_energies.py b/python/solid_dmft/dmft_tools/initial_self_energies.py index 075a9049..41893dff 100755 --- a/python/solid_dmft/dmft_tools/initial_self_energies.py +++ b/python/solid_dmft/dmft_tools/initial_self_energies.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- ################################################################################ # # solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations @@ -38,7 +37,7 @@ from triqs.gf import BlockGf, Gf -def calculate_double_counting(sum_k, density_matrix, general_params, advanced_params): +def calculate_double_counting(sum_k, density_matrix, general_params, advanced_params, solver_type_per_imp): """ Calculates the double counting, including all manipulations from advanced_params. @@ -63,35 +62,27 @@ def calculate_double_counting(sum_k, density_matrix, general_params, advanced_pa # copy the density matrix to not change it density_matrix_DC = deepcopy(density_matrix) - if general_params['solver_type'] == 'hartree': - mpi.report('\nSOLID_DMFT: Hartree solver detected, zeroing out the DC correction. This gets computed at the solver level') - for icrsh in range(sum_k.n_inequiv_shells): + # TODO: suppress print when reseting DC to zero + # and add a final print of the DC pot/energy at the end of the whole function + icrsh_hartree = [icrsh for icrsh, type in enumerate(solver_type_per_imp) if type == 'hartree'] + icrsh_not_hartree = [icrsh for icrsh, type in enumerate(solver_type_per_imp) if type != 'hartree'] + if icrsh_hartree: + mpi.report(f'\nSOLID_DMFT: Hartree solver for impurities {icrsh_hartree} detected. ' + 'Zeroing out the DC correction there, which gets computed at the solver level.') + for icrsh in icrsh_hartree: sum_k.calc_dc(density_matrix_DC[icrsh], orb=icrsh, use_dc_value=0.0) - return sum_k # Sets the DC and exits the function if advanced_params['dc_fixed_value'] is specified - if advanced_params['dc_fixed_value'] != 'none': - for icrsh in range(sum_k.n_inequiv_shells): + if advanced_params['dc_fixed_value'] is not None: + for icrsh in icrsh_not_hartree: sum_k.calc_dc(density_matrix_DC[icrsh], orb=icrsh, use_dc_value=advanced_params['dc_fixed_value']) return sum_k - # use DC for CPA - if general_params['dc'] and general_params['dc_type'] == 4: - zetas = general_params['cpa_zeta'] - for icrsh in range(sum_k.n_inequiv_shells): - sum_k.calc_dc(density_matrix_DC[icrsh], orb=icrsh, use_dc_value=zetas[icrsh]) - - return sum_k - - - if advanced_params['dc_fixed_occ'] != 'none': - mpi.report('Fixing occupation for DC potential to provided value') - - assert sum_k.n_inequiv_shells == len(advanced_params['dc_fixed_occ']), "give exactly one occupation per correlated shell" - for icrsh in range(sum_k.n_inequiv_shells): - mpi.report('fixing occupation for impurity '+str(icrsh)+' to n='+str(advanced_params['dc_fixed_occ'][icrsh])) + for icrsh in icrsh_not_hartree: + if advanced_params['dc_fixed_occ'][icrsh] is not None: + mpi.report(f'Fixing occupation for DC for imp {icrsh} to n={advanced_params["dc_fixed_occ"][icrsh]:.4f}') n_orb = sum_k.corr_shells[icrsh]['dim'] # we need to handover a matrix to calc_dc so calc occ per orb per spin channel orb_occ = advanced_params['dc_fixed_occ'][icrsh]/(n_orb*2) @@ -100,8 +91,8 @@ def calculate_double_counting(sum_k, density_matrix, general_params, advanced_pa np.fill_diagonal(inner, orb_occ+0.0j) # The regular way: calculates the DC based on U, J and the dc_type - for icrsh in range(sum_k.n_inequiv_shells): - if general_params['dc_type'] == 3: + for icrsh in icrsh_not_hartree: + if general_params['dc_type'][icrsh] == 3: # this is FLL for eg orbitals only as done in Seth PRB 96 205139 2017 eq 10 # this setting for U and J is reasonable as it is in the spirit of F0 and Javg # for the 5 orb case @@ -111,13 +102,16 @@ def calculate_double_counting(sum_k, density_matrix, general_params, advanced_pa sum_k.calc_dc(density_matrix_DC[icrsh], U_interact=Uavg, J_hund=Javg, orb=icrsh, use_dc_formula=0) else: + mpi.report(f'\nCalculating standard DC for impurity {icrsh} with U={advanced_params["dc_U"][icrsh]} and J={advanced_params["dc_J"][icrsh]}') sum_k.calc_dc(density_matrix_DC[icrsh], U_interact=advanced_params['dc_U'][icrsh], J_hund=advanced_params['dc_J'][icrsh], orb=icrsh, - use_dc_formula=general_params['dc_type']) + use_dc_formula=general_params['dc_type'][icrsh]) # for the fixed DC according to https://doi.org/10.1103/PhysRevB.90.075136 # dc_imp is calculated with fixed occ but dc_energ is calculated with given n if advanced_params['dc_nominal']: + if 'Hartree' in solver_type_per_imp: + raise NotImplementedError('dc_nominal not implemented in presence of Hartree solver') mpi.report('\ncalculating DC energy with fixed DC potential from above\n' + ' for the original density matrix doi.org/10.1103/PhysRevB.90.075136\n' + ' aka nominal DC') @@ -147,7 +141,8 @@ def calculate_double_counting(sum_k, density_matrix, general_params, advanced_pa mpi.report('DC energy for shell {} = {}'.format(icrsh, energy_per_shell)) # Rescales DC if advanced_params['dc_factor'] is given - if advanced_params['dc_factor'] != 'none': + if advanced_params['dc_factor'] is not None: + # Here, no check for Hartree since its DC is 0 and the scaling doesn't change that rescaled_dc_imp = [{spin: advanced_params['dc_factor'] * dc_per_spin for spin, dc_per_spin in dc_per_shell.items()} for dc_per_shell in sum_k.dc_imp] @@ -162,7 +157,9 @@ def calculate_double_counting(sum_k, density_matrix, general_params, advanced_pa mpi.report('DC for shell {} and block {} = {}'.format(icrsh, spin, dc_per_spin[0][0])) mpi.report('DC energy for shell {} = {}'.format(icrsh, energy_per_shell)) - if advanced_params['dc_orb_shift'] != 'none': + if advanced_params['dc_orb_shift'] is not None: + if 'Hartree' in solver_type_per_imp: + raise NotImplementedError('dc_orb_shift not implemented in presence of Hartree solver') mpi.report('adding an extra orbital dependent shift per impurity') tot_norb = 0 dc_orb_shift = [] @@ -350,7 +347,8 @@ def _set_loaded_sigma(sum_k, loaded_sigma, loaded_dc_imp, general_params): def determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, - archive, iteration_offset, density_mat_dft, solvers): + archive, iteration_offset, density_mat_dft, solvers, + solver_type_per_imp): """ Determines the double counting (DC) and the initial Sigma. This can happen in five different ways: @@ -399,7 +397,7 @@ def determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, print('\nFrom previous calculation:', end=' ') start_sigma, sum_k.dc_imp, sum_k.dc_energ, last_g0, _ = _load_sigma_from_h5(archive, -1) if general_params['csc'] and not general_params['dc_dmft']: - sum_k = calculate_double_counting(sum_k, density_mat_dft, general_params, advanced_params) + sum_k = calculate_double_counting(sum_k, density_mat_dft, general_params, advanced_params, solver_type_per_imp) # Loads Sigma from different calculation elif general_params['load_sigma']: print('\nFrom {}:'.format(general_params['path_to_sigma']), end=' ') @@ -411,25 +409,24 @@ def determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, if general_params['dc']: if general_params['dc_dmft']: sum_k = calculate_double_counting(sum_k, loaded_density_matrix, - general_params, advanced_params) + general_params, advanced_params, + solver_type_per_imp) else: sum_k = calculate_double_counting(sum_k, density_mat_dft, - general_params, advanced_params) + general_params, advanced_params, + solver_type_per_imp) start_sigma = _set_loaded_sigma(sum_k, loaded_sigma, loaded_dc_imp, general_params) # Sets DC as Sigma because no initial Sigma given elif general_params['dc']: - sum_k = calculate_double_counting(sum_k, density_mat_dft, general_params, advanced_params) + sum_k = calculate_double_counting(sum_k, density_mat_dft, general_params, advanced_params, + solver_type_per_imp) # initialize Sigma from sum_k - if general_params['solver_type'] in ['ftps']: - start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', - mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] - else: - start_sigma = [sum_k.block_structure.create_gf(ish=iineq, space='solver', mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', + mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] for icrsh in range(sum_k.n_inequiv_shells): dc_value = sum_k.dc_imp[sum_k.inequiv_to_corr[icrsh]][sum_k.spin_block_names[sum_k.SO][0]][0, 0] @@ -466,13 +463,8 @@ def determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, else: start_sigma[icrsh][spin_channel] << fac else: - if general_params['solver_type'] in ['ftps']: - start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', - mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] - else: - start_sigma = [sum_k.block_structure.create_gf(ish=iineq, space='solver', mesh=sum_k.mesh) - for iineq in range(sum_k.n_inequiv_shells)] + start_sigma = [sum_k.block_structure.create_gf(ish=iineq, gf_function=Gf, space='solver', mesh=sum_k.mesh) + for iineq in range(sum_k.n_inequiv_shells)] # Adds random, frequency-independent noise in zeroth iteration to break symmetries if not np.isclose(general_params['noise_level_initial_sigma'], 0) and iteration_offset == 0: @@ -500,11 +492,11 @@ def determine_dc_and_initial_sigma(general_params, advanced_params, sum_k, sum_k.put_Sigma([solvers[icrsh].Sigma_freq for icrsh in range(sum_k.n_inequiv_shells)]) # load sigma as first guess in the hartree solver if applicable - if general_params['solver_type'] == 'hartree': + for icrsh in range(sum_k.n_inequiv_shells): # TODO: # should this be moved to before the solve() call? Having it only here means there is a mismatch # between the mixing at the level of the solver and the sumk (solver mixes always 100%) - for icrsh in range(sum_k.n_inequiv_shells): + if solver_type_per_imp[icrsh] == 'hartree': mpi.report(f"SOLID_DMFT: setting first guess hartree solver for impurity {icrsh}") solvers[icrsh].triqs_solver.reinitialize_sigma(start_sigma[icrsh]) diff --git a/python/solid_dmft/dmft_tools/interaction_hamiltonian.py b/python/solid_dmft/dmft_tools/interaction_hamiltonian.py index 126d0ede..2cc21a57 100644 --- a/python/solid_dmft/dmft_tools/interaction_hamiltonian.py +++ b/python/solid_dmft/dmft_tools/interaction_hamiltonian.py @@ -42,33 +42,6 @@ except ImportError: pass - -def _extract_U_J_list(param_name, n_inequiv_shells, general_params): - """ - Checks if param_name ('U', 'U_prime', or 'J') are a single value or - different per inequivalent shell. If just a single value is given, - this value is applied to each shell. - """ - - if not isinstance(param_name, str): - formatted_param = ['none' if p == 'none' else '{:.2f}'.format(p) - for p in general_params[param_name]] - else: - formatted_param = general_params[param_name] - - if len(general_params[param_name]) == 1: - mpi.report('Assuming {} = '.format(param_name) - + '{} for all correlated shells'.format(formatted_param[0])) - general_params[param_name] *= n_inequiv_shells - elif len(general_params[param_name]) == n_inequiv_shells: - mpi.report('{} list for correlated shells: {}'.format(param_name, formatted_param)) - else: - raise IndexError('Property list {} '.format(general_params[param_name]) - + 'must have length 1 or n_inequiv_shells') - - return general_params - - def _load_crpa_interaction_matrix(sum_k, filename='UIJKL'): """ Loads VASP cRPA data to use as an interaction Hamiltonian. @@ -173,7 +146,7 @@ def _adapt_U_4index_for_SO(Umat_full): return Umat_full_SO -def _construct_kanamori(sum_k, general_params, icrsh): +def _construct_kanamori(sum_k, general_params, solver_type_per_imp, icrsh): """ Constructs the Kanamori interaction Hamiltonian. Only Kanamori does not need the full four-index matrix. Therefore, we can construct it directly @@ -190,12 +163,13 @@ def _construct_kanamori(sum_k, general_params, icrsh): + 'outside the t2g or eg manifold?') # check if Uprime has been specified manually - if general_params['U_prime'][icrsh] == 'U-2J': + if general_params['U_prime'][icrsh] is None: U_prime = general_params['U'][icrsh] - 2.0 * general_params['J'][icrsh] else: U_prime = general_params['U_prime'][icrsh] + mpi.report('U = {:.2f}, U\' = {:.2f}, J = {:.2f}\n'.format(general_params['U'][icrsh], U_prime, general_params['J'][icrsh])) - if general_params['solver_type'] == 'ftps': + if solver_type_per_imp[icrsh] == 'ftps': # 1-band modell requires J and U' equals zero if n_orb == 1: up, j = 0.0, 0.0 @@ -212,11 +186,11 @@ def _construct_kanamori(sum_k, general_params, icrsh): h_int = util.h_int_kanamori(sum_k.spin_block_names[sum_k.SO], n_orb, map_operator_structure=sum_k.sumk_to_solver[icrsh], U=Umat, Uprime=Upmat, J_hund=general_params['J'][icrsh], - H_dump=os.path.join(general_params['jobname'], 'H.txt')) + H_dump=os.path.join(general_params['jobname'], f'H_imp{icrsh}.txt')) else: h_int = _construct_kanamori_soc(general_params['U'][icrsh], general_params['J'][icrsh], n_orb, sum_k.sumk_to_solver[icrsh], - os.path.join(general_params['jobname'], 'H.txt')) + os.path.join(general_params['jobname'], f'H_imp{icrsh}.txt')) return h_int @@ -328,7 +302,7 @@ def _construct_dynamic(sum_k, general_params, icrsh): mpi.report('onsite interaction value for imp {}: {:.3f}'.format(icrsh, U_onsite[icrsh])) h_int = util.h_int_density(sum_k.spin_block_names[sum_k.SO], n_orb, map_operator_structure=sum_k.sumk_to_solver[icrsh], - U=np.array([[0]]), Uprime=np.array([[U_onsite[icrsh]]]), H_dump=os.path.join(general_params['jobname'], 'H.txt')) + U=np.array([[0]]), Uprime=np.array([[U_onsite[icrsh]]]), H_dump=os.path.join(general_params['jobname'], f'H_imp{icrsh}.txt')) return h_int @@ -346,16 +320,16 @@ def _generate_four_index_u_matrix(sum_k, general_params, icrsh): assert n_orb % 2 == 0 n_orb = n_orb // 2 - if sum_k.corr_shells[ish]['l'] != 2: - slater_integrals = util.U_J_to_radial_integrals(l=sum_k.corr_shells[ish]['l'], - U_int=general_params['U'][icrsh], + l = sum_k.corr_shells[ish]['l'] + if l != 2: + slater_integrals = util.U_J_to_radial_integrals(l=l, U_int=general_params['U'][icrsh], J_hund=general_params['J'][icrsh]) else: # Implements parameter R=F4/F2. For R=0.63 equivalent to util.U_J_to_radial_integrals U = general_params['U'][icrsh] J = general_params['J'][icrsh] R = general_params['ratio_F4_F2'][icrsh] - R = 0.63 if R == 'none' else R + R = 0.63 if R is None else R slater_integrals = np.array([U, 14*J/(1+R), 14*J*R/(1+R)]) mpi.report('\nImpurity {}: The corresponding slater integrals are'.format(icrsh)) @@ -369,22 +343,21 @@ def _generate_four_index_u_matrix(sum_k, general_params, icrsh): # this is consistent with the order of orbitals in the VASP interface # but not necessarily with wannier90, qe, and wien2k! # This is also true for the f-shell. - Umat_full = util.U_matrix_slater(l=sum_k.corr_shells[ish]['l'], + Umat_full = util.U_matrix_slater(l=l, radial_integrals=slater_integrals, basis='spherical') Umat_full = util.transform_U_matrix(Umat_full, - util.spherical_to_cubic(l=sum_k.corr_shells[ish]['l'], - convention=general_params['h_int_basis']) - ) + util.spherical_to_cubic(l=l, convention=general_params['h_int_basis'])) - if n_orb == 2: + if l == 2 and n_orb == 2: Umat_full = util.eg_submatrix(Umat_full) mpi.report('Using eg subspace of interaction Hamiltonian') - elif n_orb == 3: + elif l == 2 and n_orb == 3: Umat_full = util.t2g_submatrix(Umat_full) mpi.report('Using t2g subspace of interaction Hamiltonian') - elif n_orb not in (5, 7): - raise ValueError('Calculations for d shell only support 2, 3 or 5 orbitals' - + 'and for the f shell only 7 orbitals') + elif n_orb != 2*l+1: + raise ValueError(f'Imp {icrsh}: for the Slater Hamiltonian, please use either ' + f'the full shell with 2l+1={2*l+1} orbitals ' + 'or the t2g or eg subspace of the d shell with 3 or 2 orbitals.') return Umat_full @@ -420,7 +393,7 @@ def _construct_density_density(sum_k, general_params, Umat_full_rotated, icrsh): Umat, Upmat = util.reduce_4index_to_2index(Umat_full_rotated) h_int = util.h_int_density(sum_k.spin_block_names[sum_k.SO], n_orb, map_operator_structure=sum_k.sumk_to_solver[icrsh], - U=Umat, Uprime=Upmat, H_dump=os.path.join(general_params['jobname'], 'H.txt')) + U=Umat, Uprime=Upmat, H_dump=os.path.join(general_params['jobname'], f'H_imp{icrsh}.txt')) return h_int @@ -436,7 +409,7 @@ def _construct_slater(sum_k, general_params, Umat_full_rotated, icrsh): h_int = util.h_int_slater(sum_k.spin_block_names[sum_k.SO], n_orb, map_operator_structure=sum_k.sumk_to_solver[icrsh], U_matrix=Umat_full_rotated, - H_dump=os.path.join(general_params['jobname'], 'H.txt')) + H_dump=os.path.join(general_params['jobname'], f'H_imp{icrsh}.txt')) return h_int @@ -497,7 +470,7 @@ def h_int_simple_intra(spin_names,n_orb,U,off_diag=None,map_operator_structure=N return H -def construct(sum_k, general_params, advanced_params): +def construct(sum_k, general_params, solver_type_per_imp): """ Constructs the interaction Hamiltonian. Currently implemented are the Kanamori Hamiltonian (usually for 2 or 3 orbitals), the density-density and @@ -522,18 +495,6 @@ def construct(sum_k, general_params, advanced_params): # Extracts U and J mpi.report('*** interaction parameters ***') - general_params = _extract_U_J_list('h_int_type', sum_k.n_inequiv_shells, general_params) - for param_name in ('U', 'J'): - general_params = _extract_U_J_list(param_name, sum_k.n_inequiv_shells, general_params) - if 'kanamori' in general_params['h_int_type']: - general_params = _extract_U_J_list('U_prime', sum_k.n_inequiv_shells, general_params) - for param_name in ('dc_U', 'dc_J'): - advanced_params = _extract_U_J_list(param_name, sum_k.n_inequiv_shells, advanced_params) - - # Extracts ratio_F4_F2 if any shell uses a solver supporting it - if 'density_density' in general_params['h_int_type'] or 'full_slater' in general_params['h_int_type']: - general_params = _extract_U_J_list('ratio_F4_F2', sum_k.n_inequiv_shells, general_params) - # Constructs the interaction Hamiltonian. Needs to come after setting sum_k.rot_mat mpi.report('\nConstructing the interaction Hamiltonians') h_int = [None] * sum_k.n_inequiv_shells @@ -542,22 +503,22 @@ def construct(sum_k, general_params, advanced_params): # Kanamori if general_params['h_int_type'][icrsh] == 'kanamori': - h_int[icrsh] = _construct_kanamori(sum_k, general_params, icrsh) + h_int[icrsh] = _construct_kanamori(sum_k, general_params, solver_type_per_imp, icrsh) continue # for density density or full slater get full four-index U matrix if general_params['h_int_type'][icrsh] in ('density_density', 'full_slater'): mpi.report('\nNote: The input parameters U and J here are orbital-averaged parameters.') mpi.report('Note: The order of the orbitals is important. See also the doc string of this method.') - # Checks that all entries are l == 2 or R == 'none' + # Checks that all entries are l == 2 or R is None if (sum_k.corr_shells[sum_k.inequiv_to_corr[icrsh]]['l'] != 2 - and general_params['ratio_F4_F2'][icrsh] != 'none'): + and general_params['ratio_F4_F2'][icrsh] is not None): raise ValueError('Ratio F4/F2 only implemented for d-shells ' + 'but set in impurity {}'.format(icrsh)) - if general_params['h_int_type'][icrsh] == 'density_density' and general_params['solver_type'] == 'ftps': + if general_params['h_int_type'][icrsh] == 'density_density' and solver_type_per_imp[icrsh] == 'ftps': # TODO: implement - raise NotImplementedError('\nNote: Density-density not implemented for ftps.') + raise NotImplementedError('Note: Density-density not implemented for ftps.') Umat_full = _generate_four_index_u_matrix(sum_k, general_params, icrsh) @@ -589,7 +550,7 @@ def construct(sum_k, general_params, advanced_params): solver.get_n_orbitals(sum_k)[icrsh]['up'], map_operator_structure=sum_k.sumk_to_solver[icrsh], U=general_params['U'][icrsh], - H_dump=os.path.join(general_params['jobname'], 'H.txt')) + H_dump=os.path.join(general_params['jobname'], f'H_imp{icrsh}.txt')) continue diff --git a/python/solid_dmft/dmft_tools/manipulate_chemical_potential.py b/python/solid_dmft/dmft_tools/manipulate_chemical_potential.py index 188c700d..236aa552 100755 --- a/python/solid_dmft/dmft_tools/manipulate_chemical_potential.py +++ b/python/solid_dmft/dmft_tools/manipulate_chemical_potential.py @@ -31,7 +31,7 @@ import numpy as np import triqs.utility.mpi as mpi -from triqs.gf import BlockGf, GfImFreq, GfImTime, Fourier +from triqs.gf import BlockGf, GfImFreq, GfImTime, Fourier, MeshImFreq try: if mpi.is_master_node(): from solid_dmft.postprocessing import maxent_gf_latt @@ -257,7 +257,7 @@ def _set_mu_to_gap_middle_with_maxent(general_params, sum_k, gf_lattice_iw, arch return sum_k.chemical_potential + (valence_edge + conduction_edge) / 2 -def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multiplicity): +def set_initial_mu(general_params, sum_k, iteration_offset, archive, broadening): """ Handles the different ways of setting the initial chemical potential mu: * Chemical potential set to fixed value: uses this value @@ -282,8 +282,6 @@ def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multi the number of iterations executed in previous calculations. archive : HDFArchive needed to potentially load previous results and write MaxEnt results to. - shell_multiplicity : iterable of ints - number of equivalent shells per impurity. Returns ------- @@ -292,19 +290,15 @@ def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multi """ # Uses fixed_mu_value as chemical potential if parameter is given - if general_params['fixed_mu_value'] != 'none': + if general_params['fixed_mu_value'] is not None: sum_k.set_mu(general_params['fixed_mu_value']) mpi.report('+++ Keeping the chemical potential fixed at {:.3f} eV +++'.format(general_params['fixed_mu_value'])) return sum_k # In first iteration, determines mu and returns if iteration_offset == 0: - if general_params['dc'] and general_params['dc_type'] == 4: - return sum_k - if general_params['solver_type'] in ['ftps']: - sum_k.calc_mu(precision=general_params['prec_mu'], broadening=general_params['eta']) - else: - sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method'], + broadening=broadening) return sum_k # If continuing calculation and not updating mu, loads sold value @@ -324,11 +318,10 @@ def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multi previous_mu = mpi.bcast(previous_mu) # Runs maxent if spectral weight too low and occupation is close to desired one - # TODO: which solvers work? - if general_params['solver_type'] in ['cthyb', 'ctint'] and general_params['mu_gap_gb2_threshold'] != 'none': + if isinstance(sum_k.mesh, MeshImFreq) and general_params['mu_gap_gb2_threshold'] is not None: sum_k.chemical_potential = previous_mu gf_lattice_iw, g_betahalf, occupation = _initialize_lattice_gf(sum_k, general_params) - fulfills_occupation_crit = (general_params['mu_gap_occ_deviation'] == 'none' + fulfills_occupation_crit = (general_params['mu_gap_occ_deviation'] is None or np.abs(occupation - sum_k.density_required) < general_params['mu_gap_occ_deviation']) if -np.real(g_betahalf) < general_params['mu_gap_gb2_threshold'] and fulfills_occupation_crit: @@ -345,10 +338,8 @@ def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multi _, _, occupation = _initialize_lattice_gf(sum_k, general_params) # If system not gapped, gets chemical potential from dichotomy method - if general_params['solver_type'] in ['ftps']: - sum_k.calc_mu(precision=general_params['prec_mu'], broadening=general_params['eta']) - else: - sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method'], + broadening=broadening) # Applies mu mixing to dichotomy result sum_k.chemical_potential = _mix_chemical_potential(general_params, occupation, @@ -357,7 +348,7 @@ def set_initial_mu(general_params, sum_k, iteration_offset, archive, shell_multi return sum_k -def update_mu(general_params, sum_k, it, archive): +def update_mu(general_params, sum_k, it, archive, broadening): """ Handles the different ways of updating the chemical potential mu: * Chemical potential set to fixed value: uses this value @@ -386,7 +377,7 @@ def update_mu(general_params, sum_k, it, archive): """ # Uses fixed_mu_value as chemical potential if parameter is given - if general_params['fixed_mu_value'] != 'none': + if general_params['fixed_mu_value'] is not None: sum_k.set_mu(general_params['fixed_mu_value']) mpi.report('+++ Keeping the chemical potential fixed at {:.3f} eV +++'.format(general_params['fixed_mu_value'])) return sum_k @@ -399,9 +390,9 @@ def update_mu(general_params, sum_k, it, archive): # Runs maxent if spectral weight too low and occupation is close to desired one # TODO: which solvers work? - if general_params['solver_type'] in ['cthyb', 'ctint','ctseg'] and general_params['mu_gap_gb2_threshold'] != 'none': + if isinstance(sum_k.mesh, MeshImFreq) and general_params['mu_gap_gb2_threshold'] is not None: gf_lattice_iw, g_betahalf, occupation = _initialize_lattice_gf(sum_k, general_params) - fulfills_occupation_crit = (general_params['mu_gap_occ_deviation'] == 'none' + fulfills_occupation_crit = (general_params['mu_gap_occ_deviation'] is None or np.abs(occupation - sum_k.density_required) < general_params['mu_gap_occ_deviation']) if -np.real(g_betahalf) < general_params['mu_gap_gb2_threshold'] and fulfills_occupation_crit: @@ -419,10 +410,8 @@ def update_mu(general_params, sum_k, it, archive): # If system not gapped, gets chemical potential from dichotomy method previous_mu = sum_k.chemical_potential - if general_params['solver_type'] in ['ftps']: - sum_k.calc_mu(precision=general_params['prec_mu'], broadening=general_params['eta']) - else: - sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method']) + sum_k.calc_mu(precision=general_params['prec_mu'], method=general_params['calc_mu_method'], + broadening=broadening) # Applies mu mixing to dichotomy result sum_k.chemical_potential = _mix_chemical_potential(general_params, occupation, diff --git a/python/solid_dmft/dmft_tools/observables.py b/python/solid_dmft/dmft_tools/observables.py index cd37df6f..ecc29667 100644 --- a/python/solid_dmft/dmft_tools/observables.py +++ b/python/solid_dmft/dmft_tools/observables.py @@ -146,7 +146,7 @@ def write_header_to_file(general_params, sum_k): obs_file.write(header + '\n') -def add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_energy, +def add_dft_values_as_zeroth_iteration(observables, general_params, solver_type_per_imp, dft_mu, dft_energy, sum_k, G_loc_all_dft, density_mat_dft, shell_multiplicity): """ Calculates the DFT observables that should be written as the zeroth iteration. @@ -209,7 +209,7 @@ def add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_ if not spin in spin_channel: continue - if general_params['solver_type'] in ['ftps']: + if solver_type_per_imp[iineq] == 'ftps': freq_mesh = np.array([w.value for w in G_loc_all_dft[iineq][spin_channel].mesh]) fermi_idx = abs(freq_mesh).argmin() gb2_averaged = G_loc_all_dft[iineq][spin_channel].data[fermi_idx].imag @@ -257,7 +257,7 @@ def add_dft_values_as_zeroth_iteration(observables, general_params, dft_mu, dft_ return observables -def add_dmft_observables(observables, general_params, solver_params, dft_energy, it, solvers, h_int, +def add_dmft_observables(observables, general_params, solver_params, map_imp_solver, solver_type_per_imp, dft_energy, it, solvers, h_int, previous_mu, sum_k, density_mat, shell_multiplicity, E_bandcorr): """ calculates the observables for given Input, I decided to calculate the observables @@ -303,36 +303,29 @@ def add_dmft_observables(observables, general_params, solver_params, dft_energy, observables['E_bandcorr'].append(E_bandcorr) observables['E_dft'].append(dft_energy) - # if density matrix was measured store result in observables - if general_params['solver_type'] in ['cthyb', 'hubbardI'] and solver_params["measure_density_matrix"]: - mpi.report("\nextracting the impurity density matrix") - # Extract accumulated density matrix - density_matrix = [solvers[icrsh].density_matrix for icrsh in range(sum_k.n_inequiv_shells)] - # Object containing eigensystem of the local Hamiltonian - diag_local_ham = [solvers[icrsh].h_loc_diagonalization for icrsh in range(sum_k.n_inequiv_shells)] - if general_params['calc_energies']: - # dmft interaction energy with E_int = 0.5 * Tr[Sigma * G] - if (general_params['solver_type'] in ['cthyb', 'hubbardI'] - and solver_params["measure_density_matrix"]): - E_int = [trace_rho_op(density_matrix[icrsh], h_int[icrsh], diag_local_ham[icrsh]) - for icrsh in range(sum_k.n_inequiv_shells)] - elif general_params['solver_type'] == 'hartree': - E_int = [solvers[icrsh].interaction_energy for icrsh in range(sum_k.n_inequiv_shells)] - else: - warning = ( "!-------------------------------------------------------------------------------------------!\n" - "! WARNING: calculating interaction energy using Migdal formula !\n" - "! consider turning on measure density matrix to use the more stable trace_rho_op function !\n" - "!-------------------------------------------------------------------------------------------!" ) - print(warning) - # calc energy for given S and G - E_int = [0.5 * np.real((solvers[icrsh].G_freq * solvers[icrsh].Sigma_freq).total_density()) - for icrsh in range(sum_k.n_inequiv_shells)] - + mpi.report('\nCalculating interaction energies') for icrsh in range(sum_k.n_inequiv_shells): - observables['E_int'][icrsh].append(shell_multiplicity[icrsh]*E_int[icrsh].real) - E_corr_en += shell_multiplicity[icrsh] * (E_int[icrsh].real - sum_k.dc_energ[sum_k.inequiv_to_corr[icrsh]]) + if (solver_type_per_imp[icrsh] in ['cthyb', 'hubbardI'] + and solver_params[map_imp_solver[icrsh]]["measure_density_matrix"]): + mpi.report(f' Imp {icrsh}: from impurity density matrix') + # Extract accumulated density matrix + density_matrix = solvers[icrsh].density_matrix + # Object containing eigensystem of the local Hamiltonian + diag_local_ham = solvers[icrsh].h_loc_diagonalization + E_int = trace_rho_op(density_matrix, h_int[icrsh], diag_local_ham) + elif solver_type_per_imp[icrsh] == 'hartree': + mpi.report(f' Imp {icrsh}: from Hartree') + E_int = solvers[icrsh].interaction_energy + else: + mpi.report(f' Imp {icrsh}: from Migdal formula. ' + 'WARNING: less stable than measuring density matrix and using trace_rho_op!') + # calc energy for given S and G + # dmft interaction energy with E_int = 0.5 * Tr[Sigma * G] + E_int = 0.5 * np.real((solvers[icrsh].G_freq * solvers[icrsh].Sigma_freq).total_density()) + observables['E_int'][icrsh].append(shell_multiplicity[icrsh]*E_int.real) + E_corr_en += shell_multiplicity[icrsh] * (E_int.real - sum_k.dc_energ[sum_k.inequiv_to_corr[icrsh]]) observables['E_corr_en'].append(E_corr_en) @@ -346,7 +339,7 @@ def add_dmft_observables(observables, general_params, solver_params, dft_energy, else: observables['E_DC'][icrsh].append(0.0) - if general_params['solver_type'] not in ['ftps']: + if solver_type_per_imp[icrsh] != 'ftps': if solvers[icrsh].G_time: G_time = solvers[icrsh].G_time else: @@ -364,7 +357,7 @@ def add_dmft_observables(observables, general_params, solver_params, dft_energy, for spin_channel in sorted(sum_k.gf_struct_solver[icrsh].keys()): if not spin in spin_channel: continue - if general_params['solver_type'] in ['ftps']: + if solver_type_per_imp[icrsh] == 'ftps': freq_mesh = np.array([w.value for w in solvers[icrsh].G_freq[spin_channel].mesh]) fermi_idx = abs(freq_mesh).argmin() gb2_averaged = solvers[icrsh].G_freq[spin_channel].data[fermi_idx].imag diff --git a/python/solid_dmft/dmft_tools/results_to_archive.py b/python/solid_dmft/dmft_tools/results_to_archive.py index c89cc5d3..24a196f5 100644 --- a/python/solid_dmft/dmft_tools/results_to_archive.py +++ b/python/solid_dmft/dmft_tools/results_to_archive.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- ################################################################################ # # solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations @@ -27,7 +26,7 @@ import triqs.utility.mpi as mpi -def _compile_information(sum_k, general_params, solver_params, solvers, +def _compile_information(sum_k, general_params, solver_params, solvers, map_imp_solver, solver_type_per_imp, previous_mu, density_mat_pre, density_mat, deltaN, dens): """ Collects all results in a dictonary. """ @@ -45,15 +44,16 @@ def _compile_information(sum_k, general_params, solver_params, solvers, write_to_h5['deltaN_trace'] = dens for icrsh in range(sum_k.n_inequiv_shells): - if general_params['solver_type'] in ['cthyb', 'hubbardI']: + isolvsec = map_imp_solver[icrsh] + if solver_type_per_imp[icrsh] in ['cthyb', 'hubbardI']: write_to_h5['Delta_time_{}'.format(icrsh)] = solvers[icrsh].Delta_time # Write the full density matrix to last_iter only - it is large - if solver_params['measure_density_matrix']: + if solver_params[isolvsec]['measure_density_matrix']: write_to_h5['full_dens_mat_{}'.format(icrsh)] = solvers[icrsh].density_matrix write_to_h5['h_loc_diag_{}'.format(icrsh)] = solvers[icrsh].h_loc_diagonalization - elif general_params['solver_type'] in ['ftps']: + elif solver_type_per_imp[icrsh] == 'ftps': write_to_h5['Delta_freq_{}'.format(icrsh)] = solvers[icrsh].Delta_freq write_to_h5['Gimp_time_{}'.format(icrsh)] = solvers[icrsh].G_time @@ -61,46 +61,46 @@ def _compile_information(sum_k, general_params, solver_params, solvers, write_to_h5['Gimp_freq_{}'.format(icrsh)] = solvers[icrsh].G_freq write_to_h5['Sigma_freq_{}'.format(icrsh)] = solvers[icrsh].Sigma_freq - if general_params['solver_type'] == 'cthyb': - if solver_params['measure_pert_order']: + if solver_type_per_imp[icrsh] == 'cthyb': + if solver_params[isolvsec]['measure_pert_order']: write_to_h5['pert_order_imp_{}'.format(icrsh)] = solvers[icrsh].perturbation_order write_to_h5['pert_order_total_imp_{}'.format(icrsh)] = solvers[icrsh].perturbation_order_total - if general_params['measure_chi'] != 'none': - write_to_h5['O_{}_time_{}'.format(general_params['measure_chi'], icrsh)] = solvers[icrsh].O_time + if solver_params[isolvsec]['measure_chi'] is not None: + write_to_h5['O_{}_time_{}'.format(solver_params[isolvsec]['measure_chi'], icrsh)] = solvers[icrsh].O_time # if legendre was set, that we have both now! - if (solver_params['measure_G_l'] - or not solver_params['perform_tail_fit'] and general_params['legendre_fit']): + if (solver_params[isolvsec]['measure_G_l'] + 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 - if general_params['solver_type'] == 'ctint' and solver_params['measure_histogram']: + if solver_type_per_imp[icrsh] == 'ctint' and solver_params[isolvsec]['measure_histogram']: write_to_h5['pert_order_imp_{}'.format(icrsh)] = solvers[icrsh].perturbation_order - if general_params['solver_type'] == 'hubbardI': + if solver_type_per_imp[icrsh] == 'hubbardI': write_to_h5['G0_Refreq_{}'.format(icrsh)] = solvers[icrsh].G0_Refreq write_to_h5['Gimp_Refreq_{}'.format(icrsh)] = solvers[icrsh].G_Refreq write_to_h5['Sigma_Refreq_{}'.format(icrsh)] = solvers[icrsh].Sigma_Refreq - if solver_params['measure_G_l']: + if solver_params[isolvsec]['measure_G_l']: write_to_h5['Gimp_l_{}'.format(icrsh)] = solvers[icrsh].G_l - if general_params['solver_type'] == 'hartree': + if solver_type_per_imp[icrsh] == 'hartree': write_to_h5['Sigma_Refreq_{}'.format(icrsh)] = solvers[icrsh].Sigma_Refreq - if general_params['solver_type'] == 'ctseg': + if solver_type_per_imp[icrsh] == 'ctseg': # if legendre was set, that we have both now! - if (solver_params['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['measure_ft']: + if solver_params[isolvsec]['measure_ft']: write_to_h5['F_freq_{}'.format(icrsh)] = solvers[icrsh].F_freq write_to_h5['F_time_{}'.format(icrsh)] = solvers[icrsh].F_time return write_to_h5 -def write(archive, sum_k, general_params, solver_params, solvers, it, is_sampling, +def write(archive, sum_k, general_params, solver_params, solvers, map_imp_solver, solver_type_per_imp, it, is_sampling, previous_mu, density_mat_pre, density_mat, deltaN=None, dens=None): """ Collects and writes results to archive. @@ -109,7 +109,7 @@ def write(archive, sum_k, general_params, solver_params, solvers, it, is_samplin if not mpi.is_master_node(): return - write_to_h5 = _compile_information(sum_k, general_params, solver_params, solvers, + write_to_h5 = _compile_information(sum_k, general_params, solver_params, solvers, map_imp_solver, solver_type_per_imp, previous_mu, density_mat_pre, density_mat, deltaN, dens) # Saves the results to last_iter diff --git a/python/solid_dmft/dmft_tools/solver.py b/python/solid_dmft/dmft_tools/solver.py index fdfd362b..5f9d381c 100755 --- a/python/solid_dmft/dmft_tools/solver.py +++ b/python/solid_dmft/dmft_tools/solver.py @@ -31,6 +31,8 @@ import triqs.utility.mpi as mpi from h5 import HDFArchive +from solid_dmft.io_tools.dict_to_h5 import prep_params_for_h5 + from . import legendre_filter from .matheval import MathExpr @@ -113,7 +115,7 @@ class SolverStructure: solve impurity problem ''' - def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, solver_struct_ftps): + def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, h_int, iteration_offset, deg_orbs_ftps): r''' Initialisation of the solver instance with h_int for impurity "icrsh" based on soliDMFT parameters. @@ -140,14 +142,16 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.icrsh = icrsh self.h_int = h_int self.iteration_offset = iteration_offset - self.solver_struct_ftps = solver_struct_ftps - if solver_params.get("random_seed") is None: + self.deg_orbs_ftps = deg_orbs_ftps + + # Stores random_seed as MathExpr object to evaluate it at runtime + if self.solver_params.get('random_seed') is None: self.random_seed_generator = None else: - self.random_seed_generator = MathExpr(solver_params["random_seed"]) + self.random_seed_generator = MathExpr(self.solver_params['random_seed']) # initialize solver object, options are cthyb - if self.general_params['solver_type'] == 'cthyb': + if self.solver_params['type'] == 'cthyb': from triqs_cthyb.version import triqs_cthyb_hash, version # sets up necessary GF objects on ImFreq @@ -157,7 +161,7 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.git_hash = triqs_cthyb_hash self.version = version - elif self.general_params['solver_type'] == 'ctint': + elif self.solver_params['type'] == 'ctint': from triqs_ctint.version import triqs_ctint_hash, version # sets up necessary GF objects on ImFreq @@ -169,7 +173,7 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.git_hash = triqs_ctint_hash self.version = version - elif self.general_params['solver_type'] == 'hubbardI': + elif self.solver_params['type'] == 'hubbardI': from triqs_hubbardI.version import triqs_hubbardI_hash, version # sets up necessary GF objects on ImFreq @@ -180,7 +184,7 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.git_hash = triqs_hubbardI_hash self.version = version - elif self.general_params['solver_type'] == 'hartree': + elif self.solver_params['type'] == 'hartree': from triqs_hartree_fock.version import triqs_hartree_fock_hash, version # sets up necessary GF objects on ImFreq @@ -191,7 +195,7 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.git_hash = triqs_hartree_fock_hash self.version = version - elif self.general_params['solver_type'] == 'ftps': + elif self.solver_params['type'] == 'ftps': from forktps.version import forktps_hash, version # additional parameters @@ -213,7 +217,7 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.git_hash = forktps_hash self.version = version - elif self.general_params['solver_type'] == 'inchworm': + elif self.solver_params['type'] == 'inchworm': # sets up necessary GF objects on ImFreq self._init_ImFreq_objects() @@ -221,7 +225,7 @@ def __init__(self, general_params, solver_params, advanced_params, sum_k, icrsh, self.triqs_solver = self._create_inchworm_solver() # self.git_hash = inchworm_hash - elif self.general_params['solver_type'] == 'ctseg': + elif self.solver_params['type'] == 'ctseg': from triqs_ctseg.version import triqs_ctseg_hash, version # sets up necessary GF objects on ImFreq @@ -260,13 +264,11 @@ def _init_ImFreq_objects(self): self.Delta_time = self.G_time.copy() # create all Legendre instances - if (self.general_params['solver_type'] == 'cthyb' and self.solver_params['measure_G_l'] - or self.general_params['solver_type'] == 'cthyb' and self.general_params['legendre_fit'] - or self.general_params['solver_type'] == 'ctseg' and self.solver_params['measure_gl'] - or self.general_params['solver_type'] == 'ctseg' and self.general_params['legendre_fit'] - or self.general_params['solver_type'] == 'hubbardI' and self.solver_params['measure_G_l']): + if (self.solver_params['type'] in ['cthyb', 'ctseg'] + and (self.solver_params['measure_G_l'] or self.solver_params['legendre_fit']) + or self.solver_params['type'] == 'hubbardI' and self.solver_params['measure_G_l']): - self.n_l = self.general_params['n_l'] + self.n_l = self.solver_params['n_l'] self.G_l = self.sum_k.block_structure.create_gf(ish=self.icrsh, gf_function=Gf, space='solver', mesh=MeshLegendre(beta=self.general_params['beta'], max_n=self.n_l, S='Fermion') @@ -274,11 +276,11 @@ def _init_ImFreq_objects(self): # move original G_freq to G_freq_orig self.G_time_orig = self.G_time.copy() - if self.general_params['solver_type'] in ['cthyb', 'hubbardI'] and self.solver_params['measure_density_matrix']: + if self.solver_params['type'] in ['cthyb', 'hubbardI'] and self.solver_params['measure_density_matrix']: self.density_matrix = None self.h_loc_diagonalization = None - if self.general_params['solver_type'] in ['cthyb'] and self.general_params['measure_chi'] != 'none': + if self.solver_params['type'] == 'cthyb' and self.solver_params['measure_chi'] is not None: self.O_time = None def _init_ReFreq_objects(self): @@ -346,14 +348,13 @@ def solve(self, **kwargs): solve impurity problem with current solver ''' - if self.random_seed_generator is None: - random_seed = {} + if self.random_seed_generator is not None: + self.triqs_solver_params['random_seed'] = int(self.random_seed_generator(it=kwargs["it"], rank=mpi.rank)) else: - random_seed = { "random_seed": int(self.random_seed_generator(it=kwargs["it"], rank=mpi.rank)) } - - if self.general_params['solver_type'] == 'cthyb': + assert 'random_seed' not in self.triqs_solver_params - if self.general_params['cthyb_delta_interface']: + if self.solver_params['type'] == 'cthyb': + if self.solver_params['delta_interface']: mpi.report('\n Using the delta interface for cthyb passing Delta(tau) and Hloc0 directly.') # prepare solver input sumk_eal = self.sum_k.eff_atomic_levels()[self.icrsh] @@ -369,7 +370,6 @@ def solve(self, **kwargs): else: self.triqs_solver.Delta_tau[name] << make_gf_from_fourier(self.Delta_freq[name], self.triqs_solver.Delta_tau.mesh, tail) - # Make non-interacting operator for Hloc0 Hloc_0 = Operator() for spin, spin_block in solver_eal.items(): @@ -381,54 +381,54 @@ def solve(self, **kwargs): else: # TODO: adapt for SOC calculations, which should keep the imag part Hloc_0 += spin_block[o1,o2].real/2 * (c_dag(spin,o1) * c(spin,o2) + c_dag(spin,o2) * c(spin,o1)) - self.solver_params['h_loc0'] = Hloc_0 + self.triqs_solver_params['h_loc0'] = Hloc_0 else: # fill G0_freq from sum_k to solver self.triqs_solver.G0_iw << self.G0_freq # update solver in h5 archive one last time for debugging if solve command crashes - if