More precisely, I would have the (big) array already instantiated before start reading the file. I/O textĬonsidering you do have all the (RAM) memory necessary to host the whole numpy array: I would then loop over the whole (~12M lines) text file, filling the pre-existing array row-by-row. My point so far is: check the data type of your dataset, estimate the size of your future array, and guarantee you have that minimum amount of RAM memory available. The cost you pay is clear: read/writing from/to the disk is very slow. You may have enough physical (RAM) memory in your machine, but if not enough of free memory, your system will use the swap memory (i.e, disk) to keep your system stable & have the work done. It's a lot of memory (which you know, just want to emphasize).Īt this point, I would like to point out a possible swapping of the working memory. Most likely, you have - what - a dataset of Integer, Float? The size may increase quite significantly: > np.array(, dtype=bool).itemsize Notice that a simple boolean array will cost ~12 GBytes of memory: > print(" bytes".format(Īnd this is for a Boolean data type. I don't specifically know about how np.loadtxt ( genfromtxt) is operating behind the scenes, so I will tell you how I would do (after trying like you did). I assume, then, you have enough (RAM) memory to host such array - 12M x 1K. I understand you want to have the whole data set/array in memory, eventually, as a NumPy array. Unlike the HDF5, the tensorstore doesn't seem to have reading overhead issues when converting to numpy, from docs:Ĭonversion to an numpy.ndarray also implicitly performs a synchronous read (which hits the in-memory cache since the same region was just retrieved) But somehow there isn't any information on how to save the tensor/array without the exact dimensions, all of the examples seem to include configurations like 'dimensions'. There is another library tensorstore that seems to efficiently handles arrays which support conversion to numpy array when read. tensorstore) that can store and efficiently convert to numpy array when loading the saved storage format? Q (Part 3): If part 1 and part2 are not possible, are there other efficient storage (e.g. The accepted answer there suggests hdf5 but the format is not the main objective of this question and the hdf5 format isn't desired in my use-case since I've to read it back into a numpy array afterwards. But seems like the np.vstack isn't the best solution when reading the file. npy efficiently?Īlso, there's an answer here to save the csv file as numpy array iteratively. npy efficiently, is there some way to iteratively read the. Q (Part 2): If there isn't any way to to load/convert a. The above code snippet is similar to the answer from Convert CSV to numpy but that won't work for ~12M x 1024 matrix. Q (Part 1): Is there some way to load/convert a. csv file I'm working on has ~12 million lines with 1024 columns, it takes quite a lot to load everything into RAM before converting into an. While the above works for smallish file, the actual. $PMGNTRK,llll.ll,a,yyyyy.yy,a,xxxxx,a,hhmmss.How to convert a. As many additional decimal places may be added as long as the total length of the message does not exceed 82 bytes. NOTE: The Latitude and Longitude Fields are shown as having two decimal places. It is not present when a simple command of PMGNCMD,TRACK is issued. Note that this field is (and its preceding comma) is only produced by the unit when the command PMGNCMD,TRACK,2 is given. The last field contains the UTC date of the fix. The last character field is the name of the track, for those units that support named tracks. The next field consists of a status letter of “A” to indicated that the data is valid, or “V” to indicate that the data is not valid. The next field is the UTC time of the fix. The next field is the altitude followed by “F” for feet or “M” for meters. The next field is the Longitude followed by E or W. The first field in this message is the Latitude, followed by N or S. This message is to be used to transmit Track information (basically a list of previous position fixes) which is often displayed on the plotter or map screen of the unit.
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