pxl | Display images in the terminal | Command Line Interface library

by ichinaski Go Version: Current License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | pxl Summary

pxl is a Go library typically used in Utilities, Command Line Interface applications. pxl has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

pxl is a little hack to display images in the terminal.

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Quality

Security

License

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Support

pxl has a low active ecosystem.

It has 736 star(s) with 38 fork(s). There are 14 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 8 have been closed. On average issues are closed in 327 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pxl is current.

Quality

pxl has no bugs reported.

Security

pxl has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

pxl is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pxl releases are not available. You will need to build from source code and install.

Installation instructions are available. Examples and code snippets are not available.

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pxl Key Features

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pxl Examples and Code Snippets

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QUESTION

output base64 image in html nodemailer

Asked 2021-Jun-02 at 16:51

I am trying to send out an email with node mailer, and it is sending the email, but I am trying to use an image in there, a base64 image. I've converted the image to base64, and done this:

...

ANSWER

Answered 2021-Jun-02 at 16:51

var base64 = `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`
html: " 

Thank you "

Source https://stackoverflow.com/questions/67809286

QUESTION

Chrome "Can not drag" icon is interfering with mouseover events, how can I prevent this?

Asked 2021-May-26 at 04:11

Here is some code that uses javascript to create a bunch of div elements to act as pixels. I added an event listener to the mouseover event, and check to see if the mouse is clicked down. If the mouse is clicked down, I change the color of that pixel. The end result, is a simple drawing function.

I believe using something like HTML5 canvas would be more effective, but I was just playing around with the DOM and how events work.

The problem I am facing is that every so often, the chrome browser thinks I am trying to drag the body or a div, and no longer triggers mouseover events. It seems like an unusual problem, and I was wondering if anyone knew how to avoid it.

...

ANSWER

Answered 2021-Feb-05 at 06:22

I’m assuming you can just add this to your CSS

Source https://stackoverflow.com/questions/66058415

QUESTION

Why would some Angular modules import while other do not?

Asked 2021-May-16 at 06:29

I have a problem with a module that I cant seem to get to import.

Typescript 2.7 Node 10

pxl-ng-security shows an error in VSCode and VS2019. If I hover over it, it shows error 2307

Here is the import section of the file.

myfile.ts

...

ANSWER

Answered 2021-May-16 at 06:29

You are missing the include: array just add/update the include array per where you installed it.

Source https://stackoverflow.com/questions/67539610

QUESTION

Numpy for specific color section extraction

Asked 2021-Mar-17 at 19:46

I'm pretty new to python and trying to separate colored sections in an image from the grayscaled. Although this code (see below) gets me the results I'm looking for, it takes way to long (10+ seconds on normal images).

I looked for one hour and didn't find an appropriate answer, as most just separate specific colors (black, white, ...) or thresholds.

As far as I (already) know numpy is the way to go, but I was not successful :S

Looking forward for help - Thanks!!

...

ANSWER

Answered 2021-Mar-17 at 19:46

Untested, but as it's St Patrick's Day...

If you have a Numpy array na containing a 3-channel RGB image, it will have na.shape of (h,w,3).

You can get the maximum of the 3 channels at each point with:

Source https://stackoverflow.com/questions/66679633

QUESTION

I'm not able to slice and modify my array to extract desired information out of it

Asked 2021-Jan-23 at 18:23

I wanna read an excel file via pandas.read_excel but the first row is just the indication of the data in the column and I don't want it to be imported. I use this code to skip first row :

...

ANSWER

Answered 2021-Jan-23 at 18:23

I solved this!

Source https://stackoverflow.com/questions/65841211

QUESTION

Date/time in python

Asked 2021-Jan-08 at 03:53

I had 1 data frames :

...

ANSWER

Answered 2021-Jan-08 at 03:53

Use pandas.tseries.offsets.BusinessDay() instead of timedelta. I believe you have to subtract one from the number of business days, so you would use pandas.tseries.offsets.BusinessDay(3) in your case.

Source https://stackoverflow.com/questions/65623121

QUESTION

ValueError: Can only compare identically-labeled Series objects -python-pandas

Asked 2021-Jan-05 at 11:27

I had 2 data frames:

...

ANSWER

Answered 2021-Jan-05 at 05:23

Try sorting the values in the column before the comparison. The code is as follows:

Source https://stackoverflow.com/questions/65572996

QUESTION

Having Trouble Writing Table to Excel with Python

Asked 2020-Jun-19 at 05:29

Hi I am trying to create a table in excel using a dataframe from another excel spreadsheet and writing the table to a new one. I believe my code is correct but the table isn't writing to the new excel spreadsheet. Can someone take a look at my code and tell me what's wrong?

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ANSWER

Answered 2020-Jun-19 at 01:38

Possible duplicate: How to use xlsxwriter .add_table() method with a dataframe?

You can try converting the dataframe to a list of lists and use the data keyword.

Source https://stackoverflow.com/questions/62461732

QUESTION

How do I paint/draw/render a dot or color a pixel on the canvas of my window with only a few lines in Obj-C on Mac OS X using Xcode?

Asked 2020-Feb-12 at 08:20

[Edit:] Moving forward from phase 1 of my project here: How do I re-define size/origin of app window in code, overriding nib/xib file parameters, with Obj-C, Xcode 11.3.1 on Mac OS X 10.15.2 (Catalina)?

My current objective is pretty simple (at least in the abstract): I want to (re-)color a pixel in my blank Mac OS X application window. I want to do this economically, with as few lines of code as humanly possible, because looking at large chunks of code is a real eyesore.

I really did not want to deal with images and image buffers or draw the smallest visible lines and rectangles, but I was willing to give all that a try. I ended up going through more StackOverflow and Apple Kit articles than God can count.

The original agenda was to:

1) access my blank application window’s “content”

2) specify my pixel of interest within that content

3) access that pixel with its colorspace values

4) rewrite the values directly if possible

Seems pretty compact and simple, right?

This is what's happening in my AppDelegate. I open the constructing function with:

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ANSWER

Answered 2020-Feb-11 at 19:31

Create an NSView subclass for your custom drawing:

Source https://stackoverflow.com/questions/60160664

QUESTION

How to parse Binary XML post request in Python?

Asked 2019-Dec-23 at 02:02

I'm trying to create a pdf from a post request I'm making to a SOAP server. I'm sending an xml to this server and it returns me back an xml response. The problem is that I'm not able to parse this response. It seems to be returning back a binary (pdf) but also returns back xml content. I google around and spent already 6 hours trying to sort it out, but not able to. Sorry if this is trivial, but could anyone give me a direction on this?

Here's my code:

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ANSWER

Answered 2019-Jul-22 at 07:08

You have binaries in data so better use response.content instead of response.text which can convert "new line" in all data.

You can get first line and use it to split data to separated files with headers.

Using empty line "\n\n" you can split to headers and file`s content

Source https://stackoverflow.com/questions/57140115

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