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210302 lf clean examples See merge request multiscale-wdm/surrogate-models/fesl/fesl!40
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| Original file line number | Diff line number | Diff line change |
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| import fesl | ||
| from fesl import printout | ||
| from data_repo_path import get_data_repo_path | ||
| import numpy as np | ||
| data_path = get_data_repo_path()+"Al36/" | ||
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| """ | ||
| ex02_preprocess_data.py: Shows how this framework can be used to preprocess | ||
| data. Preprocessing here means converting raw DFT calculation output into | ||
| numpy arrays of the correct size. For the input data, this means descriptor | ||
| calculation. | ||
| Further preprocessing steps (scaling, unit conversion) is done later. | ||
| """ | ||
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| def run_example02(): | ||
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| #################### | ||
| # PARAMETERS | ||
| # All parameters are handled from a central parameters class that | ||
| # contains subclasses. | ||
| #################### | ||
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| test_parameters = fesl.Parameters() | ||
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| # Specify input data options, i.e. which descriptors are calculated | ||
| # with which parameters. These are the standard parameters for | ||
| # the calculation of SNAP descriptors. | ||
| test_parameters.descriptors.descriptor_type = "SNAP" | ||
| test_parameters.descriptors.twojmax = 10 | ||
| test_parameters.descriptors.rcutfac = 4.67637 | ||
| test_parameters.data.descriptors_contain_xyz = True | ||
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| # Specify output data options, i.e. how the LDOS is parsed. | ||
| # The Al system used as an example here actually has 250 energy levels. | ||
| # But for the convenience of the user, only 10 energy levels will be | ||
| # used for this example. | ||
| test_parameters.targets.target_type = "LDOS" | ||
| test_parameters.targets.ldos_gridsize = 10 | ||
| test_parameters.targets.ldos_gridspacing_ev = 0.1 | ||
| test_parameters.targets.ldos_gridoffset_ev = -10 | ||
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| #################### | ||
| # DATA | ||
| # Create a DataConverter, and add snapshots to it. | ||
| #################### | ||
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| data_converter = fesl.DataConverter(test_parameters) | ||
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| # Take care to choose the "add_snapshot" function correct for | ||
| # the type of data you want to preprocess. | ||
| data_converter.add_snapshot_qeout_cube("Al.pw.scf.out", data_path, | ||
| "cubes/tmp.pp*Al_ldos.cube", | ||
| data_path, output_units="1/Ry") | ||
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| # Convert all the snapshots and save them in the current directory. | ||
| # data_converter.convert_snapshots("./", naming_scheme="Al_snapshot*") | ||
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| #################### | ||
| # RESULTS. | ||
| # Print the used parameters and check whether the preprocessed data | ||
| # has the desired dimensions. | ||
| #################### | ||
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| printout("Parameters used for this experiment:") | ||
| test_parameters.show() | ||
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| input_data = np.load("Al_snapshot0.in.npy") | ||
| input_data_shape = np.shape(input_data) | ||
| if input_data_shape[0] != 108 or input_data_shape[1] != 108 or \ | ||
| input_data_shape[2] != 100 or input_data_shape[3] != 94: | ||
| return False | ||
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| output_data = np.load("Al_snapshot0.out.npy") | ||
| output_data_shape = np.shape(output_data) | ||
| if output_data_shape[0] != 108 or output_data_shape[1] != 108 or \ | ||
| output_data_shape[2] != 100 or output_data_shape[3] != 10: | ||
| return False | ||
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| return True | ||
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| if __name__ == "__main__": | ||
| if run_example02(): | ||
| printout("Successfully ran ex02_preprocess_data.") | ||
| else: | ||
| raise Exception("Ran ex02_preprocess_data but something was off." | ||
| " If you haven't changed any parameters in " | ||
| "the example, there might be a problem with your" | ||
| " installation.") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,110 @@ | ||
| import fesl | ||
| from fesl import printout | ||
| import numpy as np | ||
| from data_repo_path import get_data_repo_path | ||
| data_path = get_data_repo_path()+"Al36/" | ||
