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Added working skeleton code for the TFS-specific TIFF parser, which f…
…ishes successfully metadata and converts into standardized python dict, ods document added to collect IKZ feedback how to map specific TFS using assumptions onto specific concepts in NXem to consume in e.g. OASIS
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130 changes: 130 additions & 0 deletions
130
pynxtools/dataconverter/readers/em/subparsers/image_tiff_tfs.py
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| # | ||
| # 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. | ||
| # | ||
| """Subparser for harmonizing ThermoFisher-specific content in TIFF files.""" | ||
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| import mmap | ||
| import numpy as np | ||
| from typing import Dict | ||
| from PIL import Image | ||
| from PIL.TiffTags import TAGS | ||
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| from pynxtools.dataconverter.readers.em.subparsers.image_tiff import TiffSubParser | ||
| from pynxtools.dataconverter.readers.em.subparsers.image_tiff_tfs_cfg import \ | ||
| tfs_section_names, tfs_section_details | ||
| from pynxtools.dataconverter.readers.em.utils.image_utils import \ | ||
| sort_tuple, if_str_represents_float | ||
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| class TfsTiffSubParser(TiffSubParser): | ||
| def __init__(self, file_path: str = ""): | ||
| super().__init__(file_path) | ||
| self.prfx = None | ||
| self.tmp: Dict = {} | ||
| self.supported_version: Dict = {} | ||
| self.version: Dict = {} | ||
| self.tags: Dict = {} | ||
| self.supported = False | ||
| self.check_if_tiff() | ||
| self.tfs: Dict = {} | ||
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| def check_if_tiff_tfs(self): | ||
| """Check if resource behind self.file_path is a TaggedImageFormat file.""" | ||
| self.supported = 0 # voting-based | ||
| with open(self.file_path, 'rb', 0) as file: | ||
| s = mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ) | ||
| magic = s.read(4) | ||
| if magic == b'II*\x00': # https://en.wikipedia.org/wiki/TIFF | ||
| self.supported += 1 | ||
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| with Image.open(self.fiele_path, mode="r") as fp: | ||
| tfs_keys = [34682] | ||
| for tfs_key in tfs_keys: | ||
| if tfs_key in fp.tag_v2: | ||
| if len(fp.tag[tfs_key]) == 1: | ||
| self.supported += 1 # found TFS-specific tag | ||
| if self.supported == 2: | ||
| self.supported = True | ||
| else: | ||
| self.supported = False | ||
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| def get_metadata(self): | ||
| """Extract metadata in TFS specific tags if present.""" | ||
| print("Reporting the tags found in this TIFF file...") | ||
| # for an overview of tags | ||
| # https://www.loc.gov/preservation/digital/formats/content/tiff_tags.shtml | ||
| # with Image.open(self.file_path, mode="r") as fp: | ||
| # self.tags = {TAGS[key] : fp.tag[key] for key in fp.tag_v2} | ||
| # for key, val in self.tags.items(): | ||
| # print(f"{key}, {val}") | ||
| tfs_section_offsets = {} | ||
| with open(self.file_path, 'rb', 0) as fp: | ||
| s = mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ) | ||
| for section_name in tfs_section_names: | ||
| pos = s.find(bytes(section_name, "utf8")) # != -1 | ||
| tfs_section_offsets[section_name] = pos | ||
| print(tfs_section_offsets) | ||
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| # define search offsets | ||
| tpl = [] | ||
| for key, value in tfs_section_offsets.items(): | ||
| tpl.append((key, value)) | ||
| tpl = sort_tuple(tpl) | ||
| print(tpl) | ||
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| # exemplar parsing of specific TFS section content into a dict | ||
| # here for section_name == "[System]": | ||
| pos_s = None | ||
| pos_e = None | ||
| for idx in np.arange(0, len(tpl)): | ||
