I get the following error when I’m reading my .pkl files on spyder (python 3.6.5): The context: My program is made of one file: program.py In the program, a class Signal is defined as well as many functions. A simplified overview of the program is provided below: The function compute_data will return a list of tuples of the form: With,
Tag: pickle
Python 3 alternatives for __getinitargs__
Is there a short enough way to call the __init__ constructor of a class during unpickling in Python 3? The usual way to do this was using __getinitargs__ like so However, the __getinitargs__ will be ignored in new style classes and in Python 3+, all classes can only be new style classes. There is the __getnewargs__ but it will pass
python 3.6 socket pickle data was truncated
I can not send my numpy array in socket. I use pickle but my client pickle crashes with this error: pickle data was truncated My server : I create a numpy array and I want to send in my client with pickle (it’s work) My client He has my numpy array, but I can not load with pickle. I have
installing cPickle with python 3.5
This might be silly but I am unable to install cPickle with python 3.5 docker image Dockerfile requirements.txt When I try to build the image Answer cPickle comes with the standard library… in python 2.x. You are on python 3.x, so if you want cPickle, you can do this: However, in 3.x, it’s easier just to use pickle. No need
Python2: Should I use Pickle or cPickle?
Python 2 has both the pickle and cPickle modules for serialization. cPickle has an obvious advantage over pickle: speed. What, if any, advantage does pickle have over cPickle? Answer The pickle module implements an algorithm for turning an arbitrary Python object into a series of bytes. This process is also called serializing” the object. The byte stream representing the object
Python – How can I make this un-pickleable object pickleable?
So, I have an object that has quite a bit of non-pickleable things in it (pygame events, orderedDicts, clock, etc.) and I need to save it to disk. Thing is, if I can just get this thing to store a string that has the progress (a single integer is all I need), then I can pass it to the object’s
best way to preserve numpy arrays on disk
I am looking for a fast way to preserve large numpy arrays. I want to save them to the disk in a binary format, then read them back into memory relatively fastly. cPickle is not fast enough, unfortunately. I found numpy.savez and numpy.load. But the weird thing is, numpy.load loads a npy file into “memory-map”. That means regular manipulating of