I would answer with slight modification with aforementioned answers and that is to write a prettified JSON file which human eyes can read better. For this, pass sort_keys as True and indent with 4 space characters and you are good to go. Also take care of ensuring that the ascii codes will not be written in your JSON file:
How to make JSON more readable by human eyes
You may also want to return a string instead of a list in case of a np.array() since arrays are printed as lists that are spread over rows which will blow up the output if you have large or many arrays. The caveat: it is more difficult to access the items from the dumped dictionary later to get them back as the original array. Yet, if you do not mind having just a string of an array, this makes the dictionary more readable. Then exchange:
Computer Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.
JSON logs are formatted to be easily digestible by human eyes in a series of key-value pairs. For example, a log message might include a "user ID" field to indicate which user was involved during that log and a log levels field to indicate the severity of the log message.
This type of formatting is more common when encountering a JSON string or within another file type. When working with huge data sets, writing out the colons and key-value pairs on individual lines makes the list more readable to human eyes since JSON lacks the separative assistance of white spaces:
JSON (JavaScript Object Notation) files are lightweight and human-readable to store and exchange data. It is easy for machines to parse and generate these files and are based on the JavaScript programming language.
One thing to notice is that the code is less repetitive and it encourages the developer to include all the data rather than only what seems important while writing the code. Also note that in this format, the log lines are still reasonably easy to follow for a human during development. When moving to production, however, it makes more sense to use the JSON renderer:
One major aspect of modern programming is to make your code readable to other humans. This makes a lot of sense, given the process of turning high-level code into low-level machine instructions means your well-formatted code takes a turn for the worst.
JSON is a way to straddle the line between human-readable and machine-readable data. Before structured data objects like JSON, most operating systems relied on text parsing tools that used ugly regular expressions (regex) and other methods to bring structure to simple text.
The produced JSON is normally optimized for space, i.e. there are no white-spaces added. This makes it hard to read for the human eye. If you want to be able to read your JSON on screen easily and storage space is not an issue, set the StorePrettyPrint propery to true.
Historically, efforts to make government information available to the public have focused on pushing static information about government programs and services to the web. The intended user has been a human who can read, print, and take actions based on reading the material or by engaging in a form-based transaction. In some cases, users were able to query the data or map the results using sophisticated geospatial displays. Access to the data itself, on the other hand, was rarely provided.
Similar factors are now coming into play with the advent of machine readable formats for government data. The expectation of similar benefits in usability, payback, and extensibility should provide the motivation. To realize these benefits, similar conditions also apply. Uniformity and standardization in data formats and processing are needed. Simplicity in creating and embedding the formats must be achieved. Cost advantages must be realized through their use to justify their creation. When these conditions are met, machine readable data become more prevalent leading in turn to increased capabilities.
For these reasons and as commitments to open government and transparency increase, efforts to make information available must include machine readable versions of datasets and not only reports about this information in document form (such as PDF, HTML, and JPG). Consider a bar chart in a government report. You can read the report in PDF format and understand the analysis the chart provides. However, neither the chart itself nor its underlying data is available in a way that allows further processing of that information. Next generation efforts in opening government must ensure that users have access, for example, not just to a static bar chart image, but also to information about the source of that bar chart and the underlying data itself, much as the magazine bar code described above reveals far more data about the magazine that can be leveraged for additional benefits.
API: (Application Programming Interface) When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
HTML: (HyperText Markup Language) The main markup language for displaying web pages and other information in a web browser. HTML is an open standard by the W3C. The tags and markup of HTML mainly relate to how information is displayed to a human user, not to the information itself. In general, putting a document in HTML, without some form of tagging or metadata, is not considered making it machine readable in a useful sense.
PDF: (Portable Document Format) A file format used mainly to represent documents such that layout will stay the same independent of the system environment. As of 2008, Adobe released the PDF format as an open standard. PDF is mainly a human readable format, concerned with layout and organization. Tools for connecting metadata to PDF or extracting the text from it make PDF more machine readable than many other document formats.
XML: (eXtensible Markup Language) A markup language that defines a set of rules for encoding documents in a format that can be both human and machine readable. XML is an open format created and maintained by the W3C. XML is enhanced by proper use of XML schemas or by use of metadata embedded in the XML.
I noticed that the preferences are written in json which is nice and easy to edit. However I think yaml would be a better choice because it relies on indentation which makes it easy to read like python syntax. I see json more of a transport protocol and yaml for human readable markup.
[quote]YAML syntax was designed to be easily mapped to data types common to most high-level languages: list, associative array, and scalar.[4] Its familiar indented outline and lean appearance make it especially suited for tasks where humans are likely to view or edit data structures, such as configuration files, dumping during debugging, and document headers (e.g. the headers found on most e-mails are very close to YAML). Although well-suited for hierarchical data representation, it also has a compact syntax for relational data as well.[5] Its line and whitespace delimiters make it friendly to ad hoc grep/Python/Perl/Ruby operations. A major part of its accessibility comes from eschewing the use of enclosures like quotation marks, brackets, braces, and open/close-tags which can be hard for the human eye to balance in nested hierarchies.[/quote]
JSON (JavaScript Object Notation) is a text-based, human-readable data interchange format used to exchange data between web clients and web servers. The format defines a set of structuring rules for the representation of structured data. JSON is used as an alternative to Extensible Markup Language (XML).
In the United States, the OPEN Government Data Act of 14 January 2019 defines machine-readable data as "data in a format that can be easily processed by a computer without human intervention while ensuring no semantic meaning is lost." The law directs U.S. federal agencies to publish public data in such a manner,[2] ensuring that "any public data asset of the agency is machine-readable".[3]
Machine-readable data may be classified into two groups: human-readable data that is marked up so that it can also be read by machines (e.g. microformats, RDFa, HTML), and data file formats intended principally for processing by machines (CSV, RDF, XML, JSON). These formats are only machine readable if the data contained within them is formally structured; exporting a CSV file from a badly structured spreadsheet does not meet the definition.
Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for humans to access via computers, but its content is much harder to extract, transform, and process via computer programming logic if it is not machine-readable.[4]
Extensible Markup Language (XML) is designed to be both human- and machine-readable, and Extensible Stylesheet Language Transformation (XSLT) is used to improve the presentation of the data for human readability. For example, XSLT can be used to automatically render XML in Portable Document Format (PDF). Machine-readable data can be automatically transformed for human-readability but, generally speaking, the reverse is not true.
For purposes of implementation of the Government Performance and Results Act (GPRA) Modernization Act, the Office of Management and Budget (OMB) defines "machine readable format" as follows: "Format in a standard computer language (not English text) that can be read automatically by a web browser or computer system. (e.g.; xml). Traditional word processing documents and portable document format (PDF) files are easily read by humans but typically are difficult for machines to interpret. Other formats such as extensible markup language (XML), (JSON), or spreadsheets with header columns that can be exported as comma separated values (CSV) are machine readable formats. As HTML is a structural markup language, discreetly labeling parts of the document, computers are able to gather document components to assemble tables of contents, outlines, literature search bibliographies, etc. It is possible to make traditional word processing documents and other formats machine readable but the documents must include enhanced structural elements."[5] 2ff7e9595c
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