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Added support for pyccapt file formats and tested successfully with M…
…ehrpad's example
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| # POS file format reader used by atom probe microscopists. | ||
| # | ||
| # Copyright The NOMAD Authors. | ||
| # | ||
| # This file is part of NOMAD. See https://nomad-lab.eu for further info. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
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| # pylint: disable=no-member,duplicate-code | ||
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| import os | ||
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| import h5py | ||
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| import numpy as np | ||
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| import pandas as pd | ||
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| from ase.data import atomic_numbers, chemical_symbols | ||
| from ifes_apt_tc_data_modeling.nexus.nx_ion import NxIon | ||
| from ifes_apt_tc_data_modeling.nexus.nx_field import NxField | ||
| from ifes_apt_tc_data_modeling.utils.utils import \ | ||
| isotope_to_hash, isotope_vector_to_nuclid_list, MAX_NUMBER_OF_ATOMS_PER_ION | ||
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| # this implementation focuses on the following state of the pyccapt repository | ||
| # https://github.com/mmonajem/pyccapt/commit/e955beb4f2627befb8b4d26f2e74e4c52e00394e | ||
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| # during the course of an atom probe measurement and analysis with FAU/Erlangen's Oxcart instrument | ||
| # several HDF5 files are generated with essentially two software tools. One is pyccapt which has a | ||
| # a control module, a calibration module (where the voltage/bowl calibration and reconstruction is performed), | ||
| # and a module/functionalities to document ranging i.e. ion type identification made | ||
| # The other software typically used by the FAU/Erlangen atom probe group is Atom Probe Toolbox; | ||
| # instructed as a set of Matlab live scripts this toolbox offers data analysis functionalities, | ||
| # results are stored via an HDF5 file | ||
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| # specific comments | ||
| # pyccapt/control | ||
| # an HDF5 file keeping relevant quantities | ||
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| # pyccapt/calibration | ||
| # unfortunately the generated HDF5 file has internally no provenance information | ||
| # with which pyccapt version it was generated, therefore developers of pyccapt should | ||
| # rather write the content of the HDF5 file explicitly dset by dset e.g. using h5py instead | ||
| # of the pandas HDF5 dump convenience functionality | ||
| # of course pandas stores its own version but that is not conclusive enough to infer with | ||
| # which pyccapt version and most importantly from which other context the file was generated | ||
| # this is an aspect of the FAIR RDM principles which the pyccapt approach currently ignores | ||
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| class ReadPyccaptControlFileFormat(): | ||
| """Read FAU/Erlangen pyccapt (controle module) HDF5 file format.""" | ||
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| def __init__(self, filename: str): | ||
| assert len(filename) > 2, "H5 file incorrect filename ending!" | ||
| assert filename.lower().endswith(".h5") or filename.lower().endswith(".hdf5"), \ | ||
| "HDF5 file incorrect file type!" | ||
| self.filename = filename | ||
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| self.filesize = os.path.getsize(self.filename) | ||
| self.number_of_events = None | ||
| self.version = "e955beb4f2627befb8b4d26f2e74e4c52e00394e" | ||
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| # check that the formatting matches that of an pyccapt control module output HDF5 file | ||
| with h5py.File(self.filename, "r") as h5r: | ||
| self.supported = 0 # voting-based | ||
| required_groups = ["apt", "dld", "tdc"] | ||
| for req_grpnm in required_groups: | ||
| if req_grpnm in h5r.keys(): | ||
| self.supported += 1 | ||
| if self.supported == 3: | ||
| print(f"{self.filename} is a supported pyccapt/control HDF5 file!") | ||
| else: | ||
| print(f"{self.filename} is not a supported pyccapt/control HDF5 file!") | ||
| return | ||
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| class ReadPyccaptCalibrationFileFormat(): | ||
| """Read FAU/Erlangen pyccapt (calibration module) HDF5 file format.""" | ||
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| def __init__(self, filename: str): | ||
| assert len(filename) > 2, "H5 file incorrect filename ending!" | ||
| assert filename.lower().endswith(".h5") or filename.lower().endswith(".hdf5"), \ | ||
| "HDF5 file incorrect file type!" | ||
| self.filename = filename | ||
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| self.filesize = os.path.getsize(self.filename) | ||
| self.number_of_events = None | ||
| self.version = "e955beb4f2627befb8b4d26f2e74e4c52e00394e" | ||
