textfiles | Setup Files , References Files , Nail Files | File Utils library

You can use NumPy to create a Boolean vector indicating which predictions are wrong, and then use that vector to index your array of file names. For example:

I cannot reproduce this.

The only hunch I have is that on your platform, perhaps the string() accessor is not returning the plain string, but e.g. the quoted path. That would break the search. Consider using the native() accessor instead.

(In fact, since file_name is NOT a path, but a string pattern, suggest passing the argument as std::string__view or similar instead.)

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you can use this code . You wrote the number of for loops in the file, which is wrong. Put the text in a string and put it in the file

Example of how to split your procedures (so each of them does less things): The first one GetAllTextFilesFromNonNumericSubFolders just collects the text files of all non numeric sub folders and collects them in a FileList. The second one ProceedTextFiles then uses the FileList to work with these text files.

Now you can easily check out which one is the bottleneck. Just comment out ProceedTextFiles FileList in your Main() procedure. If this runs fast the bottleneck is not looping through the folders. If it is slow, you can try to find an more optimized way of collecting the text files.

Try:

Make an array/list, and add them to the list with .append()

To save in different names you have to use open() and write() inside for-loop when you read data.

It would good to read all names to list and later generate urls and also keep on list so you would not have to read them.

When I see code which you use to save csv then it looks like you get csv from server so you could save all at once using open() write() without csv module.

I see it in this way.

With a "does nothing" program like the one you're testing, most of the execution run will be in the guest dynamic linker and libc. Those do a lot of work behind the scenes before your program gets control, and some of that work varies between a "native" run and a "QEMU" run. There are two main sources of divergence, judging by some of the extra detail you give in the comments:

1. The environment QEMU provides to the guest binary is not 100% identical to that which a real host kernel provides; it's only intended to be "close enough that correct guest binaries behave in a reasonable way". For instance, there is a data structure passed to the guest called the "ELF auxiliary vector"; this contains information including "what CPU features are supported", "what user ID are you executing as", and so on. The dynamic linker iterates through this data structure at startup, so minor harmless differences in what entries are in the vector in what order will cause slightly different execution paths in the guest code.

2. The CPU QEMU emulates does not provide exactly the same features that your host CPU does. There's no support for emulation of AVX or SSE2, for instance. The guest libc will adjust its behaviour so that it takes advantage of CPU features when they're available, so it picks different optimised versions of functions like memcpy() or strlen() under the hood. Since the dynamic linker will end up calling these functions, this also results in divergences of execution.

You may be able to work around some of this by restricting the area of instruction tracing you look at to just starting from the beginning of the 'main' function to avoid tracing all of the dynamic linker startup. I can't think of a way to work around the differences in what CPU features are available on the host vs QEMU, though.

Try this:

Unfortunately, that example is out of date. The reason is, that we decided that YAML files should be loaded via the safe mode, so that contained references to python objects are not executed. This means that you can always load an unknown catalogue and examine it, before deciding if you want to access any of its contents and possibly executing code.

As things stand, your workaround would be to get the data into memory and then decode it by hand, e.g.,