A time ago I started learning Common Lisp, but now I have come to my first real stumbling block, understanding a concept. I started to change my learning projects to move from single file sources to packages. Everything so far went as expected, but then, I stumbled upon one file, a sudoku game I coded, that behaves other then I thought. You can find it here: https://github.com/Silberbogen/cl-sudoku
When I started (spiele-sudoku) after I switched inside the package via (in-package :cl-sudoku), everything works fine, but when I start it via (cl-sudoku:spiele-sudoku), only my input of coordinates is excepted, while any other input seems not to be interpreted.
What concept do I miss, so I could start the game via (cl-sudoku:spiele)?
You use read-from-string to read your input. That will intern any word encountered as a symbol into the current package.
In your main function, you use case to compare with symbols, but those are interned into the cl-sudoku package. So, if your current package is cl-sudoku, it will work, otherwise not.
You should not use read or read-form-string to parse user input (if you absolutely must, at least bind *read-eval* to nil). Instead call intern yourself (possibly in combination string-upcase) to create symbols in the right package. If you want to use package-independent symbols, intern them into the KEYWORD package, so that you can do case on keywords.
It might be helpful to use ecase or ccase, or at least log some debug information on invalid input.
Related
I'm having this weird issue where my target, which interfaces a slightly customized python module (installed with pip install --editable) through reticulate, gives different results when it's being called from an interactive session in R from when targets is being started from the command line directly, even when I make sure the other argument(s) to tar_make are identical (callr_function = NULL, which I use for interactive debugging). The function is deterministic and should be returning the exact same result but isn't.
It's tricky to provide a reproducible example but if truly necessary I'll invest the required time in it. I'd like to get tips on how to debug this and identify the exact issue. I already safeguarded against potential pointer issues; the python object is not getting passed around between different targets/environments (anymore), rather it's immediately used to compute the result of interest. I also checked that the same python version is being used by printing the result of reticulate::pyconfig() to screen. I also verified both approaches are using the same version of the customized module.
Thanks in advance..!
I need to "industrialize" an R code for a data science project, because the project will be rerun several times in the future with fresh data. The new code should be really easy to follow even for people who have not worked on the project before and they should be able to redo the whole workflow quite quickly. Therefore I am looking for tips, suggestions, resources and best-practices on how to achieve this objective.
Thank you for your help in advance!
You can make an R package out of your project, because it has everything you need for a standalone project that you want to share with others :
Easy to share, download and install
R has a very efficient documentation system for your functions and objects when you work within R Studio. Combined with roxygen2, it enables you to document precisely every function, and makes the code clearer since you can avoid commenting with inline comments (but please do so anyway if needed)
You can specify quite easily which dependancies your package will need, so that every one knows what to install for your project to work. You can also use packrat if you want to mimic python's virtualenv
R also provide a long format documentation system, which are called vignettes and are similar to a printed notebook : you can display code, text, code results, etc. This is were you will write guidelines and methods on how to use the functions, provide detailed instructions for a certain method, etc. Once the package is installed they are automatically included and available for all users.
The only downside is the following : since R is a functional programming language, a package consists of mainly functions, and some other relevant objects (data, for instance), but not really scripts.
More details about the last point if your project consists in a script that calls a set of functions to do something, it cannot directly appear within the package. Two options here : a) you make a dispatcher function that runs a set of functions to do the job, so that users just have to call one function to run the whole method (not really good for maintenance) ; b) you make the whole script appear in a vignette (see above). With this method, people just have to write a single R file (which can be copy-pasted from the vignette), which may look like this :
library(mydatascienceproject)
library(...)
...
dothis()
dothat()
finishwork()
That enables you to execute the whole work from a terminal or a distant machine with Rscript, with the following (using argparse to add arguments)
Rscript myautomatedtask.R --arg1 anargument --arg2 anotherargument
And finally if you write a bash file calling Rscript, you can automate everything !
Feel free to read Hadley Wickham's book about R packages, it is super clear, full of best practices and of great help in writing your packages.
One can get lost in the multiple files in the project's folder, so it should be structured properly: link
Naming conventions that I use: first, second.
Set up the random seed, so the outputs should be reproducible.
Documentation is important: you can use the Roxygen skeleton in rstudio (default ctrl+alt+shift+r).
I usually separate the code into smaller, logically cohesive scripts, and use a main.R script, that uses the others.
