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Jahed Stetter
The lack of float support in range doesn't seem like a big fault. The suggested link shows all kinds of ways of generating the numbers. They also point out the tricky issues. numeric_range also discusses these issues.
Both of these answers feed one generator to another, consistent with the philosophy I cited in my comment. Building complex actions by stringing together smaller building blocks is typical Python, and even more so numpy.
I have confirmed that this takes constant memory in python 3.2.1, assuming my testing methodology was correct. I created a string of very large size (1GB or so), then iterated through the iterable with a for loop (NOT a list comprehension, which would have generated extra memory). This did not result in a noticeable growth of memory (that is, if there was a growth in memory, it was far far less than the 1GB string).
The idea is that ((?!pat).)* 'negates' a group by ensuring it greedily matches until the pattern would start to match (lookaheads do not consume the string in the regex finite-state-machine). In pseudocode: repeatedly consume (begin-of-string xor sep) + as much as possible until we would be able to begin again (or hit end of string)
(One should note that str.split has an ugly behavior: it special-cases having sep=None as first doing str.strip to remove leading and trailing whitespace. The above purposefully does not do that; see the last example where sep="\s+".)
(I ran into various bugs (including an internal re.error) when trying to implement this... Negative lookbehind will restrict you to fixed-length delimiters so we don't use that. Almost anything besides the above regex seemed to result in errors with the beginning-of-string and end-of-string edge-cases (e.g. r'(.*?)($,)' on ',,,a,,b,c' returns ['', '', '', 'a', '', 'b', 'c', ''] with an extraneous empty string at the end; one can look at the edit history for another seemingly-correct regex that actually has subtle bugs.)
(If you want to implement this yourself for higher performance (although they are heavweight, regexes most importantly run in C), you'd write some code (with ctypes? not sure how to get generators working with it?), with the following pseudocode for fixed-length delimiters: Hash your delimiter of length L. Keep a running hash of length L as you scan the string using a running hash algorithm, O(1) update time. Whenever the hash might equal your delimiter, manually check if the past few characters were the delimiter; if so, then yield substring since last yield. Special case for beginning and end of string. This would be a generator version of the textbook algorithm to do O(N) text search. Multiprocessing versions are also possible. They might seem overkill, but the question implies that one is working with really huge strings... At that point you might consider crazy things like caching byte offsets if few of them, or working from disk with some disk-backed bytestring view object, buying more RAM, etc. etc.)
The most efficient way I can think of it to write one using the offset parameter of the str.find() method. This avoids lots of memory use, and relying on the overhead of a regexp when it's not needed.
When multiple delimiters are supplied, the string is split by longestpossible sequences of those delimiters by default, or, if empty is set toTrue, empty strings between the delimiters are also included. Note thatthe delimiters in this case may only be single characters.
I don't see any obvious benefit to a generator version of split(). The generator object is going to have to contain the whole string to iterate over so you're not going to save any memory by having a generator.
I wanted to show how to use the find_iter solution to return a generator for given delimiters and then use the pairwise recipe from itertools to build a previous next iteration which will get the actual words as in the original split method.
The C Code Generation installation tool error out saying that Labview 2015 had to be installed first. I verified that my 2018 Labview installation was correct (I was able to run a few block diagrams). I was not able to find a C Code Generator 2018 version, nor Labview 2015 for download.
This is not part of the Standard LabVIEW distribution. I believe it is part of the LabVIEW Tools Network, maybe designed for Embedded Processors. From what I can find, LabVIEW 2015 is the latest version of this kit.
This is the 32 bit version! You can install it and will get a 7 day trial period. The C Generator is AFAIK not officially supported in LabVIEW after 2015. It was a product that was created from the earlier LabVIEW for Embedded Toolkits for ARM and AD Blackfin embedded targets. Both proofed to be impossible to support and maintain and were discontinued and NI never really decided what they should do with the C Generator. While it was a substantial investment to create, it proofed to be to complicated to use for almost every potential user, the created code while technically quite correct, was very complex and more or less impossible to understand or maintain, and the price point was way beyond what any average user was willing to shell out. All in all a product with to many problems and to few potential users to make it a feasible solution.
When you buy a LabVIEW license you get a serial number that will not only enable you to activate the current version of LabVIEW, but you can also activate previous versions of LabVIEW with it. But if you do not buy it with an SSP and maintain it, your serial number will not let you activate later versions of LabVIEW.