self.general_params['store_solver'] and mpi.is_master_node(): + if mpi.is_master_node(): with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'a') as archive: if not 'it_-1' in archive['DMFT_input/solver']: archive['DMFT_input/solver'].create_group('it_-1') - archive['DMFT_input/solver/it_-1'][f'S_{self.icrsh}'] = self.triqs_solver - archive['DMFT_input/solver/it_-1'][f'solve_params_{self.icrsh}'] = self.solver_params + archive['DMFT_input/solver/it_-1'][f'triqs_solver_params_{self.icrsh}'] = prep_params_for_h5(self.triqs_solver_params) archive['DMFT_input/solver/it_-1']['mpi_size'] = mpi.size + if self.general_params['store_solver']: + archive['DMFT_input/solver/it_-1'][f'S_{self.icrsh}'] = self.triqs_solver + mpi.barrier() # Solve the impurity problem for icrsh shell # ************************************* - self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + self.triqs_solver.solve(h_int=self.h_int, **self.triqs_solver_params) # ************************************* # call postprocessing self._cthyb_postprocessing() - elif self.general_params['solver_type'] == 'ctint': + elif self.solver_params['type'] == 'ctint': # fill G0_freq from sum_k to solver self.triqs_solver.G0_iw << self.G0_freq - if self.general_params['h_int_type'] == 'dynamic': + if self.general_params['h_int_type'][self.icrsh] == 'dynamic': for b1, b2 in product(self.sum_k.gf_struct_solver_dict[self.icrsh].keys(), repeat=2): self.triqs_solver.D0_iw[b1,b2] << self.U_iw[self.icrsh] # Solve the impurity problem for icrsh shell # ************************************* - self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + self.triqs_solver.solve(h_int=self.h_int, **self.triqs_solver_params) # ************************************* # call postprocessing self._ctint_postprocessing() - elif self.general_params['solver_type'] == 'hubbardI': + elif self.solver_params['type'] == 'hubbardI': # fill G0_freq from sum_k to solver self.triqs_solver.G0_iw << self.G0_freq # Solve the impurity problem for icrsh shell # ************************************* # this is done on every node due to very slow bcast of the AtomDiag object as of now - self.triqs_solver.solve(h_int=self.h_int, calc_gtau=self.solver_params['measure_G_tau'], - calc_gw=True, calc_gl=self.solver_params['measure_G_l'], - calc_dm=self.solver_params['measure_density_matrix']) + self.triqs_solver.solve(h_int=self.h_int, **self.triqs_solver_params) # if density matrix is measured, get this too. Needs to be done here, # because solver property 'dm' is not initialized/broadcastable if self.solver_params['measure_density_matrix']: @@ -439,21 +439,19 @@ def solve(self, **kwargs): # call postprocessing self._hubbardI_postprocessing() - elif self.general_params['solver_type'] == 'hartree': + elif self.solver_params['type'] == 'hartree': # fill G0_freq from sum_k to solver self.triqs_solver.G0_iw << self.G0_freq # Solve the impurity problem for icrsh shell # ************************************* # this is done on every node due to very slow bcast of the AtomDiag object as of now - self.triqs_solver.solve(h_int=self.h_int, with_fock=self.solver_params['with_fock'], - one_shot=self.solver_params['one_shot'], - method=self.solver_params['method'], tol=self.solver_params['tol']) + self.triqs_solver.solve(h_int=self.h_int, **self.triqs_solver_params) # call postprocessing self._hartree_postprocessing() - elif self.general_params['solver_type'] == 'ftps': + elif self.solver_params['type'] == 'ftps': import forktps as ftps from forktps.DiscreteBath import DiscretizeBath, TimeStepEstimation from forktps.BathFitting import BathFitter @@ -486,7 +484,7 @@ def make_positive_definite(G): self.Delta_freq_solver = make_positive_definite(self.Delta_freq_solver) # remove off-diagonal terms - if self.general_params['diag_delta']: + if self.solver_params['diag_delta']: for name, delta in self.Delta_freq_solver: for i_orb, j_orb in product(range(delta.target_shape[0]),range(delta.target_shape[1])): if i_orb != j_orb: @@ -511,7 +509,7 @@ def make_positive_definite(G): Delta_discrete = fitter.FitBath(Delta=self.Delta_freq_solver, eta=self.general_params['eta'], ignoreWeight=self.solver_params['ignore_weight']) else: # discretizebath - gap_interval = self.solver_params['enforce_gap'] if self.solver_params['enforce_gap'] != 'none' else None + gap_interval = self.solver_params['enforce_gap'] if self.solver_params['enforce_gap'] is not None else None Delta_discrete = DiscretizeBath(Delta=self.Delta_freq_solver, Nb=self.solver_params['n_bath'], gap=gap_interval, SO=bool(self.sum_k.corr_shells[self.icrsh]['SO'])) @@ -546,7 +544,7 @@ def make_positive_definite(G): name = self.convert_ftps[name.split('_')[0]] solver_eal = solver_eal.real # remove off-diagonal terms - if self.general_params['diag_delta']: + if self.solver_params['diag_delta']: solver_eal = np.diag(np.diag(solver_eal)) h_loc.Fill(name, solver_eal) @@ -557,14 +555,14 @@ def make_positive_definite(G): # so for debugging it is done here again # store solver to h5 archive if self.general_params['store_solver'] and mpi.is_master_node(): - archive = HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'a') - archive['DMFT_input/solver'].create_group('it_-1') - archive['DMFT_input/solver/it_-1']['Delta'] = self.Delta_freq_solver - archive['DMFT_input/solver/it_-1']['S_'+str(self.icrsh)] = self.triqs_solver + with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'a') as archive: + archive['DMFT_input/solver'].create_group('it_-1') + archive['DMFT_input/solver/it_-1']['Delta'] = self.Delta_freq_solver + archive['DMFT_input/solver/it_-1']['S_'+str(self.icrsh)] = self.triqs_solver # Solve the impurity problem for icrsh shell # ************************************* - path_to_gs = self.solver_params['path_to_gs'] if self.solver_params['path_to_gs'] != 'none' and self.path_to_gs_accepted else None + path_to_gs = self.solver_params['path_to_gs'] if self.solver_params['path_to_gs'] is not None and self.path_to_gs_accepted else None # fix to make sure this is only done in iteration 1 if self.path_to_gs_accepted: self.path_to_gs_accepted = False @@ -581,30 +579,29 @@ def make_positive_definite(G): # call postprocessing self._ftps_postprocessing() - elif self.general_params['solver_type'] == 'inchworm': + elif self.solver_params['type'] == 'inchworm': # fill Delta_time from Delta_freq sum_k to solver self.triqs_solver.Delta_tau << make_gf_from_fourier(self.Delta_freq).real # Solve the impurity problem for icrsh shell # ************************************* - self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + self.triqs_solver.solve(h_int=self.h_int, **self.triqs_solver_params) # ************************************* # call postprocessing self._inchworm_postprocessing() - if self.general_params['solver_type'] == 'ctseg': + if self.solver_params['type'] == 'ctseg': # fill G0_freq from sum_k to solver self.triqs_solver.G0_iw << self.G0_freq - if self.general_params['h_int_type'] == 'dynamic': for b1, b2 in product(self.sum_k.gf_struct_solver_dict[self.icrsh].keys(), repeat=2): self.triqs_solver.D0_iw[b1+"|"+b2] << self.U_iw[self.icrsh] # Solve the impurity problem for icrsh shell # ************************************* - self.triqs_solver.solve(h_int=self.h_int, **{ **self.solver_params, **random_seed }) + self.triqs_solver.solve(h_int=self.h_int, **self.triqs_solver_params) # ************************************* # call postprocessing @@ -622,16 +619,45 @@ def _create_cthyb_solver(self): ''' from triqs_cthyb.solver import Solver as cthyb_solver + # Separately stores all params that go into solve() call of solver + 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', + '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'] + + # use_norm_as_weight also required to measure the density matrix + self.triqs_solver_params['use_norm_as_weight'] = self.triqs_solver_params['measure_density_matrix'] + + if self.triqs_solver_params['perform_tail_fit']: + 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] + + # 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 if self.solver_params['measure_G_l']: triqs_solver = cthyb_solver(beta=self.general_params['beta'], gf_struct=gf_struct, n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], - n_l=self.general_params['n_l'], delta_interface=self.general_params['cthyb_delta_interface']) + n_l=self.solver_params['n_l'], delta_interface=self.solver_params['delta_interface']) else: triqs_solver = cthyb_solver(beta=self.general_params['beta'], gf_struct=gf_struct, n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], - delta_interface=self.general_params['cthyb_delta_interface']) + delta_interface=self.solver_params['delta_interface']) return triqs_solver @@ -641,9 +667,18 @@ def _create_ctint_solver(self): ''' from triqs_ctint import Solver as ctint_solver + # Separately stores all params that go into solve() call of solver + self.triqs_solver_params = {} + keys_to_pass = ('length_cycle', 'max_time', 'measure_pert_order', 'move_double', 'n_warmup_cycles') + 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) + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] - if self.general_params['h_int_type'] == 'dynamic': + if self.general_params['h_int_type'][self.icrsh] == 'dynamic': self.U_iw = None if mpi.is_master_node(): with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'r') as archive: @@ -667,12 +702,20 @@ def _create_hubbardI_solver(self): ''' from triqs_hubbardI import Solver as hubbardI_solver + # Separately stores all params that go into solve() call of solver + # All params need to be renamed + self.triqs_solver_params = {} + self.triqs_solver_params['calc_gtau'] = self.solver_params['measure_G_tau'] + self.triqs_solver_params['calc_gw'] = True + self.triqs_solver_params['calc_gl'] = self.solver_params['measure_G_l'] + self.triqs_solver_params['calc_dm'] = self.solver_params['measure_density_matrix'] + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] # Construct the triqs_solver instances if self.solver_params['measure_G_l']: triqs_solver = hubbardI_solver(beta=self.general_params['beta'], gf_struct=gf_struct, n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], - n_l=self.general_params['n_l'], n_w=self.general_params['n_w'], + n_l=self.solver_params['n_l'], n_w=self.general_params['n_w'], w_min=self.general_params['w_range'][0], w_max=self.general_params['w_range'][1], idelta=self.general_params['eta']) else: @@ -698,8 +741,13 @@ def _create_hartree_solver(self): ''' from triqs_hartree_fock import ImpuritySolver as hartree_solver - gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] + # Separately stores all params that go into solve() call of solver + self.triqs_solver_params = {} + keys_to_pass = ('method', 'one_shot', 'tol', 'with_fock') + for key in keys_to_pass: + self.triqs_solver_params[key] = self.solver_params[key] + gf_struct = self.sum_k.gf_struct_solver_list[self.icrsh] # Construct the triqs_solver instances # Always initialize the solver with dc_U and dc_J equal to U and J and let the _interface_hartree_dc function # take care of changing the parameters @@ -723,17 +771,17 @@ def _interface_hartree_dc(hartree_instance, general_params, advanced_params, icr icrsh : int correlated shell number """ - for key in ['dc', 'dc_type']: - if key in general_params and general_params[key] != 'none': - setattr(hartree_instance, key, general_params[key]) + setattr(hartree_instance, 'dc', general_params['dc']) + if general_params['dc_type'][icrsh] is not None: + setattr(hartree_instance, 'dc_type', general_params['dc_type'][icrsh]) for key in ['dc_factor', 'dc_fixed_value']: - if key in advanced_params and advanced_params[key] != 'none': + if key in advanced_params and advanced_params[key] is not None: setattr(hartree_instance, key, advanced_params[key]) #list valued keys for key in ['dc_U', 'dc_J', 'dc_fixed_occ']: - if key in advanced_params and advanced_params[key] != 'none': + if key in advanced_params and advanced_params[key][icrsh] is not None: setattr(hartree_instance, key, advanced_params[key][icrsh]) # Handle special cases @@ -775,7 +823,29 @@ def _create_ctseg_solver(self): ''' from triqs_ctseg import Solver as ctseg_solver - if self.general_params['h_int_type'] == 'dynamic': + # Separately stores all params that go into solve() call of solver + self.triqs_solver_params = {} + keys_to_pass = ('improved_estimator', 'length_cycle', 'max_time', 'measure_pert_order', 'n_warmup_cycles') + 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) + + # Rename parameters that are differentin ctseg than cthyb + self.triqs_solver_params['measure_gt'] = self.solver_params['measure_G_tau'] + self.triqs_solver_params['measure_gw'] = self.solver_params['measure_G_iw'] + self.triqs_solver_params['measure_gl'] = self.solver_params['measure_G_l'] + self.triqs_solver_params['measure_hist'] = self.solver_params['measure_pert_order'] + + # Makes sure measure_gw is true if improved estimators are used + if self.triqs_solver_params['improved_estimator']: + self.triqs_solver_params['measure_gt'] = True + self.triqs_solver_params['measure_ft'] = True + else: + self.triqs_solver_params['measure_ft'] = False + + if self.general_params['h_int_type'][self.icrsh] == 'dynamic': self.U_iw = None if mpi.is_master_node(): with HDFArchive(self.general_params['jobname']+'/'+self.general_params['seedname']+'.h5', 'r') as archive: @@ -790,7 +860,7 @@ def _create_ctseg_solver(self): if self.solver_params['measure_gl']: triqs_solver = ctseg_solver(beta=self.general_params['beta'], gf_struct=gf_struct, n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], - n_legendre_g=self.general_params['n_l'], n_w_b_nn = n_w_b_nn, n_tau_k=int(n_w_b_nn*2.5), n_tau_jperp=int(n_w_b_nn*2.5)) + n_legendre_g=self.solver_params['n_l'], n_w_b_nn = n_w_b_nn, n_tau_k=int(n_w_b_nn*2.5), n_tau_jperp=int(n_w_b_nn*2.5)) else: triqs_solver = ctseg_solver(beta=self.general_params['beta'], gf_struct=gf_struct, n_iw=self.general_params['n_iw'], n_tau=self.general_params['n_tau'], @@ -804,11 +874,15 @@ def _create_ftps_solver(self): ''' import forktps as ftps - # convert self.solver_struct_ftps to mapping and solver-friendly list + # TODO: add triqs_solver_params for ftps as well to make it analogous to other similars + # Not necessary but nicer. For now, just keep an empty dummy dict + self.triqs_solver_params = {} + + # convert self.deg_orbs_ftps to mapping and solver-friendly list if not self.sum_k.corr_shells[self.icrsh]['SO']: # mapping dictionary - calc_mapping = {self.solver_struct_ftps[self.icrsh][deg_shell][0]: - self.solver_struct_ftps[self.icrsh][deg_shell][1:] for deg_shell in range(len(self.solver_struct_ftps[self.icrsh]))} + calc_mapping = {self.deg_orbs_ftps[self.icrsh][deg_shell][0]: + self.deg_orbs_ftps[self.icrsh][deg_shell][1:] for deg_shell in range(len(self.deg_orbs_ftps[self.icrsh]))} # make solver-friendly list from mapping keys calc_me = [[item.split('_')[0], int(item.split('_')[1])] for item in calc_mapping.keys()] # replace 'down' with 'dn' @@ -889,13 +963,13 @@ def set_Gs_from_G_l(): self.G_time << self.triqs_solver.G_tau self.sum_k.symm_deg_gf(self.G_time, ish=self.icrsh) - if self.general_params['legendre_fit']: + if self.solver_params['legendre_fit']: self.G_time_orig << self.triqs_solver.G_tau # run the filter - self.G_l << legendre_filter.apply(self.G_time, self.general_params['n_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'] and not self.general_params['legendre_fit']: + elif self.solver_params['perform_tail_fit'] and not self.solver_params['legendre_fit']: # if tailfit has been used replace Sigma with the tail fitted Sigma from cthyb self.Sigma_freq << self.triqs_solver.Sigma_iw self.sum_k.symm_deg_gf(self.Sigma_freq, ish=self.icrsh) @@ -912,7 +986,7 @@ def set_Gs_from_G_l(): self.perturbation_order = self.triqs_solver.perturbation_order self.perturbation_order_total = self.triqs_solver.perturbation_order_total - if self.general_params['measure_chi'] != 'none': + if self.solver_params['measure_chi'] is not None: self.O_time = self.triqs_solver.O_tau return @@ -945,10 +1019,10 @@ def _ctint_postprocessing(self): self.G_time << Fourier(self.G_freq) # TODO: probably not needed/sensible - # if self.general_params['legendre_fit']: + # if self.solver_params['legendre_fit']: # self.G_freq_orig << self.triqs_solver.G_iw # # run the filter - # self.G_l << legendre_filter.apply(self.G_time, self.general_params['n_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() @@ -1139,9 +1213,9 @@ def set_Gs_from_G_l(): self.G_l << self.triqs_solver.G_l # get G_time, G_freq, Sigma_freq from G_l set_Gs_from_G_l() - elif self.general_params['legendre_fit']: + elif self.solver_params['legendre_fit']: self.G_time_orig << self.triqs_solver.G_tau - self.G_l << legendre_filter.apply(self.G_time, self.general_params['n_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() # if improved estimators are turned on calc Sigma from F_tau, otherwise: diff --git a/python/solid_dmft/io_tools/default.toml b/python/solid_dmft/io_tools/default.toml new file mode 100644 index 00000000..828d31a9 --- /dev/null +++ b/python/solid_dmft/io_tools/default.toml @@ -0,0 +1,184 @@ +[general] +afm_order = false +beta = "" +block_threshold = 1e-5 +broy_max_it = -1 +calc_energies = false +calc_mu_method = "dichotomy" +csc = false +dc = true +dc_dmft = "" +dc_type = "" +enforce_off_diag = true +eta = "" +fixed_mu_value = "" +g0_conv_crit = -1.0 +g0_mix = 1.0 +g0_mix_type = "linear" +gimp_conv_crit = -1.0 +h_field = 0.0 +h_field_it = 0 +h_int_basis = "triqs" +h_int_type = "" +h5_save_freq = 5 +J = "" +jobname = "dmft_dir" +load_sigma = false +load_sigma_iter = -1 +magmom = "" +magnetic = false +mu_gap_gb2_threshold = "" +mu_gap_occ_deviation = "" +mu_initial_guess = "" +mu_mix_const = 1.0 +mu_mix_per_occupation_offset = 0.0 +mu_update_freq = 1 +n_iter_dmft = "" +n_iter_dmft_first = 10 +n_iter_dmft_per = 2 +n_iw = 1025 +n_tau = 10001 +n_w = 5001 +noise_level_initial_sigma = 0.0 +occ_conv_crit = -1.0 +path_to_sigma = "" +prec_mu = 1e-4 +ratio_F4_F2 = "" +sampling_h5_save_freq = 5 +sampling_iterations = 0 +seedname = "" +set_rot = "" +sigma_conv_crit = -1.0 +sigma_mix = 1.0 +store_solver = false +U = "" +U_prime = "" +w_range = [-10, 10] + +# Keys in this section that should be passed to the triqs solver +# explicitly need to be added to the triqs_solver_params +# when initializing the solver.py class from solid_dmft +[[solver]] +type = "cthyb" +idx_impurities = "" +delta_interface = false +fit_max_moment = "" +fit_max_n = "" +fit_max_w = "" +fit_min_n = "" +fit_min_w = "" +imag_threshold = 1.0e-14 +legendre_fit = false +length_cycle = "" +loc_n_max = "" +loc_n_min = "" +max_time = -1 +measure_chi_insertions = 100 +measure_chi = "" +measure_density_matrix = false +measure_G_l = false +measure_pert_order = false +move_double = true +move_shift = false +n_cycles_tot = "" +n_l = "" +n_warmup_cycles = "" +off_diag_threshold = 0.0 +perform_tail_fit = false +random_seed = "" + +[[solver]] +type = "ctint" +idx_impurities = "" +length_cycle = "" +max_time = -1 +measure_pert_order = false +move_double = true +n_cycles_tot = "" +n_warmup_cycles = "" +random_seed = "" + +[[solver]] +type = "ctseg" +idx_impurities = "" +improved_estimator = false +legendre_fit = false +length_cycle = "" +max_time = -1 +measure_G_iw = false +measure_G_l = false +measure_G_tau = true +measure_pert_order = false +n_cycles_tot = "" +n_l = "" +n_warmup_cycles = "" +random_seed = "" + +[[solver]] +type = "hubbardI" +idx_impurities = "" +legendre_fit = false +measure_density_matrix = false +measure_G_l = false +measure_G_tau = true +n_l = "" + +[[solver]] +type = "ftps" +idx_impurities = "" +bath_fit = "" +calc_me = true +diag_delta = false +dmrg_maxm = 100 +dmrg_maxmB = 100 +dmrg_maxmI = 100 +dmrg_maxmIB = 100 +dmrg_tw = 1e-9 +dt = "" +enforce_gap = "" +ignore_weight = 0.0 +maxm = 100 +maxmB = 100 +maxmI = 100 +maxmIB = 100 +n_bath = 0 +path_to_gs = "" +ph_symm = false +refine_factor = 1 +state_storage = "./" +sweeps = 10 +tw = 1e-9 + +[[solver]] +type = "hartree" +idx_impurities = "" +force_real = true +method = "krylov" +one_shot = false +tol = 1e-5 +with_fock = false + +[dft] +dft_code = "" +dft_exec = "vasp_std" +mpi_env = "default" +n_cores = "" +n_iter = 4 +n_iter_first = "" +plo_cfg = "plo.cfg" +projector_type = "w90" +store_eigenvals = false +w90_exec = "wannier90.x" +w90_tolerance = 1e-6 + +[advanced] +dc_factor = "" +dc_fixed_occ = "" +dc_fixed_value = "" +dc_nominal = false +dc_orb_shift = "" +dc_J = "" +dc_U = "" +map_solver_struct = "" +mapped_solver_struct_degeneracies = "" +pick_solver_struct = "" diff --git a/python/solid_dmft/io_tools/dict_to_h5.py b/python/solid_dmft/io_tools/dict_to_h5.py new file mode 100644 index 00000000..de0f2f1d --- /dev/null +++ b/python/solid_dmft/io_tools/dict_to_h5.py @@ -0,0 +1,25 @@ +from copy import deepcopy + +def _iteratively_replace_none(to_write, replace_from, replace_with): + """ Limitation: can only replace None with a string, or a string with None. """ + # First two checks needed because comparison to triqs many-body operator fails + if (isinstance(to_write, str) or to_write is None) and to_write == replace_from: + return replace_with + + if isinstance(to_write, dict): + for key, value in to_write.items(): + to_write[key] = _iteratively_replace_none(value, replace_from, replace_with) + elif isinstance(to_write, list): + for i, value in enumerate(to_write): + to_write[i] = _iteratively_replace_none(value, replace_from, replace_with) + + return to_write + +def prep_params_for_h5(dict_to_write): + """ Replace all NoneType with a string 'none' to be