ox | An independent Rust text editor that runs in your terminal! | Text Editor library
kandi X-RAY | ox Summary
kandi X-RAY | ox Summary
Ox is a code editor. It was written in Rust using ANSI escape sequences. It assists developers with programming by providing several tools to speed up and make programming easier and a refreshing alternative to heavily bloated and resource hungry editors such as VS Code and JetBrains. Ox is lightweight so it can be used on older computers. Bear in mind, this is a personal project and is nowhere near ready to replace your existing tools just yet. It runs in the terminal and runs on platforms like Linux and macOS but doesn't work on Windows directly (it works if you use WSL) due to a lack of a good command line. There are many text editors out there and each one of them has their flaws and I hope to have a text editor that overcomes many of the burdens and issues. Ox is not based on any other editor and has been built from the ground up without any base at all.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of ox
ox Key Features
ox Examples and Code Snippets
Community Discussions
Trending Discussions on ox
QUESTION
PS C:\Users\Lenovo> pip install pickle5
Collecting pickle5
Using cached pickle5-0.0.11.tar.gz (132 kB)
Preparing metadata (setup.py) ... done
Using legacy 'setup.py install' for pickle5, since package 'wheel' is not installed.
Installing collected packages: pickle5
Running setup.py install for pickle5 ... error
error: subprocess-exited-with-error
× Running setup.py install for pickle5 did not run successfully.
│ exit code: 1
╰─> [36 lines of output]
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.10
creating build\lib.win-amd64-3.10\pickle5
copying pickle5\pickle.py -> build\lib.win-amd64-3.10\pickle5
copying pickle5\pickletools.py -> build\lib.win-amd64-3.10\pickle5
copying pickle5\__init__.py -> build\lib.win-amd64-3.10\pickle5
creating build\lib.win-amd64-3.10\pickle5\test
copying pickle5\test\pickletester.py -> build\lib.win-amd64-3.10\pickle5\test
copying pickle5\test\test_pickle.py -> build\lib.win-amd64-3.10\pickle5\test
copying pickle5\test\test_picklebuffer.py -> build\lib.win-amd64-3.10\pickle5\test
copying pickle5\test\__init__.py -> build\lib.win-amd64-3.10\pickle5\test
running build_ext
building 'pickle5._pickle' extension
creating build\temp.win-amd64-3.10
creating build\temp.win-amd64-3.10\Release
creating build\temp.win-amd64-3.10\Release\pickle5
C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\bin\HostX86\x64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -IC:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\include -IC:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\Include -IC:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\include -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\ucrt -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\shared -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\um -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\winrt -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\cppwinrt /Tcpickle5/_pickle.c /Fobuild\temp.win-amd64-3.10\Release\pickle5/_pickle.obj
_pickle.c
C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\bin\HostX86\x64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -IC:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\include -IC:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\Include -IC:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\include -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\ucrt -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\shared -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\um -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\winrt -IC:\Program Files (x86)\Windows Kits\10\include\10.0.22000.0\cppwinrt /Tcpickle5/picklebufobject.c /Fobuild\temp.win-amd64-3.10\Release\pickle5/picklebufobject.obj
picklebufobject.c
pickle5/picklebufobject.c(20): warning C4273: 'PyPickleBuffer_FromObject': inconsistent dll linkage
C:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\include\cpython/picklebufobject.h(18): note: see previous definition of 'PyPickleBuffer_FromObject'
pickle5/picklebufobject.c(39): warning C4273: 'PyPickleBuffer_GetBuffer': inconsistent dll linkage
C:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\include\cpython/picklebufobject.h(22): note: see previous definition of 'PyPickleBuffer_GetBuffer'
pickle5/picklebufobject.c(58): warning C4273: 'PyPickleBuffer_Release': inconsistent dll linkage
C:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\include\cpython/picklebufobject.h(24): note: see previous definition of 'PyPickleBuffer_Release'
pickle5/picklebufobject.c(208): warning C4273: 'PyPickleBuffer_Type': inconsistent dll linkage
C:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\include\cpython/picklebufobject.h(13): note: see previous definition of 'PyPickleBuffer_Type'
C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\bin\HostX86\x64\link.exe /nologo /INCREMENTAL:NO /LTCG /DLL /MANIFEST:EMBED,ID=2 /MANIFESTUAC:NO /LIBPATH:C:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\libs /LIBPATH:C:\Users\Lenonvo\AppData\Local\Programs\Python\Python310\PCbuild\amd64 /LIBPATH:C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Tools\MSVC\14.29.30133\lib\x64 /LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.22000.0\ucrt\x64 /LIBPATH:C:\Program Files (x86)\Windows Kits\10\lib\10.0.22000.0\um\x64 /EXPORT:PyInit__pickle build\temp.win-amd64-3.10\Release\pickle5/_pickle.obj build\temp.win-amd64-3.10\Release\pickle5/picklebufobject.obj /OUT:build\lib.win-amd64-3.10\pickle5\_pickle.cp310-win_amd64.pyd /IMPLIB:build\temp.win-amd64-3.10\Release\pickle5\_pickle.cp310-win_amd64.lib
python310.lib(python310.dll) : error LNK2005: PyPickleBuffer_GetBuffer already defined in picklebufobject.obj
Creating library build\temp.win-amd64-3.10\Release\pickle5\_pickle.cp310-win_amd64.lib and object build\temp.win-amd64-3.10\Release\pickle5\_pickle.cp310-win_amd64.exp
build\lib.win-amd64-3.10\pickle5\_pickle.cp310-win_amd64.pyd : fatal error LNK1169: one or more multiply defined symbols found
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\BuildTools\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX86\\x64\\link.exe' failed with exit code 1169
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: legacy-install-failure
× Encountered error while trying to install package.
╰─> pickle5
note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.
ANSWER
Answered 2022-Apr-11 at 12:19You only need pickle5, a module backporting Pickle protocol 5 features to older Pythons when running on Python versions older than 3.8.
As evident from Python310 and -3.10 in the output, you're on Python 3.10. You don't need pickle5.
Thus, the answer to "what should you do", without us not knowing more details about your situation, is "not try to install pickle5".
QUESTION
I am trying to install pyhash with pip. On Ubuntu 20.04.3 with Python 3.8 I was able to install after changing setuptools to 57.5.0 (python -m pip install 'setuptools~=57.5.0')
But on Windows 10 and Python 3.10 I get a compilation error. There are multiple questions here on SO about installing pyhash; based on this answer I made the following changes:
python -m pip install 'setuptools~=57.5.0'$env:PYTHON_HOME='C:\Users\I063510\AppData\Local\Programs\Python\Python310'pip install wheel- From Microsoft Build Tools install "Desktop development with C++"
Now I get warning and errors as follows (complete output at the bottom):
C:\Users\USERID\AppData\Local\Programs\Python\Python310\lib\site-packages\setuptools\lib2to3_ex.py:36: SetuptoolsDeprecationWarning: 2to3 support is deprecated. If the project still requires Python 2 support, please migrate to a single-codebase solution or employ an independent conversion process.
I don't care about Python 2 so this in not an issue, but I don't know how to disable it.
This error aborts the installation:
...ANSWER
Answered 2022-Mar-31 at 00:40Try installing it from the Git repository. There are some fixes there that are not released on PyPI yet.
QUESTION
I am interested in retieving machine readable meta information about R packages.
For example, when I go to CRAN I can see a short description about the package, before I download it: https://cran.r-project.org/web/packages/MASS/
I could not find any way to retrieve a different output from the CRAN server than HTML. I would like to avoid parsing HTML and instead somehow retrieve meta information about packages in a more convenient format (e.g., JSON).
I saw that each R package (at least to my knowledge) has a yaml-like (?) description text inside its source code package (the file is called DESCRIPTION). However, so far I could only find this kind of description inside tar archives, which means that I would have to download the package before I can access its description.
Here an example of the DESCRIPTION from the MASS package:
ANSWER
Answered 2022-Mar-22 at 14:38An acceptable solution is the METACRAN API that is available here: https://crandb.r-pkg.org/
QUESTION
I have implemented a Convolutional Neural Network in C and have been studying what parts of it have the longest latency.
Based on my research, the massive amounts of matricial multiplication required by CNNs makes running them on CPUs and even GPUs very inefficient. However, when I actually profiled my code (on an unoptimized build) I found out that something other than the multiplication itself was the bottleneck of the implementation.
After turning on optimization (-O3 -march=native -ffast-math, gcc cross compiler), the Gprof result was the following:
Clearly, the convolution2D function takes the largest amount of time to run, followed by the batch normalization and depthwise convolution functions.
The convolution function in question looks like this:
...ANSWER
Answered 2022-Mar-10 at 13:57Looking at the result of Cachegrind, it doesn't look like the memory is your bottleneck. The NN has to be stored in memory anyway, but if it's too large that your program's having a lot of L1 cache misses, then it's worth thinking to try to minimize L1 misses, but 1.7% of L1 (data) miss rate is not a problem.
So you're trying to make this run fast anyway. Looking at your code, what's happening at the most inner loop is very simple (load-> multiply -> add -> store), and it doesn't have any side effect other than the final store. This kind of code is easily parallelizable, for example, by multithreading or vectorizing. I think you'll know how to make this run in multiple threads seeing that you can write code with some complexity, and you asked in comments how to manually vectorize the code.
I will explain that part, but one thing to bear in mind is that once you choose to manually vectorize the code, it will often be tied to certain CPU architectures. Let's not consider non-AMD64 compatible CPUs like ARM. Still, you have the option of MMX, SSE, AVX, and AVX512 to choose as an extension for vectorized computation, and each extension has multiple versions. If you want maximum portability, SSE2 is a reasonable choice. SSE2 appeared with Pentium 4, and it supports 128-bit vectors. For this post I'll use AVX2, which supports 128-bit and 256-bit vectors. It runs fine on your CPU, and has reasonable portability these days, supported from Haswell (2013) and Excavator (2015).
