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I have a framework which parses XML for its configuration. I have removed old 1.0 support and am now trying to parse "validators" config. The content of the validators.xsd is the same (apart from the keyword validators) as in other parts of the framework, which doesn't have any problems. I am only ever told the content model is not determinist hence am finding it hard to problem-solve. If you could point me in the right direction to getting better errors or "sanity-checks" that would be brilliant.

Here is the XSD configuration along with the matching xml notation being used. I'm not sure what to put here but I am going to give everything cited for clarity.

validators.xsd

I'm using python-docx to create a document with a table I want to populate from textual data. My text looks like this:

Looking into UTF8 decoding performance, I noticed the performance of protobuf's UnsafeProcessor::decodeUtf8 is better than String(byte[] bytes, int offset, int length, Charset charset) for the following non ascii string: "Quizdeltagerne spiste jordbær med flØde, mens cirkusklovnen".

I tried to figure out why, so I copied the relevant code in String and replaced the array accesses with unsafe array accesses, same as UnsafeProcessor::decodeUtf8. Here are the JMH benchmark results:

Unambiguous DNA sequences consist only of the nucleobases adenine (A), cytosine (C), guanine (G), thymine (T). For human consumption, the bases may be represented by the corresponding char in either uppercase or lowercase: A, C, G, T, or a, c, g, t. This representation is inefficient, however, when long sequences need to be stored. Since only four symbols need to be stored, each symbol can be assigned a 2-bit code. The commonly used .2bit-format specified by UCSC does exactly that, using the following encoding: T = 0b00, C = 0b01, A = 0b10, G = 0b11.

The C code below shows a reference implementation written for clarity. Various open-source software that converts genomic sequences represented as a char sequence typically uses a 256-entry lookup table indexed by each char in sequence. This also isolates from the internal representation of char. However, memory access is energetically expensive, even if the access is to an on-chip cache, and generic table look-ups are difficult to SIMDize. It would therefore be advantageous if the conversion could be accomplished by simple integer arithmetic. Given that ASCII is the dominating char encoding, one can restrict to that.

What are efficient computational approaches to convert nucleobases given as ASCII characters to their .2bit-representation?

I try to make an ESDT token issuance transaction using the following Python code

I'm new to Python and I'm learning coding/encoding, unicode, ascii and so on. I would like to print ASCII characters according to their codes and using chr() function.

I'm using below code to do some cleaning of a string. However, it is not able to remove emoticons like " 🌕💕 ". Is there a way to do it?