able to write to h5. """ + return _iteratively_replace_none(deepcopy(dict_to_write), None, 'none') + +# Not sure if the reverse route is actually needed +def prep_params_from_h5(dict_to_read): + """ Replace all 'none' strings with NoneType to parse the dict coming from h5. """ + return _iteratively_replace_none(deepcopy(dict_to_read), 'none', None) diff --git a/python/solid_dmft/io_tools/documentation.txt b/python/solid_dmft/io_tools/documentation.txt new file mode 100644 index 00000000..cd0daf81 --- /dev/null +++ b/python/solid_dmft/io_tools/documentation.txt @@ -0,0 +1,481 @@ +[ general ] +------------- + +afm_order : bool, default = False + copy self energies instead of solving explicitly for afm order +beta : float, default = None + inverse temperature. If set, solid_dmft stores all Greens functions etc on an imaginary-frequency grid + and also n_iw and n_tau have to be specified. + If not set, it uses a real-frequency grid and eta, n_w and w_range have to be set +block_threshold : float, default = 1e-5 + threshold for finding block structures in the input data (off-diag yes or no) +broy_max_it : int, default = -1 + maximum number of iteration to be considered for broyden mixing. + Only used if general.g0_mix_type='broyden'. + 1 corresponds to simple linear mixing +calc_energies : bool, default = False + Calculate the energies explicitly within the dmft loop and write them to the observables file. + Not compatible with 'ftps' solver +calc_mu_method : string, default = 'dichotomy' + optimization method used for finding the chemical potential: + + * 'dichotomy': usual method from TRIQS, should always converge but may be slow + * 'newton': scipy Newton root finder, much faster but might be unstable + * 'brent': scipy hyperbolic Brent root finder preconditioned with dichotomy to find edge, a compromise between speed and stability +csc : bool, default = False + are we doing a CSC calculation? +dc : bool, default = True + is the double-counting correction on? +dc_dmft : bool, default = None + Calculate the double-counting (DC) correction from DMFT or DFT occupations. + Needs to be set if general.dc = True. + + * True: DC with DMFT occupation in each iteration + * False: DC with DFT occupations after each DFT cycle +dc_type : int or list of int, default = None + Type of double counting correction considered. + Can be a list per impurity to have different types for different impurities. + Needs to be set if general.dc = True. + + * 0: FLL + * 1: Held formula, needs to be used with slater-kanamori h_int_type=2 + * 2: AMF + * 3: FLL for eg orbitals only with U,J for Kanamori +enforce_off_diag : bool or list of bool, default = True + only if False, the block structure can be reduced to ignore off-diagonal elements + if they are below the general.block_threshold of the block structure finder +eta : float, default = None + broadening of Green's function. Used when Green's functions are stored on real-frequency grid, + i.e., general.beta is None, and for the real-frequency solvers +fixed_mu_value : float, default = None + If given, the chemical potential remains fixed in calculations +g0_conv_crit : float, default = -1.0 + stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold +g0_mix : float, default = 1.0 + Mixing the weiss field G0 with previous iteration G0 for better convergency. 1.0 means no mixing. + Setting g0_mix to 0.0 with linear mixing can be used for statistic sampling when + restarting a calculation +g0_mix_type : string, default = 'linear' + which type of mixing is used. Possible values are: + linear: linear mixing + broyden: broyden mixing +gimp_conv_crit : float, default = -1.0 + stop the calculation if sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold +h_field : float, default = 0.0 + magnetic field +h_field_it : int, default = 0 + number of iterations the magnetic field is kept on +h_int_basis : string, default = 'triqs' + cubic basis convention to compute the interaction U matrix + + * 'triqs' + * 'vasp' (equivalent to 'triqs') + * 'wien2k' + * 'wannier90' + * 'qe' (equivalent to 'wannier90') +h_int_type : string, mandatory + interaction type: + + * density_density: used for full d-shell or eg- or t2g-subset + * kanamori: only physical for the t2g or the eg subset + * full_slater: used for full d-shell or eg- or t2g-subset + * ntot: U/2 (Ntot^2 - Ntot) interaction + * simple_intra: density-density like but only intra orbital with given U value (no rotations applied) + * crpa: use the cRPA matrix as interaction Hamiltonian + * crpa_density_density: use the density-density terms of the cRPA matrix + * dynamic: use dynamic U from h5 archive + + Needs to be stored as Matsubara Gf under dynamic_U/U_iw in the input h5 +h5_save_freq : int, default = 5 + how often is the output saved to the h5 archive +J : float or list of float, mandatory + J interaction value. If it is a float, the same J is assumed for all impurities, + otherwise as a list a different J can be specified per impurity. +jobname : str, default = 'dmft_dir' + the output directory for one-shot calculations +load_sigma : bool, default = False + load a old sigma from h5 file +load_sigma_iter : int, default = -1 + load the sigma from a specific iteration if wanted. + If it is -1, loads from the last iteration. +magmom : list of float, default = None + Initialize magnetic moments if magnetic is on. length must be #imps. + List composed of energetic shifts written in electronvolts. + This will initialize the spin blocks of the sigma with a diagonal shift + With -shift for the up block, and +shift for the down block + (positive shift favours the up spin component, not compatible with spin-orbit coupling) +magnetic : bool, default = False + are we doing a magnetic calculations? If yes put magnetic to True. + Not implemented for CSC calculations +mu_gap_gb2_threshold : float, default = None + Threshold of the absolute of the lattice GF at tau=beta/2 for use + of MaxEnt's lattice spectral function to put the chemical potential + into the middle of the gap. Does not work if system completely full + or empty, mu mixing is not applied to it. Recommended value 0.01. +mu_gap_occ_deviation : float, default = None + Only used if mu_gap_gb2_threshold != None. Sets additional criterion + for finding the middle of the gap through occupation deviation to + avoid getting stuck in an insulating state with wrong occupation. +mu_initial_guess : float, default = None + The chemical potential of the DFT calculation. + If not given, mu will be calculated from the DFT bands +mu_mix_const : float, default = 1.0 + Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset. +mu_mix_per_occupation_offset : float, default = 0.0 + Mu mixing proportional to the occupation offset. + Mixing between the dichotomy result and the previous mui, + + mu_next = factor * mu_dichotomy + (1-factor) * mu_previous, with + factor = mu_mix_per_occupation_offset * abs(n - n\_target) + mu_mix_const. + + The program ensures that 0 <= factor <= 1. + mu_mix_const = 1.0 and mu_mix_per_occupation_offset = 0.0 means no mixing. +mu_update_freq : int, default = 1 + The chemical potential will be updated every # iteration +n_iter_dmft : int, mandatory + number of iterations per dmft cycle after first cycle +n_iter_dmft_first : int, default = 10 + number of iterations in first dmft cycle to converge dmft solution +n_iter_dmft_per : int, default = 2 + number of iterations per dmft step in CSC calculations +n_iw : int, default = 1025 + number of Matsubara frequencies for the imaginary-frequency grid +n_tau : int, default = 10001 + number of imaginary time points for the imaginary-time grid +n_w : int, default = 5001 + number of real frequency points for the real-frequency grid +noise_level_initial_sigma : float, default = 0.0 + spread of Gaussian noise applied to the initial Sigma +occ_conv_crit : float, default = -1.0 + stop the calculation if a certain threshold for the imp occ change is reached +path_to_sigma : str, default = None + path to h5 file from which the sigma should be loaded. + Needed if load_sigma is true +prec_mu : float, default = 1e-4 + general precision for determining the chemical potential at any time calc_mu is called +ratio_F4_F2 : float or list of float, default = None + Ratio between the Slater integrals F_4 and F_2. Only used for the + interaction Hamiltonians 'density_density' and 'full_slater' and + only for d-shell impurities, where the default is 0.63. +sampling_h5_save_freq : int, default = 5 + overwrites h5_save_freq when sampling has started +sampling_iterations : int, default = 0 + for how many iterations should the solution sampled after the CSC loop is converged +seedname : str, mandatory + seedname for h5 archive with DMFT input and output +set_rot : string, default = None + Local orbital rotations added by solid_dmft + + * None: keep the rotations stored in the h5 archive + * 'den' use the DFT occupations density_mat_dft for diagonalization + * 'hloc': use the DFT local Hamiltonian hloc_dft for diagonalization +sigma_conv_crit : float, default = -1.0 + stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold +sigma_mix : float, default = 1.0 + careful: Sigma mixing can break orbital symmetries, use G0 mixing. + mixing sigma with previous iteration sigma for better convergency. 1.0 means no mixing +store_solver : bool, default = False + whether to store the whole solver object under DMFT_input in h5 archive +U : float or list of float, mandatory + U interaction value. If it is a float, the same U is assumed for all impurities, + otherwise as a list a different U can be specified per impurity. +U_prime : float or list of floats, default = None + U prime interaction value. + Only used for impurities where general.h_int_type is kanamori. + If it is a float, the same U prime is assumed for all impurities, + otherwise as a list a different U prime can be specified per impurity. + For None, the default of U prime = U-2J is used. +w_range : list of int, default = [-10, 10] + Minimal and maximal range of the real-frequency grid + +[ solver ] +------------ +type : str, mandatory + type of solver chosen for the calculation, currently supports: + + * 'cthyb' + * 'ctint' + * 'ctseg' + * 'hubbardI' + * 'ftps' + * 'hartree' +idx_impurities : list of int, default = None + list of impurities this solver is supposed to solve + +cthyb +===== +delta_interface : bool, default = False + use delta interface in cthyb instead of input G0 +fit_max_moment : int, default = None + max moment to be fitted. Only used if solver.perform_tail_fit = True +fit_max_n : int, default = None + number of highest matsubara frequency to fit. Only used if solver.perform_tail_fit = True +fit_max_w : float, default = None + highest matsubara frequency to fit. Only used if solver.perform_tail_fit = True +fit_min_n : int, default = None + number of start matsubara frequency to start with. Only used if solver.perform_tail_fit = True +fit_min_w : float, default = None + start matsubara frequency to start with. Only used if solver.perform_tail_fit = True +imag_threshold : float, default = 1e-14 + threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau +legendre_fit : bool, default = False + filter noise of G(tau) with G_l, cutoff is taken from n_l +length_cycle : int, mandatory + length of each cycle; number of sweeps before measurement is taken +loc_n_max : int, default = None + Restrict local Hilbert space to states with at most this number of particles +loc_n_min : int, default = None + Restrict local Hilbert space to states with at least this number of particles +max_time : int, default = -1 + maximum amount the solver is allowed to spend in each iteration +measure_chi_insertions : int, default = 100 + number of insertation for measurement of chi +measure_chi : str, default = None + measure the dynamic suszeptibility of an operator O, chi(O,O(tau)) + triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html + Possible values for this flag are: + + * 'SzSz': spin susceptibility + * 'NN': occupation susceptibility +measure_density_matrix : bool, default = False + measures the impurity density matrix and sets also + use_norm_as_weight to true +measure_G_l : bool, default = False + measure Legendre Greens function +measure_pert_order : bool, default = False + measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html. + The result is stored in the h5 archive under 'DMFT_results' at every iteration + in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X' +move_double : bool, default = True + double moves in solver +move_shift : bool, default = False + shift moves in solver +n_cycles_tot : int, mandatory + total number of sweeps +n_l : int, default = None + number of Legendre coefficients. + Needed if measure_G_l=True or legendre_fit=True +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 + numbers on all mpi ranks. + You can also pass a string that will convert the keywords it or rank on runtime, e.g., + 34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random + seed + +ctint +===== +length_cycle : int, mandatory + length of each cycle; number of sweeps before measurement is taken +max_time : int, default = -1 + maximum amount the solver is allowed to spend in each iteration +measure_pert_order : bool, default = False + measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html. + The result is stored in the h5 archive under 'DMFT_results' at every iteration + in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X' +move_double : bool, default = True + double moves in solver +n_cycles_tot : int, mandatory + total number of sweeps +n_warmup_cycles : int, mandatory + number of warmup cycles before real measurement sets in +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 + numbers on all mpi ranks. + You can also pass a string that will convert the keywords it or rank on runtime, e.g., + 34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random + seed + +ctseg +===== +improved_estimator : bool, default = False + measure improved estimators + Sigma_iw will automatically be calculated via + http://dx.doi.org/10.1103/PhysRevB.85.205106 +legendre_fit : bool, default = False + filter noise of G(tau) with G_l, cutoff is taken from n_l +length_cycle : int, mandatory + length of each cycle; number of sweeps before measurement is taken +max_time : int, default = -1 + maximum amount the solver is allowed to spend in each iteration +measure_G_iw : bool, default = False + should the solver measure G(iw)? +measure_G_l : bool, default = False + measure Legendre Greens function +measure_G_tau : bool, default = True + should the solver measure G(tau)? +measure_pert_order : bool, default = False + measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html. + The result is stored in the h5 archive under 'DMFT_results' at every iteration + in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X' +n_cycles_tot : int, mandatory + total number of sweeps +n_l : int, default = None + number of Legendre coefficients. + Needed if measure_G_l=True or legendre_fit=True +n_warmup_cycles : int, mandatory + number of warmup cycles before real measurement sets in +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 + numbers on all mpi ranks. + You can also pass a string that will convert the keywords it or rank on runtime, e.g., + 34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random + seed + +hubbardI +======== +legendre_fit : bool, default = False + filter noise of G(tau) with G_l, cutoff is taken from n_l +measure_density_matrix : bool, default = False + measures the impurity density matrix and sets also + use_norm_as_weight to true +measure_G_l : bool, default = False + measure Legendre Greens function +measure_G_tau : bool, default = True + should the solver measure G(tau)? +n_l : int, default = None + number of Legendre coefficients. + needed if measure_G_l=True or legendre_fit=True + +ftps parameters +=============== +bath_fit : bool, mandatory + DiscretizeBath vs BathFitter +calc_me : bool, default = True + calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards +diag_delta : bool, default = False + option to remove off-diagonal terms in the hybridization function + available for ftps +dmrg_maxm : int, default = 100 + TODO: add description +dmrg_maxmB : int, default = 100 + maximal bath-bath bond dimensions +dmrg_maxmI : int, default = 100 + maximal imp-imp bond dimensions +dmrg_maxmIB : int, default = 100 + maximal imp-bath bond dimensions +dmrg_tw : float, default 1e-9 + truncated weight for every link +dt : float, mandatory + time step +enforce_gap : list of float, default = None + enforce gap in DiscretizeBath between interval +ignore_weight : float, default = 0.0 + ignore weight of peaks for bath fitter +maxm : int, default = 100 + TODO: write description +maxmB : int, default = 100 + maximal bath-bath bond dimensions +maxmI : int, default = 100 + maximal imp-imp bond dimensions +maxmIB : int, default = 100 + maximal imp-bath bond dimensions +n_bath : int, default = 0 + number of bath sites +path_to_gs : string, default = None + location of GS if already present. Use 'postprocess' to skip solver and go directly to post-processing + of previously terminated time-evolved state +ph_symm : bool, default = False + particle-hole symmetric problem +refine_factor : int, default = 1 + rerun ftps cycle with increased accuracy +state_storage : string, default = './' + location of large MPS states +sweeps : int, default = 10 + Number of DMRG sweeps +tw : float, default 1e-9 + truncated weight for every link + +hartree +================ +force_real : bool, default = True + force the self energy from Hartree fock to be real +method : str, default = "krylov" + method for root finder. Only used if one_shot=False, see scipy.optimize.root for options. +one_shot : bool, default = False + Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver. +tol : float, default = 1e-5 + tolerance for root finder if one_shot=False. +with_fock : bool, default = False + include Fock exchange terms in the self-energy + +[ dft ] +--------- +dft_code : string + choose the DFT code interface, for now Quantum Espresso and Vasp are available. + Possible values: + + * 'vasp' + * 'qe' +dft_exec : string, default = 'vasp_std' + command for the DFT executable +mpi_env : string, default = 'default' + selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec + Possible values: + + * 'default' + * 'openmpi' + * 'openmpi-intra' + * 'mpich' +n_cores : int, None + number of cores for the DFT code (VASP). Mandatory for charge-self-consistent calculations +n_iter : int, default = 4 + only needed for VASP. Number of DFT iterations to feed the DMFT + charge density into DFT, which generally takes multiple Davidson steps. + For every DFT iterations, the charge-density correction is recalculated + using newly generated projectors and hoppings from the previous DFT run +n_iter_first : int, default = dft.n_iter + number of DFT iterations in the first charge correction because this + first charge correction usually changes the DFT wave functions the most. +plo_cfg : str, default = 'plo.cfg' + config file for PLOs for the converter +projector_type : string, default = 'w90' + plo: uses VASP's PLO formalism, requires LOCPROJ in the INCAR + w90: uses Wannier90 (for VASP and QuantumEspresso) +store_eigenvals : bool, default = False + stores the dft eigenvals from LOCPROJ (projector_type=plo) or + wannier90.eig (projector_type=w90) file in h5 archive +w90_exec : string, default ='wannier90.x' + the command to start a single-core wannier run +w90_tolerance : float, default = 1e-6 + threshold for mapping of shells and checks of the Hamiltonian + +[ advanced ] +-------------- +dc_factor : float, default = None + If given, scales the dc energy by multiplying with this factor, usually < 1. + If None, the dc is left unchanged, which is equivalent to dc_factor=1. +dc_fixed_occ : list of float, default = None + If given, the occupation for the DC for each impurity is set to the provided value. + Still uses the same kind of DC! +dc_fixed_value : float, default = None + If given, it sets the DC (energy/imp) to this fixed value. Overwrites EVERY other DC configuration parameter if DC is turned on +dc_nominal : bool, default = False + TODO: write +dc_orb_shift : list of float, default = None + extra potential shift per orbital per impurity added to the DC +dc_J : float or list of float, default = general.J + J values for DC determination. If only one value is given, the same J is assumed for all impurities +dc_U : float or list of float, default = general.U + U values for DC determination. If only one value is given, the same U is assumed for all impurities +map_solver_struct : list of dict, default = None + Additional manual mapping of the solver block structure, applied + after the block structure finder for each impurity. + Give exactly one dict per ineq impurity. + see also triqs.github.io/dft_tools/latest/_python_api/triqs_dft_tools.block_structure.BlockStructure.map_gf_struct_solver.html +mapped_solver_struct_degeneracies : list, default = None + Degeneracies applied when using map_solver_struct, for each impurity. + If not given and map_solver_struct is used, no symmetrization will happen. +pick_solver_struct : list of dict, default = None + input a solver dictionary for each ineq impurity to reduce dimensionality of + solver block structure. Similar to to map_solver_struct, but with simpler syntax. + Not listed blocks / orbitals will be not treated in impurity solver. + Keeps degenerate shells. diff --git a/python/solid_dmft/io_tools/postproc_toml_dict.py