I am trying to extract data from HDF files and compare the data. Is it possible to automate the process using Squish? Also how to compare data of 2 HDf files of different versions? I am very new to this and have no clue how to start. Any help is appreciated.
Thank you!
If the HDF files are basically plain text, it may be possible to use test.compareTextFiles().
If you can convert the HDF files to XML and you can use test.compareXMLFiles(), which gains you the ability to ignore dynamic portions more easily.
And in general, if you can find any tools that do what you need (extraction, comparison/diff) then you can use them in Squish test scripts (see Article - Executing external applications).
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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!
I have some VLP16 LiDar data in .csv file format, have to load the data in Ros Rviz for which I need the Rosbag file(.bag). I have tried finding it in the Ros tutorial, what I got was to convert .bag to .csv
I'm not actually expert in processing .bag files but I think you need to go through your CSV file and manually add the values using rosbag Python API
Not direct answer but check this script in python, which might help you.
Regarding C++ I propose this repository: convert_csv_to_rosbag which is even closer to what you asked.
However, it seems that you need to do it by yourself based on these examples.
I've been looking for a fast and relatively easy way of searching (grep-ish) for user-defined strings in files of varying formats, i.e xlsx, docx, pptx, pdf using Python.
My research has led me to believe that there might not be a convenient way of doing this, as per a single module or similar. Am I forced to use a separate module for each file type? And if so are these approriate?
docx
openpyxl
pptx
slate
I also looked at forms of decompression to get to the xml-files containing actual text but it seems unwieldy. I just want to be sure that there is no simple, uniform way of handling all of these different filetypes.
Well, I've mostly figured it out. In the end I decided to use powershell combined with "itextsharp.dll" to process the files. It turned out to be simpler than using portable python. Thanks for the answers:-)
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.
I want to enter data into a Microsoft Excel Spreadsheet, and for that data to interact and write itself to other documents and webforms.
With success, I am pulling data from an Excel spreadsheet using xlwings. Right now, I’m stuck working with .docx files. The goal here is to write the Excel data into specific parts of a Microsoft Word .docx file template and create a new file.
My specific question is:
Can you modify just a text string(s) in a word/document.xml file and still maintain the integrity and functionality of its .docx encasement? It seems that there are numerous things that can change in the XML code when making even the slightest change to a Word document. I've been working with python-docx and lxml, but I'm not sure if what I seek to do is possible via this route.
Any suggestions or experiences to share would be greatly appreciated. I feel I've read every article that is easily discoverable through a google search at least 5 times.
Let me know if anything needs clarification.
Some things to note:
I started getting into coding about 2 months ago. I’ve been doing it intensively for that time and I feel I’m picking up the essential concepts, but there are severe gaps in my knowledge.
Here are my tools:
Yosemite 10.10,
Microsoft Office 2011 for Mac
You probably need to be more specific, but the short answer is, in principle, yes.
At a certain level, all python-docx does is modify strings in the XML. A couple things though:
The XML you create needs to remain well-formed and valid according to the schema. So if you change the text enclosed in a <w:t> element, for example, that works fine. Conversely, if you inject a bunch of random XML at an arbitrary point in one of the .xml parts, that will corrupt the file.
The XML "files", known as parts that make up a .docx file are contained in a Zip archive known as a package. You must unpackage and repackage that set of parts properly in order to have a valid .docx file afterward. python-docx takes care of all those details for you, but if you're going directly at the .docx file you'll need to take care of that yourself.