| self.df = None | ||
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| with h5py.File(self.filename, "r") as h5r: | ||
| self.supported = 0 # voting-based | ||
| required_entries = ["df", | ||
| "df/axis0", "df/axis1", | ||
| "df/block0_items", "df/block0_values", | ||
| "df/block1_items", "df/block1_values"] | ||
| for entry in required_entries: | ||
| if entry in h5r.keys(): | ||
| self.supported += 1 | ||
| if self.supported == 7: | ||
| print(f"{self.filename} is a supported pyccapt/calibration HDF5 file!") | ||
| else: | ||
| print(f"{self.filename} is not a supported pyccapt/calibration HDF5 file!") | ||
| return | ||
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| self.df = pd.read_hdf(self.filename) | ||
| self.number_of_events = np.shape(self.df)[0] | ||
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| def get_named_quantities(self, term: str): | ||
| if term in self.df.keys(): | ||
| return self.df[term] | ||
| return None | ||
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| def get_reconstructed_positions(self): | ||
| """Read xyz columns.""" | ||
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| xyz = NxField() | ||
| xyz.typed_value = np.zeros( | ||
| [self.number_of_events, 3], np.float32) | ||
| xyz.unit = "nm" | ||
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| dim = 0 | ||
| for quant in ["x (nm)", "y (nm)", "z (nm)"]: | ||
| xyz.typed_value[:, dim] = np.asarray(self.get_named_quantities(quant), np.float32) | ||
| dim += 1 | ||
| return xyz | ||
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| def get_mass_to_charge_state_ratio(self): | ||
| """Read (calibrated) mass-to-charge-state-ratio column.""" | ||
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| m_n = NxField() | ||
| m_n.typed_value = np.zeros( | ||
| [self.number_of_events, 1], np.float32) | ||
| m_n.unit = "Da" | ||
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| m_n.typed_value[:, 0] = np.asarray(self.get_named_quantities("mc_c (Da)"), np.float32) | ||
| return m_n | ||
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| class ReadPyccaptRangingFileFormat(): | ||
| """Read FAU/Erlangen pyccapt (ranging module) HDF5 file format.""" | ||
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| def __init__(self, filename: str): | ||
| assert len(filename) > 2, "H5 file incorrect filename ending!" | ||
| assert filename.lower().endswith(".h5") or filename.lower().endswith(".hdf5"), \ | ||
| "HDF5 file incorrect file type!" | ||
| self.filename = filename | ||
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| self.filesize = os.path.getsize(self.filename) | ||
| self.number_of_events = None | ||
| self.version = "e955beb4f2627befb8b4d26f2e74e4c52e00394e" | ||
| self.df = None | ||
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| with h5py.File(self.filename, "r") as h5r: | ||
| self.supported = 0 # voting-based | ||
| required_entries = ["df", | ||
| "df/axis0", "df/axis1", | ||
| "df/block0_items", "df/block0_values", | ||
| "df/block1_items", "df/block1_values", | ||
| "df/block2_items", "df/block2_values"] | ||
| for entry in required_entries: | ||
| if entry in h5r.keys(): | ||
| self.supported += 1 | ||
| if self.supported == 9: | ||
| print(f"{self.filename} is a supported pyccapt/ranging HDF5 file!") | ||
| else: | ||
| print(f"{self.filename} is not a supported pyccapt/ranging HDF5 file!") | ||
| return | ||
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| self.df = pd.read_hdf(self.filename) | ||
| self.rng = {} | ||
| self.rng["molecular_ions"] = [] | ||
| print(np.shape(self.df)[0]) | ||
| for idx in np.arange(0, np.shape(self.df)[0]): | ||
| if isinstance(self.df.iloc[idx, 6], str) is True: | ||
| if self.df.iloc[idx, 6] == "unranged": | ||
| continue | ||
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| elements = self.df.iloc[idx, 6] | ||
| complexs = self.df.iloc[idx, 7] | ||
| isotopes = self.df.iloc[idx, 8] | ||
| # assertions | ||
| ivec = np.zeros((MAX_NUMBER_OF_ATOMS_PER_ION,), np.uint16) | ||
| hashvector = [] | ||
| for idxj in np.arange(0, len(elements)): | ||
| symbol = elements[idxj] | ||
| if symbol in chemical_symbols and symbol != "X": | ||
| proton_number = atomic_numbers[symbol] | ||
| neutron_number = isotopes[idxj] - proton_number | ||
| for mult in np.arange(0, complexs[idxj]): | ||
| hashvector.append(isotope_to_hash(proton_number, neutron_number)) | ||
| ivec[0:len(hashvector)] = np.sort(np.asarray(hashvector, np.uint16), kind="stable")[::-1] | ||
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| m_ion = NxIon() | ||
| m_ion.isotope_vector.typed_value = ivec | ||
| m_ion.nuclid_list.typed_value = isotope_vector_to_nuclid_list(ivec) | ||
| m_ion.charge_state.typed_value = np.int8(self.df.iloc[idx, 9]) | ||
| m_ion.add_range(self.df.iloc[idx, 3], self.df.iloc[idx, 4]) | ||
| m_ion.update_human_readable_name() | ||
| # m_ion.report() | ||
| self.rng["molecular_ions"].append(m_ion) | ||
| print(f"{self.filename} parsed successfully") |
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