b/python/solid_dmft/io_tools/postproc_toml_dict.py new file mode 100644 index 00000000..d51f6158 --- /dev/null +++ b/python/solid_dmft/io_tools/postproc_toml_dict.py @@ -0,0 +1,188 @@ +from typing import Any, Dict, List, Union +import copy + +ParamDict = Dict[str, Any] +FullConfig = Dict[str, Union[ParamDict, List[ParamDict]]] + +def _verify_dict_is_param_dict(d: Any) -> None: + """ Checks that the input is of type ParamDict. """ + if not isinstance(d, dict): + raise ValueError(f'Expected a dict, but got {d} of type {type(d)}.') + for key in d: + if not isinstance(key, str): + raise ValueError(f'Expected a string as key, but got {key} of type {type(key)}.') + +def _verify_dict_is_full_config(d: Dict[str, Any]) -> None: + """ Checks that dict is of type FullConfig. """ + # Checks that no keys outside a section + # This is, that d is of type Dict[str, Union[Dict, List]] + for section_name, section in d.items(): + if not isinstance(section, (dict, list)): + raise ValueError(f'Key "{section_name}" found outside of a section.') + + # Checks that entries are ParamDicts or List[ParamDict] + for section in d.values(): + if isinstance(section, list): + for entry in section: + _verify_dict_is_param_dict(entry) + else: + _verify_dict_is_param_dict(section) + +def _verify_restrictions_on_default_and_config(cfg_inp: Dict[str, Any], cfg_def: Dict[str, Any], match_key: Dict[str, str]) -> None: + """ Checks that the restrictions described in the docstring of merge_config_with_default are met. """ + # Checks that type of cfg_def dict is FullConfig + _verify_dict_is_full_config(cfg_def) + + # Checks that keys listed in match_key are lists and all other are dicts + for section_name, section in cfg_def.items(): + if section_name in match_key and not isinstance(section, list): + raise ValueError(f'"{section_name}" is in match_key so it should be a list in the default config.') + if section_name not in match_key and not isinstance(section, dict): + raise ValueError(f'"{section_name}" is not in match_key so it should be a dict in the default config.') + + # Checks that no sections in config that are not in default + unknown_sections = set(cfg_inp.keys()) - set(cfg_def.keys()) + if unknown_sections: + raise ValueError('Unknown sections were found in the config file: ' + str(unknown_sections) + + '. Please refer to the default config file for valid keys.') + + # Checks that all sections listed in match_key are in default and config + unmatched_sections = set(match_key.keys()) - set(cfg_def.keys()) + if unmatched_sections: + raise ValueError('Sections ' + str(unmatched_sections) + ' found in match_key ' + + 'that are not in the default config.') + + unmatched_sections = set(match_key.keys()) - set(cfg_inp.keys()) + if unmatched_sections: + raise ValueError('Sections ' + str(unmatched_sections) + ' found in match_key ' + + 'that are not in the config.') + + # Checks type of config dict + _verify_dict_is_full_config(cfg_inp) + +def _apply_default_values(cfg_inp: FullConfig, cfg_def: FullConfig, match_key: Dict[str, str]) -> FullConfig: + """ Fills in the default values where the input config does not specify a value. """ + output: FullConfig = {} + for section_name, section in cfg_def.items(): + if isinstance(section, list): + key = match_key[section_name] + output[section_name] = [] + for entry in cfg_inp[section_name]: + # Finds matching section through match_key in cfg_def + for default_entry in section: + if default_entry[key] == entry[key]: + output[section_name].append(copy.deepcopy(default_entry)) + break + else: + raise ValueError(f'No matching section with same "{section_name}.{key}"="{entry[key]}" found in defaults.') + # Updates config values in output + unknown_keys = set(entry.keys()) - set(output[section_name][-1].keys()) + if unknown_keys: + raise ValueError(f'Unknown keys {unknown_keys} found in section "{section_name}". ' + 'All valid keys have to be in the default config.') + output[section_name][-1].update(entry) + else: + entry = cfg_inp.get(section_name, {}) + output[section_name] = copy.deepcopy(section) + unknown_keys = set(entry.keys()) - set(output[section_name].keys()) + if unknown_keys: + raise ValueError(f'Unknown keys {unknown_keys} found in section "{section_name}". ' + 'All valid keys have to be in the default config.') + output[section_name].update(entry) + + return output + +def _replace_none(d: ParamDict) -> None: + """ Replace '' by None in a ParamDict. This also works inside lists. """ + for key, value in d.items(): + if value == '': + d[key] = None + elif isinstance(value, list): + for i, v in enumerate(value): + if v == '': + value[i] = None + +def _verify_all_mandatory_fields_present(d: ParamDict, section_name: str) -> None: + """ Verifies that all fields with "" have been replaced after reading in the config. """ + for key, value in d.items(): + if value == '': + raise ValueError(f'"{key}" in section "{section_name}" is mandatory and was left empty.') + +def _resolve_references(d: ParamDict, section_name: str, output: FullConfig) -> None: + """ Resolve all references of type "" in a ParamDict. """ + for key, value in d.items(): + if isinstance(value, str) and value.startswith('<') and value.endswith('>'): + ref_key = value[1:-1].split('.') + if len(ref_key) != 2: + raise ValueError(f'Invalid reference "{value}" in section "{section_name}".') + if isinstance(output[ref_key[0]], list): + raise ValueError(f'Invalid reference "{value}" to listed section "{section_name}".') + + referenced_val = output[ref_key[0]][ref_key[1]] + if isinstance(referenced_val, str) and referenced_val.startswith('<') and referenced_val.endswith('>'): + raise ValueError(f'"{ref_key[1]}" in section "{ref_key[0]}" is a reference itself.') + d[key] = referenced_val + +# type hints currently not supported by sphinx autodoc +# def merge_config_with_default(cfg_inp: Dict[str, Any], cfg_def: Dict[str, Any], +# match_key: Dict[str, str] = {}) -> FullConfig: +def merge_config_with_default(cfg_inp, cfg_def, match_key={}): + """ + Merge a TOML config dict with a default TOML dict. + The default dict dictates the structure of the input: + + - Only sections and keys in the default are allowed in the input + - All sections listed in match_key must be lists of dicts in the default + and can be lists of dicts or dicts in the config + + The dicts allows for the following extensions: + + - Mandatory inputs for all calculations indicated by "" + - None indicated by "". Also works inside lists + - References within the dictionary indicated by "" + + Parameters + ---------- + cfg_inp : dict + The input config dict + cfg_def : dict + The default config dict + match_key : dict, optional + A dictionary that contains section/key pairs to map entries in listed sections + between the input and default config. + + Returns + ------- + dict + The merged config dict + """ + + # Check restrictions and makes sure that config and default are of type FullConfig + _verify_restrictions_on_default_and_config(cfg_inp, cfg_def, match_key) + + # Checks that keys not listed in match_key are dicts + # The others can be lists or dicts. This differs from cfg_def + # to allow users to use multiple sections or not + for section_name, section in cfg_inp.items(): + if section_name in match_key and not isinstance(section, list): + cfg_inp[section_name] = [section] + if section_name not in match_key and not isinstance(section, dict): + raise ValueError(f'"{section_name}" should be a dict and not a list in the config.') + + # Merges config with default + output = _apply_default_values(cfg_inp, cfg_def, match_key) + + # Converts "" to None, checks that no mandatory fields were left empty + # and resolves referencing defaults + for section_name, section in output.items(): + if isinstance(section, dict): + _replace_none(section) + _verify_all_mandatory_fields_present(section, section_name) + _resolve_references(section, section_name, output) + else: + for entry in section: + _replace_none(entry) + _verify_all_mandatory_fields_present(entry, section_name) + _resolve_references(entry, section_name, output) + + return output diff --git a/python/solid_dmft/io_tools/verify_input_params.py b/python/solid_dmft/io_tools/verify_input_params.py new file mode 100644 index 00000000..5c1bddc0 --- /dev/null +++ b/python/solid_dmft/io_tools/verify_input_params.py @@ -0,0 +1,111 @@ +from typing import Any, Dict, List, Union +import numpy as np + +ParamDict = Dict[str, Any] +FullConfig = Dict[str, Union[ParamDict, List[ParamDict]]] + +def _verify_input_params_general(params: FullConfig) -> None: + # Checks that grid parameters are specified completely + if params['general']['beta'] is not None and (params['general']['n_iw'] is None + or params['general']['n_tau'] is None): + raise ValueError('Imaginary-frequency grid chosen, specify "n_iw" and "n_tau".') + + if params['general']['beta'] is None and (params['general']['eta'] is None or params['general']['n_w'] is None + or params['general']['w_range'] is None): + raise ValueError('Real-frequency grid chosen, specify "eta", "n_w", and "w_range".') + + # warning if sigma mixing is used, remove in future versions + if params['general']['sigma_mix'] < 1.0 and params['general']['g0_mix'] < 1.0: + raise ValueError('You shall not use Sigma and G0 mixing together!') + + if params['general']['calc_energies'] and any(entry['type'] == 'ftps' for entry in params['solver']): + raise ValueError('"calc_energies" is not valid for solver of type = "ftps"') + + # Checks validity of other general params + h_int_type_options = ('density_density', 'kanamori', 'full_slater', 'crpa', + 'crpa_density_density', 'dynamic', 'ntot', 'simple_intra') + if isinstance(params['general']['h_int_type'], str): + if not params['general']['h_int_type'] in h_int_type_options: + raise ValueError(f'Invalid "h_int_type" = {params["general"]["h_int_type"]}.') + elif isinstance(params['general']['h_int_type'], list): + if any(entry not in h_int_type_options for entry in params['general']['h_int_type']): + raise ValueError('Invalid "h_int_type" in input list.') + else: + raise ValueError('Invalid "h_int_type" input type. String or list expected.') + + if params['general']['g0_mix_type'] not in ('linear', 'broyden'): + raise ValueError(f'Invalid "g0_mix_type" = {params["general"]["g0_mix_type"]}.') + + if params['general']['calc_mu_method'] not in ('dichotomy', 'newton', 'brent'): + raise ValueError(f'Invalid "calc_mu_method" = {params["general"]["calc_mu_method"]}.') + + if params['general']['set_rot'] not in (None, 'den', 'hloc'): + raise ValueError(f'Invalid "set_rot" = {params["general"]["set_rot"]}.') + + if params['general']['h_int_basis'] not in ('triqs', 'wien2k', 'wannier90', 'qe', 'vasp'): + raise ValueError(f'Invalid "h_int_basis" = {params["general"]["h_int_basis"]}.') + +def _verify_input_params_dft(params: FullConfig) -> None: + if params['dft']['dft_code'] not in ('vasp', 'qe', None): + raise ValueError(f'Invalid "dft.dft_code" = {params["dft"]["dft_code"]}.') + + if params['dft']['mpi_env'] not in ('default', 'openmpi', 'openmpi-intra', 'mpich'): + raise ValueError(f'Invalid "dft.mpi_env" = {params["dft"]["mpi_env"]}.') + + if params['dft']['projector_type'] not in ('w90', 'plo'): + raise ValueError(f'Invalid "dft.projector_type" = {params["dft"]["projector_type"]}.') + +def _verify_input_params_solver(params: FullConfig) -> None: + solver_params = params['solver'] + + # Checks that the solver impurities index are all lists if there are multiple solvers + if len(solver_params) > 1 or solver_params[0]['idx_impurities'] is not None: + if any(not isinstance(entry['idx_impurities'], list) for entry in solver_params): + raise ValueError('All "solver.idx_impurities" need to specify the impurities ' + 'as a list of ints when there are multiple solvers.') + for entry in solver_params: + if any(not isinstance(imp, int) for imp in entry['idx_impurities']): + raise ValueError('All "solver.idx_impurities" need to specify the impurities ' + 'as a list of ints when there are multiple solvers.') + + # Checks that all solvers support the specified grid + # TODO: add real-frequency support for solvers that do both (e.g., hartree) + supported_grids = {'real': ['ftps'], + 'imag': ['cthyb', 'ctint', 'ctseg', 'hubbardI', 'hartree']} + if params['general']['beta'] is not None: + for entry in solver_params: + if entry['type'] not in supported_grids['imag']: + raise ValueError(f'Solver {entry["type"]} does not support real-frequency grid.') + else: + for entry in solver_params: + if entry['type'] not in supported_grids['real']: + raise ValueError(f'Solver {entry["type"]} does not support imaginary-frequency grid.') + +def _verify_input_params_advanced(params: FullConfig) -> None: + pass + +def verify_before_dmft_cycle(params): + """ All checks of params that can be done before dmft_cycle is called. """ + _verify_input_params_general(params) + _verify_input_params_dft(params) + _verify_input_params_solver(params) + _verify_input_params_advanced(params) + +def verify_h5_dependent(sum_k, solver_type_per_imp, general_params): + """ All checks of params that depend on the h5 file content that is stored in the SumkDFT object. """ + # Incompatabilities for SO coupling + if sum_k.SO == 1 and general_params['magnetic'] and general_params['afm_order']: + raise ValueError('AFM order not supported with SO coupling') + + # Checks that enforce_off_diag true for ftps and hartree + if any(s in ['ftps', 'hartree'] and not e for s, e in zip(solver_type_per_imp, general_params['enforce_off_diag'])): + raise ValueError('enforce_off_diag must be True for a impurities solver by ftps or hartree solvers') + + # Checks that the interaction Hamiltonian and the parameters match + if any(h not in ['density_density', 'slater'] and r is not None + for h, r in zip(general_params['h_int_type'], general_params['ratio_F4_F2'])): + raise ValueError('"ratio_F4_F2" only considered for interaction Hamiltonians "density_density" and "slater". ' + 'Please set to None for all other Hamiltonians.') + if any(h != 'kanamori' and up is not None for h, up in zip(general_params['h_int_type'], general_params['U_prime'])): + raise ValueError('"U_prime" only considered for interaction Hamiltonian "kanamori". ' + 'Please set to None for all other Hamiltonians.') diff --git a/python/solid_dmft/main.py b/python/solid_dmft/main.py index 839accfc..88481db9 100644 --- a/python/solid_dmft/main.py +++ b/python/solid_dmft/main.py @@ -36,55 +36,57 @@ import shutil from timeit import default_timer as timer +try: + import tomllib +except ImportError: + import tomli as tomllib + # triqs import triqs.utility.mpi as mpi # own modules -from solid_dmft.read_config import read_config from solid_dmft.dmft_cycle import dmft_cycle from solid_dmft.csc_flow import csc_flow_control +from solid_dmft.io_tools import postproc_toml_dict, verify_input_params - -def main(argv=sys.argv): - """The main function for one-shot and charge-self-consistent calculations""" +def run_dmft(params, config_file_name=None): # timing information if mpi.is_master_node(): global_start = timer() - # reading configuration for calculation - general_params = None - solver_params = None - dft_params = None - advanced_params = None - if len(argv) > 1: - config_file_name = str(argv[1]) - else: - config_file_name = 'dmft_config.ini' - if not os.path.isfile(config_file_name): - raise FileNotFoundError(f'Could not find config file {config_file_name}.') - + # Reads in default params file and merges with params + full_params = None + default_config_name = os.path.join(os.path.dirname(__file__), + 'io_tools', 'default.toml') if mpi.is_master_node(): - print('Reading the config file ' + config_file_name) - general_params, solver_params, dft_params, advanced_params = read_config(config_file_name) - general_params['config_file'] = config_file_name - - print('-'*25 + '\nGeneral parameters:') - for key, value in general_params.items(): - print('{0: <20} {1: <4}'.format(key, str(value))) - print('-'*25 + '\nSolver parameters:') - for key, value in solver_params.items(): - print('{0: <20} {1: <4}'.format(key, str(value))) - print('-'*25 + '\nDFT parameters:') - for key, value in dft_params.items(): - print('{0: <20} {1: <4}'.format(key, str(value))) - print('-'*25 + '\nAdvanced parameters, don\'t change them unless you know what you are doing:') - for key, value in advanced_params.items(): - print('{0: <20} {1: <4}'.format(key, str(value))) - - general_params = mpi.bcast(general_params) - solver_params = mpi.bcast(solver_params) - dft_params = mpi.bcast(dft_params) - advanced_params = mpi.bcast(advanced_params) + with open(default_config_name, 'rb') as file: + default_params = tomllib.load(file) + full_params = postproc_toml_dict.merge_config_with_default(params, default_params, + {'solver': 'type'}) + verify_input_params.verify_before_dmft_cycle(full_params) + full_params = mpi.bcast(full_params) + + # Prints parameters + if mpi.is_master_node(): + # Prints only sections that are in the input file + for section_name in params: + section = full_params[section_name] + print(f'\n{section_name} parameters') + if isinstance(section, dict): + for key, value in section.items(): + print(f' {key: <30} {str(value)}') + else: + for i, entry in enumerate(section): + print(f'entry {i+1}') + for key, value in entry.items(): + print(f' {key: <30} {str(value)}') + print('') + mpi.barrier() + + general_params = full_params['general'] + solver_params = full_params['solver'] + dft_params = full_params['dft'] + advanced_params = full_params['advanced'] if general_params['csc']: # Start CSC calculation, always in same folder as dmft_config @@ -93,8 +95,8 @@ def main(argv=sys.argv): else: # Sets up one-shot calculation mpi.report('', '#'*80) - mpi.report(f'Using input file {general_params["seedname"]}.h5 ' - + f'and running in folder {general_params["jobname"]}') + mpi.report(f'Using input file "{general_params["seedname"]}.h5" ' + + f'and running in folder "{general_params["jobname"]}".') if mpi.is_master_node(): # Checks for h5 file @@ -105,11 +107,14 @@ def main(argv=sys.argv): if not os.path.exists(general_params['jobname']): os.makedirs(general_params['jobname']) - # Copies h5 archive and config file to subfolder if are not there - for file in (general_params['seedname']+'.h5', - general_params['config_file']): - if not os.path.isfile(general_params['jobname']+'/'+os.path.basename(file)): - shutil.copyfile(file, general_params['jobname']+'/'+os.path.basename(file)) + # Copies h5 archive to subfolder if it is not there + h5_name = general_params['seedname']+'.h5' + if not os.path.isfile(general_params['jobname']+'/'+os.path.basename(h5_name)): + shutil.copyfile(h5_name, general_params['jobname']+'/'+os.path.basename(h5_name)) + + # Copies config file to subfolder + if config_file_name is not None: + shutil.copyfile(config_file_name, general_params['jobname']+'/'+os.path.basename(config_file_name)) mpi.barrier() # Runs dmft_cycle @@ -119,9 +124,21 @@ def main(argv=sys.argv): mpi.barrier() if mpi.is_master_node(): global_end = timer() - print('-------------------------------') + print('-'*80) print('overall elapsed time: %10.4f seconds'%(global_end-global_start)) +def main(argv=sys.argv): + """The main function for one-shot and charge-self-consistent calculations""" + config_file_name = str(argv[1]) if len(argv) > 1 else 'dmft_config.toml' + + params = None + if mpi.is_master_node(): + print('Reading the config file', config_file_name) + with open(config_file_name, 'rb') as file: + params = tomllib.load(file) + params = mpi.bcast(params) + + run_dmft(params, config_file_name) if __name__ == '__main__': main(sys.argv) diff --git a/python/solid_dmft/read_config.py b/python/solid_dmft/read_config.py deleted file mode 100755 index a4d88e89..00000000 --- a/python/solid_dmft/read_config.py +++ /dev/null @@ -1,1294 +0,0 @@ -################################################################################ -# -# solid_dmft - A versatile python wrapper to perform DFT+DMFT calculations -# utilizing the TRIQS software library -# -# Copyright (C) 2018-2020, ETH Zurich -# Copyright (C) 2021, The Simons Foundation -# authors: A. Hampel, M. Merkel, and S. Beck -# -# solid_dmft is free software: you can redistribute it and/or modify it under the -# terms of the GNU General Public License as published by the Free Software -# Foundation, either version 3 of the License, or (at your option) any later -# version. -# -# solid_dmft is distributed in the hope that it will be useful, but WITHOUT ANY -# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A -# PARTICULAR PURPOSE. See the GNU General Public License for more details. - -# You should have received a copy of the GNU General Public License along with -# solid_dmft (in the file COPYING.txt in this directory). If not, see -# . -# -################################################################################ -""" -Provides the read_config function to read the config file - -Reads the config file (default dmft_config.ini) with python's configparser -module. It consists of at least the section 'general', and optionally of the -sections 'solver', 'dft' and 'advanced'. -Comments are in general possible with with the delimiters ';' or -'#'. However, this is only possible in the beginning of a line not within -the line! For default values, the string 'none' is used. NoneType cannot be -saved in an h5 archive (in the framework that we are using). - -List of all parameters, sorted by sections: - ----XXX---start -List of all parameters, sorted by sections: - -[ general ] -------------- - -seedname : str - seedname for h5 archive with DMFT input and output -jobname : str, optional, default='dmft_dir' - the output directory for one-shot calculations -csc : bool, optional, default=False - are we doing a CSC calculation? -plo_cfg : str, optional, default='plo.cfg' - config file for PLOs for the converter -h_int_type : string - interaction type: - - * density_density: used for full d-shell or eg- or t2g-subset - * kanamori: only physical for the t2g or the eg subset - * full_slater: used for full d-shell or eg- or t2g-subset - * ntot: U/2 (Ntot^2 - Ntot) interaction - * simple_intra: density-density like but only intra orbital with given U value (no rotations applied) - * crpa: use the cRPA matrix as interaction Hamiltonian - * crpa_density_density: use the density-density terms of the cRPA matrix - * dynamic: use dynamic U from h5 archive - - Needs to be stored as Matsubara Gf under dynamic_U/U_iw in the input h5 -h_int_basis : string - cubic basis convention to compute the interaction U matrix - * 'triqs' - * 'vasp' (equivalent to 'triqs') - * 'wien2k' - * 'wannier90' - * 'qe' (equivalent to 'wannier90') -U : float or comma separated list of floats - U values for impurities if only one value is given, the same U is assumed for all impurities -U_prime : float or comma separated list of floats - U prime values for impurities if only one value is given, the same U prime is assumed for all impurities - only used if h_int_type is kanamori -J : float or comma separated list of floats - J values for impurities if only one value is given, the same J is assumed for all impurities -ratio_F4_F2 : float or comma separated list of floats, optional, default='none' - Ratio between the Slater integrals F_4 and F_2. Only used for the - interaction Hamiltonians 'density_density' and 'full_slater' and - only for d-shell impurities, where the default is 0.63. -beta : float, only used if solver ImFreq - inverse temperature for Greens function etc -n_iter_dmft_first : int, optional, default= 10 - number of iterations in first dmft cycle to converge dmft solution -n_iter_dmft_per : int, optional, default= 2 - number of iterations per dmft step in CSC calculations -n_iter_dmft : int - number of iterations per dmft cycle after first cycle -dc_type : int - Type of double counting correction considered: - * 0: FLL - * 1: held formula, needs to be used with slater-kanamori h_int_type=2 - * 2: AMF - * 3: FLL for eg orbitals only with U,J for Kanamori -dc_dmft : bool - Whether to use DMFT or DFT occupations: - - * DC with DMFT occupation in each iteration -> True - * DC with DFT occupations after each DFT cycle -> False -cpa_zeta : float or comma separated list of floats - shift of local levels per impurity in CPA -cpa_x : float or comma separated list of floats - probability distribution for summing G(tau) in CPA -solver_type : str - type of solver chosen for the calculation, currently supports: - - * 'cthyb' - * 'ctint' - * 'ftps' - * 'hubbardI' - * 'hartree' - * 'ctseg' - -n_iw : int, optional, default=1025 - number of Matsubara frequencies -n_tau : int, optional, default=10001 - number of imaginary time points -n_l : int, needed if measure_G_l=True or legendre_fit=True - number of Legendre coefficients -n_w : int, optional, default=5001 - number of real frequency points -w_range : tuple, optional, default=(-10, 10) - w_min and w_max, example: w_range = -10, 10 -eta : float, only used if solver ReFreq - broadening of Green's function -diag_delta : bool, optional, default=False - option to remove off-diagonal terms in the hybridization function - - -h5_save_freq : int, optional, default=5 - how often is the output saved to the h5 archive -magnetic : bool, optional, default=False - are we doing a magnetic calculations? If yes put magnetic to True. - Not implemented for CSC calculations -magmom : list of float seperated by comma, optional default=[] - Initialize magnetic moments if magnetic is on. length must be #imps. - List composed of energetic shifts written in electronvolts. - This will initialize the spin blocks of the sigma with a diagonal shift - With -shift for the up block, and +shift for the down block - (positive shift favours the up spin component, not compatible with spin-orbit coupling) -enforce_off_diag : bool, optional, default=False - enforce off diagonal elements in block structure finder -h_field : float, optional, default=0.0 - magnetic field -h_field_it : int, optional, default=0 - number of iterations the magnetic field is kept on -sigma_mix : float, optional, default=1.0 - careful: Sigma mixing can break orbital symmetries, use G0 mixing - mixing sigma with previous iteration sigma for better convergency. 1.0 means no mixing -g0_mix : float, optional, default=1.0 - Mixing the weiss field G0 with previous iteration G0 for better convergency. 1.0 means no mixing. - Setting g0_mix to 0.0 with linear mixing can be used for statistic sampling when - restarting a calculation -g0_mix_type : string, optional, default='linear' - which type of mixing is used. Possible values are: - linear: linear mixing - broyden: broyden mixing -broy_max_it : int, optional, default=1 - maximum number of iteration to be considered for broyden mixing - 1 corresponds to simple linear mixing -dc : bool, optional, default=True - dc correction on yes or no? -calc_energies : bool, optional, default=False, not compatible with 'ftps' solver - calc energies explicitly within the dmft loop -block_threshold : float, optional, default=1e-05 - threshold for finding block structures in the input data (off-diag yes or no) -block_suppress_orbital_symm : bool, optional, default=False - should blocks be checked if symmetry-equiv. between orbitals? - Does not affect spin symmetries. -load_sigma : bool, optional, default=False - load a old sigma from h5 file -path_to_sigma : str, needed if load_sigma is true - path to h5 file from which the sigma should be loaded -load_sigma_iter : int, optional, default= last iteration - load the sigma from a specific iteration if wanted -noise_level_initial_sigma : float, optional, default=0.0 - spread of Gaussian noise applied to the initial Sigma -occ_conv_crit : float, optional, default= -1 - stop the calculation if a certain threshold for the imp occ change is reached -gimp_conv_crit : float, optional, default= -1 - stop the calculation if sum_w 1/(w^0.6) ||Gimp-Gloc|| is smaller than threshold -g0_conv_crit : float, optional, default= -1 - stop the calculation if sum_w 1/(w^0.6) ||G0-G0_prev|| is smaller than threshold -sigma_conv_crit : float, optional, default= -1 - stop the calculation if sum_w 1/(w^0.6) ||Sigma-Sigma_prev|| is smaller than threshold -sampling_iterations : int, optional, default= 0 - for how many iterations should the solution sampled after the CSC loop is converged -sampling_h5_save_freq : int, optional, default= 5 - overwrites h5_save_freq when sampling has started -calc_mu_method : string, optional, default = 'dichotomy' - optimization method used for finding the chemical potential: - - * 'dichotomy': usual method from TRIQS, should always converge but may be slow - * 'newton': scipy Newton root finder, much faster but might be unstable - * 'brent': scipy hyperbolic Brent root finder preconditioned with dichotomy to find edge, a compromise between speed and stability -prec_mu : float - general precision for determining the chemical potential at any time calc_mu is called -fixed_mu_value : float, optional, default= 'none' - If given, the chemical potential remains fixed in calculations -mu_update_freq : int, optional, default= 1 - The chemical potential will be updated every # iteration -mu_initial_guess : float, optional, default= 'none' - The chemical potential of the DFT calculation. - If not given, mu will be calculated from the DFT bands -mu_mix_const : float, optional, default= 1.0 - Constant term of the mixing of the chemical potential. See mu_mix_per_occupation_offset. -mu_mix_per_occupation_offset : float, optional, default= 0.0 - Mu mixing proportional to the occupation offset. - Mixing between the dichotomy result and the previous mui, - - mu_next = factor * mu_dichotomy + (1-factor) * mu_previous, with - factor = mu_mix_per_occupation_offset * abs(n - n\_target) + mu_mix_const. - - The program ensures that 0 <= factor <= 1. - mu_mix_const = 1.0 and mu_mix_per_occupation_offset = 0.0 means no mixing. -afm_order : bool, optional, default=False - copy self energies instead of solving explicitly for afm order -set_rot : string, optional, default='none' - use density_mat_dft to diagonalize occupations = 'den' - use hloc_dft to diagonalize occupations = 'hloc' -measure_chi_SzSz : bool, optional, default=False - measure the dynamic spin suszeptibility chi(sz,sz(tau)) - triqs.github.io/cthyb/unstable/guide/dynamic_susceptibility_notebook.html -measure_chi_insertions : int, optional, default=100 - number of insertation for measurement of chi -mu_gap_gb2_threshold : float, optional, default=none - Threshold of the absolute of the lattice GF at tau=beta/2 for use - of MaxEnt's lattice spectral function to put the chemical potential - into the middle of the gap. Does not work if system completely full - or empty, mu mixing is not applied to it. Recommended value 0.01. -mu_gap_occ_deviation : float, optional, default=none - Only used if mu_gap_gb2_threshold != none. Sets additional criterion - for finding the middle of the gap through occupation deviation to - avoid getting stuck in an insulating state with wrong occupation. - -[ solver ] ------------- -store_solver : bool, optional default= False - store the whole solver object under DMFT_input in h5 archive - -cthyb parameters -================ -length_cycle : int - length of each cycle; number of sweeps before measurement is taken -n_warmup_cycles : int - number of warmup cycles before real measurement sets in -n_cycles_tot : int - total number of sweeps -measure_G_l : bool - measure Legendre Greens function -measure_G_tau : bool,optional, default=True - should the solver measure G(tau)? -measure_G_iw : bool,optional, default=False - should the solver measure G(iw)? -measure_density_matrix : bool, optional, default=False - measures the impurity density matrix and sets also - use_norm_as_weight to true -measure_pert_order : bool, optional, default=False - measure perturbation order histograms: triqs.github.io/cthyb/latest/guide/perturbation_order_notebook.html - - The result is stored in the h5 archive under 'DMFT_results' at every iteration - in the subgroups 'pert_order_imp_X' and 'pert_order_total_imp_X' -max_time : int, optional, default=-1 - maximum amount the solver is allowed to spend in each iteration -imag_threshold : float, optional, default= 10e-15 - threshold for imag part of G0_tau. be warned if symmetries are off in projection scheme imag parts can occur in G0_tau -off_diag_threshold : float, optional - threshold for off-diag elements in Hloc0 -delta_interface : bool, optional, default=False - use new delta interface in cthyb instead of input G0 -move_double : bool, optional, default=True - double moves in solver -perform_tail_fit : bool, optional, default=False - tail fitting if legendre is off? -fit_max_moment : int, optional - max moment to be fitted -fit_min_n : int, optional - number of start matsubara frequency to start with -fit_max_n : int, optional - number of highest matsubara frequency to fit -fit_min_w : float, optional - start matsubara frequency to start with -fit_max_w : float, optional - highest matsubara frequency to fit -random_seed : str, optional default by triqs - if specified the int will be used for random seeds! Careful, this will give the same random - numbers on all mpi ranks - You can also pass a string that will convert the keywords it or rank on runtime, e.g. - 34788 * it + 928374 * rank will convert each iteration the variables it and rank for the random - seed -legendre_fit : bool, optional default= False - filter noise of G(tau) with G_l, cutoff is taken from n_l -loc_n_min : int, optional - Restrict local Hilbert space to states with at least this number of particles -loc_n_max : int, optional - Restrict local Hilbert space to states with at most this number of particles - -ftps parameters -=============== -n_bath : int - number of bath sites -bath_fit : bool, default=False - DiscretizeBath vs BathFitter -refine_factor : int, optional, default=1 - rerun ftps cycle with increased accuracy -ph_symm : bool, optional, default=False - particle-hole symmetric problem -calc_me : bool, optional, default=True - calculate only symmetry-inequivalent spins/orbitals, symmetrized afterwards -enforce_gap : list of floats, optional, default='none' - enforce gap in DiscretizeBath between interval -ignore_weight : float, optional, default=0.0 - ignore weight of peaks for bath fitter -dt : float - time step -state_storage : string, default= './' - location of large MPS states -path_to_gs : string, default= 'none' - location of GS if already present. Use 'postprocess' to skip solver and go directly to post-processing - of previously terminated time-evolved state -sweeps : int, optional, default= 10 - Number of DMRG sweeps -maxmI : int, optional, default= 100 - maximal imp-imp bond dimensions -maxmIB : int, optional, default= 100 - maximal imp-bath bond dimensions -maxmB : int, optional, default= 100 - maximal bath-bath bond dimensions -tw : float, default 1E-9 - truncated weight for every link -dmrg_maxmI : int, optional, default= 100 - maximal imp-imp bond dimensions -dmrg_maxmIB : int, optional, default= 100 - maximal imp-bath bond dimensions -dmrg_maxmB : int, optional, default= 100 - maximal bath-bath bond dimensions -dmrg_tw : float, default 1E-9 - truncated weight for every link - -ctseg parameters -================ -measure_hist : bool, optional, default=False - measure perturbation_order histograms -improved_estimator : bool, optional, default=False - measure improved estimators - Sigma_iw will automatically be calculated via - http://dx.doi.org/10.1103/PhysRevB.85.205106 - -hartree parameters -================ -with_fock : bool, optional, default=False - include Fock exchange terms in the self-energy -force_real : bool, optional, default=True - force the self energy from Hartree fock to be real -one_shot : bool, optional, default=True - Perform a one-shot or self-consitent root finding in each DMFT step of the Hartree solver. -method : bool, optional, default=True - method for root finder. Only used if one_shot=False, see scipy.optimize.root for options. -tol : float, optional, default=1e-5 - tolerance for root finder if one_shot=False. - -[ dft ] ---------- -dft_code : string - Choose the DFT code interface, for now Quantum Espresso and Vasp are available. - - Possible values: - - * 'vasp' - * 'qe' -n_cores : int - number of cores for the DFT code (VASP) -n_iter : int, optional, default= 6 - only needed for VASP. Number of DFT iterations to feed the DMFT - charge density into DFT, which generally takes multiple Davidson steps. - For every DFT iterations, the charge-density correction is recalculated - using newly generated projectors and hoppings from the previous DFT run -n_iter_first : int, optional, default= dft/n_iter - number of DFT iterations in the first charge correction because this - first charge correction usually changes the DFT wave functions the most. -dft_exec : string, default= 'vasp_std' - command for the DFT executable -store_eigenvals : bool, optional, default= False - stores the dft eigenvals from LOCPROJ (projector_type=plo) or - wannier90.eig (projector_type=w90) file in h5 archive -mpi_env : string, default= 'local' - selection for mpi env for DFT / VASP in default this will only call VASP as mpirun -np n_cores_dft dft_exec -projector_type : string, optional, default= 'w90' - plo: uses VASP's PLO formalism, requires LOCPROJ in the INCAR - w90: uses Wannier90 (for VASP and QuantumEspresso) -w90_exec : string, default='wannier90.x' - the command to start a single-core wannier run -w90_tolerance : float, default=1e-6 - threshold for mapping of shells and checks of the Hamiltonian - -[ advanced ] --------------- -dc_factor : float, optional, default= 'none' (corresponds to 1) - If given, scales the dc energy by multiplying with this factor, usually < 1 -dc_fixed_value : float, optional, default= 'none' - If given, it sets the DC (energy/imp) to this fixed value. Overwrites EVERY other DC configuration parameter if DC is turned on -dc_fixed_occ : list of float, optional, default= 'none' - If given, the occupation for the DC for each impurity is set to the provided value. - Still uses the same kind of DC! -dc_orb_shift : list of float, optional, default= 'none' - extra potential shift per orbital per impurity added to the DC -dc_U : float or comma seperated list of floats, optional, default= general_params['U'] - U values for DC determination if only one value is given, the same U is assumed for all impurities -dc_J : float or comma seperated list of floats, optional, default= general_params['J'] - J values for DC determination if only one value is given, the same J is assumed for all impurities -map_solver_struct : list of dict, optional, default=no additional mapping - Additional manual mapping of the solver block structure, applied - after the block structure finder for each impurity. - Give exactly one dict per ineq impurity. - see also triqs.github.io/dft_tools/latest/_python_api/triqs_dft_tools.block_structure.BlockStructure.map_gf_struct_solver.html -mapped_solver_struct_degeneracies : list, optional, default=none - Degeneracies applied when using map_solver_struct, for each impurity. - If not given and map_solver_struct is used, no symmetrization will happen. -pick_solver_struct : list of dict, optional, default=no additional picking - input a solver dictionary for each ineq impurity to reduce dimensionality of - solver block structure. Similar to to map_solver_struct, but with simpler syntax. - Not listed blocks / orbitals will be not treated in impurity solver. - Keeps degenerate shells. - - ----XXX---end - -""" - -from configparser import ConfigParser -import triqs.utility.mpi as mpi -import numpy as np - -# Workaround to get the default configparser boolean converter -BOOL_PARSER = lambda b: ConfigParser()._convert_to_boolean(b) - -# TODO: it might be nicer to not have optional parameters at all and instead use -# explicit default values - -# Dictionary for the parameters. Contains the four sections general, dft, solver -# and advanced. Inside, all parameters are listed with their properties: -# - converter: converter applied on the string value of the parameter -# - valid for: a criterion for validity. If not fulfilled, the program crashes. -# Always of form lambda x, params: ..., with x being the current parameter -# - used: determines if parameter is used (and if not given, set to default value) -# or unused and ignored. If 'used' and no default given, the program crashes. -# If 'used' and default=None, this is an optional parameter -# - default: default value for parameter. Can be a function of params but can only -# use values that have NO default value. If it is None but 'used' -# is True, the parameter becomes an optional parameter -PROPERTIES_PARAMS = {'general': {'seedname': {'used': True}, - - 'h_int_type': {'valid for': lambda x, _: all(hint in ('density_density', - 'kanamori', - 'full_slater', - 'crpa', - 'crpa_density_density', - 'dynamic', - 'ntot', - 'simple_intra') for hint in x), - 'converter': lambda s: list(map(str, s.replace(" ", "").split(','))), - 'used': True}, - - 'U': {'converter': lambda s: list(map(float, s.split(','))), 'used': True}, - - 'U_prime': {'converter': lambda s: list(map(float, s.split(','))), - 'default': ['U-2J'], - 'valid for': lambda x, params: all(r == 'U-2J' or hint in ('kanamori') - for r, hint in zip(x, params['general']['h_int_type'])), - 'used': True}, - - 'J': {'converter': lambda s: list(map(float, s.split(','))), 'used': True}, - - 'ratio_F4_F2': {'converter': lambda s: list(map(float, s.split(','))), - 'default': ['none'], - 'valid for': lambda x, params: all(r == 'none' or hint in ('density_density','full_slater') - for r, hint in zip(x, params['general']['h_int_type'])), - 'used': True}, - - 'beta': {'converter': float, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'inchworm', 'hubbardI','ctseg','hartree']}, - - 'n_iter_dmft': {'converter': int, 'valid for': lambda x, _: x >= 0, 'used': True}, - - 'dc': {'converter': BOOL_PARSER, 'used': True, 'default': True}, - - 'dc_type': {'converter': int, 'valid for': lambda x, _: x in (0, 1, 2, 3, 4), - 'used': lambda params: params['general']['dc']}, - - 'prec_mu': {'converter': float, 'valid for': lambda x, _: x > 0, 'used': True}, - - 'dc_dmft': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['dc']}, - - 'cpa_zeta': {'converter': lambda s: list(map(float, s.split(','))), - 'used': lambda params: params['general']['dc'] and params['general']['dc_type'] == 4}, - - 'cpa_x': {'converter': lambda s: list(map(float, s.split(','))), - 'used': lambda params: params['general']['dc'] and params['general']['dc_type'] == 4}, - - 'solver_type': {'valid for': lambda x, _: x in ['cthyb', 'ctint', 'ftps', 'hubbardI','ctseg', 'hartree'], - 'used': True}, - - - 'n_l': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'inchworm', 'hubbardI', 'ctseg'] - and (params['solver']['measure_G_l'] or params['solver']['legendre_fit'])}, - - 'n_iw': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'inchworm', 'hubbardI','ctseg','hartree'], 'default': 1025}, - - 'n_tau': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'inchworm', 'hubbardI','ctseg','hartree'], 'default': 10001}, - - 'n_w': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps', 'hubbardI', 'hartree'], 'default': 5001}, - - 'w_range': {'converter': lambda s: tuple(map(float, s.split(','))), - 'valid for': lambda x, _: x[0] < x[1], - 'used': lambda params: params['general']['solver_type'] in ['ftps', 'hubbardI', 'hartree'], 'default': (-10, 10)}, - - 'eta': {'converter': float, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps', 'hubbardI', 'hartree']}, - - 'diag_delta': {'converter': BOOL_PARSER, 'used': True, 'default': False}, - - 'csc': {'converter': BOOL_PARSER, - 'used': True, 'default': False}, - - 'n_iter_dmft_first': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['csc'], 'default': 10}, - - 'n_iter_dmft_per': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['csc'], 'default': 2}, - - 'plo_cfg': {'used': lambda params: (params['general']['csc'] - and params['dft']['projector_type'] == 'plo'), - 'default': 'plo.cfg'}, - - 'jobname': {'used': lambda params: not params['general']['csc'], 'default': lambda params: 'dmft_dir'}, - - 'h5_save_freq': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': True, 'default': 5}, - - 'magnetic': {'converter': BOOL_PARSER, - 'used': True, 'default': False}, - - 'magmom': {'converter': lambda s: list(map(float, s.split(','))), - 'used': lambda params: params['general']['magnetic'], - 'default': []}, - - 'h_field': {'converter': float, 'used': True, 'default': 0.0}, - - 'h_field_it': {'converter': int, 'used': True, 'default': 0}, - - 'afm_order': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['magnetic'], - 'default': False}, - - 'sigma_mix': {'converter': float, - 'valid for': lambda x, params: x >= 0 and (np.isclose(params['general']['g0_mix'], 1) - or np.isclose(x, 1)), - 'used': True, 'default': 1.0}, - - 'g0_mix': {'converter': float, 'valid for': lambda x, _: x >= 0, - 'used': True, 'default': 1.0}, - - 'g0_mix_type': {'valid for': lambda x, _: x in ('linear', 'broyden'), - 'used': True, 'default': 'linear'}, - - 'broy_max_it': {'converter': int, 'valid for': lambda x, _: x >= 1 or x==-1 , - 'used': lambda params: params['general']['g0_mix_type'] == 'broyden', 'default': -1}, - - 'calc_energies': {'converter': BOOL_PARSER, 'used': True, 'default': False}, - - 'block_threshold': {'converter': float, 'valid for': lambda x, _: x > 0, - 'used': True, 'default': 1e-5}, - - 'block_suppress_orbital_symm': {'converter': BOOL_PARSER, - 'used': lambda params: not params['general']['enforce_off_diag'], 'default': False}, - - 'enforce_off_diag': {'converter': BOOL_PARSER, 'used': True, 'default': False}, - - 'load_sigma': {'converter': BOOL_PARSER, 'used': True, 'default': False}, - - 'path_to_sigma': {'used': lambda params: params['general']['load_sigma']}, - - 'load_sigma_iter': {'converter': int, - 'used': lambda params: params['general']['load_sigma'], 'default': -1}, - - 'noise_level_initial_sigma': {'converter': float, - 'valid for': lambda x, _: x > 0 or np.isclose(x, 0), - 'used': True, 'default': 0.}, - - # TODO: change default to 'none' - 'occ_conv_crit': {'converter': float, 'used': True, 'default': -1}, - 'gimp_conv_crit': {'converter': float, 'used': True, 'default': -1}, - 'g0_conv_crit': {'converter': float, 'used': True, 'default': -1}, - 'sigma_conv_crit': {'converter': float, 'used': True, 'default': -1}, - - 'sampling_iterations': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['occ_conv_crit'] > 0 or - params['general']['gimp_conv_crit'] > 0 or - params['general']['g0_conv_crit'] > 0, - 'default': 0}, - - 'sampling_h5_save_freq': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: (params['general']['occ_conv_crit'] > 0 - and params['general']['sampling_iterations'] > 0), - 'default': 5}, - - 'fixed_mu_value': {'converter': float, 'used': True, 'default': 'none'}, - - 'calc_mu_method': {'valid for': lambda x, _: x in ['dichotomy', 'newton', 'brent'], - 'used': True, - 'default': 'dichotomy', - }, - - 'mu_update_freq': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['fixed_mu_value'] == 'none', - 'default': 1}, - - 'mu_initial_guess': {'converter': float, 'used': True, 'default': 'none'}, - - 'mu_mix_const': {'converter': float, - 'valid for': lambda x, _: x > 0 or np.isclose(x, 0), - 'used': lambda params: params['general']['fixed_mu_value'] == 'none', - 'default': 1.}, - - 'mu_mix_per_occupation_offset': {'converter': float, - 'valid for': lambda x, _: x > 0 or np.isclose(x, 0), - 'used': lambda params: params['general']['fixed_mu_value'] == 'none', - 'default': 0.}, - - 'set_rot': {'valid for': lambda x, _: x in ('none', 'den', 'hloc'), - 'used': True, 'default': 'none'}, - - 'measure_chi': {'valid for': lambda x, _: x in ('SzSz', 'NN', 'none'), 'used': True, 'default': 'none'}, - - 'measure_chi_insertions': {'converter': int, 'used': True, 'default': 100}, - - # TODO: used for which solvers? Generalize to real freq. solvers without maxent? - 'mu_gap_gb2_threshold': {'converter': float, - 'valid for': lambda x, _: x == 'none' or x > 0 or np.isclose(x, 0), - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint','ctseg'], - 'default': 'none'}, - - 'mu_gap_occ_deviation': {'converter': float, - 'valid for': lambda x, _: x == 'none' or x > 0 or np.isclose(x, 0), - 'used': lambda params: (params['general']['solver_type'] in ['cthyb', 'ctint','ctseg'] - and params['general']['mu_gap_gb2_threshold'] != 'none'), - 'default': 'none'}, - - 'h_int_basis' : {'valid for': lambda x, _: x in ('triqs', 'wien2k', 'wannier90', 'qe', 'vasp'), - 'used': True, 'default' : 'triqs'} - - }, - 'dft': {'dft_code': {'used': lambda params: params['general']['csc'], - 'valid for': lambda x, _: x in ('vasp', 'qe')}, - - 'n_cores': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['csc']}, - - 'n_iter': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['csc'] and params['dft']['dft_code'] == 'vasp', - 'default': 4}, - - 'n_iter_first': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['csc'] and params['dft']['dft_code'] == 'vasp', - 'default': lambda params: params['dft']['n_iter']}, - - 'dft_exec': {'used': lambda params: params['general']['csc'], 'default': 'vasp_std'}, - - 'store_eigenvals': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['csc'], - 'default': False}, - - 'mpi_env': {'valid for': lambda x, _: x in ('default', 'openmpi', 'openmpi-intra', 'mpich'), - 'used': lambda params: params['general']['csc'], 'default': 'default'}, - - 'projector_type': {'valid for': lambda x, params: x == 'w90' or x == 'plo' and params['dft']['dft_code'] == 'vasp', - 'used': lambda params: params['general']['csc'], 'default': 'w90'}, - - 'w90_exec': {'used': lambda params: (params['general']['csc'] - and params['dft']['dft_code'] == 'vasp' and params['dft']['projector_type'] == 'w90'), - 'default': 'wannier90.x'}, - - 'w90_tolerance': {'converter': lambda s: float(s), - 'used': lambda params: params['general']['csc'] and params['dft']['projector_type'] == 'w90', - 'default': 1e-6}, - }, - 'solver': { - # - 'store_solver': {'converter': BOOL_PARSER, 'used': True, 'default': False}, - # - # cthyb parameters - # - 'length_cycle': {'converter': int, 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']}, - - 'n_warmup_cycles': {'converter': lambda s: int(float(s)), - 'valid for': lambda x, _: x > 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']}, - - 'n_cycles_tot': {'converter': lambda s: int(float(s)), - 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']}, - - 'max_time': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'default': None, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']}, - - 'imag_threshold': {'converter': float, 'default': None, - 'used': lambda params: params['general']['solver_type'] in ['cthyb']}, - - 'off_diag_threshold': {'converter': float, 'default': 0.0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb']}, - - 'delta_interface': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['cthyb']}, - - 'measure_G_tau': {'converter': BOOL_PARSER, 'default': True, - 'used': lambda params: params['general']['solver_type'] in ['hubbardI', 'ctseg']}, - - 'measure_G_iw': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['ctseg']}, - - 'measure_G_l': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'hubbardI', 'ctseg']}, - - 'measure_density_matrix': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'hubbardI']}, - - 'move_double': {'converter': BOOL_PARSER, 'default': True, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint']}, - - 'measure_pert_order': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']}, - - 'move_shift': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['cthyb']}, - - 'random_seed': {'converter': str, 'default': None, - 'used': lambda params: params['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']}, - - 'perform_tail_fit': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['cthyb'] - and not params['solver']['measure_G_l'], - 'default': False}, - - 'fit_max_moment': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: 'perform_tail_fit' in params['solver'] - and params['solver']['perform_tail_fit'] - and params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - 'fit_min_n': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: 'perform_tail_fit' in params['solver'] - and params['solver']['perform_tail_fit'] - and params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - 'fit_max_n': {'converter': int, 'valid for': lambda x, params: x >= params['solver']['fit_min_n'], - 'used': lambda params: 'perform_tail_fit' in params['solver'] - and params['solver']['perform_tail_fit'] - and params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - 'fit_min_w': {'converter': float, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: 'perform_tail_fit' in params['solver'] - and params['solver']['perform_tail_fit'] - and params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - 'fit_max_w': {'converter': float, 'valid for': lambda x, params: x >= params['solver']['fit_min_w'], - 'used': lambda params: 'perform_tail_fit' in params['solver'] - and params['solver']['perform_tail_fit'] - and params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - 'legendre_fit': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['cthyb','ctseg'], - 'default': False}, - - 'loc_n_min': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - 'loc_n_max': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['cthyb'], - 'default': None}, - - - # - # extra ctseg params - # - 'improved_estimator': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['ctseg'], - 'default': False}, - - # - # extra hartree params - # - 'with_fock': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['hartree'], - 'default': False}, - 'one_shot': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['hartree'], - 'default': False}, - 'force_real': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['hartree'], - 'default': True}, - 'method': {'valid for': lambda x, _: x in ['krylov', 'broyden1', 'broyden2', 'hybr', 'linearmixing'], - 'used': lambda params: params['general']['solver_type'] in ['hartree'], - 'default': 'krylov'}, - 'tol': {'converter': float, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['hartree'], - 'default': 1e-5}, - - # - # ftps parameters - # - 'n_bath': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': 0}, - 'bath_fit': {'converter': BOOL_PARSER, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'refine_factor': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': 1}, - 'ph_symm': {'converter': BOOL_PARSER, 'default': False, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'calc_me': {'converter': BOOL_PARSER, 'default': True, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'enforce_gap': {'converter': lambda s: [float(eval(x)) for x in s.split(',')], - 'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': 'none'}, - 'ignore_weight': {'converter': float, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': 0.0}, - 'dt': {'converter': float, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'state_storage': {'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': './'}, - 'path_to_gs': {'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': 'none'}, - 'sweeps': {'converter': int, 'valid for': lambda x, _: x >= 0, - 'used': lambda params: params['general']['solver_type'] in ['ftps'], - 'default': 10}, - 'maxm': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'maxmI': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'maxmIB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'maxmB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'tw': {'converter': float, 'valid for': lambda x, _: x > 0, 'default': 1e-9, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'dmrg_maxm': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'dmrg_maxmI': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'dmrg_maxmIB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'dmrg_maxmB': {'converter': int, 'valid for': lambda x, _: x > 0, 'default': 100, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - 'dmrg_tw': {'converter': float, 'valid for': lambda x, _: x > 0, 'default': 1e-9, - 'used': lambda params: params['general']['solver_type'] in ['ftps']}, - }, - 'advanced': {'dc_factor': {'converter': float, 'used': True, 'default': 'none'}, - - 'dc_fixed_value': {'converter': float, 'used': True, 'default': 'none'}, - - 'dc_fixed_occ': {'converter': lambda s: list(map(float, s.split(','))), - 'used': True, 'default': 'none'}, - - 'dc_orb_shift': {'converter': lambda s: list(map(float, s.split(','))), - 'used': True, 'default': 'none'}, - - 'dc_nominal': {'converter': BOOL_PARSER, 'used': True, 'default': False}, - - 'dc_U': {'converter': lambda s: list(map(float, s.split(','))), - 'used': True, 'default': lambda params: params['general']['U']}, - - 'dc_J': {'converter': lambda s: list(map(float, s.split(','))), - 'used': True, 'default': lambda params: params['general']['J']}, - - 'map_solver_struct': {'converter': eval, - 'valid for': lambda x, _: x =='none' or isinstance(x, dict) or isinstance(x, list), - 'used': True, 'default': 'none'}, - - 'mapped_solver_struct_degeneracies': {'converter': eval, - 'valid for': lambda x, _: x=='none' or isinstance(x, list), - 'used': lambda params: params['advanced']['map_solver_struct'] != 'none', - 'default': 'none'}, - 'pick_solver_struct': {'converter': eval, - 'valid for': lambda x, _: x =='none' or isinstance(x, dict) or isinstance(x, list), - 'used': True, 'default': 'none'}, - } - } - - -# -------------------------- config section cleanup -------------------------- -def _config_find_default_section_entries(config): - """ - Returns all items in the default section. - - Parameters - ---------- - config : ConfigParser - A configparser instance that has read the config file already. - - Returns - ------- - list - All entries in the default section. - """ - return list(config['DEFAULT'].keys()) - -def _config_add_empty_sections(config): - """ - Adds empty sections if no parameters in the whole section were given. - - Parameters - ---------- - config : ConfigParser - A configparser instance that has read the config file already. - - Returns - ------- - config : ConfigParser - The config parser with all required sections. - """ - for section_name in PROPERTIES_PARAMS: - if section_name not in config: - config.add_section(section_name) - - return config - -def _config_remove_unused_sections(config): - """ - Removes sections that are not supported by this program. - - Parameters - ---------- - config : ConfigParser - A configparser instance that has read the config file already. - - Returns - ------- - config : ConfigParser - The config parser without unnecessary sections. - unused_sections : list - All sections that are not supported. - """ - unused_sections = [] - for section_name in list(config.keys()): - if section_name != 'DEFAULT' and section_name not in PROPERTIES_PARAMS.keys(): - unused_sections.append(section_name) - config.remove_section(section_name) - - return config, unused_sections - -# -------------------------- parameter reading -------------------------- -def _convert_params(config): - """ - Applies the converter given in the PROPERTIES_PARAMS to the config. If no - converter is given, a default string conversion is used. - - Parameters - ---------- - config : ConfigParser - A configparser instance that has passed through the above clean-up - methods. - - Returns - ------- - parameters : dict - Contains dicts for each section. These dicts contain all parameter - names and their respective value that are in the configparser and in - the PROPERTIES_PARAMS. - """ - parameters = {name: {} for name in PROPERTIES_PARAMS} - - for section_name, section_params in parameters.items(): - for param_name, param_props in PROPERTIES_PARAMS[section_name].items(): - if param_name not in config[section_name]: - continue - - # Uses converter for parameters - if 'converter' in param_props: - section_params[param_name] = param_props['converter'](str(config[section_name][param_name])) - else: - section_params[param_name] = str(config[section_name][param_name]) - - return parameters - - -def _find_nonexistent_params(config): - """ - Returns all parameters that are in the config but not in the - PROPERTIES_PARAMS and are therefore not recognized by the program. - - Parameters - ---------- - config : ConfigParser - A configparser instance that has passed through the above clean-up - methods. - - Returns - ------- - nonexistent_params : dict - Contains a list for each section, which contains all unused parameter - names. - """ - nonexistent_params = {section_name: [] for section_name in PROPERTIES_PARAMS} - - for section_name, section_params in PROPERTIES_PARAMS.items(): - for param_name in config[section_name]: - if param_name not in (key.lower() for key in section_params): - nonexistent_params[section_name].append(param_name) - - return nonexistent_params - - -def _apply_default_values(parameters): - """ - Applies default values to all parameters that were not given in the config. - - Parameters - ---------- - parameters : dict - Contains dicts for each section, which contain the parameter names and - their values as read from the config file. - - Returns - ------- - parameters : dict - The parameters dict including the default values. - default_values_used : dict - Contains a list for each section, which contains all parameters that - were set to their default values. Used to find out later which - unnecessary parameters were actually given in the config file. - """ - default_values_used = {section_name: [] for section_name in PROPERTIES_PARAMS} - - for section_name, section_params in PROPERTIES_PARAMS.items(): - for param_name, param_props in section_params.items(): - if 'default' not in param_props: - continue - - if param_name in parameters[section_name]: - continue - - default_values_used[section_name].append(param_name) - if callable(param_props['default']): - parameters[section_name][param_name] = param_props['default'](parameters) - else: - parameters[section_name][param_name] = param_props['default'] - - return parameters, default_values_used - - -def _check_if_params_used(parameters, default_values_used): - """ - Checks if the parameters in the config file are used or unnecessary. - - Parameters - ---------- - parameters : dict - Contains dicts for each section, which contain the parameter names and - their values as read from the config file or otherwise set to default. - default_values_used : dict - Contains a list for each section, which contains all parameters that - were set to their default values. - - Returns - ------- - parameters : dict - The parameters dict where all unnecessary parameters were removed. - unnecessary_params : dict - Contains a list for each section, which contains all parameters that - were given in the config file but are unnecessary. - missing_required_params : dict - Contains a list for each section, which contains all parameters that - are required, have no default and are missing from the config file. - """ - unnecessary_params = {section_name: [] for section_name in PROPERTIES_PARAMS} - missing_required_params = {section_name: [] for section_name in PROPERTIES_PARAMS} - - for section_name, section_params in PROPERTIES_PARAMS.items(): - for param_name, param_props in section_params.items(): - # 'used' could be bool or function returning bool - if callable(param_props['used']): - required = param_props['used'](parameters) - else: - required = param_props['used'] - - if required: - if param_name not in parameters[section_name]: - missing_required_params[section_name].append(param_name) - elif parameters[section_name][param_name] is None: - del parameters[section_name][param_name] - continue - - if param_name in parameters[section_name]: - del parameters[section_name][param_name] - - if param_name not in default_values_used[section_name]: - unnecessary_params[section_name].append(param_name) - - return parameters, unnecessary_params, missing_required_params - -def _checks_validity_criterion(parameters): - """ - Checks the validity criterion from the PROPERTIES_PARAMS. - - Parameters - ---------- - parameters : dict - Contains dicts for each section, which contain the parameter names and - their values as read from the config file, set to default if required - or removed if they are unnecessary. - - Returns - ------- - invalid_params : dict - Contains a list for each section, which