Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

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Metadata extractor for engineering simulation data

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

About

Metadata extractor for engineering simulation data

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5 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

About

Metadata extractor for engineering simulation data

Resources

Stars

0 stars

Watchers

5 watching

Forks

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Packages

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

About

Metadata extractor for engineering simulation data

Resources

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0 stars

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5 watching

Forks

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
Skip to content

Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

About

Metadata extractor for engineering simulation data

Resources

Stars

0 stars

Watchers

5 watching

Forks

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Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

About

Metadata extractor for engineering simulation data

Resources

Stars

0 stars

Watchers

5 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

DOI

metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

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metaExtractIng

metaExtractIng is a tool for extracting important metadata from CSV, NetCDF, OpenDiHu and GROMACS files and storing it into a JSON-LD file, according to Metadata4Ing ontology. This README provides instructions on how to run metaExtractIng and generate the desired JSON-LD file.

Installing metaExtractIng via package manager

Currently, you can download the package meta_extractIng via PyPI Test repository:

pip install -i https://test.pypi.org/simple/ meta-extractIng

After successful installation, you can import meta_extractIng and use your desired software.

from meta_extractIng import csv_extractor, gromacs_extractor, open_dihu_extractor, netcdf_extractor
csv_extractor.extract()
open_dihu_extractor.extract()
netcdf_extractor.extract()
gromacs_extractor.extract()

After running each one of the extract() methods, you will be asked to give the path of your simulations folder. Only for the GROMACS simulations, the files should be given in separate folders inside the given path. The program uses given template file already in __output__ folder. If this file is not given, the program asks the user to create a template interactively. Final Json-LD files will be saved at the __output__ folder as well.

The program uses Metadata4Ing ontology as default. If you want to switch to another ontology, you can change the URL and context_URL values in the config.json file in lib folder, where your package is installed on your computer.

Running metaExtractIng via source code

Navigate to src/meta_extractIng folder and run in terminal:

python main.py

Requirements

The following Python libraries are required to run the program:

  • requests
  • rdflib

To install the required libraries, run the following command:

pip install -r requirements.txt

Source code folder structure

Main repository consists of four main folders:

  • meta_extractIng/src: Contains source code, and shared libraries in lib folder

    • lib: Contains shared libraries
    • simulations: Simulation examples for each software
  • tests: Contains a unit test for checking the correctness of all simulations softwares.

  • docs: Helpful documents to understand the flow of the program, for example, the interactive-template-generation step.

Expected files

  • CSV: It extracts all the data in header and rows. It expects the csv file has a header row, with one or more rows of data, and one column with id.
  • NetCDF: It extracts dimensions, variables, and global attributes from a CDL content file.
  • OpenDiHu: It processes an OpenDiHu log file, extracting metadata between specific markers.
  • GROMACS: It processes a folder containing GROMACS output files, including job, log, usermd and mdp files, extracting metadata from them.

Authors

The code was developed by Mahdi Jafarkhani, based on a prior development by Mohammed Asjadulla. The development was supervised by Björn Schembera.

Acknowledgements

The work has been funded by the DFG (German Research Foundation), project number 460135501, NFDI 29/1 “MaRDI – Mathematische Forschungsdateninitiative”.

About

Metadata extractor for engineering simulation data

Resources

Stars

0 stars

Watchers

5 watching

Forks

Releases

Packages

Contributors

Languages