| if tpl[idx][0] != "[System]": | ||
| continue | ||
| else: | ||
| pos_s = tpl[idx][1] | ||
| if idx <= len(tpl) - 1: | ||
| pos_e = tpl[idx + 1][1] | ||
| break | ||
| print(f"Search for [System] in between byte offsets {pos_s} and {pos_e}") | ||
| if pos_s is None or pos_e is None: | ||
| raise ValueError(f"Search for [System] was unsuccessful !") | ||
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| # fish metadata of e.g. the system section | ||
| for term in tfs_section_details["[System]"]: | ||
| s.seek(pos_s, 0) | ||
| pos = s.find(bytes(term, "utf8")) | ||
| if pos < pos_e: # check if pos_e is None | ||
| s.seek(pos, 0) | ||
| value = f"{s.readline().strip().decode('utf8').replace(f'{term}=', '')}" | ||
| if value != "": | ||
| if if_str_represents_float(value) is True: | ||
| self.tfs[f"system/{term}"] = np.float64(value) | ||
| elif value.isdigit() is True: | ||
| self.tfs[f"system/{term}"] = np.int64(value) | ||
| else: | ||
| self.tfs[f"system/{term}"] = None | ||
| else: | ||
| pass | ||
| print(self.tfs) | ||
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| def parse_and_normalize(self): | ||
| """Perform actual parsing filling cache self.tmp.""" | ||
| if self.supported is True: | ||
| print(f"Parsing via ThermoFisher-specific metadata...") | ||
| self.get_metadata() | ||
| else: | ||
| print(f"{self.file_path} is not a ThermoFisher-specific " | ||
| f"TIFF file that this parser can process !") |
62 changes: 62 additions & 0 deletions
62
pynxtools/dataconverter/readers/em/subparsers/image_tiff_tfs_cfg.py
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| # | ||
| # 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. | ||
| # | ||
| """Configuration of the image_tiff_tfs subparser.""" | ||
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| tfs_section_names = ["[User]", | ||
| "[System]", | ||
| "[Beam]", | ||
| "[EBeam]", | ||
| "[GIS]", | ||
| "[Scan]", | ||
| "[EScan]", | ||
| "[Stage]", | ||
| "[Image]", | ||
| "[Vacuum]", | ||
| "[Specimen]", | ||
| "[Detectors]", | ||
| "[T2]", | ||
| "[Accessories]", | ||
| "[EBeamDeceleration]", | ||
| "[CompoundLensFilter]", | ||
| "[PrivateFei]", | ||
| "[HiResIllumination]", | ||
| "[EasyLift]", | ||
| "[HotStageMEMS]", | ||
| "[HotStage]", | ||
| "[HotStageHVHS]", | ||
| "[ColdStage]"] | ||
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| tfs_section_details = {"[System]": ["Type", | ||
| "Dnumber", | ||
| "Software", | ||
| "BuildNr", | ||
| "Source", | ||
| "Column", | ||
| "FinalLens", | ||
| "Chamber", | ||
| "Stage", | ||
| "Pump", | ||
| "ESEM", | ||
| "Aperture", | ||
| "Scan", | ||
| "Acq", | ||
| "EucWD", | ||
| "SystemType", | ||
| "DisplayWidth", | ||
| "DisplayHeight"]} |
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| Original file line number | Diff line number | Diff line change |
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| # | ||
| # 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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| import numpy as np | ||
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| # https://www.geeksforgeeks.org/python-program-to-sort-a-list-of-tuples-by-second-item/ | ||
| def sort_tuple(tup): | ||
| # convert the list of tuples to a numpy array with data type (object, int) | ||
| arr = np.array(tup, dtype=[('col1', object), ('col2', int)]) | ||
| # get the indices that would sort the array based on the second column | ||
| indices = np.argsort(arr['col2']) | ||
| # use the resulting indices to sort the array | ||
| sorted_arr = arr[indices] | ||
| # convert the sorted numpy array back to a list of tuples | ||
| sorted_tup = [(row['col1'], row['col2']) for row in sorted_arr] | ||
| return sorted_tup | ||
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| def if_str_represents_float(s): | ||
| try: | ||
| float(s) | ||
| return str(float(s)) == s | ||
| except ValueError: | ||
| return False |