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I've currently got a project running on PiCloud that involves multiple iterations of an ODE Solver. Each iteration produces a NumPy array of about 30 rows and 1500 columns, with each iterations being appended to the bottom of the array of the previous results.
Normally, I'd just let these fairly big arrays be returned by the function, hold them in memory and deal with them all at one. Except PiCloud has a fairly restrictive cap on the size of the data that can be out and out returned by a function, to keep down on transmission costs. Which is fine, except that means I'd have to launch thousands of jobs, each running on iteration, with considerable overhead.
It appears the best solution to this is to write the output to a file, and then collect the file using another function they have that doesn't have a transfer limit.
Is my best bet to do this just dumping it into a CSV file? Should I add to the CSV file each iteration, or hold it all in an array until the end and then just write once? Is there something terribly clever I'm missing?
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You can save your NumPy arrays to CSV files using the savetxt() function. This function takes a filename and array as arguments and saves the array into CSV format. You must also specify the delimiter; this is the character used to separate each variable in the file, most commonly a comma.
Sometimes, we need to deal with NumPy arrays that are too big to fit in the system memory. A common solution is to use memory mapping and implement out-of-core computations. The array is stored in a file on the hard drive, and we create a memory-mapped object to this file that can be used as a regular NumPy array.
Saving NumPy Array to a . However, if you want to efficiently store data present in a NumPy array and use it only for other Python programs then you can store it in . npy files. Saving the NumPy data into . npy files increases the efficiency of saving and loading data as it is stored in a native binary format.
Unless there is a reason for the intermediate files to be human-readable, do not use CSV, as this will inevitably involve a loss of precision.
The most efficient is probably tofile (doc) which is intended for quick dumps of file to disk when you know all of the attributes of the data ahead of time.
For platform-independent, but numpy-specific, saves, you can use save (doc).
Numpy and scipy also have support for various scientific data formats like HDF5 if you need portability.
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I would recommend looking at the pickle module. The pickle module allows you to serialize python objects as streams of bytes (e.g., strings). This allows you to write them to a file or send them over a network, and then reinstantiate the objects later.
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Try Joblib - Fast compressed persistence
One of the key components of joblib is it’s ability to persist arbitrary Python objects, and read them back very quickly. It is particularly efficient for containers that do their heavy lifting with numpy arrays. The trick to achieving great speed has been to save in separate files the numpy arrays, and load them via memmapping.
Edit: Newer (2016) blog entry on data persistence in Joblib
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