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| """ | ||
| ex03_postprocess_data.py: Shows how this framework can be used to | ||
| postprocess data. Usually, this framework outputs LDOS data, thefore, | ||
| post processing of LDOS data will be shown in the following. | ||
| Set do_total_energy to False, if you don't have the QuantumEspresso | ||
| Python module installed. | ||
| """ | ||
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| def run_example03(do_total_energy=True, accuracy_electrons = 1e-11, | ||
| accuracy_total_energy=50): | ||
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| #################### | ||
| # PARAMETERS | ||
| # All parameters are handled from a central parameters class that | ||
| # contains subclasses. | ||
| #################### | ||
| test_parameters = fesl.Parameters() | ||
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| # Specify the correct LDOS parameters. | ||
| test_parameters.targets.target_type = "LDOS" | ||
| test_parameters.targets.ldos_gridsize = 250 | ||
| test_parameters.targets.ldos_gridspacing_ev = 0.1 | ||
| test_parameters.targets.ldos_gridoffset_ev = -10 | ||
|
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| #################### | ||
| # TARGETS | ||
| # Create a target calculator to postprocess data. | ||
| # Use this calculator to perform various operations. | ||
| #################### | ||
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| ldos = fesl.TargetInterface(test_parameters) | ||
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| # Read additional information about the calculation. | ||
| # By doing this, the calculator is able to know e.g. the temperature | ||
| # at which the calculation took place or the lattice constant used. | ||
| ldos.read_additional_calculation_data("qe.out", | ||
| data_path+"Al.pw.scf.out") | ||
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| # Read in LDOS data. For actual workflows, this part will come | ||
| # from a network. | ||
| ldos_data = np.load(data_path+"Al_ldos.npy") | ||
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| # Get quantities of interest. | ||
| # For better values in the post processing, it is recommended to | ||
| # calculate the "self-consistent Fermi energy", i.e. the Fermi energy | ||
| # at which the (L)DOS reproduces the exact number of electrons. | ||
| # This Fermi energy usually differs from the one outputted by the | ||
| # QuantumEspresso calculation, due to numerical reasons. The difference | ||
| # is usually very small. | ||
| self_consistent_fermi_energy = ldos.\ | ||
| get_self_consistent_fermi_energy_ev(ldos_data) | ||
| number_of_electrons = ldos.\ | ||
| get_number_of_electrons(ldos_data, fermi_energy_eV= | ||
| self_consistent_fermi_energy) | ||
| band_energy = ldos.get_band_energy(ldos_data, | ||
| fermi_energy_eV= | ||
| self_consistent_fermi_energy) | ||
| if do_total_energy: | ||
| # To perform a total energy calculation one also needs to provide | ||
| # a pseudopotential(path). | ||
| ldos.set_pseudopotential_path(data_path) | ||
| total_energy = ldos.get_total_energy(ldos_data, | ||
| fermi_energy_eV= | ||
| self_consistent_fermi_energy) | ||
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| #################### | ||
| # RESULTS. | ||
| # Print the used parameters and check whether LDOS based results | ||
| # are consistent with the actual DFT results. | ||
| #################### | ||
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| printout("Parameters used for this experiment:") | ||
| test_parameters.show() | ||
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| print("Number of electrons:", number_of_electrons) | ||
| print("Band energy:", band_energy) | ||
| if do_total_energy: | ||
| print("Total energy:", total_energy) | ||
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| if np.abs(number_of_electrons - ldos.number_of_electrons) > \ | ||
| accuracy_electrons: | ||
| return False | ||
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| # FIXME: Add as soon as band_energy_dft_calculation is fixed. | ||
| # if np.abs(number_of_electrons - ldos.number_of_electrons) > accuracy: | ||
| # return True | ||
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| if do_total_energy: | ||
| if np.abs(total_energy - ldos.total_energy_dft_calculation) > \ | ||
| accuracy_total_energy: | ||
| return False | ||
| return True | ||
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| if __name__ == "__main__": | ||
| if run_example03(): | ||
| printout("Successfully ran ex03_postprocess_data.") | ||
| else: | ||
| raise Exception("Ran ex03_postprocess_data but something was off." | ||
| " If you haven't changed any parameters in " | ||
| "the example, there might be a problem with your" | ||
| " installation.") |
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