We use a linter (for Golang) that run through a Github Actions workflow every time we open or update a Pull Request on our repository.
It recently started to return the following error:
File is not `gofmt`-ed with `-s` (gofmt)
After what happened in this other PR to the file pkg/api/api/go.
(EDIT: link added to evaluate and eventually reproduce the error)
Evidences:
I would like to understand what was the source of this error, as well as how to resolve it?
Source of the error
It seems this error can be returned when the file is not properly formatted according to Go rules.
For example: If you accidentally used tab indentation rather than spaces.
EDIT: blackgreen's answer gives more accurate details about the source of the error
How to resolve it
You can use the following Go command:
gofmt -s -w <path_to_file>.go
... then commit the code.
Note that in my case: gofmt -w pkg/api/api.go was enough to resolve the problem (without the -s flag, which I found strange as the error specifically asked for the -s).
Source 1 + Source 2
The -s flag in gofmt has nothing to do with formatting. It's about simplifying the code:
Try to simplify code (after applying the rewrite rule, if any).
The warning you see comes from the linter golangci-lint. Since you claim to have fixed the error by running gofmt -w, the presence of the hint "with -s" may be due to this bug: https://github.com/golangci/golangci-lint/issues/513.
The linked issue was fixed in 2019 and released with v1.17.0. You might want to check if your pipeline is using an older version.
Assuming that your file pkg/api/api.go triggered the warning just because it was not formatted, gofmt -w solves the issue because -w overwrites the file:
If a file's formatting is different from gofmt's, overwrite it with gofmt's version.
I'm switching to neovim and try to get nvim-go running. My Plug section in my init.vim looks like this:
call plug#begin('~/.vim/plugged')
Plug 'zchee/nvim-go', { 'do': 'make'}
Plug 'sebdah/vim-delve'
call plug#end()
If I open nvim and run PlugInstall, I get the following errors:
Error detected while processing
/home/domma/.vim/plugged/nvim-go/plugin/nvim-go. vim: line 20:
E121: Undefined variable: g:go#debug
I checked the file and the error makes sense. But I have no idea where this variable comes from, how it should be set. How can I fix this?
Temporary edit the line in /home/domma/.vim/plugged/nvim-go/plugin/nvim-go.vim: line 20
if g:go#debug -> if exists('g:go#debug')
TL;DR
How to find where exactly vim or nvim error started (which file?) when I'm interested in fixing the actual issue and not just removing the bad plugin? Anything better than strace and guesswork to find the error origin?
Issue
I often add a plugin to my vim or nvim config and end up getting errors on hooks (buffer open, close, write):
"test.py" [New] 0L, 0C written
Error detected while processing function 343[12]..272:
line 8:
E716: Key not present in Dictionary: _exec
E116: Invalid arguments for function get(a:args, 'exec', a:1['_exec'])
E15: Invalid expression: get(a:args, 'exec', a:1['_exec'])
The problem is, I have no idea where those come from, only get some line number of unknown file and I know it's not my vim/nvim config file.
Somewhere, you have a plugin that has defined a dictionary with anonymous-functions (check the help related to this tag).
For the curious ones, it's done this way:
let d = {}
function! d.whatever() abort
throw "blah"
endfunction
When you execute this function, you'll get the kind of error you're currently observing. That's why I stopped working this way to prefer:
let d = {}
function s:whatever() abort
throw "blah"
endfunction
let d.whatever = function('s:whatever') " a workaround is required for older versions of vim
" At least this way I'll get a `<SNR>42_whatever` in the exception throwpoint, and thus a scriptname.
That's the why. Now, back to your problem, AFAIK, the only things you'll be able to know are the two functions that have been called:
in line 12 of :function {343}, you've called
:function {272} which contains an error at line 8.
Thanks to these two commands (may be prefixed with :verbose, I don't remember exactly), you'll get the source code of the two functions, which you should be able to use in order to grep your plugins to know where it appears.
I'm dealing with some GB-sized numpy arrays in IPython. When I delete them, I definitely want them gone, in order to recover the memory. IPythons output cache is quite annoying there, as it keeps the objects alive even after deleting the last actively intended reference to them. I already set
c.TerminalInteractiveShell.cache_size = 0
in the IPython configuration, but this only disables caching of entries to _oh, the other variables like _, __ and so on are still created. I'm also aware of %xdel, but anyways, I'd prefer to disable it completely, as I rarely use the output history anyways, so that a plain del would work again right away.
Looking at IPython/core/displayhook.py Line 209-214 I would say that it is not configurable. You could try making a PR to add an option to disable it totally.
Enter
echo "__builtin__._ = True" > ~/.config/ipython/profile_default/startup/00-disable-history.py
and your history should be gone.
Edit:
Seems like the path to the config directory is sometimes a bit different, either ~/.config/ipython or just ~/.ipython/. So just check which one you got and adjust the path accordingly. The solution still works with jupyter console.
Seems that we can suppress the output cache by putting a ";" at the end of the line now.
See http://ipython.org/ipython-doc/stable/interactive/tips.html#suppress-output
Create an ipython profile:
!ipython profile create
The output might be (for ipython v4.0):
[ProfileCreate] Generating default config file: '/root/.ipython/profile_default/ipython_config.py'
[ProfileCreate] Generating default config file: '/root/.ipython/profile_default/ipython_kernel_config.py'
Then add the line 'c.InteractiveShell.cache_size = 0' to the ipython_kernel_config.py file by
!echo 'c.InteractiveShell.cache_size = 0' >> /root/.ipython/profile_default/ipython_kernel_config.py
Load another ipython kernel and check if this work
In [1]: 123
Out[1]: 123
In [2]: _1
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-51-21553803e553> in <module>()
----> 1 _1
NameError: name '_1' is not defined
In [3]: len(Out)
Out[3]: 0
If I am running a long R script from the command line (R --slave script.R), then how can I get it to give line numbers at errors?
I don't want to add debug commands to the script if at all possible; I just want R to behave like most other scripting languages.
This won't give you the line number, but it will tell you where the failure happens in the call stack which is very helpful:
traceback()
[Edit:] When running a script from the command line you will have to skip one or two calls, see traceback() for interactive and non-interactive R sessions
I'm not aware of another way to do this without the usual debugging suspects:
debug()
browser()
options(error=recover) [followed by options(error = NULL) to revert it]
You might want to look at this related post.
[Edit:] Sorry...just saw that you're running this from the command line. In that case I would suggest working with the options(error) functionality. Here's a simple example:
options(error = quote({dump.frames(to.file=TRUE); q()}))
You can create as elaborate a script as you want on an error condition, so you should just decide what information you need for debugging.
Otherwise, if there are specific areas you're concerned about (e.g. connecting to a database), then wrap them in a tryCatch() function.
Doing options(error=traceback) provides a little more information about the content of the lines leading up to the error. It causes a traceback to appear if there is an error, and for some errors it has the line number, prefixed by #. But it's hit or miss, many errors won't get line numbers.
Support for this will be forthcoming in R 2.10 and later. Duncan Murdoch just posted to r-devel on Sep 10 2009 about findLineNum and setBreapoint:
I've just added a couple of functions to R-devel to help with
debugging. findLineNum() finds which line of which function
corresponds to a particular line of source code; setBreakpoint() takes
the output of findLineNum, and calls trace() to set a breakpoint
there.
These rely on having source reference debug information in the code.
This is the default for code read by source(), but not for packages.
To get the source references in package code, set the environment
variable R_KEEP_PKG_SOURCE=yes, or within R, set
options(keep.source.pkgs=TRUE), then install the package from source
code. Read ?findLineNum for details on how to tell it to search
within packages, rather than limiting the search to the global
environment.
For example,
x <- " f <- function(a, b) {
if (a > b) {
a
} else {
b
}
}"
eval(parse(text=x)) # Normally you'd use source() to read a file...
findLineNum("<text>#3") # <text> is a dummy filename used by
parse(text=)
This will print
f step 2,3,2 in <environment: R_GlobalEnv>
and you can use
setBreakpoint("<text>#3")
to set a breakpoint there.
There are still some limitations (and probably bugs) in the code; I'll
be fixing thos
You do it by setting
options(show.error.locations = TRUE)
I just wonder why this setting is not a default in R? It should be, as it is in every other language.
Specifying the global R option for handling non-catastrophic errors worked for me, along with a customized workflow for retaining info about the error and examining this info after the failure. I am currently running R version 3.4.1.
Below, I've included a description of the workflow that worked for me, as well as some code I used to set the global error handling option in R.
As I have it configured, the error handling also creates an RData file containing all objects in working memory at the time of the error. This dump can be read back into R using load() and then the various environments as they existed at the time of the error can be inspected interactively using debugger(errorDump).
