I have daily stock data as an HDF5 file created using PyTables. I would like to get a group of rows, process it as an array and then write it back to disk (update rows) using PyTables. I couldn\'t fig
I do a lot of statistical work and use Python as my main language. Some of the data sets I work with though can take 20GB of memory, which开发者_Go百科 makes operating on them using in-memory function
I have a vast quantity of data (>800Mb) that takes an age to load into Matlab mainly because it\'s split up into tiny files each <20kB.They are all in a prop开发者_如何学JAVArietary format which I
ptrepack is almost what i want except that it only has the options to overwrite or ignore duplicate paths. the example below illustrates what i want to happen with the structures
I need help to make decision. I have a need to transfer some data in my application and have to make a choice between these 3 technologies.
I need to store a 512^3 array on disk in some way and I\'m currently using HDF5. Since the array is sparse a lot of d开发者_如何学Pythonisk space gets wasted.
viTables only seems to work with python 2.5.I have downloaded HDFView, but when I try to open a table I created following this tutorial, I get the following error message:
Ok, I have the HDF5 library downloaded from the official site, and I have a few DLLs, including hdf5dll.dll, and hdf5_hldll.dll.
I have a 3rd party utility written in C++ that creates an HDF5 fi开发者_StackOverflow社区le with a single data group.
Is HDF5 able to handle multiple threads on its own, or does it have to beexternally synchronized? The OpenMP example suggests the latte开发者_如何学Cr.
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