We're creating gamma-cat, an open data collection for gamma-ray astronomy, and are looking for advice (here, or links to resources, formats, tools, packages) how to best set it up.
The data we have consists of measurements for different sources, from different papers. It's pretty heterogeneous, sometimes there's data for multiple sources in one paper, for each source there's usually several papers, sometimes there's no spectrum, sometimes one, sometimes many, ...
Currently we just collect the data in an input folder as YAML and CSV files, and now we'd like to expose it to users. Mainly access from Python, but also from Javascript and accessible from a static website.
The question is what format and organisation we should use for the data, and if there's any Python packages that will help us generate the output files as a set of linked data, as well as Python and Javascript packages that will help us access it?
We would like to get multiple "views" or simple "queries" of the data, e.g. "list of all sources", "list of all papers", "list of all spectra for source X", "spectrum A from paper B for source C".
For format, probably JSON would be a good choice? Although YAML is a bit nicer to read, and it's possible to have comments and ordered maps. We're storing the output files in a git repo, and have had a lot of meaningless diffs for JSON files because key order changes all the time.
To make the datasets discoverable and linked, I don't know what to use. I found e.g. http://jsonapi.org/ but that seems to be for REST APIs, not for just a series of flat JSON files on a static webserver? Maybe it could still be used that way?
I also found http://json-ld.org/ which looks relevant, but also pretty complex. Would either of those or something else be a good choice?
And finally, we'd like to generate the linked and discoverable files in output from just a bunch of somewhat organised YAML and CSV files in input using Python scripts. So far we just wrote a bunch of Python classes or scripts based on Python dicts / lists and YAML / JSON files. Is there a Python package that would help with that task of generating the linked data files?
Apologies for the long and complex question! I hope it's still in scope for SO and someone will have some advice to share.
Judging from the breadth of your question, you are new to linked data. The least "strange" format for you might be the Data Package. In the most common case it's just a zip archive of a CSV file and JSON metadata. It has a Python package.
If you have queries to the data, you should settle for a database (triplestore) with a SPARQL endpoint. Take a look at Fuseki. You can then use Turtle or RDF/XML for file export.
If the data comes from some kind of a tool, you can model the domain it represents using Eclipse Lyo (tutorial).
These tools are maintained by 3 different communities, you can reach out to their user mailing lists separately if you have further questions about them.
Related
I used to upload csv, excel, json or geojson files in my a postegreSQL using Python/Django.
I noticed that the scripts is redundant and sometimes difficult to maintain when we need to update key or columns. Is there a way to use design pattern? I have never used it before.
Any suggestion or links could be hep!
Can any one help me on how to convert pdf file to xml file using python code? My pdf contains:
Unstructured data
It has images
Mathematical equations
Chemical Equations
Table Data
Logo's tag's etc.
I tried using PDFMiner, but my pdf data was not converted into .xml/json file format. Are there any libraries other than PDFMiner? PyPDF2, Tabula-py, PDFQuery, comelot, PyMuPDF, pdf to dox, pandas- these other libraries/utilities all not suitable for my requirement.
Please advise me on any other options. Thank you.
The first thing I would recommend you trying is GROBID (see here for the full documentation). You can play with an online demo here to see if fits your needs (select TEI -> Process Fulltext Document, and upload a PDF). You can also check out this from the Allen Institute (it is based on GROBID and has a handy function for converting TEI.XML to JSON).
The other package which--obviously--does a good job is the Adobe PDF Extract API (see here). It's of course a paid service but when you register for an account you get 1.000 document transactions for free. It's easy to implement in Python, well documented, and a good way for experimenting and getting a feel for the difficulties of reliable data extraction from PDF.
I worked with both options to extract text, figures, tables etc. from scientific papers. Both yielded good results. The main problem with out-of-the-box solutions is that, when you work with complex formats (or badly formatted docs), erroneously identified document elements are quite common (for example a footnote or a header gets merged with the main text). Both options are based on machine learning models and, at least for GROBID, it is possible to retrain these models for your specific task (I haven't tried this so far, so I don't know how worthwhile it is).
However, if your target PDFs are all of the same (simple) format (or if you can control their format) you should be fine with either option.
I work in Python and have to generate spreadsheets frequently to share my data with programming-naive colleagues. I embed large blocks of text explaining the contents of the spreadsheet and how it was generated into the first page of these spreadsheets routinely. I don't like relying on an associated document to explain definitions, criteria, algorithms, and reliability when I send my results out into the world.
It's really awkward to edit and store the long strings that make up these blocks of text. I'd love to store them in dedicated files that I can work with using a tool INTENDED to edit large blocks of text. I'm wondering how other people deal with this kind of situation. JSON files? YAML? Some obvious built-in functionality in Python I don't know about?
This is obviously a very open-ended question. I'm sure there a lot of different approaches and solutions out there. It's a difficult thing to search for online as there are a lot of obfuscating factors when you search for things like 'python large strings' or 'python text files'. I'm hoping to hear about a number of different approaches.
This is my first post on stack.
I'm looking to gather a large amount of data from a multitude of files on PW so I can quantify a few things about the records.
The directories I'm working with have unique numbers and offer files that are all similar to files in other folders.
Is there a library from python I can use or any other useful tips for taking on this task?
It could potentially save many hours of work if I can do this with code.
A pseudocode example may look like.
for element in dataField:
search(folder)
if folder found:
search(file)
if file found
extract certain data from file X
extractedData.append(data)
Thank you,
R
Based off a quick web search for projectwise api, there is a web-based REST API available, so you'll definitely want to look into that more. You'll need to read the docs carefully to figure out which endpoint does what, but once you know what information you need to send and what kind of data you'll receive, programming a basic Python interface shouldn't be too difficult. One may already exist, I didn't look too hard.
I have huge collection of .json files containing hundreds or thousands of documents I want to import to arangodb collections. Can I do it using python and if the answer is yes, can anyone send an example on how to do it from a list of files? i.e:
for i in filelist:
import i to collection
I have read the documentation but I couldn't find anything even resembling that
So after a lot of trial and error I found out that I had the answer in front of me. So I didn't need to import the .json file, I just needed to read it and then do a bulk import of documents. The code is like this:
a = db.collection('collection_name')
for x in list_of_json_files:
with open(x,'r') as json_file:
data = json.load(json_file)
a.import_bulk(data)
So actually it was quite simple. In my implementation I am collecting the .json files from multiple folders and importing them to multiple collections. I am using the python-arango 5.4.0 driver
I had this same problem. Though your implementation will be slightly different, the answer you need (maybe not the one you're looking for) is to use the "bulk import" functionality.
Since ArangoDB doesn't have an "official" Python driver (that I know of), you will have to peruse other sources to give you a good idea on how to solve this.
The HTTP bulk import/export docs provide curl commands, which can be neatly translated to Python web requests. Also see the section on headers and values.
ArangoJS has a bulk import function, which works with an array of objects, so there's no special processing or preparation required.
I have also used the arangoimport tool to great effect. It's command-line, so it could be controlled from Python, or used stand-alone in a script. For me, the key here was making sure my data was in JSONL or "JSON Lines" format (each line of the file is a self-contained JSON object, no bounding array or comma separators).