I will note that I was able to get line numbers in the traceback() output from any custom functions within the stack, but only if I used the keep.source=TRUE option when calling source() for any custom functions used in my script. Without this option, setting the global error handling option as below sent the full output of the traceback() to an error log named error.log, but line numbers were not available.
Here's the general steps I took in my workflow and how I was able to access the memory dump and error log after a non-interactive R failure.
I put the following at the top of the main script I was calling from the command line. This sets the global error handling option for the R session. My main script was called myMainScript.R. The various lines in the code have comments after them describing what they do. Basically, with this option, when R encounters an error that triggers stop(), it will create an RData (*.rda) dump file of working memory across all active environments in the directory ~/myUsername/directoryForDump and will also write an error log named error.log with some useful information to the same directory. You can modify this snippet to add other handling on error (e.g., add a timestamp to the dump file and error log filenames, etc.).
options(error = quote({
setwd('~/myUsername/directoryForDump'); # Set working directory where you want the dump to go, since dump.frames() doesn't seem to accept absolute file paths.
dump.frames("errorDump", to.file=TRUE, include.GlobalEnv=TRUE); # First dump to file; this dump is not accessible by the R session.
sink(file="error.log"); # Specify sink file to redirect all output.
dump.frames(); # Dump again to be able to retrieve error message and write to error log; this dump is accessible by the R session since not dumped to file.
cat(attr(last.dump,"error.message")); # Print error message to file, along with simplified stack trace.
cat('\nTraceback:');
cat('\n');
traceback(2); # Print full traceback of function calls with all parameters. The 2 passed to traceback omits the outermost two function calls.
sink();
q()}))
Make sure that from the main script and any subsequent function calls, anytime a function is sourced, the option keep.source=TRUE is used. That is, to source a function, you would use source('~/path/to/myFunction.R', keep.source=TRUE). This is required for the traceback() output to contain line numbers. It looks like you may also be able to set this option globally using options( keep.source=TRUE ), but I have not tested this to see if it works. If you don't need line numbers, you can omit this option.
From the terminal (outside R), call the main script in batch mode using Rscript myMainScript.R. This starts a new non-interactive R session and runs the script myMainScript.R. The code snippet given in step 1 that has been placed at the top of myMainScript.R sets the error handling option for the non-interactive R session.
Encounter an error somewhere within the execution of myMainScript.R. This may be in the main script itself, or nested several functions deep. When the error is encountered, handling will be performed as specified in step 1, and the R session will terminate.
An RData dump file named errorDump.rda and and error log named error.log are created in the directory specified by '~/myUsername/directoryForDump' in the global error handling option setting.
At your leisure, inspect error.log to review information about the error, including the error message itself and the full stack trace leading to the error. Here's an example of the log that's generated on error; note the numbers after the # character are the line numbers of the error at various points in the call stack:
Error in callNonExistFunc() : could not find function "callNonExistFunc"
Calls: test_multi_commodity_flow_cmd -> getExtendedConfigDF -> extendConfigDF
Traceback:
3: extendConfigDF(info_df, data_dir = user_dir, dlevel = dlevel) at test_multi_commodity_flow.R#304
2: getExtendedConfigDF(config_file_path, out_dir, dlevel) at test_multi_commodity_flow.R#352
1: test_multi_commodity_flow_cmd(config_file_path = config_file_path,
spot_file_path = spot_file_path, forward_file_path = forward_file_path,
data_dir = "../", user_dir = "Output", sim_type = "spot",
sim_scheme = "shape", sim_gran = "hourly", sim_adjust = "raw",
nsim = 5, start_date = "2017-07-01", end_date = "2017-12-31",
compute_averages = opt$compute_averages, compute_shapes = opt$compute_shapes,
overwrite = opt$overwrite, nmonths = opt$nmonths, forward_regime = opt$fregime,
ltfv_ratio = opt$ltfv_ratio, method = opt$method, dlevel = 0)
At your leisure, you may load errorDump.rda into an interactive R session using load('~/path/to/errorDump.rda'). Once loaded, call debugger(errorDump) to browse all R objects in memory in any of the active environments. See the R help on debugger() for more info.
This workflow is enormously helpful when running R in some type of production environment where you have non-interactive R sessions being initiated at the command line and you want information retained about unexpected errors. The ability to dump memory to a file you can use to inspect working memory at the time of the error, along with having the line numbers of the error in the call stack, facilitate speedy post-mortem debugging of what caused the error.
First, options(show.error.locations = TRUE) and then traceback(). The error line number will be displayed after #