contains all parameters that - do not fulfill their validity criterion. - """ - invalid_params = {section_name: [] for section_name in PROPERTIES_PARAMS} - - for section_name, section_params in PROPERTIES_PARAMS.items(): - for name, value in parameters[section_name].items(): - if 'valid for' not in section_params[name]: - continue - - condition = section_params[name]['valid for'] - if not condition(value, parameters): - invalid_params[section_name].append(name) - - return invalid_params - - -def read_config(config_file): - """ - Reads in the config file, checks its sections and parameters and returns - the parameters sorted by their categories. - - Parameters - ---------- - config_file : string - File name of the config file usable for configparser. - - Raises - ------ - ValueError - Required parameters are missing or parameters do not fulfill their - validity criterion. - - Returns - ------- - general_params : dict - - solver_params : dict - - dft_params : dict - - advanced_params : dict - """ - config = ConfigParser() - config.read(config_file) - - # Checks if default section is empty - config_default_entries = _config_find_default_section_entries(config) - if config_default_entries: - print('Warning: the following parameters are not in any section and will be ignored:') - print(', '.join(config_default_entries)) - - # Adds empty sections if they don't exist - config = _config_add_empty_sections(config) - - # Removes unused sections and prints a warning - config, unused_sections = _config_remove_unused_sections(config) - if unused_sections: - print('Warning: ignoring parameters in following unexpected sections:') - print(', '.join(unused_sections)) - - # Reads and converts all valid parameters - parameters = _convert_params(config) - - # Checks for unused parameters given in the config file - nonexistent_params = _find_nonexistent_params(config) - if any(nonexistent_params.values()): - print('Warning: the following parameters are not supported:') - for section_name, section_params in nonexistent_params.items(): - if section_params: - print('- Section "{}": '.format(section_name) + ', '.join(section_params)) - - # Applies default values - parameters, default_values_used = _apply_default_values(parameters) - - # Prints warning if unnecessary parameters are given - parameters, unnecessary_params, missing_required_params = _check_if_params_used(parameters, default_values_used) - if any(unnecessary_params.values()): - print('Warning: the following parameters are given but not used in this calculation:') - for section_name, section_params in unnecessary_params.items(): - if section_params: - print('- Section "{}": '.format(section_name) + ', '.join(section_params)) - - # Raises error if required parameters are not given - if any(missing_required_params.values()): - required_error_string = '' - for section_name, section_params in missing_required_params.items(): - if section_params: - required_error_string += '\n- Section "{}": '.format(section_name) + ', '.join(section_params) - raise ValueError('The following parameters are required but not given:' - + required_error_string) - - # Raises error if parameters invalid - invalid_params = _checks_validity_criterion(parameters) - if any(invalid_params.values()): - invalid_error_string = '' - for section_name, section_params in invalid_params.items(): - if section_params: - invalid_error_string += '\n- Section "{}": '.format(section_name) + ', '.join(section_params) - raise ValueError('The following parameters are not valid:' - + invalid_error_string) - - # warning if sigma mixing is used, remove in future versions - if parameters['general']['sigma_mix'] < 1.0: - if parameters['general']['g0_mix'] < 1.0: - raise ValueError('You shall not use Sigma and G0 mixing together!') - - # Workarounds for some parameters - - # make sure that pick_solver_struct and map_solver_struct are a list of dict - if isinstance(parameters['advanced']['pick_solver_struct'], dict): - parameters['advanced']['pick_solver_struct'] = [parameters['advanced']['pick_solver_struct']] - - if isinstance(parameters['advanced']['map_solver_struct'], dict): - parameters['advanced']['map_solver_struct'] = [parameters['advanced']['map_solver_struct']] - - if parameters['general']['solver_type'] in ['cthyb', 'ctint', 'ctseg']: - parameters['solver']['n_cycles'] = parameters['solver']['n_cycles_tot'] // mpi.size - del parameters['solver']['n_cycles_tot'] - - if parameters['general']['solver_type'] in ['cthyb']: - parameters['general']['cthyb_delta_interface'] = parameters['solver']['delta_interface'] - del parameters['solver']['delta_interface'] - - if parameters['general']['solver_type'] in ['ctseg']: - # some parameters have different names for ctseg - parameters['solver']['measure_gt'] = parameters['solver']['measure_G_tau'] - del parameters['solver']['measure_G_tau'] - - parameters['solver']['measure_gw'] = parameters['solver']['measure_G_iw'] - del parameters['solver']['measure_G_iw'] - - # make sure measure_gw is true if improved estimators are used - if parameters['solver']['improved_estimator']: - parameters['solver']['measure_gt'] = True - parameters['solver']['measure_ft'] = True - else: - parameters['solver']['measure_ft'] = False - del parameters['solver']['improved_estimator'] - - parameters['solver']['measure_gl'] = parameters['solver']['measure_G_l'] - del parameters['solver']['measure_G_l'] - - parameters['solver']['measure_hist'] = parameters['solver']['measure_pert_order'] - del parameters['solver']['measure_pert_order'] - - if parameters['general']['solver_type'] in ['cthyb'] and parameters['solver']['measure_density_matrix']: - # also required to measure the density matrix - parameters['solver']['use_norm_as_weight'] = True - - if parameters['general']['solver_type'] in ['ftps'] and parameters['general']['calc_energies']: - raise ValueError('"calc_energies" is not valid for solver_type = "ftps"') - - if parameters['general']['dc'] and parameters['general']['dc_type'] == 4: - assert sum(parameters['general']['cpa_x']) == 1., 'Probability distribution for CPA must equal 1.' - - # little workaround since #leg coefficients is not directly a solver parameter - if 'legendre_fit' in parameters['solver']: - parameters['general']['legendre_fit'] = parameters['solver']['legendre_fit'] - del parameters['solver']['legendre_fit'] - - parameters['general']['store_solver'] = parameters['solver']['store_solver'] - del parameters['solver']['store_solver'] - - return parameters['general'], parameters['solver'], parameters['dft'], parameters['advanced'] diff --git a/python/solid_dmft/util/update_dmft_config.py b/python/solid_dmft/util/update_dmft_config.py deleted file mode 100644 index e661628b..00000000 --- a/python/solid_dmft/util/update_dmft_config.py +++ /dev/null @@ -1,149 +0,0 @@ -# -*- coding: utf-8 -*- -################################################################################ -# -# TRIQS: a Toolbox for Research in Interacting Quantum Systems -# -# Copyright (C) 2016-2018, N. Wentzell -# Copyright (C) 2018-2019, Simons Foundation -# author: N. Wentzell -# -# TRIQS is free software: you can redistribute it and/or modify it under the -# terms of the GNU General Public License as published by the Free Software -# Foundation, either version 3 of the License, or (at your option) any later -# version. -# -# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY -# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS -# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more -# details. -# -# You should have received a copy of the GNU General Public License along with -# TRIQS. If not, see . -# -################################################################################ -""" -Updates the config file (usually dmft_config.ini) for continuing old calculations. -""" - -import os.path -import shutil -import sys -from configparser import ConfigParser - -def _backup_old_file(path): - """ Copies file to same folder with the prefix "backup_". """ - directory, filename = os.path.split(path) - shutil.copy2(path, os.path.join(directory, 'backup_'+filename)) - -def _load_config_file(path): - """ Loads file with the configparser module. Returns ConfigParser object. """ - config = ConfigParser() - config.read(path) - return config - -def _update_section_names(config): - """ - Applies the mapping between legacy names of sections and new names. The - mapping is saved in LEGACY_SECTION_NAME_MAPPING. - - Parameters - ---------- - config : ConfigParser - A configparser instance that has read the config file already. - - Returns - ------- - config : ConfigParser - The configparser with correct section names. - """ - - LEGACY_SECTION_NAME_MAPPING = {'solver': 'solver_parameters', 'advanced': 'advanced_parameters'} - - for new_name, legacy_name in LEGACY_SECTION_NAME_MAPPING.items(): - # Only new name in there, everything is okay - if legacy_name not in config.keys(): - continue - - # Only legacy name exists, creates updated section - if new_name not in config.keys(): - config.add_section(new_name) - - # Transfers parameters in legacy section to updated section - for param_name, param_value in config[legacy_name].items(): - config.set(new_name, param_name, param_value) - config.remove_section(legacy_name) - - return config - -def _update_params(config): - """ - Updates parameter names/values and adds new required parameters. - - Parameters - ---------- - config : ConfigParser - The config parser with correct section names. - - Returns - ------- - config : ConfigParser - The completely updated config parser. - """ - - # Updates h_int_type - if config['general']['h_int_type'] in ('1', '2', '3'): - config['general']['h_int_type'] = {'1': 'density_density', - '2': 'kanamori', - '3': 'full_slater' - }[config['general']['h_int_type']] - - # ---new params - # Updates solver_type - if not existent, uses previous default cthyb - if 'solver_type' not in config['general']: - config['general']['solver_type'] = 'cthyb' - - if 'dft_code' not in config['dft']: - config['dft']['dft_code'] = 'vasp' - - # ---updated params - # Updates legendre coefficients - if 'n_LegCoeff' in config['solver']: - config['general']['n_l'] = config['solver']['n_LegCoeff'] - del config['solver']['n_LegCoeff'] - - # Updates dft_executable - if 'executable' in config['dft']: - config['dft']['dft_exec'] = config['dft']['executable'] - del config['dft']['executable'] - - # Updates dft_executable - if 'wannier90_exec' in config['dft']: - config['dft']['w90_exec'] = config['dft']['wannier90_exec'] - del config['dft']['wannier90_exec'] - - return config - -def _write_config_file(config, path): - """ Writes config parser content to a file. """ - with open(path, 'w') as file: - config.write(file) - -def main(path='dmft_config.ini'): - """ Combines methods in the full work flow for updating the config file. """ - if not os.path.isfile(path): - raise ValueError('File {} does not exist.'.format(path)) - - _backup_old_file(path) - - config = _load_config_file(path) - config = _update_section_names(config) - config = _update_params(config) - _write_config_file(config, path) - -if __name__ == '__main__': - if len(sys.argv) == 1: - main() - elif len(sys.argv) == 2: - main(sys.argv[1]) - else: - raise TypeError('Maximally one argument supported: the config file name') diff --git a/python/solid_dmft/util/update_results_h5.py b/python/solid_dmft/util/update_results_h5.py deleted file mode 100644 index 4d80138a..00000000 --- a/python/solid_dmft/util/update_results_h5.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- coding: utf-8 -*- -################################################################################ -# -# TRIQS: a Toolbox for Research in Interacting Quantum Systems -# -# Copyright (C) 2016-2018, N. Wentzell -# Copyright (C) 2018-2019, Simons Foundation -# author: N. Wentzell -# -# TRIQS is free software: you can redistribute it and/or modify it under the -# terms of the GNU General Public License as published by the Free Software -# Foundation, either version 3 of the License, or (at your option) any later -# version. -# -# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY -# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS -# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more -# details. -# -# You should have received a copy of the GNU General Public License along with -# TRIQS. If not, see . -# -################################################################################ -""" -Updates the h5 archives with soliDMFT results for continuing old calculations. -""" -import os.path -import shutil -import sys -import numpy as np -from h5 import HDFArchive -# Import needed for transfering Green's functions in _update_results_per_iteration -from triqs.gf import BlockGf - -def _backup_old_file(path): - """ Copies file to same folder with the prefix "backup_". """ - directory, filename = os.path.split(path) - shutil.copy2(path, os.path.join(directory, 'backup_'+filename)) - -def _update_results_per_iteration(archive): - iterations = [key for key in archive['DMFT_results'] if key.startswith('it_')] - iterations.append('last_iter') - - for it in iterations: - keys = list(archive['DMFT_results'][it].keys()) - for key in keys: - if '_iw' in key: - new_key = key.replace('_iw_', '_freq_').replace('_iw', '_freq_') - archive['DMFT_results'][it][new_key] = archive['DMFT_results'][it][key] - del archive['DMFT_results'][it][key] - elif '_tau' in key: - new_key = key.replace('_tau_', '_time_').replace('_tau', '_time_') - archive['DMFT_results'][it][new_key] = archive['DMFT_results'][it][key] - del archive['DMFT_results'][it][key] - # Updates chemical potential to _post - elif key == 'chemical_potential': - it_number = int(it[3:]) if it.startswith('it_') else -1 - archive['DMFT_results'][it]['chemical_potential_post'] = archive['DMFT_results'][it]['chemical_potential'] - archive['DMFT_results'][it]['chemical_potential_pre'] = archive['DMFT_results']['observables']['mu'][it_number] - del archive['DMFT_results'][it]['chemical_potential'] - -def _update_observables(archive): - if 'orb_Z' not in archive['DMFT_results']['observables']: - orb_gb2 = archive['DMFT_results']['observables']['orb_gb2'] - orb_Z = [] - - for i in range(len(orb_gb2)): - orb_Z.append({}) - for spin in orb_gb2[i]: - orb_Z[i][spin] = [np.full(np.shape(z_per_it), 'none') - for z_per_it in orb_gb2[i][spin]] - - archive['DMFT_results']['observables']['orb_Z'] = orb_Z - -def main(path='dmft_config.ini'): - """ Combines methods in the full work flow for updating the h5 archive. """ - if not os.path.isfile(path): - raise ValueError('File {} does not exist.'.format(path)) - - _backup_old_file(path) - - with HDFArchive(path, 'a') as archive: - _update_results_per_iteration(archive) - _update_observables(archive) - -if __name__ == '__main__': - if len(sys.argv) == 1: - main() - elif len(sys.argv) == 2: - main(sys.argv[1]) - else: - raise TypeError('Maximally one argument supported: the archive file name') diff --git a/test/python/CMakeLists.txt b/test/python/CMakeLists.txt index 75012c70..be565aad 100644 --- a/test/python/CMakeLists.txt +++ b/test/python/CMakeLists.txt @@ -1,12 +1,12 @@ # all pytest unittests set (all_pytests test_afm_mapping + test_dict_to_h5 test_interaction_hamiltonian test_manipulate_chemical_potential.py test_observables.py - test_read_config.py - test_update_dmft_config.py - test_update_results_h5.py + test_postproc_toml_dict.py + test_postproc_toml_dict_integration.py ) foreach(test ${all_pytests}) @@ -58,6 +58,8 @@ set (integration_tests lno_hubbardI_mag svo_cthyb_basic_gl svo_cthyb_basic_tf + lco_ftps + nio_cthyb_hartree ) # copy other test files @@ -66,7 +68,7 @@ FILE(COPY UIJKL DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) foreach(test ${integration_tests}) set (test_dir ${CMAKE_CURRENT_BINARY_DIR}/${test}) - foreach(file dmft_config.ini inp.h5 ref.h5 test.py) + foreach(file dmft_config.toml inp.h5 ref.h5 test.py) FILE(COPY ${test}/${file} DESTINATION ${test_dir}) endforeach() @@ -94,3 +96,24 @@ add_test(NAME maxent_${test} ) set_property(TEST maxent_${test} APPEND PROPERTY ENVIRONMENT PYTHONPATH=${PROJECT_BINARY_DIR}/python:$ENV{PYTHONPATH}) + +# ------------------------------# + +# integration test for restarting svo_hartree +set (restart_integration_test + svo_hubbardI_basic) + +foreach(test ${restart_integration_test}) + set (test_dir ${CMAKE_CURRENT_BINARY_DIR}/${test}) + + foreach(file ref_restart.h5 test_restart.py) + FILE(COPY ${test}/${file} DESTINATION ${test_dir}) + endforeach() + + add_test(NAME restart_${test} + COMMAND ${MPIEXEC_EXECUTABLE} ${MPIEXEC_PREFLAGS} ${TRIQS_PYTHON_EXECUTABLE} test_restart.py + WORKING_DIRECTORY ${test_dir} + ) + + set_property(TEST restart_${test} APPEND PROPERTY ENVIRONMENT PYTHONPATH=${PROJECT_BINARY_DIR}/python:$ENV{PYTHONPATH}) +endforeach() diff --git a/test/python/lco_ftps/dmft_config.toml b/test/python/lco_ftps/dmft_config.toml new file mode 100644 index 00000000..e9e28383 --- /dev/null +++ b/test/python/lco_ftps/dmft_config.toml @@ -0,0 +1,51 @@ +[general] +seedname = "inp" +jobname = "out" + +# solver ftps +# freq range of all objects +w_range = [-15,15] +n_w = 1001 +# broadening for k sum and discretization of hyb function +eta=0.2 + +n_iter_dmft = 1 + +prec_mu = 0.001 +mu_initial_guess = 12.865491 + +h_int_type = "kanamori" +U = 3.6 +J = 0.0 + +dc_type = 0 +dc_dmft = false + +h5_save_freq = 1 +store_solver = true + +[solver] +type = "ftps" +# time step sizes, larger less accurant +dt = 0.2 +# automatic bath fit +bath_fit = true +# only 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ForkTPS needs to be installed to run this test') + sys.exit() + +if mpi.is_master_node(): + shutil.rmtree('out', ignore_errors=True) + +mpi.barrier() + +solid.main([None, 'dmft_config.toml']) + +mpi.barrier() + +if mpi.is_master_node(): + with HDFArchive('out/inp.h5', 'r')['DMFT_results']['last_iter'] as out, \ + HDFArchive('ref.h5', 'r')['DMFT_results']['last_iter'] as ref: + for key in ['G0_freq_0', 'Gimp_freq_0', 'Gimp_time_0', 'Sigma_freq_0']: + print(key) + assert_block_gfs_are_close(out[key],ref[key],precision=1e-05) + + for key in ['chemical_potential_pre', 'chemical_potential_post']: + print(key) + assert abs(out[key]-ref[key]) < 0.001, "chemical potential mismatch" diff --git a/test/python/lno_hubbardI_mag/dmft_config.ini b/test/python/lno_hubbardI_mag/dmft_config.ini deleted file mode 100644 index 45530898..00000000 --- a/test/python/lno_hubbardI_mag/dmft_config.ini +++ /dev/null @@ -1,31 +0,0 @@ -[general] -seedname = inp -jobname = out -block_threshold= 0.001 -mu_initial_guess = -0.003377 - -solver_type = hubbardI -n_w = 501 -w_range = -5,5 -eta = 0.05 -n_iw = 501 -beta = 40 - -prec_mu = 0.001 - -h_int_type = kanamori -U = 1.83 -J = 0.37 - -magnetic= True -magmom = -0.2,0.2 - -n_iter_dmft = 1 - -dc_type = 1 -dc = True -dc_dmft = False - -[solver] -measure_G_tau =True -measure_density_matrix = True diff --git a/test/python/lno_hubbardI_mag/dmft_config.toml b/test/python/lno_hubbardI_mag/dmft_config.toml new file mode 100644 index 00000000..3eb34d57 --- /dev/null +++ b/test/python/lno_hubbardI_mag/dmft_config.toml @@ -0,0 +1,33 @@ +[general] +seedname = "inp" +jobname = "out" +block_threshold = 0.001 +mu_initial_guess = -0.003377 + +n_iw = 501 +beta = 40 +n_w = 501 +w_range = [-5, 5] +eta = 0.05 +n_tau = 10001 + +prec_mu = 0.001 + +h_int_type = "kanamori" +U = 1.83 +J = 0.37 + +magnetic = true +magmom = [-0.2, 0.2] + +n_iter_dmft = 1 + +dc_type = 1 +dc = true +dc_dmft = false + +[solver] +type = "hubbardI" +idx_impurities = [0, 1] +measure_G_tau = true +measure_density_matrix = true diff --git a/test/python/lno_hubbardI_mag/test.py b/test/python/lno_hubbardI_mag/test.py index 656c3e74..0d21f834 100644 --- a/test/python/lno_hubbardI_mag/test.py +++ b/test/python/lno_hubbardI_mag/test.py @@ -14,7 +14,7 @@ mpi.barrier() -solid.main([None, 'dmft_config.ini']) +solid.main([None, 'dmft_config.toml']) mpi.barrier() @@ -33,4 +33,3 @@ for key in ['chemical_potential_pre', 'chemical_potential_post']: print(key) assert abs(out[key]-ref[key]) < 0.001, "chemical potential mismatch" - diff --git a/test/python/nio_cthyb_hartree/dmft_config.toml b/test/python/nio_cthyb_hartree/dmft_config.toml new file mode 100644 index 00000000..6c0d8e1e --- /dev/null +++ b/test/python/nio_cthyb_hartree/dmft_config.toml @@ -0,0 +1,46 @@ +[general] +seedname = "inp" +jobname = "out" + +mu_initial_guess = 13.751 +enforce_off_diag = [false, true] + +n_iw = 501 +beta = 20 +w_range = [-10, 10] +eta = 0.05 +n_w = 501 + +calc_mu_method = "brent" + +h_int_type = ["density_density", "kanamori"] +U = [8.32, 6.8] +U_prime = ["", 0] +J = [1.19, 0.8] +ratio_F4_F2 = [0.68, ""] + +calc_energies = true + +n_iter_dmft = 1 + +dc = true +dc_type = [0, 1] +dc_dmft = false + +[[solver]] +type = "cthyb" +idx_impurities = [0] +n_l = 25 +length_cycle = 1000 +n_warmup_cycles = 1e+3 +n_cycles_tot = 1e+4 +imag_threshold = 1e-5 +legendre_fit = true +measure_density_matrix = true + +[[solver]] +type = "hartree" +idx_impurities = [1] +with_fock = false +force_real = true +one_shot = true diff --git a/test/python/nio_cthyb_hartree/inp.h5 b/test/python/nio_cthyb_hartree/inp.h5 new file mode 100644 index 0000000000000000000000000000000000000000..cfa0d5f320d043851fa9f702eab4ee73f0a9e017 GIT binary patch literal 133750 zcmeEv2|QHY|39fHm9$Z0Dix)YB+4>vXje%}7$uct7h{`IDza88Sw~bznJgjOB+HOx z?0dGs*tap5_5Nqf+~;}neR}%-elOqO|LOb8c)91Eb3XSh@B2CDd_JG^x%WOia#&;W z!ZizdSU&*)p1C|rr%%BH!1>uPoHLyS#=(3e&IvCk^yGvwY;qpn=|3JGQBJxrr~H|8 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