![]() ![]() To make this management easier, all newly created objects will be selected at Top of this post will generate two Table objects and two Strings objects! The mapping between JSON data structures and Praat objects is direct, but canĪt times become rather cumbersome: even the relatively simple example at the Properly in a single go, which makes the process significantly simpler. This is where the beauty of JSON really shines,īecause when parsing JSON it is never necessary to back-track: it can be read Processes it character by character, populating the necessary objects along the Parsing is done by the procedure, which takes a JSON string and Take a look at that file for more information. They allow the user to control almost all of the finer aspects of the read and There are other variables like this, which are defined in the vars.proc file. That can be redefined at runtime, the user can set this to whatever fits theirĭata (if, for example, they need the hash character as a plain character). This has the benefit that, if numified with number(), it will turn into theĬorrect value ( 32), and because the carat is stored in a separate variable, #, such that a reference to object 32 would be stored as 32#. In this case, the so-called “id carat” defaults to This marker is not unlike the $ marker already used for strings in Praat, Let us know that that value represents a reference to an existing object. Insert them into the Strings and Table objects with a special marker to Represent dictionaries and lists: we need to be able to have dictionaries ofįor this, the json plugin also includes procedures that take object IDs and But even aĬase like the first example above already breaks this: there is a top levelĭictionary with a single key, whose value is another dictionary with two keys,Įach of which have a list of strings. This is all well and good when having flat lists or dictionaries. Procedures to standardise the handling of Table objects as dictionaries: theyĬan be created with the procedure, and new values can be added with and procedures, that add numeric and string values Similarly, the json plugin includes a hash.proc file that includes some The list which extracts a set of elements from the list etc. Objects as arrays, and implements some common methods like which addsĪn element at the end of the list which takes the last element from The strutils plugin already includes some procedures to handle Strings Hashes, etc), while Strings objects represent lists. These data structures: Table objects represent objects (or dictionaries, or Instead, the json plugin makes use of two existing Praat objects to represent Purpose of what we need here, they are useless. To get the keys in one of the new ones using strings. So there is no way to pass a whole set of indexed variables to a procedure, or ![]() Unrelated variables, with nothing in common but part of their identifiers. The so-called indexed variables, and for a couple of versions now these haveĪllowed using strings as keys. One of the first problems had to do with representing data structures in Praat.Īlthough JSON uses fairly straightforward data structures (lists and maps), Structure while the latter is used in line 16, to print the selected dataīelow, I’ll go into a little more detail about how this all works. One is then put to use in line 12, to convert a JSON string into a data ![]() Two main procedures of the plugin: and The first In the above snippet, lines 4 and 5 “import” the library, which provides the /plugin_json/procedures/write.proc form Args. /plugin_json/procedures/read.proc include. #!/usr/bin/env praat # Procedures are in separate files for parsing and producing JSON include. I give you the appropriately named “json” plugin for Praat: 1 One of them, making data transport difficult and turning Praat into a lonely Using a relatively small set of characters: for lists, Īlthough there are JSON libraries for most languages out there, Praat is not Of the relations between the different data items are expressed explicitly Probably the most widely spread data serialisation format out there is ,Īnd with good reason: it is powerful and simple, and easy to parse because all With the problem of importing and exporting data to and from Praat. Particularly since the advent of the so-called “barren” releases of Praat, theįirst part of that task has become a lot easier to do. Some time ago, I was asked to set up a web service that received some data onĪ web request, passed it to a Praat process, and got some data out of it. ![]()
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