The C Code Generation installation tool error out saying that Labview 2015 had to be installed first. I verified that my 2018 Labview installation was correct site (I was able to run a few block diagrams). I was not able to find a C Code Generator 2018 version here, nor Labview 2015 for download.
I finally got success not only in installing, but also in running LabVIEW C Generator 2017 module on LV 2018 and 2019. So you guys could try the steps below, if you don't want to install fresh LabVIEW 2017 for it.
7. Make sure, that you have a valid C Gen license in C:\ProgramData\National Instruments\License Manager\Licenses (trial or common one) and make sure, that it's issued for that exact LabVIEW version (2019, for example). Honestly, I don't know, if you could purchase C Gen license for LV 2018 or 2019 as it's unsupported for C Gen module, but I'm saying it anyway.
It means, that C Gen module is fine and wants the rest of the parameters to work on. But if you get "Target is not supported." message, then you have some problems with the module license. The same message is displayed, when trying to make a build in the project properties. Kinda unobvious message, it took many hours from me to know, what's the deal.
It appears that the original lvcg doesn't behave well sometimes, so I attached that slightly modded version, I mentioned above. Place it into [LabVIEW]\CCodeGen\bin folder and replace lvcg there. It also requires FF RTE 2019 to be installed, that is ni-labview-2019-ff-runtime-engine-x86_19.* package from pool folder of LV19 image.
The data types that were selected in the first section of the window should have their version numbers incremented by one. You can check this by opening one of the data types and checking the version number on the General Pane.
The data types that were selected in the first section of the window should have their version numbers restored to the version number of the data referenced in the Track in Step 3. You can check this by opening one of the data types and checking the version number on the General Pane.
Appends a build number to your product version number, which will be unique every 2 minutes and which will show the build time in a human-readable way. Or provided with the build number, reveals the date and time of the build. For example:
There are a number of ways to use OpenAPI Generator. This page documents how to install the CLI artifact.Installing OpenAPI Generator's CLI tool allows users to generate all available generators from the command line.
The npm package wrapper is cross-platform wrapper around the .jar artifact. It works by providing a CLI wrapper atop the JAR's command line options. This gives a simple interface layer which normalizes usage of the command line across operating systems, removing some differences in how options or switches are passed to the tool (depending on OS).Install the latest version of the tool globally, exposing the CLI on the command line:
npx will execute a globally available openapi-generator, and if not found it will fall back to project-local commands. The result is that the above command will work regardless of which installation method you've chosen.
The OpenAPI Generator Docker image acts as a standalone executable. It can be used as an alternative to installing via homebrew, or for developers who are unable to install Java or upgrade the installed version.
To generate code from a valid petstore.yaml doc with this image, you'll need to mount a local location as a volume.You'll then need to output the generated code to this mapped volume. Everything else works the same as if you ran the command on your host machine.
One downside to manual JAR downloads is that you don't keep up-to-date with the latest released version. We have a Bash launcher script at bin/utils/openapi-generator-cli.sh which solves this problem.
Now, openapi-generator-cli is "installed". On invocation, it will query the GitHub repository for the most recently released version. If this matches the last downloaded jar,it will execute as normal. If a newer version is found, the script will download the latest release and execute it.
If you need to invoke an older version of the generator, you can define the variable OPENAPI_GENERATOR_VERSION either ad hoc or globally. You can export this variable if you'd like to persist a specific release version.
SWIG is a software development tool that connects programs written inC and C++ with a variety of high-level programminglanguages. SWIG is used with different types of target languages including common scripting languages such asJavascript, Perl, PHP, Python, Tcl and Ruby. The list of supported languages also includes non-scripting languages such as C#, D, Go language,Java including Android, Lua, OCaml, Octave, Scilab and R.Also several interpreted and compiled Scheme implementations (Guile, MzScheme/Racket)are supported. SWIG is mostcommonly used to create high-level interpreted or compiled programmingenvironments, user interfaces, and as a tool for testing and prototyping C/C++ software. SWIG is typically used to parse C/C++ interfaces and generate the 'glue code' required for the above target languages to call into the C/C++ code.SWIG can also export its parse tree in the form of XML.SWIG is free software and the code that SWIG generates is compatible with both commercial and non-commercial projects.
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