If you use a special set of libraries, you can consider using packrat. Once you set it up, you can manage the installed project-specific libraries.
As already mentioned I'm using the Atom text editor.
I'm currently working on a project written in c++. Of course it is desirable to jump to the definition of a function (in another project file), or other uses of this function (within the project). As far as I know this can be achieved with the packages I'll mention below. I want the package to display me the definition along with the path to the corresponding file which holds the definition and ideally the line where it occurs.
I'll welcome any comments and suggestions on how to solve the below mentioned problem(s) I have with (one of) the packages. Moreover I'm also thankful about pointers to possible solutions or posts concerning my problem(s), or how I can achieve this with another package.
Here is what I found / tried / did so far.
goto
Currently I'm using this package, although it is rather slow and does not show the arguments of the function as e.g. atom-ctags does, but it's the only package which displays me the files I need to see.
It shows me where the function is defined as well as where it is also used. However it does not show me the path to the file corresponding file it refers to.
atom-ctags
I also tried this package, building the tags is quite fast and moreover it show me the path to the file. But this package only lists the .cc files and not the .h files. It appears to me as if it only shows me the other uses but not the definition, which is obviously a problem.
I also tried generating the ctags myself and changing the command options in the settings of the package, unfortunately without any success.
Atoms built-in symbols-view
In order to get this to work, one needs to generate the symbols. This can be, for example, achieved with the symbol-gen package. However, it shows me some of the definitions, but also no .h files. Moreover, jumping to the definition results in a Selected file does not exist., therefore it is not usable at all.
goto-definition
Just for completeness, there is also this package. It does not work for me, since c++ is not supported but maybe others will find it useful.
symbols-plus
Again, for completeness, this should be a replacement for the atom built-in, but when disabling the build-in it does not show me any jump functionality nor is a short cut mentioned.
So, basically, nothing really works well. I have tried Symbol Tree View but it but barely works.
Since in http://julia.readthedocs.org/en/latest/manual/modules/ there's no much info about modules, I would like to ask the following.
I want to try two modules via ijulia. Both modules are in my working directory as
name-of-files.jul. I will call them generically module_1.jul and module_2.jul.
module_1.jul uses module_2.jul and I load it with
using module_2
On ijulia session, if I try
using module_1
gives an error. I also tried
include("module_1.jul")
This last sentence, when executed, rises an error because the module_1.jul cannot find
variable "x" that I know is contained in module_1.jul (in this case I "loaded" the module
using include("module2.jul") inside module_1.jul
Julias module system assumes some things that aren't necessarily obvious from the documenation at first.
Julia files should end with .jl extensions.
Julia looks for module files in directories defined in the LOAD_PATH variable.
Julia looks for files in those directories in the form ModuleName/src/file.jl
If using module_1 fails then I'm guessing it's because it's source files fail one of the above criteria.
Some time has passed since this question. Recently, Noah_S wrote the solution in the comments of the previous answer; this means that it is a recurrent doubt for people starting to learn the language. For their sake, I will re-write it here Noah_S' answer along with my most novel solution.
I am a mess with the julia versions and which commands work with the specific ones, so for older julia versions we have to look for the \path and then include in the julia module
push!(LOAD_PATH, "/path")
In newer versions this can be improved. Forget about looking by hand the path and just do
path = readstring(`pwd`)
push!(LOAD_PATH, chomp(path))
I hope this can be useful to many julians newcomers.
I have got a memory image, that i can't find the source for and I want to get the code out of it again. What do i have to do to achieve that? I can obviously load the image, but then i'd need to guess the function names.
You may get "interesting" symbols with (apropos ""), and the function names with WITH-PACKAGE-ITERATOR and FBOUNDP. But the source code is (probably) lost: try DISASSEMBLE on functions and see the information which is there.
In addition to DISASSEMBLE, you might try EXT:UNCOMPILE. Note, however, that it will only work on functions compiled in an interactive session (i.e., from REPL), not on those loaded from a compiled .fas file.
So, the suggested procedure is:
LIST-ALL-PACKAGES - figure out which packages are interesting.
DO-EXTERNAL-SYMBOLS - figure out which symbols in the interesting packages are interesting.
DISASSEMBLE or EXT:UNCOMPILE on those interesting symbols.
However, the easiest way is to contact your vendor. Remember, CLISP is distributed under the GNU GPL.