natsort | natural sort order string comparison : a1 | Natural Language Processing library

Use usort() with a custom comparison function to split the strings and compare the portions.

Your problem is not the sorting algorithm but the fact that .z comes after -0. The extensions are part of the string and this may mess up your sorting order.

To prevent that from happening, you have to remove the .zip extensions from the filename prior to sorting. You can store the filename without extension in the same list as the correct filename and sort it zipped to just extract the real filenames in the end:

For me, your code works fine for 10, 150, or even 225 images – as long as I open the resulting video in Windows Media Player. If I open the video in VLC media player, I get distorted playback, not only skipping the last frame. (I have numbers counting from 0 to 224, so every mistaken frame is noticed.) So, if you use VLC media player, your problem most likely is the one discussed in this StackOverflow Q&A.

On the imageio GitHub issue tracker, there's also this issue, linking to this other StackOverflow question, which seems to be same issue as you have. But, still, I think it's the afore-mentioned issue with the VLC media player.

Unfortunately, I couldn't get the workaround from the first linked Q&A working using the output_params from imageio, i.e. setting -framerate or -r. So, my workaround here would be to set up a desired fps (here: 2), and a fps for the actual playback (in VLC media player), e.g. 30, and then simply add as many identical frames as needed to fake the desired fps, i.e. 30 // 2 = 15 here.

Here's some code:

It seems that you are confusing the use-cases for PythonVirtualenvOperator and PythonOperator.

If you simply want to run a Python callable in a task (callable_virtualenv() in your case) you can use PythonOperator. In this case, it does not matter if you installed Airflow in a virtual environment, system wide, or using Docker.

What happens in your code is the following: PythonVirtualenvOperator creates another virtual environment (which is completely unrelated to the one in which you run Airflow), installs Airflow into it, and tries to import Variable. But this another Airflow installation is not configured and that is why you get those exceptions. You could set the AIRFLOW_HOME environment variable for this second Airflow installation to the same directory as used by the first Airflow installation, and this should actually work, but it looks like an overkill to me.

So, what you can do is install colorama into the same environment in which you installed Airflow and replace PythonVirtualenvOperator by PythonOperator.

BTW, those print() inside the callable would be redirected into a log file and not printed to terminal, so it probably does not make much sense to use colorama with them.

I'm assuming you're using python >= 3.5, you're using aiofiles.open as a context manager, so you shouldn't worry about closing the file yourself. What you need to do is simply exit the context manager block, when your condition has determined that the file should be removed, and then remove the file after the context manager block (and yes, os.remove is the right function for the job, just make sure you don't need an absolute path).

Unfortunately, you can't use break with a context manager, but this question shows various methods for achieving the same result.

From the source,

[description & metadata are] saved with the first page of a series only.

EDIT: I updated this example to include the two options suggested by @cgohlke

By default using TiffWriter.write or TiffWriter.save sequentially like this will create a separate series in the resulting image and save the description to the first page in each series.

I found out the (silly) reason behind the long training epoch:

Data consists of train_size training data and val_size validation data without considering batches. for example, training data consists of 4886 data samples which would be 76 data batches (with batch_size=64).

when I use for batch_idx, (x, y) in enumerate(train_gen):, I have a total number of 76 batches but I loop through 4886 batches in the loop by mistake.

I rewrote the following lines to these:

Conda does not work well with large environments in which everything pinned to specific versions (in contrast to other ecosystems in which pinning everything is the standard). The result of conda env export, which is what this probably is, here also includes the build numbers, which are almost always too specific (and often platform-specific) for the purpose of installing the right version of the software. It's great for things like reproducibility of scientific work (specific versions and builds of everything need to be known), but not great for installing software (there is plenty of flexibility in versions that should work with any package).

I'd start by removing the build pins (dropping everything after the second = in each line) so that only the versions are pinned. After that, I'd start removing version pins.

I managed to stitch together a solution by merging everything into a tuple and then sorting that with natsort, and then "stitch" it back together.

Here's the code: