The example below demonstrates that even if you break the sequence up into its different parts, it would still be a real challenge to understand without any documentation handy:. Once you learn what each section represents, you can start to interpret the data. For example, if you know that this is a bit per pixel image that is two pixels wide, and two pixels high, you might be able to make sense of the pixel array data shown below:.
By experimenting with changing some of the values in the pixel array by hand, you will fairly quickly discover the overall structure of the array and the way pixels are represented. After figuring this out, you might also be able to look back on the rest of the file and determine what a few of the fields in the headers are without looking at the documentation. After exploring a bit on your own, you should check out the field-by-field walkthrough of a 2x2 bitmap file that this example was based on.
Like most binary formats, the bitmap format has a tremendous amount of options that make building a complete implementation a whole lot more complicated than just building a tool which is suitable for generating a single type of image. I realized shortly after skimming the format description that you can skip out on a lot of the boilerplate information if you stick to 24bit-per-pixel images, so I decided to do exactly that.
Looking at the implementation from the outside-in, you can see the general structure of the BMP::Writer class. Pixels are stored in a two-dimensional array, and all the interesting things happen at the time you write the image out to file:. The following code shows how BMP::Writer builds up this header and writes it to file:. Out of the five fields in this header, only the file size ended up being dynamic.
I was able to treat the pixel array offset as a constant because the headers for 24 bit color images take up a fixed amount of space. The file size computations 1 will make sense later once we examine the way that the pixel array gets encoded. The tool that makes it possible for us to convert these various field values into binary sequences is Array pack.
If you note that the file size of our reference image is 2x2 bitmap is 70 bytes, it becomes clear what pack is actually doing for us when we examine the byte by byte values in the following example:. The byte sequence for the file header exactly matches that of our reference image, which indicates that the proper bitmap file header is being generated.
After figuring out how to encode the file header, the next step was to work on the DIB header, which includes some metadata about the image and how it should be displayed on the screen:. The pack statement in the above code works in a very similar fashion as the code that writes out the BMP file header, with one exception: it needs to handle signed bit little endian integers.
The most interesting thing to note about this code is that each row of pixels ends up getting padded with some null characters. This is to ensure that each row of pixels is aligned on WORD boundaries 4 byte sequences. This is a semi-arbitrary limitation that has to do with file storage constraints, but things like this are common in binary files.
In the future, adding support for Binary Ninja is also planned. DeepDiff tells the difference between 2 collections and the changes as edit steps.
It works on any collection of Equatable and Hashable items. By default, there is no. But since. The provided pgdiff. You alone are responsible for verifying the generated SQL before running it against your database.
Go ahead and see what SQL gets generated. It's common to work with lists of elements on the DOM. Adding, removing or reordering elements in a list can be rather expensive. To optimize this you can add an id attribute to a DOM node. When reordering nodes it will compare nodes with the same ID against each other, resulting in far fewer re-renders.
This is especially potent when coupled with DOM node caching. Sometimes we want to tell the algorithm to not evaluate certain nodes and its children.
This can be because we're sure they haven't changed, or perhaps because another piece of code is managing that part of the DOM tree. To achieve this nanomorph evaluates the. A solution to this problem is to write your code as if you were recreating the entire DOM whenever state changes. Of course, if you actually recreated the entire DOM every time your application state changed, your app would be very slow and your input fields would lose focus. So instead of updating the DOM when your application state changes, you simply create a virtual tree or VTree, which looks like the DOM state that you want.
In our experience it makes UI-related code easier to build and maintain. Our aim is to make the UI a function of state i. This module was created to solve the problem of updating the DOM in response to a UI component or page being rerendered. The cost is that all of the internal state associated with the existing DOM nodes scroll positions, input caret positions, CSS transition states, etc.
This is exactly what the morphdom module does! Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. How do I compare two files in Ruby 1. Ask Question. Asked 10 years, 1 month ago. Active 6 years, 3 months ago.
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