The pattern you're using in the inner loop is called FMA (fused multiply and add). AVX2 has an instruction for this. Have a look at this function and the compiled output.
QUESTION
This program, built with -std=c++20 flag:
ANSWER
Answered 2022-Mar-05 at 09:20The overload of std::minmax taking two arguments returns a pair of references to the arguments. The lifetime of the arguments however end at the end of the full expression since they are temporaries.
Therefore the output line is reading dangling references, causing your program to have undefined behavior.
Instead you can use std::tie to receive by-value:
QUESTION
I was coding a BST Tree, and first i made it with integer key, everything worked fine. Then i copied my code and made some changes, i switched integer key to string key and also added one new pointer (because my goal is to create two trees, one with English words and one with their Polish translation) so i tested it just on single tree with string key first and insert function works fine like in the interger tree, but search function is returning some garbage insted of NULL or pointer to node. I dont really know what is a problem here.
I put the code of Integer tree below:
...ANSWER
Answered 2021-Dec-23 at 20:17The recursive function bstSearch is incorrect because it does not return a node in each its path of execution
QUESTION
I am using a dataset with the following columns: date, counts, country, engine, and type.
I have created a view with three charts using the repeat operator. The charts show dates on the X axis, counts on Y, and then the bars are split by either country, engine, or type.
I am happy with how things look but I would like to have three separate color legends, one for each domain (so a legend for countries, a legend for type, and a legend for engine). How do I do that?
Here is the link to the editor.
...ANSWER
Answered 2021-Dec-18 at 20:18To have independent color scales & legends, add the following at the top level of the chart specification:
QUESTION
I have a C++ function which I want to run from Python. For this I use Cython. My C++ function relies heavily on Eigen matrices which I map to Python's Numpy matrices using Eigency.
I cannot get this to work for the case where I have a list of Numpy matrices.
What does works (mapping a plain Numpy matrix to an Eigen matrix):
I have a C++ function which in the header (Header.h) looks like:
...ANSWER
Answered 2021-Dec-01 at 18:34Thanks to @ead I found a solution.
FlattenedMapWithOrder has implementation so it can be assinged to an Eigen::Matrix.
However, std::vector does not have such functionality and since std::vector and std::vector are of a different type, they cannot be assigned to one another.
More about this here.
The implementation in FlattenedMapWithOrder mentioned above is here.
To solve this, the function in the C++ code called from Cython need to simply have as input argument the matching type: std::vector.
To do this, the C++ code needs to know the definition of type FlattenedMapWithOrder.
To do this, you need to #include "eigency_cpp.h". Unfortunately, this header is not self contained.
Therefore, (credits to @ead) I added these lines:
QUESTION
I have a follow-up question on a previous answer that can be found here: Split uneven string in R - variable substring and delimiters
In summary, I wanted to extract the bolded text in a string that follows this pattern:
...ANSWER
Answered 2021-Nov-17 at 17:40This can be solved as follows:
QUESTION
I'm trying to download the map of Mexico to avoid querying using save_graphml and avoiding long response times in the graph_from_place, but I've already left this code running for almost six hours and absolutely nothing happens.
ANSWER
Answered 2021-Oct-14 at 20:09I've already left this code running for almost six hours and absolutely nothing happens.
A lot has been happening! Don't believe me? You ran ox.config(log_console=True), so look at your terminal and watch what's happening while it runs. You'll see a line like "2021-10-14 13:05:39 Requesting data within polygon from API in 1827 request(s)"... so you are making 1,827 requests to the Overpass server and the server is asking you to pause for rate limiting between many of those requests.
I know that due to the stipulated area the time is long, but what I wanted to know is if there is an alternative to this procedure or if there is a way to optimize so that the creation of the map is a little faster or if there is another way to load maps to route with osmnx and networkx without using queries to servers
Yes. This answer provides more details. There are tradeoffs between 1) model precision vs 2) area size vs 3) memory/speed. For faster modeling, you can load the network data from a .osm XML file instead of having to make numerous calls to the Overpass API. I'd also recommend using a custom_filter as described in the linked answer. OSMnx by default divides your query area into 50km x 50km pieces, then queries Overpass for each piece one a time to not exceed the server's per-query memory limits. You can configure this max_query_area_size parameter, as well as the server memory allocation, if you prefer to use OSMnx's API querying functions rather than its from-file functionality.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ox
Ox uses NerdFonts to display icons. You can install nerdfonts from https://nerdfonts.com If you use Arch Linux, you can install it by installing the package ttf-nerd-fonts-symbols-mono. There is a potential that you will need to add it to your terminal emulator. Install ox-bin or ox-git from the Arch User Repository.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
